May 2023 arXiv papers — page 112

Youze Wang, Wenbo Hu, Richang Hong

Multimodal learning involves developing models that can integrate information from various sources like images and texts. In this field, multimodal text generation is a crucial aspect that involves processing data from multiple modalities and outputting text. The image-guided story ending generation (IgSEG) is a particularly significant task, targeting on an

Paolo Liberatore

Iterated belief revision requires information about the current beliefs. This information is represented by mathematical structures called doxastic states. Most literature concentrates on how to revise a doxastic state and neglects that it may exponentially grow. This problem is studied for the most common ways of storing a doxastic state. All four methods a

Junming Duan, Qian Wang, Jan S. Hesthaven

Accurate prediction of aerodynamic forces in real-time is crucial for autonomous navigation of unmanned aerial vehicles (UAVs). This paper presents a data-driven aerodynamic force prediction model based on a small number of pressure sensors located on the surface of UAV. The model is built on a linear term that can make a reasonably accurate prediction and a

Yang Liu

This paper proposes a capacitor voltage synchronizing control (CVSC) system for the regulation of full-scale wind power generator-supplied power systems (FWPS). The capacitor combined with the inverter of a full-scale wind power generator (WPG) is controlled with a CVSC system to mimic the rotor dynamics of a synchronous generator (SG). WPGs are enabled to o

Al Khan, Remudin Reshid Mekuria, Ruslan Isaev

One of the biggest expense in software development is the maintenance. Therefore, it is critical to comprehend what triggers maintenance and if it may be predicted. Numerous research have demonstrated that specific methods of assessing the complexity of created programs may produce useful prediction models to ascertain the possibility of maintenance due to s

Kyusung Hwang

Quantum spin liquids and anyons, used to be subjects of condensed matter physics, now are realized in various platforms of qubits, offering unprecedented opportunities to investigate fundamental physics of many-body quantum entangled states. Qubits are inevitably exposed to environment effects such as decoherence and dissipation, which are believed to be det

Ujun Jeong, Paras Sheth, Anique Tahir, Faisal Alatawi

A recent surge of users migrating from Twitter to alternative platforms, such as Mastodon, raised questions regarding what migration patterns are, how different platforms impact user behaviors, and how migrated users settle in the migration process. In this study, we elaborate on how we investigate these questions by collecting data over 10,000 users who mig

Mengmeng Wang, Teli Ma, Xingxing Zuo, Jiajun Lv

3D LiDAR-based single object tracking (SOT) has gained increasing attention as it plays a crucial role in 3D applications such as autonomous driving. The central problem is how to learn a target-aware representation from the sparse and incomplete point clouds. In this paper, we propose a novel Correlation Pyramid Network (CorpNet) with a unified encoder and

G. G. L. Nashed

We investigate how to derive an isotropic stellar model in the framework of mimetic gravitational theory. Recently, this theory has gained big interest due to its difference from Einstein's general relativity (GR), especially in the domain non-vacuum solutions. In this regard, we apply the field equation of mimetic gravitational theory to a spherically symme

Bibandhan Poudyal, Gourab Ghoshal, Alec Kirkley

The spatial configuration of urban amenities and the streets connecting them collectively provide the structural backbone of a city, influencing its accessibility, vitality, and ultimately the well-being of its residents. Most accessibility measures focus on the proximity of amenities in space or along transportation networks, resulting in metrics largely de

Chang Gao, Wenxuan Zhang, Wai Lam, Lidong Bing

Information extraction (IE) systems aim to automatically extract structured information, such as named entities, relations between entities, and events, from unstructured texts. While most existing work addresses a particular IE task, universally modeling various IE tasks with one model has achieved great success recently. Despite their success, they employ

Nathan Bowler, Florian Gut, Meike Hatzel, Ken-ichi Kawarabayashi

We introduce torsoids, a canonical structure in matching covered graphs, corresponding to the bricks and braces of the graph. This allows a more fine-grained understanding of the structure of finite and infinite directed graphs with respect to their 1-separations.

