December 2024 arXiv papers — page 51

Ronald L. Kam, Luca Binci, Aaron D. Kaplan, Kristin A. Persson

The development of Mn-rich cathodes for Li-ion batteries promises to alleviate supply chain bottlenecks in battery manufacturing. Challenges in Mn-rich cathodes arise from Jahn-Teller (JT) distortions of Mn$^{3+}$, Mn migration, and phase transformations to spinel-like order, which can affect the electrochemical performance. These phenomena motivate an ab in

Renjie Zhang, Bocheng Yu, Hengxin Tan, Yiwei Cheng

Orbital selectivity is pivotal in dictating the phase diagrams of multiorbital systems, with prominent examples including the orbital-selective Mott phase and superconductivity, etc. The intercalation of anisotropic layers represents an effective method for enhancing orbital selectivity and, thereby shaping the low-energy physics of multiorbital systems. Des

Michael Weiss

This paper takes an exploratory approach to examine the use of ChatGPT for pattern mining. It proposes an eight-step collaborative process that combines human insight with AI capabilities to extract patterns from known uses. The paper offers a practical demonstration of this process by creating a pattern language for integrating Large Language Models (LLMs)

Dibyendu Adak, Felipe Lepe, Gonzalo Rivera

In this paper we analyze a nonconforming virtual element method to approximate the eigenfunctions and eigenvalues of the two dimensional Oseen eigenvalue problem. The spaces under consideration lead to a divergence-free method which is capable to capture properly the divergence at discrete level and the eigenvalues and eigenfunctions. Under the compact theor

Paulo Akira F. Enabe

This work explores the equivalence of two sequential properties, $D$ and $D'$, for dual Banach spaces under the weak* topology. Property $D$ ensures that any totally scalarly measurable function is also scalarly measurable, while property $D'$ states that every weakly* sequentially closed subspace of $X^*$ is weakly* closed. These properties, which are centr

Kasra Hejazi, Jay Soni, Modjtaba Shokrian Zini, Juan Miguel Arrazola

Quantum phase estimation requires simulating the evolution of the Hamiltonian, for which product formulas are attractive due to their smaller qubit cost and ease of implementation. However, the estimation of the error incurred by product formulas is usually pessimistic and task-agnostic, which poses problems for assessing their performance in practice for pr

Dawei Chen, Quentin Gendron, Miguel Prado, Guillaume Tahar

Given a partition $\mu$ of $-2$, the stratum $\mathcal{H}(\mu)$ parametrizes meromorphic differential one-forms on the Riemann sphere $\mathbb{CP}^{1}$ with~$n$ zeros and $p$ poles of orders prescribed by $\mu$. The isoresidual fibration is defined by assigning to each differential in $\mathcal{H}(\mu)$ its configuration of residues at the poles. In the case

Shaojie Zhang, Ozgur B. Akan

Molecular communication (MC) is an emerging paradigm that takes inspiration from biological processes, enabling communication at the nanoscale and facilitating the development of the Internet of Bio-Nano Things (IoBNT). Traditional models of MC often rely on idealized assumptions that overlook practical challenges related to noise and signal behavior. This p

Hanqing Jiang, Xiaojun Xiang, Han Sun, Hongjie Li

3D Gaussian Splatting (3DGS) has recently attracted wide attentions in various areas such as 3D navigation, Virtual Reality (VR) and 3D simulation, due to its photorealistic and efficient rendering performance. High-quality reconstrution of 3DGS relies on sufficient splats and a reasonable distribution of these splats to fit real geometric surface and textur

Ioannis Diamantis, Sofia Lambropoulou, Sonia Mahmoudi

Doubly periodic tangles (DP tangles) are configurations of curves embedded in the thickened plane, invariant under translations in two transversal directions. In this paper we extend the classical theory of DP tangles by introducing the theory of {\it doubly periodic pseudo tangles} (pseudo DP tangles), which incorporate undetermined crossings called {\it pr

Rahul Shah, Purba Mukherjee, Supratik Pal

We do a careful investigation of the prospects of dark energy (DE) interacting with cold dark matter in alleviating the $S_8$ clustering tension. To this end, we consider various well-known parametrizations of the DE equation of state (EoS) and consider perturbations in both the dark sectors, along with an interaction term. Moreover, we perform a separate st

Mushfiqur Rahman Abir, Md. Tanzib Hosain, Md. Abdullah-Al-Jubair, M. F. Mridha

Human behavior and interactions are profoundly influenced by visual stimuli present in their surroundings. This influence extends to various aspects of life, notably food consumption and selection. In our study, we employed various models to extract different attributes from the environmental images. Specifically, we identify five key attributes and employ a

