November 2024 arXiv papers — page 138

Yuewen Sun, Lingjing Kong, Guangyi Chen, Loka Li

Prevalent in biomedical applications (e.g., human phenotype research), multimodal datasets can provide valuable insights into the underlying physiological mechanisms. However, current machine learning (ML) models designed to analyze these datasets often lack interpretability and identifiability guarantees, which are essential for biomedical research. Recent

Faruk Temur, Cihan Sahillioğulları

In this article we study two fundamental problems on exponential sums via randomization of frequencies with stochastic processes. These are the Hardy-Littlewood majorant problem, and $L^{2n}(\mathbb{T}), \ n\in \mathbb{N}$ norms of exponential sums, which can also be interpreted as solutions of diophantine equations or lattice points on surfaces. We establis

Niccolò Cribiori, Dieter Lust

We review the remarkable interplay between modular symmetries and supergravity, which has led to major advances in both physics and mathematics in recent decades. Our focus will be on four-dimensional models with $\mathcal{N}=1$ and $\mathcal{N}=2$ local supersymmetry. We will look at the early articles on the topic, but also touch on recent developments. Th

Vladimir A. Sablikov

We study Friedel oscillations (FOs) in two-dimensional topological materials with Mexican hat band dispersion, which attract great interest due to the bunch of its inherent non-trivial features, including the Van Hove singularity, doubly connected Fermi surface, non-trivial quantum-geometric properties, and the presence of states with negative effective mass

Marianna Vuorinen, Arto Aho, Elina Anttola, Antti Fihlman

We report an improved device fabrication process employed in the development of an advanced front contact grid design employing external busbars. The advanced fabrication process results in enhanced solar cell performance measured at one-sun illumination. In this grid configuration the busbar area is located outside the active solar cell and the grid fingers

Sarang Galada, Tanurima Halder, Kunal Deo, Ram P Krish

Multi-site MRI studies often suffer from site-specific variations arising from differences in methodology, hardware, and acquisition protocols, thereby compromising accuracy and reliability in clinical AI/ML tasks. We present PRISM (Privacy-preserving Inter-Site MRI Harmonization), a novel Deep Learning framework for harmonizing structural brain MRI across m

Aziz Amaaz, Abderrahman Mouradi, Moahamed Erradi, Ali Allouch

The purpose of this study was to explore Moroccan pre-service elementary teachers' attitudes toward integrated science, technology, engineering, and mathematics (STEM) education and the use of mobile devices in integrated STEM education. The research sample was selected using convenience sampling. Data were collected from 226 pre-service teachers in the Bach

Xiaocan Li, Xiaoyu Wang, Ilia Smirnov, Scott Sanner

Coordination in traffic signal control is crucial for managing congestion in urban networks. Existing pressure-based control methods focus only on immediate upstream links, leading to suboptimal green time allocation and increased network delays. However, effective signal control inherently requires coordination across a broader spatial scope, as the effect

Sriram Narayanan, Mohamed Naveed Gul Mohamed, Indranil Nayak, Suman Chakravorty

In recent years, the development of the Lunar Gateway and Artemis missions has renewed interest in lunar exploration, including both manned and unmanned missions. This interest necessitates accurate initial orbit determination (IOD) and orbit prediction (OP) in this domain, which faces significant challenges such as severe nonlinearity, sensitivity to initia

Rui Xu, Mengya Hu, Deren Lei, Yaxi Li

The proliferation of AI-generated images has intensified the need for robust content authentication methods. We present InvisMark, a novel watermarking technique designed for high-resolution AI-generated images. Our approach leverages advanced neural network architectures and training strategies to embed imperceptible yet highly robust watermarks. InvisMark

Kecia G. de Moura, Rafael M. O. Cruz, Robert Sabourin

Handwritten Signature Verification (HSV) systems distinguish between genuine and forged signatures. Traditional HSV development involves a static batch configuration, constraining the system's ability to model signatures to the limited data available. Signatures exhibit high intra-class variability and are sensitive to various factors, including time and ext

Fang Qin, Ruizhe Shen, Ching Hua Lee

In non-Hermitian band structures, exceptional points generically form gapless lines or loops that give rise to extensively many defective eigenstates. In this work, we investigate how they nontrivially contribute to higher-order nonlinear responses by introducing unique singularities in the Berry curvature dipole (BCD) or Berry connection polarizability (BCP

Yifei Wang, Kaiwen Hu, Sharut Gupta, Ziyu Ye

Contrastive learning has been a leading paradigm for self-supervised learning, but it is widely observed that it comes at the price of sacrificing useful features (\eg colors) by being invariant to data augmentations. Given this limitation, there has been a surge of interest in equivariant self-supervised learning (E-SSL) that learns features to be augmentat

