December 2023 arXiv papers — page 98

Gregory Baimetov, Ryan Bushling, Ansel Goh, Raymond Guo

Let $G = (V,E)$ be a connected graph. A probability measure $\mu$ on $V$ is called "balanced" if it has the following property: if $T_\mu(v)$ denotes the "earth mover's" cost of transporting all the mass of $\mu$ from all over the graph to the vertex $v$, then $T_\mu$ attains its global maximum at each point in the support of $\mu$. We prove a decomposition

Jiangming Shi, Shanshan Zheng, Xiangbo Yin, Yang Lu

Federated learning (FL) provides a decentralized machine learning paradigm where a server collaborates with a group of clients to learn a global model without accessing the clients' data. User heterogeneity is a significant challenge for FL, which together with the class-distribution imbalance further enhances the difficulty of FL. Great progress has been ma

Michael Clancy

To address quantum computation of quantities in quantum chromodynamics (QCD) for which chiral symmetry is important, it would be useful to have the Hamiltonian for a fermion satisfying the Ginsparg-Wilson (GW) equation. I work with an approximate solution to the GW equation which is fractional linear in time derivatives. The resulting Hamiltonian is non-loca

Thusitha Dayaratne, Carsten Rudolph, Ariel Liebman, Mahsa Salehi

Utility companies are increasingly leveraging residential demand flexibility and the proliferation of smart/IoT devices to enhance the effectiveness of residential demand response (DR) programs through automated device scheduling. However, the adoption of distributed architectures in these systems exposes them to the risk of false data injection attacks (FDI

Ding Wang, Danhao Wang, Mahlet Molla, Yujie Liu

Wurtzite ferroelectrics possess transformative potential for next-generation microelectronics. A comprehensive understanding of their ferroelectric properties and domain energetics is crucial for tailoring their ferroelectric characteristics and exploiting their functional properties in practical devices. Despite burgeoning interest, the exact configurations

Guiqin Wang, Peng Zhao, Yanjiang Shi, Cong Zhao

Knowledge distillation (KD), a technique widely employed in computer vision, has emerged as a de facto standard for improving the performance of small neural networks. However, prevailing KD-based approaches in video tasks primarily focus on designing loss functions and fusing cross-modal information. This overlooks the spatial-temporal feature semantics, re

Ilje Cho, José L. Gómez, Rocco Lico, Guang-Yao Zhao

We present total intensity and linear polarization images of OJ287 at 1.68GHz, obtained through space-based VLBI observations with RadioAstron on April 16, 2016. The observations were conducted using a ground array consisting of the VLBA and the EVN. Ground-space fringes were detected with a maximum projected baseline length of 5.6 Earth's diameter, resultin

Yu Ji, Wen Wu, Yi Hu, Hong Zheng

Few-shot prompting elicits the remarkable abilities of large language models by equipping them with a few demonstration examples in the input. However, the traditional method of providing large language models with all demonstration input-output pairs at once may not effectively guide large language models to learn the specific input-output mapping relations

Zengrui Jin, Xurong Xie, Tianzi Wang, Mengzhe Geng

Automatic recognition of disordered speech remains a highly challenging task to date due to data scarcity. This paper presents a reinforcement learning (RL) based on-the-fly data augmentation approach for training state-of-the-art PyChain TDNN and end-to-end Conformer ASR systems on such data. The handcrafted temporal and spectral mask operations in the stan

Yuvraj Gupta, Zhewei Liu, Ali Mostafavi

The resilience of internet service is crucial for ensuring consistent communication, facilitating emergency response in digitally-dependent society. Due to empirical data constraints, there has been limited research on internet service disruptions during extreme weather events. To bridge this gap, this study utilizes observational datasets on internet perfor

Yi-Chun Chen, Arnav Jhala

We present a theory-inspired visual narrative generator that incorporates comic-authoring idioms, which transfers the conceptual principles of comics into system layers that integrate the theories to create comic content. The generator creates comics through sequential decision-making across layers from panel composition, object positions, panel transitions,

Tatsuya Gima, Tesshu Hanaka, Yasuaki Kobayashi, Yota Otachi

The problem of packing as many subgraphs isomorphic to $H \in \mathcal H$ as possible in a graph for a class $\mathcal H$ of graphs is well studied in the literature. Both vertex-disjoint and edge-disjoint versions are known to be NP-complete for $H$ that contains at least three vertices and at least three edges, respectively. In this paper, we consider ``li

