April 2024 arXiv papers — page 92

Toqi Tahamid Sarker, Mohamed G Embaby, Khaled R Ahmed, Amer AbuGhazaleh

Methane emissions from livestock, particularly cattle, significantly contribute to climate change. Effective methane emission mitigation strategies are crucial as the global population and demand for livestock products increase. We introduce Gasformer, a novel semantic segmentation architecture for detecting low-flow rate methane emissions from livestock, an

David Weik, Zehua Dou, Dirk Räbiger, Tobias Vogt

Convection of liquid metals drives large natural processes and is important in technical processes. Model experiments are conducted for research purposes where simulations are expensive and the clarification of open questions requires novel flow mapping methods with an increased spatial resolution. In this work, the method of Ultrasound Localization Microsco

Robert Andrews, Avi Wigderson

We design polynomial size, constant depth (namely, $\mathsf{AC}^0$) arithmetic formulae for the greatest common divisor (GCD) of two polynomials, as well as the related problems of the discriminant, resultant, B\'ezout coefficients, squarefree decomposition, and the inversion of structured matrices like Sylvester and B\'ezout matrices. Our GCD algorithm exte

Zhengyang Liang, Meiyu Liang, Wei Huang, Yawen Li

In recent years, pre-trained multimodal large models have attracted widespread attention due to their outstanding performance in various multimodal applications. Nonetheless, the extensive computational resources and vast datasets required for their training present significant hurdles for deployment in environments with limited computational resources. To a

Soumyendu Sarkar, Avisek Naug, Antonio Guillen, Ricardo Luna

The rapid growth of machine learning (ML) has led to an increased demand for computational power, resulting in larger data centers (DCs) and higher energy consumption. To address this issue and reduce carbon emissions, intelligent design and control of DC components such as IT servers, cabinets, HVAC cooling, flexible load shifting, and battery energy storag

Henry C. Burridge, Sen Liu, Sara Mohamed, Samuel G. A. Wood

The quality of the classroom environment, including ventilation, air quality and thermal conditions, has an important impact on children's health and academic achievements. The use of portable HEPA filter air cleaners is widely suggested as a strategy to mitigate exposure to particulate matter and airborne viruses. However, there is a need to quantify the re

João Luzio, Alexandre Bernardino, Plinio Moreno

The aim of this work is to establish how accurately a recent semantic-based foveal active perception model is able to complete visual tasks that are regularly performed by humans, namely, scene exploration and visual search. This model exploits the ability of current object detectors to localize and classify a large number of object classes and to update a s

Yi Dai, Andreas Kreisel, Brian M. Andersen

We perform a theoretical investigation of the spin susceptibility of unconventional superconductivity on the kagome lattice. Despite the existence of a sign-changing gap structure, which sums to zero over the Fermi surface, we show that such unconventional pairing states may exhibit a Hebel-Slichter peak in the temperature-dependent spin-lattice relaxation r

Lucas C. Parra, Aimar Silvan Ortubay, Maximilian Nentwich, Jens Madsen

Complex systems, such as brains, markets, and societies, exhibit internal dynamics influenced by external factors. Disentangling delayed external effects from internal dynamics within these systems is often challenging. We propose using a Vector Autoregressive model with eXogenous input (VARX) to capture delayed interactions between internal and external var

Björn Schenke, Heikki Mäntysaari, Farid Salazar, Chun Shen

We review model calculations of exclusive vector meson production in ultraperipheral heavy ion collisions. We highlight differences and similarities between different dipole models and leading twist shadowing calculations. Recent color glass condensate calculations are presented with focus on effects from nuclear structure and azimuthal anisotropies driven b

Alsu G. Bagdatova, Sergey P. Baranov

We consider the processes $g\to W{+}\,q'$ and $q\to Z{+}\,q$ and derive the respective splitting functions as functions of two kinematic variables: the longitudinal momentum fraction $z$ and transverse momentum $p_T$ of the produced bosons with respect to the parent quark. We take into account kinematic (phase space) restrictions connected with nonzero masse

Andre Izidoro, Rogerio Deienno, Sean N. Raymond, Matthew S. Clement

The dynamical architecture and compositional diversity of the asteroid belt strongly constrain planet formation models. Recent Solar System formation models have shown that the asteroid belt may have been born empty and later filled with objects from the inner ($<$2~au) and outer regions (>5 au) of the solar system. In this work, we focus on the implantation

Hantian Ding, Zijian Wang, Giovanni Paolini, Varun Kumar

In large language model training, input documents are typically concatenated together and then split into sequences of equal length to avoid padding tokens. Despite its efficiency, the concatenation approach compromises data integrity -- it inevitably breaks many documents into incomplete pieces, leading to excessive truncations that hinder the model from le

