May 2023 arXiv papers — page 19

Nico Schick

Unfortunately, many people die in car accidents. To reduce these accidents, cars are equipped with driving safety systems. With autonomous vehicles, the driver's behavior becomes irrelevant as the car drives autonomously. All autonomous driving algorithms must undergo extensive testing and validation, especially for safety-critical scenarios. Therefore, the

Hind Al Baba, Bilal Al Taki, Amru Hussein

We discuss in this short note the local-in-time strong well-posedness of the compressible Navier-Stokes system for non-Newtonian fluids on the three dimensional torus. We show that the result established recently by Kalousek, M\'{a}cha, and Ne\v{c}asova in \doi{10.1007/s00208-021-02301-8} can be extended to the case where vanishing density is allowed initial

Mustafa Burak Gurbuz, Jean Michael Moorman, Constantine Dovrolis

Deep neural networks (DNNs) struggle to learn in dynamic environments since they rely on fixed datasets or stationary environments. Continual learning (CL) aims to address this limitation and enable DNNs to accumulate knowledge incrementally, similar to human learning. Inspired by how our brain consolidates memories, a powerful strategy in CL is replay, whic

Mitch Randall, Alex Delacroix, Carson Ezell, Ezra Kelderman

(Abridged) Quantitative three-dimensional (3D) position and velocity estimates obtained by passive radar will assist the Galileo Project in the detection and classification of aerial objects by providing critical measurements of range, location, and kinematics. These parameters will be combined with those derived from the Project{\textquoteright}s suite of e

Rochelle E. Tractenberg

This 2024 chapter gives a brief overview of cognitive and educational sciences' perspectives on learning outcomes (LOs) to facilitate the integration of LOs specific to ethical reasoning into any mathematics or quantitative course. The target is undergraduate (adult) learners but these LOs can be adapted for earlier and later stages of learning. Core content

Wenqi Yang, Linghan Zhu, Yan Lu, Erik Henriksen

Defects are crucial in determining a variety of material properties especially in low dimensions. In this work, we study point defects in monolayer alpha-phase Ruthenium (III) chloride (alpha-RuCl3), a promising candidate to realize quantum spin liquid with nearly degenerate magnetic states. Our first-principles simulations reveal that Cl vacancies, Ru vacan

Kai Klocke, Michael Buchhold

Projective measurements in random quantum circuits lead to a rich breadth of entanglement phases and extend the realm of non-unitary quantum dynamics. Here we explore the connection between measurement-only quantum circuits in one spatial dimension and the statistical mechanics of loop models in two dimensions. While Gaussian Majorana circuits admit a micros

Raya Elsaleh, Guy Katz

Deep neural networks (DNNs) are becoming a key component in diverse systems across the board. However, despite their success, they often err miserably; and this has triggered significant interest in formally verifying them. Unfortunately, DNN verifiers are intricate tools, and are themselves susceptible to soundness bugs. Due to the complexity of DNN verifie

Yanqi Zhou, Nan Du, Yanping Huang, Daiyi Peng

Transformers are central to recent successes in natural language processing and computer vision. Transformers have a mostly uniform backbone where layers alternate between feed-forward and self-attention in order to build a deep network. Here we investigate this design choice and find that more complex blocks that have different permutations of layer primiti

Supriya Gadi Patil, Angel X. Chang, Manolis Savva

This paper analyzes the robustness of recent 3D shape descriptors to SO(3) rotations, something that is fundamental to shape modeling. Specifically, we formulate the task of rotated 3D object instance detection. To do so, we consider a database of 3D indoor scenes, where objects occur in different orientations. We benchmark different methods for feature extr

Shubham Kumar, Deepmala, Milan Hladik, Hossein Moosaei

This paper provides an overview of the necessary and sufficient conditions for guaranteeing the unique solvability of absolute value equations. In addition to discussing the basic form of these equations, we also address several generalizations, including generalized absolute value equations and matrix absolute value equations. Our survey encompasses known r

Matthew Szenher, Alex Delacroix, Eric Keto, Sarah Little

To date, there are little reliable data on the position, velocity and acceleration characteristics of Unidentified Aerial Phenomena (UAP). The dual hardware and software system described in this document provides a means to address this gap. We describe a weatherized multi-camera system which can capture images in the visible, infrared and near infrared wave

Janvijay Singh, Mukund Rungta, Diyi Yang, Saif M. Mohammad

Citing papers is the primary method through which modern scientific writing discusses and builds on past work. Collectively, citing a diverse set of papers (in time and area of study) is an indicator of how widely the community is reading. Yet, there is little work looking at broad temporal patterns of citation. This work systematically and empirically exami

