April 2024 arXiv papers — page 169

C. Y. Ma, J. Yu, T. Legero, S. Herbers

We have investigated crystalline AlGaAs/GaAs optical coatings with three ultra-stable cavities operating at 4 K, 16 K, 124 K and 297 K. The response of the resonance frequencies of cavities to variations in optical power indicates effects beyond the photo-thermo-optic effect observed in dielectric coatings. These effects are strongly dependent on the intensi

Johannes Bracher, Barbora Sobolová

INAR (integer-valued autoregressive) and INGARCH (integer-valued GARCH) models are among the most commonly employed approaches for count time series modelling, but have been studied in largely distinct strands of literature. In this paper, a new class of generalized integer-valued ARMA (GINARMA) models is introduced which unifies a large number of compound P

Chuang Li, Shuai Shao, Willian Mikason, Rubing Lin

The demand for improved efficiency and accuracy in vaccine safety assessments is increasing. Here, we explore the application of computer vision technologies to automate the monitoring of experimental mice for potential side effects after vaccine administration. Traditional observation methods are labor-intensive and lack the capability for continuous monito

Asma Shakil, Paul Denny

Computer science (CS) capstone courses offer students a valuable opportunity to gain hands-on experience in software development, practice essential soft skills, and enhance their employability prospects. They are a core component in many CS undergraduate degrees and address the ACM curricula requirements of inculcating professional dispositions in students

Hamad El Kahza, William Taitano, Jing-Mei Qiu, Luis Chacón

We propose a high order adaptive-rank implicit integrators for stiff time-dependent PDEs, leveraging extended Krylov subspaces to efficiently and adaptively populate low-rank solution bases. This allows for the accurate representation of solutions with significantly reduced computational costs. We further introduce an efficient mechanism for residual evaluat

Gabriela Ben Melech Stan, Estelle Aflalo, Raanan Yehezkel Rohekar, Anahita Bhiwandiwalla

In the rapidly evolving landscape of artificial intelligence, multi-modal large language models are emerging as a significant area of interest. These models, which combine various forms of data input, are becoming increasingly popular. However, understanding their internal mechanisms remains a complex task. Numerous advancements have been made in the field o

Z. Ding, A. Variu, S. Alam, Y. Yu

Ongoing and upcoming galaxy redshift surveys, such as the Dark Energy Spectroscopic Instrument (DESI) survey, will observe vast regions of sky and a wide range of redshifts. In order to model the observations and address various systematic uncertainties, N-body simulations are routinely adopted, however, the number of large simulations with sufficiently high

Alex Stivala, Peng Wang, Alessandro Lomi

The autologistic actor attribute model (ALAAM) is a model for social influence, derived from the more widely known exponential-family random graph model (ERGM). ALAAMs can be used to estimate parameters corresponding to multiple forms of social contagion associated with network structure and actor covariates. This work introduces ALAAMEE, open-source Python

Chunyuan Deng, Xiangru Tang, Yilun Zhao, Hanming Wang

Recently, large language models (LLMs) have evolved into interactive agents, proficient in planning, tool use, and task execution across a wide variety of tasks. However, without specific agent tuning, open-source models like LLaMA currently struggle to match the efficiency of GPT- 4, particularly given the scarcity of agent-tuning datasets for fine-tuning.

Xuesong Wang, Nina Fatehi, Caisheng Wang, Masoud H. Nazari

This paper presents a deep learning-based approach for hourly power outage probability prediction within census tracts encompassing a utility company's service territory. Two distinct deep learning models, conditional Multi-Layer Perceptron (MLP) and unconditional MLP, were developed to forecast power outage probabilities, leveraging a rich array of input fe

William Macke, Michael Doyle

Large Language Models (LLMs) have demonstrated impressive abilities in recent years with regards to code generation and understanding. However, little work has investigated how documentation and other code properties affect an LLM's ability to understand and generate code or documentation. We present an empirical analysis of how underlying properties of code

Gokulan Ravi, Xiaokang Qiu, Mithuna Thottethodi, T. N. Vijaykumar

Memory consistency model (MCM) issues in out-of-order-issue microprocessor-based shared-memory systems are notoriously non-intuitive and a source of hardware design bugs. Prior hardware verification work is limited to in-order-issue processors, to proving the correctness only of some test cases, or to bounded verification that does not scale in practice beyo