Siqi Liu, A-Li Luo, Wei Zhang, Xiao Kong

This article reports a sample of 1830 strong [O III] {\lambda}5007 emission-line compact galaxies discovered with the LAMOST spectroscopic survey and the photometric catalog of SDSS. We newly identify 402 spectra of 346 strong [O III]{\lambda}5007 emission-line compact galaxies by finding compact isolated point sources. Combined with the samples in our previ

Marie Amalore Nambi, Neeraj Kumar, Chitra Venugopal

We discuss the property of (almost) complete intersection of LSS-ideals of graphs of some special forms, like trees, unicyclic, and bicyclic graphs. Further, we give a sufficient condition for the complete intersection property of twisted LSS-ideals in terms of a new graph theoretical invariant called twisted positive matching decomposition number denoted by

Pei Tan, Christopher E Miles

Collective motion of locally interacting agents is found ubiquitously throughout nature. The inability to probe individuals has driven longstanding interest in the development of methods for inferring the underlying interactions. In the context of heterogeneous collectives, where the population consists of individuals driven by different interactions, existi

Dilip Kumar Ghosh, Anish Ghoshal, Sk Jeesun

The relic density of Dark Matter (DM) in the freeze-in scenario is highly dependent on the evolution history of the universe and changes significantly in a non-standard (NS) cosmological framework prior to Big Bang Nucleosynthesis (BBN). In this scenario, an additional species dominates the energy budget of the universe at early times (before BBN), resulting

R. A. Konoplya

The Effective Field Theory (EFT) of perturbations on an arbitrary background geometry with a timelike scalar profile has been recently constructed in the context of scalar-tensor theories. Unlike General Relativity, the regular Hayward metric is realized as an exact background metric in the Effective Field Theory with timelike scalar profile without resortin

Yongcheng Yao

The study's objective is to explore the distinctions in the functional brain network connectivity between Alzheimer's Disease (AD) patients and normal controls using Functional Magnetic Resonance Imaging (fMRI). The study included 590 individuals, with 175 having AD dementia and 415 age-, gender-, and handedness-matched normal controls. The connectivity of f

Xiaohu Ge, Muyao Ruan, Xiaoxuan Peng, Yong Xiao

As the size of transistors approaches the mesoscopic scale, existing energy consumption analysis methods exhibit various limits, especially when being applied to describe the non-equilibrium information processing of transistors at ultra-low voltages. The stochastic thermodynamics offers a theoretic tool to analyze the energy consumption of transistor during

Anton Biryukov, Gregory Beskin

Numerical simulations predict that the spin-down rate of a single rotation-powered neutron star depends on the angle $\alpha$ between its spin and magnetic axes as $P\dot P \propto \mu^2 (k_0 + k_1\sin^2\alpha)$, where $P$ is the star spin period, $\mu$ is its magnetic moment, while $k_0 \sim k_1 \sim 1$. Here we describe a simple observational test for this

Xucong Wang, Pengchao Han, Lei Guo

Knowledge Distillation (KD) is a powerful technique for transferring knowledge between neural network models, where a pre-trained teacher model is used to facilitate the training of the target student model. However, the availability of a suitable teacher model is not always guaranteed. To address this challenge, Self-Knowledge Distillation (SKD) attempts to

Y. Yamaguchi, W. Horiuchi, N. Itagaki

We explore the structure of the ground state of $^{20}$Ne by investigating various density profiles. Four candidates for the ground state configurations, (a) $j$-$j$ coupling and (b) SU(3) shell model and (c) $5\alpha$ and (d) $^{16}{\rm O}+\alpha$ cluster model configurations are generated by utilizing the antisymmetrized quasicluster model. A high-energy r

Jinhui Hu, Guo Chen, Huaqing Li, Zixiang Shen

Distributed stochastic optimization, arising in the crossing and integration of traditional stochastic optimization, distributed computing and storage, and network science, has advantages of high efficiency and a low per-iteration computational complexity in resolving large-scale optimization problems. This paper concentrates on resolving a large-scale conve

Andrea Bevilacqua, Jerzy Kowalski-Glikman, Wojciech Wiślicki

In this paper we consider the \k{appa}-deformed boost acting on a two-particles states. Using techniques developed in the case of infinitesimal boost we compute explicit expression for the components of the finite boost matrices acting on the first and second particle. We briefly discuss phenomenological consequences of our findings.

