April 2023 arXiv papers — page 48

Yoshihiko Kobayashi, Masayuki Matsuo

Background: The pairing correlation in weakly bound nuclei causes a mixing among bound and unbound configurations. A remarkable consequence is emergence of the quasiparticle resonance, which has been predicted with the coordinate space Hartree-Fock-Bogoliubov (HFB) theory, but not yet observed experimentally. Purpose: We discuss possible observation of quasi

Nouhaila Innan, Muhammad Al-Zafar Khan

DNA sequencing allows for the determination of the genetic code of an organism, and therefore is an indispensable tool that has applications in Medicine, Life Sciences, Evolutionary Biology, Food Sciences and Technology, and Agriculture. In this paper, we present several novel methods of performing classical-to-quantum data encoding inspired by various mathe

Eason Chen

In this paper, we proposed a conceptual model to predict the chat experience in a natural language generation dialog system. We evaluated the model with 120 participants with Partial Least Squares Structural Equation Modeling (PLS-SEM) and obtained an R-square (R2) with 0.541. The model considers various factors, including the prompts used for generation; co

Shuwen Deng, David R. Reich, Paul Prasse, Patrick Haller

Eye movements during reading offer insights into both the reader's cognitive processes and the characteristics of the text that is being read. Hence, the analysis of scanpaths in reading have attracted increasing attention across fields, ranging from cognitive science over linguistics to computer science. In particular, eye-tracking-while-reading data has be

Mohammed Majthoub Almoghrabi, Guillaume Sagnol

We study the elective surgery planning problem in a hospital with operation rooms shared by elective and emergency patients. This problem can be split in two distinct phases. First, a subset of patients to be operated in the next planning period has to be selected, and the selected patients have to be assigned to a block and a tentative starting time. Then,

Hangtao Zhang, Zeming Yao, Leo Yu Zhang, Shengshan Hu

Federated learning (FL) is vulnerable to poisoning attacks, where adversaries corrupt the global aggregation results and cause denial-of-service (DoS). Unlike recent model poisoning attacks that optimize the amplitude of malicious perturbations along certain prescribed directions to cause DoS, we propose a Flexible Model Poisoning Attack (FMPA) that can achi

Krishan Rana, Andrew Melnik, Niko Sünderhauf

In this paper, we introduce a method for unifying language, action, and state information in a shared embedding space to facilitate a range of downstream tasks in robot learning. Our method, Contrastive Language, Action, and State Pre-training (CLASP), extends the CLIP formulation by incorporating distributional learning, capturing the inherent complexities

Zhen Zhang, Simona Ispas, Walter Kob

Using large-scale molecular dynamics simulations, we investigate the surface properties of lithium, sodium, and potassium silicate glasses containing 25 mole % of alkali oxide. The comparison of two types of surfaces, a melt-formed surface (MS) and a fracture surface (FS), demonstrates that the influence of the alkali modifier on the surface properties depen

Chetan Waghela, Shubhrangshu Dasgupta

There has been debate around applicability of exceptional points (EP) for quantum sensing. To resolve this, we first explore how to experimentally implement the nonhermitian non-diagonalizable Hamiltonians, that exhibit EPs, in quantum computers which run on unitary gates. We propose to use an ancilla-based method in this regard. Next, we show how such Hamil

Binbin Huang, Xingyue Peng, Siyuan Shen, Suan Xia

We introduce Omni-LOS, a neural computational imaging method for conducting holistic shape reconstruction (HSR) of complex objects utilizing a Single-Photon Avalanche Diode (SPAD)-based time-of-flight sensor. As illustrated in Fig. 1, our method enables new capabilities to reconstruct near-$360^\circ$ surrounding geometry of an object from a single scan spot

Weiyao Ke, Yuan-Yuan Zhang, Hongxi Xing, Xin-Nian Wang

We develop the first event generator, the electron-Heavy-Ion-Jet-INteraction-Generator (eHIJING), for the jet tomography study of electron-ion collisions. In this generator, energetic jet partons produced from the initial hard scattering undergo multiple collisions with the nuclear target. The collision rate is proportional to the transverse-momentum-depende

Burak Yetiştiren, Işık Özsoy, Miray Ayerdem, Eray Tüzün

Context: AI-assisted code generation tools have become increasingly prevalent in software engineering, offering the ability to generate code from natural language prompts or partial code inputs. Notable examples of these tools include GitHub Copilot, Amazon CodeWhisperer, and OpenAI's ChatGPT. Objective: This study aims to compare the performance of these pr

