August 2022 arXiv papers — page 69

Léo Bigorgne

The purpose of this paper is twofold. In the first part, we provide a new proof of the global existence of the solutions to the Vlasov-Maxwell system with a small initial distribution function. Our approach relies on vector field methods, together with the Glassey-Strauss decomposition of the electromagnetic field, and does not require any support restrictio

Zhen Zhao, Claudio Verdozzi, Ferdi Aryasetiawan

The magnon Hedin's equations are derived via the Schwinger functional derivative technique, and the resulting self-consistent Green's function method is used to calculate ground state spin patterns and magnetic structure factors for 2-dimensional magnetic systems with frustrated spin-1/2 Heisenberg exchange coupling. Compared to random-phase approximation tr

Andrei Afanasev, Carl E. Carlson, Asmita Mukherjee

Spinor fields with a vortex structure in free space that allow them to have arbitrary integer orbital angular momentum along the direction of motion have been studied for some time. Relatively new is the observation in a certain context that the vortex center of this field structure is, unlike a classical whirlpool, not singular. We point out that there are

Markus Lippl, Michael H. Frosz, Nicolas Y. Joly

We present the advantages of supercontinuum generation in chiral, therefore circularly birefringent, all-normal dispersion fibers. Due to the absence of nonlinear power transfer between the polarization eigenstates of the fiber, chiral all-normal dispersion fibers do not exhibit any polarization instabilities and thus are an ideal platform for low-noise supe

Alex Fontana, Ludovic Bellon

For systems in equilibrium at a temperature $T$, thermal noise and energy damping are related to $T$ through the fluctuation-dissipation theorem (FDT). We study here an extension of the FDT to an out of equilibrium steady state: a microcantilever subject to a constant heat flux. The resulting thermal profile in this spatially extended system interplays with

Ardavan Afshar

In the setting of the integers, Granville, Harper and Soundararajan showed that the upper bound in Hal\'{a}sz's Theorem can be improved for smoothly supported functions. We derive the analogous result for Hal\'{a}sz's Theorem in $\mathbb{F}_q[t]$, and then consider the converse question of when the general upper bound in this version of Hal\'{a}sz's Theorem

Ruowei Xing, Shengchen Li

This paper refers to the extraction of multiple fundamental frequencies (multiple F0) based on PYIN, an algorithm for extracting the fundamental frequency (F0) of monophonic music, and a trained convolutional neural networks (CNN) model, where a pitch salience function of the input signal is produced to estimate the multiple F0. The implementation of these t

Reuben R. W. Wang, John L. Bohn

We present a hydrodynamic model of ultracold, but not yet quantum condensed, dipolar Bosonic gases. Such systems present both $s$-wave and dipolar scattering, the latter of which results in anisotropic transport tensors of thermal conductivity and viscosity. This work presents an analytic derivation of the viscosity tensor coefficients, utilizing the methods

Edward Sanderson, Bogdan J. Matuszewski

Colonoscopy is widely recognised as the gold standard procedure for the early detection of colorectal cancer (CRC). Segmentation is valuable for two significant clinical applications, namely lesion detection and classification, providing means to improve accuracy and robustness. The manual segmentation of polyps in colonoscopy images is time-consuming. As a

Hessa Al-Thani, Yubing Cui, Viswanath Nagarajan

Adaptive submodularity is a fundamental concept in stochastic optimization, with numerous applications such as sensor placement, hypothesis identification and viral marketing. We consider the problem of minimum cost cover of adaptive-submodular functions, and provide a $4(1+\ln Q)$-approximation algorithm, where $Q$ is the goal value. In fact, we consider a

Tomasz Łuczak, Joanna Polcyn, Zahra Rahimi

For graphs $H_1,H_2$ by $r^*(H_1,H_2)$ we denote the minimum number of edges in a graph $G$ on $r(H_1,H_2)$ vertices such that $G\to (H_1,H_2)$. We show that for each pair of natural numbers $k,n$, $k\le n$, where $k$ is odd and $n$ is large enough, we have $$r^*(C_n,C_k)=\lceil (n+1)(2n-1)/2\rceil \,.$$

Ziwei Liu, Zhongqi Miao, Xiaohang Zhan, Jiayun Wang

Real world data often exhibits a long-tailed and open-ended (with unseen classes) distribution. A practical recognition system must balance between majority (head) and minority (tail) classes, generalize across the distribution, and acknowledge novelty upon the instances of unseen classes (open classes). We define Open Long-Tailed Recognition++ (OLTR++) as l

