May 2024 arXiv papers — page 11

Shayan Sepahvand, Guanghui Wang, Farrokh Janabi-Sharifi

In this work, a deep learning-based technique is used to study the image-to-joint inverse kinematics of a tendon-driven supportive continuum arm. An eye-off-hand configuration is considered by mounting a camera at a fixed pose with respect to the inertial frame attached at the arm base. This camera captures an image for each distinct joint variable at each s

Matthew Watson, Divyashree Shivakumar Sreepathihalli, Francois Chollet, Martin Gorner

We present the Keras domain packages KerasCV and KerasNLP, extensions of the Keras API for Computer Vision and Natural Language Processing workflows, capable of running on either JAX, TensorFlow, or PyTorch. These domain packages are designed to enable fast experimentation, with a focus on ease-of-use and performance. We adopt a modular, layered design: at t

David McNutt, Eivind Schneider

Let $\mathfrak{k}$ be a nontrivial finite-dimensional Lie algebra of vector fields on a manifold M, and consider the family of Lorentzian metrics on M whose Killing algebra contains $\mathfrak{k}$. We show that scalar relative differential invariants, with respect to a Lie algebra of vector fields on M preserving $\mathfrak{k}$, can be used to detect the hor

Franz Louis Cesista, Rui Aguiar, Jason Kim, Paolo Acilo

Business Document Information Extraction (BDIE) is the problem of transforming a blob of unstructured information (raw text, scanned documents, etc.) into a structured format that downstream systems can parse and use. It has two main tasks: Key-Information Extraction (KIE) and Line Items Recognition (LIR). In this paper, we argue that BDIE is best modeled as

Nishant Gupta, Nemani V. Suryanarayana

We propose mixed boundary conditions for 3d conformal gravity consistent with variational principle in its second-order formalism that admit the chiral $\Lambda$-$\mathfrak{bms}_4$ algebra as their asymptotic symmetry algebra. This algebra is one of the four chiral $\mathcal W$-algebra extensions of $\mathfrak{so}(2,3)$ and is a generalisation of the chiral

MANTA Collaboration, G. Rutherford, H. S. Wilson, A. Saltzman

The MANTA (Modular Adjustable Negative Triangularity ARC-class) design study investigated how negative-triangularity (NT) may be leveraged in a compact, fusion pilot plant (FPP) to take a ``power-handling first" approach. The result is a pulsed, radiative, ELM-free tokamak that satisfies and exceeds the FPP requirements described in the 2021 National Academi

Andrew C. Nine, Robert D. Mathieu, Simon C. Schuler, Katelyn E. Milliman

We investigate barium (Ba) abundances in blue straggler stars (BSSs) in two open clusters, NGC 7789 (1.6 Gyr) and M67 (4 Gyr), as signatures of asymptotic-giant-branch (AGB) mass transfer. We combine our findings with previous Ba abundance analyses in NGC 6819 (2.5 Gyr) and NGC 188 (7 Gyr). Out of 35 BSSs studied in NGC 7789, NGC 6819, and M67, 15 (43$\pm$11

Brenden McDearmon

Considering a fluctuating scalar field on momentum space, some relativistic statistical field theories are constructed. A Hilbert space of observables is then constructed from functionals of the fluctuating scalar field with an inner product defined in terms of expectation values of the functionals. A bosonic Fock space is then constructed from the Hilbert s

Aviv Karnieli, Offek Tziperman, Charles Roques-Carmes, Shanhui Fan

Enhancing interactions in many-body quantum systems, while protecting them from environmental decoherence, is at the heart of many quantum technologies. Waveguide quantum electrodynamics is a promising platform for achieving this, as it hosts infinite-range interactions and decoherence-free subspaces of quantum emitters. However, as coherent interactions bet

Sihem zabat, Youcef Kehal, Khireddine Nouicer

The interaction between dark matter and dark energy has become a focal point in contemporary cosmological research, particularly in addressing current cosmological tensions. This study explores the cubic Galileon model's interaction with dark matter, where the interaction potential in the dark sector is proportional to the dark energy density of the Galileon

Cooper Tezak, Jacob Clary, Sophie Gerits, Joshua Quinton

We present BEAST DB, an open-source database comprised of ab initio electrochemical data computed using grand-canonical density functional theory in implicit solvent at consistent calculation parameters. The database contains over 20,000 surface calculations and covers a broad set of heterogeneous catalyst materials and electrochemical reactions. Calculation

Brenden McDearmon

A relativistic statistical field theory is constructed for a fluctuating complex-valued scalar field on a discretized Minkowski lattice. A Hilbert space of observables is then constructed from functionals of the fluctuating complex-valued scalar field with an inner product defined in terms of expectation values of the functionals. A bosonic Fock space is the