Kin Ian Lo, Mehrnoosh Sadrzadeh, Shane Mansfield

Contextuality is a distinguishing feature of quantum mechanics and there is growing evidence that it is a necessary condition for quantum advantage. In order to make use of it, researchers have been asking whether similar phenomena arise in other domains. The answer has been yes, e.g. in behavioural sciences. However, one has to move to frameworks that take

Sean Kalaycioglu, Daniel Ding

This paper presents a novel approach for vibration control of satellite-based flexible beam-type antennas using Nonlinear Model Predictive Control (NMPC) and Deep Learning techniques. The developed control system leverages piezoelectric (PZT) actuators and sensors to manage the coupled attitude and structural dynamics of the satellite, improving precision an

Evan B. Golden, Vasili K. Semenov, Sergey K. Tolpygo

Superconductor electronics (SCE) appear promising for low energy applications. However, the achieved and projected circuit densities are insufficient for direct competition with CMOS technology. Original algorithms and nontraditional architectures are required for realizing SCE energy advantages for computing. Neuromorphic computing (NMC) is a commonly discu

Ahad M. Rauf, Sean Follmer

Electroadhesive clutches are electrically controllable switchable adhesives commonly used in soft robots and haptic user interfaces. They can form strong bonds to a wide variety of surfaces at low power consumption. However, electroadhesive clutches in the literature engage to and release from substrates several orders of magnitude slower than a traditional

Lynn Chua, Badih Ghazi, Charlie Harrison, Ethan Leeman

We introduce the Balls-and-Bins sampling for differentially private (DP) optimization methods such as DP-SGD. While it has been common practice to use some form of shuffling in DP-SGD implementations, privacy accounting algorithms have typically assumed that Poisson subsampling is used instead. Recent work by Chua et al. (ICML 2024), however, pointed out tha

Leonid Nizhnik

The Hamiltonian form of the (2+1) nonlinear integrable Schr\"odinger equation and the system of two (2+1) nonlinear analogue of the mKdV equation is proved. A well--posed Cauchy problem is formulated and the solvability of such a problem for the (2+1) nonlinear analogue of the mKdV equation is proved.

Paolo Antonelli, Pierangelo Marcati, Hao Zheng

This paper analyzes weak solutions of the quantum hydrodynamics (QHD) system with a collisional term posed on the one-dimensional torus. The main goal of our analysis is to rigorously prove the time-relaxation limit towards solutions to the quantum drift-diffusion (QDD) equation. \newline The existence of global in time, finite energy weak solutions can be p

J. Kahlbow

Neutron-rich nuclei exhibit a variety of intriguing features associated with nuclear structure evolution, deformation, and other phenomena. Particularly interesting is the region in the chart of nuclides around Z = 12 and N = 20, commonly referred to as "Island of Inversion", which is profoundly influenced by these features. Recent cutting-edge experiments p

Aravindh Krishnamoorthy, Hossein Safi, Othman Younus, Hossein Kazemi

Optical wireless communication (OWC) is a promising technology anticipated to play a key role in the next-generation network of networks, especially as a complementary technology to traditional radio frequency communications, for enhancing networking capabilities beyond conventional terrestrial networks. OWC is already a mature technology with diverse usage

Michael Tang, Miroslav Krstic, Jorge Poveda

We study singularly perturbed systems that exhibit input-to-state stability (ISS) with fixed-time properties in the presence of bounded disturbances. In these systems, solutions converge to the origin within a time frame independent of initial conditions when undisturbed, and to a vicinity of the origin when subjected to bounded disturbances. First, we exten

Tabata Aira Ferreira, Gabriela González

Glitches are non-Gaussian noise transients originating from environmental and instrumental sources that contaminate data from gravitational wave detectors. Some glitches can even mimic gravitational wave signals from compact object mergers, which are the primary targets of terrestrial observatories. In this study, we present a method to analyze noise transie

Abhishake, Nicole Mücke, Tapio Helin

We study statistical inverse learning in the context of nonlinear inverse problems under random design. Specifically, we address a class of nonlinear problems by employing gradient descent (GD) and stochastic gradient descent (SGD) with mini-batching, both using constant step sizes. Our analysis derives convergence rates for both algorithms under classical a

Olivier Idir, Karoliina Lehtinen

The parity index problem of tree automata asks, given a regular tree language L, what is the least number of priorities of a nondeterministic parity tree automaton that recognises L. This is a long-standing open problem, also known as the Mostowski or Rabin-Mostowski index problem, of which only a few sub-cases and variations are known to be decidable. In a