John W. Patty, Elizabeth Maggie Penn

"Banning the Box" refers to a policy campaign aimed at prohibiting employers from soliciting applicant information that could be used to statistically discriminate against categories of applicants (in particular, those with criminal records). In this article, we examine how the concealing or revealing of informative features about an applicant's identity aff

Yoshinori Kanamura, Hyuga Yoshizaki

Periodic integer continued fractions (PICFs) are generalization of the regular periodic continued fractions (RPCFs). It is classical that a RPCF expansion of an irrational number is unique. However, it is no longer unique for a PICF expansion. Hence it is a natural problem to determine all PICF expansions of irrational numbers. In this paper, we determine ce

Peter Gracar, Arne Grauer

We study the contact process on a class of geometric random graphs with scale-free degree distribution, defined on a Poisson point process on $\mathbb{R}^d$. This class includes the age-dependent random connection model and the soft Boolean model. In the ultrasmall regime of these random graphs we provide exact asymptotics for the non-extinction probability

Zachary Kenton, Ramana Kumar, Sebastian Farquhar, Jonathan Richens

Causal models of agents have been used to analyse the safety aspects of machine learning systems. But identifying agents is non-trivial -- often the causal model is just assumed by the modeler without much justification -- and modelling failures can lead to mistakes in the safety analysis. This paper proposes the first formal causal definition of agents -- r

Debaprasanna Kar

In this note, we discuss the following problem: Given a smoothly bounded strongly pseudoconvex domain $D$ in $\mathbb{C}^n$, can we guarantee the existence of geodesics for the Kobayashi--Fuks metric which ``spiral around" in the interior of $D$? We find an affirmative answer to the above question for $n=1$ when $D$ is not simply connected.

Yulin Shao, Deniz Gunduz

Recent progress in deep learning (DL)-based joint source-channel coding (DeepJSCC) has led to a new paradigm of semantic communications. Two salient features of DeepJSCC-based semantic communications are the exploitation of semantic-aware features directly from the source signal, and the discrete-time analog transmission (DTAT) of these features. Compared wi

John W. Patty, Elizabeth Maggie Penn

Algorithmic fairness is a new interdisciplinary field of study focused on how to measure whether a process, or algorithm, may unintentionally produce unfair outcomes, as well as whether or how the potential unfairness of such processes can be mitigated. Statistical discrimination describes a set of informational issues that can induce rational (i.e., Bayesia

Yinghui Xing, Qirui Wu, De Cheng, Shizhou Zhang

With the emergence of large pre-trained vison-language model like CLIP, transferable representations can be adapted to a wide range of downstream tasks via prompt tuning. Prompt tuning tries to probe the beneficial information for downstream tasks from the general knowledge stored in the pre-trained model. A recently proposed method named Context Optimizatio

Alain Connes, Caterina Consani

We give a historical perspective on the role of the cyclic category in the development of cyclic theory. This involves a continuous interplay between the extension in characteristic one and in S-algebras, of the traditional development of cyclic theory, and the geometry of the toposes associated with several small categories involved. We clarify the link bet

Haoran Pan, Jun Zhou, Yuanpeng Liu, Xuequan Lu

6D pose estimation of rigid objects from RGB-D images is crucial for object grasping and manipulation in robotics. Although RGB channels and the depth (D) channel are often complementary, providing respectively the appearance and geometry information, it is still non-trivial how to fully benefit from the two cross-modal data. From the simple yet new observat

Yuge Hu, Joseph Musielewicz, Zachary Ulissi, Andrew J. Medford

Uncertainty quantification (UQ) is important to machine learning (ML) force fields to assess the level of confidence during prediction, as ML models are not inherently physical and can therefore yield catastrophically incorrect predictions. Established a-posteriori UQ methods, including ensemble methods, the dropout method, the delta method, and various heur

Surajit Basak, Andrzej Ptok

The Majorana bound state can be realized in one dimensional chain, in form of two well localized and separated states at both ends of the chain. In this paper, we discuss the case when the potential is smeared at one end of the system. In our investigation, we assume the smearing in form of a quadratic function of position. We show that the smearing potentia

Iskander Mukatayev, Florient Moevus, Benoît Sklénard, Valerio Olevano

X-ray photoemission spectroscopy (XPS) provides direct information on the atomic composition and stoichiometry by measuring core electron binding energies. Moreover, according to the shift of the binding energy, so-called chemical shift, the precise chemical type of bonds can be inferred, which brings additional information on the local structure. In this wo

Zhengyang Zhou, Yang Kuo, Wei Sun, Binwu Wang

Spatiotemporal forecasting is an imperative topic in data science due to its diverse and critical applications in smart cities. Existing works mostly perform consecutive predictions of following steps with observations completely and continuously obtained, where nearest observations can be exploited as key knowledge for instantaneous status estimation. Howev

Daniel W. Boutros, Simon Markfelder, Edriss S. Titi

Onsager's conjecture, which relates the conservation of energy to the regularity of weak solutions of the Euler equations, was completely resolved in recent years. In this work, we pursue an analogue of Onsager's conjecture in the context of the hydrostatic Euler equations (also known as the inviscid primitive equations of oceanic and atmospheric dynamics).

