May 2024 arXiv papers — page 17

Lei Xiong, Xin Luo, Zihao Wang, Chaofan He

With the rapid development of machine vision technology in recent years, many researchers have begun to focus on feature compression that is better suited for machine vision tasks. The target of feature compression is deep features, which arise from convolution in the middle layer of a pre-trained convolutional neural network. However, due to the large volum

Jiawei Chen, Xiao Yang, Zhengwei Fang, Yu Tian

Despite the widespread application of large language models (LLMs) across various tasks, recent studies indicate that they are susceptible to jailbreak attacks, which can render their defense mechanisms ineffective. However, previous jailbreak research has frequently been constrained by limited universality, suboptimal efficiency, and a reliance on manual cr

Ergon Cugler de Moraes Silva

Understanding distance metrics in high-dimensional spaces is crucial for various fields such as data analysis, machine learning, and optimization. The Manhattan distance, a fundamental metric in multi-dimensional settings, measures the distance between two points by summing the absolute differences along each dimension. This study investigates the behavior o

Ally Yalei Du, Dung Daniel Ngo, Zhiwei Steven Wu

We consider the problem of model multiplicity in downstream decision-making, a setting where two predictive models of equivalent accuracy cannot agree on the best-response action for a downstream loss function. We show that even when the two predictive models approximately agree on their individual predictions almost everywhere, it is still possible for thei

Wen Teng, Niall D. Ferguson, Ewan C. Goligher, Anna Heath

In various biomedical studies, analysis often focuses on data magnitudes, particularly when algebraic signs are irrelevant or lost. For repeated measures studies involving magnitude outcomes, incorporating random effects is essential as they account for individual heterogeneity, thereby enhancing parameter estimation precision. However, established regressio

Jialong Huang, Junlin Song, Penglong Lian, Mengjie Gan

Due to the scarcity of fault samples and the complexity of non-linear and non-smooth characteristics data in hydroelectric units, most of the traditional hydroelectric unit fault localization methods are difficult to carry out accurate localization. To address these problems, a sparse autoencoder (SAE)-generative adversarial network (GAN)-wavelet noise reduc

Zi-Yu Xiong, Yong-Jun Xiao, Ye-Qi Zhang, Qi-Liang He

As a precious global resource in quantum information, genuine tripartite nonlocality(GTN) can be quantified by violating Svetlichny inequality. However, there is still no analytical expression for the general three-qubit states due to the difficulty of theoretical calculations. In this paper, we achieve highly accurate quantization of GTN for arbitrary three

M. Hani Zaheer, M. Bilal Khan, S M Zeeshan Iqbal, Zaheer Uddin

The available software to study the spectroscopic characteristics of atoms, ions, and molecules runs on a server, e.g., the general-purpose atomic structure package (GRASP) and R-matrix method. A Python program has been developed to compute Transition Probabilities, oscillator strengths, Line strengths, matrix elements, and radii of the orbit for lithium and

Junpei Harada

A gauge-invariant formulation for the gravitational wave equations is presented. Using this approach, weak, plane wave solutions in a vacuum are derived in various theories. These include general relativity with two modes of polarization with helicity $\pm 2$, Yang's theory with three modes of polarization with helicity $\pm 2$ and $0$, and so-called "genera

Zhicheng Chen, Xi Xiao, Ke Xu, Zhong Zhang

Multivariate time series prediction is widely used in daily life, which poses significant challenges due to the complex correlations that exist at multi-grained levels. Unfortunately, the majority of current time series prediction models fail to simultaneously learn the correlations of multivariate time series at multi-grained levels, resulting in suboptimal

Ruiyi Wang, Stephanie Milani, Jamie C. Chiu, Jiayin Zhi

Mental illness remains one of the most critical public health issues. Despite its importance, many mental health professionals highlight a disconnect between their training and actual real-world patient practice. To help bridge this gap, we propose PATIENT-{\Psi}, a novel patient simulation framework for cognitive behavior therapy (CBT) training. To build PA

Yilin Liu, Xuezhou Guo, Xinqi Wang, Fangzhou Du

Our project proposes an end-to-end 3D face alignment and reconstruction network. The backbone of our model is built by Bottle-Neck structure via Depth-wise Separable Convolution. We integrate Coordinate Attention mechanism and Spatial Group-wise Enhancement to extract more representative features. For more stable training process and better convergence, we j

