December 2024 arXiv papers — page 165

Jiang Yang, Yuxiang Zhao, Quanhui Zhu

Understanding the training dynamics of deep neural networks (DNNs), particularly how they evolve low-dimensional features from high-dimensional data, remains a central challenge in deep learning theory. In this work, we introduce the concept of $\epsilon$-rank, a novel metric quantifying the effective feature of neuron functions in the terminal hidden layer.

Kaarthik Sundar, Deepjyoti Deka, Russell Bent

The article develops a parametric model of fairness called "$\varepsilon$-fairness" that can be represented using a single second-order cone constraint and incorporated into existing decision-making problem formulations without impacting the complexity of solution techniques. We develop the model from the fundamental result of finite-dimensional norm equival

Chengcheng Ling

We study the strong approximation of the solutions to singular stochastic kinetic equations (also referred to as second-order SDEs) driven by $\alpha$-stable processes, using an Euler-type scheme inspired by [11]. For these equations, the stability index $\alpha$ lies in the range $(1,2)$, and the drift term exhibits anisotropic $\beta$-H\"older continuity w

Bindi S. Brook, Mathew Bulpett, Robin Curnow, Emily Fraser

This report relates to a study group hosted by the EPSRC funded network, Integrating data-driven BIOphysical models into REspiratory MEdicine (BIOREME), and supported by SofTMech and Innovate UK, Business Connect. This report summarises the work undertaken on a challenge presented by two of the authors, Mathew Bulpett and Dr Emily Fraser. The aim was to iden

Jillian M. Scudder, Aidan Khelil, Jonah Z. Ordower

In this work, we use gas phase metallicities calculated from the Sloan Digital Sky Survey (SDSS) Mapping Nearby Galaxies at Apache Point (MaNGA) Data Release 17 (DR17) to assess the extent of potential biases in spaxels which are spatially adjacent to spaxels identified as non-star forming (non-SF) on a BPT diagram. We identify a sample of $\sim21,000$ such

Brian Tufts, Xuandong Zhao, Lei Li

The proliferation of large language models has raised growing concerns about their misuse, particularly in cases where AI-generated text is falsely attributed to human authors. Machine-generated content detectors claim to effectively identify such text under various conditions and from any language model. This paper critically evaluates these claims by asses

V. Adami, S. Emdadi-Mahdimahalleh, H. J. Herrmann, M. N. Najafi

We propose a novel paradigm for modeling real-world scale-free networks, where the integration of new nodes is driven by the combined attractiveness of degree and betweenness centralities, the competition of which (expressed by a parameter $0\le p\le 1$) shapes the structure of the evolving network. We reveal the ability to seamlessly explore a vast landscap

Finn Klessascheck, Luise Pufahl

Organizations need to manage numerous business processes for delivering their services and products to customers. One important consideration thereby lies in the adherence to regulations such as laws, guidelines, or industry standards. In order to monitor adherence of their business processes to regulations -- in other words, their regulatory compliance -- o

Can Ozkan, Xinhai Zou, Dave Singelee

The SolarWinds attack, which exploited weaknesses in a software update mechanism, highlights the critical need for organizations to have better visibility into their software dependencies and potential vulnerabilities associated with them. The Software Bill of Materials (SBOM) is paramount in ensuring software supply chain security. Under the Executive Order

Seyed Amin Tabatabaei, Sarah Fancher, Michael Parsons, Arian Askari

We address the task of hierarchical multi-label classification (HMC) of scientific documents at an industrial scale, where hundreds of thousands of documents must be classified across thousands of dynamic labels. The rapid growth of scientific publications necessitates scalable and efficient methods for classification, further complicated by the evolving nat

Tianning Han, Ying Wang, Yanlong Zhao

This paper is concerned with parameter identification problem for finite impulse response (FIR) systems with binary-valued observations under low computational complexity. Most of the existing algorithms under binary-valued observations rely on projection operators, which leads to a high computational complexity of much higher than O(n^2). In response, this

Narayan Srinivasan, Matthew Sutton, Christopher Drovandi, Leah F South

We propose a novel method for measuring the discrepancy between a set of samples and a desired posterior distribution for Bayesian inference. Classical methods for assessing sample quality like the effective sample size are not appropriate for scalable Bayesian sampling algorithms, such as stochastic gradient Langevin dynamics, that are asymptotically biased

