December 2024 arXiv papers — page 180

Cristobal Eyzaguirre, Eric Tang, Shyamal Buch, Adrien Gaidon

Robotics, autonomous driving, augmented reality, and many embodied computer vision applications must quickly react to user-defined events unfolding in real time. We address this setting by proposing a novel task for multimodal video understanding-Streaming Detection of Queried Event Start (SDQES). The goal of SDQES is to identify the beginning of a complex e

Lue Fan, Hao Zhang, Qitai Wang, Hongsheng Li

We propose FreeSim, a camera simulation method for autonomous driving. FreeSim emphasizes high-quality rendering from viewpoints beyond the recorded ego trajectories. In such viewpoints, previous methods have unacceptable degradation because the training data of these viewpoints is unavailable. To address such data scarcity, we first propose a generative enh

Wujian Peng, Lingchen Meng, Yitong Chen, Yiweng Xie

Large Multimodal Models (LMMs) have made significant breakthroughs with the advancement of instruction tuning. However, while existing models can understand images and videos at a holistic level, they still struggle with instance-level understanding that requires a more fine-grained comprehension and alignment. Instance-level understanding is crucial for LMM

M. B. Hastings

The sum-of-squares method can give rigorous lower bounds on the energy of quantum Hamiltonians. Unfortunately, typically using this method requires solving a semidefinite program, which can be computationally expensive. Further, the typically used degree-$4$ sum-of-squares (also known as the 2RDM method) does not correctly reproduce second order perturbation

Xinyi Mou, Xuanwen Ding, Qi He, Liang Wang

Traditional sociological research often relies on human participation, which, though effective, is expensive, challenging to scale, and with ethical concerns. Recent advancements in large language models (LLMs) highlight their potential to simulate human behavior, enabling the replication of individual responses and facilitating studies on many interdiscipli

Cassandra Marcussen, Aaron Putterman, Salil Vadhan

Given a sequence of samples $x_1, \dots , x_k$ promised to be drawn from one of two distributions $X_0, X_1$, a well-studied problem in statistics is to decide $\textit{which}$ distribution the samples are from. Information theoretically, the maximum advantage in distinguishing the two distributions given $k$ samples is captured by the total variation distan

Rui Xiao, Sanghwan Kim, Mariana-Iuliana Georgescu, Zeynep Akata

CLIP has shown impressive results in aligning images and texts at scale. However, its ability to capture detailed visual features remains limited because CLIP matches images and texts at a global level. To address this issue, we propose FLAIR, Fine-grained Language-informed Image Representations, an approach that utilizes long and detailed image descriptions

Louis Airale, Adrien Pajot, Juliette Linossier

The persisting threats on migratory bird populations highlight the urgent need for effective monitoring techniques that could assist in their conservation. Among these, passive acoustic monitoring is an essential tool, particularly for nocturnal migratory species that are difficult to track otherwise. This work presents the Nocturnal Bird Migration (NBM) dat

Pierre Monmarché, Katharina Schuh

The problem of sampling according to the probability distribution minimizing a given free energy, using interacting particles unadjusted kinetic Langevin Monte Carlo, is addressed. In this setting, three sources of error arise, related to three parameters: the number of particles $N$, the discretization step size $h$, and the length of the trajectory $n$. Th

Michael J. Schlosser

Using Bailey's very-well-poised $_6\psi_6$ summation, we show that a specific sequence of well-poised bilateral basic hypergeometric $_3\psi_3$ series form a family of orthogonal functions on the unit circle. We further extract a bilateral matrix inverse from Dougall's ${}_2H_2$ summation which we use, in combination with the Pfaff--Saalsch\"utz summation, t

Zehuan Huang, Yuan-Chen Guo, Xingqiao An, Yunhan Yang

This paper introduces MIDI, a novel paradigm for compositional 3D scene generation from a single image. Unlike existing methods that rely on reconstruction or retrieval techniques or recent approaches that employ multi-stage object-by-object generation, MIDI extends pre-trained image-to-3D object generation models to multi-instance diffusion models, enabling

Zihe Wang, Jian Wu

In this paper, we revisit cognitive extent, originally defined as the number of unique phrases in a quota. We introduce Freshness and Informative Weighted Cognitive Extent (FICE), calculated based on two novel weighting factors, the lifetime ratio and informativity of scientific entities. We model the lifetime of each scientific entity as the time-dependent

John Hughes, Sara Price, Aengus Lynch, Rylan Schaeffer

We introduce Best-of-N (BoN) Jailbreaking, a simple black-box algorithm that jailbreaks frontier AI systems across modalities. BoN Jailbreaking works by repeatedly sampling variations of a prompt with a combination of augmentations - such as random shuffling or capitalization for textual prompts - until a harmful response is elicited. We find that BoN Jailbr

