May 2024 arXiv papers — page 42

Elmar Große-Klönne

Let $F/{\mathbb Q}_p$ be a finite field extension, let $k$ be a finite field extension of the residue field of $F$. Generalizing the $\psi$-lattices which Colmez constructed in \'{e}tale $(\varphi,\Gamma)$-modules over $k[[t]][t^{-1}]$, we define, study and exemplify $\psi$-lattices in \'{e}tale $(\varphi,\Gamma)$-modules over $k[[t_1,\ldots,t_d]][\prod_it_i

Masaki Toyoda, Yoshimasa Uematsu

We propose a novel methodology for discovering the presence of relationships realized as binary time series between variables in high dimension. To make it visually intuitive, we regard the existence of a relationship as an edge connection, and call a collection of such edges a network. Our objective is thus rephrased as uncovering the network by selecting r

Peratham Wiriyathammabhum

This paper presents a mixture version of the method-of-moment unsupervised lexicon classification by an incorporation of a Dirichlet process.

Atmadev Rai, Danilo Triggiani, Paolo Facchi, Vincenzo Tamma

Achieving the ultimate quantum precision in the estimation of multiple physical parameters simultaneously is a challenge in quantum metrology due to fundamental limitations and experimental challenges in harnessing the necessary quantum resources. We propose an experimentally feasible scheme to reach Heisenberg limited sensitivity in the simultaneous estimat

Yuanbin Chen, Ying Wang, Zhaocheng Wang, Ping Zhang

Holographic multiple-input multiple-output (MIMO) systems constitute a promising technology in support of next-generation wireless communications, thus paving the way for a smart programmable radio environment. However, despite its significant potential, further fundamental issues remain to be addressed, such as the acquisition of accurate channel informatio

Pedro L. del Angel R., Frank Neumann

For a semisimple complex algebraic group $G$ we determine the rational cohomology and the Hodge-Tate structure of the moduli stack ${\mathscr B}un_{G,X}$ of principal $G$-bundles over a connected smooth complex projective variety $X$ of special type using the homotopy theory of the underlying topological stack.

Jacob Lamers, Guy Verschaffelt, Guy Van der Sande

Ising machines are dedicated hardware solvers of NP-hard optimization problems. However, they do not always find the most optimal solution. The probability of finding this optimal solution depends on the problem at hand. Using continuation methods, we show that this is closely linked to the bifurcation sequence of the optimal solution. From this bifurcation

Yeongmin Kim, Kwanghyeon Lee, Minsang Park, Byeonghu Na

Diffusion-based representation learning has achieved substantial attention due to its promising capabilities in latent representation and sample generation. Recent studies have employed an auxiliary encoder to identify a corresponding representation from a sample and to adjust the dimensionality of a latent variable z. Meanwhile, this auxiliary structure inv

Shujun Yang, Yu Zhang, Yao Ding, Danfeng Hong

Insufficient prior knowledge of a captured hyperspectral image (HSI) scene may lead the experts or the automatic labeling systems to offer incorrect labels or ambiguous labels (i.e., assigning each training sample to a group of candidate labels, among which only one of them is valid; this is also known as partial label learning) during the labeling process.

Johannes Niederhauser, Nao Hirokawa, Aart Middeldorp

We revisit completion modulo equational theories for left-linear term rewrite systems where unification modulo the theory is avoided and the normal rewrite relation can be used in order to decide validity questions. To that end, we give a new correctness proof for finite runs and establish a simulation result between the two inference systems known from the

Adrienne Tuynman, Rémy Degenne, Emilie Kaufmann

We revisit the identification of an $\varepsilon$-optimal policy in average-reward Markov Decision Processes (MDP). In such MDPs, two measures of complexity have appeared in the literature: the diameter, $D$, and the optimal bias span, $H$, which satisfy $H\leq D$. Prior work have studied the complexity of $\varepsilon$-optimal policy identification only whe

Andrew Murdza, Khai T. Nguyen

The paper establishes a sharp quantitative estimate for the $(d-1)$-Hausdorff measure of the critical set of $\mathcal{C}^1$ vector-valued functions on $\mathbb{R}^d$. Additionally, we prove that for a generic $\mathcal{C}^2$ function where ``generic" is understood in the topological sense of Baire category, the critical set has a locally finite $(d-1)$-Haus

Marius Mönch, Nicole Marheineke

In this paper, we develop high-order splitting methods for linear port-Hamiltonian systems, focusing on preserving their intrinsic structure, particularly the dissipation inequality. Port-Hamiltonian systems are characterized by their ability to describe energy-conserving and dissipative processes, which is essential for the accurate simulation of physical s