Tiago Roxo, Joana C. Costa, Pedro R. M. Inácio, Hugo Proença

Deep learning models are widely used nowadays for their reliability in performing various tasks. However, they do not typically provide the reasoning behind their decision, which is a significant drawback, particularly for more sensitive areas such as biometrics, security and healthcare. The most commonly used approaches to provide interpretability create vi

Ajay Kushwaha, Eralp Demir, Amrita Basak

Crystal plasticity (CP) modeling is a vital tool for predicting the mechanical behavior of materials, but its calibration involves numerous (>8) constitutive parameters, often requiring time-consuming trial-and-error methods. This paper proposes a robust calibration approach using Bayesian optimization (BO) to identify optimal CP model parameters under fatig

Dibakar Roy Sarkar, Vijay Kag, Birupaksha Pal, Somdatta Goswami

Discovering hidden physical laws and identifying governing system parameters from sparse observations are central challenges in computational science and engineering. Existing data-driven methods, such as physics-informed neural networks (PINNs) and sparse regression, are limited by their need for extensive retraining, sensitivity to noise, or inability to g

Runyue Wang, Yu Tian, Pietro Liò, Ginestra Bianconi

Topological signals are variables or features associated with both nodes and edges of a network. Recently, in the context of Topological Machine Learning, great attention has been devoted to signal processing of such topological signals. Most of the previous topological signal processing algorithms treat node and edge signals separately and work under the hy

Sadegh Nadimi, Vasileios Angelidakis, Sadaf Maramizonouz, Chao Zhang

We introduce OCULAR, an innovative hardware and software solution for three-dimensional dynamic image analysis of fine particles. Current state-of-the art instruments for dynamic image analysis are largely limited to two-dimensional imaging. However, extensive literature has demonstrated that relying on a single two-dimensional projection for particle charac

Tanner Sether, Elena Giusarma, Mauricio Reyes-Hurtado

In the era of precision cosmology, the ability to generate accurate and large-scale galaxy catalogs is crucial for advancing our understanding of the universe. With the flood of cosmological data from current and upcoming missions, generating theoretical predictions to compare with these observations is essential for constraining key cosmological parameters.

Alexander Martin Mussgnug

Recent research illustrates how AI can be developed and deployed in a manner detached from the concrete social context of application. By abstracting from the contexts of AI application, practitioners also disengage from the distinct normative structures that govern them. Building upon Helen Nissenbaum's framework of contextual integrity, I illustrate how di

Fernando Herrera

We establish the integral kernel associated with the Koecher-Maass series of degree three twisted by an Eisenstein series. We prove that such a kernel admits an analytic continuation and determine its functional equations. We find a second representation of this kernel using Poincar\'e series. As an application, we give another proof of the analytic properti

Dorothea Strecker

With the move towards open research information, the DOI registration agency DataCite is increasingly used as a source for metadata describing research data, for example to perform scientometric analyses. However, there is a lack of research on how DataCite metadata describing research data are created and maintained. This paper adresses this gap by using Da

Michael Hewing, Vincent Leinhos

The rise of large language models (LLMs) has highlighted the importance of prompt engineering as a crucial technique for optimizing model outputs. While experimentation with various prompting methods, such as Few-shot, Chain-of-Thought, and role-based techniques, has yielded promising results, these advancements remain fragmented across academic papers, blog

Arash Golmohammadi, Jannik Luboeinski, Christian Tetzlaff

Heterogeneity is a ubiquitous property of many biological systems and has profound implications for computation. While it is conceivable to optimize neuronal and synaptic heterogeneity for a specific task, such top-down optimization is biologically implausible, prone to catastrophic forgetting, and both data- and energy-intensive. In contrast, biological org

Rene Henrion, Georg Stadler, Florian Wechsung

We study optimal control of PDEs under uncertainty with the state variable subject to joint chance constraints. The controls are deterministic, but the states are probabilistic due to random variables in the governing equation. Joint chance constraints ensure that the random state variable meets pointwise bounds with high probability. For linear governing PD

Christian Franssen, Joost Berkhout, Bernd Heidergott

Networks are characterized by structural features, such as degree distribution, triangular closures, and assortativity. This paper addresses the problem of reconstructing instances of continuously (and non-negatively) weighted networks from given feature values. We introduce the gradient-based Feature-Based Network Construction (FBNC) framework. FBNC allows

Joseph G. Conlon, Michael Dabkowski

This paper is concerned with lattice field models in dimension at least 2. The action is a uniformly convex function of the gradient of the field. The main result Theorem 1.4 proves that charge-charge correlations in the Coulomb dipole gas are close to Gaussian. These results go beyond previous results of Dimock-Hurd and Conlon-Spencer. The approach in the p

