October 2024 arXiv papers — page 97

Luca Vincenzo Ballestra, Enzo D'Innocenzo, Christian Tezza

We introduce a novel GARCH model that integrates two sources of uncertainty to better capture the rich, multi-component dynamics often observed in the volatility of financial assets. This model provides a quasi closed-form representation of the characteristic function for future log-returns, from which semi-analytical formulas for option pricing can be deriv

Wei Ai, Wen Deng, Hongyi Chen, Jiayi Du

Multi-modal entity alignment (MMEA) is essential for enhancing knowledge graphs and improving information retrieval and question-answering systems. Existing methods often focus on integrating modalities through their complementarity but overlook the specificity of each modality, which can obscure crucial features and reduce alignment accuracy. To solve this,

Philipp Werner, Sayed Ali Akbar Ghorashi

Single-orbital Hubbard models exhibit remarkably nontrivial correlation phenomena, even on nonfrustrated bipartite lattices. Some of these, like non-Fermi-liquid metal states, or the coexistence of heavy and light quasi-particles, are reminiscent of the properties of more complex multi-orbital or Kondo-lattice systems. Here, we use basis transformations to m

Juyeon Heo, Miao Xiong, Christina Heinze-Deml, Jaya Narain

Large language models (LLMs) could be valuable personal AI agents across various domains, provided they can precisely follow user instructions. However, recent studies have shown significant limitations in LLMs' instruction-following capabilities, raising concerns about their reliability in high-stakes applications. Accurately estimating LLMs' uncertainty in

Addison Kristanto Julistiono, Davoud Ataee Tarzanagh, Navid Azizan

Attention mechanisms have revolutionized several domains of artificial intelligence, such as natural language processing and computer vision, by enabling models to selectively focus on relevant parts of the input data. While recent work has characterized the optimization dynamics of gradient descent (GD) in attention-based models and the structural propertie

Victor Ale, Nora M. Bauer, Raghav G. Jha, Felix Ringer

We present a quantum computational framework for SU(2) lattice gauge theory, leveraging continuous variables instead of discrete qubits to represent the infinite-dimensional Hilbert space of the gauge fields. We consider a ladder as well as a two-dimensional grid of plaquettes, detailing the use of gauge fixing to reduce the degrees of freedom and simplify t

Lihi Idan

Unsupervised validation of anomaly-detection models is a highly challenging task. While the common practices for model validation involve a labeled validation set, such validation sets cannot be constructed when the underlying datasets are unlabeled. The lack of robust and efficient unsupervised model-validation techniques presents an acute challenge in the

Thennal D K, Tim Fischer, Chris Biemann

Large language models (LLMs) demonstrate strong performance as text embedding models when finetuned with supervised contrastive training. However, their large size balloons inference time and memory requirements. In this paper, we show that by pruning the last $p\%$ layers of an LLM before supervised training for only 1000 steps, we can achieve a proportiona

Y. Wang, J. D. Opfermann, J. Yu, H. Yi

Autonomous surgical robots have demonstrated significant potential to standardize surgical outcomes, driving innovations that enhance safety and consistency regardless of individual surgeon experience. Deep anterior lamellar keratoplasty (DALK), a partial thickness corneal transplant surgery aimed at replacing the anterior part of cornea above Descemet membr

Suman Mondal, Dhiren Kumar Basnet

We know that if there are $k$ distinct prime factors of $n \in \mathbb{N}$, then the ring $\mathbb{Z}_n$ of integers modulo $n$ has exactly $2^k$ idempotent elements. In this article, we try to describe all the idempotents of $\mathbb{Z}_n$ for any given $n \in \mathbb{N}$.

Giuseppe Maria Coclite, Ingenuin Gasser

Parabolic trough power plants transform solar radiative energy into thermal energy which is then typically used to produce electricity. We consider a model derived in \cite{BGSP} to describe parabolic trough power plants. In particular, the thermo-fluid dynamics is studied in a single collector pipe where the solar radiation is concentrated. The model is the

Rachel S. Y. Teo, Tan M. Nguyen

Sparse Mixture of Experts (SMoE) has become the key to unlocking unparalleled scalability in deep learning. SMoE has the potential to exponentially increase parameter count while maintaining the efficiency of the model by only activating a small subset of these parameters for a given sample. However, it has been observed that SMoE suffers from unstable train

Chun Huang, Laura Tolos, Constança Providência, Anna Watts

We investigate whether measurements of the neutron star mass and radius or the tidal deformability can provide information about the presence of hyperons inside a neutron star. This is achieved by considering two inference models, with and without hyperons, based on a field-theoretical approach. While current observations do not distinguish between the two s

