March 2023 arXiv papers — page 53

Hemal Naik, Alex Hoi Hang Chan, Junran Yang, Mathilde Delacoux

Recent advances in machine learning and computer vision are revolutionizing the field of animal behavior by enabling researchers to track the poses and locations of freely moving animals without any marker attachment. However, large datasets of annotated images of animals for markerless pose tracking, especially high-resolution images taken from multiple ang

Lukas Heiland, Marius Hauser, Justus Bogner

Systems with artificial intelligence components, so-called AI-based systems, have gained considerable attention recently. However, many organizations have issues with achieving production readiness with such systems. As a means to improve certain software quality attributes and to address frequently occurring problems, design patterns represent proven soluti

H. Beuther, E. F. van Dishoeck, L. Tychoniec, C. Gieser

Aims: The JWST program JOYS (JWST Observations of Young protoStars) aims at characterizing the physical and chemical properties of young high- and low-mass star-forming regions, in particular the unique mid-infrared diagnostics of the warmer gas and solid-state components. We present early results from the high-mass star formation region IRAS23385+6053. Meth

Łukasz Stępień

In this paper we deal with global approximation of solutions of stochastic differential equations (SDEs) driven by countably dimensional Wiener process. Under certain regularity conditions imposed on the coefficients, we show lower bounds for exact asymptotic error behaviour. For that reason, we analyse separately two classes of admissible algorithms: based

Charlene Kalle, Evgeny Verbitskiy, Benthen Zeegers

The $\beta$-encoder is an analog circuit that converts an input signal $x \in [0,1]$ into a finite bit stream $\{b_i\}$. The bits $\{b_i\}$ are correlated and therefore are not immediately suitable for random number generation, but they can be used to generate bits $\{a_i\}$ that are (nearly) uniformly distributed. In this article we study two such methods.

Shohei Imai, Atsushi Ono

The generation of ultrashort light pulses is essential for the advancement of attosecond science. Here, we show that attosecond pulses approaching the Fourier limit can be generated through optimized optical driving of tunneling particles in solids. We propose an ansatz for the wave function of tunneling electron-hole pairs based on a rigorous expression for

Didier Dubois, Lluis Godo, Henri Prade

Non-additive uncertainty theories, typically possibility theory, belief functions and imprecise probabilities share a common feature with modal logic: the duality properties between possibility and necessity measures, belief and plausibility functions as well as between upper and lower probabilities extend the duality between possibility and necessity modali

Aravind Puthirath Balan, Aditya Kumar, Tanja Scholz, Zhongchong Lin

Two-dimensional van der Waals (vdW) heterostructures are an attractive platform for studying exchange bias due to their defect free and atomically flat interfaces. Chromium thiophosphate (CrPS4), an antiferromagnetic material, possesses uncompensated magnetic spins in a single layer, rendering it a promising candidate for exploring exchange bias phenomena. R

Thomas Norrenbrock, Marco Rudolph, Bodo Rosenhahn

Deep Neural Networks use thousands of mostly incomprehensible features to identify a single class, a decision no human can follow. We propose an interpretable sparse and low dimensional final decision layer in a deep neural network with measurable aspects of interpretability and demonstrate it on fine-grained image classification. We argue that a human can o

Cezary Gonera, Joanna Gonera, Artur Jasiński, Piotr Kosiński

We describe a general procedure which allows to construct, starting from a given Hamiltonian, the whole family of new ones sharing the same set of unparameterized trajectories in phase space. The symmetry structure of this family can be completely characterized provided the symmetries of initial Hamiltonian are known. Our approach covers numerous models cons

Muhammad Asgher, Anosha Karamat, Rimsha Babar, Riasat Ali

In this work, we study the parameterized black hole solution by applying the Newman-Janis approach and also examine the Hawking temperature. We consider a Lagrangian field equation associated with the generalized uncertainty principle to study the motion of boson particles. By using semi-classical phenomenon, we analyze the modified Hawking temperature and g

Samuele Giannini, Lucia Di Virgilio, Marco Bardini, Julian Hausch

There is compelling evidence that charge carriers in organic semiconductors (OSs) self-localize in nano-scale space because of dynamic disorder. Yet, some OSs, in particular recently emerged high-mobility organic molecular crystals, feature reduced mobility at increasing temperature, a hallmark for delocalized band transport. Here we present the temperature-

Alessio Lapponi, Dimitris Moustos, David Edward Bruschi, Stefano Mancini

We propose a model of communication employing two harmonic oscillator detectors interacting through a scalar field in a background Minkowski spacetime. In this way, the scalar field plays the role of a quantum channel, namely a Bosonic Gaussian channel. The classical and quantum capacities of the communication channel are found, assuming that the detectors'

