December 2024 arXiv papers — page 88

Venus Keus, Edward W. Kolb

We present a novel Baryogenesis mechanism in which an asymmetry of scalars in a three-Higgs doublet model produced exiting a CP-violating inflationary set-up is translated into an asymmetry of baryons through electroweak instantons.

Mike Zhang, Max Müller-Eberstein, Elisa Bassignana, Rob van der Goot

We present SnakModel, a Danish large language model (LLM) based on Llama2-7B, which we continuously pre-train on 13.6B Danish words, and further tune on 3.7M Danish instructions. As best practices for creating LLMs for smaller language communities have yet to be established, we examine the effects of early modeling and training decisions on downstream perfor

Michael Heck, Christian Geishauser, Nurul Lubis, Carel van Niekerk

Correct labels are indispensable for training effective machine learning models. However, creating high-quality labels is expensive, and even professionally labeled data contains errors and ambiguities. Filtering and denoising can be applied to curate labeled data prior to training, at the cost of additional processing and loss of information. An alternative

Martin Borquez, Mikaela Keller, Michael Perrot, Damien Sileo

It has been shown in the field of Author Profiling that texts may inadvertently reveal sensitive information about their authors, such as gender or age. This raises important privacy concerns that have been extensively addressed in the literature, in particular with the development of methods to hide such information. We argue that, when these texts are in f

Moritz Reuss, Jyothish Pari, Pulkit Agrawal, Rudolf Lioutikov

Diffusion Policies have become widely used in Imitation Learning, offering several appealing properties, such as generating multimodal and discontinuous behavior. As models are becoming larger to capture more complex capabilities, their computational demands increase, as shown by recent scaling laws. Therefore, continuing with the current architectures will

Slobodan Filipovski

Let $G$ be a simple graph with $n$ vertices and $m$ edges and let $k$ be a natural number such that $k\leq n.$ Brouwer conjectured that the sum of the $k$ largest Laplacian eigenvalues of $G$ is at most $m+{k+1 \choose 2}.$ In this paper we prove that this conjecture is true for simple $(m,n)$-graphs where $n\leq m\leq \frac{\sqrt{3}-1}{4}(n-1)n$ and $k\in \

Jonathan Svirsky, Yehonathan Refael, Ofir Lindenbaum

Large Language Models (LLMs), with billions of parameters, present significant challenges for full finetuning due to the high computational demands, memory requirements, and impracticality of many real-world applications. When faced with limited computational resources or small datasets, updating all model parameters can often result in overfitting. To addre

Mohammed Ali Mohammed Alghamdi, Hichem Chtioui

We apply a topological method to prove existence of positive solutions for the nonlineair Choquard equation with upper critical exponent in the sense of Hardy-Littlewood-Sobolev inquality on bounded domains having nontrivial homology group.

Ionut Marian Motoi, Valerio Belli, Alberto Carpineto, Daniele Nardi

Early detection of illnesses and pest infestations in fruit cultivation is critical for maintaining yield quality and plant health. Computer vision and robotics are increasingly employed for the automatic detection of such issues, particularly using data-driven solutions. However, the rarity of these problems makes acquiring and processing the necessary data

Yarik Menchaca Resendiz, Roman Klinger

How emotions are expressed depends on the context and domain. On X (formerly Twitter), for instance, an author might simply use the hashtag #anger, while in a news headline, emotions are typically written in a more polite, indirect manner. To enable conditional text generation models to create emotionally connotated texts that fit a domain, users need to hav

Claus Kiefer, Hamid Mohaddes

We investigate the quantum fate of the classical singularities that occur by gravitational collapse of a dust cloud. For this purpose, we address the quantization of a model first proposed by Georges Lemaitre in 1933. We find that the singularities can generically be avoided. This is a consequence of unitary evolution in the quantum theory, whereby the quant

Annalisa Calini, Thomas A. Ivey

We show that the Yajima-Oikawa (YO) equations, a model of short wave-long wave interaction, arise from a simple geometric flow on curves in the 3-dimensional sphere $S^3$ that are transverse to the standard contact structure. For the family of periodic plane wave solutions of the YO equations studied by Wright, we construct the associated transverse curves,

Kanella Panagiotopoulou, Theodoros Evrenoglou, Christopher H Schmid, Silvia Metelli

Random effects meta-analysis is widely used for synthesizing studies under the assumption that underlying effects come from a normal distribution. However, under certain conditions the use of alternative distributions might be more appropriate. We conducted a systematic review to identify articles introducing alternative meta-analysis models assuming non-nor