Tanureza Jaya, Benjamin Michalak, Marcel Radke, Kevin Haninger

Cabling tasks (pulling, clipping, and plug insertion) are today mostly manual work, limiting the cost-effectiveness of electrification. Feasibility for the robotic grasping and insertion of plugs, as well as the manipulation of cables, have been shown in research settings. However, in many industrial tasks the complete process from picking, insertion, routin

Andre Izidoro, Rogerio Deienno, Sean N. Raymond, Matthew S. Clement

The asteroid Athor, residing today in the inner main asteroid belt, has been recently associated as the source of EL enstatite meteorites to Earth. It has been argued that Athor formed in the terrestrial region -- as indicated by similarity in isotopic compositions between Earth and EL meteorites -- and was implanted in the belt $\gtrsim$60 Myr after the for

W. He, Y. Shen, K. Wohlfeld, J. Sears

Magnetic van der Waals (vdW) materials have opened new frontiers for realizing novel many-body phenomena. Recently NiPS3 has received intense interest since it hosts an excitonic quasiparticle whose properties appear to be intimately linked to the magnetic state of the lattice. Despite extensive studies, the electronic character, mobility, and magnetic inter

François Greer, Carl Lian

We show that two natural cycle classes on the moduli space of compact type stable maps to a varying elliptic curve agree. The first is the virtual fundamental class from Gromov-Witten theory, and the second is the Torelli pullback of the special cycle on A_g of principally polarized abelian varieties admitting an elliptic isogeny factor.

Selim C. Hotinli, Gilbert P. Holder

We study the prospects for measuring the cosmological distribution and abundance of ionized electrons in the intergalactic medium using galaxy surveys. Optical light from distant galaxies is subject to Thomson screening by intervening electrons which distorts the observed galaxy number density, similar to the effect of weak gravitational lensing magnificatio

Nadav Joseph Outmezguine, Noam Levi

With the success of deep neural networks (NNs) in a variety of domains, the computational and storage requirements for training and deploying large NNs have become a bottleneck for further improvements. Sparsification has consequently emerged as a leading approach to tackle these issues. In this work, we consider a simple yet effective approach to sparsifica

Ana M. Conrado, Rosa M. González Delgado, Rubén García-Benito, Isabel Pérez

The Universe is shaped as a web-like structure, formed by clusters, filaments, and walls that leave large volumes in between named voids. Galaxies in voids have been found to be of a later type, bluer, less massive, and to have a slower evolution than galaxies in denser environments (filaments and walls). However, the effect of the void environment on their

Shachar Fraenkel, Moshe Goldstein

Thermal equilibrium states of local quantum many-body systems are notorious for their spatially decaying correlations, which place severe restrictions on the types of many-body entanglement structures that may be observed at finite temperatures. These restrictions may however be defied when an out-of-equilibrium steady state is considered instead. In this pa

V. Pelgrims, N. Mandarakas, R. Skalidis, K. Tassis

We present the first degree-scale tomography map of the dusty magnetized interstellar medium (ISM) from stellar polarimetry and distance measurements. We used the RoboPol polarimeter at Skinakas Observatory to conduct a survey of starlight polarization in a region of the sky of 4 square degrees. We propose a Bayesian method to decompose the stellar-polarizat

Damian Rovara, Nils Quetschlich, Robert Wille

With the applications of quantum computing becoming more and more widespread, finding ways that allow end users without experience in the field to apply quantum computers to solve their individual problems is becoming a crucial task. However, current optimization algorithms require problem instances to be posed in complex formats that are challenging to form

Ryan Abbott, Michael S. Albergo, Denis Boyda, Daniel C. Hackett

Scale separation is an important physical principle that has previously enabled algorithmic advances such as multigrid solvers. Previous work on normalizing flows has been able to utilize scale separation in the context of scalar field theories, but the principle has been largely unexploited in the context of gauge theories. This work gives an overview of a

Connor A. Occhialini, Yi Tseng, Hebatalla Elnaggar, Qian Song

The fundamental optical excitations of correlated transition-metal compounds are typically identified with multielectronic transitions localized at the transition-metal site, such as $dd$ transitions. In this vein, intense interest has surrounded the appearance of sharp, below band-gap optical transitions, i.e. excitons, within the magnetic phase of correlat

Emma Lundqvist, Erik Zackrisson, Calum Hawcroft, Anish M. Amarsi

Deep imaging of galaxy cluster fields have in recent years revealed tens of candidates for gravitationally lensed stars at redshifts $z\approx$ 1-6, and future searches are expected to reveal highly magnified stars from even earlier epochs. Multi-band photometric observations may be used to constrain the redshift, effective temperature $T_\mathrm{eff}$ and d

