April 2023 arXiv papers — page 31

Changhao Shi, Haomiao Ni, Kai Li, Shaobo Han

Diffusion probabilistic models have achieved enormous success in the field of image generation and manipulation. In this paper, we explore a novel paradigm of using the diffusion model and classifier guidance in the latent semantic space for compositional visual tasks. Specifically, we train latent diffusion models and auxiliary latent classifiers to facilit

Heng Pan, Chenyang Liu, Wenxiao Wang, Li Yuan

We present a pipeline of Image to Vector (Img2Vec) for masked image modeling (MIM) with deep features. To study which type of deep features is appropriate for MIM as a learning target, we propose a simple MIM framework with serials of well-trained self-supervised models to convert an Image to a feature Vector as the learning target of MIM, where the feature

Ziba Parsons, Fei Dou, Houyi Du, Zheng Song

This paper explores the challenges of implementing Federated Learning (FL) in practical scenarios featuring isolated nodes with data heterogeneity, which can only be connected to the server through wireless links in an infrastructure-less environment. To overcome these challenges, we propose a novel mobilizing personalized FL approach, which aims to facilita

Lujie Yang, Hongkai Dai, Alexandre Amice, Russ Tedrake

Sums-of-squares (SOS) optimization is a promising tool to synthesize certifiable controllers for nonlinear dynamical systems. Building upon prior works, we demonstrate that SOS can synthesize dynamic controllers with bounded suboptimal performance for various underactuated robotic systems by finding good approximations of the value function. We summarize a u

Dapeng Li, Zhiwei Xu, Bin Zhang, Guoliang Fan

Spatial information is essential in various fields. How to explicitly model according to the spatial location of agents is also very important for the multi-agent problem, especially when the number of agents is changing and the scale is enormous. Inspired by the point cloud task in computer vision, we propose a spatial information extraction structure for m

Longju Yu, Hong Jian Zhao, Peng Chen, Laurent Bellaiche

The anti-symmetric and anisotropic symmetric exchange interactions between two magnetic dipole moments - responsible for intriguing magnetic textures (e.g., magnetic skyrmions) - have been discovered since last century, while their electric analogues were either hidden for a long time or still not known. As a matter of fact, it is only recently that the anti

Zachary Grannan, Alexander J. Summers

Specifications for modular program verifiers are expressed as constraints on program states (e.g. preconditions) and relations on program states (e.g. postconditions). For programs whose domain is managing resources of any kind (e.g. cryptocurrencies), such state-based specifications must make explicit properties that a human would implicitly understand for

Yang Ye, Hengxu You, Jing Du

Human robot collaboration is becoming increasingly important as robots become more involved in various aspects of human life in the era of Artificial Intelligence. However, the issue of human operators trust in robots remains a significant concern, primarily due to the lack of adequate semantic understanding and communication between humans and robots. The e

Bochao Liu, Pengju Wang, Shikun Li, Dan Zeng

While massive valuable deep models trained on large-scale data have been released to facilitate the artificial intelligence community, they may encounter attacks in deployment which leads to privacy leakage of training data. In this work, we propose a learning approach termed differentially private data-free distillation (DPDFD) for model conversion that can

Carla Biggio, Lorenzo Calibbi, Toshihiko Ota, Samuele Zanchini

We discuss in detail the possibility that the ``type-II majoron'' -- that is, the pseudo Nambu-Goldstone boson that arises in the context of the type-II seesaw mechanism if the lepton number is spontaneously broken by an additional singlet scalar -- account for the dark matter (DM) observed in the universe. We study the requirements the model's parameters ha

Zhendong Wang, Yifan Jiang, Huangjie Zheng, Peihao Wang

Diffusion models are powerful, but they require a lot of time and data to train. We propose Patch Diffusion, a generic patch-wise training framework, to significantly reduce the training time costs while improving data efficiency, which thus helps democratize diffusion model training to broader users. At the core of our innovations is a new conditional score

Tong Li, Chang-Jie Dai, Rui-Jia Zhang

The so-called Witten effect implies the existence of electromagnetic interactions between axion and magnetic monopole due to the axion-photon coupling. A sound quantization in the presence of magnetic monopoles, called quantum electromagnetodynamics (QEMD), was utilized to construct a more generic axion-photon Lagrangian in the low-energy axion effective fie

Alister W. Graham

This work advances the (galaxy morphology)-dependent (black hole mass, $M_{\rm bh}$)-(spheroid/galaxy stellar mass, $M_*$) scaling relations by introducing `dust bins' for lenticular (S0) galaxies. Doing so has led to the discovery of $M_{\rm bh}$-$M_{\rm *,sph}$ and $M_{\rm bh}$-$M_{\rm *,gal}$ relations for dusty S0 galaxies - built by major wet mergers an

