April 2024 arXiv papers — page 161

Alessio Caminata, Enrico Carlini, Luca Schaffler

A classical result of von Staudt states that if eight planes osculate a twisted cubic curve and we divide them into two groups of four, then the eight vertices of the corresponding tetrahedra lie on a twisted cubic curve. In the current paper, we give an alternative proof of this result using modern tools, and at the same time we prove the analogous result f

Giancarlo Pereira, Yidan Gao, Yurii Piadyk, David Fouhey

High speed, high-resolution, and accurate 3D scanning would open doors to many new applications in graphics, robotics, science, and medicine by enabling the accurate scanning of deformable objects during interactions. Past attempts to use structured light, time-of-flight, and stereo in high-speed settings have usually required tradeoffs in resolution or inac

Bowen Zhang, Kehua Chang, Chunping Li

Sentence Embedding stands as a fundamental task within the realm of Natural Language Processing, finding extensive application in search engines, expert systems, and question-and-answer platforms. With the continuous evolution of large language models such as LLaMA and Mistral, research on sentence embedding has recently achieved notable breakthroughs. Howev

Imen Benabbas, Belkacem Said-Houari

In this work, we investigate the global existence and asymptotic behavior of a mathematical model of nonlinear ultrasonic heating based on a coupled system of the Westervelt equation and the hyperbolic Pennes bioheat equation (Westervelt--Pennes--Cattaneo model). First, we prove that the solution exists globally in time, provided that the lower-order Sobolev

Sukanya Kudva, Kshitij Kulkarni, Chinmay Maheshwari, Anil Aswani

The rapid growth of electric vehicles (EVs) is driving the expansion of charging infrastructure globally. As charging stations become ubiquitous, their substantial electricity consumption can influence grid operation and electricity pricing. Naturally, \textit{some} groups of charging stations, which could be jointly operated by a company, may coordinate to

Chao-Ping Dong, Yongzhi Luan, Haojun Xu

The idea of using Dirac cohomology to study branching laws was initiated by Huang, Pandzi\'c and Zhu in 2013 [HPZ]. One of their results says that the Dirac cohomology of $\pi$ completely determines $\pi|_{K}$, where $\pi$ is any irreducible unitarizable highest weight $(\mathfrak{g}, K)$ module. This paper aims to develop this idea for the exceptional Lie g

Konstantin Yu. Osipenko

The paper concerns problems of the recovery of differential operators from a noisy Fourier transform. In particular, optimal methods are obtained for the recovery of powers of generalized Laplace operators from a noisy Fourier transform in the $L_2$-metric.

Huan Qing

Community detection in multi-layer networks has emerged as a crucial area of modern network analysis. However, conventional approaches often assume that nodes belong exclusively to a single community, which fails to capture the complex structure of real-world networks where nodes may belong to multiple communities simultaneously. To address this limitation,

Jiaming Wang, Xinyu Geng, Jun Xu

The traditional Kalman filter (KF) is widely applied in control systems, but it relies heavily on the accuracy of the system model and noise parameters, leading to potential performance degradation when facing inaccuracies. To address this issue, introducing neural networks into the KF framework offers a data-driven solution to compensate for these inaccurac

Christophe Revillion, Pascal Mouquet, Jeremy Commins, Juliette Miranville

The increasing availability of free high-resolution earth observation data covering any point on the globe every few days led to the emergence of new remote sensing tools that can manipulate the very large volumes of data generated by those satellites. We present Sen2Chain, an open-source Python tool that can automate the processing of large time series of S

Kesavaraj V, Anil Kumar Vuppala

Identifying keywords in an open-vocabulary context is crucial for personalizing interactions with smart devices. Previous approaches to open vocabulary keyword spotting dependon a shared embedding space created by audio and text encoders. However, these approaches suffer from heterogeneous modality representations (i.e., audio-text mismatch). To address this

Gihyun Kwon, Simon Jenni, Dingzeyu Li, Joon-Young Lee

While there has been significant progress in customizing text-to-image generation models, generating images that combine multiple personalized concepts remains challenging. In this work, we introduce Concept Weaver, a method for composing customized text-to-image diffusion models at inference time. Specifically, the method breaks the process into two steps:

Fred Philippy, Shohreh Haddadan, Siwen Guo

In NLP, zero-shot classification (ZSC) is the task of assigning labels to textual data without any labeled examples for the target classes. A common method for ZSC is to fine-tune a language model on a Natural Language Inference (NLI) dataset and then use it to infer the entailment between the input document and the target labels. However, this approach face