Subhrajyoti Roy, Rhombik Roy, Andrea Trombettoni, Barnali Chakrabarti

We investigate the dynamics of one-dimensional interacting bosons in an optical lattice after a sudden quench in the Bose-Hubbard (BH) and sine-Gordon (SG) regimes. While in higher dimension, the Mott-superfluid phase transition is observed for weakly interacting bosons in deep lattices, in 1D an instability is generated also for shallow lattices with a comm

Pedram Rostami, Mohammad Javad Dousti

Multilingual machine translation models often outperform traditional bilingual models by leveraging translation knowledge transfer. Recent advancements have led to these models supporting hundreds of languages and achieving state-of-the-art results across various translation directions. However, as these models grow larger, their inference operations become

Giulia Saccà

We suggest a general framework for compactifing quasi-projective Lagrangian fibrations of geometric origin by holomorphic symplectic varieties. This framework includes a compactification criterion, which we then apply to various fibrations of geometric origin, and a discussion on holomorphic forms that are defined via correspondences in geometric examples. A

Alessandro D'Angelo

In this paper we are going to compute the $ \mathrm{KW} $-Euler classes for rank 2 vector bundles on the classifying stack $ \mathcal{B}N $, where $N$ is the normaliser of the standard torus in $SL_2$ and $\mathrm{KW}$ represents Balmer's derived Witt groups. Using these computations we will recover, through a new and different strategy, the formulas previou

Guangyi Wang, Wei Peng, Lijiang Li, Wenyu Chen

While powerful for generation, Diffusion Probabilistic Models (DPMs) face slow sampling challenges, for which various distillation-based methods have been proposed. However, they typically require significant additional training costs and model parameter storage, limiting their practicality. In this work, we propose PCA-based Adaptive Search (PAS), which opt

Jakob Nogler, Adam Polak, Barna Saha, Virginia Vassilevska Williams

The tree edit distance (TED) between two rooted ordered trees with $n$ nodes labeled from an alphabet $\Sigma$ is the minimum cost of transforming one tree into the other by a sequence of valid operations consisting of insertions, deletions and relabeling of nodes. The tree edit distance is a well-known generalization of string edit distance and has been stu

Idan Barnea, Tal Lancewicki, Yishay Mansour

We study the regret in stochastic Multi-Armed Bandits (MAB) with multiple agents that communicate over an arbitrary connected communication graph. We analyzed a variant of Cooperative Successive Elimination algorithm, COOP-SE, and show an individual regret bound of $O(R/ m + A^2 + A \sqrt{\log T})$ and a nearly matching lower bound. Here $A$ is the number of

Agatha Schmidt, Henrik Zunker, Alexander Heinlein, Martin J. Kühn

During the COVID-19 crisis, mechanistic models have guided evidence-based decision making. However, time-critical decisions in a dynamical environment limit the time available to gather supporting evidence. We address this bottleneck by developing a graph neural network (GNN) surrogate of an age-structured and spatially resolved mechanistic metapopulation si

Behraj Khan, Behroz Mirza, Nouman Durrani, Tahir Syed

When training data are distributed across{ time or space,} covariate shift across fragments of training data biases cross-validation, compromising model selection and assessment. We present \textit{Fragmentation-Induced covariate-shift Remediation} ($FIcsR$), which minimizes an $f$-divergence between a fragment's covariate distribution and that of the standa

Michael Guerzhoy

A recent paper (van Rooij et al. 2024) claims to have proved that achieving human-like intelligence using learning from data is intractable in a complexity-theoretic sense. We point out that the proof relies on an unjustified assumption about the distribution of (input, output) tuples in the data. We briefly discuss that assumption in the context of two fund

Mathew George

A complex Monge-Amp\`ere equation for differential $(p,p)$-forms is introduced on compact K\"ahler manifolds. For any $1 \leq p < n$, we show the existence of smooth solutions unique up to adding constants. For $p=1$, this corresponds to the Calabi-Yau theorem proved by S. T. Yau, and for $p=n-1$, this gives the Monge-Amp\`ere equation for $(n-1)$ plurisubha

Raghunath Sahoo

Charmonia suppression has been considered as a smoking gun signature of quark-gluon plasma. However, the Large Hadron Collider has observed a lower degree of suppression as compared to the Relativistic Heavy Ion Collider energies, due to regeneration effects in heavy-ion collisions. Though proton collisions are considered to be the baseline measurements to c

