December 2024 arXiv papers — page 124

Daniele Villa, Nicolas Dubuit, Olivier Agullo, Xavier Garbet

A novel coalescence process is shown to take place in plasma fluid simulations, leading to the formation of large-scale magnetic islands that become dynamically important in the system. The parametric dependence of the process on the plasma $\beta$ and the background magnetic shear is studied, and the process is broken down at a fundamental level, allowing t

T. Benest Couzinou, O. Mousis, G. Danger, A. Schneeberger

Complex organic molecules serve as indicators of molecular diversity. Their detection on comets, planets, and moons has prompted inquiries into their origins, particularly the conditions conducive to their formation. One hypothesis suggests that the UV irradiation of icy grains in the protosolar nebula generates significant molecular complexity, a hypothesis

Michalis Stavrou, Benjamin Chacon, Maria Farsari, Anna Maria Pappa

Ultrafast nonlinear optical (NLO) response, fast carrier recovery, broadband absorption, and resistance to radiation and heat make 2D materials promising for photonic technologies. However, low electronic conductivity and carrier concentration limit the performance of semiconducting or semimetallic materials. This work investigates the ultrafast NLO properti

Manav Chaudhary, Harshit Gupta, Savita Bhat, Vasudeva Varma

Traditional evaluation metrics like BLEU and ROUGE fall short when capturing the nuanced qualities of generated text, particularly when there is no single ground truth. In this paper, we explore the potential of Large Language Models (LLMs), specifically Google Gemini 1, to serve as automatic evaluators for non-standardized metrics in summarization and dialo

Kiran Bajar, Rounak Chatterjee, Vikas S. Bhat, Sushil Mujumdar

High-dimensional quantum entanglement in photons offers notable technological advancements over traditional qubit-based systems, including increased information density and enhanced security. However, such high-dimensional states are vulnerable to disruption by complex disordered media, presenting significant challenges in practical applications. Spatially-e

Ayda Najafzadeh

The detection of quantum aspects of gravity remains one of the most elusive challenges in modern physics. In this paper, we develop a comprehensive theoretical framework for the gravitational Aharonov-Bohm (AB) effect, extending previous classical models to a fully quantum description. By quantizing the gravitational field and modeling its interaction with a

Weipeng Yao, Ronan Lelièvre, Itamar Cohen, Tessa Waltenspiel

We present the results of the second commissioning phase of the short-focal-length area of the Apollon laser facility (located in Saclay, France), which was performed with the main laser beam (F1), scaled to a peak power of 2 PetaWatt. Under the conditions that were tested, this beam delivered on-target pulses of maximum energy up to 45 J and 22 fs duration.

Gláuber C. Dorsch, Thomas Konstandin, Enrico Perboni, Daniel A. Pinto

Cosmological phase transitions can give rise to intriguing phenomena, such as baryogenesis or a stochastic gravitational wave background, due to nucleation and percolation of vacuum bubbles in the primordial plasma. A key parameter for predicting these relics is the bubble wall velocity, whose computation relies on solving the Boltzmann equations of the vari

Bofang Jia, Pengxiang Ding, Can Cui, Mingyang Sun

Visual-motor policy learning has advanced with architectures like diffusion-based policies, known for modeling complex robotic trajectories. However, their prolonged inference times hinder high-frequency control tasks requiring real-time feedback. While consistency distillation (CD) accelerates inference, it introduces errors that compromise action quality.

Johan Kwisthout, Andrew Schroeder

The MAP problem in Bayesian networks is notoriously intractable, even when approximated. In an earlier paper we introduced the Most Frugal Explanation heuristic approach to solving MAP, by partitioning the set of intermediate variables (neither observed nor part of the MAP variables) into a set of relevant variables, which are marginalized out, and irrelevan

Sourav Banerjee, Anush Mahajan, Ayushi Agarwal, Eishkaran Singh

Natural Language Inference (NLI) tasks require identifying the relationship between sentence pairs, typically classified as entailment, contradiction, or neutrality. While the current state-of-the-art (SOTA) model, Entailment Few-Shot Learning (EFL), achieves a 93.1% accuracy on the Stanford Natural Language Inference (SNLI) dataset, further advancements are

Chunyu Li, Chao Zhang, Weikai Xu, Jingyu Lin

End-to-end audio-conditioned latent diffusion models (LDMs) have been widely adopted for audio-driven portrait animation, demonstrating their effectiveness in generating lifelike and high-resolution talking videos. However, direct application of audio-conditioned LDMs to lip-synchronization (lip-sync) tasks results in suboptimal lip-sync accuracy. Through an