Veniamin Veselovsky, Ashton Anderson

When the COVID-19 pandemic hit, much of life moved online. Platforms of all types reported surges of activity, and people remarked on the various important functions that online platforms suddenly fulfilled. However, researchers lack a rigorous understanding of the pandemic's impacts on social platforms, and whether they were temporary or long-lasting. We pr

Massimo Finocchiaro Castroa, Calogero Guccio, Ilde Rizzo

Public infrastructure procurement is crucial as a prerequisite for public and private investments and for economic and social capital growth. However, low performance in execution severely hinders infrastructure provision and benefits delivery. One of the most sensitive phases in public infrastructure procurement is the design because of the strategic relati

Alexei Ber, Matthijs Borst, Fedor Sukochev

For a normal measurable operator $a$ affiliated with a von Neumann factor $\mathcal{M}$ we show: If $\mathcal{M}$ is infinite, then there is $\lambda_0\in \mathbb{C}$ so that for $\varepsilon>0$ there are $u_{\varepsilon}=u_{\varepsilon}^*$, $v_{\varepsilon}\in \mathcal{U}(\mathcal{M})$ with $$v_\varepsilon|[a,u_\varepsilon]|v_\varepsilon^*\geq(1-\varepsilon

Peter Kristel, Eric Schippers

This paper is an introduction to polarizations in the symplectic and orthogonal settings. They arise in association to a triple of compatible structures on a real vector space, consisting of an inner product, a symplectic form, and a complex structure. A polarization is a decomposition of the complexified vector space into the eigenspaces of the complex stru

Hongcheng Wang, Yuxuan Wang, Fangwei Zhong, Mingdong Wu

Visual-audio navigation (VAN) is attracting more and more attention from the robotic community due to its broad applications, \emph{e.g.}, household robots and rescue robots. In this task, an embodied agent must search for and navigate to the sound source with egocentric visual and audio observations. However, the existing methods are limited in two aspects:

Bellazzini M., Massari D., De Angeli F., Mucciarelli A.

We use the calibrations by Calamida et al. and by Hilker et al., and the standardised synthetic photometry in the v, b, and y Stromgren passbands from Gaia DR3 BP/RP spectra, to obtain photometric metallicities for a selected sample of 694233 old Galactic giant stars having |b|>20.0 and parallax uncertainties lower than 10%. The zero point of both sets of ph

Bin Fan, Yuchao Dai, Yongduek Seo, Mingyi He

The normalized eight-point algorithm has been widely viewed as the cornerstone in two-view geometry computation, where the seminal Hartley's normalization has greatly improved the performance of the direct linear transformation algorithm. A natural question is, whether there exists and how to find other normalization methods that may further improve the perf

Shanchuan Wan, Yujin Tang, Yingtao Tian, Tomoyuki Kaneko

Exploration is a fundamental aspect of reinforcement learning (RL), and its effectiveness is a deciding factor in the performance of RL algorithms, especially when facing sparse extrinsic rewards. Recent studies have shown the effectiveness of encouraging exploration with intrinsic rewards estimated from novelties in observations. However, there is a gap bet

Jie Chen, Hua Mao, Wai Lok Woo, Xi Peng

Multiview clustering (MVC) aims to reveal the underlying structure of multiview data by categorizing data samples into clusters. Deep learning-based methods exhibit strong feature learning capabilities on large-scale datasets. For most existing deep MVC methods, exploring the invariant representations of multiple views is still an intractable problem. In thi

Yongho Yoon, Woosuk Lee, Kwangkeun Yi

A key challenge in example-based program synthesis is the gigantic search space of programs. To address this challenge, various work proposed to use abstract interpretation to prune the search space. However, most of existing approaches have focused only on forward abstract interpretation, and thus cannot fully exploit the power of abstract interpretation. I

Lisa Bonheme, Marek Grzes

Variational autoencoders (VAEs) are used for transfer learning across various research domains such as music generation or medical image analysis. However, there is no principled way to assess before transfer which components to retrain or whether transfer learning is likely to help on a target task. We propose to explore this question through the lens of re

Shuyue Ding, Dongming Zhao, Tianxing Jiang, Haitao Wang

V3Si, a classical silicide superconductor with relatively high TC (~16 K), is promising for constructing silicon-based superconducting devices and hetero-structures. However, real space characterization on its surfaces and superconducting properties are still limited. Here we report the first low-temperature scanning tunnelling microscopy (STM) study on clea

Huayi Zhou, Fei Jiang, Jiaxin Si, Yue Ding

Detection of human body and its parts has been intensively studied. However, most of CNNs-based detectors are trained independently, making it difficult to associate detected parts with body. In this paper, we focus on the joint detection of human body and its parts. Specifically, we propose a novel extended object representation integrating center-offsets o

Pengfei Fang, Mehrtash Harandi, Trung Le, Dinh Phung

Hyperbolic geometry, a Riemannian manifold endowed with constant sectional negative curvature, has been considered an alternative embedding space in many learning scenarios, \eg, natural language processing, graph learning, \etc, as a result of its intriguing property of encoding the data's hierarchical structure (like irregular graph or tree-likeness data).

