December 2024 arXiv papers — page 89

Xin Zhang, Ting Su, Jiongtao Zhu, Hairong Zheng

Objective: The aim of this study is to validate the effectiveness of an energy-modulated scatter correction method in suppressing scatter in photon-counting detector (PCD)-based cone beam CT (CBCT) imaging. Approach: The scatter correction method, named e-Grid, which was initially applied to dual-layer flat-panel detector (DLFPD)-based CBCT imaging, was test

Zhengdi Yu, Stefanos Zafeiriou, Tolga Birdal

We propose Dyn-HaMR, to the best of our knowledge, the first approach to reconstruct 4D global hand motion from monocular videos recorded by dynamic cameras in the wild. Reconstructing accurate 3D hand meshes from monocular videos is a crucial task for understanding human behaviour, with significant applications in augmented and virtual reality (AR/VR). Howe

Sora Miyashita, Matteo Varbaro

In this paper we prove that nearly Gorenstein Stanley-Reisner rings of dimension at least 3 are indeed Gorenstein. By previous work of the first author this yields a complete characterization of nearly Gorenstein Stanley-Reisner rings. We also show that a Cohen-Macaulay Stanley-Reisner ring is Gorenstein on the punctured spectrum if and only if either it is

Jonathan Shaki, Jiarui Gan, Sarit Kraus

We study a Bayesian persuasion problem with externalities. In this model, a principal sends signals to inform multiple agents about the state of the world. Simultaneously, due to the existence of externalities in the agents' utilities, the principal also acts as a correlation device to correlate the agents' actions. We consider the setting where the agents a

Jiaqi Wang, Liutao Yu, Liwei Huang, Chenlin Zhou

The intrinsic dynamics and event-driven nature of spiking neural networks (SNNs) make them excel in processing temporal information by naturally utilizing embedded time sequences as time steps. Recent studies adopting this approach have demonstrated SNNs' effectiveness in speech command recognition, achieving high performance by employing large time steps fo

Subhashree Swain, Vaidehi S. Paliya, D. J. Saikia, C. S. Stalin

The Gamma-ray detection from an astrophysical object indicates the presence of an extreme environment where high-energy radiation is produced. With the continuous monitoring of the Gamma-ray sky by the Fermi Large Area Telescope (LAT), leading to deeper sensitivity, the high-energy Gamma-ray emission has now been detected from a diverse class of jetted activ

Shilun Zhang, Alberto Ceria, Huijuan Wang

Temporal higher-order networks, where each hyperlink involving a group of nodes are activated or deactivated over time, are recently used to represent complex systems such as social contacts, interactions or collaborations that occur at specific times. Such networks are substrates for social contagion processes like the diffusion of information and opinions.

Xiaolong Du, Yun Liang, Wencheng Yan, Demin Li

The scalar meson $f_{0}(980)$ has long posed a perplexing puzzle within the realm of light hadron physics. Conventionally, its mass and width in normal decay processes have been estimated as $M=990\pm20$~MeV/$c^2$ and $\Gamma=40-100$~MeV, respectively. Theoretical explanations regarding the internal structure of $f_{0}(980)$ range from it being a conventiona

Matthew Dickson

A non-uniqueness phase for infinite clusters is proven for a class of marked random connection models on the $d$-dimensional hyperbolic space, ${\mathbb{H}^d}$, in a high volume-scaling regime. The approach taken in this paper utilizes the spherical transform on ${\mathbb{H}^d}$ to diagonalize convolution by the adjacency function and the two-point function

Yuyu Guo, Lei Bi, Zhengbin Zhu, David Dagan Feng

Automated segmentation of left ventricular cavity (LVC) in temporal cardiac image sequences (multiple time points) is a fundamental requirement for quantitative analysis of its structural and functional changes. Deep learning based methods for the segmentation of LVC are the state of the art; however, these methods are generally formulated to work on single

Paheli Bhattacharya, Rishabh Gupta

Code explanation plays a crucial role in the software engineering domain, aiding developers in grasping code functionality efficiently. Recent work shows that the performance of LLMs for code explanation improves in a few-shot setting, especially when the few-shot examples are selected intelligently. State-of-the-art approaches for such Selective Shot Learni

Jinrong Hu

The uniqueness of solutions to the isotropic $L_{p}$ Gaussian Minkowski problem in $\mathbb{R}^{n+1}$ is established when $-(n+1)<p<-1$ with $n\geq 1$, without requiring the origin-centred assumption on convex bodies.