Qingqiang Sun, Chaoqi Chen, Ziyue Qiao, Xubin Zheng

Most graph contrastive learning (GCL) methods heavily rely on cross-view contrast, thus facing several concomitant challenges, such as the complexity of designing effective augmentations, the potential for information loss between views, and increased computational costs. To mitigate reliance on cross-view contrasts, we propose \ttt{SIGNA}, a novel single-vi

Arup Biswas, Johan L. A. Dubbeldam, Trifce Sandev, Arnab Pal

We examine the behavior of a colloidal particle immersed in a viscoelastic bath undergoing stochastic resetting at a rate $r$. Microscopic probes suspended in viscoelastic environment do not follow the classical theory of Brownian motion. This is primarily because the memory from successive collisions between the medium particles and the probes does not nece

Ruyuan Zhang, Jinguang Han

Federated learning (FL) based on cloud servers is a distributed machine learning framework that involves an aggregator and multiple clients, which allows multiple clients to collaborate in training a shared model without exchanging data. Considering the confidentiality of training data, several schemes employing functional encryption (FE) have been presented

Ke Li, Di Wang, Zhangyuan Hu, Shaofeng Li

Infrared-visible object detection (IVOD) seeks to harness the complementary information in infrared and visible images, thereby enhancing the performance of detectors in complex environments. However, existing methods often neglect the frequency characteristics of complementary information, such as the abundant high-frequency details in visible images and th

Houpu Li, Ye Yang, Mengzhu Shi, Yingcai Qian

Topological metals possess various types of symmetry-protected degenerate band crossings. When a topological metal becomes superconducting, the low-energy electronic excitations stemming from the band crossings located close to the Fermi level may contribute to highly unusual pairing symmetry and superconducting states. In this work, we study the electronic

Fabrizio Boninsegna, Francesco Silvestri

This paper presents a novel method for generating differentially private tabular datasets for hierarchical data, specifically focusing on origin-destination (O/D) trips. The approach builds upon the TopDown algorithm, a constraint-based mechanism developed by the U.S. Census to incorporate invariant queries into tabular data. O/D hierarchical data refers to

Jan Mezger, Michele Bergamaschi, Lucas Ranc, Alban Sublet

We describe the implementation of light diagnostics for studying the self-modulation instability of a long relativistic proton bunch in a 10m-long plasma. The wakefields driven by the proton bunch dissipate their energy in the surrounding plasma. The amount of light emitted as atomic line radiation is related to the amount of energy dissipated in the plasma.

Bob Pepin, Christian Igel, Raghavendra Selvan

We study to which extent additive fairness metrics (statistical parity, equal opportunity and equalized odds) can be influenced in a multi-class classification problem by memorizing a subset of the population. We give explicit expressions for the bias resulting from memorization in terms of the label and group membership distribution of the memorized dataset

Xiaowen Li, Dongfang Li, Jingyu Li, Ming Mei

This paper is concerned with a chemotaxis model with logarithmic sensitivity and fast diffusion, which possesses strong singularities for the sensitivity at zero-concentration of chemical signal, and for the diffusion at zero-population of cells, respectively. The main purpose is to show the existence of traveling waves connecting the singular zero-end-state

Kyu-Young Kim, Jin Hee Lee, Woong Bae Jeon, Dong Hyun Park

Cooperative effects such as super(sub)radiance in quantum systems arise from the interplay among quantum emitters. While bright superradiant states have been extensively studied and yielded significant insights into cooperative phenomena, subradiant states have remained less explored due to their inherently dark state nature. However, subradiance holds signi

Joni-Roy Piispanen, Rebekah Rousi

Emotion AI is an emerging field of artificial intelligence intended to be utilized by organizations to manage and monitor employees emotional states supporting employee wellbeing and organizational goals. The current paper presents a case study that took place in a Finnish research institute in which 11 research participants were interviewed about their expe

Abdessalam Ed-dib, Zhanibek Datbayev, Amine Mohamed Aboussalah

Fine-tuning large language models (LLMs) is computationally intensive because it requires updating all parameters. Low-Rank Adaptation (LoRA) improves efficiency by modifying only a subset of weights but introduces a trade-off between expressivity and computational cost: lower ranks reduce resources but limit expressiveness, while higher ranks enhance expres

A. Kovalenko, L. Lachman, T. Pham, K. Singh

Quantum coherence between energy eigenstates of harmonic oscillators is essential for quantum physics. Even the most elementary binary superpositions of the ground and the higher eigenstate are highly required for quantum sensing, thermodynamics, and computing. We derive upper bounds for quantum coherences achieved by classical and Gaussian states and operat

