December 2024 arXiv papers — page 33

Sarvesh Ravichandran Iyer

We prove that the martingale problem is well posed for pure-jump L\'evy-type operators of the form $$ (\mathcal Lf)(x) = \int_{\mathbb R^d \setminus \{0\}} \left(f(x+h)-f(x) - (\nabla f(x) \cdot h)1_{\|h\| < 1}\right)K(x,h) dh, $$ where $K(x,\cdot)$ is a jump kernel of the form $K(x,h) \sim \frac{l(\|h\|)}{\|h\|^d}$ for each $x \in \mathbb R^d,\|h\|<1$, and

G. Bor, L. Hernández-Lamoneda, S. Tabachnikov

We find new necessary and sufficient conditions for the bicycling monodromy of a closed plane curve to be hyperbolic. Our main tool is the ``hyperbolic development" interpretation of the bicycling monodromy of plane curves. Based on computer experiments, we pose two conjectures concerning the bicycling monodromy of strictly convex closed plane curves.

Pooyan Rahmanzadehgervi, Hung Huy Nguyen, Rosanne Liu, Long Mai

Multi-head self-attention (MHSA) is a key component of Transformers, a widely popular architecture in both language and vision. Multiple heads intuitively enable different parallel processes over the same input. Yet, they also obscure the attribution of each input patch to the output of a model. We propose a novel 1-head Transformer Attention Bottleneck (TAB

J. M. Duff, B. J. Faber, C. C. Hegna, M. J. Pueschel

Turbulent transport driven by trapped electron modes (TEMs) is believed to drive significant heat and particle transport in quasihelically symmetric stellarators. Two three-dimensionally-shaped magnetic configurations with suppressed trapped-electron-mode (TEM)-driven turbulence were generated through optimization that targeted quasihelical symmetry and the

Xingjian Zhang, Ziyang Xiong, Shixuan Liu, Yutong Xie

Low-dimensional visualizations, or "projection maps," are widely used in scientific and creative domains to interpret large-scale and complex datasets. These visualizations not only aid in understanding existing knowledge spaces but also implicitly guide exploration into unknown areas. Although techniques such as t-SNE and UMAP can generate these maps, there

Ratnesh Kumar Joshi, Sagnik Sengupta, Asif Ekbal

Hallucination, a persistent challenge plaguing language models, undermines their efficacy and trustworthiness in various natural language processing endeavors by generating responses that deviate from factual accuracy or coherence. This paper addresses language model hallucination by integrating curated knowledge graph (KG) triples to anchor responses in emp

Anthony Graves-McCleary, Laurent Saloff-Coste

We prove a generalized version of the $3G$ Principle for Green's functions on bounded inner uniform domains in a wide class of Dirichlet spaces. In particular, our results apply to higher-dimensional fractals such as Sierpinski carpets in $\mathbf{R}^n$, $n\geq 3$, as well as generalized fractal-type spaces that do not have a well-defined Hausdorff dimension

Jordan Pötsch

The EU AI Act (AIA) mandates the implementation of a risk management system (RMS) and a quality management system (QMS) for high-risk AI systems. The ISO/IEC 42001 standard provides a foundation for fulfilling these requirements but does not cover all EU-specific regulatory stipulations. To enhance the implementation of the AIA in Germany, the Federal Office

Anurag Mishra

Neural Machine Translation (NMT) models have shown remarkable performance but remain largely opaque in their decision making processes. The interpretability of these models, especially their internal attention mechanisms, is critical for building trust and verifying that these systems behave as intended. In this work, we introduce a systematic framework to q

Shijun Liang, Evan Bell, Avrajit Ghosh, Saiprasad Ravishankar

Deep learning methods are highly effective for many image reconstruction tasks. However, the performance of supervised learned models can degrade when applied to distinct experimental settings at test time or in the presence of distribution shifts. In this study, we demonstrate that pruning deep image reconstruction networks at training time can improve thei

Jiajie Xu, Xabier Irigoien, Mohamed-Slim Alouini

The exploration and sustainable use of marine environments have become increasingly critical as oceans cover over 70% of surface of Earth. This paper provides a comprehensive survey and classification of state-of-the-art underwater vehicles (UVs) and supporting technologies essential for enabling a smart ocean. We categorize UVs into several types, including

A. V. Tarasov, D. A. Estyunin, A. G. Rybkin, A. S. Frolov

The presence of Rashba-like surface states (RSS) in the electronic structure of topological insulators (TIs) has been a longstanding topic of interest due to their significant impact on electronic and spin structures. In this study, we investigate the interaction between topological and Rashba-like surface states (TSS and RSS) in Mn$_{1-x}$Sn$_x$Bi$_2$Te$_4$