Lucas Carvalho de Lima, Nicholas Lawrance, Kasra Khosoussi, Paulo Borges

Autonomous navigation in unstructured natural environments poses a significant challenge. In goal navigation tasks without prior information, the limited look-ahead of onboard sensors utilised by robots compromises path efficiency. We propose a novel approach that leverages an above-the-canopy aerial map for improved ground robot navigation. Our system utili

Giusy Monzillo, Safet Penić

Let $\Gamma=\Gamma(A)$ denote a simple strongly connected digraph with vertex set $X$, diameter $D$, and let $\{A_0,A:=A_1,A_2,\ldots,A_D\}$ denote the set of distance-$i$ matrices of $\Gamma$. Let $\{R_i\}_{i=0}^D$ denote a partition of $X\times X$, where $R_i=\{(x,y)\in X\times X\mid (A_i)_{xy}=1\}$ $(0\le i\le D)$. The digraph $\Gamma$ is distance-regular

Yifan Lu, Zachary S. C. Picker, Alexander Kusenko

We investigate the formation of high-redshift supermassive black holes (SMBHs) via the direct collapse of baryonic clouds, where the unwanted formation of molecular hydrogen is successfully suppressed by a Lyman-Werner (LW) photon background from relic particle decay. We improve on existing studies by dynamically simulating the collapse, accounting for the a

Suma K, Deepali Koppad, Preethi Kumar, Neha A Kantikar

In recent years, advancements in deep learning techniques have considerably enhanced the efficiency and accuracy of medical diagnostics. In this work, a novel approach using multi-task learning (MTL) for the simultaneous classification of lung sounds and lung diseases is proposed. Our proposed model leverages MTL with four different deep learning models such

Toshiyuki Mizuki, Munetake Momose, Masataka Aizawa, Hiroshi Kobayashi

More than a thousand warm debris disks have been detected as infrared excess at mid-infrared wavelengths, and their frequencies have been obtained for various spectral types of stars. However, the dependence of the frequencies on spectral type is still debated because the number of stars with significant and detectable infrared excess is limited. Herein, we

Nimrod Shabtay, Eli Schwartz, Raja Giryes

Phase-coded imaging is a computational imaging method designed to tackle tasks such as passive depth estimation and extended depth of field (EDOF) using depth cues inserted during image capture. Most of the current deep learning-based methods for depth estimation or all-in-focus imaging require a training dataset with high-quality depth maps and an optimal f

Najiya V K, Chithra A

Let $G $ be a graph on $p$ vertices with adjacency matrix $A(G)$ and degree matrix $D(G)$. For each $\alpha \in [0, 1]$, the $A_\alpha$-matrix is defined as $A_\alpha (G) = \alpha D(G) + (1 - \alpha)A(G)$. In this paper, we compute the $A_\alpha$-characteristic polynomial, $A_\alpha$-spectra and $A_\alpha$-energy of some non-regular graphs obtained from unar

Tsuyoshi Inoue, Alexandre Marcowith, Gwenael Giacinti

Supernova blast wave shock is a very important site of cosmic-ray acceleration. However, the detailed physical process of acceleration, in particular, non-linear interplay between cosmic-ray streaming and magnetic field amplification has not been studied under a realistic environment. In this paper, using a unique and novel numerical method, we study cosmic-

Lia Papadopoulos, Suhyun Jo, Kevin Zumwalt, Michael Wehr

Past work has reported inverted-U relationships between arousal and auditory task performance, but the underlying neural network mechanisms remain unclear. To make progress, we recorded auditory cortex activity from behaving mice during passive tone presentation and simultaneously monitored pupil-indexed arousal. In these experiments, neural discriminability

Koushiki, Dipanjan Dey, Pankaj S. Joshi

This paper explores the cosmological implications of a scalar field with a specific potential, crucial for achieving the final equilibrium state of gravitational collapse. We consider a system with two fluids: minimally coupled matter representing dust-like dark matter and a scalar field acting as dark energy. Our model, akin to the top-hat collapse model, f

Jerry Yao-Chieh Hu, Bo-Yu Chen, Dennis Wu, Feng Ruan

We present a nonparametric interpretation for deep learning compatible modern Hopfield models and utilize this new perspective to debut efficient variants. Our key contribution stems from interpreting the memory storage and retrieval processes in modern Hopfield models as a nonparametric regression problem subject to a set of query-memory pairs. Interestingl

Hyunyoung Jang, Jinwook Kim, Jeongmi Lee

Retargeting methods that modify the visual representation of real movements have been widely used to expand the interaction space and create engaging virtual reality experiences. For optimal user experience and performance, it is essential to specify the perception of retargeting and utilize the appropriate range of modification parameters. However, previous

Anas Mohammad Ishfaqul Muktadir Osmani, Taimur Rahman, Salekul Islam

In this paper, we analyze the effectiveness of transfer learning on classifying electronic components. Transfer learning reuses pre-trained models to save time and resources in building a robust classifier rather than learning from scratch. Our work introduces a lightweight CNN, coined as VoltaVision, and compares its performance against more complex models.

