July 2023 arXiv papers — page 73

Praveen Mathil, Jitender Kumar

Let $R$ be a commutative ring with unity. The prime ideal sum graph of the ring $R$ is the simple undirected graph whose vertex set is the set of all nonzero proper ideals of $R$ and two distinct vertices $I$, $J$ are adjacent if and only if $I + J$ is a prime ideal of $R$. In this paper, we characterize all commutative Artinian rings whose prime ideal sum g

Wei Jiang, Qiuheng Zhou, Jiguang He, Mohammad Asif Habibi

Next-generation cellular technologies, commonly referred to as the 6G, are envisioned to support a higher system capacity, better performance, and network sensing capabilities. The THz band is one potential enabler to this end due to the large unused frequency bands and the high spatial resolution enabled by the short signal wavelength and large bandwidth. D

Wen-yu zhou, Shou-Fu Tian, Xiao-fan Zhang

In this work, the nonlinear steepest descent method is employed to study the long-time asymptotics of the integrable nonlocal Lakshmanan-Porsezian-Daniel (LPD) equation with a step-like initial data: $q_{0}(x)\rightarrow0$ as $x\rightarrow-\infty$ and $q_{0}(x)\rightarrow A$ as $x\rightarrow+\infty$, where $A$ is an arbitrary positive constant. Firstly, we d

Harald Semmelrock, Simone Kopeinik, Dieter Theiler, Tony Ross-Hellauer

Research is facing a reproducibility crisis, in which the results and findings of many studies are difficult or even impossible to reproduce. This is also the case in machine learning (ML) and artificial intelligence (AI) research. Often, this is the case due to unpublished data and/or source-code, and due to sensitivity to ML training conditions. Although d

Xiaoxia Wu, Zhewei Yao, Yuxiong He

In the complex domain of large language models (LLMs), striking a balance between computational efficiency and maintaining model quality is a formidable challenge. Navigating the inherent limitations of uniform quantization, particularly when dealing with outliers, and motivated by the launch of NVIDIA's H100 hardware, this study delves into the viability of

Xinyang Zhang, Wentian Zhao, Xin Lu, Jeff Chien

Layer compositing is one of the most popular image editing workflows among both amateurs and professionals. Motivated by the success of diffusion models, we explore layer compositing from a layered image generation perspective. Instead of generating an image, we propose to generate background, foreground, layer mask, and the composed image simultaneously. To

Andre Sopczak

We describe the status of the ATLAS Forward Proton (AFP) detectors in Run-2 and the outlook for Run-3 analyses. The performance is discussed. This includes the Tracking and Time-of-Flight detectors, the luminosity, the alignment, the trigger, and data quality monitoring. Additionally, key physics results from the first AFP analyses are showcased.

Michael Oesterle, Patrick Blöbaum, Atalanti A. Mastakouri, Elke Kirschbaum

Which set of features was responsible for a certain output of a machine learning model? Which components caused the failure of a cloud computing application? These are just two examples of questions we are addressing in this work by Identifying Coalition-based Explanations for Common and Rare Events in Any Model (ICECREAM). Specifically, we propose an inform

Anand S Bharadwaj

In this paper, we look at the pressure checkerboard problem that arises in an Eulerian meshless method that solves the incompressible Navier-Stokes equations using the generalized finite difference method (GFDM). Although, the checkerboard problem has been dealt with extensively in mesh-based methods, the literature in connection with meshless methods is com

Aranka Derzsi, Mate Vass, Ranna Masheyeva, Benedek Horvath

Phase Resolved Optical Emission Spectroscopy (PROES) measurements combined with 1d3v Particle-in-Cell/Monte Carlo Collision (PIC/MCC) simulations are performed to investigate the excitation dynamics in low-pressure capacitively coupled plasmas (CCPs) in argon-oxygen mixtures. The system used for this study is a geometrically symmetric CCP reactor operated in

Lejun Min, Junyan Jiang, Gus Xia, Jingwei Zhao

We propose Polyffusion, a diffusion model that generates polyphonic music scores by regarding music as image-like piano roll representations. The model is capable of controllable music generation with two paradigms: internal control and external control. Internal control refers to the process in which users pre-define a part of the music and then let the mod

Shuo Huang, Chengpeng Hu, Julian Togelius, Jialin Liu

Procedurally generating cities in Minecraft provides players more diverse scenarios and could help understand and improve the design of cities in other digital worlds and the real world. This paper presents a city generator that was submitted as an entry to the 2023 Edition of Minecraft Settlement Generation Competition for Minecraft. The generation procedur

