April 2024 arXiv papers — page 103

Marcos Grimsditch, Victor G. Karpov

The barometric equation predicts the molecular concentration $n(z)$ exponentially decaying with altitude $z$. Because the mean free path $l=1/n\sigma$ increases exponentially, at high altitudes $z$, the equation is no longer within the domain of applicability of the standard kinetic theory. \cite{grimsditch} Here, we predict the dependence $n(z)\propto z^{-2

Long He, Geng Sun, Dusit Niyato, Hongyang Du

With the continuous advancement of network technology, various emerging complex networking optimization problems have created a wide range of applications utilizing game theory. However, since game theory is a mathematical framework, game theory-based solutions often rely heavily on the experience and knowledge of human experts. Recently, the remarkable adva

Dinesh Chandra Maurya, K. Yesmakhanova, R. Myrzakulov, G. Nugmanova

In this paper, we investigate some exact cosmological models in Myrzakulov $F(T,Q)$ gravity or the Myrzakulov gravity-III (MG-III) proposed in [arXiv:1205.5266], with observational constraints. The MG-III gravity is some kind of unification of two known gravity theories, namely, the $F(T)$ gravity and the $F(Q)$ gravity. The field equations of the MG-III the

Yang Liu, Jiahua Xiao, Xiang Song, Yu Guo

Effectively modeling global context information in hyperspectral image (HSI) denoising is crucial, but prevailing methods using convolution or transformers still face localized or computational efficiency limitations. Inspired by the emerging Selective State Space Model (Mamba) with nearly linear computational complexity and efficient long-term modeling, we

Krzysztof Jan Ciosmak

In [K.J. Ciosmak, Applications of Strassen's theorem and Choquet theory to optimal transport problems, to uniformly convex functions and to uniformly smooth functions, Nonlinear Anal. 232 (2023), Paper No. 113267, 32 pp.], Theorem 2.3. does not suffice for its applications. We strengthen Theorem 2.1. and Theorem 2.3., so that they imply their claimed consequ

Nailia Mirzakhmedova, Marcel Gohsen, Chia Hao Chang, Benno Stein

Evaluating the quality of arguments is a crucial aspect of any system leveraging argument mining. However, it is a challenge to obtain reliable and consistent annotations regarding argument quality, as this usually requires domain-specific expertise of the annotators. Even among experts, the assessment of argument quality is often inconsistent due to the inh

Guangyan Li, Yongqiang Tang, Wensheng Zhang

Large language models (LLMs) show excellent performance in difficult tasks, but they often require massive memories and computational resources. How to reduce the parameter scale of LLMs has become research hotspots. In this study, we make an important observation that the multi-head self-attention (MHA) sub-layer of Transformer exhibits noticeable low-rank

Bernhard Ramsauer, Johannes J. Cartus, Oliver T. Hofmann

In this publication we introduce MAM-STM, a software to autonomously manipulate arbitrary moieties towards specific positions on a metal surface utilizing the tip of a scanning tunneling microscope (STM). Finding the optimal manipulation parameters for a specific moiety is challenging and time consuming, even for human experts. MAM-STM combines autonomous da

Jiayi Wang, Yi-An Mao, Xiaoyu Ma, Sicen Guo

Optic nerve head (ONH) detection has been a crucial area of study in ophthalmology for years. However, the significant discrepancy between fundus image datasets, each generated using a single type of fundus camera, poses challenges to the generalizability of ONH detection approaches developed based on semantic segmentation networks. Despite the numerous rece

Maria Kelidou, Mohammad Fazelzadeh, Baptiste Parage, Sara Jabbari Farouji

Self-propelled particles possessing permanent magnetic dipole moments occur naturally in magnetotactic bacteria and in man-made systems like active colloids or micro-robots. Yet, the interplay between self-propulsion and anisotropic dipole-dipole interactions on dynamic self-assembly in three dimensions (3D) remains poorly understood. We conduct Brownian dyn

Önder Tuzcuoğlu, Aybora Köksal, Buğra Sofu, Sinan Kalkan

We introduce, XoFTR, a cross-modal cross-view method for local feature matching between thermal infrared (TIR) and visible images. Unlike visible images, TIR images are less susceptible to adverse lighting and weather conditions but present difficulties in matching due to significant texture and intensity differences. Current hand-crafted and learning-based

Argha Sen, Soham Chakraborty, Soham Tripathy, Sandip Chakraborty

Precise ego-motion measurement is crucial for various applications, including robotics, augmented reality, and autonomous navigation. In this poster, we propose mmPhase, an odometry framework based on single-chip millimetre-wave (mmWave) radar for robust ego-motion estimation in mobile platforms without requiring additional modalities like the visual, wheel,