Andreas Steiner, André Susano Pinto, Michael Tschannen, Daniel Keysers

PaliGemma 2 is an upgrade of the PaliGemma open Vision-Language Model (VLM) based on the Gemma 2 family of language models. We combine the SigLIP-So400m vision encoder that was also used by PaliGemma with the whole range of Gemma 2 models, from the 2B one all the way up to the 27B model. We train these models at three resolutions (224px, 448px, and 896px) in

Ester Sudano

We model stochastic choices with categorization. The agent preliminarly groups alternatives in homogenous disjoint classes, then randomly chooses one class and randomly picks an item within the selected class. We give a formal definition of a choice generated by this procedure, and provide an axiomatic characterization. The characterizing properties allow an

Akul Malhotra, Sumeet Kumar Gupta

Compute-in-memory (CiM)-based binary neural network (CiM-BNN) accelerators marry the benefits of CiM and ultra-low precision quantization, making them highly suitable for edge computing. However, CiM-enabled crossbar (Xbar) arrays are plagued with hardware non-idealities like parasitic resistances and device non-linearities that impair inference accuracy, es

Jing Tan, Shuai Yang, Tong Wu, Jingwen He

$360^\circ$ videos offer a hyper-immersive experience that allows the viewers to explore a dynamic scene from full 360 degrees. To achieve more user-friendly and personalized content creation in $360^\circ$ video format, we seek to lift standard perspective videos into $360^\circ$ equirectangular videos. To this end, we introduce Imagine360, the first perspe

Vera Prohaska, Eduardo Castelló Ferrer

Tangible User Interfaces (TUI) for human--computer interaction (HCI) provide the user with physical representations of digital information with the aim to overcome the limitations of screen-based interfaces. Although many compelling demonstrations of TUIs exist in the literature, there is a lack of research on TUIs intended for daily two-handed tasks and pro

Zehuan Huang, Yuan-Chen Guo, Haoran Wang, Ran Yi

Existing multi-view image generation methods often make invasive modifications to pre-trained text-to-image (T2I) models and require full fine-tuning, leading to (1) high computational costs, especially with large base models and high-resolution images, and (2) degradation in image quality due to optimization difficulties and scarce high-quality 3D data. In

Jules Drean, Fisher Jepsen, Edward Suh, Srini Devadas

We present Argos, a simple approach for adding verifiability to fully homomorphic encryption (FHE) schemes using trusted hardware. Traditional approaches to verifiable FHE require expensive cryptographic proofs, which incur an overhead of up to seven orders of magnitude on top of FHE, making them impractical. With Argos, we show that trusted hardware can be

D. Cerrone, D. Riccobelli, S. Gazzoni, P. Vitullo

Glioblastoma is among the most aggressive brain tumors in adults, characterized by patient-specific invasion patterns driven by the underlying brain microstructure. In this work, we present a proof-of-concept for a mathematical model of GBL growth, enabling real-time prediction and patient-specific parameter identification from longitudinal neuroimaging data

Mahtab Bigverdi, Zelun Luo, Cheng-Yu Hsieh, Ethan Shen

Multimodal language models (MLMs) still face challenges in fundamental visual perception tasks where specialized models excel. Tasks requiring reasoning about 3D structures benefit from depth estimation, and reasoning about 2D object instances benefits from object detection. Yet, MLMs can not produce intermediate depth or boxes to reason over. Finetuning MLM

Bihag Dave, Gaurav Goswami

For a galaxy, given its observed rotation curve, can one directly infer parameters of the dark matter density profile (such as dark matter particle mass $m$, scaling parameter $s$, core-to-envelope transition radius $r_t$ and NFW scale radius $r_s$), along with Baryonic parameters (such as the stellar mass-to-light ratio $\Upsilon_*$)? In this work, using si

Laura Herold, Marc Kamionkowski

Recent results from DESI combined with cosmic microwave background data give the tightest constraints on the sum of neutrino masses to date. However, these analyses approximate the neutrino mass hierarchy by three degenerate-mass (DM) neutrinos, instead of the normal (NH) and inverted hierarchies (IH) informed by terrestrial neutrino oscillation experiments.

