March 2023 arXiv papers — page 23

Li Ern Chern, Claudio Castelnovo

While the existence of a magnetic field induced quantum spin liquid in Kitaev magnets remains under debate, its topological properties often extend to proximal phases where they can lead to unusual behaviors of both fundamental and applied interests. Subjecting a generic nearest neighbor spin model of Kitaev magnets to a sufficiently strong in-plane magnetic

Elisabeth Wybo, Alvise Bastianello, Monika Aidelsburger, Immanuel Bloch

The sine-Gordon model emerges as a low-energy theory in a plethora of quantum many-body systems. Here, we theoretically investigate tunnel-coupled Bose-Hubbard chains with strong repulsive interactions as a realization of the sine-Gordon model deep in the quantum regime. We propose protocols for quantum gas microscopes of ultracold atoms to prepare and analy

Alonso Perez-Lona, Eric Sharpe

In this paper we generalize previous work on decomposition in three-dimensional orbifolds by 2-groups realized as analogues of central extensions, to orbifolds by more general 2-groups. We describe the computation of such orbifolds in physics, state a version of the decomposition conjecture, and then compute in numerous examples, checking that decomposition

Christopher R. Mankovich, Janosz W. Dewberry, Jim Fuller

Normal mode seismology is a promising means of measuring rotation in gas giant interiors, and ring seismology presents a singular opportunity to do so at Saturn. We calculate Saturn's normal modes of oscillation and zonal gravity field, using nonperturbative methods for normal modes in the rigidly rotating approximation, and perturbative methods for the shif

Markus B. Fröb, William C. C. Lima

We consider the relational approach to construct gauge-invariant observables in cosmological perturbation theory using synchronous coordinates. We construct dynamical synchronous coordinates as non-local scalar functionals of the metric perturbation in the fully non-linear theory in an arbitrary gauge. We show that the observables defined in this dynamical c

Annalisa Pillepich, Diego Sotillo-Ramos, Rahul Ramesh, Dylan Nelson

We present the properties of Milky Way- and Andromeda-like (MW/M31-like) galaxies simulated within TNG50, the highest-resolution run of the IllustrisTNG suite of $\Lambda$CDM magneto-hydrodynamical simulations. We introduce our fiducial selection for MW/M31 analogs, which we propose for direct usage as well as for reference in future analyses. TNG50 contains

Federico Carta, Simone Giacomelli, Noppadol Mekareeya, Alessandro Mininno

We demonstrate the presence of non-invertible symmetries in an infinite family of superconformal Argyres-Douglas theories. This class of theories arises from diagonal gauging of the flavor symmetry of a collection of multiple copies of $D_p(\mathrm{SU}(N))$ theories. The same set of theories that we study can also be realized from 6d $\mathcal{N}=(1,0)$ comp

Rahul Ramesh, Dylan Nelson, Annalisa Pillepich

We use the TNG50 simulation of the IllustrisTNG project to study cold, dense clouds of gas in the circumgalactic media (CGM) of Milky Way-like galaxies. We find that their CGM is typically filled with of order one hundred (thousand) reasonably (marginally) resolved clouds, possible analogs of high-velocity clouds (HVCs). There is a large variation in cloud a

Alexander J. Dittmann, Geoffrey Ryan, M. Coleman Miller

We have investigated, both analytically and numerically, accreting supermassive black hole binaries as they inspiral due to gravitational radiation to elucidate the decoupling of binaries from their disks and inform future multi-messenger observations of these systems. Our numerical studies evolve equal-mass binaries from initial separations of $100 GM/c^2$

Alexander C. Li, Mihir Prabhudesai, Shivam Duggal, Ellis Brown

The recent wave of large-scale text-to-image diffusion models has dramatically increased our text-based image generation abilities. These models can generate realistic images for a staggering variety of prompts and exhibit impressive compositional generalization abilities. Almost all use cases thus far have solely focused on sampling; however, diffusion mode

Harshil Bhatia, Edith Tretschk, Zorah Lähner, Marcel Seelbach Benkner

Jointly matching multiple, non-rigidly deformed 3D shapes is a challenging, $\mathcal{NP}$-hard problem. A perfect matching is necessarily cycle-consistent: Following the pairwise point correspondences along several shapes must end up at the starting vertex of the original shape. Unfortunately, existing quantum shape-matching methods do not support multiple

Kamal Gupta, Varun Jampani, Carlos Esteves, Abhinav Shrivastava

We present a method for joint alignment of sparse in-the-wild image collections of an object category. Most prior works assume either ground-truth keypoint annotations or a large dataset of images of a single object category. However, neither of the above assumptions hold true for the long-tail of the objects present in the world. We present a self-supervise