Matthew Siper, Sam Earle, Zehua Jiang, Ahmed Khalifa

Path of Destruction (PoD) is a self-supervised method for learning iterative generators. The core idea is to produce a training set by destroying a set of artifacts, and for each destructive step create a training instance based on the corresponding repair action. A generator trained on this dataset can then generate new artifacts by repairing from arbitrary

Emmanouil Theodosis, Karim Helwani, Demba Ba

Employing equivariance in neural networks leads to greater parameter efficiency and improved generalization performance through the encoding of domain knowledge in the architecture; however, the majority of existing approaches require an a priori specification of the desired symmetries. We present a neural network architecture, Linear Group Networks (LGNs),

Daniel da Silva Junior, Paulo Roberto dos S. Corval, Aline Paes, Daniel de Oliveira

The Brazilian judiciary has a large workload, resulting in a long time to finish legal proceedings. Brazilian National Council of Justice has established in Resolution 469/2022 formal guidance for document and process digitalization opening up the possibility of using automatic techniques to help with everyday tasks in the legal field, particularly in a larg

Andrew Mead, Sarah Little, Paul Sail, Michelle Tu

The Galileo Project's acoustic monitoring, omni-directional system (AMOS) aids in the detection and characterization of aerial phenomena. It uses a multi-band microphone suite spanning infrasonic to ultrasonic frequencies, providing an independent signal modality for validation and characterization of detected objects. The system utilizes infrasonic, audible

Konstantin Hopf, Hannah Hartstang, Thorsten Staake

Forecasting electricity demand plays a critical role in ensuring reliable and cost-efficient operation of the electricity supply. With the global transition to distributed renewable energy sources and the electrification of heating and transportation, accurate load forecasts become even more important. While numerous empirical studies and a handful of review

Holt Bodish

We use bordered Floer homology, specifically the immersed curve interpretation of the bordered pairing theorem, to compute various three- and four-dimensional invariants of satellite knots with arbitrary companions and patterns from a family of knots in the solid torus that have the knot type of the trefoil in $S^3$. We compute the three-genus, and bound the

Danylo Radchenko, João P. G. Ramos

We prove a conjecture by Vemuri by proving sharp bounds on $\ell^{\kappa}$ sums of Hermite functions multiplied by an exponentially decaying factor. More explicitly, we prove that, for each $y>0,$ we have \[ \sum_{n \ge 1} |h_n(x)|^{\kappa} \frac{e^{-\kappa n y}}{n^{\beta}} \ll_y x^{\frac{1}{2} - 2\beta} e^{-\kappa x^2 \tanh(y)/2}, \] for all $x \in \mathbb{

Marten Lienen, David Lüdke, Jan Hansen-Palmus, Stephan Günnemann

Simulations of turbulent flows in 3D are one of the most expensive simulations in computational fluid dynamics (CFD). Many works have been written on surrogate models to replace numerical solvers for fluid flows with faster, learned, autoregressive models. However, the intricacies of turbulence in three dimensions necessitate training these models with very

Anton Wahrstätter, Jens Ernstberger, Aviv Yaish, Liyi Zhou

Permissionless blockchains promise to be resilient against censorship by a single entity. This suggests that deterministic rules, and not third-party actors, are responsible for deciding if a transaction is appended to the blockchain or not. In 2022, the U.S. Office of Foreign Assets Control (OFAC) sanctioned a Bitcoin mixer and an Ethereum application, putt

Mehdi Rezaei, Supriya Pan, Weiqiang Yang, David F. Mota

We investigate the dark energy phenomenology in an extended parameter space where we allow the curvature density of our universe as a free-to-vary parameter. The inclusion of the curvature density parameter is motivated from the recently released observational evidences indicating the closed universe model at many standard deviations. Here we assume that the

Yue Kang, Cho-Jui Hsieh, Thomas C. M. Lee

Lipschitz bandit is a variant of stochastic bandits that deals with a continuous arm set defined on a metric space, where the reward function is subject to a Lipschitz constraint. In this paper, we introduce a new problem of Lipschitz bandits in the presence of adversarial corruptions where an adaptive adversary corrupts the stochastic rewards up to a total

H. J. Deeg, I. Y. Georgieva, G. Nowak, C. M. Persson

TOI 1416 (BD+42 2504, HIP 70705) is a V=10 late G or early K-type dwarf star with transits detected by TESS. Radial velocities verify the presence of the transiting planet TOI-1416 b, with a period of 1.07d, a mass of $3.48 M_{Earth}$ and a radius of $1.62 R_{Earth}$, implying a slightly sub-Earth density of $4.50$ g cm$^{-3}$. The RV data also further indic

