March 2024 arXiv papers — page 158

Zikang Xu, Fenghe Tang, Quan Quan, Qingsong Yao

Ensuring fairness in deep-learning-based segmentors is crucial for health equity. Much effort has been dedicated to mitigating unfairness in the training datasets or procedures. However, with the increasing prevalence of foundation models in medical image analysis, it is hard to train fair models from scratch while preserving utility. In this paper, we propo

Yunseo Choi, Katelyn Gan, Andrew Li, Tiffany Zhu

Recently, Xia introduced a deterministic variation $\phi_{\sigma}$ of Defant and Kravitz's stack-sorting maps for set partitions and showed that any set partition $p$ is sorted by $\phi^{N(p)}_{aba}$, where $N(p)$ is the number of distinct alphabets in $p$. Xia then asked which set partitions $p$ are not sorted by $\phi_{aba}^{N(p)-1}$. In this note, we prov

Tanvi Verma, Linh Le Dinh, Nicholas Tan, Xinxing Xu

Visual perimetry is an important eye examination that helps detect vision problems caused by ocular or neurological conditions. During the test, a patient's gaze is fixed at a specific location while light stimuli of varying intensities are presented in central and peripheral vision. Based on the patient's responses to the stimuli, the visual field mapping a

Junyu Chen, Yihao Liu, Shuwen Wei, Zhangxing Bian

Understanding the uncertainty inherent in deep learning-based image registration models has been an ongoing area of research. Existing methods have been developed to quantify both transformation and appearance uncertainties related to the registration process, elucidating areas where the model may exhibit ambiguity regarding the generated deformation. Howeve

Yifan Wu, Yang Liu, Yue Yang, Michael S. Yao

Diagnosing rare diseases presents a common challenge in clinical practice, necessitating the expertise of specialists for accurate identification. The advent of machine learning offers a promising solution, while the development of such technologies is hindered by the scarcity of data on rare conditions and the demand for models that are both interpretable a

Jensen Gao, Annie Xie, Ted Xiao, Chelsea Finn

Data collection has become an increasingly important problem in robotic manipulation, yet there still lacks much understanding of how to effectively collect data to facilitate broad generalization. Recent works on large-scale robotic data collection typically vary many environmental factors of variation (e.g., object types, table textures) during data collec

Amrutha P, Amritanshu Prasad, Velmurugan S

We determine the eigenvalues with multiplicity of each element of an alternating group in any irreducible representation. This is equivalent to determining the decomposition of cyclic representations of alternating groups into irreducibles. We characterize pairs $(w, V)$, where $w$ is an element and $V$ is an irreducible representation of an alternating grou

Xinpeng Lu, Heng Song, Huailing Ma, Junwu Zhu

With the rapid advancement of UAV technology, the problem of UAV coalition formation has become a hotspot. Therefore, designing task-driven multi-UAV coalition formation mechanism has become a challenging problem. However, existing coalition formation mechanisms suffer from low relevance between UAVs and task requirements, resulting in overall low coalition

Jungin Lee, Gyeonghyeon Nam

In this paper, we prove the converse of the dynamical Mordell--Lang conjecture in positive characteristic: For every subset $S \subseteq \mathbb{N}_0$ which is a union of finitely many arithmetic progressions along with finitely many $p$-sets of the form $\left \{ \sum_{j=1}^{m} c_j p^{k_jn_j} : n_j \in \mathbb{N}_0 \right \}$ ($c_j \in \mathbb{Q}$, $k_j \in

Jared M. Ping, Ken J. Nixon

Advances in Tiny Machine Learning (TinyML) have bolstered the creation of smart industry solutions, including smart agriculture, healthcare and smart cities. Whilst related research contributes to enabling TinyML solutions on constrained hardware, there is a need to amplify real-world applications by optimising energy consumption in battery-powered systems.

Haochen Han, Qinghua Zheng, Guang Dai, Minnan Luo

Collecting well-matched multimedia datasets is crucial for training cross-modal retrieval models. However, in real-world scenarios, massive multimodal data are harvested from the Internet, which inevitably contains Partially Mismatched Pairs (PMPs). Undoubtedly, such semantical irrelevant data will remarkably harm the cross-modal retrieval performance. Previ

Xiao Ge, Chunchen Xu, Daigo Misaki, Hazel Rose Markus

There is an urgent need to incorporate the perspectives of culturally diverse groups into AI developments. We present a novel conceptual framework for research that aims to expand, reimagine, and reground mainstream visions of AI using independent and interdependent cultural models of the self and the environment. Two survey studies support this framework an

