October 2022 arXiv papers — page 136

Yue Li, Yijian Wang, Hengxin Sun, Kui Liu

Strong coupling in optomechanical systems is the basic condition for observing many quantum phenomena such as optomechanical squeezing and entanglement. Normal-mode splitting (NMS) is the most evident signature of strong coupling systems. Here we show the NMS in the spectra of the movable mirror and the output field in an optomechanical system can be flexibl

Alexander Osinsky, Nikolay Brilliantov

We report a number of exact solutions for temperature-dependent Smoluchowski equations. These equations quantify the ballistic agglomeration, where the evolution of densities of agglomerates of different size is entangled with the evolution of the mean kinetic energy (partial temperatures) of such clusters. The obtained exact solutions may be used as a bench

Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Di He

Spiking neural networks (SNNs) are promising brain-inspired energy-efficient models. Recent progress in training methods has enabled successful deep SNNs on large-scale tasks with low latency. Particularly, backpropagation through time (BPTT) with surrogate gradients (SG) is popularly used to achieve high performance in a very small number of time steps. How

Jiahua Xu, Yebo Feng

Yield farming represents an immensely popular asset management activity in decentralized finance (DeFi). It involves supplying, borrowing, or staking crypto assets to earn an income in forms of transaction fees, interest, or participation rewards at different DeFi marketplaces. In this systematic survey, we present yield farming protocols as an aggregation-l

Rui Gao, Rajeev K. Jaiman

We present a rotation equivariant, quasi-monolithic graph neural network framework for the reduced-order modeling of fluid-structure interaction systems. With the aid of an arbitrary Lagrangian-Eulerian formulation, the system states are evolved temporally with two sub-networks. The movement of the mesh is reduced to the evolution of several coefficients via

Biswabibek Bandyopadhyay, Tanmoy Banerjee

Aging transition is an emergent behavior observed in networks consisting of active (self-oscillatory) and inactive (non self-oscillatory) nodes, where the network transits from a global oscillatory state to an oscillation collapsed state when the fraction of inactive oscillators surpasses a critical value. However, the aging transition in quantum domain has

Steven Y. Feng, Vivek Khetan, Bogdan Sacaleanu, Anatole Gershman

We motivate and introduce CHARD: Clinical Health-Aware Reasoning across Dimensions, to investigate the capability of text generation models to act as implicit clinical knowledge bases and generate free-flow textual explanations about various health-related conditions across several dimensions. We collect and present an associated dataset, CHARDat, consisting

Wenjie Zhang, Xinwen Shu, Jin-Hong Chen, Luming Sun

The fast blue optical transients (FBOTs) are a new population of extragalactic transients of unclear physical origin. A variety of mechanisms have been proposed including failed supernova explosion, shock interaction with a dense medium, young magnetar, accretion onto a compact object, and stellar tidal disruption event, but none is conclusive. Here we repor

S. Li, R. Cruz-Torres, N. Santiesteban, Z. H. Ye

When protons and neutrons (nucleons) are bound into atomic nuclei, they are close enough together to feel significant attraction, or repulsion, from the strong, short-distance part of the nucleon-nucleon interaction. These strong interactions lead to hard collisions between nucleons, generating pairs of highly-energetic nucleons referred to as short-range co

Mingqing Xiao, Shuxin Zheng, Chang Liu, Zhouchen Lin

Image rescaling is a commonly used bidirectional operation, which first downscales high-resolution images to fit various display screens or to be storage- and bandwidth-friendly, and afterward upscales the corresponding low-resolution images to recover the original resolution or the details in the zoom-in images. However, the non-injective downscaling mappin

Zhen Huang, Emanuel Gull, Lin Lin

Green's functions of fermions are described by matrix-valued Herglotz-Nevanlinna functions. Since analytic continuation is fundamentally an ill-posed problem, the causal space described by the matrix-valued Herglotz-Nevanlinna structure can be instrumental in improving the accuracy and in enhancing the robustness with respect to noise. We demonstrate a three

Bhavya Bhavya, Jinjun Xiong, Chengxiang Zhai

We propose a novel application of prompting Pre-trained Language Models (PLMs) to generate analogies and study how to design effective prompts for two task settings: generating a source concept analogous to a given target concept (aka Analogous Concept Generation or ACG), and generating an explanation of the similarity between a given pair of target concept

Zekun Li, Wenhu Chen, Shiyang Li, Hong Wang

Building dialogue systems requires a large corpus of annotated dialogues. Such datasets are usually created via crowdsourcing, which is expensive and time-consuming. In this paper, we propose \textsc{Dialogic}, a novel dialogue simulation method based on large language model in-context learning to automate dataset creation. Seeded with a few annotated dialog

