March 2023 arXiv papers — page 127

Manzoor Hussain, Nazakat Ali, Jang-Eui Hong

Collaborative Cyber-Physical Systems (CCPS) are systems that contain tightly coupled physical and cyber components, massively interconnected subsystems, and collaborate to achieve a common goal. The safety of a single Cyber-Physical System (CPS) can be achieved by following the safety standards such as ISO 26262 and IEC 61508 or by applying hazard analysis t

Naoki Yoshida, Sumio Watanabe

Tensor decomposition is now being used for data analysis, information compression, and knowledge recovery. However, the mathematical property of tensor decomposition is not yet fully clarified because it is one of singular learning machines. In this paper, we give the upper bound of its real log canonical threshold (RLCT) of the tensor decomposition by using

Zipeng Lin, Zhenguo Nie

In this paper, we propose an approach to address the problem of classifying 3D industrial components by introducing a novel framework named IC-classifier (Industrial Component classifier). Our framework is designed to focus on the object's local and global structures, emphasizing the former by incorporating specific local features for embedding the model. By

Thao Le, Tim Miller, Ronal Singh, Liz Sonenberg

Displaying confidence scores in human-AI interaction has been shown to help build trust between humans and AI systems. However, most existing research uses only the confidence score as a form of communication. As confidence scores are just another model output, users may want to understand why the algorithm is confident to determine whether to accept the con

Sameera Ramasinghe, Hemanth Saratchandran, Violetta Shevchenko, Simon Lucey

Modelling dynamical systems is an integral component for understanding the natural world. To this end, neural networks are becoming an increasingly popular candidate owing to their ability to learn complex functions from large amounts of data. Despite this recent progress, there has not been an adequate discussion on the architectural regularization that neu

Binghao Sun, Huiling Li, Quanyin Li, Hui Liang

Muon scattering tomography is a non-destructive technique used to image different materials by utilizing natural cosmic ray muons. Typically it requires position-sensitive detectors with a sub-millimeter resolution to effectively distinguish high-$Z$ materials in a compact system. The plastic scintillating fiber detector is a feasible candidate and is curren

Tetsuya Ito

We show that a group whose generalized torsion elements are torsion elements (which we call a $TR^{*}$-group) is torsion-by-$R^{*}$ group, an extension of torsion group by a group without generalized torsion elements. We also discuss a generalized torsion group, a group all of whose non-trivial elements are generalized torsion elements.

Jiangbin Zheng, Yile Wang, Cheng Tan, Siyuan Li

Sign language recognition (SLR) is a weakly supervised task that annotates sign videos as textual glosses. Recent studies show that insufficient training caused by the lack of large-scale available sign datasets becomes the main bottleneck for SLR. Most SLR works thereby adopt pretrained visual modules and develop two mainstream solutions. The multi-stream a

Anna Leontjeva, Genevieve Richards, Kaavya Sriskandaraja, Jessica Perchman

Since introducing changes to the New Payments Platform (NPP) to include longer messages as payment descriptions, it has been identified that people are now using it for communication, and in some cases, the system was being used as a targeted form of domestic and family violence. This type of tech-assisted abuse poses new challenges in terms of identificatio

Xingyi Li, Zhiguo Cao, Huiqiang Sun, Jianming Zhang

We present 3D Cinemagraphy, a new technique that marries 2D image animation with 3D photography. Given a single still image as input, our goal is to generate a video that contains both visual content animation and camera motion. We empirically find that naively combining existing 2D image animation and 3D photography methods leads to obvious artifacts or inc

Fernanda Couto, Diego Amaro Ferraz, Sulamita Klein

A split graph is a graph whose vertex set can be partitioned into a clique and an independent set. A connected graph $G$ is said to be $t$-admissible if admits a special spanning tree in which the distance between any two adjacent vertices is at most $t$. Given a graph $G$, determining the smallest $t$ for which $G$ is $t$-admissible, i.e. the stretch index

Akhileswar Chowdary, Ahmad Bazzi, Marwa Chafii

The following paper introduces a novel integrated sensing and communication (ISAC) scenario termed hybrid radar fusion. In this setting, the dual-functional radar and communications (DFRC) base station (BS) acts as a mono-static radar in the downlink (DL), for sensing purposes, while performing its DL communication tasks. Meanwhile, the communication users a

Yuanhui Sun, Maosheng Miao

Metal superhydrides, known for their high hydrogen content and polyhedral hydrogen cages, are promising candidates for high-temperature superconductivity. Recent research has emphasized "chemical pre-compression," enabling hydrogen metallization at lower pressures and facilitating the discovery of superconductors with near-room temperature transitions. Despi