Maxwell Wisne, Yanpei Deng, Hilal Cansizoglu, Cameron Kopas

Josephson junctions supply the nonlinear inductance element in superconducting qubits. In the widely used transmon configuration, where the junction is shunted by a large capacitor, the low charging energy minimizes the sensitivity of the qubit to charge noise while maintaining the necessary anharmonicity to qubit states. We report here low-frequency transpo

Wei Sun, Qi Zhang, Yanzhao Zhou, Qixiang Ye

3D Gaussian splatting has demonstrated impressive performance in real-time novel view synthesis. However, achieving successful reconstruction from RGB images generally requires multiple input views captured under static conditions. To address the challenge of sparse input views, previous approaches have incorporated depth supervision into the training of 3D

Han Liu, Peng Cui, Bingning Wang, Jun Zhu

Deep Neural Networks (DNNs) have achieved remarkable success in a variety of tasks, especially when it comes to prediction accuracy. However, in complex real-world scenarios, particularly in safety-critical applications, high accuracy alone is not enough. Reliable uncertainty estimates are crucial. Modern DNNs, often trained with cross-entropy loss, tend to

Roman Ya. Kezerashvili, Shalva M. Tsiklauri, Anastasia Spiridonova

We predict the formation of intravalley controllable trions in buckled two-dimensional (2D) materials such as silicene, germanene, and stanene monolayers in an external electric field. Performing a study within the framework of a nonrelativistic potential model using the method of hyperspherical harmonics (HH), the three-body Schr\"{o}dinger equation is solv

Jinxia Yang, Bing Su, Wayne Xin Zhao, Ji-Rong Wen

Medical vision-language pre-training methods mainly leverage the correspondence between paired medical images and radiological reports. Although multi-view spatial images and temporal sequences of image-report pairs are available in off-the-shelf multi-modal medical datasets, most existing methods have not thoroughly tapped into such extensive supervision si

Patrick Emami, Zhaonan Li, Saumya Sinha, Truc Nguyen

Surrogate models are used to predict the behavior of complex energy systems that are too expensive to simulate with traditional numerical methods. Our work introduces the use of language descriptions, which we call ``system captions'' or SysCaps, to interface with such surrogates. We argue that interacting with surrogates through text, particularly natural l

Xiaolong Yu, Cong Tian

Although deep convolutional neural networks have achieved rapid development, it is challenging to widely promote and apply these models on low-power devices, due to computational and storage limitations. To address this issue, researchers have proposed techniques such as model compression, activation sparsity induction, and hardware accelerators. This paper

Tiehong Zhao, Zhen-Hang Yang

Let $\mathcal{K}\left( x\right) $ be the complete elliptic integral of the first kind and \begin{equation*} \mathcal{G}_{p}\left( x\right) =e^{\mathcal{K}\left( \sqrt{x} \right) }-\frac{p}{\sqrt{1-x}} \end{equation*} for $p\in \mathbb{R}$ and $x\in \left( 0,1\right) $. In this paper we find the necessary and sufficient conditions for the functions $\pm \math

Xi Lin, Yilu Liu, Xiaoyuan Zhang, Fei Liu

Multi-objective optimization can be found in many real-world applications where some conflicting objectives can not be optimized by a single solution. Existing optimization methods often focus on finding a set of Pareto solutions with different optimal trade-offs among the objectives. However, the required number of solutions to well approximate the whole Pa

Xingyi Zhang, Zixuan Weng, Sibo Wang

Node embedding learns low-dimensional vectors for nodes in the graph. Recent state-of-the-art embedding approaches take Personalized PageRank (PPR) as the proximity measure and factorize the PPR matrix or its adaptation to generate embeddings. However, little previous work analyzes what information is encoded by these approaches, and how the information corr

Ernesto Quevedo, Jorge Yero, Rachel Koerner, Pablo Rivas

Concerns regarding the propensity of Large Language Models (LLMs) to produce inaccurate outputs, also known as hallucinations, have escalated. Detecting them is vital for ensuring the reliability of applications relying on LLM-generated content. Current methods often demand substantial resources and rely on extensive LLMs or employ supervised learning with m

Songning Lai, Ninghui Feng, Haochen Sui, Ze Ma

The field of time series forecasting has garnered significant attention in recent years, prompting the development of advanced models like TimeSieve, which demonstrates impressive performance. However, an analysis reveals certain unfaithfulness issues, including high sensitivity to random seeds and minute input noise perturbations. Recognizing these challeng

David Ferman, Pablo Garrido, Gaurav Bharaj

3D facial landmark localization has proven to be of particular use for applications, such as face tracking, 3D face modeling, and image-based 3D face reconstruction. In the supervised learning case, such methods usually rely on 3D landmark datasets derived from 3DMM-based registration that often lack spatial definition alignment, as compared with that chosen