Poetri Sonya Tarabunga, Emanuele Tirrito, Titas Chanda, Marcello Dalmonte

We introduce a method to measure many-body magic in quantum systems based on a statistical exploration of Pauli strings via Markov chains. We demonstrate that sampling such Pauli-Markov chains gives ample flexibility in terms of partitions where to sample from: in particular, it enables to efficiently extract the magic contained in the correlations between w

Alexis Boudon, Philippe Brax, Patrick Valageas, Leong Khim Wong

We investigate the imprints of accretion and dynamical friction on the gravitational-wave signals emitted by binary black holes embedded in a scalar dark matter cloud. As a key feature in this work, we focus on scalar fields with a repulsive self-interaction that balances against the self-gravity of the cloud. To a first approximation, the phase of the gravi

Matthew Mould, Davide Gerosa, Marco Dall'Amico, Michela Mapelli

Gravitational-wave observations have revealed sources whose unusual properties challenge our understanding of compact-binary formation. Inferring the formation processes that are best able to reproduce such events may therefore yield key astrophysical insights. A common approach is to count the fraction of synthetic events from a simulated population that ar

Mark J. Avara, Julian H. Krolik, Manuela Campanelli, Scott C. Noble

While supermassive binary black holes inspiral toward merger they may also experience significant accretion of matter from a surrounding disk. We study the dynamics of this system, simultaneously describing the evolving spacetime and magnetized plasma, and present the first relativistic calculation simulating two equal-mass, non-spinning black holes as they

Damianos Iosifidis, Konstantinos Pallikaris

We formulate a bi-Connection Theory of Gravity whose Gravitational action consists of a recently defined mutual curvature scalar. Namely, we build a gravitational theory consisting of one metric and two affine connections, in a Metric-Affine Gravity setup. Consequently, coupling the two connections on an equal footing with matter, we show that the geometry o

Maria Chiara Brambilla, Olivia Dumitrescu, Elisa Postinghel, Luis José Santana Sánchez

Our goal is twofold. On one hand we show that the cones of divisors ample in codimension $k$ on a Mori dream space are rational polyhedral. On the other hand we study the duality between such cones and the cones of $k$-moving curves by means of the Mori chamber decomposition of the former. We give a new proof of the weak duality property (already proved by P

Rachel Kinard, Razvan Gelca, Paul T. Schrader

We present a series of algorithms for skein manipulation in a genus-2 handlebody, implementing a novel strand sorting method to reduce any skein to a skein in a 2-punctured disk. This reduction guarantees resolution as a linear combination of basis elements of the Kauffman Bracket Skein Module. Manually, these skein manipulations prove to be computationally

Tim Chan, Simon C. Benjamin

Fault-tolerant quantum computing requires classical hardware to perform the decoding necessary for error correction. The Union-Find decoder is one of the best candidates for this. It has remarkably organic characteristics, involving the growth and merger of data structures through nearest-neighbour steps; this naturally suggests the possibility of its realis

Ashwin Rao, Siyi Guo, Sze-Yuh Nina Wang, Fred Morstatter

Effective response to pandemics requires coordinated adoption of mitigation measures, like masking and quarantines, to curb a virus's spread. However, as the COVID-19 pandemic demonstrated, political divisions can hinder consensus on the appropriate response. To better understand these divisions, our study examines a vast collection of COVID-19-related tweet

Ilyoun Na, Jack Kemp, Robert-Jan Slager, Yang Peng

We consider nontrivial topological phases in Floquet systems using unitary loops and stroboscopic evolutions under a static Floquet Hamiltonian $H_F$ in the presence of dynamical space-time symmetries $G$. While the latter has been subject of out-of-equilibrium classifications that extend the ten-fold way and systems with additional crystalline symmetries to

Eleonora Troja

Swift has now completed 18 years of mission, during which it discovered thousands of gamma-ray bursts (GRBs) as well as new classes of high-energy transient phenomena. Its first breakthrough result was the localization of short duration GRBs, which enabled for redshift measurements and kilonova searches. Swift, in synergy with the Hubble Space Telescope and

Evan Deddo, James T. Liu, Leopoldo A. Pando Zayas, Robert J. Saskowski

In the context of gravitational theories describing renormalization group flows across dimensions via AdS/CFT, we study the role of higher-derivative corrections to Einstein gravity. We use the Null Energy Condition to derive monotonicity properties of candidate holographic central charges formed by combinations of metric functions. We also implement an entr

