October 2024 arXiv papers — page 37

Xiaoyang Chen, Masayuki Akiyama, Kohei Ichikawa, Yoshiki Toba

Powerful, galactic outflows driven by Active Galactic Nuclei (AGNs) are commonly considered as a main mechanism to regulate star formation in massive galaxies. Ultra- and hyper-luminous IR galaxies (U/HyLIRGs) are thought to represent a transition phase of galaxies from a rapidly growing period to a quiescent status as gas swept out by outflows, providing a

Huan-Yu Liu, Xi-Ning Zhuang, Chao Wang, Qing-Song Li

In recent years, quantum computation has been rapidly advancing, driving a technological revolution with significant potential across various sectors, particularly in finance. Despite this, the insurance industry, an essential tool for mitigating unforeseen risks and losses, has received limited attention. This paper provides an initial exploration into the

Chia-Yi Lin, Chuan-Zhe Yao, Hon-Lam Lai, Chin-Chun Tsai

Dissipationless localized bound states of open quantum systems are significantly robust to decoherence and have potential applications in quantum technologies. In this work, the decoherence dynamics and dissipationless localized bound states of a two-mode open quantum system are investigated. The conditions for the emergence of dissipationless localized boun

Dario Izzo, Marcus Märtens, Laurent Beauregard, Max Bannach

In 2023, the 12th edition of Global Trajectory Competition was organised around the problem referred to as "Sustainable Asteroid Mining". This paper reports the developments that led to the solution proposed by ESA's Advanced Concepts Team. Beyond the fact that the proposed approach failed to rank higher than fourth in the final competition leader-board, sev

Nankai Lin, Meiyu Zeng, Wentao Huang, Shengyi Jiang

Currently, the majority of research in grammatical error correction (GEC) is concentrated on universal languages, such as English and Chinese. Many low-resource languages lack accessible evaluation corpora. How to efficiently construct high-quality evaluation corpora for GEC in low-resource languages has become a significant challenge. To fill these gaps, in

Rafał Gruszczyński, Zhiguang Zhao

The paper is devoted to modal properties of the ternary strict betweenness relation as used in the development of various systems of geometry. We show that such a relation is non-definable in a basic similarity type with a binary operator of possibility, and we put forward two systems of hybrid logic, one of them complete with respect to the class of dense l

Joaquín Ossorio-Castillo, Alexandre Rodríguez-Coello

The procedure for simulating the nuclear magnetic resonance spectrum linked to the spin system of a molecule for a certain nucleus entails diagonalizing the associated Hamiltonian matrix. As the dimensions of said matrix grow exponentially with respect to the spin system's atom count, the calculation of the eigenvalues and eigenvectors marks the performance

O. Kashuba, R. Mummadavarapu, R. -P. Riwar

Compact scalar field theories on lattices are capable of describing a large class of many-body systems, such as interacting bosons, superconducting circuit networks, spin systems and more. We show that a generic quantum geometric many-body coupling induces quantized Chern couplings, implementing a lattice network version of a Floreanini-Jackiw theory. Quantu

Lixian Shen, Ali Esamdin, Chenglong Lv, Haozhi Wang

We investigated the pulsating behavior of KIC 10855535 using Kepler 4-year long cadence data. Two independent frequencies were detected: a pulsation frequency F0 = 17.733260(5)d-1 and a low frequency f8=0.412643(8)d-1 We identify F0 as the fundamental frequency, at which a equidistant quintuplet is centered, suggesting that the star orbits in a binary system

Yen-Shan Chen, Jing Jin, Peng-Ting Kuo, Chao-Wei Huang

Recent studies have demonstrated that large language models (LLMs) exhibit significant biases in evaluation tasks, particularly in preferentially rating and favoring self-generated content. However, the extent to which this bias manifests in fact-oriented tasks, especially within retrieval-augmented generation (RAG) frameworks, where keyword extraction and f

Xizhi Liu, Sijie Ren, Jian Wang

The celebrated Andr\'{a}sfai--Erd\H{o}s--S\'{o}s Theorem from 1974 shows that every $n$-vertex triangle-free graph with minimum degree greater than $2n/5$ must be bipartite. Its extensions to $3$-uniform hypergraphs without the generalized triangle $F_5 = \{abc, abd, cde\}$ have been explored in several previous works such as~\cite{LMR23unif,HLZ24}, demonstr