Sei Saito, Masaomi Tanaka, Paolo A. Mazzali, Stephan Hachinger

H-poor superluminous supernovae (SLSNe-I) are characterized by O II lines around 4,000 - 4,500 A in pre-/near-maximum spectra, so-called W-shaped O II lines. As these lines are from relatively high excitation levels, they have been considered a sign of non-thermal processes, which may give a hint of power sources of SLSNe-I. However, the conditions for these

Dries Van De Putte, Raphael Meshaka, Boris Trahin, Emilie Habart

Mid-infrared emission features probe the properties of ionized gas, and hot or warm molecular gas. The Orion Bar is a frequently studied photodissociation region (PDR) containing large amounts of gas under these conditions, and was observed with the MIRI IFU aboard JWST as part of the "PDRs4All" program. The resulting IR spectroscopic images of high angular

Navid Mahdian, Mohammad Jani, Amir M. Soufi Enayati, Homayoun Najjaran

Multi-object tracking (MOT) is a prominent task in computer vision with application in autonomous driving, responsible for the simultaneous tracking of multiple object trajectories. Detection-based multi-object tracking (DBT) algorithms detect objects using an independent object detector and predict the imminent location of each target. Conventional predicti

Ying Shen, Yizhe Zhang, Shuangfei Zhai, Lifu Huang

Recent advancements in image generation have made significant progress, yet existing models present limitations in perceiving and generating an arbitrary number of interrelated images within a broad context. This limitation becomes increasingly critical as the demand for multi-image scenarios, such as multi-view images and visual narratives, grows with the e

Nikhita Joshi, Daniel Vogel

The feeling of something belonging to someone is called "psychological ownership." A common assumption is that writing with generative AI lowers psychological ownership, but the extent to which this occurs and the role of prompt length are unclear. We report on two experiments to examine the relationship between psychological ownership and prompt length. Par

Nicolau Manubens, Simon D. Smart, Emanuele Danovaro, Tiago Quintino

Operational Numerical Weather Prediction (NWP) workflows are highly data-intensive. Data volumes have increased by many orders of magnitude over the last 40 years, and are expected to continue to do so, especially given the upcoming adoption of Machine Learning in forecast processes. Parallel POSIX-compliant file systems have been the dominant paradigm in da

R. B. Baker, Ch. Elster, T. Dytrych, K. D. Launey

Based on the Watson expansion of the multiple scattering series, we employ a nonlocal translationally invariant nuclear density derived within the symmetry-adapted no-core shell model (SA-NCSM) framework from a chiral next-to-next-to-leading order (NNLO) nucleon-nucleon interaction and the very same interaction for a consistent full-folding calculation of th

Joo Seung Lee, Malini Mahendra, Anil Aswani

Mechanical ventilation is a critical life support intervention that delivers controlled air and oxygen to a patient's lungs, assisting or replacing spontaneous breathing. While several data-driven approaches have been proposed to optimize ventilator control strategies, they often lack interpretability and alignment with domain knowledge, hindering clinical a

Jordi Delgado, Mallika Roy, Enric Ventura

We introduce the new notion of quotient-saturation as a measure of the immensity of the quotient structure of a group. We present a sufficient condition for a finitely presented group to be quotient-saturated, and use it to deduce that non-elementary finitely presented subgroups of a hyperbolic group (in particular, non-elementary hyperbolic groups themselve

Skyler Hughes, Rebecca Martin, Micah Corah, Sebastian Scherer

Observing and filming a group of moving actors with a team of aerial robots is a challenging problem that combines elements of multi-robot coordination, coverage, and view planning. A single camera may observe multiple actors at once, and a robot team may observe individual actors from multiple views. As actors move about, groups may split, merge, and reform

Joseph Smolsky, Kyle G Leach, Ryan Abells, Pedro Amaro

Despite their high relative abundance in our Universe, neutrinos are the least understood fundamental particles of nature. They also provide a unique system to study quantum coherence and the wavelike nature of particles in fundamental systems due to their extremely weak interaction probabilities. In fact, the quantum properties of neutrinos emitted in exper

Weizhe Chen, Sven Koenig, Bistra Dilkina

Cooperative multi-agent reinforcement learning (MARL) has been an increasingly important research topic in the last half-decade because of its great potential for real-world applications. Because of the curse of dimensionality, the popular "centralized training decentralized execution" framework requires a long time in training, yet still cannot converge eff

Andries E. Brouwer, Dean Crnković, Andrea Švob

In this paper, we prove the existence of directed strongly regular graphs with parameters $(63,11,8,1,2)$. We construct a pair of nonisomorphic dsrg(63,11,8,1,2), where one is obtained from the other by reversing all arrows. Both directed strongly regular graphs have $L_2(8):3$ as the full automorphism group.