Anthony DiGiovanni, Jesse Clifton, Nicolas Macé

Agents in mixed-motive coordination problems such as Chicken may fail to coordinate on a Pareto-efficient outcome. Safe Pareto improvements (SPIs) were originally proposed to mitigate miscoordination in cases where players lack probabilistic beliefs as to how their delegates will play a game; delegates are instructed to behave so as to guarantee a Pareto imp

Kuo Xu, Maoyu Wang, Muyu Wang, Lincong Feng

The recent advancements in 2D generation technology have sparked a widespread discussion on using 2D priors for 3D shape and texture content generation. However, these methods often overlook the subsequent user operations, such as texture aliasing and blurring that occur when the user acquires the 3D model and simplifies its structure. Traditional graphics m

Ning Xu, Tingting Zhang, Hongshuo Tian, An-An Liu

News captioning task aims to generate sentences by describing named entities or concrete events for an image with its news article. Existing methods have achieved remarkable results by relying on the large-scale pre-trained models, which primarily focus on the correlations between the input news content and the output predictions. However, the news captionin

Yuriy A. Akimov

Scattering problems are the classical tools for modeling of light-matter interaction. In this paper, we investigate the solution of the dipole scattering problem under different incident radiation.s In particular, we compare the two cases of incident plane and spherically incoming fields. With this comparison, we disclose the two distinct groups of current-s

Ping Guo, Cheng Gong, Xi Lin, Zhiyuan Yang

The escalating threat of adversarial attacks on deep learning models, particularly in security-critical fields, has underscored the need for robust deep learning systems. Conventional robustness evaluations have relied on adversarial accuracy, which measures a model's performance under a specific perturbation intensity. However, this singular metric does not

Nikolaos Galatos, Isis A. Gallardo

In [11] it is shown that the variety $\mathsf{DLP}$ of distributive l-pregroups is generated by a single algebra, the functional algebra $\mathbf{F}(Z)$ over the integers. Here, we show that $\mathsf{DLP}$ is equal to the join of its subvarieties $\mathsf{LPn}$, for $n\in\mathbb{Z}$, consisting of n-periodic l-pregroups. We also prove that every algebra in $

Andrew Hundt, Gabrielle Ohlson, Pieter Wolfert, Lux Miranda

Previous work has observed how Neurodivergence is often harmfully pathologized in Human-Computer Interaction (HCI) and Human-Robot interaction (HRI) research. We conduct a review of autism robot reviews and find the dominant research direction is Autistic people's second to lowest (24 of 25) research priority: interventions and treatments purporting to 'help

Tony J. Puthenpurakal

In this paper we give bountiful examples of Gorenstein local rings $A$ and $B$ such that there is a triangle equivalence between the stable categories \underline{CM}($A$), \underline{CM}($B$).

Fernando de Ávila Silva, Marco Cappiello, Alexandre Kirilov

This paper explores the global properties of time-independent systems of operators in the framework of Gelfand-Shilov spaces. Our main results provide both necessary and sufficient conditions for global solvability and global hypoellipticity, based on analysis of the symbols of operators. We also present a class of time-dependent operators whose solvability

Xianghai Zhou, Haiyan Su

This paper proposes a novel first-order and a novel second-order fully discrete virtual element schemes based on the scalar auxiliary variable method for the three dimensional inductionless magnetohydrodynamics problem. The backward Eular formula and the backward differential formula are used for the time discretization and two types conservation virtual ele

Kaede Shiohara, Toshihiko Yamasaki

Face personalization aims to insert specific faces, taken from images, into pretrained text-to-image diffusion models. However, it is still challenging for previous methods to preserve both the identity similarity and editability due to overfitting to training samples. In this paper, we propose Face2Diffusion (F2D) for high-editability face personalization.

Seokjun Lee, Seung-Won Jung, Hyunseok Seo

Currently, image generation and synthesis have remarkably progressed with generative models. Despite photo-realistic results, intrinsic discrepancies are still observed in the frequency domain. The spectral discrepancy appeared not only in generative adversarial networks but in diffusion models. In this study, we propose a framework to effectively mitigate t

Xuejing Zheng, Chao Yu

In this paper, we study the cooperative Multi-Agent Reinforcement Learning (MARL) problems using Reward Machines (RMs) to specify the reward functions such that the prior knowledge of high-level events in a task can be leveraged to facilitate the learning efficiency. Unlike the existing work that RMs have been incorporated into MARL for task decomposition an

