July 2023 arXiv papers — page 2

Weikang Yu, Yonghao Xu, Pedram Ghamisi

Deep neural networks (DNNs) have risen to prominence as key solutions in numerous AI applications for earth observation (AI4EO). However, their susceptibility to adversarial examples poses a critical challenge, compromising the reliability of AI4EO algorithms. This paper presents a novel Universal Adversarial Defense approach in Remote Sensing Imagery (UAD-R

Miguel A. Martínez-García, Diego Martín-Cano

In this work we identify coherent electron-vibron interactions between near-resonant and non-resonant electronic levels that contribute beyond standard optomechanical models for off-resonant or resonance SERS. By developing an open-system quantum model using first molecular interaction principles, we show how the Raman interference of both resonant and non-r

Cole Jeznach

We provide several equivalent characterizations of locally flat, $d$-Ahlfors regular, uniformly rectifiable sets $E$ in $\mathbb{R}^n$ with density close to $1$ for any dimension $d \in \mathbb{N}$ with $1 \le d \le n-1$. In particular, we show that when $E$ is Reifenberg flat with small constant and has Ahlfors regularity constant close to $1$, then the Tol

Márton Naszódi, Zsombor Szilágyi, Mihály Weiner

Motivated by general probability theory, we say that the set $S$ in $\mathbb{R}^d$ is \emph{antipodal of rank $k$}, if for any $k+1$ elements $q_1,\ldots q_{k+1}\in S$, there is an affine map from $\mathrm{conv}(S)$ to the $k$-dimensional simplex $Δ_k$ that maps $q_1,\ldots q_{k+1}$ bijectively onto the $k+1$ vertices of $Δ_k$. For $k=1$, it coincides with t

Paranjoy Chaki, Aparajita Bhattacharyya, Kornikar Sen, Ujjwal Sen

We discuss the idea of extracting energy from a quantum battery, applying a projective measurement on an auxiliary system. The battery is initially connected to the auxiliary system and allowed to interact with it. After some time, we execute a measurement on the auxiliary system which probabilistically projects the setup to a particular state, and the corre

Krzysztof Andrzejewski, Katarzyna Bolonek-Lasoń, Piotr Kosiński

For vanishing fidelity between initial and final states two important quantum speed limits, the Mandelstam-Tamm limit (involving energy dispersion) and Margolus-Levitin one (involving excitation energy expectation value) have been derived. While the generalization of the former limit to the case of arbitrary fidelity is straightforward, the relevant generali

Bastian Pradenas, Oleg Tchernyshyov

We present a nonlinear field theory of a three-sublattice hexagonal antiferromagnet. The order parameter is the spin frame, an orthogonal triplet of vectors related to sublattice magnetizations and spin chirality. The exchange energy, quadratic in spin-frame gradients, has three coupling constants, only two of which manifest themselves in the bulk. As a resu

Shohreh Deldari, Dimitris Spathis, Mohammad Malekzadeh, Fahim Kawsar

Limited availability of labeled data for machine learning on multimodal time-series extensively hampers progress in the field. Self-supervised learning (SSL) is a promising approach to learning data representations without relying on labels. However, existing SSL methods require expensive computations of negative pairs and are typically designed for single m

Alexander Alecio

Phase transitions and critical behaviour of a class of MV-SDEs, whose concomitant non-local Fokker-Planck equation includes the Granular Media equation with quadratic interaction potential as a special case, is studied. By careful analysis of an implicit auxiliary integral equation, it is shown for a wide class of potentials that below a certain `critical th

Amy L. Rankine, James Aird, Angel Ruiz, Antonis Georgakakis

In active galactic nuclei, the relationship between UV and X-ray luminosity is well studied (often characterised by $α_\text{ox}$) but often with heterogeneous samples. We have parametrized the intrinsic distribution of X-ray luminosity, $L_\text{X}$, for the optically-selected sample of SDSS quasars in the Stripe 82 and XXL fields across redshifts 0.5-3.5.