Lucas E. Resck, Marcos M. Raimundo, Jorge Poco

Saliency post-hoc explainability methods are important tools for understanding increasingly complex NLP models. While these methods can reflect the model's reasoning, they may not align with human intuition, making the explanations not plausible. In this work, we present a methodology for incorporating rationales, which are text annotations explaining human

Morteza Moradi, Mohammad Moradi, Francesco Rundo, Concetto Spampinato

Recent advancements in video saliency prediction (VSP) have shown promising performance compared to the human visual system, whose emulation is the primary goal of VSP. However, current state-of-the-art models employ spatio-temporal transformers trained on limited amounts of data, hindering generalizability adaptation to downstream tasks. The benefits of vis

T. Muscheid, R. Gartmann, N. Karcher, F. Schuderer

Recent advances in the development of cryogenic particle detectors such as magnetic microcalorimeters (MMCs) allow the fabrication of sensor arrays with an increasing number of pixels. Since these detectors must be operated at the lowest temperatures, the readout of large detector arrays is still quite challenging. This is especially true for the ECHo experi

Christopher Helmerich, Jared Fuchs, Alexey Bobrick, Luke Sellers

The field of warp research has been dominated by analytical methods to investigate potential solutions. However, these approaches often favor simple metric forms that facilitate analysis but ultimately limit the range of exploration of novel solutions. So far the proposed solutions have been unphysical, requiring energy condition violations and large energy

Bogdan Vlahov, Jason Gibson, David D. Fan, Patrick Spieler

Sampling-based model-predictive controllers have become a powerful optimization tool for planning and control problems in various challenging environments. In this paper, we show how the default choice of uncorrelated Gaussian distributions can be improved upon with the use of a colored noise distribution. Our choice of distribution allows for the emphasis o

Alexander D. White, Geun Ho Ahn, Richard Luhtaru, Joel Guo

Rapid progress in photonics has led to an explosion of integrated devices that promise to deliver the same performance as table-top technology at the nanoscale; heralding the next generation of optical communications, sensing and metrology, and quantum technologies. However, the challenge of co-integrating the multiple components of high-performance laser sy

Catherine Henry, Casey Kennington

Towards addressing the Symbol Grounding Problem and motivated by early childhood language development, we leverage a robot which has been equipped with an approximate model of curiosity with particular focus on bottom-up building of unsupervised categories grounded in the physical world. That is, rather than starting with a top-down symbol (e.g., a word refe

Litao Li, Steven H. H. Ding, Andrew Walenstein, Philippe Charland

Software vulnerabilities are a challenge in cybersecurity. Manual security patches are often difficult and slow to be deployed, while new vulnerabilities are created. Binary code vulnerability detection is less studied and more complex compared to source code, and this has important practical implications. Deep learning has become an efficient and powerful t

Aida Khajavirad

We consider the problem of packing congruent circles with the maximum radius in a unit square as a mathematical optimization problem. Due to the presence of non-overlapping constraints, this problem is a notoriously difficult nonconvex quadratically constrained optimization problem, which possesses many local optima. We consider several popular convexificati

Pablo Figueroa, Gonzalo Herrera, Fredy Ochoa

A possible extension of the Standard Model able to explain the recent measurement of the anomalous magnetic moment of the muon consists in adding a gauged $U(1)_{L_{\mu}-L_{\tau}}$ symmetry. If the dark matter particle is charged under this symmetry, the kinetic mixing between the new gauge boson and the photon induces dark matter-electron interactions. We d

Mohamed Swailem, Uwe C. Täuber

Stochastic reaction-diffusion models are employed to represent many complex physical, biological, societal, and ecological systems. The macroscopic reaction rates describing the large-scale kinetics in such systems are effective, scale-dependent parameters that need to be either measured experimentally or computed using a microscopic model. In a Monte Carlo

Zexin Fang, Bin Han, Hans D. Schotten

Federated learning (FL) offers a privacy-preserving collaborative approach for training models in wireless networks, with channel estimation emerging as a promising application. Despite extensive studies on FL-empowered channel estimation, the security concerns associated with FL require meticulous attention. In a scenario where small base stations (SBSs) se

