December 2023 arXiv papers — page 109

C. R. Arguelles, J. A. Rueda, R. Ruffini

Non-linear structure formation for fermionic dark matter particles leads to dark matter density profiles with a degenerate compact core surrounded by a diluted halo. For a given fermion mass, the core has a critical mass that collapses into a supermassive black hole (SMBH). Galactic dynamics constraints suggest a $\sim 100$ keV/$c^2$ fermion, which leads to

Mohd Ibrahim Sheikh, Mohamed Elhamdadi, Danish Ali

We introduce and investigate oriented dichromatic singular links. We also introduce oriented disingquandles and use them to define counting invariants for oriented dichromatic singular links. We provide some examples to show that these invariants distinguish some oriented dichromatic singular links.

Jamil Fayyad, Shadi Alijani, Homayoun Najjaran

Background and objective: Uncertainty quantification is a pivotal field that contributes to realizing reliable and robust systems. It becomes instrumental in fortifying safe decisions by providing complementary information, particularly within high-risk applications. existing studies have explored various methods that often operate under specific assumptions

Carlotta Sartore, Lorenzo Rapetti, Fabio Bergonti, Stefano Dafarra

Ergonomics is a key factor to consider when designing control architectures for effective physical collaborations between humans and humanoid robots. In contrast, ergonomic indexes are often overlooked in the robot design phase, which leads to suboptimal performance in physical human-robot interaction tasks. This paper proposes a novel methodology for optimi

Bin Yan

The inclusion of gravitation within the framework of quantum theory remains one of the most prominent open problem in physics. To date, the absence of empirical evidence hampers conclusions regarding the fundamental nature of gravity -- whether it adheres to quantum principles or remains a classical field manifests solely in the macroscopic domain. This arti

Daniel Ordoñez-Apraez, Vladimir Kostic, Giulio Turrisi, Pietro Novelli

We introduce the use of harmonic analysis to decompose the state space of symmetric robotic systems into orthogonal isotypic subspaces. These are lower-dimensional spaces that capture distinct, symmetric, and synergistic motions. For linear dynamics, we characterize how this decomposition leads to a subdivision of the dynamics into independent linear systems

Gabriel Ng

We study the class of differentially henselian fields, which are henselian valued fields equipped with generic derivations in the sense of Cubides Kovacics and Point, and are special cases of differentially large fields in the sense of Le\'on S\'anchez and Tressl. We prove that many results from henselian valued fields as well as differentially large fields

Xun Tang, Lexing Ying

This work is concerned with solving high-dimensional Fokker-Planck equations with the novel perspective that solving the PDE can be reduced to independent instances of density estimation tasks based on the trajectories sampled from its associated particle dynamics. With this approach, one sidesteps error accumulation occurring from integrating the PDE dynami

Kaitlynn Taylor Pineda, Amama Mahmood, Juo-Tung Chen, Chien-Ming Huang

Social communication between people and social robots has been studied extensively and found to have various notable benefits, including the enhancement of human-robot team cohesion and the development of rapport and trust. However, the potential of social communication between people and non-social robots, such as non-anthropomorphic robot manipulators comm

Laura Guislain, Eric Bertin

In a statistical physics context, inverse problems consist in determining microscopic interactions such that a system reaches a predefined collective state. A complex collective state may be prescribed by specifying the overlap distribution between microscopic configurations, a notion originally introduced in the context of disordered systems like spin-glass

Alessandro Georgoudis, Carlo Heissenberg, Rodolfo Russo

We revisit the amplitude-based derivation of gravitational waveform for the scattering of two scalar black holes at subleading post-Minkowskian (PM) order. We take an eikonal-inspired approach to the two-massive-particle cut needed in the KMOC framework, as highlighted in arXiv:2308.02125, and show that its effect is to implement a simple change of frame. Th

Kento Kawaharazuka, Naoaki Kanazawa, Yoshiki Obinata, Kei Okada

In this study, we develop a simple daily assistive robot that controls its own vision according to linguistic instructions. The robot performs several daily tasks such as recording a user's face, hands, or screen, and remotely capturing images of desired locations. To construct such a robot, we combine a pre-trained large-scale vision-language model with a l

Joseph Cummings, Elizabeth Gross, Benjamin Hollering, Samuel Martin

Algebraic techniques in phylogenetics have historically been successful at proving identifiability results and have also led to novel reconstruction algorithms. In this paper, we study the ideal of phylogenetic invariants of the Cavender-Farris-Neyman (CFN) model on a phylogenetic network with the goal of providing a description of the invariants which is us