Chenwei Lin, Hanjia Lyu, Jiebo Luo, Xian Xu

The emergence of Large Multimodal Models (LMMs) marks a significant milestone in the development of artificial intelligence. Insurance, as a vast and complex discipline, involves a wide variety of data forms in its operational processes, including text, images, and videos, thereby giving rise to diverse multimodal tasks. Despite this, there has been limited

Anna N. Morozovska, Hanna V. Shevliakova, Liubomyr M. Korolevych, Victoria Khist

Using the Landau-Ginzburg-Devonshire phenomenological approach we explore the strain-polarization coupling in the low-dimensional van der Waals ferrielectrics. We evolve the analytical model of the piezoelectric susceptibility of the material in response to the periodic strain modulation, such as caused by a surface acoustic wave. Numerical calculations are

Lorenzo Cano, Alejandro R. Mosteo, Danilo Tardioli

Navigation of UAVs in challenging environments like tunnels or mines, where it is not possible to use GNSS methods to self-localize, illumination may be uneven or nonexistent, and wall features are likely to be scarce, is a complex task, especially if the navigation has to be done at high speed. In this paper we propose a novel proof-of-concept navigation te

Johannes Lengler, Leon Schiller, Oliver Sieberling

We compare the $(1,\lambda)$-EA and the $(1 + \lambda)$-EA on the recently introduced benchmark DisOM, which is the OneMax function with randomly planted local optima. Previous work showed that if all local optima have the same relative height, then the plus strategy never loses more than a factor $O(n\log n)$ compared to the comma strategy. Here we show tha

Siyuan Li, Youshao Xiao, Fanzhuang Meng, Lin Ju

Offline batch inference is a common task in the industry for deep learning applications, but it can be challenging to ensure stability and performance when dealing with large amounts of data and complicated inference pipelines. This paper demonstrated AntBatchInfer, an elastic batch inference framework, which is specially optimized for the non-dedicated clus

Yong Ma, Oda Elise Nordberg, Jessica Hubbers, Yuchong Zhang

Dementia has serious consequences for the daily life of the person affected due to the decline in the their cognitive, behavioral and functional abilities. Caring for people living with dementia can be challenging and distressing. Innovative solutions are becoming essential to enrich the lives of those impacted and alleviate caregiver burdens. This scoping r

Matheus V. Scherer, Alexandre D. Ribeiro, Renato M. Angelo

It is notorious that quantum mechanics cannot predict well-defined values for all physical quantities. Less well-known, however, is the fact that quantum mechanics is unable to furnish -- without additional assumptions -- probabilistic predictions even in emblematic scenarios such as the double-slit experiment. In contrast, trajectory-equipped theories natur

Tobias Weber, Jakob Dexl, David Rügamer, Michael Ingrisch

We address the computational barrier of deploying advanced deep learning segmentation models in clinical settings by studying the efficacy of network compression through tensor decomposition. We propose a post-training Tucker factorization that enables the decomposition of pre-existing models to reduce computational requirements without impeding segmentation

Juhwan Choi, Jungmin Yun, Kyohoon Jin, YoungBin Kim

The quality of the dataset is crucial for ensuring optimal performance and reliability of downstream task models. However, datasets often contain noisy data inadvertently included during the construction process. Numerous attempts have been made to correct this issue through human annotators. However, hiring and managing human annotators is expensive and tim

Guoming Li, Jian Yang, Shangsong Liang, Dongsheng Luo

Spectral graph neural networks are proposed to harness spectral information inherent in graph-structured data through the application of polynomial-defined graph filters, recently achieving notable success in graph-based web applications. Existing studies reveal that various polynomial choices greatly impact spectral GNN performance, underscoring the importa

Yu Bi, Mingshuo Yang, Yong Fang, Xianghang Mi

Emerging in recent years, open edge computing platforms (OECPs) claim large-scale edge nodes, the extensive usage and adoption, as well as the openness to any third parties to join as edge nodes. For instance, OneThingCloud, a major OECP operated in China, advertises 5 million edge nodes, 70TB bandwidth, and 1,500PB storage. However, little information is pu

O. Ya. Yakovlev, A. F. Valeev, G. G. Valyavin, V. N. Aitov

We present a catalog of 150 variable stars, including 13 stars with exoplanet candidates. 37 stars were identified as variables for the first time. As a result of a 2.5-year photometric survey of exoplanets, we have obtained and analyzed light curves for almost 50 thousand stars in fields around white dwarfs WD 0009+501 and GRW +708247. Here we describe obse