Vincent C. Morano, Zeno Maesen, Stanislav E. Nikitin, Jakob Lass

Altermagnets are collinear compensated magnets in which the magnetic sublattices are related by rotation rather than translation or inversion. One of the quintessential properties of altermagnets is the presence of split chiral magnon modes. Recently, such modes have been predicted in MnF$_2$. Here, we report inelastic neutron scattering results on an MnF$_2

Boryana Hadzhiyska, Simone Ferraro, Rongpu Zhou

In this work, we leverage CMB data from the Atacama Cosmology Telescope (ACT) and LSS data from the imaging survey conducted by the Dark Energy Spectroscopic Instrument (DESI) to study the distribution of gas around galaxy groups at low redshift, $z \approx 0.3$, via the kinematic Sunyaev-Zel'dovich (kSZ) effect. In particular, we perform velocity-weighted s

Noble Gluscevich, J. A. Sauls

In equilibrium, confined films of superfluid $^3$He-A have the chiral axis, $\hat{\ell}$, locked normal to the surface of the film. There are two degenerate ground states $\hat{\ell}\;||\pm\hat{z}$. However, for a temperature quench, i.e. cool down through the phase transition at a finite rate, causally disconnected regions of order parameter fluctuations de

Pavlos Kassotakis, Theodoros Kouloukas, Maciej Nieszporski

We extend the equations of motion that describe non-relativistic elastic collision of two particles in one dimension to an arbitrary associative algebra. Relativistic elastic collision equations turn out to be a particular case of these generic equations. Furthermore, we show that these equations can be reinterpreted as difference systems defined on the ${\m

Maximilian Detering, Tevong You

Self-organised criticality, realised through cosmological dynamics in the early universe, is an alternative paradigm for addressing the electroweak hierarchy problem. In this scenario, an unnaturally light Higgs boson is the result of dynamics driving the electroweak vacuum towards a near-critical metastable point where the Higgs mass is bounded from above b

Alex Kutana, Koki Yoshimochi, Ryoji Asahi

Atomistic simulations of properties of materials at finite temperatures are computationally demanding and require models that are more efficient than the ab initio approaches. Machine learning (ML) and artificial intelligence (AI) address this issue by enabling accurate models with close to ab initio accuracy. Here, we demonstrate the utility of ML models in

Huy Tuan Pham

We prove a sharp version of Talagrand's selector process conjecture. Roughly speaking, given any collection of nonnegative weight vectors whose support form a family that is not $p$-small, a random set of density $O(sp)$ captures all but a $2^{-s}$ fraction of weight of some vector with high probability. This gives a common strengthening of Talagrand&#39

Xinheng Xie, Yue Wu, Cuiyu He

Understanding adversarial examples is crucial for improving model robustness, as they introduce imperceptible perturbations to deceive models. Effective adversarial examples, therefore, offer the potential to train more robust models by eliminating model singularities. We propose NODE-AdvGAN, a novel approach that treats adversarial generation as a continuou

Jianping He, Laila Rasmy, Haifang Li, Jianfu Li

Objective: This paper aims to prompt large language models (LLMs) for clinical temporal relation extraction (CTRE) in both few-shot and fully supervised settings. Materials and Methods: This study utilizes four LLMs: Encoder-based GatorTron-Base (345M)/Large (8.9B); Decoder-based LLaMA3-8B/MeLLaMA-13B. We developed full (FFT) and parameter-efficient (PEFT) f

F. R. Klinkhamer

We review the suggestion that it is possible to eliminate the Big Bang curvature singularity of the Friedmann cosmological solution by considering a particular type of degenerate spacetime metric. Specifically, we take the 4-dimensional spacetime metric to have a spacelike 3-dimensional defect with a vanishing determinant of the metric. This new solution sug

Natalie Mackraz, Nivedha Sivakumar, Samira Khorshidi, Krishna Patel

Large language models (LLMs) are increasingly being adapted to achieve task-specificity for deployment in real-world decision systems. Several previous works have investigated the bias transfer hypothesis (BTH) by studying the effect of the fine-tuning adaptation strategy on model fairness to find that fairness in pre-trained masked language models have limi

Agnid Banerjee, Nicola Garofalo

We prove a theorem of unique continuation in measure for nonlocal equations of the type $(\partial_t - \Delta)^s u= V(x,t) u$, for $0<s <1$. Our main result, Theorem 1.1, establishes a delicate nonlocal counterpart of the unique continuation in measure for the local case $s=1$.

Dami Lee, Josh Southerland

We study a class of finite-area, infinite-type translation surfaces, and find an explicit cylinder decomposition on these surfaces which do not manifest on finite-type translation surfaces. Each cylinder decomposition contains a special curve which we show is an obstruction to the existence of certain affine diffeomorphisms.