Dan Hendrycks

For billions of years, evolution has been the driving force behind the development of life, including humans. Evolution endowed humans with high intelligence, which allowed us to become one of the most successful species on the planet. Today, humans aim to create artificial intelligence systems that surpass even our own intelligence. As artificial intelligen

Renrui Zhang, Jiaming Han, Chris Liu, Peng Gao

We present LLaMA-Adapter, a lightweight adaption method to efficiently fine-tune LLaMA into an instruction-following model. Using 52K self-instruct demonstrations, LLaMA-Adapter only introduces 1.2M learnable parameters upon the frozen LLaMA 7B model, and costs less than one hour for fine-tuning on 8 A100 GPUs. Specifically, we adopt a set of learnable adapt

Vitus Benson, Claire Robin, Christian Requena-Mesa, Lazaro Alonso

The innovative application of precise geospatial vegetation forecasting holds immense potential across diverse sectors, including agriculture, forestry, humanitarian aid, and carbon accounting. To leverage the vast availability of satellite imagery for this task, various works have applied deep neural networks for predicting multispectral images in photoreal

Stéphane Coen, Bruno Garbin, Gang Xu, Liam Quinn

We report an experimental and theoretical investigation of a system whose dynamics is dominated by an intricate interplay between three key concepts of modern physics: topology, nonlinearity, and spontaneous symmetry breaking. The experiment is based on a two-mode coherently-driven optical resonator in which photons interact through the Kerr nonlinearity. In

Guangcong Wang, Zhaoxi Chen, Chen Change Loy, Ziwei Liu

Neural Radiance Field (NeRF) significantly degrades when only a limited number of views are available. To complement the lack of 3D information, depth-based models, such as DSNeRF and MonoSDF, explicitly assume the availability of accurate depth maps of multiple views. They linearly scale the accurate depth maps as supervision to guide the predicted depth of

Sina Khajehabdollahi, Jan Prosi, Emmanouil Giannakakis, Georg Martius

It has long been hypothesized that operating close to the critical state is beneficial for natural, artificial and their evolutionary systems. We put this hypothesis to test in a system of evolving foraging agents controlled by neural networks that can adapt agents' dynamical regime throughout evolution. Surprisingly, we find that all populations that discov

Andrew Szot, Amy Zhang, Dhruv Batra, Zsolt Kira

How well do reward functions learned with inverse reinforcement learning (IRL) generalize? We illustrate that state-of-the-art IRL algorithms, which maximize a maximum-entropy objective, learn rewards that overfit to the demonstrations. Such rewards struggle to provide meaningful rewards for states not covered by the demonstrations, a major detriment when us

Mika Lambert, Chad F. Bender, Shubham Kanodia, Caleb I. Cañas

The TESS mission detected a companion orbiting TIC 71268730, categorized it as a planet candidate, and designated the system TOI-5375. Our follow-up analysis using radial velocity data from the Habitable-zone Planet Finder (HPF), photometric data from Red Buttes Observatory (RBO), and speckle imaging with NN-EXPLORE Exoplanet Stellar Speckle Imager (NESSI) d

A. K. Hartmann, A. P. Young

Using an efficient polynomial-time ground state algorithm we investigate the Ising spin glass state at zero temperature in two dimensions. For large sizes, we show that the spin state in a central region is independent of the interactions far away, indicating a ``single-state" picture, presumably the droplet model. Surprisingly, a single power law describes

Zixuan Chen, Xiaohua Xie, Lingxiao Yang, Jianhuang Lai

Anomaly detectors are widely used in industrial manufacturing to detect and localize unknown defects in query images. These detectors are trained on anomaly-free samples and have successfully distinguished anomalies from most normal samples. However, hard-normal examples are scattered and far apart from most normal samples, and thus they are often mistaken f

Christian Peterson, Shalabh Gautam, Inês Rainho, Alex Vañó-Viñuales

The Good-Bad-Ugly-F model is a system of semi-linear wave equations that mimics the asymptotic form of the Einstein field equations in generalized harmonic gauge with specific constraint damping and suitable gauge source functions. These constraint additions and gauge source functions eliminate logarithmic divergences appearing at the leading order in the as

Hongyi Chen, Yilun Du, Yiye Chen, Joshua Tenenbaum

Recent works have shown that sequence modeling can be effectively used to train reinforcement learning (RL) policies. However, the success of applying existing sequence models to planning, in which we wish to obtain a trajectory of actions to reach some goal, is less straightforward. The typical autoregressive generation procedures of sequence models preclud