Mohabat Tarkeshian

In a seminal paper in 2009, Borcea, Br\"and\'en, and Liggett described the connection between probability distributions and the geometry of their generating polynomials. Namely, they characterized that stable generating polynomials correspond to distributions with the strongest form of negative dependence. This motivates us to investigate other distributions

Youshao Xiao, Lin Ju, Zhenglei Zhou, Siyuan Li

Many distributed training techniques like Parameter Server and AllReduce have been proposed to take advantage of the increasingly large data and rich features. However, stragglers frequently occur in distributed training due to resource contention and hardware heterogeneity, which significantly hampers the training efficiency. Previous works only address par

Michiel Sprik

Non-hydrostatic stress has a peculiar effect on the phase equilibrium between solids and liquids. This was already pointed out by Gibbs. Gibbs derived his formulation of the condition for liquid-solid coexistence applying a surface accretion process without imposing chemical equilibrium between liquid and solid. Adding particles to the bulk of a solid was no

Ahmet Kazan, Mustafa Altın, Nurettin Cenk Turgay

In this study, we deal with the Gauss map of tubular hypersurfaces in 4-dimensional Lorentz-Minkowski space concerning the linearized operators $\mathcal{L}_{1}$ (Cheng-Yau) and $\mathcal{L}_{2}$. We obtain the $\mathcal{L}_{1}$ (Cheng-Yau) operator of the Gauss map of tubular hypersurfaces that are formed as the envelope of a family of pseudo hyperspheres {

Ren Xin, Hongji Liu, Yingbing Chen, Jie Cheng

This paper presents a generic trajectory planning method for wheeled robots with fixed steering axes while the steering angle of each wheel is constrained. In the existing literatures, All-Wheel-Steering (AWS) robots, incorporating modes such as rotation-free translation maneuvers, in-situ rotational maneuvers, and proportional steering, exhibit inefficient

Maria Ley-Flores, Riccardo Alessandri, Sean Najmi, Michele Valsecchi

The thermodynamics and transport properties of polymeric materials are essential for the design of reactors and for the development of polymer deconstruction processes. Existing property prediction tools such as correlations based on entropy scaling, kinetic gas theory, and free-volume model are inadequate for polymers. In this paper, we introduce a data-dri

Katsuaki Asano

We simulate the emission in the shallow decay phase of gamma-ray burst afterglows using a time-dependent code. We test four models: the energy injection model, evolving the injection efficiency of non-thermal electrons, evolving the amplification of the magnetic field, and the wind model with a relatively low bulk Lorentz factor. All of the four models can r

Antoine Amarilli, Marcelo Arenas, YooJung Choi, Mikaël Monet

This document is an introduction to two related formalisms to define Boolean functions: binary decision diagrams, and Boolean circuits. It presents these formalisms and several of their variants studied in the setting of knowledge compilation. Last, it explains how these formalisms can be connected to the notions of automata over words and trees.

Francesca Perrotta, Martina Torsello, Marika Giulietti, Andrea Lapi

FIR and submm observations have established the fundamental role of dust-obscured star formation in the assembly of stellar mass over the past 12 billion years. At z between 2 and 4, the bulk of star formation is enshrouded in dust, and dusty star forming galaxies (DSFGs) contain about half of the total stellar mass density. Star formation develops in dense

Shin'ichi Hirano, Masashi Kimura, Masahide Yamaguchi, Jiale Zhang

We investigate the parametrized black hole quasinormal ringdown formalism, which is a robust framework used to analyze quasinormal modes in systems that closely resemble general relativity, paying particular attention to the higher overtones. We find that larger deviations from the general relativity case typically appear in the quasinormal frequencies for t

Ion Simaciu, Viorel Drafta, Zoltan Borsos, Gheorghe Dumitrescu

In this paper we study the properties of vortexes, as systems specific to the Acoustic World, using both hydrodynamic theory and the corresponding hydrodynamic Maxwell equations. According to this study, it follows that the vortex behaves like an acoustic dipole that has intrinsic/internal angular momentum. The system of two identical vortices also has orbit

Hiroki Yokozuka, Tomoharu Oka, Shiho Tsujimoto, Yuto Watanabe

We report the results of the CO $\textit J$=1-0 and SiO $\textit J$=2-1 mapping observations towards the broad-velocity-width molecular feature CO 16.134-0.553 with the Nobeyama Radio Observatory 45 m telescope. The high quality CO map shows that the 5-pc size broad-velocity-width feature bridges two separate velocity components at $\textit V_{\rm{LSR}}$$\qu

Daniel Beitner, Asaf Farhi, Ravindra Kumar Nitharwal, Tejendra Dixit

The discovery of localized plasmon polariton resonances has been pivotal in enabling tunability of the optical resonance. Recently, extensive research efforts have aimed to expand these achievements to other polaritonic states that exhibit less loss and in other spectral regions. However, these efforts were limited to isotropic or uniaxial structures, and an