Yi-Zheng Fan

Hu and Ye conjectured that for an $n$-dimensional tensor $\mathcal{A}$ of order $k$ with an eigenvalue $λ$ and the corresponding eigenvariety $\mathcal{V}_λ(\mathcal{A})$, the algebraic multiplicity $\mathrm{am}(λ)$ of $λ$ satisfies: $$\mathrm{am}(λ) \ge \sum_{i=1}^κ\dim(V_i)(k-1)^{\dim(V_i)-1},$$ where $V_1,\ldots,V_κ$ are all irreducible components of $\ma

Thomas Maunder, Fionntan P. Callan, Stuart A. Sim, Alexander Heger

We present synthetic light curves and spectra from three-dimensional (3D) Monte Carlo radiative transfer simulations based on a 3D core-collapse supernova explosion model of an ultra-stripped $3.5\,\mathrm{M}_{\odot}$ progenitor. Our calculations predict a fast and faint transient with $\Delta m_{15} \sim 1\texttt{-} 2\,\mathrm{mag}$ and peak bolometric lumi

Surabhi Rathore, Pasquale Claudio Africa, Francesco Ballarin, Federico Pichi

This paper presents a projection-based reduced order modelling (ROM) framework for unsteady parametrized optimal control problems (OCP$_{(\mu)}$s) arising from cardiovascular (CV) applications. In real-life scenarios, accurately defining outflow boundary conditions in patient-specific models poses significant challenges due to complex vascular morphologies,

Mohammad Soleymani, Alessio Zappone, Eduard Jorswieck, Marco Di Renzo

We analyze the finite-block-length rate region of wireless systems aided by reconfigurable intelligent surfaces (RISs), employing treating interference as noise. We consider three nearly passive RIS architectures, including locally passive (LP) diagonal (D), globally passive (GP) D, and GP beyond diagonal (BD) RISs. In a GP RIS, the power constraint is appli

Xu Xu, Yinghe Qi, Shijie Zhong, Shiyong Tan

The deformation of finite-sized bubbles in intense turbulence exhibits complex geometries beyond simple spheroids as the bubbles exchange energy with the surrounding eddies across a wide range of scales. This study investigates deformation via the velocity of the most stretched tip of the deformed bubble in 3D, as the tip extension results from the compressi

Zezheng Qin, Congcong Sun, Taiyi He, Yuke He

In the context of widespread global information sharing, information security and privacy protection have become focal points. Steganographic systems enhance information security by embedding confidential information into public carriers; however, existing generative text steganography methods face challenges in handling the long-tail distribution of candida

Yiyang Guo, Ruizhe Li, Mude Hui, Hanzhong Guo

Invisible watermarking is essential for safeguarding digital content, enabling copyright protection and content authentication. However, existing watermarking methods fall short in robustness against regeneration attacks. In this paper, we propose a novel method called FreqMark that involves unconstrained optimization of the image latent frequency space obta

Zeren Xiong, Zedong Zhang, Zikun Chen, Shuo Chen

In this paper, we study an object synthesis task that combines an object text with an object image to create a new object image. However, most diffusion models struggle with this task, \textit{i.e.}, often generating an object that predominantly reflects either the text or the image due to an imbalance between their inputs. To address this issue, we propose

Arisa Ikeda, Ryo Higuchi, Tomohiro Yokozeki, Katsuhiro Endo

In this study, we develop a conditional diffusion model that proposes the optimal process parameters and predicts the microstructure for the desired mechanical properties. In materials development, it is costly to try many samples with different parameters in experiments and numerical simulations. The use of data-driven inverse design method can reduce the c

Yahua Liu, Seyed Ali Hosseini, Cong Liu, Milo Feinberg

Contact time of bouncing drops is one of the most essential parameters to quantify the water-repellency of surfaces. Generally, the contact time on superhydrophobic surfaces is known to be Weber number-independent. Here, we probe an additional characteristic time, \emph{transition time} inherent in water drop impacting on superhydrophobic surfaces, marking a

Enshuo Yan, Huachuan Wang, Weihao Xia

In multivariate time series classification, although current sequence analysis models have excellent classification capabilities, they show significant shortcomings when dealing with long sequence multivariate data, such as prolonged training times and decreased accuracy. This paper focuses on optimizing model performance for long-sequence multivariate data

Ahmad Nemer, J. E. Mendez-Delgado, Natascha Sattler, Guillermo A. Blanc

Understanding the complex ionization structure and chemical composition of \hii\ regions poses a significant challenge in astrophysics. The abundance discrepancy problem, characterized by inconsistencies between abundances derived from recombination lines (RLs) and collisionally excited lines (CELs), has long been a puzzle in the field. In this theoretical s