J. W. Moffat

Recent observations by the James Webb Space Telescope (JWST) have revealed the presence of bright and well-formed galaxies at high redshifts, challenging the predictions of the standard Lambda-Cold Dark Matter (LCDM) cosmological model. This paper explores the potential of Modified Gravity (MOG), specifically Scalar-Tensor-Vector Gravity (STVG), to account f

Jon Gutiérrez-Zaballa, Koldo Basterretxea, Javier Echanobe

As the deployment of artifical intelligence (AI) algorithms at edge devices becomes increasingly prevalent, enhancing the robustness and reliability of autonomous AI-based perception and decision systems is becoming as relevant as precision and performance, especially in applications areas considered safety-critical such as autonomous driving and aerospace.

Stephen Li, Maggie Ju, A. C. Quillen, Adam E. Rubinstein

Young and forming planetesimals experience impacts from particles present in a protostellar disk. Using crater scaling laws, we integrate ejecta distributions for oblique impacts. For impacts at 10 to 65 m/s, expected for impacts associated with a disk wind, we estimate the erosion rate and torque exerted on the planetesimal. We find that the mechanism for a

Marco Fava, Nicola Pagani, Filippo Viviani

We introduce and study a new class of compactified Jacobians for nodal curves, that we call compactified Jacobians of vine type, or simply V-compactified Jacobians. This class is strictly larger than the class of classical compactified Jacobians, as constructed by Oda-Seshadri, Simpson, Caporaso and Esteves. We characterize V-compactified Jacobians as the co

Gabriel Lino Garcia, João Renato Ribeiro Manesco, Pedro Henrique Paiola, Lucas Miranda

The rapid advancement of large language models (LLMs) has opened new boundaries in the extraction and synthesis of medical knowledge, particularly within evidence synthesis. This paper reviews the state-of-the-art applications of LLMs in the biomedical domain, exploring their effectiveness in automating complex tasks such as evidence synthesis and data extra

Mohamad Shalaby

Energy dissipation in collisionless shocks is a key mechanism in various astrophysical environments. Its non-linear nature complicates analytical understanding and necessitate Particle-in-Cell (PIC) simulations. This study examines the impact of reducing the ion-to-electron mass ratio ($m_r$), to decrease computational cost, on energy partitioning in 1D3V (o

Balázs Bárány, Károly Simon, Adam Śpiewak

We prove a universal projection theorem, giving conditions on a parametrized family of maps $\Pi_\lambda : X \to \mathbb{R}^d$ and a collection M of measures on X under which for almost every $\lambda$ equality $\dim_H \Pi_\lambda \mu = \min\{d, \dim_H \mu\}$ holds for all measures $\mu \in M$ simultaneously (i.e. on a full measure set of $\lambda$'s indepen

Dimitris Bertsimas, Angelos G. Koulouras, Hiroshi Nagata, Carol Gao

Observational studies provide the only evidence on the effectiveness of interventions when randomized controlled trials (RCTs) are impractical due to cost, ethical concerns, or time constraints. While many methodologies aim to draw causal inferences from observational data, there is a growing trend to model observational study designs after RCTs, a strategy

Urjitkumar Patel, Fang-Chun Yeh, Chinmay Gondhalekar, Hari Nalluri

In the rapidly evolving financial sector, the accurate and timely interpretation of market news is essential for stakeholders needing to navigate unpredictable events. This paper introduces FANAL (Financial Activity News Alerting Language Modeling Framework), a specialized BERT-based framework engineered for real-time financial event detection and analysis,

Hanxue Liang, Jiawei Ren, Ashkan Mirzaei, Antonio Torralba

Recent advancements in static feed-forward scene reconstruction have demonstrated significant progress in high-quality novel view synthesis. However, these models often struggle with generalizability across diverse environments and fail to effectively handle dynamic content. We present BTimer (short for BulletTimer), the first motion-aware feed-forward model

Robert Brignall, Ben Jarvis

Pin permutations play an important role in the structural study of permutation classes, most notably in relation to simple permutations and well-quasi-ordering, and in enumerative consequences arising from these. In this paper, we continue our study of pin classes, which are permutation classes that comprise all the finite subpermutations contained in an inf

Zachary W. Anderson, Yang Tang, Vikram Nagarajan, Mun K. Chan

It is a distinct possibility that spin fluctuations are the pairing interactions in a wide range of unconventional superconductors. In the case of the high-transition-temperature (high-$T_c$) cuprates, in which superconductivity emerges upon doping an antiferromagnetic Mott-insulating state, spin correlations might furthermore drive unusual pseudogap phenome

Laura Angeloni, Domenico Daniele Bloisi, Paolo Burghignoli, Davide Comite

Human actions have accelerated changes in global temperature, precipitation patterns, and other critical Earth systems. Key markers of these changes can be linked to the dynamic of Essential Climate Variables (ECVs) and related quantities, such as Soil Moisture (SM), Above Ground Biomass (AGB), and Freeze-Thaw (FT) Dynamics. These variables are crucial for u