Chloe Neufeld, Pieter van Dokkum, Yasmeen Asali, Alba Covelo-Paz

We present results from the JWST First Reionization Epoch Spectroscopically Complete Observations survey (FRESCO) on the star forming sequence of galaxies at $1.0<z<1.7$, around the peak of the cosmic star formation history. Star formation rates (SFRs) are measured from the redshifted, nearly dust-insensitive Paschen-$\alpha$ emission line, and stellar mass

Carlos Henrique de Lima, Daniel Stolarski

In this work, we explore how constraints based on interference effects for the negative gauge-Higgs coupling scenario are affected by new physics. Models that achieve this wrong-sign gauge-Higgs coupling inevitably have new charged Higgs states. These states affect the interpretation of interference effects. We recast the ATLAS analysis for VBF $Wh$, showing

Ryohei Kobayashi, Taige Wang, Tomohiro Soejima, Roger S. K. Mong

A (2+1)D topological ordered phase with U(1) symmetry may or may not have a symmetric gapped edge state, even if both thermal and electric Hall conductivity are vanishing. It is recently discovered that there are "higher" versions of Hall conductivity valid for fermionic fractional quantum Hall (FQH) states, which obstructs symmetry-preserving gapped edge st

William E. Harris, Marta Reina-Campos

Deep JWST imaging of the giant galaxy cluster Abell 2744, at redshift $z=0.308$, is used to explore the features of its rich population of globular clusters (GCs), building on our initial survey of the system (Harris & Reina-Campos 2023). We use the photometry of more than $10,000$ GCs over a three-magnitude range to construct the GC luminosity function (GCL

Andrea Guerrieri, Alexandre Homrich, Pedro Vieira

We introduce the notion of branon jets, states of collinear flux tube excitations. We argue for the analyticity, crossing and unitarity of the multi-particle scattering of these jets and, through the S-matrix bootstrap, place bounds on a set of finite energy multi-particle sum rules. Such bounds define a matrioska of sorts with a smaller and smaller allowed

G. Mazzolari, H. Übler, R. Maiolino, X. Ji

The James Webb Space Telescope (JWST) is revolutionizing our understanding of black holes formation and growth in the early Universe. However, JWST has also revealed that some of the classical diagnostics, such as the BPT diagrams and X-ray emission, often fail to identify narrow line TypeII active galactic nuclei (AGN) at high redshift. Here we present thre

Hong Zhe Chen

If entanglement builds spacetime, then conversely, disentanglement ought to destroy spacetime. From the quantum null energy condition and quantum focusing conjecture, we obtain disentanglement criteria which necessitate infinite energies and strong spacetime singularities. We apply our results to the strong cosmic censorship proposal, where singularities at

Hiroki Takeda, Takahiro Tanaka

We explore the impact of corrections to the propagation on the waveforms of gravitationally lensed gravitational waves under the geometrical optics approximation, focusing on both uniform cosmological modifications and local modifications localized around lensing objects. By adopting a model-independent phenomenological approach, we systematically investigat

Qiwei Di, Jiafan He, Quanquan Gu

Learning from human feedback plays an important role in aligning generative models, such as large language models (LLM). However, the effectiveness of this approach can be influenced by adversaries, who may intentionally provide misleading preferences to manipulate the output in an undesirable or harmful direction. To tackle this challenge, we study a specif

Hongxin Zhang, Zeyuan Wang, Qiushi Lyu, Zheyuan Zhang

In this paper, we investigate the problem of embodied multi-agent cooperation, where decentralized agents must cooperate given only egocentric views of the world. To effectively plan in this setting, in contrast to learning world dynamics in a single-agent scenario, we must simulate world dynamics conditioned on an arbitrary number of agents' actions given o

Liyan Tang, Philippe Laban, Greg Durrett

Recognizing if LLM output can be grounded in evidence is central to many tasks in NLP: retrieval-augmented generation, summarization, document-grounded dialogue, and more. Current approaches to this kind of fact-checking are based on verifying each piece of a model generation against potential evidence using an LLM. However, this process can be very computat

A. Y. Fresco, C. Peroux, A. Merloni, J. Comparat

Almost 50% of galaxies in the local Universe are in clusters or groups coexisting with both hot and cold gas components. In the present study, we observationally probed the cold-gas content of X-ray-selected massive galaxy clusters with spectroscopic redshift measured from the SDSS/SPIDERS survey. This paper focuses on the most massive structures: galaxy clu

