October 2024 arXiv papers — page 57

Alba Carballo-Castro, Sonia Laguna, Moritz Vandenhirtz, Julia E. Vogt

Concept-based machine learning methods have increasingly gained importance due to the growing interest in making neural networks interpretable. However, concept annotations are generally challenging to obtain, making it crucial to leverage all their prior knowledge. By creating concept-enriched models that incorporate concept information into existing archit

Aditya Anand, Thatchaphol Saranurak, Yunfan Wang

We give the first deterministic algorithm that makes sub-quadratic queries to find the global min-cut of a simple graph in the cut query model. Given an $n$-vertex graph $G$, our algorithm makes $\widetilde{O}(n^{5/3})$ queries to compute the global min-cut in $G$. As a key ingredient, we also show an algorithm for finding $s$-$t$ max-flows of size $\widetil

Peisen Lin, Yuntong Zhang, Andreea Costea, Abhik Roychoudhury

In modern software development, multiple software components, often sourced from different contributors, including AI assistants, are combined to create a cohesive system. Although these components might each be individually safe, their composition might not be so. At the core of this issue is often a misalignment between the requirements and assumptions mad

Rita Ramos, Everlyn Asiko Chimoto, Maartje ter Hoeve, Natalie Schluter

We introduce GrammaMT, a grammatically-aware prompting approach for machine translation that uses Interlinear Glossed Text (IGT), a common form of linguistic description providing morphological and lexical annotations for source sentences. GrammaMT proposes three prompting strategies: gloss-shot, chain-gloss and model-gloss. All are training-free, requiring

Peizhuang Cong, Qizhi Chen, Haochen Zhao, Tong Yang

The advanced capabilities of Large Language Models (LLMs) have inspired the development of various interactive web services or applications, such as ChatGPT, which offer query inference services for users. Unlike traditional DNN model, the inference of LLM entails different iterations of forward computation for different queries, which result in efficiency c

Antonina Maj

We consider four-dimensional non-Abelian gauge theory living on a complex projective space $\mathbb{CP}^2$ as a way of gaining insights into (3+1)-dimensional QCD. In particular, we use a complex parametrization of gauge fields on which gauge transformations act homogeneously. This allows us to factor out the gauge degrees of freedom from the volume element

Julien Brémont, Léo Régnier, Olivier Bénichou, Raphaël Voituriez

The persistence exponent, which characterises the long-time decay of the survival probability of stochastic processes in the presence of an absorbing target, plays a key role in quantifying the dynamics of fluctuating systems. Determining this exponent for non-Markovian processes is known to be a difficult task, and exact results remain scarce despite sustai

Yanguang Zhao, Long Bai, Zhaoxi Zhang, Yanan Wu

Glioma, a common and deadly brain tumor, requires early diagnosis for improved prognosis. However, low-quality Magnetic Resonance Imaging (MRI) technology in Sub-Saharan Africa (SSA) hinders accurate diagnosis. This paper presents our work in the BraTS Challenge on SSA Adult Glioma. We adopt the model from the BraTS-GLI 2021 winning solution and utilize it w

Ran Zhang, Wei Zhao, Steffen Eger

Recent research has focused on literary machine translation (MT) as a new challenge in MT. However, the evaluation of literary MT remains an open problem. We contribute to this ongoing discussion by introducing LITEVAL-CORPUS, a paragraph-level parallel corpus containing verified human translations and outputs from 9 MT systems, which totals over 2k translat

Lucas Sort, Laurent Le Brusquet, Arthur Tenenhaus

In numerous settings, it is increasingly common to deal with longitudinal data organized as high-dimensional multi-dimensional arrays, also known as tensors. Within this framework, the time-continuous property of longitudinal data often implies a smooth functional structure on one of the tensor modes. To help researchers investigate such data, we introduce a

Wang-Wang Yu, Kai-Fu Yang, Xiangrui Hu, Jingwen Jiang

The task of macro- and micro-expression spotting aims to precisely localize and categorize temporal expression instances within untrimmed videos. Given the sparse distribution and varying durations of expressions, existing anchor-based methods often represent instances by encoding their deviations from predefined anchors. Additionally, these methods typicall

Yuyang Ding, Xinyu Shi, Xiaobo Liang, Juntao Li

Improving the mathematical reasoning capabilities of Large Language Models (LLMs) is critical for advancing artificial intelligence. However, access to extensive, diverse, and high-quality reasoning datasets remains a significant challenge, particularly for the open-source community. In this paper, we propose ScaleQuest, a novel, scalable, and cost-effective

Shiro Kuriwaki, Mason Reece, Samuel Baltz, Aleksandra Conevska

Ballots are the core records of elections. Electronic records of actual ballots cast (cast vote records) are available to the public in some jurisdictions. However, they have been released in a variety of formats and have not been independently evaluated. Here we introduce a database of cast vote records from the 2020 U.S. general election. We downloaded pub