D. G. Suárez-Forero, M. Jalali Mehrabad, C. Vega, A. González-Tudela

Chiral quantum optics is a growing field of research where light-matter interactions become asymmetrically dependent on momentum and spin, offering novel control over photonic and electronic degrees of freedom. Recently, the platforms for investigating chiral light-matter interactions have expanded from laser-cooled atoms and quantum dots to various solid-st

Francesco De Anna, Xingyu Li, Marius Paicu, Arghir Zarnescu

In this paper we consider the 3D co-rotational Beris-Edwards system modeling the hydrodynamic motion of nematic liquid crystals in a thin strip. The system contains the incompressible Navier-Stokes, coupled with a parabolic system for matrix-valued functions, the $Q$-tensors. We show that under a suitable scaling, corresponding, in the Navier-Stokes part, to

Ze Sheng, Fenghua Wu, Xiangwu Zuo, Chao Li

This paper presents LProtector, an automated vulnerability detection system for C/C++ codebases driven by the large language model (LLM) GPT-4o and Retrieval-Augmented Generation (RAG). As software complexity grows, traditional methods face challenges in detecting vulnerabilities effectively. LProtector leverages GPT-4o's powerful code comprehension and gene

Samuel Feldman, Andrey Rogachev

Theoretically, Josephson junction (JJ) arrays can exhibit either a superconducting or insulating state, separated by a quantum phase transition (QPT). In this work, we analyzed published data on QPTs in three one-dimensional arrays and two two-dimensional arrays using a recently developed phenomenological model of QPTs. The model is based on the insight that

Kazuki Fujii, Taishi Nakamura, Rio Yokota

Large Language Models (LLMs) have attracted significant attention due to their human-like language understanding and generation capabilities, as well as their applicability across various domains. These models, characterized by their massive scale and extensive training data, continue to push the boundaries of what is possible in natural language processing.

Jiaxin Chen, Jinliang Ding, Kay Chen Tan, Jiancheng Qian

Cross-project defect prediction (CPDP) leverages machine learning (ML) techniques to proactively identify software defects, especially where project-specific data is scarce. However, developing a robust ML pipeline with optimal hyperparameters that effectively use cross-project information and yield satisfactory performance remains challenging. In this paper

Fadhel Ayed, Ali Maatouk, Nicola Piovesan, Antonio De Domenico

The drive toward automating cellular network operations has grown with the increasing complexity of these systems. Despite advancements, full autonomy currently remains out of reach due to reliance on human intervention for modeling network behaviors and defining policies to meet target requirements. Network Digital Twins (NDTs) have shown promise in enhanci

Saurya Das, Sourav Sur

We show that a slowly varying Newton's constant, consistent with existing bounds, can potentially explain a host of observations pertaining to gravitational effects or phenomena across distances spanning from planetary to the cosmological, relying neither on the existence of Dark Matter or (and) Dark Energy, nor on any expected high proportions of either of

Boyi Wang, Naresh Kumar, Jinyun Yuan

This paper introduces a stabilized finite element scheme for the Cahn--Hilliard cross-diffusion model, which is characterized by strongly coupled mobilities, nonlinear diffusion, and complex cross-diffusion terms. These features pose significant analytical and computational challenges, particularly due to the destabilizing effects of cross-diffusion and the

Ali Çelik

Detecting Beyond Standard Model (BSM) signals in high-energy particle collisions presents significant challenges due to complex data and the need to differentiate rare signal events from Standard Model (SM) backgrounds. This study investigates the efficacy of deep learning models, specifically Deep Neural Networks (DNNs) and Graph Neural Networks (GNNs), in

Mingyu Yu, Haonan Miao, Zhengping Jin, Sujuan Qin

With the advancement of information hiding techniques, generation-based coverless steganography has emerged as an alternative to traditional methods, leveraging generative models to transform secret information into stego-objects rather than embedding it within the redundancy of the cover. However, existing generation-based approaches require pseudo-keys tha

Benoît Dubus, Joseph Cunningham, Jérémie Roland

Many quantum algorithms, such as adiabatic algorithms (e.g. AQC) and phase randomisation, require simulating Hamiltonian evolution. In addition, the simulation of physical systems is an important objective in its own right. In many cases, the Hamiltonian is complex at first sight, but can be decomposed as a linear combination of simple ones; for instance, a

Zahra Najafabadi Samani, Matthias Gassner, Thomas Fahringer, Juan Aznar Poveda

With the growth of real-time applications and IoT devices, computation is moving from cloud-based services to the low latency edge, creating a computing continuum. This continuum includes diverse cloud, edge, and endpoint devices, posing challenges for software design due to varied hardware options. To tackle this, a unified resource manager is needed to aut