Arpit Raj, Abigail Postlewaite, Swati Chaudhary, Gregory A. Fiete

We theoretically study first and second-order optical responses in a transition metal dichalcogenide monolayer with distinct trivial, nodal, and time-reversal invariant topological superconducting (TRITOPS) phases. We show that the second-order DC response, also known as the photogalvanic response, contains signatures for differentiating these phases while t

Gennadiy Feldman

We consider a certain convolution semigroup $\Theta$ of probability distributions on the group $\mathbb{R}\times \mathbb{Z}(2)$, where $\mathbb{R}$ is the group of real numbers and $\mathbb{Z}(2)$ is the additive group of the integers modulo 2. This semigroup appeared in connection with the study of a characterization problem of mathematical statistics on $a

Yi Xin, Junlong Du, Qiang Wang, Ke Yan

Multi-Task Learning (MTL) is designed to train multiple correlated tasks simultaneously, thereby enhancing the performance of individual tasks. Typically, a multi-task network structure consists of a shared backbone and task-specific decoders. However, the complexity of the decoders increases with the number of tasks. To tackle this challenge, we integrate t

Ruocheng Zhai, Radosław Poleski, Weicheng Zang, Youn Kil Jung

The gravitational microlensing technique is most sensitive to planets in a Jupiter-like orbit and has detected more than 200 planets. However, only a few wide-orbit ($s > 2$) microlensing planets have been discovered, where $s$ is the planet-to-host separation normalized to the angular Einstein ring radius, $\theta_{\rm E}$. Here we present the discovery and

Olaiyan Alolaiyan, Shahad Albwardi, Sarah Alsaggaf, Thamer Tabbakh

Recent reports on machine learning (ML) and machine vision (MV) devices have demonstrated the potentials of 2D materials and devices. Yet, scalable 2D devices are being challenged by contact resistance and Fermi Level Pinning (FLP), power consumption, and low-cost CMOS compatible lithography processes. To enable CMOS+2D, it is essential to find a proper lith

Zhuoran Li, Jiqiang Zheng

In this paper, we study the restriction problem for one class of hypersurfaces with vanishing curvature in $\mathbb{R}^n$ with $n$ being odd. We obtain an $L^2-L^p$ restriction estimate, which is optimal except at the endpoint. Furthermore, we establish an $L^s-L^p$ restriction estimate for these hypersurfaces, which is achieved by improving the known $L^{\i

Zhi-Kang Lin, Yao Zhou, Bin Jiang, Bing-Quan Wu

Entanglement entropy is a fundamental concept with rising importance in different fields ranging from quantum information science, black holes to materials science. In complex materials and systems, entanglement entropy provides insight into the collective degrees of freedom that underlie the systems' complex behaviours. As well-known predictions, the entang

Wentao Pan, Zhe Xu, Jiangpeng Yan, Zihan Wu

Semi-supervised semantic segmentation aims to utilize limited labeled images and abundant unlabeled images to achieve label-efficient learning, wherein the weak-to-strong consistency regularization framework, popularized by FixMatch, is widely used as a benchmark scheme. Despite its effectiveness, we observe that such scheme struggles with satisfactory segme

Chien-Yeah Seng

It is well-known that the traditional treatment of radiative corrections that utilizes the "true" neutrino momentum $\vec{p}_\nu$ in the differential decay rate formula could lead to a $\sim \alpha/\pi$ systematic error in certain observables due to the mistreatment of 4-body kinematics. We investigate the theory structure of one of the proposed solutions, t

Haiyang Tang, Zhenyi Liu, Dongping Chen, Qingzhao Chu

Recent advancements in large language models (LLMs) have notably propelled natural language processing (NLP) capabilities, demonstrating significant potential in safety engineering applications. Despite these advancements, LLMs face constraints in processing specialized tasks, attributed to factors such as corpus size, input processing limitations, and priva

Xiao Yang, Enmin Song, Guangzhi Ma, Yunfeng Zhu

Colon cancer is expected to become the second leading cause of cancer death in the United States in 2023. Although colonoscopy is one of the most effective methods for early prevention of colon cancer, up to 30% of polyps may be missed by endoscopists, thereby increasing patients' risk of developing colon cancer. Though deep neural networks have been proven

Keith Pedersen, Mithila Mangedarage, Zack Sullivan

As multiplicity increases at the CERN Large Hadron Collider, an opportunity arises to explore the information contained in the full QCD power spectrum on an event-by-event basis. This paper lays the foundations for a framework to encode and extract the information contained in finite sampling of a QCD event.

