March 2024 arXiv papers — page 34

Zhiqi Shao, Michael G. H. Bell, Ze Wang, D. Glenn Geers

Accurate, and effective traffic forecasting is vital for smart traffic systems, crucial in urban traffic planning and management. Current Spatio-Temporal Transformer models, despite their prediction capabilities, struggle with balancing computational efficiency and accuracy, favoring global over local information, and handling spatial and temporal data separ

Wangyue Li, Liangzhi Li, Tong Xiang, Xiao Liu

Multiple-choice questions (MCQs) are widely used in the evaluation of large language models (LLMs) due to their simplicity and efficiency. However, there are concerns about whether MCQs can truly measure LLM's capabilities, particularly in knowledge-intensive scenarios where long-form generation (LFG) answers are required. The misalignment between the task a

Xusheng Zhu, Wen Chen, Qingqing Wu, Wen Fang

Reconfigurable intelligent surface (RIS)-assisted index modulation system schemes are considered a promising technology for sixth-generation (6G) wireless communication systems, which can enhance various system capabilities such as coverage and reliability. However, obtaining perfect channel state information (CSI) is challenging due to the lack of a radio f

Cynthia Maldonado-Garcia, Rodrigo Bonazzola, Enzo Ferrante, Thomas H Julian

Cardiovascular diseases (CVD) are the leading cause of death globally. Non-invasive, cost-effective imaging techniques play a crucial role in early detection and prevention of CVD. Optical coherence tomography (OCT) has gained recognition as a potential tool for early CVD risk prediction, though its use remains underexplored. In this study, we investigated t

M. Thoennessen

The 2023 update of the discovery of nuclide project is presented when thirteen nuclides were observed for the first time. In addition, a major update and revision of the isotope discovery project is described.

Yuqi Yang, Peng-Tao Jiang, Qibin Hou, Hao Zhang

Previous multi-task dense prediction methods based on the Mixture of Experts (MoE) have received great performance but they neglect the importance of explicitly modeling the global relations among all tasks. In this paper, we present a novel decoder-focused method for multi-task dense prediction, called Mixture-of-Low-Rank-Experts (MLoRE). To model the globa

Leonie Weissweiler, Nina Böbel, Kirian Guiller, Santiago Herrera

The Universal Dependencies (UD) project has created an invaluable collection of treebanks with contributions in over 140 languages. However, the UD annotations do not tell the full story. Grammatical constructions that convey meaning through a particular combination of several morphosyntactic elements -- for example, interrogative sentences with special mark

Laurentiu Maxim, Jörg Schürmann

We give a cohomological and geometrical interpretation for the weighted Ehrhart theory of a full-dimensional lattice polytope $P$, with Laurent polynomial weights of geometric origin. For this purpose, we calculate the motivic Chern and Hirzebruch characteristic classes of a mixed Hodge module complex $\mathcal{M}$ whose underlying cohomology sheaves are con

G. Lusztig

We study the new basis of the (complexified) Grothendieck group of unipotent representations of a split reductive group over a finite field. For exceptional types we use a definition of the new basis which differs from the earlier one.

Zihao Zhao, Yi Jing, Fuli Feng, Jiancan Wu

Medication recommendation systems have gained significant attention in healthcare as a means of providing tailored and effective drug combinations based on patients' clinical information. However, existing approaches often suffer from fairness issues, as recommendations tend to be more accurate for patients with common diseases compared to those with rare co

Jin-Man Chen, Ze-Rui Liang, De-Liang Yao

The low-energy antineutrino- and neutrino-nucleon neutral current elastic scattering is studied within the framework of the relativistic SU(2) baryon chiral perturbation theory up to the order of $\mathcal{O}(p^3)$. We have derived the model-independent hadronic amplitudes and extracted the form factors from them. It is found that differential cross sections

Muhammad Rashid, Elvio G. Amparore, Enrico Ferrari, Damiano Verda

We investigate the use of a stratified sampling approach for LIME Image, a popular model-agnostic explainable AI method for computer vision tasks, in order to reduce the artifacts generated by typical Monte Carlo sampling. Such artifacts are due to the undersampling of the dependent variable in the synthetic neighborhood around the image being explained, whi

Francesco Sorrenti, Ruth Durrer, Martin Kunz

In previous work we have shown that the dipole in the low redshift supernovae of the Pantheon+SH0ES data does not agree with the one inferred from the velocity of the solar system as obtained from CMB data. We interpreted this as the presence of significant bulk velocities. In this paper we study the monopole, dipole and quadrupole in the Pantheon+SH0ES data