Nazar Miheisi, Ryan O'Loughlin

We discuss generalizations of the Szeg\H{o} Limit Theorem to truncated Toeplitz operators. In particular, we consider compressions of Toeplitz operators to an increasing sequence of finite dimensional model spaces. We present two theorems. The first is a new variant of the Szeg\H{o} Limit Theorem in this setting. The second relates to the variant given by St

Johann D. Gaebler, Sharad Goel, Aziz Huq, Prasanna Tambe

Regulatory efforts to protect against algorithmic bias have taken on increased urgency with rapid advances in large language models (LLMs), which are machine learning models that can achieve performance rivaling human experts on a wide array of tasks. A key theme of these initiatives is algorithmic "auditing," but current regulations -- as well as the scient

Fred Hohman, Chaoqun Wang, Jinmook Lee, Jochen Görtler

On-device machine learning (ML) moves computation from the cloud to personal devices, protecting user privacy and enabling intelligent user experiences. However, fitting models on devices with limited resources presents a major technical challenge: practitioners need to optimize models and balance hardware metrics such as model size, latency, and power. To h

Manfred Diaz, Liam Paull, Andrea Tacchetti

Teacher-Student Curriculum Learning (TSCL) is a curriculum learning framework that draws inspiration from human cultural transmission and learning. It involves a teacher algorithm shaping the learning process of a learner algorithm by exposing it to controlled experiences. Despite its success, understanding the conditions under which TSCL is effective remain

Fu-Der Chen, Ankita Sharma, Tianyuan Xue, Youngho Jung

In brain activity mapping experiments using optogenetics, patterned illumination is crucial for deterministic and localized stimulation of neurons. However, due to optical scattering in brain tissue, light-emitting implantable devices are needed to bring precise patterned illumination to deep brain regions. A promising solution is silicon neural probes with

Arnauld Mesinga Mwafise

Partially ordered sets (posets) are discrete mathematical structures that formalize the notion of comparison without forcing every pair of objects to be comparable. This makes them a natural representation for the many machine learning and data-analysis settings in which objects are related by dominance, containment, priority, or refinement relations rather

Yifan Qu, Oliver Krzysik, Hans De Sterck, Omer Ege Kara

Graph Neural Networks (GNNs) have established themselves as the preferred methodology in a multitude of domains, ranging from computer vision to computational biology, especially in contexts where data inherently conform to graph structures. While many existing methods have endeavored to model GNNs using various techniques, a prevalent challenge they grapple

Romy Müller

Saliency maps can explain how deep neural networks classify images. But are they actually useful for humans? The present systematic review of 68 user studies found that while saliency maps can enhance human performance, null effects or even costs are quite common. To investigate what modulates these effects, the empirical outcomes were organised along severa

Yanpeng Ye, Jie Ren, Shaozhou Wang, Yuwei Wan

Knowledge in materials science is widely dispersed across extensive scientific literature, posing significant challenges to the efficient discovery and integration of new materials. Traditional methods, often reliant on costly and time-consuming experimental approaches, further complicate rapid innovation. Addressing these challenges, the integration of arti

Guoliang He, Gingfung Yeung, Sheriffo Ceesay, Adam Barker

Accurately predicting task performance at runtime in a cluster is advantageous for a resource management system to determine whether a task should be migrated due to performance degradation caused by interference. This is beneficial for both cluster operators and service owners. However, deploying performance prediction systems with learning methods requires

Anton Kananovich, J. Goree

The microscopic structure within a two-dimensional shock was studied using data from a dusty plasma experiment. A single layer of charged microparticles, levitated in a glow-discharge plasma, was perturbed by an electrically floating wire that was moved at a steady supersonic speed to excite a compressional shock. A rearrangement of particles was observed, f

Marcin Kolakowski

The paper presents a comparison of performance of two Kalman Filters: extended Kalman filter (EKF) and unscented Kalman filter (UKF) in a hybrid Bluetooth-Low-Energy-ultra-wideband (BLE-UWB) based localization system. In the system, the user is localized primarily based on Received Signal Strength (RSS) measurements of BLE signals. The UWB part of the system

Nazar Miheisi

For two inner functions $\vartheta,\varphi\in H^\infty$, we give a simple sufficient condition for the system $\vartheta^m,\; \varphi^n$, $m,n\in\mathbb{Z}$, to be complete in the weak-$^*$ topology of $L^\infty(\mathbb{T})$. To be precise, we show that this system is complete whenever there is an arc $I$ of the unit circle $\mathbb{T}$ such that $\vartheta$