Xiao-Yan Wang

This paper assumes that neutrino flavor conversion is induced by right-handed neutrino mixture via seesaw mechanism, which leads to apparent fake neutrino mixture with neutrino mass eigenstate consistent with flavor state of left-handed neutrino rather than mixture of flavor state. The transition probability between right-handed neutrinos due to mixture can

Haoyu Zhao, Wenhang Ge, Ying-cong Chen

Visual grounding is an essential tool that links user-provided text queries with query-specific regions within an image. Despite advancements in visual grounding models, their ability to comprehend complex queries remains limited. To overcome this limitation, we introduce LLM-Optic, an innovative method that utilizes Large Language Models (LLMs) as an optica

Houxing Ren, Mingjie Zhan, Zhongyuan Wu, Hongsheng Li

In infilling tasks, sub-tokens, representing instances where a complete token is segmented into two parts, often emerge at the boundaries of prefixes, middles, and suffixes. Traditional methods focused on training models at the token level, leading to sub-optimal performance in character-level infilling tasks during the inference stage. Alternately, some app

Yifan Mao, Ming Li, Jian Liu, Jiayang Liu

Surround-view depth estimation is a crucial task aims to acquire the depth maps of the surrounding views. It has many applications in real world scenarios such as autonomous driving, AR/VR and 3D reconstruction, etc. However, given that most of the data in the autonomous driving dataset is collected in daytime scenarios, this leads to poor depth model perfor

Zalán Molnár

The main motivation of this paper is the study of first-order model theoretic properties of structures having their roots in modal logic. We will focus on the connections between ultrafilter extensions and ultrapowers. We show that certain structures (called bounded graphs) are elementary substructures of their ultrafilter extensions, moreover their modal lo

Haozhe Xu, Cong Wu, Yangyang Gu, Xingcan Shang

The integration of Voice Control Systems (VCS) into smart devices and their growing presence in daily life accentuate the importance of their security. Current research has uncovered numerous vulnerabilities in VCS, presenting significant risks to user privacy and security. However, a cohesive and systematic examination of these vulnerabilities and the corre

Xi Zhu, Songcan Yu, Junbo Wang, Qinglin Yang

Federated learning (FL), as an emerging collaborative learning paradigm, has garnered significant attention due to its capacity to preserve privacy within distributed learning systems. In these systems, clients collaboratively train a unified neural network model using their local datasets and share model parameters rather than raw data, enhancing privacy. P

Shengchao Hu, Ziqing Fan, Chaoqin Huang, Li Shen

Recent advancements in offline reinforcement learning (RL) have underscored the capabilities of Conditional Sequence Modeling (CSM), a paradigm that learns the action distribution based on history trajectory and target returns for each state. However, these methods often struggle with stitching together optimal trajectories from sub-optimal ones due to the i

Steven Landgraf, Markus Hillemann, Theodor Kapler, Markus Ulrich

Deep neural networks excel in perception tasks such as semantic segmentation and monocular depth estimation, making them indispensable in safety-critical applications like autonomous driving and industrial inspection. However, they often suffer from overconfidence and poor explainability, especially for out-of-domain data. While uncertainty quantification ha

Chandan Bhaumik, Md Abu Raihan, Husney Parvez Sarwar

Let $A$ be a Rees-like algebra of dimension $d$ and $N$ a commutative partially cancellative torsion-free seminormal monoid. We prove the following results. \begin{enumerate} \item Let $P$ be a finitely generated projective $A$-module of $\rank\geq d$. Then $(i)$ $P$ has a unimodular element; $(ii)$ The action of $\EL(A\oplus P)$ on $\Um(A\oplus P)$ is trans

Jongchul Chae, Michiel van Noort, Maria S. Madjarska, Kyeore Lee

The investigation of plasma motions in the solar chromosphere is crucial for understanding the transport of mechanical energy from the interior of the Sun to the outer atmosphere and into interplanetary space. We report the finding of large-amplitude oscillatory transverse motions prevailing in the non-spicular Halpha chromosphere of a small quiet region nea

Fabio Feser, Marina Evangelou

The sparse-group lasso performs both variable and group selection, simultaneously using the strengths of the lasso and group lasso. It has found widespread use in genetics, a field that regularly involves the analysis of high-dimensional data, due to its sparse-group penalty, which allows it to utilize grouping information. However, the sparse-group lasso ca