Yixuan Weng, Minjun Zhu, Guangsheng Bao, Hongbo Zhang

The automation of scientific discovery has been a long-standing goal within the research community, driven by the potential to accelerate knowledge creation. While significant progress has been made using commercial large language models (LLMs) as research assistants or idea generators, the possibility of automating the entire research process with open-sour

Davood Farhadi, Laura Pernigoni, David Melancon, Katia Bertoldi

The ancient art of origami, traditionally used to transform simple sheets into intricate objects, also holds potential for diverse engineering applications, such as shape morphing and robotics. In this study, we demonstrate that one of the most basic origami structures (i.e., a rigid, foldable degree-four vertex) can be engineered to create a crawler capable

Hellina Hailu Nigatu, Atnafu Lambebo Tonja, Benjamin Rosman, Thamar Solorio

The disparity in the languages commonly studied in Natural Language Processing (NLP) is typically reflected by referring to languages as low vs high-resourced. However, there is limited consensus on what exactly qualifies as a `low-resource language.' To understand how NLP papers define and study `low resource' languages, we qualitatively analyzed 150 papers

Tushar Jain, Madeline Lubien, Jerome Gilles

A variety of neural networks architectures are being studied to tackle blur in images and videos caused by a non-steady camera and objects being captured. In this paper, we present an overview of these existing networks and perform experiments to remove the blur caused by atmospheric turbulence. Our experiments aim to examine the reusability of existing netw

Jiawei Xu, Zexin Fan, Jian Yang, Jin Xie

Recently, Gaussian splatting has received more and more attention in the field of static scene rendering. Due to the low computational overhead and inherent flexibility of explicit representations, plane-based explicit methods are popular ways to predict deformations for Gaussian-based dynamic scene rendering models. However, plane-based methods rely on the

Hexuan Deng, Wenxiang Jiao, Xuebo Liu, Min Zhang

Despite their remarkable abilities in various tasks, large language models (LLMs) still struggle with real-time information (e.g., new facts and terms) due to the knowledge cutoff in their development process. However, existing benchmarks focus on outdated content and limited fields, facing difficulties in real-time updating and leaving new terms unexplored.

Rostyslav Kozhan

We introduce Nikishin system of $r$ probability measures on the unit circle. We show that such systems satisfy the AT property and therefore normality, introduced in~\cite{KVMLOPUC}, for any multi-index $(n_1,\ldots,n_r)\in\mathbb{N}^r$ with same-parity components satisfying $n_1 \ge n_2 \ge\ldots\ge n_r$. In the case of $r=2$, we demonstrate that the same p

Jiacheng Wang, Xiang Chen, Renjiu Hu, Rongguang Wang

Co-examination of second-harmonic generation (SHG) and bright-field (BF) microscopy enables the differentiation of tissue components and collagen fibers, aiding the analysis of human breast and pancreatic cancer tissues. However, large discrepancies between SHG and BF images pose challenges for current learning-based registration models in aligning SHG to BF

Jaechang Kim, Jinmin Goh, Inseok Hwang, Jaewoong Cho

Deep learning-based expert models have reached superhuman performance in decision-making domains such as chess and Go. However, it is under-explored to explain or comment on given decisions although it is important for model explainability and human education. The outputs of expert models are accurate, but yet difficult to interpret for humans. On the other

Licheng Zhang, Yuanqiu Huang

A bipartite graph is chordal bipartite if every cycle of length at least six contains a chord. We determine the minimum size in 2-connected chordal bipartite graphs with given order.

Toshiki Kai, Yuta Teruya, Kazuhisa Nakasho

In this paper, we present a remote verification environment for Mizar and its integration with a web platform. Although a VSCode extension for Mizar is already available, it requires installing the Mizar verification tools locally. Our newly developed system implements these verification environments on a server, eliminating this requirement. First, we expla

Azizjon Azimi, Bonu Boboeva, Ilyas Varshavskiy, Shuhrat Khalilbekov

The phenomenon of "black swans" has posed a fundamental challenge to performance of classical machine learning models. The perceived rise in frequency of outlier conditions, especially in post-pandemic environment, has necessitated exploration of synthetic data as a complement to real data in model training. This article provides a general overview and exper

Wenjun Miao, Guansong Pang, Jin Zheng, Xiao Bai

One key challenge in Out-of-Distribution (OOD) detection is the absence of ground-truth OOD samples during training. One principled approach to address this issue is to use samples from external datasets as outliers (i.e., pseudo OOD samples) to train OOD detectors. However, we find empirically that the outlier samples often present a distribution shift comp

ZhenXing Dong, JiaZhou Chen, YangHui Xu

The planning of digital orthodontic treatment requires providing tooth alignment, which not only consumes a lot of time and labor to determine manually but also relays clinical experiences heavily. In this work, we proposed a lightweight tooth alignment neural network based on Swin-transformer. We first re-organized 3D point clouds based on virtual arch line

