March 2024 arXiv papers — page 68

Esteban Gabory, Chang Liu, Grigorios Loukides, Solon P. Pissis

Strings in the real world are often encoded with some level of uncertainty. In the character-level uncertainty model, an uncertain string $X$ of length $n$ on an alphabet $\Sigma$ is a sequence of $n$ probability distributions over $\Sigma$. Given an uncertain string $X$ and a weight threshold $\frac{1}{z}\in(0,1]$, we say that pattern $P$ occurs in $X$ at p

Sehee Lim, Yejin Kim, Chi-Hyun Choi, Jy-yong Sohn

Improving the accessibility of psychotherapy with the aid of Large Language Models (LLMs) is garnering a significant attention in recent years. Recognizing cognitive distortions from the interviewee's utterances can be an essential part of psychotherapy, especially for cognitive behavioral therapy. In this paper, we propose ERD, which improves LLM-based cogn

Luca Martinoia

At its core, hydrodynamics is a many-body low-energy effective theory for the long-wavelength, long-timescale dynamics of conserved charges in systems close to thermodynamic equilibrium. It has a wide range of applications spanning from nuclear physics, astrophysics, cosmology, and more recently strongly-interacting electronic phases of matter. In solid-stat

Kyuhee Kim, Surin Lee, Sangah Lee

In many literary texts, emotions are indirectly conveyed through descriptions of actions, facial expressions, and appearances, necessitating emotion inference for narrative understanding. In this paper, we introduce K-Act2Emo, a Korean commonsense knowledge graph (CSKG) comprising 1,900 indirect emotional expressions and the emotions inferable from them. We

L. Gamberale, G. Modanese

The Schr\"odinger equation and Bloch theorem are applied to examine a system of protons confined within a periodic potential, accounting for deviations from ideal harmonic behavior due to real-world conditions like truncated and non-quadratic potentials, in both one-dimensional and three-dimensional scenarios. Numerical computation of the energy spectrum of

Masato Fujitake

This paper proposes LayoutLLM, a more flexible document analysis method for understanding imaged documents. Visually Rich Document Understanding tasks, such as document image classification and information extraction, have gained significant attention due to their importance. Existing methods have been developed to enhance document comprehension by incorpora

Eduardo Abi Jaber, Christa Cuchiero, Luca Pelizzari, Sergio Pulido

We study the class of continuous polynomial Volterra processes, which we define as solutions to stochastic Volterra equations driven by a continuous semimartingale with affine drift and quadratic diffusion matrix in the state of the Volterra process. To demonstrate the versatility of possible state spaces within our framework, we construct polynomial Volterr

Maddalena Donà, Pieter Trapman

We model the impact of local vaccine mandates on the spread of vaccine-preventable infectious diseases, which in the absence of vaccines will mainly affect children. Examples of such diseases are measles, rubella, mumps and pertussis. To model the spread of the pathogen, we use a stochastic SIR (Susceptible, Infectious, Recovered) model with two levels of mi

Xun Lin, Yi Yu, Song Xia, Jue Jiang

The widespread availability of publicly accessible medical images has significantly propelled advancements in various research and clinical fields. Nonetheless, concerns regarding unauthorized training of AI systems for commercial purposes and the duties of patient privacy protection have led numerous institutions to hesitate to share their images. This is p

Tianqi Chen, Hai-Tao Ding, Ruizhe Shen, Shi-Liang Zhu

The concepts of topology and geometry are of critical importance in exploring exotic phases of quantum matter. Though they have been investigated on various experimental platforms, to date a direct probe of topological and geometric properties on a universal quantum computer even for a minimum model is still in vain. In this work, we first show that a densit

Sibasish Dhibar

Skin cancer is a crucial health issue that requires timely detection for higher survival rates. Traditional computer vision techniques face challenges in addressing the advanced variability of skin lesion features, a gap partially bridged by convolutional neural networks (CNNs). To overcome the existing issues, we introduce an innovative convolutional ensemb

Amlan Chakraborty, Tulip Ray, Subinoy Das, Arka Banerjee

We present new constraints on an interacting dark matter-dark energy scenario motivated by string compactification, where a scalar field adiabatically tracks the minimum of an effective potential sourced by dark matter density. In this study, we focus on the Chameleon dark energy model and numerically solve the Klein-Gordon equation using a shooting algorith

Shrishail Baligar, Mikolaj Kegler, Bryce Irvin, Marko Stamenovic

Target Sound Extraction (TSE) focuses on the problem of separating sources of interest, indicated by a user's cue, from the input mixture. Most existing solutions operate in an offline fashion and are not suited to the low-latency causal processing constraints imposed by applications in live-streamed content such as augmented hearing. We introduce a family o

Adam Grzela, Jacek Jezierski, Tomasz Smołka

We construct electromagnetic field with non-trivial topological properties on de Sitter background. The field is closely related with Hopf fibration. We analyze energy, angular momentum and topological charges for this solution. The paper is a generalization of CQG \textbf{35} (2018), no. 24, 245010 to de Sitter spacetime.