Jialong Cui, Chen Qing, Lishuang Feng, Dengke Zhang

The generation of optical vortex beams is pivotal for a myriad of applications, encompassing optical tweezing, optical communications, and quantum information, among others. The metasurface-based approach has realized significant advancements in vortex production, utilizing either dynamic or geometric phases. The dynamic design exhibits indifference to the p

Yuri Prokhorov

We investigate birational properties of hypersurfaces of degree $6$ in the weighted projective space $\mathbf{P}(1,1,2,2,3)$. In particular, we prove that any such quasi-smooth hypersurface is not rational.

Xiaoxiao Wang, Jingjing Zhang, Huize Wan, Weiwei Zhang

Immigration and aging have always been significant topics of discussion in society, concerning the stability and future development of a country and its people. Research in the field of HCI on immigration and aging has primarily focused on their practical needs but has paid less attention to the adaptability issues of older internal migrants moving with thei

Zygmunt Bak

The aim of our paper is to study the multi-quanta Abrikosov vortices injected into a superconductor layer by the twisted light impulses. We predict that the condensate circulating around the core of a multi-quanta vortex may differ in nature from that of the bulk superconductor. Special attention is paid to possibility of appearance of the quasi angular mome

Niklas Nolte, Ouail Kitouni, Adina Williams, Mike Rabbat

Despite their remarkable success in language modeling, transformers trained to predict the next token in a sequence struggle with long-term planning. This limitation is particularly evident in tasks requiring foresight to plan multiple steps ahead such as maze navigation. The standard next single token prediction objective, however, offers no explicit mechan

Erwan Lucas, Gang Xu, Pengxiang Wang, Gian-Luca Oppo

We report on numerical predictions and experimental observations of a novel type of temporal localized dissipative structures that manifest themselves in the self-defocusing regime of driven nonlinear optical resonators with two polarization modes. These chiral dissipative solitons, which we term polarization faticons, break both temporal and polarization sy

Joshua Viszlai, Jason D. Chadwick, Sarang Joshi, Gokul Subramanian Ravi

Real-time decoding is a key ingredient in future fault-tolerant quantum systems, yet many decoders are too slow to run in real time. Prior work has shown that parallel window decoding schemes can scalably meet throughput requirements in the presence of increasing decoding times, given enough classical resources. However, windowed decoding schemes require tha

Patrick Betz, Nathanael Stelzner, Christian Meilicke, Heiner Stuckenschmidt

In this technical report, we investigate the predictive performance differences of a rule-based approach and the GNN architectures NBFNet and A*Net with respect to knowledge graph completion. For the two most common benchmarks, we find that a substantial fraction of the performance difference can be explained by one unique negative pattern on each dataset th

Jozef Strecka, Fadwa Benabdallah, Mohammed Daoud

We demonstrate that two independent spin-1/2 Ising-Heisenberg trimer chains provide an effective platform for the quantum teleportation of any entangled two-qubit state through the quantum communication channel formed by two Heisenberg dimers. The reliability of this quantum channel is assessed by comparing the concurrences, which quantify a strength of the

Kazuma Nagashima, Jumpei Nishikawa, Junya Morita

Immersion in a task is a prerequisite for creativity. However, excessive arousal in a single task has drawbacks, such as overlooking events outside of the task. To examine such a negative aspect, this study constructs a computational model of arousal dynamics where the excessively increased arousal makes the task transition difficult. The model was developed

Maciej Kowalczyk, Tomasz Pawłowski

We revise the technique of semiclassical effective dynamics, in particular reexamining the evaluation of Poisson structure of the so-called central moments capturing quantum corrections, providing a systematic, pedagogical, and efficient algorithm for evaluation of said structure. The resulting closed formulae for Poisson brackets involve less summatios than

Hao Sun, Ashkan Ertefaie, Luke Duttweiler, Brent A. Johnson

Real-world clinical decision making is a complex process that involves balancing the risks and benefits of treatments. Quality-adjusted lifetime is a composite outcome that combines patient quantity and quality of life, making it an attractive outcome in clinical research. We propose methods for constructing optimal treatment length strategies to maximize th