Md Khairul Islam, Ayush Karmacharya, Timothy Sue, Judy Fox

Considering the difficulty of financial time series forecasting in financial aid, much of the current research focuses on leveraging big data analytics in financial services. One modern approach is to utilize "predictive analysis", analogous to forecasting financial trends. However, many of these time series data in Financial Aid (FA) pose unique challenges

Zoé Haskell-Craig, Kevin P. Josey, Patrick L. Kinney, Priyanka deSouza

Unequal exposure to air pollution by race and socioeconomic status is well-documented in the U.S. However, there has been relatively little research on inequities in the collection of PM2.5 data, creating a critical gap in understanding which neighborhood exposures are represented in these datasets. In this study we use multilevel models with random intercep

Zheheng Wang

This study introduces an advanced transient stability assessment (TSA) method for power systems, addressing the challenges of sample class imbalance and data noise through a novel CatBoost algorithm framework. By implementing a Gradient Harmonizing Mechanism (GHM), this method adjusts the gradient norm distribution across samples by incorporating a coordinat

Ankur Garg, Meenakshi Sarkar, S. Manthira Moorthi, Debajyoti Dhar

Hyperspectral images enable precise identification of ground objects by capturing their spectral signatures with fine spectral resolution.While high spatial resolution further enhances this capability, increasing spatial resolution through hardware like larger telescopes is costly and inefficient. A more optimal solution is using ground processing techniques

Ankur Garg, Tushar Shukla, Purvee Joshi, Debojyoti Ganguly

The Ocean Color Monitor-3 (OCM-3) sensor is instrumental in Earth observation, achieving a critical balance between high-resolution imaging and broad coverage. This paper explores innovative imaging methods employed in OCM-3 and the transformative potential of super-resolution techniques to enhance image quality. The super-resolution model for OCM-3 (SOCM-3)

Chunhao Bao, Xiaolong Yin, Jifeng Shao, Longxiang Li

Orbital two-channel Kondo (2CK) effect is one of the crucial systems with non- Fermi liquid (NFL) behaviors. But the full three-regime transport evidence has never been observed in one sample. Here, all three-resistive regimes for the orbital 2CK effect induced by two-level systems (TLSs) have been observed in the van der Waals ferromagnet Fe3GaTe2. The elec

Renshuai Tao, Manyi Le, Chuangchuang Tan, Huan Liu

Despite significant advances in deepfake detection, handling varying image quality, especially due to different compressions on online social networks (OSNs), remains challenging. Current methods succeed by leveraging correlations between paired images, whether raw or compressed. However, in open-world scenarios, paired data is scarce, with compressed images

Xiaoyu Tao, Tingyue Pan, Mingyue Cheng, Yucong Luo

Time series classification plays a fundamental role in a wide range of real-world applications. Recently, large language models (LLMs) have demonstrated strong generalization and reasoning capacities, but directly applying them to time series classification remains non-trivial due to the representation gap between numerical sequences and linguistic semantics

Robin Ollive, Stephane Louise

This article proposes a formalism which unifies Hamiltonian simulation techniques from different fields. This formalism leads to a competitive method to construct the Hamiltonian simulation with a comprehensible, simple-to-implement circuit generation technique. It leads to a gate decomposition and a scaling different from the usual strategy based on a Linea

Alexander Jaus, Constantin Seibold, Simon Reiß, Zdravko Marinov

We present Connected-Component~(CC)-Metrics, a novel semantic segmentation evaluation protocol, targeted to align existing semantic segmentation metrics to a multi-instance detection scenario in which each connected component matters. We motivate this setup in the common medical scenario of semantic metastases segmentation in a full-body PET/CT. We show how

Stefan Brandstätter, Philipp Seeböck, Christoph Fürböck, Svitlana Pochepnia

2D to 3D registration is essential in tasks such as diagnosis, surgical navigation, environmental understanding, navigation in robotics, autonomous systems, or augmented reality. In medical imaging, the aim is often to place a 2D image in a 3D volumetric observation to w. Current approaches for rigid single slice in volume registration are limited by require

Yuting Guo, Pengcheng Tang

It is well known that the Hilbert matrix operator $\mathcal {H}$ is bounded from $H^{\infty}$ to the mean Lipschitz spaces $\Lambda^{p}_{\frac{1}{p}}$ for all $1<p<\infty$. In this paper, we prove that the range of Hilbert matrix operator $\mathcal {H}$ acting on $H^{\infty}$ is contained in certain Zygmund-type space (denoted by $\Lambda^{1.*}_{1}$), which