Nicola Cavallucci, Andrea Sambusetti

We prove some finiteness results for discrete isometry groups $\Gamma$ of uniformly packed CAT$(0)$-spaces $X$ with uniformly bounded codiameter (up to group isomorphism), and for CAT$(0)$-orbispaces $M = \Gamma \backslash X$ (up to equivariant homotopy equivalence or equivariant diffeomorphism); these results generalize, in nonpositive curvature, classical

mengqun Jin, Kai Li, Shuyan Li, Chunming He

Semi-Supervised Domain Adaptation (SSDA) is a recently emerging research topic that extends from the widely-investigated Unsupervised Domain Adaptation (UDA) by further having a few target samples labeled, i.e., the model is trained with labeled source samples, unlabeled target samples as well as a few labeled target samples. Compared with UDA, the key to SS

Adeel Muneer, Tobias Schikarski, Lukas Pflug

The unique properties of anisotropic and composite particles are increasingly being leveraged in modern particulate products. However, tailored synthesis of particles characterized by multi-dimensional dispersed properties remains in its infancy and few mathematical models for their synthesis exist. Here, we present a novel, accurate and highly efficient num

Qi Guo, Jiong Cheng, Huatang Tan, Jie Li

We propose a scheme for preparing magnon squeezed states in a hybrid cavity-magnon-qubit system. The system consists of a microwave cavity that simultaneously couples to a magnon mode of a macroscopic yttrium-iron-garnet (YIG) sphere via the magnetic-dipole interaction and to a transmon-type superconducting qubit via the electric-dipole interaction. By far d

Chuwei Luo, Changxu Cheng, Qi Zheng, Cong Yao

Visual information extraction (VIE) plays an important role in Document Intelligence. Generally, it is divided into two tasks: semantic entity recognition (SER) and relation extraction (RE). Recently, pre-trained models for documents have achieved substantial progress in VIE, particularly in SER. However, most of the existing models learn the geometric repre

Md Rasel Sarkar, Sreenatha G. Anavatti, Tanmoy Dam, Mahardhika Pratama

The main objective of this study is to propose an enhanced wind power forecasting (EWPF) transformer model for handling power grid operations and boosting power market competition. It helps reliable large-scale integration of wind power relies in large part on accurate wind power forecasting (WPF). The proposed model is evaluated for single-step and multi-st

Yun Yong Terh, Rimi Banerjee, Haoran Xue, Y. D. Chong

We study a non-Hermitian variant of the (2+1)-dimensional Dirac wave equation, which hosts a real energy spectrum with pairwise-orthogonal eigenstates. In the spatially uniform case, the Hamiltonian's non-Hermitian symmetries allow its eigenstates to be mapped to a pair of Hermitian Dirac subsystems. When a wave is transmitted across an interface between two

Harsh Maheshwari, Yen-Cheng Liu, Zsolt Kira

Using multiple spatial modalities has been proven helpful in improving semantic segmentation performance. However, there are several real-world challenges that have yet to be addressed: (a) improving label efficiency and (b) enhancing robustness in realistic scenarios where modalities are missing at the test time. To address these challenges, we first propos

Xinliang Zhou, Chenyu Liu, Jinan Zhou, Zhongruo Wang

The close coupling of artificial intelligence (AI) and electroencephalography (EEG) has substantially advanced human-computer interaction (HCI) technologies in the AI era. Different from traditional EEG systems, the interpretability and robustness of AI-based EEG systems are becoming particularly crucial. The interpretability clarifies the inner working mech

Jianli Xiang, Guanghui Hu

This paper is concerned with an inverse transmission problem for recovering the shape of a penetrable rectangular grating sitting on a perfectly conducting plate. We consider a general transmission problem with the coefficient \lambda\neq 1 which covers the TM polarization case. It is proved that a rectangular grating profile can be uniquely determined by th

Werner Porod

Composite Higgs models with a fermionic ultraviolet completion predict in general additional pseudo Nambu Goldstone bosons beside the Higgs multiplet. In this contribution we discuss their LHC signatures and present first bounds in simplified models which can also be applied to generic models like multi-Higgs models. We then demonstrate how these can be comb

