April 2023 arXiv papers — page 3

Zhe Chen, Yang Yang, Anne Bettens, Youngho Eun

Detecting Resident Space Objects (RSOs) and preventing collisions with other satellites is crucial. Recently, deep convolutional neural networks (DCNNs) have shown superior performance in object detection when large-scale datasets are available. However, collecting rich data of RSOs is difficult due to very few occurrences in the space images. Without suffic

R. Selvaraj, T. Satheesh, V. Suresh, V. Yathavaraj

Globally, Coronary Heart Disease (CHD) is one of the main causes of death. Early detection of CHD can improve patient outcomes and reduce mortality rates. We propose a novel framework for predicting the presence of CHD using a combination of machine learning and image processing techniques. The framework comprises various phases, including analyzing the data

Vishesh Mittal, Rahul Meshram, Deepak Dev, Surya Prakash

We consider finite state restless multi-armed bandit problem. The decision maker can act on M bandits out of N bandits in each time step. The play of arm (active arm) yields state dependent rewards based on action and when the arm is not played, it also provides rewards based on the state and action. The objective of the decision maker is to maximize the inf

Hiba Bibi, Dorel Fetcu, Cezar Oniciuc

We consider the Segre embedding of the product $\mathbb{C}P^p\times\mathbb{C}P^q$ into $\mathbb{C}P^{p+q+pq}$ and study the biharmonicity of $M^p\times\mathbb{C}P^q$ and $M^p_1\times M^q_2$ as submanifolds of $\mathbb{C}P^{p+q+pq}$, where $M$ and $M_1$ are Lagrangian submanifolds of $\mathbb{C}P^p$ and $M_2$ is a Lagrangian submanifold of $\mathbb{C}P^q$. We

Kaiqiang Liu, Zhengchun Zhou, Avik Ranjan Adhikary, Rong Luo

Extended Boolean functions (EBFs) are one of the most important tools in cryptography and spreading sequence design in communication systems. In this paper, we use EBFs to design new sets of spreading sequences for non-orthogonal multiple access (NOMA), which is an emerging technique capable of supporting massive machine-type communications (mMTC) in 5G and

Bishnu P Lamichhane

We consider a mixed finite element method for a biharmonic equation with clamped boundary conditions based on biorthogonal systems with weakly imposed Dirichlet boundary condition. We show that the weak imposition of the boundary condition arising from a natural minimisation formulation allows to get an optimal a priori error estimate for the finite element

Zhongyang Zhu, Junqiao Zhao, Kai Huang, Xuebo Tian

Simultaneous localization and mapping (SLAM) is critical to the implementation of autonomous driving. Most LiDAR-inertial SLAM algorithms assume a static environment, leading to unreliable localization in dynamic environments. Moreover, the accurate tracking of moving objects is of great significance for the control and planning of autonomous vehicles. This

Graham H. Norton

Rueppel's conjecture on the linear complexity of the first $n$ terms of the sequence $(1,1,0,1,0^3,1,0^7,1,0^{15},\ldots)$ was first proved by Dai using the Euclidean algorithm. We have previously shown that we can attach a homogeneous (annihilator) ideal of $F[x,z]$ to the first $n$ terms of a sequence over a field $F$ and construct a pair of generating for

Zhejie Ding, Yu Yu, Pengjie Zhang

The China Space Station Telescope (CSST) is a forthcoming Stage IV galaxy survey. It will simultaneously undertake the photometric redshift (photo-z) and slitless spectroscopic redshift (spec-z) surveys mainly for weak lensing and galaxy clustering studies. The two surveys cover the same sky area and overlap on the redshift range. At $z>1$, due to the sparse

Karun Adusumilli

Recent years have seen tremendous advances in the theory and application of sequential experiments. While these experiments are not always designed with hypothesis testing in mind, researchers may still be interested in performing tests after the experiment is completed. The purpose of this paper is to aid in the development of optimal tests for sequential e

Khai Nguyen, Nhat Ho

The sliced Wasserstein (SW) distances between two probability measures are defined as the expectation of the Wasserstein distance between two one-dimensional projections of the two measures. The randomness comes from a projecting direction that is used to project the two input measures to one dimension. Due to the intractability of the expectation, Monte Car

Dana Jones, Ling Sun, Nils Siemonsen, William E. East

Ultralight bosons are predicted in many extensions to the Standard Model and are popular dark matter candidates. The black hole superradiance mechanism allows for these particles to be probed using only their gravitational interaction. In this scenario, an ultralight boson cloud may form spontaneously around a spinning black hole and extract a non-negligible

Nam Nguyen

In this article, I present a novel and computational-efficient approach for treatment-response modeling of tumor progression-free survival (PFS) probability using the physical phenomenon of a quantum particle walking on a one-dimensional lattice with the presence of a proximate trap.