Prashant Kocherlakota, Luciano Rezzolla, Rittick Roy, Maciek Wielgus

Astrophysical black holes (BHs) are universally expected to be described by the Kerr metric, a stationary, vacuum solution of general relativity (GR). Indeed, by imaging M87$^\star$ and Sgr A$^\star$ and measuring the size of their shadows, we have substantiated this hypothesis through successful null tests. Here we discuss the potential of upcoming improved

Arthur Vesperini, Ghofrane Bel-Hadj-Aissa, Lorenzo Capra, Roberto Franzosi

We show that the manifold of quantum states is endowed with a rich and nontrivial geometric structure. We derive the Fubini-Study metric of the projective Hilbert space of a multi-qubit quantum system, endowing it with a Riemannian metric structure, and investigate its deep link with the entanglement of the states of this space. As a measure, we adopt the En

Sungho Shin, François Pacaud, Mihai Anitescu

This paper introduces a framework for solving alternating current optimal power flow (ACOPF) problems using graphics processing units (GPUs). While GPUs have demonstrated remarkable performance in various computing domains, their application in ACOPF has been limited due to challenges associated with porting sparse automatic differentiation (AD) and sparse l

Nguyen Chau Giang, Thiem Hoang

Thermal dust polarization is a powerful tool to probe magnetic fields ($\textbf{B}$) and grain properties. However, a systematic study of the dependence of dust polarization on grain properties in protostellar environments is not yet available. In this paper, we post-process a non-ideal MHD simulation of a collapsing protostellar core with our updated POLARI

ATLAS Collaboration

The identification of jets originating from quarks and gluons, often referred to as quark/gluon tagging, plays an important role in various analyses performed at the Large Hadron Collider, as Standard Model measurements and searches for new particles decaying to quarks often rely on suppressing a large gluon-induced background. This paper describes the measu

Susmita Ghosh, Abhiroop Chatterjee

The rapid spread of COVID-19 has necessitated efficient and accurate diagnostic methods. Computed Tomography (CT) scan images have emerged as a valuable tool for detecting the disease. In this article, we present a novel deep learning approach for automated COVID-19 CT scan classification where a modified Xception model is proposed which incorporates a newly

Saizhuo Wang, Hang Yuan, Leon Zhou, Lionel M. Ni

One of the most important tasks in quantitative investment research is mining new alphas (effective trading signals or factors). Traditional alpha mining methods, either hand-crafted factor synthesizing or algorithmic factor mining (e.g., search with genetic programming), have inherent limitations, especially in implementing the ideas of quants. In this work

Amador Martin-Pizarro, Martin Ziegler

A first-order theory is Noetherian with respect to the collection of formulae $\mathcal{F}$ if every definable set is a Boolean combination of instances of formulae in $\mathcal{F}$ and the topology whose subbasis of closed sets is the collection of instances of arbitrary formulae in $\mathcal{F}$ is Noetherian. Noetherianity is a strengthening of equational

J. G. De la Varga, S. Pineda, J. M. Morales, Á. Porras

The task of state estimation in active distribution systems faces a major challenge due to the integration of different measurements with multiple reporting rates. As a result, distribution systems are essentially unobservable in real time, indicating the existence of multiple states that result in identical values for the available measurements. Certain exi

Yani Ziani, Nikolai Kosmatov, Frédéric Loulergue, Daniel Gracia Pérez

The Trusted Platform Module (TPM) is a cryptoprocessor designed to protect integrity and security of modern computers. Communications with the TPM go through the TPM Software Stack (TSS), a popular implementation of which is the open-source library tpm2-tss. Vulnerabilities in its code could allow attackers to recover sensitive information and take control o

Richard D. James, Kunlun Qi, Li Wang

In this paper, we develop a multiscale hierarchy framework for objective molecular dynamics (OMD), a reduced order molecular dynamics with a certain symmetry, that connects it to the statistical kinetic equation, and the macroscopic hydrodynamic model. In the mesoscopic regime, we exploit two interaction scalings that lead, respectively, to either a mean-fie

Angus R. Williams, Hannah Rose Kirk, Liam Burke, Yi-Ling Chung

Public figures receive a disproportionate amount of abuse on social media, impacting their active participation in public life. Automated systems can identify abuse at scale but labelling training data is expensive, complex and potentially harmful. So, it is desirable that systems are efficient and generalisable, handling both shared and specific aspects of

Souheib Allout, Abderrahmane Belkacem, Abdelghani Zeghib

We construct compact Lorentz manifolds without closed geodesics.

Samuel A. Lopes

This is an abridged version of our Habilitation thesis. In these notes, we aim to summarize our research interests and achievements as well as motivate what drives our work: symmetry, structure and invariants. The paradigmatic example which permeates and often inspires our research is the Weyl algebra $\mathbb{A}_{1}$.