Chandramouli Chowdhury, Kajal Singh

This paper presents an evaluation of the wave function coefficients for conformally coupled scalars at both one and two-loop levels at leading order in the coupling constant, in momentum space. We take cues from time-dependent interactions in flat spacetime and under suitable approximations, these can also be used to study the wave function coefficients for

Marco Crisostomi, Kallol Dey, Enrico Barausse, Roberto Trotta

We analyze the ringdown phase of the first detected black-hole merger, GW150914, using a simulation-based inference pipeline based on masked autoregressive flows. We obtain approximate marginal posterior distributions for the ringdown parameters, namely the mass, spin, and the amplitude and phases of the dominant mode and its first overtone. Thanks to the lo

Akshay Suresh, Vishal Gajjar, Pranav Nagarajan, Sofia Z. Sheikh

Radio searches for extraterrestrial intelligence have mainly targeted the discovery of narrowband continuous-wave beacons and artificially dispersed broadband bursts. Periodic pulse trains, in comparison to the above technosignature morphologies, offer an energetically efficient means of interstellar transmission. A rotating beacon at the Galactic Center (GC

Kevin Michalewicz, Martin Millon, Frédéric Dux, Frédéric Courbin

The spatial resolution of astronomical images is limited by atmospheric turbulence and diffraction in the telescope optics, resulting in blurred images. This makes it difficult to accurately measure the brightness of blended objects because the contributions from adjacent objects are mixed in a time-variable manner due to changes in the atmospheric condition

Lina Johnsen Kamra, Jacob Linder

When a spin-splitting field is introduced to a thin film superconductor, the spin currents polarized along the field couples to energy currents that can only decay via inelastic scattering. We study spin and energy injection into such a superconductor where spin-orbit impurity scattering yields inverse spin-Hall and spin-swapping currents. We show that the c

Davide Dal Cin, Takeshi Kobayashi

The radial direction of the Peccei--Quinn field can drive cosmic inflation, given a non-minimal coupling to gravity. This scenario has been considered to simultaneously explain inflation, the strong $CP$ problem, and dark matter. We argue that Peccei--Quinn inflation is extremely sensitive to higher-dimensional operators. Further combining with the discussio

Ilija Buric, Francesco Russo, Alessandro Vichi

Partial wave decomposition is one of the main tools within the modern S-matrix studies. We present a method to compute partial waves for $2\to2$ scattering of spinning particles in arbitrary spacetime dimension. We identify partial waves as matrix elements of the rotation group with definite covariance properties under a subgroup. This allows to use a variet

Kyriakos Destounis, Kostas D. Kokkotas

X-ray astronomy provides information regarding the electromagnetic emission of active galactic nuclei and X-ray binaries. These events provide details regarding the astrophysical environment of black holes and stars, and help us understand gamma-ray bursts. They produce estimates for the maximum mass of neutron stars and eventually will contribute to the dis

Pengfei Li

The interplay between dark matter (DM) and baryons has long been ignored when building galaxies semi-empirically and observationally. Here I show that baryonic gravity leads to an adiabatic contraction of DM halos, which is most significant in massive galaxies. Ignoring this effect, the derived DM halos are not guaranteed in dynamical equilibrium. I present

Michael F. Wondrak, Walter D. van Suijlekom, Heino Falcke

We present a new avenue to black hole evaporation using a heat-kernel approach analogous as for the Schwinger effect. Applying this method to an uncharged massless scalar field in a Schwarzschild spacetime, we show that spacetime curvature takes a similar role as the electric field strength in the Schwinger effect. We interpret our results as local pair prod

Gudrun Hiller, Tim Höhne, Daniel F. Litim, Tom Steudtner

We explain why vector-like fermions are natural candidates to lift the Standard Model vacuum instability. Results are further discussed from the viewpoint of criticality. Several models allow for vector-like quarks and leptons in the TeV-range which can be searched for at the LHC.

Steven T. Flammia, Ryan O'Donnell

For quantum state tomography on rank-$r$ dimension-$d$ states, we show that $\widetilde{O}(r^{.5}d^{1.5}/\epsilon) \leq \widetilde{O}(d^2/\epsilon)$ copies suffice for accuracy~$\epsilon$ with respect to (Bures) $\chi^2$-divergence, and $\widetilde{O}(rd/\epsilon)$ copies suffice for accuracy~$\epsilon$ with respect to quantum relative entropy. The best prev