Peter Tatum, David Malaspina, Alexandros Chasapis, Benjamin Short

An outstanding gap in our knowledge of the solar wind is the relationship between switchbacks and solar wind turbulence. Switchbacks are large fluctuations, even reversals, of the background magnetic field embedded in the solar wind flow. It has been proposed that switchbacks may form as a product of turbulence and decay via coupling with the turbulent casca

Jose Daniel Lara, Clayton Barrows, Daniel Thom, Sourabh Dalvi

PowerSimulations.jl is a Julia-based BSD-licensed power system operations simulation tool developed as a flexible and open source software for quasi-static power systems simulations including Production Cost Models. PowerSimulations.jl tackles the issues of developing a simulation model in a modular way providing tools for the formulation of decision models

Kaavya Chaparala, Guido Zarrella, Bruce Torres Fischer, Larry Kimura

In this paper we address the challenge of improving Automatic Speech Recognition (ASR) for a low-resource language, Hawaiian, by incorporating large amounts of independent text data into an ASR foundation model, Whisper. To do this, we train an external language model (LM) on ~1.5M words of Hawaiian text. We then use the LM to rescore Whisper and compute wor

Marcin Kolakowski

Localization systems intended for home use by people with mild cognitive impairment should comply with specific requirements. They should provide the users with sub-meter accuracy allowing for analyzing patient's movement trajectory and be energy effective, so the devices do not need frequent charging. Such requirements could be satisfied by employing a hybr

Kishore Vasan, Marton Karsai, Albert-Laszlo Barabasi

The metaverse promises a shift in the way humans interact with each other, and with their digital and physical environments. The lack of geographical boundaries and travel costs in the metaverse prompts us to ask if the fundamental laws that govern human mobility in the physical world apply. We collected data on avatar movements, along with their network mob

S C Tiwari

Vast literature on the experiments and mathematical formulations on the geometric phases signifies the importance of this subject. Physical mechanism for the origin of the geometric phases in optics was suggested in 1992 by the author in terms of the exchange of the angular momentum. Some of the literature has taken notice of it, however, the real import of

Su Sun, Cheng Zhao, Yuliang Guo, Ruoyu Wang

In this paper, we present a novel indoor 3D reconstruction method with occluded surface completion, given a sequence of depth readings. Prior state-of-the-art (SOTA) methods only focus on the reconstruction of the visible areas in a scene, neglecting the invisible areas due to the occlusions, e.g., the contact surface between furniture, occluded wall and flo

V. Mishnyakov, A. Morozov, M. Reva, P. Suprun

We continue the development of a position space approach to equations for Feynman multi-loop integrals. The key idea of the approach is that unintegrated products of Greens functions in position space are still loop integral in momentum space. The natural place to start are the famous banana diagrams, which we explore in this paper. In position space, these

Mobeen Mahmood, Yicheng Yuan, Tho Le-Ngoc

This work considers a multi-user massive multiple-input multiple-output (MU-mMIMO) Internet-of-Things (IoT) system, where multiple unmanned aerial vehicles (UAVs) operating as decode-and-forward (DF) relays connect the base station (BS) to a large number of IoT devices. To maximize the total achievable rate, we propose a novel joint optimization problem of h

Xiwen Dengxiong, Xueting Wang, Shi Bai, Yunbo Zhang

Most existing 6-DoF robot grasping solutions depend on strong supervision on grasp pose to ensure satisfactory performance, which could be laborious and impractical when the robot works in some restricted area. To this end, we propose a self-supervised 6-DoF grasp pose detection framework via an Augmented Reality (AR) teleoperation system that can efficientl

Matin Macktoobian, Zhan Shu, Qing Zhao

Traffic dynamics is universally crucial in analyzing and designing almost any network. This article introduces a novel theoretical approach to analyzing network traffic dynamics. This theory's machinery is based on the notion of traffic divergence, which captures the flow (im)balance of network nodes and links. It features various analytical probes to invest

Daniel Alpay, Ilwoo Cho, Mihaela Vajiac

In this paper we describe the rise of global operators in the scaled quaternionic case, an important extension from the quaternionic case to the family of scaled hypercomplex numbers $\mathbb{H}_t,\, t\in\mathbb{R}^*$, of which the $\mathbb{H}_{-1}=\mathbb{H}$ is the space of quaternions and $\mathbb{H}_{1}$ is the space of split quaternions. We also describ