Christopher Reichling, Leo van Iersel, Yukihiro Murakami

Phylogenetic networks are useful in representing the evolutionary history of taxa. In certain scenarios, one requires a way to compare different networks. In practice, this can be rather difficult, except within specific classes of networks. In this paper, we derive metrics for the class of \emph{orchard networks} and the class of \emph{strongly reticulation

P. D. Grigoriev, V. D. Kochev, V. A. Tsyplukhin, A. M. Dyugaev

Modeling the motion of ultracold neutrons (UCNs) is crucial for assessing their losses, accurately measuring their lifetime, and describing other experiments. In material traps, it is necessary to account not only for specular but also for diffuse elastic reflection of UCNs from the trap walls. Typically, the Lambert cosine law is used to describe the angula

Arthur Bartels, Wolfgang Lueck, Stefan Witzel

We extend results for the K-theory of Hecke algebras of reductive $p$-adic groups to completed Kac-Moody groups.

Andrea López-Incera, Wolfgang Dür, Stefan Heusler

Schr\"odinger's cat is an iconic example for the problem of the transition from the microscopic quantum world to the macroscopic, classical one. It opened many interesting questions such as, could a macroscopic superposition like a dead and alive cat ever exist? What would be the characteristic features of such a system? The field of macroscopicity aims at p

Edgar Beck, Hsuan-Yu Lin, Patrick Rückert, Yongping Bao

As early as 1949, Weaver defined communication in a very broad sense to include all procedures by which one mind or technical system can influence another, thus establishing the idea of semantic communication. With the recent success of machine learning in expert assistance systems where sensed information is wirelessly provided to a human to assist task exe

Tommaso Grigoletto, Yukuan Tao, Francesco Ticozzi, Lorenza Viola

We consider finite-dimensional many-body quantum systems described by time-independent Hamiltonians and Markovian master equations, and present a systematic method for constructing smaller-dimensional, reduced models that exactly reproduce the time evolution of a set of initial conditions or observables of interest. Our approach exploits Krylov operator spac

Ruoyu Wang, Huayang Huang, Ye Zhu, Olga Russakovsky

In this work, we introduce NoiseQuery as a novel method for enhanced noise initialization in versatile goal-driven text-to-image (T2I) generation. Specifically, we propose to leverage an aligned Gaussian noise as implicit guidance to complement explicit user-defined inputs, such as text prompts, for better generation quality and controllability. Unlike exist

Yanna J. Kraakman, Clara Stegehuis

Comparative analysis between a network and a random graph model can uncover network properties that significantly deviate from those in random networks. The standard random graph model used for comparison uniformly samples random graphs with the same degrees as the network data, often achieved through edge-swap algorithms. However, for hypergraphs, fewer suc

Tamara Molyarova, Eduard Vorobyov, Vitaly Akimkin

Elemental abundances, particularly the C/O ratio, are seen as a way to connect the composition of planetary atmospheres with planet formation scenario and the disc chemical environment. We model the chemical composition of gas and ices in a self-gravitating disc on timescales of 0.5\,Myr since its formation to study the evolution of C/O ratio due to dust dyn

Alex, Liu, Vivian, Chi

This manuscript signals a new era in the integration of artificial intelligence with software engineering, placing machines at the pinnacle of coding capability. We present a formalized, iterative methodology proving that AI can fully replace human programmers in all aspects of code creation and refinement. Our approach, combining large language models with

Haowu Duan, Alex Kovner, Michael Lublinsky

In \cite{one}, we have introduced the Born-Oppenheimer (BO) renormalization group approach to high energy hadronic collisions and derived the BO approximation for the light cone wave function of a fast moving projectile hadron. In this second paper, we utilize this wave function to derive the BO evolution of partonic distributions in the hadron -- the gluon

Tim Downing

Pangenome variation graphs (PVGs) allow for the representation of genetic diversity in a more nuanced way than traditional reference-based approaches. Here we focus on how PVGs are a powerful tool for studying genetic variation in viruses, offering insights into the complexities of viral quasispecies, mutation rates, and population dynamics. PVGs originated

Xiaofeng Tan, Hongsong Wang, Xin Geng, Pan Zhou

Text-to-motion generation is essential for advancing the creative industry but often presents challenges in producing consistent, realistic motions. To address this, we focus on fine-tuning text-to-motion models to consistently favor high-quality, human-preferred motions, a critical yet largely unexplored problem. In this work, we theoretically investigate t