Kathrin Wardatzky, Oana Inel, Luca Rossetto, Abraham Bernstein

Adding explanations to recommender systems is said to have multiple benefits, such as increasing user trust or system transparency. Previous work from other application areas suggests that specific user characteristics impact the users' perception of the explanation. However, we rarely find this type of evaluation for recommender systems explanations. This p

Yongwan Cho, Rabia Emhamed AlMamlook, Tasnim Gharaibeh

Knowledge Tracing (KT) is a research field that aims to estimate a student's knowledge state through learning interactions-a crucial component of Intelligent Tutoring Systems (ITSs). Despite significant advancements, no current KT models excel in both predictive accuracy and interpretability. Meanwhile, Large Language Models (LLMs), pre-trained on vast natur

Asli Umay Ozturk, Recep Firat Cekinel, Pinar Karagoz

Satire detection is essential for accurately extracting opinions from textual data and combating misinformation online. However, the lack of diverse corpora for satire leads to the problem of stylistic bias which impacts the models' detection performances. This study proposes a debiasing approach for satire detection, focusing on reducing biases in training

Andres Stump, Jeremy R. Green

Distillation in lattice QCD is a smearing method that uses the lowest eigenvectors of the spatial Laplacian to construct a subspace in which the Dirac operator can be fully inverted. However, local multiquark interpolators are expensive in this framework because the cost of the contractions scales with a high power of the number of Laplacian eigenvectors. To

A. Kudlis, D. Novokreschenov, I. A. Shelykh

The classic lattice XY model is one of the universal models of statistical mechanics appearing in a broad variety of optical and condensed matter systems. One of its possible realizations is a system of tunnel-coupled spinor polariton condensates, where phases of individual condensates play a role of the two-dimensional spins. We show that the account of the

Alexander K. Hartmann, Satya N. Majumdar

We consider three global characteristic times for a one-dimensional Brownian motion $x(\tau)$ in the interval $\tau\in [0,t]$: the occupation time $t_{\rm o}$ denoting the cumulative time where $x(\tau)>0$, the time $t_{\rm m}$ at which the process achieves its global maximum in $[0,t]$ and the last-passage time $t_l$ through the origin before $t$. All three

Chongming Gao, Ruijun Chen, Shuai Yuan, Kexin Huang

Large language models (LLMs) have attracted significant attention in recommendation systems. Current work primarily applies supervised fine-tuning (SFT) to adapt the model for recommendation tasks. However, SFT on positive examples only limits the model's ability to align with user preference. To address this, researchers recently introduced Direct Preferenc

Xiaowen Li, Jingyu Li

We are interested in the dynamical behaviors of solutions to a parabolic-parabolic chemotaxis-consumption model with a volume-filling effect on a bounded interval, where the physical no-flux boundary condition for the bacteria and mixed Dirichlet-Neumann boundary condition for the oxygen are prescribed. By taking a continuity argument, we first show that the

Denis Kleyko, Dmitri A. Rachkovskij

Expand & Sparsify is a principle that is observed in anatomically similar neural circuits found in the mushroom body (insects) and the cerebellum (mammals). Sensory data are projected randomly to much higher-dimensionality (expand part) where only few the most strongly excited neurons are activated (sparsify part). This principle has been leveraged to design

Tom Steudtner

The question of stability of the Higgs potential in the Standard Model is revisited employing advanced theoretical precision and recent experimental results. We show that the top mass and strong coupling constants are key observables in order to reach or refute absolute stability. We highlight new physics scenarios that lead to a decisive stabilisation of th

Roberto Alcover-Couso, Marcos Escudero-Viñolo, Juan C. SanMiguel, Jesus Bescos

Segmentation models are typically constrained by the categories defined during training. To address this, researchers have explored two independent approaches: adapting Vision-Language Models (VLMs) and leveraging synthetic data. However, VLMs often struggle with granularity, failing to disentangle fine-grained concepts, while synthetic data-based methods re

Moritz Willmann, Marcel Albus, Jan Schnabel, Marco Roth

The even distribution and optimization of tasks across resources and workstations is a critical process in manufacturing aimed at maximizing efficiency, productivity, and profitability, known as Robotic Assembly Line Balancing (RALB). With the increasing complexity of manufacturing required by mass customization, traditional computational approaches struggle

Hayk L. Gevorgyan, Koryun L. Gevorgyan, Anahit H. Shamamian, Lekdar A. Gevorgian

The problem of undulator radiation from a bunch of charged particles, taking into account a medium polarization, is considered. In a dispersive medium, at a zero angle, in addition to hard photons, soft photons are also generated. If the wavelength of the soft photons is greater than or equal to the longitudinal size of the microbunches formed during the FEL