Yan Liu, Kemeng Wu, Shutao Liu, Yan-Cheng Wang

We extend the single-mode Approximation (SMA) into quantum Monte Carlo simulations to provides an efficient and fast method to obtain the dynamical dispersion of quantum many-body systems. Based on stochastic series expansion (SSE) and its projector algorithms, the SMA + SSE method can simply extract the dispersion of the dynamical dispersion in the long wav

Takanori Sakai, Kohei Santo, Shinya Tanaka, Tetsuro Hyodo

The rapid growth of the e-commerce market creates new dynamics in the logistics landscape, which has been evolving for decades in cities around the world. It is a challenge for businesses and planners to meet the high demand for logistics facilities for e-commerce order fulfillment and goods handling. In the Tokyo Metropolitan Area, mega-scale multi-tenant l

Haoyu Tang, Louis J. Durlofsky

The optimization of well locations and controls is an important step in the design of subsurface flow operations such as oil production or geological CO2 storage. These optimization problems can be computationally expensive, however, as many potential candidate solutions must be evaluated. In this study, we propose a graph network surrogate model (GNSM) for

Manuel Rebol, Krzysztof Pietroszek, Claudia Ranniger, Colton Hood

Medical procedures are an essential part of healthcare delivery, and the acquisition of procedural skills is a critical component of medical education. Unfortunately, procedural skill is not evenly distributed among medical providers. Skills may vary within departments or institutions, and across geographic regions, depending on the provider's training and o

Ben Bobell, Danny Sun, Allan H. Reiman

A nonlinear effect, RF (radio frequency) condensation, can be used to facilitate RF stabilization of magnetic islands. Previously studied semi-analytical models for RF condensation are suited mainly for broad deposition profiles and are unable to handle power depositions that are localized in the interior of a magnetic island. Here, a model is developed that

Haibin Wu, Heng-Cheng Kuo, Yu Tsao, Hung-yi Lee

Automatic speaker verification (ASV) is highly susceptible to adversarial attacks. Purification modules are usually adopted as a pre-processing to mitigate adversarial noise. However, they are commonly implemented across diverse experimental settings, rendering direct comparisons challenging. This paper comprehensively compares mainstream purification techni

Hideyoshi Yanagisawa, Shimon Honda

Epistemic emotions, such as curiosity and interest, drive the inquiry process. This study proposes a novel formulation of epistemic emotions such as curiosity and interest using two types of information gain generated by the principle of free energy minimization: Kullback-Leibler divergence(KLD) from Bayesian posterior to prior, which represents free energy

Adarsh Salagame, Maria Gianello, Chenghao Wang, Kaushik Venkatesh

Inspired by Chukars wing-assisted incline running (WAIR), in this work, we employ a high-fidelity model of our Husky Carbon quadrupedal-legged robot to walk over steep slopes of up to 45 degrees. Chukars use the aerodynamic forces generated by their flapping wings to manipulate ground contact forces and traverse steep slopes and even overhangs. By exploiting

Hiroto Ishida

We consider the Dirichlet Laplacian $\mathcal{A}_\varepsilon=-\Delta$ in the domain $\Omega\setminus\bigcup_i K_{i\varepsilon}\subset\mathbb{R}^n$ with holes $K_{i\varepsilon}$ and the Schr\"{o}dinger operator $\mathcal{A}=-\Delta+V$ in $\Omega$ where $V$ is the $L^n(\Omega)$ limit of the density of the capacities $\operatorname{cap}(K_{i\varepsilon}).$ Stro

Qian-Ze Zhu, Chrisy Xiyu Du, Ella M. King, Michael P. Brenner

Designing components that can robustly self-assemble into structures with biological complexity is a grand challenge for material science. Proofreading and error correction is required to improve assembly yield beyond equilibrium limits, using energy to avoid kinetic traps in the energy landscape. Here we introduce an explicit two staged proofreading scheme

Kaiqiang Song, Xiaoyang Wang, Sangwoo Cho, Xiaoman Pan

This paper introduces a novel approach to enhance the capabilities of Large Language Models (LLMs) in processing and understanding extensive text sequences, a critical aspect in applications requiring deep comprehension and synthesis of large volumes of information. Recognizing the inherent challenges in extending the context window for LLMs, primarily built

Shang Liu, Wenji Fang, Yao Lu, Qijun Zhang

The automatic generation of RTL code (e.g., Verilog) using natural language instructions and large language models (LLMs) has attracted significant research interest recently. However, most existing approaches heavily rely on commercial LLMs such as ChatGPT, while open-source LLMs tailored for this specific design generation task exhibit notably inferior per