Lynn Buchele, Earl P. Bellinger, Saskia Hekker, Sarbani Basu

Asteroseismic inferences of main-sequence solar-like oscillators often rely on best-fit models. However, these models cannot fully reproduce the observed mode frequencies, suggesting that the internal structure of the model does not fully match that of the star. Asteroseismic structure inversions provide a way to test the interior of our stellar models. Rece

Da Ju, Adina Williams, Brian Karrer, Maximilian Nickel

Large language models have increasingly been proposed as a powerful replacement for classical agent-based models (ABMs) to simulate social dynamics. By using LLMs as a proxy for human behavior, the hope of this new approach is to be able to simulate significantly more complex dynamics than with classical ABMs and gain new insights in fields such as social sc

Ashutosh Roy Choudhury, Tanmay Deshpande

Let $G$ be a connected reductive algebraic group over an algebraically closed field $k$ of characteristic $p > 0$ and let $\ell$ be a prime number different from $p$. Let $U \subseteq G$ be a maximal unipotent subgroup, $T$ a maximal torus normalizing $U$ and $W$ the Weyl group of $G$. Let $\mathcal{L}$ be a non-degenerate multiplicative $\overline{\mathbb{Q

Prakash Aryan, Sujala Deepak Shetty

This paper presents a novel approach to cloud storage challenges by integrating NextCloud, TrueNAS, and QEMU/KVM. Our research demonstrates how this combination creates a robust, flexible, and economical cloud storage system suitable for various applications. We detail the architecture, highlighting TrueNAS's ZFS-based storage, QEMU/KVM's virtualization, and

Henry A. Thompson

I argue that generative AI will have an uneven effect on the evolution of the law. To do so, I consider generative AI as a labor-augmenting technology that reduces the cost of both writing more complete contracts and litigating in court. The contracting effect reduces the demand for court services by making contracts more complete. The litigation effect, by

Duanny Silva Onório, Flavio A. M. Marques, Alexandre A. C. Cotta, Jefferson E. Tsuchida

Hollow-core photonic crystal fibers (HCPCFs) have become a key enabling technology for addressing a broad spectrum of fundamental and applied needs. Indeed, recent advancements achieved by the HCPCF research community have led to significant progress, establishing these fibers as the lowest-loss optical fibers currently available for use in the visible and u

Oscar Fuentealba, Marc Henneaux

Matching conditions relating the fields at the future of past null infinity with the fields at the past of future null infinity play a central role in the analysis of asymptotic symmetries and conservation laws in asymptotically flat spacetimes. These matching conditions can be derived from initial data given on a Cauchy hypersurface by integrating forward a

Angelina Sherman, Jessie Thwaites, Ke Fang, Justin Vandenbroucke

Although Cherenkov detectors of high-energy neutrinos in ice and water are often optimized to detect TeV-PeV neutrinos, they may also be sensitive to transient neutrino sources in the 1-100~GeV energy range. A wide variety of transient sources have been predicted to emit GeV neutrinos. In light of the upcoming IceCube-Upgrade, which will extend the IceCube d

H. Kim, Y. Bae, C. Heo, J. Seo

Beam Drift Chamber (BDC) is designed to reconstruct the trajectories of incident rare isotope beams provided by RAON (Rare isotope Accelerator complex for ON-line experiments) into the experimental target of LAMPS (Large Acceptance Multi-Purpose Spectrometer). To conduct the performance test of the BDC, the prototype BDC (pBDC) is manufactured and evaluated

Yasin Qazi, Anvar. Shukurov, Frederick. A. Gent, Devika. Tharakkal

Magnetic buoyancy (MBI) and Parker instabilities are strong, generic instabilities expected to occur in most astrophysical systems with sufficiently strong magnetic fields. In galactic and accretion discs, large-scale magnetic fields are thought to arise from mean-field dynamo action, particularly the $\alpha^2\Omega$-dynamo. Using non-ideal MHD equations, w

Haowu Duan, Alex Kovner, Michael Lublinsky

We develop an approach to QCD evolution based on the sequential Born-Oppenheimer approximations that include higher and higher frequency modes as the evolution parameter is increased. This Born-Oppenheimer renormalization group is a general approach which is valid for the high energy evolution as well as the evolution in transverse resolution scale $Q^2$. In

Haotian Zhao, Ruifeng Chen, Jing Yan, Juan Feng

To shorten the door-to-puncture time for better treating patients with acute ischemic stroke, it is highly desired to obtain quantitative cerebral perfusion images using C-arm cone-beam computed tomography (CBCT) equipped in the interventional suite. However, limited by the slow gantry rotation speed, the temporal resolution and temporal sampling density of

