April 2024 arXiv papers — page 143

Heyuan Li, Ce Chen, Tianhao Shi, Yuda Qiu

While recent advances in 3D-aware Generative Adversarial Networks (GANs) have aided the development of near-frontal view human face synthesis, the challenge of comprehensively synthesizing a full 3D head viewable from all angles still persists. Although PanoHead proves the possibilities of using a large-scale dataset with images of both frontal and back view

Diego Barberena, Aaron J. Friedman

We provide an overview of standard "projective" quantum measurements with the goal of elucidating connections between theory and experiment. We make use of a unitary "Stinespring" representation of measurements on a dilated Hilbert space that includes both the physical degrees of freedom and those of the measurement apparatus. We explain how this unitary rep

Yuheng Lai, Leying Guan

$\textit{Equalized odds}$, an important notion of algorithmic fairness, aims to ensure that sensitive variables, such as race and gender, do not unfairly influence the algorithm's prediction when conditioning on the true outcome. Despite rapid advancements, current research primarily focuses on equalized odds violations caused by a single sensitive attribute

Bruce Allen

Gravitational waves (GWs) influence the arrival times of radio signals coming from pulsars. Here, we investigate the harmonic space approach to describing a pulsar's response to GWs. We derive and discuss the "diagonalized form" of the response, which is a sum of spin-2-weighted spherical harmonics of the GW direction multiplied by normal (spin-weight 0) sph

V. Lora, R. Smith, J. Fritz, A. Pasquali

When falling into a galaxy cluster, galaxies experience a loss of gas due to ram pressure stripping. In particular, disk galaxies lose gas from their disks and very large tentacles of gas can be formed. Because of the morphology of these stripped galaxies they have been referred to as Jellyfish galaxies. It has been found that star formation is triggered not

Olaf Dünkel, Tim Salzmann, Florian Pfaff

Normalizing flows have proven their efficacy for density estimation in Euclidean space, but their application to rotational representations, crucial in various domains such as robotics or human pose modeling, remains underexplored. Probabilistic models of the human pose can benefit from approaches that rigorously consider the rotational nature of human joint

Kunpeng Song, Yizhe Zhu, Bingchen Liu, Qing Yan

In this paper, we present MoMA: an open-vocabulary, training-free personalized image model that boasts flexible zero-shot capabilities. As foundational text-to-image models rapidly evolve, the demand for robust image-to-image translation grows. Addressing this need, MoMA specializes in subject-driven personalized image generation. Utilizing an open-source, M

Xiaoyi Bao, Siyang Sun, Shuailei Ma, Kecheng Zheng

The reasoning segmentation task, which demands a nuanced comprehension of intricate queries to accurately pinpoint object regions, is attracting increasing attention. However, Multi-modal Large Language Models (MLLM) often find it difficult to accurately localize the objects described in complex reasoning contexts. We believe that the act of reasoning segmen

Jean-Luc Baril, Pamela E. Harris, Kimberly J. Harry, Matt McClinton

We provide generating functions, formulas, and asymptotic expressions for the number of Catalan words based on the number of runs of ascents (descents), runs of weak ascents (descents), $\ell$-valleys, valleys, symmetric valleys, $\ell$-peaks, peaks, and symmetric peaks. We also establish some bijections with restricted Dyck paths and ordered trees that tran

Godwin Osabutey, Robert Richardson, Garritt L. Page

We address the inverse problem for the mean-field Ising model with two- and three-body interactions using a Bayesian framework. Parameter recovery in this setting is notoriously difficult, particularly near phase transitions, at criticality, and under non-identifiability, where conventional estimators and standard MCMC samplers fail. To overcome these challe

Nastassia Grimm, Martin Pijnenburg, Giulia Cusin, Camille Bonvin

While pulsar timing array experiments have recently found evidence for the existence of a stochastic gravitational wave (GW) background, its origin is still unclear. If this background is of astrophysical origin, we expect the distribution of GW sources to follow the one of galaxies. Since galaxies are not perfectly isotropically distributed at large scales,

Giordano Cicchetti, Danilo Comminiello

Real-world documents may suffer various forms of degradation, often resulting in lower accuracy in optical character recognition (OCR) systems. Therefore, a crucial preprocessing step is essential to eliminate noise while preserving text and key features of documents. In this paper, we propose NAF-DPM, a novel generative framework based on a diffusion probab