Shaun Azzopardi, Luca Di Stefano, Nir Piterman, Gerardo Schneider

Recently, interest has increased in applying reactive synthesis to richer-than-Boolean domains. A major (undecidable) challenge in this area is to establish when certain repeating behaviour terminates in a desired state when the number of steps is unbounded. Existing approaches struggle with this problem, or can handle at most deterministic games with B\"uch

Jiahao Xun, Shengyu Zhang, Yanting Yang, Jieming Zhu

In the field of music information retrieval (MIR), cover song identification (CSI) is a challenging task that aims to identify cover versions of a query song from a massive collection. Existing works still suffer from high intra-song variances and inter-song correlations, due to the entangled nature of version-specific and version-invariant factors in their

Qianjun Zhang, Wei Dong, Qingsong Xu, Bimal J. Goteea

Decoupled parallel $xyz$ positioning stages with large stroke have been desired in high-speed and precise positioning fields. However, currently such stages are either short in stroke or unqualified in parasitic motion and coupling rate. This paper proposes a novel flexure-based decoupled parallel $xyz$ positioning stage (FlexDelta) and conducts its conceptu

Hideaki Takahashi, Jingjing Liu, Yang Liu

Vertical federated learning (VFL) enables multiple parties with disjoint features of a common user set to train a machine learning model without sharing their private data. Tree-based models have become prevalent in VFL due to their interpretability and efficiency. However, the vulnerability of tree-based VFL has not been sufficiently investigated. In this s

Thomas D. Honeychurch, Daniel S. Kosov

We study the dynamics of two capacitively coupled quantum dots, each coupled to a lead. A Floquet Green's function approach described the system's dynamics, with the electron-electron interactions handled with the fluctuation-exchange approximation. While electrons cannot move between the separate sections of the device, energy transfer occurs with the perio

M. S. Nadirbekov, O. A. Bozarov, N. Minkov

The quantization of the kinetic energy of a deformed nucleus in curvilinear coordinates in the case of octupole oscillations of its surface firstly has been carried out. The obtained form of the Hamiltonian differs from the previously obtained Hamiltonian for quadrupole oscillations only by factors in front of the derivatives $\partial/\partial\gamma$ and $\

Ke Xu, Jiangtao Wang, Hongyuan Zhu, Dingchang Zheng

Recently, with the advancement of the Internet of Things (IoT), WiFi CSI-based HAR has gained increasing attention from academic and industry communities. By integrating the deep learning technology with CSI-based HAR, researchers achieve state-of-the-art performance without the need of expert knowledge. However, the scarcity of labeled CSI data remains the

William Béthune

Fluid discontinuities, such as shock fronts and vortex sheets, can reflect waves and become unstable to corrugation. Analytical calculations of these phenomena are tractable in the simplest cases only, while their numerical simulations are biased by truncation errors inherent to discretization schemes. The author lays down a computational framework to study

Jinyang Li, Zhepeng Wang, Zhirui Hu, Prasanna Date

Quantum computing presents a promising approach for machine learning with its capability for extremely parallel computation in high-dimension through superposition and entanglement. Despite its potential, existing quantum learning algorithms, such as Variational Quantum Circuits(VQCs), face challenges in handling more complex datasets, particularly those tha

Peizhen Yang, Xinke Shen, Zongsheng Li, Zixiang Luo

Identifying causal relationships among distinct brain areas, known as effective connectivity, holds key insights into the brain's information processing and cognitive functions. Electroencephalogram (EEG) signals exhibit intricate dynamics and inter-areal interactions within the brain. However, methods for characterizing nonlinear causal interactions among m

Mahendra K. Verma

Turbulent flows exhibit intriguing energy transfers. In this paper, we compute the renormalized viscosities, mode-to-mode energy transfers, energy fluxes, and shell-to-shell energy transfers for the two-dimensional (2D) and three-dimensional (3D) hydrodynamic turbulence (HDT) using field-theoretic methods. We employ Craya-Herring basis that provides separate

Qinji Yu, Nan Xi, Junsong Yuan, Ziyu Zhou

Unsupervised domain adaptation (UDA) has increasingly gained interests for its capacity to transfer the knowledge learned from a labeled source domain to an unlabeled target domain. However, typical UDA methods require concurrent access to both the source and target domain data, which largely limits its application in medical scenarios where source data is o