Akerele Olofin Segun

The so-called Riemann sums have their origin in the efforts of Greek mathematicians to find the center of gravity or the volume of a solid body. These researches led to the method of exhaustion, discovered by Archimedes and described using modern ideas by MacLaurin in his \textit{Treatise of Fluxions} in 1742. At this times the sums were only a practical met

Yuanhao Gong, Lantao Yu, Guanghui Yue

The 3D Gaussian splatting method has drawn a lot of attention, thanks to its high performance in training and high quality of the rendered image. However, it uses anisotropic Gaussian kernels to represent the scene. Although such anisotropic kernels have advantages in representing the geometry, they lead to difficulties in terms of computation, such as split

Dimitrios P. Panagoulias, Evridiki Tsoureli-Nikita, Maria Virvou, George A. Tsihrintzis

The rise of Artificial Intelligence creates great promise in the field of medical discovery, diagnostics and patient management. However, the vast complexity of all medical domains require a more complex approach that combines machine learning algorithms, classifiers, segmentation algorithms and, lately, large language models. In this paper, we describe, imp

Chen Chen, Guangyu Hu, Dongsheng Zuo, Cunxi Yu

Logic synthesis plays a crucial role in the digital design flow. It has a decisive influence on the final Quality of Results (QoR) of the circuit implementations. However, existing multi-level logic optimization algorithms often employ greedy approaches with a series of local optimization steps. Each step breaks the circuit into small pieces (e.g., k-feasibl

Ilya V. Kashin, Sergei N. Andreev

In this work we suggest a theoretical approach, that allows to study the effects of magnetocrystalline anisotropy (MCA) in metallic systems using the Green`s functions formalism. We demonstrate that employment of the reciprocal space resolution instead of its reduction in the inter-site variant essentially improves the numerical stability of MCA energy by me

Wang-Wang Yu, Xian-Shi Zhang, Fu-Ya Luo, Yijun Cao

Frame-level micro- and macro-expression spotting methods require time-consuming frame-by-frame observation during annotation. Meanwhile, video-level spotting lacks sufficient information about the location and number of expressions during training, resulting in significantly inferior performance compared with fully-supervised spotting. To bridge this gap, we

Alessandro Balestri, Justin Ball, Stefano Coda, Diego Jose Cruz-Zabala

In this work, we study the impact of aspect ratio A = R0 /r (the ratio of major radius R0 to minor radius r) on the confinement benefits of Negative Triangularity (NT) plasma shaping. We use high-fidelity flux tube gyrokinetic GENE simulations and consider several different scenarios: four of them inspired by TCV experimental data, a scenario inspired by DII

Kyungjae Lee, Dasol Hwang, Sunghyun Park, Youngsoo Jang

Despite the promise of RLHF in aligning LLMs with human preferences, it often leads to superficial alignment, prioritizing stylistic changes over improving downstream performance of LLMs. Underspecified preferences could obscure directions to align the models. Lacking exploration restricts identification of desirable outputs to improve the models. To overcom

Tomas Katkus, Soon Hock Ng, Haoran Mu, Nguyen Hoai An Le

Large surface area applications such as high-efficiency > 26% solar cells require surface patterning with 1-10 micrometers periodic patterns at high fidelity over 1-10 cm^2 areas (before up scaling to 1 m^2) to perform at, or exceed, the Lambertian (ray optics) limit of light trapping. Here we show a pathway to high-resolution sub-1 micrometer etch mask patt

Qiushi Sun, Zhirui Chen, Fangzhi Xu, Kanzhi Cheng

Neural Code Intelligence -- leveraging deep learning to understand, generate, and optimize code -- holds immense potential for transformative impacts on the whole society. Bridging the gap between Natural Language and Programming Language, this domain has drawn significant attention from researchers in both research communities over the past few years. This

Akshat Gupta, Dev Sajnani, Gopala Anumanchipalli

ROME and MEMIT are largely believed to be two different model editing algorithms, with the major difference between them being the ability to perform batched edits. In this paper, we unify these two algorithms under a single conceptual umbrella, optimizing for the same goal, which we call the preservation-memorization objective. ROME uses an equality constra