Hui Yang, Jiaoyan Chen, Uli Sattler

OWL (Web Ontology Language) ontologies, which are able to represent both relational and type facts as standard knowledge graphs and complex domain knowledge in Description Logic (DL) axioms, are widely adopted in domains such as healthcare and bioinformatics. Inspired by the success of knowledge graph embeddings, embedding OWL ontologies has gained significa

Zifei Xu, Sayeh Sharify, Wanzin Yazar, Tristan Webb

Large language models of high parameter counts are computationally expensive, yet can be made much more efficient by compressing their weights to very low numerical precision. This can be achieved either through post-training quantization by minimizing local, layer-wise quantization errors, or through quantization-aware fine-tuning by minimizing the global l

Hanna Kim, Minkyoo Song, Seung Ho Na, Seungwon Shin

Recent advancements in Large Language Models (LLMs) have established them as agentic systems capable of planning and interacting with various tools. These LLM agents are often paired with web-based tools, enabling access to diverse sources and real-time information. Although these advancements offer significant benefits across various applications, they also

Andronikos Paliathanasis

The connection between the Equivalence Principle and Noether's theorem was discussed in S. Capozziello and C. Ferrara, Int. J. Geom. Meth. Mod. Phys. 21, 2440014 (2024). However, it is known that the Noether symmetry condition is independent of the equations of motions as follows from Hamilton's Principle. In this paper, we critically examine the analysis pr

Zhiyuan Peng, Jinming Nian, Alexandre Evfimievski, Yi Fang

Retrieval-augmented generation (RAG) has become integral to large language models (LLMs), particularly for conversational AI systems where user questions may reference knowledge beyond the LLMs' training cutoff. However, many natural user questions lack well-defined answers, either due to limited domain knowledge or because the retrieval system returns docum

Xiaxin Li, Arya Mazumdar

In Group Testing, the objective is to identify $K$ defective items out of $N$, $K\ll N$, by testing pools of items together and using the least amount of tests possible. Recently, a fast decoding method based on binary splitting (Price and Scarlett, 2020) has been proposed that simultaneously achieve optimal number of tests and decoding complexity for Non-Ad

Jianping Li, Thien-Minh Nguyen, Muqing Cao, Shenghai Yuan

Large-scale LiDAR Bundle Adjustment (LBA) to refine sensor orientation and point cloud accuracy simultaneously to build the navigation map is a fundamental task in logistics and robotics. Unlike pose-graph-based methods that rely solely on pairwise relationships between LiDAR frames, LBA leverages raw LiDAR correspondences to achieve more precise results, es

Ondřej Kincl, Ilya Peshkov, Walter Boscheri

This paper contributes to the recent investigations of Lagrangian methods based on Voronoi meshes. The aim is to design a new conservative numerical scheme that can simulate complex flows and multi-phase problems with more accuracy than SPH (Smoothed Particle Hydrodynamics) methods but, unlike diffuse interface models on fixed grid topology, does not suffer

Luiz A. C. A. Schiavo, James Stewart, Philippa K. Browning

Quasi-periodic pulsations (QPPs) are often observed in flare emissions. While these may reveal much about the time-dependent reconnection involved in flare energy release, the underlying mechanisms are still poorly understood. In this paper, we use 2D magnetohydrodynamic simulations to investigate the magnetic reconnection in two merging flux ropes, focusing

Anna A. Anisimova, Yaroslav M. Plotnikov, Dmitry M. Korotin

We explore the electronic and magnetic properties of NH$_4^+$-intercalated vanadium pentoxide (NH$_4^+$-V$_2$O$_5$), a material that has been identified as a promising cathode for aqueous zinc-ion batteries. Density Functional Theory (DFT) calculations incorporating the Hubbard U correction reveal that NH$_4^+$-V$_2$O$_5$ is an antiferromagnetic insulator wi

Anton Klimek, Johannes C. J. Heyn, Debasmita Mondal, Sophia Schwartz

When analyzing the individual positional dynamics of an ensemble of moving objects, the extracted parameters that characterize the motion of individual objects, such as the mean-squared instantaneous velocity or the diffusivity, exhibit a spread that is due to the convolution of three different effects: i) Motion stochasticity, caused by the fluctuating envi

E. J. B. Ferreira, H. F. Santana Mota

In this paper, the Quantum Brownian motion of a point particle induced by the quantum vacuum fluctuations of a real massless scalar field in Einstein universe under Dirichlet and Neumann boundary conditions is studied. Using the Wightman functions, general expressions for the renormalized dispersion of the physical momentum are derived. Distinct expressions