Dai Shi, Yi Guo, Zhiqi Shao, Junbin Gao

Graph neural network (GNN) has been demonstrated powerful in modeling graph-structured data. However, despite many successful cases of applying GNNs to various graph classification and prediction tasks, whether the graph geometrical information has been fully exploited to enhance the learning performance of GNNs is not yet well understood. This paper introdu

Magnus Wiese, Phillip Murray, Ralf Korn

We propose a novel generative model for multivariate discrete-time time series data. Drawing inspiration from the construction of neural spline flows, our algorithm incorporates linear transformations and the signature transform as a seamless substitution for traditional neural networks. This approach enables us to achieve not only the universality property

Itsuki Iwata, Takuya Sakamoto, Takuya Matsumoto, Satoshi Hirata

This study proposes a method to determine the filter parameters required for the topology method, which is a radar-based noncontact method for measurement of heart inter-beat intervals. The effectiveness of the proposed method is evaluated by performing radar measurements involving both human participants and chimpanzee subjects. The proposed method is desig

Bowen Xu, Thanh-Dat Nguyen, Thanh Le-Cong, Thong Hoang

Stack Overflow, the world's largest software Q&A (SQA) website, is facing a significant traffic drop due to the emergence of generative AI techniques. ChatGPT is banned by Stack Overflow after only 6 days from its release. The main reason provided by the official Stack Overflow is that the answers generated by ChatGPT are of low quality. To verify this, we c

Felix Nickel, André Kubetzka, Soumyajyoti Haldar, Roland Wiesendanger

We identify the triple-Q (3Q) state as magnetic ground state in Pd/Mn and Rh/Mn bilayers on Re(0001) using spin-polarized scanning tunneling microscopy and density functional theory. An atomistic model reveals that in general the 3Q state with tetrahedral magnetic order and zero net spin moment is coupled to a hexagonal atomic lattice in a highly symmetric o

Qingwen Bu, Dong Huang, Heming Cui

The vulnerability of deep neural networks to adversarial samples has been a major impediment to their broad applications, despite their success in various fields. Recently, some works suggested that adversarially-trained models emphasize the importance of low-frequency information to achieve higher robustness. While several attempts have been made to leverag

Abhishek Ajayakumar, Soumyendu Raha

Many real-world systems are modelled using complex ordinary differential equations (ODEs). However, the dimensionality of these systems can make them challenging to analyze. Dimensionality reduction techniques like Proper Orthogonal Decomposition (POD) can be used in such cases. However, these reduced order models are susceptible to perturbations in the inpu

Chia-Hsiang Kao, Yu-Chiang Frank Wang

Federated Learning (FL) offers a collaborative training framework, allowing multiple clients to contribute to a shared model without compromising data privacy. Due to the heterogeneous nature of local datasets, updated client models may overfit and diverge from one another, commonly known as the problem of client drift. In this paper, we propose FedBug (Fede

Nicholas F. Wogan, David C. Catling, Kevin J. Zahnle, Roxana Lupu

The origin of life on Earth would benefit from a prebiotic atmosphere that produced nitriles, like HCN, which enable ribonucleotide synthesis. However, geochemical evidence suggests that Hadean air was relatively oxidizing with negligible photochemical production of prebiotic molecules. These paradoxes are resolved by iron-rich asteroid impacts that transien

Kaumudi Joshi, Vukka Snigdha, Arya Kumar Bhattacharya

The phenomena of Spectral Bias, where the higher frequency components of a function being learnt in a feedforward Artificial Neural Network (ANN) are seen to converge more slowly than the lower frequencies, is observed ubiquitously across ANNs. This has created technology challenges in fields where resolution of higher frequencies is crucial, like in Physics

Aaron Nicolson, Jason Dowling, Bevan Koopman

Radiologists face high burnout rates, partially due to the increasing volume of Chest X-rays (CXRs) requiring interpretation and reporting. Automated CXR report generation holds promise for reducing this burden and improving patient care. While current models show potential, their diagnostic accuracy is limited. Our proposed CXR report generator integrates e

Ronika Sarkar, Ayan Banerjee, Awadhesh Narayan

The union of topology and non-Hermiticity has led to the unveiling of many intriguing phenomena. We introduce a synthetic spin-engineered model belonging to symmetry class AI, which is a rare occurrence, and demonstrate the emergence of a multi-fold spectral topology. As an example of our proposal, we engineer non-Hermiticity in the paradigmatic Su-Schrieffe