Davide Orsucci, Philipp Kleinpaß, Jaspar Meister, Innocenzo De Marco

Quantum key distribution (QKD) allows the generation of cryptographic keys beyond the computational hardness paradigm and is befitting for secure data transmission requiring long-term security. The communication distance of fibre-based QKD, however, is limited to a few hundred kilometers due to the exponential scaling of signal attenuation. Satellite QKD (Sa

Jiannan Ge, Lingxi Xie, Hongtao Xie, Pandeng Li

A serious issue that harms the performance of zero-shot visual recognition is named objective misalignment, i.e., the learning objective prioritizes improving the recognition accuracy of seen classes rather than unseen classes, while the latter is the true target to pursue. This issue becomes more significant in zero-shot image segmentation because the stron

Sergey Kastryulin, Artem Konev, Alexander Shishenya, Eugene Lyapustin

In the rapidly progressing field of generative models, the development of efficient and high-fidelity text-to-image diffusion systems represents a significant frontier. This study introduces YaART, a novel production-grade text-to-image cascaded diffusion model aligned to human preferences using Reinforcement Learning from Human Feedback (RLHF). During the d

Riccardo Martini, Gregorio Paci, Dario Sauro, Gian Paolo Vacca

We study substructures of the Weyl group of conformal transformations of the metric of (pseudo)Riemannian manifolds. These substructures are identified by differential constraints on the conformal factors of the transformations which are chosen such that their composition is associative. Mathematically, apart from rare exceptions, they are partial associativ

Liming Wang, Gideon Kassa, Jifeng Liu, Guangrui Xia

Bulk Ge crystals, characterized by significantly lower threading dislocation densities than their epitaxial counterparts, emerge as optimal candidates for studying and improving Ge laser performance. Our study focused on the Ge thickness and TDD impacts on Ge photoluminescence.

Xingyu Zheng, Xianglong Liu, Haotong Qin, Xudong Ma

With the advancement of diffusion models (DMs) and the substantially increased computational requirements, quantization emerges as a practical solution to obtain compact and efficient low-bit DMs. However, the highly discrete representation leads to severe accuracy degradation, hindering the quantization of diffusion models to ultra-low bit-widths. This pape

Xiaoyan Cong, Yue Wu, Qifeng Chen, Chenyang Lei

We propose a framework for automatic colorization that allows for iterative editing and modifications. The core of our framework lies in an imagination module: by understanding the content within a grayscale image, we utilize a pre-trained image generation model to generate multiple images that contain the same content. These images serve as references for c

Jonah A. Quirk, Carol E. Tanner, D. S. Elliott

We report measurements of the ratio of the scalar polarizability $\alpha$ to the vector polarizability $\beta$ for the $6s ^2S_{1/2} \rightarrow 7s ^2S_{1/2}$ transition in atomic cesium. These measurements are motivated by a discrepancy between the values of the vector transition polarizability as determined using two separate methods. In the present measur

Khai Le-Duc

Due to privacy restrictions, there's a shortage of publicly available speech recognition datasets in the medical domain. In this work, we present VietMed - a Vietnamese speech recognition dataset in the medical domain comprising 16h of labeled medical speech, 1000h of unlabeled medical speech and 1200h of unlabeled general-domain speech. To our best knowledg

Eduardo Casas, Konstantinos Chrysafinos, Mariano Mateos

This paper studies an optimal control problem governed by a semilinear elliptic equation, in which the control acts in a multiplicative or bilinear way as the reaction coefficient of the equation. We focus on the numerical discretization of the problem. The discretization is carried out by using the finite element method, with piecewise constant functions fo

Sihao Lin, Pumeng Lyu, Dongrui Liu, Tao Tang

Self-attention mechanism is the key of the Transformer but often criticized for its computation demands. Previous token pruning works motivate their methods from the view of computation redundancy but still need to load the full network and require same memory costs. This paper introduces a novel strategy that simplifies vision transformers and reduces compu

Shahidur Rahoman Sohag, Sai Zhang, Min Xian, Shoukun Sun

Industry-wide nuclear power plant operating experience is a critical source of raw data for performing parameter estimations in reliability and risk models. Much operating experience information pertains to failure events and is stored as reports containing unstructured data, such as narratives. Event reports are essential for understanding how failures are

Niklas Sapountzoglou, Aleksandra Zimmermann

In this contribution, we provide convergence rates for a finite volume scheme of the stochastic heat equation with multiplicative Lipschitz noise and homogeneous Neumann boundary conditions (SHE). More precisely, we give an error estimate for the $L^2$-norm of the space-time discretization of SHE by a semi-implicit Euler scheme with respect to time and a TPF