Yubo Qi, Karin M. Rabe

We carry out first-principles calculations to demonstrate that the complex energy landscape and competing phases of HfO$_2$ can be understood from the four unstable flat phonon bands of an unconventional high-symmetry structure of HfO$_2$ with the space group $Cmma$. We consider structures generated from the $Cmma$ reference structure by all possible combina

Daniel Urda, Branly Martínez, Nuño Basurto, Meelis Kull

Websites, as essential digital assets, are highly vulnerable to cyberattacks because of their high traffic volume and the significant impact of breaches. This study aims to enhance the identification of web traffic attacks by leveraging machine learning techniques. A methodology was proposed to extract relevant features from HTTP traces using the CSIC2010 v2

Hemanshu Kaul, Jeffrey A. Mudrock, Gunjan Sharma

The chromatic polynomial of a graph is an important notion in algebraic combinatorics that was introduced by Birkhoff in 1912; denoted $P(G,k)$, it equals the number of proper $k$-colorings of graph $G$. Enumerative analogues of the chromatic polynomial of a graph have been introduced for two well-studied generalizations of ordinary coloring, namely, list co

Imants Svalbe, Rob Tijdeman

Projection ghosts are discrete arrays of signed values positioned so that their discrete projections vanish for some chosen set of n projection angles. Minimal ghosts are designed to be compact, with no internal pixels having value zero. Here we control the shape, number of boundary pixels and area that each minimal ghost encloses. Binary minimal ghosts and

Hossein Rafieizadeh, Hadi Zare, Mohsen Ghassemi Parsa, Hadi Davardoust

Anomaly detection using a network-based approach is one of the most efficient ways to identify abnormal events such as fraud, security breaches, and system faults in a variety of applied domains. While most of the earlier works address the complex nature of graph-structured data and predefined anomalies, the impact of data attributes and emerging anomalies a

Priscilla Canizares, Davide Murari, Carola-Bibiane Schönlieb, Ferdia Sherry

Hamilton's equations are fundamental for modeling complex physical systems, where preserving key properties such as energy and momentum is crucial for reliable long-term simulations. Geometric integrators are widely used for this purpose, but neural network-based methods that incorporate these principles remain underexplored. This work introduces SympFlow, a

Ergon Cugler de Moraes Silva

[WhitePaper] The TelegramScrap tool provides a robust and versatile solution for extracting and analyzing data from Telegram channels and groups, addressing the increasing demand for efficient methods to study digital ecosystems. This white paper outlines the tool's development, capabilities, and applications in academic and scientific research, including st

Sabine Chu, Giada Franz

We study unknottedness for free boundary minimal surfaces in a three-dimensional Riemannian manifold with nonnegative Ricci curvature and strictly convex boundary, and for self-shrinkers in the three-dimensional Euclidean space. For doing so, we introduce the concepts of boundary graph for free boundary minimal surfaces and of graph at infinity for self-shri

Sena Ghobadi

Modeling the orbital dynamics of objects in galactic disks is crucial to understanding the stability and evolution of disk galaxies. While studies of galactic orbits are largely dominated by $N$-body simulations, perturbative analytical models offer a computationally inexpensive and conceptually insightful way of analyzing galactic dynamics. We utilize pertu

Maciej Ogrodnik, Adam Widomski, Dagmar Bruß, Giovanni Chesi

While quantum key distribution (QKD) based on two-dimensional (qubit) encoding is a mature, field-tested technology, its performance is lacking for many cryptographic applications. High-dimensional encoding for QKD enables increased achievable key rates and robustness as compared to the standard qubit-based systems. However, experimental implementations of s

Isao Sauzedde

We construct and study the renormalised Amperean area of a Brownian motion. First studied by W.Werner, the Amperean area is related to L\'evy area and stochastic integrals in a way akin to the relation between self-intersection measure and occupation measure. As we explain, it plays a central role in the Symanzik's polymer representation of the continuous Ab

Changchang Sun, Ren Wang, Yihua Zhang, Jinghan Jia

Machine unlearning (MU), which seeks to erase the influence of specific unwanted data from already-trained models, is becoming increasingly vital in model editing, particularly to comply with evolving data regulations like the ``right to be forgotten''. Conventional approaches are predominantly model-based, typically requiring retraining or fine-tuning the m

Jonggyu Jang, Hyeonsu Lyu, David J. Love, Hyun Jong Yang

As 6G and beyond networks grow increasingly complex and interconnected, federated learning (FL) emerges as an indispensable paradigm for securely and efficiently leveraging decentralized edge data for AI. By virtue of the superposition property of communication signals, over-the-air FL (OtA-FL) achieves constant communication overhead irrespective of the num