D. D. Pawar, S. D. Patil, V. J. Dagwal

We have explored cold dark matter and holographic dark energy cosmological models with big rip singularity. To obtain the solution of the field equation, we have supposed that scalar expansion $\theta$ is proportional to shear scalar $\sigma ^2 $ which leads to $p_1=(p_2) ^n $, where $A_1 $, $A_2$ are metric potentials and $ n$ is constant. Big bang and Big

Boheng Zeng, LianLi Gao, QiLong Zhang, ChaoQun Li

Due to the gap between a substitute model and a victim model, the gradient-based noise generated from a substitute model may have low transferability for a victim model since their gradients are different. Inspired by the fact that the decision boundaries of different models do not differ much, we conduct experiments and discover that the gradients of differ

Gilhan Kim, Hojun Lee, Junghyo Jo, Yongjoo Baek

Finding the optimal model complexity that minimizes the generalization error (GE) is a key issue of machine learning. For the conventional supervised learning, this task typically involves the bias-variance tradeoff: lowering the bias by making the model more complex entails an increase in the variance. Meanwhile, little has been studied about whether the sa

Zhongxu Zhai, Will J. Percival, Zhejie Ding

The source of the tension between local SN Ia based Hubble constant measurements and those from the CMB or BAO+BBN measurements is one of the most interesting unknowns of modern cosmology. Sample variance forms a key component of the error on the local measurements, and will dominate the error budget in the future as more SNe Ia are observed. Many methods ha

A. Chainani, M. Horio, C. -M. Cheng, D. Malterre

We study the electronic structure of electron-doped Pr$_{1.3-x}$La$_{0.7}$Ce$_{x}$CuO$_{4}$ (PLCCO ; $T_{c}$ = 27 K, x = 0.1) and hole-doped Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$ (Bi2212 ; $T_{c}$ = 90 K) cuprate superconductors using x-ray absorption spectroscopy (XAS) and resonant photoemission spectroscopy (Res-PES). From Res-PES across the O K-edge and Cu L

Seungmin Jeon, Kwang Pyo Choi, Youngo Park, Chang-Su Kim

Trit-plane coding enables deep progressive image compression, but it cannot use autoregressive context models. In this paper, we propose the context-based trit-plane coding (CTC) algorithm to achieve progressive compression more compactly. First, we develop the context-based rate reduction module to estimate trit probabilities of latent elements accurately a

Zhiyan Ding, Lin Lin

We introduce a multi-modal, multi-level quantum complex exponential least squares (MM-QCELS) method to simultaneously estimate multiple eigenvalues of a quantum Hamiltonian on early fault-tolerant quantum computers. Our theoretical analysis demonstrates that the algorithm exhibits Heisenberg-limited scaling in terms of circuit depth and total cost. Notably,

Pieter Vandenberge, Jay Gopalakrishnan, Jacob Grosek

A prime objective of modeling optical fibers is capturing mode confinement losses correctly. This paper demonstrates that specific modeling choices, especially regarding the outer fiber cladding regions and the placement of the computational boundary, have significant impacts on the calculated mode losses. Our results illustrate that one can obtain disparate

Eduardo de la Fuente, Iván Toledano-Juárez, Kazumasa Kawata, Miguel A. Trinidad

PeVatrons are the most powerful naturally occurring particle accelerators in the Universe. The identification of counterparts associated to astrophysical objects such as dying massive stars, molecular gas, star-forming regions, and star clusters is essential to clarify the underlying nature of the PeV emission, i.e., hadronic or leptonic. We present $^{12,13

Lokesh Krishna, Quan Nguyen

In this paper, we propose a novel framework for synthesizing a single multimodal control policy capable of generating diverse behaviors (or modes) and emergent inherent transition maneuvers for bipedal locomotion. In our method, we first learn efficient latent encodings for each behavior by training an autoencoder from a dataset of rough reference motions. T

Xinyi Zhang, Zhuo Chang, Hong Wu, Yang Li

Recently using machine learning (ML) based techniques to optimize modern database management systems has attracted intensive interest from both industry and academia. With an objective to tune a specific component of a DBMS (e.g., index selection, knobs tuning), the ML-based tuning agents have shown to be able to find better configurations than experienced d