Zehao Yu, Torsten Sattler, Andreas Geiger

Recently, 3D Gaussian Splatting (3DGS) has demonstrated impressive novel view synthesis results, while allowing the rendering of high-resolution images in real-time. However, leveraging 3D Gaussians for surface reconstruction poses significant challenges due to the explicit and disconnected nature of 3D Gaussians. In this work, we present Gaussian Opacity Fi

Zhuo Chen, Jacob McCarran, Esteban Vizcaino, Marin Soljačić

Partial differential equations (PDEs) are instrumental for modeling dynamical systems in science and engineering. The advent of neural networks has initiated a significant shift in tackling these complexities though challenges in accuracy persist, especially for initial value problems. In this paper, we introduce the $\textit{Time-Evolving Natural Gradient (

Larry D. Bradley, Angela Adamo, Eros Vanzella, Keren Sharon

We present recent JWST NIRCam imaging observations of SPT0615-JD (also known as the Cosmic Gems Arc), lensed by the galaxy cluster SPT-CL J0615-5746. The 5 arcsec long arc is the most highly magnified $z>10$ galaxy known. It straddles the lensing critical curve and reveals five star clusters with radii of $\sim 1$ pc or less. We measure the full arc to have

Isao Ishikawa

This paper develops a functional-analytic framework for approximating the push-forward induced by an analytic map from finitely many samples. Instead of working directly with the map, we study the push-forward on the space of locally analytic functionals and identify it, via the Fourier--Borel transform, with an operator on the space of entire functions of e

Amir Eskandari-asl, Adolfo Avella

We derive the two-time linear response theory for out-of-equilibrium pumped systems, generic pump-probe delays and probe frequencies. Such a theory enormously simplifies the numerical calculations, for instance, of the optical conductivity with respect to the actual procedure, which requires computing the effect of the probe pulse for each time delay with re

Nivasini Ananthakrishnan, Tiffany Ding, Mariel Werner, Sai Praneeth Karimireddy

We study price-discrimination games between buyers and a seller where privacy arises endogenously--that is, utility maximization yields equilibrium strategies where privacy occurs naturally. In this game, buyers with a high valuation for a good have an incentive to keep their valuation private, lest the seller charge them a higher price. This yields an equil

Mark C. Eid, Pak-Hei Yeung, Madeleine K. Wyburd, João F. Henriques

Two-dimensional (2D) freehand ultrasonography is one of the most commonly used medical imaging modalities, particularly in obstetrics and gynaecology. However, it only captures 2D cross-sectional views of inherently 3D anatomies, losing valuable contextual information. As an alternative to requiring costly and complex 3D ultrasound scanners, 3D volumes can b

Ashkan Mirzaei, Riccardo De Lutio, Seung Wook Kim, David Acuna

Neural reconstruction approaches are rapidly emerging as the preferred representation for 3D scenes, but their limited editability is still posing a challenge. In this work, we propose an approach for 3D scene inpainting -- the task of coherently replacing parts of the reconstructed scene with desired content. Scene inpainting is an inherently ill-posed task

Da-Wei Zhou, Zhi-Hong Qi, Han-Jia Ye, De-Chuan Zhan

The era of pre-trained models has ushered in a wealth of new insights for the machine learning community. Among the myriad of questions that arise, one of paramount importance is: 'Do pre-trained models possess comprehensive knowledge?' This paper seeks to address this crucial inquiry. In line with our objective, we have made publicly available a novel datas

Hubert Eichner, Daniel Ramage, Kallista Bonawitz, Dzmitry Huba

Federated Learning and Analytics (FLA) have seen widespread adoption by technology platforms for processing sensitive on-device data. However, basic FLA systems have privacy limitations: they do not necessarily require anonymization mechanisms like differential privacy (DP), and provide limited protections against a potentially malicious service provider. Ad

Yuchi Wang, Shuhuai Ren, Rundong Gao, Linli Yao

Diffusion models have exhibited remarkable capabilities in text-to-image generation. However, their performance in image-to-text generation, specifically image captioning, has lagged behind Auto-Regressive (AR) models, casting doubt on their applicability for such tasks. In this work, we revisit diffusion models, highlighting their capacity for holistic cont

Ness Mayker Chen, Adam K. Leroy, Sumit K. Sarbadhicary, Laura A. Lopez

We present a statistical analysis of the local, approximately 50-100 pc scale, H-alpha emission at the locations of recent (less than 125 years) supernovae (SNe) in nearby star-forming galaxies. Our sample consists of 32 SNe in 10 galaxies that are targets of the PHANGS-MUSE survey. We find that 41% (13/32) of these SNe occur coincident with a previously ide