Zhou Tang, Ted Westling

The bootstrap is a popular method of constructing confidence intervals due to its ease of use and broad applicability. Theoretical properties of bootstrap procedures have been established in a variety of settings. However, there is limited theoretical research on the use of the bootstrap in the context of estimation of a differentiable functional in a nonpar

Felix Rydell, Isak Sundelius

We present an algebraic study of the projection of plane curves and twisted cubics in space onto multiple images of pinhole cameras. The Zariski closure of the image of the projection of conics is a conic multiview varieties. Extending previous work for point and line multiview varieties, we use back-projected cones to describe these varieties. For two views

Santanu Dey, Anffany Chen, Pablo Basteiro, Alexander Fritzsche

We demonstrate how table-top settings combining hyperbolic lattices with nonlinear dynamics universally encode aspects of the bulk-boundary-correspondence between gravity in anti-de-Sitter (AdS) space and conformal field theory (CFT). Our concrete and broadly applicable holographic toy model simulates gravitational self-interactions in the bulk and features

Maicon Azevedo da Luz, Kleinner Farias

Companies developing Web applications have faced an increasing demand for high-quality products with low cost and production time ever smaller. However, developing such applications is still considered a time-consuming and error-prone task, mainly due to the difficulty of promoting the reuse of features (or functionalities) and modules, and the heterogeneity

Damião J. Araújo, Ginaldo S. Sá, Eduardo V. Teixeira, José Miguel Urbano

We investigate a class of free boundary problems with oscillatory singularities within stochastic materials. Our main result yields sharp regularity estimates along the free boundary, provided the power of the singularity varies in a Dini-continuous fashion below a certain threshold. We also reveal an interesting repelling estimate preventing the free bounda

Yumeng Wang, Snigdha Panigrahi, Xuming He

In modern data analysis, it is common to select a model before performing statistical inference. Selective inference tools make adjustments for the model selection process in order to ensure reliable inference post selection. In this paper, we introduce an asymptotic pivot to infer about the effects of selected variables on conditional quantile functions. Ut

Krzysztof Siminski, Konrad Wnuk

Neuro-fuzzy systems are a technique of explainable artificial intelligence (XAI). They elaborate knowledge models as a set of fuzzy rules. Fuzzy sets are crucial components of fuzzy rules. They are used to model linguistic terms. In this paper, we present an automatic extraction of fuzzy rules in the natural English language. Full implementation is available

Peter Asenbaum, Chris Overstreet, Mark A. Kasevich

In a uniform gravitational field, classical test objects fall universally. Any reference object or observer will fall in the same universal manner. Therefore, a uniform gravitational field cannot create dynamics between observers and classical test objects. The influence of a uniform gravitational field on matter waves and clocks, however, is described incon

Johannes Greiner, Anurag Singh, Merle I. S. Röhr

In computational chemistry, accurately predicting molecular configurations that exhibit specific properties remains a critical challenge. Its intricacies become especially evident in the study of molecular aggregates, where the light-induced functionality is tied to highly structure-dependent electronic couplings between molecules. Here, we present an effici

Ashod Khederlarian, Jeffrey A. Newman, Brett H. Andrews, Biprateep Dey

We present a simple, differentiable method for predicting emission line strengths from rest-frame optical continua using an empirically-determined mapping. Extensive work has been done to develop mock galaxy catalogues that include robust predictions for galaxy photometry, but reliably predicting the strengths of emission lines has remained challenging. Our

Robert Kasumba, Guanghui Yu, Chien-Ju Ho, Sarah Keren

Goal recognition design (GRD) aims to make limited modifications to decision-making environments to make it easier to infer the goals of agents acting within those environments. Although various research efforts have been made in goal recognition design, existing approaches are computationally demanding and often assume that agents are (near-)optimal in thei

Yahya Almumin

Properties such as the radius, charge and energy of ${U}(1)$ gauged Q-balls are analytically complicated to characterize. A mapping relation is known between the ground state of gauged Q-balls and global Q-balls that reduces the complexity of the analysis of the ground state of gauged Q-balls. Extending the map to excited states is a powerful tool to determi

Ali Pesaranghader, Nikhil Verma, Manasa Bharadwaj

Harmful and offensive communication or content is detrimental to social bonding and the mental state of users on social media platforms. Text detoxification is a crucial task in natural language processing (NLP), where the goal is removing profanity and toxicity from text while preserving its content. Supervised and unsupervised learning are common approache