Yao Qiao, Kaiwen Guo, Marco Salviato

This work investigated how the structure size affects the quasi-static and fatigue behaviors of graphene polymer nanocomposites, a topic that has been often overlooked. The results showed that both quasi-static and fatigue failure of these materials scale nonlinearly with the structure size due to the presence of a significant Fracture Process Zone (FPZ) ahe

Juan Song, Zhilei Liu

Facial action unit (AU) detection is a challenging task due to the scarcity of manual annotations. Recent works on AU detection with self-supervised learning have emerged to address this problem, aiming to learn meaningful AU representations from numerous unlabeled data. However, most existing AU detection works with self-supervised learning utilize global f

Jiaqi Xu, Bo Liu, Yunkuo Chen, Mengli Cheng

Video-and-language understanding has a variety of applications in the industry, such as video question answering, text-video retrieval, and multi-label classification. Existing video-and-language understanding methods generally adopt heavy multi-modal encoders and feature fusion modules, which consume high computational costs. Specially, they have difficulty

Hiroko Arai, Hiroshi Imamura

Voltage controlled magnetoresistive random access memory (VC MRAM) is a promising candidate for a future low-power high-density memory. The main causes of bit errors in VC MRAM are write error and retention error. As the size of the memory cell decreases, the data retention time decreases, which causes a transition from the write-error-dominant region to the

Jiakang Han, Bin Hu, Shamik Ghosh, Siyu Li

Cosmic microwave background radiation (CMB) observations are unavoidably contaminated by emission from various extra-galactic foregrounds, which must be removed to obtain reliable measurements of the cosmological signal. In this paper, we demonstrate CMB lensing reconstruction in AliCPT-1 after foreground removal, combine the two bands of AliCPT-1 (90 and 15

Mojtaba Esfandiari, Yanlin Zhou, Shervin Dehghani, Muhammad Hadi

Retinal microsurgery is a high-precision surgery performed on an exceedingly delicate tissue. It now requires extensively trained and highly skilled surgeons. Given the restricted range of instrument motion in the confined intraocular space, and also potentially restricting instrument contact with the sclera, snake-like robots may prove to be a promising tec

Kaizhi Yang, Xiaoshuai Zhang, Zhiao Huang, Xuejin Chen

We present MovingParts, a NeRF-based method for dynamic scene reconstruction and part discovery. We consider motion as an important cue for identifying parts, that all particles on the same part share the common motion pattern. From the perspective of fluid simulation, existing deformation-based methods for dynamic NeRF can be seen as parameterizing the scen

Li Xiaoyue, Mao Xuerong, Song guoting

To our knowledge, the existing measure approximation theory requires the diffusion term of the stochastic delay differential equations (SDDEs) to be globally Lipschitz continuous. Our work is to develop a new explicit numerical method for SDDEs with the nonlinear diffusion term and establish the measure approximation theory. Precisely, we construct a functio

Zahra Esmaeilbeig, Arian Eamaz, Kumar Vijay Mishra, Mojtaba Soltanalian

Integrated sensing and communications (ISAC) is a spectrum-sharing paradigm that allows different users to jointly utilize and access the crowded electromagnetic spectrum. In this context, intelligent reflecting surfaces (IRSs) have lately emerged as an enabler for non-line-of-sight (NLoS) ISAC. Prior IRS-aided ISAC studies assume passive surfaces and rely o

Saemi Moon, Seunghyuk Cho, Dongwoo Kim

We tackle the problem of feature unlearning from a pre-trained image generative model: GANs and VAEs. Unlike a common unlearning task where an unlearning target is a subset of the training set, we aim to unlearn a specific feature, such as hairstyle from facial images, from the pre-trained generative models. As the target feature is only presented in a local

Yunhan Zheng, Qingyi Wang, Dingyi Zhuang, Shenhao Wang

Short-term demand forecasting for on-demand ride-hailing services is one of the fundamental issues in intelligent transportation systems. However, previous travel demand forecasting research predominantly focused on improving prediction accuracy, ignoring fairness issues such as systematic underestimations of travel demand in disadvantaged neighborhoods. Thi

Yiwei Yang, Brian Zhao, Yusheng Zheng, Pooneh Safayenikoo

CXLMemSim is a fast, lightweight simulation framework that enables performance characterization of memory systems based on Compute Express Link (CXL) .mem technology. CXL.mem allows disaggregation and pooling of memory to mitigate memory stranding (underutilized memory trapped on fully loaded servers) in cloud and datacenter environments. However, CXL-attach