Woo-han Yun, Minsu Jang, Jaehong Kim

In restaurants, many aspects of customer service, such as greeting customers, taking orders, and processing payments, are automated. Due to the various cuisines, required services, and different standards of each restaurant, one challenging part of making the entire automated process is inspecting and providing appropriate services at the table during a meal

Xiu-Lei Ren, Igor Danilkin, Marc Vanderhaeghen

We present a phenomenological study of the $\gamma\gamma^*\to K\bar{K}^*(892)$ process by including the $s$-channel production of the $\eta(1475)$ and $f_1(1420)$ resonances. The non-resonant channel via $K$- and $K^*$-exchanges is investigated carefully by performing the Lorentz tensor decomposition and is constructed to yield a correct high-energy Regge be

Dongxu Wang, Yanbin Lu, Weilong Liu, Hao Zuo

In this paper, we propose an Openspace Collision-freE trAjectory plaNner (OCEAN) for autonomous parking. OCEAN is an optimization-based trajectory planner accelerated by Alternating Direction Method of Multiplier (ADMM) with enhanced computational efficiency and robustness, and is suitable for all scenes with few dynamic obstacles. Starting from a hierarchic

Soonki Hong

Let $\mathcal{T}$ be a locally finite tree whose geometric boundary has infinitely many points. Suppose that a non-amenable group $\G$ acts isometrically and geometrically on the tree $\mathcal{T}$. In this paper, we show that if the length spectrum is Diophantine, then there exists a continuous function $C$ on $\mathcal{T}^2$ such that the heat kernel $p(t,

Yusuke Inoue, Kenji Hashimoto, Hiroyuki Seki

The definition of period in finite-state Markov chains can be extended to regular languages by considering the transitions of DFAs accepting them. For example, the language $(\Sigma\Sigma)^*$ has period two because the length of a recursion (cycle) in its DFA must be even. This paper shows that the period of a regular language appears as a cyclic group withi

Zhijing Shao, Zhaolong Wang, Zhuang Li, Duotun Wang

We present SplattingAvatar, a hybrid 3D representation of photorealistic human avatars with Gaussian Splatting embedded on a triangle mesh, which renders over 300 FPS on a modern GPU and 30 FPS on a mobile device. We disentangle the motion and appearance of a virtual human with explicit mesh geometry and implicit appearance modeling with Gaussian Splatting.

Youngju Na, Woo Jae Kim, Kyu Beom Han, Suhyeon Ha

Generalizable neural implicit surface reconstruction aims to obtain an accurate underlying geometry given a limited number of multi-view images from unseen scenes. However, existing methods select only informative and relevant views using predefined scores for training and testing phases. This constraint renders the model impractical in real-world scenarios,

Bill Sands

For each finite poset $F$ with $|F| > 1$, $\chi_{ac}(F)$ denotes the smallest integer $n$ (if it exists) such that the elements of every finite poset $P$ with $|P| > 1$ can be coloured with at most $n$ colours so that every maximal $F$-free subset of $P$ with more than one element gets at least two colours. In this note we discuss the problem of determining

Liam Blake, John Maclean, Sanjeeva Balasuriya

The Lyapunov exponent is well-known in deterministic dynamical systems as a measure for quantifying chaos and detecting coherent regions in physically evolving systems. In this Letter, we show how the Lyapunov exponent can be unified with stochastic sensitivity (which quantifies the uncertainty of an evolving uncertain system whose initial condition is certa

Sanghoon Lee, Alexei Andreanov, Tigran Sedrakyan, Sergej Flach

We investigate 1D and 2D cross-stitch lattices with hard-core bosons and analytically construct exact groundstates that feature macroscopic degeneracy. The construction relies on the presence of a flatband in the single particle spectrum and the orthogonality of the associated compact localized states (CLS). Up to filling fraction $\nu=1/2$, the groundstate

Batuhan Sayis, Hatice Gunes

Conversational agents have a potential in improving student mental wellbeing while assisting them in self-disclosure activities such as journalling. Their embodiment might have an effect on what students disclose, and how they disclose this, and students overall adherence to the disclosure activity. However, the effect of embodiment in the context of agent a

Max Hirschberger, Bertalan G. Szigeti, Mamoun Hemmida, Moritz M. Hirschmann

Skyrmion lattices (SkL) in centrosymmetric materials typically have a magnetic period on the nanometer-scale, so that the coupling between magnetic superstructures and the underlying crystal lattice cannot be neglected. Here, we reveal the commensurate locking of a SkL to the atomic lattice in Gd$_3$Ru$_4$Al$_{12}$ via high-resolution resonant elastic x-ray