Yibo Li, Xiao Wang, Hongrui Liu, Chuan Shi

Recent studies reveal the connection between GNNs and the diffusion process, which motivates many diffusion-based GNNs to be proposed. However, since these two mechanisms are closely related, one fundamental question naturally arises: Is there a general diffusion framework that can formally unify these GNNs? The answer to this question can not only deepen ou

Farzad Maghsoudi, Babak Miraftab, Sho Suda

In this paper, we explore the concept of the ``matrix product of graphs," initially introduced by Prasad, Sudhakara, Sujatha, and M. Vinay. This operation involves the multiplication of adjacency matrices of two graphs with assigned labels, resulting in a weighted digraph. Our primary focus is on identifying graphs that can be expressed as the graphical matr

Haolin Qin, Daquan Zhou, Tingfa Xu, Ziyang Bian

Transformers have astounding representational power but typically consume considerable computation which is quadratic with image resolution. The prevailing Swin transformer reduces computational costs through a local window strategy. However, this strategy inevitably causes two drawbacks: (1) the local window-based self-attention hinders global dependency mo

Tomoki Fujita, Yanwen Sun, Haoyuan Li, Thies J. Albert

Glass forming liquids exhibit structural relaxation behaviors, reflecting underlying atomic rearrangements on a wide range of timescales. These behaviors play a crucial role in determining many material properties. However, the relaxation processes on the atomic scale are not well understood due to the experimental difficulties in directly characterizing the

Yuchan Lee

For the unitary group defined over the ring of integers in a non Archimedean local field, we give a correction for a Kostant section provided in G.Laumon and B.C. Ng\^o's paper; Le lemme fondamental pour les groupes unitaires.

Andrew Melnik, Michael Büttner, Leon Harz, Lyon Brown

This report introduces our UniTeam agent - an improved baseline for the "HomeRobot: Open Vocabulary Mobile Manipulation" challenge. The challenge poses problems of navigation in unfamiliar environments, manipulation of novel objects, and recognition of open-vocabulary object classes. This challenge aims to facilitate cross-cutting research in embodied AI usi

Kunxing Lu, Xianrui Wang, Tetsuya Ueda, Shoji Makino

While the semi-blind source separation-based acoustic echo cancellation (SBSS-AEC) has received much research attention due to its promising performance during double-talk compared to the traditional adaptive algorithms, it suffers from system latency and nonlinear distortions. To circumvent these drawbacks, the recently developed ideas on convolutive transf

Pedro D. Manrique, Frank Yingjie Huo, Sara El Oud, Neil F. Johnson

Self-assembly is a key process in living systems - from the microscopic biological level (e.g. assembly of proteins into fibrils within biomolecular condensates in a human cell) through to the macroscopic societal level (e.g. assembly of humans into common-interest communities across online social media platforms). The components in such systems (e.g. macrom

Kento Akamatsu, Takuya Hirose, Nobuhito Maru, Akio Nago

Electroweak symmetry breaking is explored in a two Higgs doublet model based on a six dimensional $SU(4)$ gauge-Higgs unification compactified on an orbifold $T^2/Z_2$. The remarkable property of this model is a prediction of realistic weak mixing angle $\sin^2 \theta_W = 1/4$ at the compactification scale. We calculate one-loop effective potential of the St

Xiao Liang

The parabolic Anderson model (PAM) is one of the most interesting and challenging SPDEs related to various physical phenomena, and can be described mathematically as a stochastic heat equation driven by linear multiplicative noise. In this paper, we consider PAM with initial condition given by a signed Borel measure on $R^d$. The forcing term under investiga

Wenbin Zou, Hongxia Gao, Tian Ye, Liang Chen

Night photography often struggles with challenges like low light and blurring, stemming from dark environments and prolonged exposures. Current methods either disregard priors and directly fitting end-to-end networks, leading to inconsistent illumination, or rely on unreliable handcrafted priors to constrain the network, thereby bringing the greater error to

Guillaume Le Treut, Sarah Ancheta, Greg Huber, Henri Orland

We present a method to sample Markov-chain trajectories constrained to both the initial and final conditions, which we term Markov bridges. The trajectories are conditioned to end in a specific state at a given time. We derive the master equation for Markov bridges, which exhibits the original transition rates scaled by a time-dependent factor. Trajectories

Albert Lin, Somil Bansal

Learning-based approaches for controlling safety-critical systems are rapidly growing in popularity; thus, it is important to assure their performance and safety. Hamilton-Jacobi (HJ) reachability analysis is a popular formal verification tool for providing such guarantees, since it can handle general nonlinear system dynamics, bounded adversarial system dis