Natalya Weber, Werner Koch, Ozan Erdem, Tom Froese

Hopfield networks are an attractive choice for solving many types of computational problems because they provide a biologically plausible mechanism. The Self-Optimization (SO) model adds to the Hopfield network by using a biologically founded Hebbian learning rule, in combination with repeated network resets to arbitrary initial states, for optimizing its ow

Esra Sablevice, Peter Millington

This paper explores quantum field theories with pseudo-Hermitian Hamiltonians, where PT-symmetric Hamiltonians serve as a special case. In specific regimes, these pseudo-Hermitian Hamiltonians have real eigenspectra, orthogonal eigenstates, and unitary time evolution. So far, most pseudo-Hermitian quantum field theories have been constructed using analytic c

Lucy Martinez, Doron Zeilberger

We use three kinds of computations: simulation, numeric, and symbolic, to guide risk-averse gamblers in general, and offer particular advice on how to resolve the famous St. Petersburg paradox.

Yachong Yang, Arun Kumar Kuchibhotla, Eric Tchetgen Tchetgen

Series or orthogonal basis regression is one of the most popular non-parametric regression techniques in practice, obtained by regressing the response on features generated by evaluating the basis functions at observed covariate values. The most routinely used series estimator is based on ordinary least squares fitting, which is known to be minimax rate opti

Andrea Lama, Mario di Bernardo

We study the shepherding control problem where a group of "herders" need to orchestrate their collective behaviour in order to steer the dynamics of a group of "target" agents towards a desired goal. We relax the strong assumptions of targets showing cohesive collective behavior in the absence of the herders, and herders owning global sensing

Diana Waldmannstetter, Ivan Ezhov, Benedikt Wiestler, Francesco Campi

Accurate image registration is pivotal in biomedical image analysis, where selecting suitable registration algorithms demands careful consideration. While numerous algorithms are available, the evaluation metrics to assess their performance have remained relatively static. This study addresses this challenge by introducing a novel evaluation metric termed La

Abraham Holtermann, Jacquelyn Noronha-Hostler, Anne M. Sickles, Xiaoning Wang

Correlations of two or more particles have been an essential tool for understanding the hydrodynamic behavior of the quark-gluon plasma created in ultra-relativistic nuclear collisions. In this paper, we extend that framework to introduce a mathematical construction of multi-particle correlators that utilize correlations between arbitrary numbers of particle

Pavel Galashin, Thomas Lam

We define monotone links on a torus, obtained as projections of curves in the plane whose coordinates are monotone increasing. Using the work of Morton-Samuelson, to each monotone link we associate elements in the double affine Hecke algebra and the elliptic Hall algebra. In the case of torus knots (when the curve is a straight line), we recover symmetric fu

Joshua D. Martin, Duff Neill, A. Roggero, Huaiyu Duan

Neutrino gases are expected to form in high density astrophysical environments, and accurately modeling their flavor evolution is critical to understanding such environments. In this work we study a simplified model of such a dense neutrino gas in the regime for which neutrino-neutrino coherent forward scattering is the dominant mechanism contributing to the

Zihan Zhang, Lei Shi, Ding-Xuan Zhou

Deep neural networks (DNNs) trained with the logistic loss (i.e., the cross entropy loss) have made impressive advancements in various binary classification tasks. However, generalization analysis for binary classification with DNNs and logistic loss remains scarce. The unboundedness of the target function for the logistic loss is the main obstacle to derivi

Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu

Despite the advancements of open-source large language models (LLMs), e.g., LLaMA, they remain significantly limited in tool-use capabilities, i.e., using external tools (APIs) to fulfill human instructions. The reason is that current instruction tuning largely focuses on basic language tasks but ignores the tool-use domain. This is in contrast to the excell

Blair Attard-Frost, David Gray Widder

Researchers, practitioners, and policymakers with an interest in AI ethics need more integrative approaches for studying and intervening in AI systems across many contexts and scales of activity. This paper presents AI value chains as an integrative concept that satisfies that need. To more clearly theorize AI value chains and conceptually distinguish them f

Olivier Lamarre, Shantanu Malhotra, Jonathan Kelly

The success of a multi-kilometre drive by a solar-powered rover at the lunar south pole depends upon careful planning in space and time due to highly dynamic solar illumination conditions. An additional challenge is that the rover may be subject to random faults that can temporarily delay long-range traverses. The majority of existing global spatiotemporal p

Kun Zhang, Kwangmin Yu, Kun Hao, Vladimir Korepin

Quantum computers provide a promising method to study the dynamics of many-body systems beyond classical simulation. On the other hand, the analytical methods developed and results obtained from the integrable systems provide deep insights on the many-body system. Quantum simulation of the integrable system not only provides a valid benchmark for quantum com