Glauco Amigo, Daniel Andrés Díaz-Pachón, Robert J. Marks, Charles Baylis

The outcome of all time series cannot be forecast, e.g. the flipping of a fair coin. Others, like the repeated {01} sequence {010101...} can be forecast exactly. Algorithmic information theory can provide a measure of forecastability that lies between these extremes. The degree of forecastability is a function of only the data. For prediction (or classificat

Ziquan Yang

Let $(G, \Omega)$ be a Shimura datum of abelian type. It is well known that the corresponding Shimura variety $\mathrm{Sh}(G, \Omega)$ should be a moduli space of abelian motives equipped with some additional structures. In this half-expository note, we give under some simplifying assumptions a moduli interpretation of $\mathrm{Sh}(G, \Omega)$ over the refle

Nikhil Mehta, Milagro Teruel, Patricio Figueroa Sanz, Xin Deng

Many approaches to Natural Language Processing (NLP) tasks often treat them as single-step problems, where an agent receives an instruction, executes it, and is evaluated based on the final outcome. However, human language is inherently interactive, as evidenced by the back-and-forth nature of human conversations. In light of this, we posit that human-AI col

Wenxuan Pan, Feifei Zhao, Guobin Shen, Yi Zeng

Spiking Neural Networks (SNNs) have received considerable attention not only for their superiority in energy efficiency with discrete signal processing but also for their natural suitability to integrate multi-scale biological plasticity. However, most SNNs directly adopt the structure of the well-established Deep Neural Networks (DNNs), and rarely automatic

Yang-Yang Xie, Feng-Hua Ren, Arapat Ablimit, Xiang-Han Liang

Quantum state transfer (QST) through spin chains has been extensively investigated. Two schemes, the coupling set for perfect state transfer (PST) or adding a leakage elimination operator (LEO) Hamiltonian have been proposed to boost the transmission fidelity. However, these ideal schemes are only suitable for closed systems and will lose their effectiveness

Adithya Rajan, Tom G. Saunderson, Fabian R. Lux, Rocío Yanes Díaz

Noncollinear antiferromagnets can generate a transverse electrical response known as the anomalous Hall effect, even though they possess almost no net magnetization. The microscopic origin of this behaviour, however, has remained unclear because conventional measurement geometries mix different contributions to the measured response. Here, we show that apply

Muhammad Darwis Umar, Lalu Dalilul Falihin, Arief Lukmantoro, Harsojo

The experimental evidence of the ultra-high electron mobility and strong spin-orbit coupling in the two-dimensional (2D) layered bismuth-based oxyselenide, Bi$_{2}$O$_{2}$Se, makes it a potential material for spintronic devices. However, its spin-related properties have not been extensively studied due to the centrosymmetric nature of its crystal structure.

Prithwish Jana, Romit Roy Choudhury, Niloy Ganguly

In the era of social media, people frequently share their own opinions online on various issues and also in the way, get exposed to others' opinions. Be it for selective exposure of news feed recommendation algorithms or our own inclination to listen to opinions that support ours, the result is that we get more and more exposed to opinions closer to ours. Fu

Jieming Bian, Cong Shen, Jie Xu

Federated Learning (FL) is a machine learning approach that enables the creation of shared models for powerful applications while allowing data to remain on devices. This approach provides benefits such as improved data privacy, security, and reduced latency. However, in some systems, direct communication between clients and servers may not be possible, such

Yuhao Kang, Qianheng Zhang, Robert Roth

The rapid advancement of artificial intelligence (AI) such as the emergence of large language models including ChatGPT and DALLE 2 has brought both opportunities for improving productivity and raised ethical concerns. This paper investigates the ethics of using artificial intelligence (AI) in cartography, with a particular focus on the generation of maps usi

Candi Zheng, Yang Wang, Shiyi Chen

Extended thermodynamics commonly uses polynomial moments to model non-equilibrium transportation, but faces a crisis due to sub-shocks, which are anomalous discontinuities in gas properties when predicting shock waves. The cause of sub-shocks is still unclear, challenging the validity of extended thermodynamics. This paper reveals, for the first time, that s

Shahrukh Salim, Rahul Marathe, Sankalpa Ghosh

In view of the recent progress in experiments on charge transport through various Josephson junctions made out of graphene, we have made a careful comparison between the theory and some of the available experimental results. Within the framework of a transfer matrix approach, we have first analytically derived the spectrum of Andreev bound states (ABS) in a