Hyun-Jun Heo, Ui-Hyeop Shin, Ran Lee, YoungJu Cheon

In speaker verification, ECAPA-TDNN has shown remarkable improvement by utilizing one-dimensional(1D) Res2Net block and squeeze-and-excitation(SE) module, along with multi-layer feature aggregation (MFA). Meanwhile, in vision tasks, ConvNet structures have been modernized by referring to Transformer, resulting in improved performance. In this paper, we prese

Ernst Moritz Hahn, Mateo Perez, Sven Schewe, Fabio Somenzi

Regular decision processes (RDPs) are a subclass of non-Markovian decision processes where the transition and reward functions are guarded by some regular property of the past (a lookback). While RDPs enable intuitive and succinct representation of non-Markovian decision processes, their expressive power coincides with finite-state Markov decision processes

Brian J. J. Khor, D. M. Kürkçüoglu, T. J. Hobbs, G. N. Perdue

In this work, we explore the interplay of confinement, string breaking and entanglement asymmetry on a 1D quantum Ising chain. We consider the evolution of an initial domain wall and show that, surprisingly, while the introduction of confinement through a longitudinal field typically suppresses entanglement, it can also serve to increase it beyond a bound se

Xiran Zhou, Yi Wen, Honghao Li, Kaiyuan Li

Maps are fundamental medium to visualize and represent the real word in a simple and 16 philosophical way. The emergence of the 3rd wave information has made a proportion of maps are available to be generated ubiquitously, which would significantly enrich the dimensions and perspectives to understand the characteristics of the real world. However, a majority

Elie El Haber, Mohamed Elhattab, Chadi Assi, Sanaa Sharafeddine

Although multi-access edge computing (MEC) has allowed for computation offloading at the network edge, weak wireless signals in the radio access network caused by obstacles and high network load are still preventing efficient edge computation offloading, especially for user requests with stringent latency and reliability requirements. Intelligent reflective

Andreas Müller, Carlo Curino, Raghu Ramakrishnan

Foundation models are transforming machine learning across many modalities, with in-context learning replacing classical model training. Recent work on tabular data hints at a similar opportunity to build foundation models for classification for numerical data. However, existing meta-learning approaches can not compete with tree-based methods in terms of inf

Elizabeth Himwich, Monica Pate

In four-dimensional asymptotically flat spacetimes, an infinite tower of soft graviton modes is known to generate the symmetry algebra of ${\rm w}_{1+\infty}$ at tree-level. Here we demonstrate that the symmetry action follows from soft graviton theorems and acts non-trivially on massive scalar particles. By generalizing previous analyses that were specifica

Changhan Zou

We extend the support theory of Benson--Iyengar--Krause to the non-Noetherian setting by introducing a new notion of small support for modules. This enables us to prove that the stable module category of a finite group is canonically stratified by the action of the Tate cohomology ring, despite the fact that this ring is rarely Noetherian. In the tensor tria

Anson Ho, Ege Erdil, Tamay Besiroglu

CMOS microprocessors have achieved massive energy efficiency gains but may reach limits soon. This paper presents an approach to estimating the limits on the maximum floating point operations per Joule (FLOP/J) for CMOS microprocessors. We analyze the three primary sources of energy dissipation: transistor switching, interconnect capacitances and leakage pow

Sicheng Wang, Hao Jiang, Lei Xiang

Recent deep multi-view stereo (MVS) methods have widely incorporated transformers into cascade network for high-resolution depth estimation, achieving impressive results. However, existing transformer-based methods are constrained by their computational costs, preventing their extension to finer stages. In this paper, we propose a novel cross-scale transform

Jean-Paul Mazellier, Antoine Boujon, Méline Bour-Lang, Maël Erharhd

This technical report presents MOSaiC 3.6.2, a web-based collaborative platform designed for the annotation and evaluation of medical videos. MOSaiC is engineered to facilitate video-based assessment and accelerate surgical data science projects. We provide an overview of MOSaiC's key functionalities, encompassing group and video management, annotation tools

Frank P. -W. Lo, Jianing Qiu, Zeyu Wang, Junhong Chen

Conventional approaches to dietary assessment are primarily grounded in self-reporting methods or structured interviews conducted under the supervision of dietitians. These methods, however, are often subjective, potentially inaccurate, and time-intensive. Although artificial intelligence (AI)-based solutions have been devised to automate the dietary assessm