Frederik Schröder, Martin P. van Exter, Meng Xiong, George Kountouris

We employ polarization tomography to characterize the modal properties of a dielectric nanocavity with sub-wavelength mode confinement. Our analysis of reflection spectra shows that the Fano-lineshape depends strongly on the polarization in a confocal configuration, and that the lineshape can be transformed into a Lorentzian-like peak for a certain polarizat

Sumit Sharma, Girish Rongali, Kaushik Mitra

Traditional CMOS sensors suffer from restricted dynamic range and sub optimal performance under extreme lighting conditions. They are affected by electronic noise in low light conditions and pixel saturation while capturing high illumination. Recent High Dynamic Range (HDR) Imaging methods, often designed for CMOS Sensors, attempt to address these issues by

Elena Battipede, Antonella Giangualano, Paolo Boffi, Monica Clerici

Physics Playground is an immersive Virtual Reality (VR) application designed for educational purposes, featuring a virtual laboratory where users interact with various physics phenomena through guided experiments. This study aims to evaluate the application's design and educational content to facilitate its integration into classroom settings. A quantitative

Dasol Choi, Guijin Son, Soo Yong Kim, Gio Paik

Visual-Language Models (VLMs) have become a powerful tool for bridging the gap between visual and linguistic understanding. However, the conventional learning approaches for VLMs often suffer from limitations, such as the high resource requirements of collecting and training image-text paired data. Recent research has suggested that language understanding pl

S. I. Ipatov, G. J. Hahn

A numerical integration of the equations of motion of the Sun-planets-an object system is used to study the evolution of orbits close to the orbit of the P/1996 R2 object, which is a Jupiter-crossing object, and to the asteroidal orbit of the P/1996 N2 object, which, at the moment of its detection, had a tail similar to a cometary one. Small variations in th

Nima Yazdani, Hamed Alimohammadzadeh, Shahram Ghandeharizadeh

A Flying Light Speck, FLS, is a miniature sized drone configured with light sources to illuminate 3D multimedia objects in a fixed volume, an FLS display. A swarm of FLSs may provide haptic interactions by exerting force back at a user's touch. This paper presents a conceptual model for the multimedia data to enable content-based queries. The model empowers

Thomas Royen

An integral over the interval $(0,\pi)$ is given for the cumulative distribution function of a sum of independent gamma random variables with different scale and shape parameters. The cumulative distribution function of a positive definite quadratic form is obtained as a special case with identical shape parameters $\alpha = 1/2$.

Yuki Harada, Shuichi Maeda, Masato Kiyama, Shinichiro Nakamura

Although experimental design and methodological surveys for mono-molecular activity/property has been extensively investigated, those for chemical composition have received little attention, with the exception of a few prior studies. In this study, we configured three simple DNN regressors to predict essential oil property based on chemical composition. Desp

Mainak Poddar, Abhishek Sarkar

Lie algebroids over a topological ringed space provides a unified framework to study various geometric structures. This geometric concept is intimately connected with well-known algebraic structures, including Gerstenhaber algebras and Batalin-Vilkovisky algebras. We introduce more general concepts such as $\mathcal{L}$-Lie algebroids and $\mathcal{A}$-Gerst

Ioannis Tzachristas

Large Language Models (LLMs) have revolutionized various aspects of engineering and science. Their utility is often bottlenecked by the lack of interaction with the external digital environment. To overcome this limitation and achieve integration of LLMs and Artificial Intelligence (AI) into real-world applications, customized AI agents are being constructed

Ritwick Sarkar, Urna Basu

We show that heterogeneity in self-propulsion speed can lead to the emergence of a robust effective short-range repulsion among active particles interacting via long-range attractive potentials. Using the example of harmonically coupled active Brownian particles, we analytically derive the stationary distribution of the pairwise distances and reveal that the

Yudong Han, Haocong Wang, Yupeng Hu, Yongshun Gong

Due to the superior ability of global dependency, transformer and its variants have become the primary choice in Masked Time-series Modeling (MTM) towards time-series classification task. In this paper, we experimentally analyze that existing transformer-based MTM methods encounter with two under-explored issues when dealing with time series data: (1) they e

He Yu, Jing Liu

Community structures are critical for understanding the mesoscopic organization of networks, bridging local and global patterns. While methods such as DeepWalk and node2vec capture local positional information through random walks, they fail to preserve community structures. Other approaches like modularized nonnegative matrix factorization and evolutionary