Payam Dibaeinia, Saurabh Sinha

The discovery of causal relationships from high-dimensional data is a major open problem in bioinformatics. Machine learning and feature attribution models have shown great promise in this context but lack causal interpretation. Here, we show that a popular feature attribution model estimates a causal quantity reflecting the influence of one variable on anot

Zhong Zheng, Shiqian Ma, Lingzhou Xue

This paper considers the robust phase retrieval problem, which can be cast as a nonsmooth and nonconvex optimization problem. We propose a new inexact proximal linear algorithm with the subproblem being solved inexactly. Our contributions are two adaptive stopping criteria for the subproblem. The convergence behavior of the proposed methods is analyzed. Thro

Keunwoo Choi, Jaekwon Im, Laurie Heller, Brian McFee

The addition of Foley sound effects during post-production is a common technique used to enhance the perceived acoustic properties of multimedia content. Traditionally, Foley sound has been produced by human Foley artists, which involves manual recording and mixing of sound. However, recent advances in sound synthesis and generative models have generated int

Zhongzhi Yu, Shang Wu, Yonggan Fu, Shunyao Zhang

Despite the growing demand for tuning foundation vision transformers (FViTs) on downstream tasks, fully unleashing FViTs' potential under data-limited scenarios (e.g., few-shot tuning) remains a challenge due to FViTs' data-hungry nature. Common data augmentation techniques fall short in this context due to the limited features contained in the few-shot tuni

Junyi Li, Wayne Xin Zhao, Jian-Yun Nie, Ji-Rong Wen

Diffusion models have become a new generative paradigm for text generation. Considering the discrete categorical nature of text, in this paper, we propose GlyphDiffusion, a novel diffusion approach for text generation via text-guided image generation. Our key idea is to render the target text as a glyph image containing visual language content. In this way,

Vimal Mollyn, Riku Arakawa, Mayank Goel, Chris Harrison

Tracking body pose on-the-go could have powerful uses in fitness, mobile gaming, context-aware virtual assistants, and rehabilitation. However, users are unlikely to buy and wear special suits or sensor arrays to achieve this end. Instead, in this work, we explore the feasibility of estimating body pose using IMUs already in devices that many users own -- na

Humphrey Leung, Zhuocong Li, Baike She, Philip E. Paré

We introduce a multi-layer networked compartmental $SIRS-V_o$ model that captures opinion dynamics, disease spread, risk perception, and self-interest vaccine-uptake behavior in an epidemic process. We characterize the target vaccination criterion of the proposed model and conditions that guarantee the criterion is obtainable by influencing opinions on disea

Tadashi Fujioka, Ayato Mitsuishi, Takao Yamaguchi

We establish a quantitative version of the Lipschitz homotopy convergence introduced by Mitsuishi and Yamaguchi for a moduli space of compact Alexandrov spaces without collapsing. Along the way, we obtain a Lipschitz version of Petersen's homotopy stability theorem that is applicable to more general settings, including CAT spaces. We also show that the Lipsc

Krisada Rawiraswattana, Simon P. Goodwin

Close encounters between stars in star forming regions are important as they can perturb or destroy protoplanetary discs, young planetary systems, and stellar multiple systems. We simulate simple, viralised, equal-mass $N$-body star clusters and find that both the rate and total number of encounters between stars varies by factors of several in statistically

Zhenchuan Ma, Xiaohai He, Pengcheng Yan, Fan Zhang

The microstructure analyses of porous media have considerable research value for the study of macroscopic properties. As the premise of conducting these analyses, the accurate reconstruction of microstructure digital model is also an important component of the research. Computational reconstruction algorithms of microstructure have attracted much attention d

Henry Gilbert, Michael Sandborn, Douglas C. Schmidt, Jesse Spencer-Smith

The rise of large language models (LLMs) is revolutionizing information retrieval, question answering, summarization, and code generation tasks. However, in addition to confidently presenting factually inaccurate information at times (known as "hallucinations"), LLMs are also inherently limited by the number of input and output tokens that can be processed a

Huong T. T. Ta, Nam V. Tran, M. C. Righi

Tribological phenomena such as adhesion, friction, and wear can undermine the functionality of devices and applications based on the diamond-silica interface. Controlling these phenomena is highly desirable, but difficult since extrinsic factors, such as the surface termination by adsorbed species, can deeply affect the reactivity of diamond and its resistan