John Hawkins, David Tivey

Identifying critical research within the growing body of academic work is an intrinsic aspect of conducting quality research. Systematic review processes used in evidence-based medicine formalise this as a procedure that must be followed in a research program. However, it comes with an increasing burden in terms of the time required to identify the important

Jie Yang, Jiankun Wang, Max Q. -H. Meng

Automated Cobb angle estimation based on X-ray images plays an important role in scoliosis diagnosis, treatment, and progression surveillance. The inadequate feature extraction and the noise in X-ray images are the main difficulties of automated Cobb angle estimation, and it is challenging to ensure that the calculated Cobb angle meets clinical requirements.

Ryo Fujii, Masashi Hatano, Hideo Saito, Hiroki Kajita

Surgical phase recognition has gained significant attention due to its potential to offer solutions to numerous demands of the modern operating room. However, most existing methods concentrate on minimally invasive surgery (MIS), leaving surgical phase recognition for open surgery understudied. This discrepancy is primarily attributed to the scarcity of publ

Alon Kukliansky, Brad Lackey

We extend the recently introduced notion of tensor enumerator to the circuit enumerator. We provide a mathematical framework that offers a novel method for analyzing circuits and error models without resorting to Monte Carlo techniques. We introduce an analogue of the Poisson summation formula for stabilizer codes, facilitating a method for the exact computa

Mengjie Gan, Penglong Lian, Zhiheng Su, Jiyang Zhang

Industrial equipment fault diagnosis often encounter challenges such as the scarcity of fault data, complex operating conditions, and varied types of failures. Signal analysis, data statistical learning, and conventional deep learning techniques face constraints under these conditions due to their substantial data requirements and the necessity for transfer

Yuhao Tong, Steven W. Su

This note explores the extension of D-stability to non-square matrices, applicable to distributed/decentralized controllability analysis. We first present a definition of D-stability for non-square matrices, directly extending from square matrices. We propose sufficient conditions for specific configurations of non-square matrices. Finally, we consider the s

Ewen Denney, Ganesh Pai

We propose a new framework to facilitate dynamic assurance within a safety case approach by associating safety performance measurement with the core assurance artifacts of a safety case. The focus is mainly on the safety architecture, whose underlying risk assessment model gives the concrete link from safety measurement to operational risk. Using an aviation

Tomasz Rzepecki

We define and study the class of inner ultrahomogeneous groups, which includes Hall's universal group and the universal locally recursively presentable group. We provide simple criteria for ample generic automorphisms, straight maximality, uniform simplicity and divisibility (all of which apply to both Hall's universal group and the universal locally recursi

Mahmoud AlaaEldin, Emad Alsusa, Mohammad Al-Jarrah, Karim G. Seddik

Non-orthogonal multiple access (NOMA) is widely recognized for its spectral and energy efficiency, which allows more users to share the network resources more effectively. This paper provides a generalized bit error rate (BER) performance analysis of successive interference cancellation (SIC)-based uplink NOMA systems under Rayleigh fading channels, taking i

Xinyang Huang, Chuang Zhu, Kebin Liu, Ruiying Ren

Existing few-shot segmentation (FSS) only considers learning support-query correlation and segmenting unseen categories under the precise pixel masks. However, the cost of a large number of pixel masks during training is expensive. This paper considers a more challenging scenario, weakly-supervised few-shot segmentation (WS-FSS), which only provides category

Lianqiang Qu, Lu Chen, Ting Yan, Yuguo Chen

We propose a semiparametric model for dyadic link formations in directed networks. The model contains a set of degree parameters that measure different effects of popularity or outgoingness across nodes, a regression parameter vector that reflects the homophily effect resulting from the nodal attributes or pairwise covariates associated with edges, and a set

Xianghao Xu, Aditya Ganeshan, Karl D. D. Willis, Yewen Pu

Editing 2D icon images can require significant manual effort from designers. It involves manipulating multiple geometries while maintaining the logical or physical coherence of the objects depicted in the image. Previous language driven image editing methods can change the texture and geometry of objects in the image but fail at producing spatial variations,

Yao Yao, Zuchao Li, Hai Zhao

The burgeoning size of Large Language Models (LLMs) has led to enhanced capabilities in generating responses, albeit at the expense of increased inference times and elevated resource demands. Existing methods of acceleration, predominantly hinged on knowledge distillation, generally necessitate fine-tuning of considerably large models, such as Llama-7B, posi