Tara N. Tošić, Arkadiy Simonov, Nicola A. Spaldin

We use a combination of first-principles density functional calculations and spin-dynamics simulations to explain the unusual diffuse inelastic neutron scattering in the hexagonal multiferroic yttrium manganite, YMnO$_3$. Using symmetry considerations, we construct a model spin Hamiltonian with parameters derived from our density functional calculations and

Marlom Ramalho, Jouni Suhonen

In the present paper we treat the second-forbidden non-unique (2nd-nu) ground-state-to-ground-state $\beta^-$ decay $^{99}\textrm{Tc}(9/2^+)\to\,^{99}\textrm{Ru}(5/2^+)$, with a 100$\%$ branching ratio, within the framework of the nuclear shell model (NSM). The energy spectrum of the electrons emitted in this $\beta$-decay transition ($\beta$-electron spectr

Emil Boman, Andrew Lifson, Malin Sjodahl, Adam Warnerbring

The chirality-flow formalism, combined with good choices of gauge reference vectors, simplifies tree-level calculations to the extent that it is often possible to write down amplitudes corresponding to Feynman diagrams immediately. It has also proven to give a very sizable speedup in a proof of concept implementation of massless tree-level QED in MadGraph5_a

John Brownfield, Huy Q. Nguyen

We study surface gravity waves for viscous fluid flows governed by Darcy's law. The free boundary is acted upon by an external pressure posited to be in traveling wave form with a periodic profile. It has been proven that for any given speed, small external pressures generate small periodic traveling waves that are asymptotically stable. In this work, we con

Hjalti Isleifsson

The dimension of cycles in the Tits boundary of a proper Hadamard space is bounded by the asymptotic rank m of the space minus one. Kleiner and Lang proved that for (m-1)-dimensional cycles in the Tits boundary, the asymptotic Plateau problem can be solved. We prove that the asymptotic Plateau problem can be solved for so called strongly immovable cycles in

Aljowhara H. Honain, Khaled M. Furati, Ibrahim O. Sarumi, Abdul Q. M. Khaliq

The two-parameter Mittag-Leffler function $E_{\alpha, \beta}$ is of fundamental importance in fractional calculus. It appears frequently in the solutions of fractional differential and integral equations. Nonetheless, this vital function is often expensive to compute. Several attempts have been made to construct cost-effective and accurate approximations. Th

Jan Sbierski

This paper studies the singularity structure of FLRW spacetimes without particle horizons at the $C^0$-level of the metric. We show that in the case of constant spatial curvature $K=+1$, and without any further assumptions on the scale factor, the big bang singularity is sufficiently strong to exclude continuous spacetime extensions to the past. On the other

Nicki Mullins, Mauricio Hippert, Lorenzo Gavassino, Jorge Noronha

We present a new general formalism for introducing thermal fluctuations in relativistic hydrodynamics, which incorporates recent developments on the causality and stability of relativistic hydrodynamic theories. Our approach is based on the information current, which measures the net amount of information carried by perturbations around equilibrium in a rela

Domala Sai Suhas, Vikrant Khullar

The realm of latent heat storage has witnessed emergence of optical charging as a promising route of solar thermal latent heat storage. However, it is still in its initial stages of development and warrants further investigations to take it to the next level i.e., realization of optical charging based real-world systems. Engineering efficient optical chargin

Zenny Wettersten, Olivier Mattelaer, Stefan Roiser, Robert Schöfbeck

An important area of high energy physics studies at the Large Hadron Collider (LHC) currently concerns the need for more extensive and precise comparison data. Important tools in this realm are event reweighing and evaluation of more precise next-to-leading order (NLO) processes via Monte Carlo event generators, especially in the context of the upcoming High

Jenny Hamer, Eleni Triantafillou, Bart van Merriënboer, Stefan Kahl

The ability for a machine learning model to cope with differences in training and deployment conditions--e.g. in the presence of distribution shift or the generalization to new classes altogether--is crucial for real-world use cases. However, most empirical work in this area has focused on the image domain with artificial benchmarks constructed to measure in

Mehdi Karimi, Levent Tuncel

Quantum Relative Entropy (QRE) programming is a recently popular and challenging class of convex optimization problems with significant applications in quantum computing and quantum information theory. We are interested in modern interior point (IP) methods based on optimal self-concordant barriers for the QRE cone. A range of theoretical and numerical chall