Kanghyun Ryu, Negar Mehr

Ensuring safe navigation in human-populated environments is crucial for autonomous mobile robots. Although recent advances in machine learning offer promising methods to predict human trajectories in crowded areas, it remains unclear how one can safely incorporate these learned models into a control loop due to the uncertain nature of human motion, which can

Sanjay Chaudhuri, Subhroshekhar Ghosh, Kim Cuc Pham

Approximate Bayesian Computation (ABC) methods are applicable to statistical models specified by generative processes with analytically intractable likelihoods. These methods try to approximate the posterior density of a model parameter by comparing the observed data with additional process-generated simulated datasets. For computational benefit, only the va

Jing Xu, Yongbo Zhang

The quality of power grid equipment forms the material foundation for the safety of the large power grid. Ensuring the quality of equipment entering the grid is a core task in material management. Currently, the inspection of incoming materials involves the generation of sampling plans, sampling, sealing, sample delivery, and testing. Due to the lack of a co

Eugene Strahov

We consider an infinitely-many neutral allelic model of population genetics where all alleles are divided into a finite number of classes, and each class is characterized by its own mutation rate. For this model the allelic composition of a sample taken from a very large population of genes is characterized by a random matrix, and the problem is to describe

Jing Xu, Yongbo Zhang

As one of the key equipment in the distribution system, the distribution transformer directly affects the reliability of the user power supply. The probability of accidents occurring in the operation of transformer equipment is high, so it has become a focus of material inspection in recent years. However, the large amount of raw data from sample testing is

Zhiqiang Zhong, Kuangyu Zhou, Davide Mottin

Large Language Models (LLMs) stand at the forefront of a number of Natural Language Processing (NLP) tasks. Despite the widespread adoption of LLMs in NLP, much of their potential in broader fields remains largely unexplored, and significant limitations persist in their design and implementation. Notably, LLMs struggle with structured data, such as graphs, a

Kengo Nakamura, Masaaki Nishino, Shuhei Denzumi

Binary decision diagram (BDD) and zero-suppressed binary decision diagram (ZDD) are data structures to represent a family of (sub)sets compactly, and it can be used as succinct indexes for a family of sets. To build BDD/ZDD representing a desired family of sets, there are many transformation operations that take BDDs/ZDDs as inputs and output BDD/ZDD represe

Ryan Rogers

We present a data analytics system that ensures accurate counts can be released with differential privacy and minimal onboarding effort while showing instances that outperform other approaches that require more onboarding effort. The primary difference between our proposal and existing approaches is that it does not rely on user contribution bounds over dist

Krishna Menon

It is known that for the Young diagram of any partition of an integer $n$, the sum of squares of the hook lengths of its cells is exactly $n^2$ more than that of the contents of its cells. That is, for any partition $\lambda$ of an integer $n$, \begin{equation*} \sum_{u \in \lambda} h(u)^2 = n^2 + \sum_{u \in \lambda} c(u)^2. \end{equation*} We provide a bij

Arup Majumdar, P. Sam Johnson

This paper delves into several characterizations of $A$-approximate point spectrum of A-bounded operators acting on a complex semi-Hilbertian space $H$ and also investigates properties of the $A$-approximate point spectrum for the tensor product of two $A^{\frac{1}{2}}$-adjoint operators. Furthermore, several properties of $A$-normal operators have been esta

Yingxue Yang

In this paper, we consider the unconstrained multiobjective optimization problem. In recent years, researchers pointed out that the steepest decent method may generate small stepsize which leads to slow convergence rates. To address the issue, we propose a global Barzilai and Borwein's gradient normalization descent method for multiobjective optimization (GB

Daegyu Kim, Jooyoung Choi, Chaehun Shin, Uiwon Hwang

We introduce the Approximated Optimal Transport (AOT) technique, a novel training scheme for diffusion-based generative models. Our approach aims to approximate and integrate optimal transport into the training process, significantly enhancing the ability of diffusion models to estimate the denoiser outputs accurately. This improvement leads to ODE trajector

Hui Yang, Jeremy Hare, Oleg Kargaltsev

A large fraction of Fermi-Large Area Telescope (LAT) sources in the fourth Fermi-LAT 14 yr catalog (4FGL) still remain unidentified (unIDed). We continued to improve our machine-learning pipeline and used it to classify 1206 X-ray sources with signal-to-noise ratios >3 located within the extent of 73 unIDed 4FGL sources with Chandra X-ray Observatory observa

Yitong Fan, Bo Chen, Weipeng Li

A modelling framework based on the resolvent analysis and machine learning is proposed to predict the turbulent energy in incompressible channel flows. In the framework, the optimal resolvent response modes are selected as the basis functions modelling the low-rank behaviour of high-dimensional nonlinear turbulent flow-fields, and the corresponding weight fu

