February 2024 arXiv papers — page 162

Hans Riess, Manolis Veveakis, Michael M. Zavlanos

The path signature, having enjoyed recent success in the machine learning community, is a theoretically-driven method for engineering features from irregular paths. On the other hand, graph neural networks (GNN), neural architectures for processing data on graphs, excel on tasks with irregular domains, such as sensor networks. In this paper, we introduce a n

Dayou Mao, Yuhao Chen, Yifan Wu, Maximilian Gilles

One of the main motivations of MTL is to develop neural networks capable of inferring multiple tasks simultaneously. While countless methods have been proposed in the past decade investigating robust model architectures and efficient training algorithms, there is still lack of understanding of these methods when applied on smaller feature extraction backbone

Henry Bradford

In [K. Bou-Rabee, B. Seward, J. Reine Angwe. Math. 2016] Bou-Rabee and Seward constructed examples of finitely generated residually finite groups $G$ whose residual finiteness growth function $\mathcal{F}_G$ can be at least as fast as any prescribed function. In this note we describe a modified version of their construction, which allows us to give a complem

Antonio López Vivar, Ana Lucila Sandoval Orozco, Luis Javier García Villalba

The use of blockchain and smart contracts have not stopped growing in recent years. Like all software that begins to expand its use, it is also beginning to be targeted by hackers who will try to exploit vulnerabilities in both the underlying technology and the smart contract code itself. While many tools already exist for analyzing vulnerabilities in smart

Aobo Lyu, Andrew Clark, Netanel Raviv

Mutual information between two random variables is a well-studied notion, whose understanding is fairly complete. Mutual information between one random variable and a pair of other random variables, however, is a far more involved notion. Specifically, Shannon's mutual information does not capture fine-grained interactions between those three variables, resu

Stella Bounareli, Christos Tzelepis, Vasileios Argyriou, Ioannis Patras

In this paper, we present our framework for neural face/head reenactment whose goal is to transfer the 3D head orientation and expression of a target face to a source face. Previous methods focus on learning embedding networks for identity and head pose/expression disentanglement which proves to be a rather hard task, degrading the quality of the generated i

Jeffrey Adams, Alexandre Afgoustidis

Consider the irreducible representations of a real reductive group $G(\mathbb{R})$, and their parametrization by the local Langlands correspondence. We ask: does the parametrization give easily accessible information on the restriction of representations to a maximal compact subgroup $K(\mathbb{R})$ of $G(\mathbb{R})$? We find a natural connection between th

Kelly McKinnie, Erin Szalda-Petree

The 2020 decennial census data resulted in an increase from one to two congressional representatives in the state of Montana. The state underwent its redistricting process in 2021 in time for the November 2022 congressional elections, carving the state into two districts. This paper analyzes the redistricting process and compares the adopted congressional ma

Diptyaroop Maji, Noman Bashir, David Irwin, Prashant Shenoy

In recent years, there has been an increased emphasis on reducing the carbon emissions from electricity consumption. Many organizations have set ambitious targets to reduce the carbon footprint of their operations as a part of their sustainability goals. The carbon footprint of any consumer of electricity is computed as the product of the total energy consum

Maham Tanveer, Yizhi Wang, Ruiqi Wang, Nanxuan Zhao

We present AnaMoDiff, a novel diffusion-based method for 2D motion analogies that is applied to raw, unannotated videos of articulated characters. Our goal is to accurately transfer motions from a 2D driving video onto a source character, with its identity, in terms of appearance and natural movement, well preserved, even when there may be significant discre

Yidong Gong, Arnab Tarafder, Saima Afrin, Pradeep Kumar

The current graph neural network (GNN) systems have established a clear trend of not showing training accuracy results, and directly or indirectly relying on smaller datasets for evaluations majorly. Our in-depth analysis shows that it leads to a chain of pitfalls in the system design and evaluation process, questioning the practicality of many of the propos

Khashayar Namdar, Matthias W. Wagner, Cynthia Hawkins, Uri Tabori

Pediatric Low-Grade Neuroepithelial Tumors (PLGNT) are the most common pediatric cancer type, accounting for 40% of brain tumors in children, and identifying PLGNT molecular subtype is crucial for treatment planning. However, the gold standard to determine the PLGNT subtype is biopsy, which can be impractical or dangerous for patients. This research improves

Andrew Ho

We show that if $Y$ is a compact topological manifold and $X$ is a locally flat submanifold, then the complement $Y - X$ is homotopy equivalent to a finite CW complex. This is a direct proof, and does not rely on much of the theory of topological manifolds.

