March 2024 arXiv papers — page 66

Illya Koval

The problem of the existence of an analytic normal form near an equilibrium point of an area-preserving map and analyticity of the associated coordinate change is a classical problem in dynamical systems going back to Poincar\'e and Siegel. One important class of examples of area-preserving maps consists of the collision maps for planar billiards. Recently,

Andrea Avogaro, Andrea Toaiari, Federico Cunico, Xiangmin Xu

We introduce HARPER, a novel dataset for 3D body pose estimation and forecast in dyadic interactions between users and Spot, the quadruped robot manufactured by Boston Dynamics. The key-novelty is the focus on the robot's perspective, i.e., on the data captured by the robot's sensors. These make 3D body pose analysis challenging because being close to the gr

I. L. Buchbinder, S. A. Fedoruk, A. P. Isaev, V. A. Krykhtin

We generalize the first class constraints that describe the infinite spin irreducible $4D$ Poincar\'{e} group representation in flat space to new first class constraints in $AdS_4$ space. The constraints are realized as operators acting in Fock space spanned by the creation and annihilation operators with two-component spinor indices. As a result, we obtain

Hagit Attiya, Michael A. Bender, Martin Farach-Colton, Rotem Oshman

A data structure is called history independent if its internal memory representation does not reveal the history of operations applied to it, only its current state. In this paper we study history independence for concurrent data structures, and establish foundational possibility and impossibility results. We show that a large class of concurrent objects can

Ashvini Varatharaj, Simon Todd

Non-M\=aori-speaking New Zealanders (NMS)are able to segment M\=aori words in a highlysimilar way to fluent speakers (Panther et al.,2024). This ability is assumed to derive through the identification and extraction of statistically recurrent forms. We examine this assumption by asking how NMS segmentations compare to those produced by Morfessor, an unsuperv

Mona Bielig, Celina Kacperski, Florian Kutzner

Energy efficient technologies are particularly important for social housing settings: they offer the potential to improve tenants' wellbeing through monetary savings and comfort, while reducing emissions of entire communities. Slow uptake of innovative energy technology in social housing has been associated with a lack of trust and the perceived risks of ado

Nasim Rahaman, Martin Weiss, Manuel Wüthrich, Yoshua Bengio

This work addresses the buyer's inspection paradox for information markets. The paradox is that buyers need to access information to determine its value, while sellers need to limit access to prevent theft. To study this, we introduce an open-source simulated digital marketplace where intelligent agents, powered by language models, buy and sell information o

Yufan Chen, Jiaming Zhang, Kunyu Peng, Junwei Zheng

Before developing a Document Layout Analysis (DLA) model in real-world applications, conducting comprehensive robustness testing is essential. However, the robustness of DLA models remains underexplored in the literature. To address this, we are the first to introduce a robustness benchmark for DLA models, which includes 450K document images of three dataset

Leo Köberlein, Dominik Probst, Richard Lenz

Quantifying the semantic similarity between database queries is a critical challenge with broad applications, ranging from query log analysis to automated educational assessment of SQL skills. Traditional methods often rely solely on syntactic comparisons or are limited to checking for semantic equivalence. This paper introduces a novel graph-based approach

Mathias Öttl, Siyuan Mei, Frauke Wilm, Jana Steenpass

Denoising Diffusion Probabilistic models have become increasingly popular due to their ability to offer probabilistic modeling and generate diverse outputs. This versatility inspired their adaptation for image segmentation, where multiple predictions of the model can produce segmentation results that not only achieve high quality but also capture the uncerta

Christos Kantas, Bjørk Antoniussen, Mathias V. Andersen, Rasmus Munksø

Using RAW-images in computer vision problems is surprisingly underexplored considering that converting from RAW to RGB does not introduce any new capture information. In this paper, we show that a sufficiently advanced classifier can yield equivalent results on RAW input compared to RGB and present a new public dataset consisting of RAW images and the corres

Dominik Wagner, Alexander Churchill, Siddharth Sigtia, Panayiotis Georgiou

Interactions with virtual assistants typically start with a predefined trigger phrase followed by the user command. To make interactions with the assistant more intuitive, we explore whether it is feasible to drop the requirement that users must begin each command with a trigger phrase. We explore this task in three ways: First, we train classifiers using on

Donia Ben Amor, Michael Joham, Wolfgang Utschick

In this work, we develop an efficient precoding strategy for a multi-user multiple-input-single output (MU MISO) system operating in frequency-division-duplex (FDD) mode, where rate splitting multiple access (RSMA) is implemented. To this end, we consider one-layer RS and show its significant impact on the system performance, specifically in the case where t