Thais Maria Dalbelo, Maria Elenice Rodrigues Hernandes, Maria Aparecida Soares Ruas

In this work, we describe a prenormal form for the generators of the semigroup of a toric variety $X \subset \mathbb{C}^p$ with isolated singularity at the origin and smooth normalization. A complete description of the semigroup is given when $X$ is a variety of dimension $n$ in $\mathbb{C}^{2n}$. Moreover, for toric surfaces in $\mathbb{C}^4$, we provide a

Julius Witte, Cu Cui, Francesca Bonizzoni, Guido Kanschat

We discuss vertex patch smoothers as overlapping domain decomposition methods for fourth order elliptic partial differential equations. We show that they are numerically very efficient and yield high convergence rates. Furthermore, we discuss low rank tensor approximations for their efficient implementation. Our experiments demonstrate that the inexact local

Ivanna Kramer, Lara Blomenkamp, Kevin Weirauch, Sabine Bauer

Spinal ligaments are crucial elements in the complex biomechanical simulation models as they transfer forces on the bony structure, guide and limit movements and stabilize the spine. The spinal ligaments encompass seven major groups being responsible for maintaining functional interrelationships among the other spinal components. Determination of the ligamen

Ekaterina Amerik, Mikhail Lozhkin

We give a new example of potential density of rational points on the third punctual Hilbert scheme of a K3 surface.

YingXing Cheng, Benjamin Stamm

In this work, we introduce several approximations of the Iterative Stockholder Analysis (ISA) method based on exponential basis functions. These approximations are categorized into linear and non-linear models, referred to as LISA and NLIS, respectively. By particular choices of hyperparameters in the NLIS model, both LISA and the Minimal-Basis Iterative Sto

Prakash Aryan, Radhika Khatri, Vijayakumar Balakrishnan

This paper presents an experimental framework for implementing Decentralized Autonomous Database Systems (DADBS) using the Rust programming language. As traditional centralized databases face challenges in scalability, security, and autonomy, DADBS emerge as a promising solution, using blockchain principles to create distributed, self-governing database syst

Niels Langeveld, David Ralston

Proper continued fractions are generalized continued fractions with positive integer numerators $a_i$ and integer denominators with $b_i\geq a_i$. In this paper we study the strength of approximation of irrational numbers to their convergents and classify which pairs of integers $p,q$ yield a convergent $p/q$ to some irrational $x$. Notably, we reduce the pr

Nikita Gabdullin

This paper studies a combined person reidentification (re-id) method that uses human parsing, analytical feature extraction and similarity estimation schemes. One of its prominent features is its low computational requirements so it can be implemented on edge devices. The method allows direct comparison of specific image regions using interpretable features

Zhan Bai, Xiangyan An, Yuqi Chen, Baifei Shen

We investigate the interaction between an optical laser and an ionic crystal and reveal coherent emission of axions through phase-match between laser and axion fields. Such emission is further enhanced by stacking thin crystal layers of half-wavelength thickness. Based on these findings, we propose a novel method for generating and detecting axions in terres

Utsav Choudhury, Neeraj Deshmukh, Amit Hogadi

We develop a motivic cohomology theory, representable in the Voevodsky's triangulated category of motives, for smooth separated Deligne-Mumford stacks and show that the resulting higher Chow groups are canonically isomorphic to the higher $K$-theory of such stacks. This generalises the Grothendieck-Riemann-Roch theorem to the category of smooth Deligne-Mumfo

Gero Junike, Hauke Stier

Fourier pricing methods such as the Carr-Madan formula or the COS method are classic tools for pricing European options for advanced models such as the Heston model. These methods require tuning parameters such as a damping factor, a truncation range, a number of terms, etc. Estimating these tuning parameters is difficult or computationally expensive. Recent

Theodore Erler

We study covariant open bosonic string field theory in lightcone gauge. When lightcone gauge is well-defined, we find two results. First, the vertices of the gauge-fixed action consist of Mandelstam diagrams with stubs covering specific portions of the moduli spaces of Riemann surfaces. This is true regardless of how the vertices of the original covariant st

Martin Kilian

We consider the local theory of constant mean curvature surfaces that satisfy one or two integrable boundary conditions and determine the corresponding potentials for the generalized Weierstrass representation.