Aroonkumar Beesham

We place observational constraints on an FLRW cosmological model in $f(R,L_m)$ gravity with a specific deceleration parameter that depends on the scale factor. This form of the deceleration parameter has been discussed by authors in several papers, but none of them have applied observations to constrain the variables of the model. We carry this out with the

Samo Novák, Raúl García-Patrón

In a Boson Sampling quantum optical experiment we send $n$ individual photons into an $m$-mode interferometer and we measure the occupation pattern on the output. The statistics of this process depending on the permanent of a matrix representing the experiment, a \#P-hard problem to compute, is the reason behind ideal and fully general Boson Sampling being h

Nikolaos Evangelou, Alexander M. Stankovic, Ioannis G. Kevrekidis, Mark K. Transtrum

Parameter identifiability refers to the capability of accurately inferring the parameter values of a model from its observations (data). Traditional analysis methods exploit analytical properties of the closed form model, in particular sensitivity analysis, to quantify the response of the model predictions to variations in parameters. Techniques developed to

Kasturie D. Jatkar, Tien-Tien Yeh, Matteo Pancaldi, Stefano Bonetti

We introduce a systematic approach that enables two robust methods for performing terahertz time-domain spectroscopy in reflection geometry. Using the Kramers-Kronig relations in connection to accurate experimental measurements of the amplitude of the terahertz electric field, we show how the correct phase of the same field can be retrieved, even in the case

Joseph R. Livesey, Juliette Becker

It has been demonstrated that systems of tightly packed inner planets with giant exterior companions tend to have less regular orbital spacings than those without such companions. We investigate whether this observed increase in the gap complexity of the inner systems can be explained solely as the result of secular dynamics caused by the disturbing potentia

Ayodele James Oyejide, Chidera Samuella Okeke, Jesuloluwa Emmanuel Zaccheus, Ebenezer Olubunmi Ige

Homogeneity and efficient oxygen transfer are crucial for aerobic cultures, which is popularly performed in Stirred Tank Bioreactors, through internal mechanical agitation of the impellers.Although there are a number of impeller designs for achieving this purpose, there are still concerns about the ability of the impellers to yield homogeneity and mitigate o

C. Vázquez-García, F. J. Martínez-Murcia, F. Segovia Román, Juan M. Górriz

Neuroimaging data, particularly from techniques like MRI or PET, offer rich but complex information about brain structure and activity. To manage this complexity, latent representation models - such as Autoencoders, Generative Adversarial Networks (GANs), and Latent Diffusion Models (LDMs) - are increasingly applied. These models are designed to reduce high-

Marco A. A. de Paula, Haroldo C. D. Lima Junior, Pedro V. P. Cunha, Carlos A. R. Herdeiro

In relativistic mechanics, the 4-velocity and the 4-momentum need not be parallel. This allows their norm to have a different sign. This possibility occurs in nonlinear electrodynamics (NED) models minimally coupled to Einstein's theory. Surprisingly, for a large class of NED models with a Maxwell limit, for weak fields, the causal (acausal) photons, as dete

J. Arroyo, U. Garg, H. Akimune, G. P. A. Berg

The incompressibility of infinite nuclear matter (K_\infty) is a parameter in the description of the nuclear equation of state that governs the energy cost associated with density oscillations near the saturation density. The most direct experimental method for studying this property of infinite nuclear matter is to probe the isoscalar giant monopole resonan

Catherine Ott, Vaibhav Verma, Adam Peters, Ian McCue

Ultra-high temperature ceramics (UHTCs) are promising materials for use in next-generation aerospace structures but have primarily been used as monolithic materials or coatings due to processing limitations. Here, new functionality (e.g., ablation resistance) is introduced to these materials by developing a porous form factor that can be later infiltrated wi

Thomas Trogdon

We consider the asymptotics of orthogonal polynomials for measures that are differentiable, but not necessarily analytic, multiplicative perturbations of Jacobi-like measures supported on disjoint intervals. We analyze the Fokas-Its-Kitaev Riemann-Hilbert problem using the Deift-Zhou method of nonlinear steepest descent and its $\overline{\partial}$ extensio

Laura Vásquez-Rodríguez, Nhung T. H. Nguyen, Piotr Przybyła, Matthew Shardlow

In this paper, we present the SimDoc system, a simplification model considering simplicity, readability, and discourse aspects, such as coherence. In the past decade, the progress of the Text Simplification (TS) field has been mostly shown at a sentence level, rather than considering paragraphs or documents, a setting from which most TS audiences would benef