Zhipeng Huang, Wangbo Yu, Xinhua Cheng, ChengShu Zhao

Indoor scene texture synthesis has garnered significant interest due to its important potential applications in virtual reality, digital media and creative arts. Existing diffusion-model-based researches either rely on per-view inpainting techniques, which are plagued by severe cross-view inconsistencies and conspicuous seams, or adopt optimization-based app

Victor Akinwande, Mohammad Sadegh Norouzzadeh, Devin Willmott, Anna Bair

Self-supervised vision-language models trained with contrastive objectives form the basis of current state-of-the-art methods in AI vision tasks. The success of these models is a direct consequence of the huge web-scale datasets used to train them, but they require correspondingly large vision components to properly learn powerful and general representations

Sanghyun Son, Matheus Gadelha, Yang Zhou, Matthew Fisher

Recent probabilistic methods for 3D triangular meshes capture diverse shapes by differentiable mesh connectivity, but face high computational costs with increased shape details. We introduce a new differentiable mesh processing method that addresses this challenge and efficiently handles meshes with intricate structures. Our method reduces time complexity fr

Zain Khaliq, Ahmed Refaey Hussein

Researchers all over the world are employing a variety of analysis approaches in attempt to provide a safer and faster solution for sharing resources via a Multi-access Edge Computing system. Multi-access Edge Computing (MEC) is a job-sharing method within the edge server network whose main aim is to maximize the pace of the computing process, resulting in a

Evren Gokcen, Anna I. Jasper, Adam Kohn, Christian K. Machens

Gaussian processes are now commonly used in dimensionality reduction approaches tailored to neuroscience, especially to describe changes in high-dimensional neural activity over time. As recording capabilities expand to include neuronal populations across multiple brain areas, cortical layers, and cell types, interest in extending Gaussian process factor mod

Ivan Zakazov, Mikolaj Boronski, Lorenzo Drudi, Robert West

The ongoing revolution in language modeling has led to various novel applications, some of which rely on the emerging social abilities of large language models (LLMs). Already, many turn to the new cyber friends for advice during the pivotal moments of their lives and trust them with the deepest secrets, implying that accurate shaping of the LLM's personalit

Lars L. Schaaf, Ilyes Batatia, Christoph Brunken, Thomas D. Barrett

Simulating atomic-scale processes, such as protein dynamics and catalytic reactions, is crucial for advancements in biology, chemistry, and materials science. Machine learning force fields (MLFFs) have emerged as powerful tools that achieve near quantum mechanical accuracy, with promising generalization capabilities. However, their practical use is often lim

Tan-Hanh Pham, Hoang-Nam Le, Phu-Vinh Nguyen, Chris Ngo

Visual Language Models have demonstrated remarkable capabilities across tasks, including visual question answering and image captioning. However, most models rely on text-based instructions, limiting their effectiveness in human-machine interactions. Moreover, the quality of language models depends on reasoning and prompting techniques, such as COT, which re

Yi-Cheng Wang, Jiong Li, Li-Wei Duan, Qing-Hu Chen

A natural extension of the non-Hermitian qubit is to place it in a single-mode cavity. This setup corresponds to the quantum Rabi model (QRM) with a purely imaginary bias on the qubit, exhibiting parity-time ($\mathcal{P}\mathcal{T}$) symmetry. In this work, we first solve the $\mathcal{P} \mathcal{T}$-symmetric QRM using the Bogoliubov operator approach. We

Lily Hu

Discussions of statistical criteria for fairness commonly convey the normative significance of calibration within groups by invoking what risk scores "mean." On the Same Meaning picture, group-calibrated scores "mean the same thing" (on average) across individuals from different groups and accordingly, guard against disparate treatment of individuals based o

Archishman Gupta, Ankit Arora

In this work, we present a comprehensive investigation of graphene's thermal conductivity using first-principles density functional perturbation theory calculations, with a focus on the phonon and lattice vibrational properties underlying its superior heat transport capabilities. The study highlights the role of phonon frequencies, lifetimes and mode-resolve

Elham Musaaed, Nabil Hewahi, Abdulla Alasaadi

In recent years, ML algorithms have been shown to be useful for predicting diseases based on health data and posed a potential application area for these algorithms such as modeling of diseases. The majority of these applications employ supervised rather than unsupervised ML algorithms. In addition, each year, the amount of data in medical science grows rapi