William Harvey, Frank Wood

Recent progress with conditional image diffusion models has been stunning, and this holds true whether we are speaking about models conditioned on a text description, a scene layout, or a sketch. Unconditional image diffusion models are also improving but lag behind, as do diffusion models which are conditioned on lower-dimensional features like class labels

Yue Yao, Huan Lei, Tom Gedeon, Liang Zheng

We consider a scenario where we have access to the target domain, but cannot afford on-the-fly training data annotation, and instead would like to construct an alternative training set from a large-scale data pool such that a competitive model can be obtained. We propose a search and pruning (SnP) solution to this training data search problem, tailored to ob

Andrew King

Quasiperiodic eruptions (QPEs) from low-mass galaxy centres may result from accretion from a white dwarf in a very eccentric orbit about the central massive black hole. Evolution under gravitational radiation losses reduces the separation and eccentricity. I note that below a critical eccentricity $e_{\rm crit} \simeq 0.97$, the accretion disc's viscous time

Yuan-Chen Guo, Yan-Pei Cao, Chen Wang, Yu He

With the emergence of neural radiance fields (NeRFs), view synthesis quality has reached an unprecedented level. Compared to traditional mesh-based assets, this volumetric representation is more powerful in expressing scene geometry but inevitably suffers from high rendering costs and can hardly be involved in further processes like editing, posing significa

P. M. W. Kalberla, U. Haud

Dusty magnetized structures observable in the far-infrared (FIR) at high Galactic latitudes are ubiquitous and found to be closely related to HI filaments with coherent velocity structures. Considering dimensionless morphological characteristics based on Minkowski functionals, we determine the distribution of filamentarities $F$ and aspect ratios $A$ for the

Cleonice F. Bracciali, Karina S. Rampazzi, Luana L. Silva Ribeiro

We consider orthogonal polynomials on the unit circle associated with certain semi-classical weight functions. This means that the Pearson-type differential equations satisfied by these weight functions involve two polynomials of degree at most 2. We determine all such semi-classical weight functions and this also includes an extension of the Jacobi weight f

Chun-Mei Feng, Bangjun Li, Xinxing Xu, Yong Liu

Federated Magnetic Resonance Imaging (MRI) reconstruction enables multiple hospitals to collaborate distributedly without aggregating local data, thereby protecting patient privacy. However, the data heterogeneity caused by different MRI protocols, insufficient local training data, and limited communication bandwidth inevitably impair global model convergenc

Irina Kostitsyna, David Liedtke, Christian Scheideler

Motivated by the prospect of nano-robots that assist human physiological functions at the nanoscale, we investigate the coating problem in the three-dimensional model for hybrid programmable matter. In this model, a single agent with strictly limited viewing range and the computational capability of a deterministic finite automaton can act on passive tiles b

Nazim Khelifa, David Urbanik

Motivated by a question of Baldi-Klingler-Ullmo, we provide a general sufficient criterion for the existence and analytic density of typical Hodge loci associated to a polarizable $\mathbb{Z}$-variation of Hodge structures $\mathbb{V}$. Our criterion reproves the existing results in the literature on density of Noether-Lefschetz loci. It also applies to unde

Sakib Haque, Aakash Bansal, Collin McMillan

Label smoothing is a regularization technique for neural networks. Normally neural models are trained to an output distribution that is a vector with a single 1 for the correct prediction, and 0 for all other elements. Label smoothing converts the correct prediction location to something slightly less than 1, then distributes the remainder to the other eleme

Vedant Mundheda, Damodar Datta K, Harikumar Kandath

This paper introduces a method for effectively controlling the movement of an Unmanned Aerial Vehicle (UAV) within a tunnel. The primary challenge of this problem lies in the UAV's exposure to nonlinear distance-dependent torques and forces generated by the tunnel walls, along with the need to operate safely within a defined region while in close proximity t

David Beers, Jacob Leygonie

Consider the space of continuous functions on a geometric tree $X$ whose persistent homology gives rise to a finite generic barcode $D$. We show that there are exactly as many path connected components in this space as there are merge trees whose barcode is $D$. We find that each component is homotopy equivalent to a configuration space on $X$ with specializ

Sophie Chiang, Gyorgy Denes

Machine learning, classification and prediction models have applications across a range of fields. Sport analytics is an increasingly popular application, but most existing work is focused on automated refereeing in mainstream sports and injury prevention. Research on other sports, such as table tennis, has only recently started gaining more traction. This p