Sin-Ei Takahasi, Kiyoshi Shirayanagi, Makoto Tsukada

An endo-commutative algebra is a nonassociative algebra in which the square mapping preserves multiplication. In this paper, we give a complete classification of endo-commutative curled algebras of dimension 2 over an arbitrary non-trivial field, where a curled algebra satisfies the condition that the square of any element is a scalar multiple of that elemen

Simon Machado

Approximate lattices of Euclidean spaces, also known as Meyer sets, are aperiodic subsets with fascinating properties. In general, approximate lattices are defined as approximate subgroups of locally compact groups that are discrete and have finite co-volume. A theorem of Lagarias provides a criterion for discrete subsets of Euclidean spaces to be approximat

Mengyu Liu, Pengyuan Lu, Xin Chen, Fanxin Kong

We propose a model-free reinforcement learning solution, namely the ASAP-Phi framework, to encourage an agent to fulfill a formal specification ASAP. The framework leverages a piece-wise reward function that assigns quantitative semantic reward to traces not satisfying the specification, and a high constant reward to the remaining. Then, it trains an agent w

Zihui Wu, Tianwei Yin, Yu Sun, Robert Frost

Compressed sensing magnetic resonance imaging (CS-MRI) seeks to recover visual information from subsampled measurements for diagnostic tasks. Traditional CS-MRI methods often separately address measurement subsampling, image reconstruction, and task prediction, resulting in a suboptimal end-to-end performance. In this work, we propose TACKLE as a unified co-

Jiahao Weng, Xusheng Du, Haoran Xie

Online learning and academic conferences have become pervasive and essential for education and professional development, especially since the onset of pandemics. Academic presentations usually require well-designed slides that are easily understood. Sketches that visually represent design intentions and are readily accessible to the average users. To assist

Enakshi Saha

Bayesian Additive Regression Trees (BART) are a powerful ensemble learning technique for modeling nonlinear regression functions. Although initially BART was proposed for predicting only continuous and binary response variables, over the years multiple extensions have emerged that are suitable for estimating a wider class of response variables (e.g. categori

Lia Yeh

We identify a novel qudit gate which we call the $\sqrt[d]{Z}$ gate. This is an alternate generalization of the qutrit $T$ gate to any odd prime dimension $d$, in the $d^{\text{th}}$ level of the Clifford hierarchy. Using this gate which is efficiently realizable fault-tolerantly should a certain conjecture hold, we deterministically construct in the Cliffor

Andrew Havard, Theodore Manikas, Eric C. Larson, Mitchell A. Thornton

We propose a method to improve steganography by increasing the resilience of stego-media to discovery through steganalysis. Our approach enhances a class of steganographic approaches through the inclusion of a steganographic assistant convolutional neural network (SA-CNN). Previous research showed success in discovering the presence of hidden information wit

Christo Kurisummoottil Thomas, Walid Saad, Yong Xiao

A digital twin (DT) leverages a virtual representation of the physical world, along with communication (e.g., 6G), computing (e.g., edge computing), and artificial intelligence (AI) technologies to enable many connected intelligence services. In order to handle the large amounts of network data based on digital twins (DTs), wireless systems can exploit the p

Ishaan Ganti, Srinivasan S. Iyengar

We present a physically appealing and elegant picture for quantum computing using rules constructed for a game of darts. A dartboard is used to represent the state space in quantum mechanics and the act of throwing the dart is shown to have close similarities to the concept of measurement, or collapse of the wavefunction in quantum mechanics. The analogy is

Nozomu Kobayashi, Yoshiyuki Suimon, Koichi Miyamoto, Kosuke Mitarai

In this paper, we investigate the application of quantum and quantum-inspired machine learning algorithms to stock return predictions. Specifically, we evaluate the performance of quantum neural network, an algorithm suited for noisy intermediate-scale quantum computers, and tensor network, a quantum-inspired machine learning algorithm, against classical mod

Tullia Dymarz, David Fisher, Xiangdong Xie

We establish a Tukia-type theorem for uniform quasiconformal groups of a Carnot group. More generally we establish a fiber bundle version (or foliated version) of Tukia theorem for uniform quasiconformal groups of a nilpotent Lie group whose Lie algebra admits a diagonalizable derivation with positive eigenvalues. These results have applications to quasi-iso

Fan Fei, Andre Costa, John E. Dolbow, Randolph R. Settgast

Many geo-engineering applications, e.g., enhanced geothermal systems, rely on hydraulic fracturing to enhance the permeability of natural formations and allow for sufficient fluid circulation. Over the past few decades, the phase-field method has grown in popularity as a valid approach to modeling hydraulic fracturing because of the ease of handling complex