Unni V. S., Pravin Nair, Kunal N. Chaudhury

In multiband fusion, an image with a high spatial and low spectral resolution is combined with an image with a low spatial but high spectral resolution to produce a single multiband image having high spatial and spectral resolutions. This comes up in remote sensing applications such as pansharpening~(MS+PAN), hyperspectral sharpening~(HS+PAN), and HS-MS fusi

Zijia Zhao, Longteng Guo, Xingjian He, Shuai Shao

Multimodal representation learning has shown promising improvements on various vision-language tasks. Most existing methods excel at building global-level alignment between vision and language while lacking effective fine-grained image-text interaction. In this paper, we propose a jointly masked multimodal modeling method to learn fine-grained multimodal rep

Enwei Zhu, Yiyang Liu, Jinpeng Li

Span-based models are one of the most straightforward methods for named entity recognition (NER). Existing span-based NER systems shallowly aggregate the token representations to span representations. However, this typically results in significant ineffectiveness for long-span entities, a coupling between the representations of overlapping spans, and ultimat

Qiuxiao Wang, Yongzhi Ren, Mingming Gu, Bowen Yu

This paper presents an experimental study on the emergence of the central recirculation zone (CRZ) in a V-shaped premixed swirling flame, using simultaneous measurement of particle image velocimetry (PIV) and CH* chemiluminescence. The results show that either increasing the Reynolds number (Re) or decreasing the equivalence ratio ({\Phi}) would facilitate t

Shichao Kan, Yixiong Liang, Min Li, Yigang Cen

A fundamental challenge in deep metric learning is the generalization capability of the feature embedding network model since the embedding network learned on training classes need to be evaluated on new test classes. To address this challenge, in this paper, we introduce a new method called coded residual transform (CRT) for deep metric learning to signific

Jun Nemoto, Takashi Kambayashi, Takashi Hoshino, Hideyuki Kawashima

This paper proposes Oze, a concurrency control protocol that handles heterogeneous workloads, including long-running update transactions. Oze explores a large scheduling space using a multi-version serialization graph to reduce false positives. Oze manages the graph in a decentralized manner to exploit many cores in modern servers. We further propose an OLTP

Aliakbar Alijani

A locally compact abelian group $G$ is called a generalized $\unicode{x00A3}$-cotosion group if $G$ contains an open $\unicode{x00A3}$-cotosion subgroup $H$ such that $G/H$ is a cotorsion group. In this paper, we determine the generalized $\unicode{x00A3}$-cotorsion LCA groups.

Andronikos Paliathanasis

We investigate the cosmological dynamics in a spatially flat Friedmann--Lema\^{\i}tre--Robertson--Walker geometry in scalar-tensor and scalar-torsion theories where the nonminimally coupled scalar field is a complex field. We derive the cosmological field equations and we make use of dimensionless variables in order to determine the stationary points and det

Shuyi Chen, Bochao Zhao, Mingjun Zhong, Wenpeng Luan

Deep learning models for non-intrusive load monitoring (NILM) tend to require a large amount of labeled data for training. However, it is difficult to generalize the trained models to unseen sites due to different load characteristics and operating patterns of appliances between data sets. For addressing such problems, self-supervised learning (SSL) is propo

Zhen Liang, Dejin Ren, Wanwei Liu, Ji Wang

Neural networks (NNs) are increasingly applied in safety-critical systems such as autonomous vehicles. However, they are fragile and are often ill-behaved. Consequently, their behaviors should undergo rigorous guarantees before deployment in practice. In this paper we propose a set-boundary reachability method to investigate the safety verification problem o

Awnon Bhowmik

Every day, millions of credit cards are swiped and transactions are carried out across the world. Due to numerous forms of unethical digital activities, users are vulnerable to credit card fraud, phishing, identity theft, etc. This paper outlines a novel block encryption algorithm involving multiple private keys and a resilient trapdoor function that ensures

Xinwei Zhang, Jianwen Jiang, Yutong Feng, Zhi-Fan Wu

Although a number of studies are devoted to novel category discovery, most of them assume a static setting where both labeled and unlabeled data are given at once for finding new categories. In this work, we focus on the application scenarios where unlabeled data are continuously fed into the category discovery system. We refer to it as the {\bf Continuous C