Nikhel Gupta, Ray P. Norris, Zeeshan Hayder, Minh Huynh

We present source detection and catalogue construction pipelines to build the first catalogue of radio galaxies from the 270 $\rm deg^2$ pilot survey of the Evolutionary Map of the Universe (EMU-PS) conducted with the Australian Square Kilometre Array Pathfinder (ASKAP) telescope. The detection pipeline uses Gal-DINO computer-vision networks (Gupta et al., 2

Itsuki Ogami, Yutaka Komiyama, Masashi Chiba, Mikito Tanaka

We analyze the outer regions of M33, beyond 15 kpc in projected distance from its center using Subaru/HSC multi-color imaging. We identify Red Giant Branch (RGB) stars and Red Clump (RC) stars using the surface gravity sensitive $NB515$ filter for the RGB sample, and a multi-color selection for both samples. We construct the radial surface density profile of

Xi Jiang, Ying Chen, Qiang Nie, Yong Liu

Although mainstream unsupervised anomaly detection (AD) algorithms perform well in academic datasets, their performance is limited in practical application due to the ideal experimental setting of clean training data. Training with noisy data is an inevitable problem in real-world anomaly detection but is seldom discussed. This paper considers label-level no

Minqin Zhu, Anpeng Wu, Haoxuan Li, Ruoxuan Xiong

Estimating the individuals' potential response to varying treatment doses is crucial for decision-making in areas such as precision medicine and management science. Most recent studies predict counterfactual outcomes by learning a covariate representation that is independent of the treatment variable. However, such independence constraints neglect much of th

Sébastien Bossu, Stéphane Crépey, Hoang-Dung Nguyen

We propose a distributional formulation of the spanning problem of a multi-asset payoff by vanilla basket options. This problem is shown to have a unique solution if and only if the payoff function is even and absolutely homogeneous, and we establish a Fourier-based formula to calculate the solution. Financial payoffs are typically piecewise linear, resultin

Mai Oguchi, Mao Shinoda

In this paper we consider the specification property for $(\alpha,\beta)$-shifts. When $\alpha=0$, Schmeling shows that the set of $\beta>1$ for which the $\beta$-shift has the specification property has the Lebesgue measure zero but has the full Hausdorff dimension\cite{Schmeling}. So it is natural to ask what happens when $\alpha>0$. Buzzi shows that for f

Markus Bachmayr, Riccardo Bardin, Matthias Schlottbom

The radiative transfer equation (RTE) has been established as a fundamental tool for the description of energy transport, absorption and scattering in many relevant societal applications, and requires numerical approximations. However, classical numerical algorithms scale unfavorably with respect to the dimensionality of such radiative transfer problems, whe

Nathan Mankovich, Homer Durand, Emiliano Diaz, Gherardo Varando

Detecting latent confounders from proxy variables is an essential problem in causal effect estimation. Previous approaches are limited to low-dimensional proxies, sorted proxies, and binary treatments. We remove these assumptions and present a novel Proxy Confounder Factorization (PCF) framework for continuous treatment effect estimation when latent confound

Jiawen Liu, Yuanyuan Yao, Pengcheng An, Qi Wang

In children's collaborative learning, effective peer conversations can significantly enhance the quality of children's collaborative interactions. The integration of Large Language Model (LLM) agents into this setting explores their novel role as peers, assessing impacts as team moderators and participants. We invited two groups of participants to engage in

Jarne J. H. van Gemert, Mircea Lazar, Siep Weiland

A common tool in system theory for formulating control laws that achieve local asymptotic stability are Control Lyapunov functions (CLFs), while Control Barrier functions (CBFs) are typically employed to enforce safety constraints. Combining these two types of functions is of interest, because it leads to stabilizing controllers with safety guarantees. A com

Insung Kong, Yongdai Kim

Bayesian approaches for training deep neural networks (BNNs) have received significant interest and have been effectively utilized in a wide range of applications. There have been several studies on the properties of posterior concentrations of BNNs. However, most of these studies only demonstrate results in BNN models with sparse or heavy-tailed priors. Sur

Arthur Guijt, Dirk Thierens, Tanja Alderliesten, Peter A. N. Bosman

Traditional approaches to neuroevolution often start from scratch. This becomes prohibitively expensive in terms of computational and data requirements when targeting modern, deep neural networks. Using a warm start could be highly advantageous, e.g., using previously trained networks, potentially from different sources. This moreover enables leveraging the