Lei Han, Qian Wang, Ying Lu, Sheng Tao

Two-dimensional (2D) hybrid organic-inorganic perovskite (HOIP) demonstates great potential for developing flexible and wearable spintronic devices, by serving as spin sources via the bulk Rashba effect (BRE). However, the practical application of BRE in 2D HOIP faces huge challenges, particularly due to the toxicity of lead, which is crucial for achieving l

S. M. Tschopp, H. Vahid, A. Sharma, J. M. Brader

Classical density functional theory (DFT) is a powerful framework to study inhomogeneous fluids. Its standard form is based on the knowledge of a generating free energy functional. If this is known exactly, then the results obtained by using standard DFT or its alternative, recently developed version, force-DFT, are the same. If the free energy functional is

Sena Watanabe, Haruki Watanabe

Superconductors are often discussed in the mean-field approximation that breaks $U(1)$ symmetry. Since the $U(1)$ symmetry underlies the charge conservation, naive application of response theory sometimes gives results that are not gauge-invariant. We study the effect of vertex corrections on the electromagnetic responses of superconductors by employing the

Ali Hamza, Aizea Lojo, Adrian Núñez-Marcos, Aitziber Atutxa

This paper introduces Ali-AUG, a novel single-step diffusion model for efficient labeled data augmentation in industrial applications. Our method addresses the challenge of limited labeled data by generating synthetic, labeled images with precise feature insertion. Ali-AUG utilizes a stable diffusion architecture enhanced with skip connections and LoRA modul

Gábor Hidy, Bence Bakos, András Lukács

In medical image segmentation tasks, the scarcity of labeled training data poses a significant challenge when training deep neural networks. When using U-Net-style architectures, it is common practice to address this problem by pretraining the encoder part on a large general-purpose dataset like ImageNet. However, these methods are resource-intensive and do

Linus Bao, Emily Jin, Michael Bronstein, İsmail İlkan Ceylan

Graph Transformers are popular neural networks that extend the well-known Transformer architecture to the graph domain. These architectures operate by applying self-attention on graph nodes and incorporating graph structure through the use of positional encodings (e.g., Laplacian positional encoding) or structural encodings (e.g., random-walk structural enco

Aykut Özdönmez, Murat Tekkeşinoğlu

This study presents an analysis of the optical variability of the blazar 1E 1458.8+2249 on diverse timescales using multi-band observations, including observations in the optical BVRI bands carried out with the T60 and T100 telescopes from 2020 to 2023 and ZTF gri data from 2018 to 2023. On seven nights, we searched for intraday variability using the power-e

Long Chen, Michał Czakon, Marco Niggetiedt

We present the renormalization constant of the pseudoscalar operator defined with a non-anticommuting $\gamma_5$ in dimensional regularization up to four-loop order in perturbative Quantum Chromodynamics (QCD). Furthermore, by virtue of renormalization-group invariance of the relation between the scalar and the pseudoscalar operator, we predict the $\overlin

Maialen Orte-García, César Esteban, José Eduardo Méndez-Delgado, Jorge García-Rojas

Chlorine (Cl) is a chemical element of the group of the halogens and is between the 17th and the 20th most abundant elements in the Solar System. It is thought to be produced from the capture of a proton or neutron by specific alpha-element isotopes during both hydrostatic and explosive oxygen burning, though some contribution may come from Type Ia supernova

Andreas Wagner, Daniel J. Price, Slava Bourgeois, Farhad Daei

We investigate the effect of data-driving on flux rope eruptivity in magnetic field simulations by analysing fully data-driven modelling results of active region (AR) 12473 and AR11176, as well as preforming relaxation runs for AR12473 (found to be eruptive). Here, the driving is switched off systematically at different time steps. We analyse the behaviour o

Neta Elad, Sharon Shoham

First-order logic (FOL) has proved to be a versatile and expressive tool as the basis of abstract modeling languages. Used to verify complex systems with unbounded domains, such as heap-manipulating programs and distributed protocols, FOL, and specifically uninterpreted functions and quantifiers, strike a balance between expressiveness and amenity to automat

Vasiliki Papanikou, Panagiotis Papadakos, Theodora Karamanidou, Thanos G. Stavropoulos

In this paper, we present a comprehensive survey on the pervasive issue of medical misinformation in social networks from the perspective of information technology. The survey aims at providing a systematic review of related research and helping researchers and practitioners navigate through this fast-changing field. Specifically, we first present manual and

Yingbo Fu, Ziwen Yang, Liang Xu, Yi Guo

This paper studies trajectory optimization of an autonomous underwater vehicle (AUV) to track an unknown maneuvering target both in the 2D and 3D space. Due to the restrictions on sensing capabilities in the underwater scenario, the AUV is limited to collecting only bearing measurements to the target. A framework called {\it GP-based Bearing-only Tracking (G