Yin-Jie Li, Shao-Peng Tang, Shi-Jie Gao, Dao-Cheng Wu

We investigate formation channels for merging binary black holes (BBHs) in GWTC-3, with a dedicated semiparametric population model. The model first describes or excludes a high-spin (with magnitudes of $\sim0.7$) and high-mass (ranging in $\sim 20-80M_{\odot}$) subpopulation, which was identified by previous works and can be interpreted as hierarchical merg

D. O. Kudryavtsev, Yu. V. Sotnikova, V. A. Stolyarov, T. V. Mufakharov

Based on the collected multiwavelength data, namely in the radio (NVSS, FIRST, RATAN-600), IR (WISE), optical (Pan-STARRS), UV (GALEX), and X-ray (ROSAT, Swift-XRT) ranges, we have performed a cluster analysis for the blazars of the Roma-BZCAT catalog. Using two machine learning methods, namely a combination of PCA with k-means clustering and Kohonen's self-

Alessa Hering, Sarah de Boer, Anindo Saha, Jasper J. Twilt

The PI-CAI (Prostate Imaging: Cancer AI) challenge led to expert-level diagnostic algorithms for clinically significant prostate cancer detection. The algorithms receive biparametric MRI scans as input, which consist of T2-weighted and diffusion-weighted scans. These scans can be misaligned due to multiple factors in the scanning process. Image registration

Leonardo Severi, Matteo Sacchetto, Andrea Bianco, Cristina Rottondi

In this paper we present a Networked Music Performance system currently under development at Politecnico di Torino. We demonstrate its use in a distributed concert held in June 2023, which featured three musicians in Turin (Italy) and three musicians in Wroc{\l}aw (Poland). Although in its early stages, the system proved to be already stable enough to appear

Biswajit Rout, Ananya B. Sai, Arun Rajkumar

The rapid developments of various machine learning models and their deployments in several applications has led to discussions around the importance of looking beyond the accuracies of these models. Fairness of such models is one such aspect that is deservedly gaining more attention. In this work, we analyse the natural language representations of documents

Fabrizio Cleri

Quantum thermodynamics aims at extending standard thermodynamics and non-equilibrium statistical physics to systems with sizes well below the thermodynamic limit. A rapidly evolving research field, which promises to change our understanding of the foundations of physics, while enabling the discovery of novel thermodynamic techniques and applications at the n

M. Roppongi, T. Arakawa, Y. Yoshino, K. Ishihara

We have developed a circularly polarized dielectric rutile (TiO$_2$) cavity with a high quality-factor that can generate circularly polarized microwaves from two orthogonal linearly polarized microwaves with a phase difference of $\pm \pi/2$ using a hybrid coupler. Using this cavity, we have established a new methodology to measure the microwave Hall conduct

Diana Marin, Filippo Maggioli, Simone Melzi, Stefan Ohrhallinger

Reconstructing 2D curves from sample points has long been a critical challenge in computer graphics, finding essential applications in vector graphics. The design and editing of curves on surfaces has only recently begun to receive attention, primarily relying on human assistance, and where not, limited by very strict sampling conditions. In this work, we fo

Yash Deshpande, Xianglong Wang, Wolfgang Kellerer

This paper presents OpenAirLink(OAL), an open-source channel emulator for reproducible testing of wireless scenarios. OAL is implemented on off-the-shelf software-defined radios (SDR) and presents a smaller-scale alternative to expensive commercially available channel emulators. Path loss and propagation delay are the fundamental aspects of emulating a wirel

L. -M. Seillé, V. Buat, V. Fernández, M. Boquien

The ultraviolet continuum traces young stars while the near-infrared unveils older stellar populations and dust-obscured regions. Balmer emission lines provide insights on gas properties and young stellar objects but are highly affected by dust attenuation. The near-infrared Paschen lines suffer less dust attenuation and can be used to measure star formation

Fiona K. Seibold, Arkady A. Tseytlin

We compute the one-loop $2 \rightarrow 2$ scattering amplitude of massless scalars on the world volume of an infinite $D = 11$ supermembrane quantized in the static gauge. The resulting expression is manifestly finite and turns out to be much simpler than in the bosonic membrane case in arXiv:2308.12189 being simply proportional to the tree-level scattering

Georg Rabenstein, Lars Ullrich, Knut Graichen

Alongside optimization-based planners, sampling-based approaches are often used in trajectory planning for autonomous driving due to their simplicity. Model predictive path integral control is a framework that builds upon optimization principles while incorporating stochastic sampling of input trajectories. This paper investigates several sampling approaches