Christopher C. Stark, Natasha Latouf, Avi M. Mandell, Amber Young

A primary scientific goal of the future Habitable Worlds Observatory will be the direct detection and characterization of Earth-like planets. Estimates of the exoplanet yields for this concept will help guide mission design through detailed trade studies. It is therefore critical that yield estimation codes optimally adapt observations to the mission's perfo

Nikolaos Kouvatsos, Mairi Sakellariadou

We probe the astrophysical gravitational-wave background resulting from compact binary coalescences, focusing on Population III binary black holes. We exploit results of state-of-the-art simulations on the evolution of Population I-II and III binaries, considering a variety of initial condition and star formation rate models for the latter. The contribution

Shuo Zhou, Junhao Luo, Yaya Jiang, Haolin Wang

Lateralization is a fundamental feature of the human brain, where sex differences have been observed. Conventional studies in neuroscience on sex-specific lateralization are typically conducted on univariate statistical comparisons between male and female groups. However, these analyses often lack effective validation of group specificity. Here, we formulate

S. M. Matthews, C. A. Watson, E. J. W. de Mooij, T. R. Marsh

High-resolution Doppler spectroscopy provides an avenue to study the atmosphere of both transiting and non-transiting planets. This powerful method has also yielded some of the most robust atmospheric detections to date. Currently, high-resolution Doppler spectroscopy detects atmospheric signals by cross-correlating observed data with a model atmospheric spe

Lewis McCallum, Kenneth Wood, Robert Benjamin, Camilo Peñaloza

Widespread, high altitude, diffuse ionized gas with scale heights of around a kiloparsec is observed in the Milky Way and other star forming galaxies. Numerical radiation-magnetohydrodynamic simulations of a supernova-driven turbulent interstellar medium show that gas can be driven to high altitudes above the galactic midplane, but the degree of ionization i

Ziyao Wang, Yan Meng, Bei Yan, Dong Zhao

The discovery of photonic higher-order topological insulators (HOTIs) has significantly expanded our understanding of band topology and provided unprecedented lower-dimensional topological boundary states for robust photonic devices. However, due to the vectorial and leaky nature of electromagnetic waves, it is challenging to discover three-dimensional (3D)

Jichang Yang, Hegan Chen, Jia Chen, Songqi Wang

Human brains image complicated scenes when reading a novel. Replicating this imagination is one of the ultimate goals of AI-Generated Content (AIGC). However, current AIGC methods, such as score-based diffusion, are still deficient in terms of rapidity and efficiency. This deficiency is rooted in the difference between the brain and digital computers. Digita

Jérôme Pétri

In this paper, we compute a full atlas of radio, X-ray and $\gamma$-ray pulse profiles relying on the force-free magnetosphere model. Our goal is to use such data bank of multi-wavelength profiles to fit a substantial number of radio-loud $\gamma$-ray pulsars also detected in non-thermal X-rays to decipher the X-ray radiation mechanism and sites. Using resul

Arghadeep Pal, Alekhya Ghosh, Shuangyou Zhang, Lewis Hill

Nonlinear effects in microresonators are efficient building blocks for all-optical computing and telecom systems. With the latest advances in microfabrication, coupled microresonators are used in a rapidly growing number of applications. In this work, we investigate the coupling between twin-resonators in the presence of Kerr-nonlinearity. We use an experime

Fernando Micena, Ryo Moore, Jana Rodriguez Hertz, Raul Ures

A smooth conservative DA-diffeomorphism is smoothly conjugated to its Anosov linear part if and only if all Lyapunov exponents coincide almost everywhere with those of its linear part. A more general result for entropy maximizing measures of $C^{1+\alpha}$ partially hyperbolic diffeomorphisms isotopic to Anosov (DA-diffeomorphisms) on $T^3$ is that they are

Gian Alexandre Michaelsen, Renato P. dos Santos

Background: The integration of artificial intelligence (AI) into daily life, particularly through chatbots utilizing natural language processing (NLP), presents both revolutionary potential and unique challenges. This intended to investigate how different input forms impact ChatGPT, a leading language model by OpenAI, performance in understanding and executi

Paramvir Ahlawat

The efficiency and stability of halide perovskite-based solar cells and light-emitting diodes directly depend on the intricate dynamics of solid-solid crystallization[1-23]. In this study, we employ a multi-scale approach using random phase approximation, density functional theory, machine learning potentials, reduced charge force fields, and both enhanced s