Michael Schopf-Kuester, Zorah Lähner, Michael Moeller

This work addresses the problem of \textit{shape completion}, i.e., the task of restoring incomplete shapes by predicting their missing parts. While previous works have often predicted the fractured and restored shape in one step, we approach the task by separately predicting the fractured and newly restored parts, but ensuring these predictions are intercon

Jianfeng Wu, Lurong Ding, Hongtao Lin, Qi Gao

This study develops an effective theoretical framework that couples two vector fields: the velocity field $\mathbf{u}$ and an auxiliary vorticity field $\boldsymbol{\xi}$. Together, these fields form a larger conserved dynamical system. Within this framework, the incompressible Navier-Stokes (NS) equation and a complementary vorticity equation with negative

Yuang Ai, Xiaoqiang Zhou, Huaibo Huang, Xiaotian Han

Image restoration (IR) in real-world scenarios presents significant challenges due to the lack of high-capacity models and comprehensive datasets. To tackle these issues, we present a dual strategy: GenIR, an innovative data curation pipeline, and DreamClear, a cutting-edge Diffusion Transformer (DiT)-based image restoration model. GenIR, our pioneering cont

A. J. Goodwin, A. Mummery, T. Laskar, K. D. Alexander

We present the discovery of a second radio flare from the tidal disruption event (TDE) AT2020vwl via long-term monitoring radio observations. Late-time radio flares from TDEs are being discovered more commonly, with many TDEs showing radio emission 1000s of days after the stellar disruption, but the mechanism that powers these late-time flares is uncertain.

Sander C. Hille, Hanna Oppelmayer, Tomasz Szarek

We study random dynamical systems of certain continuous functions on the unit interval. We use bounded variation to provide sufficient conditions for unique ergodicity of these systems. Several classes of examples are provided.

D. J. M. Petit dit de la Roche, H. Chakraborty, M. Lendl, D. Kitzmann

Context: Transmission spectroscopy is a powerful tool for understanding exoplanet atmospheres. At optical wavelengths, it makes it possible to infer the composition and the presence of aerosols in the atmosphere. However, unocculted stellar activity can result in contamination of atmospheric transmission spectra by introducing spurious slopes and molecular s

Jesus M. Alvarez-Llorente, Vicente P. Guerrero-Bote, Felix de Moya-Anegon

A reference-based classification system for individual Scopus publications is presented which takes into account the categories of the papers citing those references instead of the journals in which those cited papers are published. It supports multiple assignments of up to 5 categories within the Scopus ASJC structure, but eliminates the Multidisciplinary A

Amir Abboud, Nick Fischer, Ron Safier, Nathan Wallheimer

Sumsets are central objects in additive combinatorics. In 2007, Granville asked whether one can efficiently recognize whether a given set $S$ is a sumset, i.e. whether there is a set $A$ such that $A+A=S$. Granville suggested an algorithm that takes exponential time in the size of the given set, but can we do polynomial or even linear time? This basic comput

Dechi Peng, Shujie Cheng, Gao Xianlong

In this paper, we investigate a non-Hermitian Aubry-Andr\'e-Harper model characterized by power-law hoppings ($1/s^{a}$) and a quasi-periodic parameter $\beta$, where $a$ denotes the power-law index, $s$ represents the hopping distance, and $\beta$ belongs to the metallic mean family. In the intermediate phases, we find that ergodic states correspond to comp

Gabriel d'Andrade Furlanetto, Riccardo Della Monica, Ivan De Martino

Fuzzy Dark Matter (FDM) is among the most suitable candidates to replace WIMPs and to resolve the puzzling mystery of dark matter. A galactic dark matter halo made of these ultralight bosonic particles leads to the formation of a solitonic core surrounded by quantum interference patterns that, on average, reproduce a Navarro-Frenk-White-like mass density pro

Changshi Zhou, Haichuan Xu, Jiarui Hu, Feng Luan

Robotic cloth manipulation faces challenges due to the fabric's complex dynamics and the high dimensionality of configuration spaces. Previous methods have largely focused on isolated smoothing or folding tasks and overly reliant on simulations, often failing to bridge the significant sim-to-real gap in deformable object manipulation. To overcome these chall

Anton Raskovalov, Nikita Gabdullin, Ilya Androsov

This paper presents neural networks for network intrusion detection systems (NIDS), that operate on flow data preprocessed with a time window. It requires only eleven features which do not rely on deep packet inspection and can be found in most NIDS datasets and easily obtained from conventional flow collectors. The time window aggregates information with re

Darkhan Shadykul, Hrishikesh Chakrabarty, Daniele Malafarina

We study eccentric equatorial orbits of a stellar-mass black hole around an intermediate-mass slowly rotating Kerr black hole in the presence of gravitational radiation and a dark matter halo. The stellar-mass companion will inspiral towards the central black hole while emitting gravitational waves. The evolution is affected by the dynamical friction force c