Zhiyuan Ji, Christina C. Williams, Sandro Tacchella, Katherine A. Suess

We present a spatially resolved study of stellar populations in 6 galaxies with stellar masses $M_*\sim10^{10}M_\odot$ at $z\sim3.7$ using 14-filter JWST/NIRCam imaging from the JADES and JEMS surveys. The 6 galaxies are visually selected to have clumpy substructures with distinct colors over rest-frame $3600-4100\r{A}$, including a red, dominant stellar cor

Alaric Erschfeld, Stefan Floerchinger

Large-scale structure formation is studied in a kinetic theory approach, extending the standard perfect pressureless fluid description for dark matter by including the velocity dispersion tensor as a dynamical degree of freedom. The evolution of power spectra for density, velocity and velocity dispersion degrees of freedom is investigated in a non-perturbati

Elyse Incha, Andrew Vanderburg, Tom Jacobs, Daryll LaCourse

The Kepler space telescope was responsible for the discovery of over 2,700 confirmed exoplanets, more than half of the total number of exoplanets known today. These discoveries took place during both Kepler's primary mission, when it spent 4 years staring at the same part of the sky, and its extended K2 mission, when a mechanical failure forced it to observe

Veronica Pasquarella

Exploiting the symmetry topological field theory/topological order correspondence (SymTFT/TO), together with the higher-categorical structure of 6D N =(2,0) SCFTs, we prove that the total quantum dimension of the relative gaugeable algebra leading to intrinsic non-invertible symmetries between class S theories is greater with respect to the non-intrinsic cas

Chao Yin, Andrew Lucas

The computational complexity of simulating quantum many-body systems generally scales exponentially with the number of particles. This enormous computational cost prohibits first principles simulations of many important problems throughout science, ranging from simulating quantum chemistry to discovering the thermodynamic phase diagram of quantum materials o

Sakura Schafer-Nameki

What comprises a global symmetry of a Quantum Field Theory (QFT) has been vastly expanded in the past 10 years to include not only symmetries acting on higher-dimensional defects, but also most recently symmetries which do not have an inverse. The principle that enables this generalization is the identification of symmetries with topological defects in the Q

Zeyue Xue, Guanglu Song, Qiushan Guo, Boxiao Liu

Text-to-image generation has recently witnessed remarkable achievements. We introduce a text-conditional image diffusion model, termed RAPHAEL, to generate highly artistic images, which accurately portray the text prompts, encompassing multiple nouns, adjectives, and verbs. This is achieved by stacking tens of mixture-of-experts (MoEs) layers, i.e., space-Mo

Goro Kobayashi, Tatsuki Kuribayashi, Sho Yokoi, Kentaro Inui

Prediction head is a crucial component of Transformer language models. Despite its direct impact on prediction, this component has often been overlooked in analyzing Transformers. In this study, we investigate the inner workings of the prediction head, specifically focusing on bias parameters. Our experiments with BERT and GPT-2 models reveal that the biases

Musfar Muhamed Kozhikkal, Siyuan Chen, Gilles Theureau, Melanie Habouzit

Studying how the black hole (BH) - (galaxy) bulge mass relation evolves with redshift provides valuable insights into the co-evolution of supermassive black holes and their host galaxies. However, obtaining accurate measurement of BH masses is challenging due to the bias towards the most massive and luminous galaxies. Instead we focus on the BH and bulge mas

Yuchao Gu, Xintao Wang, Jay Zhangjie Wu, Yujun Shi

Public large-scale text-to-image diffusion models, such as Stable Diffusion, have gained significant attention from the community. These models can be easily customized for new concepts using low-rank adaptations (LoRAs). However, the utilization of multiple concept LoRAs to jointly support multiple customized concepts presents a challenge. We refer to this

Hugo Molinares, Bing He, Vitalie Eremeev

We present a systematic study on the effects of dynamical transfer and steady-state synchronization of quantum states in a hybrid optomechanical network, consisting of two cavities with atoms inside and interacting via a common moving mirror (i.e. mechanical oscillator), are studied. It is found that high fidelity transfer of Schr\"{o}dinger's cat and squeez

Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon

While large-scale unsupervised language models (LMs) learn broad world knowledge and some reasoning skills, achieving precise control of their behavior is difficult due to the completely unsupervised nature of their training. Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the uns

Zachary Slepian

Here we show how to produce a 3D density field with a given set of higher-order correlation functions. Our algorithm enables producing any desired two-point, three-point, and four-point functions, including odd-parity for the latter. We note that this algorithm produces the desired correlations about a set of ``primary'' points, matched to how the spherical-

Elena Redaelli, Luca Bizzocchi, Paola Caselli, Jaime E. Pineda

Context. Observations of $\rm ^{14}N/^{15}N$ in the interstellar medium are becoming more frequent thanks to the increased telescope capabilities. However, interpreting these data is still puzzling. In particular, measurements of $\rm ^{14}N/^{15}N$ in diazenylium revealed high levels of anti-fractionation in cold cores. Aims. Furuya & Aikawa (2018), using a