Atsushi Matsuo, Hiroki Shimakura

A series of integral lattices parametrised by integers $k,m,n$ are introduced and investigated, where $n$ is the rank of the lattice, including the root lattices described in a uniform way and unimodular lattices such as the Niemeier lattices of type $A_{24}$ and $D_{24}$. The lattices are characterised by means of a sublattice isomorphic to the root lattice

Khawar Islam, Muhammad Zaigham Zaheer, Arif Mahmood, Karthik Nandakumar

Recently, a number of image-mixing-based augmentation techniques have been introduced to improve the generalization of deep neural networks. In these techniques, two or more randomly selected natural images are mixed together to generate an augmented image. Such methods may not only omit important portions of the input images but also introduce label ambigui

Nils Hüsken, Richard F. Lebed, Ryan E. Mitchell, Eric S. Swanson

A recent report of $e^+ e^- \to D\bar D$ events by the BESIII Collaboration suggests the presence of a structure $R$ at 3900~MeV\@. We argue that this structure, called $G(3900)$ in the past, is not in fact due to a new $c\bar c$ resonance, but rather naturally emerges as a threshold enhancement due to the opening of the $D^*\bar D$ channel. We further find

Parveen, Manisha, Jitender Kumar

Let $H$ be a normal subgroup of a group $G$. The normal subgroup based power graph $\Gamma_H(G)$ of $G$ is the simple undirected graph with vertex set $V(\Gamma_H(G))= (G\setminus H)\cup \{e\}$ and two distinct vertices $a$ and $b$ are adjacent if either $aH = b^m H$ or $bH=a^nH$ for some $m,n \in \mathbb{N}$. In this paper, we continue the study of normal s

Hwiyeol Jo, Taiwoo Park, Hyunwoo Lee, Nayoung Choi

Although there has been a growing interest among industries in integrating generative LLMs into their services, limited experience and scarcity of resources act as a barrier in launching and servicing large-scale LLM-based services. In this paper, we share our experiences in developing and operating generative AI models within a national-scale search engine,

Jon McCormack, Elliott Wilson

This paper presents an interactive artwork, "Holon", a collection of 130 autonomous, cybernetic organisms that listen and make sound in collaboration with the natural environment. The work was developed for installation on water at a heritage-listed dock in Melbourne, Australia. Conceptual issues informing the work are presented, along with a detailed techni

Tengfei Ma, Xiang song, Wen Tao, Mufei Li

Knowledge graph completion (KGC) aims to alleviate the inherent incompleteness of knowledge graphs (KGs), which is a critical task for various applications, such as recommendations on the web. Although knowledge graph embedding (KGE) models have demonstrated superior predictive performance on KGC tasks, these models infer missing links in a black-box manner

Maryam Ahmed, Tooba Bibi, Rizwan Ahmed Khan, Sidra Nasir

The Deep learning (DL) models for diagnosing breast cancer from mammographic images often operate as "black boxes", making it difficult for healthcare professionals to trust and understand their decision-making processes. The study presents an integrated framework combining Convolutional Neural Networks (CNNs) and Explainable Artificial Intelligence (XAI) fo

Gawon Choi, Hyemin Ahn

In robotics, the use of Large Language Models (LLMs) is becoming prevalent, especially for understanding human commands. In particular, LLMs are utilized as domain-agnostic task planners for high-level human commands. LLMs are capable of Chain-of-Thought (CoT) reasoning, and this allows LLMs to be task planners. However, we need to consider that modern robot

Giovanni Soligo, Marco Edoardo Rosti

We perform numerical simulations of planar jets of elastoviscoplastic (EVP) fluid (\citet{saramito2007new} model) at low Reynolds number. Three different configurations are considered: $(i)$ EVP jet in EVP ambient fluid, $(ii)$ EVP jet in Newtonian ambient fluid (miscible), and $(iii)$ EVP jet in Newtonian ambient fluid (immiscible). We investigate the effec

Yan Li, Keyi Liu, Garnett W. Bryant

Nagaoka ferromagnetism (NF) is a long-predicted example of itinerant ferromagnetism (IF) in the Hubbard model that has been studied theoretically for many years. The condition for NF, an infinite on-site Coulomb repulsion and a single hole in a half-filled band, does not arise naturally in materials. NF was only realized recently for the first time in experi