Sergey Titov, Agnia Sergeyuk, Timofey Bryksin

With the development of artificial intelligence, writing assistants (WAs) are changing the way people interact with text, creating lengthy outputs that can be overwhelming for users. The programming field has long addressed this issue, and Integrated Development Environments (IDEs) have been created for efficient software development, helping programmers red

Tejinder P. Singh

The exceptional Jordan algebra is the algebra of $3\times 3$ Hermitian matrices with octonionic entries. It is the only one from Jordan's algebraic formulation of quantum mechanics which is not equivalent to the conventional formulation of quantum theory. It has often been suggested that this exceptional algebra could explain physical phenomena not currently

Philippe Gaucher

The previous paper of this series shows that the q-model categories of $\mathcal{G}$-multipointed $d$-spaces and of $\mathcal{G}$-flows are Quillen equivalent. In this paper, the same result is established by replacing the reparametrization category $\mathcal{G}$ by the reparametrization category $\mathcal{M}$. Unlike the case of $\mathcal{G}$, the execution

Emily Allaway, Nina Taneja, Sarah-Jane Leslie, Maarten Sap

Essentialist beliefs (i.e., believing that members of the same group are fundamentally alike) play a central role in social stereotypes and can lead to harm when left unchallenged. In our work, we conduct exploratory studies into the task of countering essentialist beliefs (e.g., ``liberals are stupid''). Drawing on prior work from psychology and NLP, we con

Lucas Böttcher, Tom Chou, Maria R. D'Orsogna

Drug overdose deaths continue to increase in the United States for all major drug categories. Over the past two decades the total number of overdose fatalities has increased more than five-fold; since 2013 the surge in overdose rates is primarily driven by fentanyl and methamphetamines. Different drug categories and factors such as age, gender, and ethnicity

Maximilian F. I. Kieler, Arnd Bäcker

A bipartite spin system is proposed for which a fast transfer from one defined state into another exists. For sufficient coupling between the spins, this implements a bit-flipping mechanism which is much faster than that induced by tunneling. The states correspond in the semiclassical limit to equilibrium points with a stability transition from elliptic-elli

Igor Dolgachev, Gebhard Martin

We complete the classification of automorphism groups of del Pezzo surfaces over algebraically closed fields of odd positive characteristic.

Paulina Hoyos, Joe Kileel

In this article, we consider the manifold learning problem when the data set is invariant under the action of a compact Lie group $K$. Our approach consists in augmenting the data-induced graph Laplacian by integrating over the $K$-orbits of the existing data points, which yields a $K$-invariant graph Laplacian $L$. We prove that $L$ can be diagonalized by u

A. Avakyan, M. Neumann, A. Zainab, V. Doroshenko

We present a new catalogue of low-mass X-ray binaries (LMXBs) in the Galaxy. The catalogue contains source names, coordinates, source types, fluxes, distances, system parameters, and other characteristic properties of 349 LMXBs, including systems that have been newly discovered or reclassified since the most recently reported LMXB catalogues. The aim of this

Roberta Bianchini, Lars Eric Hientzsch, Felice Iandoli

We prove the strong ill-posedness in the sense of Hadamard of the two-dimensional Boussinesq equations in $W^{1, \infty}(\mathbb{R}^2)$ without boundary, extending to the case of systems the method that Shikh Khalil \& Elgindi arXiv:2207.04556v1 developed for scalar equations. We provide a large class of initial data with velocity and density of small $W^{1,

Sara Papi, Marco Gaido, Andrea Pilzer, Matteo Negri

Despite its crucial role in research experiments, code correctness is often presumed only on the basis of the perceived quality of results. This assumption comes with the risk of erroneous outcomes and potentially misleading findings. To address this issue, we posit that the current focus on reproducibility should go hand in hand with the emphasis on softwar

Kunal Sanjay Narkhede, Mohamad Shafiee Motahar, Sushant Veer, Ioannis Poulakakis

This paper presents a modular approach to motion planning with provable stability guarantees for robots that move through changing environments via periodic locomotion behaviors. We focus on dynamic walkers as a paradigm for such systems, although the tools developed in this paper can be used to support general compositional approaches to robot motion planni

Wallace H. Montaño, Jesús A. Maytorena

We investigate the spectrum of energy and eigenstates of a hybrid cavity optomechanical system, where a cavity field mode interacts with a mechanical mode of a vibrating end mirror via radiation pressure and with a two level atom via electric dipole interaction. In the spirit of approximations developed for the quantum Rabi model beyond rotating-wave approxi