Nikolina Milanović, Lakshmi Pradeep Chitta, Hardi Peter

In addition to a component of the emission that originates from clearly distinguishable coronal loops, the solar corona also exhibits extreme-ultraviolet (EUV) and X-ray ambient emission that is rather diffuse and is often considered undesirable background. Importantly, unlike the generally more structured transition region and chromosphere, the diffuse coro

Lucas Journel, Pierre Monmarché

We introduce and investigate stochastic processes designed to find local minimizers and saddle points of non-convex functions, exploring the landscape more efficiently than the standard noisy gradient descent. The processes switch between two behaviours, a noisy gradient descent and a noisy saddle point search. It is proven to be well-defined and to converge

Riccardo Falcone, Claudio Conti

We consider a free Dirac field in flat spacetime and we derive the representation of the Minkowski vacuum as an element of the Rindler-Fock space. We also compute the statistical operator obtained by tracing away the left wedge. We detail the resulting thermal state for fermionic particles.

Hassan Mohamed Muhi-Aldeen, Maha Ammar Mustafa, Asma A. Abdulrahman, Jabbar Abed Eleiwy

This work is unique in the use of discrete wavelets that were built from or derived from Chebyshev polynomials of the second and third kind, filter the Discrete Second Chebyshev Wavelets Transform (DSCWT), and derive two effective filters. The Filter Discrete Third Chebyshev Wavelets Transform (FDTCWT) is used in the process of analyzing color images and rem

Luke Mosley, Tak-Shing Chan, Alex Gibberd

sparseDFM is an R package for the implementation of popular estimation methods for dynamic factor models (DFMs) including the novel Sparse DFM approach of Mosley et al. (2023). The Sparse DFM ameliorates interpretability issues of factor structure in classic DFMs by constraining the loading matrices to have few non-zero entries (i.e. are sparse). Mosley et a

Alexander Krawczyk, Alexander Gepperth

Conventional replay-based approaches to continual learning (CL) require, for each learning phase with new data, the replay of samples representing all of the previously learned knowledge in order to avoid catastrophic forgetting. Since the amount of learned knowledge grows over time in CL problems, generative replay spends an increasing amount of time just r

Zi-Yong Ge, Yu-Ran Zhang, Franco Nori

We investigate the meson excitations (particle-antiparticle bound states) in quantum many-body scars of a 1D $\mathbb{Z}_2$ lattice gauge theory coupled to a dynamical spin-$\frac{1}{2}$ chain as a matter field. By introducing a string representation of the physical Hilbert space, we express a scar state $\ket {\Psi_{n,l}}$ as a superposition of all string b

Takuya Hirose, Hiroto Shibuya

In this work, we investigate the relation between higher-dimensional gauge theories and stochastic gravitational wave (GW) spectrums caused by their potential. It is known that the higher-dimensional gauge theories can induce the spontaneous symmetry breaking of the gauge symmetry. If the spontaneous symmetry breaking induces the first-order phase transition

David H. Brooks, Jeffrey W. Reep, Ignacio Ugarte-Urra, Harry P. Warren

We assess the on-orbit performance of the flare event trigger for the Hinode EUV Imaging Spectrometer. Our goal is to understand the time-delay between the occurrence of a flare, as defined by a prompt rise in soft X-ray emission, and the initiation of the response observing study. Wide (266$''$) slit patrol images in the He II 256.32A spectral line are used

Tomasz Brzeziński, Małgorzata Hryniewicka

To every Hopf heap or quantum cotorsor of Grunspan a Hopf algebra of translations is associated. This translation Hopf algebra acts on the Hopf heap making it a Hopf-Galois co-object. Conversely, any Hopf-Galois co-object has the natural structure of a Hopf heap with the translation Hopf algebra isomorphic to the acting Hopf algebra. It is then shown that th

Jean-Christophe Pain

We derive a closed-form solution for the Kullback-Leibler divergence between two Fr\'echet extreme-value distributions. The resulting expression is rather simple and involves the Euler-Mascheroni constant.