Yan-Feng Wu, Jian-Qiang Hu

We introduce ajdmom, a Python package designed for automatically deriving moment formulae for the well-established affine jump diffusion processes with state-independent jump intensities. ajdmom can produce explicit closed-form expressions for conditional and unconditional moments of any order, significantly enhancing the usability of these models. Additiona

Ruilin Hu, Phuoc-Tai Nguyen, Quoc-Hung Nguyen, Ping Zhang

Building on Tao&#39;s quantitative regularity theory and triple-logarithmic blow-up estimate in $L^3$ in \cite{Tao_20}, we consider classical solutions $(u,P)$ of the three-dimensional incompressible Navier--Stokes equations on $[0,T]\times\mathbb{R}^3$. For $3<p<\infty$, under simultaneous uniform control of the two scaling-critical quantities $\|u\|_{L_T^\

Riccardo Busetto, Valentina Breschi, Marco Forgione, Dario Piga

Imagine having a system to control and only know that it belongs to a certain class of dynamical systems. Would it not be amazing to simply plug in a controller and have it work as intended? With the rise of in-context learning and powerful architectures like Transformers, this might be possible, and we want to show it. In this work, within the model referen

Zeyu Zhang, Hang Gao, Akide Liu, Qi Chen

Human motion generation is a cut-edge area of research in generative computer vision, with promising applications in video creation, game development, and robotic manipulation. The recent Mamba architecture shows promising results in efficiently modeling long and complex sequences, yet two significant challenges remain: Firstly, directly applying Mamba to ex

Valentin Semkin, Aleksandr Shabanov, Kirill Kapralov, Mikhail Kashchenko

Two-dimensional materials offering ultrafast photoresponse suffer from low intrinsic absorbance, especially in the mid-infrared wavelength range. Challenges in 2d material doping further complicate the creation of light-sensitive $p-n$ junctions. Here, we experimentally demonstrate a graphene-based infrared detector with simultaneously enhanced absorption an

Julien-Pooya Weihs, Vegard Gjerde, Helge Drange

Understanding how learners conceptualize complex scientific systems remains a key challenge in geoscience education. We investigate the evolution of conceptual understanding in cloud physics among 153 learners, ranging from bachelor students to disciplinary experts and representing diverse academic backgrounds across STEM. To do so, we trace how knowledge st

Rémi Giraud, Michaël Clément

For many years, image over-segmentation into superpixels has been essential to computer vision pipelines, by creating homogeneous and identifiable regions of similar sizes. Such constrained segmentation problem would require a clear definition and specific evaluation criteria. However, the validation framework for superpixel methods, typically viewed as stan

Sunday Oluyele, Juwon Akingbade, Victor Akinode

Musicians frequently use social media to express their opinions, but they often convey different messages in their music compared to their posts online. Some utilize these platforms to abuse their colleagues, while others use it to show support for political candidates or engage in activism, as seen during the #EndSars protest. There are extensive research d

Lehan Chen, Yuji Nakatsukasa

Stochastic gradient descent (SGD) is a workhorse algorithm for solving large-scale optimization problems in data science and machine learning. Understanding the convergence of SGD is hence of fundamental importance. In this work we examine the SGD convergence (with various step sizes) when applied to unconstrained convex quadratic programming (essentially le

Youshan Tao, Michael Winkler

In bounded $n$-dimensional domains with $n\ge 3$, this manuscript considers an initial-boundary problem for a quasilinear chemotaxis system with indirect attractant production, as arising, inter alia, in the modeling of effects due to phenotypical heterogeneity in microbial populations. Under the assumption that the rates $D$ and $S$ of diffusion and cross-d

John Armstrong, James Dalby, Catherine Donnelly

We evaluate the performance and level of intergenerational cross-subsidy in flat-accrual and dynamic-accrual collective defined contribution (CDC) schemes which have been designed to be compatible with UK legislation. In the flat-accrual scheme, all members accrue the benefits at the same rate irrespective of age. This captures the most significant feature o

Matus Rybak, J. T. Jansen, M. Frias Castillo, J. A. Hodge

Massive, intensely star-forming galaxies at high redshift require a supply of molecular gas from their gas reservoirs, replenished by infall from the surrounding circumgalactic medium, to sustain their immense star-formation rates. However, our knowledge of the extent and morphology of their cold-gas reservoirs is still in its infancy. We present the results