Hongjoon Ahn, Jinu Hyeon, Youngmin Oh, Bosun Hwang

We argue that the negative transfer problem occurring when the new task to learn arrives is an important problem that needs not be overlooked when developing effective Continual Reinforcement Learning (CRL) algorithms. Through comprehensive experimental validation, we demonstrate that such issue frequently exists in CRL and cannot be effectively addressed by

Aru Maekawa, Tsutomu Hirao, Hidetaka Kamigaito, Manabu Okumura

Recently, decoder-only pre-trained large language models (LLMs), with several tens of billion parameters, have significantly impacted a wide range of natural language processing (NLP) tasks. While encoder-only or encoder-decoder pre-trained language models have already proved to be effective in discourse parsing, the extent to which LLMs can perform this tas

Zeyang Zhang, Xin Wang, Ziwei Zhang, Guangyao Shen

The existing graph neural architecture search (GNAS) methods heavily rely on supervised labels during the search process, failing to handle ubiquitous scenarios where supervisions are not available. In this paper, we study the problem of unsupervised graph neural architecture search, which remains unexplored in the literature. The key problem is to discover

Wensheng Lu, Jianxun Lian, Wei Zhang, Guanghua Li

Inspired by the exceptional general intelligence of Large Language Models (LLMs), researchers have begun to explore their application in pioneering the next generation of recommender systems - systems that are conversational, explainable, and controllable. However, existing literature primarily concentrates on integrating domain-specific knowledge into LLMs

Xinyao Li, Jingjing Li, Fengling Li, Lei Zhu

Efficiently utilizing rich knowledge in pretrained models has become a critical topic in the era of large models. This work focuses on adaptively utilizing knowledge from multiple source-pretrained models to an unlabeled target domain without accessing the source data. Despite being a practically useful setting, existing methods require extensive parameter t

Geonho Bang, Kwangjin Choi, Jisong Kim, Dongsuk Kum

The inherent noisy and sparse characteristics of radar data pose challenges in finding effective representations for 3D object detection. In this paper, we propose RadarDistill, a novel knowledge distillation (KD) method, which can improve the representation of radar data by leveraging LiDAR data. RadarDistill successfully transfers desirable characteristics

Hao Sun, Yu Song, Xinyao Yu, Jiaqing Liu

Recent advancements in large-scale models have showcased remarkable generalization capabilities in various tasks. However, integrating multimodal processing into these models presents a significant challenge, as it often comes with a high computational burden. To address this challenge, we introduce a new parameter-efficient multimodal tuning strategy for la

Zengyang Li, Guangzong Cai, Qinyi Yu, Peng Liang

In issue tracking systems, each bug is assigned a priority level (e.g., Blocker, Critical, Major, Minor, or Trivial in JIRA from highest to lowest), which indicates the urgency level of the bug. In this sense, understanding bug priority changes helps to arrange the work schedule of participants reasonably, and facilitates a better analysis and resolution of

Yuki Arai, Junichi Hayashi, Keiki Takeda, Hideki Tou

The large anomalous Hall effect (AHE) in antiferromagnetic (AF) materials arises from symmetry breaking equivalent to a ferromagnetic (FM) state. Consequently, this suggests that the observed AHE is induced by the intrinsic mechanism of the band structure effect, which in turn induces dissipationless transverse conductivity. Confirmation of impurity-insensit

P. L. Krapivsky

We consider an impurity in a sea of zero-temperature fermions uniformly distributed throughout the space. The impurity scatters on fermions. On average, the momentum of impurity decreases with time as $t^{-1/(d+1)}$ in $d$ dimensions, and the momentum distribution acquires a scaling form in the long time limit. We solve the Lorentz-Boltzmann equation for the

Yifan Mao, Jian Liu, Xianming Liu

Monocular depth estimation is a crucial task in computer vision. While existing methods have shown impressive results under standard conditions, they often face challenges in reliably performing in scenarios such as low-light or rainy conditions due to the absence of diverse training data. This paper introduces a novel approach named Stealing Stable Diffusio

Yitao Zhu, Sheng Wang, Mengjie Xu, Zixu Zhuang

Multiple cameras can provide comprehensive multi-view video coverage of a person. Fusing this multi-view data is crucial for tasks like behavioral analysis, although it traditionally requires camera calibration, a process that is often complex. Moreover, previous studies have overlooked the challenges posed by self-occlusion under multiple views and the cont