Iva Šnidarić, Vibor Jelić, Maaijke Mevius, Michiel Brentjens

We present the first deep polarimetric study of Galactic synchrotron emission at low radio frequencies. Our study is based on 21 observations of the European Large Area Infrared Space Observatory Survey-North 1 (ELAIS-N1) field using the Low-Frequency Array (LOFAR) at frequencies from 114.9 to 177.4 MHz. These data are a part of the LOFAR Two-metre Sky Surve

Petra Heck, Gerard Schouten

For an AI solution to evolve from a trained machine learning model into a production-ready AI system, many more things need to be considered than just the performance of the machine learning model. A production-ready AI system needs to be trustworthy, i.e. of high quality. But how to determine this in practice? For traditional software, ISO25000 and its pred

Sreelekshmi Mohan, Sarita Vig, Samir Mandal

Protostellar jets are one of the primary signposts of star formation. A handful of protostellar objects exhibit radio emission from ionized jets, of which a few display negative spectral indices, indicating the presence of synchrotron emission. In this study, we characterize the radio spectra of HH80-81 jet with the help of a numerical model that we have dev

Alessandro D'Andrea, Salvatore Stella

Let $K_n$ denote Ganyushkin-Kudryavtseva-Mazorchuk's generalization of Kiselman's semigroups. We show that the sequence $2^{-n/2}\cdot \log|K_n|$ admits finite limits as $n$ grows to infinity both on odd and even values.

Tomas Vojir, Jan Sochman, Rahaf Aljundi, Jiri Matas

Out-of-distribution detection is a common issue in deploying vision models in practice and solving it is an essential building block in safety critical applications. Most of the existing OOD detection solutions focus on improving the OOD robustness of a classification model trained exclusively on in-distribution (ID) data. In this work, we take a different a

A. A. Vasil'eva

Order estimates for the Kolmogorov widths of an intersection of two finite-dimensional balls in a mixed norm under some conditions on the parameters are obtained.

Rubén Campoy

Different notions on regularity of sets and of collection of sets play an important role in the analysis of the convergence of projection algorithms in nonconvex scenarios. While some projection algorithms can be applied to feasibility problems defined by finitely many sets, some other require the use of a product space reformulation to construct equivalent

Leyou Xu, Bo Zhou

In this paper, we give tight bounds for the normalized Laplacian eigenvalues of hypergraphs that are not necessarily uniform, and provide an edge version interlacing theorem, a Cheeger inequality, and a discrepancy inequality that are related to the normalized Laplacian eigenvalues for uniform hypergraphs.

Maximilian Kotz, Carsten Timm

We develop a topological classification of non-Hermitian effective Hamiltonians that depend on momentum and frequency. Such effective Hamiltonians are in one-to-one correspondence to single-particle Green's functions of systems that satisfy translational invariance in space and time but may be interacting or open. We employ K-theory, which for the special ca

Jan Draisma, Sarah Eggleston, Rudi Pendavingh, Johannes Rau

Given a complex vector subspace $V$ of $\mathbb{C}^n$, the dimension of the amoeba of $V \cap (\mathbb{C}^*)^n$ depends only on the matroid that $V$ defines on the ground set $\{1,\ldots,n\}$. Here we prove that this dimension is given by the minimum of a certain function over all partitions of the ground set, as previously conjectured by Rau. We also prove

M. M. Chernyavskij, A. V. Lebedev, Yu. V. Trubnikov

In the article we develop Euler-Lagrange method and calculate all the roots of an arbitrary complex polynomial $P(z)$ on the base of calculation (similar to the Bernoulli-Aitken-Nikiporets methods) of the limits of ratios of Hadamard determinants built by means of coefficients of expansions into Taylor and Laurent series of the function~$\frac{P'(z)}{P(z)}$.

Andrew Gibbs, David P. Hewett, Botond Major

We consider the numerical evaluation of a class of double integrals with respect to a pair of self-similar measures over a self-similar fractal set (the attractor of an iterated function system), with a weakly singular integrand of logarithmic or algebraic type. In a recent paper [Gibbs, Hewett and Moiola, Numer. Alg., 2023] it was shown that when the fracta

Matthias Himmelmann, Myfanwy E. Evans

Framework materials and their deformations provide a compelling relation between materials science and algebraic geometry. Physical distance constraints within the material transform into polynomial constraints, making algebraic geometry and associated numerical strategies feasible for finding equilibrium configurations and deformation pathways. In this pape

Kiriko Tomita, Shuichi Makita, Naoki Fukutake, Rion Morishita

A new formulation of lateral imaging process of point-scanning optical coherence tomography (OCT) and a new differential contrast method designed by using this formulation are presented. The formulation is based on a mathematical sample model called the dispersed scatterer model (DSM), in which the sample is represented as a material with a spatially slowly