Shuheng Fang, Kangfei Zhao, Yu Rong, Zhixun Li

Cold-start rating prediction is a fundamental problem in recommender systems that has been extensively studied. Many methods have been proposed that exploit explicit relations among existing data, such as collaborative filtering, social recommendations and heterogeneous information network, to alleviate the data insufficiency issue for cold-start users and i

Leonardo de Lima Santos, Samuel Bueno Soltau

The Unified Model of Active Galactic Nuclei (UMAGN) is a comprehensive theoretical framework aimed at elucidating the diverse observations of AGN, encompassing quasars and Seyfert galaxies. The Model attributes the observed variations to different orientations of a surrounding matter disk around a supermassive black hole (SMBH), with the primary factor influ

Martin Beneke, Yao Ji, Xing Wang

We calculate directly in position space the one-loop renormalization kernels of the soft operators $O_\gamma$ and $O_g$ that appear in the soft-quark contributions to, respectively, the subleading-power $\gamma\gamma\to h$ and $gg\to h$ form factors mediated by the $b$-quark. We present an IR/rapidity divergence-free definition for $O_g$ and demonstrate that

Marcin Rosmus, Natalia Olszowska, Zbigniew Bukowski, Przemysław Piekarz

LaCuSb$_{2}$ is a superconductor with a transition temperature of about $T_\text{c} = 0.9$K and is a potential platform where Dirac fermions can be experimentally observed. In this paper, we report systematic high-resolution studies of its electronic structure using the angle-resolved photoemission spectroscopy (ARPES) technique supported by the DFT calculat

Fatemeh Mohammadi

We explore a family of monomial ideals derived as Gr\"obner degenerations of determinantal ideals. These ideals, previously examined as block diagonal matching field ideals within the realm of toric degenerations of Grassmannians, are identified as monomial initial ideals of determinantal ideals. We establish their linear quotient property and calculate thei

Xiang Tao, Mingqing Zhang, Qiang Liu, Shu Wu

Due to the rapid spread of rumors on social media, rumor detection has become an extremely important challenge. Existing methods for rumor detection have achieved good performance, as they have collected enough corpus from the same data distribution for model training. However, significant distribution shifts between the training data and real-world test dat

Rowan Bradbury, Katherine A. Vallis, Bartlomiej W. Papiez

The rapid advancement of Artificial Intelligence (AI) in biomedical imaging and radiotherapy is hindered by the limited availability of large imaging data repositories. With recent research and improvements in denoising diffusion probabilistic models (DDPM), high quality synthetic medical scans are now possible. Despite this, there is currently no way of gen

Chenlong Zhang, Pengfei Cao, Yubo Chen, Kang Liu

Traditional continual event detection relies on abundant labeled data for training, which is often impractical to obtain in real-world applications. In this paper, we introduce continual few-shot event detection (CFED), a more commonly encountered scenario when a substantial number of labeled samples are not accessible. The CFED task is challenging as it inv

Richard De la cruz, Wladimir Neves

In this paper, we study two systems with a time-variable coefficient and general time-gradually-degenerate damping. More explicitly, we construct the Riemann solutions to the time-variable coefficient Zeldovich approximation and time-variable coefficient pressureless gas systems both with general time-gradually-degenerate damping. Applying the method of simi

Ignacio Labarca-Figueroa, Ralf Hiptmair

We study frequency domain electromagnetic scattering at a bounded, penetrable, and inhomogeneous obstacle $ \Omega \subset \mathbb{R}^3 $. From the Stratton-Chu integral representation, we derive a new representation formula when constant reference coefficients are given for the interior domain. The resulting integral representation contains the usual layer

Venkatesh G. S.

The affine feedback connection of SISO nonlinear systems modeled by Chen--Fliess series is shown to be a group action on the plant which is isomorphic to the semi-direct product of shuffle and additive group of non-commutative formal power series. The additive and multiplicative feedback loops in an affine feedback connection are thus proven to be structural

Zhen Tian, Wayne Xin Zhao, Changwang Zhang, Xin Zhao

To capture user preference, transformer models have been widely applied to model sequential user behavior data. The core of transformer architecture lies in the self-attention mechanism, which computes the pairwise attention scores in a sequence. Due to the permutation-equivariant nature, positional encoding is used to enhance the attention between token rep