Jicheng Shi, Christophe Salzmann, Colin N. Jones

Model Predictive Control (MPC) has demonstrated significant potential in improving energy efficiency in building climate control, outperforming traditional controllers commonly used in modern building management systems. Among MPC variants, Data-driven Predictive Control (DPC) offers the advantage of modeling building dynamics directly from data, thereby sub

Yijun Liu, Wu Liu, Xiaoyan Gu, Yong Rui

The believable simulation of multi-user behavior is crucial for understanding complex social systems. Recently, large language models (LLMs)-based AI agents have made significant progress, enabling them to achieve human-like intelligence across various tasks. However, real human societies are often dynamic and complex, involving numerous individuals engaging

Takumi Anzawa

In this paper, we show that the cyclotomic symmetric multiple zeta values, independently proposed by Jarossay, Singar and Zhao, and Tasaka, span the space of the cyclotomic multiple zeta values modulo $\pi i$.

Alberto Chiarini, Giovanni Conforti, Giacomo Greco, Luca Tamanini

We study stability of optimizers and convergence of Sinkhorn's algorithm for the entropic optimal transport problem. In the special case of the quadratic cost, our stability bounds imply that if one of the two entropic potentials is semiconcave, then the relative entropy between optimal plans is controlled by the squared Wasserstein distance between their ma

Trambak Bhattacharyya, Maciej Rybczyński, Grzegorz Wilk, Zbigniew Włodarczyk

It is widely observed that particles produced in high-energy collisions follow a power-law distribution. One such power-law distribution used extensively in the phenomenological studies owes its origin to nonadditive statistics proposed by C. Tsallis. In this article, we derive a novel nonadditive generalization of the conventional Bose-Einstein distribution

Dafydd Owen-Newns, Lina Jaurigue, Josh Robertson, Andrew Adair

Photonic technologies hold significant potential for creating innovative, high-speed, efficient and hardware-friendly neuromorphic computing platforms. Neuromorphic photonic methods leveraging ubiquitous, technologically mature and cost-effective Vertical-Cavity Surface Emitting Lasers (VCSELs) are of notable interest. VCSELs have demonstrated the capability

Simon De Vos, Christopher Bockel-Rickermann, Stefan Lessmann, Wouter Verbeke

The goal of uplift modeling is to recommend actions that optimize specific outcomes by determining which entities should receive treatment. One common approach involves two steps: first, an inference step that estimates conditional average treatment effects (CATEs), and second, an optimization step that ranks entities based on their CATE values and assigns t

Jietao Chen, Weijie Chen, Qianjian Xing, Feng Yu

Neural image compression (NIC) has received considerable attention due to its significant advantages in feature representation and data optimization. However, most existing NIC methods for volumetric medical images focus solely on improving human-oriented perception. For these methods, data need to be decoded back to pixels for downstream machine learning an

Sai Bhargav Rongali, Mohamad Hassan N C, Ankit Jha, Neha Bhargava

This paper tackles the intricate challenge of video question-answering (VideoQA). Despite notable progress, current methods fall short of effectively integrating questions with video frames and semantic object-level abstractions to create question-aware video representations. We introduce Local-Global Question Aware Video Embedding (LGQAVE), which incorporat

Silin Cheng, Yuanpei Liu, Kai Han

We tackle the challenging problem of Open-Set Object Detection (OSOD), which aims to detect both known and unknown objects in unlabelled images. The main difficulty arises from the absence of supervision for these unknown classes, making it challenging to distinguish them from the background. Existing OSOD detectors either fail to properly exploit or inadequ

Nailya Ganiyeva

In this thesis, we investigate traversable wormhole spacetimes within the context of a covariant generalization of Einstein's General Relativity, namely the energy-momentum squared gravity, denoted as $f\left(R,T_{ab}T^{ab}\right)$. Here, $R$ represents the Ricci scalar and $T_{ab}$ is the energy-momentum tensor. Specifically considering the linear form $f\l

Christophe Hohlweg, Viviane Pons

In this article, we discuss the notion of partition of elements in an arbitrary Coxeter system $(W,S)$: a partition of an element $w$ is a subset $\mathcal P\subseteq W$ such that the left inversion set of $w$ is the disjoint union of the left inversion set of the elements in $\mathcal P$. Partitions of elements of $W$ arises in the study of the Belkale-Kuma