Yavdat Il'yasov

This paper provides a direct method of establishing the existence and uniqueness of saddle-node bifurcations for nonlinear equations in general domains. The method employs the scaled extended quotient whose saddle points correspond to the saddle-node bifurcations. The uniqueness of the saddle-node bifurcation point directly stems from the uniqueness of the s

Joshua A. Dijksman, Tom Mullin

Flow in soft materials encompasses a wide range of viscous, plastic and elastic phenomena which provide challenges to modelling at the microscopic level. To create a controlled flow, we perform falling ball viscometry tests on packings of soft, frictionless hydrogel spheres. Systematic creep flow is found when a controlled driving stress is applied to a sink

Maxence Bideaux, Alice Phe, Mohamed Chaouch, Bertrand Luvison

We introduce 3D-COCO, an extension of the original MS-COCO dataset providing 3D models and 2D-3D alignment annotations. 3D-COCO was designed to achieve computer vision tasks such as 3D reconstruction or image detection configurable with textual, 2D image, and 3D CAD model queries. We complete the existing MS-COCO dataset with 28K 3D models collected on Shape

Naser Souri, Ali Mehrizi-Sani, Kambiz Tehrani

An LCL filter offers superior attenuation for high-frequency harmonics for three-phase grid-following inverters compared to LC and L filters. However, it also introduces an inherent resonance peak, which can lead to power quality issues or even instability of the inverter control system. Active damping (AD) is widely employed to effectively mitigate this res

Qun Li, Yuan Meng, Chen Tang, Jiacheng Jiang

Quantization is a promising technique for reducing the bit-width of deep models to improve their runtime performance and storage efficiency, and thus becomes a fundamental step for deployment. In real-world scenarios, quantized models are often faced with adversarial attacks which cause the model to make incorrect inferences by introducing slight perturbatio

Jie Ren, Dao-Quan Sun

We study holographic supersymmetric Renyi entropies from a family of hyperbolic black holes in an Einstein-Maxwell-dilaton (EMD) system under the BPS condition. We calculate the thermodynamic quantities of these hyperbolic black holes. We find a remarkably simple formula of the supersymmetric Renyi entropy that unifies (interpolates) 11 cases embeddable to 1

Lucas José Gonçalves Freitas, Thaís Rodrigues, Guilherme Rodrigues, Pamella Edokawa

Data analysis and machine learning are of preeminent importance in the legal domain, especially in tasks like clustering and text classification. In this study, we harnessed the power of natural language processing tools to enhance datasets meticulously curated by experts. This process significantly improved the classification workflow for legal texts using

Oleg Komoltsev

I explore various scenarios for the phase transition within neutron-star matter. I do so by generating large model-agnostic ensemble using Gaussian processes, both with and without explicit inclusion of first-order phase transitions (FOPTs). The ensemble is conditioned with state-of-the-art astrophysical and theoretical inputs in a fully Bayesian approach. I

Joyce Charles Shikali, Paul Samwel Muneja

The study examined access to library information resources by university students during the outbreak of the COVID-19 pandemic. The study investigated measures that were adopted by academic libraries for smooth delivery of library information resources to their patrons. It also identified technological tools that were employed by libraries to facilitate acce

Hong Qin, Eric Palmerduca

Photons in vacuum are transverse in any inertial frame; longitudinal photons only exist virtually. By developing a manifestly covariant theory for electromagnetic excitations in relativistic plasmas and applying Wigner's little group method for elementary particle classifications, we show that photons in plasmas are neither transverse nor longitudinal; they

Jad Wehbeh, Eric C. Kerrigan

Discrete-time robust optimal control problems generally take a min-max structure over continuous variable spaces, which can be difficult to solve in practice. In this paper, we extend the class of such problems that can be solved through a previously proposed local reduction method to consider those with existence constraints on the uncountable variables. We

Emily Oliphant, Veda Mantena, Madison Brod, G. Jeffrey Snyder

It is difficult to intuit how electronic structure features$-$such as band gap magnitude, location of band extrema, effective masses, etc.$-$arise from the underlying crystal chemistry of a material. Here we present a strategy to distill sparse and chemically-interpretable tight-binding models from density functional theory calculations, enabling us to inter

Ovidiu Cristinel Stoica

Applications of quantum mechanics rely on the accuracy of reading and writing data. This requires accurate measurements and preparations of the quantum states. I show that accurate measurements and preparations are impossible if the total Hamiltonian is bounded from below (as thought to be in our universe). This result invites a reevaluation of the limitatio