Torbjørn Smith, Olav Egeland

This paper presents a new method for learning dissipative Hamiltonian dynamics from a limited and noisy dataset. The method uses the Helmholtz decomposition to learn a vector field as the sum of a symplectic and a dissipative vector field. The two vector fields are learned using two reproducing kernel Hilbert spaces, defined by a symplectic and a curl-free k

Mahyar Afshinmehr, Matin Ansaripour, Alireza Danaei, Kurt Mehlhorn

We explore the fair distribution of a set of $m$ indivisible chores among $n$ agents, where each agent's costs are evaluated using a monotone cost function. Our focus lies on two fairness criteria: envy-freeness up to any item (EFX) and a relaxed notion, namely envy-freeness up to the transfer of any item (tEFX). We demonstrate that a 2-approximate EFX alloc

Xue-Ying Han, Jun Hua, Xiangdong Ji, Cai-Dian Lü

We develop an approach for calculating heavy quark effective theory (HQET) light-cone distribution amplitudes (LCDAs) by employing a sequential effective theory methodology. The theoretical foundation of the framework is established, elucidating how the quasi distribution amplitudes (quasi DAs) with three scales can be utilized to compute HQET LCDAs. We prov

Marius Costandin

For $S \in \mathbb{N}^n$ and $T \in \mathbb{N}$, the Subset Sum Problem (SSP) $\exists^? x \in \{0,1\}^n $ such that $S^T\cdot x = T$ can be interpreted as the problem of deciding whether the intersection of the positive unit hypercube $Q_n = [0,1]^n$ with the hyperplane $S^T\cdot \left(x - \frac{S}{\|S\|^2 }\cdot T \right) = 0$ contains at least a vertex. I

Esteban Garces Arias, Hannah Blocher, Julian Rodemann, Meimingwei Li

Open-ended text generation has become a prominent task in natural language processing due to the rise of powerful (large) language models. However, evaluating the quality of these models and the employed decoding strategies remains challenging due to trade-offs among widely used metrics such as coherence, diversity, and perplexity. This paper addresses the s

Woosung Koh, Jang Han Yoon, MinHyung Lee, Youngjin Song

Generating high-quality charts with Large Language Models (LLMs) presents significant challenges due to limited data and the high cost of scaling through human curation. $\langle \text{instruction}, \text{data}, \text{code} \rangle$ triplets are scarce and expensive to manually curate as their creation demands technical expertise. To address this scalability

Leo Brauner, Georg C. Hofstätter, Oscar Ortega-Moreno

We show an analogue of the Klain-Schneider theorem for valuations that are invariant under rotations around a fixed axis, called zonal. Using this, we establish a new integral representation of zonal valuations involving mixed area measures with a disk. In our argument, we introduce an easy way to translate between this representation and the one involving a

Bodo Manthey, Jesse van Rhijn

We show that the problem of counting the number of 2-optimal tours in instances of the Travelling Salesperson Problem (TSP) on complete graphs is #P-complete. In addition, we show that the expected number of 2-optimal tours in random instances of the TSP on complete graphs is $O(1.2098^n \sqrt{n!})$. Based on numerical experiments, we conjecture that the tru

Luis A. Anchordoqui, Ignatios Antoniadis, Daniele Bielli, Auttakit Chatrabhuti

The proposal of a rapid sign-switching cosmological constant in the late universe, mirroring a transition from anti-de Sitter (AdS) to de Sitter (dS) space, has significantly improved the fit to observational data and provides a compelling framework for ameliorating major cosmological tensions, such as the $H_0$ and $S_8$ tensions. An attractive theoretical

Yating Ma, Xiaogang Xu, Liming Fang, Zhe Liu

Current Transferable Adversarial Examples (TAE) are primarily generated by adding Adversarial Noise (AN). Recent studies emphasize the importance of optimizing Data Augmentation (DA) parameters along with AN, which poses a greater threat to real-world AI applications. However, existing DA-based strategies often struggle to find optimal solutions due to the c

Adam Brzosko, Robert I Woodward, Yuen San Lo, Mirko Pittaluga

We report a proof-of-principle realisation of a decoy-state BB84 QKD protocol with phase encoding over a record-breaking 17 km of MMF at a rate of 193 kbits/s, as well as over 1 Mbit/s at a distance of 1 km. These results suggest that QKD can be deployed over MMF in metropolitan-scale telecommunication connections. Such MMF metropolitan networks are ubiquito