S. A. Venikoudis, F. P. Fronimos

In the present article we showcase how the reheating era can be described properly in the context of Einstein-Gauss-Bonnet gravity assuming that the primordial gravitational waves propagate with the velocity of light. The equations of the duration of reheating along with the reheating temperature are derived and as demonstrated, their expressions are quite s

Farhan Nur Kholid, Dominik Hamara, Ahmad Faisal Bin Hamdan, Guillermo Nava Antonio

Interfaces between heavy metals (HMs) and antiferromagnetic insulators (AFIs) have recently become highly investigated and debated systems in the effort to create spintronic devices able to function at terahertz frequencies. Such heterostructures have great technological potential because AFIs can generate sub-picosecond spin currents which the HMs can conve

Litao Yang, Deval Mehta, Dwarikanath Mahapatra, Zongyuan Ge

Recently, a lot of automated white blood cells (WBC) or leukocyte classification techniques have been developed. However, all of these methods only utilize a single modality microscopic image i.e. either blood smear or fluorescence based, thus missing the potential of a better learning from multimodal images. In this work, we develop an efficient multimodal

Jungho Ahn, Seonghyuk Im, Sang-il Oum

A proper coloring of a graph is \emph{proper conflict-free} if every non-isolated vertex $v$ has a neighbor whose color is unique in the neighborhood of $v$. A proper coloring of a graph is \emph{odd} if for every non-isolated vertex $v$, there is a color appearing an odd number of times in the neighborhood of $v$. For an integer $k$, the \textsc{PCF $k$-Col

Benjamin Cramer, Markus Kreft, Sebastian Billaudelle, Vitali Karasenko

A unique feature of neuromorphic computing is that memory is an implicit part of processing through traces of past information in the system's collective dynamics. The extent of memory about past inputs is commonly quantified by the autocorrelation time of collective dynamics. Based on past experimental evidence, a potential explanation for the underlying au

Juha Kinnunen, Kim Myyryläinen, Dachun Yang, Chenfeng Zhu

This work discusses parabolic Muckenhoupt weights on spaces of homogeneous type, i.e.\ quasi-metric spaces with both a doubling measure and an additional monotone geodesic property. The main results include a characterization in terms of weighted norm inequalities for parabolic maximal operators, a reverse H\"older inequality, and a Jones-type factorization

Yuexin Wang, Sébastien Descotes-Genon, Olivier Deschamps, Lingfeng Li

The physics potential of measuring $B^0_{(s)}\to\pi^0\pi^0$ and $B^0_{(s)}\to\eta\eta$ decays via four-photon final states at Tera-$Z$ phase of CEPC or FCC-ee is investigated in this paper. We propose an electromagnetic calorimeter (ECAL) with both high energy resolution and excellent separation power to efficiently reconstruct $\pi^0$ and $\eta$ from hadron

Ana-Maria Acu, Margareta Heilmann, Ioan Rasa, Andra Seserman

The idea behind Poisson approximation to the binomial distribution was used in [J. de la Cal, F. Luquin, J. Approx. Theory, 68(3), 1992, 322-329] and subsequent papers in order to establish the convergence of suitable sequences of positive linear operators. The proofs in these papers are given using probabilistic methods. We use similar methods, but in analy

Ramesh Sreekantan

We construct indecomposable cycles in the motivic cohomology group $H^3_{{\mathcal M}}(A,{\mathbb Q}(2))$ where $A$ is an Abelian surface over a number field or the function field of a base. When $A$ is the self product of the universal elliptic curve over a modular curve, these cycles can be used to prove algebraicity results for values of higher Green's fu

Tamás Darvas, Kewei Zhang

We prove existence of twisted K\"ahler-Einstein metrics in big cohomology classes, using a divisorial stability condition. In particular, when $-K_X$ is big, we obtain a uniform Yau-Tian-Donaldson existence theorem for K\"ahler-Einstein metrics. To achieve this, we build up from scratch the theory of Fujita-Odaka type delta invariants in the transcendental b