Ademar Crotti Junior, Christophe Debruyne

Knowledge graph construction (KGC) from (semi-)structured data is challenging, and facilitating user involvement is an issue frequently brought up within this community. We cannot deny the progress we have made with respect to (declarative) knowledge graph construction languages and tools to help build such mappings. However, it is surprising that no two stu

Hippolyte Labarrière, Cesare Molinari, Lorenzo Rosasco, Cristian Vega

Gradient-based methods successfully train highly overparameterized models in practice, even though the associated optimization problems are markedly nonconvex. Understanding the mechanisms that make such methods effective has become a central problem in modern optimization. To investigate this question in a tractable setting, we study Deep Diagonal Linear Ne

Iqra Aslam, Igor Anpilogov, Andreas Rausch

Autonomous driving technology is progressing rapidly, largely due to complex End To End systems based on deep neural networks. While these systems are effective, their complexity can make it difficult to understand their behavior, raising safety concerns. This paper presents a new solution a Behavior Selector that uses multiple smaller artificial neural netw

Shuochen Wang, Nishant Yadav, Auroop R. Ganguly

One of the major sources of uncertainty in the current generation of Global Climate Models (GCMs) is the representation of sub-grid scale physical processes. Over the years, a series of deep-learning-based parameterization schemes have been developed and tested on both idealized and real-geography GCMs. However, datasets on which previous deep-learning model

Iqra Aslam, Abhishek Buragohain, Daniel Bamal, Adina Aniculaesei

Environment perception is a fundamental part of the dynamic driving task executed by Autonomous Driving Systems (ADS). Artificial Intelligence (AI)-based approaches have prevailed over classical techniques for realizing the environment perception. Current safety-relevant standards for automotive systems, International Organization for Standardization (ISO) 2

Shuai Sun

The subspace identification method (SIM) has been extensively employed in the identification of discrete-time multiple-input multiple-output (MIMO) linear time-invariant (LTI) systems. This paper focuses on the analysis of perturbation errors for the system matrices in state-space models and the corresponding system poles, under two unified SIMs, based on a

Klaus Ziegler, Roman Ya. Kezerashvili

We study the quasiparticles in chiral double layers with electron pairing within the framework of the Bogoliubov de Gennes equation. In the presence of an edge it is demonstrated that the quasiparticle modes can be distinguished as edge modes and bulk modes, which appear at different energies. The bulk-edge correspondence is obtained by an analytic continuat

Takumi Maegawa

Using the six-functor formalism for sheaves of spectra on topological spaces, we provide a novel construction of the Bauer--Furuta invariant, as well as its family version. This approach avoids the conventional arguments based on approximations by finite-dimensional subspaces, and we instead employ the Borel--Moore homology spectra relative to Fredholm maps

Georgios Zervakis, Fabio Frustaci, Ourania Spantidi, Iraklis Anagnostopoulos

In this work, we present a control variate approximation technique that enables the exploitation of highly approximate multipliers in Deep Neural Network (DNN) accelerators. Our approach does not require retraining and significantly decreases the induced error due to approximate multiplications, improving the overall inference accuracy. As a result, our appr

Petr Zemánek

The spectrum of an arbitrary self-adjoint extension of the minimal linear relation associated with the discrete symplectic system in the limit point case is completely characterized by using the limiting Weyl--Titchmarsh $M_+(\lambda)$-function. Furthermore, a dependence of the spectrum on a boundary condition is investigated and, consequently, several resul

Shahid Ansari, Mahendra Kumar Gohil, Bishakh Bhattacharya

Current agriculture and farming industries are able to reap advancements in robotics and automation technology to harvest fruits and vegetables using robots with adaptive grasping forces based on the compliance or softness of the fruit or vegetable. A successful operation depends on using a gripper that can adapt to the mechanical properties of the crops. Th

Yongzhe Huang, Kaiming Huang, Matthew Ennis, Vikram Narayanan

Device driver isolation is a promising approach for protecting the kernel from faulty or malicious drivers, but the actual security provided by such frameworks is often not well understood. Recent research has identified Compartment Interface Vulnerabilities (CIVs) in userspace compartmentalized applications, yet their impact on driver isolation frameworks r

Ariel Rapaport

We extend Hochman's work on exponentially separated self-similar measures on $\mathbb{R}$ to the real analytic setting. More precisely, let $\Phi=\left\{ \varphi_{i}\right\} _{i\in\Lambda}$ be an iterated function system on $I:=[0,1]$ consisting of real analytic contractions, let $p=(p_{i})_{i\in\Lambda}$ be a positive probability vector, and let $\mu$ be th