Yuzhong Zhao, Qixiang Ye, Weijia Wu, Chunhua Shen

Weakly supervised object localization (WSOL) remains challenging when learning object localization models from image category labels. Conventional methods that discriminatively train activation models ignore representative yet less discriminative object parts. In this study, we propose a generative prompt model (GenPromp), defining the first generative pipel

Changqi Wang, Haoyu Xie, Yuhui Yuan, Chong Fu

Semi-Supervised Semantic Segmentation (S4) aims to train a segmentation model with limited labeled images and a substantial volume of unlabeled images. To improve the robustness of representations, powerful methods introduce a pixel-wise contrastive learning approach in latent space (i.e., representation space) that aggregates the representations to their pr

Mohamed Elnoor, Adarsh Jagan Sathyamoorthy, Kasun Weerakoon, Dinesh Manocha

We propose a novel method, ProNav, which uses proprioceptive signals for traversability estimation in challenging outdoor terrains for autonomous legged robot navigation. Our approach uses sensor data from a legged robot's joint encoders, force, and current sensors to measure the joint positions, forces, and current consumption respectively to accurately ass

Luke Munn, Liam Magee, Vanicka Arora

AI image models are rapidly evolving, disrupting aesthetic production in many industries. However, understanding of their underlying archives, their logic of image reproduction, and their persistent biases remains limited. What kind of methods and approaches could open up these black boxes? In this paper, we provide three methodological approaches for invest

Dean Crnković, Daniel R. Hawtin, Nina Mostarac, Andrea Švob

A code $C$ is a subset of the vertex set of a graph and $C$ is $s$-neighbour-transitive if its automorphism group ${\rm Aut}(C)$ acts transitively on each of the first $s+1$ parts $C_0,C_1,\ldots,C_s$ of the distance partition $\{C=C_0,C_1,\ldots,C_\rho\}$, where $\rho$ is the covering radius of $C$. While codes have traditionally been studied in the Hamming

Qingyao Ai, Ting Bai, Zhao Cao, Yi Chang

The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs. Recently, Large Language Models (LLMs) have demonstrated exceptional capabilities in text understanding, generation, and knowledge inference, opening up exciting avenues for IR research. LLMs not only facilit

Tom Joly-Jehenne, Artur R. Davoyan

Harnessing solar radiation pressure is key to transforming space exploration with multiple low cost sunlight propelled spacecraft to outer reaches of space. By controlling the direction of sunlight momentum transfer new missions and better maneuvering in space can be accessed. Here, we discuss design principles for taming in-plane radiation pressure under am

Hang Guo, Tao Dai, Guanghao Meng, Shu-Tao Xia

Scene text image super-resolution (STISR), aiming to improve image quality while boosting downstream scene text recognition accuracy, has recently achieved great success. However, most existing methods treat the foreground (character regions) and background (non-character regions) equally in the forward process, and neglect the disturbance from the complex b

Feiran Hu, Peng Wang, Yangyang Li, Chenlong Duan

The SnakeCLEF2023 competition aims to the development of advanced algorithms for snake species identification through the analysis of images and accompanying metadata. This paper presents a method leveraging utilization of both images and metadata. Modern CNN models and strong data augmentation are utilized to learn better representation of images. To reliev

Heinz H. Bauschke, Walaa M. Moursi, Shambhavi Singh, Xianfu Wang

Monotone inclusion problems occur in many areas of optimization and variational analysis. Splitting methods, which utilize resolvents or proximal mappings of the underlying operators, are often applied to solve these problems. In 2022, Bredies, Chenchene, Lorenz, and Naldi introduced a new elegant algorithmic framework that encompasses various well known alg

Sébastien Vievard, Elsa Huby, Sylvestre Lacour, Olivier Guyon

FIRST is a spectro-interferometer combining, in the visible, the techniques of aperture masking and spatial filtering thanks to single-mode fibers. This instrument aims to deliver high contrast capabilities at spatial resolutions that are inaccessible to classical coronagraphic instruments. The technique implemented is called pupil remapping: the telescope i

Lizhao Liu, Zhuangwei Zhuang, Shangxin Huang, Xunlong Xiao

We study the task of weakly-supervised point cloud semantic segmentation with sparse annotations (e.g., less than 0.1% points are labeled), aiming to reduce the expensive cost of dense annotations. Unfortunately, with extremely sparse annotated points, it is very difficult to extract both contextual and object information for scene understanding such as sema