Sean Cox

\emph{Approximation Theory} uses nicely-behaved subcategories to understand entire categories, just as projective modules are used to approximate arbitrary modules in classical homological algebra. We use set-theoretic \emph{elementary submodel arguments} to give new, short proofs of well-known theorems in approximation theory, sometimes with stronger result

Yilun Xu, Feng-Xiao Sun, Wei Zhang, Qiongyi He

The exotic phase transitions and multistabilities in atom-cavity coupled systems have attracted tremendous interests recently. In this work, we investigate the effect of photon hopping between two Dicke cavities, which induces rich quantum phases for steady states and dynamic process. Starting from a generic dimer system where the two cavities are not necess

Paula Kirya, Aida Mestre-Farrera, Jing Yang, Lisa V. Poulikakos

Changes in the density and organization of fibrous biological tissues often accompany the progression of serious diseases ranging from fibrosis to neurodegenerative diseases, heart disease and cancer. However, challenges in cost, complexity, or precision faced by existing imaging methodologies pose barriers to elucidating the role of tissue microstructure in

Akul Goel, Surya Narayanan Hari, Belinda Waltman, Matt Thomson

Social Determinants of Health (SDOH), also known as Health-Related Social Needs (HSRN), play a significant role in patient health outcomes. The Centers for Disease Control and Prevention (CDC) introduced a subset of ICD-10 codes called Z-codes to recognize and measure SDOH. However, Z-codes are infrequently coded in a patient's Electronic Health Record (EHR)

Dongjun Wu, Lihui Pu, Jun Jo, Rene Hexel

This paper presents an interdisciplinary PhD project using a humanoid robot to encourage interactive activities for people with dementia living in two aged care facilities. The aim of the project was to develop software and use technologies to achieve successful robot-led engagement with older people with dementia. This paper outlines the qualitative finding

José Ignacio Díaz Villa, Patricio Loncomilla, Javier Ruiz-del-Solar

This paper introduces YotoR (You Only Transform One Representation), a novel deep learning model for object detection that combines Swin Transformers and YoloR architectures. Transformers, a revolutionary technology in natural language processing, have also significantly impacted computer vision, offering the potential to enhance accuracy and computational e

Suwannee Adsavakulchai, Mawin Prommasaeng

This research aims to detect the physical characteristics of corn kernels and analyze images using a deep learning model. The data analysis based on the CRISP-DM framework which consists of six steps, business understanding, data understanding, data preparation, modelling, evaluation, and deployment. The business goal reduces the cost of the separation of ab

Valentin V. Andreev, Miron B. Bekker, Joseph A. Cima

We discuss the Ces`aro operator on the Hardy space in the upper half-plane. We provide a new simple proof of the boundedness of this operator, prove that this operator is equal to the sum of the identity operator and a unitary operator, which implies its normality.

Yi Xu, Suyash Mahar, Ziheng Liu, Mingyao Shen

While Compute Express Link (CXL) enables support for cache-coherent shared memory among multiple nodes, it also introduces new types of failures--processes can fail before data does, or data might fail before a process does. The lack of a failure model for CXL-based shared memory makes it challenging to understand and mitigate these failures. To solve these

Hao-Hang Xu, Jian Liu, L. L. Tao, Xian-Jie Wang

We investigated the spin-lattice coupling in orthorhombic pseudobrookite FeTi2O5 single crystal with highly ordered $ Fe^{2+} / Ti^{4+}$ occupation, which consists of quasi-1D S=2 chains running along a-axis. Both the magnetization and specific heat measurements confirm that the antiferromagnetic phase transition of FeTi2O5 occurs at TN = 42 K. The structura

Ren Yang, Yangyang Yu, Qihang Zhou, Gang Li

The paper calculates the helicity-dependent dihadron fragmentation function (DiFF), by extending the dihadron spectator model and examine the single longitudinal spin asymmetry $A^{\sin(2\phi_h)}_{UL}$ from dihadron in semi-inclusive inelastic scattering (SIDIS). This function elucidates the relationship between the longitudinal polarization of the fragmente

Jiuang Zhao, Zitian Yang, Li Zhang, Xiaoli Lian

Software architectures are usually meticulously designed to address multiple quality concerns and support long-term maintenance. However, due to the imbalance between the cost and value for developers to document design rationales (i.e., the design alternatives and the underlying arguments for making or rejecting decisions), these rationales are often obsole

Andrew Ryzhikov

Given a finite set of matrices with integer entries, the matrix mortality problem asks if there exists a product of these matrices equal to the zero matrix. We consider a special case of this problem where all entries of the matrices are nonnegative. This case is equivalent to the NFA mortality problem, which, given an NFA, asks for a word $w$ such that the