Kode Creer, Imitiaz Parvez

In the smart grid, the prosumers can sell unused electricity back to the power grid, assuming the prosumers own renewable energy sources and storage units. The maximizing of their profits under a dynamic electricity market is a problem that requires intelligent planning. To address this, we propose a framework based on Proximal Policy Optimization (PPO) usin

Hyeonwoo Kim, Gyoungjin Gim, Yungi Kim, Jihoo Kim

This study presents a novel learning approach designed to enhance both mathematical reasoning and problem-solving abilities of Large Language Models (LLMs). We focus on integrating the Chain-of-Thought (CoT) and the Program-of-Thought (PoT) learning, hypothesizing that prioritizing the learning of mathematical reasoning ability is helpful for the amplificati

Xiyun Xu, Ming Xu

In this paper, we introduce the geodesic orbit and weakly symmetric properties in homogeneous spray geometry. When a homogeneous spray manifold is endowed with a reductive decomposition, we can use the spray vector field to describe these properties, and prove that a weakly symmetric spray manifold must be geodesic orbit, which generalizes its analog in homo

Zhiyan Ding, Ethan N. Epperly, Lin Lin, Ruizhe Zhang

Subspace-based signal processing techniques, such as the Estimation of Signal Parameters via Rotational Invariant Techniques (ESPRIT) algorithm, are popular methods for spectral estimation. These algorithms can achieve the so-called super-resolution scaling under low noise conditions, surpassing the well-known Nyquist limit. However, the performance of these

Eunja Ha, Myung-Ki Cheoun, H. Sagawa, Gianluca Colo

We investigate the Gamow-Teller (GT) transition strength distributions of {strongly} deformed nuclei, $^{24,26}$Mg, as well as of $^{18}$O. The calculations are performed within a deformed quasi-particle random phase approximation (DQRPA) which explicitly includes the deformation degree of freedom in the Skyrme-Hartree-Fock (SHF) and RPA calculations. The re

Judy X Yang, Jun Zhou, Jing Wang, Hui Tian

The fusion of hyperspectral and LiDAR data has been an active research topic. Existing fusion methods have ignored the high-dimensionality and redundancy challenges in hyperspectral images, despite that band selection methods have been intensively studied for hyperspectral image (HSI) processing. This paper addresses this significant gap by introducing a cro

Yun-Ning Fan, Kun Xu, Wen-Cong Chen

Recently, it discovered two ultra-long period radio transients GLEAM-X J162759.5-523504.3 (J1627) and GPM J1839$-$10 (J1839) with spin periods longer than 1000 s. The origin of these two ultra-long period radio transients is intriguing in understanding the spin evolution of neutron stars (NSs). In this work, we diagnose whether the interaction between strong

Xiaocheng Luo, Yanping Chen, Ruixue Tang, Caiwei Yang

Current methods to extract relational triples directly make a prediction based on a possible entity pair in a raw sentence without depending on entity recognition. The task suffers from a serious semantic overlapping problem, in which several relation triples may share one or two entities in a sentence. In this paper, based on a two-dimensional sentence repr

Akash Mittal, Anshul Bheemreddy, Huili Tao

In recent years, the surge in unstructured data analysis, facilitated by advancements in Machine Learning (ML), has prompted diverse approaches for handling images, text documents, and videos. Analysts, leveraging ML models, can extract meaningful information from unstructured data and store it in relational databases, allowing the execution of SQL queries f

Jiang-Tao Li, Wei Sun, Li Ji, Yang Yang

Superbubbles in the nuclear region of galaxies could be produced by the AGN or nuclear starburst via different driving forces. We report analysis of the multi-wavelength data of the kpc-scale nuclear superbubble in NGC 3079, in order to probe the mechanisms driving the expansion of the superbubble. Based on the Chandra X-ray observations, we derive the hot g

Haoshu Xu, Hongzhe Li

This paper considers the problem of regression analysis with random covariance matrix as outcome and Euclidean covariates in the framework of Fr\'echet regression on the Bures-Wasserstein manifold. Such regression problems have many applications in single cell genomics and neuroscience, where we have covariance matrix measured over a large set of samples. Fr

Yicheng Zhang, Ravan Nazaraliyev, Sankha Baran Dutta, Nael Abu-Ghazaleh

High-speed interconnects, such as NVLink, are integral to modern multi-GPU systems, acting as a vital link between CPUs and GPUs. This study highlights the vulnerability of multi-GPU systems to covert and side channel attacks due to congestion on interconnects. An adversary can infer private information about a victim's activities by monitoring NVLink conges

Cristiano Fanelli, James Giroux, Patrick Moran, Hemalata Nayak

The 2023 AI4EIC hackathon was the culmination of the third annual AI4EIC workshop at The Catholic University of America. This workshop brought together researchers from physics, data science and computer science to discuss the latest developments in Artificial Intelligence (AI) and Machine Learning (ML) for the Electron Ion Collider (EIC), including applicat