Ales Wodecki, Jakub Marecek, Vyacheslav Kungurtsev, Pavel Eichler

The problem of quantum state preparation is one of the main challenges in achieving the quantum advantage. Furthermore, classically, for multi-level problems, our ability to solve the corresponding quantum optimal control problems is rather limited. The ability of the latter to feed into the former may result in significant progress in quantum computing. To

Xinyi Zhang, Johanna Sophie Bieri, Manuel Günther

To visualize the regions of interest that classifiers base their decisions on, different Class Activation Mapping (CAM) methods have been developed. However, all of these techniques target categorical classifiers only, though most real-world tasks are binary classification. In this paper, we extend gradient-based CAM techniques to work with binary classifier

Johannes Schleischitz

In the early 1900's, Maillet proved that the image of any Liouville number under a rational function with rational coefficients is again a Liouville number. The analogous result for quadratic Liouville matrices in higher dimension turns out to fail. In fact, using a result by Kleinbock and Margulis, we show that among analytic matrix functions in dimension $

Naichen Shi, Salar Fattahi, Raed Al Kontar

In this work, we study the problem of common and unique feature extraction from noisy data. When we have N observation matrices from N different and associated sources corrupted by sparse and potentially gross noise, can we recover the common and unique components from these noisy observations? This is a challenging task as the number of parameters to estima

Dylon Chow, Daniel Loughran, Ramin Takloo-Bighash, Sho Tanimoto

We prove a variant of Manin's conjecture for Campana points on wonderful compactifications of semi-simple algebraic groups of adjoint type. We use this to provide evidence for a new conjecture on the leading constant in Manin's conjecture for Campana points.

Hans Harder, Sebastian Peitz

The value function plays a crucial role as a measure for the cumulative future reward an agent receives in both reinforcement learning and optimal control. It is therefore of interest to study how similar the values of neighboring states are, i.e., to investigate the continuity of the value function. We do so by providing and verifying upper bounds on the va

Tianming Liang, Chaolei Tan, Beihao Xia, Wei-Shi Zheng

This paper focuses on open-ended video question answering, which aims to find the correct answers from a large answer set in response to a video-related question. This is essentially a multi-label classification task, since a question may have multiple answers. However, due to annotation costs, the labels in existing benchmarks are always extremely insuffici

Mathias Öttl, Frauke Wilm, Jana Steenpass, Jingna Qiu

Deep learning-based image generation has seen significant advancements with diffusion models, notably improving the quality of generated images. Despite these developments, generating images with unseen characteristics beneficial for downstream tasks has received limited attention. To bridge this gap, we propose Style-Extracting Diffusion Models, featuring t

Cem Ata Baykara, Ali Burak Ünal, Mete Akgün

Federated learning has become increasingly widespread due to its ability to train models collaboratively without centralizing sensitive data. While most research on FL emphasizes privacy-preserving techniques during training, the evaluation phase also presents significant privacy risks that have not been adequately addressed in the literature. In particular,

Lukas Galke, Limor Raviv

Finding and facilitating commonalities between the linguistic behaviors of large language models and humans could lead to major breakthroughs in our understanding of the acquisition, processing, and evolution of language. However, most findings on human-LLM similarity can be attributed to training on human data. The field of emergent machine-to-machine commu

Benjamin Delarue, Joachim Hilgert

For negatively curved symmetric spaces it is known from [Hansen-Hilgert-Parthasarathy,2019] that the poles of the scattering matrices defined via the standard intertwining operators for the spherical principal representations of the isometry group are either given as poles of the intertwining operators or as quantum resonances, i.e. poles of the meromorphica

Daniel Mayfrank, Na Young Ahn, Alexander Mitsos, Manuel Dahmen

Mechanistic dynamic process models may be too computationally expensive to be usable as part of a real-time capable predictive controller. We present a method for end-to-end learning of Koopman surrogate models for optimal performance in a specific control task. In contrast to previous contributions that employ standard reinforcement learning (RL) algorithms

Zilong Shao

When the equipment is working, real-time collection of environmental sensor data for anomaly detection is one of the key links to prevent industrial process accidents and network attacks and ensure system security. However, under the environment with specific real-time requirements, the anomaly detection for environmental sensors still faces the following di

Masahiro Watari

The present paper addresses a semimodule counting conjecture of Moreno-Fr\'{i}as and Rosales for numerical semigroups. Applying Pfister and Steenbrink's Theory for punctual Hilbert schemes of curve singularities, we show that this conjecture is true for any numerical semigroup.

