November 2024 arXiv papers — page 13

Jin-Yang Li, An-Dong Fan, Yong-Kun Wang, Ying Zhang

Altermagnets (AM) are a recently discovered third class of collinear magnets, and have been attracting significant interest in the field of condensed matter physics. Here, based on first-principles calculations and theoretical analysis, we propose four two-dimensional (2D) magnetic materials--monolayer V$_2$Te$_2$O, V$_2$STeO, V$_2$SSeO, and V$_2$S$_2$O--as

Chang-Sik Choi

Although a significant number satellites are deemed essential for facilitating diverse applications of satellite networks, aerial platforms are emerging as excellent alternatives for enabling reliable communications with fewer satellites. In scenarios with sparse satellite networks, aerial platforms participate in downlink communications, serving effectively

Haijie Li, Yanmin Wu, Jiarui Meng, Qiankun Gao

3D scene understanding has become an essential area of research with applications in autonomous driving, robotics, and augmented reality. Recently, 3D Gaussian Splatting (3DGS) has emerged as a powerful approach, combining explicit modeling with neural adaptability to provide efficient and detailed scene representations. However, three major challenges remai

Carlo Marinelli, Stefano D'Addona

We examine the empirical performance of some parametric and nonparametric estimators of prices of options with a fixed time to maturity, focusing on variance-gamma and Heston models on one side, and on expansions in Hermite functions on the other side. The latter class of estimators can be seen as perturbations of the classical Black-Scholes model. The compa

Oualid Zaazaa, Hanan El Bakkali

Smart contracts are essential for managing digital assets in blockchain networks, highlighting the need for effective security measures. This paper introduces SmartLLMSentry, a novel framework that leverages large language models (LLMs), specifically ChatGPT with in-context training, to advance smart contract vulnerability detection. Traditional rule-based f

Thomas Wimmer, Michael Oechsle, Michael Niemeyer, Federico Tombari

State-of-the-art novel view synthesis methods achieve impressive results for multi-view captures of static 3D scenes. However, the reconstructed scenes still lack "liveliness," a key component for creating engaging 3D experiences. Recently, novel video diffusion models generate realistic videos with complex motion and enable animations of 2D images, however

Xi Lu, Xiao-Jia Zhang, Anton V. Artemyev, Vassilis Angelopoulos

Electromagnetic whistler-mode waves play a crucial role in the acceleration and precipitation of radiation belt electrons. Statistical surveys of wave characteristics suggest that these waves should preferentially scatter and precipitate relativistic electrons on the day side. However, the night-side region is expected to be primarily associated with electro

Yingying Deng, Xiangyu He, Fan Tang, Weiming Dong

Style transfer presents a significant challenge, primarily centered on identifying an appropriate style representation. Conventional methods employ style loss, derived from second-order statistics or contrastive learning, to constrain style representation in the stylized result. However, these pre-defined style representations often limit stylistic expressio

Xinxu Wei, Kanhao Zhao, Yong Jiao, Hua Xie

Effectively utilizing extensive unlabeled high-density EEG data to improve performance in scenarios with limited labeled low-density EEG data presents a significant challenge. In this paper, we address this challenge by formulating it as a graph transfer learning and knowledge distillation problem. We propose a Unified Pre-trained Graph Contrastive Masked Au

Arian Fooroogh Mand Arabi, Cansu Koyuturk, Michael O'Mahony, Raffaella Calati

This paper presents the iterative development of Habit Coach, a GPT-based chatbot designed to support users in habit change through personalized interaction. Employing a user-centered design approach, we developed the chatbot using a Retrieval-Augmented Generation (RAG) system, which enables behavior personalization without retraining the underlying language

S. Rosswog

We present and explore a new shock-capturing particle hydrodynamics approach. Our starting point is a commonly used discretization of smoothed particle hydrodynamics. We enhance this discretization with Roe's approximate Riemann solver, we identify its dissipative terms, and in these terms, we use slope-limited linear reconstruction. All gradients needed for

Laura Sanità, Lucy Verberk

Cooperative 2-matching games are a generalization of cooperative matching games, where the value function is given by maximum-weight b-matchings, for a vertex capacity vector $b \leq 2$. We show how to separate over the core of 2-matching games in polynomial time, fixing a small flaw in the literature, and prove the existence of a compact extended formulatio

Laura Carini, Isabella Furci, Sara Sommariva

In this work we present a computationally efficient linear optimization approach for estimating the cross--power spectrum of an hidden multivariate stochastic process from that of another observed process. Sparsity in the resulting estimator of the cross--power is induced through $\ell_1$ regularization and the Fast Iterative Shrinkage-Thresholding Algorithm