Qingqing Fang, Qinliang Su, Wenxi Lv, Wenchao Xu

Many unsupervised visual anomaly detection methods train an auto-encoder to reconstruct normal samples and then leverage the reconstruction error map to detect and localize the anomalies. However, due to the powerful modeling and generalization ability of neural networks, some anomalies can also be well reconstructed, resulting in unsatisfactory detection an

Christopher Thirgood, Oscar Mendez, Erin Chao Ling, Jon Storey

We introduce HyperGS, a novel framework for Hyperspectral Novel View Synthesis (HNVS), based on a new latent 3D Gaussian Splatting (3DGS) technique. Our approach enables simultaneous spatial and spectral renderings by encoding material properties from multi-view 3D hyperspectral datasets. HyperGS reconstructs high-fidelity views from arbitrary perspectives w

Yuxi Sun, Wei Gao, Jing Ma, Hongzhan Lin

With the rise and widespread use of Large Language Models (LLMs), ensuring their safety is crucial to prevent harm to humans and promote ethical behaviors. However, directly assessing value valence (i.e., support or oppose) by leveraging large-scale data training is untrustworthy and inexplainable. We assume that emulating humans to rely on social norms to m

Bernardo Ballotta, Jacopo Lupi, Leandro Ayarde-Henríquez, Stephen Dooley

Xylopyranose is the principal monosaccharide unit of hemicellulose, one of the three major biopolymers of lignocellulosic biomass. Understanding its decomposition mechanism is increasingly relevant for thermochemical biorefinery research such as pyrolysis. Significant efforts have been made to study its chemical and structural properties using both computati

Vipin K E, Prahallad Padhan

The transition to sustainable green hydrogen production demands innovative electrocatalyst design strategies that can overcome current technological limitations. This study introduces a comprehensive data-driven approach to predicting and understanding catalytic performance for Hydrogen Evolution Reaction (HER) and Oxygen Evolution Reaction (OER) using advan

Hendrik Geisler, Emmanuel Baranger, Philipp Junker

Uncertainty quantification is not yet widely adapted in the design process of engineering components despite its importance for achieving sustainable and resource-efficient structures. This is mainly due to two reasons: 1) Tracing the effect of uncertainty in engineering simulations is a computationally challenging task. This is especially true for inelastic

Jose L Salmeron, Irina Arévalo

Data privacy is a major concern in industries such as healthcare or finance. The requirement to safeguard privacy is essential to prevent data breaches and misuse, which can have severe consequences for individuals and organisations. Federated learning is a distributed machine learning approach where multiple participants collaboratively train a model withou

Xianlei Long, Xiaxin Zhu, Fangming Guo, Wanyi Zhang

Event-based semantic segmentation has great potential in autonomous driving and robotics due to the advantages of event cameras, such as high dynamic range, low latency, and low power cost. Unfortunately, current artificial neural network (ANN)-based segmentation methods suffer from high computational demands, the requirements for image frames, and massive e

Marc Bauer, Renzo Kapust, Jan M. Pawlowski, Finn L. Temmen

We propose a renormalisation group inspired normalising flow that combines benefits from traditional Markov chain Monte Carlo methods and standard normalising flows to sample lattice field theories. Specifically, we use samples from a coarse lattice field theory and learn a stochastic map to the targeted fine theory. The devised architecture allows for syste

Ruikang Ni, Da Xiao, Qingye Meng, Xiangyu Li

Compositional relational reasoning (CRR) is a hallmark of human intelligence, but we lack a clear understanding of whether and how existing transformer large language models (LLMs) can solve CRR tasks. To enable systematic exploration of the CRR capability of LLMs, we first propose a new synthetic benchmark called Generalized Associative Recall (GAR) by inte

Luis M. San-José-Revuelta, Pablo Casaseca-de-la-Higuera

This paper proposes a novel memetic algorithm (MA) for the blind equalization of digital multiuser channels with Direct-Sequence / Code-Division Multiple-Access (DS/CDMA) sharing scheme. Equalization involves two different tasks, the estimation of: (1) channel response and (2) transmitted data. The corresponding channel model is first analyzed and then the M

Kaustubh Vyas, Damien Graux, Yijun Yang, Sébastien Montella

In response to the call for agent-based solutions that leverage the ever-increasing capabilities of the deep models' ecosystem, we introduce Hive -- a comprehensive solution for knowledge-aware planning of a set of atomic actions to address input queries and subsequently selecting appropriate models accordingly. Hive operates over sets of models and, upon re

Jose Antonio Álvarez-Sanchis, Borja Vidal, Ana Díaz-Rubio

We investigate a terahertz (THz) gas sensing platform based on all-dielectric metasurfaces that support quasi-bound states in the continuum (quasi-BIC) with both electric and magnetic dipole resonances. The structure is designed to achieve the first Kerker condition, minimizing backscattering and maximizing light-matter interaction, which significantly enhan