Gonzalo Agustin Lucero, Argelia Bernal, Juan Barranco, Andres Aceña

It is shown that the data from the orbital period decay of binary pulsars give strong constraints on the dark matter-nucleons cross section. The limits are robust and competitive because this new method for testing dark matter interactions with standard model particles has a minimal number of assumptions combined with the extremely high accuracy on the measu

Elia Fantini

This thesis presents an innovative approach to automate video thumbnail selection for traditional broadcast content. Our methodology establishes stringent criteria for diverse, representative, and aesthetically pleasing thumbnails, considering factors like logo placement space, incorporation of vertical aspect ratios, and accurate recognition of facial ident

Wei Ai, Jianbin Li, Ze Wang, Jiayi Du

Graph contrastive learning (GCL) has been widely applied to text classification tasks due to its ability to generate self-supervised signals from unlabeled data, thus facilitating model training. However, existing GCL-based text classification methods often suffer from negative sampling bias, where similar nodes are incorrectly paired as negative pairs. This

Leonardo A. Navarro-Labastida

Thermal effects in a one-dimensional Su-Schrieffer-Hegger (SSH) topological insulator are studied. Particularly, we focus on quantum information processing (QIP) capacity for thermal ensembles. To evaluate QIP an optimized quantum Fisher information (OQFI) is introduced as a quantifier of entanglement and topological phases are calculated by a definition in

Konstantin Kalinin, Govind Menon, Bian Wu

We derive robust long-time a-priori estimates for the Navier-Stokes equation in a two-dimensional infinite strip which are uniform in the Reynolds number. These estimates provide several new scale invariant upper bounds for the size of the mixing layer in the Kelvin-Helmholtz instability.

Mikhail Mironov, Liudmila Prokhorenkova

This paper addresses the problem of quantifying diversity for a set of objects. First, we conduct a systematic review of existing diversity measures and explore their undesirable behavior in certain cases. Based on this review, we formulate three desirable properties (axioms) of a reliable diversity measure: monotonicity, uniqueness, and continuity. We show

Salvatore Tirone, Gian Marcello Andolina, Giuseppe Calajò, Vittorio Giovannetti

Quantum batteries have demonstrated remarkable charging properties, showing that a quantum advantage is possible in the realm of quantum thermodynamics. However, finding an effective strategy to store energy for long periods remains crucial in these systems. Here, we investigate different configurations of a waveguide-QED system acting as a quantum battery a

Javier De Loera

For cluster algebras of surface type, Musiker, Schiffler and Williams gave a formula for cluster variables in terms of perfect matchings of snake graphs. Building on this, we provide a simple determinantal formula for cluster variables via the weighted biadjacency matrix of the associated snake graphs, thus circumventing the enumeration of their perfect matc

Ming Yu, Lihao Zhao, Yibin Du, Xianxu Yuan

In the present study, we conduct direct numerical simulations to investigate the near-wall dynamics of compressible turbulent boundary layers at the free-stream Mach number of 6 laden with heavy particles. By inspecting the instantaneous near-wall flow structures, Reynolds stresses and the impacts of particle forces on solenoidal and dilatational motions, we

Mengqi Wang, Krishna Kumar, Y. T. Feng, Tongming Qu

Artificial intelligence (AI) has become a buzz word since Google's AlphaGo beat a world champion in 2017. In the past five years, machine learning as a subset of the broader category of AI has obtained considerable attention in the research community of granular materials. This work offers a detailed review of the recent advances in machine learning-aided st

Enkhbold Nyamsuren

Democratization of AI is an important topic within the broader topic of the digital divide. This issue is relevant to LLMs, which are becoming popular as AI co-pilots but suffer from a lack of accessibility due to high computational demand. In this study, we evaluate whether quantization is a viable approach toward enabling LLMs on generic consumer devices.