Matthew D. Kvalheim, Philip Arathoon

We consider the problem of determining the class of continuous-time dynamical systems that can be globally linearized in the sense of admitting an embedding into a linear system on a higher-dimensional Euclidean space. We solve this problem for dynamical systems on connected state spaces that are either compact or contain at least one nonempty compact attrac

M. Jehanzeb Mirza, Leonid Karlinsky, Wei Lin, Mateusz Kozinski

Recently, large-scale pre-trained Vision and Language (VL) models have set a new state-of-the-art (SOTA) in zero-shot visual classification enabling open-vocabulary recognition of potentially unlimited set of categories defined as simple language prompts. However, despite these great advances, the performance of these zeroshot classifiers still falls short o

Jing Gu, Yilin Wang, Nanxuan Zhao, Tsu-Jui Fu

In an era where images and visual content dominate our digital landscape, the ability to manipulate and personalize these images has become a necessity. Envision seamlessly substituting a tabby cat lounging on a sunlit window sill in a photograph with your own playful puppy, all while preserving the original charm and composition of the image. We present Pho

Konstantin Mishchenko, Rustem Islamov, Eduard Gorbunov, Samuel Horváth

We present a partially personalized formulation of Federated Learning (FL) that strikes a balance between the flexibility of personalization and cooperativeness of global training. In our framework, we split the variables into global parameters, which are shared across all clients, and individual local parameters, which are kept private. We prove that under

Fabio Bergamin, James Lough, Emil Schreiber, Hartmut Grote

Squeezed light is injected into the dark port of gravitational wave interferometers, in order to reduce the quantum noise. A fraction of the interferometer output light can reach the OPO due to sub-optimal isolation of the squeezing injection path. This backscattered light interacts with squeezed light generation process, introducing additional measurement n

Juan Zuluaga-Gomez, Sara Ahmed, Danielius Visockas, Cem Subakan

Despite the recent advancements in Automatic Speech Recognition (ASR), the recognition of accented speech still remains a dominant problem. In order to create more inclusive ASR systems, research has shown that the integration of accent information, as part of a larger ASR framework, can lead to the mitigation of accented speech errors. We address multilingu

Christian Copetti, Michele Del Zotto, Kantaro Ohmori, Yifan Wang

A recent development in our understanding of the theory of quantum fields is the fact that familiar gauge theories in spacetime dimensions greater than two can have non-invertible symmetries generated by topological defects. The hallmark of these non-invertible symmetries is that the fusion rule deviates from the usual group-like structure, and in particular

Florian Mai, Juan Zuluaga-Gomez, Titouan Parcollet, Petr Motlicek

State-of-the-art ASR systems have achieved promising results by modeling local and global interactions separately. While the former can be computed efficiently, global interactions are usually modeled via attention mechanisms, which are expensive for long input sequences. Here, we address this by extending HyperMixer, an efficient alternative to attention ex

Pietro Caputo, Shirshendu Ganguly

We consider non-colliding Brownian lines above a hard wall, which are subject to geometrically growing (given by a parameter $\lambda>1$) area tilts, which we call the $\lambda$-tilted line ensemble (LE). The model was introduced by Caputo, Ioffe, Wachtel [CIW] in 2019 as a putative scaling limit for the level lines of low-temperature 3D Ising interfaces. Wh

Arash Ardakani, Altan Haan, Shangyin Tan, Doru Thom Popovici

Transformer-based models, such as BERT and ViT, have achieved state-of-the-art results across different natural language processing (NLP) and computer vision (CV) tasks. However, these models are extremely memory intensive during their fine-tuning process, making them difficult to deploy on GPUs with limited memory resources. To address this issue, we introd

Yuhang Zang, Wei Li, Jun Han, Kaiyang Zhou

Recent Multimodal Large Language Models (MLLMs) are remarkable in vision-language tasks, such as image captioning and question answering, but lack the essential perception ability, i.e., object detection. In this work, we address this limitation by introducing a novel research problem of contextual object detection -- understanding visible objects within dif

Matilde Marcolli, Noam Chomsky, Robert Berwick

The syntactic Merge operation of the Minimalist Program in linguistics can be described mathematically in terms of Hopf algebras, with a formalism similar to the one arising in the physics of renormalization. This mathematical formulation of Merge has good descriptive power, as phenomena empirically observed in linguistics can be justified from simple mathem