Zihui Cheng, Qiguang Chen, Jin Zhang, Hao Fei

Large Vision-Language Models (LVLMs) have recently demonstrated amazing success in multi-modal tasks, including advancements in Multi-modal Chain-of-Thought (MCoT) reasoning. Despite these successes, current benchmarks still follow a traditional paradigm with multi-modal input and text-modal output, which leads to significant drawbacks such as missing visual

Krystian Polczyński, Maksymilian Bednarek, Jan Awrejcewicz

This study investigates the dynamics of a magnetic pendulum under time-varying magnetic excitation with a position-dependent phase. The system exhibits complex chaotic and regular dynamics, validated through simulations and experiments. The mathematical model, based on a physical setup, includes a magnetic excitation torque with phase dependence on the dynam

Liangqi Xie, Jicong Fan

This paper aims to recover a multi-subspace matrix from permuted data: given a matrix, in which the columns are drawn from a union of low-dimensional subspaces and some columns are corrupted by permutations on their entries, recover the original matrix. The task has numerous practical applications such as data cleaning, integration, and de-anonymization, but

Quentin Manière, Marcin Przybyłko

Recent works have explored the use of counting queries coupled with Description Logic ontologies. The answer to such a query in a model of a knowledge base is either an integer or $\infty$, and its spectrum is the set of its answers over all models. While it is unclear how to compute and manipulate such a set in general, we identify a class of counting queri

Ildikó Pilán, Benet Manzanares-Salor, David Sánchez, Pierre Lison

Text sanitization aims to rewrite parts of a document to prevent disclosure of personal information. The central challenge of text sanitization is to strike a balance between privacy protection (avoiding the leakage of personal information) and utility preservation (retaining as much as possible of the document's original content). To this end, we introduce

Martin Bock, Usman Sapaev, Ji Eun Bae, Anton Husakou

We experimentally and numerically investigate self-compression of pulses around 5 $\mu$m wavelength in a noble-gas-filled hollow waveguides. We demonstrate spectral broadening of multi-mJ pulses at 4.9 $\mu$m and associated pulse compression from 85 fs to 47 fs in the solitonic pulse compression regime. The self-compression resulted in sub-three-cycle pulses

Ali Mesbah, Seid H. Pourtakdoust, Alireza Sharifi, Afshin Banazadeh

Control barrier functions (CBFs) have emerged as a popular topic in safety critical control due to their ability to provide formal safety guarantees for dynamical systems. Despite their powerful capabilities, the determination of feasible CBFs for input-constrained systems is still a formidable task and a challenging research issue. The present work aims to

S. I. Ipatov

Limits and characteristic periods of variations in orbital elements of planets were studied by numerical integration of equations of motion. Interrelations between the characteristic periods of variations in orbital elements of some planets were found.

Eduardo Flandez, Victor Munoz

We apply a complex network approach to analyse the time series of five solar parameters, and propose an strategy to predict the number of sunspots for the next solar maximum, and when will this maximum will occur. The approach is based on the Visibility Graph (VG) algorithm, and a slightly modified version of it, the Horizontal Visibility Graph (HVG), which

Johannes Martin, Jeremiah Lübke, Tianyi Li, Michele Buzzicotti

Models for the transport of high energy charged particles through strong magnetic turbulence play a key role in space and astrophysical studies, such as describing the propagation of solar energetic particles and high energy cosmic rays. Inspired by the recent advances in high-performance machine learning techniques, we investigate the application of generat

Arthur Clairembaud, Marlene Turner, Patric Muggli

We use numerical simulations to determine whether the saturation length of the self-modulation (SM) instability of a long proton bunch in plasma could be determined by measuring the radius of the bunch halo SM produces. Results show that defocused protons acquire their maximum transverse momentum and exit the wakefields at a distance approximately equal to t

Ramandeep Singh Arora, Navnath Daundkar

For a $G$-equivariant fibration $p \colon E\to B$, we introduce and study the invariant analogue of Cohen, Farber and Weinberger's parametrized topological complexity, called the invariant parametrized topological complexity. This notion generalizes the invariant topological complexity introduced by Lubawski and Marzantowicz. When $G$ is a compact Lie gr

Junwen Liu, Luming Yao, Lun Zhang

A special type of geometric situation in ensembles of non-intersecting paths occurs when the non-intersecting trajectories are required to be nonnegative so that the limit shape becomes tangential to the hard-edge $0$. The local fluctuation is governed by the universal hard edge tacnode process, which also arises from some tiling problems. It is the aim of t