Alex Gower, Oliver Hart, Claudio Castelnovo

Slow relaxation and glassiness have been the focus of extensive research attention, along with popular and technological interest, for many decades. While much understanding has been attained through mean-field and mode-coupling models, energy landscape paradigms, and real-space descriptions of dynamical heterogeneities and facilitation, a complete framework

Jingjing Chen, Mikael Afzelius

Long-duration and efficient quantum memories for photons are key components of quantum repeater and network applications. To achieve long duration storage in atomic systems, a short-lived optical coherence can be mapped into a long-lived spin coherence, which is the basis for many quantum memory schemes. In this work, we present modeling and measurements of

Rafał Bistroń, Jakub Czartowski

In quantum thermodynamics, understanding the interplay between locality, thermal constraints, and communication remains an open challenge. In this manuscript, we introduce Local Thermal Operations and Classical Communication (LTOCC), a novel operational framework that unifies the distant laboratories paradigm with thermodynamic restrictions, defining the fun

Lukas A. Codispoti, Daniel W. Meyer, Patrick Jenny

In this work, we aim to deepen the understanding of inertial clustering and the role of sling events in high-Reynolds number ($Re$) particle-laden turbulence. To this end, we perform one-way coupled particle tracking in flow fields obtained from direct numerical simulations (DNS) of forced homogeneous isotropic turbulence. Additionally, we examine the impact

Ravil Mussabayev, Rustam Mussabayev

K-means clustering is a cornerstone of data mining, but its efficiency deteriorates when confronted with massive datasets. To address this limitation, we propose a novel heuristic algorithm that leverages the Variable Neighborhood Search (VNS) metaheuristic to optimize K-means clustering for big data. Our approach is based on the sequential optimization of t

Kun Fang, Zi-Wen Liu

Quantum resource distillation is a fundamental task in quantum information science and technology. Minimizing the overhead of distillation is crucial for the realization of quantum computation and other technologies. Here we explicitly demonstrate how, for general quantum resources, suitably designed quantum catalysts (i.e., auxiliary systems that remain unc

Alexandra Doytcheva, Filomela V. Gerou, Johannes U. Lange

Cosmological simulations of galaxy formation are an invaluable tool for understanding galaxy formation and its impact on cosmological parameter inference from large-scale structure. However, their high computational cost is a significant obstacle for running simulations that probe cosmological volumes comparable to those analyzed by contemporary large-scale

Frederic Kirstein, Terry Ruas, Robert Kratel, Bela Gipp

Meeting summarization is crucial in digital communication, but existing solutions struggle with salience identification to generate personalized, workable summaries, and context understanding to fully comprehend the meetings' content. Previous attempts to address these issues by considering related supplementary resources (e.g., presentation slides) alongsid

Giuseppe De Giacomo, Emiliano Lorini, Timothy Parker, Gianmarco Parretti

Responsibility is one of the key notions in machine ethics and in the area of autonomous systems. It is a multi-faceted notion involving counterfactual reasoning about actions and strategies. In this paper, we study different variants of responsibility in a strategic setting based on LTLf. We show a connection with notions in reactive synthesis, including sy

Hiroki Kawai, Massimo Meneghetti

The galaxy-galaxy strong lensing (GGSL) cross-section in observed galaxy clusters has been reported to be more than an order of magnitude higher than the theoretical prediction by the standard cold dark matter (CDM) model. In this study, we focus on the fuzzy dark matter (FDM) model and study the GGSL cross-section numerically and analytically. We find that

Chao-Chun Wei, Xiaoyin Li, Sabrina Hatt, Xudong Huai

Altermagnets represent a new class of magnetic phases without net magnetization that are invariant under a combination of rotation and time reversal. Unlike conventional collinear antiferromagnets (AFM), altermagnets could lead to new correlated states and important material properties deriving from their non-relativistic spin-split band structure. Indeed, t

Václav Pavlík, Vladimír Karas, Bhavana Bhat, Florian Peißker

Context: The existence of intermediate-mass black holes (IMBHs) still poses challenges to theoretical and observational astronomers. Several candidates have been proposed, including the one in the IRS13 cluster in the Galactic centre, where the evidence is based on the velocity dispersion of its members, however, none have been confirmed to date. Aims: We ai

Calvin-Khang Ta, Arindam Dutta, Rohit Kundu, Rohit Lal

The Skinned Multi-Person Linear (SMPL) model plays a crucial role in 3D human pose estimation, providing a streamlined yet effective representation of the human body. However, ensuring the validity of SMPL configurations during tasks such as human mesh regression remains a significant challenge , highlighting the necessity for a robust human pose prior capab

Weichun Xia, Jiaxin Jiang, Lei Shi

We introduce a novel diffusion-based spectral algorithm to tackle regression analysis on high-dimensional data, particularly data embedded within lower-dimensional manifolds. Traditional spectral algorithms often fall short in such contexts, primarily due to the reliance on predetermined kernel functions, which inadequately address the complex structures inh