I. C. Bandac, A. S. Barabash, L. Bergé, Yu. A. Borovlev

We report on the development of scintillating bolometers based on lithium molybdate crystals containing molybdenum depleted in the double-$β$ active isotope $^{100}$Mo (Li$_2$$^{100\textrm{depl}}$MoO$_4$). We used two Li$_2$$^{100\textrm{depl}}$MoO$_4$ cubic samples, 45 mm side and 0.28 kg each, produced following purification and crystallization protocols d

Vladislav Lomtev, Alexander Kovalev, Alexey Timchenko

Motor brain-computer interface (BCI) development relies critically on neural time series decoding algorithms. Recent advances in deep learning architectures allow for automatic feature selection to approximate higher-order dependencies in data. This article presents the FingerFlex model - a convolutional encoder-decoder architecture adapted for finger moveme

Kevin L. Chen, Falco J. Bargagli Stoffi, Raphael C. Kim, Rachel C. Nethery

Emissions generators, such as coal-fired power plants, are key contributors to air pollution and thus environmental policies to reduce their emissions have been proposed. Furthermore, marginalized groups are exposed to disproportionately high levels of this pollution and have heightened susceptibility to its adverse health impacts. As a result, robust evalua

Arda Yigit, David Breton, Zhou Zhou, Thierry Laliberte

A novel kinematically redundant (6+3)-DoF parallel robot is presented in this paper. Three identical 3-DoF RU/2-RUS legs are attached to a configurable platform through spherical joints. With the selected leg mechanism, the motors are mounted at the base, reducing the reflected inertia. The robot is intended to be actuated with direct-drive motors in order t

Tullia Dymarz, David Fisher, Xiangdong Xie

In this paper we study uniform quasiconformal groups of Carnot-by-Carnot groups. We show that they can be conjugated into conformal groups provided the induced action on the space of distinct pairs is cocompact. Following the approach of Eskin-Fisher-Whyte these results have applications to quasi-isometric rigidity of certain solvable groups.

Rodolfo Brandão

Spontaneous motion due to symmetry breaking has been theoretically predicted for both active droplets and isotropically active particles in an unbounded fluid domain, provided their intrinsic P\'eclet number $Pe$ exceeds a critical value. However, due to their inherently small $Pe$, this phenomenon has yet to be experimentally observed for active particles.

Abdelmalek Bouzenada, Abdelmalek Boumali, Omar Mustafa, Hassan Hassanabadi

Our research aims to investigate how the gravitational field influences the spectroscopic structure of the Feshbach-Villars oscillator in G\"urses space-time. To achieve this, we utilize the first-order Feshbach-Villars version of the Klein-Gordon equation, which is a relativistic wave equation for spinless particles. We examine the oscillator's quantum mech

Yu Xing, Karl H. Johansson

We study the transient behavior of a gossip model, in which agents randomly interact pairwise over a weighted graph with two communities. Edges within each community have identical weights, different from the weights between communities. It is shown that, at the early stage of the opinion evolution, the expected agent states in the same community have identi

Usmi Mukherjee, Mohammad Masudur Rahman

Software bug reports often lack crucial information (e.g., steps to reproduce), which makes bug resolution challenging. Developers thus ask follow-up questions to capture additional information. However, according to existing evidence, bug reporters often face difficulties answering them, which leads to the premature closing of bug reports without any resolu

Mohammad Javad Salariseddigh, Vahid Jamali, Holger Boche, Christian Deppe

The Binomial channel serves as a fundamental model for molecular communication (MC) systems employing molecule-counting receivers. Here, deterministic identification (DI) is addressed for the discrete-time Binomial channels (DTBC), subject to an average and a peak constraint on the molecule release rate. We establish that the number of different messages tha

Lucas Pascotti Valem, Daniel Carlos Guimarães Pedronette, Longin Jan Latecki

Due to a huge volume of information in many domains, the need for classification methods is imperious. In spite of many advances, most of the approaches require a large amount of labeled data, which is often not available, due to costs and difficulties of manual labeling processes. In this scenario, unsupervised and semi-supervised approaches have been gaini

Daniel A. Dale, Mederic Boquien, Jordan A. Turner, Daniela Calzetti

We present model spectral energy distribution (SED) fits to ultraviolet/optical/infrared observations for the 258 nearby galaxies in the Local Volume Legacy survey, a sample dominated by lower-luminosity dwarf irregular systems. The data for each galaxy include up to 26 spatially-integrated broadband and narrowband fluxes from the Galaxy Evolution Explorer,