Gianluca Barone, Aashrit Cunchala, Rudy Nunez

Standard classification theory assumes that the distribution of images in the test and training sets are identical. Unfortunately, real-life scenarios typically feature unseen data (``out-of-distribution data") which is different from data in the training distribution (``in-distribution"). This issue is most prevalent in social justice problems where data fr

Wei-Yang Liu, Ismail Zahed

We analyze the photo-production of $\eta_{c,b}$ off a proton in the threshold region, in terms of C-odd gluonic correlations in the off-forward proton matrix element. Near threshold, the skewness is large leading to a production amplitude that is dominated by four C-odd twist-3 gluon GPDs. We use the QCD instanton vacuum to estimate these C-odd contributions

Hiroki Watanabe, Kohei Ichihara, Takumi Aita

The blockchain ecosystem, particularly with the rise of Web3 and Non-Fungible Tokens (NFTs), has experienced a significant increase in users and applications. However, this expansion is challenged by the need to connect early adopters with a wider user base. A notable difficulty in this process is the complex interfaces of blockchain wallets, which can be da

Yulian Mao, Qingqing Ye, Haibo Hu, Qi Wang

Time series have numerous applications in finance, healthcare, IoT, and smart city. In many of these applications, time series typically contain personal data, so privacy infringement may occur if they are released directly to the public. Recently, local differential privacy (LDP) has emerged as the state-of-the-art approach to protecting data privacy. Howev

Nader Inan, Ahmed Farag Ali, Kimet Jusufi, Abdelrahman Yasser

It is well known that the cosmological constant term in the Einstein field equations can be interpreted as a stress tensor for dark energy. This stress tensor is formally analogous to an elastic constitutive equation in continuum mechanics. As a result, the cosmological constant leads to a "shear modulus" and "bulk modulus" affecting all gravitational fields

Sangwon Lee, Taro Yaoyama, Yuma Matsumoto, Takenori Hida

This study presents a novel approach to quantifying uncertainties in Bayesian model updating, which is effective in sparse or single observations. Conventional uncertainty quantification metrics such as the Euclidean and Bhattacharyya distance-based metrics are potential in scenarios with ample observations. However, their validation is limited in situations

Arnav Swaroop

IL-3 is a hemopoietic growth factor that usually targets blood cell precursors; IL-3R is a cytokine receptor that binds to IL-3. However, IL-3 takes on a different role in the context of glial cells in the nervous system, where studies show that the protein IL-3 protects against Alzheimer's disease by activating microglia at their IL-3R receptors, causing th

Arnav Swaroop

In North America, there are many diverse species of native bees crucial for the environment, who are the primary pollinators of most native floral species. The Californian agriculture industry imports European honeybees (Apis Mellifera) primarily for pollinating almonds. Unfortunately, this has resulted in the unintended consequence of disrupting the native

Xudong Guo, Daming Shi, Junjie Yu, Wenhui Fan

The emergence of multi-agent reinforcement learning (MARL) is significantly transforming various fields like autonomous vehicle networks. However, real-world multi-agent systems typically contain multiple roles, and the scale of these systems dynamically fluctuates. Consequently, in order to achieve zero-shot scalable collaboration, it is essential that stra

Bowen Zhang, Harold Soh

In this work, we are interested in automated methods for knowledge graph creation (KGC) from input text. Progress on large language models (LLMs) has prompted a series of recent works applying them to KGC, e.g., via zero/few-shot prompting. Despite successes on small domain-specific datasets, these models face difficulties scaling up to text common in many r

Hyunwoong Chang, Quan Zhou

Convergence analysis of Markov chain Monte Carlo methods in high-dimensional statistical applications is increasingly recognized. In this paper, we develop general mixing time bounds for Metropolis-Hastings algorithms on discrete spaces by building upon and refining some recent theoretical advancements in Bayesian model selection problems. We establish suffi

Lihuan Yu, Jiangdan Li, Jinliang Wang, Jiajia Li

The development of spectroscopic survey telescopes like Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), Apache Point Observatory Galactic Evolution Experiment and Sloan Digital Sky Survey has opened up unprecedented opportunities for stellar classification. Specific types of stars, such as early-type emission-line stars and those with ste

Ajay Jaiswal, Bodun Hu, Lu Yin, Yeonju Ro

Autoregressive Large Language Models (e.g., LLaMa, GPTs) are omnipresent achieving remarkable success in language understanding and generation. However, such impressive capability typically comes with a substantial model size, which presents significant challenges for autoregressive token-by-token generation. To mitigate computation overload incurred during