Soyuj Basnet, Jerry Gou, Antonio Mallia, Torsten Suel

A lot of recent work has focused on sparse learned indexes that use deep neural architectures to significantly improve retrieval quality while keeping the efficiency benefits of the inverted index. While such sparse learned structures achieve effectiveness far beyond those of traditional inverted index-based rankers, there is still a gap in effectiveness to

Ling-Zheng Xia, Wei-Jia Li

In this paper we investigate the shear viscoelasticity and the hydrodynamic modes in a holographic solid model with several sets of axions that all break the translations spontaneously on boundary. Comparing with the single-axion model, the shear modulus is enhanced at high temperatures and the shear viscosity is always suppressed in the presence of addition

Anton Rarovskii

According to the classification of quasihomogeneus singularities, any polynomial $f$ defining such singularity has a decomposition $f = f_\kappa + f_{add}$. The polynomial $f_\kappa$ is of the certain form while $f_{add}$ is only restricted by the condition that the singularity of $f$ should be isolated. The polynomial $f_{add}$ is zero if and only if $f$ is

Moritz Hauck, Yizhou Liang, Daniel Peterseim

We propose a positivity preserving finite element discretization for the nonlinear Gross-Pitaevskii eigenvalue problem. The method employs mass lumping techniques, which allow to transfer the uniqueness up to sign and positivity properties of the continuous ground state to the discrete setting. We further prove that every non-negative discrete excited state

P. Bernát Szabó, Zeno Schätzle, Mike T. Entwistle, Frank Noé

We introduce several improvements to the penalty-based variational quantum Monte Carlo (VMC) algorithm for computing electronic excited states of Entwistle $\textit{et al.}$ [M. T. Entwistle $\textit{et al.}$, Nat. Commun. $\textbf{14}$, 274 (2023)], and demonstrate that the accuracy of the updated method is competitive with other available excited-state VMC

Julian Arnold, Flemming Holtorf, Frank Schäfer, Niels Lörch

In a physical system, changing parameters such as temperature can induce a phase transition: an abrupt change from one state of matter to another. Analogous phenomena have recently been observed in large language models. Typically, the task of identifying phase transitions requires human analysis and some prior understanding of the system to narrow down whic

Daniel Wilczak, Piotr Zgliczyński

We prove the existence of infinite number of homoclinic and heteroclinic orbits to two periodic orbits for the Kuramoto-Sivashinsky PDE on the line with odd and periodic boundary conditions and for some fixed parameter value of the system. The proof is computer assisted and it is based on a new algorithm for rigorous integration of the variational equation f

Dylan Galt, Langte Ma

We study generalized anti-self-dual instantons defined over Riemannian manifolds equipped with a parallel codimension-$4$ differential form. In particular, for product Riemannian manifolds possessing such a form, we study dimension reduction phenomena, finding a topological criterion for bundles which, when satisfied, allows for a complete characterization o

Zhongshi Sun, Guangyan Jia

This article studies inverse reinforcement learning (IRL) for the stochastic linear-quadratic optimal control problem, where two agents are considered. A learner agent does not know the expert agent's performance cost function, but it imitates the behavior of the expert agent by constructing an underlying cost function that obtains the same optimal feedback

Katarzyna Mazowiecka, Armin Schikorra

For any $M, n \geq 2$ and any open set $\Omega \subset \mathbb{R}^n$ we find a smooth, strongly polyconvex function $F\colon \mathbb{R}^{M\times n}\to \mathbb{R}$ and a Lipschitz map $u\colon \mathbb{R}^n \to \mathbb{R}^M$ that is a weak local minimizer of the energy \[ \int_{\Omega} F(Du). \] but with nowhere continuous partial derivatives. This extends cel

Andris Erglis, Milan Radonjić, Stefan Yoshi Buhmann

We investigate the properties of the photon Bose-Einstein condensate in the limit of small mode spacing. Alongside the well-known threshold of the phase transition at large mode spacings, we find an emergence of a second threshold for sufficiently small mode spacings, defining the crossover to a fully condensed state. Furthermore, we present our findings for

Xiangyu Sun, Joo Chan Lee, Daniel Rho, Jong Hwan Ko

The neural radiance field (NeRF) has made significant strides in representing 3D scenes and synthesizing novel views. Despite its advancements, the high computational costs of NeRF have posed challenges for its deployment in resource-constrained environments and real-time applications. As an alternative to NeRF-like neural rendering methods, 3D Gaussian Spla