Anthony Zhou, Amir Barati Farimani

Neural solvers for partial differential equations (PDEs) have great potential to generate fast and accurate physics solutions, yet their practicality is currently limited by their generalizability. PDEs evolve over broad scales and exhibit diverse behaviors; predicting these phenomena will require learning representations across a wide variety of inputs whic

Kazuki Kawamura, Jun Rekimoto

Quickly understanding lengthy lecture videos is essential for learners with limited time and interest in various topics to improve their learning efficiency. To this end, video summarization has been actively researched to enable users to view only important scenes from a video. However, these studies focus on either the visual or audio information of a vide

Qingyuan Wang, Barry Cardiff, Antoine Frappé, Benoit Larras

This paper introduces TinySaver, an early-exit-like dynamic model compression approach which employs tiny models to substitute large models adaptively. Distinct from traditional compression techniques, dynamic methods like TinySaver can leverage the difficulty differences to allow certain inputs to complete their inference processes early, thereby conserving

Andrii Kompanets, Gautam Pai, Remco Duits, Davide Leonetti

Automating the current bridge visual inspection practices using drones and image processing techniques is a prominent way to make these inspections more effective, robust, and less expensive. In this paper, we investigate the development of a novel deep-learning method for the detection of fatigue cracks in high-resolution images of steel bridges. First, we

Noura Zenbaa, Claas Abert, Fabian Majcen, Michael Kerber

In the field of magnonics, which uses magnons, the quanta of spin waves, for energy-efficient data processing, significant progress has been made leveraging the capabilities of the inverse design concept. This approach involves defining a desired functionality and employing a feedback-loop algorithm to optimise the device design. In this study, we present th

Xavier Ros-Oton, Marvin Weidner

In this article we establish fine results on the boundary behavior of solutions to nonlocal equations in $C^{k,\gamma}$ domains which satisfy local Neumann conditions on the boundary. Such solutions typically blow up at the boundary like $v \asymp d^{s-1}$ and are sometimes called large solutions. In this setup we prove optimal regularity results for the quo

Jingyuan Wang, Shengdong Xu, Yang Yang

This report provide a detailed description of the method that we proposed in the TRAC-2024 Offline Harm Potential dentification which encloses two sub-tasks. The investigation utilized a rich dataset comprised of social media comments in several Indian languages, annotated with precision by expert judges to capture the nuanced implications for offline contex

C. R. Ding, Gang Li, J. M. Yao

Nuclear matrix elements (NMEs) for neutrinoless double-beta ($0\nu\beta\beta$) decay in candidate nuclei play a crucial role in interpreting results from current experiments and in designing future ones. Accurate NME values serve as important nuclear inputs for constraining parameters in new physics, such as neutrino mass and the Wilson coefficients of lepto

William Bergan

Coherent electron cooling is a novel method to cool dense hadron beams on timescales of a few hours. This method uses a copropagating beam of electrons to pick up the density fluctuations within the hadron beam in one straight section and then provide corrective energy kicks to the hadrons in a downstream straight, cooling the beam. Microbunched electron coo

Elias Roos Hansen, Ferdinand Kuemmeth, Joost van der Heijden

Low-frequency qubit control wires require non-trivial thermal anchoring and low-pass filtering. The resulting electron temperature serves as a quality benchmark for these signal lines. In this technical note, we make use of a primary electron thermometry technique, using a Coulomb blockade thermometer, to establish the electron temperature in the millikelvin

Rafael M. Santos, Rafael C. Nunes, Jose C. N. de Araujo

The Kerr spacetime is a fundamental solution of general relativity (GR), describing the gravitational field around a rotating, uncharged black hole (BH). Kerr spacetime has been crucial in modern astrophysics and it serves as a foundation for the study of gravitational waves (GWs). Possible deviations in Kerr geometry may indicate deviations from GR predicti

Nausica Aldeghi, Jonathan Rohleder

We consider the eigenvalue problem for the Laplacian with mixed Dirichlet and Neumann boundary conditions. For a certain class of bounded, simply connected planar domains we prove monotonicity properties of the first eigenfunction. As a consequence, we establish a variant of the hot spots conjecture for mixed boundary conditions. Moreover, we obtain an inequ

Sneha Pradhan, P. K. Sahoo

This article primarily investigates the existence of the charged compact star under the conformal motion treatment within the context of f(Q) gravity. We have developed two models by implementing the power-law and linear form of conformal factor, enabling an in-depth comparison in our study. We have selected the MIT Bag model equation of state to describe th