David Damanik, Long Li

We prove that dynamically defined Jacobi and CMV matrices associated with generic continuous sampling functions have all gaps predicted by the Gap Labelling Theorem open. We also give a mechanism for generic gap opening for quasi-periodic analytic sampling functions in the subcritical region following from the analyticity of resonance tongue boundaries for b

Lloyd van Wees, Karthik Shankar, Jan N. Fuhg, Nikolaos Bouklas

In this study, we present a methodology to predict the macroscopic yield surface of metals and metallic alloys with general crystallographic textures. In previous work, we have established the use of partially input convex neural networks (pICNN) as macroscopic yield functions of crystal plasticity simulations. However, this work was performed with an over-a

Jingyu Zhang, Marc Marone, Tianjian Li, Benjamin Van Durme

To trust the fluent generations of large language models (LLMs), humans must be able to verify their correctness against trusted, external sources. Recent efforts, such as providing citations via retrieved documents or post-hoc provenance, enhance verifiability but provide no guarantees on their correctness. To address these limitations, we tackle the verifi

Alex H. Rubin, Brian Marinelli, Victoria A. Norman, Zainab Rizvi

We explore the potential for hybrid development of quantum hardware where currently available quantum computers simulate open Cavity Quantum Electrodynamical (CQED) systems for applications in optical quantum communication, simulation and computing. Our simulations make use of a recent quantum algorithm that maps the dynamics of a singly excited open Tavis-C

B. Hassoun, R. Al-Saphory, S. Hassan

The purpose of this article is to introduce the original results which devoted with the nonlinear control system problems involves of nonlinear differential equations of fractional orders. Thus, this system is described with a mixed of ordinary derivatives in the first and second order that, are unstable before feedback gain. More precisely, we investigate a

João Gouveia, Masaru Ito, Bruno F. Lourenço

The starting point of this paper is the computation of minimal hyperbolic polynomials of duals of cones arising from chordal sparsity patterns. From that, we investigate the relation between ranks of homogeneous cones and their minimal polynomials. Along the way, we answer in the negative a question posed in an earlier paper and show examples of homogeneous

Victoria Desyatka, Evgeny Sevost'yanov

The manuscript is devoted to the boundary behavior of mappings with bounded and finite distortion. We consider mappings of domains of the Euclidean space that satisfy weighted Poletsky inequality. Assume that, the definition domain is finitely connected on its boundary and, in addition, on the set of all points which are pre-images of the cluster set of this

Yu Cheng, Xing Lu, Patricio Sanhueza, Hauyu Baobab Liu

Filamentary structures are ubiquitously found in high-mass star-forming clouds. To investigate the relationship between filaments and star formation, we carry out the INFANT (INvestigations of massive Filaments ANd sTar formation) survey, a multi-scale, multi-wavelength survey of massive filamentary clouds with ALMA band 3/band 6 and VLA K band. In this firs

Yi Qiao, Yue-Xin Liu, Yuan-Guo Xu, Ru-Min Wang

Semileptonic three-body $D \to M\ell^+\nu_\ell$ decays, non-leptonic $M \to VP$ decays, and semileptonic four-body $D \to M(M \to VP)\ell^+\nu_\ell$ decays are analyzed using the SU(3) flavor symmetry/breaking approach, where $\ell=e/\mu$, $M=A/T$, and $A/T/V/P$ denote the axial-vector/tensor/vector/pseudoscalar mesons, respectively. In terms of SU(3) flavor

Prashant R. Ghediya, Yusaku Magari, Hikaru Sadahira, Takashi Endo

Transparent oxide semiconductors (TOSs) based thin-film transistors (TFTs) that exhibit higher field effect mobility (uFE) are highly required toward the realization of next-generation displays. Among numerous types of TOS-TFTs, In2O3-based TFTs are the front-running candidate because they exhibit the highest uFE ~100 cm2/Vs. However, the device operation of

Vyacheslav Futorny, Xiangqian Guo, Yaohui Xue, Kaiming Zhao

Smooth modules for affine Kac-Moody algebras have a prime importance for the quantum field theory as they correspond to the representations of the universal affine vertex algebras. But, very little is known about such modules beyond the category of positive energy representations. We construct a new class of smooth modules over affine Kac-Moody algebras. In

Zitao Shuai, Chenwei Wu, Zhengxu Tang, Liyue Shen

Vision-language pre-training (VLP) has emerged as an effective scheme for multimodal representation learning, but its reliance on large-scale multimodal data poses significant challenges for medical applications. Federated learning (FL) offers a promising solution to scale up the dataset for medical VLP while preserving data privacy. However, we observe that

