March 2023 arXiv papers — page 75

Thien Hoang Nguyen, Shenghai Yuan, Lihua Xie

In this work, we propose a simultaneous localization and mapping (SLAM) system using a monocular camera and Ultra-wideband (UWB) sensors. Our system, referred to as VRSLAM, is a multi-stage framework that leverages the strengths and compensates for the weaknesses of each sensor. Firstly, we introduce a UWB-aided 7 degree-of-freedom (scale factor, 3D position

Shuai Wang, Daoan Zhang, Zipei Yan, Jianguo Zhang

Test time adaptation (TTA) aims to adapt deep neural networks when receiving out of distribution test domain samples. In this setting, the model can only access online unlabeled test samples and pre-trained models on the training domains. We first address TTA as a feature revision problem due to the domain gap between source domains and target domains. After

Valiollah Khalili

We study the structure of a graded $3$-Lie-Rinehart algebra $\mathcal{L}$ over an associative and commutative graded algebra $A.$ For $G$ an abelian group, we show that if $(L, A)$ is a tight $G$-graded 3-Lie-Rinehart algebra, then $\mathcal{L}$ and $A$ decompose as $\mathcal{L} =\bigoplus_{i\in I}\mathcal{L}_i$ and $A =\bigoplus_{j\in J}A_j,$ where any $\ma

Ali Mokhtari, Drake Rawls, Tony Huynh, Jeremiah Green

With the increasing popularity of accelerator technologies (e.g., GPUs and TPUs) and the emergence of domain-specific computing via ASICs and FPGA, the matter of heterogeneity and understanding its ramifications on the performance has become more critical than ever before. However, it is challenging to effectively educate students about the potential impacts

Apurba Biswas, V . V. Prasad, R. Rajesh

Mpemba effect refers to the counterintuitive effect where a system which is initially further from the final steady state equilibrates faster than an identical system that is initially closer. The closeness to the final state is defined in terms of a distance measure. For driven granular systems, the Mpemba effect has been illustrated in terms of an ad-hoc m

Shen Han, Shengnan Dai, Jie Ma, Qingyong Ren

Aliovalent doping has been adopted to optimize the electrical properties of semiconductors, while its impact on the phonon structure and propagation is seldom paid proper attention to. This work reveals that aliovalent doping can be much more effective in reducing the lattice thermal conductivity of thermoelectric semiconductors than the commonly employed is

Min Zhang, Jintang Xue, Pranav Kadam, Hardik Prajapati

The design of a tiny machine learning model, which can be deployed in mobile and edge devices, for point cloud object classification is investigated in this work. To achieve this objective, we replace the multi-scale representation of a point cloud object with a single-scale representation for complexity reduction, and exploit rich 3D geometric information o

Fan Cui, Liyong Guo, Lang He, Jiyao Liu

Electroencephalography (EEG) plays a vital role in detecting how brain responses to different stimulus. In this paper, we propose a novel Shallow-Deep Attention-based Network (SDANet) to classify the correct auditory stimulus evoking the EEG signal. It adopts the Attention-based Correlation Module (ACM) to discover the connection between auditory speech and

Chang Yu, Xiangyu Zhu, Xiaomei Zhang, Zhaoxiang Zhang

The function of constructing the hierarchy of objects is important to the visual process of the human brain. Previous studies have successfully adopted capsule networks to decompose the digits and faces into parts in an unsupervised manner to investigate the similar perception mechanism of neural networks. However, their descriptions are restricted to the 2D

Weibo Mao, Chenxin Xu, Qi Zhu, Siheng Chen

To model the indeterminacy of human behaviors, stochastic trajectory prediction requires a sophisticated multi-modal distribution of future trajectories. Emerging diffusion models have revealed their tremendous representation capacities in numerous generation tasks, showing potential for stochastic trajectory prediction. However, expensive time consumption p

Xiaoqi Zhao, Hongpeng Jia, Youwei Pang, Long Lv

Accurate medical image segmentation is critical for early medical diagnosis. Most existing methods are based on U-shape structure and use element-wise addition or concatenation to fuse different level features progressively in decoder. However, both the two operations easily generate plenty of redundant information, which will weaken the complementarity betw

Xinnian Liang, Zefan Zhou, Hui Huang, Shuangzhi Wu

Pretrained language models (PLMs) have shown marvelous improvements across various NLP tasks. Most Chinese PLMs simply treat an input text as a sequence of characters, and completely ignore word information. Although Whole Word Masking can alleviate this, the semantics in words is still not well represented. In this paper, we revisit the segmentation granula