Shourya Bose, Kejun Chen, Yu Zhang

The optimal power flow (OPF) problem is an important mathematical program that aims at obtaining the best operating point of an electric power grid. The optimization problem typically minimizes the total generation cost subject to certain physical constraints of the system. The so-called linearized distribution flow (LinDistFlow) model leverages a set of lin

Jingfeng Zhang, Bo Song, Bo Han, Lei Liu

Adversarial training (AT) is a robust learning algorithm that can defend against adversarial attacks in the inference phase and mitigate the side effects of corrupted data in the training phase. As such, it has become an indispensable component of many artificial intelligence (AI) systems. However, in high-stake AI applications, it is crucial to understand A

Minghui Yang, Jing Liu, Zhiwei Yang, Zhaoyang Wu

Industrial image anomaly detection under the setting of one-class classification has significant practical value. However, most existing models struggle to extract separable feature representations when performing feature embedding and struggle to build compact descriptions of normal features when performing one-class classification. One direct consequence o

Su Pang, Daniel Morris, Hayder Radha

Despite radar's popularity in the automotive industry, for fusion-based 3D object detection, most existing works focus on LiDAR and camera fusion. In this paper, we propose TransCAR, a Transformer-based Camera-And-Radar fusion solution for 3D object detection. Our TransCAR consists of two modules. The first module learns 2D features from surround-view camera

Yifan Chen, Xiaoxia Wang

In this paper, we present several new $q$-congruences on the $q$-trinomial coefficients introduced by Andrews and Baxter. As a conclusion, we obtain the following congruence: \begin{align*} \bigg(\!\!\binom{ap+b}{cp+d}\!\!\bigg)\equiv\bigg(\!\!\binom{a}{c}\!\!\bigg)\bigg(\!\!\binom{b}{d}\!\!\bigg)+\bigg(\!\!\binom{a}{c+1}\!\!\bigg)\bigg(\!\!\binom{b}{d-p}\!\

Yoyo Hinuma, Shinya Mine, Takashi Toyao, Takashi Kamachi

Spinel oxides are an important class of materials for heterogeneous catalysis including photocatalysis and electrocatalysis. The surface O vacancy formation energy (EOvac) is a critical quantity on catalyst performance because the surface of metal oxide catalysts often acts as reaction sites, for example, in the Mars-van Krevelen mechanism. However, experime

Nikhil Kalyanapuram

We elaborate upon and consolidate various recent developments focusing on the triality of questions offered by issues of basis building, unitarity and non-polylogarithmicity in quantum field theory, specifically for planar two loops. The interplay between the dual questions of setting up bases of integrands and accurately preparing a complete set of cuts to

Yanpeng Zhao, Siyu Gao, Yunbo Wang, Xiaokang Yang

Unsupervised learning of object-centric representations in dynamic visual scenes is challenging. Unlike most previous approaches that learn to decompose 2D images, we present DynaVol, a 3D scene generative model that unifies geometric structures and object-centric learning in a differentiable volume rendering framework. The key idea is to perform object-cent

Xu-Run Huang, Chuan-Le Sun, Lie-Wen Chen, Jun Gao

We implement the Bayesian inference to retrieve energy spectra of all neutrinos from a galactic core-collapse supernova (CCSN). To achieve high statistics and full sensitivity to all flavours of neutrinos, we adopt a combination of several reaction channels from different large-scale neutrino observatories, namely inverse beta decay on proton and elastic sca

Dong Xiao, Zuoqiang Shi, Bin Wang

We propose a new strategy to bridge point cloud denoising and surface reconstruction by alternately updating the denoised point clouds and the reconstructed surfaces. In Poisson surface reconstruction, the implicit function is generated by a set of smooth basis functions centered at the octnodes. When the octree depth is properly selected, the reconstructed

Yoyo Hinuma, Shinya Mine, Takashi Toyao, Zen Maeno

Metal/oxide support perimeter sites are known to provide unique properties because the nearby metal changes the local environment on the support surface. In particular, the electron scavenger effect reduces the energy necessary for surface anion desorption, thereby contributes to activation of the (reverse) Mars-van Krevelen mechanism. This study investigate