Malihe Heydari-Fard, Sara Ghassemi Honarvar, Mohaddese Heydari-Fard

Motivated by the fact that the universe is dominated by dark matter and dark energy, we consider rotating black holes surrounded by perfect fluid dark matter and study the accretion process in thin disk around such black holes. Here, we are interested in how the presence of dark matter affects the properties of the electromagnetic radiation emitted from a th

D. D. Pawar, D. K. Raut, W. D. Patil

Present paper deals with flat Friedmann - Robertson - Walker (FRW) model with two - fluid source in fractal cosmology. In this model one fluid represents matter content of the universe and another fluid is radiation field modeling the cosmic microwave background. To get the deterministic model, we have used the relation between pressure and density for matte

Jianbo Chen, Yangsong Zhang, Yudong Pan, Peng Xu

Steady-state visual evoked potential (SSVEP) is one of the most commonly used control signal in the brain-computer interface (BCI) systems. However, the conventional spatial filtering methods for SSVEP classification highly depend on the subject-specific calibration data. The need for the methods that can alleviate the demand for the calibration data become

S. A. Wolf, I. Meirzada, G. Haim, N. Bar-Gill

The singlet states of the negatively-charged nitrogen-vacancy centers in diamond play a key role in its optical spin control and readout. In this work, the hitherto unknown ionization energy of the singlet is measured experimentally and found to be between 1.91-2.25 eV. This is obtained by analyzing photoluminescence measurements incorporating spin control a

Yukun Zheng, Jiang Bian, Guanghao Meng, Chao Zhang

In large-scale e-commerce platforms like Taobao, it is a big challenge to retrieve products that satisfy users from billions of candidates. This has been a common concern of academia and industry. Recently, plenty of works in this domain have achieved significant improvements by enhancing embedding-based retrieval (EBR) methods, including the Multi-Grained D

Yuan Wang, Sebin Gracy, César A. Uribe, Hideaki Ishii

The paper considers continuous-time networked susceptible-infected-susceptible (SIS) diseases spreading over a population. Each agent represents a sub-population and has its own healing rate and infection rate; the state of the agent at a time instant denotes what fraction of the said sub-population is infected with the disease at the said time instant. By t

He Jia, Hong-Ming Zhu, Ue-Li Pen

The angular momentum of galaxies (galaxy spin) contains rich information about the initial condition of the Universe, yet it is challenging to efficiently measure the spin direction for the tremendous amount of galaxies that are being mapped by the ongoing and forthcoming cosmological surveys. We present a machine learning based classifier for the Z-wise vs

Rene Carmona, Claire Zeng

This paper investigates the impact of anonymous trading on the agents' strategy in an optimal execution framework. It mainly explores the specificity of order attribution on the Toronto Stock Exchange, where brokers can choose to either trade with their own identity or under a generic anonymous code that is common to all the brokers. We formulate a stochasti

Wagdi F. S. Ahmed, D. D. Pawar

ABSTRACT. The triple Shehu transform, a new generalisation of the triple Laplace transforms and triple Sumudu transform, has recently been introduced. The triple Shehu transform formulas for fractional Caputo operators were obtained in this study. The generalised integral transform was subsequently applied to solve fractional partial differential equations i

Fatih Furkan Yilmaz, Reinhard Heckel

Modern image classifiers are very accurate, but the predictions come without uncertainty estimates. Conformal predictors provide uncertainty estimates by computing a set of classes containing the correct class with a user-specified probability based on the classifier's probability estimates. To provide such sets, conformal predictors often estimate a cutoff

Wei Liu, Zhilu Lai, Kiran Bacsa, Eleni Chatzi

Accurate structural response prediction forms a main driver for structural health monitoring and control applications. This often requires the proposed model to adequately capture the underlying dynamics of complex structural systems. In this work, we utilize a learnable Extended Kalman Filter (EKF), named the Neural Extended Kalman Filter (Neural EKF) throu

Nir Avni, Itay Glazer

Given a word $w(x_{1},\ldots,x_{r})$, i.e., an element in the free group on $r$ elements, and an integer $d\geq1$, we study the characteristic polynomial of the random matrix $w(X_{1},\ldots,X_{r})$, where $X_{i}$ are Haar-random independent $d\times d$ unitary matrices. If $c_{m}(X)$ denotes the $m$-th coefficient of the characteristic polynomial of $X$, ou

Yan He, Chih-Chun Chien

We apply the Lindblad quantum master equation to two examples of one-dimensional topological systems, the Su-Schrieffer-Heeger (SSH) model and Kitaev chain, to study their particle and thermal transport. The steady-state properties are obtained by decomposing fermions into Majorana fermions and extracting their correlation functions. We focus on the particle