Zhentao Liu, Ruyi Zha, Huangxuan Zhao, Hongdong Li

Reconstructing 3D vessel structures from sparse-view dynamic digital subtraction angiography (DSA) images enables accurate medical assessment while reducing radiation exposure. Existing methods often produce suboptimal results or require excessive computation time. In this work, we propose 4D radiative Gaussian splatting (4DRGS) to achieve high-quality recon

Lam Ngo, Huong Ha, Jeffrey Chan, Hongyu Zhang

When it comes to expensive black-box optimization problems, Bayesian Optimization (BO) is a well-known and powerful solution. Many real-world applications involve a large number of dimensions, hence scaling BO to high dimension is of much interest. However, state-of-the-art high-dimensional BO methods still suffer from the curse of dimensionality, highlighti

Maria Melamed, Florian Peißker

The Galactic Center provides a unique opportunity to observe a galactic core, objects in close proximity to a supermassive black hole (SMBH), and star formation channels that exhibit imprints of this peculiar environment. This habitat hosts, in addition to the SMBH Sgr A*, a surprisingly young cluster with the so-called S-stars. These stars orbit the SMBH on

Andrin Rehmann, Alexandre Bovet

Signed graphs allow for encoding positive and negative relations between nodes and are used to model various online activities. Node representation learning for signed graphs is a well-studied task with important applications such as sign prediction. While the size of datasets is ever-increasing, recent methods often sacrifice scalability for accuracy. We pr

Arsalan Akram Malik, Dmytro Savchuk

We provide sufficient conditions for the multi-EGS groups to be liftable and thus produce new examples of groups acting transitively on regular trees of finite degree stabilizing one of the ends, whose closures are scale groups as defined by Willis. Additionally, we explicitly compute the contracting nuclei of the groups in this class. We also specialize our

Aymen Hamrouni, Sofie Pollin, Hazem Sallouha

With the surge in IoT devices ranging from wearables to smart homes, prompt transmission is crucial. The Age of Information (AoI) emerges as a critical metric in this context, representing the freshness of the information transmitted across the network. This paper studies hybrid IoT networks that employ Optical Communication (OC) as a reinforcement medium to

Anwesha Kanjilal, Shamsa Aliramaji, Deborah Neuß, Marcus Hans

While intermetallic (IM)-metal interfaces in metallic alloys are critical for tuning mechanical properties, they can also act as failure sites, underscoring the importance of determining their strength. This study reports on a novel microshear geometry, and demonstrates its applicability for testing the strength and deformation behavior of IM-metal interface

Zixiang Li, Yue Song, Renshuai Tao, Xiaohong Jia

Although diffusion models have achieved remarkable success in the field of image generation, their latent space remains under-explored. Current methods for identifying semantics within latent space often rely on external supervision, such as textual information and segmentation masks. In this paper, we propose a method to identify semantic attributes in the

Antoine Bret, Asaf Pe'er

Collisionless shocks are complex nonlinear structures that are not yet fully understood. In particular, the interaction between these shocks and the particles they accelerate remains elusive. Based on an instability analysis that relates the shock width to the spectrum of the accelerated particle and the shock density ratio, we find that the acceleration pro

Zichen Wang, Hao Miao, Senzhang Wang, Renzhi Wang

Accurately predicting the trajectory of vehicles is critically important for ensuring safety and reliability in autonomous driving. Although considerable research efforts have been made recently, the inherent trajectory uncertainty caused by various factors including the dynamic driving intends and the diverse driving scenarios still poses significant challe

Salim I. Amoukou, Tom Bewley, Saumitra Mishra, Freddy Lecue

We introduce a novel approach for detecting distribution shifts that negatively impact the performance of machine learning models in continuous production environments, which requires no access to ground truth data labels. It builds upon the work of Podkopaev and Ramdas [2022], who address scenarios where labels are available for tracking model errors over t

Zhenyi Fan, Jiaqi Li, Dongyu Luo, Yuqi Yuan

Hospital readmission prediction is critical for clinical decision support, aiming to identify patients at risk of returning within 30 days post-discharge. High readmission rates often indicate inadequate treatment or post-discharge care, making effective prediction models essential for optimizing resources and improving patient outcomes. We propose PT, a Tra

S. V. Kozyrev

Explanation of grokking (delayed generalization) in learning is given by modeling grokking by the stochastic gradient Langevin dynamics (Brownian motion) and applying the ideas of thermodynamics.