Cong Wang, Kuan Tian, Yonghang Guan, Fei Shen

The success of the text-guided diffusion model has inspired the development and release of numerous powerful diffusion models within the open-source community. These models are typically fine-tuned on various expert datasets, showcasing diverse denoising capabilities. Leveraging multiple high-quality models to produce stronger generation ability is valuable,

Ian Pons, Bruno Yamamoto, Anna H. Reali Costa, Artur Jordao

Deep neural networks have been the predominant paradigm in machine learning for solving cognitive tasks. Such models, however, are restricted by a high computational overhead, limiting their applicability and hindering advancements in the field. Extensive research demonstrated that pruning structures from these models is a straightforward approach to reducin

Nicole Neis, Juergen Beyerer

The lateral position of vehicles within their lane is a decisive factor for the range of vision of vehicle sensors. This, in turn, is crucial for a vehicle's ability to perceive its environment and gain a high situational awareness by processing the collected information. When aiming for increasing levels of vehicle autonomy, this situational awareness becom

Puning Zhao, Li Shen, Rongfei Fan, Qingming Li

User-level privacy is important in distributed systems. Previous research primarily focuses on the central model, while the local models have received much less attention. Under the central model, user-level DP is strictly stronger than the item-level one. However, under the local model, the relationship between user-level and item-level LDP becomes more com

Raffaele Reda, Valentina Penza

The solar activity, which is driven by a variable magnetic field, exhibits changes along several time scales, the 11-year being the most known. In addition to the SunSpot Number, the Ca II K index and the Mg II index are indices widely employed among those proposed to quantify the solar activity, also because of their ability to trace the solar UV emission.

Zhao-Feng Wu, Dimitrios Giannios

In 2023, the Pulsar Timing Array (PTA) Collaborations announced the discovery of a gravitational wave background (GWB), predominantly attributed to supermassive black hole binary (SMBHB) mergers. However, the detected GWB is several times stronger than the default value expected from galactic observations at low and moderate redshifts. Recent findings by the

Hubert D. Zając, Jorge M. N. Ribeiro, Silvia Ingala, Simona Gentile

Artificial Intelligence (AI) repeatedly match or outperform radiologists in lab experiments. However, real-world implementations of radiological AI-based systems are found to provide little to no clinical value. This paper explores how to design AI for clinical usefulness in different contexts. We conducted 19 design sessions and design interventions with 13

Felix Brei, Johannes Frey, Lars-Peter Meyer

In this work we will show that language models with less than one billion parameters can be used to translate natural language to SPARQL queries after fine-tuning. Using three different datasets ranging from academic to real world, we identify prerequisites that the training data must fulfill in order for the training to be successful. The goal is to empower

Egor Gladin, Pavel Dvurechensky, Alexander Mielke, Jia-Jie Zhu

This paper presents a new gradient flow dissipation geometry over non-negative and probability measures. This is motivated by a principled construction that combines the unbalanced optimal transport and interaction forces modeled by reproducing kernels. Using a precise connection between the Hellinger geometry and the maximum mean discrepancy (MMD), we propo

Hongming Chen, Xiang Chen, Chen Wu, Zhuoran Zheng

Despite significant progress has been made in image deraining, existing approaches are mostly carried out on low-resolution images. The effectiveness of these methods on high-resolution images is still unknown, especially for ultra-high-definition (UHD) images, given the continuous advancement of imaging devices. In this paper, we focus on the task of UHD im

Alexandre Girard, Jean-Philippe Lucking Bigué, Benjamin M. O'Brien, Todd A. Gisby

Soft robots could bring robotic systems to new horizons, by enabling safe human-machine interaction. For precise control, these soft structures require high level position feedback that is not easily achieved through conventional one-degree-of-freedom (DOF) sensing apparatus. In this paper, a soft two-DOF dielectric elastomer (DE) sensor is specifically desi

Jordina Francès de Mas, Juliana Bowles

This paper presents a novel simplification calculus for propositional logic derived from Peirce's existential graphs' rules of inference and implication graphs. Our rules can be applied to propositional logic formulae in nested form, are equivalence-preserving, guarantee a monotonically decreasing number of variables, clauses and literals, and maximise the p

Hyojin Lee, Sangwoo Park, Osvaldo Simeone, Yonina C. Eldar

Detecting occupied subbands is a key task for wireless applications such as unlicensed spectrum access. Recently, detection methods were proposed that extract per-subband features from sub-Nyquist baseband samples and then apply thresholding mechanisms based on held-out data. Such existing solutions can only provide guarantees in terms of false negative rate