Vibhoothi, François Pitié, Angeliki Katsenou, Yeping Su

The complexity of modern codecs along with the increased need of delivering high-quality videos at low bitrates has reinforced the idea of a per-clip tailoring of parameters for optimised rate-distortion performance. While the objective quality metrics used for Standard Dynamic Range (SDR) videos have been well studied, the transitioning of consumer displays

Hyungrok Kim, Christian Saemann

We provide a pedagogical introduction to the theory of principal 2-bundles with adjusted connections and show how they enter the description of geometric and non-geometric T-dualities as proposed in arXiv:2204.01783. This description combines the torus fibrations as well as the gerbe containing the Kalb-Ramond $B$-field into a single geometric object, a part

Arnab Barman Ray, Arunabh Mukherjee, Liangyu Qiu, Renee Sailus

Moir\'e heterobilayers host interlayer excitons in a natural, periodic array of trapping potentials. Recent work has elucidated the structure of the trapped interlayer excitons and the nature of photoluminescence (PL) from trapped and itinerant charged complexes such as interlayer trions in these structures. In this paper, our results serve to add to the und

Jing Lin, Ailing Zeng, Haoqian Wang, Lei Zhang

Whole-body mesh recovery aims to estimate the 3D human body, face, and hands parameters from a single image. It is challenging to perform this task with a single network due to resolution issues, i.e., the face and hands are usually located in extremely small regions. Existing works usually detect hands and faces, enlarge their resolution to feed in a specif

Wolf-Patrick Düll, Dominik Engl, Carolin Kreisbeck

The main result of this work is a homogenization theorem via variational convergence for elastic materials with stiff checkerboard-type heterogeneities under the assumptions of physical growth and non-self-interpenetration. While the obtained energy estimates are rather standard, determining the effective deformation behavior, or in other words, characterizi

Alp Müyesser

An orthomorphism of a finite group $G$ is a bijection $\phi\colon G\to G$ such that $g\mapsto g^{-1}\phi(g)$ is also a bijection. In 1981, Friedlander, Gordon, and Tannenbaum conjectured that when $G$ is abelian, for any $k\geq 2$ dividing $|G|-1$, there exists an orthomorphism of $G$ fixing the identity and permuting the remaining elements as products of di

Stefanos Baziotis, Daniel Kang, Charith Mendis

In recent years, dataframe libraries, such as pandas have exploded in popularity. Due to their flexibility, they are increasingly used in ad-hoc exploratory data analysis (EDA) workloads. These workloads are diverse, including custom functions which can span libraries or be written in pure Python. The majority of systems available to accelerate EDA workloads

Vitor Jeronymo, Roberto Lotufo, Rodrigo Nogueira

This paper reports on a study of cross-lingual information retrieval (CLIR) using the mT5-XXL reranker on the NeuCLIR track of TREC 2022. Perhaps the biggest contribution of this study is the finding that despite the mT5 model being fine-tuned only on query-document pairs of the same language it proved to be viable for CLIR tasks, where query-document pairs

Arpita Das, Philip D. Gregory, Tetsu Takekoshi, Luke Fernley

We identify a route for the production of $^{87}$Rb$^{133}$Cs molecules in the $\textrm{X} \, ^1\Sigma^+$ rovibronic ground state that is compatible with efficient mixing of the atoms in optical lattices. We first construct a model for the excited-state structure using constants found by fitting to spectroscopy of the relevant $\textrm{a} \, ^3\Sigma^+ \righ

Gangadhar Karevvanavar, Rajshekhar V Bhat

We consider a scenario where multiple users, powered by energy harvesting, send version updates over a fading multiple access channel (MAC) to an access point (AP). Version updates having random importance weights arrive at a user according to an exogenous arrival process, and a new version renders all previous versions obsolete. As energy harvesting imposes

Hyeonsu Kim, Jeheon Woo, Seonghwan Kim, Seokhyun Moon

As quantum chemical properties have a dependence on their geometries, graph neural networks (GNNs) using 3D geometric information have achieved high prediction accuracy in many tasks. However, they often require 3D geometries obtained from high-level quantum mechanical calculations, which are practically infeasible, limiting their applicability to real-world

M. Capsoni, A. Schiffrin, K. A. Cochrane, C. -G. Wang

The electronic properties of metal-molecule interfaces can in principle be controlled by molecular design and self-assembly, yielding great potential for future nano- and optoelectronic technologies. However, the coupling between molecular orbitals and the electronic states of the surface can significantly influence molecular states. In particular, molecules