Xun Tang, Holakou Rahmanian, Michael Shavlovsky, Kiran Koshy Thekumparampil

Entropic optimal transport (OT) and the Sinkhorn algorithm have made it practical for machine learning practitioners to perform the fundamental task of calculating transport distance between statistical distributions. In this work, we focus on a general class of OT problems under a combination of equality and inequality constraints. We derive the correspondi

Yibin Wang, Weizhong Zhang, Jianwei Zheng, Cheng Jin

Image composition involves seamlessly integrating given objects into a specific visual context. Current training-free methods rely on composing attention weights from several samplers to guide the generator. However, since these weights are derived from disparate contexts, their combination leads to coherence confusion and loss of appearance information. The

Ross D. Monaghan, Giuseppe C. Tettamanzi

This work reports the theoretical investigation into the mechanism underpinning the anomalous Josephson effect. The prototypical system we study is a ballistic two-dimensional junction containing a two-dimensional Rashba spin-orbit interaction. In this paper we demonstrate how this two-dimensional Rashba interaction mixes the spins of adjacent transverse sub

Ran Wang, Jiayang Li, Xinliang Huang, Lichuan Wang

In two-dimensional system with Rashba spin-orbit coupling, it is well-known that superconductivity can have mixed spin-singlet and -triplet parity, and the $\boldsymbol{d}$-vector of spin-triplet pairing is parallel to $\boldsymbol{g}$-vector of Rashba spin-orbit coupling. Here, we propose a model to describe a two-dimensional system with unconventional Rash

Xiang Huang, Zhi-Qi Cheng, Jun-Yan He, Chenyang Li

The advancement of autonomous driving systems hinges on the ability to achieve low-latency and high-accuracy perception. To address this critical need, this paper introduces Dynamic Routing Network (DyRoNet), a low-rank enhanced dynamic routing framework designed for streaming perception in autonomous driving systems. DyRoNet integrates a suite of pre-traine

Yunpeng Qu, Kun Yuan, Kai Zhao, Qizhi Xie

Diffusion-based methods, endowed with a formidable generative prior, have received increasing attention in Image Super-Resolution (ISR) recently. However, as low-resolution (LR) images often undergo severe degradation, it is challenging for ISR models to perceive the semantic and degradation information, resulting in restoration images with incorrect content

Chen Wang, Hailong Yu, Chao Wang

$k$-diagonal circulant matrices and cyclic banded matrices are widely used in numerical simulations and signal processing of circular linear systems. Algorithms that directly involve or specify linear or quadratic complexity for the inverses of these two types of matrices are rare. We find that the inverse of a $k$-diagonal circulant matrix can be uniquely d

Guoqing Zhang, Wenbo Zhao, Jian Liu, Xianming Liu

Sampling is widely used in various point cloud tasks as it can effectively reduce resource consumption. Recently, some methods have proposed utilizing neural networks to optimize the sampling process for various task requirements. Currently, deep downsampling methods can be categorized into two main types: generative-based and score-based. Generative-based m

Kaijun Li, Aigen Li, Xuejuan Yang, Taotao Fang

Polycyclic aromatic hydrocarbon (PAH) molecules have long been suggested to be present in the interstellar medium (ISM). Nevertheless, despite their expected ubiquity and sustained searching efforts, identifying specific interstellar PAH molecules from their infrared (IR) spectroscopy has so far been unsuccessful. However, due to its unprecedented sensitivit

Irving Fang, Yuzhong Chen, Yifan Wang, Jianghan Zhang

A robot's ability to anticipate the 3D action target location of a hand's movement from egocentric videos can greatly improve safety and efficiency in human-robot interaction (HRI). While previous research predominantly focused on semantic action classification or 2D target region prediction, we argue that predicting the action target's 3D coordinate could p

Toshish Jawale, Chaitanya Animesh, Sekhar Vallath, Kartik Talamadupula

This study analyzes changes in the attention mechanisms of large language models (LLMs) when used to understand natural conversations between humans (human-human). We analyze three use cases of LLMs: interactions over web content, code, and mathematical texts. By analyzing attention distance, dispersion, and interdependency across these domains, we highlight

Eddy Kwessi

Using information theory, we propose an estimation method for traits parameters in a Darwinian evolution model for species with on trait or multiple traits. We use the Fisher's information to obtain the errors on the estimation for one species with one or multiple traits. We perform simulations to illustrate the method.