Eleuterio F. Toro, Svetlana A. Tokareva

HLL-type schemes constitute a large hierarchy of numerical methods, in the finite volume and discontinuous Galerkin finite element frameworks, for solving hyperbolic equations. The hierarchy of fluxes includes Rusanov schemes, HLL schemes, HLLC schemes, and other variations. All of these schemes rely on wave speed estimates. Recent work has shown that most w

Le Chen, Cheng Ouyang, Samy Tindel, Panqiu Xia

In this note we review several situations in which stochastic PDEs exhibit ergodic properties. We begin with the basic dissipative conditions, as stated by Da Prato and Zabczyk in their classical monograph. Then we describe the singular case of SPDEs with reflection. Next we move to some degenerate (and thus more demanding) settings. Namely we recall some re

Hannan Lu, Xiaohe Wu, Shudong Wang, Xiameng Qin

Generating multi-view videos for autonomous driving training has recently gained much attention, with the challenge of addressing both cross-view and cross-frame consistency. Existing methods typically apply decoupled attention mechanisms for spatial, temporal, and view dimensions. However, these approaches often struggle to maintain consistency across dimen

Hermes McGriff, Renato Martins, Nicolas Andreff, Cedric Demonceaux

This paper presents a dense depth estimation approach from light-field (LF) images that is able to compensate for strong rolling shutter (RS) effects. Our method estimates RS compensated views and dense RS compensated disparity maps. We present a two-stage method based on a 2D Gaussians Splatting that allows for a ``render and compare" strategy with a point

Lingen Li, Zhaoyang Zhang, Yaowei Li, Jiale Xu

Recent advancements in generative models have significantly improved novel view synthesis (NVS) from multi-view data. However, existing methods depend on external multi-view alignment processes, such as explicit pose estimation or pre-reconstruction, which limits their flexibility and accessibility, especially when alignment is unstable due to insufficient o

Dominic Lohr, Hieke Keuning, Natalie Kiesler

Background: Feedback as one of the most influential factors for learning has been subject to a great body of research. It plays a key role in the development of educational technology systems and is traditionally rooted in deterministic feedback defined by experts and their experience. However, with the rise of generative AI and especially Large Language Mod

Shengyuan Zhang, An Zhao, Ling Yang, Zejian Li

Diffusion models have been applied to 3D LiDAR scene completion due to their strong training stability and high completion quality. However, the slow sampling speed limits the practical application of diffusion-based scene completion models since autonomous vehicles require an efficient perception of surrounding environments. This paper proposes a novel dist

Jiayu Mao, Aylin Yener

Over-the-air federated learning (OTA-FL) unifies communication and model aggregation by leveraging the inherent superposition property of the wireless medium. This strategy can enable scalable and bandwidth-efficient learning via simultaneous transmission of model updates using the same frequency resources, if care is exercised to design the physical layer j

Kuei-Chun Kao

Recently, CLIP has become an important model for aligning images and text in multi-modal contexts. However, researchers have identified limitations in the ability of CLIP's text and image encoders to extract detailed knowledge from pairs of captions and images. In response, this paper presents Knowledge-CLIP, an innovative approach designed to improve CLIP's

Frank Fundel, Johannes Schusterbauer, Vincent Tao Hu, Björn Ommer

Semantic correspondence, the task of determining relationships between different parts of images, underpins various applications including 3D reconstruction, image-to-image translation, object tracking, and visual place recognition. Recent studies have begun to explore representations learned in large generative image models for semantic correspondence, demo

Ahmed El Alaoui

We show that in the Ising pure $p$-spin model of spin glasses, shattering takes place at all inverse temperatures $\beta \in (\sqrt{(2 \log p)/p}, \sqrt{2\log 2})$ when $p$ is sufficiently large as a function of $\beta$. Of special interest is the lower boundary of this interval which matches the large $p$ asymptotics of the inverse temperature marking the h

Emily Y. Chen, Christopher A. Browne, Simon J. Haward, Amy Q. Shen

Many environmental, energy, and industrial processes involve the flow of polymer solutions in three-dimensional (3D) porous media where fluid is confined to navigate through complex pore space geometries. As polymers are transported through the tortuous pore space, elastic stresses accumulate, leading to the onset of unsteady flow fluctuations above a thresh

Amit Kumar, Raya Dastidar, Justyn R. Maund, Adam J. Singleton

Red supergiants may lose significant mass during the final 100-1000 years before core collapse, shaping their circumstellar environment. The supernova (SN) shockwave propagating through this environment forms a shock-swept dense shell that interacts with the surrounding circumstellar material (CSM), generating secondary shocks that energise the ejecta and ma