Mahsa Tavakoli, Rohitash Chandra, Fengrui Tian, Cristián Bravo

Knowing which factors are significant in credit rating assignment leads to better decision-making. However, the focus of the literature thus far has been mostly on structured data, and fewer studies have addressed unstructured or multi-modal datasets. In this paper, we present an analysis of the most effective architectures for the fusion of deep learning mo

Donghee Choi, Mogan Gim, Samy Badreddine, Hajung Kim

Determining proper quantities for ingredients is an essential part of cooking practice from the perspective of enriching tastiness and promoting healthiness. We introduce KitchenScale, a fine-tuned Pre-trained Language Model (PLM) that predicts a target ingredient's quantity and measurement unit given its recipe context. To effectively train our KitchenScale

François Legrand, Elad Paran

Let $H$ be a division ring of finite dimension over its center, let $H[T]$ be the ring of polynomials in a central variable over $H$, and let $H(T)$ be its quotient skew field. We show that every intermediate division ring between $H$ and $H(T)$ is itself of the form $H(f)$, for some $f$ in the center of $H(T)$. This generalizes the classical L\"uroth's theo

Tamer Abdelaziz, Aquinas Hobor

We introduce SCooLS, our Smart Contract Learning (Semi-supervised) engine. SCooLS uses neural networks to analyze Ethereum contract bytecode and identifies specific vulnerable functions. SCooLS incorporates two key elements: semi-supervised learning and graph neural networks (GNNs). Semi-supervised learning produces more accurate models than unsupervised lea

Zitong Lu, Julie D. Golomb

Most models in cognitive and computational neuroscience trained on one subject do not generalize to other subjects due to individual differences. An ideal individual-to-individual neural converter is expected to generate real neural signals of one subject from those of another one, which can overcome the problem of individual differences for cognitive and co

Baiyi Yu, Yaoming Chu, Ralf Betzholz, Shaoliang Zhang

The electric dipole moment (EDM) plays a crucial role in determining the interaction strength of an atom with electric fields, making it paramount to quantum technologies based on coherent atomic control. We propose a scheme for engineering the potential in a Paul trap to realize a two-level quantum system with a giant EDM formed by the motional states of a

Fumihiko Nakano, Hoang Dung Trinh, Khanh Duy Trinh

In a high temperature regime, it was shown in Trinh--Trinh (\emph{J.\ Stat.\ Phys.}\ \textbf{185}(1), Paper No.\ 4, 15 (2021)) that the empirical distribution of beta Jacobi ensembles converges to a limiting probability measure which is related to Model III of associated Jacobi polynomials. In this paper, we establish Gaussian fluctuations around the limit w

Wei Zhiwei, Xiao Yi, Tong Ying, Xu Wenjia

Building patterns are important urban structures that reflect the effect of the urban material and social-economic on a region. Previous researches are mostly based on the graph isomorphism method and use rules to recognize building patterns, which are not efficient. The knowledge graph uses the graph to model the relationship between entities, and specific

Shisong Li, Yongchao Ma, Wei Zhao, Songling Huang

The Kibble balance is a precision instrument for realizing the mass unit, the kilogram, in the new international system of units (SI). In recent years, an important trend for Kibble balance experiments is to go tabletop, in which the instrument's size is notably reduced while retaining a measurement accuracy of $10^{-8}$. In this paper, we report a new desig

Blair Davey, Mariana Smit Vega Garcia

This paper continues the study initiated in [B. Davey, Parabolic theory as a high-dimensional limit of elliptic theory, Arch Rational Mech Anal 228 (2018)], where a high-dimensional limiting technique was developed and used to prove certain parabolic theorems from their elliptic counterparts. In this article, we extend these ideas to the variable-coefficient

Jia Wang, Ming Zeng, Dazhang Li, Xiaoning Wang

We propose to use tightly focused lasers to generate high quality electron beams in laser wakefield accelerators. In this scheme, the expansion of the laser beam after the focal position enlarges the size of wakefield bubble, which reduces the effective phase velocity of the wake and triggers injection of plasma electrons. This scheme injects a relatively lo

Yuanyuan Wei, Julian Jang-Jaccard, Fariza Sabrina, Wen Xu

A Distributed Denial-of-service (DDoS) attack is a malicious attempt to disrupt the regular traffic of a targeted server, service, or network by sending a flood of traffic to overwhelm the target or its surrounding infrastructure. As technology improves, new attacks have been developed by hackers. Traditional statistical and shallow machine learning techniqu

Kang Wang

The use of 3D printing, or additive manufacturing, has gained significant attention in recent years due to its potential for revolutionizing traditional manufacturing processes. One key challenge in 3D printing is managing energy consumption, as it directly impacts the cost, efficiency, and sustainability of the process. In this paper, we propose an energy m