Shan-Liang Zhang, Jinfeng Liao, Guang-You Qin, Enke Wang

Jet quenching has long been regarded as one of the key signatures for the formation of quark-gluon plasma in heavy-ion collisions. Despite significant efforts, the separate identification of quark and gluon jet quenching has remained as a challenge. Here we show that $J/\psi$ in high transverse momentum ($p_\text{T}$) region provides a uniquely sensitive pro

Ignacio Sanchez-Burgos, Jorge R. Espinosa

Salty water is the most abundant electrolyte aqueous mixture on Earth, however, very little is known about the NaCl-saturated solution interfacial free energy. Here, we provide the first direct estimation of this magnitude for several NaCl crystallographic planes by means of the Mold Integration technique, a highly efficient computational method to evaluate

Weiquan Chen, Zhao Dong, Xiangchan Zhu

In this paper we establish a sharp non-uniqueness result for stochastic $d$-dimensional ($d\geq2$) incompressible Navier-Stokes equations. First, for every divergence free initial condition in $L^2$ we show existence of infinite many global in time probabilistically strong and analytically weak solutions in the class $L^\alpha\big(\Omega,L^p_tL^\infty\big)$

Zhenyu Lei, Herun Wan, Wenqian Zhang, Shangbin Feng

Twitter bots are automatic programs operated by malicious actors to manipulate public opinion and spread misinformation. Research efforts have been made to automatically identify bots based on texts and networks on social media. Existing methods only leverage texts or networks alone, and while few works explored the shallow combination of the two modalities,

R. Nakanishi, Y. Takata, H. Seki

Nominal set plays a central role in a group-theoretic extension of finite automata to those over an infinite set of data values. Moerman et al. proposed an active learning algorithm for nominal word automata with the equality symmetry. In this paper, we introduce deterministic bottom-up nominal tree automata (DBNTA), which operate on trees whose nodes are la

Ramesh Sreekantan

If $X$ is a smooth projective variety over ${\mathbb R}$, the Hodge ${\mathcal D}$-conjecture of Beilinson asserts the surjectivity of the regulator map to Deligne cohomology with real coefficients. It is known to be false in general but is true in some special cases like Abelian surfaces and $K3$-surfaces - and still expected to be true when the variety is

Van Duong Dinh, Dinh-Thi Nguyen, Nicolas Rougerie

We study the ground states of a 2D focusing non-linear Schr\"odinger equation with rotation and harmonic trapping. When the strength of the interaction approaches a critical value from below, the system collapses to a profile obtained from the optimizer of a Gagliardo--Nirenberg interpolation inequality. This was established before in the case of fixed rotat

Wenfeng Huang, Xinyun Liang, Baiqiang Zhu, Yuhan Yan

Interferometers are crucial for precision measurements, including gravitational waves, laser ranging, radar, and imaging. The phase sensitivity, the core parameter, can be quantum-enhanced to break the standard quantum limit (SQL) using quantum states. However, quantum states are highly fragile and quickly degrade with losses. We design and demonstrate a qua

Chengxi Zeng, Xinyu Yang, Majid Mirmehdi, Alberto M Gambaruto

We propose Video-TransUNet, a deep architecture for instance segmentation in medical CT videos constructed by integrating temporal feature blending into the TransUNet deep learning framework. In particular, our approach amalgamates strong frame representation via a ResNet CNN backbone, multi-frame feature blending via a Temporal Context Module (TCM), non-loc

Silvia Tosi, Flavia Dell'Agli, Devika Kamath, Paolo Ventura

Aims: We aim to understand the variation of the surface chemistry that occurs during the AGB phase by analysing results from observations of single post-AGB stars in the Magellanic Clouds. We also aim at reconstruct dust formation processes, that are active in the circumstellar envelope of AGB stars. Methods: We study likely single post-AGB sources in the Ma

Najwa Ghannoum

In this paper we study grouplike monoids, these are monoids that contain a group to which we add an ordered set of idempotents. We classify finite categories with two objects having grouplike endomorphism monoids, and we give a count of certain categories with grouplike monoids.

Hao Tang, Feng-Yu Wang

In this paper we focus on nonlinear SPDEs with singularities included in both drift and noise coefficients, for which the Gelfand-triple argument developed for (local) monotone SPDEs turns out to be invalid. We propose a general framework of \emph{proper regularization} to solve such singular SPDEs. As applications, the (local and global) existence is presen

Changxing Miao, Weikui Ye

In this paper, we prove the non-uniqueness of three-dimensional magneto-hydrodynamic (MHD) system in $C([0,T];L^2(\mathbb{T}^3))$ for any initial data in $H^{\bar{\beta}}(\mathbb{T}^3)$~($\bar{\beta}>0$), by exhibiting that the total energy and the cross helicity can be controlled in a given positive time interval. Our results extend the non-uniqueness resul