Brian Coyle, Snehal Raj, Natansh Mathur, El Amine Cherrat

Quantum machine learning (QML) requires powerful, flexible and efficiently trainable models to be successful in solving challenging problems. We introduce density quantum neural networks, a model family that prepares mixtures of trainable unitaries, with a distributional constraint over coefficients. This framework balances expressivity and efficient trainab

Naoki Hiratani

Continual learning of partially similar tasks poses a challenge for artificial neural networks, as task similarity presents both an opportunity for knowledge transfer and a risk of interference and catastrophic forgetting. However, it remains unclear how task similarity in input features and readout patterns influences knowledge transfer and forgetting, as w

Tommaso Fazio, Anna Napoli, Benedetto Militello

The Stimulated Raman Adiabatic Passage (STIRAP) is applied to a system coupled to a bath made of fully-interacting two-level systems, whose dynamics is studied exploiting the generalized Cluster Correlation Expansion (gCCE) theory. We specialize our analysis to a negatively charged silicon vacancy (SiV-1) in non-purified 4H-SiC to assess the possibility of t

Cheng'an Wei, Yue Zhao, Yujia Gong, Kai Chen

Large Language Models (LLMs) such as ChatGPT and Llama have become prevalent in real-world applications, exhibiting impressive text generation performance. LLMs are fundamentally developed from a scenario where the input data remains static and unstructured. To behave interactively, LLM-based chat systems must integrate prior chat history as context into the

Jaerin Lee, Bong Gyun Kang, Kihoon Kim, Kyoung Mu Lee

One puzzling artifact in machine learning dubbed grokking is where delayed generalization is achieved tenfolds of iterations after near perfect overfitting to the training data. Focusing on the long delay itself on behalf of machine learning practitioners, our goal is to accelerate generalization of a model under grokking phenomenon. By regarding a series of

Manousos Linardakis, Iraklis Varlamis, Georgios Th. Papadopoulos

Robotic exploration has long captivated researchers aiming to map complex environments efficiently. Techniques such as potential fields and frontier exploration have traditionally been employed in this pursuit, primarily focusing on solitary agents. Recent advancements have shifted towards optimizing exploration efficiency through multiagent systems. However

Derek Lim, Theo Moe Putterman, Robin Walters, Haggai Maron

Many algorithms and observed phenomena in deep learning appear to be affected by parameter symmetries -- transformations of neural network parameters that do not change the underlying neural network function. These include linear mode connectivity, model merging, Bayesian neural network inference, metanetworks, and several other characteristics of optimizati

Zoey Zhiyuan Dong, Joshua Cole Faggert, Shu Yan Lau, Kent Yagi

Neutron stars (NSs) provide a unique laboratory to study matter under extreme densities. Recent observations from gravitational and electromagnetic waves have enabled constraints on NS properties, such as tidal deformability (related to the tidal Love number) and stellar compactness. Although each of these two NS observables depends strongly on the stellar i

Laura Classen, Joseph J. Betouras

The flattening of single-particle band structures plays an important role in the quest for novel quantum states of matter due to the crucial role of interactions. Recent advances in theory and experiment made it possible to construct and tune systems with nearly flat bands, ranging from graphene multilayers and moire' materials to kagome' metals and ruthenat

Ákos Nagy, Cindy Zhang

We present new designs for quantum random access memory. More precisely, for each function, $f : \mathbb{F}_2^n \rightarrow \mathbb{F}_2^d$, we construct oracles, $\mathcal{O}_f$, with the property \begin{equation} \mathcal{O}_f \left| x \right\rangle_n \left| 0 \right\rangle_d = \left| x \right\rangle_n \left| f(x) \right\rangle_d. \end{equation} Our method

Soumya Saha

The Carbon-Nitrogen-Oxygen (CNO) cycle is fundamental to the process of hydrogen burning in stars, serving as a pivotal mechanism. At its core, the primary reaction involves the radiative capture of a proton by $^{12}$C, crucially influencing the isotopic ratio of $^{12}$C to $^{13}$C observed in celestial bodies, including our Solar System. We have addresse

Muyao Niu, Xiaodong Cun, Xintao Wang, Yong Zhang

We present MOFA-Video, an advanced controllable image animation method that generates video from the given image using various additional controllable signals (such as human landmarks reference, manual trajectories, and another even provided video) or their combinations. This is different from previous methods which only can work on a specific motion domain