Alhassan Mabrouk, Abdelghani Dahou, Mohamed Abd Elaziz, Rebeca P. Díaz Redondo

The Internet of Medical Things (IoMT) has dramatically benefited medical professionals that patients and physicians can access from all regions. Although the automatic detection and prediction of diseases such as melanoma and leukemia is still being researched and studied in IoMT, existing approaches are not able to achieve a high degree of efficiency. Thus,

Ziyang Zheng, Ziad Sakr, Luca Amendola

We show how one can test the cosmological Poisson equation by requiring only the validity of three main assumptions: the energy-momentum conservation equations of matter, the equivalence principle, and the cosmological principle. We first point out that one can only measure the combination ${\mathcal M}\equiv \Omega_m^{(0)}\mu$, where $\mu$ quantifies the de

Love Panta, Prashant Shrestha, Brabeem Sapkota, Amrita Bhattarai

Video moment retrieval is a challenging task requiring fine-grained interactions between video and text modalities. Recent work in image-text pretraining has demonstrated that most existing pretrained models suffer from information asymmetry due to the difference in length between visual and textual sequences. We question whether the same problem also exists

Renukanandan Tumu, Matthew Cleaveland, Rahul Mangharam, George J. Pappas

Conformal prediction is a statistical tool for producing prediction regions for machine learning models that are valid with high probability. A key component of conformal prediction algorithms is a \emph{non-conformity score function} that quantifies how different a model's prediction is from the unknown ground truth value. Essentially, these functions deter

Romuald Tylenda, Tomek Kamiński, Radek Smolec

Nova 1670 is a historical transient bearing strong similarities to a recently-recognized type of stellar eruptions known as red novae, which are thought to be powered by stellar mergers. The remnant of the transient, CK Vul, is observable today mainly through cool circumstellar gas and dust, and recombining plasma, but we have no direct view on the stellar o

Christopher Krapu, Mark Borsuk

Data analysis and individual policy-level modeling for insurance involves handling large data sets with strong spatiotemporal correlations, non-Gaussian distributions, and complex hierarchical structures. In this research, we demonstrate that by utilizing gradient-based Markov chain Monte Carlo (MCMC) techniques accelerated by graphics processing units, the

Jiehua Chen, Jiong Guo, Yinghui Wen

We study the congested assignment problem as introduced by Bogomolnaia and Moulin (2023). We show that deciding whether a competitive assignment exists can be done in polynomial time, while deciding whether an envy-free assignment exists is NP-complete.

Claudia Hagedorn, M. L. López-Ibáñez, M. Jay Pérez, Moinul Hossain Rahat

In flavor models the vacuum alignment of flavons is typically achieved via the $F$-terms of certain fields in the supersymmetric limit. We propose a method for preserving such alignments, up to a rescaling of the vacuum expectation values, even after supersymmetry (and the flavor symmetry) are softly broken, facilitating the vacuum alignment in models which

Mikhail Khovanov, Vyacheslav Krushkal, John Nicholson

The universal pairing for manifolds was defined and shown to lack positivity in dimension 4 by Freedman et al. We prove an analogous result for 2-complexes, and also show that the universal pairing does not detect the difference between simple homotopy equivalence and 3-deformations. The question of whether these two equivalence relations are different for 2

Alhassan Mabrouk, Rebeca P. Díaz Redondo, Mohamed Abd Elaziz, Mohammed Kayed

Federated learning is a very convenient approach for scenarios where (i) the exchange of data implies privacy concerns and/or (ii) a quick reaction is needed. In smart healthcare systems, both aspects are usually required. In this paper, we work on the first scenario, where preserving privacy is key and, consequently, building a unique and massive medical im

Zhonglu Lin, Dongfang Liang, Yu Zhang

This study examines the impact of in-phase schooling on the hydrodynamic efficiency during linear acceleration in a simplified model using two undulating NACA0012 hydrofoils arranged in phalanx formation as a minimal representation of a fish school. The research focuses on key parameters, namely Strouhal (0.2-0.7) and Reynolds (1000-2000) numbers, and explor

Mehrab Momennia, Pritam Banerjee, Alfredo Herrera-Aguilar, Ulises Nucamendi

Motivated by recent achievements of a full general relativistic method in estimating the mass-to-distance ratio of supermassive black holes hosted at the core of active galactic nuclei, we introduce the new concept redshift rapidity in order to express the Schwarzschild black hole mass and its distance from the Earth just in terms of observational quantities

Tom Tirer, Raja Giryes, Se Young Chun, Yonina C. Eldar

Deep learning, in general, focuses on training a neural network from large labeled datasets. Yet, in many cases there is value in training a network just from the input at hand. This is particularly relevant in many signal and image processing problems where training data is scarce and diversity is large on the one hand, and on the other, there is a lot of s