Azza Bouleimen, Luca Luceri, Felipe Cardoso, Luca Botturi

With the spread of high-speed Internet and portable smart devices, the way people access and consume information has drastically changed. However, this presents many challenges, including information overload, personal data leakage, and misinformation diffusion. Across the spectrum of risks that Internet users face nowadays, this work focuses on understandin

Liping Yi, Gang Wang, Xiaoguang Liu, Zhuan Shi

Federated learning (FL) is an emerging machine learning paradigm that allows multiple parties to train a shared model collaboratively in a privacy-preserving manner. Existing horizontal FL methods generally assume that the FL server and clients hold the same model structure. However, due to system heterogeneity and the need for personalization, enabling clie

Roman Dmytryshyn, Serhii Sharyn

The paper deals with the problem of approximating the functions of several variables by branched continued fractions, in particular, multidimensional A- and J-fractions with independent variables. A generalization of Gragg's algorithm is constructed that enables us to compute, by the coefficients of the given formal multiple power series, the coefficients of

Toshio Suzuki

Comparing the moments of the charge distribution in the mean-field models with experimental values from electron scattering, each value of the related moments of the point proton and neutron distributions is estimated in $^{40}$Ca, $^{48}$Ca and $^{208}$Pb by the least-squares analysis(LSA).

Tommaso Bruno, Marco M. Peloso, Maria Vallarino

On a general Lie group $G$ endowed with a sub-Riemannian structure and of local dimension $d$, we characterize the pointwise multipliers of Triebel--Lizorkin spaces $F^{p,q}_{\alpha}$ for $p,q\in (1,\infty)$ and $\alpha>d/p$, and those of Besov spaces $B^{p,q}_{\alpha}$ for $q\in [1,\infty]$, $p>d$ and $d/p< \alpha<1$. When $G$ is stratified, we extend the l

Zhiwen Zuo, Lei Zhao, Ailin Li, Zhizhong Wang

This paper presents a new adversarial training framework for image inpainting with segmentation confusion adversarial training (SCAT) and contrastive learning. SCAT plays an adversarial game between an inpainting generator and a segmentation network, which provides pixel-level local training signals and can adapt to images with free-form holes. By combining

Haoyu Chen, Jinjin Gu, Yihao Liu, Salma Abdel Magid

When capturing and storing images, devices inevitably introduce noise. Reducing this noise is a critical task called image denoising. Deep learning has become the de facto method for image denoising, especially with the emergence of Transformer-based models that have achieved notable state-of-the-art results on various image tasks. However, deep learning-bas

Yuxuan Duan, Xuhong Zhang, Chuer Yu, Zonghui Wang

Recently, face swapping has been developing rapidly and achieved a surprising reality, raising concerns about fake content. As a countermeasure, various detection approaches have been proposed and achieved promising performance. However, most existing detectors struggle to maintain performance on unseen face swapping methods and low-quality images. Apart fro

Y. S. Solanke, Sandhya Mhaske, D. D. Pawar, V. J. Dagwal

Present work deals with the two fluid Bianchi Type-V cosmological models consisting of matter and radiating source in the $f(R, T)$ theory of gravity studied by Harko et al. (2011). In this paper, we developed a new idea about $f(R, T)$ gravity with the help of two fluids: one fluid is matter field modeling material content of the Universe and other fluid is

Quanzhou Li, Jingbo Wang, Chen Change Loy, Bo Dai

Digital human motion synthesis is a vibrant research field with applications in movies, AR/VR, and video games. Whereas methods were proposed to generate natural and realistic human motions, most only focus on modeling humans and largely ignore object movements. Generating task-oriented human-object interaction motions in simulation is challenging. For diffe

Piotr Kotko, Leszek Motyka, Anna Stasto

We investigate production of $J/\psi$ mesons in hadron-hadron collisions, defined as low invariant mass $c\bar{c}$ singlets produced in a mixture of perturbative and nonperturbative mechanisms provided by the PYTHIA Monte Carlo. We find that in this model the color reconnection mechanism, which breaks the factorization, is essential to reasonably describe th

Sven Jandura, Vineesha Srivastava, Laura Pecorari, Gavin Brennen

We present two protocols for implementing deterministic non-local multi-qubit quantum gates on qubits coupled to a common cavity mode. The protocols rely only on a classical drive of the cavity modes, while no external drive of the qubits is required. In the first protocol, the state of the cavity follows a closed trajectory in phase space and accumulates a

Jing Zhao, Heliang Zheng, Chaoyue Wang, Long Lan

The advent of open-source AI communities has produced a cornucopia of powerful text-guided diffusion models that are trained on various datasets. While few explorations have been conducted on ensembling such models to combine their strengths. In this work, we propose a simple yet effective method called Saliency-aware Noise Blending (SNB) that can empower th