Sarula Chang, Jianxi Li, Yirong Zheng

Let $m(G,\lambda)$ be the multiplicity of an eigenvalue $\lambda$ of a connected graph $G$. Wang et al. [Linear Algebra Appl. 584(2020), 257-266] proved that for any connected graph $G\neq C_n$, $m(G, \lambda) \leq 2c(G) + p(G) -1$, where $c (G) = |E(G)| - |V (G)| + 1$ and $p(G)$ are the cyclomatic number and the number of pendant vertices of $G$, respective

Frederic Salmen, Ulrik Schroeder

Explorables with interactive, multimodal content, openly available on the web, are a promising medium for education. Yet authoring such explorables requires web development expertise, excluding most educators and students from the authoring and remixing process. Some tools are available to reduce this barrier of entry and others are in development, making a

Alexandre Benatti, Luciano da F. Costa

Graphs and complex networks can be successively separated into connected components associated to respective seed nodes, therefore establishing a respective hierarchical organization. In the present work, we study the properties of the hierarchical structure implied by distance-based cutting of Erd\H{o}s-R\'enyi, Barab\'asi-Albert, and a specific geometric n

Jue Wang, Yuxiang Lin, Qi Zhao, Dong Luo

The widespread use of various chemical gases in industrial processes necessitates effective measures to prevent their leakage during transportation and storage, given their high toxicity. Thermal infrared-based computer vision detection techniques provide a straightforward approach to identify gas leakage areas. However, the development of high-quality algor

Jan Schneider, Julian Berberich

Quantum computing provides a powerful framework for tackling computational problems that are classically intractable. The goal of this paper is to explore the use of quantum computers for solving relevant problems in systems and control theory. In the recent literature, different quantum algorithms have been developed to tackle binary optimization, which pla

Jiawen Shi, Zenghui Yuan, Yinuo Liu, Yue Huang

LLM-as-a-Judge uses a large language model (LLM) to select the best response from a set of candidates for a given question. LLM-as-a-Judge has many applications such as LLM-powered search, reinforcement learning with AI feedback (RLAIF), and tool selection. In this work, we propose JudgeDeceiver, an optimization-based prompt injection attack to LLM-as-a-Judg

Jongha Kim, Jihwan Park, Jinyoung Park, Jinyoung Kim

Visual Relationship Detection (VRD) has seen significant advancements with Transformer-based architectures recently. However, we identify two key limitations in a conventional label assignment for training Transformer-based VRD models, which is a process of mapping a ground-truth (GT) to a prediction. Under the conventional assignment, an unspecialized query

Yutong Xu, Junhao Du, Jiahe Wang, Yuwei Ning

With the rapid development and widespread application of VR/AR technology, maximizing the quality of immersive panoramic video services that match users' personal preferences and habits has become a long-standing challenge. Understanding the saliency region where users focus, based on data collected with HMDs, can promote multimedia encoding, transmission, a

Anirban Misra, Urbashi Satpathi, Supurna Sinha, Sanjukta Roy

The position response of a particle subjected to a perturbation is of general interest in physics. We study the modification of the position response function of an ensemble of cold atoms in a magneto-optical trap in the presence of tunable light-assisted interactions. We subject the cold atoms to an intense laser light tuned near the photoassociation resona

Hongpeng Pan, Yang Yang, Zhongtian Fu, Yuxuan Zhang

This report proposes an improved method for the Tracking Any Point (TAP) task, which tracks any physical surface through a video. Several existing approaches have explored the TAP by considering the temporal relationships to obtain smooth point motion trajectories, however, they still suffer from the cumulative error caused by temporal prediction. To address

Shuyu Chang, Rui Wang, Peng Ren, Qi Wang

Modeling topics effectively in short texts, such as tweets and news snippets, is crucial to capturing rapidly evolving social trends. Existing topic models often struggle to accurately capture the underlying semantic patterns of short texts, primarily due to the sparse nature of such data. This nature of texts leads to an unavoidable lack of co-occurrence in

Daniela Ivanković, Tomislav Kralj, Nikola Sandrić, Stjepan Šebek

In this paper, we study the limiting behavior of the perimeter and diameter functionals of the convex hull spanned by the first $n$ steps of two planar random walks. As the main results, we obtain the strong law of large numbers and the central limit theorem for the perimeter and diameter of these random sets.