Peifeng Peng, Hongyi Xiong, He Li, Felix Li

The ultra-short-period (USP) planets are exoplanets with very short orbital periods ($\textit{P} < 1$ day), and TOI-1807b is one such planet recently discovered by the TESS mission where it orbits in the TOI-1807 system that is still little known nowadays. In this paper, we re-analyzed the transit light curves of TOI-1807 using the latest TESS data from Sect

Yueyue Huang, Chu-Ren Huang

This study attempts to investigate cross-strait variations on two typical synonymous loanwords in Chinese, i.e. xie2shang1 and tan2pan4, drawn on MARVS theory. Through a comparative analysis, the study found some distributional, eventual, and contextual similarities and differences across Taiwan and Mainland Mandarin. Compared with the underused tan2pan4, xi

Xiaona Fang, Lihua You, Rangwei Wu, Yufei Huang

Let $G$ be a graph of order $n$ and $\mu$ be an adjacency eigenvalue of $G$ with multiplicity $k\geq 1$. A star complement $H$ for $\mu$ in $G$ is an induced subgraph of $G$ of order $n-k$ with no eigenvalue $\mu$, and the vertex subset $X=V(G-H)$ is called a star set for $\mu$ in $G$. The study of star complements and star sets provides a strong link betwee

Jia Xu Brian Sia, Xiang Li, X. Guo, Jiawei Wang

The 2 {\mu}m waveband is an area that could have significant technological consequences, with applications ranging from spectroscopy, LIDAR and free-space communications. The development of the thulium-doped fiber amplifier, hollow-core photonic bandgap fiber and 2 {\mu}m GaSb-based diode lasers has highlighted the ability of the waveband in alleviating the

Yuzhe Ma, Yulong Wang, Yuxin Wang, Soham Manni

The combination with superconductivity and topological nontrivial band structure provides a promising route towards novel quantum states such as topological superconductivity. Here, we report the first observation of superconductivity (4.1 K) in Ba5In4Bi5 single crystal, a noncentrosymmetric topological semimetal featuring nodal chain loops at the high-symme

Ao-Xiang Zhang, Yuan-Gen Wang, Weixuan Tang, Leida Li

Video quality is a primary concern for video service providers. In recent years, the techniques of video quality assessment (VQA) based on deep convolutional neural networks (CNNs) have been developed rapidly. Although existing works attempt to introduce the knowledge of the human visual system (HVS) into VQA, there still exhibit limitations that prevent the

Tengyang Xie, Dylan J. Foster, Yu Bai, Nan Jiang

Coverage conditions -- which assert that the data logging distribution adequately covers the state space -- play a fundamental role in determining the sample complexity of offline reinforcement learning. While such conditions might seem irrelevant to online reinforcement learning at first glance, we establish a new connection by showing -- somewhat surprisin

Marian Temprana Alonso, Farhad Shirani, S. Sitharama Iyengar

Distributed estimation in the context of sensor networks is considered, where distributed agents are given a set of sensor measurements, and are tasked with estimating a target variable. A subset of sensors are assumed to be faulty. The objective is to minimize i) the mean square estimation error at each node (accuracy objective), and ii) the mean square dis

Jianxin Lin, Yongqiang Tang, Junping Wang, Wensheng Zhang

As a recent noticeable topic, domain generalization aims to learn a generalizable model on multiple source domains, which is expected to perform well on unseen test domains. Great efforts have been made to learn domain-invariant features by aligning distributions across domains. However, existing works are often designed based on some relaxed conditions whic

Haosen Yang, Deng Huang, Bin Wen, Jiannan Wu

Masked autoencoders (MAEs) have emerged recently as art self-supervised spatiotemporal representation learners. Inheriting from the image counterparts, however, existing video MAEs still focus largely on static appearance learning whilst are limited in learning dynamic temporal information hence less effective for video downstream tasks. To resolve this draw

Peng Ye, Shengji Tang, Baopu Li, Tao Chen

Residual networks have shown great success and become indispensable in today's deep models. In this work, we aim to re-investigate the training process of residual networks from a novel social psychology perspective of loafing, and further propose a new training strategy to strengthen the performance of residual networks. As residual networks can be viewed a

Yufan Zhang, Honglin Wen, Qiuwei Wu

Prediction interval (PI) is an effective tool to quantify uncertainty and usually serves as an input to downstream robust optimization. Traditional approaches focus on improving the quality of PI in the view of statistical scores and assume the improvement in quality will lead to a higher value in the power systems operation. However, such an assumption cann