Kaijun Li, Aigen Li, Xuejuan Yang, Taotao Fang

The unidentified infrared emission (UIE) features at 3.3, 6.2, 7.7, 8.6, 11.3 and 12.7 micron are ubiquitously seen in a wide variety of astrophysical regions and commonly attributed to polycyclic aromatic hydrocarbon (PAH) molecules. However, the unambiguous identification of any individual, specific PAH molecules has proven elusive until very recently two

F. Urbina, J. Miley, M. Kama, L. Keyte

In protoplanetary disks, atomic carbon is expected to originate from the PDR at the disk surface where CO is dissociated by UV photons coming from the stellar, or external interstellar, radiation field. Even though atomic carbon has been detected in several protoplanetary disks, there is a lack of spatially resolved observations of it. For HD 163296 protopla

Rajesh Jayaram, Erik Waingarten, Tian Zhang

We give new data-dependent locality sensitive hashing schemes (LSH) for the Earth Mover's Distance ($\mathsf{EMD}$), and as a result, improve the best approximation for nearest neighbor search under $\mathsf{EMD}$ by a quadratic factor. Here, the metric $\mathsf{EMD}_s(\mathbb{R}^d,\ell_p)$ consists of sets of $s$ vectors in $\mathbb{R}^d$, and for any two s

Te Pi, Rui Sun, Long-Tu Yuan

We determine the maximum number of a graph without containing the 2-power of a Hamilton path. Using this result, we establish a spectral condition for a graph containing the 2-power of a Hamilton path.

Wen-Cheng Jiang, Hong Wu, Jian Li, Qing-Xu Li

We investigate theoretically tunable non-Hermitian skin effect in systems with gain and loss, and find that bipolar (quadripolar) non-Hermitian skin effect characterized by topological invariants in one (two)-dimensional system. We also find the partial non-Hermitian skin effect with the coexistence of localized states and extended states. Both types of the

Sebastian Krantz

collapse is a large C/C++-based infrastructure package facilitating complex statistical computing, data transformation, and exploration tasks in R - at outstanding levels of performance and memory efficiency. It also implements a class-agnostic approach to R programming, supporting vector, matrix and data frame-like objects and their popular extensions ('uni

Mehrdad Morsali, Brendan Reidy, Deniz Najafi, Sepehr Tabrizchi

This paper proposes a high-performance and energy-efficient optical near-sensor accelerator for vision applications, called Lightator. Harnessing the promising efficiency offered by photonic devices, Lightator features innovative compressive acquisition of input frames and fine-grained convolution operations for low-power and versatile image processing at th

Li Liang, Xiao Zhang, Chuan Wang, Jie Liu

Nonreciprocity, which denotes the asymmetric or even unidirectional transmission of light, constitutes the cornerstone of modern photonic circuits. In the realm of photonic devices, it has been widely utilized in isolators, circulators and so on. Recent topology in artificial materials, an unprecedented degree of freedom, has been proposed to solve the effec

Yang Xu, Saumya Choudhary, Robert W. Boyd

Stimulated emission tomography (SET) is an excellent tool for characterizing the process of spontaneous parametric down-conversion (SPDC), which is commonly used to create pairs of entangled photons for use in quantum information protocols. The use of stimulated emission increases the average number of detected photons by several orders of magnitude compared

Nobuhiro Honda, Jeff Viaclovsky

Let $Z$ be a compact, connected $3$-dimensional complex manifold with vanishing first and second Betti numbers and non-vanishing Euler characteristic. We prove that there is no surjective holomorphic mapping from $Z$ onto any $2$-dimensional complex space. In other words, $Z$ can only possibly fiber over a curve. This result applies in particular to a class

Zhengyi Wang, Yikai Wang, Yifei Chen, Chendong Xiang

Feed-forward 3D generative models like the Large Reconstruction Model (LRM) have demonstrated exceptional generation speed. However, the transformer-based methods do not leverage the geometric priors of the triplane component in their architecture, often leading to sub-optimal quality given the limited size of 3D data and slow training. In this work, we pres

Anupam Chaudhuri, Anj Simmons, Mohamed Abdelrazek

This paper presents our experiments to quantify the manifolds learned by ML models (in our experiment, we use a GAN model) as they train. We compare the manifolds learned at each epoch to the real manifolds representing the real data. To quantify a manifold, we study the intrinsic dimensions and topological features of the manifold learned by the ML model, h

José A. Vélez-Marulanda

Let $\mathbf{k}$ be a field and let $V: \mathscr{C} \to \mathbf{k}\textup{-Mod}$ be a point-wise finite dimensional persistence modules, where $\mathscr{C}$ is a small category. Assume that for all local Artinian $\mathbf{k}$-algebras $R$ with residue field isomorphic to $\mathbf{k}$, there is a generalized persistence module $M: \mathscr{C} \to R\textup{-Mo