Caroline Bauzet, Flore Nabet, Kerstin Schmitz, Aleksandra Zimmermann

We propose a two-point flux approximation finite-volume scheme for a stochastic non-linear parabolic equation with a multiplicative noise. The time discretization is implicit except for the stochastic noise term in order to be compatible with stochastic integration in the sense of It\^{o}. We show existence and uniqueness of solutions to the scheme and the a

Natalia P. Bondarenko

This paper is concerned with the inverse spectral problem for the third-order differential equation with distribution coefficient. The inverse problem consists in the recovery of the differential expression coefficients from the spectral data of two boundary value problems with separated boundary conditions. For this inverse problem, we solve the most fundam

Kilian Zepf, Selma Wanna, Marco Miani, Juston Moore

Image segmentation relies heavily on neural networks which are known to be overconfident, especially when making predictions on out-of-distribution (OOD) images. This is a common scenario in the medical domain due to variations in equipment, acquisition sites, or image corruptions. This work addresses the challenge of OOD detection by proposing Laplacian Seg

Yi Lin, Zhongchen Zhao, Zhengjie ZHU, Lisheng Wang

Multiple instance learning (MIL) has emerged as a popular method for classifying histopathology whole slide images (WSIs). However, existing approaches typically rely on pre-trained models from large natural image datasets, such as ImageNet, to generate instance features, which can be sub-optimal due to the significant differences between natural images and

Yuiko Sakuma, Masato Ishii, Takuya Narihira

We address the challenge of training a large supernet for the object detection task, using a relatively small amount of training data. Specifically, we propose an efficient supernet-based neural architecture search (NAS) method that uses search space pruning. The search space defined by the supernet is pruned by removing candidate models that are predicted t

Dániel Németh

This is a Ph.D. thesis that presents the author's findings in the area of Causal Dynamical Triangulations. In compliance with Jagiellonian University of Krak\'ow regulations, the document consists of six publications and a general summary, which serves as a guide to assist readers in navigating through the publications. Although the six publications that con

Marcel Cech, Igor Lesanovsky, Beatriz Olmos

Atomic emitter ensembles couple collectively to the radiation field. Although an excitation on a single emitter may be short-lived, a collection of them can contain a photon several orders of magnitude longer than the single emitter lifetime. We provide the exact conditions for optimal absorption, long-lived and dispersionless storage, and release, of a sing

Fumiaki Nakai, Takashi Uneyama

Non-Gaussian diffusion was recently observed in gas mixtures with mass and fraction contrast [F. Nakai et al, Phys. Rev. E 107, 014605 (2023)]. The mean square displacement of a minor gas particle with a small mass is linear in time, while the displacement distribution deviates from the Gaussian distribution, which is called the Brownian yet non-Gaussian dif

Xiang-Quan Deng, Shan-Gui Zhou

The multidimensionally-constrained covariant density functional theories (MDC-CDFTs) have been developed to study the influence of octupole and triaxial deformations on the ground state and fission properties. In this paper, we present a brief review of the applications of MDC-CDFTs and discuss the results of a systematical study of even-$A$ uranium isotopes

Ching Pui Wan, Tung Li, Jason Min Wang

Reinforcement learning has been applied in operation research and has shown promise in solving large combinatorial optimization problems. However, existing works focus on developing neural network architectures for certain problems. These works lack the flexibility to incorporate recent advances in reinforcement learning, as well as the flexibility of custom

Naruo Ohga, Sosuke Ito, Artemy Kolchinsky

The principle of microscopic reversibility says that, in equilibrium, two-time cross-correlations are symmetric under the exchange of observables. Thus, the asymmetry of cross-correlations is a fundamental, measurable, and often-used statistical signature of deviation from equilibrium. Here we find a simple and universal inequality that bounds the magnitude

Eli Bagno, Estrella Eisenberg, Shulamit Reches, Moriah Sigron

A permutation is called {\it {block-wise simple}} if it contains no interval of the form $p_1\oplus p_2$ or $p_1 \ominus p_2$. We present this new set of permutations and explore some of its combinatorial properties. We present a generating function for this set, as well as a recursive formula for counting block-wise simple permutations. Following Tenner, wh

Pierre Guetschel, Théodore Papadopoulo, Michael Tangermann

Neurophysiological time series recordings like the electroencephalogram (EEG) or local field potentials are obtained from multiple sensors. They can be decoded by machine learning models in order to estimate the ongoing brain state of a patient or healthy user. In a brain-computer interface (BCI), this decoded brain state information can be used with minimal