Satish Kumar, Anirban Pathak

The no-quantum broadcasting theorem which is a weaker version of the nocloning theorem restricts us from broadcasting completely unknown quantum information to multiple users. However, if the sender is aware of the quantum information (state) to be broadcasted then the above restriction disappears and the task reduces to a multiparty remote state preparation

Yoyo Hinuma, Masanori Kohyama, Shingo Tanaka

This study proposes algorithms for building tilt grain boundary (GB) models with a boundary plane-oriented approach that does not rely on existence of a coincidence site lattice (CSL). As conventional GB model generation uses the CSL of superimposed grains as the starting point, our totally different approach allows systematic treatment of diverse grain boun

Razieh Emami, Sheperd S. Doeleman, Maciek Wielgus, Dominic Chang

We present an in-depth analysis of a newly proposed correlation function in visibility space, between the E and B modes of the linear polarization, hereafter the EB-correlation, for a set of time-averaged GRMHD simulations compared with the phase map from different semi-analytic models as well as the Event Horizon Telescope (EHT) 2017 data for M87* source. W

Zhenqiao Song, Lei Li

Designing protein sequences with desired biological function is crucial in biology and chemistry. Recent machine learning methods use a surrogate sequence-function model to replace the expensive wet-lab validation. How can we efficiently generate diverse and novel protein sequences with high fitness? In this paper, we propose IsEM-Pro, an approach to generat

Yuheng Li, Jacob Wynne, Jing Wang, Richard L. J. Qiu

Biparametric magnetic resonance imaging (bpMRI) has demonstrated promising results in prostate cancer (PCa) detection using convolutional neural networks (CNNs). Recently, transformers have achieved competitive performance compared to CNNs in computer vision. Large scale transformers need abundant annotated data for training, which are difficult to obtain in

Guijin Son, Hanearl Jung, Moonjeong Hahm, Keonju Na

Large Language Models (LLMs), consisting of 100 billion or more parameters, have demonstrated remarkable ability in complex multi-step reasoning tasks. However, the application of such generic advancements has been limited to a few fields, such as clinical or legal, with the field of financial reasoning remaining largely unexplored. To the best of our knowle

Haihui Xie, Minghua Xia, Peiran Wu, Shuai Wang

In the Internet of Things (IoT) networks, edge learning for data-driven tasks provides intelligent applications and services. As the network size becomes large, different users may generate distinct datasets. Thus, to suit multiple edge learning tasks for large-scale IoT networks, this paper performs efficient communication under the task-oriented principle

Anders Mølmen Høst, Pierre Lison, Leon Moonen

Knowledge graphs have shown promise for several cybersecurity tasks, such as vulnerability assessment and threat analysis. In this work, we present a new method for constructing a vulnerability knowledge graph from information in the National Vulnerability Database (NVD). Our approach combines named entity recognition (NER), relation extraction (RE), and ent

Dong Li, Alexander Warmuth, Jincheng Wang, Haisheng Zhao

Solar flares and coronal mass ejections (CMEs) are thought to be the most powerful events on the Sun. They can release energy as high as 10^32 erg in tens of minutes,and could produce solar energetic particles (SEPs) in the interplanetary space. We explore global energy budgets of solar major eruptions on 6 September 2017, including the energy partition of a

Zifeng Wang, Zheng Zhan, Yifan Gong, Yucai Shao

Rehearsal-based approaches are a mainstay of continual learning (CL). They mitigate the catastrophic forgetting problem by maintaining a small fixed-size buffer with a subset of data from past tasks. While most rehearsal-based approaches study how to effectively exploit the knowledge from the buffered past data, little attention is paid to the inter-task rel

Pourya Shamsolmoali, Masoumeh Zareapoor, Eric Granger

Given the recent advances with image-generating algorithms, deep image completion methods have made significant progress. However, state-of-art methods typically provide poor cross-scene generalization, and generated masked areas often contain blurry artifacts. Predictive filtering is a method for restoring images, which predicts the most effective kernels b

Michael E. Cuffaro, Stephan Hartmann

It is argued that those who defend the Everett, or `Many Worlds', interpretation of quantum mechanics should embrace what we call the general quantum theory of open systems (GT) as the proper framework in which to conduct foundational and philosophical investigation in quantum physics. GT is a wider dynamical framework than its alternative, standard quantum

Xiaoyu Cheng, J. J. W. Van der Vegt, Yan Xu, H. J. Zwart

In this paper, we present port-Hamiltonian formulations of the incompressible Euler equations with a free surface governed by surface tension and gravity forces, modelling e.g. capillary and gravity waves and the evolution of droplets in air. Three sets of variables are considered, namely $(v,\Sigma)$, $(\eta,\phi_{\partial},\Sigma)$ and $(\omega,\phi_{\part