Changwei Xiong, Jinglong Yang, Jinchao Yu

We study three types of fourth-order Steklov eigenvalue problems. For the first two of them, we derive the asymptotic expansion of their spectra on Euclidean annular domains $\mathbb{B}^n_1\setminus \overline{\mathbb{B}^n_\epsilon}$ as $\epsilon \to 0$, leading to conclusions on shape optimization. For these two problems, we also compute their spectra on cyl

S. B. Hong, J. S. Park

Pulse Shape Discrimination (PSD) is a widely used technique in many experimental analysis. In this study, we specifically aimed to assess the effectiveness of PSD in accurately measuring decay time. We measured the decay times of a 0.1 wt% Gd-loaded liquid scintillator (Gd-LS) with 5 vol% Ultimagold-F added when irradiated with neutrons and gamma rays, which

Mengxuan Ma, Liping Yang, Fang Shen, Chenglong Shen

The magnetic orientation of coronal mass ejections (CMEs) is of great importance to understand their space weather effects. Although many evidences suggest that CMEs can undergo significant rotation during the early phases of evolution in the solar corona, there are few reports that CMEs rotate in the interplanetary space. In this work, we use multi-spacecra

Morgan R. Jones, Charles Klewicki, Oliver Khan, Steven L. Brunton

We consider the relationship between Eulerian modal decompositions and Lagrangian coherent structures (LCSs). The model sensitivity framework developed by Kaszás and Haller (2020) is used to express data-driven modal representations of fluid flow in a Lagrangian space. The method, based on the computation of the finite-time Lyapunov exponent, computes the am

Jassem Abbasi, Ben Moseley, Takeshi Kurotori, Ameya D. Jagtap

We propose a workflow based on physics-informed neural networks (PINNs) to model multiphase fluid flow in fractured porous media. After validating the workflow in forward and inverse modeling of a synthetic problem of flow in fractured porous media, we applied it to a real experimental dataset in which brine is injected at a constant pressure drop into a CO2

Kangqiao Liu, Jie Gu

The exploration of far-from-equilibrium systems has been at the forefront of nonequilibrium thermodynamics, with a particular focus on understanding the fluctuations and response of thermodynamic systems to external perturbations. In this study, we introduce a universal response kinetic uncertainty relation, which provides a fundamental trade-off between the

Zhe Su, Chang-Han Rhee

The large deviations theory for heavy-tailed processes has seen significant advances in the recent past. In particular, Rhee et al. (2019) and Bazhba et al. (2020) established large deviation asymptotics at the sample-path level for L\'evy processes and random walks with regularly varying and (heavy-tailed) Weibull-type increments. This leaves the lognormal

Kazuhiko Minami

An infinite number of solvable Hamiltonians, including the transverse Ising chain, the XY chain with an external field, the cluster model with next-nearest-neighbor x-x interactions, or with next-nearest-neighbor z-z interactions, and other solvable models that can be mapped to the free fermion system are considered. All the conserved charges of these models

Congyu Qiao, Ning Xu, Yihao Hu, Xin Geng

Instance-dependent Partial Label Learning (ID-PLL) aims to learn a multi-class predictive model given training instances annotated with candidate labels related to features, among which correct labels are hidden fixed but unknown. The previous works involve leveraging the identification capability of the training model itself to iteratively refine supervisio

Michael Pieler, Marco Bellagente, Hannah Teufel, Duy Phung

Recently published work on rephrasing natural text data for pre-training LLMs has shown promising results when combining the original dataset with the synthetically rephrased data. We build upon previous work by replicating existing results on C4 and extending them with our optimized rephrasing pipeline to the English, German, Italian, and Spanish Oscar subs

Li Li

We formulate an inverse problem for an uncoupled space-time fractional Schr\"odinger equation on closed manifolds. Our main goal is to determine the fractional powers and the Riemannian metric (up to an isometry) simultaneously from the knowledge of the associated source-to-solution map. Our argument relies on the asymptotic behavior of Mittag-Leffler functi

Tingyu Zhang

The Goldstino, which is a fermionic Nambu-Goldstone mode, has been predicted in a Bose-Fermi mixture when the supersymmetry is broken. To detect this excitation mode, we theoretically investigate the shot noise of the supersymmetry-like tunneling current in a weakly interacting ultracold Bose-Fermi mixture. The Fano factor, which is defined by the noise-to-c