Ousmane Touat, Jezekael Brunon, Yacine Belal, Julien Nicolas

The primary promise of decentralized learning is to allow users to engage in the training of machine learning models in a collaborative manner while keeping their data on their premises and without relying on any central entity. However, this paradigm necessitates the exchange of model parameters or gradients between peers. Such exchanges can be exploited to

Yuyuan Li, Xiaohua Feng, Chaochao Chen, Qiang Yang

Recommender systems have become increasingly influential in shaping user behavior and decision-making, highlighting their growing impact in various domains. Meanwhile, the widespread adoption of machine learning models in recommender systems has raised significant concerns regarding user privacy and security. As compliance with privacy regulations becomes mo

Gergely Ambrus, Barnabás Gárgyán

The Laplace--P\'olya integral, defined by $J_n(r) = \frac1\pi\int_{-\infty}^\infty \mathrm{sinc}^n t \cos(rt) \mathrm{d} \, t$, appears in several areas of mathematics. We study this quantity by combinatorial methods; accordingly, our investigation focuses on the values at integer $r$'s. Our main result establishes a lower bound for the ratio $\frac{J_n(r+2)

Nan Lin, Dong Yun, Weijie Xia, Peter Palensky

Short-term load prediction (STLP) is critical for modern power distribution system operations, particularly as demand and generation uncertainties grow with the integration of low-carbon technologies, such as electric vehicles and photovoltaics. In this study, we evaluate the zero-shot prediction capabilities of five Time-Series Foundation Models (TSFMs)-a n

Zheng Cheng, Rendong Wang, Zhicheng Wang

Recently, multi-modal large language models have made significant progress. However, visual information lacking of guidance from the user's intention may lead to redundant computation and involve unnecessary visual noise, especially in long, untrimmed videos. To address this issue, we propose FocusChat, a text-guided multi-modal large language model (LLM) th

Jinxiang Xie, Yilin Li, Xunjian Yin, Xiaojun Wan

Evaluating the performance of Grammatical Error Correction (GEC) models has become increasingly challenging, as large language model (LLM)-based GEC systems often produce corrections that diverge from provided gold references. This discrepancy undermines the reliability of traditional reference-based evaluation metrics. In this study, we propose a novel eval

Aldo Gamboa, Mohammed Khalil, Alessandra Buonanno

Accurate waveform models for coalescing binaries on eccentric orbits are crucial for avoiding biases in the analysis of eccentric gravitational-wave signals. The effective-one-body (EOB) formalism combines various analytical approximation methods with information derived from numerical-relativity simulations, and it has proven reliable in modeling the inspir

Xinyu He, Xinhui Li, Xiaojie Guo

Domain adaptive object detection (DAOD) aims to generalize an object detector trained on labeled source-domain data to a target domain without annotations, the core principle of which is \emph{source-target feature alignment}. Typically, existing approaches employ adversarial learning to align the distributions of the source and target domains as a whole, ba

Elena Bueno-Benito, Mariella Dimiccoli

This paper presents a simple yet effective approach for the poorly investigated task of global action segmentation, aiming at grouping frames capturing the same action across videos of different activities. Unlike the case of videos depicting all the same activity, the temporal order of actions is not roughly shared among all videos, making the task even mor

Dongik Lee, Seunghun Lee

Physical insight into a material can be first gained by its color since the reflectance spectrum from an object reflects its microstructure and complex reflective indices. We here present a comprehensive overview of electrodynamics and optics related to reflectance spectra and color and provide an open-source Python code for simulating reflectance spectra an

Abhishek Trivedi, Sourajit Mukherjee, Rajat Kumar Singh, Vani Agarwal

Extraction of transaction information from bank statements is required to assess one's financial well-being for credit rating and underwriting decisions. Unlike other financial documents such as tax forms or financial statements, extracting the transaction descriptions from bank statements can provide a comprehensive and recent view into the cash flows and s

J. Álvarez-Márquez, A. Crespo Gómez, L. Colina, D. Langeroodi

This paper presents a deep MIRI/JWST medium resolution spectroscopy (MRS) covering the rest-frame optical spectrum of the GN-z11 galaxy. The [OIII]5008 and H$\alpha$ emission lines are detected and spectroscopically resolved. The line profiles are well-modeled by a narrow Gaussian component with intrinsic FWHMs of 189$\pm$25 and 231$\pm$52 kms$^{-1}$, respec