Yunhui Jiang, Fan Zhang, Yuwei Zheng, Ruixi Sun

Photoacoustic imaging (PAI) and ultrasound imaging (USI) are important biomedical imaging techniques, due to their unique and complementary advantages in tissue's structure and function visualization. In this Letter, we proposed a coaxial photoacoustic-ultrasound dual-modal imaging system (coPAUS) with disposable opto-acoustic window. This opto-acoustic wind

Saori Morimoto, Makoto Katori, Tomoyuki Shirai

We consider nonnormal matrix-valued dynamical systems with discrete time. For an eigenvalue of matrix, the number of times it appears as a root of the characteristic polynomial is called the algebraic multiplicity. On the other hand, the geometric multiplicity is the dimension of the linear space of eigenvectors associated with that eigenvalue. If the former

Pengfei Wang, Jiantao Song, Lei Wang, Shiqing Xin

Extraction of a high-fidelity 3D medial axis is a crucial operation in CAD. When dealing with a polygonal model as input, ensuring accuracy and tidiness becomes challenging due to discretization errors inherent in the mesh surface. Commonly, existing approaches yield medial-axis surfaces with various artifacts, including zigzag boundaries, bumpy surfaces, un

Steven R. Costenoble, Thomas Hudson

We calculate the ordinary $C_2$-cohomology of $BT^2$ with Burnside ring coefficients, using an extended grading that allows us to capture a more natural set of generators. We discuss how this cohomology is related to those of $BT^1$ and $BU(2)$, calculated previously, both relationships being more complicated than in the nonequivariant case.

Charu Goel, Sarah Hess, Salma Kuhlmann

In 1888, Hilbert proved that the cone $\mathcal{P}_{n+1,2d}$ of positive semidefinite forms in $n+1$ variables of degree $2d$ coincides with its subcone $\Sigma_{n+1,2d}$ of those forms that are representable as finite sums of squares if and only if $(n+1,2d) = (2,2d)_{d\geq1}$ or $(n+1,2)_{n\geq1}$ or $(3,4)$. These are the Hilbert cases. In [GHK23, GHK24],

Dahyana Farias, Eduardo Fernández

We provide a topological characterization for a family of bypasses with a fixed attaching arc to be contractible. This characterization is formulated in terms of the existence of a bypass that is disjoint from the given family away from the attaching region. As an application, we provide new proofs of several h-principles in overtwisted contact 3-manifolds.

Timm Michel, Jürgen Behr, Hamed Sabzalipoor, Gisela Ibáñez-Redín

Large-scale synthesis of DNA and RNA is a crucial technology for modern biological research ranging from genomics to nucleic acid therapeutics and for technological research ranging from nanofabrication of materials to molecular-level writing of digital data. Maskless Array Synthesis (MAS) is a versatile and efficient approach for creating the required compl

Kazuki Fujii, Kohei Watanabe, Rio Yokota

In large language model (LLM) training, several parallelization strategies, including Tensor Parallelism (TP), Pipeline Parallelism (PP), Data Parallelism (DP), as well as Sequence Parallelism (SP) and Context Parallelism (CP), are employed to distribute model parameters, activations, and optimizer states across devices. Identifying the optimal parallelizati

Robert Löffler, Lukas Siedentop, Peter Keim

Melting in 2D is described by the celebrated Kosterlitz-Thouless-Halperin-Nelson-Young (KTHNY) theory. The unbinding of two different types of topological defects destroys translational and orientational order at different temperatures. The intermediate phase is called hexatic and has been measured in 2D colloidal monolayers of isotropic particles. The hexat

Boyao Wang, Volodymyr Kindratenko

Convolutional Neural Networks (CNNs) have demonstrated exceptional performance in recent years. Compressing these models not only reduces storage requirements, making deployment to edge devices feasible, but also accelerates inference, thereby reducing latency and computational costs. Structured pruning, which removes filters at the layer level, directly mod

Luca Benatti, Alessandra Pluda, Marco Pozzetta

We rigorously show that a large family of monotone quantities along the weak inverse mean curvature flow is the limit case of the corresponding ones along the level sets of $p$-capacitary potentials. Such monotone quantities include Willmore and Minkowski-type functionals on Riemannian manifolds with nonnegative Ricci curvature. In $3$-dimensional manifolds

Sebastian Brierley-Croft, Peter D. Olmsted, Peter J. Hine, Richard J. Mandle

We present a new method that successfully predicts the glass transition temperature $T_{\! \textrm{g}}$ of polymers based on their monomer structure. The model combines ideas from Group Additive Properties (GAP) and Quantitative Structure Property Relationship (QSPR) methods, where GAP (or Group Contributions) assumes that sub-monomer motifs contribute addit