Jianfeng Lu, Yue Wu, Yang Xiang

We use the score-based transport modeling method to solve the mean-field Fokker-Planck equations, which we call MSBTM. We establish an upper bound on the time derivative of the Kullback-Leibler (KL) divergence to MSBTM numerical estimation from the exact solution, thus validates the MSBTM approach. Besides, we provide an error analysis for the algorithm. In

Shengqian Wang, Amirali Salehi-Abari, Julie Thorpe

Passwords, a first line of defense against unauthorized access, must be secure and memorable. However, people often struggle to create secure passwords they can recall. To address this problem, we design Password inspiration by eXploring information (PiXi), a novel approach to nudge users towards creating secure passwords. PiXi is the first of its kind that

Cheng Zhang, Nilam Nur Amir Sjarif, Roslina Ibrahim

Accurately predicting the prices of financial time series is essential and challenging for the financial sector. Owing to recent advancements in deep learning techniques, deep learning models are gradually replacing traditional statistical and machine learning models as the first choice for price forecasting tasks. This shift in model selection has led to a

Seulki Park, Daeho Um, Hajung Yoon, Sanghyuk Chun

With the extensive use of vision-language models in various downstream tasks, evaluating their robustness is crucial. In this paper, we propose a benchmark for assessing the robustness of vision-language models. We believe that a robust model should properly understand both linguistic and visual semantics and be resilient to explicit variations. In pursuit o

Magnus Bordewich, Charles Semple

Phylogenetic diversity is a popular measure for quantifying the biodiversity of a collection $Y$ of species, while phylogenetic diversity indices provide a way to apportion phylogenetic diversity to individual species. Typically, for some specific diversity index, the phylogenetic diversity of $Y$ is not equal to the sum of the diversity indices of the speci

Koki Ikeda, Isao Ono

This paper proposes a natural evolution strategy (NES) for mixed-integer black-box optimization (MI-BBO) that appears in real-world problems such as hyperparameter optimization of machine learning and materials design. This problem is difficult to optimize because plateaus where the values do not change appear when the integer variables are relaxed to the co

Chang Liu, Shuangyang Li, Weijie Yuan, Xuemeng Liu

To guarantee excellent reliability performance in ultra-reliable low-latency communications (URLLC), pragmatic precoder design is an effective approach. However, an efficient precoder design highly depends on the accurate instantaneous channel state information at the transmitter (ICSIT), which however, is not always available in practice. To overcome this p

Hao Mei, Junxian Li, Bin Shi, Hua Wei

The emergence of reinforcement learning (RL) methods in traffic signal control tasks has achieved better performance than conventional rule-based approaches. Most RL approaches require the observation of the environment for the agent to decide which action is optimal for a long-term reward. However, in real-world urban scenarios, missing observation of traff

Y. H. Li, D. Wu, J. Zhang

In the double-cone ignition scheme, compressed fuels in two head-on cones are ejected to collide, forming a colliding plasma with an isochoric distribution for rapid heating by high flux fast electrons from picosecond petawatt laser beams in the perpendicular direction from the cone axis. In this work, we investigate the effects of quantum degeneracy on the

Linwei Sang, Yinliang Xu, Zhongkai Yi, Lun Yang

The increasing penetration of distributed energy resources (DERs) will decrease the rotational inertia of the power system and further degrade the system frequency stability. To address the above issues, this paper leverages the advanced neural network (NN) to learn the frequency dynamics and incorporates NN to facilitate system reliable operation. This pape

Yuxuan Liu, Zhenhua Xu, Huaiyang Huang, Lujia Wang

Predicting accurate depth with monocular images is important for low-cost robotic applications and autonomous driving. This study proposes a comprehensive self-supervised framework for accurate scale-aware depth prediction on autonomous driving scenes utilizing inter-frame poses obtained from inertial measurements. In particular, we introduce a Full-Scale de

Weihao Li, Wenkai Zhu, Gaojie Zhang, Hao Wu

Emerging wide varieties of the two-dimensional (2D) van der Waals (vdW) magnets with atomically thin and smooth interfaces holds great promise for next-generation spintronic devices. However, due to the lower Curie temperature of the vdW 2D ferromagnets than room temperature, electrically manipulating its magnetization at room temperature has not been realiz

Shuang Hu, Meiqin Liu, Senlin Zhang, Shanling Dong

In this paper, physics-informed neural network (PINN) based on characteristic-based split (CBS) is proposed, which can be used to solve the time-dependent Navier-Stokes equations (N-S equations). In this method, The output parameters and corresponding losses are separated, so the weights between output parameters are not considered. Not all partial derivativ