Amogh Mannekote

Compositional generalization is the ability of a model to generalize to complex, previously unseen types of combinations of entities from just having seen the primitives. This type of generalization is particularly relevant to the semantic parsing community for applications such as task-oriented dialogue, text-to-SQL parsing, and information retrieval, as th

Alexey Gorbatovski, Boris Shaposhnikov, Alexey Malakhov, Nikita Surnachev

Despite the fact that offline methods for Large Language Models (LLMs) alignment do not require a direct reward model, they remain susceptible to overoptimization. This issue arises when the trained model deviates excessively from the reference policy, leading to a decrease in sample quality. We propose a new paradigm of offline alignment methods, called Tru

Ali Imaanpur

The metric of $S^7$ can be written as an $SU(2)$-instanton bundle over $S^4$. It is also possible to write it differently as an anti-instanton bundle. We use this observation to construct an instanton--anti-instanton, $SU(2)\times SU(2)$, bundle over $S^4$. We show that this 10d manifold admits two Einstein metrics. We then rewrite the metric to isolate two

Jiaqi Zhu, Shaofeng Cai, Fang Deng, Beng Chin Ooi

Large vision-language models (LVLMs) are markedly proficient in deriving visual representations guided by natural language. Recent explorations have utilized LVLMs to tackle zero-shot visual anomaly detection (VAD) challenges by pairing images with textual descriptions indicative of normal and abnormal conditions, referred to as anomaly prompts. However, exi

Renato Golin, Lorenzo Chelini, Adam Siemieniuk, Kavitha Madhu

This work proposes a compilation flow using open-source compiler passes to build a framework to achieve ninja performance from a generic linear algebra high-level abstraction. We demonstrate this flow with a proof-of-concept MLIR project that uses input IR in Linalg-on-Tensor from TensorFlow and PyTorch, performs cache-level optimizations and lowering to mic

Angus B. Clark, Nicolas Rojas

Robotic arms built from stiffness-adjustable, continuously bending segments serially connected with revolute joints have the ability to change their mechanical architecture and workspace, thus allowing high flexibility and adaptation to different tasks with less than six degrees of freedom, a concept that we call malleable robots. Known stiffening mechanisms

Raven Beutner, Bernd Finkbeiner, Hadar Frenkel, Niklas Metzger

Hyperproperties express the relationship between multiple executions of a system. This is needed in many AI-related fields, such as knowledge representation and planning, to capture system properties related to knowledge, information flow, and privacy. In this paper, we study the monitoring of complex hyperproperties at runtime. Previous work in this area ha

Sandeep Sharma, Seongi Hong, Andrey S. Moskalenko

We propose a scheme to create and transfer thermal squeezed states and random-phase coherent states in a system of two interacting levitated nanoparticles. In this coupled levitated system, we create a thermal squeezed state of motion in one of the nanoparticles by parametrically driving it and then transferring the state to the other nanoparticle with high

Anweshika Pattanayak, Amir Shee, Debasish Chaudhuri, Abhishek Chaudhuri

In this work, we investigate the effects of chirality, accounting for translational diffusion, on active Brownian particles in two and three dimensions. Despite the inherent complexity in solving the Fokker-Planck equation, we demonstrate a Laplace transform method for precisely calculating the temporal evolution of various dynamic moments. Our analysis yiel

Stefan Friedl, Tejas Kalelkar, José Pedro Quintanilha

We give a necessary condition for two diagrams of $3$-regular spatial graphs with the same underlying abstract graph $G$ to represent isotopic spatial graphs. The test works by reading off the writhes of the knot diagrams coming from a collection of cycles in $G$ in each diagram, and checking whether the writhe tuples differ by an element in the image of a c

Samyak Pratyush Prasad, Maria Maffei, Patrice A. Camati, Cyril Elouard

Optical Bloch Equations (OBEs) are canonical equations describing the dynamics of a classically driven atom coupled to a thermal bath. Their thermodynamics is highly relevant to establish fundamental energetic bounds of key quantum processes. A consistent framework is available in the regime where the drives and baths can be treated classically, i.e. remains

Xutao Han, Zhiyi Li, Yue Xu

Considering widely dispersed uncertain renewable energy sources (RESs), scenario-based stochastic optimization is an effective method for the economic dispatch of renewables-rich power systems. However, on classic computers, to simulate RES uncertainties with high accuracy, the massive scenario generation is very time-consuming, and the pertinent optimizatio

Angus B. Clark, Nicolas Rojas

Through the implementation of reconfigurability to achieve flexibility and adaptation to tasks by morphology changes rather than by increasing the number of joints, malleable robots present advantages over traditional serial robot arms in regards to reduced weight, size, and cost. While limited in degrees of freedom (DOF), malleable robots still provide vers