Zhongyi Han, Guanglin Zhou, Rundong He, Jindong Wang

In machine learning, generalization against distribution shifts -- where deployment conditions diverge from the training scenarios -- is crucial, particularly in fields like climate modeling, biomedicine, and autonomous driving. The emergence of foundation models, distinguished by their extensive pretraining and task versatility, has led to an increased inte

Ashwath Shetty, Marc Habermann, Guoxing Sun, Diogo Luvizon

We present the first approach to render highly realistic free-viewpoint videos of a human actor in general apparel, from sparse multi-view recording to display, in real-time at an unprecedented 4K resolution. At inference, our method only requires four camera views of the moving actor and the respective 3D skeletal pose. It handles actors in wide clothing, a

Anh Duong Vo, Elisabeth Abs, Pau Vilimelis Aceituno, Benjamin Friedrich Grewe

Recent work has provided new insights into the temporal specialization of Intratelencephalic (IT) and Pyramidal tract neurons (PT). However, functional and anatomical differences of IT and PT have not been connected yet. This perspective article contributes by highlighting empirical studies about the connectivity of IT and PT as well as their specialization

Daniele Toller, Mirco Tribastone, Max Tschaikowski, Andrea Vandin

The ability to control complex networks is of crucial importance across a wide range of applications in natural and engineering sciences. However, issues of both theoretical and numerical nature introduce fundamental limitations to controlling large-scale networks. In this paper, we cope with this problem by introducing a coarse-graining algorithm. It leads

Swanand Ravindra Kadhe, Anisa Halimi, Ambrish Rawat, Nathalie Baracaldo

Training large language models (LLMs) is a costly endeavour in terms of time and computational resources. The large amount of training data used during the unsupervised pre-training phase makes it difficult to verify all data and, unfortunately, undesirable data may be ingested during training. Re-training from scratch is impractical and has led to the creat

Xiangyu Shi, Yunlong Liang, Jinan Xu, Yufeng Chen

Recent works have proven the effectiveness of k-nearest-neighbor machine translation(a.k.a kNN-MT) approaches to produce remarkable improvement in cross-domain translations. However, these models suffer from heavy retrieve overhead on the entire datastore when decoding each token. We observe that during the decoding phase, about 67% to 84% of tokens are unva

Kabita Parajuli, Shashidhar Ram Joshi

Video captioning in Nepali, a language written in the Devanagari script, presents a unique challenge due to the lack of existing academic work in this domain. This work develops a novel encoder-decoder paradigm for Nepali video captioning to tackle this difficulty. LSTM and GRU sequence-to-sequence models are used in the model to produce related textual desc

Biplab Bawali, Alokmay Datta, Jayashree Saha

In this study, we present a comprehensive exploration of formation of different phases in lipid molecules using a coarse-grained implicit solvent model, where each lipid molecule is represented as a rigid, three-bead rod-like structure. Our study not only successfully replicates the spontaneous self-assembly of lipid bilayers but also elucidates the intricat

J. van der Kolk, M. Á. Serrano, M. Boguñá

Geometry can be used to explain many properties commonly observed in real networks. It is therefore often assumed that real networks, especially those with high average local clustering, live in an underlying hidden geometric space. However, it has been shown that finite size effects can also induce substantial clustering, even when the coupling to this spac

Christopher Plumberg, Dekrayat Almaalol, Travis Dore, Jordi Salinas San Martin

At the Large Hadron Collider it is possible to generate BSQ (baryon, strangeness, and electric) charge density fluctuations from gluon splittings into quark/anti-quark pairs, generated within the ICCING model. In this work, we implement BSQ charge dynamics in a fully integrated framework. We propagate these conserved charges within an upgraded version of the

Efraín Palacios Jara, Ahmad Mohamad Mezhe, Mónica Aguilar Igartua, Rebeca P. Díaz Redondo

A mobile ad hoc network (MANET) is a set of autonomous mobile devices connected by wireless links in a distributed manner and without a fixed infrastructure. Real-time multimedia services, such as video-streaming over MANETs, offers very promising applications, e.g. two members of a group of tourists who want to share a video transmitted through the MANET th

Tom Davidson, Jean-Stanislas Denain, Pablo Villalobos, Guillem Bas

State-of-the-art AI systems can be significantly improved without expensive retraining via "post-training enhancements"-techniques applied after initial training like fine-tuning the system to use a web browser. We review recent post-training enhancements, categorizing them into five types: tool-use, prompting methods, scaffolding, solution selection, and da