Siyuan Wei, Tianzhu Ye, Shen Zhang, Yao Tang

Although vision transformers (ViTs) have shown promising results in various computer vision tasks recently, their high computational cost limits their practical applications. Previous approaches that prune redundant tokens have demonstrated a good trade-off between performance and computation costs. Nevertheless, errors caused by pruning strategies can lead

Shu-Xu Yi, Zhen Zhang, Xilu Wang

Long-lived massive magnetars are expected to be remnants of some binary neutron star (BNS) mergers. In this paper, we argue that the magnetic powered flaring activities of these merged magnetars would occur dominantly in their early millisecond-period-spin phase, which is in the timescale of days. Such flares endure significant absorption by the ejecta from

Li Ma, Peixi Peng, Guangyao Chen, Yifan Zhao

The sensitivity of deep neural networks to compressed images hinders their usage in many real applications, which means classification networks may fail just after taking a screenshot and saving it as a compressed file. In this paper, we argue that neglected disposable coding parameters stored in compressed files could be picked up to reduce the sensitivity

Anderson José de Oliveira, Giuliano Gadioli La Guardia, Reginaldo Palazzo, Clarice Dias de Albuquerque

In this paper, we consider the steps to be followed in the analysis and interpretation of the quantization problem related to the $C_{2,8}$ channel, where the Fuchsian differential equations, the generators of the Fuchsian groups, and the tessellations associated with the cases $g=2$ and $g=3$, related to the hyperbolic case, are determined. In order to obta

Xin Liu, Chunhao Liang, Yangjian Cai, Sergey A. Ponomarenko

We advance a general theory of field correlation revivals of structured random wave packets, composed of superpositions of propagation-invariant modes, at pairs of planes transverse to the packet propagation direction. We derive an elegant analytical relation between the normalized intensity autocorrelation function of thus structured paraxial light fields a

Chengyin Hu, Weiwen Shi, Tingsong Jiang, Wen Yao

Infrared imaging systems have a vast array of potential applications in pedestrian detection and autonomous driving, and their safety performance is of great concern. However, few studies have explored the safety of infrared imaging systems in real-world settings. Previous research has used physical perturbations such as small bulbs and thermal "QR codes" to

Zhen Tian, Ting Bai, Wayne Xin Zhao, Ji-Rong Wen

Learning effective high-order feature interactions is very crucial in the CTR prediction task. However, it is very time-consuming to calculate high-order feature interactions with massive features in online e-commerce platforms. Most existing methods manually design a maximal order and further filter out the useless interactions from them. Although they redu

Xin Wang, Huai-Bing Zhu, Tao Liu, Franco Nori

To go beyond quantum optics in free-space setups, atom-light interfaces with structured photonic environments are often employed to realize unconventional quantum electrodynamics (QED) phenomena. However, when employed as quantum buses, those long-distance nanostructures are limited by fabrication disorders. In this work, we alternatively propose to realize

G. E. W. Bauer, P. Tang, M. Elyasi, Y. M. Blanter

We discuss spin-wave transport in anisotropic ferromagnets with an emphasis on the zeroes of the band edges as a function of a magnetic field. An associated divergence of the magnon spin should be observable by enhanced magnon conductivities in non-local experiments, especially in two-dimensional ferromagnets.

Masahiro Sugimoto

K. Harada conjectured for any finite group $G$, the product of sizes of all conjugacy classes is divisible by the product of degrees of all irreducible characters. We study this conjecture when $G$ is the general linear group over a finite field. We show the conjecture holds if the order of the field is sufficiently large.

Xinwei Zhang, Zhiqiang Tan, Zhijian Ou

Maximum likelihood (ML) learning for energy-based models (EBMs) is challenging, partly due to non-convergence of Markov chain Monte Carlo.Several variations of ML learning have been proposed, but existing methods all fail to achieve both post-training image generation and proper density estimation. We propose to introduce diffusion data and learn a joint EBM

Xiaosong Yuan, Ke Chen, Wanli Zuo, Yijia Zhang

The present study explores the intricacies of causal relationship extraction, a vital component in the pursuit of causality knowledge. Causality is frequently intertwined with temporal elements, as the progression from cause to effect is not instantaneous but rather ensconced in a temporal dimension. Thus, the extraction of temporal causality holds paramount

Houcheng Su, Jintao Huang, Daixian Liu, Rui Yan

Multi-instance multi-label (MIML) learning is widely applicated in numerous domains, such as the image classification where one image contains multiple instances correlated with multiple logic labels simultaneously. The related labels in existing MIML are all assumed as logical labels with equal significance. However, in practical applications in MIML, signi