Vishal Purohit

Neural Ordinary Differential Equations (NODEs) probed the usage of numerical solvers to solve the differential equation characterized by a Neural Network (NN), therefore initiating a new paradigm of deep learning models with infinite depth. NODEs were designed to tackle the irregular time series problem. However, NODEs have demonstrated robustness against va

Taiga Ishii, Ryo Ueda, Yusuke Miyao

A unique feature of Recurrent Neural Networks (RNNs) is that it incrementally processes input sequences. In this research, we aim to uncover the inherent generalization properties, i.e., inductive bias, of RNNs with respect to how frequently RNNs switch the outputs through time steps in the sequence classification task, which we call output sequence frequenc

G. Maciejewski, J. Golonka, W. Łoboda, J. Ohlert

Hot Jupiters have been perceived as loners devoid of planetary companions in close orbital proximity. However, recent discoveries based on space-borne precise photometry have revealed that at least some fraction of giant planets coexists with low-mass planets in compact orbital architectures. We report detecting a 1.446-day transit-like signal in the photome

Zhongren Wang, Lihao Tian, Xiaokang Liu, Andrei Sharf

Stochastic porous structures are ubiquitous in natural phenomena and have gained considerable traction across diverse domains owing to their exceptional physical properties. The recent surge in interest in microstructures can be attributed to their impressive attributes, such as a high strength-to-weight ratio, isotropic elasticity, and bio-inspired design p

Ali Akbar Rezaei Lori, Mohammad Danesh, Iman Izadi

In this paper, first the full dynamics of aerial transportation of a rigid body with arbitrary number of quadrotors is derived. Then a control strategy is proposed to convey the nonuniform rigid body appropriately to the desired trajectory. In the dynamical model of this transportation system, not only the load is considered as a nonuniform and non-homogeneo

Yu-Liang Tao, Mou Yan, Mian Peng, Qiang Wei

Topological phases of matter are classified based on symmetries, with nonsymmorphic symmetries like glide reflections and screw rotations being of particular importance in the classification. In contrast to extensively studied glide reflections in real space, introducing space-dependent gauge transformations can lead to momentum-space glide reflection symmet

Jeong-Min Ma, Hyung-Gon Lee, Kevin L. Moore, Hyo-Sung Ahn

We study clustering properties of networks of single integrator nodes over a directed graph, in which the nodes converge to steady-state values. These values define clustering groups of nodes, which depend on interaction topology, edge weights, and initial values. Focusing on the interaction topology of the network, we introduce the notion of topological clu

Supragyan Priyadarshinee, Subhash Mahapatra

We present and discuss new families of primary hair charged black hole solutions in asymptotically anti-de Sitter space in three dimensions. The coupled Einstein-Maxwell-scalar gravity system, that carries the coupling $f(\phi)$ between the scalar and Maxwell fields is solved, and exact hairy black hole solutions are obtained analytically. The hairy solution

Xin Wang

We investigate the topological string correspondence of the five-dimensional half-BPS Wilson loops on $S^1$. First, we propose the refined holomorphic anomaly equations for the BPS sectors of the Wilson loop expectation values. We then solve these equations and obtain many non-trivial novel integral refined BPS invariants for rank-one models. By studying the

Ryu Sasaki

The multivariate Hahn polynomials are constructed explicitly as the common eigenvectors of a family of second order difference operators. They are orthogonal with respect to the hypergeometric multinomial distribution. The main difference operator is adopted from the work of Karlin-McGregor in 1975. The minor ones are the subsets of the main one containing l

Keyu Li, Xinyu Mao, Chengwei Ye, Ang Li

Robotic ultrasound (US) systems have shown great potential to make US examinations easier and more accurate. Recently, various machine learning techniques have been proposed to realize automatic US image interpretation for robotic US acquisition tasks. However, obtaining large amounts of real US imaging data for training is usually expensive or even unfeasib

Jacob Bergquist, Adam N. Elmachtoub

We consider a general queueing system with price-sensitive customers in which the service provider seeks to balance two objectives, maximizing the average revenue rate and minimizing the average queue length. Customers arrive according to a Poisson process, observe an offered price, and decide to join the queue if their valuation exceeds the price. The queue

Adolfo S. Carvalho, Christopher M. Johns-Krull

In high dispersion spectra of rotating bodies such as stars and planets, the rotation contributes significantly to, and sometimes dominates, the line broadening. We present a simple method for rotationally broadening large wavelength ranges of high-dispersion spectra. The broadening is rapid and scales linearly with the length of the spectrum array. For larg

Xintao Zhao, Shuai Wang, Yang Chao, Zhiyong Wu

Nowadays, recognition-synthesis-based methods have been quite popular with voice conversion (VC). By introducing linguistics features with good disentangling characters extracted from an automatic speech recognition (ASR) model, the VC performance achieved considerable breakthroughs. Recently, self-supervised learning (SSL) methods trained with a large-scale