Tianye Yu, Junwen Lai, Xiangyang Liu, Peitao Liu

As one class of typical quantum materials, Kagome metals in $A$V$_3$Sb$_5$($A$ = K, Rb, Cs) have attracted extensive attentions due to their interesting physical properties and different quantum phases of charge density wave (CDW), superconductivity and nontrivial topology. Recently, a new CDW phase in ScV$_6$Sn$_6$ was experimentally observed and inspired a

Akihiko Sekine, Ryo Murakami, Yoshiyasu Doi

The quantum transduction, or equivalently quantum frequency conversion, between microwave and optical photons is essential for realizing scalable quantum computers with superconducting qubits. Due to the large frequency difference between microwave and optical ranges, the transduction needs to be done via intermediate bosonic modes or nonlinear processes. Re

Wonseok Roh, Hwanhee Jung, Jong Wook Kim, Seunggwan Lee

Recently, generalizable feed-forward methods based on 3D Gaussian Splatting have gained significant attention for their potential to reconstruct 3D scenes using finite resources. These approaches create a 3D radiance field, parameterized by per-pixel 3D Gaussian primitives, from just a few images in a single forward pass. However, unlike multi-view methods t

Eoin Ó Colgáin, M. M. Sheikh-Jabbari

Dark Energy Spectroscopic Instrument (DESI) observations, when combined with Cosmic Microwave Background (CMB) and Type Ia supernovae (SNe), have led to statistically significant dynamical dark energy (DDE) claims. In this letter we reconstruct the $\Lambda$CDM parameter $\Omega_m$ from the $w_0 w_a$CDM cosmologies advocated by the DESI collaboration. We ide

Aldo Kiem, Olaf Parczyk, Christoph Spiegel

Sidorenko's conjecture states that the number of copies of any given bipartite graph in another graph of given density is asymptotically minimized by a random graph. The forcing conjecture further strengthens this, claiming that any minimizer in fact needs to be quasi-random. Here we extend the family of bipartite graphs for which the forcing conjecture is k

Haruto Murata

We prove that the category of finitely generated graded modules over the quiver Hecke algebra of arbitrary type admits numerous stratifications in the sense of Kleshchev. A direct consequence is that the full subcategory corresponding to the quantum unipotent subgroup associated with any Weyl group element is an affine highest weight category. Our results si

Nikitha SR, Tarun Ram Menta, Mausoom Sarkar

The advent of multimodal learning has brought a significant improvement in document AI. Documents are now treated as multimodal entities, incorporating both textual and visual information for downstream analysis. However, works in this space are often focused on the textual aspect, using the visual space as auxiliary information. While some works have explor

A. G. Cesar, Mario Novello

We analyze the effect of higher order viscosity corrections in a spatially homogeneous and isotropic universe. The viscous effects are modeled by introducing a second-order term to the energy density in the equation of state and on the expansion factor. An autonomous dynamical system is then constructed to study the qualitative behavior of the energy density

Seok-Young Chung, Qiyu Sun

In this paper, we introduce the concept of graph shift-invariant space (GSIS) on an undirected finite graph, which is the linear space of graph signals being invariant under graph shifts, and we study its bandlimiting, kernel reproducing and sampling properties. Graph bandlimited spaces have been widely applied where large datasets on networks need to be han

Li-wen Zhou, Min Tang, Ya-ling Yi, Yao-Jia Zhang

In this paper, a generalized optimization problem on the local sphere is established by the cone order relation on the tangent space, and solved by an improved conditional gradient method (for short, ICGM). The auxiliary subproblems are constructed by the directed distance function on the tangent space, the iteration step size is updated by the Armijo rule,

Shreeyash Gowaikar, Srinivasan Iyengar, Sameer Segal, Shivkumar Kalyanaraman

The Piping and Instrumentation Diagrams (P&IDs) are foundational to the design, construction, and operation of workflows in the engineering and process industries. However, their manual creation is often labor-intensive, error-prone, and lacks robust mechanisms for error detection and correction. While recent advancements in Generative AI, particularly Large

Jiahui Zhu, Jianliang Zhai

In this paper, we study the stochastic logrithmic Schr\"odinger equation with saturated nonlinear multiplicative L\'evy noise. The global well-posedness is established for the stochastic logrithmic Schr\"odinger equation in an appropriate Orlicz space by construct solutions of a regularized equation converging strongly to a solution to the original equation.