Yulan Liu, Robert A. Main, Joris P. W. Verbiest, Ziwei Wu

Annual variations of interstellar scintillation can be modelled to constrain parameters of the ionized interstellar medium. If a pulsar is in a binary system, then investigating the orbital parameters is possible through analysis of the orbital variation of scintillation. In observations carried out from 2011 January to 2020 August by the European Pulsar Tim

Long Mai, Julie Carson-Berndsen

The integration of natural language processing (NLP) technologies into educational applications has shown promising results, particularly in the language learning domain. Recently, many spoken open-domain chatbots have been used as speaking partners, helping language learners improve their language skills. However, one of the significant challenges is the hi

Sayan Bagchi, Riju Basak, Rahul Garg, Abhishek Ghosh

This is a continuation of our work [BBGG23, BBGG22] where we have initiated the study of sparse domination and quantitative weighted estimates for Grushin pseudo-multipliers. In this article, we further extend this analysis to study analogous estimates for a family of operator-valued Fourier pseudo-multipliers associated to Grushin operators $G = - \Delta_{x

Ganlong Zhao, Guanbin Li, Yipeng Qin, Yizhou Yu

Dataset Condensation aims to condense a large dataset into a smaller one while maintaining its ability to train a well-performing model, thus reducing the storage cost and training effort in deep learning applications. However, conventional dataset condensation methods are optimization-oriented and condense the dataset by performing gradient or parameter mat

Rinnosuke Matsuhira, Toshiki Matsusaka, Koki Tsuchida

Inspired by Episode 3 of the Japanese manga "Seisu-tan" by Doom Kobayashi and Shin-ichiro Seki, we investigate the $k$-G\"{o}bel sequence $(g_{k,n})_n$ named after Fritz G\"{o}bel. Although the sequence is generally defined as rational, quite a few initial terms behave like an integer sequence. This article addresses a question raised in Seisu-tan and shows

Yiqi Xing, Yu Liu, Dayou Lu, Xinchen Zou

Data driven transmission line fault location methods have the potential to more accurately locate faults by extracting fault information from available data. However, most of the data driven fault location methods in the literature are not validated by field data for the following reasons. On one hand, the available field data during faults are very limited

Naoki Seto

Two groups recently analyzed the long-term orbital evolution of HM Cancri, which is one of the most important verification binaries for the space gravitational wave detector LISA. By using the reported first and second derivatives of its orbital frequency $f$, we discuss potential tertiary effects on this binary. We found that, in contrast to the first deriv

Mitchell Barrington

This paper argues that training AI systems with absolute constraints -- which forbid certain acts irrespective of the amount of value they might produce -- may make considerable progress on many AI safety problems in principle. First, it provides a guardrail for avoiding the very worst outcomes of misalignment. Second, it could prevent AIs from causing catas

Zecheng Zhang, Wing Tat Leung, Hayden Schaeffer

We present a generalized version of the discretization-invariant neural operator and prove that the network is a universal approximation in the operator sense. Moreover, by incorporating additional terms in the architecture, we establish a connection between this discretization-invariant neural operator network and those discussed before. The discretization-

Kento Asai, Arindam Das, Jinmian Li, Takaaki Nomura

Motivated by the observation of tiny neutrino mass can not be explained within the framework of Standard Model (SM), we consider extra gauge extended scenarios in which tiny neutrino masses are generated through seesaw mechanism. These scenarios are equipped with beyond the standard model (BSM) neutral gauge boson called $Z^\prime$ in the general $U(1)_X$ sy

Maliha Mahajebin, Mohammad Rifat Ahmmad Rashid, Nafees Mansoor

Music can evoke various emotions, and with the advancement of technology, it has become more accessible to people. Bangla music, which portrays different human emotions, lacks sufficient research. The authors of this article aim to analyze Bangla songs and classify their moods based on the lyrics. To achieve this, this research has compiled a dataset of 4000

I Wayan Palton Anuwiksa, Rinovia Simanjuntak, Edy Tri Baskoro

In 2019, Perondi and Carmelo determined the set multipartite Ramsey number of particular complete bipartite graphs by establishing a relationship between the set multipartite Ramsey number, Hadamard matrices, and strongly regular graphs, which is a breakthrough in Ramsey theory. However, since Hadamard matrices of order not divisible by 4 do not exist, many

Xue Han, Huafeng Liu

In this paper, we prove that every pair of sufficiently large odd integers can be represented in the form of a pair of one prime, four prime cubes and $48$ powers of $2$.