Taichi Sakaguchi, Akira Taniguchi, Yoshinobu Hagiwara, Lotfi El Hafi

Robots that assist humans in their daily lives should be able to locate specific instances of objects in an environment that match a user's desired objects. This task is known as instance-specific image goal navigation (InstanceImageNav), which requires a model that can distinguish different instances of an object within the same class. A significant challen

Battulga Gankhuu

This paper provides the first and second order derivatives of any risk measures, including VaR and ES for continuous and discrete portfolio loss random variable variables. Also, we give asymptotic results of the first and second order conditional moments for heavy-tailed portfolio loss random variable.

Taichi Sakaguchi, Akira Taniguchi, Yoshinobu Hagiwara, Lotfi El Hafi

Improving instance-specific image goal navigation (InstanceImageNav), which locates the identical object in a real-world environment from a query image, is essential for robotic systems to assist users in finding desired objects. The challenge lies in the domain gap between low-quality images observed by the moving robot, characterized by motion blur and low

Qiujie Lu, Angus B. Clark, Matthew Shen, Nicolas Rojas

While the grasping capability of robotic grippers has shown significant development, the ability to manipulate objects within the hand is still limited. One explanation for this limitation is the lack of controlled contact variation between the grasped object and the gripper. For instance, human hands have the ability to firmly grip object surfaces, as well

Santanu Mondal, S. Pujitha Suribhatla, Kaushik Chatterjee, Chandra B. Singh

We study the spectro-polarimetric properties of a newly discovered black hole X-ray binary Swift\,J151857.0-572147 jointly using {\it IXPE} and {\it NuSTAR} observations during March 2024. The analysis of {\it IXPE} data reports the first detection of X-ray polarization with degree (PD) $1.34\pm0.27$ and polarization angle (PA) $-13.69^\circ\pm5.85^\circ$ us

Martin Beneke, Gael Finauri, Alexey A. Petrov

The non-observation of baryon number violation suggests that the scale of baryon-number violating interactions at zero temperature is comparable to the GUT scale. However, the pertinent measurements involve hadrons made of the first-generation quarks, such as protons and neutrons. One may therefore entertain the idea that new flavour physics breaks baryon nu

H. Z. Shen, Y. Chen, T. Z. Luan, X. X. Yi

In this paper, we show how to generate the multiple single-photon wavepackets of arbitrary temporal shape from an optical cavity coupled with $N$ three-level atoms driven by a driving field in the non-Markovian regime. We derive an exact analytical expression of the optimal driving field for generating such wavepackets, which depends on two detunings of the

Haojian Huang, Xiaozhen Qiao, Zhuo Chen, Haodong Chen

Zero-shot learning (ZSL) enables the recognition of novel classes by leveraging semantic knowledge transfer from known to unknown categories. This knowledge, typically encapsulated in attribute descriptions, aids in identifying class-specific visual features, thus facilitating visual-semantic alignment and improving ZSL performance. However, real-world chall

Jyun-Jie Liao

A conjecture of Marton, widely known as the polynomial Freiman-Ruzsa conjecture, was recently proved by Gowers, Green, Manners and Tao for any bounded-torsion Abelian group $G$. In this paper we show a few simple modifications that improve their bound in $G=\mathbb{F}_2^n$. Specifically, for $G=\mathbb{F}_2^n$, they proved that any set $A\subseteq G$ with $|

Angelos Anastopoulos, Marco Benini

It has been observed that, given an algebraic quantum field theory (AQFT) on a manifold $M$ and an open cover $\{M_\alpha\}$ of $M$, it is typically not possible to recover the global algebra of observables on $M$ by simply gluing the underlying local algebras subordinate to $\{M_\alpha\}$. Instead of gluing local algebras, we introduce a gluing construction

Liang Zhang, Mohamed Y. Eltabakh, Elke A. Rundensteiner, Khalid Alnuaim

The generation and collection of big data series are becoming an integral part of many emerging applications in sciences, IoT, finance, and web applications among several others. The terabyte-scale of data series has motivated recent efforts to design fully distributed techniques for supporting operations such as approximate kNN similarity search, which is a

Manuel Gloeckler, Michael Deistler, Christian Weilbach, Frank Wood

Amortized Bayesian inference trains neural networks to solve stochastic inference problems using model simulations, thereby making it possible to rapidly perform Bayesian inference for any newly observed data. However, current simulation-based amortized inference methods are simulation-hungry and inflexible: They require the specification of a fixed parametr