Petr Zemánek

Eigenfunctions expansion for discrete symplectic systems on a finite discrete interval is established in the case of a general linear dependence on the spectral parameter as a significant generalization of the Expansion theorem given by Bohner, Do\v{s}l\'{y} and Kratz in [Trans. Amer. Math. Soc. 361 (2009), 3109--3123]. Subsequently, an integral representati

Zahra Babaiee, Peyman M. Kiasari, Daniela Rus, Radu Grosu

This paper challenges the prevailing view that convolutional neural network (CNN) filters become increasingly specialized in deeper layers. Motivated by recent observations of clusterable repeating patterns in depthwise separable CNNs (DS-CNNs) trained on ImageNet, we extend this investigation across various domains and datasets. Our analysis of DS-CNNs reve

Wei-Chih Li, Chih-Yao Shih, Tzu-Liang Chang, Tzay-Ming Hong

We combine experiments and theoretical derivations to study the evolution of a stretched soap bubble and compare it with an open film to highlight the effect of volume conservation. We identify a critical length for both surfaces, beyond which a bottleneck develops in the middle and begins to shrink irreversibly, ultimately pinching off into multiple compart

Sanghyun Son, Laura Zheng, Brian Clipp, Connor Greenwell

We present a parallelized differentiable traffic simulator based on the Intelligent Driver Model (IDM), a car-following framework that incorporates driver behavior as key variables. Our vehicle simulator efficiently models vehicle motion, generating trajectories that can be supervised to fit real-world data. By leveraging its differentiable nature, IDM param

Michael Gutperle, Charlie Hultgreen-Mena

In this paper we find solutions of minimal $d=3,N=2$ gauged supergravity corresponding to Janus and RG-flow interfaces. We use holography to calculate symmetric and interface entanglement entropy as well as reflection coefficients and confirm that a recently proposed [1] inequality involving these quantities is satisfied for the solutions found here.

Henry Li, Marcus Pereira

Existing approaches to diffusion-based inverse problem solvers frame the signal recovery task as a probabilistic sampling episode, where the solution is drawn from the desired posterior distribution. This framework suffers from several critical drawbacks, including the intractability of the conditional likelihood function, strict dependence on the score netw

Chao Zhang, Qingchao Li, Chao Xu, Lie-Liang Yang

Given their extensive geographic coverage, low Earth orbit (LEO) satellites are envisioned to find their way into next-generation (6G) wireless communications. This paper explores space-air-ground integrated networks (SAGINs) leveraging LEOs to support terrestrial and non-terrestrial users. We first propose a practical satellite-ground channel model that inc

Tai-Quan Peng, Kaiqi Yang, Sanguk Lee, Hang Li

As large language models (LLMs) become increasingly embedded in civic, educational, and political information environments, concerns about their potential political bias have grown. Prior research often evaluates such bias through simulated personas or predefined ideological typologies, which may introduce artificial framing effects or overlook how models be

Maheswar Bora, Tushar Anand, Saurabh Atreya, Aritra Mukherjee

In this work we propose a Visual Mamba (ViM) based architecture, to dissolve the existing trade-off for real-time and accurate model with low computation overhead for disparity map generation (DMG). Moreover, we proposed a performance measure that can jointly evaluate the inference speed, computation overhead and the accurateness of a DMG model. The code imp

Ruochun Zhao, Yue Hao, Xuechen Li

In the post-pandemic era, the hotel industry plays a crucial role in economic recovery, with consumer sentiment increasingly influencing market trends. This study utilizes advanced natural language processing (NLP) and the BERT model to analyze user reviews, extracting insights into customer satisfaction and guiding service improvements. By transforming revi

Yoshiaki Maeda, Steven Rosenberg

We prove that a closed regular $(4k+1)$-Sasakian manifold $(M,h_0)$ admits a family of non-isometric metrics $h_\rho, \rho\geq 0,$ such that $\pi_1({\rm Isom}(M, h_\rho))$, the fundamental group of the isometry group, is infinite for $\rho>0.$ For $M= S^{4k+1}$, this result holds for all $\rho>0$, but fails at $\rho=0.$

Yung-Hong Sun, Gefei Shen, Jiangang Chen, Jayer Fernandes

EasyVis2 is a system designed to provide hands-free, real-time 3D visualization for laparoscopic surgery. It incorporates a surgical trocar equipped with an array of micro-cameras, which can be inserted into the body cavity to offer an enhanced field of view and a 3D perspective of the surgical procedure. A specialized deep neural network algorithm, YOLOv8-P

Irea Mosquera-Lois, Johan Klarbring, Aron Walsh

Point defects dictate the properties of many functional materials. The standard approach to modelling the thermodynamics of defects relies on a static description, where the change in Gibbs free energy is approximated by the internal energy. This approach has a low computational cost, but ignores contributions from atomic vibrations and structural configurat