Zihao Li, Changhao Yi, You Zhou, Huangjun Zhu

Classical shadow estimation (CSE) is a powerful tool for learning the properties of quantum states and quantum processes. Here we consider the CSE task for quantum unitary channels. By querying an unknown unitary channel $\mathcal{U}$ multiple times in quantum experiments, the goal is to learn a classical description from which one can accurately predict man

Fatemeh Banani Ardecani, Amit Kumar, Omidreza Shoghli

Recent data from the Federal Highway Administration highlights an alarming increase in fatalities and injuries in roadway work zones, emphasizing the need for enhanced worker safety measures. This study addresses this concern by evaluating stress levels among roadway workers equipped with AR-assisted multi-sensory warning technology during varying work inten

Masaru Isonuma, Ivan Titov

Fine-tuning is widely used to adapt language models for specific goals, often leveraging real-world data such as patient records, customer-service interactions, or web content in languages not covered in pre-training. These datasets are typically massive, noisy, and often confidential, making their direct inspection challenging. However, understanding them i

Amanda A. Howard, Bruno Jacob, Panos Stinis

We develop a method for multifidelity Kolmogorov-Arnold networks (KANs), which use a low-fidelity model along with a small amount of high-fidelity data to train a model for the high-fidelity data accurately. Multifidelity KANs (MFKANs) reduce the amount of expensive high-fidelity data needed to accurately train a KAN by exploiting the correlations between th

Maksuda Akter, Rabea Khatun, Md Manowarul Islam

Acute lymphoblastic leukemia (ALL) is the most malignant form of leukemia and the most common cancer in adults and children. Traditionally, leukemia is diagnosed by analyzing blood and bone marrow smears under a microscope, with additional cytochemical tests for confirmation. However, these methods are expensive, time consuming, and highly dependent on exper

Jérome Eertmans, Enrico Maria Vitucci, Vittorio Degli-Esposti, Laurent Jacques

With the increasing presence of dynamic scenarios, such as Vehicle-to-Vehicle communications, radio propagation modeling tools must adapt to the rapidly changing nature of the radio channel. Recently, both Differentiable and Dynamic Ray Tracing frameworks have emerged to address these challenges. However, there is often confusion about how these approaches d

Vedant Das Swain, Qiuyue "Joy" Zhong, Jash Rajesh Parekh, Yechan Jeon

Client-Service Representatives (CSRs) are vital to organizations. Frequent interactions with disgruntled clients, however, disrupt their mental well-being. To help CSRs regulate their emotions while interacting with uncivil clients, we designed Care-Pilot, an LLM-powered assistant, and evaluated its efficacy, perception, and use. Our comparative analyses bet

Olivia Chandrasekhar, Christopher K. R. T. Jones, Blake Barker, Rodman Linn

For certain values of the wave speed parameter, evolution equations for the temperature of a region of fuel admit traveling wave solutions describing fire fronts. We consider such a system in the form of a nonlinear reaction-diffusion equation with a first-order forcing term capturing the combined effects of ambient and fire-induced wind. The fire-induced wi

Marc Meléndez

Sorin Co\c{s}ofre\c{t} has argued that kinetic molecular theory does not correctly describe thermal equilibrium in ideal gases and has provided some examples that purportedly show that the standard laws imply consequences that contradict experimental results. This paper considers a few of the examples in detail and concludes that Co\c{s}ofre\c{t}'s analysis

Qiyuan Chen, Raed Al Kontar

This paper introduces a framework for Bayesian Optimization (BO) with metric movement costs, addressing a critical challenge in practical applications where input alterations incur varying costs. Our approach is a convenient plug-in that seamlessly integrates with the existing literature on batched algorithms, where designs within batches are observed follow

Arthur Emanuel de Oliveira Carosia

Cryptocurrencies have gained significant attention in recent years due to their decentralized nature and potential for financial innovation. Thus, the ability to accurately predict its price has become a subject of great interest for investors, traders, and researchers. Some works in the literature show how Bitcoin's market sentiment correlates with its pric

Ahmet Oguz Sakin, Hamza Kurt, Mehmet Unlu

We present, for the first time, power splitters with multiple channel configurations in one-dimensional grating waveguides (1DGWs) that maintain crystal lattice-sensitive Bloch mode profiles without perturbation across all output channels, all within an ultra-miniaturized footprint of just 2.1 x 2.2 {\mu}m^2. This novel capability reduces the need for transi

Astero Provata, Yannis Almirantis, Wentian Li

Chimera states and bump states are collective synchronization phenomena observed independently (at different parameter regions) in networks of coupled nonlinear oscillators. And while chimera states are characterized by coexistence of coherent and incoherent domains, bump states consist of active domains operating on a silent background. Multistable plastici