Bowen Ye, Jianing Zhao, Shaoyuan Li, Xiang Yin

In this paper, we investigate the problem of linear temporal logic (LTL) path planning for multi-agent systems, introducing the new concept of \emph{ordering constraints}. Specifically, we consider a generic objective function that is defined for the path of each individual agent. The primary objective is to find a global plan for the team of agents, ensurin

Neeraj Kumar Dhanwani, Deepanshi Saraf, Mahender Singh

In this paper, we investigate the residual finiteness and subquandle separability of quandles, properties that respectively imply the solvability of the word problem and the generalized word problem for quandles. From Winker's work, we know that fundamental $n$-quandles of oriented links, which are canonical quotients of their fundamental quandles, are close

Haonan Xu, Yurui Huang, Sishun Pan, Zhihao Guan

In this paper, we propose a solution for cross-modal transportation retrieval. Due to the cross-domain problem of traffic images, we divide the problem into two sub-tasks of pedestrian retrieval and vehicle retrieval through a simple strategy. In pedestrian retrieval tasks, we use IRRA as the base model and specifically design an Attribute Classification to

Hao Tang, Lianglun Cheng, Guoheng Huang, Zhengguang Tan

Image segmentation holds a vital position in the realms of diagnosis and treatment within the medical domain. Traditional convolutional neural networks (CNNs) and Transformer models have made significant advancements in this realm, but they still encounter challenges because of limited receptive field or high computing complexity. Recently, State Space Model

Claudio Bonanno, Roberto Castorrini

This paper explores the domain of meromorphic extension for the dynamical zeta function associated to a class of one-dimensional differentiable parabolic maps featuring an indifferent fixed point. We establish the connection between this domain and the spectrum of the weighted transfer operators of the induced map. Furthermore, we discuss scenarios where mer

Siddharth Gautam, David R. Cole, Zoltán Imre Dudás, Indu Dhiman

Adsorption in nanoporous materials is one strategy that can be used to store hydrogen at conditions of temperature and pressure that are economically viable. Adsorption capacity of nanoporous materials depends on surface area which can be enhanced by incorporating a hierarchical pore structure. We report grand canonical Monte Carlo (GCMC) simulation results

Weiguo Gao

When the predicted sequence length exceeds the length seen during training, the transformer's inference accuracy diminishes. Existing relative position encoding methods, such as those based on the ALiBi technique, address the length extrapolation challenge exclusively through the implementation of a single kernel function, which introduces a constant bias to

Ciaran A. J. O'Hare

I present an introduction and topical review on axions as a dark matter candidate. Emphasis is placed on issues surrounding the cosmology of axion dark matter that are relevant for present-day searches, including: early-Universe production mechanisms, predictions of the axion mass, bounds on axion properties derived from cosmological data, as well as the dir

Iegor Riepin, Tom Brown, Victor Zavala

Companies with datacenters are procuring significant amounts of renewable energy to reduce their carbon footprint. There is increasing interest in achieving 24/7 Carbon-Free Energy (CFE) matching in electricity usage, aiming to eliminate all carbon footprints associated with electricity consumption on an hourly basis. However, the variability of renewable en

Luis Ferroni, Benjamin Schröter

We provide a full classification of all families of matroids that are closed under duality and minors, and for which the Tutte polynomial is a universal valuative invariant. There are four inclusion-wise maximal families, two of which are the class of elementary split matroids and the class of graphic Schubert matroids. As a consequence of our framework, we

Chenhongyi Yang, Zehui Chen, Miguel Espinosa, Linus Ericsson

We present PlainMamba: a simple non-hierarchical state space model (SSM) designed for general visual recognition. The recent Mamba model has shown how SSMs can be highly competitive with other architectures on sequential data and initial attempts have been made to apply it to images. In this paper, we further adapt the selective scanning process of Mamba to

Huawei Wei, Zejun Yang, Zhisheng Wang

In this study, we propose AniPortrait, a novel framework for generating high-quality animation driven by audio and a reference portrait image. Our methodology is divided into two stages. Initially, we extract 3D intermediate representations from audio and project them into a sequence of 2D facial landmarks. Subsequently, we employ a robust diffusion model, c

Bekzat Tilekbay, Saelyne Yang, Michal Lewkowicz, Alex Suryapranata

Informational videos serve as a crucial source for explaining conceptual and procedural knowledge to novices and experts alike. When producing informational videos, editors edit videos by overlaying text/images or trimming footage to enhance the video quality and make it more engaging. However, video editing can be difficult and time-consuming, especially fo