Alexandra Tofful, Charles F. A. Baynham, E. Anne Curtis, Adam O. Parsons

A full evaluation of the uncertainty budget for the ytterbium ion optical clock at the National Physical Laboratory (NPL) was performed on the electric octupole (E3) $^2\mathrm{S}_{1/2}\,\rightarrow\, ^2\mathrm{F}_{7/2}$ transition. The total systematic frequency shift was measured with a fractional standard systematic uncertainty of $2.2\times 10^{-18}$. Fu

Carla Cederbaum, Albachiara Cogo, Benedito Leandro, João Paulo dos Santos

In this paper, we combine and generalize to higher dimensions the approaches to proving the uniqueness of connected (3+1)-dimensional static vacuum asymptotically flat black hole spacetimes by M\"uller zum Hagen--Robinson--Seifert and by Robinson. Applying these techniques, we prove and/or reprove geometric inequalities for connected (n + 1)-dimensional stat

Jonathan Lebensold, Maziar Sanjabi, Pietro Astolfi, Adriana Romero-Soriano

Text-to-image diffusion models have been shown to suffer from sample-level memorization, possibly reproducing near-perfect replica of images that they are trained on, which may be undesirable. To remedy this issue, we develop the first differentially private (DP) retrieval-augmented generation algorithm that is capable of generating high-quality image sample

Yannick J. Franzke, Christof Holzer

Spin-orbit coupling induces a current density in the ground state, which consequently requires a generalization for meta-generalized gradient approximations. That is, the exchange-correlation energy has to be constructed as an explicit functional of the current density and a generalized kinetic energy density has to be formed to satisfy theoretical constrain

Rikkert Frederix, Tetiana Moskalets

We compute top quark pair production in association with a bottom quark pair at the LHC within the five-flavour scheme, matched to a parton shower, employing the FxFx merging scheme for $t\bar{t}+\textrm{jets}$ production with up to 2 jets at NLO accuracy. To enhance the selection efficiency for the events with $b$-jets within the inclusive five-flavour samp

Bohao Peng, Xiaoyang Wu, Li Jiang, Yukang Chen

The booming of 3D recognition in the 2020s began with the introduction of point cloud transformers. They quickly overwhelmed sparse CNNs and became state-of-the-art models, especially in 3D semantic segmentation. However, sparse CNNs are still valuable networks, due to their efficiency treasure, and ease of application. In this work, we reexamine the design

Kyungeun Kim, J. M. Schwarz, Martine Ben Amar

Cross-sections of cell shapes in a tissue monolayer typically resemble a tiling of convex polygons. Yet, examples exist where the polygons are not convex with curved cell-cell interfaces, as seen in the adaxial epidermis. To date, two-dimensional vertex models predicting the structure and mechanics of cell monolayers have been mostly limited to convex polygo

Michael X. Cao, Rahul Jain, Marco Tomamichel

Characterizing the minimal communication needed for the quantum channel simulation is a fundamental task in the quantum information theory. In this paper, we show that, in fidelity, the quantum channel simulation can be directly achieved via quantum state splitting without using a technique known as the de~Finetti reduction, and thus provide a pair of tighte

Mikhail Drobyshevskiy, Denis Turdakov

Random graph (RG) models play a central role in the complex networks analysis. They help to understand, control, and predict phenomena occurring, for instance, in social networks, biological networks, the Internet, etc. Despite a large number of RG models presented in the literature, there are few concepts underlying them. Instead of trying to classify a wid

Yongyi Jia, Shu Miao, Junjian Zhou, Niandong Jiao

Magnetic microrobots can be navigated by an external magnetic field to autonomously move within living organisms with complex and unstructured environments. Potential applications include drug delivery, diagnostics, and therapeutic interventions. Existing techniques commonly impart magnetic properties to the target object,or drive the robot to contact and th

Hao Hao, Xiaoqun Zhang, Aimin Zhou

Black-box optimization problems, which are common in many real-world applications, require optimization through input-output interactions without access to internal workings. This often leads to significant computational resources being consumed for simulations. Bayesian Optimization (BO) and Surrogate-Assisted Evolutionary Algorithm (SAEA) are two widely us

Matteo Bonotto, Luigi Sarrocco, Daniele Evangelista, Marco Imperoli

Neural Radiance Fields (NeRFs) have shown impressive results for novel view synthesis when a sufficiently large amount of views are available. When dealing with few-shot settings, i.e. with a small set of input views, the training could overfit those views, leading to artifacts and geometric and chromatic inconsistencies in the resulting rendering. Regulariz