Ruihan Wu, Siddhartha Datta, Yi Su, Dheeraj Baby

This paper addresses the prevalent issue of label shift in an online setting with missing labels, where data distributions change over time and obtaining timely labels is challenging. While existing methods primarily focus on adjusting or updating the final layer of a pre-trained classifier, we explore the untapped potential of enhancing feature representati

Young-Joon Song, Charlotte Gallenkamp, Genís Lleopart, Vera Krewald

Although iron-based single atom catalysts are regarded as a promising alternative to precious metal catalysts, their precise electronic structures during catalysis still pose challenges for computational descriptions. A particularly urgent question is the influence of the environment on the electronic structure, and how to describe this properly with computa

Giorgio Bacci, Radu Mardare, Prakash Panangaden, Gordon Plotkin

We study Polynomial Lawvere logic PL, a logic defined over the Lawvere quantale of extended positive reals with sum as tensor, to which we add multiplication, thereby obtaining a semiring structure. PL is designed for complex quantitative reasoning, allowing judgements that express inequalities between polynomials on the extended positive reals. We introduce

Luis E. Padilla, Juan Carlos Hidalgo, Tadeo D. Gomez-Aguilar, Karim A. Malik

In this paper we review the possible mechanisms for the production of primordial black holes (PBHs) during a slow-reheating period {in which the energy transfer of the inflaton field to standard model particles becomes effective at slow temperatures}, offering a comprehensive examination of the theoretical foundations and conditions required for each of form

Andrey Bryutkin, Jiahao Huang, Zhongying Deng, Guang Yang

We present a novel graph transformer framework, HAMLET, designed to address the challenges in solving partial differential equations (PDEs) using neural networks. The framework uses graph transformers with modular input encoders to directly incorporate differential equation information into the solution process. This modularity enhances parameter corresponde

Mohammad Yaghini, Patty Liu, Franziska Boenisch, Nicolas Papernot

Existing work on trustworthy machine learning (ML) often concentrates on individual aspects of trust, such as fairness or privacy. Additionally, many techniques overlook the distinction between those who train ML models and those responsible for assessing their trustworthiness. To address these issues, we propose a framework that views trustworthy ML as a mu

Markus Hecher, Rafael Kiesel

Answer Set Programming (ASP) is a generic problem modeling and solving framework with a strong focus on knowledge representation and a rapid growth of industrial applications. So far, the study of complexity resulted in characterizing hardness and determining their sources, fine-grained insights in the form of dichotomy-style results, as well as detailed par

Yolanda Gomez, Jesus Rios, David Rios Insua, Jose Vila

In domains such as homeland security, cybersecurity and competitive marketing, it is frequently the case that analysts need to forecast adversarial actions that impact the problem of interest. Standard structured expert judgement elicitation techniques may fall short as they do not explicitly take into account intentionality. We present a decomposition techn

Saheli Sarkar

Hole-doped high-temperature copper oxide-based superconductors (cuprates) exhibit complex phase diagrams where electronic orders like a charge density wave (CDW) and superconductivity (SC) appear at low temperatures. The origins of these electronic orders are still open questions due to their complex interplay and correlated nature. These electronic orders c

Sigbjorn Hervik

We study left-invariant pseudo-Riemannian metrics on Lie groups using the bracket flow of the corresponding Lie algebra. We focus on metrics where the Lie algebra is in the null cone of the $G=O(p,q)$-action; i.e., Lie algebras $\mu$ where zero is in the closure of the orbits: $0\in\overline{G\cdot \mu}$. We provide examples of such Lie groups in various sig

Andrew Engel, Gautham Narayan, Nell Byler

The availability of large, public, multi-modal astronomical datasets presents an opportunity to execute novel research that straddles the line between science of AI and science of astronomy. Photometric redshift estimation is a well-established subfield of astronomy. Prior works show that computer vision models typically outperform catalog-based models, but

Jose-Carlos Gamazo-Real, Victor Martinez-Martinez, Jaime Gomez-Gil

BLDC motor applications require precise position and speed measurements, traditionally obtained with sensors. This article presents a method for estimating those measurements without position sensors using terminal phase voltages with attenuated spurious, acquired with a FPGA that also operates a PWM-controlled inverter. Voltages are labelled with electrical

Kwantae Kim, Changhyeon Kim, Sungpill Choi, Hoi-Jun Yoo

This paper presents the first sub-10$\mu$W, sub-0.1% total harmonic distortion (THD) sinusoidal current generator (CG) integrated circuit (IC) that is capable of 20kHz output for the bio-impedance (Bio-Z) sensing applications. To benefit from the ultra-low-power nature of near-threshold operation, a 9b pseudo-sine lookup table (LUT) is 3b $\Delta\Sigma$ modu

Martin Schmalzried

This paper leverages various philosophical and ontological frameworks to explore the concept of embodied artificial general intelligence (AGI), its relationship to human consciousness, and the key role of the metaverse in facilitating this relationship. Several theoretical frameworks underpin this exploration, such as embodied cognition, Michael Levin's comp

Yi-Fan Yao, Neil Qiang Su

Reduced density matrix functional theory (RDMFT) calculations are usually implemented in a decoupled manner, where the orbital and occupation optimizations are repeated alternately. Typically, orbital updates are performed using the unitary optimization method, while occupations are optimized through the explicit-by-implicit (EBI) method. The EBI method addr