Jiho Jin, Jiseon Kim, Nayeon Lee, Haneul Yoo

The Bias Benchmark for Question Answering (BBQ) is designed to evaluate social biases of language models (LMs), but it is not simple to adapt this benchmark to cultural contexts other than the US because social biases depend heavily on the cultural context. In this paper, we present KoBBQ, a Korean bias benchmark dataset, and we propose a general framework t

Marie-Hélène Tomé

Deep work by Shintani in the 1970's describes Hecke $L$-functions associated to narrow ray class group characters of totally real fields $F$ in terms of what are now known as Shintani zeta functions. However, for $[F:\mathbb{Q}] = n \geq 3$, Shintani's method was ineffective due to its crucial dependence on abstract fundamental domains for the action

Yisheng Tu, Zhi-Yun Li, Ka Ho Lam, Kengo Tomida

Stars form from the gravitational collapse of turbulent, magnetized molecular cloud cores. Our non-ideal MHD simulations reveal that the intrinsically anisotropic magnetic resistance to gravity during the core collapse naturally generates dense gravo-magneto-sheetlets within inner protostellar envelopes -- disrupted versions of classical sheet-like pseudodis

Tianxing Wu, Xudong Cao, Yipeng Zhu, Feiyue Wu

To easily obtain the knowledge about autism spectrum disorder and help its early screening and diagnosis, we create AsdKB, a Chinese knowledge base on autism spectrum disorder. The knowledge base is built on top of various sources, including 1) the disease knowledge from SNOMED CT and ICD-10 clinical descriptions on mental and behavioural disorders, 2) the d

Monika Henzinger, Barna Saha, Martin P. Seybold, Christopher Ye

{\em Algorithms with predictions} incorporate machine learning predictions into algorithm design. A plethora of recent works incorporated predictions to improve on worst-case optimal bounds for online problems. In this paper, we initiate the study of complexity of dynamic data structures with predictions, including dynamic graph algorithms. Unlike in online

Bo Li, David Saad

In the context of epidemic spreading, many intricate dynamical patterns can emerge due to the cooperation of different types of pathogens or the interaction between the disease spread and other failure propagation mechanism. To unravel such patterns, simulation frameworks are usually adopted, but they are computationally demanding on big networks and subject

Candida Bowtell, Robert Hancock, Joseph Hyde

For graphs $G, H_1,\dots,H_r$, write $G \to (H_1, \ldots, H_r)$ to denote the property that whenever we $r$-colour the edges of $G$, there is a monochromatic copy of $H_i$ in colour $i$ for some $i \in \{1,\dots,r\}$. Mousset, Nenadov and Samotij proved an upper bound on the threshold function for the property that $G_{n,p} \to (H_1,\dots,H_r)$, thereby reso

Rihito Nagase, Takahiro Sagawa

The tradeoff relation between speed and cost is a central issue in designing fast and efficient information processing devices. We derive an achievable bound on thermodynamic cost for obtaining information through finite-time (non-quasi-static) measurements. Our proof is based on optimal transport theory, which enables us to identify the explicit protocol to

Andres Fernandez Herrero, Siqing Zhang

Fix a smooth projective family of curves $C \to S$ and a split reductive group scheme $G$ over a Noetherian base scheme $S$. For any (possibly nonreduced) fixed relative Cartier divisor $D$, we provide a treatment of the moduli of $G$-bundles on the fibers of $C$ equipped with $t$-connections with pole orders bounded by $D$. Under mild assumptions on the cha

Dmytro Pavlichenko, Sven Behnke

Many objects, such as tools and household items, can be used only if grasped in a very specific way - grasped functionally. Often, a direct functional grasp is not possible, though. We propose a method for learning a dexterous pre-grasp manipulation policy to achieve human-like functional grasps using deep reinforcement learning. We introduce a dense multi-c

Lin Huang, Weisheng Li, Yujuan Tan, Linlin Shen

In this study, the structural problems of the YOLOv5 model were analyzed emphatically. Based on the characteristics of fine defects in artificial leather, four innovative structures, namely DFP, IFF, AMP, and EOS, were designed. These advancements led to the proposal of a high-performance artificial leather fine defect detection model named YOLOD. YOLOD demo

Mingzhi Wang, Guanghai Guo, Pengfei Yan, Songbai Chen

We investigate the shadows of Konoplya-Zhidenko naked singularity. In the spacetime of Konoplya-Zhidenko naked singularity, not only can unstable retrograde light ring (LR) exist, but also unstable prograde LR, leading to the formation of a complete photon sphere (PS). Due to the absence of an event horizon, a dark disc-shaped shadow does not appear; instead