Jose Beltrán Jiménez, Dario Bettoni, David Figueruelo, Florencia A. Teppa Pannia

The increasing quality of cosmological data has revealed some tensions that could be signalling the necessity of incorporating new physics into our cosmological model. One particularly intriguing possibility is the existence of elastic interactions between dark matter and dark energy. Not only do these interactions provide a natural mechanism to relief cosmo

Paolo Lungaroni, Andrea Mayer, Stefano Salsano, Pierpaolo Loreti

Virtualized environments offer a flexible and scalable platform for evaluating network performance, but they can introduce significant variability that complicates accurate measurement. This paper presents PASTRAMI, a methodology designed to assess the stability of software routers, which is critical to accurately evaluate performance metrics such as the Par

Theodore Andronikos

This article introduces a novel Quantum Secret Sharing scheme with $( k, n )$ threshold and endowed with verification capability. The new protocol exploits the power of entanglement and evolves in three phases. The primary novelty of the new protocol lies in its ability to operate completely parallelly in a fully distributed setup, where the spymaster and he

R. Tufan Erdogan, Georgy A. Filonenko, Stephen J. Picken, Peter G. Steeneken

Ultrasound is widely used in medical imaging, and emerging photo-acoustic imaging is crucial for disease diagnosis. Currently, high-end photo-acoustic imaging systems rely on piezo-electric materials for detecting ultrasound waves, which come with sensitivity, noise, and bandwidth limitations. Advanced applications demand a large matrix of broadband, high-re

Diogo Cosme, António Galvão, Fernando Brito e Abreu

Purpose: Our research project focuses on improving the content update of the online European Smart Tourism Tools (STTs) Observatory by incorporating and categorizing STTs. The categorization is based on their taxonomy, and it facilitates the end user's search process. The use of a Smart ETL (Extract, Transform, and Load) process, where \emph{Smart} indicates

Wenhong Zhu, Zhiwei He, Xiaofeng Wang, Pengfei Liu

Aligning language models (LMs) with human preferences has become a key area of research, enabling these models to meet diverse user needs better. Inspired by weak-to-strong generalization, where a strong LM fine-tuned on labels generated by a weaker model can consistently outperform its weak supervisor, we extend this idea to model alignment. In this work, w

Jinxu Lin, Linwei Tao, Minjing Dong, Chang Xu

As diffusion models become increasingly popular, the misuse of copyrighted and private images has emerged as a major concern. One promising solution to mitigate this issue is identifying the contribution of specific training samples in generative models, a process known as data attribution. Existing data attribution methods for diffusion models typically qua

Vedant Bhandari, Jasmin James, Tyson Phillips, P. Ross McAree

Autonomous agents require the capability to identify dynamic objects in their environment for safe planning and navigation. Incomplete and erroneous dynamic detections jeopardize the agent's ability to accomplish its task. Dynamic detection is a challenging problem due to the numerous sources of uncertainty inherent in the problem's inputs and the wide varie

Alexander Shurakov, Margarita Ershova, Abdukodir Khakimov, Anatoliy Prikhodko

Beam tracking is an essential functionality of millimeter wave (mmWave, 30-100 GHz) and sub-terahertz (sub-THz, 100-300 GHz) 5G/6G systems. It operates by performing antenna sweeping at both base station (BS) and user equipment (UE) sides using the Synchronization Signal Blocks (SSB). The optimal frequency of beam tracking events is not specified by 3GPP sta

Alexander Meulemans, Seijin Kobayashi, Johannes von Oswald, Nino Scherrer

Self-interested individuals often fail to cooperate, posing a fundamental challenge for multi-agent learning. How can we achieve cooperation among self-interested, independent learning agents? Promising recent work has shown that in certain tasks cooperation can be established between learning-aware agents who model the learning dynamics of each other. Here,

Haonan Chen, Liang Wang, Nan Yang, Yutao Zhu

Synthetic data generation has become an increasingly popular way of training models without the need for large, manually labeled datasets. For tasks like text embedding, synthetic data offers diverse and scalable training examples, significantly reducing the cost of human annotation. However, most current approaches rely heavily on proprietary models like GP

Kieran Gilday, Chapa Sirithunge, Fumiya Iida, Josie Hughes

A human-shaped robotic hand offers unparalleled versatility and fine motor skills, enabling it to perform a broad spectrum of tasks with precision, power and robustness. Across the paleontological record and animal kingdom we see a wide range of alternative hand and actuation designs. Understanding the morphological design space and the resulting emergent be

Pintu Debnath, Sayan Goswami

Using the methods from topological dynamics, H. Furstenberg introduced the notion of a central set and proved the famous Central Sets Theorem. Later D. De, Neil Hindman, and D. Strauss [Fund. Math.199 (2008), 155-175.] established a stronger version of the Central Sets Theorem and then introduced the notion of $C$-sets satisfying the Central Sets Theorem and