Mohammadhossein Momeni, Vivek Gopalakrishnan, Neel Dey, Polina Golland

We present DiffVox, a self-supervised framework for Cone-Beam Computed Tomography (CBCT) reconstruction by directly optimizing a voxelgrid representation using physics-based differentiable X-ray rendering. Further, we investigate how the different implementations of the X-ray image formation model in the renderer affect the quality of 3D reconstruction and n

Jiani Yan, Charles Rahal

We propose a rigorous decomposition of predictive error, highlighting that not all 'irreducible' error is genuinely immutable. Many domains stand to benefit from iterative enhancements in measurement, construct validity, and modeling. Our approach demonstrates how apparently 'unpredictable' outcomes can become more tractable with improved data (across both t

ShuaiHeng Li, Qing Cai, Fan Zhang, Menghuan Zhang

Self-supervised learning is emerging in fine-grained visual recognition with promising results. However, existing self-supervised learning methods are often susceptible to irrelevant patterns in self-supervised tasks and lack the capability to represent the subtle differences inherent in fine-grained visual recognition (FGVR), resulting in generally poorer p

I. V. Krainov, M. V. Rakhlin, A. I. Veretennikov, T. V. Shubina

Currently, two optical processes are mainly used to realize single photon sources: deterministic transitions in a semiconductor quantum dot (QD) placed in a microcavity and spontaneous frequency down-conversion in materials with intrinsic nonlinearity. In this work, we consider another approach that combines the advantages of both, such as high power with on

Moh. Adhib Ulil Absor, Muhammad Arifin, Iman Santoso, Harsojo

Two-dimensional tungsten-based transition metal dichalcogenides (TMDCs), $MX_{2}$ ($M$: W, Mo; $X$: S, Se, Te) monolayers (MLs) with a $1T'$ structure, serve as significant-gap quantum spin Hall insulators. However, due to the centrosymmetric nature of these crystals, spin degeneracy persists throughout their electronic band structures, limiting their potent

Tsun-Hin Cheung, Ka-Chun Fung, Songjiang Lai, Kwan-Ho Lin

Identifying defects and anomalies in industrial products is a critical quality control task. Traditional manual inspection methods are slow, subjective, and error-prone. In this work, we propose a novel zero-shot training-free approach for automated industrial image anomaly detection using a multimodal machine learning pipeline, consisting of three foundatio

Lorenzo Iacobacci, Kevin Nguyen

We provide a detailed decomposition of Wigner's particles, defined as unitary irreducible representations of the Poincar\'e group, in terms of unitary representations of its Lorentz subgroup. As pointed out before us, this decomposition only involves Lorentz representations belonging to the principal continuous series, and further underpins the connection be

Wayne Zheng, Zheng-Yuan Yue, Jian-Hao Zhang, Zheng-Cheng Gu

Over the last two decades, the competing orders in high-$T_{c}$ cuprates have been intensely studied, such as pseudogap phase, charge density waves (CDW), and pair density waves (PDW), which are thought to play a crucial role in high-temperature superconductivity. Using the $t$-$J$ model on a square lattice as the simplest model for high-$T_{c}$ cuprates, we

Guo-En Nian, Stefan Vandoren

In this work, we explore the feasibility of realizing the Dark Dimension Scenario through T-fold compactifications on $T^5 \times S^1$, where the base of the internal space $S^1$ naturally acts as the requisite mesoscopic extra dimension. Utilizing Scherk--Schwarz reduction from 5D to 4D, and applying duality twists from both elliptic and parabolic conjugacy

Kshitij Nikhal, Cedric Nimpa Fondje, Benjamin S. Riggan

Cross-spectral biometrics, such as matching imagery of faces or persons from visible (RGB) and infrared (IR) bands, have rapidly advanced over the last decade due to increasing sensitivity, size, quality, and ubiquity of IR focal plane arrays and enhanced analytics beyond the visible spectrum. Current techniques for mitigating large spectral disparities betw

Kento Nakada, Kazuki Kawamura, Ryosuke Furukawa

Reciprocal recommender systems (RRSs) are crucial in online two-sided matching platforms, such as online job or dating markets, as they need to consider the preferences of both sides of the match. The concentration of recommendations to a subset of users on these platforms undermines their match opportunities and reduces the total number of matches. To maxim

Venkata Satya Sai Ajay Daliparthi

Inspired by the Many-Worlds Interpretation (MWI), this work introduces a novel neural network architecture that splits the same input signal into parallel branches at each layer, utilizing a Hyper Rectified Activation, referred to as ANDHRA. The branched layers do not merge and form separate network paths, leading to multiple network heads for output predict