Alexander Gomilko, Yuri Tomilov

We improve the classical results by Brenner and Thom\'ee on rational approximations of operator semigroups. In the setting of Hilbert spaces, we introduce a finer regularity scale for initial data, provide sharper stability estimates, and obtain optimal approximation rates. Moreover, we strengthen a result due to Egert-Rozendaal on subdiagonal Pad\'e approxi

Sanqing Qu, Tianpei Zou, Florian Röhrbein, Cewu Lu

Deep neural networks often exhibit sub-optimal performance under covariate and category shifts. Source-Free Domain Adaptation (SFDA) presents a promising solution to this dilemma, yet most SFDA approaches are restricted to closed-set scenarios. In this paper, we explore Source-Free Universal Domain Adaptation (SF-UniDA) aiming to accurately classify "known"

Yuchen Cai, Ding Cao, Rongxi Guo, Yaqin Wen

Large language models(LLM) are pre-trained on extensive corpora to learn facts and human cognition which contain human preferences. However, this process can inadvertently lead to these models acquiring biases and stereotypes prevalent in society. Prior research has typically tackled the issue of bias through a one-dimensional perspective, concentrating eith

Didier Robert

We consider the time dependent Schr\"odinger equation with a coupling spin-orbit in the semi-classical regime $\hbar\searrow 0$ and large spin number $\spin\rightarrow +\infty$ such that $\hbar^\delta\spin=c$ where $c>0$ and $\delta>0$ are constant. The initial state $\Psi(0)$ is a product of an orbital coherent state in $L^2(\R^d)$ and a spin coherent state

Ali Arslan, Ruben E. Rojas

We prove new bounds on the heat flux out of the bottom boundary, $\mathcal{F}_B$, for a fluid at infinite Prandtl number, heated internally between isothermal parallel plates under two kinematic boundary conditions. In uniform internally heated convection, the supply of heat equally leaves the domain by conduction when there is no flow. When the heating, qua

Stian Bilek

Quantum circuit partitioning (QCP) is a hybrid quantum-classical approach that aims to simulate large quantum systems on smaller quantum computers. A quantum computation is divided into smaller subsystems and results of measurements on these subsystems are combined using classical processing. In this paper, we propose a QCP strategy to measure an observable

Hong Huang, Weiming Zhuang, Chen Chen, Lingjuan Lyu

Federated learning (FL) promotes decentralized training while prioritizing data confidentiality. However, its application on resource-constrained devices is challenging due to the high demand for computation and memory resources to train deep learning models. Neural network pruning techniques, such as dynamic pruning, could enhance model efficiency, but dire

Yuji Zou, Jin-Kao Hao, Qinghua Wu

The latency location routing problem integrates the facility location problem and the multi-depot cumulative capacitated vehicle routing problem. This problem involves making simultaneous decisions about depot locations and vehicle routes to serve customers while aiming to minimize the sum of waiting (arriving) times for all customers. To address this comput

Jan-Hendrik Bastek, WaiChing Sun, Dennis M. Kochmann

Generative models such as denoising diffusion models are quickly advancing their ability to approximate highly complex data distributions. They are also increasingly leveraged in scientific machine learning, where samples from the implied data distribution are expected to adhere to specific governing equations. We present a framework that unifies generative

Soyeong Jeong, Jinheon Baek, Sukmin Cho, Sung Ju Hwang

Retrieval-Augmented Large Language Models (LLMs), which incorporate the non-parametric knowledge from external knowledge bases into LLMs, have emerged as a promising approach to enhancing response accuracy in several tasks, such as Question-Answering (QA). However, even though there are various approaches dealing with queries of different complexities, they

HyoJung Han, Mohamed Anwar, Juan Pino, Wei-Ning Hsu

Speech recognition and translation systems perform poorly on noisy inputs, which are frequent in realistic environments. Augmenting these systems with visual signals has the potential to improve robustness to noise. However, audio-visual (AV) data is only available in limited amounts and for fewer languages than audio-only resources. To address this gap, we

Dingchen Yang, Bowen Cao, Guang Chen, Changjun Jiang

Multi-modal Large Language Models (MLLMs) demonstrate remarkable success across various vision-language tasks. However, they suffer from visual hallucination, where the generated responses diverge from the provided image. Are MLLMs oblivious to the accurate visual cues when they hallucinate? Our investigation reveals that the visual branch may equally advoca