Qi Xiong, Zhenqiu Zhang, Lingwei Ma

In this paper, we consider the solutions to the non-homogeneous double obstacle problems with Orlicz growth involving measure data. After establishing the existence of the solutions to this problem in the Orlicz-Sobolev space, we derive a pointwise gradient estimate for these solutions by Riesz potential, which leads to the result on the $C^1$ regularity cri

Wenchao Sun, Xuewu Lin, Yining Shi, Chuang Zhang

The well-established modular autonomous driving system is decoupled into different standalone tasks, e.g. perception, prediction and planning, suffering from information loss and error accumulation across modules. In contrast, end-to-end paradigms unify multi-tasks into a fully differentiable framework, allowing for optimization in a planning-oriented spirit

Kenji Kajiwara, Shota Shigetomi, Seiichi Udagawa

We give a simple and explicit constructions of various semi-discrete surfaces and discrete $K$-surfaces in terms of the Jacobi elliptic functions using $\tau$-functions. Their periodicities are also determined.

Qijun You, Lingshuo Meng, Yun Gao, Qing Liao

We have demonstrated the capability of spectral multiplexing in multi-distance diffractive imaging, enabling the reconstruction of samples with diverse spectral responses. While previous methods like ptychography utilize redundancy in radial diffraction data to achieve information multiplexing, they typically require capturing a substantial amount of diffrac

Hernan Olguin, Pascale Domingo, Luc Vervisch, Christian Hasse

So far, flamelet theory has treated curvature as an independent parameter requiring specific means for closure. In this work, it is shown how the adoption of a two-dimensional orthogonal composition space allows obtaining formal mathematical relations between the flame curvatures and the gradients of the conditioning scalars (also called flamelet coordinates

Sean Williams, James Huckle

We introduce a comprehensive Linguistic Benchmark designed to evaluate the limitations of Large Language Models (LLMs) in domains such as logical reasoning, spatial intelligence, and linguistic understanding, among others. Through a series of straightforward questions, it uncovers the significant limitations of well-regarded models to perform tasks that huma

Federico R. Noguer, Suber Corley, Kyle A. Pearson, Robert T. Zellem

We present an updated ephemeris and physical parameters for the exoplanet WASP-77 A b. In this effort, we combine 64 ground- and space-based transit observations, 6 space-based eclipse observations, and 32 radial velocity observations to produce the most precise orbital solution to date for this target, aiding in the planning of James Webb Space Telescope (J

Peifeng Jiang, Hong Liu, Xia Li, Ti Wang

The limited robustness of 3D Gaussian Splatting (3DGS) to motion blur and camera noise, along with its poor real-time performance, restricts its application in robotic SLAM tasks. Upon analysis, the primary causes of these issues are the density of views with motion blur and the cumulative errors in dense pose estimation from calculating losses based on nois

Germán Fonseca, Oscar Riaño, Guillermo Rodriguez-Blanco

We consider the initial value problem associated to the low dispersion fractionary Benjamin-Bona-Mahony equation, fBBM. Our aim is to establish local persistence results in weighted Sobolev spaces and to obtain unique continuation results that imply that those results above are sharp. Hence, arbitrary polynomial type decay is not preserved by the fBBM flow.

Hai-Dang Kieu, Minh Duc Nguyen, Thanh-Son Nguyen, Dung D. Le

Recent advancements in Large Language Models (LLMs) have shown significant potential in enhancing recommender systems. However, addressing the cold-start recommendation problem, where users lack historical data, remains a considerable challenge. In this paper, we introduce KALM4Rec (Keyword-driven Retrieval-Augmented Large Language Models for Cold-start User

Huan Yu, Qi Xia, Jun Fang

Energetic positrons have been observed in the interstellar medium, and high-energy positrons with relativistic energies up to approximately 1 TeV have been detected in Galactic cosmic rays. We conducted a study on the acceleration of particles, specifically positrons, in a nonrelativistic quasiparallel collisionless shock induced by a plasma consisting of pr

Guanhao Zhou, Yuefeng Han, Xiufan Yu

This paper studies the prediction task of tensor-on-tensor regression in which both covariates and responses are multi-dimensional arrays (a.k.a., tensors) across time with arbitrary tensor order and data dimension. Existing methods either focused on linear models without accounting for possibly nonlinear relationships between covariates and responses, or di