Khrystyna O. Levchenko, Kristýna Davídková, Jan Mikkelsen, Andrii V. Chumak

This review explores the development of spin-wave technology, highlighting magnonics as a promising route for radio frequency (RF) communication systems. The rollout of 5G and the upcoming 6G networks intensifies the demand for devices that can operate at higher frequencies while remaining scalable, compact, and energy-efficient - requirements that spin wave

N'yoma Diamond, Soumya Banerjee

The Generative Agents framework recently developed by Park et al. has enabled numerous new technical solutions and problem-solving approaches. Academic and industrial interest in generative agents has been explosive as a result of the effectiveness of generative agents toward emulating human behaviour. However, it is necessary to consider the ethical challen

Finlay G. C. Hudson, William A. P. Smith

We present Track Anything Behind Everything (TABE), a novel pipeline for zero-shot amodal video object segmentation. Unlike existing methods that require pretrained class labels, our approach uses a single query mask from the first frame where the object is visible, enabling flexible, zero-shot inference. We pose amodal segmentation as generative outpainting

Ria Talukder, Anas Skalli, Xavier Porte, Simon Thorpe

Spiking neural networks are neuromorphic systems that emulate certain aspects of biological neurons, offering potential advantages in energy efficiency and speed by for example leveraging sparsity. While CMOS-based electronic SNN hardware has shown promise, scalability and parallelism challenges remain. Photonics provides a promising platform for SNNs due to

Helena Bergold, Lukas Egeling, Hung. P. Hoang

Arrangements of pseudohyperplanes are widely studied in computational geometry. A rich subclass of pseudohyerplane arrangements, which has gained more attention in recent years, is the so-called signotopes. Introduced by Manin and Schechtman (1989), the higher Bruhat order is a natural order of $r$-signotopes on $n$ elements, with the signotope corresponding

Xin-Xiang Ju, Wen-Bin Pan, Ya-Wen Sun, Yuan-Tai Wang

We show that there exists a huge amount of multipartite entanglement in holography by studying the upper bound for holographic $n$-partite information $I_n$ that $n-1$ fixed boundary subregions participate. We develop methods to find the $n$-th region $E$ that makes $I_n$ reach the upper bound. Through the explicit evaluation, it is shown that $I_n$, an IR t

Huy N. Chau, Miklos Rasonyi

In this paper, a new approach for solving the problems of pricing and hedging derivatives is introduced in a general frictionless market setting. The method is applicable even in cases where an equivalent local martingale measure fails to exist. Our main results include a new superhedging duality for American options when wealth processes can be negative and

Katarina Halaj, Bernhard Klar, Bojana Milošević, Mirjana Veljović

We study a circular-circular multiplicative regression model, characterized by an angular error distribution assumed to be wrapped Cauchy. We propose a specification procedure for this model, focusing on adapting a recently proposed goodness-of-fit test for circular distributions. We derive its limiting properties and study the power performance of the test

Kelvin Summoogum, Debayan Das, Sathish Kumaran, Sumit Bhagra

Incorporating cloud technology with Internet of Medical Things for ubiquitous healthcare has seen many successful applications in the last decade with the advent of machine learning and deep learning techniques. One of these applications, namely voice-based pathology, has yet to receive notable attention from academia and industry. Applying voice analysis to

Joy Mahapatra, Utpal Garain

Large Language Models (LLMs) have shown exceptional performance across various Data-to-Text Generation (DTG) tasks. However, generating factually consistent text in DTG remains challenging for LLMs. Despite this, in-depth evaluations of LLM factual consistency for DTG remain missing in the current literature. This paper addresses this gap by providing an ext

Sam Adriaensen, Zsuzsa Weiner

The elements of a finite field of prime order canonically correspond to the integers in an interval. This induces an ordering on the elements of the field. Using this ordering, Kiss and Somlai recently proved interesting properties of the set of points below the diagonal line. In this paper, we investigate the set of points lying below a parabola. We prove t

Sam Adriaensen, Tamás Szőnyi, Zsuzsa Weiner

In this paper, we tie together two well studied topics related to finite Desarguesian affine and projective planes. The first topic concerns directions determined by a set, or even a multiset, of points in an affine plane. The second topic concerns the linear code generated by the incidence matrix of a projective plane. We show how a multiset determining onl

Yongzheng Zhu, Shiji Zhao, Yuanye Zhou, Hong Liang

Mesh generation is essential for accurate and efficient computational fluid dynamics simulations. To resolve critical features in the flow, adaptive mesh refinement (AMR) is routinely employed in certain regions of the computational domain, where gradients or error estimates of the solution are often considered as the refining criteria. In many scenarios, ho