Seunghwan Kim, Heejung Shin, Gaeun Yim, Changseung Kim

Autonomous exploration is a crucial aspect of robotics, enabling robots to explore unknown environments and generate maps without prior knowledge. This paper proposes a method to enhance exploration efficiency by integrating neural network-based occupancy grid map prediction with uncertainty-aware Bayesian neural network. Uncertainty from neural network-base

Yi-Fang Ren, Janarbek Yuanbek, Yusuf Turek

We investigate the effects of postselected von Neumann measurements on the nonclassical properties of pair coherent states (PCS). We calculated key quantum characteristics, such as squeezing, photon statistics, and entanglement between the two PCS modes. Our results demonstrate that postselected von Neumann measurements enhance both the non-Gaussianity and n

Aldo Gamboa, Alessandra Buonanno, Raffi Enficiaud, Mohammed Khalil

The measurement of orbital eccentricity in gravitational-wave (GW) signals will provide unique insights into the astrophysical origin of binary systems, while ignoring eccentricity in waveform models could introduce significant biases in parameter estimation and tests of General Relativity. Upcoming LIGO-Virgo-KAGRA observing runs are expected to detect a su

José M. Conde Alonso, Nathan A. Wagner

We prove $\mathrm{H}^1$ and $\mathrm{BMO}$ endpoint inequalities for generic cancellative Haar shifts defined with respect to a possibly non-homogeneous Borel measure $\mu$ satisfying a weak regularity condition. This immediately yields a new, highly streamlined proof of the $L^p$-results for the same operators due to L\'opez-Sanchez, Martell, and Parcet. We

Yaohui Ma, Xiaopeng Hong, Shizhou Zhang, Huiyun Li

Large multimodal language models (MLLMs) have revolutionized natural language processing and visual understanding, but often contain outdated or inaccurate information. Current multimodal knowledge editing evaluations are limited in scope and potentially biased, focusing on narrow tasks and failing to assess the impact on in-domain samples. To address these

Zongxin Liu, Zhe Zhao, Fu Song, Jun Sun

Formal verification provides critical security assurances for neural networks, yet its practical application suffers from the long verification time. This work introduces a novel method for training verification-friendly neural networks, which are robust, easy to verify, and relatively accurate. Our method integrates neuron behavior consistency into the trai

Camino Martín-Sánchez, Ana Sánchez-Iglesias, José Antonio Barreda-Argüeso, Jean-Paul Itié

We report on the crystallographic structure of penta-twinned gold nanoparticles. Although gold typically exhibits a face-centered cubic (fcc) lattice, other phases have been reported in some nanoscale systems. We show that the crystallographic system and the lattice parameters of the gold unit cell strongly depend on the nanoparticle geometry, for a wide siz

Rupert L. Frank, Jonas W. Peteranderl

Among all metrics on $\mathbb S^d$ with $d>4$ that are conformal to the standard metric and have positive scalar curvature, the total $\sigma_2$-curvature, normalized by the volume, is uniquely (up to M\"obius transformations) minimized by the standard metric. We show that if a metric almost minimizes, then it is almost the standard metric (up to M\"obius tr

Shuyi Wang, Huan Zhao, Yuji Cao, Zibin Pan

The Wind Storage Integrated System with Power Smoothing Control (PSC) has emerged as a promising solution to ensure both efficient and reliable wind energy generation. However, existing PSC strategies overlook the intricate interplay and distinct control frequencies between batteries and wind turbines, and lack consideration of wake effect and battery degrad

Yu Li, Bryce Wang, Xinyu Luan

We present XPath Agent, a production-ready XPath programming agent specifically designed for web crawling and web GUI testing. A key feature of XPath Agent is its ability to automatically generate XPath queries from a set of sampled web pages using a single natural language query. To demonstrate its effectiveness, we benchmark XPath Agent against a state-of-

D. Kreikemeyer-Lorenzo, T. Koettig, P. Borges de Sousa, C. Gooch

MADMAX, an axion dark matter search experiment, is currently in the prototype testing phase. Its working principle is based on the conversion of axions in a magnetic field into photons. This signal is then enhanced by a booster made of dielectric disks placed in front of a mirror. In order to test MADMAX prototypes at cryogenic temperatures in a magnetic fie

Jun-Xiang Huang, Chandra B. Singh

We present simulation results examining the presence and behavior of standing shocks in zero-energy low angular momentum advective accretion flows and explore their (in)stabilities properties taking into account various specific angular momentum, $\lambda_0$. Within the range $10-50R_g$ (where $R_g$ denotes the Schwarzschild radius), shocks are discernible f