J. A. Carrillo, X. Chen, B. Du, A. Jüngel

The Busenberg--Travis cross-diffusion system for segregating populations is approximated by the compressible Navier--Stokes--Korteweg equations on the torus, including a density-dependent viscosity and drag forces. The Korteweg term can be associated to the quantum Bohm potential. The singular asymptotic limit is proved rigorously using compactness and relat

Nian Liu, Libin Liu, Zilong Zhang, Zi Wang

Learning natural and diverse behaviors from human motion datasets remains challenging in physics-based character control. Existing conditional adversarial models often suffer from tight and biased embedding distributions where embeddings from the same motion are closely grouped in a small area and shorter motions occupy even less space. Our empirical observa

Andreas Athanasiou, Kangsoo Jung, Catuscia Palamidessi

Federated Learning (FL) enables clients to train a joint model without disclosing their local data. Instead, they share their local model updates with a central server that moderates the process and creates a joint model. However, FL is susceptible to a series of privacy attacks. Recently, the source inference attack (SIA) has been proposed where an honest-b

Kodai Sakurai, Fuminobu Takahashi

We discuss the thermal production of axions in renormalizable models involving two Higgs doublet fields and a complex singlet field with a global $U(1)$ Peccei-Quinn symmetry, i.e., DFSZ type axion models. We demonstrate that, when the reheating temperature exceeds the mass scale of heavy Higgs bosons, axions are efficiently produced through heavy Higgs boso

Chen Wu, Ling Wang, Long Peng, Dianjie Lu

With the popularization of high-end mobile devices, Ultra-high-definition (UHD) images have become ubiquitous in our lives. The restoration of UHD images is a highly challenging problem due to the exaggerated pixel count, which often leads to memory overflow during processing. Existing methods either downsample UHD images at a high rate before processing or

Hikaru Manabe, Ryohei Miyadera, Yuji Sasaki, Shoei Takahashi

We present a new O(k log n) algorithm of the Josephus problem. The time complexity of our algorithm is O(k log n), and this time complexity is on a par with the existing O(k log n) algorithm. We do not have any recursion overhead or stack overflow because we do not use any recursion. Therefore, the space complexity of our algorithm is O(1), and ours is bette

Chengkun Ye, Milena Radenkovic

This study aims to optimise the "spray and wait" protocol in delay tolerant networks (DTNs) to improve the performance of information transmission in emergency situations, especially in car accident scenarios. Due to the intermittent connectivity and dynamic environment of DTNs, traditional routing protocols often do not work effectively. In this study, a ma

San-Dong Guo, Ping Li, Guangzhao Wang

Superior to ferromagnetic (FM) valleytronics, antiferromagnetic (AFM) counterpart exhibits ultradense and ultrafast potential due to their intrinsic advantages of zero stray field, terahertz dynamics, and compensated moment of antiferromagnets. However, the physics of spontaneous valley polarization is mainly rooted in FM hexagonal lattices and is rarely use

Yuxiang Li, Hao Yin, Jie Zhou

We study the compactness of Willmore surfaces without assuming the convergence of the induced complex structures. In particular, we compute the energy loss in the neck in terms of the residue and we prove that the limit of the image of the Gauss map is a geodesic in the Grassmannian $G(2,n)$ whose length can also be computed in terms of the residue. Moreover

Zhixing Liu, Bonan Pu

Secular impact-parameter variations encode dynamical perturbations and can help reveal unseen companions or constrain planetary masses when combined with dynamical interpretation. Existing methods either fit transits independently or jointly model the light curves and orbital dynamics. We introduce Simultaneous Impact Parameter Variation Analysis (SIPVA), wh

Gayatri Ghosh

The Randall-Sundrum (RS) model offers a compelling framework to address the hierarchy problem and provides new sources of CP violation beyond the Standard Model (SM). The motivation for studying CP violation in the RS model arises from the insufficiency of CP-violating phases in the SM to account for the observed matter-antimatter asymmetry in the universe.

Felix Finster, Sebastian Kindermann, Jan-Hendrik Treude

This textbook introduces the basic concepts of the theory of causal fermion systems, a recent approach to the description of fundamental physics. The theory yields quantum mechanics, general relativity and quantum field theory as limiting cases and is therefore a candidate for a unified physical theory. From the mathematical perspective, causal fermion syste

Pingyu Wu, Kai Zhu, Yu Liu, Liming Zhao

Variational Autoencoder (VAE) aims to compress pixel data into low-dimensional latent space, playing an important role in OpenAI's Sora and other latent video diffusion generation models. While most of existing video VAEs inflate a pretrained image VAE into the 3D causal structure for temporal-spatial compression, this paper presents two astonishing findings