Aparajita Bhattacharyya, Kornikar Sen, Ujjwal Sen

Energy extraction from quantum batteries by means of completely positive trace-preserving (CPTP) maps leads to the concept of CPTP-local passive states, which identify bipartite states from which no energy can be squeezed out by applying any CPTP map to a particular subsystem. We prove, for arbitrary dimension, that if a state is CPTP-local passive with resp

Sonali Gangwar, Rajamanickam Ravisankar, S. I. Mistakidis, Paulsamy Muruganandam

We investigate the ground state and dynamics of one-dimensional spin-orbit coupled (SOC) quantum droplets within the extended Gross-Pitaevskii approach. As the SOC wavenumber increases, stripe droplet patterns emerge, with a flat-top background, for larger particle numbers. The surface energy decays following a power-law with respect to the interactions. At

Jiaqi Miao

Through the utilization of smart materials and well-designed structures, functional surfaces have been developed to enable small-scale liquid/solid manipulation tasks, thereby facilitating crucial applications in the fields of microfluidics, soft robotics, and biomedical engineering. However, the design of functional systems with flexible, tunable, and multi

M. Zendra, F. Borgonovi, G. L. Celardo, S. Gurvitz

We present a non-standard Hubbard model applicable to arbitrary single-particle potential profiles and inter-particle interactions. Our approach involves a novel treatment of Wannier functions, free from the ambiguities of conventional methods and applicable to finite systems without periodicity constraints. To ensure the consistent evaluation of Wannier fun

László Györfi, Tamás Linder, Harro Walk

We study the excess minimum risk in statistical inference, defined as the difference between the minimum expected loss in estimating a random variable from an observed feature vector and the minimum expected loss in estimating the same random variable from a transformation (statistic) of the feature vector. After characterizing lossless transformations, i.e.

Muruhan Rathinam, Mingkai Yu

Stochastic reaction network models arise in intracellular chemical reactions, epidemiological models and other population process models, and are a class of continuous time Markov chains which have the nonnegative integer lattice as state space. We consider the problem of estimating the conditional probability distribution of a stochastic reaction network gi

Urmila Chadayammuri, Michelle Ntampaka, John ZuHone, Àkos Bogdàn

Galaxy cluster mass functions are a function of cosmology, but mass is not a direct observable, and systematic errors abound in all its observable proxies. Mass-free inference can bypass this challenge, but it requires large suites of simulations spanning a range of cosmologies and models for directly observable quantities. In this work, we devise a U-net -

Yining Ma, Qadeer Khan, Daniel Cremers

In this work, we propose a learning based neural model that provides both the longitudinal and lateral control commands to simultaneously navigate multiple vehicles. The goal is to ensure that each vehicle reaches a desired target state without colliding with any other vehicle or obstacle in an unconstrained environment. The model utilizes an attention based

L. Peri, G. A. Oakes, L. Cochrane, C. J. B. Ford

Semiconductor quantum dots operated dynamically are the basis of many quantum technologies such as quantum sensors and computers. Hence, modelling their electrical properties at microwave frequencies becomes essential to simulate their performance in larger electronic circuits. Here, we develop a self-consistent quantum master equation formalism to obtain th

Michal Hrbek, Lorenzo Martini

We show that the cotilting heart associated to a tilting complex $T$ is a locally coherent and locally coperfect Grothendieck category (i.e. an Ind-completion of a small artinian abelian category) if and only if $T$ is product-complete. We then apply this to the specific setting of the derived category of a commutative noetherian ring $R$. If $\dim(R)<\infty

Zbigniew Palmowski, Meral Şimşek, Apostolos D. Papaioannou

In this paper, we solve exit problems for a level-dependent Lévy process which is exponentially killed with a killing intensity that depends on the present state of the process. Moreover, we analyse the respective resolvents. All identities are given in terms of new generalisations of scale functions (counterparts of the scale function from the theory of Lév

Belén Pulido, Alba M. Franco-Pereira, Rosa E. Lillo

With the rapid growth of data generation, advancements in functional data analysis (FDA) have become essential, especially for approaches that handle multiple variables at the same time. This paper introduces a novel formulation of the epigraph and hypograph indices, along with their generalized expressions, specifically designed for multivariate functional

Kevin Qinghong Lin, Pengchuan Zhang, Joya Chen, Shraman Pramanick

Video Temporal Grounding (VTG), which aims to ground target clips from videos (such as consecutive intervals or disjoint shots) according to custom language queries (e.g., sentences or words), is key for video browsing on social media. Most methods in this direction develop taskspecific models that are trained with type-specific labels, such as moment retrie