Mikkel Bennedsen, Eric Hillebrand, Morten Ørregaard Nielsen

The Global Carbon Budget, maintained by the Global Carbon Project, summarizes Earth's global carbon cycle through four annual time series beginning in 1959: atmospheric CO$_2$ concentrations, anthropogenic CO$_2$ emissions, and CO$_2$ uptake by land and by ocean. We analyze these four time series as a multivariate (cointegrated) system. Statistical tests sho

Olatunji Johnson, Bedilu A Ejigu, Ezra Gayawan

Model-based geostatistics (MBG) is a subfield of spatial statistics focused on predicting spatially continuous phenomena using data collected at discrete locations. Geostatistical models often rely on the assumptions of stationarity and isotropy for practical and conceptual simplicity. However, an alternative perspective involves considering non-stationarity

Kunlun Xu, Chenghao Jiang, Peixi Xiong, Yuxin Peng

Lifelong person re-identification (LReID) is an important but challenging task that suffers from catastrophic forgetting due to significant domain gaps between training steps. Existing LReID approaches typically rely on data replay and knowledge distillation to mitigate this issue. However, data replay methods compromise data privacy by storing historical ex

Subhashis Das, Debashis Naskar, Sara Rodriguez Gonzalez

The rise of digital platforms has led to an increasing reliance on technology-driven, home-based healthcare solutions, enabling individuals to monitor their health and share information with healthcare professionals as needed. However, creating an efficient care plan management system requires more than just analyzing hospital summaries and Electronic Health

Novoneel Chakraborty, Anshoo Tandon, Kailash Reddy, Kaushal Kirpekar

Data de-identification makes it possible to glean insights from data while preserving user privacy. The use of Trusted Execution Environments (TEEs) allow for the execution of de-identification applications on the cloud without the need for a user to trust the third-party application provider. In this paper, we present \textit{SPIDEr - Secure Pipeline for In

Ishaan Kannan, Robbie King, Leo Zhou

Local Hamiltonian Problems (LHPs) are important problems that are computationally QMA-complete and physically relevant for many-body quantum systems. Quantum MaxCut (QMC), which equates to finding ground states of the quantum Heisenberg model, is the canonical LHP for which various algorithms have been proposed, including semidefinite programs and variationa

Wanjiang Weng, Hongsong Wang, Junbo Wang, Lei He

Contrastive learning has achieved great success in skeleton-based representation learning recently. However, the prevailing methods are predominantly negative-based, necessitating additional momentum encoder and memory bank to get negative samples, which increases the difficulty of model training. Furthermore, these methods primarily concentrate on learning

Zhong-Hao Tu, Ang Li

We connect nuclear forces to one of the most notable irregular behaviors observed in pulsars, already detected in approximately 6\% known pulsars, with increasingly accurate data expected from upcoming high-precision timing instruments on both ground and space. Built on Shang & Li (2021), we conduct a case study on the 2001 glitch of the Vela pulsar. For our

Yong Ji, Junye Li, Rui Yang

From a geometric perspective, we employ metric mean dimension to investigate the set of generic points of invariant measures and saturated sets in infinite entropy systems. For systems with the specification property, we establish certain variational principles for the Bowen and packing metric mean dimensions of saturated sets in terms of Kolmogorov-Sinai $\

Chenyu He, Chengyuan Li, Gang Li

Extended main-sequence stars that are dim in the ultraviolet passbands of Hubble Space Telescope (UV-dim stars) are found in several young and intermediate-age Magellanic Cloud star clusters. The obscuring of the dust in the discs of stars expelled due to fast rotation have been suggested to be responsible for the appearance of UV-dim stars, and play an impo

Agustín Muñoz González, Juan I. Sequeira y Ariel Dembling

This article analytically characterizes the impermanent loss for automatic market makers in decentralized exchanges such as Uniswap or Balancer (CPMM). We present a theoretical static replication formula for the pool value using a combination of European calls and puts. We will formulate a result to guarantee coverage for any final price that falls within a

M. Foschi, J. L. Gómez, A. Fuentes, I. Cho

We present high resolution images of the radio source 3C 84 at 43 GHz, from 121 observations conducted by the BEAM-ME monitoring program between 2010 and 2023. Imaging was performed using the recent forward modeling imaging method eht-imaging, which achieved a resolution of 80 $\mu$as, a factor of $\sim$2-3 better than traditional imaging methods such as CLE

Andrzej Gawlik, Marta Bernaś, Kinga Żołnacz, Karol Tarnowski

In this paper, we use a birefringent few-mode fiber to demonstrate an intermodal-vectorial four-wave mixing process that generates two pairs of spectrally overlapping signal-idler bands. Using phase-matching conditions, we show that the pairs of bands become spectrally indistinguishable when the group refractive indices of the signal and idler modes intersec