Kevin K. Hardegree-Ullman, Dániel Apai, Galen J. Bergsten, Ilaria Pascucci

Molecular oxygen is a strong indicator of life on Earth, and may indicate biological processes on exoplanets too. Recent studies proposed that Earth-like O$_\mathrm{2}$ levels might be detectable on nearby exoplanets using high-resolution spectrographs on future extremely large telescopes (ELTs). However, these studies did not consider constraints like relat

N. D. Padilla, S. Carneiro, J. Chaves-Montero, C. J. Donzelli

Context: Gravitational redshift is a classical effect of Einstein's General Relativity, already measured in stars, quasars and clusters of galaxies. Aims: We here aim to identify the signature of gravitational redshift in the emission lines of active galaxies due to supermassive black holes, and compare to what is found for inactive galaxies. Methods: Using

Anwei Luo, Chenqi Kong, Jiwu Huang, Yongjian Hu

Face forgery detection is essential in combating malicious digital face attacks. Previous methods mainly rely on prior expert knowledge to capture specific forgery clues, such as noise patterns, blending boundaries, and frequency artifacts. However, these methods tend to get trapped in local optima, resulting in limited robustness and generalization capabili

Christian Krullikowski, Candace Chow, Marc Wieland, Sandro Martinis

The Global Flood Monitoring (GFM) system of the Copernicus Emergency Management Service (CEMS) addresses the challenges and impacts that are caused by flooding. The GFM system provides global, near-real time flood extent masks for each newly acquired Sentinel-1 Interferometric Wide Swath Synthetic Aperture Radar (SAR) image, as well as flood information from

Yaren Bilge Kaya, Kayse Lee Maass

Runaway and homeless youth (RHY) are a group of youth and young adults who are at high risk of being exploited through human trafficking. Although access to housing and support services is an effective way to decrease their vulnerability to being exploited, research reveals that coordination of these services provided to RHY by non-profit and government orga

Timothée Fronteau, Arnaud Paran, Aymen Shabou

Adversarial attacks and defenses have gained increasing interest on computer vision systems in recent years, but as of today, most investigations are limited to images. However, many artificial intelligence models actually handle documentary data, which is very different from real world images. Hence, in this work, we try to apply the adversarial attack phil

L. Shen, V. Esposito, N. G. Burdet, M. Zhu

In the cuprate superconductors, the spatial coherence of the charge density wave (CDW) state grows rapidly below a characteristic temperature $T_\mathrm{CDW}$, the nature of which is debated. We have combined a set of x-ray scattering techniques to study La$_{1.88}$Sr$_{0.12}$CuO$_{4}$ ($T_\mathrm{CDW}$~$\approx$~80\,K) to shed light on this discussion. We o

Mohamed Dhouib, Ghassen Bettaieb, Aymen Shabou

Information Extraction from visually rich documents is a challenging task that has gained a lot of attention in recent years due to its importance in several document-control based applications and its widespread commercial value. The majority of the research work conducted on this topic to date follow a two-step pipeline. First, they read the text using an

Aashish Rai, Hiresh Gupta, Ayush Pandey, Francisco Vicente Carrasco

In recent years, there has been significant progress in 2D generative face models fueled by applications such as animation, synthetic data generation, and digital avatars. However, due to the absence of 3D information, these 2D models often struggle to accurately disentangle facial attributes like pose, expression, and illumination, limiting their editing ca

Thomas F. Varley

In the 21st century, many of the crucial scientific and technical issues facing humanity can be understood as problems associated with understanding, modelling, and ultimately controlling complex systems: systems comprised of a large number of non-trivially interacting components whose collective behaviour can be difficult to predict. Information theory, a b

Jian-Hui Zhang

We show that double parton distributions, which are important in describing double parton scattering processes in hadron collisions, can be directly computed from correlations of equal-time nonlocal Euclidean operators on the lattice in the large hadron momentum limit. We demonstrate this by taking the unpolarized color singlet quark double parton distributi

Haneya Naeem Qureshi, Usama Masood, Marvin Manalastas, Syed Muhammad Asad Zaidi

The future of cellular networks is contingent on artificial intelligence (AI) based automation, particularly for radio access network (RAN) operation, optimization, and troubleshooting. To achieve such zero-touch automation, a myriad of AI-based solutions are being proposed in literature for modeling and optimizing network behavior to achieve the zero-touch