Boris Ryabko

Perfect ciphers have been a very attractive cryptographic tool ever since C. Shannon described them. Note that, by definition, if a perfect cipher is used, no one can get any information about the encrypted message without knowing the secret key. We consider the problem of reducing the key length of perfect ciphers, because in many applications the length of

Yoshihiro Nishiyama

The transverse-field $XY$ spin chain with competing antiferromagnetic long-range interactions, $J_r \propto 1/r^\alpha$ ($r$: distance between spins), and the exponent $\alpha$ was investigated numerically. The main concern is to clarify the character of the transverse-field-driven phase transition for the small-$\alpha$ regime around the $XX$-symmetric poin

Jared Marx-Kuo

We compute the second inner variation of the Abelian Yang--Mills--Higgs and Ginzburg--Landau energies. Given a sequence of critical points with energy measures converging to a codimension $2$ minimal submanifold, we use the second inner variation formula to bound the morse index of the submanifold by the index of the critical points. The key tools are the co

Leyao Liu, Tao Kong, Minzhao Zhu, Jiashuo Fan

3D instance segmentation methods often require fully-annotated dense labels for training, which are costly to obtain. In this paper, we present ClickSeg, a novel click-level weakly supervised 3D instance segmentation method that requires one point per instance annotation merely. Such a problem is very challenging due to the extremely limited labels, which ha

Jiarui Zhang, Jiguo Cao, Liangliang Wang

We develop a robust Bayesian functional principal component analysis (RB-FPCA) method that utilizes the skew elliptical class of distributions to model functional data, which are observed over a continuous domain. This approach effectively captures the primary sources of variation among curves, even in the presence of outliers, and provides a more robust and

Monica S. Li, Hannah S. Stuart

Sound is a rich information medium that transmits through air; people communicate through speech and can even discern material through tapping and listening. To capture frequencies in the human hearing range, commercial microphones typically have a sampling rate of over 40kHz. These accessible acoustic technologies are not yet widely adopted for the explicit

Shalaleh Rismani, Renee Shelby, Andrew Smart, Renelito Delos Santos

Identifying potential social and ethical risks in emerging machine learning (ML) models and their applications remains challenging. In this work, we applied two well-established safety engineering frameworks (FMEA, STPA) to a case study involving text-to-image models at three stages of the ML product development pipeline: data processing, integration of a T2

Xiaohong Liu, Xiongkuo Min, Wei Sun, Yulun Zhang

This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos. The chall

Ming Tong, Xuefeng Yan, Yongzhen Wang

Visual-based measurement systems are frequently affected by rainy weather due to the degradation caused by rain streaks in captured images, and existing imaging devices struggle to address this issue in real-time. While most efforts leverage deep networks for image deraining and have made progress, their large parameter sizes hinder deployment on resource-co

Zi Li, Lin Tian, Tony C. W. Mok, Xiaoyu Bai

Estimating displacement vector field via a cost volume computed in the feature space has shown great success in image registration, but it suffers excessive computation burdens. Moreover, existing feature descriptors only extract local features incapable of representing the global semantic information, which is especially important for solving large transfor

Chen Liu, Beatrice Riviere, Jie Shen, Xiangxiong Zhang

For time-dependent PDEs, the numerical schemes can be rendered bound-preserving without losing conservation and accuracy, by a post processing procedure of solving a constrained minimization in each time step. Such a constrained optimization can be formulated as a nonsmooth convex minimization, which can be efficiently solved by first order optimization meth

Yuxiang Li, Jens-Christian Svenning, Weiqi Zhou, Kai Zhu

Heat extremes are projected to severely impact humanity and with increasing geographic disparities. Global South countries are more exposed to heat extremes and have reduced adaptation capacity. One documented source of such adaptation inequality is a lack of resources to cool down indoor temperatures. Less is known about the capacity to ameliorate outdoor h

Kibeom Hong, Seogkyu Jeon, Junsoo Lee, Namhyuk Ahn

To deliver the artistic expression of the target style, recent studies exploit the attention mechanism owing to its ability to map the local patches of the style image to the corresponding patches of the content image. However, because of the low semantic correspondence between arbitrary content and artworks, the attention module repeatedly abuses specific l

Honglin Mu, Wentian Xia, Wanxiang Che

Sound classification models' performance suffers from generalizing on out-of-distribution (OOD) data. Numerous methods have been proposed to help the model generalize. However, most either introduce inference overheads or focus on long-lasting CNN-variants, while Transformers has been proven to outperform CNNs on numerous natural language processing and comp