François Doré, Enrico Formenti, Antonio E. Porreca, Sara Riva

Functional graphs (FGs) model the graph structures used to analyse the behaviour of functions from a discrete set to itself. In turn, such functions are used to study real complex phenomena evolving in time. As the systems involved can be quite large, it is interesting to decompose and factorise them into several subgraphs acting together. Polynomial equatio

Horatiu Nastase, Caio Luiz Tiedt

In this paper we consider the effect of a topological Maxwell term $W(\Phi)F_{\mu\nu}\tilde F^{\mu\nu}$ on holographic transport and thermodynamics in 2+1 dimensions, in the case with a dyonic black hole in the gravity dual. We find that for a constant $W$ the modifications to the thermodynamics are easily quantified, and transport is affected only for $\sig

Fabao Zhang, Xinwen Shu, Luming Sun, Lei Yang

We present the discovery of a sample of 18 low-redshift (z<0.3) galaxies with transient nuclear radio emission. These galaxies are not or weakly detected in the Faint Images of the Radio Sky at Twenty cm survey performed on 1993-2009, but have brightened significantly in the radio flux (by a factor of >5) in the epoch I (2017-2019) observations of Very Large

Lukas Schmid, Mansoor Nasir Cheema, Victor Reijgwart, Roland Siegwart

Exploration of unknown environments is a fundamental problem in robotics and an essential component in numerous applications of autonomous systems. A major challenge in exploring unknown environments is that the robot has to plan with the limited information available at each time step. While most current approaches rely on heuristics and assumption to plan

Dor Elboim, Dominik Schmid

We study the totally asymmetric simple exclusion process with open boundaries in the high density and the low density phase. In the bulk of the two phases, we show that the process on a segment of length $N$ exhibits cutoff at order $N$, while in the intersection of the phases, the coexistence line, the mixing time is of order $N^2$, and no cutoff occurs. In

Sambuddha Majumder, Sooraj Ravindran

In this paper, we have investigated the optical properties of GaAs nanocone (NC), inverted nanocone (INC), and hourglass (HG) shaped solar cells by performing Finite Difference Time Domain (FDTD) simulations. The different structures are compared for their light absorptance, reflectance, transmittance, and photogeneration with traditional cylindrical nanowir

Gang Ma, Jianfeng Wang, Sandi Klavžar

A connected graph $G$ of diameter ${\rm diam}(G) \ge \ell$ is $\ell$-distance-balanced if $|W_{xy}|=|W_{yx}|$ for every $x,y\in V(G)$ with $d_{G}(x,y)=\ell$, where $W_{xy}$ is the set of vertices of $G$ that are closer to $x$ than to $y$. We prove that the generalized Petersen graph $GP(n,k)$ is ${\rm diam}(GP(n,k))$-distance-balanced provided that $n$ is la

John W. Simpson-Porco

We consider the problem of designing a feedback controller which robustly regulates an LTI system to an optimal operating point in the presence of unmeasured disturbances. A general design framework based on so-called optimality models was previously put forward for this class of problems, effectively reducing the problem to that of stabilization of an assoc

Dotan Gazith, Barak Zackay

In High Contrast Imaging, a large instrumental, technological and algorithmic effort is made to reduce residual speckle noise and improve the detection capabilities. In this work, we explore the potential of using a precise physical description of speckle images, in conjunction with the optimal detection statistic to perform High Contrast Imaging. Our method

Qingyun Sun, Jianxin Li, Haonan Yuan, Xingcheng Fu

Topology-imbalance is a graph-specific imbalance problem caused by the uneven topology positions of labeled nodes, which significantly damages the performance of GNNs. What topology-imbalance means and how to measure its impact on graph learning remain under-explored. In this paper, we provide a new understanding of topology-imbalance from a global view of t

Tobias Reinhart, Gemma De les Coves

How complex is an Ising model? Usually, this is measured by the computational complexity of its ground state energy problem. Yet, this complexity measure only distinguishes between planar and non-planar interaction graphs, and thus fails to capture properties such as the average node degree, the number of long range interactions, or the dimensionality of the

Tashreef Muhammad, Anika Bintee Aftab, Md. Mainul Ahsan, Maishameem Meherin Muhu

In modern capital market the price of a stock is often considered to be highly volatile and unpredictable because of various social, financial, political and other dynamic factors. With calculated and thoughtful investment, stock market can ensure a handsome profit with minimal capital investment, while incorrect prediction can easily bring catastrophic fina