Haitham Hammami, Louis Baligand, Bojan Petrovski

In the financial industry, identifying the location of parties involved in payments is a major challenge in the context of various regulatory requirements. For this purpose address parsing entails extracting fields such as street, postal code, or country from free text message attributes. While payment processing platforms are updating their standards with m

Abhishek Kumar Singh, Kyle Jamieson

The last couple of years have seen an ever-increasing interest in using different Ising solvers, like Quantum annealers, Coherent Ising machines, and Oscillator-based Ising machines, for solving tough computational problems in various domains. Although the simulations predict massive performance improvements for several tough computational problems, the real

Simon Ellmeyer, Georg C. Hofstätter

Busemann-Petty type problems for the recently introduced complex projection, centroid and $L_p$-intersection body operators are examined. Moreover, it is shown that, as their real counterparts, they can be linked to the spherical Fourier transform.

Jiang-Tao Li, Li-Yuan Lu, Zhijie Qu, Robert A. Benjamin

The extraplanar diffuse ionized gas (eDIG) represents ionized gases traced by optical/UV lines beyond the stellar extent of galaxies. We herein introduce a novel multi-slit narrow-band spectroscopy method to conduct spatially resolved spectroscopy of the eDIG around a sample of nearby edge-on disk galaxies (eDIG-CHANGES). In this paper, we introduce the proj

Thomas M. C. Sears, M. Riley Cooper, Sabrina R. Button, Joshua A. Marshall

There exist a wide range of options for field robotics research using ground and aerial mobile robots, but there are comparatively few robust and research-ready uncrewed surface vessels (USVs). This workshop paper starts with a snapshot of USVs currently available to the research community and then describes "OtterROS", an open source ROS 2 solution for the

Tianlong Xu, Richard Tong, Jing Liang, Xing Fan

With the advent of foundation models like ChatGPT, educators are excited about the transformative role that AI might play in propelling the next education revolution. The developing speed and the profound impact of foundation models in various industries force us to think deeply about the changes they will make to education, a domain that is critically impor

Fengrui Tian, Yaoyao Liu, Adam Kortylewski, Yueqi Duan

3D object pose estimation is a challenging task. Previous works always require thousands of object images with annotated poses for learning the 3D pose correspondence, which is laborious and time-consuming for labeling. In this paper, we propose to learn a category-level 3D object pose estimator without pose annotations. Instead of using manually annotated i

Grigore Călugăreanu, Horia F. Pop, Adrian Vasiu

A unimodular $2\times 2$ matrix with entries in a commutative $R$ is called extendable (resp.\ simply extendable) if it extends to an invertible $3\times 3$ matrix (resp.\ invertible $3\times 3$ matrix whose $(3,3)$ entry is $0$). We obtain necessary and sufficient conditions for a unimodular $2\times 2$ matrix to be extendable (resp.\ simply extendable) and

Chun-Ming Yang, Pranav A. Bhounsule

The paper presents a robust control technique that combines the Control Lyapunov function and Hamilton-Jacobi Reachability to compute a controller and its Region of Attraction (ROA). The Control Lyapunov function uses a linear system model with an assumed additive uncertainty to calculate a control gain and the level sets of the ROA as a function of the unce

Faren Yan, Peng Yu, Xin Chen

The use of LLMs for natural language processing has become a popular trend in the past two years, driven by their formidable capacity for context comprehension and learning, which has inspired a wave of research from academics and industry professionals. However, for certain NLP tasks, such as NER, the performance of LLMs still falls short when compared to s

Pietro Lesci, Andreas Vlachos

Active learning for imbalanced classification tasks is challenging as the minority classes naturally occur rarely. Gathering a large pool of unlabelled data is thus essential to capture minority instances. Standard pool-based active learning is computationally expensive on large pools and often reaches low accuracy by overfitting the initial decision boundar

Olivier Binette, Youngsoo Baek, Siddharth Engineer, Christina Jones

Entity resolution (record linkage, microclustering) systems are notoriously difficult to evaluate. Looking for a needle in a haystack, traditional evaluation methods use sophisticated, application-specific sampling schemes to find matching pairs of records among an immense number of non-matches. We propose an alternative that facilitates the creation of repr

Matteo Farina, Massimiliano Mancini, Elia Cunegatti, Gaowen Liu

While excellent in transfer learning, Vision-Language models (VLMs) come with high computational costs due to their large number of parameters. To address this issue, removing parameters via model pruning is a viable solution. However, existing techniques for VLMs are task-specific, and thus require pruning the network from scratch for each new task of inter