Ehsan Latif, Gengchen Mai, Matthew Nyaaba, Xuansheng Wu

Artificial general intelligence (AGI) has gained global recognition as a future technology due to the emergence of breakthrough large language models and chatbots such as GPT-4 and ChatGPT, respectively. Compared to conventional AI models, typically designed for a limited range of tasks, demand significant amounts of domain-specific data for training and may

Joshua Comden, Jing Wang, Andrey Bernstein

With the increased adoption of distributed energy resources (DERs) in distribution networks, their coordinated control with a DER management system (DERMS) that provides grid services (e.g., voltage regulation, virtual power plant) is becoming more necessary. One particular type of DERMS using primal-dual control has recently been found to be very effective

Jia Lin Hau, Erick Delage, Mohammad Ghavamzadeh, Marek Petrik

Optimizing static risk-averse objectives in Markov decision processes is difficult because they do not admit standard dynamic programming equations common in Reinforcement Learning (RL) algorithms. Dynamic programming decompositions that augment the state space with discrete risk levels have recently gained popularity in the RL community. Prior work has show

Lucas O. Lima, Andressa R. Medeiros-Silva, Raimundo R. dos Santos, Thereza Paiva

The interest in the physical properties of kagome lattices has risen considerably. In addition to the synthesis of new materials, the possibility of realizing ultracold atoms on an optical kagome lattice (KL) raises interesting issues. For instance, by considering the Hubbard model on an anisotropic KL, with a hopping $t^\prime$ along one of the directions,

C. A. Bertulani

This article presents a very brief review of the physics of Ultra-Peripheral Collisions (UPC) at the Large Hadron Collider (LHC) and other nuclear facilities. I discuss several processes of interest such as electron-position pair production, the anti-hydrogen atom, giant resonances, exotic meson production and parton distribution functions.

Murat Isik, Kayode Inadagbo, Hakan Aktas

Reconfigurable architectures like Field Programmable Gate Arrays (FPGAs) have been used for accelerating computations in several domains because of their unique combination of flexibility, performance, and power efficiency. However, FPGAs have not been widely used for high-performance computing, primarily because of their programming complexity and difficult

Christian Kuehn, Cinzia Soresina

The concept of Turing instability, namely that diffusion can destabilize the uniform steady state, is well known either in the context of partial differential equations (PDEs) or in networks of dynamical systems. Recently reaction-diffusion equations with cross-diffusion terms have been investigated, showing an analogous effect called cross-diffusion induced

Abhishek Som, Nahom K. Yirga, David K. Campbell

We study the extent to which a three-dimensional dispersing Periodic Anderson Model (PAM) can explain the emergence of novel superconductivity in the Infinite-Layer Nickelate compounds. By going beyond frequently used 2D models, the 3D dispersing PAM allows us to incorporate effects of finite out-of-plane hopping and orbital hybridization in describing these

Haruki Oga, Takeshi Omori, Laurent Joly, Yasutaka Yamaguchi

Solid-liquid friction plays a key role in nanofluidic systems. Yet, despite decades of method development to quantify solid-liquid friction using molecular dynamics (MD) simulations, an accurate and widely applicable method is still missing. Here, we propose a method to quantify the solid-liquid friction coefficient (FC) from equilibrium MD simulations of a

Yanchen Wang, Adam Turnbull, Yunlong Xu, Kathi Heffner

Computerized cognitive training (CCT) is a scalable, well-tolerated intervention that has promise for slowing cognitive decline. Outcomes from CCT are limited by a lack of effective engagement, which is decreased by factors such as mental fatigue, particularly in older adults at risk for dementia. There is a need for scalable, automated measures that can mon

Peng Cheng, Yang Yu, Hui-Yu Xing, Chen Chen

Supposing there exists an effective charge which defines an evolution scheme for both unpolarised and polarised parton distribution functions (DFs) that is all-orders exact and using Ans\"atze for hadron-scale proton polarised valence quark DFs, constrained by flavour-separated axial charges and insights from perturbative quantum chromodynamics, predictions

Andrew P. Rotunno, Amy K. Robinson, Nikunjkumar Prajapati, Samuel Berweger

We present a thermal velocity sampling method for calculating Doppler-broadened atomic spectra, which more efficiently reaches a smooth limit than regular velocity weighted sampling. The method uses equal-population sampling of the 1-D thermal distribution, sampling the 'inverse transform' of the cumulative distribution function, and is broadly applicable to

Sixu Li, Chaojian Li, Wenbo Zhu, Boyang Yu

Neural Radiance Field (NeRF) based 3D reconstruction is highly desirable for immersive Augmented and Virtual Reality (AR/VR) applications, but achieving instant (i.e., < 5 seconds) on-device NeRF training remains a challenge. In this work, we first identify the inefficiency bottleneck: the need to interpolate NeRF embeddings up to 200,000 times from a 3D emb