Niki Kotecha, Antonio del Rio Chanona

Inventory control in modern supply chains has attracted significant attention due to the increasing number of disruptive shocks and the challenges posed by complex dynamics, uncertainties, and limited collaboration. Traditional methods, which often rely on static parameters, struggle to adapt to changing environments. This paper proposes a Multi-Agent Reinfo

Xiaofeng Lin, Saúl Alexis Heredia Pérez, Kanako Harada

Biological specimens exhibit significant variations in size and shape, challenging autonomous robotic manipulation. We focus on the mouse skull window creation task to illustrate these challenges. The study introduces a microscopic stereo camera system (MSCS) enhanced by the linear model for depth perception. Alongside this, a precise registration scheme is

Sanjay Singh, Amaresh Chakrabarti

This paper proposes a framework for assessing the novelty of design problems using the SAPPhIRE model of causality. The novelty of a problem is measured as its minimum distance from the problems in a reference problem database. The distance is calculated by comparing the current problem and each reference past problem at the various levels of abstraction in

Tsugumasa Yutani, Yuya Yamamoto, Shuyo Nakatani, Hiroko Terasawa

Synthesizers are essential in modern music production. However, their complex timbre parameters, often filled with technical terms, require expertise. This research introduces a method of timbre control in wavetable synthesis that is intuitive and sensible and utilizes semantic labels. Using a conditional variational autoencoder (CVAE), users can select a wa

Ankita Koley, Chandramani Singh

We consider a system with a local cache connected to a backend server and an end user population. A set of contents are stored at the the server where they continuously get updated. The local cache keeps copies, potentially stale, of a subset of the contents. The users make content requests to the local cache which either can serve the local version if avail

Liyu Zhang, Haochi Wu, Xu Wan, Quan Kong

Offline-to-online (O2O) reinforcement learning (RL) pre-trains models on offline data and refines policies through online fine-tuning. However, existing O2O RL algorithms typically require maintaining the tedious offline datasets to mitigate the effects of out-of-distribution (OOD) data, which significantly limits their efficiency in exploiting online sample

Huiying Pan, Jianing Sun, Xu Jiang, Xing Zhao

Hybrid spectral CT integrates energy integrating detectors (EID) and photon counting detectors (PCD) into a single system, combining the large field-of-view advantage of EID with the high energy and spatial resolution of PCD. This represents a new research direction in spectral CT imaging. However, the different imaging principles and inconsistent geometric

David Thulke, Yingbo Gao, Rricha Jalota, Christian Dugast

This paper explores the rapid development of a telephone call summarization system utilizing large language models (LLMs). Our approach involves initial experiments with prompting existing LLMs to generate summaries of telephone conversations, followed by the creation of a tailored synthetic training dataset utilizing stronger frontier models. We place speci

Carlo Bellavita, Eugenio Alberto Dellepiane, Javad Mashreghi

We conduct a spectral analysis of the difference quotient operator $Q^u_\zeta$, associated with a boundary point $\zeta \in \partial \mathbb{D}$, on the model space $K_u$. We describe the operator's spectrum and provide both upper and lower estimates for its norm, and furthermore discussing the sharpness of these bounds. Notably, the upper estimate offers a

Antonio D'Orazio, Davide Sforza, Fabio Pellacini, Iacopo Masi

Editing High Dynamic Range (HDR) environment maps using an inverse differentiable rendering architecture is a complex inverse problem due to the sparsity of relevant pixels and the challenges in balancing light sources and background. The pixels illuminating the objects are a small fraction of the total image, leading to noise and convergence issues when the

Wonhyung Choi, Inkyung Ahn

Understanding species dynamics in heterogeneous environments is essential for ecosystem studies. Traditional models assumed homogeneous habitats, but recent approaches include spatial and temporal variability, highlighting species migration. We adopt starvation-driven diffusion (SDD) models as nonlinear diffusion to describe species dispersal based on local

Vasudevarao Allu, Subhadip Pal

In this paper, we investigate the arithmetic Bohr radius of bounded linear operators between arbitrary complex Banach spaces. We establish the close connection between the classical Bohr radius and the arithmetic Bohr radius of bounded linear operators. Further, we study the asymptotic estimates of arithmetic Bohr radius for identity operator on infinite dim

G. Rojas García, D. Graczyk, G. Pietrzyński, C. Gałan

Detached eclipsing binaries (DEBs) allow for the possibility of precise characterization of its stellar components. They offer a unique opportunity to derive their physical parameters in a near-model-independent way for a number of systems consisting of late-type giant stars. Here we aim to expand the sample of low-metallicity late-type giant stars with prec