Haoran Mu, Daniel Smith, Tomas Katkus, Nguyen Hoai An Le

In astronomy, multi-object spectrographs employ fibre positioning robots to couple the light from multiple astronomy sources (stars or galaxies) into multiple multi-mode fibres, which are distributed across the focal plane of the telescope. These fibres transport the celestial light to the entrance slit of a spectrograph (or bank of spectrographs) for analys

Li-wen Zhou, Min Tang, Ya-ling Yi, Yao-Jia Zhang

This paper introduces and studies the generalized optimization problem (for short, GOP) defined by the conic order relation on a local sphere. The existence of solution to this problem is studied by using image space analysis (for short, ISA), and a class of regular weak separation functions on the local sphere is established. Moreover, a Lagrangian-type suf

Taisuke Kobayashi, Takumi Aotani

This paper proposes a new design method for a stochastic control policy using a normalizing flow (NF). In reinforcement learning (RL), the policy is usually modeled as a distribution model with trainable parameters. When this parameterization has less expressiveness, it would fail to acquiring the optimal policy. A mixture model has capability of a universal

Kevin Fischer, Darren Fürst, Sebastian Steindl, Jakob Lindner

Large Language Models (LLMs) have been showing promising results for various NLP-tasks without the explicit need to be trained for these tasks by using few-shot or zero-shot prompting techniques. A common NLP-task is question-answering (QA). In this study, we propose a comprehensive performance comparison between smaller fine-tuned models and out-of-the-box

Xing Liufu, Chaolei Tan, Xiaotong Lin, Yonggang Qi

Edge labels are typically at various granularity levels owing to the varying preferences of annotators, thus handling the subjectivity of per-pixel labels has been a focal point for edge detection. Previous methods often employ a simple voting strategy to diminish such label uncertainty or impose a strong assumption of labels with a pre-defined distribution,

Lewis E. MacKenzie, Peter Kirton

Superfluorescence (SF) in lanthanide doped upconversion nanoparticles (UCNPs) is a room-temperature quantum phenomenon, first discovered in 2022. In a SF process, the many emissive lanthanide ions within a single UCNP are coherently coupled by an ultra-short (ns or fs) high-power excitation laser pulse. This leads to a superposition of excited emissive state

Shijing Wang, Yaping Huang

Uncertainty in gaze estimation manifests in two aspects: 1) low-quality images caused by occlusion, blurriness, inconsistent eye movements, or even non-face images; 2) incorrect labels resulting from the misalignment between the labeled and actual gaze points during the annotation process. Allowing these uncertainties to participate in training hinders the i

Peng Gao

Our goal in this paper is to investigate the ergodicity of the white-forced wave equation on the whole line. Assuming that sufficiently many directions of the phase space are stochastically forced, we prove the uniqueness of the stationary measure and polynomial mixing in the dual-Lipschitz metric. The difficulties in our proof are twofold. On the one hand,

Antoine Detaille, Jean Van Schaftingen

For any integer $ p \geq 2 $, we construct a compact Riemannian manifold $ \mathcal{N} $ such that if $ \dim \mathcal{M} > p $, there is a map in the Sobolev space of mappings $ W^{1,p} (\mathcal{M}, \mathcal{N})$ which is not a weak limit of smooth maps into $ \mathcal{N} $ due to a mechanism of analytical obstruction. For $ p = 4n - 1 $, the target manifol

Haydn Belfield

In the Frontier AI Safety Commitments, sixteen companies committed to "Assess the risks posed by their frontier models or systems across the AI lifecycle, including [...] as appropriate, before and during training" (I) and to "Provide public transparency on the implementation of the above (I-VI), except insofar as doing so would increase risk or divulge sens

Zhongjie Duan, Qianyi Zhao, Cen Chen, Daoyuan Chen

The emergence of diffusion models has significantly advanced image synthesis. The recent studies of model interaction and self-corrective reasoning approach in large language models offer new insights for enhancing text-to-image models. Inspired by these studies, we propose a novel method called ArtAug for enhancing text-to-image models in this paper. To the

Hichem Sahbi

Magnitude Pruning is a staple lightweight network design method which seeks to remove connections with the smallest magnitude. This process is either achieved in a structured or unstructured manner. While structured pruning allows reaching high efficiency, unstructured one is more flexible and leads to better accuracy, but this is achieved at the expense of