Peng Hu, Peinan Zhao, Yuan Qu, Konstantin Maslov

Objective: Strain elastography and shear wave elastography are two commonly used methods to quantify cervical elasticity; however, they have limitations. Strain elastography is effective in showing tissue elasticity distribution in a single image, but the absence of stress information causes difficulty in comparing the results acquired from different imaging

Wei Dai, Siyu Liu, Craig B. Engstrom, Shekhar S. Chandra

Semantic medical image segmentation using deep learning has recently achieved high accuracy, making it appealing to clinical problems such as radiation therapy. However, the lack of high-quality semantically labelled data remains a challenge leading to model brittleness to small shifts to input data. Most works require extra data for semi-supervised learning

Ziqian Wu, Xingzhe He, Yijun Li, Cheng Yang

We present a lightweighted neural PDE representation to discover the hidden structure and predict the solution of different nonlinear PDEs. Our key idea is to leverage the prior of ``translational similarity'' of numerical PDE differential operators to drastically reduce the scale of learning model and training data. We implemented three central network comp

Xiuxiu Bai, Shuaishuai Zhao, Yao Gao, Zhe Liu

Visual long-range interaction refers to modeling dependencies between distant feature points or blocks within an image, which can significantly enhance the model's robustness. Both CNN and Transformer can establish long-range interactions through layering and patch calculations. However, the underlying mechanism of long-range interaction in visual space rema

Haitong Ma, Tianpeng Zhang, Yixuan Wu, Flavio P. Calmon

We study the multi-agent Bayesian optimization (BO) problem, where multiple agents maximize a black-box function via iterative queries. We focus on Entropy Search (ES), a sample-efficient BO algorithm that selects queries to maximize the mutual information about the maximum of the black-box function. One of the main challenges of ES is that calculating the m

Shengxuan Ding, Mohamed Abdel-Aty, Ou Zheng, Zijin Wang

The utilization of traffic conflict indicators is crucial for assessing traffic safety, especially when the crash data is unavailable. To identify traffic conflicts based on traffic flow characteristics across various traffic states, we propose a framework that utilizes unsupervised learning to automatically establish surrogate safety measures (SSM) threshol

A Bharali, B Bhattacharjya, S Borah, I J Gogoi

Let $X$ be a mixed graph and $\omega=\frac{1+\i \sqrt{3}}{2}$. We write $i\rightarrow j$, if there is an oriented edge from a vertex $v_i$ to another vertex $v_j$, and $i\sim j$ for an un-oriented edge between the vertices $v_i$ and $v_j$. The degree of a vertex $v_i$ is denoted by $d_i$. We propose the Hermitian Randi\'c matrix of second kind $R^\omeg(X)\co

Sunwoo Kim, Kyuhong Shim, Luong Trung Nguyen, Byonghyo Shim

Image text retrieval is a task to search for the proper textual descriptions of the visual world and vice versa. One challenge of this task is the vulnerability to input image and text corruptions. Such corruptions are often unobserved during the training, and degrade the retrieval model decision quality substantially. In this paper, we propose a novel image

Vandad Davoodnia, Ali Etemad

Despite the impressive performance of vision-based pose estimators, they generally fail to perform well under adverse vision conditions and often don't satisfy the privacy demands of customers. As a result, researchers have begun to study tactile sensing systems as an alternative. However, these systems suffer from noisy and ambiguous recordings. To tackle t

G. C. Anthal, J. M. Do Ó, J. Giacomoni, K. Sreenadh

This article investigates the existence and properties of ground state solutions to the following nonlocal Hamiltonian elliptic system: \begin{align*} \begin{cases} (-\Delta)^\frac12 u +V_0 u =g(v),~x\in \mathbb{R} (-\Delta)^\frac12 v +V_0 v =f(u),~x\in \mathbb{R}, \end{cases} \end{align*} where $(-\Delta)^\frac12$ is the square root Laplacian operator, $V_0

Seng-Beng Ho

The understanding of the functional aspects of objects and tools is of paramount importance in supporting an intelligent system in navigating around in the environment and interacting with various objects, structures, and systems, to help fulfil its goals. A detailed understanding of functionalities can also lead to design improvements and novel designs that

Tong Liang, Jim Davis

There is a recently discovered and intriguing phenomenon called Neural Collapse: at the terminal phase of training a deep neural network for classification, the within-class penultimate feature means and the associated classifier vectors of all flat classes collapse to the vertices of a simplex Equiangular Tight Frame (ETF). Recent work has tried to exploit