Jiawei Huo

This article presents a simple but effective and efficient approach to improve the accuracy and stability of Least-Squares Monte Carlo. The key idea is to construct the ansatz of conditional expected continuation payoff using the finite-difference solution from one dimension, to be used in linear regression. This approach bridges between solving backward par

Tong Wu, Zhiyong Chen, Meixia Tao, Bin Xia

Degraded broadcast channels (DBC) are a typical multi-user communications scenario. There exist classic transmission methods, such as superposition coding with successive interference cancellation, to achieve the DBC capacity region. However, semantic communications method over DBC remains lack of in-depth research. To address this, we design a fusion-based

E. Harikumar, Harsha Sreekumar, Suman Kumar Panja

Considering space--time to be non-commutative, we study the evolution of the universe employing the approach of Newtonian cosmology. Generalizing the conservation of energy and the first law of thermodynamics to $\kappa$-deformed space--time, we derive the modified Friedmann equations, valid up to the first order, in the deformation parameter. Analyzing thes

Anand Radhakrishnan, Henry Le Berre, Benjamin Wilfong, Jean-Sebastien Spratt

Multiphase compressible flows are often characterized by a broad range of space and time scales. Thus entailing large grids and small time steps, simulations of these flows on CPU-based clusters can thus take several wall-clock days. Offloading the compute kernels to GPUs appears attractive but is memory-bound for standard finite-volume and -difference metho

Armin Maleki, Malihe Ghodrat, Ignacio Pagonabarraga

Microorganisms, such as E.Coli, are known to display upstream behavior and respond rheotactically to shear flows. In particular, E.Coli suspensions have been shown to display strong sensitivity to spatial constrictions, leading to an anomalous densification past the constriction for incoming fluid velocities comparable to the microoganism's self propulsion s

Boris Kosyakov

We outline the course of affairs in the experimental and theoretical fields of nuclear and particle physics which determined its finale, and give several fragmentary remarks on its present state. The essay tells about events and their participants, known from the literature, but presented here from the perspective of a person whose 50-year labor activity, 19

Tong Wu, Zhiyong Chen, Dazhi He, Liang Qian

Diffusion models (DM) can gradually learn to remove noise, which have been widely used in artificial intelligence generated content (AIGC) in recent years. The property of DM for removing noise leads us to wonder whether DM can be applied to wireless communications to help the receiver eliminate the channel noise. To address this, we propose channel denoisin

Siyuan Huang, Bo Zhang, Botian Shi, Peng Gao

Although Domain Generalization (DG) problem has been fast-growing in the 2D image tasks, its exploration on 3D point cloud data is still insufficient and challenged by more complex and uncertain cross-domain variances with uneven inter-class modality distribution. In this paper, different from previous 2D DG works, we focus on the 3D DG problem and propose a

Pradeep Das, Umesh V. Dubey, N. Raghavendra

In this article, we define the tensor product $V\otimes W$ of a representation $V$ of a quiver $Q$ with a representation $W$ of an another quiver $Q'$, and show that the representation $V\otimes W$ is semistable if $V$ and $W$ are semistable. Over the field of complex numbers, we also describe a relation between the natural line bundles, and between the univ

S. Mallik

In this work, Canonical Thermodynamical model for nuclear multifragmentation has been updated with realistic nuclear equation of state. Mass distribution, intermediate mass fragment multiplicity as well as isospin sensitive observables have been investigated with semi-microscopic approach of determining nuclear binding and excitation energies. Production of

Nashiba Parbin, Dhruba Jyoti Gogoi, Umananda Dev Goswami

We investigate the impact of the axionic coupling parameter on the bending angle of light and the shadow cast by slowly rotating black holes in Chern-Simons modified gravity. We utilize the Ishihara \etal method to derive the deflection angle of light for an observer and source located at finite distances from a lens object in an asymptotically flat spacetim

Zitang Sun, Yen-Ju Chen, Yung-hao Yang, Shin'ya Nishida

Visual motion processing is essential for humans to perceive and interact with dynamic environments. Despite extensive research in cognitive neuroscience, image-computable models that can extract informative motion flow from natural scenes in a manner consistent with human visual processing have yet to be established. Meanwhile, recent advancements in comput