Friedhelm Hamann, Daniel Gehrig, Filbert Febryanto, Kostas Daniilidis

Tracking any point (TAP) recently shifted the motion estimation paradigm from focusing on individual salient points with local templates to tracking arbitrary points with global image contexts. However, while research has mostly focused on driving the accuracy of models in nominal settings, addressing scenarios with difficult lighting conditions and high-spe

Pierre Fima, Malay Mandal, Issan Patri

Given two unital C*-algebras $A$ and $B$, we study, when it exists, the universal unital $C^*$-algebra $\mathcal{U}(A,B)$ generated by the coefficients of a unital $*$-homomorphism $\rho\,:\, A\rightarrow B\otimes\mathcal{U}(A,B)$. When $B$ is finite dimensional, it is well known that $\mathcal{U}(A,B)$ exists and we study in this case properties LP, RFD, pr

J. M. Díaz-Bañez, J. M. Higes-López, M. A. Pérez-Cutiño, J. Valverde

In Concentrated Solar Power (CSP) plants based on Parabolic Trough Collectors (PTC), the Sun is tracked at discrete time intervals, with each interval representing a movement of the collector system. The act of moving heavy mechanical structures can lead to the development of cracks, bending, and/or displacements of components from their optimal optical posi

Jeremy Mitchell

We investigate 1-factorisations in which the 2-regular graphs that occur as the union of a pair of 1-factors appear an equal number of times across the unions of all pairs of 1-factors in the 1-factorisation. We call such 1-factorisations balanced 1-factorisations (B1Fs) and we present some results on B1Fs of 3- and 4-regular circulant graphs.

Krzysztof Jodłowski

Dark axion portal connects an axion-like-particle (ALP), which can be the QCD axion, with the SM by the coupling to a photon and a dark photon, leading to a rich and distinct phenomenology related to dark matter, astrophysics, and cosmology. We note that due to the gauge invariance, the $Z$ boson-dark photon-ALP coupling should also be generated with sizable

G. Garza, K. Gurevich, A. Iosevich, A. Mayeli

Let $f: {\mathbb Z}_N^d \to {\mathbb C}$ be a signal with the Fourier transform $\widehat{f}: \Bbb Z_N^d\to \Bbb C$. A classical result due to Matolcsi and Szucs (\cite{MS73}), and, independently, to Donoho and Stark (\cite{DS89}) states if a subset of frequencies ${\{\widehat{f}(m)\}}_{m \in S}$ of $f$ are unobserved due to noise or other interference, then

Yan Zhang, Chenyang Huang, Ankang Hu, Yucheng Wang

Purpose: Our aim was to elucidate the critical factors responsible for inducing the FLASH effect, focusing on the role of free radicals through simulation and experimental approaches. Methods and Materials: The whole abdomen of C57BL/6 mice was irradiated with 6 MeV electron beam. The endpoint was acute intestinal toxicity quantified by histological score. T

Pekka Malo, Lauri Viitasaari, Antti Suominen, Eeva Vilkkumaa

This paper examines reinforcement learning (RL) in infinite-horizon decision processes with almost-sure safety constraints, crucial for applications like autonomous systems, finance, and resource management. We propose a doubly-regularized RL framework combining reward and parameter regularization to address safety constraints in continuous state-action spac

Rofeide Jabbari, Pablo Olivares

In this paper we extend models for the dynamic of the temperatures by considering random switching between Levy noises instead of Brownian motions, with a mean-reverting movement towards a seasonal periodic function. The use of Levy noises allows for jumps, capturing, together with the regime changes, sudden and relatively persistent oscillations in the weat

Prajakta Bedekar, Megan A. Catterton, Matthew DiSalvo, Gregory A. Cooksey

Flow cytometry measurements are widely used in diagnostics and medical decision making. Incomplete understanding of sources of measurement uncertainty can make it difficult to distinguish autofluorescence and background sources from signals of interest. Moreover, established methods for modeling uncertainty overlook the fact that the apparent distribution of

Anna Gierzkiewicz, Robert Szczelina

We present an adaptation of a relatively simple topological argument to show the existence of many periodic orbits in an infinite dimensional dynamical system, provided that the system is close to a one-dimensional map in a certain sense. Namely, we prove a Sharkovskii-type theorem: if the system has a periodic orbit of basic period $m$, then it must have al

Lennart Köpper, Thomas Wieland

Real-world traffic involves diverse road users, ranging from pedestrians to heavy trucks, necessitating effective road user classification for various applications within Intelligent Transport Systems (ITS). Traditional approaches often rely on intrusive and/or expensive external hardware sensors. These systems typically have limited spatial coverage. In res