Zhenfeng Ouyang, Miao Gao, Zhong-Yi Lu

A recent experimental study announced the emergence of superconductivity in La$_3$Ni$_2$O$_7$ under pressure, with the highest observed superconducting transition temperature ($T_c$) reaching approximately 80 K beyond 14 GPa. While extensive studies have been devoted to the electronic correlations and potential superconducting pairing mechanisms, there lack

Changtong Zan, Liang Ding, Li Shen, Yibing Zhen

Translation-tailored Large language models (LLMs) exhibit remarkable translation capabilities, even competing with supervised-trained commercial translation systems. However, off-target translation remains an unsolved problem, especially for low-resource languages, hindering us from developing accurate LLMs-based translation models. To mitigate the off-targe

Zih-Syuan Huang, Ching-pei Lee

We propose a Regularized Adaptive Momentum Dual Averaging (RAMDA) algorithm for training structured neural networks. Similar to existing regularized adaptive methods, the subproblem for computing the update direction of RAMDA involves a nonsmooth regularizer and a diagonal preconditioner, and therefore does not possess a closed-form solution in general. We t

Michal Cuadrat-Grzybowski, Sébastien Clesse, Pascale Defraigne, Michel Van Camp

We show that Global Navigation Satellite Systems (GNSS) and gravimeters on Earth and in space can potentially offer the most accurate direct measurement of local density of near-Earth asteroid-mass Primordial Black Holes (PBHs) and Dark Matter (DM) clumps in the solar system by means of gravitational influence. Using semi-analytical methods and Monte Carlo s

Tianjiao Hua, Peng Luo

In this paper, we study a class of infinite horizon fully coupled McKean-Vlasov forward-backward stochastic differential equations (FBSDEs). We propose a generalized monotonicity condition involving two flexible functions. Under this condition, we establish the well-posedness results for infinite horizon McKean-Vlasov FBSDEs by the method of continuation, in

Tran-Vu La, Thanh Huy Nguyen, Patrick Matgen, Marco Chini

This paper addresses the challenges of an early flood warning caused by complex convective systems (CSs), by using Low-Earth Orbit and Geostationary satellite data. We focus on a sequence of extreme events that took place in central Vietnam during October 2020, with a specific emphasis on the events leading up to the floods, i.e., those occurring before Octo

Thanh Huy Nguyen, Sophie Ricci, Andrea Piacentini, Charlotte Emery

In spite of astonishing advances and developments in remote sensing technologies, meeting the spatio-temporal requirements for flood hydrodynamic modeling remains a great challenge for Earth Observation. The assimilation of multi-source remote sensing data in 2D hydrodynamic models participates to overcome such a challenge. The recently launched Surface Wate

Pengfei Sun, Jorg De Winne, Paul Devos, Dick Botteldooren

Decoding EEG signals is crucial for unraveling human brain and advancing brain-computer interfaces. Traditional machine learning algorithms have been hindered by the high noise levels and inherent inter-person variations in EEG signals. Recent advances in deep neural networks (DNNs) have shown promise, owing to their advanced nonlinear modeling capabilities.

Alexander Rothstein, Christoph Schattauer, Robin J. Dolleman, Stefan Trellenkamp

We report on the investigation of periodic superstructures in twisted bilayer graphene (tBLG) van-der-Waals heterostructures, where one of the graphene layers is aligned to hexagonal boron nitride (hBN). Our theoretical simulations reveal that if the ratio of the resulting two moir\'e unit cell areas is a simple fraction, the graphene/hBN moir\'e lattice act

Shuvendu Roy, Chunjong Park, Aldi Fahrezi, Ali Etemad

We present a bag of tricks framework for few-shot class-incremental learning (FSCIL), which is a challenging form of continual learning that involves continuous adaptation to new tasks with limited samples. FSCIL requires both stability and adaptability, i.e., preserving proficiency in previously learned tasks while learning new ones. Our proposed bag of tri

Someshwar Sanjay Ade, Deepa Gupta, Lakshmana Dora Chandrala, Kirti Chandra Sahu

We examine five machine learning-based architectures to estimate the droplet size distributions obtained using digital inline holography. The architectures, namely, U-Net, R2 U-Net, Attention U-Net, V-Net, and Residual U-Net are trained using synthetic holographic images. Our assessment focuses on evaluating the training, validation, and prediction performan

Qingwen Lin, Boyan Xu, Zhengting Huang, Ruichu Cai

Addressing the challenge of high annotation costs in solving Math Word Problems (MWPs) through full supervision with intermediate equations, recent works have proposed weakly supervised task settings that rely solely on the final answer as a supervised signal. Existing leading approaches typically employ various search techniques to infer intermediate equati

Immanuel van Santen

Let $X$ be an affine toric variety and let $D(X)$ be the set of weights of all root subgroups. It is known that $D(X)$ together with its embedding into the character group determines $X$ as a toric variety. In this article we prove that $X$ is already determined by the abstract set $D(X)$ together with some additional combinatorial data.