Tomohiro Fujimoto, Takayuki Kurihara, Yuta Murotani, Tomohiro Tamaya

We report the first observation of the spin Hall conductivity spectrum in GaAs at room temperature. Our terahertz polarimetry with a precision of several $\mu$rads resolves the Faraday rotation of terahertz pulses arising from the inverse spin Hall effect of optically injected spin-polarized electrons. The obtained spin Hall conductivity spectrum exhibits an

Chaojun Wang, Yang Liu, Wai Lam

Previous studies show that intermediate supervision signals benefit various Natural Language Processing tasks. However, it is not clear whether there exist intermediate signals that benefit Neural Machine Translation (NMT). Borrowing techniques from Statistical Machine Translation, we propose intermediate signals which are intermediate sequences from the "so

Michele Veronesi

We report updates on time-dependent $CP$-violation observables at Belle II. The benchmark measurements of the $B^0$ lifetime $\tau_{B^0}$ and mixing frequency $\Delta m_d$ using flavor specific hadronic decays and the determination of the $CP$-violating phase $\sin2\phi_1$ in $b\to c\overline{c}s$ transitions have been performed using data collected between

Sheng Liu, Shuang Wei, Wei Wang, Chao Lei

We propose a QNSC pre-coding scheme based on probabilistic shaping of the basis, to reduce the probability of ciphertext bits that are easier to be intercepted. Experiment results show this scheme can improve the security performance by 100% in terms of Eve's cipher text BER.

Tomohiro Taniguchi

Input-driven dynamical systems have attracted attention because their dynamics can be used as resources for brain-inspired computing. The recent achievement of human-voice recognition by spintronic oscillator also utilizes an input-driven magnetization dynamics. Here, we investigate an excitation of input-driven chaos in magnetization dynamics by voltage con

Víctor A. Vicente-Benítez

The construction of a pair of transmutation operators for the radial main Vekua equation with a Bicomplex-valued coefficient is presented. The pair of operators transform the Bicomplex analytic functions into the solutions of the main Vekua equation. The analytical properties of the operators in the space of classical solutions and the pseudoanalytic Bergman

Ashutosh Jindal, Ravi Banavar, David Martin Diego

Control laws for continuous-time dynamical systems are most often implemented via digital controllers using a sample-and-hold technique. Numerical discretization of the continuous system is an integral part of subsequent analysis. Feedback linearizability of such sampled systems is dependent upon the choice of discretization map or technique. In this article

Ziheng Li, Shaohan Huang, Zihan Zhang, Zhi-Hong Deng

Recent studies have shown that dual encoder models trained with the sentence-level translation ranking task are effective methods for cross-lingual sentence embedding. However, our research indicates that token-level alignment is also crucial in multilingual scenarios, which has not been fully explored previously. Based on our findings, we propose a dual-ali

Wenbo Shao, Jun Li, Hong Wang

Trajectory prediction is one of the key components of the autonomous driving software stack. Accurate prediction for the future movement of surrounding traffic participants is an important prerequisite for ensuring the driving efficiency and safety of intelligent vehicles. Trajectory prediction algorithms based on artificial intelligence have been widely stu

Richard F. Lebed, Steven R. Martinez

The diabatic framework generalizes the adiabatic approximation built into the Born-Oppenheimer (BO) formalism, and is devised to rigorously incorporate the mixing of BO-approximation eigenstates with two-particle thresholds. We recently applied this framework in a bound-state approximation to the mixing of hidden-charm dynamical-diquark tetraquark states wit

Feng-Lei Fan, Wei Huang, Xiangru Zhong, Lecheng Ruan

A ReLU network is a piecewise linear function over polytopes. Figuring out the properties of such polytopes is of fundamental importance for the research and development of neural networks. So far, either theoretical or empirical studies on polytopes only stay at the level of counting their number, which is far from a complete characterization. Here, we prop

Boxi Cao, Qiaoyu Tang, Hongyu Lin, Shanshan Jiang

Memory is one of the most essential cognitive functions serving as a repository of world knowledge and episodes of activities. In recent years, large-scale pre-trained language models have shown remarkable memorizing ability. On the contrary, vanilla neural networks without pre-training have been long observed suffering from the catastrophic forgetting probl

Tomohiro Taniguchi

A recent experimental demonstration of a parametric magnetization oscillation excited by applying a microwave voltage to a ferromagnetic metal will be applicable not only to a new magnetization switching method but also to bio-inspired computing. It should be, however, noted that a phase of the parametric magnetization oscillation is not uniquely locked, rel

Xiang Li, Zhongci Hang, Zhanpeng Gu, Dunyan Yan

In this paper, we obtained the sharp bounds for $m$-linear $n$-dimensional Hardy-Littlewood-P\'{o}lya operator and Hilbert operator in two power weighted Morrey space on Heisenberg group.