Hongyan Ran, Caiyan Jia

Massive rumors usually appear along with breaking news or trending topics, seriously hindering the truth. Existing rumor detection methods are mostly focused on the same domain, and thus have poor performance in cross-domain scenarios due to domain shift. In this work, we propose an end-to-end instance-wise and prototype-wise contrastive learning model with

Deepak Raikwal, Sandhya Choubey, Monojit Ghosh

In this paper, we have presented a comprehensive study of Lorentz Invariance Violation (LIV) in the context of atmospheric neutrino experiment ICAL and long-baseline experiments T2HK and DUNE. Our study consists of the full parameter space of the LIV parameters (isotropic), i.e., six CPT violating LIV parameters ($a_{\alpha \beta}$) and six CPT conserving LI

Fushuo Huo, Wenchao Xu, Jingcai Guo, Haozhao Wang

This paper investigates a new, practical, but challenging problem named Non-exemplar Online Class-incremental continual Learning (NO-CL), which aims to preserve the discernibility of base classes without buffering data examples and efficiently learn novel classes continuously in a single-pass (i.e., online) data stream. The challenges of this task are mainly

Maresuke Shiraishi, Teppei Okumura, Kazuyuki Akitsu

As well as the galaxy number density and peculiar velocity, the galaxy intrinsic alignment can be used to test the cosmic isotropy. We study distinctive impacts of the isotropy breaking on the configuration-space two-point correlation functions (2PCFs) composed of the spin-2 galaxy ellipticity field. For this purpose, we build a formalism for general types o

Shurojit Chatterji, Huaxia Zeng

We introduce the notion of a multidimensional hybrid preference domain on a (finite) set of alternatives that is a Cartesian product of finitely many components. We demonstrate that in a model of public goods provision, multidimensional hybrid preferences arise naturally through assembling marginal preferences under the condition of semi-separability - a wea

Chanjun Park, Hyeonseok Moon, Seolhwa Lee, Jaehyung Seo

Leaderboard systems allow researchers to objectively evaluate Natural Language Processing (NLP) models and are typically used to identify models that exhibit superior performance on a given task in a predetermined setting. However, we argue that evaluation on a given test dataset is just one of many performance indications of the model. In this paper, we cla

Hongyu Gao, Jiaxin Han, Matthew Fong, Y. P. Jing

We investigate the build-up of the halo profile out to large scale in a cosmological simulation, focusing on the roles played by the recently proposed depletion radii. We explicitly show that halo growth is accompanied by the depletion of the environment, with the inner depletion radius demarcating the two. This evolution process is also observed via the for

Yifeng Zhou, Jiaxin Han

We develop a self-consistent and accurate halo model by partitioning matter according to the depletion radii of haloes. Unlike conventional models that define haloes with the virial radius while relying on a separate exclusion radius or ad-hoc fixes to account for halo exclusion, our model distributes mass across all scales self-consistently and accounts for

Peng Ye, Wei Chen, Guo-Wei Zhang, Feng-Yu Lu

Lithium niobate (LiNbO3, LN) devices play critical roles in quantum information processing. However, for special applications like quantum key distribution (QKD), the characteristics of materials and devices and their impact on practical systems must be intensively inquired. For the first time, we reveal that the photorefractive effect in LN can be utilized

Tee-How Loo, M. Koussour, Avik De

$f(Q,T)$ theory of gravity is very recently proposed to incorporate within the action Lagrangian, the trace $T$ of the energy-momentum tensor along with the non-metricity scalar $Q$. The cosmological application of this theory in a spatially flat isotropic and homogeneous Universe is well-studied. However, our Universe is not isotropic since the Planck era a

Weihuang Liu, Xi Shen, Chi-Man Pun, Xiaodong Cun

We consider the generic problem of detecting low-level structures in images, which includes segmenting the manipulated parts, identifying out-of-focus pixels, separating shadow regions, and detecting concealed objects. Whereas each such topic has been typically addressed with a domain-specific solution, we show that a unified approach performs well across al

Xiaoyu Zhang, Yun-Hui Liu

Localization in a pre-built map is a basic technique for robot autonomous navigation. Existing mapping and localization methods commonly work well in small-scale environments. As a map grows larger, however, more memory is required and localization becomes inefficient. To solve these problems, map sparsification becomes a practical necessity to acquire a sub