Anmol Kumar

K{\"o}hler, in [1], presented a weight 1 newform on $\Gamma_0(576)$ constructed from a linear combination of weight 1 eta quotients and asked, ``What would be a suitable $L$ and representation $\rho$ such that Deligne\text{-}Serre correspondence holds?" In this paper, we find the Galois field extension $L$ and representation $\rho$ such that the Deligne\text

Wanyue Zhang, Rishabh Dabral, Vladislav Golyanik, Vasileios Choutas

We present BimArt, a novel generative approach for synthesizing 3D bimanual hand interactions with articulated objects. Unlike prior works, we do not rely on a reference grasp, a coarse hand trajectory, or separate modes for grasping and articulating. To achieve this, we first generate distance-based contact maps conditioned on the object trajectory with an

Lara Blomenkamp, Ivanna Kramer, Sabine Bauer, Kevin Weirauch

Patient-specific 3D spine models serve as a foundation for spinal treatment and surgery planning as well as analysis of loading conditions in biomechanical and biomedical research. Despite advancements in imaging technologies, the reconstruction of complete 3D spine models often faces challenges due to limitations in imaging modalities such as planar X-Ray a

Xiaofeng Xue

In this paper, we extend the central limit theorem of the occupation time of the voter model on the lattice $\mathbb{Z}^d$ given in \cite{Cox1983} to the sample path case for $d\geq 3$. The proof of our main result utilizes the resolvent strategy and the Poisson flow strategy introduced in previous literatures, where the duality relationship between the vote

Klaas Willems, Vince Maes, Zhirui Tang, Giovanni Samaey

Monte Carlo methods are state-of-the-art when it comes to dosimetric computations in radiotherapy. However, the execution time of these methods suffers in high-collisional regimes. We address this problem by introducing a kinetic-diffusion particle tracing scheme. This algorithm, first proposed in the context of neutral transport in fusion energy, relies on

Lukas Legenstein, Lukas Reicht, Sandro Wieser, Michele Simoncelli

Understanding heat transport in organic semiconductors is of fundamental and practical relevance. Therefore, we study the lattice thermal conductivities of a series of (oligo)acenes, where an increasing number of rings per molecule leads to a systematic increase of the crystals' complexity. Temperature-dependent thermal conductivity experiments in these syst

Marina Masson, Johan Bregeon

Orphan gamma-ray burst afterglows are good candidates to learn more about the GRB physics and progenitors or for the development of multi-messenger analysis with gravitational waves. Our objective is to identify orphan afterglows in Rubin LSST data, by using the characteristic features of their light curves. In this work, we generated a population of short G

Athari Alotaibi, Maxwell T. Hansen

We present a numerical investigation of the relativistic-field-theoretic (RFT) formalism, used to predict the discrete energy spectrum of three pions in a finite volume. Applying the previously derived generalization, we extract results for all non-maximal isospin values ($I_{\pi\pi\pi} = 2,1,$ and $0$), for different total momenta $\boldsymbol{P}$, and for

Benjamin von der Heyde, Anand Srinivasan, Sumit Kumar Birwa, Eva Laura von der Heyde

The evolution of multicellularity involved the transformation of a simple cell wall of unicellular ancestors into a complex, multifunctional extracellular matrix (ECM). A suitable model organism to study the formation and expansion of an ECM during ontogenesis is the multicellular green alga $Volvox~carteri$, which, along with the related volvocine algae, pr

Zhen Wang, Yijin Pan, Jun-Bo Wang, Yijian Chen

This paper analyzes the spatial bandwidth of line-of-sight (LoS) channels in massive MIMO systems. For the linear large-scale antenna arrays (LSAA) of transceivers placed in random locations in 3D space, a simple but accurate closed-form expression is derived to characterize the spatial bandwidth. Subsequent analysis of the LSAA's spatial bandwidth propertie

Alexandre Cloots, Tanguy Colleu, Vincent Liégeois, Gian-Marco Rignanese

Surface-plasmon polaritons (SPPs) are electromagnetic waves that propagate along metal-dielectric interfaces, with important applications in sensing, energy, and nanotechnology. While the behavior of SPPs in single metal slabs is well understood, the coupling between plasmon modes in multilayer systems has received less attention. In this paper, we explore t

D. M. Naplekov, V. V. Yanovsky

We have considered a model of a small finite system with internal particles and surface degrees of freedom. All the main statistical distributions were explicitly obtained, on a pre thermodynamic limit basis. The concept of temperature or any thermodynamic equations was not used. The distribution of coordinates of a surface element allows the rigorous determ