Moussa Barro, K. Ernest Bognini, Boucaré Kientéga

Let us consider an infinite word and $k\geq 1$ an integer. By steps of $k$, we substitute a letter ofthis infinite word by the power of an external letter. The new word obtaining by this process is called $k$ to $k$ substitution of a power letter. After the application of this new notion on modulo-reccurent words and in particular on Sturmian words. We estab

Brandon Colelough, Andrew Zheng

Background: Active noise cancellation has been a subject of research for decades. Traditional techniques, like the Fast Fourier Transform, have limitations in certain scenarios. This research explores the use of deep neural networks (DNNs) as a superior alternative. Objective: The study aims to determine the effect sampling rate within training data has on l

Natalie H. Allen, David K. Sing, Néstor Espinoza, Richard O'Steen

The Hubble Space Telescope (HST) has been our most prolific tool to study exoplanet atmospheres. As the age of JWST begins, there is a wealth of HST archival data that is useful to strengthen our inferences from JWST. Notably, HST/STIS and its 0.3-1 $\mu$m wavelength coverage extends past JWST's 0.6 $\mu$m wavelength cutoff and holds an abundance of potentia

Guodong Jin, Zihan Zhou, Wenzheng Tang, Kanglei Yu

In an era of increasing concerns over intellectual property rights, traditional peer review systems face challenges including plagiarism, malicious attacks, and unauthorized data access. BeerReview, a blockchain-enabled peer review platform, offers a robust solution, enabling experts and scholars to participate actively in the review process without concerns

Hao Tu, Xinfan Lin, Yebin Wang, Huazhen Fang

Essential to various practical applications of lithium-ion batteries is the availability of accurate equivalent circuit models. This paper presents a new coupled electro-thermal model for batteries and studies how to extract it from data. We consider the problem of maximum likelihood parameter estimation, which, however, is nontrivial to solve as the model i

Mariya Pavlova, Bernard Casey, Miaosen Wang

We present ESG-FTSE, the first corpus comprised of news articles with Environmental, Social and Governance (ESG) relevance annotations. In recent years, investors and regulators have pushed ESG investing to the mainstream due to the urgency of climate change. This has led to the rise of ESG scores to evaluate an investment's credentials as socially responsib

Harveen Kaur, Flaviano Della Pia, Ilyes Batatia, Xavier R. Advincula

Calculating sublimation enthalpies of molecular crystal polymorphs is relevant to a wide range of technological applications. However, predicting these quantities at first-principles accuracy -- even with the aid of machine learning potentials -- is a challenge that requires sub-kJ/mol accuracy in the potential energy surface and finite-temperature sampling.

Sanghyeon Na, Yonggyu Kim, Hyunjoon Lee

Human image generation is a key focus in image synthesis due to its broad applications, but even slight inaccuracies in anatomy, pose, or details can compromise realism. To address these challenges, we explore Direct Preference Optimization (DPO), which trains models to generate preferred (winning) images while diverging from non-preferred (losing) ones. How

Chen Zhang, Chengguang Tang, Dading Chong, Ke Shi

Mainstream approaches to aligning large language models (LLMs) heavily rely on human preference data, particularly when models require periodic updates. The standard process for iterative alignment of LLMs involves collecting new human feedback for each update. However, the data collection process is costly and challenging to scale. To address this issue, we

Marcos Mazari-Armida

We study limit models in the abstract elementary class of modules with embeddings as algebraic objects. We characterize parametrized noetherian rings using the degree of injectivity of certain limit models. We show that the number of limit models and how close a ring is from being noetherian are inversely proportional. $\textbf{Theorem.}$ Let $n \geq 0$ The

Vijay Jaisankar, Sambaran Bandyopadhyay, Kalp Vyas, Varre Chaitanya

A poster from a long input document can be considered as a one-page easy-to-read multimodal (text and images) summary presented on a nice template with good design elements. Automatic transformation of a long document into a poster is a very less studied but challenging task. It involves content summarization of the input document followed by template genera

Kai Xiao, Bowen Huang, Yang Huang, Haibo Yuan

We search for an optimal filter design for the estimation of stellar metallicity, based on synthetic photometry from Gaia XP spectra convolved with a series of filter-transmission curves defined by different central wavelengths and bandwidths. Unlike previous designs based solely on maximizing metallicity sensitivity, we find that the optimal solution provid

Kiran Sagar Kollepara

(Rephrased) Non-conformance decision-making processes in high-precision manufacturing of engineering structures are often delayed due to numerical simulations that are needed for analyzing the defective parts and assemblies. Interfaces between parts of assemblies can only be simulated using the modeling of contact. Thus, efficient parametric ROMs are necessa