Shan Ali, Chaima Boufaied, Domenico Bianculli, Paula Branco

Growth in system complexity increases the need for automated log analysis techniques, such as Log-based Anomaly Detection (LAD). While deep learning (DL) methods have been widely used for LAD, traditional machine learning (ML) techniques can also perform well depending on the context and dataset. Semi-supervised techniques deserve the same attention as they

Per Berglund, Tristan Hübsch, Djordje Minic

We elaborate on the new understanding of the cosmological constant and the gauge hierarchy problems in the context of string theory in its metastring formulation, based on the concepts of modular spacetime and Born geometry. The interplay of phase space (and Born geometry), the Bekenstein bound, the mixing between ultraviolet (UV) and infrared (IR) physics a

Zahra Esmaeilbeig, Mojtaba Soltanalian

This paper revisits two prominent adaptive filtering algorithms, namely recursive least squares (RLS) and equivariant adaptive source separation (EASI), through the lens of algorithm unrolling. Building upon the unrolling methodology, we introduce novel task-based deep learning frameworks, denoted as Deep RLS and Deep EASI. These architectures transform the

Donghwan Lee, Han-Dong Lim, Do Wan Kim

The main goal of this paper is to investigate continuous-time distributed dynamic programming (DP) algorithms for networked multi-agent Markov decision problems (MAMDPs). In our study, we adopt a distributed multi-agent framework where individual agents have access only to their own rewards, lacking insights into the rewards of other agents. Moreover, each a

Gonçalo Mordido, Pranshu Malviya, Aristide Baratin, Sarath Chandar

Sharpness-aware minimization (SAM) methods have gained increasing popularity by formulating the problem of minimizing both loss value and loss sharpness as a minimax objective. In this work, we increase the efficiency of the maximization and minimization parts of SAM&#39;s objective to achieve a better loss-sharpness trade-off. By taking inspiration from the

Yuuya Chiba

Absence of local conserved quantities is often required, such as for thermalization or for the validity of response theory. Although many studies have discussed whether thermalization occurs in the Ising chain with longitudinal and transverse fields, rigorous results on local conserved quantities of this model have still been lacking. Here, we rigorously pro

Yun Zeng, Deren Han, Yansheng Su, Jiaxin Xie

The problem of finding a solution to the linear system $Ax = b$ with certain minimization properties arises in numerous scientific and engineering areas. In the era of big data, the stochastic optimization algorithms become increasingly significant due to their scalability for problems of unprecedented size. This paper focuses on the problem of minimizing a

Giovanni Pighizzini, Luca Prigioniero

We introduce and investigate forgetting 1-limited automata, which are single-tape Turing machines that, when visiting a cell for the first time, replace the input symbol in it by a fixed symbol, so forgetting the original contents. These devices have the same computational power as finite automata, namely they characterize the class of regular languages. We

Zhiguang Liao, Lei Chen, Guijing Duan, Yiming Wang

Motivated by the recent discovery of superconductivity in La$_3$Ni$_2$O$_7$ under pressure, we discuss the basic ingredients of a model that captures its microscopic physics under pressure tuning. We anchor our description in terms of the spectroscopic evidence of strong correlations in this system. In a bilayer Hubbard model including the Ni $3d$ $x^2-y^2$

Gustavo Pinto, Isadora Cardoso-Pereira, Danilo Monteiro Ribeiro, Danilo Lucena

As a way of addressing increasingly sophisticated problems, software professionals face the constant challenge of seeking improvement. However, for these individuals to enhance their skills, their process of studying and training must involve feedback that is both immediate and accurate. In the context of software companies, where the scale of professionals

Javed Lindner, David Dahmen, Michael Krämer, Moritz Helias

Bayesian inference and kernel methods are well established in machine learning. The neural network Gaussian process in particular provides a concept to investigate neural networks in the limit of infinitely wide hidden layers by using kernel and inference methods. Here we build upon this limit and provide a field-theoretic formalism which covers the generali

M. M. Amaan Valiuddin, Christiaan G. A. Viviers, Ruud J. G. van Sloun, Peter H. N. de With

Data uncertainties, such as sensor noise, occlusions or limitations in the acquisition method can introduce irreducible ambiguities in images, which result in varying, yet plausible, semantic hypotheses. In Machine Learning, this ambiguity is commonly referred to as aleatoric uncertainty. In image segmentation, latent density models can be utilized to addres