Mélodie Monod, Peter Krusche, Qian Cao, Berkman Sahiner

TorchSurv is a Python package that serves as a companion tool to perform deep survival modeling within the PyTorch environment. Unlike existing libraries that impose specific parametric forms, TorchSurv enables the use of custom PyTorch-based deep survival models. With its lightweight design, minimal input requirements, full PyTorch backend, and freedom from

Jiangning Zhang, Chengjie Wang, Xiangtai Li, Guanzhong Tian

Anomaly detection (AD) is often focused on detecting anomaly areas for industrial quality inspection and medical lesion examination. However, due to the specific scenario targets, the data scale for AD is relatively small, and evaluation metrics are still deficient compared to classic vision tasks, such as object detection and semantic segmentation. To fill

Saeid Pourmand, Wyatt D. Whiting, Alireza Aghasi, Nicholas F. Marshall

This paper studies the geometry of binary hyperdimensional computing (HDC), a computational scheme in which data are encoded using high-dimensional binary vectors. We establish a result about the similarity structure induced by the HDC binding operator and show that the Laplace kernel naturally arises in this setting, motivating our new encoding method Lapla

Truman Hickok, Dhireesha Kudithipudi

In continual learning, a model learns incrementally over time while minimizing interference between old and new tasks. One of the most widely used approaches in continual learning is referred to as replay. Replay methods support interleaved learning by storing past experiences in a replay buffer. Although there are methods for selectively constructing the bu

Yu-Yang Li, Yu Bai, Cunshi Wang, Mengwei Qu

Light curves serve as a valuable source of information on stellar formation and evolution. With the rapid advancement of machine learning techniques, it can be effectively processed to extract astronomical patterns and information. In this study, we present a comprehensive evaluation of deep-learning and large language model (LLM) based models for the automa

Sebastian Myrbäck, Sara Zahedi

A mass-conservative high-order unfitted finite element method for convection-diffusion equations in evolving domains is proposed. The space-time method presented in [P. Hansbo, M. G. Larson, S. Zahedi, Comput. Methods Appl. Mech. Engrg. 307 (2016)] is extended to naturally achieve mass conservation by utilizing Reynold's transport theorem. Furthermore, by pa

Massimo Pascale, Liang Dai

We suggest that "Godzilla" of the lensed Sunburst galaxy ($z=2.37$) is a young super star cluster powering a nebula of gravitationally trapped stellar ejecta. Employing HST photometry and spectroscopy from VLT/MUSE and VLT/X-Shooter, we infer physical and chemical properties of the cluster and nebula, finding Godzilla is young $4$-$6$Myr, massive $2 \times 1

James Michael Tooth, Nilufer Tuptuk, Jeremy Daniel McKendrick Watson

Ontologies are widely used for achieving interoperable Digital Twins (DTws), yet competing DTw definitions compound interoperability issues. Semantically linking these differing twins is feasible through ontologies and Cognitive Digital Twins (CDTws). However, it is often unclear how ontology use bolsters broader DTw advancements. This article presents a sys

Rohit Kumar Mishra, Suman Kumar Sahoo, Chandni Thakkar

In this article, we study various aspects of the mixed ray transform of $(k + \ell)$-tensor fields that are symmetric in its first $k$ and last $\ell$ indices. As a first result, we derive an inversion algorithm to recover the solenoidal part of the unknown tensor field using the normal operator of the mixed ray transform. Next, we establish a set of unique

Philipp Czerner, Javier Esparza, Valentin Krasotin, Christoph Welzel-Mohr

Regular transition systems (RTS) are a popular formalism for modeling infinite-state systems in general, and parameterised systems in particular. In a CONCUR 22 paper, Esparza et al. introduce a novel approach to the verification of RTS, based on inductive invariants. The approach computes the intersection of all inductive invariants of a given RTS that can

Duncan Austin, Christopher J. Conselice, Nathan J. Adams, Thomas Harvey

We present an analysis of rest-frame UV continuum slopes, $\beta$, using a sample of 1011 galaxies at $6.5<z<13$ from the EPOCHS photometric sample collated from the GTO PEARLS and public ERS/GTO/GO (JADES, CEERS, NGDEEP, GLASS) JWST NIRCam imaging across $178.9~\mathrm{arcmin}^2$ of unmasked blank sky. We correct our UV slopes for the photometric error coup

Maya Stein, Ana Trujillo-Negrete

We show that if $D$ is an $n$-vertex digraph with more than $(k-1)n$ arcs that does not contain any of three forbidden digraphs, then $D$ contains every antidirected tree on $k$ arcs. The forbidden digraphs are those orientations of $K_{2, \lceil k/12\rceil}$ where each of the vertices in the class of size two has either out-degree $0$ or in-degree $0$. This