Marc Hovemann

In this paper we deduce new characterizations for bivariate Bessel-Potential spaces defined on the unit square via B-spline quarklets. For that purpose in a first step we use univariate boundary adapted quarklets to describe univariate Bessel-Potential spaces on intervals. To obtain the bivariate characterizations a recent result of Hansen and Sickel is appl

Yi-Shan Lan, Pin-Yu Chen, Tsung-Yi Ho

Protein classification tasks are essential in drug discovery. Real-world protein structures are dynamic, which will determine the properties of proteins. However, the existing machine learning methods, like ProNet (Wang et al., 2022a), only access limited conformational characteristics and protein side-chain features, leading to impractical protein structure

Xiu-Cai Jiang, Yi-Yuan Zhao, Yu-Zhong Zhang

At large commensurate angles, twisted bilayer graphene which holds even parity under sublattice exchange exhibits a tiny gap. Here, we point out a way to tune this tiny gap into a large gap. We start from comprehensive understanding of the physical origin of gap opening by density functional theory calculations. We reveal that the effective inter-layer hoppi

Michael Epp, Fabio Molinari, Joerg Raisch

This paper introduces a distributed control method for multi-agent robotic systems employing Over the Air Consensus (OtA-Consensus). Designed for agents with decoupled single-integrator dynamics, this approach aims at efficient formation achievement and collision avoidance. As a distinctive feature, it leverages OtA's ability to exploit interference in wirel

Jonathan Fuhr, Philipp Berens, Dominik Papies

The estimation of causal effects with observational data continues to be a very active research area. In recent years, researchers have developed new frameworks which use machine learning to relax classical assumptions necessary for the estimation of causal effects. In this paper, we review one of the most prominent methods - "double/debiased machine learnin

Heiko Georg Menzler, Rishabh Jha

Krylov complexity has recently gained attention where the growth of operator complexity in time is measured in terms of the off-diagonal operator Lanczos coefficients. The operator Lanczos algorithm reduces the problem of complexity growth to a single-particle semi-infinite tight-binding chain (known as the Krylov chain). Employing the phenomenon of Anderson

Ryoichi Saito, Takashi Mukaiyama

We investigate the control mechanism of trap frequencies and determine the orientation of ion trap principal axes. The application of DC voltage to the ground electrodes, commonly employed to finely tune trap frequencies in ion traps, leads to the rotation of the trap principal axes. Analyzing the ion matter-wave interference signal enables us to determine t

L. Friedland, A. G Shagalov

An autoresonant approach for exciting space-time quasicrystals in Bose-Einstein condensates is proposed by employing two-component chirped frequency parametric driving or modulation of the interaction strength within Gross-Pitaevskii equation. A weakly nonlinear theory of the process is developed using Whitham's averaged variational principle yielding reduct

Yuchen Cai, Ding Cao, Rongxi Guo, Yaqin Wen

Neural language models (LMs) have been extensively trained on vast corpora to store factual knowledge about various aspects of the world described in texts. Current technologies typically employ knowledge editing methods or specific prompts to modify LM outputs. However, existing knowledge editing methods are costly and inefficient, struggling to produce app

Francesco Salvi, Manoel Horta Ribeiro, Riccardo Gallotti, Robert West

The development and popularization of large language models (LLMs) have raised concerns that they will be used to create tailor-made, convincing arguments to push false or misleading narratives online. Early work has found that language models can generate content perceived as at least on par and often more persuasive than human-written messages. However, th

Sukhbinder Singh, Saeed S. Jahromi, Roman Orus

Convolutional neural networks (CNNs) are one of the most widely used neural network architectures, showcasing state-of-the-art performance in computer vision tasks. Although larger CNNs generally exhibit higher accuracy, their size can be effectively reduced by ``tensorization'' while maintaining accuracy, namely, replacing the convolution kernels with compa