Fan-Ying Wu, Qi-Yi Wu, Chen Zhang, Yang Luo

The three-dimensional electronic structure and the nature of Ce 4f electrons of the Kondo insulator CeRu4Sn6 are investigated by angle-resolved photoemission spectroscopy, utilizing tunable photon energies. Our results reveal (i) the three-dimensional k-space nature of the Fermi surface, (ii) the localized-to-itinerant transition of f electrons occurs at a m

Mohamed Debbagh

Neural Radiance Field (NeRF) is a framework that represents a 3D scene in the weights of a fully connected neural network, known as the Multi-Layer Perception(MLP). The method was introduced for the task of novel view synthesis and is able to achieve state-of-the-art photorealistic image renderings from a given continuous viewpoint. NeRFs have become a popul

Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama

Adversarial contrastive learning (ACL) is a technique that enhances standard contrastive learning (SCL) by incorporating adversarial data to learn a robust representation that can withstand adversarial attacks and common corruptions without requiring costly annotations. To improve transferability, the existing work introduced the standard invariant regulariz

Kaihong Sun, Raphael F. Ribeiro

We introduce a theory of chemical equilibrium in optical microcavities, which allows us to relate equilibrium reaction quotients in different electromagnetic environments. Our theory shows that in planar microcavities under strong coupling with polyatomic molecules, hybrid modes formed between all dipole-active vibrations and cavity resonances contribute to

Qingguo Li, Hualin Miao

M. Escard\'o et al. asked whether the core compactly generated topology of a sober space is again sober and the sobrification of a core compactly generated space again core compactly generated. In this note, we answer the problem by displaying a counterexample, which reveals that the core compactly generated spaces are not closed under sobrifications. Meanti

S. Q. Hou, J. B. Liu, T. C. L. Trueman, J. G. Li

Accurate nuclear reaction rates for 26P(p,{\gamma})27S are pivotal for a comprehensive understanding of rp-process nucleosynthesis path in the region of proton-rich sulfur and phosphorus isotopes. However, large uncertainties still exist in the current rate of 26P(p,{\gamma})27S because of the lack of the nuclear mass and the energy level structure informati

Wei-Hao Huang, Shih-Hsuan Chen, Chun-Hao Chang, Tzu-Liang Hsu

Einstein-Podolsky-Rosen (EPR) steering and Bell nonlocality illustrate two different kinds of correlations predicted by quantum mechanics. They not only motivate the exploration of the foundation of quantum mechanics, but also serve as important resources for quantum-information processing in the presence of untrusted measurement apparatuses. Herein, we intr

Jiachen Li, Jincheng Yu, Zehua Chen, Weitao Yang

We developed an efficient active-space particle-particle random phase approximation (ppRPA) approach to calculate accurate charge-neutral excitation energies of molecular systems. The active-space ppRPA approach constrains both indexes in particle and hole pairs in the ppRPA matrix, which only selects frontier orbitals with dominant contributions to low-lyin

Sowmitra Das

We give a concise and self-contained introduction to the theory of Quantum Games by reviewing the seminal works of Meyer, Eisert-Wilkens-Lewenstein, Marinatto-Weber and Landsburg, which initiated the study of this field. By generalizing this body of work, we formulate a protocol to $\textit{Quantumize}$ any finite classical $n$-player game, and use a novel a

Cong T. Nguyen, Dinh Thai Hoang, Diep N. Nguyen, Yong Xiao

Due to its security, transparency, and flexibility in verifying virtual assets, blockchain has been identified as one of the key technologies for Metaverse. Unfortunately, blockchain-based Metaverse faces serious challenges such as massive resource demands, scalability, and security concerns. To address these issues, this paper proposes a novel sharding-base

Yuze Lou, Bailey Kuehl, Erin Bransom, Sergey Feldman

Entity linking (EL) is the task of linking a textual mention to its corresponding entry in a knowledge base, and is critical for many knowledge-intensive NLP applications. When applied to tables in scientific papers, EL is a step toward large-scale scientific knowledge bases that could enable advanced scientific question answering and analytics. We present t

Wilkie Olin-Ammentorp

It has been well-established that within conventional neural networks, many of the values produced at each layer are zero. In this work, I demonstrate that spiking neural networks can prevent the transmission of spikes representing values close to zero using local information. This can reduce the amount of energy required for communication and computation in