Syed Javed, Tariq M. Khan, Abdul Qayyum, Hamid Alinejad-Rokny

Accurate segmentation of anatomical structures and abnormalities in medical images is crucial for computer-aided diagnosis and analysis. While deep learning techniques excel at this task, their computational demands pose challenges. Additionally, some cutting-edge segmentation methods, though effective for general object segmentation, may not be optimised fo

Monika Zimmermann, Florian Ziel

Accurate mid-term (weeks to one year) hourly electricity load forecasts are essential for strategic decision-making in power plant operation, ensuring supply security and grid stability, planning and building energy storage systems, and energy trading. While numerous models effectively predict short-term (hours to a few days) hourly load, mid-term forecastin

Jinqi Wang, Yunfei Fu, Zhangcan Ding, Bailin Deng

Inspired by the software industry's practice of offering different editions or versions of a product tailored to specific user groups or use cases, we propose a novel task, namely, training-free editioning, for text-to-image models. Specifically, we aim to create variations of a base text-to-image model without retraining, enabling the model to cater to the

Saravanan Kandasamy, Dheeraj Nagaraj

Langevin Dynamics is a Stochastic Differential Equation (SDE) central to sampling and generative modeling and is implemented via time discretization. Langevin Monte Carlo (LMC), based on the Euler-Maruyama discretization, is the simplest and most studied algorithm. LMC can suffer from slow convergence - requiring a large number of steps of small step-size to

Dixuan Wang, Yanda Li, Junyuan Jiang, Zepeng Ding

Large Language Models (LLMs) have shown remarkable capabilities in language understanding and generation. Nonetheless, it was also witnessed that LLMs tend to produce inaccurate responses to specific queries. This deficiency can be traced to the tokenization step LLMs must undergo, which is an inevitable limitation inherent to all LLMs. In fact, incorrect to

Jeff Guo, Philippe Schwaller

Generative molecular design for drug discovery has very recently achieved a wave of experimental validation, with language-based backbones being the most common architectures employed. The most important factor for downstream success is whether an in silico oracle is well correlated with the desired end-point. To this end, current methods use cheaper proxy o

Furkan Polat, Hasan Tuncer, Armin Moin, Moharram Challenger

This study introduces a novel framework that brings together two main Quantum Programming methodologies, gate-based Quantum Computing and Quantum Annealing, by applying the Model-Driven Engineering principles. This aims to enhance the adaptability, design and scalability of quantum programs, facilitating their design and operation across diverse computing pl

Olivier Thas, Stijn Jaspers

In an attempt to provide an answer to the increasing criticism against p-values and to bridge the gap between statistical inference and prediction modelling, we introduce the probability of improved prediction (PIP). In general, the PIP is a probabilistic measure for comparing two competing models. Three versions of the PIP and several estimators are introdu

Pavel Strunz

Orthogonal coordinate systems enable expressing the boundary conditions of differential equations in accord with the physical boundaries of the problem. It can significantly simplify calculations. The orthogonal similar oblate spheroidal (SOS) coordinate system can be particularly useful for a physical processes description inside or in the vicinity of the b

Jun Gao, Qi Lv, Zili Wang, Tianxiang Wu

In-context learning (ICL) enhances the reasoning abilities of Large Language Models (LLMs) by prepending a few demonstrations. It motivates researchers to introduce more examples to provide additional contextual information for the generation. However, existing methods show a significant limitation due to the problem of excessive growth in context length, wh

Long-Fei Li, Yu-Jie Zhang, Peng Zhao, Zhi-Hua Zhou

We study a new class of MDPs that employs multinomial logit (MNL) function approximation to ensure valid probability distributions over the state space. Despite its significant benefits, incorporating the non-linear function raises substantial challenges in both statistical and computational efficiency. The best-known result of Hwang and Oh [2023] has achiev

Yidong Liao, Xiao-Ming Zhang, Chris Ferrie

Graph Neural Networks (GNNs) are powerful machine learning models that excel at analyzing structured data represented as graphs, demonstrating remarkable performance in applications like social network analysis and recommendation systems. However, classical GNNs face scalability challenges when dealing with large-scale graphs. This paper proposes frameworks

Dayana K, S. Nandini, Sanjjushri Varshini R

The detection of cardiovascular diseases (CVD) using machine learning techniques represents a significant advancement in medical diagnostics, aiming to enhance early detection, accuracy, and efficiency. This study explores a comparative analysis of various machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, Gradient Boos