Ho-Joon Kim, Soojoon Lee

Quantum dynamics governs the transformation of static quantum resources, such as coherence and entanglement, in both quantum states and measurements. Prior studies have established that a quantum channel's state-cohering power can be converted into the state-entangling power without additional coherence. Here, we complete this coherence-to-entanglement parad

Rishan Mehta, Param Rajpura, Yogesh Kumar Meena

Resting-state EEG data in neuroscience research serve as reliable markers for user identification and reveal individual-specific traits. Despite this, the use of resting-state data in EEG classification models is limited. In this work, we propose a feature concatenation approach to enhance decoding models' generalization by integrating resting-state EEG, aim

Jiacheng Hu, Yiru Cang, Guiran Liu, Meiqi Wang

This paper proposes a medical literature summary generation method based on the BERT model to address the challenges brought by the current explosion of medical information. By fine-tuning and optimizing the BERT model, we develop an efficient summary generation system that can quickly extract key information from medical literature and generate coherent, ac

Gopi Krishnan Rajbahadur, Gustavo A. Oliva, Dayi Lin, Jiho Shin

The rapid expansion of foundation models (FMs), such as large language models (LLMs), has given rise to FMware, software systems that integrate FM(s) as core components. While building demonstration-level FMware is relatively straightforward, transitioning to production-ready systems presents numerous challenges, including reliability, high implementation co

Kunyun Wang, Shuo Yang, Jieru Zhao, Wenchao Ding

Deep learning models have become pivotal in the field of video processing and is increasingly critical in practical applications such as autonomous driving and object detection. Although Vision Transformers (ViTs) have demonstrated their power, Convolutional Neural Networks (CNNs) remain a highly efficient and high-performance choice for feature extraction a

S. K. Tripathy, Sasmita Pal, B. Mishra

Teleparallel description of gravity theories where the gravity is mediated through the tetrad field and consequent torsion provide an alternative route to explain the late time cosmic speed up issue. Generalization of the teleparallel gravity theory with different functional forms of the torsion scalar $T$ leads to $f(T)$ gravity. The role of scalar field pl

Xiaonuo Dongye, Hanzhi Guo, Le Luo, Haiyan Jiang

With the advancement of virtual reality, the demand for 3D human avatars is increasing. The emergence of Gaussian Splatting technology has enabled the rendering of Gaussian avatars with superior visual quality and reduced computational costs. Despite numerous methods researchers propose for implementing drivable Gaussian avatars, limited attention has been g

Shanu Kumar, Akhila Yesantarao Venkata, Shubhanshu Khandelwal, Bishal Santra

Prompt optimization is essential for effective utilization of large language models (LLMs) across diverse tasks. While existing optimization methods are effective in optimizing short prompts, they struggle with longer, more complex ones, often risking information loss and being sensitive to small perturbations. To address these challenges, we propose SCULPT

Shiyong Zeng, Ping Zhu, Eric C. Howell

The physics of neoclassical tearing mode (NTM) is of great concern to the tokamak plasma stability and performance, especially in the burning plasma regime. Whereas a great deal about the different seeding mechanisms have been understood, and in many situations the seed event can be clearly identified, the potential seeding process of NTM due to the resistiv

Jianmina Ma, Jingtian Ji, Yue Gao

Constrained reinforcement learning has achieved promising progress in safety-critical fields where both rewards and constraints are considered. However, constrained reinforcement learning methods face challenges in striking the right balance between task performance and constraint satisfaction and it is prone for them to get stuck in over-conservative or con

Jianchun Chu, Man-Chun Lee, Jintian Zhu

In this paper, we prove an optimal systolic inequality and the corresponding rigidity in the equality case on closed manifolds with positive bi-Ricci curvature, which generalizes the work of Bray-Brendle-Neves. The proof is given in all dimensions based on the method of minimal surfaces under the Generic Regularity Hypothesis, which is known to be true up to

Zehui Li, Yuhao Ni, Guoxuan Xia, William Beardall

Abstract Recent advances in immunology and synthetic biology have accelerated the development of deep generative methods for DNA sequence design. Two dominant approaches in this field are AutoRegressive (AR) models and Diffusion Models (DMs). However, genomic sequences are functionally heterogeneous, consisting of multiple connected regions (e.g., Promoter R

Zhe Jiao, Yaxu Li, Lijing Zhao

This paper is concerned about the implicit-explicit (IMEX) methods for a class of dissipative wave systems with time-varying velocity feedbacks and nonlinear potential energies, equipped with different boundary conditions. Firstly, we approximate the problems by using a vanilla IMEX method, which is a second-order scheme for the problems when the damping ter