Thomas Schwemberger, Volodymyr Takhistov

Dark stars (DSs) -- first stars powered by dark-matter (DM) heating rather than fusion -- could form in the early Universe. They can grow to $\gtrsim 10^5 M_{\odot}$ masses and collapse into seeds of supermassive black holes (SMBHs). We demonstrate that diffuse neutrino flux generated by DSs can be observable in existing experiments and have energies reachin

Chenglin Yang, Celong Liu, Xueqing Deng, Dongwon Kim

We present 1.58-bit FLUX, the first successful approach to quantizing the state-of-the-art text-to-image generation model, FLUX.1-dev, using 1.58-bit weights (i.e., values in {-1, 0, +1}) while maintaining comparable performance for generating 1024 x 1024 images. Notably, our quantization method operates without access to image data, relying solely on self-s

Andrea Antinucci, Christian Copetti, Giovanni Galati, Giovanni Rizi

Extended objects (defects) in Quantum Field Theory exhibit rich, nontrivial dynamics describing a variety of physical phenomena. These systems often involve strong coupling at long distances, where the bulk and defects interact, making analytical studies challenging. By carefully analyzing the behavior of bulk symmetries in the presence of defects, we uncove

Laura Pezzella, Kyriakos Destounis, Andrea Maselli, Vitor Cardoso

We investigate the (axial) quasinormal modes of black holes embedded in generic matter profiles. Our results reveal that the axial QNMs experience a redshift when the black hole is surrounded by various matter environments, proportional to the compactness of the matter halo. Our calculations demonstrate that for static black holes embedded in galactic matter

Jinhui Yi, Syed Talal Wasim, Yanan Luo, Muzammal Naseer

We present an efficient encoder-free approach for video-language understanding that achieves competitive performance while significantly reducing computational overhead. Current video-language models typically rely on heavyweight image encoders (300M-1.1B parameters) or video encoders (1B-1.4B parameters), creating a substantial computational burden when pro

Steven B. Giddings

This contribution overviews the information paradox, or perhaps more aptly "unitarity crisis," and a proposed resolution called nonviolent unitarization. It begins by examining the conflict of principles that yields the crisis, which can be phrased in terms of a "black hole theorem" summarizing how basic assumptions come into conflict. Proposed resolutions o

Minghao Chen, Roman Shapovalov, Iro Laina, Tom Monnier

Text- or image-to-3D generators and 3D scanners can now produce 3D assets with high-quality shapes and textures. These assets typically consist of a single, fused representation, like an implicit neural field, a Gaussian mixture, or a mesh, without any useful structure. However, most applications and creative workflows require assets to be made of several me

Yuntao Chen, Yuqi Wang, Zhaoxiang Zhang

World model-based searching and planning are widely recognized as a promising path toward human-level physical intelligence. However, current driving world models primarily rely on video diffusion models, which specialize in visual generation but lack the flexibility to incorporate other modalities like action. In contrast, autoregressive transformers have d

Salvatore D. Pace, Arkya Chatterjee, Shu-Heng Shao

Dualities of quantum field theories are challenging to realize in lattice models of qubits. In this work, we explore one of the simplest dualities, T-duality of the compact boson CFT, and its realization in quantum spin chains. In the special case of the XX model, we uncover an exact lattice T-duality, which is associated with a non-invertible symmetry that

Daan M. Pool, Yasemin Vardar

Touch interfaces are replacing physical buttons, dials, and switches in the new generation of cars, aircraft, and vessels. However, vehicle vibrations and accelerations perturb finger movements and cause erroneous touchscreen inputs by users. Furthermore, unlike physical buttons, touchscreens cannot be operated by touch alone and always require users' visual

Zehan Wang, Ziang Zhang, Tianyu Pang, Chao Du

Orientation is a key attribute of objects, crucial for understanding their spatial pose and arrangement in images. However, practical solutions for accurate orientation estimation from a single image remain underexplored. In this work, we introduce Orient Anything, the first expert and foundational model designed to estimate object orientation in a single- a

Tahira Kazimi, Ritika Allada, Pinar Yanardag

Classifiers are important components in many computer vision tasks, serving as the foundational backbone of a wide variety of models employed across diverse applications. However, understanding the decision-making process of classifiers remains a significant challenge. We propose DiffEx, a novel method that leverages the capabilities of text-to-image diffusi

Se Jin Park, Julian Salazar, Aren Jansen, Keisuke Kinoshita

We consider the generative modeling of speech over multiple minutes, a requirement for long-form multimedia generation and audio-native voice assistants. However, textless spoken language models struggle to generate plausible speech past tens of seconds, due to high temporal resolution of speech tokens causing loss of coherence, architectural issues with lon