Noel T. Fortun, Angelyn R. Lao, Eduardo R. Mendoza, Luis F. Razon

The potential for multistationarity, or the existence of steady-state multiplicity, in the Earth System raises concerns that the planet could reach a climatic `tipping point,' rapidly transitioning to a warmer steady-state from which recovery may be practically unattainable. In detailed Earth models that require extensive computation time, it is difficult to

Houxing Ren, Mingjie Zhan, Zhongyuan Wu, Aojun Zhou

Code generation plays a crucial role in various tasks, such as code auto-completion and mathematical reasoning. Previous work has proposed numerous methods to enhance code generation performance, including integrating feedback from the compiler. Inspired by this, we present ReflectionCoder, a novel approach that effectively leverages reflection sequences con

Joon-Hwi Kim, Jung-Wook Kim, Sangmin Lee

We study the (ambi-)twistor model for spinning particles interacting via electromagnetic field, as a toy model for studying classical dynamics of gravitating bodies including effects of both spins to all orders. We compute the momentum kick and spin kick up to one-loop order and show precisely how they are encoded in the classical eikonal. The all-orders-in-

Giovanni Anello, Luca Vilasi

We consider a Kirchhoff problem of Brezis-Nirenberg type in a smooth bounded domain of $\mathbb{R}^4$ with Dirichlet boundary conditions. Our approach, novel in this framework and based upon approximation arguments, allows us to cope with the interaction between the higher order Kirchhoff term and the critical nonlinearity, typical of the dimension four. We

Hanxi Xiao, Fan Lyu

The goal of Continual Learning (CL) task is to continuously learn multiple new tasks sequentially while achieving a balance between the plasticity and stability of new and old knowledge. This paper analyzes that this insufficiency arises from the ineffective handling of outliers, leading to abnormal gradients and unexpected model updates. To address this iss

Jiawei Shao, Jingwen Tong, Qiong Wu, Wei Guo

The rapid evolution of wireless technologies and the growing complexity of network infrastructures necessitate a paradigm shift in how communication networks are designed, configured, and managed. Recent advancements in Large Language Models (LLMs) have sparked interest in their potential to revolutionize wireless communication systems. However, existing stu

Jun Gao, Ziqiang Cao, Wenjie Li

Long prompt leads to huge hardware costs when using transformer-based Large Language Models (LLMs). Unfortunately, many tasks, such as summarization, inevitably introduce long documents, and the wide application of in-context learning easily makes the prompt length explode. This paper proposes a Self-Compressor (SelfCP), which employs the target LLM itself t

Jiadong Dan, Cheng Zhang, Xiaoxu Zhao, N. Duane Loh

We present a method using Zernike moments for quantifying rotational and reflectional symmetries in scanning transmission electron microscopy (STEM) images, aimed at improving structural analysis of materials at the atomic scale. This technique is effective against common imaging noises and is potentially suited for low-dose imaging and identifying quantum d

Hao Wu, Xingjian Shi, Ziyue Huang, Penghao Zhao

Data-driven deep learning has emerged as the new paradigm to model complex physical space-time systems. These data-driven methods learn patterns by optimizing statistical metrics and tend to overlook the adherence to physical laws, unlike traditional model-driven numerical methods. Thus, they often generate predictions that are not physically realistic. On t

Sonny Achten, Zander Op de Beeck, Francesco Tonin, Volkan Cevher

Clustering nodes in heterophilous graphs is challenging as traditional methods assume that effective clustering is characterized by high intra-cluster and low inter-cluster connectivity. To address this, we introduce HeNCler-a novel approach for Heterophilous Node Clustering. HeNCler learns a similarity graph by optimizing a clustering-specific objective bas

Verena Wolf

Young stellar objects (YSOs) accrete up to half of their material in short periods of enhanced mass accretion. For massive YSOs (MYSOs with more than 8 solar masses), accretion outbursts are of special importance, as they serve as diagnostics in highly obscured regions. Within this work, two outbursting MYSOs within different evolutionary stages, the young s

Boyuan Zheng, Jianlong Zhou, Fang Chen

Humans naturally employ linguistic instructions to convey knowledge, a process that proves significantly more complex for machines, especially within the context of multitask robotic manipulation environments. Natural language, moreover, serves as the primary medium through which humans acquire new knowledge, presenting a potentially intuitive bridge for tra

Rashi Jain, Satyabrata Adhikari

In a realistic situation, it is very difficult to communicate securely between two distant parties without introducing any disturbances. These disturbances might occur either due to external noise or may be due to the interference of an eavesdropper sitting in between the sender and the receiver. In this work, we probe here the existence of the possibility o

Xuemei Gu, Mario Krenn

The rapid growth of scientific literature makes it challenging for researchers to identify novel and impactful ideas, especially across disciplines. Modern artificial intelligence (AI) systems offer new approaches, potentially inspiring ideas not conceived by humans alone. But how compelling are these AI-generated ideas, and how can we improve their quality?