Andrew Wagenmaker, Dylan J. Foster

We consider the development of adaptive, instance-dependent algorithms for interactive decision making (bandits, reinforcement learning, and beyond) that, rather than only performing well in the worst case, adapt to favorable properties of real-world instances for improved performance. We aim for instance-optimality, a strong notion of adaptivity which asser

Mateo Díaz, Ethan N. Epperly, Zachary Frangella, Joel A. Tropp

We investigate preconditioned conjugate gradient methods for kernel ridge regression (KRR) problems with a moderate to large number of data points ($10^4 \leq N \leq 10^7$). We develop and analyze two randomized preconditioners with complementary guarantees. For full-data KRR, RPCholesky preconditioning requires $O(N^2)$ arithmetic operations to achieve fixe

Aleksandr N. Mikheev, Ido Siovitz, Thomas Gasenzer

Closed quantum systems far from thermal equilibrium can show universal dynamics near attractor solutions, known as non-thermal fixed points, generically in the form of scaling behavior in space and time. A systematic classification and comprehensive understanding of such scaling solutions are tasks of future developments in non-equilibrium quantum many-body

Tingwei Shen, Ganning Zhao, Suya You

Synthetic-to-real data translation using generative adversarial learning has achieved significant success in improving synthetic data. Yet, limited studies focus on deep evaluation and comparison of adversarial training on general-purpose synthetic data for machine learning. This work aims to train and evaluate a synthetic-to-real generative model that trans

Grégoire Véchambre

We consider a class of L\'evy-type processes on which spectral analysis technics can be made to produce optimal results, in particular for the decay rate of their survival probability and for the spectral gap of their ground state transform. This class is defined by killed symmetric L\'evy processes under general random time-changes satisfying some integrabi

Haian Jin, Isabella Liu, Peijia Xu, Xiaoshuai Zhang

We propose TensoIR, a novel inverse rendering approach based on tensor factorization and neural fields. Unlike previous works that use purely MLP-based neural fields, thus suffering from low capacity and high computation costs, we extend TensoRF, a state-of-the-art approach for radiance field modeling, to estimate scene geometry, surface reflectance, and env

Xiyuan Gao, Jiayi Wang, Guanyu Hu, Jianguo Sun

Functional data is a powerful tool for capturing and analyzing complex patterns and relationships in a variety of fields, allowing for more precise modeling, visualization, and decision-making. For example, in healthcare, functional data such as medical images can help doctors make more accurate diagnoses and develop more effective treatment plans. However,

Sadjad Arzash, Anupama Gannavarapu, Fred C. MacKintosh

At zero temperature, spring networks with connectivity below Maxwell's isostatic threshold undergo a mechanical phase transition from a floppy state at small strains to a rigid state for applied shear strain above a critical strain threshold. Disordered networks in the floppy mechanical regime can be stabilized by entropic effects at finite temperature. We d

Carmel Fiscko, Soummya Kar, Bruno Sinopoli

This work studies a multi-agent Markov decision process (MDP) that can undergo agent dropout and the computation of policies for the post-dropout system based on control and sampling of the pre-dropout system. The central planner's objective is to find an optimal policy that maximizes the value of the expected system given a priori knowledge of the agents' d

Christian Nelson, Xuhong Li, Thomas Wilding, Benjamin Deutschmann

Multiple concepts for future generations of wireless communication standards utilize coherent processing of signals from many distributed antennas. Names for these concepts include distributed MIMO, cell-free massive MIMO, XL-MIMO, and large intelligent surfaces. They aim to improve communication reliability, capacity, as well as energy efficiency and provid

Luka Pavešič, Ramón Aguado, Rok Žitko

We consider an interacting quantum dot strongly coupled to two superconducting leads in a Josephson junction geometry. By defining symmetry-adapted superpositions of states from the leads, we formulate an effective Hamiltonian for the strong-hybridisation regime with a single orbital directly coupled to the dot and three additional indirectly coupled orbital

Heng Yu, Zoltan A. Milacski, Laszlo A. Jeni

Inferring 3D object structures from a single image is an ill-posed task due to depth ambiguity and occlusion. Typical resolutions in the literature include leveraging 2D or 3D ground truth for supervised learning, as well as imposing hand-crafted symmetry priors or using an implicit representation to hallucinate novel viewpoints for unsupervised methods. In