Erwin Mönch, Sergey O. Potashin, Katja Lindner, Ivan Yahniuk

We report on a tunable - by magnetic field and gate voltage - conversion of terahertz radiation into a dc current in spatially modulated bilayer graphene. We experimentally demonstrate that the underlying physics is related to the so-called ratchet effect. Our key findings are the direct observation of a sharp cyclotron resonance in the photocurrent and the

Mylène Sauty, Natalia Alyabyeva, Cheyenne Lynsky, Yi Chao Chow

Scanning tunneling electroluminescence microscopy is performed on a 3-nm-thick InGaN/GaN quantum well with x = 0.23 such that the main light emission occurs in the green. The technique is used to map the local recombination properties at a scale of ~10 nm and to correlate them with the surface topography imaged by scanning tunneling microscopy. A 500 nm x 50

Andrea Ferigo, Leonardo Lucio Custode, Giovanni Iacca

Addressing the need for explainable Machine Learning has emerged as one of the most important research directions in modern Artificial Intelligence (AI). While the current dominant paradigm in the field is based on black-box models, typically in the form of (deep) neural networks, these models lack direct interpretability for human users, i.e., their outcome

Raz Lapid, Zvika Haramaty, Moshe Sipper

Deep neural networks (DNNs) are sensitive to adversarial data in a variety of scenarios, including the black-box scenario, where the attacker is only allowed to query the trained model and receive an output. Existing black-box methods for creating adversarial instances are costly, often using gradient estimation or training a replacement network. This paper

Tomo Takahashi, Toshifumi Yamada, Shuichiro Yokoyama

We argue that sneutrinos can be embedded in a multi-field inflation framework where two inflatons and a curvaton simultaneously contribute to primordial fluctuations, which is consistent with current constraints on the spectral index and the tensor-to-scalar ratio from Planck and BICEP/Keck 2018. We also show that the same framework can also explain the bary

Himanshu Kumar, Abeer Banerjee, Sumeet Saurav, Sanjay Singh

Grayscale image colorization is a fascinating application of AI for information restoration. The inherently ill-posed nature of the problem makes it even more challenging since the outputs could be multi-modal. The learning-based methods currently in use produce acceptable results for straightforward cases but usually fail to restore the contextual informati

Marta Tornago, Flavio Giobergia, Luca Menzio, Federico Siviero

Resistive AC-coupled Silicon Detectors (RSDs) are based on the Low Gain Avalanche Diode (LGAD) technology, characterized by a continuous gain layer, and by the innovative introduction of resistive read-out. Thanks to a novel electrode design aimed at maximizing signal sharing, RSD2, the second RSD production by Fondazione Bruno Kessler (FBK), achieves a posi

Ya'acov Peterzil, Anand Pillay, Francoise Point

We study finite-dimensional groups definable in models of the theory of real closed fields with a generic derivation (also known as CODF). We prove that any such group definably embeds in a semialgebraic group. We extend the results to several more general contexts; strongly model complete theories of large geometric fields with a generic derivation, model c

Hongkun Liu, Zican Hu, Qichen Ding, Xueyun Chen

Remote sensing image change detection is of great importance in disaster assessment and urban planning. The mainstream method is to use encoder-decoder models to detect the change region of two input images. Since the change content of remote sensing images has the characteristics of wide scale range and variety, it is necessary to improve the detection accu

I. Martin-de-Santos

Se repasa brevemente la historia y las finanzas islandesas de manera diacr\'onica. Se presenta a Islandia como basti\'on del estallido de la crisis financiera internacional que comienza a gestarse a principios del siglo XXI y cuyo origen se hace evidente en la fecha simb\'olica del a\~no 2008. Se analizan las razones fundamentales de esta crisis, centrandono

Yuhao Ren, Peishen Li, Zhuojun Liu, Zihao Chen

The pursuit of compact lasers with low-thresholds has imposed strict requirements on tight light confinements with minimized radiation losses. Bound states in the continuum (BICs) have been recently demonstrated as an effective mechanism to trap light along the out-of-plane direction, paving the way to low-threshold lasers. To date, most reported BIC lasers

Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey

In a variety of applications, including nonparametric instrumental variable (NPIV) analysis, proximal causal inference under unmeasured confounding, and missing-not-at-random data with shadow variables, we are interested in inference on a continuous linear functional (e.g., average causal effects) of nuisance function (e.g., NPIV regression) defined by condi

Martina Hofmanová, Rongchan Zhu, Xiangchan Zhu

We establish the existence of infinitely many stationary solutions, as well as ergodic stationary solutions, to the three dimensional Navier--Stokes and Euler equations in both deterministic and stochastic settings, driven by additive noise. These solutions belong to the regularity class $C(\mathbb{R};H^{\vartheta})\cap C^{\vartheta}(\mathbb{R};L^{2})$ for s