Mieszko Baszczak

We describe the action of the Weyl group of a semi simple linear group $G$ on cohomological and K-theoretic invariants of the generalized flag variety $G/B$. We study the automorphism $s_i$, induced by the reflection in the simple root, on the equivariant $K$-theory ring $K_T(G/B)$ using divided difference operators. Using the localization theorem for torus

Ying He, Mingyang Niu, Jingyu Hua, Yunlong Mao

Split Neural Network, as one of the most common architectures used in vertical federated learning, is popular in industry due to its privacy-preserving characteristics. In this architecture, the party holding the labels seeks cooperation from other parties to improve model performance due to insufficient feature data. Each of these participants has a self-de

Yinghao Zhu, Changyu Ren, Zixiang Wang, Xiaochen Zheng

The integration of multimodal Electronic Health Records (EHR) data has significantly advanced clinical predictive capabilities. Existing models, which utilize clinical notes and multivariate time-series EHR data, often fall short of incorporating the necessary medical context for accurate clinical tasks, while previous approaches with knowledge graphs (KGs)

Sayan Das, Li-Cheng Tsai

We prove the upper-tail Large Deviation Principle (LDP) for the parabolic Airy process and characterize the limit shape of the directed landscape under the upper-tail conditioning. The LDP result answers Conjecture 10.1 in Das, Dauvergne, and Vir\'{a}g (2024). The starting point of our proof is the metric-level LDP for the directed landscape from Das, Dauver

Yipei Zhang, Xiumei Wang, Jinjiang Yuan, C. T. Ng

A matching covered graph $G$ is minimal if for each edge $e$ of $G$, $G-e$ is not matching covered. An edge $e$ of a matching covered graph $G$ is removable if $G-e$ is also matching covered. Thus a matching covered graph is minimal if and only if it is free of removable edges. For bipartite graphs, Lov\'{a}sz and Plummer gave a characterization of bipartite

Chengxing Jia, Pengyuan Wang, Ziniu Li, Yi-Chen Li

Large language models (LLMs) have catalyzed a paradigm shift in natural language processing, yet their limited controllability poses a significant challenge for downstream applications. We aim to address this by drawing inspiration from the neural mechanisms of the human brain, specifically Broca's and Wernicke's areas, which are crucial for language generat

Chiara Fumelli, Anirvan Dutta, Mohsen Kaboli

Motivated by the growing interest in enhancing intuitive physical Human-Machine Interaction (HRI/HVI), this study aims to propose a robust tactile hand gesture recognition system. We performed a comprehensive evaluation of different hand gesture recognition approaches for a large area tactile sensing interface (touch interface) constructed from conductive te

Zongkai Zhang, Zidong Xu, Wenming Yang, Qingmin Liao

Existing 3D occupancy networks demand significant hardware resources, hindering the deployment of edge devices. Binarized Neural Networks (BNN) offer substantially reduced computational and memory requirements. However, their performance decreases notably compared to full-precision networks. Moreover, it is challenging to enhance the performance of binarized

Christian Stefan Gruber, Mahmoud Abdel-Hafiez

Topological quantum materials hold great promise for future technological applications. Their unique electronic properties, such as protected surface states and exotic quasiparticles, offer opportunities for designing novel electronic devices, spintronics, and quantum information processing. The origin of the interplay between various electronic orders in to

Harshit Varma, Dheeraj Nagaraj, Karthikeyan Shanmugam

We introduce the Glauber Generative Model (GGM), a new class of discrete diffusion models, to obtain new samples from a distribution given samples from a discrete space. GGM deploys a discrete Markov chain called the heat bath dynamics (or the Glauber dynamics) to denoise a sequence of noisy tokens to a sample from a joint distribution of discrete tokens. Ou

Renqiang Luo, Huafei Huang, Shuo Yu, Zhuoyang Han

Fairness-aware Graph Neural Networks (GNNs) often face a challenging trade-off, where prioritizing fairness may require compromising utility. In this work, we re-examine fairness through the lens of spectral graph theory, aiming to reconcile fairness and utility within the framework of spectral graph learning. We explore the correlation between sensitive fea