S. van Golden, C. Kalle, S. Kombrink, T. Samuel

For countably infinite IFSs on $\mathbb R^2$ consisting of affine contractions with diagonal linear parts, we give conditions under which the affinity dimension is an upper bound for the Hausdorff dimension and a lower bound for the lower box-counting dimension. Moreover, we identify a family of countably infinite IFSs for which the Hausdorff and affinity di

Phillip Kerger, David E. Bernal Neira, Zoe Gonzalez Izquierdo, Eleanor G. Rieffel

We investigate distributed classical and quantum approaches for the survivable network design problem (SNDP), sometimes called the generalized Steiner problem. These problems generalize many complex graph problems of interest, such as the traveling salesperson problem, the Steiner tree problem, and the k-connected network problem. To our knowledge, no classi

Mario Gleirscher, Rehab Massoud, Dieter Hutter, Christoph Lüth

Mathematical proofs are a cornerstone of control theory, and it is important to get them right. Deduction systems can help with this by mechanically checking the proofs. However, the structure and level of detail at which a proof is represented in a deduction system differ significantly from a proof read and written by mathematicians and engineers, hampering

Juno Nam, Jiayu Peng, Rafael Gómez-Bombarelli

Machine learning interatomic potentials (MLIPs) have become a workhorse of modern atomistic simulations, and recently published universal MLIPs, pre-trained on large datasets, have demonstrated remarkable accuracy and generalizability. However, the computational cost of MLIPs limits their applicability to chemically disordered systems requiring large simulat

Weitong Zhang, Zhiyuan Fan, Jiafan He, Quanquan Gu

We study the constant regret guarantees in reinforcement learning (RL). Our objective is to design an algorithm that incurs only finite regret over infinite episodes with high probability. We introduce an algorithm, Cert-LSVI-UCB, for misspecified linear Markov decision processes (MDPs) where both the transition kernel and the reward function can be approxim

Christa Cuchiero, Florian Huber

Motivated by the robustness of the capital distribution curves, we study the behavior of a certain polynomial equity market model as the number of companies goes to infinity. More precisely, we extend volatility-stabilized market models introduced by Fernholz et al. by allowing for a common noise term such that the models remain polynomial. As the number of

Anthony Wilkie, James Ostrowski, Rebekah Herrman

The multi-angle quantum approximate optimization algorithm (ma-QAOA) is a recently introduced algorithm that gives at least the same approximation ratio as the quantum approximate optimization algorithm (QAOA) and, in most cases, gives a significantly higher approximation ratio than QAOA. One drawback to ma-QAOA is that it uses significantly more classical p

Zhong-Hao Luo, Yun-Long Zhang

Ultralight bosons can condense to form the so-called bosonic clouds around spinning black holes by superradiance instability. When quantum effects are taken into account, the classical black holes were replaced by exotic compact objects including area quantized black holes. In this work, we consider the superradiant instabilities of massive scalar fields aro

M. D. Gray, S. Etoka, B. Pimpanuwat, A. M. S. Richards

We use 3D computer modelling to investigate the timescales and radiative output from maser flares generated by the impact of shock-waves on astronomical unit-scale clouds in interstellar and star-forming regions, and in circumstellar regions in some circumstances. Physical conditions are derived from simple models of isothermal hydrodynamic (single-fluid) an

Caroline Wang, Arrasy Rahman, Ishan Durugkar, Elad Liebman

Current approaches to learning cooperative multi-agent behaviors assume relatively restrictive settings. In standard fully cooperative multi-agent reinforcement learning, the learning algorithm controls $\textit{all}$ agents in the scenario, while in ad hoc teamwork, the learning algorithm usually assumes control over only a $\textit{single}$ agent in the sc

Johannes Frank, Elham Kashefi, Dominik Leichtle, Michael de Oliveira

In this paper, we introduce a new approach to quantum benchmarking inspired by quantum verification motivating new paradigms of quantum benchmarking. Our proposed benchmark not only serves as a robust indicator of computational capability but also offers scalability, customizability, and universality. By providing formal statements regarding the quality of q

Jordan M. Thomas, Fei I. Yeh, Jim Hao Chen, Joe J. Mambretti

The ability for quantum and conventional networks to operate in the same optical fibers would aid the deployment of quantum network technology on a large scale. Quantum teleportation is a fundamental operation in quantum networking, but has yet to be demonstrated in fibers populated with high-power conventional optical signals. Here we report to the best of