Xiaodong Zhao, YiXuan Luo, Juejing Liu, Wenjun Liu

Manual analysis of XRD data is usually laborious and time consuming. The deep neural network (DNN) based models trained by synthetic XRD patterns are proved to be an automatic, accurate, and high throughput method to analysis common XRD data collected from solid sample in ambient environment. However, it remains unknown that whether synthetic XRD based model

Zhao-Heng Yin, Binghao Huang, Yuzhe Qin, Qifeng Chen

Tactile information plays a critical role in human dexterity. It reveals useful contact information that may not be inferred directly from vision. In fact, humans can even perform in-hand dexterous manipulation without using vision. Can we enable the same ability for the multi-finger robot hand? In this paper, we present Touch Dexterity, a new system that ca

Yi Luo, Yee Sin Ang, L. K. Ang

We develop analytical models of optical-field electron tunneling from the edge and surface of two-dimensional (2D) materials, including the effects of reduced dimensionality, non-parabolic energy dispersion, band anisotropy, quasi-time dependent tunneling and emission dynamics indueced by the laser field. We discover a universal scaling between the tunneling

Pallab Maiti, Asrifa Sultana

Fixed points for uniformly local asymptotic nonexpansive maps are discussed in this article. An approximate fixed point sequence for such a map over a uniformly convex Banach space is derived. At the end, we study the unique fixed point for uniformly local asymptotic contraction.

Yanwei Gong, Xiaolin Chang, Jelena Mišić, Vojislav B. Mišić

Fully homomorphic encryption (FHE) has experienced significant development and continuous breakthroughs in theory, enabling its widespread application in various fields, like outsourcing computation and secure multi-party computing, in order to preserve privacy. Nonetheless, the application of FHE is constrained by its substantial computing overhead and stor

Chenxin Xu, Robby T. Tan, Yuhong Tan, Siheng Chen

Learning to predict agent motions with relationship reasoning is important for many applications. In motion prediction tasks, maintaining motion equivariance under Euclidean geometric transformations and invariance of agent interaction is a critical and fundamental principle. However, such equivariance and invariance properties are overlooked by most existin

Ao Zhou, Jianlei Yang, Yingjie Qi, Yumeng Shi

Graph neural networks (GNNs) have emerged as a popular strategy for handling non-Euclidean data due to their state-of-the-art performance. However, most of the current GNN model designs mainly focus on task accuracy, lacking in considering hardware resources limitation and real-time requirements of edge application scenarios. Comprehensive profiling of typic

Francesco Schiliro

This research paper proposes a framework for building a resilient cybersecurity posture that leverages prevent, detect, and respond functions and law enforcement collaboration. The Cybersecurity Resilience and Law Enforcement Collaboration (CyRLEC) Framework is designed to provide a comprehensive and integrated approach to cybersecurity that emphasizes colla

Tomoki Inoue, Hisayoshi Toyokawa

We show the existence of Lebesgue-equivalent conservative and ergodic $\sigma$-finite invariant measures for a wide class of one-dimensional random maps consisting of piecewise convex maps. We also estimate the size of invariant measures around a small neighborhood of a fixed point where the invariant density functions may diverge. Application covers random

Haoyuan Pan, Jian Feng, Tse-Tin Chan, Victor C. M. Leung

We investigate the age of information (AoI) of a relay-assisted cooperative communication system, where a source node sends status update packets to the destination node as timely as possible with the aid of a relay node. For time-slotted systems without relaying, prior works have shown that the source should generate and send a new packet to the destination

Roelien C. Timmer, Fech Scen Khoo, Megan Mark, Marcella Scoczynski Ribeiro Martins

The size of the National Aeronautics and Space Administration (NASA) Science Mission Directorate (SMD) is growing exponentially, allowing researchers to make discoveries. However, making discoveries is challenging and time-consuming due to the size of the data catalogs, and as many concepts and data are indirectly connected. This paper proposes a pipeline to

Ying Mo, Hongyin Tang, Jiahao Liu, Qifan Wang

Named entity recognition (NER) is an important research problem in natural language processing. There are three types of NER tasks, including flat, nested and discontinuous entity recognition. Most previous sequential labeling models are task-specific, while recent years have witnessed the rising of generative models due to the advantage of unifying all NER

Chuang Huang, Yang-yang Tan, Rui Wen, Shi Yin

The maximum entropy method (MEM) and the Gaussian process (GP) regression, which are both well-suited for the treatment of inverse problems, are used to reconstruct net-baryon number distributions based on a finite number of cumulants of the distribution. Baryon number distributions across the chiral phase transition are reconstructed. It is found that with