Achraf Ben-Hamadou, Oussama Smaoui, Ahmed Rekik, Sergi Pujades

Teeth localization, segmentation, and labeling from intra-oral 3D scans are essential tasks in modern dentistry to enhance dental diagnostics, treatment planning, and population-based studies on oral health. However, developing automated algorithms for teeth analysis presents significant challenges due to variations in dental anatomy, imaging protocols, and

Georg Novtony, Walter Morales-Alvarez, Nikita Smirnov, Cristina Olaverri-Monreal

This paper presents the development of the JKU-ITS Last Mile Delivery Robot. The proposed approach utilizes a combination of one 3D LIDAR, RGB-D camera, IMU and GPS sensor on top of a mobile robot slope mower. An embedded computer, running ROS1, is utilized to process the sensor data streams to enable 2D and 3D Simultaneous Localization and Mapping, 2D local

Refat Abdelmawla Khaled Assaad, Mohammed Tamekkante, Lixin Mao

Let $R$ be a commutative ring. An $R$-module $M$ is said to be $w$-split if Ext$_{R}^1(M,N)$ is a GV-torsion $R$-module for all $R$-modules $N$. It is known that every projective module is $w$-split, but the converse is not true in general. In this paper, we study the w-split dimension of a flat module. To do so, we introduce and study the so-called $w$-copu

Paul S. Scotti, Atmadeep Banerjee, Jimmie Goode, Stepan Shabalin

We present MindEye, a novel fMRI-to-image approach to retrieve and reconstruct viewed images from brain activity. Our model comprises two parallel submodules that are specialized for retrieval (using contrastive learning) and reconstruction (using a diffusion prior). MindEye can map fMRI brain activity to any high dimensional multimodal latent space, like CL

Xinchao Song, Nikolas Lamb, Sean Banerjee, Natasha Kholgade Banerjee

We present Pix2Repair, an automated shape repair approach that generates restoration shapes from images to repair fractured objects. Prior repair approaches require a high-resolution watertight 3D mesh of the fractured object as input. Input 3D meshes must be obtained using expensive 3D scanners, and scanned meshes require manual cleanup, limiting accessibil

Yemon Choi, Mahya Ghandehari, Hung Le Pham

The AMNM property for commutative Banach algebras is a form of Ulam stability for multiplicative linear functionals. We show that on any semilattice of infinite breadth, one may construct a weight for which the resulting weighted convolution algebra fails to have the AMNM property. Our work is the culmination of a trilogy started in [Semigroup Forum 102 (202

Elon Lindenstrauss, Amir Mohammadi, Zhiren Wang, Lei Yang

We prove an effective version of the Oppenheim conjecture with a polynomial error rate. The proof is based on an effective equidistribution theorem which in turn relies on recent progress towards restricted projection problem.

Yatin Dandi, Florent Krzakala, Bruno Loureiro, Luca Pesce

For high-dimensional Gaussian data, we investigate theoretically how the features of a two-layer neural network adapt to the structure of the target function through a few large batch gradient descent steps, leading to an improvement in the approximation capacity from initialization. First, we compare the influence of batch size to that of multiple steps. Fo

Yiran Mao, Madeline G. Reinecke, Markus Kunesch, Edgar A. Duéñez-Guzmán

Is it possible to evaluate the moral cognition of complex artificial agents? In this work, we take a look at one aspect of morality: `doing the right thing for the right reasons.' We propose a behavior-based analysis of artificial moral cognition which could also be applied to humans to facilitate like-for-like comparison. Morally-motivated behavior should p

Radford M. Neal, Jeffrey S. Rosenthal

We review criteria for comparing the efficiency of Markov chain Monte Carlo (MCMC) methods with respect to the asymptotic variance of estimates of expectations of functions of state, and show how such criteria can justify ways of combining improvements to MCMC methods. We say that a chain on a finite state space with transition matrix $P$ efficiency-dominate

Tobias Friedrich, Timo Kötzing, Aneta Neumann, Frank Neumann

Understanding how evolutionary algorithms perform on constrained problems has gained increasing attention in recent years. In this paper, we study how evolutionary algorithms optimize constrained versions of the classical LeadingOnes problem. We first provide a run time analysis for the classical (1+1) EA on the LeadingOnes problem with a deterministic cardi

Morris Ang, Guillaume Remy, Xin Sun, Tunan Zhu

We prove that the probabilistic definition of the most general boundary three-point and bulk-boundary structure constants in Liouville conformal field theory (LCFT) agree respectively with the formula proposed by Ponsot-Techsner (2002) and by Hosomichi (2001). These formulas also respectively describe the fusion kernel and modular kernel of the Virasoro conf