Neer Bhardwaj, Raymond McCulloch, Nandagopal Ramachandran, Katharine Woo

We study $\mathbb{R}_{\textrm{an},\exp}$-definable functions $f:\mathbb{R}\to \mathbb{R}$ that take integer values at all sufficiently large positive integers. If $|f(x)|= O\big(2^{(1+10^{-5})x}\big)$, then we find polynomials $P_1, P_2$ such that $f(x)=P_1(x)+P_2(x)2^x$ for all sufficiently large $x$. Our result parallels classical theorems of P\'olya and S

Diego Lima Bomfim, Charles Morgan, Samuel Gomes da Silva

We prove forcing axiom equivalents of two families of weakenings of the axiom of choice: a trichotomy principle for cardinals isolated by L\'evy, ${\rm H\hskip0.05pt}_\kappa$, and ${\rm DC}_\kappa$, the principle of dependent choices generalized to cardinals $\kappa$, for regular cardinals $\kappa$. Using these equivalents we obtain new forcing axiom formula

Henrik Rüping, Marc Stephan

We provide an equivariant extension of Carlsson's BGG correspondence in characteristic two. As an application we classify perfect cochain complexes of $(\mathbb{Z}/2\times \mathbb{Z}/2)\rtimes Q$-representations with four-dimensional total homology for finite groups $Q$ of odd order. We deduce that cochain complexes of finite, free $A_4$-CW complexes with fo

Tim-Lukas Habich, Jonas Haack, Mehdi Belhadj, Dustin Lehmann

Soft-robot designs are manifold, but only a few are publicly available. Often, these are only briefly described in their publications. This complicates reproduction, and hinders the reproducibility and comparability of research results. If the designs were uniform and open source, validating researched methods on real benchmark systems would be possible. To

Benjamin A Newman, Chris Paxton, Kris Kitani, Henny Admoni

Agents that assist people need to have well-initialized policies that can adapt quickly to align with their partners' reward functions. Initializing policies to maximize performance with unknown partners can be achieved by bootstrapping nonlinear models using imitation learning over large, offline datasets. Such policies can require prohibitive computation t

Arvind Srinivasan, Johannes Ellemose, Peter W. S. Butcher, Panagiotis D. Ritsos

We propose the notion of Attention-Aware Visualizations (AAVs) that track the user's perception of a visual representation over time and feed this information back to the visualization. Such context awareness is particularly useful for ubiquitous and immersive analytics where knowing which embedded visualizations the user is looking at can be used to make vi

Bowen Xu

This paper aims to establish a consensus on AGI's definition. General intelligence refers to the adaptation to open environments according to certain principles using limited resources. It emphasizes that adaptation or learning is an indispensable property of intelligence, and places the controversial part within the principles of intelligence, which can be

Hieu Le, Zhenhua He, Mai Le, Dhruva K. Chakravorty

The discoveries in this paper show that Intelligence Processing Units (IPUs) offer a viable accelerator alternative to GPUs for machine learning (ML) applications within the fields of materials science and battery research. We investigate the process of migrating a model from GPU to IPU and explore several optimization techniques, including pipelining and gr

Oliver Clarke, Fatemeh Mohammadi

We study a family of monomial ideals, called block diagonal matching field ideals, which arise as monomial Gr\"obner degenerations of determinantal ideals. Our focus is on the minimal free resolutions of these ideals and all of their powers. Initially, we establish their linear quotient property and compute their Betti numbers, illustrating that their minima

Hao-Lun Hsu, Weixin Wang, Miroslav Pajic, Pan Xu

We present the first study on provably efficient randomized exploration in cooperative multi-agent reinforcement learning (MARL). We propose a unified algorithm framework for randomized exploration in parallel Markov Decision Processes (MDPs), and two Thompson Sampling (TS)-type algorithms, CoopTS-PHE and CoopTS-LMC, incorporating the perturbed-history explo

Umberto Tomasini, Matthieu Wyart

Understanding what makes high-dimensional data learnable is a fundamental question in machine learning. On the one hand, it is believed that the success of deep learning lies in its ability to build a hierarchy of representations that become increasingly more abstract with depth, going from simple features like edges to more complex concepts. On the other ha

T. Crosta, L. Rebón, F. Vilariño, J. M. Matera

During their operation, due to shifts in environmental conditions, devices undergo various forms of detuning from their optimal settings. Typically, this is addressed through control loops, which monitor variables and the device performance, to maintain settings at their optimal values. Quantum devices are particularly challenging since their functionality r

Xhek Turkeshi, Piotr Sierant

Unitary dynamics of a quantum system initialized in a selected basis state yields, generically, a state that is a superposition of all the basis states. This process, associated with the quantum information scrambling and intimately tied to the resource theory of coherence, may be viewed as a gradual delocalization of the system's state in the Hilbert space.