Nikolay Sukhov

Solitonic boson stars (SBS) are compact shell-like objects with an inside having a nearly constant value of scalar field bounded by a thin shell where the scalar field rapidly changes. While the spherically symmetric SBS can be described by an analytical approximation that works well in the thin-shell case, no such approximation exists for rotating SBS and t

Nikolay Sukhov

We present a novel numerical solver for the systems of coupled non-linear elliptical differential equations. The solver partitions the computational domain into a set of rectangular pseudo-spectral collocation subdomains and is especially well-suited for working with stiff solutions such as almost shell-like solitonic boson stars. The method can be used in a

Shashank Kanade, Matthew C. Russell, Shunsuke Tsuchioka, S. Ole Warnaar

In a series of two papers, S. Capparelli, A. Meurman, A. Primc, M. Primc (CMPP) and then M. Primc put forth three remarkable sets of conjectures, stating that the generating functions of coloured integer partition in which the parts satisfy restrictions on the multiplicities admit simple infinite product forms. While CMPP related one set of conjectures to th

Cai-Na Hao, Xiaoyang Xia, Yong Shi, Rui Guo

Quiescent galaxies generally possess denser cores than star-forming galaxies with similar mass. As a measurement of the core density, the central stellar mass surface density within a radius of 1 kpc ($\Sigma_1$) was thus suggested to be closely related to galaxy quenching. Massive star-forming galaxies with high $\Sigma_1$ do not fit into this picture. To u

Daichi Kozawa, Shun Fujii, Yuichiro K. Kato

We investigate the intrinsic microscopic mechanism of photon upconversion in air-suspended single-walled carbon nanotubes through photoluminescence and upconversion photoluminescence spectroscopy. Nearly linear excitation power dependence of upconversion photoluminescence intensity is observed, indicating a one-photon process as the underlying mechanism. In

Xiongping Dai

Let $H$ be a subnormal co-compact closed subgroup of a Hausdorff topological group $T$ and $X$ a compact Hausdorff space. We prove the inheritance theorem: A point of $X$ is almost periodic (a.p.) for $T\curvearrowright X$ iff it is a.p. for $H\curvearrowright X$. Moreover, if $T\curvearrowright X$ is minimal with $H\lhd T$, then $\mathscr{O}_H\colon X\right

Meghal Gupta, Mihir Singhal, Hongxun Wu

Estimating quantiles is one of the foundational problems of data sketching. Given $n$ elements $x_1, x_2, \dots, x_n$ from some universe of size $U$ arriving in a data stream, a quantile sketch estimates the rank of any element with additive error at most $\varepsilon n$. A low-space algorithm solving this task has applications in database systems, network m

Yifu Cao, Chandan Setty, Andreas Kreisel, Laura Fanfarillo

The iron-based superconductor FeSe isovalently substituted with S displays an abundance of remarkable phenomena that have not been fully understood, at the center of which are apparent zero-energy excitations in the superconducting state in the tetragonal phase. The phenomenology has been generally consistent with the proposal of the so-called ultranodal sta

Eason Chen, Zimo Xiao, Justa Liang, Damien Chen

In this paper, we developed a blockchain application to demonstrate the functionality of Sui's recent innovations: Zero Knowledge Login and Sponsored Transactions. Zero Knowledge Login allows users to create and access their blockchain wallets just with their OAuth accounts (e.g., Google, Facebook, Twitch), while Sponsored Transactions eliminate the need for

Dmitriy Zhuk

The Quantified Constraint Satisfaction Problem is the problem of evaluating a sentence with both quantifiers, over relations from some constraint language, with conjunction as the only connective. We show that for any constraint language on a finite domain the Quantified Constraint Satisfaction Problem is either in $\Pi_{2}^{P}$, or PSpace-complete. Addition

Scott Ettinger, Kratarth Goel, Avikalp Srivastava, Rami Al-Rfou

Motion forecasting has become an increasingly critical component of autonomous robotic systems. Onboard compute budgets typically limit the accuracy of real-time systems. In this work we propose methods of improving motion forecasting systems subject to limited compute budgets by combining model ensemble and distillation techniques. The use of ensembles of d

Abhishek Dhawan, Yuzhou Wang

We study the algorithmic task of finding large independent sets in Erdos-Renyi $r$-uniform hypergraphs on $n$ vertices having average degree $d$. Krivelevich and Sudakov showed that the maximum independent set has density $\left(\frac{r\log d}{(r-1)d}\right)^{1/(r-1)}$. We show that the class of low-degree polynomial algorithms can find independent sets of d