Ruochen Zhao, Hailin Chen, Weishi Wang, Fangkai Jiao

As Large Language Models (LLMs) become popular, there emerged an important trend of using multimodality to augment the LLMs' generation ability, which enables LLMs to better interact with the world. However, there lacks a unified perception of at which stage and how to incorporate different modalities. In this survey, we review methods that assist and augmen

Tilahun M. Getu, Georges Kaddoum, Mehdi Bennis

Semantic communication (SemCom) aims to convey the meaning behind a transmitted message by transmitting only semantically-relevant information. This semantic-centric design helps to minimize power usage, bandwidth consumption, and transmission delay. SemCom and goal-oriented SemCom (or effectiveness-level SemCom) are therefore promising enablers of 6G and de

Ajaykrishnan E S, Soumen Maity, Abhishek Sahu, Saket Saurabh

A knot $K$ in a directed graph $D$ is a strongly connected component of size at least two such that there is no arc $(u,v)$ with $u \in V(K)$ and $v\notin V(K)$. Given a directed graph $D=(V,E)$, we study Knot-Free Vertex Deletion (KFVD), where the goal is to remove the minimum number of vertices such that the resulting graph contains no knots. This problem

Shiyu Xu, Tianyuan Liu, Michael Wong, Dana Kulić

We propose a robotic manipulation system that can pivot objects on a surface using vision, wrist force and tactile sensing. We aim to control the rotation of an object around the grip point of a parallel gripper by allowing rotational slip, while maintaining a desired wrist force profile. Our approach runs an end-effector position controller and a gripper wi

Han Xu, Xiaoyan Zhang

In this paper, we study the boundedness, compactness and Schatten class membership of composition operators on the weighted $L^{p}$-space of a tree $L^{p}_{\lambda}(T)$ with $1\leq p <\infty$.

Xingchen Li, Jun Xiao, Guikun Chen, Yinfu Feng

Today's scene graph generation (SGG) models typically require abundant manual annotations to learn new predicate types. Therefore, it is difficult to apply them to real-world applications with massive uncommon predicate categories whose annotations are hard to collect. In this paper, we focus on Few-Shot SGG (FSSGG), which encourages SGG models to be able to

Isaac J. Arnquist, Maria Laura di Vacri, Nicole Rocco, Richard Saldanha

Flexible printed cables and circuitry based on copper-polyimide materials are widely used in experiments looking for rare events due to their unique electrical and mechanical characteristics. However, past studies have found copper-polyimide flexible cables to contain 400-4700 pg $^{238}$U/g, 16-3700 pg $^{232}$Th/g, and 170-2100 ng $^{nat}$K/g, which can be

Akifumi Chitose, Masahiro Ibe

We consider a model of dark photon which appears as a result of the successive symmetry breaking SU(2)$\,\to\,$U(1)$\,\to \mathbb{Z}_2$, where various types of topological defects appear in the dark sector. In this paper, we study the interactions between QED charges and the dark topological defects through mixing between QED photon and dark photon. In parti

Seiseki Akibue, Go Kato, Seiichiro Tani

When preparing a pure state with a quantum circuit, there is an unavoidable approximation error due to the compilation error in fault-tolerant implementation. A recently proposed approach called probabilistic state synthesis, where the circuit is probabilistically sampled, is able to reduce the approximation error compared to conventional deterministic synth

Yuan Cheng, Ruiquan Huang, Jing Yang, Yingbin Liang

In reward-free reinforcement learning (RL), an agent explores the environment first without any reward information, in order to achieve certain learning goals afterwards for any given reward. In this paper we focus on reward-free RL under low-rank MDP models, in which both the representation and linear weight vectors are unknown. Although various algorithms

Jiawei Zuo, Jing Bai, Shinhyuk Choi, Ali Basiri

Polarimetric imaging has a wide range of applications for uncovering features invisible to human eyes and conventional imaging sensors. Compact, fast, cost-effective and accurate full-Stokes polarimetric imaging sensors are highly desirable in many applications, which, however, remain elusive due to fundamental material limitations. Here we present a Metasur

Wenlong Wang, Thomas Pfeiffer

Information that is of relevance for decision-making is often distributed, and held by self-interested agents. Decision markets are well-suited mechanisms to elicit such information and aggregate it into conditional forecasts that can be used for decision-making. However, for incentive-compatible elicitation, decision markets rely on stochastic decision rule