Yu-Liang Zhan, Zhong-Yi Lu, Hao Sun, Ze-Feng Gao

Increased training parameters have enabled large pre-trained models to excel in various downstream tasks. Nevertheless, the extensive computational requirements associated with these models hinder their widespread adoption within the community. We focus on Knowledge Distillation (KD), where a compact student model is trained to mimic a larger teacher model,

Alex Finkelstein, Nikita Vladimirov, Moritz Zaiss, Or Perlman

Biophysical model fitting plays a key role in obtaining quantitative parameters from physiological signals and images. However, the model complexity for molecular magnetic resonance imaging (MRI) often translates into excessive computation time, which makes clinical use impractical. Here, we present a generic computational approach for solving the parameter

William Huddie, Laura Filion, Marjolein Dijkstra, Rembert Duine

Nanomagnetism concerns the engineering of magnetic interactions in heterostructures that consist of layers of magnetic and non-magnetic materials. Mostly, these interactions are dominated by the minimization of energy. Here, we propose an effective magnetic interlayer coupling that is dominated by the maximization of entropy. As an example, we consider the s

Daniil Sulimov

Large Language Models (LLMs) are increasingly adopted for complex scientific text generation tasks, yet they often suffer from limitations in accuracy, consistency, and hallucination control. This thesis introduces a Parameter-Efficient Fine-Tuning (PEFT) approach tailored for GPT-like models, aiming to mitigate hallucinations and enhance reproducibility, pa

Taohui Xiao, Jian Cheng, Wenxin Fan, Enqing Dong

Neurite Orientation Dispersion and Density Imaging (NODDI) microstructure estimation from diffusion magnetic resonance imaging (dMRI) is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods accelerate the speed of NODDI parameter estimation and improve the accuracy. However, most methods

S. Meljanac, S. Mignemi

We discuss, at leading order in $\hbar$, the quantum mechanics of a specific realization in phase space of the Yang model describing noncommutative geometry in a curved background. In particular, we show how the deformation of the Heisenberg uncertainty relations crucially depends on the signs of the coupling constants of the model. We also discuss the dynam

Ziqi Ni, Yahao Li, Kaijia Hu, Kunyuan Han

The rapid evolution of artificial intelligence, particularly large language models, presents unprecedented opportunities for materials science research. We proposed and developed an AI materials scientist named MatPilot, which has shown encouraging abilities in the discovery of new materials. The core strength of MatPilot is its natural language interactive

Minghong Duan, Linhao Qu, Shaolei Liu, Manning Wang

Implicit neural representations have recently demonstrated promising potential in arbitrary-scale Super-Resolution (SR) of images. Most existing methods predict the pixel in the SR image based on the queried coordinate and ensemble nearby features, overlooking the importance of incorporating high-frequency prior information in images, which results in limite

Dmitry Vesnin, Dmitry Levshun, Andrey Chechulin

In recent years, diffusion models have become one of the main methods for generating images. However, detecting images generated by these models remains a challenging task. This paper proposes a novel method for detecting images generated by Latent Diffusion Models (LDM) by identifying artifacts introduced by their autoencoders. By training a detector to dis

Etkin Getir

While there is a plethora of cybersecurity and risk management frameworks for different target audiences and use cases, micro-businesses (MBs) are often overlooked. As the smallest business entities, MBs represent a special case with regard to cybersecurity for two reasons: (1) Having fewer than 10 employees, they tend to lack cybersecurity expertise. (2) Be

Rong-Lan Li, Chengchao Yuan, Hao-Ning He, Yun Wang

Tidal disruption events (TDEs), in which stars are disrupted by supermassive black holes, have been proposed as potential sources of high-energy neutrinos through hadronic interactions. X-ray-bright TDEs provide dense photon fields conducive to neutrino production via proton-photon ($p\gamma$) processes. We conducted a time-dependent unbinned likelihood anal

Yuezhou Luo, Andrew John Flewitt

Amorphous semiconductors are important channel semiconductors in thin film transistors (TFTs) which serve not only active-matrix displays, but also flexible electronics for Internet of Things (IoT) applications. Nevertheless, a great limitation of amorphous semiconductors is their low carrier mobilities relative to their monocrystalline counterparts. Based o

Hyukhun Koh, Minha Jhang, Dohyung Kim, Sangmook Lee

Although auto-regressive models excel in natural language processing, they often struggle to generate diverse text and provide limited controllability. Non-auto-regressive methods could be an alternative but often produce degenerate outputs and exhibit shortcomings in conditional generation. To address these challenges, we propose Diffusion-EAGS, a novel fra