Muxin Zhang, Qiao Feng, Zhuo Su, Chao Wen

3D human generation is increasingly significant in various applications. However, the direct use of 2D generative methods in 3D generation often results in losing local details, while methods that reconstruct geometry from generated images struggle with global view consistency. In this work, we introduce Joint2Human, a novel method that leverages 2D diffusio

Teodora Popordanoska, Sebastian G. Gruber, Aleksei Tiulpin, Florian Buettner

Proper scoring rules evaluate the quality of probabilistic predictions, playing an essential role in the pursuit of accurate and well-calibrated models. Every proper score decomposes into two fundamental components -- proper calibration error and refinement -- utilizing a Bregman divergence. While uncertainty calibration has gained significant attention, cur

Hengrui Zhu, Harrison Siegel, Keefe Mitman, Maximiliano Isi

The spectroscopic study of black hole quasinormal modes in gravitational-wave ringdown observations is hindered by our ignorance of which modes should dominate astrophysical signals for different binary configurations, limiting tests of general relativity and astrophysics. In this work, we present a description of the quasinormal modes that are excited in th

Rongke Lyu, Marina Vannucci, Suprateek Kundu

Tensor-based representations are being increasingly used to represent complex data types such as imaging data, due to their appealing properties such as dimension reduction and the preservation of spatial information. Recently, there is a growing literature on using Bayesian scalar-on-tensor regression techniques that use tensor-based representations for hig

Teodora Popordanoska, Gorjan Radevski, Tinne Tuytelaars, Matthew B. Blaschko

In the face of dataset shift, model calibration plays a pivotal role in ensuring the reliability of machine learning systems. Calibration error (CE) is an indicator of the alignment between the predicted probabilities and the classifier accuracy. While prior works have delved into the implications of dataset shift on calibration, existing CE estimators assum

Jingxuan Wei, Linzhuang Sun, Xu Tan, Bihui Yu

Knowledge distillation, a technique for model compression and performance enhancement, has gained significant traction in Neural Machine Translation (NMT). However, existing research primarily focuses on empirical applications, and there is a lack of comprehensive understanding of how student model capacity, data complexity, and decoding strategies collectiv

Thiago Bellotti Furtado, Ahmed Esmin

The technological evolution of the library in the academic environment brought a lot of information and documents that are available to access, but these systems do not always have mechanisms to search in an integrated way the relevant information for the user. To alleviate this problem, we propose a recommendation system that generates the user profile thro

Xiaoxia Wu, Haojun Xia, Stephen Youn, Zhen Zheng

This study examines 4-bit quantization methods like GPTQ in large language models (LLMs), highlighting GPTQ's overfitting and limited enhancement in Zero-Shot tasks. While prior works merely focusing on zero-shot measurement, we extend task scope to more generative categories such as code generation and abstractive summarization, in which we found that INT4

Gabriel Catalini, Nicolás A. García, Daniel A. Vega, Arash Nikoubashman

We report molecular dynamics simulation results on the equilibrium properties of polymer thin films adsorbed onto flat and curved substrates. We first systematically determine the contact angle of polymer droplets on flat substrates as a function of the substrate-monomer adsorption strength and degree of polymerization. Focussing on the fully wetted regime,

J. Alonso-Santiago, A. Frasca, G. Catanzaro, A. Bragaglia

M 39 is a nearby young open cluster hardly studied in the last decades. No giant is known among its members and its chemical composition has never been studied. In order to investigate it we performed high-resolution spectroscopy of 20 expected cluster members with the HARPS and FIES spectrographs. By combining our observations with archival photometry and $

Qianyun Li, Jiaolong Chen

The main purpose of this paper is to establish a Schwarz lemma for the solutions to the Dirichlet problems for the invariant Laplacians. The obtained result of this paper is a generalization of the corresponding known results [11, Theorem 1.1] and [15, Theorem 2.1].

Golnaz Shapurian, Michael J Kurtz, Alberto Accomazzi

The automatic identification of planetary feature names in astronomy publications presents numerous challenges. These features include craters, defined as roughly circular depressions resulting from impact or volcanic activity; dorsas, which are elongate raised structures or wrinkle ridges; and lacus, small irregular patches of dark, smooth material on the M

Shane Storm Strachan

This research explores the application of a genetic algorithm metaheuristic enriched by the wisdom of crowds in order to address the NP-Complete matrix domination problem (henceforth: TMDP) which is itself a constraint on related problems applied in graphs. Matrix domination involves accurately placing a subset of cells, referred to as dominators, within a m