Stanley Yao Xiao

In this paper we complete B\"{u}chi's proof that there is no decision algorithm for the solubility in integers of arbitrary systems of diagonal quadratic form equations, by proving the assertion that whenever $x_1^2, \cdots, x_5^2$ are five squares such that the second differences satisfy \[x_{k+2}^2 - 2 x_{k+1}^2 + x_k^2 = 2\] for $k = 1,2,3$, then they mus

Long Zhou, Fereshteh Shakeri, Aymen Sadraoui, Mounir Kaaniche

Transductive few-shot learning has recently triggered wide attention in computer vision. Yet, current methods introduce key hyper-parameters, which control the prediction statistics of the test batches, such as the level of class balance, affecting performances significantly. Such hyper-parameters are empirically grid-searched over validation data, and their

Juan Diego Toscano, Li-Lian Wang, George Em Karniadakis

Inspired by the Kolmogorov-Arnold representation theorem and Kurkova's principle of using approximate representations, we propose the Kurkova-Kolmogorov-Arnold Network (KKAN), a new two-block architecture that combines robust multi-layer perceptron (MLP) based inner functions with flexible linear combinations of basis functions as outer functions. We first p

Fotios Logothetis, Ignas Budvytis, Stephan Liwicki, Roberto Cipolla

The biggest improvements in Photometric Stereo (PS) field has recently come from adoption of differentiable volumetric rendering techniques such as NeRF or Neural SDF achieving impressive reconstruction error of 0.2mm on DiLiGenT-MV benchmark. However, while there are sizeable datasets for environment lit objects such as Digital Twin Catalogue (DTS), there a

Yuanbin Liu, Joe D. Morrow, Christina Ertural, Natascia L. Fragapane

Machine learning has become ubiquitous in materials modelling and now routinely enables large-scale atomistic simulations with quantum-mechanical accuracy. However, developing machine-learned interatomic potentials requires high-quality training data, and the manual generation and curation of such data can be a major bottleneck. Here, we introduce an automat

Daoming Liu, Jonathan Klein, Florian Rist, Wojciech Pałubicki

We propose a unified volumetric combustion simulator that supports general wooden structures capturing the multi-phase combustion of charring materials. Complex geometric structures can conveniently be represented in a voxel grid for the effective evaluation of volumetric effects. In addition, a signed distance field is introduced to efficiently query the su

Kelvin Dsouza, Patrick Del Vecchio, Nicolas Rotaru, Oussama Moutanabbir

This work investigates and compares the impact of strain on heavy hole (HH) spin qubits in SiGe/Ge and light hole (LH) spin qubits in GeSn/Ge heterostructures, focusing on energy states, g-factor, Rabi frequency, spin relaxation, and dephasing times. By exploring the distinct properties of HH and LH spin qubits under strain, we demonstrate how strain serves

Chiara Ribaldone, Jacques Kontak Desmarais

Spherical Harmonic Gaussian type orbitals and Slater functions can be expressed using spherical coordinates or a linear combinations of the appropriate Cartesian functions. General expressions for the transformation coefficients between the two representations are provided. Values for the transformation coefficients are tabulated up to the quantum number $\e

Øystein Slagtern Fjellvåg, Helmer Fjellvåg, Julie Hessevik, Anja Olafsen Sjåstad

A new platinate was recently discovered when Nd2O3 was explored as a platinum capture material in the Ostwald process, formed by a direct reaction between PtO2(g) and Nd2O3. The crystal structure of this new platinate and its composition, Nd10.67Pt4O24 , are here reported for the first time. The compound is synthesized either by a direct reaction using PtO2(

Sergio Rodríguez-Aparicio, Conrado Ferrera, María Eugenia Fuentes-Cañamero, Javier García García

Background: Despite the significant advances made in the field of computational fluid dynamics (CFD) to simulate the left atrium (LA) in atrial fibrillation (AF) conditions, the connection between atrial structure, flow dynamics, and blood stagnation in the left atrial appendage (LAA) remains unclear. Deepening our understanding of this relationship would ha

Anthony P. McFadden, Jinsu Oh, Lin Zhou, Trevyn F. Q. Larson

Quantum coherence in superconducting circuits has increased steadily over the last decades as a result of a growing understanding of the various loss mechanisms. Recently, tantalum (Ta) emerged as a promising material to address microscopic sources of loss found on niobium (Nb) or aluminum (Al) surfaces. However, the effects of film and interface microstruct