Bingxin Ke, Dominik Narnhofer, Shengyu Huang, Lei Ke

Video depth estimation lifts monocular video clips to 3D by inferring dense depth at every frame. Recent advances in single-image depth estimation, brought about by the rise of large foundation models and the use of synthetic training data, have fueled a renewed interest in video depth. However, naively applying a single-image depth estimator to every frame

Alex S. A. Alochukwu, Audace A. V. Dossou-Olory, Fadekemi J. Osaye, Valisoa R. M. Rakotonarivo

The rank (also known as protection number or leaf-height) of a vertex in a rooted tree is the minimum distance between the vertex and any of its leaf descendants. We consider the sum of ranks over all vertices (known as the security) in binary trees, and produce a classification of families of binary trees for which the security is maximized. In addition, ex

Anirudh Phukan, Divyansh, Harshit Kumar Morj, Vaishnavi

The rapid development of Large Multimodal Models (LMMs) has significantly advanced multimodal understanding by harnessing the language abilities of Large Language Models (LLMs) and integrating modality-specific encoders. However, LMMs are plagued by hallucinations that limit their reliability and adoption. While traditional methods to detect and mitigate the

Debraj Dutta, Anupam Kundu, Urna Basu

We study the dynamics of a single inertial run-and-tumble particle on a straight line. The motion of this particle is characterized by two intrinsic time-scales, namely, an inertial and an active time-scale. We show that interplay of these two time-scales leads to the emergence of four distinct regimes, characterized by different dynamical behaviour of mean-

Aiying Zhao, Richard A Klemm, Qiang Gu

The influence of the Zeeman energy and the Landau levels (LLs) arising from an applied magnetic field ${\bf B}$ upon the critical temperature $T_c$ is studied using a fully quantum mechanical method within the framework of the Bardeen-Cooper-Schrieffer (BCS) theory of superconductivity that forms from an ultraclean metal. As in semiclassical treatments, we f

Lilong Zhou

An automatically differentiable, high-order non-oscillatory finite volume shallow water dynamic core has been constructed on a cubed sphere grid. This dynamic core has four advantageous properties: high order accuracy, essential non-oscillation, mass conservation, and scalability. Besides, the code development is based on PyTorch, enabling the model to run s

Lorenzo Dell'Oro, Carlo Gaetan

Extreme value analysis is an essential methodology in the study of rare and extreme events, which hold significant interest in various fields, particularly in the context of environmental sciences. Models that employ the exceedances of values above suitably selected high thresholds possess the advantage of capturing the "sub-asymptotic" dependence of data. T

Girtrude Hamm, Johannes Hofscheier, Alexander Kasprzyk

We present an algorithm for growing the denominator $r$ polygons containing a fixed number of lattice points and enumerate such polygons containing few lattice points for small $r$. We describe the Ehrhart quasi-polynomial of a rational polygon in terms of boundary and interior point counts. Using this, we bound the coefficients of Ehrhart quasi-polynomials

Yuhan Pei, Ruoyu Wang, Yongqi Yang, Ye Zhu

Originating from the diffusion phenomenon in physics, which describes the random movement and collisions of particles, diffusion generative models simulate a random walk in the data space along the denoising trajectory. This allows information to diffuse across regions, yielding harmonious outcomes. However, the chaotic and disordered nature of information d

Worachit Amnuaypongsa, Jitkomut Songsiri

Forecasting accuracy in highly uncertain environments is challenging due to the stochastic nature of systems. Deterministic forecasting provides only point estimates and cannot capture potential outcomes. Therefore, probabilistic forecasting has gained significant attention due to its ability to quantify uncertainty, where one of the approaches is to express

Felipe J. Llanes-Estrada

Hints of toponium might be incipient in LHC data, as given the vast numbers of t quarks produced, some survive on the exponential-decay tail long enough to fasten ttbar together. I here discuss a few differences between the standard Quantum Chromodynamics (QCD) binding (the ``glue'') and exotic short-range binding (the ``nail''). If the binding energy below

Javier Oliva del Moral, Olatz Sanz Larrarte, Reza Dastbasteh, Josu Etxezarreta Martinez

Spin qubits in quantum dots are a promising technology for quantum computing due to their fast response time and long coherence times. An electromagnetic pulse is applied to the system for a specific duration to perform a desired rotation. To avoid decoherence, the amplitude and gate time must be highly accurate. In this work, we aim to study the impact of l