Mingjian Jiang, Yangjun Ruan, Prasanna Sattigeri, Salim Roukos

Recent advancements in Large Language Models (LLMs) have significantly improved text generation capabilities, but these systems are still known to hallucinate, and granular uncertainty estimation for long-form LLM generations remains challenging. In this work, we propose Graph Uncertainty -- which represents the relationship between LLM generations and claim

Yuan Cai, Zhen Lei

The magnetohydrodynamic current-vortex sheet is a free boundary problem involving a moving free surface separating two plasma regions. We prove the global nonlinear stability of current-vortex sheet in the two dimensional ideal incompressible magnetohydrodynamics under the strong horizontal background magnetic field. This appears to be the first result on th

Yang Liu, Jie Gao, Xiaonan Zhang, Xiaomin Fang

Messenger RNA (mRNA) vaccines and therapeutics are emerging as powerful tools against a variety of diseases, including infectious diseases and cancer. The design of mRNA molecules, particularly the untranslated region (UTR) and coding sequence (CDS) is crucial for optimizing translation efficiency and stability. Current design approaches generally focus sole

Yoshitaka Koike, Takumi Nakagawa, Hiroki Waida, Takafumi Kanamori

This paper studies stable learning methods for generative models that enable high-quality data generation. Noise injection is commonly used to stabilize learning. However, selecting a suitable noise distribution is challenging. Diffusion-GAN, a recently developed method, addresses this by using the diffusion process with a timestep-dependent discriminator. W

Chaeyun Jang, Deukhwan Cho, Seanie Lee, Hyungi Lee

Recently, Large Language Models (LLMs) have been increasingly used to support various decision-making tasks, assisting humans in making informed decisions. However, when LLMs confidently provide incorrect information, it can lead humans to make suboptimal decisions. To prevent LLMs from generating incorrect information on topics they are unsure of and to imp

Omer Shubi, Cfir Avraham Hadar, Yevgeni Berzak

Readers can have different goals with respect to the text that they are reading. Can these goals be decoded from their eye movements over the text? In this work, we examine for the first time whether it is possible to distinguish between two types of common reading goals: information seeking and ordinary reading for comprehension. Using large-scale eye track

Muyan Weng, Yunjia Xi, Weiwen Liu, Bo Chen

As the last stage of recommender systems, re-ranking generates a re-ordered list that aligns with the user's preference. However, previous works generally focus on item-level positive feedback as history (e.g., only clicked items) and ignore that users provide positive or negative feedback on items in the entire list. This list-level hybrid feedback can reve

Rambod Azimi, Rishav Rishav, Marek Teichmann, Samira Ebrahimi Kahou

Large language models (LLMs) have demonstrated remarkable performance across various downstream tasks. However, the high computational and memory requirements of LLMs are a major bottleneck. To address this, parameter-efficient fine-tuning (PEFT) methods such as low-rank adaptation (LoRA) have been proposed to reduce computational costs while ensuring minima

David A. Croydon

The $\lambda$-biased random walk on a binary tree of depth $n$ is the continuous-time Markov chain that has unit mean holding times and, when at a vertex other than the root or a leaf of the tree in question, has a probability of jumping to the parent vertex that is $\lambda$ times the probability of jumping to a particular child. (From the root, it chooses

Viney Kumar, Chris T Bauch, Samit Bhattacharyya

Information dissemination intricately intertwines with the dynamics of infectious diseases in the contemporary interconnected world. Recognizing the critical role of public awareness, individual vaccination choices appear to be an essential factor in collective efforts against emerging health threats. This study aims to characterize disease transmission dyna

Bing Han, Wen Huang, Zhengyang Chen, Anbai Jiang

The goal of the acoustic scene classification (ASC) task is to classify recordings into one of the predefined acoustic scene classes. However, in real-world scenarios, ASC systems often encounter challenges such as recording device mismatch, low-complexity constraints, and the limited availability of labeled data. To alleviate these issues, in this paper, a

Dongryeol Lee, Yerin Hwang, Yongil Kim, Joonsuk Park

In line with the principle of honesty, there has been a growing effort to train large language models (LLMs) to generate outputs containing epistemic markers. However, evaluation in the presence of epistemic markers has been largely overlooked, raising a critical question: Could the use of epistemic markers in LLM-generated outputs lead to unintended negativ

Dong Yao, Caizhi Tang, Qing Cui, Longfei Li

Data from observational studies (OSs) is widely available and readily obtainable yet frequently contains confounding biases. On the other hand, data derived from randomized controlled trials (RCTs) helps to reduce these biases; however, it is expensive to gather, resulting in a tiny size of randomized data. For this reason, effectively fusing observational d