Héctor Ariza, Carmen Fernández, Antonio Galbis

We analyze the behavior of the iterates of composition operators defined by polynomials acting on global classes of ultradifferentiable functions of Beurling type and being invariant under Fourier transform. We characterize the polynomials $\psi$ for which the sequence of iterates is equicontinuous between two different Gelfand-Shilov spaces. For the particu

Haoxin Lin, Yu-Yan Xu, Yihao Sun, Zhilong Zhang

Model-based methods in reinforcement learning offer a promising approach to enhance data efficiency by facilitating policy exploration within a dynamics model. However, accurately predicting sequential steps in the dynamics model remains a challenge due to the bootstrapping prediction, which attributes the next state to the prediction of the current state. T

Avinash Nittur Ramesh, Aitor Correas-Serrano, María González-Huici

We present a novel synthetically generated multi-modal dataset, SCaRL, to enable the training and validation of autonomous driving solutions. Multi-modal datasets are essential to attain the robustness and high accuracy required by autonomous systems in applications such as autonomous driving. As deep learning-based solutions are becoming more prevalent for

James L. Gray, Aous T. Naman, David S. Taubman

Variational approaches to disparity estimation typically use a linearised brightness constancy constraint, which only applies in smooth regions and over small distances. Accordingly, current variational approaches rely on a schedule to progressively include image data. This paper proposes the use of Gradient Consistency information to assess the validity of

Haoxiang Shi, Jianzong Wang, Xulong Zhang, Ning Cheng

Although current Text-To-Speech (TTS) models are able to generate high-quality speech samples, there are still challenges in developing emotion intensity controllable TTS. Most existing TTS models achieve emotion intensity control by extracting intensity information from reference speeches. Unfortunately, limited by the lack of modeling for intra-class emoti

Bilal Faye, Mustapha Lebbah, Hanane Azzag

Batch Normalization (BN), a widely-used technique in neural networks, enhances generalization and expedites training by normalizing each mini-batch to the same mean and variance. However, its effectiveness diminishes when confronted with diverse data distributions. To address this challenge, we propose Supervised Batch Normalization (SBN), a pioneering appro

Saikat Panja

If $A$ is a finite group (or a finite ring) and $\omega$ is a word map (or a polynomial map), we define the quantity $|\omega(A)|/|A|$ as the image ratio of $\omega$ on $A$ and will be denoted by $\mu(\omega,A)$. In this article, we investigate the set $\mathrm{R}(\omega)=\{\mu(\omega,A) : A \text{ is a finite group}\}$, and also consider the case of rings.

Zhenyu Bai, Pranav Dangi, Huize Li, Tulika Mitra

Efficiently supporting long context length is crucial for Transformer models. The quadratic complexity of the self-attention computation plagues traditional Transformers. Sliding window-based static sparse attention mitigates the problem by limiting the attention scope of the input tokens, reducing the theoretical complexity from quadratic to linear. Althoug

Xiran Xu, Bo Wang, Boda Xiao, Yadong Niu

Researchers have reported high decoding accuracy (>95%) using non-invasive Electroencephalogram (EEG) signals for brain-computer interface (BCI) decoding tasks like image decoding, emotion recognition, auditory spatial attention detection, etc. Since these EEG data were usually collected with well-designed paradigms in labs, the reliability and robustness of

Yael Arieli, Alexander Khain, Ehud Gavze, Orit Altaratz

This study employs a high-resolution (10m) System for Atmospheric Modeling (SAM) coupled with the Spectral Bin Microphysical (SBM) scheme to thoroughly investigate the processes governing the evolution of aerosol properties within and outside a shallow cumulus cloud. The model encompasses the complete life cycle of cloud droplets, starting from their formati

Yixiong Zou, Shanghang Zhang, Haichen Zhou, Yuhua Li

Few-shot class-incremental learning (FSCIL) is proposed to continually learn from novel classes with only a few samples after the (pre-)training on base classes with sufficient data. However, this remains a challenge. In contrast, humans can easily recognize novel classes with a few samples. Cognitive science demonstrates that an important component of such

Robert L. Singleton

Modular exponentiation (ME) operators are one of the fundamental components of Shor's algorithm, and the place where most of the quantum resources are deployed. I propose a method for constructing the ME operators that relies upon the simple observation that the work register starts in state $\vert 1 \rangle$. Therefore, we do not have to create an ME operat