Fernando Jia, Jade Zheng, Florence Li

In the rapidly evolving landscape of GameFi, a fusion of gaming and decentralized finance (DeFi), there exists a critical need to enhance player engagement and economic interaction within gaming ecosystems. Our GameFi ecosystem aims to fundamentally transform this landscape by integrating advanced embodied AI agents into GameFi platforms. These AI agents, de

Hongjie Li, Hong-Xing Yu, Jiaman Li, Jiajun Wu

Human-scene interaction (HSI) generation is crucial for applications in embodied AI, virtual reality, and robotics. Yet, existing methods cannot synthesize interactions in unseen environments such as in-the-wild scenes or reconstructed scenes, as they rely on paired 3D scenes and captured human motion data for training, which are unavailable for unseen envir

Mustafa Doger, Sennur Ulukus, Nail Akar

Theoretical guarantees for double spending probabilities for the Nakamoto consensus under the $k$-deep confirmation rule have been extensively studied for zero/bounded network delays and fixed mining rates. In this paper, we introduce a ruin-theoretical model of double spending for Nakamoto consensus under the $k$-deep confirmation rule when the honest minin

Noah Kravitz

Let $hA$ denote the $h$-fold sumset of a subset $A$ of an abelian group. Resolving a problem of Nathanson, we show that for any prescribed permutations $\sigma_1, \ldots, \sigma_H \in \mathfrak{S}_n$, there exist finite subsets $A_1, \ldots, A_n \subseteq \mathbb{Z}$ such that for each $1 \leq h \leq H$, the relative order of the quantities $|h A_1|, \ldots,

Minghong Cai, Xiaodong Cun, Xiaoyu Li, Wenze Liu

Sora-like video generation models have achieved remarkable progress with a Multi-Modal Diffusion Transformer MM-DiT architecture. However, the current video generation models predominantly focus on single-prompt, struggling to generate coherent scenes with multiple sequential prompts that better reflect real-world dynamic scenarios. While some pioneering wor

Kanchana Ranasinghe, Sadeep Jayasumana, Andreas Veit, Ayan Chakrabarti

Latent Diffusion Models (LDMs) produce high-quality, photo-realistic images, however, the latency incurred by multiple costly inference iterations can restrict their applicability. We introduce LatentCRF, a continuous Conditional Random Field (CRF) model, implemented as a neural network layer, that models the spatial and semantic relationships among the late

Saman Bazargani, Therese Biedl, Prosenjit Bose, Anil Maheshwari

Let $B$ be a set of Eulerian subgraphs of a graph $G$. We say $B$ forms a $k$-basis if it is a minimum set that generates the cycle space of $G$, and any edge of $G$ lies in at most $k$ members of $B$. The basis number of a graph $G$, denoted by $b(G)$, is the smallest integer such that $G$ has a $k$-basis. A graph is called 1-planar (resp. planar) if it can

Vignesh Tirukkonda, Anirudh Rayas, Gautam Dasarathy

Gaussian graphical model selection is usually studied under independent sampling, but in many applications observations arise from dependent dynamics. We study structure learning when the data consist of a single trajectory of Gaussian Glauber dynamics. We develop two complementary approaches. The first is a local edge-testing estimator based on an appropria

David Shoresh, Yonatan Loewenstein

The field of collective intelligence studies how teams can achieve better results than any of the team members alone. The special case of human-machine teams carries unique challenges in this regard. For example, human teams often achieve synergy by communicating to discover their relative advantages, which is not an option if the team partner is an unexplai

S. Balasubramanian, Ammu Abhishek, Yedu Krishna, Darshan Gera

Gastrointestinal (GI) bleeding is a serious medical condition that presents significant diagnostic challenges, particularly in settings with limited access to healthcare resources. Wireless Capsule Endoscopy (WCE) has emerged as a powerful diagnostic tool for visualizing the GI tract, but it requires time-consuming manual analysis by experienced gastroentero

Liuquan Wang, Huohong Zhang

Zagier observed that modular Nahm sums associated with the same matrix may form a vector-valued modular function on some congruence subgroup. We establish modular transformation formulas for several families of Nahm sums by viewing them as vector-valued functions, and thereby we show that they are indeed modular on the congruence subgroup $\Gamma_0(N)$ with

Xinran Li, Yi Shuai, Chen Liu, Qi Chen

Tumor synthesis can generate examples that AI often misses or over-detects, improving AI performance by training on these challenging cases. However, existing synthesis methods, which are typically unconditional -- generating images from random variables -- or conditioned only by tumor shapes, lack controllability over specific tumor characteristics such as

OpenMind, Shaohong Zhong, Adam Zhou, Boyuan Chen

Large Language Models (LLMs) are compact representations of all public knowledge of our physical environment and animal and human behaviors. The application of LLMs to robotics may offer a path to highly capable robots that perform well across most human tasks with limited or even zero tuning. Aside from increasingly sophisticated reasoning and task planning