Brad Stenger, Yuanyuan Feng

Increasing numbers of athletes and sports teams use data collection technologies to improve athletic development and athlete health with the goal of improving competitive performance. Personal data privacy is managed but it is not always a priority for the coaches who are in charge of athletes. There is a pressing need to investigate what are appropriate inf

Alfie Robinson, James E. Owen, Richard A. Booth

Observed IR excesses indicate that protoplanetary discs evolve slowly for the majority of their lifetime before losing their near- and mid-IR excesses on short timescales. Photoevaporation models can explain this "two-timescale" nature of disc evolution through the removal of inner regions of discs after a few million years. However, they also predict the ex

Kaizhen Sun, Jinghang Li, Kuan Dai, Bangyan Liao

Time-to-Collision (TTC) estimation lies in the core of the forward collision warning (FCW) functionality, which is key to all Automatic Emergency Braking (AEB) systems. Although the success of solutions using frame-based cameras (e.g., Mobileye's solutions) has been witnessed in normal situations, some extreme cases, such as the sudden variation in the relat

David Duenas-Cid

This paper focuses on the complex dynamics of trust and distrust in digital government technologies by approaching the cancellation of machine voting in the Netherlands (2006-07). This case describes how a previously trusted system can collapse, how paradoxical the relationship between trust and distrust is, and how it interacts with adopting and managing el

Dhal Biswabhusan, Puzari Animesh, Li-Hsien Yeh, Kalon Gopinadhan

Memristors that mimic brain functions are crucial for energy-efficient neuromorphic devices. Ion channels that emulate biological synapses are still in the early stages of development, especially the tunability of memory states. Here, we demonstrate that cations such as K+, Na+, Ca2+, and Al3+ intercalated in the interlayer spaces of vermiculite, result in h

J. Yu. Panteleeva, E. Epelbaum, J. Gegelia, U. -G. Meißner

We work out the spatial density distributions corresponding to the axial-vector charge density operator for spin-1/2 systems using states described by sharply localized wave packets in arbitrary Lorentz-frames. The static approximation, leading to the frequently assumed Breit-frame distributions, is also considered. We discuss the interpretation of the resul

Felix Mächtle, Jan-Niclas Serr, Nils Loose, Jonas Sander

In an era where cyberattacks increasingly target the software supply chain, the ability to accurately attribute code authorship in binary files is critical to improving cybersecurity measures. We propose OCEAN, a contrastive learning-based system for function-level authorship attribution. OCEAN is the first framework to explore code authorship attribution on

A. Turchi, E. Masciadri, L. Fini

In this contribution we present the FAST, which is a comprehensive software suite that aims to streamline and automatically manage the forecast of atmospheric and astroclimatic parameters (provided respectively by Meso-Nh and Astro-Meso-Nh models) on large ground-based telescope installations. The forecast of the aforementioned parameters is becoming crucial

A. Turchi, E. Pancino, F. Rossi, A. Avdeeva

We present a machine learning method to assign stellar parameters (temperature, surface gravity, metallicity) to the photometric data of large photometric surveys such as SDSS and SKYMAPPER. The method makes use of our previous effort in homogenizing and recalibrating spectroscopic data from surveys like APOGEE, GALAH, or LAMOST into a single catalog, which

Ekaterina Krupkina, Dmitrii Volkov

We show a version of Qi et al. 2023's simple fine-tuning poisoning technique strips GPT-4o's safety guardrails without degrading the model. The BadGPT attack matches best white-box jailbreaks on HarmBench and StrongREJECT. It suffers no token overhead or performance hits common to jailbreaks, as evaluated on tinyMMLU and open-ended generations. Despite havin

R. Abbasi, M. Ackermann, J. Adams, S. K. Agarwalla

We analyzed the 7.92$\times 10^{11}$ cosmic-ray-induced muon events collected by the IceCube Neutrino Observatory from May 13, 2011, when the fully constructed experiment started to take data, to May 12, 2023. This dataset provides an up-to-date cosmic-ray arrival direction distribution in the Southern Hemisphere with unprecedented statistical accuracy cover

Qiang Hou, Wei Wei, Xin Zhou, Wenhui Liu

Recent investigations of FeSe-based superconductors have revealed the presence of two superconducting domes, and suggest possible distinct pairing mechanisms. Two superconducting domes are commonly found in unconventional superconductors and exhibit unique normal states and electronic structures. In this study, we conducted electromagnetic transport measurem