William W. Hager

In an earlier paper (https://doi.org/10.1137/21M1393315), the Switch Point Algorithm was developed for solving optimal control problems whose solutions are either singular or bang-bang or both singular and bang-bang, and which possess a finite number of jump discontinuities in an optimal control at the points in time where the solution structure changes. The

Pengfei Luo, Tong Xu, Shiwei Wu, Chen Zhu

Multimodal entity linking (MEL) task, which aims at resolving ambiguous mentions to a multimodal knowledge graph, has attracted wide attention in recent years. Though large efforts have been made to explore the complementary effect among multiple modalities, however, they may fail to fully absorb the comprehensive expression of abbreviated textual context an

Golnar Gharooni-Fard, Morgan Byers, Varad Deshmukh, Elizabeth Bradley

A primary challenge in understanding collective behavior is characterizing the spatiotemporal dynamics of the group. We employ topological data analysis to explore the structure of honeybee aggregations that form during trophallaxis, which is the direct exchange of food among nestmates. From the positions of individual bees, we build topological summaries ca

Juanjuan Xu, Jingmei Liu, Zhaorong Zhang, Wei Wang

This paper is concerned with the linear quadratic optimal control of discrete-time time-varying system with terminal state constraint. The main contribution is to propose a Q-learning algorithm for the optimal controller when the time-varying system matrices and input matrices are both unknown. Different from the existing Q-learning algorithms in the literat

Benjamin Linowitz

In a recent paper Garoufalidis and Reid constructed pairs of 1-cusped hyperbolic 3-manifolds which are isospectral but not isometric. In this paper we extend this work to the multi-cusped setting by constructing isospectral but not isometric hyperbolic 3-manifolds with arbitrarily many cusps. The manifolds we construct have the same Eisenstein series, the sa

Wan-Lu Li, Kaixuan Chen, Elliot Rossomme, Martin Head-Gordon

We present an investigation into the transferability of pseudopotentials (PPs) with a nonlinear core correction (NLCC) using the Goedecker, Teter, and Hutter (GTH) protocol across a range of pure GGA, meta-GGA and hybrid functionals, and their impact on thermochemical and non-thermochemical properties. The GTH-NLCC PP for the PBE density functional demonstra

Shobhit Aggarwal, Arnab Purkayastha

The COVID-19 pandemic has highlighted the need for innovative solutions to monitor and control the spread of infectious diseases. With the potential for future pandemics and the risk of outbreaks particularly in academic institutions, there is a pressing need for effective approaches to monitor and manage such diseases. Contact tracing using Global Positioni

G. Pacelli Bessa, Steen Markvorsen, Leandro F. Pessoa

We show that submanifolds with infinite mean exit time can not be isometrically and minimally immersed into cylinders, horocylinders, cones, and wedges of some product spaces. Our approach is not based on the weak maximum principle at infinity, and thus it permits us to generalize previous results concerning non-immersibility of stochastically complete subma

Leilei Ma, Dengdi Sun, Lei Wang, Haifeng Zhao

Extracting image semantics effectively and assigning corresponding labels to multiple objects or attributes for natural images is challenging due to the complex scene contents and confusing label dependencies. Recent works have focused on modeling label relationships with graph and understanding object regions using class activation maps (CAM). However, thes

Minyu Fan, Kun Liu, Jie Zhu, Yu Cao

Multimode fiber (MMF) has been proven to have good potential in imaging and optical communication because of its advantages of small diameter and large mode numbers. However, due to the mode coupling and modal dispersion, it is very sensitive to environmental changes. Minor changes in the fiber shape can lead to difficulties in information reconstruction. He

Nepal Banerjee

Spontaneous symmetry breaking of 2D isotropic Heisenberg magnet is restricted by Mermin-Wagner theorem at any finite temperature in presence of short-range exchange interaction.Kosterlitz and Thouless using XY spin model showed that how an order state could developed in 2D spin system in presence of short range isotropic interaction.Very recent discovery of

Mohsen Sadatsafavi, John Petkau

Moderate calibration, the expected event probability among observations with predicted probability z being equal to z, is a desired property of risk prediction models. Current graphical and numerical techniques for evaluating moderate calibration of risk prediction models are mostly based on smoothing or grouping the data. As well, there is no widely accepte

Xiaolei Zhang

Let $R$ be a ring and $S$ a multiplicative subset of $R$. We introduce and study the notions of ($u$-)$S$-$w$-Noetherian modules and ($u$-)$S$-$w$-principal ideal modules. Some characterizations of these new concepts are given.