Sourav Bhattacharya, Ashish Yadav

We prove a sufficient condition for a \emph{pattern} $\pi$ on a \emph{triod} $T$ to have \emph{rotation number} $\rho_{\pi}$ coincide with an end-point of its \emph{forced rotation interval} $I_{\pi}$. Then, we demonstrate the existence of peculiar \emph{patterns} on \emph{triods} that are neither \emph{triod twists} nor possess a \emph{block structure} over

Haoran Chu, Linjuan Rita Men, Sixiao Liu, Shupei Yuan

This study builds on person perception and human AI interaction (HAII) theories to investigate how content and source cues, specifically race, ethnicity, and nationality, affect judgments of AI-generated content in a high-stakes self-presentation context: college applications. Results of a pre-registered experiment with a nationally representative U.S. sampl

A. R. Olamaei, S. Rostami, K. Azizi

We investigate the kinematically allowed baryon to baryon-meson strong transitions in all the light and heavy sectors. We consider only the ground state on-shell particles in the baryonic and mesonic channels. In the case of mesons, only the well-established pseudoscslar and vector nonets are involved. For the baryons, we consider the ground state spin 1/2 a

Joshua Levin, Ariel Shlosberg, Vikesh Siddhu, Graeme Smith

Information theory provides a framework for answering fundamental questions about the optimal performance of many important quantum communication and computational tasks. In many cases, the optimal rates of these tasks can be expressed in terms of regularized formulas that consist of linear combinations of von Neumann entropies optimized over state extension

Katherine A. Maxwell, Alexander A. Voronov

In 1987, Albert Schwarz suggested a formula which extends the super Mumford form from the moduli space of super Riemann surfaces into the super Sato Grassmannian. His formula is a remarkably simple combination of super tau functions. We compute the Neveu-Schwarz action on super tau functions, and show that Schwarz's extended Mumford form is invariant under t

Anselm Krainovic, Stefan Ruschke, Reinhard Heckel

Deep learning-based 3D imaging, in particular magnetic resonance imaging (MRI), is challenging because of limited availability of 3D training data. Therefore, 2D diffusion models trained on 2D slices are starting to be leveraged for 3D MRI reconstruction. However, as we show in this paper, existing methods pertain to a fixed voxel size, and performance degra

Naz Yaldiz, Amaresh Chakrabarti

Sustainability is a concept mainly assessed by the features of a product that are considered as specific metrics for sustainability in an engineering context. However, sustainability is a comprehensive process for improvement in sustainable development that can be structured with the influence of inclusivity of empowered people. We define sustainability valu

Sergey Sedov, Sumanth Bharadwaj Hachalli Karanam, Venu Gopal Kadamba

Prompt-Tuning is an efficient method for adapting pre-trained language models to new tasks with minimal computational overhead by modifying prompt embeddings. In this work, we investigate how crucial the phenomenon of embedding collapse, frequently observed in Prompt-Tuning, is for the final performance of the model. To address this question, we designed emb

Lucas G. B. de Souza, M. G. E. da Luz, E. P. Raposo, Evaldo M. F. Curado

We study sums of independent and identically distributed random velocities in special relativity. We show that the resulting one-dimensional velocity distributions are not only stable under relativistic velocity addition but define a genuinely new class of stochastic processes--relativistic L\'evy processes. Given a system, this allows identifying distinct r

Shen-Ning Tung, Tai-Ho Wang

This paper develops a rigorous mathematical framework for analyzing Concentrated Liquidity Market Makers (CLMMs) in Decentralized Finance (DeFi) within a continuous-time setting. We model the evolution of liquidity profiles as measure-valued processes and characterize their dynamics under continuous trading. Our analysis encompasses two critical aspects of C

Oliver Cassidy, Marta Andronic, Samuel Coward, George A. Constantinides

Lookup tables (LUTs) are frequently used to efficiently store arrays of precomputed values for complex mathematical computations. When used in the context of neural networks, these functions exhibit a lack of recognizable patterns which presents an unusual challenge for conventional logic synthesis techniques. Several approaches are known to break down a sin

Tejas Bhojraj

A state $\rho=(\rho_n)_{n=1}^{\infty}$ is a sequence such that $\rho_n$ is a density matrix on $n$ qubits. It formalizes the notion of an infinite sequence of qubits. The von Neumann entropy $H(d)$ of a density matrix $d$ is the Shannon entropy of its eigenvalue distribution. We show: (1) If $\rho$ is a computable quantum Schnorr random state then $\lim_n [H