Weiheng Zhong, Yuancan Huang, Da Hong, Nianfeng Shao

Since the shape of industrial endoscopes is passively altered according to the contact around it, manual inspection approaches of aeroengines through the inspection ports have unreachable areas, and it's difficult to traverse multistage blades and inspect them simultaneously, which requires engine disassembly or the cooperation of multiple operators, resulti

Praveen Manju, Rajendra Kumar Sharma

Let $A$ be a commutative ring with unity and $B = A[\theta]$ be an integral extension of $A$. Assume that $B$ is an integral domain with quotient field $\mathbb{K}$ and $\mathbb{E}$ is the minimal splitting field of $\theta$ over $\mathbb{K}$. Suppose $\sigma, \tau: B \rightarrow \mathbb{E}$ are two different ring homomorphisms that fix $A$ element-wise. In

Yuan Gao, Quanjun Lang, Fei Lu

The construction of loss functions presents a major challenge in data-driven modeling involving weak-form operators in PDEs and gradient flows, particularly due to the need to select test functions appropriately. We address this challenge by introducing self-test loss functions, which employ test functions that depend on the unknown parameters, specifically

Dimitrios Charamaras, Andreas Mountakis, Konstantinos Tsinas

In a recent article, Donoso, Le, Moreira and Sun studied sets of recurrence for actions of the multiplicative semigroup $(\mathbb{N}, \times)$ and provided some sufficient conditions for sets of the form $S=\{(an+b)/(cn+d) \colon n \in \mathbb{N} \}$ to be sets of recurrence for such actions. A necessary condition for $S$ to be a set of multiplicative recurr

S. Paradiso, M. Bonici, M. Chen, W. J. Percival

Many physical models contain nuisance parameters that quantify unknown properties of an experiment that are not of primary relevance. Typically, these cannot be measured except by fitting the models to the data from the experiment, requiring simultaneous measurement of interesting parameters that are our target of inference and nuisance terms that are not di

Ankit Chakraborty, Judit Munoz-Matute, Leszek Demkowicz, Jake Grosek

In this article, we propose a modified nonlinear Schr\"odinger equation for modeling pulse propagation in optical waveguides. The proposed model bifurcates into a system of elliptic and hyperbolic equations depending on waveguide parameters. The proposed model leads to a stable first-order system of equations, distinguishing itself from the canonical nonline

Juejing Liu, Shinhyo Bang, Natalie S. Yaw, Sam Karcher

The defect chemistry and thermal oxidation of lanthanide (Ln) incorporated-UO2 are critical for understanding and predicting their behavior as enhanced fuels, mixed oxide (MOX) fuels, spent nuclear fuels (SNF), and particles for safeguard purposes. In this study, we independently controlled the Ln type (Ce4+, Nd3+, and Gd3+) and the preparation condition (re

Praveen Manju, Rajendra Kumar Sharma

In this article, we study $(\sigma, \tau)$-derivations of number rings by considering them as commutative unital $\mathbb{Z}$-algebras. We begin by characterizing all $(\sigma, \tau)$-derivations and inner $(\sigma, \tau)$-derivations of the ring of algebraic integers of a quadratic number field. Then we characterize all $(\sigma, \tau)$-derivations of the r

Markus Aschwanden, Ersin Gogus

In this study we are testing whether the power law slopes ($\alpha_F$, $\alpha_E$) of fluxes $(F)$, fluences or energies $(E)$ are universal in their size distributions, $N(F) \propto F^{-\alpha_F}$ and $N(E) \propto E^{-\alpha_E}$, in astrophysical observations of galactic, extragalactic, and black-hole systems. This is a test of fundamental importance for

Proma Hossain Progga, Md. Jobayer Rahman, Swapnil Biswas, Md. Shakil Ahmed

Gait recognition is a significant biometric technique for person identification, particularly in scenarios where other physiological biometrics are impractical or ineffective. In this paper, we address the challenges associated with gait recognition and present a novel approach to improve its accuracy and reliability. The proposed method leverages advanced t

Youssof Nawar, Nouran Soliman, Moustafa Wassel, Mohamed ElHabebe

Glaucoma is a progressive optic neuropathy characterized by structural damage to the optic nerve head and functional changes in the visual field. Detecting glaucoma early is crucial to preventing loss of eyesight. However, medical datasets often suffer from class imbalances, making detection more difficult for deep-learning algorithms. We use a generative-ba

Casey Lauer, Robert C. Blake, Jonathan B. Freund

Trained neural networks (NN) are attractive as surrogate models to replace costly calculations in physical simulations, but are often unknowingly applied to states not adequately represented in the training dataset. We present the novel technique of soft checksums for scientific machine learning, a general-purpose method to differentiate between trustworthy