Yongyi Su, Xun Xu, Tianrui Li, Kui Jia

Deploying models on target domain data subject to distribution shift requires adaptation. Test-time training (TTT) emerges as a solution to this adaptation under a realistic scenario where access to full source domain data is not available, and instant inference on the target domain is required. Despite many efforts into TTT, there is a confusion over the ex

Ju Gao, Fang Shen

The wave spin of an electron can be fully characterized by the current density calculated from the exact four-spinor solution of the Dirac equation. In the excited states of the electron in a magnetic field-free quantum well, the current density has a multiple vortex topology. The interaction of the current with a magnetic potential produces a finer structur

Soham Mehta, Anderson Rogers, Thomas Krendl Gilbert

AI documentation is a rapidly-growing channel for coordinating the design of AI technologies with policies for transparency and accessibility. Calls to standardize and enact documentation of algorithmic harms and impacts are now commonplace. However, documentation standards for AI remain inchoate, and fail to match the capabilities and social effects of incr

Neha Elizabeth Thomas, K Vishnu Namboothiri

Using combinatorial techniques, we derive a recurrence identity that expresses an exponential power sum with negative powers in terms of another exponential power sum with positive powers. Consequently, we derive a formula for the power sum of the first $k$ natural numbers when the power is odd, which when used in combination with Faulhaber's formula for com

Jinlai Zhu, Jianfeng Huang, Lihua Yang, Qia Li

We consider a class of nonsmooth and nonconvex optimization problems over the Stiefel manifold where the objective function is the summation of a nonconvex smooth function and a nonsmooth Lipschitz continuous convex function composed with an linear mapping. We propose three numerical algorithms for solving this problem, by combining smoothing methods and som

Shaohu Lei, Shiyu Liu, Weiqing Wang, Zhigang Bu

According to the conservation of angular momentum, when a plane-wave polarized photon splits into a pair of electron-positron under the influence of the Coulomb field, the spin angular momentum (SAM) of the photon is converted into the angular momentum of the leptons. We investigate this process (the Bethe-Heitler process) by describing the final electron an

Mahmood Mazarei, Jan Astrom, Jan Westerlund, Mikko Karttunen

Tissue growth kinetics and interface dynamics depend on the properties of the tissue environment and cell-cell interactions. In cellular environments, substrate heterogeneity and geometry arise from a variety factors, such as the structure of the extracellular matrix and nutrient concentration. We used the CellSim3D model, a kinetic division simulator, to in

Wei Zhang, Bowen Ma, Feng Qiu, Yu Ding

Human affective behavior analysis focuses on analyzing human expressions or other behaviors to enhance the understanding of human psychology. The CVPR 2023 Competition on Affective Behavior Analysis in-the-wild (ABAW) is dedicated to providing high-quality and large-scale Aff-wild2 for the recognition of commonly used emotion representations, such as Action

Xinyan Zu, Haiyang Yu, Bin Li, Xiangyang Xue

Text segmentation is a challenging vision task with many downstream applications. Current text segmentation methods require pixel-level annotations, which are expensive in the cost of human labor and limited in application scenarios. In this paper, we take the first attempt to perform weakly-supervised text instance segmentation by bridging text recognition

Fiona H. Panther, Paul D. Lasky

Gravitational waves from binary neutron star post-merger remnants have the potential to uncover the physics of the hot nuclear equation of state. These gravitational-wave signals are high frequency ($\sim$ kHz) and short lived ($\mathcal{O}(10\,\mathrm{ms})$), which introduces potential problems for data-analysis algorithms due to the presence of non-station

Rui Zhang, Sha Liu, Jianfeng Chen, Chengwen Zhong

A computationally accurate and efficient numerical method under a unified framework is crucial to various multi-scale scientific and engineering problems. So far, many numerical methods have encountered various challenges in efficiently solving multi-scale non-equilibrium flows that cover a wide range of Knudsen numbers, especially the three-dimensional hype

Xiaozhe Ren, Pingyi Zhou, Xinfan Meng, Xinjing Huang

The scaling of large language models has greatly improved natural language understanding, generation, and reasoning. In this work, we develop a system that trained a trillion-parameter language model on a cluster of Ascend 910 AI processors and MindSpore framework, and present the language model with 1.085T parameters named PanGu-{\Sigma}. With parameter inh