Lu Chen, Tian Wu, Jin Yan, Yabo Yang

In this paper, we classify all positive solutions of the critical anisotropic Sobolev equation \begin{equation}\label{0.1} -\Delta^{H}_{p}u = u^{p^{*}-1}, \ \ x\in \mathbb{R}^n \end{equation} without the finite volume constraint for $n \geq 3$ and $p_n(\Lambda) < p < n$, where $p^{*} = \frac{np}{n-p}$ denotes the critical Sobolev exponent, $-\Delta^{H}_{p}=-

Prateek Anand, Samriddhi Sankar Ray

We perform direct numerical simulations of sub-Kolmogorov, inertial spheroids settling under gravity in homogeneous, isotropic turbulence and find that small-scale clustering, measured via the correlation dimension, depends sensitively on their aspect ratios. In particular, such particles are shown to cluster more as their anisotropy increases. Further, the

Luis E. Portilla P., Eric Loubeau, Henrique N. Sá Earp

This work seeks to advance the understanding of the smooth structure of the moduli space of self-dual contact instantons (SDCI) on Sasakian 7-manifolds M. A neighborhood of a smooth point of M is locally modeled on the first cohomological group of an elliptic complex (1.4). There is a cohomological obstruction to the smoothness for the moduli space, in terms

Han Xue, Qianru Sun, Li Song, Wenjun Zhang

We propose In-Context Translation (ICT), a general learning framework to unify visual recognition (e.g., semantic segmentation), low-level image processing (e.g., denoising), and conditional image generation (e.g., edge-to-image synthesis). Thanks to unification, ICT significantly reduces the inherent inductive bias that comes with designing models for speci

Jungin Park, Jiyoung Lee, Kwanghoon Sohn

This paper introduces VLAP, a novel approach that bridges pretrained vision models and large language models (LLMs) to make frozen LLMs understand the visual world. VLAP transforms the embedding space of pretrained vision models into the LLMs' word embedding space using a single linear layer for efficient and general-purpose visual and language understanding

Diego Aineto, Enrico Scala

This paper studies the problem of action model learning with full observability. Following the learning by search paradigm by Mitchell, we develop a theory for action model learning based on version spaces that interprets the task as search for hypothesis that are consistent with the learning examples. Our theoretical findings are instantiated in an online a

Wenbo Fan, Weihua Gu, Meng Xu

The technology-enabled ride-pooling (RP) is designed as an on-demand feeder service to connect remote areas to transit terminals (or activity centers). We propose the so-called ``hold-dispatch'' operation strategy, which imposes a target number of shared rides (termed the ride-pooling size) for each vehicle to enhance RP's transportation efficiency. Analytic

Thitithep Sitthiyot, Kanyarat Holasut

Given a vast concern about high income inequality in Thailand as opposed to empirical findings around the world showing people's preference for fair income inequality over unfair income equality, it is therefore important to examine whether inequality in income distribution in Thailand over the past three decades is fair, and what fair inequality in income d

Erik Duse, Andreas Rosén

We prove a Weitzenb\"ock identity for general pairs of constant coefficient homogeneous first order partial differential operators, and deduce from it sufficient algebraic conditions for coerciveness and Morrey estimates under the natural 1/2 boundary conditions. Our proof of the $W^{1,2}$ elliptic estimate relies on the Aronszajn-Necas-Smith coercive estima

Varul Srivastava, Sankarshan Damle, Sujit Gujar

Proof-of-stake (PoS) has emerged as a natural alternative to the resource-intensive Proof-of-Work (PoW) blockchain, as was recently seen with the Ethereum Merge. PoS-based blockchains require an initial stake distribution among the participants. Typically, this initial stake distribution is called bootstrapping. This paper argues that existing bootstrapping

Alexander Roth, Vivien Parmentier, Mark Hammond

The population of hot Jupiters is extremely diverse, with large variations in their irradiation, period, gravity and chemical composition. To understand the intrinsic planet diversity through the observed population level trends, we explore the a-priori scatter in the population created by the different responses of atmospheric circulation to planetary param

Martin Kodys, Zhongmin Dai, Vrizlynn L. L. Thing

Privacy-preserving analytics is designed to protect valuable assets. A common service provision involves the input data from the client and the model on the analyst's side. The importance of the privacy preservation is fuelled by legal obligations and intellectual property concerns. We explore the use case of a model owner providing an analytic service on cu

Yipo Huang, Xiangfei Sheng, Zhichao Yang, Quan Yuan

The highly abstract nature of image aesthetics perception (IAP) poses significant challenge for current multimodal large language models (MLLMs). The lack of human-annotated multi-modality aesthetic data further exacerbates this dilemma, resulting in MLLMs falling short of aesthetics perception capabilities. To address the above challenge, we first introduce