Churna Bhandari, Gavin N. Nope, T. Lograsso, Durga Paudyal

The binary manganese aluminium (MnAl) alloy with L$1_0$ crystal structure is a promising rare earth element-free permanent magnetic material because of its exceptional magnetic properties. However, experimentally synthesizing it in a stable bulk form is extremely challenging. Here, an alternative method of stabilizing the material is proposed and theoretical

Daniel Potts, Laura Weidensager

We propose two algorithms for boosting random Fourier feature models for approximating high-dimensional functions. These methods utilize the classical and generalized analysis of variance (ANOVA) decomposition to learn low-order functions, where there are few interactions between the variables. Our algorithms are able to find an index set of important input

Johnathan E. Avery

This paper explores the integration of linguistic inputs within robotic navigation systems, drawing upon the symbol interdependency hypothesis to bridge the divide between symbolic and embodied cognition. It examines previous work incorporating language and semantics into Neural Network (NN) and Simultaneous Localization and Mapping (SLAM) approaches, highli

Vibhor Agarwal, Aravindh Raman, Nishanth Sastry, Ahmed M. Abdelmoniem

The recent development of decentralised and interoperable social networks (such as the "fediverse") creates new challenges for content moderators. This is because millions of posts generated on one server can easily "spread" to another, even if the recipient server has very different moderation policies. An obvious solution would be to leverage moderation to

Wei-Yang Liu, Edward Shuryak, Ismail Zahed

We discuss a general framework for the evaluation of the gluonic form factors in light hadrons at low momentum transfer, in the QCD instanton vacuum. At medium resolution of the order of the inverse mean instanton size, the glue is mostly localized in single or pair of pseudoparticles, and globally constrained by the fluctuations of their topological charges

Tyson B. Littenberg, Ananthu K. Lali

Some electromagnetically observed ultra-compact binaries will be strong gravitational wave sources for space-based detectors like the Laser Interferometer Space Antenna (LISA). These sources have historically been referred to as "verification binaries" under the assumption that they will be exploited to assess mission performance. This paper quantitatively i

Jérôme Droniou, Ali Haidar, Roland Masson

This work performs the convergence analysis of the polytopal nodal discretisation of contact-mechanics (with Tresca friction) recently introduced in [18] in the framework of poro-elastic models in fractured porous media. The scheme is based on a mixed formulation, using face-wise constant approximations of the Lagrange multipliers along the fracture network

Marcin P. Joachimiak, Mark A. Miller, J. Harry Caufield, Ryan Ly

The Artificial Intelligence Ontology (AIO) is a systematization of artificial intelligence (AI) concepts, methodologies, and their interrelations. Developed via manual curation, with the additional assistance of large language models (LLMs), AIO aims to address the rapidly evolving landscape of AI by providing a comprehensive framework that encompasses both

Nafaa Nacereddine, Aicha Baya Goumeidane, Djemel Ziou

Accurate detection of the centerline of a thick linear structure and good estimation of its thickness are challenging topics in many real-world applications such X-ray imaging, remote sensing and lane marking detection in road traffic. Model-based approaches using Hough and Radon transforms are often used but, are not recommended for thick line detection, wh

Silvia Zuffi, Michael J. Black

Many classical parametric 3D shape models exist, but creating novel shapes with such models requires expert knowledge of their parameters. For example, imagine creating a specific type of tree using procedural graphics or a new kind of animal from a statistical shape model. Our key idea is to leverage language to control such existing models to produce novel

Alexander Dranishnikov

We study numerical invariants $d\TC(\Gamma)$ and $d\cat(\Gamma)$ of groups recently introduced in \cite{DJ} and independently in \cite{KW}. We compute $d\TC$ for finite cyclic groups $\mathbb Z_p$ with prime $p$ as well as for nonorientable surfaces of genus $g>3$ (for orientable surfaces it was computed in \cite{DJ}). We prove the formula $$d\TC(G\ast H)=\m

D. R. Ballantyne, V. Sudhakar, D. Fairfax, S. Bianchi

The soft excess in active galactic nuclei (AGNs) may arise through a combination of relativistic reflection and the effects of a warm corona at the surface of the accretion disc. Detailed examination of the soft excess can therefore constrain models of the transport and dissipation of accretion energy. Here, we analyze 34 XMM-Newton observations from 14 Type

Alastair May, Taylor J. Smith

In this article, we discuss a new software tool that interacts with Grail+, a library of automata-theoretic command-line utilities. Our software, the Grail+ Visualizer, takes the textual representation of a finite automaton produced by Grail+ and generates TikZ code to illustrate the finite automaton, with automatic layout of states and transitions. In addit

Andreas Reinhart

In this note, we discuss recently discovered counterexamples to Mordell's Pellian Equation Conjecture and the Ankeny-Artin-Chowla-Conjecture. We provide a verification of the counterexample to Mordell's Pellian Equation Conjecture that can be checked with marginal computer assistance.