Stefan Jaric, Anastasiia Kudriavtseva, Nikita Nekrasov, Alexey V. Orlov

Measuring NT-proBNP biomarker is recommended for preliminary diagnostics of the heart failure. Recent studies suggest a possibility of early screening of biomarkers in saliva for non-invasive identification of cardiac diseases at the point-of-care. However, NT-proBNP concentrations in saliva can be thousand time lower than in blood plasma, going down to pg/m

Guedong Park, Hyukjoon Kwon, Hyunseok Jeong

The Wigner function formalism has played a pivotal role in examining the non-classical aspects of quantum states and their classical simulatability. Nevertheless, its application in qubit systems faces limitations due to negativity induced by Clifford gates. In this work, we propose a novel classical simulation method for qubit Clifford circuits based on the

Runyang Feng, Yixing Gao, Tze Ho Elden Tse, Xueqing Ma

Denoising diffusion probabilistic models that were initially proposed for realistic image generation have recently shown success in various perception tasks (e.g., object detection and image segmentation) and are increasingly gaining attention in computer vision. However, extending such models to multi-frame human pose estimation is non-trivial due to the pr

Mingyuan Fan, Chengyu Wang, Cen Chen, Yang Liu

Diffusion models and large language models have emerged as leading-edge generative models, revolutionizing various aspects of human life. However, the practical implementations of these models have also exposed inherent risks, bringing to the forefront their evil sides and sparking concerns regarding their trustworthiness. Despite the wealth of literature on

Manh Hong Duong, Amit Einav

In this work we study the convergence to equilibrium for a (potentially) degenerate nonlinear and nonlocal McKean-Vlasov equation. We show that the solution to this equation is related to the solution of a linear degenerate and/or defective Fokker-Planck equation and employ recent sharp convergence results to obtain an easily computable (and many times sharp

Raghav Soni, Daniel Harnack, Hannah Isermann, Sotaro Fushimi

Legged locomotion is arguably the most suited and versatile mode to deal with natural or unstructured terrains. Intensive research into dynamic walking and running controllers has recently yielded great advances, both in the optimal control and reinforcement learning (RL) literature. Hopping is a challenging dynamic task involving a flight phase and has the

Nils Carqueville

The orbifold construction via topological defects in quantum field theory can either be understood as a state sum construction internal to a given ambient theory, or as the procedure of (identifying and) gauging ordinary and &#34;non-invertible&#34; symmetries. Here we explain how this is rigorously understood in the case of topological QFTs. We provide vari

Adrián Andrada, Alejandro Tolcachier

We study complex solvmanifolds $Γ\backslash G$ with holomorphically trivial canonical bundle. We show that the trivializing section of this bundle can be either invariant or non-invariant by the action of $G$. First we characterize the existence of invariant trivializing sections in terms of the Koszul 1-form $ψ$ canonically associated to $(\mathfrak{g},J)$,

Yin Yifan, Li Shunli, Wu Ke

This study suggests an idea that radiation in a periodic leaky-wave antenna (PLWA) should be considered to be produced with the fundamental mode, regardless of whether it is fast-wave or slow-wave. The idea is different from the conventional PLWA theory, which considers it a fact that a PLWA produces radiation with its fast-wave space harmonic when the funda

Maximilian Protte, Timon Schapeler, Jan Sperling, Tim J. Bartley

Superconducting nanowire single-photon detectors (SNSPDs) have been widely used to study the discrete nature of quantum states of light in the form of photon-counting experiments. We show that SNSPDs can also be used to study continuous variables of optical quantum states by performing homodyne detection at a bandwidth of $400~\mathrm{kHz}$. By measuring the

Marcin Briański, Jędrzej Hodor, Hoang La, Piotr Micek

For every integer $n$ with $n \geq 6$, we prove that the Boolean dimension of a poset consisting of all the subsets of $\{1,\dots,n\}$ equipped with the inclusion relation is strictly less than $n$.