Claudio Agostini, Andrea Medini

All spaces are assumed to be separable and metrizable. Building on work of van Engelen, Harrington, Michalewski and Ostrovsky, we obtain the following results: (1) Every finite-dimensional analytic space is $\sigma$-homogeneous with analytic witnesses, (2) Every finite-dimensional analytic space is $\sigma$-homogeneous with pairwise disjoint $\mathbf{\Delta}

Guangyi Liu, Quanming Yao, Yongqi Zhang, Lei Chen

Recommendation systems, as widely implemented nowadays on various platforms, recommend relevant items to users based on their preferences. The classical methods which rely on user-item interaction matrices has limitations, especially in scenarios where there is a lack of interaction data for new items. Knowledge graph (KG)-based recommendation systems have e

Jiabin Liang, Lanqing Zhang, Zhuoran Zhao, Xiangyu Xu

The conventional mesh-based Level of Detail (LoD) technique, exemplified by applications such as Google Earth and many game engines, exhibits the capability to holistically represent a large scene even the Earth, and achieves rendering with a space complexity of O(log n). This constrained data requirement not only enhances rendering efficiency but also facil

Greg McShane

We discuss the relationship between Penner's $\lambda$-length and the norms of Eisenstein integers. This leads to a geometric proof of the fact, attributed to Fermat, that every prime $p$ of the form $3k + 1$ is the norm of an Eisenstein integer that is can be written as $a^2 - ab + b^2$ for some $a,b \in \mathbb{Z}$.

Yuren Mao, Xuemei Dong, Wenyi Xu, Yunjun Gao

Due to the extraordinarily large number of parameters, fine-tuning Large Language Models (LLMs) to update long-tail or out-of-date knowledge is impractical in lots of applications. To avoid fine-tuning, we can alternatively treat a LLM as a black-box (i.e., freeze the parameters of the LLM) and augment it with a Retrieval-Augmented Generation (RAG) system, n

Ayda Kamalifar, Carlo Cenedese, Michele Cucuzzella, Antonella Ferrara

In this paper, we propose the METANET with service station (METANET-s) model, a second-order macroscopic traffic model that, compared to the classical METANET, incorporates the dynamics of service stations on highways. Specifically, we employ the (so-called) store-and-forward links to model the stop of vehicles and the possible queue forming in the process o

A. Riccardi, L. Laurenti, B. De Schutter

The European Economic Area Electricity Network Benchmark (EEA-ENB) is a multi-area power system representing the European network of transmission systems for electricity to facilitate the application of distributed control techniques. In the EEA-ENB we consider the Load Frequency Control (LFC) problem in the presence of renewable energy sources (RESs), and e

Fei Li, Chu Kiong Loo, Wei Shiung Liew, Xiaofeng Liu

In federated learning, data heterogeneity significantly impacts performance. A typical solution involves segregating these parameters into shared and personalized components, a concept also relevant in multi-task learning. Addressing this, we propose "Loop Improvement" (LI), a novel method enhancing this separation and feature extraction without necessitatin

Jaihoon Kim, Juil Koo, Kyeongmin Yeo, Minhyuk Sung

We introduce a general framework for generating diverse visual content, including ambiguous images, panorama images, mesh textures, and Gaussian splat textures, by synchronizing multiple diffusion processes. We present exhaustive investigation into all possible scenarios for synchronizing multiple diffusion processes through a canonical space and analyze the

Yujia Yang, Chris Manzie, Ye Pu

The agents within a multi-agent system (MAS) operating in marine environments often need to utilize task payloads and avoid collisions in coordination, necessitating adherence to a set of relative-pose constraints, which may include field-of-view, line-of-sight, collision-avoidance, and range constraints. A nominal controller designed for reference tracking

Aram Davtyan, Sepehr Sameni, Björn Ommer, Paolo Favaro

The field of video generation has expanded significantly in recent years, with controllable and compositional video generation garnering considerable interest. Most methods rely on leveraging annotations such as text, objects' bounding boxes, and motion cues, which require substantial human effort and thus limit their scalability. In contrast, we address the

M. J. Greenall, M. J. Derry

The nature of the transition between worm-like and spherical micelles in block copolymer dispersions varies between systems. In some formulations, heating drives a transition from worms to spheres, while in other systems the same transition is induced by cooling. In addition, a sphere-worm interconversion can be accompanied either by an increase or a decreas

Lizhe Liu, Bohua Wang, Hongwei Xie, Daqi Liu

Vision-centric 3D environment understanding is both vital and challenging for autonomous driving systems. Recently, object-free methods have attracted considerable attention. Such methods perceive the world by predicting the semantics of discrete voxel grids but fail to construct continuous and accurate obstacle surfaces. To this end, in this paper, we propo