Saarth Vardhan, Pavani R Acharya, Samarth S Rao, Oorjitha Ratna Jasthi

Music source separation (MSS) is a task that involves isolating individual sound sources, or stems, from mixed audio signals. This paper presents an ensemble approach to MSS, combining several state-of-the-art architectures to achieve superior separation performance across traditional Vocal, Drum, and Bass (VDB) stems, as well as expanding into second-level

Bong Gyun Kang, Dongjun Lee, HyunGi Kim, DoHyun Chung

Sequence modeling faces challenges in capturing long-range dependencies across diverse tasks. Recent linear and transformer-based forecasters have shown superior performance in time series forecasting. However, they are constrained by their inherent inability to effectively address long-range dependencies in time series data, primarily due to using fixed-siz

Julie Kallini, Shikhar Murty, Christopher D. Manning, Christopher Potts

Models that rely on subword tokenization have significant drawbacks, such as sensitivity to character-level noise like spelling errors and inconsistent compression rates across different languages and scripts. While character- or byte-level models like ByT5 attempt to address these concerns, they have not gained widespread adoption -- processing raw byte str

Jiewen Yang, Yiqun Lin, Bin Pu, Jiarong Guo

Echocardiogram video plays a crucial role in analysing cardiac function and diagnosing cardiac diseases. Current deep neural network methods primarily aim to enhance diagnosis accuracy by incorporating prior knowledge, such as segmenting cardiac structures or lesions annotated by human experts. However, diagnosing the inconsistent behaviours of the heart, wh

Milad Khademi Nori, Il-Min Kim

In class-incremental learning (class-IL), models must classify all previously seen classes at test time without task-IDs, leading to task confusion. Despite being a key challenge, task confusion lacks a theoretical understanding. We present a novel mathematical framework for class-IL and prove the Infeasibility Theorem, showing optimal class-IL is impossible

David Fernando Daza Urbano, René González-Martínez, Mario Huicochea Mason, Amanda Montejano Cantoral

Let $A$ and $B$ be sets of $k\ge5$ elements in $F=\mathbb{Z}/p\mathbb{Z}$ the field with $p>2k-2$ elements. We denote by $A\dot{+}B$ the set of different elements of $F$ that can be written in the form $a+b$, where $a\in A$, $b\in B$, $a\neq b$. The number of elements of this set is at least $2k-3$. K\'{a}rolyi showed that, except from some particular cases,

Wei-Nan Zhang, Yiming Cui, Kaiyan Zhang, Yifa Wang

Recently, research on open domain dialogue systems have attracted extensive interests of academic and industrial researchers. The goal of an open domain dialogue system is to imitate humans in conversations. Previous works on single turn conversation generation have greatly promoted the research of open domain dialogue systems. However, understanding multipl

Shuai Zhou, Shusu Shi

A significant difference in the elliptic flow $v_2$ for particles and their corresponding antiparticles, which is more pronounced for baryons and anti-baryons, was observed in the STAR experiment during the Beam Energy Scan I (BES-I) at RHIC. By employing the SMASH model, we study the $v_2$ difference between protons and anti-protons, as well as between $\La

Nick Fischer, Ce Jin, Yinzhan Xu

The 3SUM problem is one of the cornerstones of fine-grained complexity. Its study has led to countless lower bounds, but as has been sporadically observed before -- and as we will demonstrate again -- insights on 3SUM can also lead to algorithmic applications. The starting point of our work is that we spend a lot of technical effort to develop new algorithms

Sumit Asthana, Hannah Rashkin, Elizabeth Clark, Fantine Huot

One useful application of NLP models is to support people in reading complex text from unfamiliar domains (e.g., scientific articles). Simplifying the entire text makes it understandable but sometimes removes important details. On the contrary, helping adult readers understand difficult concepts in context can enhance their vocabulary and knowledge. In a pre

Jakkapat Seeyangnok, Udomsilp Pinsook, Graeme J Ackland

Hydrogen in its metallic form is the most common material in our solar system, found under the extreme pressure and temperature conditions found in giant planets. Such conditions are inaccessible to experiment and consequently, theoretical work has typically led experiment. Many remarkable properties are proposed for metallic hydrogen, which is expected to e

Yangbo Wei, Kedi Wei, Shangjin Li, Bo Yan

The optical tweezer experiment with neutral atoms is a focal topic in cold atom physics due to its significant potential in quantum computing and simulation. Here, we present the realization of a dual-species optical tweezer for both Rb and K atoms, marking the first step towards creating a polar molecule optical tweezer array. Initially, Rb and K atoms are