Jack Urbanek, Florian Bordes, Pietro Astolfi, Mary Williamson

Curation methods for massive vision-language datasets trade off between dataset size and quality. However, even the highest quality of available curated captions are far too short to capture the rich visual detail in an image. To show the value of dense and highly-aligned image-text pairs, we collect the Densely Captioned Images (DCI) dataset, containing 780

Varun Laxman Muttepawar, Arjun Mehra, Zubair Shaban, Ranjitha Prasad

Wireless embedded edge devices are ubiquitous in our daily lives, enabling them to gather immense data via onboard sensors and mobile applications. This offers an amazing opportunity to train machine learning (ML) models in the realm of wireless devices for decision-making. Training ML models in a wireless setting necessitates transmitting datasets collected

R. Sekhar Chivukula, Joshua A. Gill, Kirtimaan A. Mohan, Dipan Sengupta

Building on work by Hang and He, we show how the residual five-dimensional diffeomorphism symmetries of compactified gravitational theories with a warped extra dimension imply Equivalence theorems which ensure that the scattering amplitudes of helicity-0 and helicity-1 spin-2 Kaluza-Klein states equal (to leading order in scattering energy) those of the corr

Satoshi Murai, Mitsuki Shiina

In this note, we study Betti splittings of cover ideals of bipartite graphs. We prove that if $J \subset \Bbbk [x_1,\dots,x_n]$ is the cover ideal of a bipartite graph then the $x_i$-partition of $J$ is a Betti splitting for any $i$. We also prove that multigraded Betti numbers of any squarefree monomial ideal can appear in a certain part of multigraded Bett

Yuma Kitagawa, Yuta Suzuki, Shin-ichiro Tezuka, Hiroshi Akera

A method is developed to calculate the electron flow between an atomic monolayer sheet and a tube with use of tunneling matrix elements between monolayer sheets and applied to the spin current from monolayer silicene with sublattice-staggered current-induced spin polarization to silicene tube. Calculated sheet-to-tube spin current exhibits an oscillation as

George Pantazis, Filiberto Fele, Filippo Fabiani, Sergio Grammatico

We study coalitional games with exogenous uncertainty in the coalition value, in which each agent is allowed to have private samples of the uncertainty. As a consequence, the agents may have a different perception of stability of the grand coalition. In this context, we propose a novel methodology to study the out-of-sample coalitional rationality of allocat

W. Sengupta, N. Nikulsin, R. Gaur, A. Bhattacharjee

Quasisymmetry (QS), a hidden symmetry of the magnetic field strength, is known to support nested flux surfaces and provide superior particle confinement in stellarators. In this work, we study the ideal MHD equilibrium and stability of high-beta plasma in a large aspect-ratio stellarator. In particular, we show that the lowest-order description of a near-axi

Yuri Yu. Tarasevich, Irina V. Vodolazskaya, Andrei V. Eserkepov

We studied the effective electrical conductivity of dense random resistor networks (RRNs) produced using a Voronoi tessellation when its seeds are generated by means of a homogeneous Poisson point process in the two-dimensional Euclidean space. Such RRNs are isotropic and in average homogeneous, however, local fluctuations of the number of edges per unit are

Daniel Alpay, Ilwoo Cho

In this paper, we generalize the well-known hyperbolic numbers to certain numeric structures scaled by the real numbers. Under our scaling of $\mathbb{R}$, the usual hyperbolic numbers are understood to be our 1-scaled hyperbolic numbers. If a scale $t$ is not positive in $\mathbb{R}$, then our $t$-scaled hyperbolic numbers have similar numerical structures

Tobias Canavesi

We use gauge gravity duality to describe the strange metal phase of High $T_c$ superconductors.

Chengxi Lei, Satwinder Singh, Feng Hou, Xiaoyun Jia

Most of the current speech data augmentation methods operate on either the raw waveform or the amplitude spectrum of speech. In this paper, we propose a novel speech data augmentation method called PhasePerturbation that operates dynamically on the phase spectrum of speech. Instead of statically rotating a phase by a constant degree, PhasePerturbation utiliz

Gery Geenens

Some personal thoughts on Sklar's theorem and copulas after reading the original paper (Sklar, 1959) in French.