Hai-Xiang Zhu, Lu Meng, Yao Ma, Ning Li

In this study, we propose using the $Z_c(3900)$ pole position to constrain the existence of the $DDD^*$ three-body bound state within the one-boson-exchange (OBE) model. The existence of the $DDD^*$ bound state remains uncertain due to significant variations in the OBE interaction, particularly in the strength of scalar-meson-exchange interactions, which can

Valentin Flietner, Bernd Heidergott, Frank den Hollander, Ines Lindner

In this paper, we advance the network theory of aging and mortality by developing a causal mathematical model for the mortality rate. First, we show that in large networks, where health deficits accumulate at nodes representing health indicators, the modeling of network evolution with Poisson processes is universal and can be derived from fundamental princip

Céline Vanini, Chris Hargreaves, Frank Breitinger

Event reconstruction is a fundamental part of the digital forensic process, helping to answer key questions like who, what, when, and how. A common way of accomplishing that is to use tools to create timelines, which are then analyzed. However, various challenges exist, such as large volumes of data or contamination. While prior research has focused on simpl

M. A. Guerrero, E. Santamaria, G. Liberato, Q. A. Parker

The identification of the nebula HaTr 5 with the shell remnant of the historic Nova Sco 1437 around the low-accretion rate cataclysmic variable 2MASS J17022815-4306123 has been used in the framework of the hibernation scenario to set an upper limit of <580 yr to the transition time from a nova-like binary to a dwarf nova. This work aims at clarifying the nat

Ran Su, Rui Shi, Hui Cui, Ping Xuan

Molecular subtyping of cancer is recognized as a critical and challenging upstream task for personalized therapy. Existing deep learning methods have achieved significant performance in this domain when abundant data samples are available. However, the acquisition of densely labeled samples for cancer molecular subtypes remains a significant challenge for co

Magdalini Zonnios, Alec Boyd, Felix C. Binder

Stochastic processes abound in nature and accurately modeling them is essential across the quantitative sciences. They can be described by hidden Markov models (HMMs) or by their quantum extensions (QHMMs). These models explain and give rise to process outputs in terms of an observed system interacting with an unobserved memory. Although there are infinitely

Ryota Tajima

For a normalized newform $g \in S_{k}(\Gamma_{0}(N))$ with complex multiplication by an imaginary quadratic field $K$, there is a mock modular form $F^{+}$ corresponding to $g$. K. Bringmann et al. modified $F^{+}$ in order to obtain a $p$-adic modular form by a certain $p$-adic constant $\alpha_{g}$. In addition, they showed that if $p$ is split in $\mathca

M. Chellali, J. C. Valenzuela-Tripodoro, H. Golmohammadi, I. I. Takhonov

A set $S\subseteq V$ in an isolate-free graph $G$ is a total restrained dominating set, abbreviated TRD-set, if every vertex in $V$ is adjacent to a vertex in $S$, and every vertex in $V\setminus S$ is adjacent to a vertex in $V\setminus S$. A total restrained coalition is made up of two disjoint sets of vertices $X$ and $Y$ of $G$, neither of which is a TRD

Angel Paredes, Jose Guerra-Carmenate, Jose R. Salgueiro, Daniele Tommasini

We disclose a class of stable nonlinear traveling waves moving at specific constant velocities within symmetric two-dimensional quantum droplets. We present a comprehensive analysis of these traveling bubbles and identify three qualitatively distinct regions within the one-parameter family of solutions, classified by velocity: (i) well-separated phase singul

Arturo Nuñez-Castiñeyra, Matthias González, Noé Brucy, Patrick Hennebelle

The initial mass function (IMF) of stars and the corresponding cloud mass function (CMF), traditionally considered universal, exhibit variations that are influenced by the local environment. Notably, these variations are apparent in the distribution's tail, indicating a possible relationship between local dynamics and mass distribution. Our study is designed

Ziqi Qiu, Jianxing Yu, Yufeng Zhang, Hanjiang Lai

This paper focuses on sarcasm detection, which aims to identify whether given statements convey criticism, mockery, or other negative sentiment opposite to the literal meaning. To detect sarcasm, humans often require a comprehensive understanding of the semantics in the statement and even resort to external commonsense to infer the fine-grained incongruity.