Mikhail Genkin, J. J. McArthur

Energy optimization leveraging artificially intelligent algorithms has been proven effective. However, when buildings are commissioned, there is no historical data that could be used to train these algorithms. On-line Reinforcement Learning (RL) algorithms have shown significant promise, but their deployment carries a significant risk, because as the RL agen

Mahdi Anbarloei

Various expansions of prime hyperideals have been studied in a Krasner $(m,n)$-hyperring $R$. For instance, a proper hyperideal $Q$ of $R$ is called weakly $(k,n)$-absorbing primary provided that for $r_1^{kn-k+1} \in R$, $g(r_1^{kn-k+1}) \in Q-\{0\}$ implies that there are $(k-1)n-k+2$ of the $r_i^,$s whose $g$-product is in $Q$ $g(r_1^{(k-1)n-k+2}) \in Q$

Marco A. M. Guaraco, Stephen Lynch

Let $\Gamma$ be a compact codimension-two submanifold of $\mathbb{R}^n$, and let $L$ be a nontrivial real line bundle over $X = \mathbb{R}^n \setminus \Gamma$. We study the Allen--Cahn functional, \[E_\varepsilon(u) = \int_X \varepsilon \frac{|\nabla u|^2}{2} + \frac{(1-|u|^2)^2}{4\varepsilon}\,dx,\] on the space of sections $u$ of $L$. Specifically, we are

Linwei Sang, Yinliang Xu, Huan Long, Qinran Hu

Electricity price prediction plays a vital role in energy storage system (ESS) management. Current prediction models focus on reducing prediction errors but overlook their impact on downstream decision-making. So this paper proposes a decision-focused electricity price prediction approach for ESS arbitrage to bridge the gap from the downstream optimization m

Xiaofeng Xue, Xueting Yin

In this paper, we are concerned with stochastic susceptible-exposed-infected-removed epidemics on complete graphs with vertex-dependent transition rates. Large and moderate deviations of empirical density fields of our models are given. Proofs of our main results utilize exponential martingale strategies. Mathematical difficulties are mainly in checks of exp

Ilia Binder, Tomas Kojar

In this article we systematically study the general properties and the single-point moments of the inverse of the Gaussian multiplicative chaos.

Ambuj Mehrish, Navonil Majumder, Rishabh Bhardwaj, Rada Mihalcea

The field of speech processing has undergone a transformative shift with the advent of deep learning. The use of multiple processing layers has enabled the creation of models capable of extracting intricate features from speech data. This development has paved the way for unparalleled advancements in speech recognition, text-to-speech synthesis, automatic sp

Jan Niklas Kolf, Tim Rieber, Jurek Elliesen, Fadi Boutros

Many of the commonly used datasets for face recognition development are collected from the internet without proper user consent. Due to the increasing focus on privacy in the social and legal frameworks, the use and distribution of these datasets are being restricted and strongly questioned. These databases, which have a realistically high variability of dat

Benjamin Carrillo

For prime $p$ and small $n$, Jones and Roberts have developed a database recording invariants for $p$-adic extensions of degree $n$. We contributed to this database by computing the Galois slope content, Galois mean slope, and inertia subgroup for a variety of wildly ramified extensions of composite degree using the idea of Galois splitting models. We will d

Shuangping Li, Tselil Schramm

Gaussian mixture block models are distributions over graphs that strive to model modern networks: to generate a graph from such a model, we associate each vertex $i$ with a latent feature vector $u_i \in \mathbb{R}^d$ sampled from a mixture of Gaussians, and we add edge $(i,j)$ if and only if the feature vectors are sufficiently similar, in that $\langle u_i

Alper Çakan, Vipul Goyal, Chen-Da Liu-Zhang, João Ribeiro

Quantum secret sharing (QSS) allows a dealer to distribute a secret quantum state among a set of parties so that certain subsets can reconstruct the secret, while unauthorized subsets obtain no information. While QSS was introduced over twenty years ago, previous works focused only on existence of perfectly secure schemes, and the share size of the known sch

Yifang Xu, Yunzhuo Sun, Yang Li, Yilei Shi

With the increasing demand for video understanding, video moment and highlight detection (MHD) has emerged as a critical research topic. MHD aims to localize all moments and predict clip-wise saliency scores simultaneously. Despite progress made by existing DETR-based methods, we observe that these methods coarsely fuse features from different modalities, wh

Marek Karliner, Jonathan L. Rosner

Hadrons containing at least one heavy quark (charm or bottom) frequently have small enough natural widths that decay modes involving a single photon have detectable branching fractions. Photons of typical energy greater than 100 MeV have been directly detected, while those of lower energy have only been inferred. Here we discuss prospects for observing direc