Matthew Ricci, Guy Pelc, Zoe Piran, Noa Moriel

Spatiotemporal dynamics pervade the natural sciences, from the morphogen dynamics underlying patterning in animal pigmentation to the protein waves controlling cell division. A central challenge lies in understanding how controllable parameters induce qualitative changes in system behavior called bifurcations. This endeavor is particularly difficult in reali

Elvira Bilokon, Valeriia Bilokon, Dusty R. Lindberg, Lev Kaplan

Understanding quantum tunneling in many-body systems is crucial for advancing quantum technologies and nanoscale device design. Despite extensive studies of quantum tunneling, the role of interactions in determining directional transport through asymmetric barriers in discrete quantum systems remains unclear. Here we show that noninteracting fermions exhibit

Adam A. Zychowicz, Krzysztof T. Chyży

Magnetic fields, together with cosmic rays (CRs), play an important role in the dynamics and evolution of galaxies, but are difficult to estimate. Energy equipartition between magnetic fields and CRs provides a convenient way to approximate magnetic field strength from radio observations. We present a new approach for calculating the equipartition magnetic f

Missagh Mehdipour, Laura W. Brenneman, Jon M. Miller, Elisa Costantini

Black hole accretion in active galactic nuclei (AGN) is coupled to the evolution of their host galaxies. Outflowing winds in AGN can play an important role in this evolution through the resulting feedback mechanism. Multi-wavelength spectroscopy is key for probing the intertwined physics of inflows and outflows in AGN. However, with the current spectrometers

Shubhangi Lagad, Amar Kakad, Bharati Kakad

Spacecraft observations of high-energy electron flux enhancement up to 125 keV at Earth's magnetopause are typically linked to the magnetic reconnection. Here, we report the first ever observation of prolonged electron flux enhancement reaching very high energy up to 650 keV at magnetopause near the subsolar point, notably without reconnection signatures. Th

Christina Sauer, Anne-Laure Boulesteix, Luzia Hanßum, Farina Hodiamont

Adequately generating and evaluating prediction models based on supervised machine learning (ML) is often challenging, especially for less experienced users in applied research areas. Special attention is required in settings where the model generation process involves hyperparameter tuning, i.e. data-driven optimization of different types of hyperparameters

Md Abu Yusuf, Md Rezaul Karim Khan, Partha Pratim Saha, Mohammed Mahbubur Rahaman

Considerable study has already been conducted regarding autonomous driving in modern era. An autonomous driving system must be extremely good at detecting objects surrounding the car to ensure safety. In this paper, classification, and estimation of an object's (pedestrian) position (concerning an ego 3D coordinate system) are studied and the distance betwee

Zican Wang, Michael Fischer, Tobias Ritschel

We derive methods to compute higher order differentials (Hessians and Hessian-vector products) of the rendering operator. Our approach is based on importance sampling of a convolution that represents the differentials of rendering parameters and shows to be applicable to both rasterization and path tracing. We further suggest an aggregate sampling strategy t

Asif Iftekhar Omi, Emma Farina, Anyu Jiang, Adam Khalifa

Body-coupled powering (BCP) is an innovative wireless power transfer (WPT) technique, recently explored for its potential to deliver power to cutting-edge biomedical implants such as nerve and muscle stimulators. This paper demonstrates the efficient technique of designing WPT systems embedding BCP via galvanic coupling (G-BCP). The G-BCP configuration utili

Neta Shaul, Itai Gat, Marton Havasi, Daniel Severo

The design space of discrete-space diffusion or flow generative models are significantly less well-understood than their continuous-space counterparts, with many works focusing only on a simple masked construction. In this work, we aim to take a holistic approach to the construction of discrete generative models based on continuous-time Markov chains, and fo

Parshuram N Aarotale, Jaydip Desai

Long duration spaceflight missions can affect the cognitive and behavioral activities of astronauts due to changes in gravity. The microgravity significantly impacts the central nervous system physiology which causes the degradation in the performance and lead to potential risk in the space exploration. The aim of this study was to evaluate functional connec

Anna Van Elst, Debarghya Ghoshdastidar

Contrastive representation learning is a modern paradigm for learning representations of unlabeled data via augmentations -- precisely, contrastive models learn to embed semantically similar pairs of samples (positive pairs) closer than independently drawn samples (negative samples). In spite of its empirical success and widespread use in foundation models,