Azim Ospanov, Mohammad Jalali, Farzan Farnia

The use of CLIP embeddings to assess the fidelity of samples produced by text-to-image generative models has been extensively explored in the literature. While the widely adopted CLIPScore, derived from the cosine similarity of text and image embeddings, effectively measures the alignment of a generated image, it does not quantify the diversity of images gen

Yu-Hua Yao, Fang-Sheng Min, Shi Chen, Yi-Qing Guo

The study of high-energy gamma-ray emission from gamma-ray bursts (GRBs) involves complex synchrotron radiation and synchrotron self-Compton scattering (SSC) mechanisms with multiple parameters exhibiting a wide distribution. Recent advancements in GRB research, particularly the observation of very high energy (VHE, $\rm >100~GeV$) radiation, have ushered in

Angelica Babei, Barinder S. Banwait, AJ Fong, Xiaoyu Huang

We train machine learning models to predict the order of the Shafarevich-Tate group of an elliptic curve over $\mathbb{Q}$. Building on earlier work of He, Lee, and Oliver, we show that a feed-forward neural network classifier trained on subsets of the invariants arising in the Birch--Swinnerton-Dyer conjectural formula yields higher accuracies ($> 0.9$) tha

Monalisa Cavalcante, José Araújo, José Holanda

Image processing has always been a topic of significant importance to society. Recently, this field has gained considerable prominence due to the development of intelligent systems. In this work, we present a new method of image processing that utilizes statistical analysis, specifically designed for applications in intelligent systems. We tested our method

Vishwajeet Kumar, Arnab Pal, Ohad Shpielberg

Metastable states appear as long-lived intermediate states in various natural transport phenomena which are governed by energy landscapes. As such, these intermediate metastable states dominate the system's dynamics at coarse grained times. Moreover, they can strongly influence the overall pathways through which the energy landscape is explored. Thus, quanti

Dewu Zheng, Yanlin Wang, Ensheng Shi, Xilin Liu

With the rapid advancement of large language models (LLMs), extensive research has been conducted to investigate the code generation capabilities of LLMs. However, existing efforts primarily focus on general-domain tasks, leaving LLMs' code generation performance in real-world application domains underexplored. This raises a critical question: can a model's

Vladimir Chernov, Rustam Sadykov

Kauffman virtual knots are knots in thickened surfaces $F\times R$ considered up to isotopy, stabilizations and destabilizations, and diffeomorphisms of $F\times R$ induced by orientation preserving diffeomorphisms of $F$. Similarly, virtual Legendrian knots, introduced by Cahn and Levi~\cite{CahnLevi}, are Legendrian knots in $ST^*F$ with the natural contac

Co Tran, Quoc-Bao Tran, Hy Truong Son, Thang N Dinh

Hard combinatorial optimization problems, often mapped to Ising models, promise potential solutions with quantum advantage but are constrained by limited qubit counts in near-term devices. We present an innovative quantum-inspired framework that dynamically compresses large Ising models to fit available quantum hardware of different sizes. Thus, we aim to br

Katherine A. Maxwell, Alexander A. Voronov

We construct a local universal Mumford form on a product of Sato Grassmannians using the flow of the Virasoro algebra. The existence of this universal Mumford form furthers the proposal that the Sato Grassmannian provides a universal moduli space with applications to string theory. Our approach using the Virasoro flow is an alternative to using the KP flow,

Atanu Guha, Debashree Sen, Chang Ho Hyun

The composition and equation of state (EoS) of dense matter relevant to compact stars are quite inconclusive. However, certain observational constraints on the structural properties of compact stars help us constrain the EoS to a fair extent. Moreover, gravitational asteroseismology gives a notion of the composition and EoS of compact stars. The next generat

Wenqin Du, Rundong Ding, Yingying Fan, Jinchi Lv

The problem of evaluating the effectiveness of a treatment or policy commonly appears in causal inference applications under network interference. In this paper, we suggest the new method of high-dimensional network causal inference (HNCI) that provides both valid confidence interval on the average direct treatment effect on the treated (ADET) and valid conf

C. N. Azevedo, F. C. Sobrinho, F. S. Navarra

Very recently, the two-photon decay width of the $\eta_b$ meson was computed with lattice QCD methods. This decay has not yet been measured. The knowledge of this width allows for the calculation of the $\eta_b$ production cross section through photon-photon interactions in ultra-peripheral $PbPb$ collisions. In this work we present this calculation, which i

Karel Mundnich, Xing Niu, Prashant Mathur, Srikanth Ronanki

Despite recent advancements in speech processing, zero-resource speech translation (ST) and automatic speech recognition (ASR) remain challenging problems. In this work, we propose to leverage a multilingual Large Language Model (LLM) to perform ST and ASR in languages for which the model has never seen paired audio-text data. We achieve this by using a pre-