Zongjie Li, Chaozheng Wang, Zhibo Liu, Haoxuan Wang

Code completion, a highly valuable topic in the software development domain, has been increasingly promoted for use by recent advances in large language models (LLMs). To date, visible LLM-based code completion frameworks such as GitHub Copilot and GPT are trained using deep learning over vast quantities of unstructured text and open source code. As the para

Jonathan D. Smith, Samuel Hall, George Coombs, James Byrne

We introduce a method for long-distance maritime route planning in polar regions, taking into account complex changing environmental conditions. The method allows the construction of optimised routes, describing the three main stages of the process: discrete modelling of the environmental conditions using a non-uniform mesh, the construction of mesh-optimal

Harshit Agrawal, Ari Hietanen, Simo Särkkä

Metal artifact correction is a challenging problem in cone beam computed tomography (CBCT) scanning. Metal implants inserted into the anatomy cause severe artifacts in reconstructed images. Widely used inpainting-based metal artifact reduction (MAR) methods require segmentation of metal traces in the projections as a first step, which is a challenging task.

Xiongjun Zhang, Michael K. Ng

Tensor decomposition is a powerful tool for extracting physically meaningful latent factors from multi-dimensional nonnegative data, and has been an increasing interest in a variety of fields such as image processing, machine learning, and computer vision. In this paper, we propose a sparse nonnegative Tucker decomposition and completion method for the recov

M. J. Nikmehr, R. Nikandish, A. Yassine

A famous result due to L. S. Levy provides a classification of all finitely generated indecomposable modules over Dedekind-like rings. This motivates us to outline an approach to the classification of indecomposable pseudo-absorbing primary multiplication modules with finite-dimensional top over certain kinds of pullback rings. In this paper, we give a compl

Yidong Wang, Hao Wu, Ao Liu, Wenxin Hou

Target-oriented Opinion Words Extraction (TOWE) is a fine-grained sentiment analysis task that aims to extract the corresponding opinion words of a given opinion target from the sentence. Recently, deep learning approaches have made remarkable progress on this task. Nevertheless, the TOWE task still suffers from the scarcity of training data due to the expen

Maxim Susloparov, Artem Krasilov, Evgeny Khorov

Augmented and Virtual Reality (AR/VR) is often called a "killer" application of 5G systems because it imposes very strict Quality of Service (QoS) requirements related to throughput, latency, and reliability. A high-resolution AR/VR flow requires a bandwidth of dozens of MHz. Since the existing low-frequency bands (i.e., below 6 GHz) have limited bandwidth a

Mackenzie L. Blanusa, Carla J. López-Zurita, Stephan Rasp

Climate projection uncertainty can be partitioned into model uncertainty, scenario uncertainty and internal variability. Here, we investigate the different sources of uncertainty in the projected frequencies of daily maximum temperature and precipitation extremes, which are defined as events that exceed the 99.97th percentile. This is done globally using ini

Mohit Vaishnav, Thomas Fel, Ivań Felipe Rodríguez, Thomas Serre

Vision transformers are nowadays the de-facto choice for image classification tasks. There are two broad categories of classification tasks, fine-grained and coarse-grained. In fine-grained classification, the necessity is to discover subtle differences due to the high level of similarity between sub-classes. Such distinctions are often lost as we downscale

Ignacio Loaiza, Alireza Marefat Khah, Nathan Wiebe, Artur F. Izmaylov

We consider different Linear Combination of Unitaries (LCU) decompositions for molecular electronic structure Hamiltonians. Using these LCU decompositions for Hamiltonian simulation on a quantum computer, the main figure of merit is the 1-norm of their coefficients, which is associated with the quantum circuit complexity. It is derived that the lowest possib

Marcin Stawiski, Trevor M. Wilson

The distinguishing index $D'(G)$ of a graph $G$ is the least number of colors necessary to obtain an edge coloring of $G$ that is preserved only by the trivial automorphism. We show that if $G$ is a connected $\alpha$-regular graph for some infinite cardinal $\alpha$ then $D'(G) \le 2$, proving a conjecture of Lehner, Pil\'{s}niak, and Stawiski. We also show

Xiao Li, Qiongxiu Li, Zhanhao Hu, Xiaolin Hu

Machine learning poses severe privacy concerns as it has been shown that the learned models can reveal sensitive information about their training data. Many works have investigated the effect of widely adopted data augmentation and adversarial training techniques, termed data enhancement in the paper, on the privacy leakage of machine learning models. Such p