Harry Ho-Yin Ng, Carlo Musolino, Samuel D. Tootle, Luciano Rezzolla

The abundances resulting from $r$-process nucleosynthesis as predicted by simulations of binary neutron star (BNS) mergers remain an open question as the current state of the art is still restricted to three-species neutrino transport. We present the first BNS merger simulations employing a moment-based general-relativistic neutrino transport with five neutr

Benedikt Fauseweh, Ben Hermann, Falk Howar

We explore ideas for scaling verification methods for quantum circuits using SMT (Satisfiability Modulo Theories) solvers. We propose two primary strategies: (1) decomposing proof obligations via compositional verification and (2) leveraging linear over-approximation techniques for gate effects. We present two examples and demonstrate the application of thes

Alex Diana, Jackie Wong Siaw Tze, Aniketh Pittea

In recent years, a wide range of mortality models has been proposed to address the diverse factors influencing mortality rates, which has highlighted the need to perform model selection. Traditional mortality model selection methods, such as AIC and BIC, often require fitting multiple models independently and ranking them based on these criteria. This proces

Ulysse Pavloff

Blockchains have sparked global interest in recent years, gaining importance as they increasingly influence technology and finance. This thesis investigates the robustness of blockchain protocols, specifically focusing on Ethereum Proof-of-Stake. We define robustness in terms of two critical properties: Safety, which ensures that the blockchain will not have

Kerstin Schneider, Helene Krieg, Dimitri Nowak, Karl-Heinz Küfer

This work uniquely combines an affine linear decision rule known from adjustable robustness with min-max-regret robustness. By doing so, the advantages of both concepts can be obtained with an adjustable solution that is not over-conservative. This combination results in a bilevel optimization problem. For solving this problem, a three-stage algorithm which

Zach Hunter, Aleksa Milojević, Benny Sudakov, István Tomon

More than 40 years ago, Galvin, Rival and Sands showed that every $K_{s, s}$-free graph containing an $n$-vertex path must contain an induced path of length $f(n)$, where $f(n)\to \infty$ as $n\to \infty$. Recently, it was shown by Duron, Esperet and Raymond that one can take $f(n)=(\log \log n)^{1/5-o(1)}$. In this note, we give a short self-contained proof

Peng-Zhang He

This article discusses the relationship between the boundary spread complexity rate and the radial momentum in the bulk within the framework of AdS/CFT. We demonstrate that the radial momentum of a freely falling particle, as measured by a stationary observer in the bulk, is equal to the spread complexity rate of the boundary conformal field theory. For a ma

Amir Subba, Ritesh K. Singh, Rohini M. Godbole

We study $H\to ZZ^\star$ production process in final four lepton states at $13$ TeV LHC in SMEFT framework. The anomalous $HZZ$ couplings are parameterized with dimension-6 $SU(2)_L\times U(1)_Y$ gauge invariant operators. We compute the eight polarizations of each $Z$ boson and $64$ spin-correlations as asymmetries in angular functions of final decayed lept

M. A. Arroyo-Ureña, Alejandro Ibarra, Pablo Roig, T. Valencia-Pérez

We study the decay of a heavy CP-even neutral Higgs into an on-shell Standard Model-like Higgs boson and two photons, $H\to h\gamma\gamma$, in the two-Higgs doublet model. We argue that the decay channel $H\to h\gamma\gamma$, followed by the decay of the Standard Model Higgs $h\rightarrow b\bar b$, could be observed at the 5$\sigma$ level at the High-Luminos

Shiwei Wu, Mingxiang Wang, Chuhan Shi, Zhenhui Peng

Online mental health communities (OMHCs) offer rich posts and comments for viewers, who do not directly participate in the communications, to seek social support from others' experience. However, viewers could face challenges in finding helpful posts and comments and digesting the content to get needed support, as revealed in our formative study (N=10). In t

J. Butterworth, S. Martín, V. M. Rivilla, S. Viti

Deuterium was primarily created during the Big Bang Nucleosynthesis (BBN). This fact, alongside its fractionation reactions resulting in enhanced abundances of deuterated molecules, means that these abundances can be used to better understand many processes within the interstellar medium (ISM), as well as its history. Previously, observations of deuterated m

Prithviraj Banerjee, Sindi Shkodrani, Pierre Moulon, Shreyas Hampali

We introduce HOT3D, a publicly available dataset for egocentric hand and object tracking in 3D. The dataset offers over 833 minutes (3.7M+ images) of recordings that feature 19 subjects interacting with 33 diverse rigid objects. In addition to simple pick-up, observe, and put-down actions, the subjects perform actions typical for a kitchen, office, and livin