Zak Risha, Yiling Lin, Erin Leahey, Lingfei Wu

Scholars are often categorized into two types: hedgehogs (specialists), who focus on working within a specific research field, and foxes (generalists), who actively contribute to a variety of fields. Despite the familiar anecdotes and popularity of this distinction, its empirical foundation has remained largely unexamined. We examine whether the research sty

Guanchu Wang, Ninghao Liu, Daochen Zha, Xia Hu

Anomaly detection, where data instances are discovered containing feature patterns different from the majority, plays a fundamental role in various applications. However, it is challenging for existing methods to handle the scenarios where the instances are systems whose characteristics are not readily observed as data. Appropriate interactions are needed to

Archiki Prasad, Swarnadeep Saha, Xiang Zhou, Mohit Bansal

Multi-step reasoning ability is fundamental to many natural language tasks, yet it is unclear what constitutes a good reasoning chain and how to evaluate them. Most existing methods focus solely on whether the reasoning chain leads to the correct conclusion, but this answer-oriented view may confound reasoning quality with other spurious shortcuts to predict

Shimiao Li, Jan Drgona, Shrirang Abhyankar, Larry Pileggi

Recent years have seen a rich literature of data-driven approaches designed for power grid applications. However, insufficient consideration of domain knowledge can impose a high risk to the practicality of the methods. Specifically, ignoring the grid-specific spatiotemporal patterns (in load, generation, and topology, etc.) can lead to outputting infeasible

Jiaxi Yang, Wenglong Deng, Benlin Liu, Yangsibo Huang

Data valuation plays a crucial role in machine learning. Existing data valuation methods, mainly focused on discriminative models, overlook generative models that have gained attention recently. In generative models, data valuation measures the impact of training data on generated datasets. Very few existing attempts at data valuation methods designed for de

Jason J. Yu, Fereshteh Forghani, Konstantinos G. Derpanis, Marcus A. Brubaker

Novel view synthesis from a single input image is a challenging task, where the goal is to generate a new view of a scene from a desired camera pose that may be separated by a large motion. The highly uncertain nature of this synthesis task due to unobserved elements within the scene (i.e. occlusion) and outside the field-of-view makes the use of generative

Rasoul Ghadimi, Masahiro Hori, Takanori Sugimoto, Takami Tohyama

Motivated by topological equivalence between an extended Haldane model and a chiral-$\pi$-flux model on a square lattice, we apply $\pi$-flux models to two-dimensional bipartite quasicrystals with rhombus tiles in order to investigate topological properties in aperiodic systems. Topologically trivial $\pi$-flux models in the Ammann-Beenker tiling lead to mas

Talagbe Gabin Akpo, Louis-Paul Rivest

This paper proposes multivariate copula models for hierarchical data. They account for two types of correlation: one is between variables measured on the same unit and the other is a correlation between units in the same cluster. This model is used to carry out copula regression for hierarchical data that gives cluster specific prediction curves. In the simp

Anthony M. Bloch, Steven N. Karp

The Toda lattice (1967) is a Hamiltonian system given by $n$ points on a line governed by an exponential potential. Flaschka (1974) showed that the Toda lattice is integrable by interpreting it as a flow on the space of symmetric tridiagonal $n\times n$ matrices, while Moser (1975) showed that it is a gradient flow on a projective space. The symmetric Toda f

Baiqing Zhu

We establish the arithmetic Siegel-Weil formula on the modular curve $\mathcal{X}_{0}(N)$ for arbitrary level $N$, i.e., we relate the arithmetic degrees of special cycles on $\mathcal{X}_{0}(N)$ to the derivatives of Fourier coefficients of a genus 2 Eisenstein series. We prove this formula by a precise identity between the local arithmetic intersection num

Michiyasu Mori, Timothy Ziman

We introduce a simple model for the ferrimagnetic non-collinear ``magnetic umbrella" states of rare-earth iron garnets (REIG), common when the rare-earth moments have non-zero orbital angular momentum. The spin-wave excitations are calculated within linear spin wave theory and temperature effects via mean-field theory. This could be used to determine the mag

Weiyuan Tang, Mudi Wang, Shaojie Ma, C. T. Chan

Topological photonic insulators show promise for applications in compact integrated photonic circuits due to their ability to transport light robustly through sharp bendings. The number of topological edge states relies on the difference between the bulk Chern numbers across the boundary, as dictated by the bulk edge correspondence. The interference among mu