Jaela Foster-Burns, Nan Miles Xi

Torsades de pointes (TdP) is an irregular heart rhythm as a side effect of drugs and may cause sudden cardiac death. A machine learning model that can accurately identify drug TdP risk is necessary. This study uses multinomial logistic regression models to predict three-class drug TdP risks based on datasets generated from rabbit ventricular wedge assay expe

Feng Liang, Bichen Wu, Xiaoliang Dai, Kunpeng Li

Open-vocabulary semantic segmentation aims to segment an image into semantic regions according to text descriptions, which may not have been seen during training. Recent two-stage methods first generate class-agnostic mask proposals and then leverage pre-trained vision-language models, e.g., CLIP, to classify masked regions. We identify the performance bottl

Aixin Sun

Recommendation has become a prominent area of research in the field of Information Retrieval (IR). Evaluation is also a traditional research topic in this community. Motivated by a few counter-intuitive observations reported in recent studies, this perspectives paper takes a fresh look at recommender systems from an evaluation standpoint. Rather than examini

Xiang Tang, Yi Wang, Dechao Zheng

For weighted Bergman spaces on the unit disk, we give trace formulas of semicommutators of Toeplitz operators with $\mathscr{C}^2(\overline{\mathbb{D}})$ symbols. We generalize this formula to weighted Bergman spaces on the unit ball in higher dimensions. Applications and examples on the Hankel operators are also discussed.

Pei Wang, Yu Chen Tang, Lei Zu, Yuan Yuan Chen

A series of dynamical anomalies in the orbits of distant trans-Neptunian objects points to a new celestial body (usually named Planet Nine) in the solar system. In this draft, we point out that a mirror planet captured from the outer solar system or formed in the solar system is also a possible candidate. The introduction of the mirror matter model is due to

Somayeh Komeylian, Christopher Paolini

To address practical challenges in establishing and maintaining robust wireless connectivity such as multi-path effects, low latency, size reduction, and high data rate, the digital beamformer is performed by the hybrid antenna array at the frequency of operation of 10 GHz. The proposed digital beamformer, as a spatial filter, is capable of performing Direct

Luis Alvarez, Chang Chiann, Pedro Morettin

This paper studies parameter estimation using L-moments, an alternative to traditional moments with attractive statistical properties. The estimation of model parameters by matching sample L-moments is known to outperform maximum likelihood estimation (MLE) in small samples from popular distributions. The choice of the number of L-moments used in estimation

He Cheng, Depeng Xu, Shuhan Yuan, Xintao Wu

Anomaly detection in sequential data has been studied for a long time because of its potential in various applications, such as detecting abnormal system behaviors from log data. Although many approaches can achieve good performance on anomalous sequence detection, how to identify the anomalous entries in sequences is still challenging due to a lack of infor

Dandan Guo, Long Tian, He Zhao, Mingyuan Zhou

Few-shot classification aims to learn a classifier to recognize unseen classes during training, where the learned model can easily become over-fitted based on the biased distribution formed by only a few training examples. A recent solution to this problem is calibrating the distribution of these few sample classes by transferring statistics from the base cl

Kuan-Wei Huang, Geoff Chih-Fan Chen, Po-Wen Chang, Sheng-Chieh Lin

Quantifying the parameters and corresponding uncertainties of hundreds of strongly lensed quasar systems holds the key to resolving one of the most important scientific questions: the Hubble constant ($H_{0}$) tension. The commonly used Markov chain Monte Carlo (MCMC) method has been too time-consuming to achieve this goal, yet recent work has shown that con

Yazhou Ren, Jingyu Pu, Zhimeng Yang, Jie Xu

Cluster analysis plays an indispensable role in machine learning and data mining. Learning a good data representation is crucial for clustering algorithms. Recently, deep clustering, which can learn clustering-friendly representations using deep neural networks, has been broadly applied in a wide range of clustering tasks. Existing surveys for deep clusterin

Siyu Lai, Zhen Yang, Fandong Meng, Yufeng Chen

Word alignment which aims to extract lexicon translation equivalents between source and target sentences, serves as a fundamental tool for natural language processing. Recent studies in this area have yielded substantial improvements by generating alignments from contextualized embeddings of the pre-trained multilingual language models. However, we find that

Hanxi Sun, Boqian Zhang, Minhyeok Kim, Vinayak Rao

Mixture models are a standard tool in statistical analyses, widely used for density modeling and model-based clustering. In this work, we propose a Bayesian mixture model with repulsion between mixture components. Such repulsion helps address the problem of overlapping or poorly separated clusters, and assists with model interpretibility and robustness. Our

Jagdeep Singh

A graph that can be generated from $K_1$ using joins and 0-sums is called a cograph. We define a sesquicograph to be a graph that can be generated from $K_1$ using joins, 0-sums, and 1-sums. We show that, like cographs, sesquicographs are closed under induced minors. Cographs are precisely the graphs that do not have the 4-vertex path as an induced subgraph.