Izabela Agata Malinowska

Isabel Martin-Lyons and Paul J.Truman generalized the definition of a skew brace to give a new algebraic object, which they termed a skew bracoid. Their construction involves two groups interacting in a manner analogous to the compatibility condition found in the definition of a skew brace. They formulated tools for characterizing and classifying skew bracoi

Weisheng Gong, Chen He, Kaijie Su, Qingyong Li

Adverse weather conditions, low-light environments, and bumpy road surfaces pose significant challenges to SLAM in robotic navigation and autonomous driving. Existing datasets in this field predominantly rely on single sensors or combinations of LiDAR, cameras, and IMUs. However, 4D millimeter-wave radar demonstrates robustness in adverse weather, infrared c

Tuan Anh Nguyen, Taeho Kwag, Vinh Pham, Viet Nghia Nguyen

This study advanced tele-operations in Advanced Air Mobility (AAM) through the creation of a Vehicle Digital Twin (VDT) system for eVTOL aircraft, tailored to enhance remote control safety and efficiency, especially for Beyond Visual Line of Sight (BVLOS) operations. By synergizing digital twin technology with immersive Virtual Reality (VR) interfaces, we no

Florian Euchner, Phillip Stephan, Stephan ten Brink

Channel Charting is a dimensionality reduction technique that reconstructs a map of the radio environment from similarity relationships found in channel state information. Distances in the channel chart are often computed based on some dissimilarity metric, which can be derived from angular-domain information, channel impulse responses, measured phase differ

Zhaokun Zhou, Qiulin Wang, Bin Lin, Yiwei Su

As an alternative to expensive expert evaluation, Image Aesthetic Assessment (IAA) stands out as a crucial task in computer vision. However, traditional IAA methods are typically constrained to a single data source or task, restricting the universality and broader application. In this work, to better align with human aesthetics, we propose a Unified Multi-mo

Johannes A. F. Lehmeyer, Alexander D. Fuchs, Zhengming Li, Titus Bornträger

When annealing a 4H silicon carbide (SiC) crystal, a sequence of optically active defect centers occurs among which the TS center is a prominent example. Here, we present low-temperature photoluminescence analyses on the single defect level. They reveal that the three occurring spectral signatures TS1, TS2 and TS3 originate from one single defect. Their pola

Hongjia Ou, Andreas Themelis

Leveraging on recent advancements on adaptive methods for convex minimization problems, this paper provides a linesearch-free proximal gradient framework for globalizing the convergence of popular stepsize choices such as Barzilai-Borwein and one-dimensional Anderson acceleration. This framework can cope with problems in which the gradient of the differentia

Julian Lorenz, Robin Schön, Katja Ludwig, Rainer Lienhart

Scene graph generation has emerged as a prominent research field in computer vision, witnessing significant advancements in the recent years. However, despite these strides, precise and thorough definitions for the metrics used to evaluate scene graph generation models are lacking. In this paper, we address this gap in the literature by providing a review an

Matti Wiegmann, Jennifer Rakete, Magdalena Wolska, Benno Stein

Trigger warnings are labels that preface documents with sensitive content if this content could be perceived as harmful by certain groups of readers. Since warnings about a document intuitively need to be shown before reading it, authors usually assign trigger warnings at the document level. What parts of their writing prompted them to assign a warning, howe

Silja Sormunen, Lasse Leskelä, Jari Saramäki

Distinguishing power-law distributions from other heavy-tailed distributions is challenging, and this task is often further complicated by subsampling effects. In this work, we evaluate the performance of two commonly used methods for detecting power-law distributions - the maximum likelihood method of Clauset et al. and the extreme value method of Voitalov

Marco Rasetto, Himanshu Akolkar, Ryad Benosman

The Hierarchy Of Time-Surfaces (HOTS) algorithm, a neuromorphic approach for feature extraction from event data, presents promising capabilities but faces challenges in accuracy and compatibility with neuromorphic hardware. In this paper, we introduce Sup3r, a Semi-Supervised algorithm aimed at addressing these challenges. Sup3r enhances sparsity, stability,

Yifei Yu, Shaocong Wang, Woyu Zhang, Xinyuan Zhang

Human beings construct perception of space by integrating sparse observations into massively interconnected synapses and neurons, offering a superior parallelism and efficiency. Replicating this capability in AI finds wide applications in medical imaging, AR/VR, and embodied AI, where input data is often sparse and computing resources are limited. However, t

Gianluca Gorni, Mattia Scomparin, Gaetano Zampieri

Some characteristics of the Sawada-Kotera Lagrangian system lend themselves to generalization, producing a large class of separable Lagrangian systems with two degrees of freedom.