Weixiang Zhang, Shuzhao Xie, Chengwei Ren, Shijia Ge

We propose symmetric power transformation to enhance the capacity of Implicit Neural Representation~(INR) from the perspective of data transformation. Unlike prior work utilizing random permutation or index rearrangement, our method features a reversible operation that does not require additional storage consumption. Specifically, we first investigate the ch

L. I. Danilov

We prove that in a Sobolev space $H^s_{\Lambda }({\mathbb R}^2;{\mathbb R})$, $s > 0$, of periodic functions with a given period lattice $\Lambda $, there exists a dense $G_{\delta }$-set ${\mathcal O}$ such that the spectrum of the Landau Hamiltonian $H_B + V$ perturbed by any periodic electric potential $V\in {\mathcal O}$ is absolutely continuous for all

Wilfried Buchmuller, Arthur Hebecker, Alexander Westphal

Jackiw-Teitelboim (JT) gravity in two-dimensional de Sitter space is an intriguing model for cosmological "wave functions of the universe". Its minisuperspace version already contains all physical information. The size of compact slices is parametrized by a scale factor $h > 0$. The dilaton $\phi$ is chosen to have positive values, $\phi > 0$, and interprete

Dohun Kim, Seyoung Jin, Takashi Taniguchi, Kenji Watanabe

Magnetotransport of conventional semiconductor based double layer systems with barrier suppressed interlayer tunneling has been a rewarding subject due to the emergence of an interlayer coherent state that behaves as an excitonic superfluid. Large angle twisted bilayer graphene offers unprecedented strong interlayer Coulomb interaction, since both layer thic

Jad Mansour, Hayat Rajani, Rafael Garcia, Nuno Gracias

The joint use of event-based vision and Spiking Neural Networks (SNNs) is expected to have a large impact in robotics in the near future, in tasks such as, visual odometry and obstacle avoidance. While researchers have used real-world event datasets for optical flow prediction (mostly captured with Unmanned Aerial Vehicles (UAVs)), these datasets are limited

Andrey Konyukhov

The generation of quantum-correlated pulse pairs in a dispersion modulated birefringent fiber is considered. The photon-number correlations and squeezing are studied using linearized quantum fluctuation theory. Two models of the pulse propagation in an optical fiber are used. The first model is based on the Manakov equations, and the second one is based on t

Tsjerk A. Wassenaar

In this work, we show how the eigenstructures of summands are related to that of the sum. In particular, we show that the sum of two positive semidefinite matrices can be written as the inner product of two block matrices $\mathbf{C} = \mathbf{A} + \mathbf{B} = \mathbf{PD}^2\mathbf{P}^T + \mathbf{QE}^2\mathbf{Q}^T = \begin{pmatrix} \mathbf{PD} & \mathbf{QE}

Garima Karki, Brigitte Schmieder, Pooja Devi, Ramesh Chandra

The solar corona is highly structured by bunches of magnetic field lines forming either loops, or twisted flux ropes representing prominences/filaments, or very dynamic structures such as jets. The aim of this paper is to understand the interaction between filament channels and jets. We use high-resolution H$\alpha$ spectra obtained by the ground-based Teles

Isaac A. García, Jaume Giné

We analyze the structure of the Poincar\'e map $\Pi$ associated to a monodromic singularity of an analytic family of planar vector fields. We work under two assumptions. The first one is that the family possesses an inverse integrating factor that can be expanded in Laurent series centered at the singularity after a weighted polar blow-up fixed by the Newton

Virmarie Maquiling, Li Zhaoping, Enkelejda Kasneci

We introduce to VR a novel imperceptible gaze guidance technique from a recent discovery that human gaze can be attracted to a cue that contrasts from the background in its perceptually non-distinctive ocularity, defined as the relative difference between inputs to the two eyes. This cue pops out in the saliency map in the primary visual cortex without being

Dmitry Vikhorev, Daria Galimzianova, Svetlana Gorovaia, Elizaveta Zhemchuzhina

Humor generation is a challenging task in natural language processing due to limited resources and the quality of existing datasets. Available humor language resources often suffer from toxicity and duplication, limiting their effectiveness for training robust models. This paper proposes CleanComedy, a specialized, partially annotated toxicity-filtered corpu

Qiang Li, Di Liu, Jun Kong, Sen Li

Temporal action localization (TAL) involves dual tasks to classify and localize actions within untrimmed videos. However, the two tasks often have conflicting requirements for features. Existing methods typically employ separate heads for classification and localization tasks but share the same input feature, leading to suboptimal performance. To address thi