Joao Otavio Chervinski, Diego Kreutz, Xiwei Xu, Jiangshan Yu

With the increasing demand for communication between blockchains, improving the performance of cross-chain communication protocols becomes an emerging challenge. We take a first step towards analyzing the limitations of cross-chain communication protocols by comprehensively evaluating Cosmos Network's Inter-Blockchain Communication Protocol. To achieve our g

Rong Ma, Krishnan Mahesh

The influence of roughness spacing on boundary layer transition over distributed roughness elements is studied using direct numerical simulation (DNS) and global stability analysis, and compared to isolated roughness elements at the same Reh. Small spanwise spacing ($\lambda_z = 2.5h$) inhibits the formation of counter-rotating vortices (CVP) and as a result

Ye Xiong

In disordered systems, the amplitudes of the localized states will decrease exponentially away from their centers and the localization lengths are characterizing such decreasing. In this article, we find a model in which each eigenstate is decreasing at two distinct rates. The model is a one-dimensional disordered system with a constrained Hilbert space: all

Kengo Kikuchi, Kenji Nishiwaki, Kin-ya Oda

The gauge-invariant two-point function of the Higgs field at the same spacetime point can make a natural gauge-invariant order parameter for spontaneous gauge symmetry breaking. However, this composite operator is ultraviolet divergent and is not well defined. We propose using a gradient flow to cure the divergence from putting the fields at the same spaceti

Wenhang Ge, Tao Hu, Haoyu Zhao, Shu Liu

Neural implicit surface learning has shown significant progress in multi-view 3D reconstruction, where an object is represented by multilayer perceptrons that provide continuous implicit surface representation and view-dependent radiance. However, current methods often fail to accurately reconstruct reflective surfaces, leading to severe ambiguity. To overco

Ye Wang, Bowei Jiang, Changqing Zou, Rui Ma

Multifold observations are common for different data modalities, e.g., a 3D shape can be represented by multi-view images and an image can be described with different captions. Existing cross-modal contrastive representation learning (XM-CLR) methods such as CLIP are not fully suitable for multifold data as they only consider one positive pair and treat othe

Alan Lewis, Tim Miller

This paper investigates deceptive reinforcement learning for privacy preservation in model-free and continuous action space domains. In reinforcement learning, the reward function defines the agent's objective. In adversarial scenarios, an agent may need to both maximise rewards and keep its reward function private from observers. Recent research presented t

Weizhao Jin, Yuhang Yao, Shanshan Han, Jiajun Gu

Federated Learning trains machine learning models on distributed devices by aggregating local model updates instead of local data. However, privacy concerns arise as the aggregated local models on the server may reveal sensitive personal information by inversion attacks. Privacy-preserving methods, such as homomorphic encryption (HE), then become necessary f

Na Hyun Jo, Omar A. Ashour, Zhixue Shu, Chris Jozwiak

Topological insulators are characterized by spin-momentum-locked massless surface states which are robust under various perturbations. Manipulating such surface states is a topic of vigorous research, as a possible route for the realization of emergent many-body physics in topological systems. Thus far, time-reversal symmetry breaking via Coulomb and magneti

Xinyu Li

This study rigorously investigates the Keynesian cross model of a national economy with a focus on the dynamic relationship between government spending and economic equilibrium. The model consists of two ordinary differential equations regarding the rate of change of national income and the rate of consumer spending. Three dynamic relationships between natio

Jindong Jiang, Fei Deng, Gautam Singh, Sungjin Ahn

The recent success of transformer-based image generative models in object-centric learning highlights the importance of powerful image generators for handling complex scenes. However, despite the high expressiveness of diffusion models in image generation, their integration into object-centric learning remains largely unexplored in this domain. In this paper

Shudi Yang, Tonghui Zhang, Zheng-An Yao

Linear codes are the most important family of codes in cryptography and coding theory. Some codes have only a few weights and are widely used in many areas, such as authentication codes, secret sharing schemes and strongly regular graphs. By setting $ p\equiv 1 \pmod 4 $, we construct an infinite family of linear codes using two distinct weakly regular unbal

Youjia Zhang, Pingzhong Tang

This paper studies a house allocation problem in a networked housing market, where agents can invite others to join the system in order to enrich their options. Top Trading Cycle is a well-known matching mechanism that achieves a set of desirable properties in a market without invitations. However, under a tree-structured networked market, existing agents ha

Ned Cooper

This position paper encourages the Human-Computer Interaction (HCI) community to focus on designing deliberative processes to inform and coordinate technology and policy design for large language models (LLMs) -- a `societal-scale technology'. First, I propose a definition for societal-scale technology and locate LLMs within this definition. Next, I argue th