Gustave Cortal

The study of dreams has been central to understanding human (un)consciousness, cognition, and culture for centuries. Analyzing dreams quantitatively depends on labor-intensive, manual annotation of dream narratives. We automate this process through a natural language sequence-to-sequence generation framework. This paper presents the first study on character

Bikash R. Dinda, Narayan Banerjee

For the first time, we reconstruct the dark energy equation of the state parameter $w$ from the combination of background and perturbation observations, specifically combining the Hubble parameter data from cosmic chronometer observations and the logarithmic growth rate data from the growth rate observations. We do this analysis using posterior Gaussian proc

Jonas Golde, Felix Hamborg, Alan Akbik

Few-shot named entity recognition (NER) detects named entities within text using only a few annotated examples. One promising line of research is to leverage natural language descriptions of each entity type: the common label PER might, for example, be verbalized as ''person entity.'' In an initial label interpretation learning phase, the model learns to int

Yukun Zhao, Lingyong Yan, Weiwei Sun, Guoliang Xing

Large language models (LLMs) have shown tremendous success in following user instructions and generating helpful responses. Nevertheless, their robustness is still far from optimal, as they may generate significantly inconsistent responses due to minor changes in the verbalized instructions. Recent literature has explored this inconsistency issue, highlighti

Yating Wang, Wing Tat Leung

In this work, we present an efficient approach to solve nonlinear high-contrast multiscale diffusion problems. We incorporate the explicit-implicit-null (EIN) method to separate the nonlinear term into a linear term and a damping term, and then utilise the implicit and explicit time marching scheme for the two parts respectively. Due to the multiscale proper

G. Moza, R. Efrem

Subthreshold oscillations in neurons are those oscillations which do not attain the critical value of the membrane's voltage needed for triggering an action potential (a spike). Their contribution to the forming of action potentials in neurons is a current field of research in biology. The present work approaches this subject using tools from mathematical mo

Suguru Endo, Keitaro Anai, Yuichiro Matsuzaki, Yuuki Tokunaga

The design of translation symmetric bosonic codes, e.g., Gottesmann-Kitaev-Preskill and squeezed cat codes, is robust against photon loss, but the computation accuracy is limited by the available squeezing level. Here, we introduce the \textit{projective squeezing} (PS) method for computing outcomes for a higher squeezing level by revealing that a linear com

Mark Jones, Jannik Schestag

Phylogenetic Diversity (PD) is a measure of the overall biodiversity of a set of present-day species (taxa) within a phylogenetic tree. In Maximize Phylogenetic Diversity (MPD) one is asked to find a set of taxa (of bounded size/cost) for which this measure is maximized. MPD is a relevant problem in conservation planning, where there are not enough resources

Markku Lanne, Savi Virolainen

We introduce a new smooth transition vector autoregressive model with a Gaussian conditional distribution and transition weights that, for a $p$th order model, depend on the full distribution of the preceding $p$ observations. Specifically, the transition weight of each regime increases in its relative weighted likelihood. This data-driven approach facilitat

Sueyeong Kang, Matthieu Petit, Vasile Heresanu, Alexandre Altié

Structural and magnetic properties of Mn5(SixGe1-x)3 thin films were investigated. Ferromagnetic Mn5Ge3 and anti-ferromagnetic Mn5Si3 thin films have been synthesized and characterized as these compounds exhibit interesting features for the development of spintronics. Here, Mn5(SixGe1-x)3 thin films were grown on Ge(111) substrates by co-deposition using mol

Peng Zong, Jian-Ning Fu, Jie Su, Xueying Hu

We conduct an asteroseismological analysis on the non-Blazhko ab-type RR Lyrae star EPIC 248846335 employing the Radial Stellar Pulsations (RSP) module of the Modules for Experiments in Stellar Astrophysics (MESA) based on the set of stellar parameters. The atmospheric parameters as $T_\mathrm{eff}$ = 6933$\pm$70 $K$, log $g$ = 3.35$\pm$ 0.50 and [Fe/H] = -1

Xi Jiang, Ying Chen, Qiang Nie, Jianlin Liu

In the context of high usability in single-class anomaly detection models, recent academic research has become concerned about the more complex multi-class anomaly detection. Although several papers have designed unified models for this task, they often overlook the utility of class labels, a potent tool for mitigating inter-class interference. To address th

Sihao Wang, Veerendra Dhyani, Sakthi Sanjeev Mohanraj, Xiaodong Shi

Scandium aluminum nitride (ScAlN) has recently emerged as an attractive material for integrated photonics due to its favorable nonlinear optical properties and compatibility with CMOS fabrication. Despite the promising and versatile material properties, it is still an outstanding challenge to realize low-loss photonic circuits on thin-film ScAlN-on-insulator