Esther Cabezas-Rivas, Salvador Moll, Vicent Pallardó-Julià

We study a generalization of the manifold-valued Rudin-Osher-Fatemi (ROF) model, which involves an initial datum $f$ mapping from a curved compact surface with smooth boundary to a complete, connected and smooth $n$-dimensional Riemannian manifold. We prove the existence and uniqueness of minimizers under curvature restrictions on the target and topological

Bin Lin, Yunyang Ge, Xinhua Cheng, Zongjian Li

We introduce Open-Sora Plan, an open-source project that aims to contribute a large generation model for generating desired high-resolution videos with long durations based on various user inputs. Our project comprises multiple components for the entire video generation process, including a Wavelet-Flow Variational Autoencoder, a Joint Image-Video Skiparse D

Cecilia Chen, John Urschel

Krylov subspace methods are a powerful tool for efficiently solving high-dimensional linear algebra problems. In this work, we study the approximation quality that a Krylov subspace provides for estimating the numerical range of a matrix. In contrast to prior results, which often depend on the gaps between eigenvalues, our estimates depend only on the dimens

Takashi Goda, David Krieg

We present a simple universal algorithm for high-dimensional integration which has the optimal error rate (independent of the dimension) in all weighted Korobov classes both in the randomized and the deterministic setting. Our theoretical findings are complemented by numerical tests.

Florian Besau, Anna Gusakova, Christoph Thäle

We investigate the asymptotic properties of random polytopes arising as convex hulls of $n$ independent random points sampled from a family of block-beta distributions. Notably, this family includes the uniform distribution on a product of Euclidean balls of varying dimensions as a key example. As $n\to\infty$, we establish explicit growth rates for the expe

Tjark Behrens, René Zurbrügg, Marc Pollefeys, Zuria Bauer

Recent approaches have successfully focused on the segmentation of static reconstructions, thereby equipping downstream applications with semantic 3D understanding. However, the world in which we live is dynamic, characterized by numerous interactions between the environment and humans or robotic agents. Static semantic maps are unable to capture this inform

Caoliwen Wang, Bailin Deng, Juyong Zhang

Shadow art is a captivating form of sculptural expression where the projection of a sculpture in a specific direction reveals a desired shape with high precision. In this work, we introduce Neural Shadow Art, which leverages implicit occupancy function representation to significantly expand the possibilities of shadow art. This representation enables the des

Dmitri Kuzmin, Sanghyun Lee, Yi-Yung Yang

In this paper, we develop monolithic limiting techniques for enforcing nonlinear stability constraints in enriched Galerkin (EG) discretizations of nonlinear scalar hyperbolic equations. To achieve local mass conservation and gain control over the cell averages, the space of continuous (multi-)linear finite element approximations is enriched with piecewise-c

Risako Kikuchi, Ai Yamakage

We theoretically investigate the quantum transport properties of three-dimensional spin-1 chiral fermion systems with a curved trivial band. In the multiband system with two distinct characters--a linear Dirac band and a quadratic trivial band--the hybridization induced by impurity effects leads to pronounced energy and temperature dependences in the electri

Alessio Spagnoletti, Alexandre Boucaud, Marc Huertas-Company, Wassim Kabalan

Deconvolution of astronomical images is a key aspect of recovering the intrinsic properties of celestial objects, especially when considering ground-based observations. This paper explores the use of diffusion models (DMs) and the Diffusion Posterior Sampling (DPS) algorithm to solve this inverse problem task. We apply score-based DMs trained on high-resolut

Yuhong Zhou, Fubao Yang, Jinrong Liu, Gaole Dai

Manipulating particles, such as cells and tissues, in a flowing liquid environment is crucial for life science research. Traditional contactless tweezers, although widely used for single-cell manipulation, face several challenges. These include potential damage to the target, restriction to static environments, complex excitation setups, and interference out

Rolci Cipolatti, Yuri M. Lira, Giovanni L. G. Saisse

In this paper we present mathematical analysis of one-dimensional effective models proposed in [\cite{MunozDelgado}] concerning Bose-Einstein condensates in the presence of harmonic confinement. Among the demonstrated properties, we can mention: existence, uniqueness, orbital stability, symmetry and gaussian asymptotic decay of ground-state solutions in the

Xue Song, Jiequan Cui, Hanwang Zhang, Jiaxin Shi

In this paper, we propose the LoRA of Change (LoC) framework for image editing with visual instructions, i.e., before-after image pairs. Compared to the ambiguities, insufficient specificity, and diverse interpretations of natural language, visual instructions can accurately reflect users' intent. Building on the success of LoRA in text-based image editing a