Thomas Braure, Delphine Lazaro, David Hateau, Vincent Brandon

Computed Tomography (CT) is a prominent example of Imaging Inverse Problem highlighting the unrivaled performances of data-driven methods in degraded measurements setups like sparse X-ray projections. Although a significant proportion of deep learning approaches benefit from large supervised datasets, they cannot generalize to new experimental setups. In con

Osvaldo Gramaxo Freitas, Juan Calderón Bustillo, José A. Font, Solange Nunes

We evaluate several neural-network architectures, both convolutional and recurrent, for gravitational-wave time-series feature extraction by performing point parameter estimation on noisy waveforms from binary-black-hole mergers. We build datasets of 100,000 elements for each of four different waveform models (or approximants) in order to test how approximan

Ning Jiang, Ziying Zhou, Jiazheng Zhu, Yibo Wang

Recently, three optical tidal disruption event (TDE) candidates discovered by the Zwicky Transient Facility (ZTF) have been suggested to be coincident with high-energy neutrinos. They all exhibit unusually strong dust infrared (IR) echoes, with their peak times matching the neutrino arrival time even better than the optical peaks. We hereby report on two new

Giovanni Panti

We describe Gauss-type maps as geometric realizations of certain codes in the monoid of nonnegative matrices in the extended modular group. Each such code, together with an appropriate choice of unimodular intervals in P^1R, determines a dual pair of graph-directed iterated function systems, whose attractors contain intervals and constitute the domains of a

Antai Xie, Xinlei Yi, Xiaofan Wang, Ming Cao

This paper studies the privacy-preserving distributed optimization problem under limited communication, where each agent aims to keep its cost function private while minimizing the sum of all agents&#39; cost functions. To this end, we propose two differentially private distributed algorithms under compressed communication. We show that the proposed algorith

Etsuko Itou, Akira Matsumoto, Yuya Tanizaki

We consider three distinct methods to compute the mass spectrum of gauge theories in the Hamiltonian formalism: (1) correlation-function scheme, (2) one-point-function scheme, and (3) dispersion-relation scheme. The first one examines spatial correlation functions as we do in the conventional Euclidean Monte Carlo simulations. The second one uses the boundar

Hamed Babaei Giglou, Jennifer D&#39;Souza, Sören Auer

We propose the LLMs4OL approach, which utilizes Large Language Models (LLMs) for Ontology Learning (OL). LLMs have shown significant advancements in natural language processing, demonstrating their ability to capture complex language patterns in different knowledge domains. Our LLMs4OL paradigm investigates the following hypothesis: \textit{Can LLMs effectiv

Clifford M. Will, Smadar Naoz, Aurélien Hees, Alexandria Tucker

We use 23 years of astrometric and radial velocity data on the orbit of the star S0-2 to constrain a hypothetical intermediate-mass black hole orbiting the massive black hole Sgr A* at the Galactic center. The data place upper limits on variations of the orientation of the stellar orbit (inclination, nodal angle, and pericenter) at levels between 0.02 and 0.

Xin-Yu Tian, Jian Shi, Xiaotong Shen, Kai Song

The dynamic ranking, due to its increasing importance in many applications, is becoming crucial, especially with the collection of voluminous time-dependent data. One such application is sports statistics, where dynamic ranking aids in forecasting the performance of competitive teams, drawing on historical and current data. Despite its usefulness, predicting

Michał Sobieraj

In this paper, we investigate the properties of standard and multilevel Monte Carlo methods for weak approximation of solutions of stochastic differential equations (SDEs) driven by the infinite-dimensional Wiener process and Poisson random measure with Lipschitz payoff function. The error of the truncated dimension randomized numerical scheme, which is dete

Aleksandr Tuxanidy, Daniel Panario

It is proven that, in any given base, there are infinitely many palindromic numbers having at most six prime divisors, each relatively large. The work involves equidistribution estimates for the palindromes in residue classes to large moduli, offering upper bounds for moments and averages of certain products closely related to exponential sums over palindrom

Rabab Abdelfattah, Qing Guo, Xiaoguang Li, Xiaofeng Wang

This paper presents a CLIP-based unsupervised learning method for annotation-free multi-label image classification, including three stages: initialization, training, and inference. At the initialization stage, we take full advantage of the powerful CLIP model and propose a novel approach to extend CLIP for multi-label predictions based on global-local image-

M. D. Adzhymambetov, S. V. Akkelin, Yu. M. Sinyukov

The one- and two-boson momentum spectra are derived in the quantum local-equilibrium canonical ensemble of noninteracting bosons with a fixed particle number constraint. We define the canonical ensemble as a subensemble of events associated with the grand-canonical ensemble. Applying simple hydro-inspired parametrization with parameter values that correspond

Yongxu Fu, Yi Zhang

Surpassing the individual characteristics of the non-Hermitian skin effect (NHSE) and the scale-free (SF) effect observed recently, we systematically exploit the exponential decay behavior of bulk eigenstates via the transfer matrix approach in non-Hermitian systems. We concentrate on one-dimensional (1D) finite-size non-Hermitian systems with 2*2 transfer m