Tuan Nguyen, Max Mehltretter, Franz Rottensteiner

Panoptic segmentation unifies semantic and instance segmentation and thus delivers a semantic class label and, for so-called thing classes, also an instance label per pixel. The differentiation of distinct objects of the same class with a similar appearance is particularly challenging and frequently causes such objects to be incorrectly assigned to a single

G. Moza, C. Lazureanu, F. Munteanu, C. Sterbeti

We study a two-dimensional Kolmogorov system when its two parameters vary in a small neighbourhood of the value $0.$ The local behavior of the system is described in terms of bifurcation diagrams.

Tammo van der Heide, Ben Hourahine, Bálint Aradi, Thomas Frauenheim

Accurate electronic bandstructures of solids are indispensable for a wide variety of applications and should provide a sound prediction of phonon-induced band gap renormalization at finite temperatures. We employ our previously introduced formalism of general hybrid functionals within the approximate density functional method, DFTB, to present first insights

Bingchen Liu, Huang Peng, Weixin Zeng, Xiang Zhao

The construction of large open knowledge bases (OKBs) is integral to many knowledge-driven applications on the world wide web such as web search. However, noun phrases and relational phrases in OKBs often suffer from redundancy and ambiguity, which calls for the investigation on OKB canonicalization. Current solutions address OKB canonicalization by devising

Si Kheang Moeurn

Matrix analysis plays a crucial role in the field of control engineering, providing a powerful mathematical framework for the analysis and design of control systems. This research report explores various applications of matrix analysis in control engineering, focusing on its contributions to system modeling, stability analysis, controllablity, observability,

Mitja Nikolaus, Abhishek Agrawal, Petros Kaklamanis, Alex Warstadt

The acquisition of grammar has been a central question to adjudicate between theories of language acquisition. In order to conduct faster, more reproducible, and larger-scale corpus studies on grammaticality in child-caregiver conversations, tools for automatic annotation can offer an effective alternative to tedious manual annotation. We propose a coding sc

Bikramjit Kundu, Sudeep Podder

In this note, we compute the upper characteristic rank of the projective Stiefel manifolds over $\mathbb{R}, \mathbb{C}$ and $\mathbb{H}$ and the flip Stiefel manifolds. We also provide bounds for the $\mathrm{cup}$ lengths of these spaces. We also provide necessary conditions for the existence of $S^3$-map between quaternionic Stiefel manifolds using the Fa

Harald Garcke, Robert Nürnberg

Phase transition problems on curved surfaces can lead to a panopticon of fascinating patterns. In this paper we consider finite element approximations of phase field models with a spatially inhomogeneous and anisotropic surface energy density. The problems are either posed in $\mathbb R^3$ or on a two-dimensional hypersurface in $\mathbb R^3$. In the latter

Piaoran Ye, Zhihan Hong, Douglas A. Loy, Rongguang Liang

The growing interest in 3D printing of silica glass has spurred substantial research efforts. Our prior work utilizing a liquid silica resin (LSR) demonstrated high printing accuracy and resolution. However, the resin's sensitivity to moisture posed limitations, restricting the printing environment. On the other hand, polyhedral oligomeric silsesquioxane (PO

Yixun Wei, Wenlong Wang, Huibing Dong, Bingzhe Li

DNA storage is a promising archival data storage solution to today's big data problem. A DNA storage system encodes and stores digital data with synthetic DNA sequences and decodes DNA sequences back to digital data via sequencing. For efficient target data retrieving, existing Polymerase Chain Reaction PCR based DNA storage systems apply primers as specific

Swapnil Bhosale, Haosen Yang, Diptesh Kanojia, Jiangkang Deng

Audio-Visual Segmentation (AVS) aims to identify, at the pixel level, the object in a visual scene that produces a given sound. Current AVS methods rely on costly fine-grained annotations of mask-audio pairs, making them impractical for scalability. To address this, we introduce unsupervised AVS, eliminating the need for such expensive annotation. To tackle

D. E. Ferreyra, N. Thome, C. Torigino

The core-EP and BT inverses for rectangular matrices were studied recently in the literature. The main aim of this paper is to unify both concepts by means of a new kind of generalized inverse called $W$-weighted $q$-BT inverse. We analyze its existence and uniqueness by considering an adequate matrix system. Basic properties and some interesting characteriz