Xin Meng, Shuguan Ji

This paper concerns the existence of positive ground state solutions for generalized quasilinear Schr\"odinger equations in $\mathbb{R}^N$ with critical growth which arise from plasma physics, as well as high-power ultrashort laser in matter. By applying a variable replacement, the quasilinear problem reduces to a semilinear problem which the associated func

Bruce A. Corliss, Yaotian Wang, Francis P. Driscoll, Heman Shakeri

We propose a fold change visualization that demonstrates a combination of properties from log and linear plots of fold change. A useful fold change visualization can exhibit: (1) readability, where fold change values are recoverable from datapoint position; (2) proportionality, where fold change values of the same direction are proportionally distant from th

Liang Zhang, Yutong Zhang, Jianming Deng, Chen Li

Reinforcement learning (RL) has emerged as a promising solution for addressing traffic signal control (TSC) challenges. While most RL-based TSC systems typically employ an online approach, facilitating frequent active interaction with the environment, learning such strategies in the real world is impractical due to safety and risk concerns. To tackle these c

David Noever, Samantha Elizabeth Miller Noever

The AI community has embraced multi-sensory or multi-modal approaches to advance this generation of AI models to resemble expected intelligent understanding. Combining language and imagery represents a familiar method for specific tasks like image captioning or generation from descriptions. This paper compares these monolithic approaches to a lightweight and

Philip D. Mannheim

Second-order-derivative plus fourth-order-derivative gravity is the ultraviolet completion of second-order-derivative quantum Einstein gravity. While it achieves renormalizability through states of negative Dirac norm, the unitarity violation that this would entail can be postponed to Planck energies. As we show in this paper the theory has a different probl

Jiawen Zhu, Simiao Lai, Xin Chen, Dong Wang

Visible-modal object tracking gives rise to a series of downstream multi-modal tracking tributaries. To inherit the powerful representations of the foundation model, a natural modus operandi for multi-modal tracking is full fine-tuning on the RGB-based parameters. Albeit effective, this manner is not optimal due to the scarcity of downstream data and poor tr

Weitang Li, Jonathan Allcock, Lixue Cheng, Shi-Xin Zhang

TenCirChem is an open-source Python library for simulating variational quantum algorithms for quantum computational chemistry. TenCirChem shows high performance on the simulation of unitary coupled-cluster circuits, using compact representations of quantum states and excitation operators. Additionally, TenCirChem supports noisy circuit simulation and provide

Minkyu Jeon, Hyeonjin Park, Hyunwoo J. Kim, Michael Morley

The application of modern machine learning to retinal image analyses offers valuable insights into a broad range of human health conditions beyond ophthalmic diseases. Additionally, data sharing is key to fully realizing the potential of machine learning models by providing a rich and diverse collection of training data. However, the personally-identifying n

Yuwei Wu, Zhe Zhang, Xiaolan Qiu, Yao Zhao

repetition frequency (PRF). Given the system complexity and resource constraints, it is often difficult to achieve high imaging performance and low ambiguity without compromising the swath. In this paper, we propose a joint optimization framework for sparse strip SAR imaging algorithms and azimuth undersampling patterns based on a deep convolutional neural n

Ahmet A. Husainov

This paper considers non-Abelian homology groups of a group diagram introduced as homotopy groups of a simplicial change. We prove a theorem stating that the non-Abelian homology groups of a group diagram are isomorphic to the homotopy groups of the homotopy colimit of a classifying space diagram, with the dimension shifted by 1. Bousfield and Kan proved an

Marcus D. Liebenthal, Nam Vu, A. Eugene DePrince

Cavity quantum electrodynamics (QED) generalizations of time-dependent (TD) density functional theory (DFT) and equation-of-motion (EOM) coupled-cluster (CC) theory are used to model small molecules strongly coupled to optical cavity modes. We consider two types of calculations. In the first approach (termed "relaxed"), we use a coherent-state-transformed Ha

Shogo Sato, Yasuhiro Yao, Taiga Yoshida, Takuhiro Kaneko

Intrinsic image decomposition (IID) is the task that decomposes a natural image into albedo and shade. While IID is typically solved through supervised learning methods, it is not ideal due to the difficulty in observing ground truth albedo and shade in general scenes. Conversely, unsupervised learning methods are currently underperforming supervised learnin