Peter K. F. Kuhfittig

While wormholes may be just as good a prediction of Einstein's theory as black holes, they are subject to severe restrictions from quantum field theory. In particular, a wormhole can only be held open by violating the null energy condition, calling for the existence of "exotic matter." An equally serious problem is the enormous radial tension at the throat o

Ville J. Härkönen

We compute the ab-initio electron density beyond the strict Born-Oppenheimer approximation in crystalline LiH and LiD with density functional methods. By taking into account the quantum mechanical nature of the nuclei, an aspect absent in the strict Born-Oppenheimer approximation, we find significant corrections to electron density in the vicinity of nuclei

Antony Jeyaseelan A, Sandip Bysakh, Jalaja M A, Soma Dutta

This paper reports the impact of process-dependent structural deformation and lattice strain by doping, resulting in domain re-orientation along the a-axis. For this investigation, the smaller La3+ cation is introduced at A-site and the longitudinal and transverse piezocoefficient properties have been studied in Pb(Zr,Ti)O3 (PZT) film. Introducing smaller ca

Kaiwen Zhang, Yifan Zhou, Xudong Xu, Xingang Pan

Diffusion models have achieved remarkable image generation quality surpassing previous generative models. However, a notable limitation of diffusion models, in comparison to GANs, is their difficulty in smoothly interpolating between two image samples, due to their highly unstructured latent space. Such a smooth interpolation is intriguing as it naturally se

Chen Ju, Haicheng Wang, Zeqian Li, Xu Chen

Vision-Language Large Models (VLMs) have become primary backbone of AI, due to the impressive performance. However, their expensive computation costs, i.e., throughput and delay, impede potentials in real-world scenarios. To achieve acceleration for VLMs, most existing methods focus on the model perspective: pruning, distillation, quantification, but complet

Yoshihiko Hasegawa

The thermodynamic uncertainty relation posits that higher thermodynamic costs are essential for a system to function with greater precision. Recent discussions have expanded thermodynamic uncertainty relations beyond classical non-equilibrium systems, investigating how quantum characteristics can be utilized to improve precision. In this Letter, we explore h

Xiaomeng Zhang, Jinhan Guo, Yang Guo, Mingde Ding

We perform a data-constrained simulation with the zero-$\beta$ assumption to study the mechanisms of strong rotation and failed eruption of a filament in active region 11474 on 2012 May 5 observed by Solar Dynamics Observatory and Solar Terrestrial Relations Observatory. The initial magnetic field is provided by nonlinear force-free field extrapolation, whic

Marc-Etienne Brunet, Ashton Anderson, Richard Zemel

Large pretrained language models (LLMs) can be rapidly adapted to a wide variety of tasks via a text-to-text approach, where the instruction and input are fed to the model in natural language. Combined with in-context learning (ICL), this paradigm is impressively flexible and powerful. However, it also burdens users with an overwhelming number of choices, ma

Roldan Pozo

Finding the k-medianin a network involves identifying a subset of k vertices that minimize the total distance to all other vertices in a graph. This problem has been extensively studied in computer science, graph theory, operations research, and numerous areas due to its significance in a wide range of applications. While known to be computationally challeng

Michael Coons, Simon Kristensen, Mathias L. Laursen

In this paper, harkening back to ideas of Hardy and Ramanujan, Mahler and de Bruijn, with the addition of more recent results on the Fibonacci Dirichlet series, we determine the asymptotic number of ways $p_F(n)$ to write an integer as the sum of non-distinct Fibonacci numbers. This appears to be the first such asymptotic result concerning non-distinct parti

Alice Hedenlund, Tasos Moulinos

We recapture Douglas' framework for twisted parametrized stable homotopy theory in the language of $\infty$- categories. A twisted spectrum is essentially a section of a bundle of presentable stable $\infty$-categories whose fiber is the $\infty$-category of spectra, a perspective we refine in this work. We recover some of Douglas' results on classifications

I. Mohelsky, J. Wyzula, F. Le Mardele, F. Abadizaman

Here we report on Landau level spectroscopy of an epitaxially grown thin film of the topological insulator Sb2Te3, complemented by ellipsometry and magneto-transport measurements. The observed response suggests that Sb2Te3 is a direct-gap semiconductor with the fundamental band gap located at the \Gamma point, or along the trigonal axis, and its width reache

Dun Zeng, Yong Dai, Pengyu Cheng, Longyue Wang

Aligning large language models (LLMs) with human preferences has been recognized as the key to improving LLMs' interaction quality. However, in this pluralistic world, human preferences can be diversified due to annotators' different tastes, which hinders the effectiveness of LLM alignment methods. This paper presents the first quantitative analysis of the e