Kaixin Deng, Senping Luo

Let $z\in \mathbb{H}:=\{z= x+ i y\in\mathbb{C}: y>0\}$ and $\mathcal{K}(\alpha;z):=\sum_{ (m,n)\in \mathbb{Z} ^2 }\frac{{\left| mz+n \right|}^2}{{{\Im}(z)}}e^{-\pi\alpha\frac{ \left|mz+n\right|^2}{\Im(z)}}.$ In this paper, we characterize the following minimization problem$:$ $\min_{ \mathbb{H} } \big(\mathcal{K}(\alpha;z)-b\mathcal{K}(2\alpha;z)\big).$ We p

Alexander Belyaev, Pierre-Alain Fayolle

Given a bounded domain, we deal with the problem of estimating the distance function from the internal points of the domain to the boundary of the domain. Convolutional and differential distance estimation schemes are considered and, for both the schemes, accuracy improvements are proposed and evaluated. Asymptotics of Laplace integrals and Taylor series ext

Qiwen Gu, Xufei Wang, Junqiao Zhao, Siyue Tao

Visual Place Recognition (VPR) enables robust localization through image retrieval based on learned descriptors. However, drastic appearance variations of images at the same place caused by viewpoint changes can lead to inconsistent supervision signals, thereby degrading descriptor learning. Existing methods either rely on manually defined cropping rules or

E. Sharpe

We give a brief overview of recent progress in understanding Bagger-Witten line bundles, which are bundles over moduli spaces of two-dimensional N=2 SCFTs whose existence is a consequence of the global U(1)_R symmetry of the theories. Our overview includes a discussion of applications in supergravities coupled to gauge theories, a proposal for a purely geome

Isaac A. García, Jaume Giné

In this work we deal with analytic families of real planar vector fields $\mathcal{X}_\lambda$ having a monodromic singularity at the origin for any $\lambda \in \Lambda \subset \mathbb{R}^p$ and depending analytically on the parameters $\lambda$. There naturally appears the so-called center-focus problem which consists in describing the partition of $\Lambd

N. Askour, A. Belhaj, L. Chakhchi, H. El Moumni

In this work, we investigate the optical properties of a new black hole recently obtained from the Dunkl operator formalism involving a relevant parameter denoted by $\xi$. Concretely, we first investigate the shadows, the Lyapunov exponents of unstable nearly bound orbits and the spherically infalling accretion behaviors in terms of such a parameter. Then,

Chenyang Guo, Liping Chen, Zhuhai Li, Kong Aik Lee

Neural networks are commonly known to be vulnerable to adversarial attacks mounted through subtle perturbation on the input data. Recent development in voice-privacy protection has shown the positive use cases of the same technique to conceal speaker's voice attribute with additive perturbation signal generated by an adversarial network. This paper examines

Kazuki Sato, Futoshi Takahashi

In this paper, we study an overdetermined problem with Kirchhoff type nonlocal terms related to the celebrated work by Serrin. We obtain the precise number of solutions according to the value of the bifurcation parameter and study asymptotics of bifurcation curves of solutions when the bifurcation parameter is large in some cases.

Zhongbao Yang, Jiangxin Dong, Jinhui Tang, Jinshan Pan

Removing blur caused by moving objects is challenging, as the moving objects are usually significantly blurry while the static background remains clear. Existing methods that rely on local blur detection often suffer from inaccuracies and cannot generate satisfactory results when focusing solely on blurred regions. To overcome these problems, we first design

Zhigang Song, Xiuying Zhang, Julian Klein, Jonathan Curtis

Momentum-resolved spin-polarized bands are a key ingredient in many proposed spintronic devices, but their existence often relies on lattice commensurability or strong spin-orbit coupling. By a large-scale DFT calculation (up to 4212 atoms), we propose a way to realize strongly spin-polarized bands in the absence of these ingredients by twisting monolayers o

Sora Kim, Sungho Suh, Minsik Lee

Diffusion models have achieved remarkable success in image generation, with applications broadening across various domains. Inpainting is one such application that can benefit significantly from diffusion models. Existing methods either hijack the reverse process of a pretrained diffusion model or cast the problem into a larger framework, \ie, conditioned ge

A. I. Smith, C. M. Steenkamp, M. S. Tame

Path entanglement is an essential resource for photonic quantum information processing, including in quantum computing, quantum communication and quantum sensing. In this work, we experimentally study the generation and verification of bipartite path-entangled states using single photons produced by a nitrogen-vacancy center within a nanodiamond. We perform