Alexander Azarov, Javier García Fernández, Junlei Zhao, Flyura Djurabekova

Radiation tolerance is determined as the ability of crystalline materials to withstand the accumulation of the radiation induced disorder. Nevertheless, for sufficiently high fluences, in all by far known semiconductors it ends up with either very high disorder levels or amorphization. Here we show that gamma/beta double polymorph Ga2O3 structures exhibit re

Elif Ceren Gok Yildirim, Murat Onur Yildirim, Mert Kilickaya, Joaquin Vanschoren

Class-Incremental Learning aims to update a deep classifier to learn new categories while maintaining or improving its accuracy on previously observed classes. Common methods to prevent forgetting previously learned classes include regularizing the neural network updates and storing exemplars in memory, which come with hyperparameters such as the learning ra

Cheng-Shang Chang

Various recent experimental results show that large language models (LLM) exhibit emergent abilities that are not present in small models. System performance is greatly improved after passing a certain critical threshold of scale. In this letter, we provide a simple explanation for such a phase transition phenomenon. For this, we model an LLM as a sequence-t

Yi Lin, Xiao Fang, Dong Zhang, Kwang-Ting Cheng

Recently, the advent of vision Transformer (ViT) has brought substantial advancements in 3D dataset benchmarks, particularly in 3D volumetric medical image segmentation (Vol-MedSeg). Concurrently, multi-layer perceptron (MLP) network has regained popularity among researchers due to their comparable results to ViT, albeit with the exclusion of the resource-in

Yuan Zhang, Chuanyi Li, Yu Sheng, Jidong Ge

In formal procedure of civil cases, the textual materials provided by different parties describe the development process of the cases. It is a difficult but necessary task to extract the key information for the cases from these textual materials and to clarify the dispute focus of related parties. Currently, officers read the materials manually and use metho

Jeongun Ryu, Aaron Valero Puche, JaeWoong Shin, Seonwook Park

Cell detection is a fundamental task in computational pathology that can be used for extracting high-level medical information from whole-slide images. For accurate cell detection, pathologists often zoom out to understand the tissue-level structures and zoom in to classify cells based on their morphology and the surrounding context. However, there is a lack

Yanjun Ji, Kathrin F. Koenig, Ilia Polian

Vigorous optimization of quantum gates has led to bipotent quantum architectures, where the optimized gates are available for some qubits but not for others. However, such gate-level improvements limit the application of user-side pulse-level optimizations, which have proven effective for quantum circuits with a high level of regularity, such as the ansatz c

Yufeng Wei, Junzhe Cao, Xing Ji, Kun Xu

With wave-particle decomposition, a unified gas-kinetic wave-particle (UGKWP) method has been developed for the multiscale flow simulations. The UGKWP method captures the transport process in all flow regimes without kinetic solver's constraint on the numerical mesh size and time step being less than the particle mean free path and collision time. In the cur

Xiang-Quan Deng, Shan-Gui Zhou

Two actinide isotopes, $^{241}$Am and $^{244}$Cm, produced and chemically purified by the HFIR/REDC complex at ORNL are candidates for target materials of heavy-ion fusion reaction experiments for the synthesis of new superheavy elements (SHEs) with $Z>118$. In the framework of the dinuclear system model with a dynamical potential energy surface (DNS-DyPES m

Wu-Hao Cai, Ying Tian, Rui-Bo Jin

We theoretically investigate the preparation of pure-state single-photon source from 14 birefringent crystals (CMTC, THI, LiIO$_3$, AAS, HGS, CGA, TAS, AGS, AGSe, GaSe, LIS, LISe, LGS, and LGSe) and 8 periodic poling crystals (LT, LN, KTP, KN, BaTiO$_3$, MgBaF$_4$, PMN-0.38PT, and OP-ZnSe) in a wavelength range from 1224 nm to 11650 nm. The three kinds of gr

Yongbin Du, Xiangdong Zhang

In this paper, we investigate the topological number of de-Sitter black hole solutions with different charges $(q)$ and rotational $(a)$ parameters. By using generalized free energy and Duan's $\phi$-mapping topological current theory, we find that the topological numbers of black holes can still be classified as three types. In addition, we interestingly fo

Dong Wang, Pei-Chao Cao, Yanxiang Wang, Minghong Qi

Significant progress in manipulating heat diffusion has been achieved with the advent of non-Hermitian physics and topology. However, previous studies on diffusive systems have primarily concentrated on isolated cases, where fields decay exponentially over time. In practical scenarios, systems inevitably interact with external environments, making it essenti