Giuliano Giacalone, Wenbin Zhao, Benjamin Bally, Shihang Shen

The System for Measuring Overlap with Gas (SMOG2) at the LHCb detector enables the study of fixed-target ion-ion collisions at relativistic energies ($\sqrt{s_{\rm NN}}\sim100$ GeV in the centre-of-mass). With input from \textit{ab initio} calculations of the structure of $^{16}$O and $^{20}$Ne, we compute 3+1D hydrodynamic predictions for the anisotropic fl

Vito Cerone, Sophie M. Fosson, Diego Regruto, Francesco Ripa

The development of algorithms for secure state estimation in vulnerable cyber-physical systems has been gaining attention in the last years. A consolidated assumption is that an adversary can tamper a relatively small number of sensors. In the literature, block-sparsity methods exploit this prior information to recover the attack locations and the state of t

Ginevra Braga, Alice Garoffolo, Angelo Ricciardone, Nicola Bartolo

When gravitational waves travel from their source to an observer, they interact with matter structures along their path, causing distinct deformations in their waveforms. In this study we introduce a novel theoretical framework for wave optics effects in gravitational lensing, addressing the limitations of existing approaches. We achieve this by incorporatin

Marcello Musso, Giulia Despali, Ravi K. Sheth

As it collapses to form a halo, the shape of a protohalo patch is deformed by the initial shear field. This deformation is often modeled using the "deformation" tensor, constructed from second derivatives of the gravitational potential, whose trace gives the initial overdensity. However, especially for lower mass protohalos, this matrix is not always positiv

ATLAS Collaboration

Measurements of jet cross-section ratios between inclusive bins of jet multiplicity are performed in 140 fb$^{-1}$ of proton--proton collisions with $\sqrt{s}=13$ TeV center-of-mass energy, recorded with the ATLAS detector at CERN's Large Hadron Collider. Observables that are sensitive the energy-scale and angular distribution of radiation due to the strong

Robert Berghaus, Selene Sachero, Gregor Bayer, Julia Heupel

Group IV color centers in diamond are among the most promising optically active spin systems with strong optical transitions and long spin coherences. The ground-state splitting of the center is particularly important to suppress the interaction with coherence-limiting phonons, which improves the coherence properties and sets the upper limit for the operatin

Andreas Koukounas, Georgios Mastrapas, Michael Günther, Bo Wang

Contrastive Language-Image Pretraining (CLIP) is widely used to train models to align images and texts in a common embedding space by mapping them to fixed-sized vectors. These models are key to multimodal information retrieval and related tasks. However, CLIP models generally underperform in text-only tasks compared to specialized text models. This creates

Federica Padovano, Chiara Villa

We present a mathematical model of the evolutionary dynamics of a metastatic tumour under chemotherapy, comprising non-local partial differential equations for the phenotype-structured cell populations in the primary tumour and its metastasis. These equations are coupled with a physiologically-based pharmacokinetic model of drug delivery, implementing a real

Ke Yi, Yuhui Xu, Heng Chang, Chen Tang

Large Language Models (LLMs) have advanced rapidly but face significant memory demands. While quantization has shown promise for LLMs, current methods typically require lengthy training to alleviate the performance degradation from quantization loss. However, deploying LLMs across diverse scenarios with different resource constraints, e.g., servers and perso

Luis Manuel Máñez-Espina, Ana Díaz-Rubio

Reconfigurable metasurfaces are potent platforms to control the propagation properties of light dynamically. Among different reconfiguration mechanisms available at optical frequencies, using non-volatile phase change materials is one of the most prominent. Tuning the refractive index of these materials, and thus changing the electromagnetic response of the

Jacob Dineen, Don Kridel, Daniel Dolk, David Castillo

A high-velocity paradigm shift towards Explainable Artificial Intelligence (XAI) has emerged in recent years. Highly complex Machine Learning (ML) models have flourished in many tasks of intelligence, and the questions have started to shift away from traditional metrics of validity towards something deeper: What is this model telling me about my data, and ho

Alexandre Besner, Alexandre Blondin Massé, Abderrahman Bani, Mouad Morabit

Hydro-Quebec (HQ) is a vertically integrated utility that produces, transmits, and distributes most of the electricity in the province of Quebec. The power grid it operates has a particular architecture created by large hydroelectric dams located far north and the extensive 735kV transmission grid that allows the generated power to reach the majority of the

Felix Brandt

This paper proves the local-in-time strong well-posedness of a parabolic-hyperbolic regularized version of Hibler's sea ice model. Hibler's model is the most frequently used sea ice model in climate science. Lagrangian coordinates are employed to handle the hyperbolic terms in the balance laws. The resulting problem is regarded as a quasilinear non-autonomou