Yixun Wei, Bingzhe Li, David Du

DNA storage is a promising archival data storage solution to today's big data problem. A DNA storage system encodes and stores digital data with synthetic DNA sequences and decodes DNA sequences back to digital data via sequencing. For efficient target data retrieving, existing Polymerase Chain Reaction (PCR) based DNA storage systems apply primers as specif

Rémi Nahon, Ivan Luiz De Moura Matos, Van-Tam Nguyen, Enzo Tartaglione

Nowadays an ever-growing concerning phenomenon, the emergence of algorithmic biases that can lead to unfair models, emerges. Several debiasing approaches have been proposed in the realm of deep learning, employing more or less sophisticated approaches to discourage these models from massively employing these biases. However, a question emerges: is this extra

Yu Kawakami

We survey Bernstein-type theorems for graphical surfaces in the Euclidean space and the Lorentz-Minkowski space. More specifically, we explain several proofs of the Bernstein theorem for minimal graphs in the Euclidean 3-space. Furthermore, we show the Heinz-type mean curvature estimates for graphs in the Euclidean 3-space and space-like graphs in the Lorent

Guopeng Li, Ming Qian, Gui-Song Xia

This paper investigates the effective utilization of unlabeled data for large-area cross-view geo-localization (CVGL), encompassing both unsupervised and semi-supervised settings. Common approaches to CVGL rely on ground-satellite image pairs and employ label-driven supervised training. However, the cost of collecting precise cross-view image pairs hinders t

Kosuke Akimoto, Kunihiro Takeoka, Masafumi Oyamada

Retrieval-augmented generation models augment knowledge encoded in a language model by providing additional relevant external knowledge (context) during generation. Although it has been shown that the quantity and quality of context impact the performance of retrieval-augmented generation models during inference, limited research explores how these character

Junyeop Cha, Seoyun Kim, Dongjae Kim, Eunil Park

Early detection plays a crucial role in the treatment of depression. Therefore, numerous studies have focused on social media platforms, where individuals express their emotions, aiming to achieve early detection of depression. However, the majority of existing approaches often rely on specific features, leading to limited scalability across different types

D. E. Ferreyra, D. Mosic

Recently, Malik and Ferreyra introduced the $m$-weak core inverse for complex square matrices which generalizes the core-EP inverse, the WC inverse, and therefore the core inverse. The main aim of this paper is to extend the concept of $m$-weak core inverse for complex rectangular matrices. This extension is called the $W$-weighted $m$-weak core inverse. We

Harrison B. Smith, Lana Sinapayen

The search for a second instance of life is one of the greatest problems of modern science. Outside of creating an artificial origin of life on Earth, the primary targets for the search for life are planets inside or outside the solar system. Realistically, there are just a few locations to search for alien life within the solar system. Outside the solar sys

Yihuai Zhang, Huan Yu

Control problems of mixed-autonomy traffic systems that consist of both human-driven vehicles (HV) and autonomous vehicles (AV), have gained increasing attention. This paper focuses on suppressing traffic oscillations in the mixed-autonomy traffic system using boundary control design. The mixed traffic dynamics are described by 4 x 4 hyperbolic partial diffe

Takaaki Nomura, Hiroshi Okada

We propose a new inverse seesaw model based on hidden local $U(1)$ symmetry framework where inverse seesaw mechanism is induced at one loop level. A Majorana mass term of singlet fermion is forbidden by the $U(1)$ symmetry and it is generated at one-loop level by introducing relevant particle contents to get loop diagram, inducing inverse seesaw mechanism. T

Shuangyang Li, Peter Jung, Weijie Yuan, Zhiqiang Wei

The recently proposed orthogonal time frequency space (OTFS) modulation, which is a typical Delay-Doppler (DD) communication scheme, has attracted significant attention thanks to its appealing performance over doubly-selective channels. In this paper, we present the fundamentals of general DD communications from the viewpoint of the Zak transform. We start o

Dougho Park, Younghun Kim, Harim Kang, Junmyeoung Lee

Bolus segmentation is crucial for the automated detection of swallowing disorders in videofluoroscopic swallowing studies (VFSS). However, it is difficult for the model to accurately segment a bolus region in a VFSS image because VFSS images are translucent, have low contrast and unclear region boundaries, and lack color information. To overcome these challe

M. Petris, V. Aprodu, D. Bartos, D. Dorobantu

A long term operation of Multi-Strip Multi-Gap Resistive Plate Chambers (MSMGRPC) with gas mixtures based on C2H2F4 and SF6 leads to aging effects, observed as depositions on the surface of the resistive electrodes. Moreover, enhanced depositions and higher noise rates were evidenced around the nylon spacers used for defining the gas gaps between the resisti