Yihang Luo, Shangchen Zhou, Yushi Lan, Xingang Pan

Despite advances in neural rendering, due to the scarcity of high-quality 3D datasets and the inherent limitations of multi-view diffusion models, view synthesis and 3D model generation are restricted to low resolutions with suboptimal multi-view consistency. In this study, we present a novel 3D enhancement pipeline, dubbed 3DEnhancer, which employs a multi-

Apurba Sarker

Aircraft design optimization traditionally relies on computationally expensive simulation techniques such as Finite Element Method (FEM) and Finite Volume Method (FVM), which, while accurate, can significantly slow down the design iteration process. The challenge lies in reducing the computational complexity while maintaining high accuracy for quick evaluati

Gang Huang, Xiaohua Zhou, Qingyang Song

Artificial intelligence is transforming financial investment decision-making frameworks, with deep reinforcement learning demonstrating substantial potential in robo-advisory applications. This paper addresses the limitations of traditional portfolio optimization methods in dynamic asset weight adjustment through the development of a deep reinforcement learn

Bengong Lou, Zheng Zuo, Bo Ling

A Cayley graph $\Ga=\Cay(G,S)$ is said to be normal if the right-regular representation of $G$ is normal in $\Aut\Ga$. In this paper, we investigate the normality problem of the connected 13-valent symmetric Cayley graphs $\Ga$ of finite nonabelian simple groups $G$, where the vertex stabilizer $\A_v$ is soluble for $\A=\Aut\Ga$ and $v\in V\Ga$. We prove tha

R. O. Kuzian, D. V. Efremov, E. E. Krasovskii

Bound states and scattering resonances in the unoccupied continuum of a two-dimensional crystal predicted in [Phys$.$Rev$.$ B 87, 041405(R) (2013)] are considered within an exactly solvable model. A close connection of the observed resonances with those arising in the Fano theory is revealed. The resonance occurs when the lateral scattering couples the layer

Caterina Balzotti, Roberta Bianchini, Maya Briani, Benedetto Piccoli

In this paper, we present an extension of the Generic Second Order Models (GSOM) for traffic flow on road networks. We define a Riemann solver at the junction based on a priority rule and provide an iterative algorithm to construct solutions at junctions with n incoming and m outgoing roads. The logic underlying our solver is as follows: the flow is maximize

Louis H. Rowen

Continuing the study of the structure of semirings, we turn to the spectrum of prime congruences. Joo and Mincheva developed an elegant theory in the special case of idempotent semirings, which is generalized here to ``semiring pairs,'' which include supertropical semirings and various classes of hyperrings. Our main result is that the Joo-Mincheva spectrum

Jaouhar Fattahi

In the paced realms of cybersecurity and digital forensics machine learning (ML) and deep learning (DL) have emerged as game changing technologies that introduce methods to identify stop and analyze cyber risks. This review presents an overview of the ML and DL approaches used in these fields showcasing their advantages drawbacks and possibilities. It covers

Scott Baldridge, Ben McCarty

We show how ideas coming out of gauge theory can be used to prove configurations in the list of ``633 unavoidable configurations" are reducible. In this paper, we prove the smallest nontrivial example, the Birkhoff diamond, is reducible using our filtered $3$- and $4$-color homology. This is a new proof of a 111-year-old result that is a direct consequence o

Guochen Yan, Luyuan Xie, Xinyi Gao, Wentao Zhang

Federated learning has become a promising solution for collaboration among medical institutions. However, data owned by each institution would be highly heterogeneous and the distribution is always non-independent and identical distribution (non-IID), resulting in client drift and unsatisfactory performance. Despite existing federated learning methods attemp

Vishal Singh, Theshani Nuradha, Mark M. Wilde

Unextendibility of quantum states and channels is inextricably linked to the no-cloning theorem of quantum mechanics, it has played an important role in understanding and quantifying entanglement, and more recently it has found applications in providing limitations on quantum error correction and entanglement distillation. Here we generalize the framework of

L. R. Patrick, D. J. Lennon, A. Schootemeijer, L. Bianchi

Red supergiant stars (RSGs) represent the final evolutionary phase of the majority of massive stars and hold a unique role in testing the physics of stellar models. Eighty eight RSGs in the Small Magellanic Cloud (SMC) were recently found to have an ultra-violet excess that was attributed to a B-type companion. We present follow-up Hubble Space Telescope (HS

Zigeng Huang, Zhen Guo, Changsu Cao, Hung Q. Pham

Predictive simulation of surface chemistry is of paramount importance for progress in fields from catalysis to electrochemistry and clean energy generation. Ab-initio quantum many-body methods should be offering deep insights into these systems at the electronic level, but are limited in their efficacy by their steep computational cost. In this work, we buil