David B. Williams-Young, Norm M. Tubman, Carlos Mejuto-Zaera, Wibe A. de Jong

Many-body simulations of quantum systems is an active field of research that involves many different methods targeting various computing platforms. Many methods commonly employed, particularly coupled cluster methods, have been adapted to leverage the latest advances in modern high-performance computing.Selected configuration interaction (sCI) methods have s

Nicholas Chimitt, Xingguang Zhang, Yiheng Chi, Stanley H. Chan

A spatially varying blur kernel $h(\mathbf{x},\mathbf{u})$ is specified by an input coordinate $\mathbf{u} \in \mathbb{R}^2$ and an output coordinate $\mathbf{x} \in \mathbb{R}^2$. For computational efficiency, we sometimes write $h(\mathbf{x},\mathbf{u})$ as a linear combination of spatially invariant basis functions. The associated pixelwise coefficients,

Amir Sadikov, Xinlei Pan, Hannah Choi, Lanya T. Cai

Diffusion MRI is a non-invasive, in-vivo biomedical imaging method for mapping tissue microstructure. Applications include structural connectivity imaging of the human brain and detecting microstructural neural changes. However, acquiring high signal-to-noise ratio dMRI datasets with high angular and spatial resolution requires prohibitively long scan times,

Ding Wang, Wenwen Zhang

Estimating the composition and concentration of ambient gases is crucial for industrial gas safety. Even though other researchers have proposed some gas identification and con-centration estimation algorithms, these algorithms still suffer from severe flaws, particularly in fulfilling industry demands. One example is that the lengths of data collected in an

Man Yin Leung, Anson Ka Long Yip, Patrick Chi-Kit Cheong, Tjonnie Guang Feng Li

Highly magnetized neutron stars are promising candidates to explain some of the most peculiar astronomical phenomena, for instance, fast radio bursts, gamma-ray bursts, and superluminous supernovae. Pulsations of these highly magnetized neutron stars are also speculated to produce detectable gravitational waves. In addition, pulsations are important probes o

Marek Gagolewski, Anna Cena, Simon James, Gleb Beliakov

Agglomerative hierarchical clustering based on Ordered Weighted Averaging (OWA) operators not only generalises the single, complete, and average linkages, but also includes intercluster distances based on a few nearest or farthest neighbours, trimmed and winsorised means of pairwise point similarities, amongst many others. We explore the relationships betwee

Samuel Lichtenberg, Abiy Tasissa

Classical multidimensional scaling (CMDS) is a technique that embeds a set of objects in a Euclidean space given their pairwise Euclidean distances. The main part of CMDS involves double centering a squared distance matrix and using a truncated eigendecomposition to recover the point coordinates. In this paper, motivated by a study in Euclidean distance geom

Yifei Xin, Dongchao Yang, Yuexian Zou

In text-audio retrieval (TAR) tasks, due to the heterogeneity of contents between text and audio, the semantic information contained in the text is only similar to certain frames within the audio. Yet, existing works aggregate the entire audio without considering the text, such as mean-pooling over the frames, which is likely to encode misleading audio infor

Jialiang Tan, Yu Chen, Shuyin Jiao

Involving integrated development environments (IDEs) in introductory-level (CS1) programming courses is critical. However, it is difficult for instructors to find a suitable IDE that is beginner friendly and supports strong functionality. In this paper, we report the experience of using Visual Studio Code (VS Code) in a CS1 programming course. We describe ou

Yihao He, Juntao Ma, Zhendong Peng, Gang Wu

This paper investigates downlink power adaptation for the suborbital node in suborbital-ground communication systems, which are subject to extremely high reliability and ultra-low latency communications requirements. The problem is formulated as a power threshold-minimization problem, where interference from satellites is modeled as an accumulation of stocha

Marek Gagolewski, Anna Cena, Maciej Bartoszuk, Łukasz Brzozowski

Minimum spanning trees (MSTs) provide a convenient representation of datasets in numerous pattern recognition activities. Moreover, they are relatively fast to compute. In this paper, we quantify the extent to which they are meaningful in low-dimensional partitional data clustering tasks. By identifying the upper bounds for the agreement between the best (or

Yifei Xin, Dongchao Yang, Fan Cui, Yujun Wang

Existing weakly supervised sound event detection (WSSED) work has not explored both types of co-occurrences simultaneously, i.e., some sound events often co-occur, and their occurrences are usually accompanied by specific background sounds, so they would be inevitably entangled, causing misclassification and biased localization results with only clip-level s

Yingguang Yang, Renyu Yang, Hao Peng, Yangyang Li

Social bot detection is of paramount importance to the resilience and security of online social platforms. The state-of-the-art detection models are siloed and have largely overlooked a variety of data characteristics from multiple cross-lingual platforms. Meanwhile, the heterogeneity of data distribution and model architecture makes it intricate to devise a