Ningbo Fan, Jorge Íñiguez, L. Bellaiche, Bin Xu

Anomalous electrocaloric effect (ECE) with decreasing temperature upon application of an electric field is known to occur in antiferroelectrics (AFEs), and previous understanding refers to the field-induced canting of electric dipoles if there is no phase transitions. Here, we use a first-principle-based method to study the ECE in Nd-substituted BiFeO3 (BNFO

Ali Ayub, Carter Fendley

In this paper, we consider a challenging but realistic continual learning (CL) problem, Few-Shot Continual Active Learning (FoCAL), where a CL agent is provided with unlabeled data for a new or a previously learned task in each increment and the agent only has limited labeling budget available. Towards this, we build on the continual learning and active lear

Yi-Fan Zhao, Ruoxi Zhang, Jiaqi Cai, Deyi Zhuo

One-dimensional (1D) topologically protected states are usually formed at the interface between two-dimensional (2D) materials with different topological invariants. Therefore, 1D chiral interface channels (CICs) can be created at the boundary of two quantum anomalous Hall (QAH) insulators with different Chern numbers. Such a QAH junction can function as a c

Shraman Pramanick, Li Jing, Sayan Nag, Jiachen Zhu

Vision-language pre-training (VLP) has recently proven highly effective for various uni- and multi-modal downstream applications. However, most existing end-to-end VLP methods use high-resolution image-text box data to perform well on fine-grained region-level tasks, such as object detection, segmentation, and referring expression comprehension. Unfortunatel

Nobuyuki Ishibashi

Every Riemann surface with genus $g$ and $n$ punctures admits a hyperbolic metric, if $2g-2+n>0$. Such a surface can be decomposed into pairs of pants whose boundaries are geodesics. We construct a string field theory for closed bosonic strings based on this pants decomposition. In order to do so, we derive a recursion relation satisfied by the off-shell amp

Pierre Chambon, Christian Bluethgen, Curtis P. Langlotz, Akshay Chaudhari

Multi-modal foundation models are typically trained on millions of pairs of natural images and text captions, frequently obtained through web-crawling approaches. Although such models depict excellent generative capabilities, they do not typically generalize well to specific domains such as medical images that have fundamentally shifted distributions compare

Jing Bi, Vorapong Suppakitpaisarn

This study explores the robustness of learning by symmetric loss on private data. Specifically, we leverage exponential mechanism (EM) on private labels. First, we theoretically re-discussed properties of EM when it is used for private learning with symmetric loss. Then, we propose numerical guidance of privacy budgets corresponding to different data scales

Junchao Shentu, Chen Zhao

We generalize Koll\'ar's conjecture (including torsion freeness, injectivity theorem, vanishing theorem and decomposition theorem) to Saito's $S$-sheaves twisted by a $\mathbb{Q}$-divisor. This gives a uniform treatment for various kinds of Koll\'ar's package in different topics in complex geometry. As a consequence we prove Koll\'ar's package of pluricanoni

Chen Zhao

In this paper, we provide an $L^2$ fine resolution of the prolongation of a nilpotent harmonic bundle in the sense of Simpson-Mochizuki (an analytic analogue of the Kashiwara-Malgrange filtrations). This is the logarithmic analogue of Cattani-Kaplan-Schmid's and Kashiwara-Kawai's results on the $L^2$ interpretation of the intersection complex. As an applicat

Gui-Qiang G. Chen, Zhongmin Qian

We develop a new approach for regularity estimates, especially vorticity estimates, of solutions of the three-dimensional Navier-Stokes equations with periodic initial data, by exploiting carefully formulated linearized vorticity equations. An appealing feature of the linearized vorticity equations is the inheritance of the divergence-free property of soluti

Sergey Pereverzev

In solid-state dark matter detectors, energy accumulation due to ionizing radiation should produce delayed low-energy background similar to the background produced by energy deposited by mechanical stress. The tunneling two-level systems model for glasses is missing interactions between excitation; in contrast, interactions lead to emerging phenomena in Prig

Yeji Song, Chaerin Kong, Seoyoung Lee, Nojun Kwak

Neural Radiance Fields (NeRF) achieves photo-realistic image rendering from novel views, and the Neural Scene Graphs (NSG) \cite{ost2021neural} extends it to dynamic scenes (video) with multiple objects. Nevertheless, computationally heavy ray marching for every image frame becomes a huge burden. In this paper, taking advantage of significant redundancy acro