Erfan Darzidehkalani, Nanna M. Sijtsema, P. M. A van Ooijen

Deep learning is effective in diagnosing COVID-19 and requires a large amount of data to be effectively trained. Due to data and privacy regulations, hospitals generally have no access to data from other hospitals. Federated learning (FL) has been used to solve this problem, where it utilizes a distributed setting to train models in hospitals in a privacy-pr

Jérémy Scheurer, Jon Ander Campos, Tomasz Korbak, Jun Shern Chan

Pretrained language models often generate outputs that are not in line with human preferences, such as harmful text or factually incorrect summaries. Recent work approaches the above issues by learning from a simple form of human feedback: comparisons between pairs of model-generated outputs. However, comparison feedback only conveys limited information abou

Prateesh Goyal, Eashan Gupta, Ilias Marinos, Chenxingyu Zhao

In this paper, we consider the problem of hosting financial exchanges in the cloud. Financial exchanges require predictable, equal latency to all market participants to ensure fairness for various tasks, such as high speed trading. However, it is extremely difficult to ensure equal latency to all market participants in existing cloud deployments, because of

Isabella Huang, Yashraj Narang, Ruzena Bajcsy, Fabio Ramos

Robotic grasping of 3D deformable objects is critical for real-world applications such as food handling and robotic surgery. Unlike rigid and articulated objects, 3D deformable objects have infinite degrees of freedom. Fully defining their state requires 3D deformation and stress fields, which are exceptionally difficult to analytically compute or experiment

Marvin Neumann, Artur Avakyan, Victor Doroshenko, Andrea Santangelo

We present a new catalogue of high mass X-ray binaries (HMXBs) in the Galaxy that we call the Galactic High Mass X-ray Binary Catalogue (XRBcats), which improves upon the most recent of such catalogues. We include new HMXBs discovered since previous publications and revise the classification for several objects previously considered HMXBs or HMXB candidates.

Tanaya Ray, Ahana Ghoshal, Debraj Rakshit, Ujjwal Sen

We analyze the quantum resource generation of capacitively-coupled multilevel transmon circuits surrounded by bosonic baths, within the Markovian limit. In practice, the superconducting circuit elements are usually part of a larger circuit, constructed with many other linear circuit elements, which along with their environment is assumed to be mimicked by th

Andrey O. Matveev

We briefly discuss linear algebraic, combinatorial, and applied aspects of an exact model representation of binary arrays. As an illustration, we present two linear algebraic portraits of a string of characters.

Minas Karamanis

The goal of this thesis is twofold; introduce the fundamentals of Bayesian inference and computation focusing on astronomical and cosmological applications, and present recent advances in probabilistic computational methods developed by the author that aim to facilitate Bayesian data analysis for the next generation of astronomical observations and theoretic

Adyasha Maharana, Amita Kamath, Christopher Clark, Mohit Bansal

As general purpose vision models get increasingly effective at a wide set of tasks, it is imperative that they be consistent across the tasks they support. Inconsistent AI models are considered brittle and untrustworthy by human users and are more challenging to incorporate into larger systems that take dependencies on their outputs. Measuring consistency be

Jinlong Hu, Yangmin Huang, Shoubin Dong

Advanced deep learning methods, especially graph neural networks (GNNs), are increasingly expected to learn from brain functional network data and predict brain disorders. In this paper, we proposed a novel Transformer and snowball encoding networks (TSEN) for brain functional network classification, which introduced Transformer architecture with graph snowb

Rahul Jangid, Nanna Zhou Hagström, Meera Madhavi, Kyle Rockwell

Time-resolved ultrafast EUV magnetic scattering was used to test a recent prediction of >10 km/s domain wall speeds by optically exciting a magnetic sample with a nanoscale labyrinthine domain pattern. Ultrafast distortion of the diffraction pattern was observed at markedly different timescales compared to the magnetization quenching. The diffraction pattern

Mitchell Jubeir, Ina Petkova, Noah Schwartz, Zachary Winkeler

We prove that the filtered GRID invariants of Legendrian links in link Floer homology, and consequently their associated invariants in the spectral sequence, obstruct decomposable Lagrangian cobordisms in the symplectization of the standard contact structure on $\mathbb{R}^3$, strengthening a result by Baldwin, Lidman, and the fifth author.