Yasuhiko Asao

In this article, we give a framework for studying the Euler characteristic and its categorification of objects across several areas of geometry, topology and combinatorics. That is, the magnitude theory of filtered sets enriched categories. It is a unification of the Euler characteristic of finite categories and it the magnitude of metric spaces, both of whi

Weiqi Wang, Zihang Zhao, Ziyuan Jiao, Yixin Zhu

We present an optimization-based framework for rearranging indoor furniture to accommodate human-robot co-activities better. The rearrangement aims to afford sufficient accessible space for robot activities without compromising everyday human activities. To retain human activities, our algorithm preserves the functional relations among furniture by integrati

Shixiang Tang, Cheng Chen, Qingsong Xie, Meilin Chen

Human-centric perceptions include a variety of vision tasks, which have widespread industrial applications, including surveillance, autonomous driving, and the metaverse. It is desirable to have a general pretrain model for versatile human-centric downstream tasks. This paper forges ahead along this path from the aspects of both benchmark and pretraining met

Kento Kawaharazuka, Yoshiki Obinata, Naoaki Kanazawa, Kei Okada

In recent years, a number of models that learn the relations between vision and language from large datasets have been released. These models perform a variety of tasks, such as answering questions about images, retrieving sentences that best correspond to images, and finding regions in images that correspond to phrases. Although there are some examples, the

Roman Gorazd

This paper outlines a method to determine whether two label-regular directed trees, are isomorphic and when they are almost isomorphic. The approach involves reinterpreting label-regular directed trees as universal covers of rooted graphs. This allows us associate a unique graph with each isomorphism class of a label-regular directed tree. Additionally, by e

Yiheng Wang, Hao Zhan, Yu Peng, Zhe Lin

We investigate involutive commutative residuated lattices without unit, which are commutative residuated lattice-ordered semigroups enriched with a unary involutive negation operator. The logic of this structure is discussed and the Genzten-style sequent calculus of it is presented. Moreover, we prove the decidability of this logic.

Jinlu Li, Yanghai Yu, Weipeng Zhu

For the real-line case, it is shown that both the Camassa--Holm \cite{Guo} and Novikov equations \cite{Li-arx} are ill-posed in $B_{\infty,1}^{1}$. In this paper, by presenting a new construction of initial data which leads to the norm inflation phenomena, we prove that both the periodic Camassa--Holm and Novikov equations are also ill-posed in $B_{\infty,1}

Hongyin Luo, James Glass

Due to their similarity-based learning objectives, pretrained sentence encoders often internalize stereotypical assumptions that reflect the social biases that exist within their training corpora. In this paper, we describe several kinds of stereotypes concerning different communities that are present in popular sentence representation models, including pret

Gustavo M. Monteiro, Sriram Ganeshan

This letter explores the relationship between the coastal Kelvin mode observed in the shallow water model of ocean waves and the edge mode of a fractional quantum Hall (FQH) state. The hydrodynamic equations for the FQH state can be written as a generalized form of the shallow water equations with Coriolis force, where the density replaces the height of the

Ashish Seth, Sreyan Ghosh, S. Umesh, Dinesh Manocha

In this paper, we introduce UnFuSeD, a novel approach to leverage self-supervised learning and reduce the need for large amounts of labeled data for audio classification. Unlike prior works, which directly fine-tune a self-supervised pre-trained encoder on a target dataset, we use the encoder to generate pseudo-labels for unsupervised fine-tuning before the

Tadahiro Miyao

We demonstrate that electron-phonon interactions enhance the stability of charge density waves in low-temperature phases of many-electron systems. Our proof method involves an appropriate application of the Pirogov--Sinai theory to electron-phonon systems. Combining our findings with existing results, we obtain rigorous information regarding the low-temperat

Xiaobin Tang, Nuo Lei

In the past, the seed keywords for CPI prediction were often selected based on empirical summaries of research and literature studies, which were prone to select omitted and invalid variables. In this paper, we design a keyword expansion technique for CPI prediction based on the cutting-edge NLP model, PANGU. We improve the CPI prediction ability using the c

Wei Liu, Min Hua, Zhiyun Deng, Zonglin Meng

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focus

Huimin Zhang, Michael Weinert, Lian Li

Quantum materials, particularly Dirac materials with linearly dispersing bands, can be effectively tuned by strain-induced lattice distortions leading to a pseudo-magnetic field that strongly modulates their electronic properties. Here, we grow kagome magnet FeSn films, consisting of alternatingly stacked Sn$_2$ honeycomb (stanene) and Fe$_3$Sn kagome layers