Tony J. Puthenpurakal

Let $A$ be a Noetherian ring and let $I$ be an ideal in $A$. Let $\mathcal{F} = \{ J_n \}_{n \geq 0}$ be a multiplicative filtration of ideals in $A$ such that $\mathcal{R}(\mathcal{F}) = \bigoplus_{n \geq 0} J_n$ is a finitely generated $A$-algebra. Let $\mathcal{R} = A[It]$ and assume $I^n \subseteq J_n$ for all $n \geq 1$. We show the following two assert

Ronald S. Polidan, Jack O. Burns, Alex Ignatiev, Alex Hegedus

FarView is an early-stage concept for a large, low-frequency radio observatory, manufactured in-situ on the lunar far side using metals extracted from the lunar regolith. It consists of 100,000 dipole antennas in compact subarrays distributed over a large area but with empty space between subarrays in a core-halo structure. FarView covers a total area of ~20

Asmae Hardoul, Zoubida Mghazli

We develop an unstructured mathematical model describing the growth kinetics of the Trichoderma fungus and the production of enzymes (cellulase) by degradation of a substrate (cellulose) in the rhizosphere. We integrate into this model the hydrolysis step of the organic matter and analyze the asymptotic behaviour of the obtained system. We show that our syst

Javier Rodriguez-Sanchez, Kyle Johnsen, Changying Li

Traditional field phenotyping methods are often manual, time-consuming, and destructive, posing a challenge for breeding progress. To address this bottleneck, robotics and automation technologies offer efficient sensing tools to monitor field evolution and crop development throughout the season. This study aimed to develop an autonomous ground robotic system

William Q. Meeker, Luis A. Escobar, Francis G. Pascual, Yili Hong

Engineers and scientists have been collecting and analyzing fatigue data since the 1800s to ensure the reliability of life-critical structures. Applications include (but are not limited to) bridges, building structures, aircraft and spacecraft components, ships, ground-based vehicles, and medical devices. Engineers need to estimate S-N relationships (Stress

Benjamin Doerr, Joshua Knowles, Aneta Neumann, Frank Neumann

We consider whether conditions exist under which block-coordinate descent is asymptotically efficient in evolutionary multi-objective optimization, addressing an open problem. Block-coordinate descent, where an optimization problem is decomposed into $k$ blocks of decision variables and each of the blocks is optimized (with the others fixed) in a sequence, i

Yuan Sun, Zhou Zhou

We consider parameter inference for linear quantile regression with non-stationary predictors and errors, where the regression parameters are subject to inequality constraints. We show that the constrained quantile coefficient estimators are asymptotically equivalent to the metric projections of the unconstrained estimator onto the constrained parameter spac

Amrin Kareem, Jean Lahoud, Hisham Cholakkal

Recent advancements in 3D perception systems have significantly improved their ability to perform visual recognition tasks such as segmentation. However, these systems still heavily rely on explicit human instruction to identify target objects or categories, lacking the capability to actively reason and comprehend implicit user intentions. We introduce a nov

Andrey Akinshin

Traditional density and quantile estimators are often inconsistent with each other. Their simultaneous usage may lead to inconsistent results. To address this issue, we propose a novel smooth density estimator that is naturally consistent with the Harrell-Davis quantile estimator. We also provide a jittering implementation to support discrete-continuous mixt

Md Ishat-E-Rabban, Guangyao Shi, Griffin Bonner, Pratap Tokekar

Maintaining a robust communication network plays an important role in the success of a multi-robot team jointly performing an optimization task. A key characteristic of a robust cooperative multi-robot system is the ability to repair the communication topology in the case of robot failure. In this paper, we focus on the Fast k-connectivity Restoration (FCR)

Mary M. Lucas, Xiaoyang Wang, Chia-Hsuan Chang, Christopher C. Yang

Fairness of machine learning models in healthcare has drawn increasing attention from clinicians, researchers, and even at the highest level of government. On the other hand, the importance of developing and deploying interpretable or explainable models has been demonstrated, and is essential to increasing the trustworthiness and likelihood of adoption of th

Nassim Derriche, Marcel Franz, George Sawatzky

We examine a class of Hamiltonians characterized by interatomic, interorbital even-odd parity hybridization as a model for a family of topological insulators without the need for spin-orbit coupling. Non-trivial properties of these materials are exemplified by studying the topologically-protected edge states of s-p hybridized alkali and alkaline earth atoms

Jonathan F. Carter, João Jorge, Oliver Gibson, Lionel Tarassenko

Advances in camera-based physiological monitoring have enabled the robust, non-contact measurement of respiration and the cardiac pulse, which are known to be indicative of the sleep stage. This has led to research into camera-based sleep monitoring as a promising alternative to "gold-standard" polysomnography, which is cumbersome, expensive to administer, a