Minrui Xu, Hongyang Du, Dusit Niyato, Jiawen Kang

Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This survey paper focuses on the deployment of AIGC applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile AIGC networks, that provide personalized and customized

Katherine A. Cochrane, Tanya S. Roussy, Bingkai Yuan, Gary Tom

Organic semiconductors are a promising class of materials for numerous electronic and optoelectronic applications, including solar cells. However, these materials tend to be extremely sensitive to the local environment and surrounding molecular geometry, causing the energy levels near boundaries and interfaces essential to device function to differ from thos

N. P. Zaitseva, M. L. Carman, M. J. Ford, A. M. Glenn

The paper describes preparation of $^6$Li-loaded liquid scintillators by methods involving direct dissolution of $^6$Li salts in the commercial diisopropylnaphthalene (DIPN) solvent, without the formation of water-in-oil emulsions. Methods include incorporation of $^6$Li that, unlike previously reported formulations, does not require additions of water or a

Stefan Behringer, Roman V. Belavkin

We present a universal concept for the Value of Information (VoI), based on the works of Claude Shannon's and Ruslan Stratonovich that can take into account very general preferences of the agents and results in a single number. As such it is convenient for applications and also has desirable properties for decision theory and demand analysis. The Shannon/Str

Anabel Ovide, Santiago Rodrigo, Medina Bandic, Hans Van Someren

Current monolithic quantum computer architectures have limited scalability. One promising approach for scaling them up is to use a modular or multi-core architecture, in which different quantum processors (cores) are connected via quantum and classical links. This new architectural design poses new challenges such as the expensive inter-core communication. T

M. Tsantaki, E. Delgado-Mena, D. Bossini, S. G. Sousa

Lithium-rich giant stars are rare and their existence challenges our understanding of stellar structure and evolution. We profit from the high-quality sample gathered with HARPS and UVES, in order to search for Li-rich giants and to identify the Li enrichment mechanisms responsible. We derive stellar parameters for 247 stars belonging to 32 open clusters, wi

Neman Abdoli, Ke Zhang, Patrik Gilley, Xuxin Chen

2D and 3D tumor features are widely used in a variety of medical image analysis tasks. However, for chemotherapy response prediction, the effectiveness between different kinds of 2D and 3D features are not comprehensively assessed, especially in ovarian cancer-related applications. This investigation aims to accomplish such a comprehensive evaluation. For th

Hector Bombín, Chris Dawson, Naomi Nickerson, Mihir Pant

Many quantum operations are expected to exhibit bias in the structure of their errors. Recent works have shown that a fixed bias can be exploited to improve error tolerance by statically arranging the errors in beneficial configurations. In some cases an error bias can be dynamically reconfigurable, an example being linear optical fusion where the basis of a

M. Agundez, J. C. Loison, K. M. Hickson, V. Wakelam

We present the detection of ethanol (C2H5OH), acetone (CH3COCH3), and propanal (C2H5CHO) toward the cyanopolyyne peak of TMC-1. These three O-bearing complex organic molecules are known to be present in warm interstellar clouds, but had never been observed in a starless core. The addition of these three new pieces to the puzzle of complex organic molecules i

Somya Mehra, Peter G. Taylor

In this paper, we consider the occupancy distribution for an open network of infinite server queues with multivariate batch arrivals following a non-homogeneous Poisson process, and general service time distributions. We derive a probability generating function for the transient occupancy distribution of the network, and prove that it is necessary and suffic

Marissa C. Ashner, Tanya P. Garcia

Despite its drawbacks, the complete case analysis is commonly used in regression models with missing covariates. Understanding when implementing complete cases will lead to consistent parameter estimation is vital before use. Here, our aim is to demonstrate when a complete case analysis is appropriate for a nuanced type of missing covariate, the randomly rig

Lei Chen, Zhan Tong, Yibing Song, Gangshan Wu

The relation modeling between actors and scene context advances video action detection where the correlation of multiple actors makes their action recognition challenging. Existing studies model each actor and scene relation to improve action recognition. However, the scene variations and background interference limit the effectiveness of this relation model

Alexandre R. Nieto, Jesús M. Seoane, Miguel A. F. Sanjuán

In this paper, we show that the destruction of the main KAM islands in two-degree-of-freedom Hamiltonian systems occurs through a cascade of period-doubling bifurcations. We calculate the corresponding Feigenbaum constant and the accumulation point of the period-doubling sequence. By means of a systematic grid search on exit basin diagrams, we find the exist

Yavar Kian, Tony Liimatainen, Yi-Hsuan Lin

We investigate an inverse boundary value problem of determination of a nonlinear law for reaction-diffusion processes, which are modeled by general form semilinear parabolic equations. We do not assume that any solutions to these equations are known a priori, in which case the problem has a well known gauge symmetry. We determine, under additional assumption