Long-Xing Huang, Shi-Xian Sun, Yong-Qiang Wang

In this paper, we study solutions of a static spherically symmetric system, which is composed of the coupling with the Bardeen action and two Dirac fields. For the case where only the Bardeen action is present, the magnetic charge $q$ can be infinite, then when the magnetic charge is greater than a certain value $q_b$, there exists a black hole solution, whi

Taeyoon Kwon, Kai Tzu-iunn Ong, Dongjin Kang, Seungjun Moon

Machine reasoning has made great progress in recent years owing to large language models (LLMs). In the clinical domain, however, most NLP-driven projects mainly focus on clinical classification or reading comprehension, and under-explore clinical reasoning for disease diagnosis due to the expensive rationale annotation with clinicians. In this work, we pres

Yue Zhang, Ming Zhang, Haipeng Yuan, Shichun Liu

Recently, the evaluation of Large Language Models has emerged as a popular area of research. The three crucial questions for LLM evaluation are ``what, where, and how to evaluate''. However, the existing research mainly focuses on the first two questions, which are basically what tasks to give the LLM during testing and what kind of knowledge it should deal

Tao Wang, Ziv Goldfeld

Optimal transport (OT) and Gromov-Wasserstein (GW) alignment are powerful frameworks for geometrically driven matching of probability distributions, yet their large-scale usage is hampered by high statistical and computational costs. Entropic regularization has emerged as a promising solution, allowing parametric convergence rates via the plug-in estimator,

Alberto Ruiz-Biestro, Julio C. Gutierrez-Vega

We present analytical and numerical solutions of the Lippmann-Schwinger equation for the scattered wavefunctions generated by confocal parabolic billiards and parabolic segments with various $\delta$-type potential-strength functions. The analytical expressions are expressed as summations of products of parabolic cylinder functions $D_m$. We numerically inve

Patrick Charbonneau, Yi Hu, Peter K. Morse

The mean-field theory (MFT) of simple glasses, which is exact in the limit of infinite spatial dimensions, $d\rightarrow\infty$, offers theoretical insight as well as quantitative predictions about certain features of $d=3$ systems. In order to more systematically relate the behavior of physical systems to MFT, however, various finite-$d$ corrections need to

Jacob Carruth, Arie Israel

In this paper, we prove the existence of a bounded linear extension operator $T: L^{2,p}(E) \rightarrow L^{2,p}(\mathbb{R}^2)$ when $1<p<2$, where $E \subset \mathbb{R}^2$ is a certain discrete set with fractal structure. Our proof makes use of a theorem of Fefferman-Klartag on the existence of linear extension operators for radially symmetric binary trees.

Pinelopi Papalampidi, Skanda Koppula, Shreya Pathak, Justin Chiu

Understanding long, real-world videos requires modeling of long-range visual dependencies. To this end, we explore video-first architectures, building on the common paradigm of transferring large-scale, image--text models to video via shallow temporal fusion. However, we expose two limitations to the approach: (1) decreased spatial capabilities, likely due t

Dario Dennstädt, Lukas Lanza, Karl Worthmann

We propose a model predictive control (MPC) scheme with sampled-data input which ensures output-reference tracking within prescribed error bounds for relative-degree-one systems. Hereby, we explicitly deduce bounds on the required maximal control input and sampling frequency such that the MPC scheme is both initially and recursively feasible. A key feature o

Ayah Almousa, Sean Grate, Daoji Huang, Patricia Klein

We introduce the MatrixSchubert package for the computer algebra system Macaulay2. This package has tools to construct and study matrix Schubert varieties and alternating sign matrix (ASM) varieties. The package also introduces tools for quickly computing homological invariants of such varieties, finding the components of an ASM variety, and checking if a un

Xiangyu Yin, Sihao Wu, Jiaxu Liu, Meng Fang

While Goal-Conditioned Reinforcement Learning (GCRL) has gained attention, its algorithmic robustness against adversarial perturbations remains unexplored. The attacks and robust representation training methods that are designed for traditional RL become less effective when applied to GCRL. To address this challenge, we first propose the Semi-Contrastive Rep

Matteo Puviani, Sangkha Borah, Remmy Zen, Jan Olle

Bosonic codes allow the encoding of a logical qubit in a single component device, utilizing the infinitely large Hilbert space of a harmonic oscillator. In particular, the Gottesman-Kitaev-Preskill code has recently been demonstrated to be correctable well beyond the break-even point of the best passive encoding in the same system. Current approaches to quan