Grant Norman, Jacqueline Wentz, Hemanth Kolla, Kurt Maute

Throughout many fields, practitioners often rely on differential equations to model systems. Yet, for many applications, the theoretical derivation of such equations and/or accurate resolution of their solutions may be intractable. Instead, recently developed methods, including those based on parameter estimation, operator subset selection, and neural networ

Vatsal Vinay Parikh

The recent introduction of OpenAI's text-to-video model Sora has sparked widespread public discourse across online communities. This study aims to uncover the dominant themes and narratives surrounding Sora by conducting topic modeling analysis on a corpus of 1,827 Reddit comments from five relevant subreddits (r/OpenAI, r/technology, r/singularity, r/vfx, a

Yujiao Jiang, Qingmin Liao, Zhaolong Wang, Xiangru Lin

Recovering photorealistic and drivable full-body avatars is crucial for numerous applications, including virtual reality, 3D games, and tele-presence. Most methods, whether reconstruction or generation, require large numbers of human motion sequences and corresponding textured meshes. To easily learn a drivable avatar, a reasonable parametric body model with

J. C. del Valle

For a hydrogen atom subject to a constant magnetic field, we report a numerical realization of the two-dimensional Non-Linearization Procedure (NLP) to estimate the accuracy of the variational energy associated with a given trial function. Relevant equations of the NLP, which resemble those describing a dielectric medium with a space-dependent permittivity a

S. I. Mistakidis, G. Bougas, G. C. Katsimiga, P. G. Kevrekidis

We demonstrate the existence and stability of one-dimensional (1D) topological kink configurations immersed in higher-dimensional bosonic gases and nonlinear optical setups. Our analysis pertains, in particular, to the two- and three-dimensional extended Gross-Pitaevskii models with quantum fluctuations describing droplet-bearing environments but also to the

Xiaming Che, Junlin Zhang, Zhuang Qi, Xin Qi

Learning with noisy labels has become an effective strategy for enhancing the robustness of models, which enables models to better tolerate inaccurate data. Existing methods either focus on optimizing the loss function to mitigate the interference from noise, or design procedures to detect potential noise and correct errors. However, their effectiveness is o

Yuta Hyodo, Teruyuki Kitabayashi

The primitive Pythagorean triples are the three natural numbers $(a, b, c)$ that satisfy $c^2=a^2+b^2$ in a right triangle. We constructed a neutrino mixing models related to primitive Pythagorean triples that satisfy the observed values within the $3 \sigma$ region for the reactor, solar, and atmospheric neutrino mixing angles, as well as the Dirac CP phase

Al Conrad, Fernando Pedichini, Gianluca Li Causi, Simone Antoniucci

Since volcanic activity was first discovered on Io from Voyager images in 1979, changes on Io's surface have been monitored from both spacecraft and ground-based telescopes. Here, we present the highest spatial resolution images of Io ever obtained from a ground-based telescope. These images, acquired by the SHARK-VIS instrument on the Large Binocular Telesc

Oliver Knill

Both Morse theory and Lusternik-Schnirelmann theory link algebra, topology and analysis in a geometric setting. The two theories can be formulated in finite geometries like graph theory or within finite abstract simplicial complexes. We work here mostly in graph theory and review the Morse inequalities b(k)-b(k-1) + ... + b(0) less of equal than c(k)-c(k-1)

Huian Lin, Liming Ling

In this work, we analyze the asymptotic behaviors of high-order rogue wave solutions with multiple large parameters and discover novel rogue wave patterns, including claw-like, OTR-type, TTR-type, semi-modified TTR-type, and their modified patterns. A correlation is established between these rogue wave patterns and the root structures of the Adler--Moser pol

Tomoyuki Abe

Let $G$ be a finite group and $A$ be a regular local ring on which $G$ acts. Under certain assumptions on $A$ and the action, Serre defined a function $a_G\colon G\rightarrow\mathbb{Z}$ which can be viewed as a higher dimensional analogue of Artin character, and conjectured that it is associated to a $\mathbb{Q}_\ell$-rational representation of $G$ for any p

Xiangru Jian, Xinjian Zhao, Wei Pang, Chaolong Ying

The recent surge in contrast-based graph self-supervised learning has prominently featured an intensified exploration of spectral cues. Spectral augmentation, which involves modifying a graph's spectral properties such as eigenvalues or eigenvectors, is widely believed to enhance model performance. However, an intriguing paradox emerges, as methods grounded

Elliot C. Eklund, Nandini Ananth

We present a hybrid Path Integral Monte Carlo (hPIMC) algorithm to calculate real-time quantum thermal correlation functions and demonstrate its application to open quantum systems. The hPIMC algorithm leverages the successes of classical PIMC as a computational tool for high-dimensional system studies by exactly simulating dissipation using the Feynman-Vern