Ivan Junier, Elham Ghobadpour, Olivier Espeli, Ralf Everaers

DNA supercoiling is central to many fundamental processes of living organisms. Its average level along the chromosome and over time reflects the dynamic equilibrium of opposite activities of topoisomerases, which are required to relax mechanical stresses that are inevitably produced during DNA replication and gene transcription. Supercoiling affects all scal

Robin Cockett, Amolak Ratan Kalra, Priyaa Varshinee Srinivasan

Star to mesh transformations are well-known in electrical engineering, and are reminiscent of local complementation for graph states in qudit stabilizer quantum mechanics. This paper describes a rewriting system for resistor circuits over any positive division rig using general star to mesh transformations. We show how these transformations can be organized

Darryl Biggar

In regulatory proceedings, few issues are more hotly debated than the cost of capital. This article formalises the theoretical foundation of cost of capital estimation for regulatory purposes. Several common regulatory practices lack a solid foundation in the theory. For example, the common practice of estimating a single cost of capital for the regulated fi

De-Fu Bu, Liang Chen, Guobin Mou, Erlin Qiao

Winds can be launched in tidal disruption event (TDE). It has been proposed that the winds can interact with the cloud surrounding the black hole, produce bow shocks, accelerate electrons, and produce radio emission. We restudy the wind-cloud interaction model. We employ the properties of winds found by the radiation hydrodynamic simulations of super-Eddingt

Xinhang Li, Xiangyu Zhao, Jiaxing Xu, Yong Zhang

Link prediction aims to identify potential missing triples in knowledge graphs. To get better results, some recent studies have introduced multimodal information to link prediction. However, these methods utilize multimodal information separately and neglect the complicated interaction between different modalities. In this paper, we aim at better modeling th

Arkajit Mandal, Ding Xu, Ankit Mahajan, Joonho Lee

We develop a microscopic theory for the multimode polariton dispersion in materials coupled to cavity radiation modes. Starting from a microscopic light-matter Hamiltonian, we devise a general strategy for obtaining simple matrix models of polariton dispersion curves based on the structure and spatial location of multi-layered 2D materials inside the optical

Nate Harman, Andrew Snowden, Noah Snyder

Let $G$ (resp. $H$) be the group of orientation preserving self-homeomorphisms of the unit circle (resp. real line). In previous work, the first two authors constructed pre-Tannakian categories $\underline{\mathrm{Rep}}(G)$ and $\underline{\mathrm{Rep}}(H)$ associated to these groups. In the predecessor to this paper, we analyzed the category $\underline{\ma

Kensuke Arakawa

We provide details of the proof of Lurie's theorem on operadic Kan extensions. Along the way, we generalize the construction of monoidal envelopes of $\infty$-operads to families of $\infty$-operads and use it to construct the fiberwise direct sum functor, both of which we characterize by certain universal properties. Aside from their uses in the proof of Lu

Farhad Aghili

This paper presents a method for guiding a robot manipulator to capture and bring a tumbling satellite to a state of rest. The proposed approach includes developing a coordination control for the combined system of the space robot and the target satellite, where the satellite acts as the manipulator payload. This control ensures that the robot tracks the opt

Hülya Argüz

We review how log Gromov--Witten invariants of toric varieties can be used to express quiver Donaldson--Thomas invariants in terms of the simpler attractor Donaldson--Thomas invariants. This is an exposition of joint work with Pierrick Bousseau.

Honghong Wu, Jiansen He, Shiyong Huang, Liping Yang

The scaling of magnetic fluctuations provides crucial information for the understanding of solar wind turbulence. However, the observed magnetic fluctuations contain not only turbulence but also magnetic structures, leading to the violation of the time stationarity. This violation would conceal the true scaling and influence the determination of the sampling

Melissa A. Adrian, Bruce P. Ayati, Ashutosh K. Mangalam

The human gut microbiota is a complex ecosystem that affects a range of human physiology. In order to explore the dynamics of the human gut microbiota, we used a system of ordinary differential equations to model mathematically the biomass of three microorganism populations: $\textit{Bacteroides thetaiotaomicron}$, $\textit{Eubacterium rectale}$, and $\texti

James F. O'Brien, Jessica K. Hodgins

We have developed a simulation technique that uses non-linear finite element analysis and elastic fracture mechanics to compute physically plausible motion for three-dimensional, solid objects as they break, crack, or tear. When these objects deform beyond their mechanical limits, the system automatically determines where fractures should begin and in what d