Kartoue Mady Demdah, Ibrahim Nonkane

In this paper, we revisit the problem of classifying real algebraic and semialgebraic sets by their topological types, focusing on establishing the effectiveness of bounds rather than deriving new quantitative estimates. Building on Hardt's theorem and leveraging a model-theoretic framework, we prove the existence of effective bounds on the number of distinc

Shakil M. Khan, Yves Lespérance, Maryam Rostamigiv

Reasoning about the causes behind observations is crucial to the formalization of rationality. While extensive research has been conducted on root cause analysis, most studies have predominantly focused on deterministic settings. In this paper, we investigate causation in more realistic nondeterministic domains, where the agent does not have any control on a

Luis Colmenarez, Seyong Kim, Markus Müller

Quantum error correcting (QEC) codes protect quantum information against environmental noise. Computational errors caused by the environment change the quantum state within the qubit subspace, whereas quantum erasures correspond to the loss of qubits at known positions. Correcting either type of error involves different correction mechanisms, which makes stu

Yu. B. Kudasov

A theory for nontrivial topology of band structure in metallic helimagnets is developed. Two theorems on electron dispersion in helimagnets are proved. They reveal a Kramers-like degeneracy in helical magnetic field. The generalized Bloch theorem together with periodic boundary conditions lead to a nontrivial topological band structure. As a result, an unusu

Zhaoqun Li, Xiaotong Fang, Chen Chen, Mengze Li

In recent years, large language models (LLMs) have made significant advancements in developing human-like and engaging dialogue systems. However, in tasks such as consensus-building and persuasion, LLMs often struggle to resolve conflicts arising from incomplete or inconsistent information, revealing their limitations in real-world applications. Given these

Giovanni Pollo, Alessio Burrello, Enrico Macii, Massimo Poncino

Estimating the evolution of the battery's State of Charge (SoC) in response to its usage is critical for implementing effective power management policies and for ultimately improving the system's lifetime. Most existing estimation methods are either physics-based digital twins of the battery or data-driven models such as Neural Networks (NNs). In this work,

Yu-Fan Lin, Bo-Cheng Qiu, Chia-Ming Lee, Chih-Chung Hsu

Accurate detection and segmentation of gastrointestinal bleeding are critical for diagnosing diseases such as peptic ulcers and colorectal cancer. This study proposes a two-stage framework that decouples classification and grounding to address the inherent challenges posed by traditional Multi-Task Learning models, which jointly optimizes classification and

Jason Green, Dmitri Nikshych

We define the Tannakian radical of a braided fusion category $\mathcal{C}$ as the intersection of its maximal Tannakian subcategories. The localization of $\mathcal{C}$ corresponding to the Tannakian radical, termed the mantle of $\mathcal{C}$, admits a canonical central extension that serves as a complete invariant of $\mathcal{C}$. The mantle has a trivial

Marta Colleoni, Felip A. Ramis Vidal, Cecilio García-Quirós, Sarp Akçay

Gravitational waveform (GW) models are a core ingredient for the analysis of compact binary mergers observed by current ground-based interferometers. We focus here on a specific class of such models known as PhenomX, which has gained popularity in recent years thanks to its computational efficiency. We introduce a new description of the ``twisting-up'' mappi

Huanyu Chen, Jernej Barbic

In computer graphics and engineering, nonlinear elastic material properties of 3D volumetric solids are typically adjusted by selecting a material family, such as St. Venant Kirchhoff, Linear Corotational, (Stable) Neo-Hookean, Ogden, etc., and then selecting the values of the specific parameters for that family, such as the Lame parameters, Ogden exponents,

OpenAI, :, Aaron Jaech, Adam Kalai

The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought. These advanced reasoning capabilities provide new avenues for improving the safety and robustness of our models. In particular, our models can reason about our safety policies in context when responding to potentially unsafe prompts, through deliberative

Yaya Sy, Christophe Cerisara, Irina Illina

Current LLM structured pruning methods typically involve two steps: (1) compression with calibration data and (2) costly continued pretraining on billions of tokens to recover lost performance. This second step is necessary as the first significantly impacts model accuracy. Prior research suggests pretrained Transformer weights aren't inherently low-rank, un

Sithik Aliyar, Henrik Bredmose, Johan Roenby, Pietro Danilo Tomaselli

The offshore wind sector relies on floating foundations for deeper waters but faces challenges from harsh conditions, nonlinear dynamics, and low-frequency resonant motions caused by second-order hydrodynamic loads. We analyze these dynamics and extract higher harmonic motions for a semisubmersible floating foundation under extreme wave conditions using expe