Nisar Ahmed, H. M. Shahzad Asif, Abdul Rauf Bhatti, Atif Khan

Blind image quality assessment is a challenging task particularly due to the unavailability of reference information. Training a deep neural network requires a large amount of training data which is not readily available for image quality. Transfer learning is usually opted to overcome this limitation and different deep architectures are used for this purpos

Thomas Yu

It is very well known that when the exact line search gradient descent method is applied to a convex quadratic objective, the worst-case rate of convergence (ROC), among all seed vectors, deteriorates as the condition number of the Hessian of the objective grows. By an elegant analysis due to H. Akaike, it is generally believed -- but not proved -- that in t

Binwei Wu, Shuo Wang, Weiqian Tan

This paper proposes an innovative end-to-end deterministic network mechanism to achieve delay-bounded transmissions across multiple network domains. The proposed mechanism installs discrete shapers at the edge of the network domains, which serves to decouple the clock domains of different networks. Thereby, the challenges associated with cross-domain clock s

Tiancheng Sun, Xunzhou Chen, Shaolan Bi, Zhishuai Ge

Main Sequence Turn-off stars (MSTO) and subgiant stars are good tracers of galactic populations. We present a study of 41,034 MSTO and subgiant stars from the GALAH survey. Using a grid of stellar models that accounts for the variation of O abundances, we determine their ages with a median age uncertainty of $\sim$9.4 per cent. Our analysis reveals that the

Yuxian Gu, Li Dong, Furu Wei, Minlie Huang

In-context learning, where pre-trained language models learn to perform tasks from task examples and instructions in their contexts, has attracted much attention in the NLP community. However, the ability of in-context learning is not fully exploited because language models are not explicitly trained to learn in context. To this end, we propose PICL (Pre-tra

Xiao Ma, Gang Yao, Sanyi Yuan, Feng Zhang

Seismic coherent noise is often found in post-stack seismic data, which contaminates the resolution and integrity of seismic images. It is difficult to remove the coherent noise since the features of coherent noise, e.g., frequency, is highly related to signals. Recently, deep learning has proven to be uniquely advantageous in image denoise problems. To enha

Xiaotao Sun, Mingshuo Zhou

Let $C$ be a nonsingular projective curve over an algebraically closed field of characteristic $p>0$ and $I\subset C$ be a finite set. If $\mathcal{U}_{C,\,\omega}$ denotes the moduli space of semistable parabolic bundles of rank $r$ and degree $d$ on $C$ with parabolic structures determined by $\omega=(k,\{\vec n(x),\vec a(x)\}_{x\in I})$, we prove that $\m

Aditya Pribadi Kalapaaking, Ibrahim Khalil, Mohammed Atiquzzaman

The widespread adoption of Internet of Things (IoT) devices in smart cities, intelligent healthcare systems, and various real-world applications have resulted in the generation of vast amounts of data, often analyzed using different Machine Learning (ML) models. Federated learning (FL) has been acknowledged as a privacy-preserving machine learning technology

James Davies

We prove a conjecture of Kim and Oum that every proper pivot-minor-closed class of graphs has the strong Erd\H{o}s-Hajnal property. More precisely, for every graph $H$, there exists $\epsilon > 0$ such that every $n$-vertex graph with no pivot-minor isomorphic to $H$ contains two sets $A, B$ of vertices such that $|A|, |B| \ge \epsilon n$ and $A$ is complete

Pengcheng Shi, Haozhe Cheng, Xu Han, Yiyang Zhou

Point cloud completion estimates complete shapes from incomplete point clouds to obtain higher-quality point cloud data. Most existing methods only consider global object features, ignoring spatial and semantic information of adjacent points. They cannot distinguish structural information well between different object parts, and the robustness of models is p

Fengyuan Zhang, Qiongqiong Chu, Qiang Wang, Shining Zhu

Bound states in the continuum (BICs) have attracted significant interest in recent years due to their unique optical properties, such as infinite quality factor and wave localization. In order to improve the optical performance of BICs based devices, more degrees of freedom are required to tune BICs in high-dimension parameter space for practical application