Takafumi Kanamori, Kodai Yokoyama, Takayuki Kawashima

In statistical inference, we commonly assume that samples are independent and identically distributed from a probability distribution included in a pre-specified statistical model. However, such an assumption is often violated in practice. Even an unexpected extreme sample called an {\it outlier} can significantly impact classical estimators. Robust statisti

Lian-Xiang Cui, Yi-Mu Du, C. P. Sun

Quantum sensing utilize quantum effects, such as entanglement and coherence, to measure physical signals. The performance of a sensing process is characterized by error which requires comparison to a true value. However, in practice, such a true value might be inaccessible. In this study, we utilize quantum reliability as a metric to evaluate quantum sensor'

Jesús A. Álvarez López, Junhyeong Kim, Masanori Morishita

We prove a regularized determinant formula for the zeta functions of certain 3-dimensional Riemannian foliated dynamical systems, in terms of the infinitesimal operator induced by the flow acting on the reduced leafwise cohomologies. It is the formula conjectured by Deninger. The proof is based on relating the dynamical spectral $\xi$-functions, analogues of

Jacob Serpico, B. A. Zambrano-Luna, Russell Milne, Christopher M. Heggerud

Harmful cyanobacterial blooms (CBs) are increasingly prevalent worldwide, posing significant environmental and health concerns. We derive a stoichiometric model describing the population dynamics and toxicity of cyanobacteria in north-temperate freshwater ecosystems. Our model quantifies the hypoxic effects of CBs on fish mortality and evaluates the impact o

Albert Bruch

Novalike variables are a subgroup of cataclysmic variables (CVs) that -- unlike dwarf novae -- do not exhibit strong brightenings in their long-term light curves. Variations over time scales of weeks, months or years are mostly restricted to irregular low-amplitude modulations. However, some of them occasionally suffer from so-called stunted outbursts, that

Yawen Guo, Sonia Naderi, Colleen Josephson

Solar-powered base stations are a promising approach to sustainable telecommunications infrastructure. However, the successful deployment of solar-powered base stations requires precise prediction of the energy harvested by photovoltaic (PV) panels vs. anticipated energy expenditure in order to achieve affordable yet reliable deployment and operation. This p

Thang D. Bui

Non-Gaussian likelihoods are essential for modelling complex real-world observations but pose significant computational challenges in learning and inference. Even with Gaussian priors, non-Gaussian likelihoods often lead to analytically intractable posteriors, necessitating approximation methods. To this end, we propose efficient schemes to approximate the e

Susannah Kate Conroy

Autonomous weapons systems (AWS) change the way humans make decisions, the effect of those decisions and who is accountable for decisions made. We must remain vigilant, informed and human-centred as we tackle our deliberations on developing norms regarding their development, use and justification. Ways to enhance compliance in international humanitarian law

Prakhar Verma, Sukruta Prakash Midigeshi, Gaurav Sinha, Arno Solin

We introduce Plan*RAG, a novel framework that enables structured multi-hop reasoning in retrieval-augmented generation (RAG) through test-time reasoning plan generation. While existing approaches such as ReAct maintain reasoning chains within the language model's context window, we observe that this often leads to plan fragmentation and execution failures. O

Jiewen Yang, Yiqun Lin, Bin Pu, Xiaomeng Li

Quantitative analysis of cardiac motion is crucial for assessing cardiac function. This analysis typically uses imaging modalities such as MRI and Echocardiograms that capture detailed image sequences throughout the heartbeat cycle. Previous methods predominantly focused on the analysis of image pairs lacking consideration of the motion dynamics and spatial

Renato D. C. Monteiro, Honghao Zhang

This paper develops a parameter-free adaptive proximal bundle method with two important features: 1) adaptive choice of variable prox stepsizes that "closely fits" the instance under consideration; and 2) adaptive criterion for making the occurrence of serious steps easier. Computational experiments show that our method performs substantially fewer consecuti

Jiafei Lyu, Kang Xu, Jiacheng Xu, Mengbei Yan

We consider off-dynamics reinforcement learning (RL) where one needs to transfer policies across different domains with dynamics mismatch. Despite the focus on developing dynamics-aware algorithms, this field is hindered due to the lack of a standard benchmark. To bridge this gap, we introduce ODRL, the first benchmark tailored for evaluating off-dynamics RL

Changhao Li, Yuchen Zhuang, Rushi Qiang, Haotian Sun

Despite the impressive generative abilities of black-box large language models (LLMs), their inherent opacity hinders further advancements in capabilities such as reasoning, planning, and personalization. Existing works aim to enhance LLM capabilities via domain-specific adaptation, which require additional training on accessible model parameters, an infeasi