Antoine Deblais, K. R. Prathyusha, Rosa Sinaasappel, Harry Tuazon

Recently, long and slender living worms have garnered significant interest because of their impressive ability to exhibit diverse emergent behaviors in highly entangled physical and topological conditions. These worms can form an active viscoelastic, three-dimensional soft entity known as the 'blob', which can behave like a solid, flow like a liquid, and eve

Rafael Oliveira Ribeiro, João C. R. Neves, Arnout C. C. Ruifrok, Flavio de Barros Vidal

In forensic facial comparison, questioned-source images are usually captured in uncontrolled environments, with non-uniform lighting, and from non-cooperative subjects. The poor quality of such material usually compromises their value as evidence in legal matters. On the other hand, in forensic casework, multiple images of the person of interest are usually

Kaushal Kumar

Optimization techniques play a crucial role in estimating parameters and state information for nonlinear systems. However, some critical aspects of these problems have received little attention in previous research. In this paper, we address this gap by exploring optimization techniques for parameter estimation in nonlinear system modeling, with a focus on c

Lingyao Li, Zihui Ma, Lizhou Fan, Sanggyu Lee

The rapid advancements in generative AI models present new opportunities in the education sector. However, it is imperative to acknowledge and address the potential risks and concerns that may arise with their use. We analyzed Twitter data to identify key concerns related to the use of ChatGPT in education. We employed BERT-based topic modeling to conduct a

Korawat Tanwisuth, Shujian Zhang, Huangjie Zheng, Pengcheng He

Through prompting, large-scale pre-trained models have become more expressive and powerful, gaining significant attention in recent years. Though these big models have zero-shot capabilities, in general, labeled data are still required to adapt them to downstream tasks. To overcome this critical limitation, we propose an unsupervised fine-tuning framework to

Lan Wen, Aaron L. Sarvet, Mats J. Stensrud

We present new results on average causal effects in settings with unmeasured exposure-outcome confounding. Our results are motivated by a class of estimands, e.g., frequently of interest in medicine and public health, that are currently not targeted by standard approaches for average causal effects. We recognize these estimands as queries about the average c

Marius Memmel, Roman Bachmann, Amir Zamir

Effectively localizing an agent in a realistic, noisy setting is crucial for many embodied vision tasks. Visual Odometry (VO) is a practical substitute for unreliable GPS and compass sensors, especially in indoor environments. While SLAM-based methods show a solid performance without large data requirements, they are less flexible and robust w.r.t. to noise

Pierre Ohlmann

This short note establishes positionality of mean-payoff games over infinite game graphs by constructing a well-founded monotone universal graph.

Diego Caso, Pablo Tuero, Javier Garcia, Konstantin Y. Guslienko

Fast and efficient switching of nanomagnets is one of the main challenges in the development of future magnetic memories. We numerically investigate the evolution of the static and dynamic spin wave (SW) magnetization in short (50-400 nm length and 120 nm diameter) cylindrical ferromagnetic nanowires, where competing single vortex (SV) and vortex domain wall

John Rozmarynowycz, Seungki Kim

We report the finding of the new upper bound on the lowest positive integer $x$ for which the Mertens conjecture \begin{equation*} \left| \sum_{1 \leq n \leq x} \mu(n) \right| < \sqrt{x} \end{equation*} fails to hold: $x < \exp(1.017 \times 10^{29})$, an improvement over previously known $\exp(1.59 \times 10^{40})$ due to Kotnik and te Riele [7].

Lucas Lavoyer

We study the Ricci flow out of spaces with edge type conical singularities along a closed, embedded curve. Under the additional assumption that for each point of the curve, our space is locally modelled on the product of a fixed positively curved cone and a line, we show existence of a solution to Ricci flow $(M,g(t))$ for $t\in (0,T],$ which converges back

Siran Li, Hao Ni, Qianyu Zhu

Physical Brownian motion describes the dynamics of a Brownian particle experiencing frictional force. It was investigated in the classical work [L. S. Ornstein and G. E. Uhlenbeck, Phys. Rev. 36 (1930)] as a physically meaningful approach to realising the standard ``mathematical'' Brownian motion, via sending the mass $m \to 0^+$ and performing natural scali

Maximiliano Escayola, Cristóbal Rivas

Let $G$ be a torsion-free, finitely-generated, nilpotent and metabelian group. In this work we show that $G$ embeds into the group of orientation preserving $C^{1+\alpha}$-diffeomorphisms of the compact interval, for all $\alpha< 1/k$ where $k$ is the torsion-free rank of $G/A$ and $A$ is a maximal abelian subgroup. We show that in many situations the corres