Jiexi Liu, Meng Cao, Songcan Chen

Irregularly sampled multivariate time series (ISMTS) are prevalent in reality. Due to their non-uniform intervals between successive observations and varying sampling rates among series, the channel-independent (CI) strategy, which has been demonstrated more desirable for complete multivariate time series forecasting in recent studies, has failed. This failu

Stefano Berrone, Stefano Ferraris, Denise Grappein, Gioana Teora

An optimization-based strategy is proposed for coupling three-dimensional and one-dimensional problems (3D-1D coupling) in the context of soil-root interaction simulations. This strategy, originally designed to tackle generic 3D-1D coupled problems with discontinuous solutions, is here extended to the case of non-linear problems and applied, for the first ti

Nicholas Kiefer, Arvid Weyrauch, Muhammed Öz, Achim Streit

The current landscape in time-series forecasting is dominated by Transformer-based models. Their high parameter count and corresponding demand in computational resources pose a challenge to real-world deployment, especially for commercial and scientific applications with low-power embedded devices. Pruning is an established approach to reduce neural network

Tianye Yu, Ijaz Shahid, Peitao Liu, Ding-Fu Shao

Altermagnets are predicted to exhibit anomalous transport phenomena, such as the anomalous Hall and Nernst effects, as observed in ferromagnets but with a vanishing net magnetic moment, akin to antiferromagnets. Despite their potential, progress has been limited due to the scarcity of metallic altermagnets. Motivated by the recent discovery of the altermagne

Jinhao Jiang, Jiayi Chen, Junyi Li, Ruiyang Ren

Existing large language models (LLMs) show exceptional problem-solving capabilities but might struggle with complex reasoning tasks. Despite the successes of chain-of-thought and tree-based search methods, they mainly depend on the internal knowledge of LLMs to search over intermediate reasoning steps, limited to dealing with simple tasks involving fewer rea

Yujie Wang, Kui Yu, Yuhong Zhang, Fuyuan Cao

Graph Neural Networks (GNNs) perform effectively when training and testing graphs are drawn from the same distribution, but struggle to generalize well in the face of distribution shifts. To address this issue, existing mainstreaming graph rationalization methods first identify rationale and environment subgraphs from input graphs, and then diversify trainin

Fei Wu, Erik Demeulemeester, Jannik Matuschke

This paper studies the computation of robust deterministic policies for Markov Decision Processes (MDPs) in the Lightning Does Not Strike Twice (LDST) model of Mannor, Mebel and Xu (ICML '12). In this model, designed to provide robustness in the face of uncertain input data while not being overly conservative, transition probabilities and rewards are unc

Peter E. D. Love, Jane Matthews, Weili Fang, Hadi Mahamivanan

Explainable Artificial Intelligence seeks to make the reasoning processes of AI models transparent and interpretable, particularly in complex decision making environments. In the construction industry, where AI based decision support systems are increasingly adopted, limited attention has been paid to the integration of supporting evidence that underpins the

Gauthier Bertrand, Tristan Aurégan, Benjamin Thiria, Ramiro Godoy-Diana

We study experimentally a symmetrical rigid foil performing pitching oscillations around a mean incidence angle ($\alpha_{m}$) with respect to an incoming flow in a hydrodynamic channel at a constant velocity where the Reynolds number according to the chord of the foil is, $Re_{c} = \rho U_{\infty} c / \mu = 14400$. The problem is inspired from the pumping m

Samuel Teodoro, Agus Gunawan, Soo Ye Kim, Jihyong Oh

Recent AI-based video editing has enabled users to edit videos through simple text prompts, significantly simplifying the editing process. However, recent zero-shot video editing techniques primarily focus on global or single-object edits, which can lead to unintended changes in other parts of the video. When multiple objects require localized edits, existin

Weixiang Sun, Han Shen, Biwei Jiang, Xiaowei Liu

Galactic disk warp has been widely characterized by stellar distributions and stellar kinematics but has not been traced by stellar chemistry. Here, we use a sample with over 170,000 red clump (RC) stars selected from LAMOST and APOGEE first to establish a correlation between the north-south asymmetry in metallicity ([Fe/H]) and the disk warp. Our results in

Jialin Zhuang, Xuan He, Yafei Wang, Jiale Liu

In this paper, we propose a novel covariance information-assisted channel state information (CSI) feedback scheme for frequency-division duplex (FDD) massive multi-input multi-output (MIMO) systems. Unlike most existing CSI feedback schemes, which rely on instantaneous CSI only, the proposed CovNet leverages CSI covariance information to achieve high-perform