Neev Khera, Sizheng Ma, Huan Yang

Quadratic quasinormal modes encode fundamental properties of black hole spacetimes. They are also one of the key ingredients of nonlinearities of General Relativity in the ringdown stage of binary black hole coalescence. In this work, we classify all possible quadratic coupling channels of quasinormal modes for a generic Kerr black hole, and use a frequency-

Lakshmideepakreddy Manda, Shaoru Chen, Mahyar Fazlyab

Learning-based approaches for constructing Control Barrier Functions (CBFs) are increasingly being explored for safety-critical control systems. However, these methods typically require complete retraining when applied to unseen environments, limiting their adaptability. To address this, we propose a self-supervised deep operator learning framework that lear

Wenshan Chen, Kingsley Egbo, Joe Kler, Andreas Falkenstein

Rutile GeO$_2$ is a promising ultra-wide bandgap semiconductor for future power electronic devices whose alloy with the wide bandgap semiconductor rutile-SnO$_2$ enables bandgap engineering and the formation of heterostructure devices. The (Sn$_x$Ge$_{1-x}$)O$_2$ alloy system is in its infancy and molecular beam epitaxy (MBE) is a well-suited technique for i

Solen Balman, Eric M. Schlegel, Patrick Godon, Jeremy J. Drake

We present X-ray spectroscopy of the SU UMa-type dwarf nova (DN) Z Cha using the EPIC and RGS instruments onboard the XMM-Newton Observatory. The quiescent system can be modeled by collisional equilibrium or nonequilibrium plasma models, yielding a kT of 8.2-13.0 keV at a luminosity of (5.0-6.0)$\times$10$^{30}$ erg/s. The spectra yield better reduced $\chi^

Jonas Hansson, Emma Tegling

In this work, we consider the problem of coordinating a collection of $n$th-order integrator systems. The coordination is achieved through the novel serial-consensus design, which can be seen as a method for achieving a stable closed-loop while only using local relative measurements. Earlier work has shown that second-order serial consensus can stabilize a c

Daniel Wolf, Tristan Payer, Catharina Silvia Lisson, Christoph Gerhard Lisson

Self-supervised pre-training of deep learning models with contrastive learning is a widely used technique in image analysis. Current findings indicate a strong potential for contrastive pre-training on medical images. However, further research is necessary to incorporate the particular characteristics of these images. We hypothesize that the similarity of me

Diana Sukhoverkhova, Lev Shchur

In this paper, we applied a deep neural network to study the issue of knowledge transferability between statistical mechanics models. The following computer experiment was conducted. A convolutional neural network was trained to solve the problem of binary classification of snapshots of the Ising model's spin configuration on a two-dimensional lattice. Durin

Joshua Nathaniel Williams, Anurag Katakkar, Hoda Heidari, J. Zico Kolter

Counterfactual explanations have been a popular method of post-hoc explainability for a variety of settings in Machine Learning. Such methods focus on explaining classifiers by generating new data points that are similar to a given reference, while receiving a more desirable prediction. In this work, we investigate a framing for counterfactual generation met

Xian-Wei Kang, Jin-Zhe Zhang, Xin-Heng Guo

We analyze the recent data from the BESIII collaboration on the $X(3872)$ state in the $J/\psi\pi^+\pi^-$ and $D^0\bar{D}^0\pi^0$ decay channels. The quantum number and mass of the $X(3872)$ state allow us to exploit the universal feature of the very near-threshold $D\bar D^*$ scattering in the $S$ wave. The analysis of $J/\psi\pi^+\pi^-$ data and $D^0\bar{D

Gianluca Inverso, Davide Rovere

We prove necessary and sufficient algebraic conditions to determine whether a D=3 gauged maximal supergravity can be obtained from consistent Kaluza-Klein truncation of ten- or eleven-dimensional supergravity. We describe the procedure to identify the internal geometry and explicitly construct the frame encoding the reduction ansatz. As byproducts, we derive

Sridhar Chellappa, Lihong Feng, Peter Benner

The discrete empirical interpolation method (DEIM) is a well-established approach, widely used for state reconstruction using sparse sensor/measurement data, nonlinear model reduction, and interpretable feature selection. We introduce the tensor t-product Q-DEIM (t-Q-DEIM), an extension of the DEIM framework for dealing with tensor-valued data. The proposed