Yice Zhang, Guangyu Xie, Hongling Xu, Kaiheng Hou

Fine-grained sentiment analysis (FSA) aims to extract and summarize user opinions from vast opinionated text. Recent studies demonstrate that large language models (LLMs) possess exceptional sentiment understanding capabilities. However, directly deploying LLMs for FSA applications incurs high inference costs. Therefore, this paper investigates the distillat

Haonan Li, Xudong Han, Zenan Zhai, Honglin Mu

To address this gap, we introduce Libra-Leaderboard, a comprehensive framework designed to rank LLMs through a balanced evaluation of performance and safety. Combining a dynamic leaderboard with an interactive LLM arena, Libra-Leaderboard encourages the joint optimization of capability and safety. Unlike traditional approaches that average performance and sa

A. E. Cárcamo Hernández, D. T. Huong, H. N. Long, Daniel Salinas-Arizmendi

We present a extension of the 3-3-1 gauge model supplemented by an $A_4$ flavor symmetry and cyclic discrete symmetries, including $Z_2$, $Z_2'$, $Z_3$, $Z_4$, $Z_7$ and $Z_{10}$. The model successfully reproduces the observed SM fermion mass hierarchies and mixing patterns in quark and lepton sectors. The smallness of the active neutrino masses is explained

Lorenzo Lucchini, Valentina Marziano, Filippo Trentini, Chiara Chiavenna

The collection of updated data on social contact patterns following the COVID-19 pandemic disruptions is crucial for future epidemiological assessments and evaluating non-pharmaceutical interventions (NPIs) based on physical distancing. We conducted two waves of an online survey in March 2022 and March 2023 in Italy, gathering data from a representative popu

Alfredo Amador-Portes, Vahram Chavushyan, Víctor M. Patiño-Álvarez, José Ramón-Valdés

The flat spectrum radio quasar PKS 1510-089 is one of the most active blazars across the entire electromagnetic spectrum, displaying periods of flaring activity. This study explores its spectral variability over a decade. By employing the non-thermal dominance parameter, we analyze the H$\beta$ and $\lambda5100\text{ \AA}$ continuum light curves, as well as

Tingxu Han, Zhenting Wang, Chunrong Fang, Shiyu Zhao

Reasoning is critical for large language models (LLMs) to excel in a wide range of tasks. While methods like Chain-of-Thought (CoT) reasoning and enhance LLM performance by decomposing problems into intermediate steps, they also incur significant overhead in token usage, leading to increased costs. We find that the reasoning process of current LLMs is unnece

Sandro Mereghetti

I review the properties and discuss some of the puzzling aspects of the unique binary system composed of the luminous hot subdwarf HD 49798 and a white dwarf with mass of 1.2 solar masses and spin period of 13.2 s. This is one of the few massive white dwarfs with a dynamically measured mass and the one with the shortest spin period. It emits pulsed X-rays wi

Mingyuan Meng, Michael Fulham, Lei Bi, Jinman Kim

Deformable image registration is a fundamental requirement for medical image analysis. Recently, transformers have been widely used in deep learning-based registration methods for their ability to capture long-range dependency via self-attention (SA). However, the high computation and memory loads of SA (growing quadratically with the spatial resolution) hin

Daniel Paleka, Abhimanyu Pallavi Sudhir, Alejandro Alvarez, Vineeth Bhat

Forecasting is a task that is difficult to evaluate: the ground truth can only be known in the future. Recent work showing LLM forecasters rapidly approaching human-level performance begs the question: how can we benchmark and evaluate these forecasters instantaneously? Following the consistency check framework, we measure the performance of forecasters in t

Chris Verhoek, Ivan Markovsky, Sofie Haesaert, Roland Tóth

In this paper, we present a data-driven representation for linear parameter-varying (LPV) systems, which can be used for direct data-driven analysis and control of such systems. Specifically, we use the behavioral approach to develop a data-driven representation of the finite-horizon behavior of LPV systems for which there exists a kernel representation with

Rajat Majumder, Alexander Yu. Sokolov

We present algebraic diagrammatic construction theory for simulating spin-orbit coupling and electron correlation in charged electronic states and photoelectron spectra. Our implementation supports Hartree-Fock and multiconfigurational reference wavefunctions, enabling efficient correlated calculations of relativistic effects using single-reference (SR-) and

Xingjian Xu, Jiahui Chen, Chunmei Wang

Recent advances in topology-based modeling have accelerated progress in physical modeling and molecular studies, including applications to protein-ligand binding affinity. In this work, we introduce the Persistent Laplacian Decision Tree (PLD-Tree), a novel method designed to address the challenging task of predicting protein-protein interaction (PPI) affini