Nikiforos Paraskevopoulos, Matthew Steinberg, Brennan Undseth, Aritra Sarkar

The design and benchmarking of quantum computer architectures traditionally rely on practical hardware restrictions, such as gate fidelities, control, and cooling. At the theoretical and software levels, numerous approaches have been proposed for benchmarking quantum devices, ranging from, inter alia, quantum volume to randomized benchmarking. In this work,

Vinh Quang Ngo, Marina Papatriantafilou

Finding heavy hitters in databases and data streams is a fundamental problem with applications ranging from network monitoring to database query optimization, machine learning, and more. Approximation algorithms offer practical solutions, but they present trade-offs involving throughput, memory usage, and accuracy. Moreover, modern applications further compl

Alexandre M. de M. Carvalho, Alisson S. Marques, Glauber T. Silva, Gabriel Q. Garcia

In this work, we analyze the scattering of fermionic quasiparticles in the presence of radial disgyrations and symmetric vortices in the superfluid 3He-A. We consider a Volovik analog model for the description of these defects and investigate the scattering of fermionic quasiparticles in this background. Furthermore, we solve the massless Dirac equation empl

Xi Lu, Bojko N. Bakalov, Yuan Liu

While continuous-variable (CV) quantum systems are believed to be more efficient for quantum sensing and metrology than their discrete-variable (DV) counterparts due to the infinite spectrum of their native operators, our toolkit of manipulating CV systems is still limited. We introduce the quantum coherent state transform~(QCST) and a framework for implemen

Zhenjiang Mao, Mrinall Eashaan Umasudhan, Ivan Ruchkin

Learning predictive models from high-dimensional sensory observations is fundamental for cyber-physical systems, yet the latent representations learned by standard world models lack physical interpretability. This limits their reliability, generalizability, and applicability to safety-critical tasks. We introduce Physically Interpretable World Models (PIWM),

Yi-Jun He, Yunfeng Jiang

We propose a new approach to compute exact $g$-function for integrable quantum field theories with non-diagonal scattering S-matrices. The approach is based on an integrable lattice regularization of the quantum field theory. The exact $g$-function is encoded in the overlap of the integrable boundary state and the ground state on the lattice, which can be co

Alexander Mozdzen, Felicity Addo, Tamas Krisztin, Gregor Kastner

The global climate has underscored the need for effective policies to reduce greenhouse gas emissions from all sources, including those resulting from agricultural expansion, which is regulated by the Common Agricultural Policy (CAP) across the European Union (EU). To assess the effectiveness of these mitigation policies, statistical methods must account for

Krzysztof G. Hełminiak, Ayush Moharana, Tilak B. Pawar, Ganesh Pawar

The Comprehensive Research with Echelles on the Most interesting Eclipsing binaries (CR\'EME) projects was aimed to collect high-resolutions spectra of about 380 detached eclipsing binaries (DEBs), which mostly do not have literature RV data. From this vast observational material we were able to estimate masses of components of 325 double-lined system. Since

Dong Su, Hui Liu, Yangyang Shi

This paper derives the stochastic homogenization for two dimensional Navier--Stokes equations with random coefficients. By means of weak convergence method and Stratonovich--Khasminskii averaging principle approach, the solution of two dimensional Navier--Stokes equations with random coefficients converges in distribution to the solution of two dimensional N

Yuchen Fan, Yuzhong Hong, Qiushi Wang, Junwei Bao

Alignment, endowing a pre-trained Large language model (LLM) with the ability to follow instructions, is crucial for its real-world applications. Conventional supervised fine-tuning (SFT) methods formalize it as causal language modeling typically with a cross-entropy objective, requiring a large amount of high-quality instruction-response pairs. However, the

Yoontae Hwang, Yongjae Lee

Tabular data poses unique challenges due to its heterogeneous nature, combining both continuous and categorical variables. Existing approaches often struggle to effectively capture the underlying structure and relationships within such data. We propose GFTab (Geodesic Flow Kernels for Semi- Supervised Learning on Mixed-Variable Tabular Dataset), a semi-super

Ziheng Qiao, Houquan Zhou, Yumeng Liu, Zhenghua Li

One key characteristic of the Chinese spelling check (CSC) task is that incorrect characters are usually similar to the correct ones in either phonetics or glyph. To accommodate this, previous works usually leverage confusion sets, which suffer from two problems, i.e., difficulty in determining which character pairs to include and lack of probabilities to di