Thomas Llauze, Félix Montjovet-Basset, Anne Louchet-Chauvet

Achieving accurate arbitrary frequency excursions with a laser can be quite a technical challenge, especially when steep slopes (GHz/$\mu$s) are required, due to both deterministic and stochastic frequency fluctuations. In this work we present a multi-stage correction combining four techniques: pre-distorsion of the laser modulation, iterative correction, op

Hichem Ammar Khodja, Frédéric Béchet, Quentin Brabant, Alexis Nasr

The factuality of large language model (LLMs) tends to decay over time since events posterior to their training are "unknown" to them. One way to keep models up-to-date could be factual update: the task of inserting, replacing, or removing certain simple (atomic) facts within the model. To study this task, we present WikiFactDiff, a dataset that describes th

Jiaqi Yue, Chunhui Zhao, Jiancheng Zhao, Biao Huang

Generalized zero-shot learning (GZSL) focuses on recognizing seen and unseen classes against domain shift problem where data of unseen classes may be misclassified as seen classes. However, existing GZSL is still limited to seen domains. In the current work, we study cross-domain GZSL (CDGZSL) which addresses GZSL towards unseen domains. Different from exist

Sophie Rosu, Josefin Larsson, Claes Fransson, Peter Challis

Supernova (SN) 1987A offers a unique opportunity to study how a spatially resolved SN evolves into a young supernova remnant (SNR). We present and analyze Hubble Space Telescope (HST) imaging observations of SN 1987A obtained in 2022 and compare them with HST observations from 2009 to 2021. These observations allow us to follow the evolution of the equatoria

Ioannis Papoutsidakis, Robert J. Piechocki, Angela Doufexi

Feedback holds a pivotal role in practical communication schemes, even though it does not enhance channel capacity. Its main attribute includes adaptability in transmission that allows for a higher rate of convergence of the error probability to zero with respect to blocklength. Motivated by this fact, we present a non-asymptotic achievability bound for vari

Zina-Sabrina Duma, Tomas Zemcik, Simon Bilik, Tuomas Sihvonen

Hyperspectral (HS) imagery in agriculture is becoming increasingly common. These images have the advantage of higher spectral resolution. Advanced spectral processing techniques are required to unlock the information potential in these HS images. The present paper introduces a method rooted in multivariate statistics designed to detect parasitic Varroa destr

Yang Yao, Xin Wang, Zeyang Zhang, Yijian Qin

Large language models (LLMs) have achieved great success in many fields, and recent works have studied exploring LLMs for graph discriminative tasks such as node classification. However, the abilities of LLMs for graph generation remain unexplored in the literature. Graph generation requires the LLM to generate graphs with given properties, which has valuabl

Prasanta Malik, Saikat Das

In this paper we introduce the notion of I-convergence of sequences of k-dimensional subspaces of an inner product space, where I is an ideal of subsets of N, the set of all natural numbers and k in N. We also study some basic properties of this notion.

Xudong Sun, Carla Feistner, Alexej Gossmann, George Schwarz

Poor generalization performance caused by distribution shifts in unseen domains often hinders the trustworthy deployment of deep neural networks. Many domain generalization techniques address this problem by adding a domain invariant regularization loss terms during training. However, there is a lack of modular software that allows users to combine the advan

Toya Kumagai, Tomohiro Okuma

Let $V$ be an affine algebraic variety, and let $p\in V$ be a singular point. For a regular function $g$ on $V$ such that $g(p)=0$ and for a positive integer $n$, we consider the cyclic covering $\phi_n\: V_n \to V$ of degree $n$ branched along the hypersurface defined by $g$. We will prove that for sufficiently large $n$, the tangent cone of $V_n$ at $\phi_

Zhongyu Yang, Chen Shen, Wei Shao, Tengfei Xing

Despite recent advances in lane detection methods, scenarios with limited- or no-visual-clue of lanes due to factors such as lighting conditions and occlusion remain challenging and crucial for automated driving. Moreover, current lane representations require complex post-processing and struggle with specific instances. Inspired by the DETR architecture, we

Yoonsung Kim, Changhun Oh, Jinwoo Hwang, Wonung Kim

Deep neural network (DNN) video analytics is crucial for autonomous systems such as self-driving vehicles, unmanned aerial vehicles (UAVs), and security robots. However, real-world deployment faces challenges due to their limited computational resources and battery power. To tackle these challenges, continuous learning exploits a lightweight "student" model