Milo Edwardes, Daniel Heath

By classical results of Malcev, cancellative monoids need not be group-embeddable. In this paper, we describe and give presentations for and study an infinite family $\mathcal{M}_n$ of cancellative monoids which are not group-embeddable, originating from Malcev's original work. We show that $\mathcal{M}_n$ is singly aligned for $n \geq 2$, owing to applicati

Jan Niklas Reinhardt, Philipp Euringer, Olaf Hartwig, Gerald Hechenblaikner

Time-delay interferometry (TDI) is a data processing technique for space-based gravitational-wave detectors to create laser-noise-free equal-optical-path-length interferometers virtually on the ground. It relies on the interspacecraft signal propagation delays, which are delivered by intersatellite ranging monitors. Also delays due to onboard signal propagat

Zilin Ma, Susannah, Su, Nathan Zhao

Humanitarian negotiations in conflict zones, called \emph{frontline negotiation}, are often highly adversarial, complex, and high-risk. Several best-practices have emerged over the years that help negotiators extract insights from large datasets to navigate nuanced and rapidly evolving scenarios. Recent advances in large language models (LLMs) have sparked i

B. N. Kausik

Deep learning neural network models must be large enough to adapt to their problem domain, while small enough to avoid overfitting training data during gradient descent. To balance these competing demands, over-provisioned deep learning models such as transformers are trained for a single epoch on large data sets, and hence inefficient with both computing re

Kallan Berglund, Martin Bojowald, Aurora Colter, Manuel Diaz

Superpositions of black holes can be described geometrically using a combined canonical formulation for space-time and quantum states. A previously introduced black-hole model that includes quantum fluctuations of metric components is shown here to give full access to the corresponding space-time geometry of weak-field gravity in terms of suitable line eleme

Shuyang Jiang, Yusheng Liao, Ya Zhang, Yanfeng Wang

Fine-tuning on task-specific question-answer pairs is a predominant method for enhancing the performance of instruction-tuned large language models (LLMs) on downstream tasks. However, in certain specialized domains, such as healthcare or harmless content generation, it is nearly impossible to obtain a large volume of high-quality data that matches the downs

Caterina Morelli, Simone Boccaletti, Paolo Maranzano, Philipp Otto

The assessment of corporate sustainability performance is extremely relevant in facilitating the transition to a green and low-carbon intensity economy. However, companies located in different areas may be subject to different sustainability and environmental risks and policies. Henceforth, the main objective of this paper is to investigate the spatial and t

Ilaria Rossinelli

This paper delves into the study of curvilinear Hilbert schemes associated with a singular variety $(X,0)$ and the relationship between their motivic classes and the motivic measure on the arc scheme $X_\infty$ of $X$ introduced by Denef and Loeser. We introduce an Igusa zeta function specifically tailored for curvilinear Hilbert schemes for which we provide

Hangyeol Kang, Maher Ben Moussa, Nadia Magnenat-Thalmann

In this work, we describe our approach to developing an intelligent and robust social robotic system for the Nadine social robot platform. We achieve this by integrating Large Language Models (LLMs) and skilfully leveraging the powerful reasoning and instruction-following capabilities of these types of models to achieve advanced human-like affective and cogn

Yuxin Yao, Bailin Deng, Junhui Hou, Juyong Zhang

Existing optimization-based methods for non-rigid registration typically minimize an alignment error metric based on the point-to-point or point-to-plane distance between corresponding point pairs on the source surface and target surface. However, these metrics can result in slow convergence or a loss of detail. In this paper, we propose SPARE, a novel formu

Marcus Eßer, Marco Pratzer, Marc Frömming, Jonas Duffhauß

We describe a compact ultra-high vacuum (UHV) scanning tunneling microscope (STM) system that does not need any external supply of cooling liquids. It achieves temperatures down to 1.5 K and a z-noise down to 300 fmRMS for the frequency range of 0.1 Hz - 5 kHz (feedback loop off). It employs a pulse tube cryocooler (PTC) and a Joule-Thomson (JT) stage induci

Fatima Belokda, Ahmed Bouhlal, Ahmed Siari, Ahmed Jellal

We address the question of how the Aharonov-Bohm flux $\Phi_{AB}$ can affect the energy levels of graphene magnetic quantum dots (GMQDs) of radius $R$. To answer this question, we consider GMQDs induced by a magnetic field $B$ and subjected to two different gaps - an internal gap $\Delta_1$ and an external gap $\Delta_2$. After determining the eigenspinors a