Guanrou Yang, Ziyang Ma, Zhifu Gao, Shiliang Zhang

Contextual ASR or hotword customization holds substantial practical value. Despite the impressive performance of current end-to-end (E2E) automatic speech recognition (ASR) systems, they often face challenges in accurately recognizing rare words. Typical E2E contextual ASR models commonly feature complex architectures and decoding mechanisms, limited in perf

Scott Pezanowski, Etien Luc Koua, Joseph C Okeibunor, Abdou Salam Gueye

Objectives: Our research adopts computational techniques to analyze disease outbreaks weekly over a large geographic area while maintaining local-level analysis by incorporating relevant high-spatial resolution cultural and environmental datasets. The abundance of data about disease outbreaks gives scientists an excellent opportunity to uncover patterns in d

Alexander Menovschikov, Alexander Ukhlov

In this article, we study homeomorphisms $\varphi: \Omega \to \widetilde{\Omega}$ that generate embedding operators in Sobolev classes on metric measure spaces $X$ by the composition rule $\varphi^{\ast}(f)=f\circ\varphi$. In turn, this leads to Sobolev type embedding theorems for a wide class of bounded domains $\widetilde{\Omega}\subset X$.

Adrien Lefauve, Yui Hin Marvil Cheung, Xianyang Jiang, Miles M. P. Couchman

Modelling fluid turbulence using a `skeleton' of coherent structures has traditionally progressed by focusing on a few canonical laboratory experiments such as pipe flow and Taylor-Couette flow. We here consider the stratified inclined duct, a sustained shear flow whose density stratification allows for the exploration of a wealth of new coherent and intermi

Arijit Mandal, S. Satpathy, B. R. K. Nanda

The topological Hall effect is the result of spin-asymmetric deflection of charge carriers flowing through a non-collinear spin system. Effective manipulation of the topological Hall conductivity (THC) in skyrmions is currently a vigorous area of research with an eye towards potential spintronics application. Here, we show that the band topology and the THC

Huiling Chen, Shanli Ye

Let $\mu$ be a positive Borel measure on the interval $[0,1)$. The Hankel matrix $\mathcal{H}_{\mu}=(\mu_{n,k})_{n,k\geq 0}$ with entries $\mu_{n,k}=\mu_{n+k}$, where $\mu_{n}=\int_{[0,1)}t^nd\mu(t)$, induces, formally, the Derivative-Hilbert operator $$\mathcal{DH}_\mu(f)(z)=\sum_{n=0}^\infty\left(\sum_{k=0}^\infty \mu_{n,k}a_k\right)(n+1)z^n , ~z\in \mathb

Ramin Ebrahimi

Let $\mathcal{X}$ be a skeletally small additive category. Using the canonical equivalence between two different presentations of the free abelian category over $\mathcal{X}$, we give a new and simple characterization of definable subcategories of $\rm Mod\text{-}\mathcal{X}$, and in particular definable subcategories of modules over rings. In the end, we gi

Amelia Carolina Sparavigna

The discovery of the Diary of Merer (papyri Wadi al-Jarf) allows us to see the Egyptian calendar applied in a logbook. The diary is dated to the 26th year of reign of Khufu and describes Merer and his crew transporting the limestone blocks from the Tura quarries to Akhet Khufu, that is, the pyramid of Khufu (Old Kingdom). We find a calendar with 30-day month

Nikolas Vellnow, Toni I. Gossmann, David Waxman

Genetic diversity is central to the process of evolution. Both natural selection and random genetic drift are influenced by the level of genetic diversity of a population; selection acts on diversity while drift samples from it. At a given locus in a diploid population, each individual carries only two alleles, but the population as a whole can possess a muc

Mihai Prunescu, Joseph Shunia

We construct a new arithmetic-term representation for the function gcd(a,b). As a byproduct, we also deduce a representation gcd(a,b) by a modular term in integer arithmetic.

Catalin-Viorel Dinu, Thomas Moerland

Quantum Tiq-Taq-Toe is a well-known benchmark and playground for both quantum computing and machine learning. Despite its popularity, no reinforcement learning (RL) methods have been applied to Quantum Tiq-Taq-Toe. Although there has been some research on Quantum Chess this game is significantly more complex in terms of computation and analysis. Therefore, w

Sascha Xu, Nils Philipp Walter, Jilles Vreeken

Machine learning models deployed in sensitive areas such as healthcare must be interpretable to ensure accountability and fairness. Rule lists (if Age < 35 $\wedge$ Priors > 0 then Recidivism = True, else if Next Condition . . . ) offer full transparency, making them well-suited for high-stakes decisions. However, learning such rule lists presents significan