Pieter Appeltans, Wim Michiels

TDS-CONTROL is an integrated MATLAB package for the analysis and controller-design of linear time-invariant (LTI) dynamical systems with (multiple) discrete delays, supporting both systems of retarded and neutral type. TDS-CONTROL is based on a state-space representations for these TDSs, although functionality is provided to obtain such a formulation from a

Chuqin Geng, Yihan Zhang, Brigitte Pientka, Xujie Si

The recent introduction of ChatGPT has drawn significant attention from both industry and academia due to its impressive capabilities in solving a diverse range of tasks, including language translation, text summarization, and computer programming. Its capability for writing, modifying, and even correcting code together with its ease of use and access is alr

Nuno J. Alves, Athanasios E. Tzavaras

We consider a set of bipolar Euler-Poisson equations and study two asymptotic limiting processes. The first is the zero-electron-mass limit, which formally results in a non-linear adiabatic electron system. In a second step, we analyse the combined zero-electron-mass and quasi-neutral limits, which together lead to the compressible Euler equations. Using the

Wenhao Ding

Autonomous systems, such as self-driving vehicles, quadrupeds, and robot manipulators, are largely enabled by the rapid development of artificial intelligence. However, such systems involve several trustworthy challenges such as safety, robustness, and generalization, due to their deployment in open-ended and real-time environments. To evaluate and improve t

Jorge Arroyo-Esquivel, Christopher A Klausmeier, Elena Litchman

Simple models have been used to describe ecological processes for over a century. However, the complexity of ecological systems makes simple models subject to modeling bias due to simplifying assumptions or unaccounted factors, limiting their predictive power. Neural Ordinary Differential Equations (NODEs) have surged as a machine-learning algorithm that pre

ChihYun Chuang, TingFang Lee

On the Ethereum network, it is challenging to determine a gas price that ensures a transaction will be included in a block within a user's required timeline without overpaying. One way of addressing this problem is through the use of gas price oracles that utilize historical block data to recommend gas prices. However, when transaction volumes increase rapid

T. A. Khudaiberganov

We are consistency considered two cases. Firstly, we consider exciton-photon statistic radiation from pillar microcavity. We obtained a photon antibunching and small polariton antibunching. Secondly, we use two strong-coupled pillar microcavities to achieve pronounced polariton antibunching. We observed the polariton blockade effect when use a polarion dimer

Matias Vera, Martin G. Gonzalez, Leonardo Rey Vega

Image reconstruction in optoacoustic tomography (OAT) is a trending learning task highly dependent on measured physical magnitudes present at sensing time. The large number of different settings, and also the presence of uncertainties or partial knowledge of parameters, can lead to reconstructions algorithms that are specifically tailored and designed to a p

K. Aditya Mohan, Jean-Baptiste Forien, Venkatesh Sridhar, Jefferson A. Cuadra

X-ray phase-contrast tomography (XPCT) is widely used for high contrast 3D imaging using either synchrotron or laboratory microfocus X-ray sources. XPCT enables an order of magnitude improvement in image contrast of the reconstructed material interfaces with low X-ray absorption contrast. The dominant approaches to 3D reconstruction using XPCT relies on the

Charles Radin, Lorenzo Sadun

In the edge-2star model with hard constraints we prove the existence of an open set of constraint parameters, bisected by a line segment on which there are nonunique entropy-optimal graphons related by a symmetry. At each point in the open set but off the line segment there is a unique entropy-optimizer, bipodal and varying analytically with the constraints.

Jeroen Van Der Donckt, Jonas Van Der Donckt, Michael Rademaker, Sofie Van Hoecke

Visualization plays an important role in analyzing and exploring time series data. To facilitate efficient visualization of large datasets, downsampling has emerged as a well-established approach. This work concentrates on LTTB (Largest-Triangle-Three-Buckets), a widely adopted downsampling algorithm for time series data point selection. Specifically, we pro

James Mayfield, Eugene Yang, Dawn Lawrie, Samuel Barham

A key stumbling block for neural cross-language information retrieval (CLIR) systems has been the paucity of training data. The appearance of the MS MARCO monolingual training set led to significant advances in the state of the art in neural monolingual retrieval. By translating the MS MARCO documents into other languages using machine translation, this reso

S. E. A. Orrigo, B. Rubio, W. Gelletly

During the last decade we have carried out a systematic study of the $\beta$ decay of neutron-deficient nuclei, providing rich spectroscopic information of importance for both nuclear structure and nuclear astrophysics. We present an overview of the most relevant achievements, including the discovery of a new exotic decay mode in the fp-shell, the $\beta$-de