Shima Hajimirza

This work uses the mathematical machinery of Renewal/Ruin (surplus risk) theory to derive preliminary explicit estimations for the radiative properties of dilute and disperse porous media otherwise only computable accurately with Monte Carlo Ray Tracing (MCRT) simulations. Although random walk and Levy processes have been extensively used for modeling diffus

Haopeng Zhang, Xiao Liu, Jiawei Zhang

Extractive summarization for long documents is challenging due to the extended structured input context. The long-distance sentence dependency hinders cross-sentence relations modeling, the critical step of extractive summarization. This paper proposes HEGEL, a hypergraph neural network for long document summarization by capturing high-order cross-sentence r

Pei-Cheng Jiang, Xuan Wang, Da-Yong Wang

In the system of a pair of quantum-entangled neutral kaons from meson decays, when one kaon collapses into the $K_S$ state, the other will collapse instantaneously into the $K_L$ state due to entanglement and nonlocality. However, if the alternative hypothesis is correct and there's a time window during which one kaon is unaware that the other has decayed, s

Andi Han, Dai Shi, Zhiqi Shao, Junbin Gao

In this work, we provide a theoretical understanding of the framelet-based graph neural networks through the perspective of energy gradient flow. By viewing the framelet-based models as discretized gradient flows of some energy, we show it can induce both low-frequency and high-frequency-dominated dynamics, via the separate weight matrices for different freq

Ruizhong Qiu, Zhiqing Sun, Yiming Yang

Recently, deep reinforcement learning (DRL) models have shown promising results in solving NP-hard Combinatorial Optimization (CO) problems. However, most DRL solvers can only scale to a few hundreds of nodes for combinatorial optimization problems on graphs, such as the Traveling Salesman Problem (TSP). This paper addresses the scalability challenge in larg

Haodi Jiang, Qin Li, Yan Xu, Wynne Hsu

Obtaining high-quality magnetic and velocity fields through Stokes inversion is crucial in solar physics. In this paper, we present a new deep learning method, named Stacked Deep Neural Networks (SDNN), for inferring line-of-sight (LOS) velocities and Doppler widths from Stokes profiles collected by the Near InfraRed Imaging Spectropolarimeter (NIRIS) on the

Ardavan S. Nobandegani, Thomas R. Shultz, Irina Rish

To achieve desirable performance, current AI systems often require huge amounts of training data. This is especially problematic in domains where collecting data is both expensive and time-consuming, e.g., where AI systems require having numerous interactions with humans, collecting feedback from them. In this work, we substantiate the idea of $\textit{cogni

Bailey Tallman, Alexandra Boone, Caden LaFontaine, Trevor Croteau

Assuming a dark matter fraction $\Omega_{DM} = 0.27$ and a reduced Hubble constant $h = 0.73$, we obtain a value of 70 GeV/c$^2$ for the mass of the dark matter WIMP we have previously proposed. We also obtain a value for the annihilation cross section given by $\langle \sigma_{ann} v \rangle = 1.19 \times 10^{-26} $ cm$^3$/s in the present universe, consist

Viraj Shah, Ayush Sarkar, Sudharsan Krishnakumar Anitha, Svetlana Lazebnik

Image stylization aims at applying a reference style to arbitrary input images. A common scenario is one-shot stylization, where only one example is available for each reference style. Recent approaches for one-shot stylization such as JoJoGAN fine-tune a pre-trained StyleGAN2 generator on a single style reference image. However, such methods cannot generate

Tao Wang, Shan-Qin Wang, Wen-Pei Gan

Supernovae (SNe), kilonovae (KNe), tidal disruption events (TDEs), optical afterglows of gamma ray bursts (GRBs), and many other optical transients are important phenomena in time-domain astronomy. Fitting the multi-band light curves (LCs) or the synthesized (pseudo-)bolometric LCs can be used to constrain the physical properties of optical transients. The (

Alireza Tavakoli, Zahra Honjani, Hedieh Sajedi

With the growing popularity of the Internet, digital images are used and transferred more frequently. Although this phenomenon facilitates easy access to information, it also creates security concerns and violates intellectual property rights by allowing illegal use, copying, and digital content theft. Using watermarks in digital images is one of the most co