Patrick Hemmer, Max Schemmer, Niklas Kühl, Michael Vössing

Artificial intelligence (AI) has the potential to significantly enhance human performance across various domains. Ideally, collaboration between humans and AI should result in complementary team performance (CTP) -- a level of performance that neither of them can attain individually. So far, however, CTP has rarely been observed, suggesting an insufficient u

Manali Dutta, Rahul Singh

In this work, we consider a wireless networked control system (WNCS) consisting of a plant, a battery-operated sensor, a controller, and an actuator. The battery in the sensor harvests energy from the environment. The sensor then uses this energy for packet transmissions. There are two types of wireless communication channels, (i) sensor--controller channel

Zhao He, Maxim S. Elizarov, Ning Li, Fei Xiang

Quantum artificial intelligence is a frontier of artificial intelligence research, pioneering quantum AI-powered circuits to address problems beyond the reach of deep learning with classical architectures. This work implements a large-scale quantum-activated recurrent neural network possessing more than 3 trillion hardware nodes/cm$^2$, originating from repe

Jaemin Park

In this paper, we revisit asymptotic stability for the two-dimensional incompressible porous media equation and the Stokes transport system in a periodic channel. It is well-known that a stratified density, which strictly decreases in the vertical direction, is asymptotically stable under sufficiently small and smooth perturbations. We provide improvements i

Jongwoo Choi, Kwanggyoon Seo, Amirsaman Ashtari, Junyong Noh

We propose a method that can generate cinemagraphs automatically from a still landscape image using a pre-trained StyleGAN. Inspired by the success of recent unconditional video generation, we leverage a powerful pre-trained image generator to synthesize high-quality cinemagraphs. Unlike previous approaches that mainly utilize the latent space of a pre-train

Ziwei Huang, Lu Bai, Mingran Sun, Xiang Cheng

In this paper, a novel channel modeling approach, named light detection and ranging (LiDAR)-aided geometry-based stochastic modeling (LA-GBSM), is developed. Based on the developed LA-GBSM approach, a new millimeter wave (mmWave) channel model for sixth-generation (6G) vehicular intelligent sensing-communication integration is proposed, which can support the

Will Sharpless, Yat Tin Chow, Sylvia Herbert

Hamilton-Jacobi reachability (HJR) provides a value function that encodes the set of states from which a system with bounded control inputs can reach or avoid a target despite any bounded disturbance, and the corresponding robust, optimal control policy. Though powerful, traditional methods for HJR rely on dynamic programming (DP) and suffer from exponential

Kwanyoung Kim, Yujin Oh, Jong Chul Ye

The recent success of CLIP has demonstrated promising results in zero-shot semantic segmentation by transferring muiltimodal knowledge to pixel-level classification. However, leveraging pre-trained CLIP knowledge to closely align text embeddings with pixel embeddings still has limitations in existing approaches. To address this issue, we propose OTSeg, a nov

Souvik Das, Ahmed Atteya, Pralay Kumar Karmakar

A recently reported gravito-electrostatic sheath (GES) model is procedurally applied to study the turbumagnetoactive helioseismic oscillation features on the entire bi-fluidic solar plasma system. The bounded solar interior plasma (SIP, internally self-gravitating) and the unbounded solar wind plasma (SWP, externally point-gravitating) are coupled through th

Djshwar Dhahir Lateef, Nawroz Abdul-razzak Tahir

In this investigation, five separate experiments were carried out. The first experiments were examined the molecular characteristics of 59 barley accessions collected from different regions in Iraq using three different molecular markers (ISSR, CDDP, and Scot). A total of 391 amplified polymorphic bands were generated using forty-four ISSR, nine CDDP, and tw

Ping Li, Bang Huang, Wen-Qin Wang

This paper addresses the problem of detecting a moving target embedded in Gaussian noise with an unknown covariance matrix for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. To end it, assume that obtaining a set of training data is available. Moreover, we propose three adaptive detectors in accordance with the one-step generalized

Kevin Wilkinghoff

The state-of-the-art approach for semi-supervised anomalous sound detection is to first learn an embedding space by using auxiliary classification tasks based on meta information or self-supervised learning and then estimate the distribution of normal data. In this work, AdaProj a novel loss function for training the embedding model is presented. In contrast

Dmitry Ammosov, Tina Mai, Juan Galvis

For nonlinear Cosserat elasticity, we consider multiscale methods in this paper. In particular, we explore the generalized multiscale finite element method (GMsFEM) to solve an isotropic Cosserat problem with strain-limiting property (ensuring bounded linearized strains even under high stresses). Such strain-limiting Cosserat model can find potential applica