Bharath Reddy, Richard Fields

Sequence alignment is common nowadays as it is used in many fields to determine how closely two sequences are related and at times to see how little they differ. In computational biology / Bioinformatics, there are many algorithms developed over the course of time to not only align two sequences quickly but also get good laboratory results from these alignme

Ankush Meshram, Markus Karch, Christian Haas, Jürgen Beyerer

Since 2010, multiple cyber incidents on industrial infrastructure, such as Stuxnet and CrashOverride, have exposed the vulnerability of Industrial Control Systems (ICS) to cyber threats. The industrial systems are commissioned for longer duration amounting to decades, often resulting in non-compliance to technological advancements in industrial cybersecurity

Thuy Dung Nguyen, Anh Duy Nguyen, Kok-Seng Wong, Huy Hieu Pham

Federated learning (FL) enables multiple clients to train a model without compromising sensitive data. The decentralized nature of FL makes it susceptible to adversarial attacks, especially backdoor insertion during training. Recently, the edge-case backdoor attack employing the tail of the data distribution has been proposed as a powerful one, raising quest

Xiaojun Chang, Hichem Hajaiej, Zhouji Ma, Linjie Song

We prove the existence of normalized ground state solutions for the biharmonic Schr\"odinger equation with combined nonlinearities and show that all ground states correspond to the local minima of the associated energy functional restricted to the appropriate set. Moreover, we prove that the standing waves are strongly unstable by blowup. In particular, our

Andrew Mao, Sebastian Flassbeck, Cem Gultekin, Jakob Assländer

We extend the traditional framework for estimating subspace bases that maximize the preserved signal energy to additionally preserve the Cram\'er-Rao bound (CRB) of the biophysical parameters and, ultimately, improve accuracy and precision in the quantitative maps. To this end, we introduce an \textit{approximate compressed CRB} based on orthogonalized versi

Pavel Pokhilko, Dominika Zgid

Using generalizations of natural orbitals, spin-averaged natural orbitals, and two-particle charge correlators for solids, we investigate electronic structure of antiferromagnetic transition-metal oxides with a fully self-consistent, finite-temperature GW method. Our findings disagree with Goodenough-Kanamori (GK) rules, commonly used for qualitative interpr

Lu Zou, Haoyuan Chen, Liang Ding

Among generalized additive models, additive Mat\'ern Gaussian Processes (GPs) are one of the most popular for scalable high-dimensional problems. Thanks to their additive structure and stochastic differential equation representation, back-fitting-based algorithms can reduce the time complexity of computing the posterior mean from $O(n^3)$ to $O(n\log n)$ tim

AmirHossein Naghshzan, Saeed Khalilazar, Pierre Poilane, Olga Baysal

Context: Recent research has used data mining to develop techniques that can guide developers through source code changes. To the best of our knowledge, very few studies have investigated data mining techniques and--or compared their results with other algorithms or a baseline. Objectives: This paper proposes an automatic method for recommending source code

Kefan Dong, Tengyu Ma

Many machine learning applications require learning a function with a small worst-case error over the entire input domain, that is, the $L_\infty$-error, whereas most existing theoretical works only guarantee recovery in average errors such as the $L_2$-error. $L_\infty$-recovery from polynomial samples is even impossible for seemingly simple function classe

Jeffrey Kuan, Zhengye Zhou

A previous paper by the authors found explicit contour integral formulas for certain joint moments of the multi-species q-TAZRP (totally asymmetric zero range process), using algebraic methods. These contour integral formulas have a "pseudo-factorized" form which makes asymptotic analysis simpler. In this brief note, we use those contour integral formulas to

Arthur Josi, Mahdi Alehdaghi, Rafael M. O. Cruz, Eric Granger

Visible-infrared person re-identification (V-I ReID) seeks to match images of individuals captured over a distributed network of RGB and IR cameras. The task is challenging due to the significant differences between V and I modalities, especially under real-world conditions, where images are corrupted by, e.g, blur, noise, and weather. Indeed, state-of-art V

Andrés Hoyos-Idrobo

Many re-ranking strategies in search systems rely on stochastic ranking policies, encoded as Doubly-Stochastic (DS) matrices, that satisfy desired ranking constraints in expectation, e.g., Fairness of Exposure (FOE). These strategies are generally two-stage pipelines: \emph{i)} an offline re-ranking policy construction step and \emph{ii)} an online sampling