Fujiao Ji, Kiho Lee, Hyungjoon Koo, Wenhao You

Phishing attacks pose a significant threat to Internet users, with cybercriminals elaborately replicating the visual appearance of legitimate websites to deceive victims. Visual similarity-based detection systems have emerged as an effective countermeasure, but their effectiveness and robustness in real-world scenarios have been underexplored. In this paper,

Vijay Lingam, Atula Tejaswi, Aditya Vavre, Aneesh Shetty

Popular parameter-efficient fine-tuning (PEFT) methods, such as LoRA and its variants, freeze pre-trained model weights \(W\) and inject learnable matrices \(\Delta W\). These \(\Delta W\) matrices are structured for efficient parameterization, often using techniques like low-rank approximations or scaling vectors. However, these methods typically show a per

Wei Lu, Qingyao Wang, Xiaoqiang Wang, Dabin Zheng

Studying the generalized Hamming weights of linear codes is a significant research area within coding theory, as it provides valuable structural information about the codes and plays a crucial role in determining their performance in various applications. However, determining the generalized Hamming weights of linear codes, particularly their weight hierarch

Jeffrey D. Rudie, Hui-Ming Lin, Robyn L. Ball, Sabeena Jalal

The RSNA Abdominal Traumatic Injury CT (RATIC) dataset is the largest publicly available collection of adult abdominal CT studies annotated for traumatic injuries. This dataset includes 4,274 studies from 23 institutions across 14 countries. The dataset is freely available for non-commercial use via Kaggle at https://www.kaggle.com/competitions/rsna-2023-abd

B. C. He, S. R. Stroberg

We describe an approximation to the in-medium similarity renormalization group (IMSRG) method in which we include the effects of intermediate three-body operators arising within nested commutators. As an initial step, we present the relevant equations for two nested commutators, all of which can be factorized so that the method scales like the standard IMSRG

Nicolas Capitelli, Francisco Somma

In this article, we study the behavior of a broad family of real sequences derived from randomized one-pile subtraction games. For any subtraction set $S$, we allow any valid number of chips $s\in S$ to be removed at equal probability at any given position and we study the sequences $(a_n^S)_{n\in\mathbb{N}}$ representing the probability of winning the game

Zhenmei Shi, Junyi Wei, Zhuoyan Xu, Yingyu Liang

Large language models (LLM) have emerged as a powerful tool for AI, with the key ability of in-context learning (ICL), where they can perform well on unseen tasks based on a brief series of task examples without necessitating any adjustments to the model parameters. One recent interesting mysterious observation is that models of different scales may have dif

Tianyi Zhang, Yen-Wei Chang, Omar Barrera, Naveed Ahmed

This work reports the procedure for modeling piezoelectric acoustic resonators and filters at millimeter wave (mmWave). Different from conventional methods for lower frequency piezoelectric devices, we include both acoustic and electromagnetic (EM) effects, e.g., self-inductance, in both the circuit-level fitting and finite element analysis, toward higher ac

Junbin Zhuang, Guiguang Din, Yunyi Yan

Weight play an essential role in deep learning network models. Unlike network structure design, this article proposes the concept of weight augmentation, focusing on weight exploration. The core of Weight Augmentation Strategy (WAS) is to adopt random transformed weight coefficients training and transformed coefficients, named Shadow Weight(SW), for networks

Christian Táfula

On an infinite chess board, how much faster can the knight reach a square when compared to the king, in average? More generally, for coprime $b>a \in \mathbb{Z}_{\geq 1}$ such that $a+b$ is odd, define the $(a,b)$-knight and the king as \begin{equation*} \begin{aligned} \mathrm{N}_{a,b} = \{(a,b), (b,a), (-a,b), (-b,a), (-b,-a), (-a,-b), (a,-b), (b, -a)\},\n

Ming-Jing Zhao, Yuanhong Tao

The uncertainty relation is a distinctive characteristic of quantum theory. The uncertainty is essentially rooted in quantum states. In this work we regard the uncertainty as an intrinsic property of quantum state and characterize it systematically with respect to given projective measurement. Some basic concepts about uncertainty are reformulated in this co

Janosch Döcker, Simone Linz, Kristina Wicke

In comparison to phylogenetic trees, phylogenetic networks are more suitable to represent complex evolutionary histories of species whose past includes reticulation such as hybridisation or lateral gene transfer. However, the reconstruction of phylogenetic networks remains challenging and computationally expensive due to their intricate structural properties