Manon Flageat, Luca Grillotti, Antoine Cully

While standard approaches to optimisation focus on producing a single high-performing solution, Quality-Diversity (QD) algorithms allow large diverse collections of such solutions to be found. If QD has proven promising across a large variety of domains, it still struggles when faced with uncertain domains, where quantification of performance and diversity a

Nuozhou Wang, Junyu Zhang, Shuzhong Zhang

This paper studies the complexity of finding approximate stationary points for the smooth nonconvex-strongly-concave (NC-SC) saddle point problem: $\min_x\max_yf(x,y)$. Under the standard first-order smoothness conditions where $f$ is $\ell$-smooth in both arguments and $\mu_y$-strongly concave in $y$, existing literature shows that the optimal complexity fo

Othmane Islah

We show in this article in what sense viscosity solutions of the Hamilton-Jacobi equation can be restricted to a submanifold M of \mathbb{R}^{d}. We treat in this article the case of M\times\mathbb{R}^{d} being invariant by the Hamiltonian vector field.

Michał P. Karpowicz

This paper explores the relationship between matrix factorizations and linear matrix equations. It shows that every matrix factorization defines two hidden projectors, one for the column space and one for the row space of a matrix, and how to calculate them. The projectors can be applied to solve linear matrix equations, generate low-rank approximations, or

Carsten H. Chong, Viktor Todorov

We derive a nonparametric higher-order asymptotic expansion for small-time changes of conditional characteristic functions of It\^o semimartingale increments. The asymptotics setup is of joint type: both the length of the time interval of the increment of the underlying process and the time gap between evaluating the conditional characteristic function are s

Ivan A. Korneev, Ibadulla R. Ramazanov, Andrei V. Slepnev, Tatiana E. Vadivasova

Typically, the period-doubling bifurcations exhibited by nonlinear dissipative systems are observed when varying systems' parameters. In contrast, the period-doubling bifurcations considered in the current research are induced by changing the initial conditions whereas parameter values are fixed. Thus, the studied bifurcations can be classified as the period

Lucas Pascotti Valem, Daniel Carlos Guimarães Pedronette, Longin Jan Latecki

Impressive advances in acquisition and sharing technologies have made the growth of multimedia collections and their applications almost unlimited. However, the opposite is true for the availability of labeled data, which is needed for supervised training, since such data is often expensive and time-consuming to obtain. While there is a pressing need for the

Eashan Kosaraju, Praveen Kumar Pandian Shanmuganathan

Pulmonary hypertension (PH) is a condition of high blood pressure that affects the arteries in the lungs and the right side of the heart (Mayo Clinic, 2017). A mean pulmonary artery pressure greater than 25 mmHg is defined as Pulmonary hypertension. The estimated 5-year survival rate from the time of diagnosis of pulmonary hypertension is only 57% without th

Ilya Shvartsman

This paper is devoted to a study of infinite horizon optimal control problems with time discounting and time averaging criteria in discrete time. It is known that these problems are related to certain infinite-dimensional linear programming problems, but compactness of the state constraint is a common assumption imposed in analysis of these LP problems. In t

Jingwei Dong, Yucheng Liao, Haiwei Xie, Jochen Cremer

Ground fault detection in inverter-based microgrid (IBM) systems is challenging, particularly in a real-time setting, as the fault current deviates slightly from the nominal value. This difficulty is reinforced when there are partially decoupled disturbances and modeling uncertainties. The conventional solution of installing more relays to obtain additional

Juhoon Chung, Khang Tran

From a sequence $\left\{ a_{n}\right\} _{n=0}^{\infty}$ of real numbers satisfying a three-term recurrence, we form a sequence of polynomials $\left\{ P_{m}(z)\right\} _{m=0}^{\infty}$ whose coefficients are numbers in this sequence. We showed that under explicit conditions, the zeros of $P_{m}(z)$, $m\gg1$, lie on one side of the circle whose radius is give

Nan-Jiang Jiang, Chenhao Tan, Marie-Catherine de Marneffe

Human label variation (Plank 2022), or annotation disagreement, exists in many natural language processing (NLP) tasks. To be robust and trusted, NLP models need to identify such variation and be able to explain it. To this end, we created the first ecologically valid explanation dataset with diverse reasoning, LiveNLI. LiveNLI contains annotators' highlight

Prepared by the ExoPAG Science Interest Group, 2 on Exoplanet Demographics, Jessie L. Christiansen, David P. Bennett

Goal 1 of the National Academies of Science, Engineering and Mathematics Exoplanet Science Strategy is "to understand the formation and evolution of planetary systems as products of the process of star formation, and characterize and explain the diversity of planetary system architectures, planetary compositions, and planetary environments produced by these