Robert Litschko, Oliver Kraus, Verena Blaschke, Barbara Plank

A large amount of local and culture-specific knowledge (e.g., people, traditions, food) can only be found in documents written in dialects. While there has been extensive research conducted on cross-lingual information retrieval (CLIR), the field of cross-dialect retrieval (CDIR) has received limited attention. Dialect retrieval poses unique challenges due t

Gongbo Zhang, Zihan Xu, Qiao Jin, Fangyi Chen

While holding great promise for improving and facilitating healthcare, large language models (LLMs) struggle to produce up-to-date responses on evolving topics due to outdated knowledge or hallucination. Retrieval-augmented generation (RAG) is a pivotal innovation that improves the accuracy and relevance of LLM responses by integrating LLMs with a search eng

Sofia B. S. D. Castro, Alastair M. Rucklidge

Heteroclinic cycles are sequences of equilibria along with trajectories that connect them in a cyclic manner. We investigate a class of robust heteroclinic cycles that does not satisfy the usual condition that all connections between equilibria lie in flow-invariant subspaces of equal dimension. We refer to these as robust heteroclinic cycles in pluridimensi

Cameron Kemp, Robert Laugwitz, Alexander Schenkel

This paper develops a graphical calculus to determine the $n$-shifted Poisson structures on finitely generated semi-free commutative differential graded algebras. When applied to the Chevalley-Eilenberg algebra of an ordinary Lie algebra, we recover Safronov's result that the $(n=1)$- and $(n=2)$-shifted Poisson structures in this case are given by quasi-Lie

Wael Bahsoun, Maxence Phalempin

Understanding the statistics of collisions among locally confined gas particles poses a major challenge. In this work we investigate $\mathbb Z^d$-map lattices coupled by collision with simplified local dynamics that offer significant insights for the above challenging problem. We obtain a first order approximation for the first collision rate at a site $\te

Francesca Fedele, Bethany Rose Marsh

We show that the braid group associated to the complex reflection group $G(d,d,n)$ is an index $d$ subgroup of the braid group of the orbifold quotient of the complex numbers by a cyclic group of order $d$. We also give a compatible presentation of $G(d,d,n)$ and its braid group for each tagged triangulation of the disk with $n$ marked points on its boundary

Yuhong Chen, Ailin Song, Huifeng Yin, Shuai Zhong

The rapid evolution of multimedia technology has revolutionized human perception, paving the way for multi-view learning. However, traditional multi-view learning approaches are tailored for scenarios with fixed data views, falling short of emulating the intricate cognitive procedures of the human brain processing signals sequentially. Our cerebral architect

James Prather, Brent N. Reeves, Paul Denny, Juho Leinonen

Non-native English speakers (NNES) face multiple barriers to learning programming. These barriers can be obvious, such as the fact that programming language syntax and instruction are often in English, or more subtle, such as being afraid to ask for help in a classroom full of native English speakers. However, these barriers are frustrating because many NNES

Yiheng Li, Yang Yang, Zhen Lei

In radar-camera 3D object detection, the radar point clouds are sparse and noisy, which causes difficulties in fusing camera and radar modalities. To solve this, we introduce a novel query-based detection method named Radar-Camera Transformer (RCTrans). Specifically, we first design a Radar Dense Encoder to enrich the sparse valid radar tokens, and then conc

Shiqi Huang, Shuting He, Bihan Wen

Instance segmentation algorithms in remote sensing are typically based on conventional methods, limiting their application to seen scenarios and closed-set predictions. In this work, we propose a novel task called zero-shot remote sensing instance segmentation, aimed at identifying aerial objects that are absent from training data. Challenges arise when clas

Cristiano Chesi

A significant debate has emerged in response to a paper written by Steven Piantadosi (Piantadosi, 2023) and uploaded to the LingBuzz platform, the open archive for generative linguistics. Piantadosi's dismissal of Chomsky's approach is ruthless, but generative linguists deserve it. In this paper, I will adopt three idealized perspectives -- computational, th

Lukas Lüchtrath

This note provides a sufficient condition for linear lower bounds on chemical distances (compared to the Euclidean distance) in general spatial random graphs. The condition is based on the scarceness of long edges in the graph and weak correlations at large distances and is valid for all translation invariant and locally finite graphs that fulfil these condi

Dorota I. Walicka, Olivier Blacque, Karolina Gornicka, Jonathan S. White

The magnetic properties of EuAlSi, a compound comprising a honeycomb lattice of Al and Si atoms and a triangular lattice of Eu atoms, are presented. Moreover, we have prepared the Eu1-xSrxAlSi solid solution, to study the evolution of the collective quantum properties from the ferromagnetic EuAlSi towards the superconducting SrAlSi. A possible quantum critic

Aleksey N. Bolgar, Shtefan V. Sanduleanu, Aleksandr Strelnikov, Oleg V. Astafiev

Phononic crystals are a promising platform for the study of quantum acoustodynamics. In a recent experiment, the interaction of a superconducting quantum bit with modes of a phononic crystal has been demonstrated. The field of these modes is localized in a compact area, providing high values of the coupling constant with the qubit. However, the Q-factor of p