Ernesto Acosta, Carlos Cano Gutierrez, Guillermo Botella, Roberto Campos

This paper presents a new hybrid Quantum Machine Learning (QML) model composed of three elements: a classical computer in charge of the data preparation and interpretation; a Gate-based Quantum Computer running the Variational Quantum Algorithm (VQA) representing the Quantum Neural Network (QNN); and an adiabatic Quantum Computer where the optimization funct

Rosa E. Keers, Alexander I. Shapiro, Nadiia M. Kostogryz, Ana Glidden

Stellar limb darkening must be properly accounted for to accurately determine the radii of exoplanets at various wavelengths. The standard approach to address limb darkening involves either using laws with coefficients from modelled stellar spectra or determining the coefficients empirically during light curve fitting of the data. Here, we test how accuratel

Christopher T. H Teo, Milad Abdollahzadeh, Xinda Ma, Ngai-man Cheung

Recently, prompt learning has emerged as the state-of-the-art (SOTA) for fair text-to-image (T2I) generation. Specifically, this approach leverages readily available reference images to learn inclusive prompts for each target Sensitive Attribute (tSA), allowing for fair image generation. In this work, we first reveal that this prompt learning-based approach

Haojie Hou, Xicheng Zhang

In this paper, we employ probabilistic techniques to derive sharp, explicit two-sided estimates for the heat kernel of the nonlocal kinetic operator $$ \Delta^{\alpha/2}_v + v \cdot \nabla_x, \quad \alpha \in (0, 2),\ (x,v)\in {\mathbb R}^{d}\times{\mathbb R}^d,$$ where $ \Delta^{\alpha/2}_v $ represents the fractional Laplacian acting on the velocity variab

Hemanth Saratchandran, Jianqiao Zheng, Yiping Ji, Wenbo Zhang

This paper questions whether the strong performance of softmax attention in transformers stems from producing a probability distribution over inputs. Instead, we argue that softmax's effectiveness lies in its implicit regularization of the Frobenius norm of the attention matrix, which stabilizes training. Motivated by this, we explore alternative activations

Yuhua Liao, Zetian Wang, Peng Wei, Qiangqiang Nie

Deep learning and pre-trained models have shown great success in time series forecasting. However, in the tourism industry, time series data often exhibit a leading time property, presenting a 2D structure. This introduces unique challenges for forecasting in this sector. In this study, we propose a novel modelling paradigm, TripCast, which treats trip time

Zimo Hao, Chongyang Ren, Mingyan Wu

In this paper, we study the following supercritical McKean-Vlasov SDE, driven by a symmetric non-degenerate cylindrical $\alpha$-stable process in $\mathbb{R}^d$ with $\alpha \in (0,1)$: $$ \mathord{{\rm d}} X_t = (K * \mu_{t})(X_t)\mathord{{\rm d}}t + \mathord{{\rm d}} L_t^{(\alpha)}, \quad X_0 = x \in \mathbb{R}^d, $$ where $K: \mathbb{R}^d \to \mathbb{R}^

Minfeng Xu, Chen-Chen Fan, Yan-Jie Zhou, Wenchao Guo

Cardiovascular diseases (CVD) remain a leading health concern and contribute significantly to global mortality rates. While clinical advancements have led to a decline in CVD mortality, accurately identifying individuals who could benefit from preventive interventions remains an unsolved challenge in preventive cardiology. Current CVD risk prediction models,

Juan Juan Gerardo Alcázar, Carlos Hermoso, Hüsnü Anıl Çoban, Uğur Gözütok

In this paper we provide, first, a general symbolic algorithm for computing the symmetries of a given rational surface, based on the classical differential invariants of surfaces, i.e. Gauss curvature and mean curvature. In practice, the algorithm works well for sparse parametrizations (e.g. toric surfaces) and PN surfaces. Additionally, we provide a specifi

Daulet Baimukashev, Gokhan Alcan, Kevin Sebastian Luck, Ville Kyrki

In complex real-world tasks such as robotic manipulation and autonomous driving, collecting expert demonstrations is often more straightforward than specifying precise learning objectives and task descriptions. Learning from expert data can be achieved through behavioral cloning or by learning a reward function, i.e., inverse reinforcement learning. The latt

Elisa Atza, Rob Klooster, Falko Hofstra, Frank van der Werff

The vigor of potato plants, defined as the canopy area at the end of the exponential growth stage, depends on the origin and physiological state of the seed tuber. Experiments carried out with six potato varieties in three test fields over three years show that there is a 73%-90% correlation in the vigor of the plants from the same seedlot grown in different

Hawau Olamide Toyin, Hao Li, Hanan Aldarmaki

Speech recognition and speech synthesis models are typically trained separately, each with its own set of learning objectives, training data, and model parameters, resulting in two distinct large networks. We propose a parameter-efficient approach to learning ASR and TTS jointly via a multi-task learning objective and shared parameters. Our evaluation demons