Chengfan Gao, Siping Gao, Ruimeng Hu, Zimu Zhu

The optimal stopping problem is one of the core problems in financial markets, with broad applications such as pricing American and Bermudan options. The deep BSDE method [Han, Jentzen and E, PNAS, 115(34):8505-8510, 2018] has shown great power in solving high-dimensional forward-backward stochastic differential equations (FBSDEs), and inspired many applicat

B. Westbrook, P. A. R. Ade, M. Aguilar, Y. Akiba

We present on the status of POLARBEAR-2 A (PB2-A) focal plane fabrication. The PB2-A is the first of three telescopes in the Simon Array (SA), which is an array of three cosmic microwave background (CMB) polarization sensitive telescopes located at the POLARBEAR (PB) site in Northern Chile. As the successor to the PB experiment, each telescope and receiver c

Neeraj Gangwar, Nickvash Kani

Mathematical notation makes up a large portion of STEM literature, yet finding semantic representations for formulae remains a challenging problem. Because mathematical notation is precise, and its meaning changes significantly with small character shifts, the methods that work for natural text do not necessarily work well for mathematical expressions. This

Ngartelbaye Guerngar, James McCormick

We provide explicit classical solutions and stochastic analogues for distributed-order space-time fractional diffusion equations on bounded domains with zero exterior boundary conditions. We also show that our results still hold when the mixing measure in the distributed-order time-derivative is singular.

Xian-Mao Cao, Shan-Qin Wang, Wen-Pei Gan, Jing-Yao Li

This paper investigates eleven (UV-)optical-infrared (IR) spectral energy distributions (SEDs) of six tidal disruption events (TDEs), which are ASASSN-14li, ASASSN-15lh, ASASSN-18ul, ASASSN-18zj, PS18kh, and ZTF18acaqdaa. We find that all the SEDs show evident IR excesses. We invoke the blackbody plus dust emission model to fit the SEDs, and find that the mo

Deniz Gurevin, Mohsin Shan, Tong Geng, Weiwen Jiang

In recent years, graph representation learning has gained significant popularity, which aims to generate node embeddings that capture features of graphs. One of the methods to achieve this is employing a technique called random walks that captures node sequences in a graph and then learns embeddings for each node using a natural language processing technique

Rawal Khirodkar, Brandon Smith, Siddhartha Chandra, Amit Agrawal

Ensemble approaches for deep-learning-based semantic segmentation remain insufficiently explored despite the proliferation of competitive benchmarks and downstream applications. In this work, we explore and benchmark the popular ensembling approach of combining predictions of multiple, independently-trained, state-of-the-art models at test time on popular da

Qile Zhang, Fan Guo, William Daughton, Hui Li

Magnetic reconnection drives multi-species particle acceleration broadly in space and astrophysics. We perform the first 3D hybrid simulations (fluid electrons, kinetic ions) that contain sufficient scale separation to produce nonthermal heavy-ion acceleration, with fragmented flux ropes critical for accelerating all species. We demonstrate the acceleration

Honglei Zhang, Francesco Cricri, Hamed Rezazadegan Tavakoli, Emre Aksu

Deep learning is overwhelmingly dominant in the field of computer vision and image/video processing for the last decade. However, for image and video compression, it lags behind the traditional techniques based on discrete cosine transform (DCT) and linear filters. Built on top of an autoencoder architecture, learned image compression (LIC) systems have draw

Yoshinao Katsu, Xiaozhi Lin, Ruigeng Ji, Ze Chen

Lampreys are jawless fish that evolved about 550 million years ago at the base of the vertebrate line. Modern lampreys contain a corticoid receptor (CR), the common ancestor of the glucocorticoid receptor (GR) and mineralocorticoid receptor (MR), which first appear in cartilaginous fish, such as sharks. Until recently, 344 amino acids at the amino terminus o

Yan V. Sym, João Gabriel M. Campos, Fabio G. Cozman

This demo paper introduces the BLAB Reporter, a robot-journalist covering the Brazilian Blue Amazon. The Reporter is based on a pipeline architecture for Natural Language Generation; it offers daily reports, news summaries and curious facts in Brazilian Portuguese. By collecting, storing and analysing structured data from publicly available sources, the robo

Xin Guo, Anran Hu, Jiacheng Zhang

This paper proposes and studies a class of discrete-time finite-time-horizon Stackelberg mean-field games, with one leader and an infinite number of identical and indistinguishable followers. In this game, the objective of the leader is to maximize her reward considering the worst-case cost over all possible $\epsilon$-Nash equilibria among followers. A new

Lewis Stanton, Jeffrey Thompson

We characterise the form of all simple, finite graphs for which the girth of the graph is equal to the circumference of the graph. We apply this to prove a bound on the number of edges in such a graph.