Wenbin Pei, Ruohao Dai, Bing Xue, Mengjie Zhang

Class imbalance would lead to biased classifiers that favor the majority class and disadvantage the minority class. Unfortunately, from a practical perspective, the minority class is of importance in many real-life applications. Hybrid sampling methods address this by oversampling the minority class to increase the number of its instances, followed by unders

P. S. Ens, A. F. Santos

In this paper, we investigate one of the established methods for reconstructing modified gravity models from a dark energy model, with the aim of discovering relationships between these theories. In this study, we focus on the $f(R,T)$ modified gravity theory, where $R$ denotes the Ricci scalar and $T$ represents the trace of the energy-momentum tensor. We e

Ling Wang, Pengcheng Xia, Longjie Xie, Li Yang

We develop a new tool, the time inhomogeneous Poisson equation in the whole space and with a terminal condition at infinity, to study the asymptotic behavior of the non-autonomous multi-scale stochastic system with irregular coefficients, where both the fast and the slow equation depend on the highly oscillating time component. In particular, periodic, quasi

Sheng Wu, Xiaobao Wang, Longbiao Wang, Dongxiao He

Multimodal Sentiment Analysis (MSA) stands as a critical research frontier, seeking to comprehensively unravel human emotions by amalgamating text, audio, and visual data. Yet, discerning subtle emotional nuances within audio and video expressions poses a formidable challenge, particularly when emotional polarities across various segments appear similar. In

Daniel Stremmer

In this contribution we discuss recent progress in associated top-quark pair production with one or two isolated photons, $pp\to t\bar{t}\gamma(\gamma)$. The focus is the simultaneous inclusion of higher-order effects and photon radiation in the production of the top-quark pair and in the decay processes. This allows us to quantify the importance of photon r

Tomoyuki Yamakami

We discuss a nondeterministic variant of the recently introduced machine model of deterministic auxiliary depth-$k$ storage automata (or aux-$k$-sda's) by Yamakami. It was proven that all languages recognized by polynomial-time logarithmic-space aux-$k$-sda's are located between $\mathrm{LOGDCFL}$ and $\mathrm{SC}^k$ (the $k$th level of Steve's class SC). We

Xianzhe Dai, Changliang Wang, Lihe Wang, Guofang Wei

We show that a uniformly Euclidean metric with isolated singularity on $M^n = T^n \# M_0$, where $4\leq n\leq 7$ or $n\geq 4$, $M_0$ spin, and nonnegative scalar curvature on the smooth part is Ricci flat and extends smoothly over the singularity. This confirms Schoen's Conjecture in these cases. The key to the proof is to show that the space has nonnegative

Naira Grigoryan, Piotr Chudzinski

Tomonaga-Luttinger liquid (TLL) theory is a canonical formalism used to describe one-dimensional (1D) metals, where the low energy physics is determined by collective bosonic excitations. In this work, we present a theoretical model to compute the parameters of Tomonaga-Luttinger liquid (TLL) in multi-wall nanotubes (MWNTs). MWNTs introduce additional comple

Luo Long, Coralia Cartis, Paz Fink Shustin

Bayesian Optimisation (BO) is a state-of-the-art global optimisation technique for black-box problems where derivative information is unavailable, and sample efficiency is crucial. However, improving the general scalability of BO has proved challenging. Here, we explore Latent Space Bayesian Optimisation (LSBO), that applies dimensionality reduction to perfo

Robin Ghyselinck, Valentin Delchevalerie, Bruno Dumas, Benoît Frénay

Numerous studies have recently focused on incorporating different variations of equivariance in Convolutional Neural Networks (CNNs). In particular, rotation-equivariance has gathered significant attention due to its relevance in many applications related to medical imaging, microscopic imaging, satellite imaging, industrial tasks, etc. While prior research

Carlos Matamala, Goran Strbac

The vast integration of non-synchronous renewable energy sources compromises power system stability, increasing vulnerability to frequency deviations due to the lack of inertia. Current efforts to decarbonise electricity grids while maintaining frequency security still rely on Ancillary Services (AS) provision, such as inertia and frequency response, from fl

Agustín Muñoz González, Juan I. Sequeira, Rafael Orive Illera

In this work, we present an application of the probabilistic weak formulation of mean field games (MFG) for modeling liquidity pools in a constant product automated market maker (AMM) protocol in the context of decentralized finance. Our work extends one of the most conventional applications of MFG, which is the price impact model in an order book, by incorp

Nikolay Kivel

Motivated by experimental data at large momentum transfer we update the analysis of the two-photon exchange effect in the electron-nucleon scattering using the effective field theory formalism. Our approach is suitable for describing the hard region, where the hadronic model calculations are not accurate enough. We improve the estimates of various long-range