Junjie Zhang, Chenjia Bai, Haoran He, Wenke Xia

Acquiring a multi-task imitation policy in 3D manipulation poses challenges in terms of scene understanding and action prediction. Current methods employ both 3D representation and multi-view 2D representation to predict the poses of the robot's end-effector. However, they still require a considerable amount of high-quality robot trajectories, and suffer fro

Elizabeth Collins-Woodfin, Inbar Seroussi, Begoña García Malaxechebarría, Andrew W. Mackenzie

We develop a framework for analyzing the training and learning rate dynamics on a large class of high-dimensional optimization problems, which we call the high line, trained using one-pass stochastic gradient descent (SGD) with adaptive learning rates. We give exact expressions for the risk and learning rate curves in terms of a deterministic solution to a s

Daewook Kim, Youngsik Kim, Heejoo Choi, Marcos Esparza

The Single Aperture Large Telescope for Universe Studies (SALTUS) is a deployable space telescope designed to provide the astrophysics community with an extremely large far-infrared (far-IR) space observatory to explore our cosmic origins. The SALTUS observatory can observe thousands of faint astrophysical targets, including the first galaxies, protoplanetar

David Damanik, Yong Li, Fei Xu

This is the first part of a two-paper series studying nonlinear Schr\"odinger equations with quasi-periodic initial data. In this paper, we consider the standard nonlinear Schr\"odinger equation. Under the assumption that the Fourier coefficients of the initial data obey a power-law upper bound, we establish local existence of a solution that retains quasi-p

Zian Su, Xiangzhe Xu, Ziyang Huang, Kaiyuan Zhang

Human-Oriented Binary Reverse Engineering (HOBRE) lies at the intersection of binary and source code, aiming to lift binary code to human-readable content relevant to source code, thereby bridging the binary-source semantic gap. Recent advancements in uni-modal code model pre-training, particularly in generative Source Code Foundation Models (SCFMs) and bina

Jiawen Stefanie Zhu, Zibo Zhang, Jian Zhao

Data exploration is an important aspect of the workflow of mixed-methods researchers, who conduct both qualitative and quantitative analysis. However, there currently exists few tools that adequately support both types of analysis simultaneously, forcing researchers to context-switch between different tools and increasing their mental burden when integrating

José Luis Hernández-Barradas, Fernando Galaz-Fontes

In this paper we study the Y-convexity, a property which is obtained by considering a real Banach sequence lattice Y instead of $\ell^p$ for a linear operator $T : E \rightarrow X$, where E is a Banach space and X is a Banach lattice. We introduce some vector sequence spaces in order to characterize the Y-convexity of T by means of the continuity of an assoc

Denish Omondi Otieno, Faranak Abri, Sima Siami-Namini, Akbar Siami Namin

Large language models (LLMs) have attracted considerable attention as they are capable of showcasing impressive capabilities generating comparable high-quality responses to human inputs. LLMs, can not only compose textual scripts such as emails and essays but also executable programming code. Contrary, the automated reasoning capability of these LLMs in perf

Leila Gharavi, Changrui Liu, Bart De Schutter, Simone Baldi

Nonlinear Programs (NLPs) are prevalent in optimization-based control of nonlinear systems. Solving general NLPs is computationally expensive, necessitating the development of fast hardware or tractable suboptimal approximations. This paper investigates the sensitivity of the solutions of NLPs with polytopic constraints when the nonlinear continuous objectiv

Marian Boguna, Dmitri Krioukov

Causal set theory is perhaps the most minimalistic approach to quantum gravity, in the sense that it makes next to zero assumptions about the structure of spacetime below the Planck scale. Yet even with this minimalism, the continuum limit is still a major challenge in causal sets. One aspect of this challenge is the measurement of distances in causal sets.

Zuzana Masáková, Edita Pelantová

Fractions $\frac{p}{q} \in [0,1)$ with prime denominator $q$ written in decimal have a curious property described by Midy's Theorem, namely that two halves of their period (if it is of even length $2n$) sum up to $10^n-1$. A number of results generalise Midy's theorem to expansions of $\frac{p}{q}$ in different integer bases, considering non-prime de

Danica Kosanović

We show that embedding calculus invariants $ev_n$ are surjective for long knots in an arbitrary $3$-manifold. This solves some remaining open cases of Goodwillie--Klein--Weiss connectivity estimates, and at the same time confirms one half of the conjecture that for classical knots $ev_n$ are universal additive Vassiliev invariants over the integers. In addit