Amartya Mukherjee, Thanin Quartz, Jun Liu

This paper presents a novel approach to generating stabilizing controllers for a large class of dynamical systems using diffusion models. The core objective is to develop stabilizing control functions by identifying the closest asymptotically stable vector field relative to a predetermined manifold and adjusting the control function based on this finding. To

Uri Hacohen, Adi Haviv, Shahar Sarfaty, Bruria Friedman

The advent of Generative Artificial Intelligence (GenAI) models, including GitHub Copilot, OpenAI GPT, and Stable Diffusion, has revolutionized content creation, enabling non-professionals to produce high-quality content across various domains. This transformative technology has led to a surge of synthetic content and sparked legal disputes over copyright in

S. Yalçınkaya, E. M. Esmer, Ö. Baştürk, A. Muhaymin

We update the ephemerides of 16 transiting exoplanets using our ground-based observations, new TESS data, and previously published observations including those of amateur astronomers. All these light curves were modeled by making use of a set of quantitative criteria with the EXOFAST code to obtain mid-transit times. We searched for statistically significant

Stefan Schoder

The urgent need for transitioning to green energy solutions, particularly in the context of house heating and urban redensification, has brought the issue of fan noise aeroacoustics investigations to the forefront. As societies worldwide strive to mitigate climate change and reduce carbon emissions, adopting sustainable heating technologies such as air heat

Sergio Pirozzoli, Davide Modesti

We derive explicit formulas for the mean profiles of temperature (modeled as a passive scalar) in forced turbulent convection, as a function of the Reynolds and Prandtl numbers. The derivation leverages on the observed universality of the inner-layer thermal eddy diffusivity with respect to Reynolds and Prandtl number variations and across different flows, a

Zhongxiang Sun, Zihua Si, Xiaoxue Zang, Kai Zheng

Recent advancements in Large Language Models (LLMs) have attracted considerable interest among researchers to leverage these models to enhance Recommender Systems (RSs). Existing work predominantly utilizes LLMs to generate knowledge-rich texts or utilizes LLM-derived embeddings as features to improve RSs. Although the extensive world knowledge embedded in L

Jasper Albers, Anno C. Kurth, Robin Gutzen, Aitor Morales-Gregorio

Assessing the similarity of matrices is valuable for analyzing the extent to which data sets exhibit common features in tasks such as data clustering, dimensionality reduction, pattern recognition, group comparison, and graph analysis. Methods proposed for comparing vectors, such as cosine similarity, can be readily generalized to matrices. However, this app

Richard Weidmann, Thomas Weller

Carrier graphs of groups representing subgroups of a given relatively hyperbolic groups are introduced and a combination theorem for relatively quasi-convex subgroups is proven. Subsequently a theory of folds for such carrier graphs is introduced and finiteness results for subgroups of locally relatively quasiconvex relatively hyperbolic groups and Kleinian

Dzmitry Badziahin

We compute the Hausdorff dimension of the set of simultaneously $q^{-\lambda}$-well approximable points on the Veronese curve in $\mathbb{R}^n$ for $\lambda$ between $\frac{1}{n}$ and $\frac{2}{2n-1}$. For $n=3$, the same result is given for a wider range of $\lambda$ between $\frac13$ and $\frac12$. We also provide a nontrivial upper bound for this Hausdorf

Dugald Macpherson, Katrin Tent

We explore the interplay between omega-categoricity and pseudofiniteness for groups, conjecturing that omega-categorical pseudofinite groups are finite-by-abelian-by-finite. We show that the conjecture reduces to nilpotent p-groups of class 2, and give a proof that several of the known examples of omega-categorical p-groups satisfy the conjecture. In particu

Shengdong Xu, Zhouyang Chi, Yang Yang

This report provide a detailed description of the method that we explored and proposed in the WECIA Emotion Prediction Competition (EPC), which predicts a person's emotion through an artistic work with a comment. The dataset of this competition is ArtELingo, designed to encourage work on diversity across languages and cultures. The dataset has two main chall

Laurent Stolovitch, Xiaojun Wu

We consider an embedded general complex torus $C_n$ into a complex manifold $M_{n+d}$ with a unitary flat normal bundle $N_C$. We show the existence of (non-singular) holomorphic foliation in a neighborhood of $C$ in $M$ having $C$ as leaf under some conditions.