A. Makovec, I. Tropin

This benchmarking study aims to compare residual dose rate predictions from the MARS and DORIAN codes with experimental measurements taken from samples irradiated at CERN's CERF facility. The research evaluates the residual dose rates of these samples over varied cooling intervals. Additionally, it delves into the influence of parameter choices, such as spec

Michael Lanier, Aayush Dhakal, Zhexiao Xiong, Arthur Li

In critical operations where aerial imagery plays an essential role, the integrity and trustworthiness of data are paramount. The emergence of adversarial attacks, particularly those that exploit control over labels or employ physically feasible trojans, threatens to erode that trust, making the analysis and mitigation of these attacks a matter of urgency. W

Julius Köpke, Sebastian Trattnig

Blockchains and distributed ledger technology offer promising capabilities for supporting collaborative business processes across organizations. Typically, approaches in this field fall into two categories: either executing the entire process model on the blockchain or using the blockchain primarily to enforce or monitor the exchange of messages between part

T. Faulwasser, O. Molodchyk

Gaussian Processes (GPs) are a versatile method that enables different approaches towards learning for dynamics and control. Gaussianity assumptions appear in two dimensions in GPs: The positive semi-definite kernel of the underlying reproducing kernel Hilbert space is used to construct the co-variance of a Gaussian distribution over functions, while measure

Sean Chen, Nicholas Rivera, Jamison Sloan, Marin Soljacic

Quantum states of light, particularly at optical frequencies, are considered necessary to realize a host of important quantum technologies and applications, spanning Heisenberg-limited metrology, continuous-variable quantum computing, and quantum communications. Nevertheless, a wide variety of important quantum light states are currently challenging to deter

Haiming Zhang, Zhihao Yuan, Chaoda Zheng, Xu Yan

Although existing speech-driven talking face generation methods achieve significant progress, they are far from real-world application due to the avatar-specific training demand and unstable lip movements. To address the above issues, we propose the GSmoothFace, a novel two-stage generalized talking face generation model guided by a fine-grained 3d face mode

Yupeng Hu, Han Jiang, Hao Liu, Kun Wang

Recently, temporal action localization (TAL) has garnered significant interest in information retrieval community. However, existing supervised/weakly supervised methods are heavily dependent on extensive labeled temporal boundaries and action categories, which is labor-intensive and time-consuming. Although some unsupervised methods have utilized the ``iter

Aditya Sarkar, Sreelakshmi Manjunath

We analyse the performance of the IEEE 802.11bd MAC protocol, with Enhanced Distributed Channel Access (EDCA) and repeated transmissions, in terms of the MAC access delay of packets pertaining to safety-related events. We outline Markov chain models for the contention mechanism of priority-based access categories, and derive the associated steady-state proba

Mohamed Kayed, Rebeca P. Díaz-Redondo, Alhassan Mabrouk

Recently, Deep Learning (DL) approaches have been applied to solve the Sentiment Classification (SC) problem, which is a core task in reviews mining or Sentiment Analysis (SA). The performances of these approaches are affected by different factors. This paper addresses these factors and classifies them into three categories: data preparation based factors, f

Kenny A. Q. Caldas, Filipe M. Barbosa, Junior A. R. Silva, Tiago C. Santos

Off-road driving operations can be a challenging environment for human conductors as they are subject to accidents, repetitive and tedious tasks, strong vibrations, which may affect their health in the long term. Therefore, they can benefit from a successful implementation of autonomous vehicle technology, improving safety, reducing labor costs and fuel cons

Hallee E. Wong, Marianne Rakic, John Guttag, Adrian V. Dalca

Biomedical image segmentation is a crucial part of both scientific research and clinical care. With enough labelled data, deep learning models can be trained to accurately automate specific biomedical image segmentation tasks. However, manually segmenting images to create training data is highly labor intensive and requires domain expertise. We present \emph

Elia Favarelli, Elisabetta Matricardi, Lorenzo Pucci, Wen Xu

This study explores the promising potential of integrating sensing capabilities into multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM)-based networks through innovative multi-sensor fusion techniques, tracking algorithms, and resource management. A novel data fusion technique is proposed within the MIMO-OFDM system, whic

Linglin Jing, Ying Xue, Xu Yan, Chaoda Zheng

The field of 4D point cloud understanding is rapidly developing with the goal of analyzing dynamic 3D point cloud sequences. However, it remains a challenging task due to the sparsity and lack of texture in point clouds. Moreover, the irregularity of point cloud poses a difficulty in aligning temporal information within video sequences. To address these issu