H. Jung, A. Lelek, K. Moral Figueroa, S. Taheri Monfared

uPDFevolv2 is a software package designed for evolving collinear and Transverse Momentum Dependent (TMD) parton densities using the DGLAP evolution equation. A comprehensive description of both the theoretical framework and technical implementation is given, accompanied by a detailed guide on program usage, focusing on customizable parameters. This report is

Rodica Andreea Dinu, Martin Vodička

Jukes-Cantor model is one of the most meaningful statistical models from a biological perspective. We are interested in computing the algebraic degrees for phylogenetic varieties, which we call phylogenetic degrees, associated to the Jukes-Cantor model and any tree. As these varieties are toric, their geometry is hidden in the associated polytopes. For this

Eduard Frankford, Ingo Höhn, Clemens Sauerwein, Ruth Breu

This paper analyzes Large Language Models (LLMs) with regard to their programming exercise generation capabilities. Through a survey study, we defined the state of the art, extracted their strengths and weaknesses and finally proposed an evaluation matrix, helping researchers and educators to decide which LLM is the best fitting for the programming exercise

Siming Liang, Hui Sun, Richard Archibald, Feng Bao

This paper presents convergence analysis of a novel data-driven feedback control algorithm designed for generating online controls based on partial noisy observational data. The algorithm comprises a particle filter-enabled state estimation component, estimating the controlled system's state via indirect observations, alongside an efficient stochastic maximu

Feng-Li Lin, Avani Patel, Jason Payne

Soft hairs are an intrinsic infrared feature of a black hole, which may also affect near-horizon physics. In this work, we study some of the subtleties surrounding one of the primary observables with which we can study their effects in the context of Einstein's gravity: the black hole shadow. First, we clarify the singular pathology associated with black hol

Francesco Petri, Luigi Asprino, Aldo Gangemi

World modelling, i.e. building a representation of the rules that govern the world so as to predict its evolution, is an essential ability for any agent interacting with the physical world. Recent applications of the Transformer architecture to the problem of world modelling from video input show notable improvements in sample efficiency. However, existing a

Zichao Hu, Junyi Jessy Li, Arjun Guha, Joydeep Biswas

Code LLMs have shown promising results with converting tasks in natural language to programs that can be executed by service robots. We are interested in finetuning small, specialized LLMs for this purpose, but collecting datasets of task-program pairs specific to each robot is time-consuming and expensive. While approaches such as SELF-INSTRUCT and EVOL-INS

Joshua Hanson, Paul Kuberry, Biliana Paskaleva, Pavel Bochev

We demonstrate that system identification techniques can provide a basis for effective, non-intrusive model order reduction (MOR) for common circuits that are key building blocks in microelectronics. Our approach is motivated by the practical operation of these circuits and utilizes a canonical Hammerstein architecture. To demonstrate the approach we develop

Allan John Gerrard

We propose a new framework for the nested algebraic Bethe ansatz for a closed, rational spin chain with $\mathfrak{g}$-symmetry for any simple Lie algebra $\mathfrak{g}$. Starting the nesting process by removing a single simple root from $\mathfrak{g}$, we use the residual $U(1)$ charge and the block Gauss decomposition of the $R$-matrix to derive many stand

Michael Gowanlock, David E. Trilling, Daniel Kramer, Maria Chernyavskaya

The Solar System Notification Alert Processing System (SNAPS) is a ZTF and Rubin Observatory alert broker that will send alerts to the community regarding interesting events in the Solar System. SNAPS is actively monitoring Solar System objects and one of its functions is to compare objects (primarily main belt asteroids) to one another to find those that ar

Kuang-Ming Chen, Hung-yi Lee

The rapid development of large language models (LLMs) in recent years has largely focused on English, resulting in models that respond exclusively in English. To adapt these models to other languages, continual pre-training (CP) is often employed, followed by supervised fine-tuning (SFT) to maintain conversational abilities. However, CP and SFT can reduce a

Paul Lezeau, Thomas Walker, Yueqi Cao, Shiv Bhatia

We propose an algebraic geometric framework to study the expressivity of linear activation neural networks. A particular quantity of neural networks that has been actively studied is the number of linear regions, which gives a quantification of the information capacity of the architecture. To study and evaluate information capacity and expressivity, we work

Eneko Osaba, Matic Petrič, Izaskun Oregi, Raphael Seidel

This paper focuses on the presentation and evaluation of the high-level quantum programming language Eclipse Qrisp. The presented framework, used for developing and compiling quantum algorithms, is measured in terms of efficiency for its implementation of the Quantum Approximation Optimization Algorithm (QAOA) Module. We measure this efficiency and compare i