Shizhuo Deng, Bowen Han, Jiaqi Chen, Hao Wang

Noisy labels threaten the robustness of few-shot learning (FSL) due to the inexact features in a new domain. CLIP, a large-scale vision-language model, performs well in FSL on image-text embedding similarities, but it is susceptible to misclassification caused by noisy labels. How to enhance domain generalization of CLIP on noisy data within FSL tasks is a c

Ajay Kumar Saini, Gajendra Pandey

A fine abundance analysis of a recently discovered hydrogen-deficient carbon (HdC) star, A 980, is presented. Based on the observed high-resolution optical spectrum, we ascertain that A 980 is a cool extreme helium (EHe) star and not an HdC. Singly-ionized germanium Ge II lines are identified in A 980's optical spectrum. These are the first-ever detections o

Manele Ait Habouche, Mickaël Kerboeuf, Goulven Guillou, Jean-Philippe Babau

Unmanned Surface Vehicles (USVs) have become critical tools for marine exploration, environmental monitoring, and autonomous navigation. Accurate estimation of wave direction is essential for improving USV navigation and ensuring operational safety, but traditional methods often suffer from high costs and limited spatial resolution. This paper proposes a mac

Shiping Ge, Qiang Chen, Zhiwei Jiang, Yafeng Yin

Weakly-Supervised Dense Video Captioning (WSDVC) aims to localize and describe all events of interest in a video without requiring annotations of event boundaries. This setting poses a great challenge in accurately locating the temporal location of event, as the relevant supervision is unavailable. Existing methods rely on explicit alignment constraints betw

Hiroki Sayama

Hash Chemistry, a minimalistic artificial chemistry model of open-ended evolution, has recently been extended to non-spatial and cellular versions. The non-spatial version successfully demonstrated continuous adaptation and unbounded growth of complexity of self-replicating entities, but it did not simulate multiscale ecological interactions among the entiti

Markus Fidler, Flavio Gallistl, Jaya Prakash Champati, Joerg Widmer

The freshness of sensor data is critical for all types of cyber-physical systems. An established measure for quantifying data freshness is the Age-of-Information (AoI), which has been the subject of extensive research. Recently, there has been increased interest in multi-sensor systems: redundant sensors producing samples of the same physical process, sensor

Kanghoon Yoon, Kibum Kim, Jaehyung Jeon, Yeonjun In

Scene Graph Generation (SGG) research has suffered from two fundamental challenges: the long-tailed predicate distribution and semantic ambiguity between predicates. These challenges lead to a bias towards head predicates in SGG models, favoring dominant general predicates while overlooking fine-grained predicates. In this paper, we address the challenges of

Huiqiu Lin, Da Zhao

We study the maximal Steklov eigenvalues of trees with given number of boundary vertices and total number of vertices. Trees can be regarded as discrete analogue of Hadamard manifolds, namely simply-connected Riemannian manifolds of non-positive sectional curvature. Let $\sigma_{k,\text{max}}(b, n)$ be the maximal of $k$-th Steklov eigenvalue of trees with $

Deborah Haun, Laura Merker, Sergey Pupyrev

An ordered graph is a graph with a total order over its vertices. A linear layout of an ordered graph is a partition of the edges into sets of either non-crossing edges, called stacks, or non-nesting edges, called queues. The stack (queue) number of an ordered graph is the minimum number of required stacks (queues). Mixed linear layouts combine these layouts

Siyuan Wang, Dianyi Wang, Chengxing Zhou, Zejun Li

Large Vision-Language Models (LVLMs) typically learn visual capacity through visual instruction tuning, involving updates to both a projector and their LLM backbones. Inspired by the concept of a visual region in the human brain, we investigate the existence of an analogous \textit{visual region} within LLMs that functions as a cognitive core, and explore th

Johannes Lehmann, Michael Beckmann

This paper provides a comprehensive, descriptive overview of the current state of digital transformation in the Swiss economy and delineates areas that businesses should keep an eye on. Key findings illustrate that even established technologies are not universally adopted, that companies tend to overestimate their technological status compared to their compe

Kees Koenders, Leo Schnitzpan, Fabian Kammerbauer, Sinan Shu

Brain-inspired learning in physical hardware has enormous potential to learn fast at minimal energy expenditure. One of the characteristics of biological learning systems is their ability to learn in the presence of various noise sources. Inspired by this observation, we introduce a novel noise-based learning approach for physical systems implementing multi-