Alhassan Mabrouk, Rebeca P. Díaz-Redondo, Mohammed Kayed

E-Commerce (EC) websites provide a large amount of useful information that exceed human cognitive processing ability. In order to help customers in comparing alternatives when buying a product, previous studies designed opinion summarization systems based on customer reviews. They ignored templates' information provided by manufacturers, although these descr

Beth Bjorkman, Esther Conrad, Mary Flagg

Sensors called phasor measurement units (PMUs) are used to monitor the electric power network. The power domination problem seeks to minimize the number of PMUs needed to monitor the network. We extend the power domination problem and consider the minimum number of sensors and appropriate placement to ensure monitoring when $k$ sensors are allowed to fail wi

Mukesh Kumar Singh, Shasvath J Kapadia, Aditya Vijaykumar, Parameswaran Ajith

Templates modeling just the dominant mode of gravitational radiation are generally sufficient for the unbiased parameter inference of near-equal-mass compact binary mergers. However, neglecting the subdominant modes can bias the inference if the binary is significantly asymmetric, very massive, or has misaligned spins. In this work, we explore if neglecting

Jihua Chen, Alejandro Espera, Jan Michael Y. Carrillo, Rigoberto Advincula

Ion complexes hold the key for various energy transfer and communication systems in nature and industries. Achieving controllable mechanical and electrical properties in these complex systems is highly desirable but remains challenging. In this work, we propose the use of amphiphilic molecules to mediate salt crystallization. The resultant ionic interfaces c

Owen Tanner

We include a class of generalisations of Thompson's group $V$ introduced by Melanie Stein into the growing framework of topological full groups. Like $V$, Stein's groups can be described as certain piecewise linear maps with prescribed slopes and nondifferentiable points. Stein's groups include the class of groups where the group of slopes is generated by mu

Jian Hu, Jiayi Lin, Weitong Cai, Shaogang Gong

Camouflaged object detection (COD) approaches heavily rely on pixel-level annotated datasets. Weakly-supervised COD (WSCOD) approaches use sparse annotations like scribbles or points to reduce annotation effort, but this can lead to decreased accuracy. The Segment Anything Model (SAM) shows remarkable segmentation ability with sparse prompts like points. How

Nicolai Jurek Gerber, Franca Hoffmann, Urbain Vaes

For algorithms based on interacting particle systems that admit a mean-field description, convergence analysis is often more accessible at the mean-field level. In order to transfer convergence results obtained at the mean-field level to the finite ensemble size setting, it is desirable to show that the particle dynamics converge in an appropriate sense to t

Xuanyu Zhang, Runyi Li, Jiwen Yu, Youmin Xu

In the era where AI-generated content (AIGC) models can produce stunning and lifelike images, the lingering shadow of unauthorized reproductions and malicious tampering poses imminent threats to copyright integrity and information security. Current image watermarking methods, while widely accepted for safeguarding visual content, can only protect copyright a

Sergei P. Maydanyuk, Gyorgy Wolf

$\mathbf{Purpose}$: Incoherent processes in production of lepton pairs in the scattering of protons off nuclei are investigated. $\mathbf{Methods}$: New quantum mechanical model is constructed which uses generalization of the nuclear model of emission of photons in the proton-nucleus reactions in region from low up to high energies with inclusion of formalis

Sen Yan, Hongyuan Fang, Ji Li, Tomas Ward

Battery Electric Vehicles (BEVs) are increasingly significant in modern cities due to their potential to reduce air pollution. Precise and real-time estimation of energy consumption for them is imperative for effective itinerary planning and optimizing vehicle systems, which can reduce driving range anxiety and decrease energy costs. As public awareness of d

Marwa Kechaou, Mokhtar Z. Alaya, Romain Hérault, Gilles Gasso

Adversarial learning baselines for domain adaptation (DA) approaches in the context of semantic segmentation are under explored in semi-supervised framework. These baselines involve solely the available labeled target samples in the supervision loss. In this work, we propose to enhance their usefulness on both semantic segmentation and the single domain clas

Danilo P. F. Silva, Rodrigo C. V. Coelho, Ignacio Pagonabarraga, Sauro Succi

With the rapid development of studies involving droplet microfluidics, drug delivery, cell detection, and microparticle synthesis, among others, many scientists have invested significant efforts to model the flow of these fluid-filled bodies. Motivated by the intricate coupling between hydrodynamics and the interactions of fluid-filled bodies, several method