Spencer C. Wallace, Thomas R. Quinn

Formation models in which terrestrial bodies grow via the pairwise accretion of planetesimals have been reasonably successful at reproducing the general properties of the solar system, including small body populations. However, planetesimal accretion has not yet been fully explored in the context of the wide variety of recently discovered extrasolar planetar

Soohyun Park, Haemin Lee, Chanyoung Park, Soyi Jung

This paper presents the deep learning-based recent achievements to resolve the problem of autonomous mobility control and efficient resource management of autonomous vehicles and UAVs, i.e., (i) multi-agent reinforcement learning (MARL), and (ii) neural Myerson auction. Representatively, communication network (CommNet), which is one of the most popular MARL

Julian Sester

We study the influence of additional intermediate marginal distributions on the value of the martingale optimal transport problem. From a financial point of view, this corresponds to taking into account call option prices not only, as usual, for those call options where the respective future maturities coincide with the maturities of some exotic derivative b

Jiayi Liu, Xudong Sun, Peter W. Schuck, Sarah A. Jaeggli

Delta ($\delta$) sunspots sometimes host fast photospheric flows along the central magnetic polarity inversion line (PIL). Here we study the strong Doppler shift signature in the central penumbral light bridge of solar active region NOAA 12673. Observations from the Helioseismic and Magnetic Imager (HMI) indicate highly sheared, strong magnetic fields. Large

Huan-Wen Wang, Bo Fu, Shun-Qing Shen

The anomalous Hall effect has considerable impact on the progress of condensed matter physics and occurs in systems with time-reversal symmetry breaking. Here we theoretically investigate the anomalous Hall effect in nonmagnetic transition-metal pentatelluride $\mathrm{ZrTe_{5}}$ and $\mathrm{HfTe}_{5}$. In the presence of Zeeman splitting and Dirac mass, th

Chaojin Qing, Shuhai Tang, Na Yang, Chuangui Rao

Timing synchronization (TS) is vital for orthogonal frequency division multiplexing (OFDM) systems, which makes the discrete Fourier transform (DFT) window start at the inter-symbol-interference (ISI)-free region. However, the multi-path uncertainty in wireless communication scenarios degrades the TS correctness. To alleviate this degradation, we propose a l

Tianjian Gao, Phillipe Langlais

Taxonomies are an essential knowledge representation, yet most studies on automatic taxonomy construction (ATC) resort to manual evaluation to score proposed algorithms. We argue that automatic taxonomy evaluation (ATE) is just as important as taxonomy construction. We propose RaTE, an automatic label-free taxonomy scoring procedure, which relies on a large

Guohai Xu, Jiayi Liu, Ming Yan, Haotian Xu

With the rapid evolution of large language models (LLMs), there is a growing concern that they may pose risks or have negative social impacts. Therefore, evaluation of human values alignment is becoming increasingly important. Previous work mainly focuses on assessing the performance of LLMs on certain knowledge and reasoning abilities, while neglecting the

Chenyue Jiao, Kai Li, Zhichao Fang

As part of the data-driven paradigm and open science movement, the data paper is becoming a popular way for researchers to publish their research data, based on academic norms that cross knowledge domains. Data journals have also been created to host this new academic genre. The growing number of data papers and journals has made them an important large-scal

Tao Cui, Wenhao Lu, Naiyan Pan, Weiying Zheng

In this paper, we study a priori error estimates for the finite element approximation of the nonlinear Schr\"{o}dinger-Poisson model. The electron density is defined by an infinite series over all eigenvalues of the Hamiltonian operator. To establish the error estimate, we present a unified theory of error estimates for a class of nonlinear problems. The the

Brandon T. Willard, Rémi Louf

In this article we show how the problem of neural text generation can be constructively reformulated in terms of transitions between the states of a finite-state machine. This framework leads to an efficient approach to guiding text generation with regular expressions and context-free grammars by allowing the construction of an index over a language model's

Hao Peng, Qingqing Cao, Jesse Dodge, Matthew E. Peters

Rising computational demands of modern natural language processing (NLP) systems have increased the barrier to entry for cutting-edge research while posing serious environmental concerns. Yet, progress on model efficiency has been impeded by practical challenges in model evaluation and comparison. For example, hardware is challenging to control due to dispar