Denis Spiridonov, Sergei Stepanov, Tina Mai

We develop a new coarse-scale approximation strategy for the nonlinear single-continuum Richards equation as an unsaturated flow over heterogeneous non-periodic media, using the online generalized multiscale finite element method (online GMsFEM) together with deep learning. A novelty of this approach is that local online multiscale basis functions are comput

Byunghee Choi, Hogyun Kim, Younggun Cho

Radar is highlighted for robust sensing capabilities in adverse weather conditions (e.g. dense fog, heavy rain, or snowfall). In addition, Radar can cover wide areas and penetrate small particles. Despite these advantages, Radar-based place recognition remains in the early stages compared to other sensors due to its unique characteristics such as low resolut

Rakuten Group, Aaron Levine, Connie Huang, Chenguang Wang

We introduce RakutenAI-7B, a suite of Japanese-oriented large language models that achieve the best performance on the Japanese LM Harness benchmarks among the open 7B models. Along with the foundation model, we release instruction- and chat-tuned models, RakutenAI-7B-instruct and RakutenAI-7B-chat respectively, under the Apache 2.0 license.

Anat Goldman, Efrat Blumenfeld-Lieberthal

This chapter explores the concept of self-organization in urban planning and design, highlighting its role in shaping the unique characteristics of cities. It examines how various socio-economic, cultural, and political factors contribute to the development of distinct architectural styles, emphasizing the morphological patterns and self-organization princip

Jingjing Hu, Dan Guo, Kun Li, Zhan Si

Inspired by the activity-silent and persistent activity mechanisms in human visual perception biology, we design a Unified Static and Dynamic Network (UniSDNet), to learn the semantic association between the video and text/audio queries in a cross-modal environment for efficient video grounding. For static modeling, we devise a novel residual structure (ResM

Jianping Li, Shenghai Yuan, Muqing Cao, Thien-Minh Nguyen

Compact wearable mapping system (WMS) has gained significant attention due to their convenience in various applications. Specifically, it provides an efficient way to collect prior maps for 3D structure inspection and robot-based "last-mile delivery" in complex environments. However, vibrations in human motion and the uneven distribution of point cloud featu

Boyao Peng, Lexing Zhang, Enkai Li

In the upstream of the exit ramp of the expressway, the speed limit difference leads to a significant deceleration of the vehicle in the area adjacent to the off-ramp. The friction coefficient of the road surface decreases under rainy weather, and the above deceleration process can easily lead to sideslip and rollover of the vehicle. Dynamic speed guidance i

Longzheng Wang, Xiaohan Xu, Lei Zhang, Jiarui Lu

Automatic detection of multimodal misinformation has gained a widespread attention recently. However, the potential of powerful Large Language Models (LLMs) for multimodal misinformation detection remains underexplored. Besides, how to teach LLMs to interpret multimodal misinformation in cost-effective and accessible way is still an open question. To address

Barbara Drinovec Drnovšek, Uroš Kuzman

Given a bounded strictly convex domain $\Omega\Subset \mathbb{C}$ and a point $q\in \Omega$ we construct a continuous solution of the Pascali-type elliptic system of differential equations that is centered in $q$, maps the unit disc into $\Omega$ and the unit circle into $\partial \Omega$.

Santanu Mondal, Rwitika Chatterjee, Vivek K. Agrawal, Anuj Nandi

We conducted a study on the X-ray polarization properties of MCG-5-23-16 by analyzing long-term monitoring data from {\it NuSTAR} jointly with {\it IXPE} observations made in May and November 2022. The re-analysis of {\it IXPE} data gives model-dependent polarization degree, PD (\%) = $1.08\pm0.66$ in the energy band $2-8$ keV. The model-independent analysis

Zhe Chen, Heyang Liu, Wenyi Yu, Guangzhi Sun

Publishing open-source academic video recordings is an emergent and prevalent approach to sharing knowledge online. Such videos carry rich multimodal information including speech, the facial and body movements of the speakers, as well as the texts and pictures in the slides and possibly even the papers. Although multiple academic video datasets have been con

Maoxuan Zhou, Wei Kang, Kun He

In order to solve the problem that current convolutional neural networks can not capture the correlation features between the time domain signals of rolling bearings effectively, and the model accuracy is limited by the number and quality of samples, a rolling bearing fault diagnosis method based on generative adversarial enhanced multi-scale convolutional n