Leo Joon Il Moon, Paul Manuel Schindler, Yizhe Sun, Emanuel Druga

Our understanding of phases of matter relies on symmetry breaking, one example being water ice whose crystalline structure breaks the continuous translation symmetry of space. Recently, breaking of time translation symmetry was observed in systems not in thermal equilibrium. The associated notion of time crystallinity has led to a surge of interest, raising

Janik Kruse

A central task of theoretical physics is to analyse spectral properties of quantum mechanical observables. In this endeavour, Mourre's conjugate operator method emerged as an effective tool in the spectral theory of Schr\"odinger operators. This paper introduces a novel class of examples from relativistic quantum field theory that are amenable to Mourre's me

Christoffer Hindlycke, Jan-Åke Larsson

We propose a direct (non-recursive) algorithm for applying a rotation $R_{\theta^\ast}$, $\epsilon$-close to a desired rotation $R_\theta$, to a single qubit using the Clifford+Toffoli gate set. Our algorithm does not rely on repeatedly applying a fixed rotation, but immediately applies $R_{\theta^\ast}$. It succeeds with probability strictly greater than $1

Jonas Sonnenschein, Atanu Maity, Chunxiao Liu, Ronny Thomale

Motivated by recent numerical studies reporting putative quantum paramagnetic behavior in spin-$1/2$ Heisenberg models on the maple-leaf lattice, we classify Abrikosov fermion mean-field Ans\"atze of fully symmetric $U(1)$ and $\mathbb{Z}_{2}$ quantum spin liquids within the framework of projective symmetry groups. We obtain a total of $17$ $U(1)$ and $12$ $

M. Gil de Oliveira, A. L. S. Santos Junior, P. M. R. Lima, A. C. Barbosa

In this work we study the tomography of the spatial structure of light. We develop a simple technique that allows one to perform the tomography over the space of fixed order modes. The technique is based on two spatially resolved intensity measurements, the second of which is performed after the light field has undergone an astigmatic transformation implemen

Taorui Wang, Zheyuan Hu, Kenji Kawaguchi, Zhongqiang Zhang

We solve high-dimensional steady-state Fokker-Planck equations on the whole space by applying tensor neural networks. The tensor networks are a linear combination of tensor products of one-dimensional feedforward networks or a linear combination of several selected radial basis functions. The use of tensor feedforward networks allows us to efficiently exploi

Richard Korytár, Jan M. van Ruitenbeek, Ferdinand Evers

Motivated by experimental reports on chirality induced spin selectivity, we investigate a minimal model that allows us to calculate the charge and spin conductances through helical molecules analytically. The spin-orbit interaction is assumed to be non-vanishing on the molecule and negligible in the reservoirs (leads). The band-structure of the molecule feat

Suiyao Chen, Xinyi Liu, Yulei Li, Jing Wu

As the aging population grows, particularly for the baby boomer generation, the United States is witnessing a significant increase in the elderly population experiencing multifunctional disabilities. These disabilities, stemming from a variety of chronic diseases, injuries, and impairments, present a complex challenge due to their multidimensional nature, en

Marianna Chatzakou, Aidyn Kassymov, Michael Ruzhansky

In this work, we study the global well-posedeness of the heat equation with variable time-dependent nonlinearity of the form $\varphi(t)f(u)$ on unimodular Lie groups when the differential operator arises as the sum of squares of H\"ormander vector fields. For general unimodular Lie groups, we derive the necessary conditions for the nonexistence of global po

Tim Niklas Uhl, Matthias Schimek, Lukas Hübner, Demian Hespe

The Message-Passing Interface (MPI) and C++ form the backbone of high-performance computing, but MPI only provides C and Fortran bindings. While this offers great language interoperability, high-level programming languages like C++ make software development quicker and less error-prone. We propose novel C++ language bindings that cover all abstraction levels

Jiajin Liang, Di Zhao, Li Qiu

In this study, we investigate the robust feedback stability problem for multiple-input-multiple-output linear time-invariant systems involving sectored-disk uncertainty, namely, dynamic uncertainty subject to simultaneous gain and phase constraints. This problem is thereby called a sectored-disk problem. Employing a frequency-wise analysis approach, we deriv

Shangjia Zhang, Zhaohuan Zhu, Yan-Fei Jiang

Turbulence is crucial for protoplanetary disk dynamics, and Vertical Shear Instability (VSI) is a promising mechanism in outer disk regions to generate turbulence. We use Athena++ radiation module to study VSI in full and transition disks, accounting for radiation transport and stellar irradiation. We find that the thermal structure and cooling timescale sig