Zhiguang Lu, Qianqian Xu, Shilong Bao, Zhiyong Yang

This paper addresses the challenge of Granularity Competition in fine-grained classification tasks, which arises due to the semantic gap between multi-granularity labels. Existing approaches typically develop independent hierarchy-aware models based on shared features extracted from a common base encoder. However, because coarse-grained levels are inherently

Amrapali Pednekar, Alvaro Garrido, Yara Khaluf, Pieter Simoens

Time perception research has advanced significantly over the years. However, some areas remain largely unexplored. This study addresses two such under-explored areas in timing research: (1) A quantitative analysis of time perception at an individual level, and (2) Time perception in an ecological setting. In this context, we trained a machine learning model

Johannes Lehmann, Michael Beckmann

Using novel survey data from Swiss firms, this paper empirically examines the relationship between the use of digital technologies and the prevalence of performance incentives. We argue that digital technologies tend to reduce the cost of organizational monitoring through improved measurement of employee behavior and performance, as well as through employee

Veronica Phan

In this paper, we will use the entropy approach to derive a necessary and sufficient condition for the existence of an element that belongs to at least half of the sets in a finite family of sets.

Nicolas Delaissé, Peyman Havaej, Dieter Fauconnier, Joris Degroote

This paper presents a new solver developed in OpenFOAM for the modeling of lubricant in the narrow gap between two surfaces inducing hydrodynamic pressures up to few gigapascal. Cavitation is modeled using the homogeneous equilibrium model. The mechanical and thermodynamic constitutive behavior of the lubricant is accurately captured by inclusion of compress

Chengzhou Yu, Huihui Fang, Hongqiu Wang, Ting Deng

Fundus imaging is a critical tool in ophthalmology, with different imaging modalities offering unique advantages. For instance, fundus fluorescein angiography (FFA) can accurately identify eye diseases. However, traditional invasive FFA involves the injection of sodium fluorescein, which can cause discomfort and risks. Generating corresponding FFA images fro

Lauren Barnes, Boris Khusid, Lou Kondic, William V. Meyer

Colloid-polymer mixtures are an archetype for modeling phase transition processes, as they a exhibit low-density gas phase, high-density crystalline phase and an intervening liquid phase. While their equilibrium behavior has been studied extensively, the role of hydrodynamics in driving their phase separation is not yet understood. We present a theoretical m

Jose Enrique Maese, Fernando Caballero, Luis Merino

This paper presents a simulation framework able of modeling the dynamics of a hanging tether with adjustable length, connecting a UAV to a UGV. The model incorporates the interaction between the UAV, UGV, and a winch, allowing for dynamic tether adjustments based on the relative motion of the robots. The accuracy and reliability of the simulator are assessed

Yihang Cheng, Lan Zhang, Junyang Wang, Mu Yuan

Retrieval-augmented generation (RAG) improves the service quality of large language models by retrieving relevant documents from credible literature and integrating them into the context of the user query. Recently, the rise of the cloud RAG service has made it possible for users to query relevant documents conveniently. However, directly sending queries to

Amalia Foka

This paper proposes a novel interdisciplinary framework for the critical evaluation of text-to-image models, addressing the limitations of current technical metrics and bias studies. By integrating art historical analysis, artistic exploration, and critical prompt engineering, the framework offers a more nuanced understanding of these models' capabilities an

Marta Gili, Paul San Sebastian, Ane Blázquez-García

The Tail Assignment Problem (TAP) is a critical optimization challenge in airline operations, requiring the optimal assignment of aircraft to scheduled flights to maximize efficiency and minimize costs. To address the TAP, this work applies the Quantum Approximate Optimization Algorithm (QAOA), a promising quantum computing algorithm developed for tackling c

Leo Segre, Shai Avidan

Neural Radiance Fields (NeRF) have advanced photorealistic novel view synthesis, but their reliance on photometric reconstruction introduces artifacts, commonly known as "floaters". These artifacts degrade novel view quality, especially in areas unseen by the training cameras. We present a fast, post-hoc NeRF cleanup method that eliminates such artifacts by

Shoukun Sun, Min Xian, Tiankai Yao, Fei Xu

Producing large images using small diffusion models is gaining increasing popularity, as the cost of training large models could be prohibitive. A common approach involves jointly generating a series of overlapped image patches and obtaining large images by merging adjacent patches. However, results from existing methods often exhibit noticeable artifacts, e

Liwei Pan, Weike Pan, Meiyan Wei, Hongzhi Yin

Different from most conventional recommendation problems, sequential recommendation focuses on learning users' preferences by exploiting the internal order and dependency among the interacted items, which has received significant attention from both researchers and practitioners. In recent years, we have witnessed great progress and achievements in this fiel