Natalia Garanina, Sergey Staroletov, Sergei Gorlatch

The paper combines research approaches that traditionally have been disjoint: 1) model checking as used in formal verification of programs, and 2) auto-tuning as often used in high-performance computing. Auto-tuning frameworks optimize parallel programs by finding the optimal values of the performance-critical parameters -- so-called tuning parameters -- for

Daniele Gammelli, James Harrison, Kaidi Yang, Marco Pavone

Optimization problems over dynamic networks have been extensively studied and widely used in the past decades to formulate numerous real-world problems. However, (1) traditional optimization-based approaches do not scale to large networks, and (2) the design of good heuristics or approximation algorithms often requires significant manual trial-and-error. In

Z. N. Liu, X. Q. Zhao, J. Yao, C. Zhang

In recent years, twisted bilayer graphene has become a hot topic and inspired the research upsurge of photonic moir\'e lattice. Here, we designed a photonic moir\'e superlattice with two synthetic twist angles and constructed a synthetic moir\'e sphere based on these two angles. Thus, we have more degrees of freedom to design the band structure flexibly. A t

Xiaoheng Sun, Yuejie Gao, Hanyao Lin, Huaping Liu

Automatic singing evaluation independent of reference melody is a challenging task due to its subjective and multi-dimensional nature. As an essential attribute of singing voices, vocal timbre has a non-negligible effect and influence on human perception of singing quality. However, no research has been done to include timbre information explicitly in singin

Song Wei, Hanyu Zhang, Ronald Moore, Rishikesan Kamaleswaran

We present a Transfer Causal Learning (TCL) framework when target and source domains share the same covariate/feature spaces, aiming to improve causal effect estimation accuracy in limited data. Limited data is very common in medical applications, where some rare medical conditions, such as sepsis, are of interest. Our proposed method, named \texttt{$\ell_1$

Yanzhong Yao, Jiawei Guo, Tongxiang Gu

Given the facts of the extensiveness of multi-material diffusion problems and the inability of the standard PINN(Physics-Informed Neural Networks) method for such problems, in this paper we present a novel PINN method that can accurately solve the multi-material diffusion equation. The new method applies continuity conditions at the material interface derive

Peter J. Forrester

Random matrices from the elliptic Ginibre orthogonal ensemble (GinOE) are a certain linear combination of a real symmetric, and real anti-symmetric, real Gaussian random matrices and controlled by a parameter $\tau$. Our interest is in the fluctuations of the number of real eigenvalues, for fixed $\tau$ when the expected number is proportional to the square

Weizhao Tang, Peiyao Sheng, Ronghao Ni, Pronoy Roy

Crash fault tolerant (CFT) consensus algorithms are commonly used in scenarios where system components are trusted -- e.g., enterprise settings and government infrastructure. However, CFT consensus can be broken by even a single corrupt node. A desirable property in the face of such potential Byzantine faults is \emph{accountability}: if a corrupt node break

Yongli Zhu, Xiang Zhang, Renchang Dai

This paper proposes an electronic circuit simulator-based method to accelerate the power system transient simulation, where the modeling of a generic HVDC (High Voltage Direct Current) system is focused. The electronic circuit simulation equations and the backward differentiation formula for numerical solving are described. Then, the circuit modeling process

Ruoqi Wang, Zhuoyang Chen, Qiong Luo, Feng Wang

In radio astronomy, signals from radio telescopes are transformed into images of observed celestial objects, or sources. However, these images, called dirty images, contain real sources as well as artifacts due to signal sparsity and other factors. Therefore, radio interferometric image reconstruction is performed on dirty images, aiming to produce clean ima

Seth M. Davis, Sankar Das Sarma

The only kinematically-allowed phonon-scattering events for bands of subsonic fermions ($v_F < v_p$) are interband transitions, leading to different low-$T$ transport physics than in the typical supersonic case. We apply a kinetic theory of phonon-limited transport to a generic two-band system of subsonic fermions, deriving formulae for relaxation times and

Yonghe Liu, Hanqi Pi, Kenji Watanabe, Takashi Taniguchi

Interest in ZrTe5 has been reinvigorated in recent years owing to its potential for hosting versatile topological electronic states and intriguing experimental discoveries. However, the mechanism of many of its unusual transport behaviors remains controversial, for example, the characteristic peak in the temperature-dependent resistivity and the anomalous Ha