Gengyu Wang, Kate Harwood, Lawrence Chillrud, Amith Ananthram

We present a new fact-checking benchmark, Check-COVID, that requires systems to verify claims about COVID-19 from news using evidence from scientific articles. This approach to fact-checking is particularly challenging as it requires checking internet text written in everyday language against evidence from journal articles written in formal academic language

Fu-Yun Wang, Wenshuo Chen, Guanglu Song, Han-Jia Ye

Leveraging large-scale image-text datasets and advancements in diffusion models, text-driven generative models have made remarkable strides in the field of image generation and editing. This study explores the potential of extending the text-driven ability to the generation and editing of multi-text conditioned long videos. Current methodologies for video ge

S. Yaser Samadi, L. Billard, Jiin-Huarng Guo, Wei Xu

With contemporary data sets becoming too large to analyze the data directly, various forms of aggregated data are becoming common. The original individual data are points, but after aggregation, the observations are interval-valued (e.g.). While some researchers simply analyze the set of averages of the observations by aggregated class, it is easily establis

Mert Yuksekgonul, Linjun Zhang, James Zou, Carlos Guestrin

While most machine learning models can provide confidence in their predictions, confidence is insufficient to understand a prediction's reliability. For instance, the model may have a low confidence prediction if the input is not well-represented in the training dataset or if the input is inherently ambiguous. In this work, we investigate the relationship be

Simon Yves, Emanuele Galiffi, Xiang Ni, Enrico Maria Renzi

Following the discovery of moir\'e-driven superconductivity in twisted graphene multilayers, twistronics has spurred a surge of interest in tailored broken symmetries through angular rotations, enabling new properties from electronics to photonics and phononics. Analogously, in monoclinic polar crystals a nontrivial angle between non-degenerate dipolar phono

Elon Lindenstrauss, Amir Mohammadi, Zhiren Wang

We show that pair correlation function for the spectrum of a flat 2-dimensional torus satisfying an explicit Diophantine condition agrees with those of a Poisson process with a polynomial error rate. The proof is based on a quantitative equidistribution theorem and tools from geometry of numbers.

Daniel Coelho, Miguel Oliveira, Paulo Dias

Collecting real-world data is often considered the bottleneck of Artificial Intelligence, stalling the research progress in several fields, one of which is camera localization. End-to-end camera localization methods are still outperformed by traditional methods, and we argue that the inconsistencies associated with the data collection techniques are restrain

Yukang Yang, Dongnan Gui, Yuhui Yuan, Weicong Liang

Recently, there has been an increasing interest in developing diffusion-based text-to-image generative models capable of generating coherent and well-formed visual text. In this paper, we propose a novel and efficient approach called GlyphControl to address this task. Unlike existing methods that rely on character-aware text encoders like ByT5 and require re

Zhihan Liu, Miao Lu, Wei Xiong, Han Zhong

In online reinforcement learning (online RL), balancing exploration and exploitation is crucial for finding an optimal policy in a sample-efficient way. To achieve this, existing sample-efficient online RL algorithms typically consist of three components: estimation, planning, and exploration. However, in order to cope with general function approximators, mo

Antonio Morales-Pérez, Chiara Devescovi, Yoonseok Hwang, Mikel García-Díez

Tight-binding models can accurately replicate the band structure and topology of crystalline systems. They have been widely used in solid-state physics due to their versatility and low computational cost. It is straightforward to build an accurate tight-binding model of any crystalline system using the crystal's maximally localized Wannier functions as a bas

Chanyoung Chung, Jaejun Lee, Joyce Jiyoung Whang

A hyper-relational knowledge graph has been recently studied where a triplet is associated with a set of qualifiers; a qualifier is composed of a relation and an entity, providing auxiliary information for a triplet. While existing hyper-relational knowledge graph embedding methods assume that the entities are discrete objects, some information should be rep

Harald Garcke, Patrik Knopf, Julia Wittmann

The Cahn--Hilliard equation with anisotropic energy contributions frequently appears in many physical systems. Systematic analytical results for the case with the relevant logarithmic free energy have been missing so far. We close this gap and show existence, uniqueness, regularity, and separation properties of weak solutions to the anisotropic Cahn--Hilliar

L. A. Lessa, J. E. G. Silva

We investigate the effects of the Einstein cubic gravity (ECG) on regular black hole solutions driven by nonlinear electrodynamics (NLE) sources. The ECG tends to form a naked singularity at the origin for a high ECG coupling constant. Assuming that ECG provides only perturbative corrections to the regular magnetic charged solutions, we found modified regula