Yoshihiro Gunji, Koji Ishiwata, Takahiro Yoshida

We study baryogenesis in a hybrid inflation model which is embedded to the minimal supersymmetric model with right-handed neutrinos. Inflation is induced by a linear combination of the right-handed sneutrinos and its decay reheats the universe. The decay products are stored in conserved numbers, which are transported under the interactions in equilibrium as

Apoorva Jain, Soumyamouli Pal, Nicholas L. Abbott, Rong Yang

The ability to synthesize shape-controlled polymer particles will benefit a wide range of applications including targeted drug delivery and metamaterials with reconfigurable structures, but existing synthesis approaches are commonly multistep and limited to a narrow size/shape range. Using a novel single-step synthesis technique, a variety of shapes includin

Xin Yang, Hong Chang, Jinwen Wang, Yan Ma

The improvement of the multi-mode capability of quantum memory can further improve the utilization efficiency of the quantum memory and reduce the requirement of quantum communication for storage units. In this letter, we experimentally investigate the multi-mode light multiplexing storage of orbital angular momentum (OAM) mode based on rubidium vapor, and d

Tong Nie, Guoyang Qin, Yunpeng Wang, Jian Sun

Traffic volume is an indispensable ingredient to provide fine-grained information for traffic management and control. However, due to limited deployment of traffic sensors, obtaining full-scale volume information is far from easy. Existing works on this topic primarily focus on improving the overall estimation accuracy of a particular method and ignore the u

Thai-Son Tang, Zhihui Liu, Ali Hosni, John Kim

The goal of radiation therapy for cancer is to deliver prescribed radiation dose to the tumor while minimizing dose to the surrounding healthy tissues. To evaluate treatment plans, the dose distribution to healthy organs is commonly summarized as dose-volume histograms (DVHs). Normal tissue complication probability (NTCP) modelling has centered around making

G. B. de Gracia

Considering the recent advances, the weak correlation between the massive Kalb-Ramond and the Proca interacting models is investigated by means of a set of complementary quantum field techniques beyond the semi-classical approach. A consistent framework to discuss the abrupt degree of freedom variation in the massless limit is established. In this manner, th

Xinyu Huang, Youcai Zhang, Jinyu Ma, Weiwei Tian

This paper presents Tag2Text, a vision language pre-training (VLP) framework, which introduces image tagging into vision-language models to guide the learning of visual-linguistic features. In contrast to prior works which utilize object tags either manually labeled or automatically detected with an off-the-shelf detector with limited performance, our approa

Hongyi Yuan, Songchi Zhou, Sheng Yu

Electronic health records (EHR) contain a wealth of biomedical information, serving as valuable resources for the development of precision medicine systems. However, privacy concerns have resulted in limited access to high-quality and large-scale EHR data for researchers, impeding progress in methodological development. Recent research has delved into synthe

Keke Wu, Michelle H Tran, Emma Petersen, Varsha Koushik

Data is everywhere but may not be accessible to everyone. Conventional data visualization tools and guidelines often do not actively consider the specific needs and abilities of people with Intellectual and Developmental Disabilities (IDD), leaving them excluded from data-driven activities and vulnerable to ethical issues. To understand the needs and challen

Thilini V. Mahanama, Abootaleb Shirvani, Svetlozar Rachev

The financial industry should be involved in mitigating the risk of downturns in the financial wellbeing indices around the world by implementing well-developed financial tools such as insurance instruments on the underlying wellbeing indices. We define a new quantitative measure of the wellbeing of a country's population for those countries using the world

Christopher Benka, Judah Goldfeder, Carl Gross, Riya Gupta

Intelligent robots must be able to perform safe and efficient motion planning in their environments. Central to modern motion planning is the configuration space. Configuration spaces define the set of configurations of a robot that result in collisions with obstacles in the workspace, $\text{C}_{\text{clsn}}$, and the set of configurations that do not, $\te

Yingxuan You, Hong Liu, Xia Li, Wenhao Li

3D human mesh recovery from a 2D pose plays an important role in various applications. However, it is hard for existing methods to simultaneously capture the multiple relations during the evolution from skeleton to mesh, including joint-joint, joint-vertex and vertex-vertex relations, which often leads to implausible results. To address this issue, we propos