Christopher Jarzynski

A quantum impulse is a brief but strong perturbation that produces a sudden change in a wavefunction $\psi(x)$. We develop a theory of quantum impulses, distinguishing between ordinary and super impulses. An ordinary impulse paints a phase onto $\psi$, while a super impulse -- the main focus of this paper -- deforms the wavefunction under an invertible map,

Wonbong Jang, Lourdes Agapito

We propose NViST, a transformer-based model for efficient and generalizable novel-view synthesis from a single image for real-world scenes. In contrast to many methods that are trained on synthetic data, object-centred scenarios, or in a category-specific manner, NViST is trained on MVImgNet, a large-scale dataset of casually-captured real-world videos of hu

Ethan Thomas, Salman Aslam

Cardiovascular diseases, particularly heart failure, are a leading cause of death globally. The early detection of heart failure through routine echocardiogram screenings is often impeded by the high cost and labor-intensive nature of these procedures, a barrier that can mean the difference between life and death. This paper presents ConFormer, a novel deep

Lionel Wong, Jiayuan Mao, Pratyusha Sharma, Zachary S. Siegel

Effective planning in the real world requires not only world knowledge, but the ability to leverage that knowledge to build the right representation of the task at hand. Decades of hierarchical planning techniques have used domain-specific temporal action abstractions to support efficient and accurate planning, almost always relying on human priors and domai

Yuhui Liu

Let $1<c<\frac{1787}{1502}$ and $N$ be a sufficiently large real number. In this paper, it is proved that for any arbitrarily large number $E>0$ and for almost all real $R \in (N,2N]$, the Diophantine inequality $$|p_{1}^{c}+p_{2}^{c}-R|<(log N)^{-E}$$ is solvable in prime variables $p_1,p_2$ such that, each of the numbers $p_{1}+2,p_{2}+2$ has at most $[\fr

Yanzeng Zhang, Jun Li, Xianzhu Tang

Thermal quench of a nearly collisionless plasma against a cooling boundary or region is an undesirable off-normal event in magnetic fusion experiments, but an ubiquitous process of cosmological importance in astrophysical plasmas. There is a well-known mismatch that what experimentally diagnosed is the drop in perpendicular electron temperature $T_{e\perp},$

Liangchen Song, Liangliang Cao, Jiatao Gu, Yifan Jiang

The advancement of text-driven 3D content editing has been blessed by the progress from 2D generative diffusion models. However, a major obstacle hindering the widespread adoption of 3D content editing is its time-intensive processing. This challenge arises from the iterative and refining steps required to achieve consistent 3D outputs from 2D image-based ge

Piotr M. Hajac, Mariusz Tobolski

In the standard category of directed graphs, graph morphisms map edges to edges. By allowing graph morphisms to map edges to finite paths (path homomorphisms of graphs), we obtain an ambient category in which we determine subcategories enjoying covariant functors to categories of algebras given by constructions of path algebras, Cohn path algebras, and Leavi

Yuanhang Zhu, Kenneth Breuer

We propose and validate a data-driven approach for modeling large-amplitude flow-induced oscillations of elastically mounted pitching wings. We first train a neural networks regression model for the nonlinear aerodynamic moment using data obtained from experimental measurements during prescribed pitching oscillations and at fixed angles of attack. We then em

A. Gutiérrez-Rodríguez, E. Cruz-Albaro, D. Espinosa-Gómez, T. Cisneros-Pérez

The Bestest Little Higgs Model (BLHM) has attracted increasing attention in recent years, mainly because it can explain the hierarchy problem without fine-tuning by introducing one-loop corrections to the Higgs boson mass through heavy top quark partners and heavy gauge bosons. In the context of this new model, we exhaustively investigated the impact of the

Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain

In critical machine learning applications, ensuring fairness is essential to avoid perpetuating social inequities. In this work, we address the challenges of reducing bias and improving accuracy in data-scarce environments, where the cost of collecting labeled data prohibits the use of large, labeled datasets. In such settings, active learning promises to ma

M. Eren Akbiyik, Nedko Savov, Danda Pani Paudel, Nikola Popovic

Understanding drivers' decision-making is crucial for road safety. Although predicting the ego-vehicle's path is valuable for driver-assistance systems, existing methods mainly focus on external factors like other vehicles' motions, often neglecting the driver's attention and intent. To address this gap, we infer the ego-trajectory by integrating the driver'

Sigmundur Vang, Christian Thomsen, Torben Bach Pedersen

Data cubes are used for analyzing large data sets usually contained in data warehouses. The most popular data cube tools use graphical user interfaces (GUI) to do the data analysis. Traditionally this was fine since data analysts were not expected to be technical people. However, in the subsequent decades the data landscape changed dramatically requiring com

Jarand Hole, Andy Philpott, Oscar Dowson

We present a capacity expansion model for deciding the new electricity generation and transmission capacity to complement an existing hydroelectric reservoir system. The objective is to meet a forecast demand at least expected cost, namely the capital cost of the investment plus the expected discounted operating cost of the system. The optimal operating poli