Letizia Angeli, Dan Crisan, Michela Ottobre

We prove a general criterion providing sufficient conditions under which a time-discretiziation of a given Stochastic Differential Equation (SDE) is a uniform in time approximation of the SDE. The criterion is also, to a certain extent, discussed in the paper, necessary. Using such a criterion we then analyse the convergence properties of numerical methods f

Longfei Song, Juliette Cardoletti, Alfredo Blazquez Martinez, Andreja Bencan

Integration of thin-film oxide piezoelectrics on glass is imperative for the next generation of transparent electronics to attain sensing and actuating functions. However, their crystallization temperature (above 650 {\deg}C) is incompatible with most glasses. We developed a flash lamp process for growth of piezoelectric lead zirconate titanate films. The pr

Bo Zhang, Zuheng Ming, Wei Feng, Yaqian Liu

To benefit the complementary information between heterogeneous data, we introduce a new Multimodal Transformer (MMFormer) for Remote Sensing (RS) image classification using Hyperspectral Image (HSI) accompanied by another source of data such as Light Detection and Ranging (LiDAR). Compared with traditional Vision Transformer (ViT) lacking inductive biases of

Yun Liu, Xuefeng Yan, Zhilei Chen, Zhiqi Li

Self-supervised learning is attracting large attention in point cloud understanding. However, exploring discriminative and transferable features still remains challenging due to their nature of irregularity and sparsity. We propose a geometrically and adaptively masked auto-encoder for self-supervised learning on point clouds, termed \textit{PointGame}. Poin

Hyukhun Koh, Haesung Pyun, Nakyeong Yang, Kyomin Jung

In Task Oriented Dialogue (TOD) system, detecting and inducing new intents are two main challenges to apply the system in the real world. In this paper, we suggest the semantic multi-view model to resolve these two challenges: (1) SBERT for General Embedding (GE), (2) Multi Domain Batch (MDB) for dialogue domain knowledge, and (3) Proxy Gradient Transfer (PG

Janis Siebrecht, Haonan Huang, Piotr Kot, Robert Drost

Magnetic impurities on superconductors lead to bound states within the superconducting gap, so called Yu-Shiba-Rusinov (YSR) states. They are parity protected, which enhances their lifetime, but makes it more difficult to excite them. Here, we realize the excitation of YSR states by microwaves facilitated by the tunnel coupling to another superconducting ele

Yaomin Huang, Ning Liu, Zhengping Che, Zhiyuan Xu

Channel pruning can effectively reduce both computational cost and memory footprint of the original network while keeping a comparable accuracy performance. Though great success has been achieved in channel pruning for 2D image-based convolutional networks (CNNs), existing works seldom extend the channel pruning methods to 3D point-based neural networks (PNN

Hongyu Liu, Shen Zhang

We propose and study several inverse problems for the mean field games (MFG) system in a bounded domain. Our focus is on simultaneously recovering the running cost and the Hamiltonian within the MFG system by the associated boundary observation. There are several technical novelties that make the study intriguing and challenging. First, the MFG system couple

Zhibo Yang, Rujiao Long, Pengfei Wang, Sibo Song

Recently, Visual Information Extraction (VIE) has been becoming increasingly important in both the academia and industry, due to the wide range of real-world applications. Previously, numerous works have been proposed to tackle this problem. However, the benchmarks used to assess these methods are relatively plain, i.e., scenarios with real-world complexity

Diana Cholakova, Slavka Tcholakova, Nikolai Denkov

Triacylglycerols (TAGs) are among the most important ingredients in food, cosmetic and pharmaceutical products. Many physical properties of such products, incl. morphology, texture and rheology, are determined by the phase behaviour of the included TAGs. Triglycerides are also of special interest for the production of solid lipid nanoparticles, applied for c

Liu Ziyin, Botao Li, Tomer Galanti, Masahito Ueda

Characterizing and understanding the dynamics of stochastic gradient descent (SGD) around saddle points remains an open problem. We first show that saddle points in neural networks can be divided into two types, among which the Type-II saddles are especially difficult to escape from because the gradient noise vanishes at the saddle. The dynamics of SGD aroun

Xin Liang, Ren-Cang Li

This paper is concerned with establishing a trace minimization principle for two Hermitian matrix pairs. Specifically, we will answer the question: when is $\inf_X\operatorname{tr}(\widehat AX^{\rm H}AX)$ subject to $\widehat BX^{\rm H}BX=I$ (the identity matrix of apt size) finite? Sufficient and necessary conditions are obtained and, when the infimum is fi