Guokai Zhang, Lanjun Wang, Yuting Su, An-An Liu

Today, the family of latent diffusion models (LDMs) has gained prominence for its high quality outputs and scalability. This has also raised security concerns on social media, as malicious users can create and disseminate harmful content. Existing approaches typically involve training specific components or entire generative models to embed a watermark in ge

Yating Wang, Ran Yi, Xiaoning Lei, Ke Fan

Industrial 3D face assets creation typically reconstructs topology-consistent face meshes from multi-view images for downstream production. However, high-quality reconstruction usually requires manual processing or specific capture settings. Recently NeRF has shown great advantages in 3D reconstruction, by representing scenes as density and radiance fields a

Ao Zhou, Jianlei Yang, Tong Qiao, Yingjie Qi

The key to device-edge co-inference paradigm is to partition models into computation-friendly and computation-intensive parts across the device and the edge, respectively. However, for Graph Neural Networks (GNNs), we find that simply partitioning without altering their structures can hardly achieve the full potential of the co-inference paradigm due to vari

Zihan Pengmei, Zimu Li

Graph Transformers have emerged as a powerful alternative to Message-Passing Graph Neural Networks (MP-GNNs) to address limitations such as over-squashing of information exchange. However, incorporating graph inductive bias into transformer architectures remains a significant challenge. In this report, we propose the Graph Spectral Token, a novel approach to

Zhipeng Zhang, Zhimin Wei, Guolei Sun, Peng Wang

In the field of visual affordance learning, previous methods mainly used abundant images or videos that delineate human behavior patterns to identify action possibility regions for object manipulation, with a variety of applications in robotic tasks. However, they encounter a main challenge of action ambiguity, illustrated by the vagueness like whether to be

Mohammed Ashfaaq M. Farzaan, Mohamed Chahine Ghanem, Ayman El-Hajjar, Deepthi N. Ratnayake

The escalating sophistication and volume of cyber threats in cloud environments necessitate a paradigm shift in strategies. Recognising the need for an automated and precise response to cyber threats, this research explores the application of AI and ML and proposes an AI-powered cyber incident response system for cloud environments. This system, encompassing

Alain Reiser, Christopher Allan Schuh

Cold spray coatings are the sum of countless individual bonding events between single particles impacting on top of one another at high velocities. Thus, the collective behavior of microparticles must be considered to elucidate the origins of coating flaws at the scale of the particles and larger, or the dynamic evolution of the overall coating microstructur

Dong Zhang, Zhaowei Li, Shimin Li, Xin Zhang

Speech language models have significantly advanced in generating realistic speech, with neural codec language models standing out. However, the integration of human feedback to align speech outputs to human preferences is often neglected. This paper addresses this gap by first analyzing the distribution gap in codec language models, highlighting how it leads

Johannes Schreiner, Daniel Gerl, Robert Kunzelmann, Paritosh Kumar Sinha

The rapid evolution of Integrated Circuit (IC) development necessitates innovative methodologies such as code generation to manage complexity and increase productivity. Using the right methodology for generator development to maximize the capability and, most notably, the feasibility of generators is a crucial part of this work. Meta-Modeling-based approache

Kaveen Hiniduma, Suren Byna, Jean Luca Bez

Artificial Intelligence (AI) applications critically depend on data. Poor quality data produces inaccurate and ineffective AI models that may lead to incorrect or unsafe use. Evaluation of data readiness is a crucial step in improving the quality and appropriateness of data usage for AI. R&D efforts have been spent on improving data quality. However, standar

Louis Loechel, Siar-Remzi Akbayin, Elias Grünewald, Jannis Kiesel

gRPC is at the heart of modern distributed system architectures. Based on HTTP/2 and Protocol Buffers, it provides highly performant, standardized, and polyglot communication across loosely coupled microservices and is increasingly preferred over REST- or GraphQL-based service APIs in practice. Despite its widespread adoption, gRPC lacks any advanced privacy

I. Brevik, A. V. Timoshkin

We study Little Rip (LR) and Pseudo Rip (PR) cosmological models containing two coupled fluids: dark energy and dark matter. We assume a spatially flat Friedmann-Robertson-Walker (FRW) universe. The interaction between the dark energy and the dark matter fluid components is described in terms of the parameters in the generalized equation of state (EoS) in pr