Gaurav Pundir, Aseem Paranjape, Tirthankar Roy Choudhury

Building fast and accurate ways to model the distribution of neutral hydrogen during the Epoch of Reionization (EoR) is essential for interpreting upcoming 21 cm observations. A key component of semi-numerical models of reionization is the collapse fraction field $f_{\text{coll}}(\mathbf{x})$, which represents the fraction of mass within dark matter halos at

Oluwayomi Akinfenwa, Niamh Cahill, Catherine Hurley

When developing Bayesian hierarchical models, selecting the most appropriate hierarchical structure can be a challenging task, and visualisation remains an underutilised tool in this context. In this paper, we consider visualisations for the display of hierarchical models in data space and compare a collection of multiple models via their parameters and hype

Loukas Ilias, George Doukas, Vangelis Lamprou, Christos Ntanos

The advent of 6G/NextG networks comes along with a series of benefits, including extreme capacity, reliability, and efficiency. However, these networks may become vulnerable to new security threats. Therefore, 6G/NextG networks must be equipped with advanced Artificial Intelligence algorithms, in order to evade these attacks. Existing studies on the intrusio

Florian Reich

Halin showed that every thick end of every graph contains an infinite grid. We extend Halin's theorem to digraphs. More precisely, we show that for every infinite family $\mathcal{R}$ of disjoint equivalent out-rays there is a grid whose vertical rays are contained in $\mathcal{R}$. Furthermore, we obtain similar results for in-rays and necklaces.

Markus Aschwanden

The occurrence frequency distributions of fluxes (F) and fluences or energies (E) observed in astrophysical observations are found to be consistent with the predictions of the fractal-diffusive self-organized criticality (FD-SOC) model, which predicts power law slopes with universal constants of $\alpha_F=(9/5)=1.80$ for the flux and $\alpha_E=(5/3)\approx 1

Joachim Dahl Thomsen, Wissam A. Saidi, Kate Reidy, Jatin J. Patil

Titanium (Ti) is an adhesion and contact metal commonly used in nanoelectronics and two-dimensional (2D) materials research. However, when Ti is deposited on graphene (Gr), we obtain dramatically different film morphology depending on the experimental conditions. Through a combination of transmission electron microscopy, Raman spectroscopy, and ab initio den

Antoine Deza, Zhongyuan Liu, Lionel Pournin

A lattice $(d,k)$-polytope is the convex hull of a set of points in $\mathbb{R}^d$ whose coordinates are integers ranging between $0$ and $k$. We consider the smallest possible distance $\varepsilon(d,k)$ between two disjoint lattice $(d,k)$-polytopes. We propose an algebraic model for this distance and derive from it an explicit formula for $\varepsilon(2,k

Takafumi Saito, Yumiharu Nakano

We propose deep learning methods for classical Monge's optimal mass transportation problems, where where the distribution constraint is treated as penalty terms defined by the maximum mean discrepancy in the theory of Hilbert space embeddings of probability measures. We prove that the transport maps given by the proposed methods converge to optimal transport

Wasilij Barsukow, Janina Kern, Christian Klingenberg, Lisa Lechner

The degrees of freedom of Active Flux are cell averages and point values along the cell boundaries. These latter are shared between neighbouring cells, which gives rise to a globally continuous reconstruction. The semi-discrete Active Flux method uses its degrees of freedom to obtain Finite Difference approxi\-mations to the spatial derivatives which are use

Klaus Altmann, Andreas Hochenegger, Frederik Witt

Over a smooth projective toric variety we study toric sheaves, that is, reflexive sheaves equivariant with respect to the acting torus, from a polyhedral point of view. One application is the explicit construction of the torus invariant universal extension of two nef line bundles via polyhedral inclusion/exclusion sequences. Second, we link the cohomology of

Alexander Mramor, Ao Sun

In this article we show that generally almost regular flows, introduced by Bamler and Kleiner, in closed 3-manifolds will either go extinct in finite time or flow to a collection of smooth embedded minimal surfaces, possibly with multiplicity. Using a perturbative argument then we construct piecewise almost regular flows which either go extinct in finite tim

Azizul Hakim Rafi, Abdullah Al Abrar Chowdhury, Adita Sultana, Abdulla All Noman

Given the fact that climate change has become one of the most pressing problems in many countries in recent years, specialized research on how to mitigate climate change has been adopted by many countries. Within this discussion, the influence of advanced technologies in achieving carbon neutrality has been discussed. While several studies investigated how A

Michael Liu, Xinlei Wang, Minchen Li

The Material Point Method (MPM) has become a cornerstone of physics-based simulation, widely used in geomechanics and computer graphics for modeling phenomena such as granular flows, viscoelasticity, fracture mechanics, etc. Despite its versatility, the original MPM suffers from cell-crossing instabilities caused by discontinuities in particle-grid transfer