David Loeffler, Óscar Rivero

We prove algebraicity results for critical $L$-values attached to the group $\text{GSp}_4 \times \text{GL}_2$, and for Gan--Gross--Prasad periods which are conjecturally related to central $L$-values for $\text{GSp}_4 \times \text{GL}_2 \times \text{GL}_2$. Our result for $\text{GSp}_4 \times \text{GL}_2$ gives a new proof (by a very different method) of a r

Rémy Gardier, Juan Luis Villarreal Haro, Erick J. Canales-Rodriguez, Ileana O. Jelescu

Purpose: Biophysical models of diffusion MRI have been developed to characterize microstructure in various tissues, but existing models are not suitable for tissue composed of permeable spherical cells. In this study we introduce Cellular Exchange Imaging (CEXI), a model tailored for permeable spherical cells, and compares its performance to a related Ball \

Caleb Scharf, Olaf Witkowski

Computation, if treated as a set of physical processes that act on information represented by states of matter, encompasses biological systems, digital systems, and other constructs, and may be a fundamental measure of living systems. The opportunity for biological computation, represented in the propagation and selection-driven evolution of information-carr

Nick McGreivy, Ammar Hakim

Machine learned partial differential equation (PDE) solvers trade the reliability of standard numerical methods for potential gains in accuracy and/or speed. The only way for a solver to guarantee that it outputs the exact solution is to use a convergent method in the limit that the grid spacing $\Delta x$ and timestep $\Delta t$ approach zero. Machine learn

Sajjad Mozaffari, Mreza Alipour Sormoli, Konstantinos Koufos, Mehrdad Dianati

Predicting the behaviour (i.e., manoeuvre/trajectory) of other road users, including vehicles, is critical for the safe and efficient operation of autonomous vehicles (AVs), a.k.a., automated driving systems (ADSs). Due to the uncertain future behaviour of vehicles, multiple future behaviour modes are often plausible for a vehicle in a given driving scene. T

Juan Miguel Nieto García, Roberto Ruiz, Alessandro Torrielli

We construct infinite-dimensional R-matrices that generalise the relativistic scattering of massless modes with the same chirality on $\mathrm{AdS}_2$ near the Berestein-Maldacena-Nastase vacuum. We show that the infrared limit of the R-matrices reproduces finite-dimensional scattering of massless modes on $\mathrm{AdS}_2$, from which the R-matrices borrow m

Massimiliano Rinaldi, Chiara Cecchini, Anish Ghoshal, Debangshu Mukherjee

We examine a scalar-tensor model of gravity that is globally scale-invariant. When adapted to a spatially flat Robertson-Walker metric, the equations of motion describe a dynamical system that flows from an unstable de Sitter space to a stable one. We show that during this transition inflation can occur. Moreover, at the final fixed point, a mass scale natur

Minsoo Kang, Doyup Lee, Jiseob Kim, Saehoon Kim

We propose a text-to-image generation algorithm based on deep neural networks when text captions for images are unavailable during training. In this work, instead of simply generating pseudo-ground-truth sentences of training images using existing image captioning methods, we employ a pretrained CLIP model, which is capable of properly aligning embeddings of

Nuno M. Guerreiro, Duarte Alves, Jonas Waldendorf, Barry Haddow

Large-scale multilingual machine translation systems have demonstrated remarkable ability to translate directly between numerous languages, making them increasingly appealing for real-world applications. However, when deployed in the wild, these models may generate hallucinated translations which have the potential to severely undermine user trust and raise

Angelica Chen, Jérémy Scheurer, Tomasz Korbak, Jon Ander Campos

The potential for pre-trained large language models (LLMs) to use natural language feedback at inference time has been an exciting recent development. We build upon this observation by formalizing an algorithm for learning from natural language feedback at training time instead, which we call Imitation learning from Language Feedback (ILF). ILF requires only

Yongyu Wang, Shiqi Hao, Xiaoyang Wang, Xiaotian Zhuang

Current modularity-based community detection algorithms attempt to find cluster memberships that maximize modularity within a fixed graph topology. Diverging from this conventional approach, our work introduces a novel strategy that employs modularity to guide the enhancement of both graph topology and clustering quality through a maximization process. Speci

Frederik Hagelskjær, Rasmus Laurvig Haugaard

In this paper, we present KeyMatchNet, a novel network for zero-shot pose estimation in 3D point clouds. Our method uses only depth information, making it more applicable for many industrial use cases, as color information is seldom available. The network is composed of two parallel components for computing object and scene features. The features are then co