Andrés Jaramillo Puentes

We prove an analog of the wall crossing formula for Welschinger invariants relating the difference of signed curve counting of real curves passing through configurations that differ by a pair of complex conjugated points, and a correspondence Welschinger invariant of the blow up. We prove this analogue for the motivic count of rational curves of fixed degree

Thomas Le Fils

We classify the Teichm\"uller curves in the moduli space of genus three Riemann surfaces $\mathcal M_3$ that are obtained by a covering construction from a primitive Teichm\"uller curve in $\mathcal M_2$. We describe the action on homology modulo two of the affine groups of translation surfaces generating these primitive curves. We also classify the $\mathrm

Daniel Schiwietz, Marian Hörsting, Eva Maria Weig, Matthias Wenzel

Micro- and nanoelectromechanical system (MEMS and NEMS) resonators can exhibit rich nonlinear dynamics as they are often operated at large amplitudes with high quality factors and possess a high mode density with a variety of nonlinear modal couplings. Their impact is strongly influenced by internal resonance conditions and by the strength of the modal coupl

Jiageng Wu, Zhiguo Wang, Ya-Feng Liu, Fan Liu

In this paper, we propose a multi-input multi-output transmit beamforming optimization model for joint radar sensing and multi-user communications, where the design of the beamformers is formulated as an optimization problem whose objective is a weighted combination of the sum rate and the Cram\'{e}r-Rao bound, subject to the transmit power budget. Obtaining

Adrien Lafage, Mathieu Barbier, Gianni Franchi, David Filliat

Accurate trajectory forecasting is crucial for the performance of various systems, such as advanced driver-assistance systems and self-driving vehicles. These forecasts allow us to anticipate events that lead to collisions and, therefore, to mitigate them. Deep Neural Networks have excelled in motion forecasting, but overconfidence and weak uncertainty quant

Diego Valsesia, Tiziano Bianchi, Enrico Magli

Deep learning methods have traditionally been difficult to apply to compression of hyperspectral images onboard of spacecrafts, due to the large computational complexity needed to achieve adequate representational power, as well as the lack of suitable datasets for training and testing. In this paper, we depart from the traditional autoencoder approach and w

Songwei Liu, Yingyi Wen, Jingfang Pei, Yang Liu

Reservoir computing is a recurrent neural network designed for approximating complex dynamics in, for instance, motion tracking, spatial-temporal pattern recognition, and chaotic attractor reconstruction. Its implementation demands intense computation for the nonlinear transformation of the reservoir input, i.e. activating the reservoir. Configuring physical

Yunan Wang, Chuxiong Hu, Zeyang Li, Yujie Lin

Time-optimal control for high-order chain-of-integrator systems with full state constraints remains an open and challenging problem within the discipline of optimal control. The behavior of optimal control in high-order problems lacks precise characterization, and even the existence of the chattering phenomenon, i.e., the control switches for infinitely many

Yikuan Yan, Yaolun Zhang, Keman Huang

Integrating LLM and reinforcement learning (RL) agent effectively to achieve complementary performance is critical in high stake tasks like cybersecurity operations. In this study, we introduce SecurityBot, a LLM agent mentored by pre-trained RL agents, to support cybersecurity operations. In particularly, the LLM agent is supported with a profile module to

Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar

We demonstrate a substantial gap between the privacy guarantees of the Adaptive Batch Linear Queries (ABLQ) mechanism under different types of batch sampling: (i) Shuffling, and (ii) Poisson subsampling; the typical analysis of Differentially Private Stochastic Gradient Descent (DP-SGD) follows by interpreting it as a post-processing of ABLQ. While shuffling

Julia Guerrero-Viu, J. Daniel Subias, Ana Serrano, Katherine R. Storrs

Estimating perceptual attributes of materials directly from images is a challenging task due to their complex, not fully-understood interactions with external factors, such as geometry and lighting. Supervised deep learning models have recently been shown to outperform traditional approaches, but rely on large datasets of human-annotated images for accurate

C. Chen, Y. Liu, Y. Chen, Y. N. Hu

Vortex pinning is a crucial factor that determines the critical current of practical superconductors and enables their diverse applications. However, the underlying mechanism of vortex pinning has long been elusive, lacking a clear microscopic explanation. Here using high-resolution scanning tunneling microscopy, we studied single vortex pinning induced by p

Muhong Gao, Qizhai Li

Quantifying the strength of functional dependence between random scalars $X$ and $Y$ is an important statistical problem. While many existing correlation coefficients excel in identifying linear or monotone functional dependence, they fall short in capturing general non-monotone functional relationships. In response, we propose a family of correlation coeffi