Marta Frias Castillo, Matus Rybak, Jacqueline A. Hodge, Paul Van der Werk

The CO(1--0) and [\ion{C}{1}](1--0) emission lines are well-established tracers of cold molecular gas mass in local galaxies. At high redshift, where the interstellar medium (ISM) is likely to be denser, there have been limited direct comparisons of both ground state transitions. Here we present a study of CO(1--0) and [\ion{C}{1}](1--0) emission in a sample

Jiacheng Zhang, Jie Wu, Yuxi Ren, Xin Xia

Latent diffusion models (LDM) have revolutionized text-to-image generation, leading to the proliferation of various advanced models and diverse downstream applications. However, despite these significant advancements, current diffusion models still suffer from several limitations, including inferior visual quality, inadequate aesthetic appeal, and inefficien

Zhaobing Fan, Zhicheng Zhang, Haitao Ma

We commence by constructing the mirabolic quantum Schur algebra, utilizing the convolution algebra defined on the variety of triples of two $n$-step partial flags and a vector. Subsequently, we employ a stabilization procedure to derive the mirabolic quantum $\mathfrak{gl}_n$. Then we present the geometric approach of the mirabolic Schur-Weyl duality of type

Chengyuan Cai, Tao Yu

In the celebrated Stern-Gerlach experiment an inhomogeneous static magnetic field separates a beam of charge-neutral atoms with opposite spins, thereby driving a ``spin current" normal to the propagation direction. Here we generalize it to the dynamic scenario by demonstrating a spin transfer between an AC inhomogeneous magnetic field and intraband electrons

Aindriú Conroy, Peter Taylor

We study semi-classical particle production in non-singular bouncing cosmologies by employing the Unruh-DeWitt model of a particle detector propagating in this class of spacetimes. The scale factor for the bouncing cosmology is derived analytically and is inspired by the modified Friedmann equation employed in the loop quantum cosmology literature. We examin

Eeshan Kulkarni, Suresh Sundaram

This paper presents the design of Variable-Pitch-Propeller mechanism and its application on a quadcopter called Heliquad to demonstrate its unique capabilities. The input-output relationship is estimated for a generic mechanism. Various singularities and actuator sizing requirements are also analyzed. The mechanism is manufactured, and the validated input-ou

Iñigo Alonso, Maite Oronoz, Rodrigo Agerri

Large Language Models (LLMs) have the potential of facilitating the development of Artificial Intelligence technology to assist medical experts for interactive decision support, which has been demonstrated by their competitive performances in Medical QA. However, while impressive, the required quality bar for medical applications remains far from being achie

Hiromasa Takaura

We present a method to extract the low energy behavior of physical observables from their high energy expansions, systematically calculable via the operator product expansion (OPE), in asymptotically free and mass-gapped theories. By applying the inverse Laplace transform to correlation functions, their analytic structure is modified such that low-energy inf

Evienia Bazzocchi, Roberto Pagaria, Maddalena Pismataro

We give a Orlik-Solomon type presentation for the cohomology ring of arrangements in a non-compact abelian Lie group. The new insight consists in comparing arrangements in different abelian groups. Our work is based on the Varchenko-Gelfand ring for real hyperplane arrangements and from that we deduce the cohomology rings of all other abelian arrangements. A

Wolfgang Otto, Sharmila Upadhyaya, Stefan Dietze

This paper describes our participation in the Shared Task on Software Mentions Disambiguation (SOMD), with a focus on improving relation extraction in scholarly texts through generative Large Language Models (LLMs) using single-choice question-answering. The methodology prioritises the use of in-context learning capabilities of GLMs to extract software-relat

Mingzhe Guo, Tom Van Doorsselaere, Bo Li, Marcel Goossens

Kink oscillations are frequently observed in coronal loops. This work aims to numerically clarify the influence of loop curvature on horizontally and vertically polarized kink oscillations. Working within the framework of ideal MHD, we conduct 3D simulations of axial fundamental kink oscillations in curved density-enhanced loops embedded in a potential magne

Yongjun Zhang

The absorption of photons by atoms encompasses fundamental quantum mechanical aspects, particularly the emergence of randomness to account for the inherent unpredictability in absorption outcomes. We demonstrate that vacuum fluctuations can be the origin of this randomness. An illustrative example of this is the absorption of a single photon by two symmetric

Michael Deutges, Ario Sadafi, Nassir Navab, Carsten Marr

Diagnosis of hematological malignancies depends on accurate identification of white blood cells in peripheral blood smears. Deep learning techniques are emerging as a viable solution to scale and optimize this process by automatic cell classification. However, these techniques face several challenges such as limited generalizability, sensitivity to domain sh