Muhammad Adnan Samad, Yuanqing Xia, Saima Siddiqui, Muhammad Younus Bhat

The linear canonical transform (LCT) serves as a powerful generalization of the Fourier transform (FT), encapsulating various integral transforms within a unified framework. This versatility has made it a cornerstone in fields such as signal processing, optics, and quantum mechanics. Extending this concept to quaternion algebra, the Quaternion Fourier Transf

Biman Barua, M. Shamim Kaiser

This research gives a detailed analysis of the application of blockchain technology to the airline reservation systems in order to bolster trust, transparency, and operational efficiency by overcoming several challenges including customer control and data integrity issues. The study investigates the major components of blockchain technology such as decentral

Artem Dudko, Yongle Jiang

We develop a character approach to study the invariant von Neumann subalgebras rigidity property (abbreviated as the ISR property) introduced in Amrutam-Jiang's work. First, we introduce the non-factorizable regular character property for groups and show that this implies the ISR property for any infinite ICC groups with trivial amenable radical.Various exam

Juyeon Heo, Christina Heinze-Deml, Oussama Elachqar, Kwan Ho Ryan Chan

Instruction-following is crucial for building AI agents with large language models (LLMs), as these models must adhere strictly to user-provided constraints and guidelines. However, LLMs often fail to follow even simple and clear instructions. To improve instruction-following behavior and prevent undesirable outputs, a deeper understanding of how LLMs' inter

Owen Cook, Charlie Grimshaw, Ben Wu, Sophie Dillon

Misinformation spreads rapidly on social media, confusing the truth and targeting potentially vulnerable people. To effectively mitigate the negative impact of misinformation, it must first be accurately detected before applying a mitigation strategy, such as X's community notes, which is currently a manual process. This study takes a knowledge-based approac

Moritz Hauck, Alexei Lozinski

In this paper, we propose a multiscale method for heterogeneous Stokes problems. The method is based on the Localized Orthogonal Decomposition (LOD) methodology and has approximation properties independent of the regularity of the coefficients. We apply the LOD to an appropriate reformulation of the Stokes problem, which allows us to construct exponentially

Xing Mu

Flare stacks play an important role in the treatment of waste gas and waste materials in petroleum fossil energy plants. Monitoring the efficiency of flame combustion is of great significance for environmental protection. The traditional method of monitoring with sensors is not only expensive, but also easily damaged in harsh combustion environments. In this

Luca Vincenzo Ballestra, Enzo D'Innocenzo, Christian Tezza

Christoffersen, Jacobs, Ornthanalai, and Wang (2008) (CJOW) proposed an improved Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model for valuing European options, where the return volatility is comprised of two distinct components. Empirical studies indicate that the model developed by CJOW outperforms widely-used single-component GARCH m

Juan Zurita, Charles E. Creffield, Gloria Platero

In this Letter, we study a previously unexplored class of topological states protected by hidden chiral symmetries that are local, that is, that protect against any off-diagonal disorder. We derive their related topological invariant for the first time, and show that these previously unidentified symmetries can act together with standard chiral symmetries to

Mingjie Li, Masahiro Suzuki, Katherine Zhiyuan Zhang

We consider the non-isentropic compressible Navier-Stokes equation in a perturbed half space with an outflow boundary condition as well as the supersonic condition. This equation models a compressible viscous, heat-conductive, and Newtonian polytropic fluid. We show the unique existence of stationary solutions for the perturbed half-space. The stationary sol

Wolfgang Lück, Irakli Patchkoria, Stefan Schwede

Given a discrete group $G$ with a finite model for $\underline{E}G$, we study $K(n)^*(BG)$ and $E^*(BG)$, where $K(n)$ is the $n$-th Morava $K$-theory for a given prime and $E$ is the height $n$ Morava $E$-theory. In particular we generalize the character theory of Hopkins, Kuhn and Ravenel who studied these objects for finite groups. We give a formula for a

Andrea Appiani, Cigdem Beyan

Voice Activity Detection (VAD) is the process of automatically determining whether a person is speaking and identifying the timing of their speech in an audiovisual data. Traditionally, this task has been tackled by processing either audio signals or visual data, or by combining both modalities through fusion or joint learning. In our study, drawing inspirat

Nefeli Andreou, Xi Wang, Victoria Fernández Abrevaya, Marie-Paule Cani

Our goal is to generate realistic human motion from natural language. Modern methods often face a trade-off between model expressiveness and text-to-motion alignment. Some align text and motion latent spaces but sacrifice expressiveness; others rely on diffusion models producing impressive motions, but lacking semantic meaning in their latent space. This may