Pranab J. Deka, Lukas Einkemmer, Mayya Tokman

We present a publicly available software for exponential integrators that computes the $\varphi_l(z)$ functions using polynomial interpolation. The interpolation method at Leja points have recently been shown to be competitive with the traditionally-used Krylov subspace method. The developed framework facilitates easy adaptation into any Python software pack

Justin Zhang, Deborah Lee, Kylie Jungles, Diane Shaltis

Oral Food Challenges (OFCs) are essential to accurately diagnosing food allergy due to the limitations of existing clinical testing. However, some patients are hesitant to undergo OFCs, while those willing suffer from limited access to allergists in rural/community healthcare settings. Despite its success in predicting patient outcomes in other clinical sett

Sören Bartels, Balázs Kovács, Zhangxian Wang

An error estimate for a canonical discretization of the harmonic map heat flow into spheres is derived. The numerical scheme uses standard finite elements with a nodal treatment of linearized unit-length constraints. The analysis is based on elementary approximation results and only uses the discrete weak formulation.

Ying Tang, Jing Liu, Jiang Zhang, Pan Zhang

Nonequilibrium statistical mechanics exhibit a variety of complex phenomena far from equilibrium. It inherits challenges of equilibrium, including accurately describing the joint distribution of a large number of configurations, and also poses new challenges as the distribution evolves over time. Characterizing dynamical phase transitions as an emergent beha

Ko-Hui Michael Fan, Chih-Chung Chang, Kuang-Hsiao-Yin Kongguoluo

In this paper we propose a novel method for semi-supervised anomaly detection (SSAD). Our classifier is named QMS22 as its inception was dated 2022 upon the framework of quadratic multiform separation (QMS), a recently introduced classification model. QMS22 tackles SSAD by solving a multi-class classification problem involving both the training set and the t

Caimei Liu, Min Li, Zhimin Wang, Jun Hu

The Jiangmen underground neutrino observatory (JUNO) is a neutrino project with a 20-kton liquid scintillator detector located at 700-m underground. The large 20-inch PMTs are one of the crucial components of the JUNO experiment aiming to precision neutrino measurements with better than 3% energy resolution at 1 MeV. The excellent energy resolution and a lar

Haoyu Lu, Qiongyi Zhou, Nanyi Fei, Zhiwu Lu

Multimodal learning, especially large-scale multimodal pre-training, has developed rapidly over the past few years and led to the greatest advances in artificial intelligence (AI). Despite its effectiveness, understanding the underlying mechanism of multimodal pre-training models still remains a grand challenge. Revealing the explainability of such models is

Ol'ga Sipacheva

Topological groups whose underlying spaces are basically disconnected, $F$-, or $F'$-spaces but not $P$-spaces are considered. It is proved, in particular, that the existence of a Lindel\"of basically disconnected topological group which is not a $P$-space is equivalent to the existence of a Boolean basically disconnected Lindel\"of group of countable pseudo

Yang-Yang Xie, Feng-Hua Ren, Run-Hong He, Arapat Ablimit

Precise and efficient control of quantum systems is essential to perform quantum information processing tasks. In terms of adiabatic speedup via leakage elimination operator approach, for a closed system, the ideal pulse control conditions have been theoretically derived by P-Q partitioning technique. However, it is a challenge to design the corresponding co

Hedy Attouch, Radu Ioan Bot, Dang-Khoa Nguyen

In a Hilbert setting, we develop a gradient-based dynamic approach for fast solving convex optimization problems. By applying time scaling, averaging, and perturbation techniques to the continuous steepest descent (SD), we obtain high-resolution ODEs of the Nesterov and Ravine methods. These dynamics involve asymptotically vanishing viscous damping and Hessi

Niall Rodgers, Peter Tino, Samuel Johnson

In many real, directed networks, the strongly connected component of nodes which are mutually reachable is very small. This does not fit with current theory, based on random graphs, according to which strong connectivity depends on mean degree and degree-degree correlations. And it has important implications for other properties of real networks and the dyna

Liam J Ruske, Julia M Yeomans

We extend the continuum theory of active nematic fluids to study cell flows and tissue dynamics inside multicellular spheroids, spherical, self-assembled aggregates of cells that are widely used as model systems to study tumour dynamics. Cells near the surface of spheroids have better access to nutrients and therefore proliferate more rapidly than those in t

Pál András Papp, Georg Anegg, A. N. Yzelman

We study the balanced $k$-way hypergraph partitioning problem, with a special focus on its practical applications to manycore scheduling. Given a hypergraph on $n$ nodes, our goal is to partition the node set into $k$ parts of size at most $(1+\epsilon)\cdot \frac{n}{k}$ each, while minimizing the cost of the partitioning, defined as the number of cut hypere