En Xu, Zhiwen Yu, Ying Zhang, Bin Guo

Top-N recommendation aims to recommend each consumer a small set of N items from a large collection of items, and its accuracy is one of the most common indexes to evaluate the performance of a recommendation system. While a large number of algorithms are proposed to push the Top-N accuracy by learning the user preference from their history purchase data, a

Heng Cai, Shumeng Li, Lei Qi, Qian Yu

Recent trends in semi-supervised learning have significantly boosted the performance of 3D semi-supervised medical image segmentation. Compared with 2D images, 3D medical volumes involve information from different directions, e.g., transverse, sagittal, and coronal planes, so as to naturally provide complementary views. These complementary views and the intr

Mengyao Lyu, Jundong Zhou, Hui Chen, Yijie Huang

Active learning selects informative samples for annotation within budget, which has proven efficient recently on object detection. However, the widely used active detection benchmarks conduct image-level evaluation, which is unrealistic in human workload estimation and biased towards crowded images. Furthermore, existing methods still perform image-level ann

Theresa Graefe

In 2013, TSOs from the Central European Region complained to the Agency for the Cooperation of Energy Regulators because of increasing unplanned flows that were presumed to be caused by a joint German-Austrian bidding zone in the European electricity market. This paper empirically analyses the effects of the split of this bidding zone in 2018 on planned and

Koki Fukusumi, Soichiro Morisaki, Teruaki Suyama

If dark matter is a light scalar field weakly interacting with elementary particles, such a field induces oscillations of the physical constants, which results in time-varying force acting on macroscopic objects. In this paper, we report on a search for such a signal in the data of the two LIGO detectors during their third observing run (O3). We focus on the

Zhuo Huang, Miaoxi Zhu, Xiaobo Xia, Li Shen

Robust generalization aims to tackle the most challenging data distributions which are rare in the training set and contain severe noises, i.e., photon-limited corruptions. Common solutions such as distributionally robust optimization (DRO) focus on the worst-case empirical risk to ensure low training error on the uncommon noisy distributions. However, due t

Hadjer Benmeziane, Amine Ziad Ounnoughene, Imane Hamzaoui, Younes Bouhadjar

Spiking neural networks (SNNs) have gained attention as a promising alternative to traditional artificial neural networks (ANNs) due to their potential for energy efficiency and their ability to model spiking behavior in biological systems. However, the training of SNNs is still a challenging problem, and new techniques are needed to improve their performanc

Yongmin Yoo, Cheonkam Jeong, Sanguk Gim, Junwon Lee

Patent similarity analysis plays a crucial role in evaluating the risk of patent infringement. Nonetheless, this analysis is predominantly conducted manually by legal experts, often resulting in a time-consuming process. Recent advances in natural language processing technology offer a promising avenue for automating this process. However, methods for measur

Jorge S. Garcia, Tomoki Ohsawa

We extend the method of Controlled Lagrangians to nonholonomic Euler--Poincar\'e equations with advected parameters, specifically to those mechanical systems on Lie groups whose symmetry is broken not only by a potential force but also by nonholonomic constraints. We introduce advected-parameter-dependent quasivelocities in order to systematically eliminate

Aru Beri, Rahul Sharma, Pinaki Roy, Vishal Gaur

We report results from a study of XTE J1739-285, a transient neutron star low mass X-ray binary observed with AstroSat and NuSTAR during its 2019-2020 outburst. We detected accretion-powered X-ray pulsations at 386 Hz during very short intervals (0.5--1 s) of X-ray flares. These flares were observed during the 2019 observation of XTE J1739-285. During this o

Thomas Hall, Max Kölbl, Koji Matsushita, Sora Miyashita

In this paper, we study nearly Gorensteinness of Ehrhart rings arising from lattice polytopes. We give necessary conditions and sufficient conditions on lattice polytopes for their Ehrhart rings to be nearly Gorenstein. Using this, we give an efficient method for constructing nearly Gorenstein polytopes. Moreover, we determine the structure of nearly Gorenst

Jihao Liu, Yujie Luo, Fanjun Meng

In this paper, we prove the rational coefficient case of the global ACC for foliated threefolds. Specifically, we consider any lc foliated log Calabi-Yau triple $(X,\mathcal{F},B)$ of dimension $3$ whose coefficients belong to a set $\Gamma$ of rational numbers satisfying the descending chain condition, and prove that the coefficients of $B$ belong to a fini