Alaa Nfissi, Wassim Bouachir, Nizar Bouguila, Brian Mishara

In speech emotion recognition (SER), using predefined features without considering their practical importance may lead to high dimensional datasets, including redundant and irrelevant information. Consequently, high-dimensional learning often results in decreasing model accuracy while increasing computational complexity. Our work underlines the importance of

Garry Goldstein

The Projected Augmented Waves (PAW) method is based on a linear transformation between the pseudo wavefunctions and the all electron wavefunctions. To obtain high accuracy with this method, it is important that the local part of the linear transform (inside each atomic sphere) be defined over a complete basis set (with deviations from completeness leading to

Trupti Patil, Sukanta Panda

This paper investigates the effects of spatial curvature in a model where dark matter and dark energy interact. The analysis employs a range of datasets, including CMB, BAO, Type Ia Supernova, $H(z)$ from cosmic chronometers, $H_0$ measurements from Megamasers and SH0ES, growth rate data and strong lensing time delay measurements, to assess the model's fit a

Yanyan Zhang, Sida Xing

Single-cycle optical pulses offer a strong carrier-envelope-offset (CEO) dependent electric field and the highest peak intensity for a given pulse energy. Absence of demonstrated GHz single-cycle lasers constrains exploration of single/sub-cycle dynamics at this repetition rate. By leveraging fiber soliton effects and suppressing higher-order dispersion, we

Hyunsang Cho, Seonghoon Yoo, Bang Chul Jung, Joonhyuk Kang

This paper considers a joint communication and sensing technique for enhancing situational awareness in practical battlefield scenarios. In particular, we propose an aerial reconfigurable intelligent surface (ARIS)-assisted integrated sensing and communication (ISAC) system consisting of a single access point (AP), an ARIS, multiple users, and a sensing targ

G. J. Cooke, D. R. Marsh, C. Walsh, F. Sainsbury-Martinez

Ozone ($\textrm{O}_3$) is important for the survival of life on Earth because it shields the surface from ionising ultraviolet (UV) radiation. However, the existence of $\textrm{O}_3$ in Earth's atmosphere is not always beneficial. Resulting from anthropogenic activity, $\textrm{O}_3$ exists as a biologically harmful pollutant at the surface when it forms in

Erik Hormann, Renaud Lambiotte, George T. Cantwell

We propose an approximation for the first return time distribution of random walks on undirected networks. We combine a message-passing solution with a mean-field approximation, to account for the short- and long-term behaviours respectively. We test this approximation on several classes of large graphs and find excellent agreement between our approximations

Wooseong Cho, Taehyun Hwang, Joongkyu Lee, Min-hwan Oh

We study reinforcement learning with multinomial logistic (MNL) function approximation where the underlying transition probability kernel of the Markov decision processes (MDPs) is parametrized by an unknown transition core with features of state and action. For the finite horizon episodic setting with inhomogeneous state transitions, we propose provably eff

Leticia Arrington, Sebastian Ueckert

Item parameter estimation in pharmacometric item response theory (IRT) models is predominantly performed using the Laplace estimation algorithm as implemented in NONMEM. In psychometrics a wide range of different software tools, including several packages for the open-source software R for implementation of IRT are also available. Each have their own set of

Rosario Uceda-Sosa, Karthikeyan Natesan Ramamurthy, Maria Chang, Moninder Singh

The ability to summarize and organize knowledge into abstract concepts is key to learning and reasoning. Many industrial applications rely on the consistent and systematic use of concepts, especially when dealing with decision-critical knowledge. However, we demonstrate that, when methodically questioned, large language models (LLMs) often display and demons

Julia Falcone, D. Michael Crenshaw, Travis C. Fischer, Beena Meena

We have characterized the ionized, neutral, and warm molecular gas kinematics in the Seyfert 1 galaxy NGC 3227 using observations from the Hubble Space Telescope Space Telescope Imaging Spectrograph, Apache Point Observatory's Kitt Peak Ohio State Multi-Object Spectrograph, Gemini-North's Near-Infrared Integral Field Spectrometer, and the Atacama Large Milli

Tommaso Monopoli, Fabio Montello, Claudio Rossi

Landslides are one of the most critical and destructive geohazards. Widespread development of human activities and settlements combined with the effects of climate change on weather are resulting in a high increase in the frequency and destructive power of landslides, making them a major threat to human life and the economy. In this paper, we explore methodo

Justin Plogmann, Oliver Brenner, Patrick Jenny

The URANS equations provide a computationally efficient tool to simulate unsteady turbulent flows for a wide range of applications. To account for the errors introduced by the turbulence closure model, recent works have adopted data assimilation (DA) to enhance their predictive capabilities. Recognizing the challenges posed by the computational cost of 4DVar