Qiuxin Du, Zhen Song, Haiyan Jiang, Xiaoying Wei

The decline of cognitive inhibition significantly impacts older adults' quality of life and well-being, making it a vital public health problem in today's aging society. Previous research has demonstrated that Virtual reality (VR) exergames have great potential to enhance cognitive inhibition among older adults. However, existing commercial VR exergames were

Stephen Casper, Lennart Schulze, Oam Patel, Dylan Hadfield-Menell

Despite extensive diagnostics and debugging by developers, AI systems sometimes exhibit harmful unintended behaviors. Finding and fixing these is challenging because the attack surface is so large -- it is not tractable to exhaustively search for inputs that may elicit harmful behaviors. Red-teaming and adversarial training (AT) are commonly used to improve

Chengyang Zhang, Yong Zhang, Qitan Shao, Jiangtao Feng

Traffic prediction is one of the most significant foundations in Intelligent Transportation Systems (ITS). Traditional traffic prediction methods rely only on historical traffic data to predict traffic trends and face two main challenges. 1) insensitivity to unusual events. 2) limited performance in long-term prediction. In this work, we explore how generati

Shuyi Zhou, Jihai Dong, Fanghua Xu, Zhiyou Jing

Submesoscale processes, with spatio-temporal scales of O(0.01-10) km and hours to 1 day which are hardly resolved by current ocean models, are important sub-grid processes in ocean models. Due to the strong vertical currents, submesoscale processes can lead to submesoscale vertical heat flux (SVHF) in the upper ocean which plays a crucial role in the heat ex

Wen-Ai Jackson, Peter Wild

An O'Nan configuration in a unital is a set of four lines forming a quadrilateral, where the six intersections of pairs of lines are points of the unital. In 2019 Feng and Li elegantly construct O'Nan configurations in Buekenhout-Metz unitals, in particular, for odd order unitals. We extend their work by showing the existence of Triple O'Nan configurations (

Xin Zhu, Ahmet Enis Cetin

The lack of an efficient preamble detection algorithm remains a challenge for solving preamble collision problems in intelligent random access (RA) in the cellular Internet of Things (IoT). To address this problem, we present an early preamble detection scheme based on a maximum likelihood estimation (MLE) model at the first step of the grant-based RA proced

Zeyang Zhang, Xin Wang, Ziwei Zhang, Zhou Qin

Dynamic graph neural networks (DyGNNs) currently struggle with handling distribution shifts that are inherent in dynamic graphs. Existing work on DyGNNs with out-of-distribution settings only focuses on the time domain, failing to handle cases involving distribution shifts in the spectral domain. In this paper, we discover that there exist cases with distrib

Zhi Xu, Dingkang Yang, Mingcheng Li, Yuzheng Wang

Human multimodal language understanding (MLU) is an indispensable component of expression analysis (e.g., sentiment or humor) from heterogeneous modalities, including visual postures, linguistic contents, and acoustic behaviours. Existing works invariably focus on designing sophisticated structures or fusion strategies to achieve impressive improvements. Unf

Xin Zhu, Hongyi Pan, Yury Velichko, Adam B. Murphy

Magnetic field inhomogeneity correction remains a challenging task in MRI analysis. Most established techniques are designed for brain MRI by supposing that image intensities in the identical tissue follow a uniform distribution. Such an assumption cannot be easily applied to other organs, especially those that are small in size and heterogeneous in texture

Dingkang Yang, Mingcheng Li, Dongling Xiao, Yang Liu

Multimodal Sentiment Analysis (MSA) aims to understand human intentions by integrating emotion-related clues from diverse modalities, such as visual, language, and audio. Unfortunately, the current MSA task invariably suffers from unplanned dataset biases, particularly multimodal utterance-level label bias and word-level context bias. These harmful biases po

Saksham Sahai Srivastava, Arpita Dutta, Rajib Mall

Context: Fault localization (FL) is the key activity while debugging a program. Any improvement to this activity leads to significant improvement in total software development cost. There is an internal linkage between the program spectrum and test execution result. Conditional probability in statistics captures the probability of occurring one event in rela

Yunhao Li, Qin Li, Hao Wang, Xue Ma

Current multi-object tracking (MOT) aims to predict trajectories of targets (i.e., ''where'') in videos. Yet, knowing merely ''where'' is insufficient in many crucial applications. In comparison, semantic understanding such as fine-grained behaviors, interactions, and overall summarized captions (i.e., ''what'') from videos, associated with ''where'', is hig