David Randahl, Jonathan P. Williams, Håvard Hegre

Forecasting armed conflicts is a critical area of research with the potential to save lives and mitigate suffering. While existing forecasting models offer valuable point predictions, they often lack individual-level uncertainty estimates, limiting their usefulness for decision-making. Several approaches exist to estimate uncertainty, such as parametric and

Pedro Alejandro Dal Bianco, Oscar Agustín Stanchi, Facundo Manuel Quiroga, Franco Ronchetti

This paper presents the first comprehensive interpretability analysis of a Transformer-based Sign Language Translation (SLT) model, focusing on the translation from video-based Greek Sign Language to glosses and text. Leveraging the Greek Sign Language Dataset, we examine the attention mechanisms within the model to understand how it processes and aligns vis

Benyamin Mehmandar, Reza Talakoob, Charalambos Poullis

Currently, there are no learning-free or neural techniques for real-time recalibration of infrared multi-camera systems. In this paper, we address the challenge of real-time, highly-accurate calibration of multi-camera infrared systems, a critical task for time-sensitive applications. Unlike traditional calibration techniques that lack adaptability and strug

Luca Lalor, Anatoliy Swishchuk

We develop a deep reinforcement learning (RL) framework for an optimal market-making (MM) trading problem, specifically focusing on price processes with semi-Markov and Hawkes Jump-Diffusion dynamics. We begin by discussing the basics of RL and the deep RL framework used, where we deployed the state-of-the-art Soft Actor-Critic (SAC) algorithm for the deep l

Mojtaba Hassani, Zahra Faraei

Topological superconductors host gapless surface states that fundamentally alter their electromagnetic response through the axion field term $\theta \vec{E}\cdot\vec{B}$, arising from the topological magnetoelectric effect. In this work, we investigate the electromagnetic properties of a three-dimensional topological Weyl superconductor by leveraging its the

Marvin van Bekkum

In June 2024, the EU AI Act came into force. The AI Act includes obligations for the provider of an AI system. Article 10 of the AI Act includes a new obligation for providers to evaluate whether their training, validation and testing datasets meet certain quality criteria, including an appropriate examination of biases in the datasets and correction measure

Steffen Borgwardt, Nicholas Crawford, Drew Horton, Angela Morrison

We study the problem of evacuation planning for natural disasters, focusing on wildfire evacuations. By creating pre-planned evacuation routes that can be updated based on real-time data, we provide an easily adjustable approach to evacuation planning and implementation. Our method uses publicly available data and can be tailored for a particular region or c

Flavien Bureau, Louise Denis, Antoine Coudert, Mathias Fink

Transcranial ultrasound imaging is usually limited by skull-induced attenuation and high-order aberrations. By using contrast agents such as microbubbles in combination with ultrafast imaging, not only can the signal-to-noise ratio be improved, but super-resolution images down to the micrometer scale of the brain vessels can also be obtained. However, ultras

Jakub K. Sowa, Peter J. Rossky

The use of machine learning (ML) algorithms in molecular simulations has become commonplace in recent years. There now exists, for instance, a multitude of ML force field algorithms that have enabled simulations approaching ab initio level accuracy at time scales and system sizes that significantly exceed what is otherwise possible with traditional methods.

Nishant K. Singh, Arunima Ajay, S R Rajesh

Presence of a hot corona above the accretion disc can have important consequences for the evolution of magnetic fields and the Shakura-Sunyaev (SS) viscosity parameter $\alpha$ in such a strongly coupled system. In this work, we have performed three-dimensional magnetohydrodynamical shearing-box numerical simulations of accretion disc with a hot corona above

Taisei Kii, Kentaro Yaji, Hiroshi Teramoto, Kikuo Fujita

This paper proposes a selection strategy for enhancing population diversity in data-driven topology design (DDTD), a topology optimization framework based on evolutionary algorithms (EAs) using a deep generative model. While population diversity is essential for global search with EAs, conventional selection operators that preserve diverse solutions based on

Dirk Fahland, Marco Montali, Julian Lebherz, Wil M. P. van der Aalst

Process mining is shifting towards use cases that explicitly leverage the relations between data objects and events under the term of object-centric process mining. Realizing this shift and generally simplifying the exchange and transformation of data between source systems and process mining solutions requires a standardized data format for such object-cent

Petr Balek, Tomasz Bold, Michal Naworyta

Charged particle track reconstruction in silicon detectors of collider experiments in high-multiplicity events, such as heavy-ion collisions at LHC, is a difficult and resource-demanding process. The first phase of the procedure is the formation of seeds composed out of a few signals per track. A high number of actual particles results in a combinatorial exp