Dirk Erhard, Martin Hairer, Tiecheng Xu

We consider the (discrete) parabolic Anderson model $\partial u(t,x)/\partial t=\Delta u(t,x) +\xi_t(x) u(t,x)$, $t\geq 0$, $x\in \mathbb{Z}^d$. Here, the $\xi$-field is $\mathbb{R}$-valued, acting as a dynamic random environment, and $\Delta$ represents the discrete Laplacian. We focus on the case where $\xi$ is given by a rescaled symmetric simple exclusio

Tomoki Wada, Katsuaki Asano

An X-ray short burst associated with a Galactic fast radio burst was observed in 2020, distinguished by its X-ray cut-off energy significantly exceeding that of other X-ray short bursts. X-ray photons of these short bursts are believed to originate from fireballs within the magnetospheres of magnetars. If a fireball forms near a magnetic pole, it expands alo

Nils Dengler, Juan Del Aguila Ferrandis, João Moura, Sethu Vijayakumar

In complex scenarios where typical pick-and-place techniques are insufficient, often non-prehensile manipulation can ensure that a robot is able to fulfill its task. However, non-prehensile manipulation is challenging due to its underactuated nature with hybrid-dynamics, where a robot needs to reason about an object's long-term behavior and contact-switching

Brecht Vandevoort, Bas Ketsman, Frank Neven

A DBMS allows trading consistency for efficiency through the allocation of isolation levels that are strictly weaker than serializability. The robustness problem asks whether, for a given set of transactions and a given allocation of isolation levels, every possible interleaved execution of those transactions that is allowed under the provided allocation, is

Qilin Wang, Jiangning Zhang, Chengming Xu, Weijian Cao

Facial Appearance Editing (FAE) aims to modify physical attributes, such as pose, expression and lighting, of human facial images while preserving attributes like identity and background, showing great importance in photograph. In spite of the great progress in this area, current researches generally meet three challenges: low generation fidelity, poor attri

Erik I. Broman, Federico Camia

We study cover times of subsets of ${\mathbb Z}^2$ by a two-dimensional massive random walk loop soup. We consider a sequence of subsets $A_n \subset {\mathbb Z}^2$ such that $|A_n| \to \infty$ and determine the distributional limit of their cover times ${\mathcal T}(A_n).$ We allow the killing rate $\kappa_n$ (or equivalently the ``mass'') of the loop soup

Isaac Roberts, Alexander Schulz, Luca Hermes, Barbara Hammer

Attention based Large Language Models (LLMs) are the state-of-the-art in natural language processing (NLP). The two most common architectures are encoders such as BERT, and decoders like the GPT models. Despite the success of encoder models, on which we focus in this work, they also bear several risks, including issues with bias or their susceptibility for a

C. Lapre, D. Brouczek, M. Schwentenwein, K. Neumann

The use of additive manufacturing (AM) processes for industrial fabrication has grown rapidly over the last ten years. The most well-known AM technologies are fused deposition modelling and stereolithography techniques. One particular industry where 3D printing is advantageous over traditional fabrication techniques is within ceramic components due to its fl

Aleksandra Edwards, Jose Camacho-Collados

Recent foundational language models have shown state-of-the-art performance in many NLP tasks in zero- and few-shot settings. An advantage of these models over more standard approaches based on fine-tuning is the ability to understand instructions written in natural language (prompts), which helps them generalise better to different tasks and domains without

Luis Piloto, Sofia Liguori, Sephora Madjiheurem, Miha Zgubic

Optimal Power Flow (OPF) refers to a wide range of related optimization problems with the goal of operating power systems efficiently and securely. In the simplest setting, OPF determines how much power to generate in order to minimize costs while meeting demand for power and satisfying physical and operational constraints. In even the simplest case, power g

J. Rigney, P. T. Gallagher, G. Ramsay, J. G. Doyle

Shock waves are excited by coronal mass ejections (CMEs) and large-scale extreme-ultraviolet (EUV) wave fronts and can result in low-frequency radio emission under certain coronal conditions. In this work, we investigate a moving source of low-frequency radio emission as a CME and an associated EUV wave front move along a channel of a lower density, magnetic

Michael Chertkov

The paper reflects on the future role of AI in scientific research, with a special focus on turbulence studies, and examines the evolution of AI, particularly through Diffusion Models rooted in non-equilibrium statistical mechanics. It underscores the significant impact of AI on advancing reduced, Lagrangian models of turbulence through innovative use of dee