Zhenhe Zhang, Jun Zheng, Guchuan Zhu

This paper presents a new aggregate power tracking control scheme for populations of thermostatically controlled loads (TCLs). The control design is carried out in the framework of partial differential equations (PDEs) based on a late-lumping procedure without truncating the infinite-dimensional model describing the dynamics of the TCL population. An input-o

Andres Pastrana-Cruz, Manuel Lafond

Several NP-hard problems are solved exactly using exponential-time branching strategies, whether it be branch-and-bound algorithms, or bounded search trees in fixed-parameter algorithms. The number of tractable instances that can be handled by sequential algorithms is usually small, whereas massive parallelization has been shown to significantly increase the

Shirantha Welikala, Hai Lin, Panos J. Antsaklis

Symbolic control problems aim to synthesize control policies for dynamical systems under complex temporal specifications. For such problems, Signal Temporal Logic (STL) is increasingly used as the formal specification language due to its rich expressiveness. Moreover, the degree of satisfaction of STL specifications can be evaluated using ``STL robust semant

Theodoros Trochatos, Anthony Etim, Jakub Szefer

Continued expansion of cloud computing offerings now includes SmartSSDs. A SmartSSD is a solid-state disk (SSD) augmented with an FPGA. Through public cloud providers, it is now possible to rent on-demand virtual machines enabled with SmartSSDs. Because of the FPGA component of the SmartSSD, cloud users who access the SmartSSD can instantiate custom circuits

Jinhui Chen, Zuo-Tang Liang, Yu-Gang Ma, Qun Wang

A perspective for the Global spin alignment of vector mesons and strong force fields in heavy-ion collisions is provided in this short report.

Ziwei Feng, Tan Shi, Guangzhou Geng, Junjie Li

Lanthanide-doped upconversion nanoparticles emerged recently as an attractive material platform underpinning a broad range of innovative applications such as optical cryptography, luminescent probes, and lasing. However, the intricate 4f-associated electronic transition in upconversion nanoparticles leads only to a weak photoluminescence intensity and unpola

Jasper van de Kreeke

Relative Fukaya categories are hard to construct. In this paper, we provide a very explicit construction in the case of punctured surfaces. The starting point is the gentle algebra $ \operatorname{Gtl} Q $ associated with a punctured surface $ Q $. Our model for the relative Fukaya category is a deformation $ \operatorname{Gtl}_q Q $ which has one formal def

Michael Cunanan, Michael Thielscher

The recent popularity of Wordle has revived interest in guessing games. We develop a general method for finding optimal strategies for guessing games while avoiding an exhaustive search. Our main contributions are several theorems that build towards a general theory to prove the optimality of a strategy for a guessing game. This work is developed to apply to

Renhui Wan

For any nonempty set $U\subset\R^+$, we consider the maximal operator $\h^U$ defined as $\h^Uf=\sup_{u\in U}|H^{(u)} f|$, where $H^{(u)}$ represents the Hilbert transform along the monomial curve $u\gamma(s)$. We focus on the $L^p(\mathbb{R}^d)$ operator norm of $\h^U$ for $p\in (p_\circ(d),\infty)$, where $p_\circ(d)$ is the optimal exponent known for the $

Luke Kershaw, Jeremy Rickard

We give an example of a finite dimensional algebra with infinite delooping level, based on an example of a semi-Gorenstein-projective module due to Ringel and Zhang.

Sankalp Gaur, Victor Gurarie

We show that spectral form factors of unconventional gapped superconductors have singularities occurring periodically in time. These are the superconductors whose gap function vanishes somewhere in momentum space (Brillouin zone) but whose fermionic excitation spectrum is fully gapped. Many, although not all, of these superconductors are topologically nontri

Eric C. Rowell, Hannah Solomon, Qing Zhang

The construction and classification of super-modular categories is an ongoing project, of interest in algebra, topology and physics. In a recent paper, Cho, Kim, Seo and You produced two mysterious families of super-modular data, with no known realization. We show that these data are realized by modifying the Drinfeld centers of near-group fusion categories

Chenyang Lyu, Tianbo Ji, Yvette Graham, Jennifer Foster

Conventional Transformer-based Video Question Answering (VideoQA) approaches generally encode frames independently through one or more image encoders followed by interaction between frames and question. However, such schema would incur significant memory use and inevitably slow down the training and inference speed. In this work, we present a highly efficien