Sike Wang, Helen Wong

The Kauffman bracket skein algebra of a surface is a generalization of the Jones polynomial invariant for links and plays a principal role in the Witten-Reshetikhin- Turaev topological quantum field theory. However, the multiplicative structure of the skein algebra is not well understood, with a priori exponential complexity. We consider the case of one-hole

Yu-Min Hu, Xian Gao

We investigate the parity-violating scalar-tensor theory and pay special attention to terms that are free of the Ostrogradsky ghost in the unitary gauge, i.e., when the scalar field possesses a timelike gradient. We exhaustively identify the generally covariant scalar-tensor theory (GST) monomials with parity violation up to $d=4$, where $d$ is the total num

Sunday Achimugu, Abraham Usman Usman, Suleiman Zubair, Michael David

The paradigm shift in the use cases of wireless communication necessitates the need to move toward higher data rates, large bandwidths, and intelligent reconfiguration in 6G. This paper presents a novel double T-shaped antenna array that operates between 4GHz to 16GHz for 6G mobile communication. The antenna consists of a rectangular microstrip with a fracta

Fabio Ashtar Telarico

This paper seeks to bridge the gap between archival text analysis and network analysis by applying network clustering methods to analyze the coverage of Bulgaria in 123 issues of the newspaper Osservatore Romano published between January and May 1877. Utilizing optical character recognition and generalized homogeneity blockmodeling, the study constructs netw

Lukas Uzolas, Elmar Eisemann, Petr Kellnhofer

Animation techniques bring digital 3D worlds and characters to life. However, manual animation is tedious and automated techniques are often specialized to narrow shape classes. In our work, we propose a technique for automatic re-animation of various 3D shapes based on a motion prior extracted from a video diffusion model. Unlike existing 4D generation meth

Giulia Bevilacqua, Chiara Lonati, Luca Lussardi, Alfredo Marzocchi

Nematic surfaces are thin fluid structures, ideally two-dimensional, endowed with an in-plane nematic order. In 2012, two variational models have been introduced by Giomi [11] and by Napoli and Vergori [29,28]. Both penalize the area of the surface and the gradient of the director: in [11] the covariant derivative of the director is considered, while [28] de

Daniel R. Sabogal, Daniel F. Urrego, Juan Rafael Álvarez, Andrés F. Herrera

We present a theoretical and experimental study of a controllable decoherence-assisted quantum key distribution scheme. Our method is based on the possibility of introducing controllable decoherence to polarization qubits using the spatial degree of freedom of light. We show that our method reduces the amount of information that an eavesdropper can obtain in

Phillip Howard, Kathleen C. Fraser, Anahita Bhiwandiwalla, Svetlana Kiritchenko

With the advent of Large Language Models (LLMs) possessing increasingly impressive capabilities, a number of Large Vision-Language Models (LVLMs) have been proposed to augment LLMs with visual inputs. Such models condition generated text on both an input image and a text prompt, enabling a variety of use cases such as visual question answering and multimodal

Klaus Ziegler

We study the effect of random scattering in quantum walks on a finite graph and compare it with the effect of repeated measurements. To this end, a constructive approach is employed by introducing a localized and a delocalized basis for the underlying Hilbert space. This enables us to design Hamiltonians whose eigenvectors are either localized or delocalized

Andrea Adriani, Alec Jacopo Almo Schiavoni-Piazza, Stefano Serra-Capizzano, Cristina Tablino-Possio

In the current note we consider matrix-sequences $\{B_{n,t}\}_n$ of increasing sizes depending on $n$ and equipped with a parameter $t>0$. For every fixed $t>0$, we assume that each $\{B_{n,t}\}_n$ possesses a canonical spectral/singular values symbol $f_t$ defined on $D_t\subset \R^{d}$ of finite measure, $d\ge 1$. Furthermore, we assume that $ \{ \{ B_{n,t

Enrico Fabrizi, Nicola Salvati, Martin Slawski

In small area estimation different data sources are integrated in order to produce reliable estimates of target parameters (e.g., a mean or a proportion) for a collection of small subsets (areas) of a finite population. Regression models such as the linear mixed effects model or M-quantile regression are often used to improve the precision of survey sample e

Juhan Aru, Philémon Bordereau

One way to uniquely define Schramm-Loewner Evolution (SLE) in multiply connected domains is to use the restriction property. This gives an implicit definition of a $\sigma$-finite measure on curves; yet it is in general not clear how to construct such measures nor whether the mass of these measures, called the partition function, is finite. We provide an exp