Konrad Bals

We prove that the $\infty$-category of $p$-typical topological Cartier modules, recently introduced by Antieau--Nikolaus, over some base $A$ is equivalent to the $\infty$-category of modules over a ring spectrum $\mathcal R_A$, which we call the topological Cartier--Raynaud ring. Our main result is an identification of the homotopy groups of $\mathcal R_A$.

Yu Luo

We provide a moduli description of the ramified unitary local model of signature $(n-1,1)$ with arbitrary parahoric level structure, assuming the residue field has characteristic not equal to $2$, thereby confirming a conjecture of Smithling. Our approach involves writing down explicit equations for the special fiber and proving that they define a normal, Co

Alessio Sodini, Valentina D'Odorico, Stefania Salvadori, Irene Vanni

This study explores the metal enrichment signatures attributed to the first generation of stars (PopIII) in the Universe, focusing on the E-XQR-30 sample. We aim to identify traces of Pop III metal enrichment by analyzing neutral gas in the interstellar medium of primordial galaxies and their satellite clumps, detected in absorption. To chase the chemical si

A. A. Araújo Filho, N. Heidari, J. A. A. S. Reis, H. Hassanabadi

In this paper, we investigate the influence of anti-symmetric tensor effects, which trigger the Lorentz symmetry breaking, on charged spherically symmetric black holes. Initially, we address an overview of the model, laying the groundwork for deriving solutions to black holes. With this, we analyze the horizons, critical orbits, and geodesics. We compute qua

Kartav Kesri, Sahel Dey, Piyali Chatterjee, Robertus Erdelyi

Solar spicules are plasma jets observed in the interface region between the visible solar surface and the corona. At any given time, there are millions of spicules present all over the Sun. While various models attempt to elucidate their origin and characteristics, here, we consider the one driven by the magneto-convection undulations. The radiative magneto-

Shusheng Xu, Wei Fu, Jiaxuan Gao, Wenjie Ye

Reinforcement Learning from Human Feedback (RLHF) is currently the most widely used method to align large language models (LLMs) with human preferences. Existing RLHF methods can be roughly categorized as either reward-based or reward-free. Novel applications such as ChatGPT and Claude leverage reward-based methods that first learn a reward model and apply a

Zhi-Yi Lin, Jouh Yeong Chew, Jan van Gemert, Xucong Zhang

Precisely detecting which object a person is paying attention to is critical for human-robot interaction since it provides important cues for the next action from the human user. We propose an end-to-end approach for gaze target detection: predicting a head-target connection between individuals and the target image regions they are looking at. Most of the ex

Lijian Li

Recently, prototype learning has emerged in semi-supervised medical image segmentation and achieved remarkable performance. However, the scarcity of labeled data limits the expressiveness of prototypes in previous methods, potentially hindering the complete representation of prototypes for class embedding. To address this problem, we propose the Mixed Protot

Kang Liao, Zongsheng Yue, Zhonghua Wu, Chen Change Loy

While recent image warping approaches achieved remarkable success on existing benchmarks, they still require training separate models for each specific task and cannot generalize well to different camera models or customized manipulations. To address diverse types of warping in practice, we propose a Multiple-in-One image WArping model (named MOWA) in this w

Debopriya Roy Dipta, Thore Tiemann, Berk Gulmezoglu, Eduard Marin

The cloud computing landscape has evolved significantly in recent years, embracing various sandboxes to meet the diverse demands of modern cloud applications. These sandboxes encompass container-based technologies like Docker and gVisor, microVM-based solutions like Firecracker, and security-centric sandboxes relying on Trusted Execution Environments (TEEs)

Zexin Li, Yiyang Lin, Zijie Fang, Shuyan Li

Different types of staining highlight different structures in organs, thereby assisting in diagnosis. However, due to the impossibility of repeated staining, we cannot obtain different types of stained slides of the same tissue area. Translating the slide that is easy to obtain (e.g., H&E) to slides of staining types difficult to obtain (e.g., MT, PAS) is a

Tandin Dorji, Pakinee Aimmanee, Vich Yindeedej

The field of augmented reality (AR) has undergone substantial growth, finding diverse applications in the medical industry. This paper delves into various techniques employed in medical surgeries, scrutinizing factors such as cost, implementation, and accessibility. The focus of this exploration is on AR-based solutions, with a particular emphasis on address

MIT Hardness Group, Erik D. Demaine, Holden Hall, Jeffery Li

We prove NP-hardness and #P-hardness of Tetris clearing (clearing an initial board using a given sequence of pieces) with the Super Rotation System (SRS), even when the pieces are limited to any two of the seven Tetris piece types. This result is the first advance on a question posed twenty years ago: which piece sets are easy vs. hard? All previous Tetris N