Long Pan, Fei Si, Haoyu Wu

We establish the full explicit wall-crossing for K-moduli space $\overline{P}^K_c$ of degree $8$ del Pezzo pairs $(X,cC)$ where generically $X \cong \bbF_1$ and $C \sim -2K_X$. We also show K-moduli spaces $\overline{P}^K_c$ coincide with Hassett-Keel-Looijenga(HKL) models $\cF(s)$ of a $18$-dimensional locally symmetric spaces associated to the lattice $E_8

So Tanaka, Teruaki Suyama

The Kramers-Kronig relation is a well-known relation, especially in the field of optics. The key to this relation is the causality that output comes only after input. We first show that gravitational lensing obeys the causality in the sense that (electromagnetic/gravitational) waves emitted from the source arrive at an observer only after the arrival of the

Sean Patrick O'Neil, Frank C. Langbein, Edmond Jonckheere, Sophie Shermer

As shown in previous work, in some cases closed quantum systems exhibit a non-conventional trade-off in performance and robustness in the sense that controllers with the highest fidelity can also provide the best robustness to parameter uncertainty. As the dephasing induced by the interaction of the system with the environment guides the evolution to a more

Qingming Li, H. Vicky Zhao

Choice problems refer to selecting the best choices from several items, and learning users' preferences in choice problems is of great significance in understanding the decision making mechanisms and providing personalized services. Existing works typically assume that people evaluate items independently. In practice, however, users' preferences depend on th

J. J. L. R. Pinto, J. E. G. Silva, C. A. S. Almeida

We study the properties of a two-dimensional non-relativistic electron gas (TDEG) constrained on wires along a M\"{o}bius strip. We considered wires around the strip and along the transverse direction, across the width of the strip. For each direction, we investigate how the curvature modifies the electronic states and their corresponding energy spectrum. At

Haohan Wang, Liang Liu, Wuhao Zhang, Jiangning Zhang

Few-shot semantic segmentation aims to learn to segment unseen class objects with the guidance of only a few support images. Most previous methods rely on the pixel-level label of support images. In this paper, we focus on a more challenging setting, in which only the image-level labels are available. We propose a general framework to firstly generate coarse

Priyanka Das, Naveen G Venkataswamy, Laura Holsopple, Masudul H Imtiaz

The temporal stability of iris recognition performance is core to its success as a biometric modality. With the expanding horizon of applications for children, gaps in the knowledge base on the temporal stability of iris recognition performance in children have impacted decision-making during applications at the global scale. This report presents the most ex

Norio Ota, Aigen Li

Protoplanetary disk around a just born young star contains a lot of cosmic dust. especially polycyclic-aromatic-hydrocarbon (PAH), which would become basic component to create biological organics. This study classified many astronomically observed infrared spectra of protoplanetary disks to three typical spectra. Type-A show well known astronomical bands of

Bassem Tossoun, Di Liang, Stanley Cheung, Zhuoran Fang

Recently, interest in programmable photonics integrated circuits has grown as a potential hardware framework for deep neural networks, quantum computing, and field programmable arrays (FPGAs). However, these circuits are constrained by the limited tuning speed and large power consumption of the phase shifters used. In this paper, introduced for the first tim

Yide Zhang, Zhe He, Xin Tong, David C. Garrett

Quantum imaging can potentially provide certain advantages over classical imaging. Thus far, however, the signal-to-noise ratios (SNRs) are poor; the resolvable pixel counts are low; biological organisms have not been imaged; birefringence has not been quantified. Here, we introduce quantum imaging by coincidence from entanglement (ICE). Utilizing spatially

Leopoldo Mejía, Pilar Cossio, Ignacio Franco

Molecular electronics break-junction experiments are widely used to investigate fundamental physics and chemistry at the nanoscale. Reproducibility in these experiments relies on measuring conductance on thousands of freshly formed molecular junctions, yielding a broad histogram of conductance events. Experiments typically focus on the most probable conducta

Anand Subramoney

Backpropagation through time (BPTT) is the standard algorithm for training recurrent neural networks (RNNs), which requires separate simulation phases for the forward and backward passes for inference and learning, respectively. Moreover, BPTT requires storing the complete history of network states between phases, with memory consumption growing proportional

Koichi Miyamoto

The Markov chain Monte Carlo method (MCMC), especially the Metropolis-Hastings (MH) algorithm, is a widely used technique for sampling from a target probability distribution $P$ on a state space $\Omega$ and applied to various problems such as estimation of parameters in statistical models in the Bayesian approach. Quantum algorithms for MCMC have been propo