Hee-Cheol Kim, Cumrun Vafa, Kai Xu

We present a bottom-up argument showing that the number of massless fields in six-dimensional quantum gravitational theories with eight supercharges is uniformly bounded. Specifically, we show that the number of tensor multiplets is bounded by $T\leq 193$, and the rank of the gauge group is restricted to $r(V)\leq 480$. Given that F-theory compactifications

Haiyang Guo, Fei Zhu, Fanhu Zeng, Bing Liu

Continual learning aims to equip models with the ability to retain previously learned knowledge like a human. Recent work incorporating Parameter-Efficient Fine-Tuning has revitalized the field by introducing lightweight extension modules. However, existing methods usually overlook the issue of information leakage caused by the fact that the experiment data

Wen Yuan, Winfried Sickel, Dachun Yang

In this article, the authors determine the optimal regularity of characteristic functions in Besov-type and Triebel--Lizorkin-type spaces under restrictions on the measure of the $\delta$-neighborhoods of the boundary. In particular, the necessary and sufficient conditions for the membership in these spaces of characteristic functions of the snowflake domain

Adrián Pérez-Salinas, Mahtab Yaghubi Rad, Alice Barthe, Vedran Dunjko

The conventional paradigm of quantum computing is discrete: it utilizes discrete sets of gates to realize bitstring-to-bitstring mappings, some of them arguably intractable for classical computers. In parameterized quantum approaches, the input becomes continuous and the output represents real-valued functions. While the universality of discrete quantum comp

Giada Cerato, Katinka Bellomo, Roberta D Agostino, Jost von Hardenberg

Projections from global climate models reveal a significant inter-model spread in future rainfall changes in the tropical Atlantic by the end of the 21st century, including alterations to the Intertropical Convergence Zone (ITCZ) and monsoonal regions. While existing studies have identified various sources of uncertainty, our research uncovers a prominent ro

Divyansh Wadhwa, Gurharinder Singh, Krishna Bharadwaj Balasubramanian

This study examines the behavior and tunability of plasmonic lenses created from arrays of nanoslits, applicable in sub-wavelength optical microscopy and other high-resolution imaging systems. We performed simulations on COMSOL Multiphysics to assess power flow and focal shifts in plasmonic lenses with differing slit designs, refractive indices, and angular

Corentin Dumery, Noa Etté, Aoxiang Fan, Ren Li

Visual object counting is a fundamental computer vision task underpinning numerous real-world applications, from cell counting in biomedicine to traffic and wildlife monitoring. However, existing methods struggle to handle the challenge of stacked 3D objects in which most objects are hidden by those above them. To address this important yet underexplored pro

Thomas Auer, Frank Woittennek

An algorithm for planning near time-optimal trajectories for systems with an oscillatory internal dynamics has been developed in previous work. It is based on assembling a complete trajectory from motion primitives called jerk segments, which are the time-optimal solution to an optimization problem. To achieve the shortest overall transition time, it is adva

Lina Tinnerberg, Dumitra Iancu, Ove Edfors, Liang Liu

The next generation wireless systems will face stringent new requirements, including ultra-low latency, high data rates and enhanced reliability. Large Intelligent Surfaces, is one proposed solution that has the potential to solve these high demands. The real-life deployment of such systems involves different design considerations with non-trivial trade-offs

Akhiad Bercovich, Tomer Ronen, Talor Abramovich, Nir Ailon

Large language models (LLMs) offer remarkable capabilities, yet their high inference costs restrict wider adoption. While increasing parameter counts improves accuracy, it also broadens the gap between state-of-the-art capabilities and practical deployability. We present Puzzle, a hardware-aware framework that accelerates the inference of LLMs while preservi

Amine Asselah, Bruno Schapira

We study intersection properties of two or more independent tree-like random graphs. Our setting encompasses critical, possibly long range, Bernoulli percolation clusters, incipient infinite clusters, as well as critical branching random walk ranges. We obtain sharp excess deviation bounds on the number of intersection points of two or more clusters, under m

Thomas Auer, Frank Woittennek

An efficient approach to compute near time-optimal trajectories for linear kinematic systems with oscillatory internal dynamics is presented. Thereby, kinematic constraints with respect to velocity, acceleration and jerk are taken into account. The trajectories are composed of several motion primitives, the most crucial of which is termed jerk segment. Withi

Jicheng Yuan, Anh Le-Tuan, Ali Ganbarov, Manfred Hauswirth

Recently, deep learning has experienced rapid expansion, contributing significantly to the progress of supervised learning methodologies. However, acquiring labeled data in real-world settings can be costly, labor-intensive, and sometimes scarce. This challenge inhibits the extensive use of neural networks for practical tasks due to the impractical nature of