Renhao Zhang, Haotian Fu, Yilin Miao, George Konidaris

We propose a novel model-based reinforcement learning algorithm -- Dynamics Learning and predictive control with Parameterized Actions (DLPA) -- for Parameterized Action Markov Decision Processes (PAMDPs). The agent learns a parameterized-action-conditioned dynamics model and plans with a modified Model Predictive Path Integral control. We theoretically quan

Constanza Fierro, Nicolas Garneau, Emanuele Bugliarello, Yova Kementchedjhieva

Facts are subject to contingencies and can be true or false in different circumstances. One such contingency is time, wherein some facts mutate over a given period, e.g., the president of a country or the winner of a championship. Trustworthy language models ideally identify mutable facts as such and process them accordingly. We create MuLan, a benchmark for

Giuliano Lorenzoni, Cristina Tavares, Nathalia Nascimento, Paulo Alencar

Depression has affected millions of people worldwide and has become one of the most common mental disorders. Early mental disorder detection can reduce costs for public health agencies and prevent other major comorbidities. Additionally, the shortage of specialized personnel is very concerning since Depression diagnosis is highly dependent on expert professi

Petros Terzis, Michael Veale, Noëlle Gaumann

For almost a decade now, scholarship in and beyond the ACM FAccT community has been focusing on novel and innovative ways and methodologies to audit the functioning of algorithmic systems. Over the years, this research idea and technical project has matured enough to become a regulatory mandate. Today, the Digital Services Act (DSA) and the Online Safety Act

Wenqi Zhu, Coralia Cartis

High-order tensor methods that employ Taylor-based local models (of degree $p\ge 3$) within adaptive regularization frameworks have been recently proposed for both convex and nonconvex optimization problems. They have been shown to have superior, and even optimal, worst-case global convergence rates and local rates compared to Newton's method. Finding rigoro

Ellen Færgestad Mosleth, Kjell-Morten Myhr, Christian Alexander Vedeler, Frode Steingrimsen Berven

The novel data analytical platform General Effect Modelling (GEM), is an umbrella platform covering different data analytical methods that handle data with multiple design variables (or pseudo design variables) and multivariate responses. GEM is here demonstrated in an analysis of proteome data from cerebrospinal fluid (CSF) from two independent previously p

Junhao Chen, Milena Radenkovic

The popularity of online shopping has challenged the existing express tracking. How to provide customers with reliable and stable express tracking has become one of the important issues that express companies need to solve now. The current stage of courier tracking is not ideal in challenging environments such as mountain roads, tunnels and city centres. The

Bo Chen, Xiaoda Liu, Yu-Hang Li, Han Tay

Magnetic topological materials with coexisting magnetism and non-trivial band structures exhibit many novel quantum phenomena, including the quantum anomalous Hall effect, the axion insulator state, and the Weyl semimetal phase. As a stoichiometric layered antiferromagnetic topological insulator, thin films of MnBi2Te4 show fascinating even-odd layer-depende

Wynter Broussard, Edward W. Schwieterman, Sukrit Ranjan, Clara Sousa-Silva

JWST has created a new era of terrestrial exoplanet atmospheric characterization, and with it the possibility to detect potential biosignature gases like CH$_{4}$. Our interpretation of exoplanet atmospheric spectra, and the veracity of these interpretations, will be limited by our understanding of atmospheric processes and the accuracy of input modeling dat

Philip Groet, Joost Hoozemans, Andreas Grapentin, Felix Eberhardt

This paper describes a distributed implementation of Apache Arrow that can leverage cluster-shared load-store addressable memory that is hardware-coherent only within each node. The implementation is built on the ThymesisFlow prototype that leverages the OpenCAPI interface to create a shared address space across a cluster. While Apache Arrow structures are i

Ellen Færgestad Mosleth, Simon Erling Nitter Dankel, Gunnar Mellgren, Francisco Martin Barajas Olmos

General Effect Modelling (GEM) is an umbrella over different methods that utilise effects in the analyses of data with multiple design variables and multivariate responses. To demonstrate the methodology, we here use GEM in gene expression data where we use GEM to combine data from different cohorts and apply multivariate analysis of the effects of the targe