Sambhav Solanki, Shweta Jain, Sujit Gujar

This paper considers the contextual multi-armed bandit (CMAB) problem with fairness and privacy guarantees in a federated environment. We consider merit-based exposure as the desired fair outcome, which provides exposure to each action in proportion to the reward associated. We model the algorithm's effectiveness using fairness regret, which captures the dif

Lu Sun, Aaron Chan, Yun Seo Chang, Steven P. Dow

Peer review is a cornerstone of science. Research communities conduct peer reviews to assess contributions and to improve the overall quality of science work. Every year, new community members are recruited as peer reviewers for the first time. How could technology help novices adhere to their community's practices and standards for peer reviewing? To better

Carlos Calvo Tapia, Valeriy A. Makarov Slizneva, Cees van Leeuwen

The brain can be considered as a system that dynamically optimizes the structure of anatomical connections based on the efficiency requirements of functional connectivity. To illustrate the power of this principle in organizing the complexity of brain architecture, we portray the functional connectivity as diffusion on the current network structure. The diff

Adam M. Krajewski, Allison M. Beese, Wesley F. Reinhart, Zi-Kui Liu

Many disciplines of science and engineering deal with problems related to compositions, ranging from chemical compositions in materials science to portfolio compositions in economics. They exist in non-Euclidean simplex spaces, causing many standard tools to be incorrect or inefficient, which is significant in combinatorically or structurally challenging spa

David R. Burt, Yunyi Shen, Tamara Broderick

Spatial prediction tasks are key to weather forecasting, studying air pollution impacts, and other scientific endeavors. Determining how much to trust predictions made by statistical or physical methods is essential for the credibility of scientific conclusions. Unfortunately, classical approaches for validation fail to handle mismatch between locations avai

Haifan Gong, Luoyao Kang, Yitao Wang, Xiang Wan

In the field of biomedical image analysis, the quest for architectures capable of effectively capturing long-range dependencies is paramount, especially when dealing with 3D image segmentation, classification, and landmark detection. Traditional Convolutional Neural Networks (CNNs) struggle with locality respective field, and Transformers have a heavy comput

George Dunn, Hadi Charkhgard, Ali Eshragh, Sasan Mahmoudinazlou

Order Picker Routing is a critical issue in Warehouse Operations Management. Due to the complexity of the problem and the need for quick solutions, suboptimal algorithms are frequently employed in practice. However, Reinforcement Learning offers an appealing alternative to traditional heuristics, potentially outperforming existing methods in terms of speed a

Mushkan Sureka, Saikat Guha

Gaussian Boson Sampling (GBS) generate random samples of photon-click patterns from a class of probability distributions that are hard for a classical computer to sample from. Despite heroic demonstrations for quantum supremacy using GBS, Boson Sampling, and instantaneous quantum polynomial (IQP) algorithms, systematic evaluations of the power of these quant

Reinier Díaz Millán, Julien Ugon

In this paper we introduce two conceptual algorithms for minimising abstract convex functions. Both algorithms rely on solving a proximal-type subproblem with an abstract Bregman distance based proximal term. We prove their convergence when the set of abstract linear functions forms a linear space. This latter assumption can be relaxed to only require the se

Chen Frenkel

We study periodic infinite billiards in the plane. We show that for rational models, some particular obstacles can be added periodically, so that the billiard flow in the resulting table is recurrent in almost every direction.

Axel Ljungström

In Homotopy Type Theory, few constructions have proved as troublesome as the smash product. While its definition is just as direct as in classical mathematics, one quickly realises that in order to define and reason about functions over iterations of it, one has to verify an exponentially growing number of coherences. This has led to crucial results concerni

Yoshiki Takagi, Roderick Tabalba, Nurit Kirshenbaum, Jason Leigh

Explainable AI (XAI) has demonstrated the potential to help reinforcement learning (RL) practitioners to understand how RL models work. However, XAI for users who do not have RL expertise (non-RL experts), has not been studied sufficiently. This results in a difficulty for the non-RL experts to participate in the fundamental discussion of how RL models shoul

Marina Lin, Laura P. Schaposnik, Raina Wu

How would admissions look like in a university program for influencers? In the realm of social network analysis, influence maximization and link prediction stand out as pivotal challenges. Influence maximization focuses on identifying a set of key nodes to maximize information dissemination, while link prediction aims to foresee potential connections within

Thomas D Swinburne

ParSplice D. Perez, E. D. Cubuk, A. Waterland, E. Kaxiras, and A. F. Voter, Long-Time Dynamics through Parallel Trajectory Splicing, Journal of Chemical Theory and Computation, 2016 is a molecular dynamics method for parallel-in-time trajectory generation, allowing this workhorse of in silico science to strong scale on massively parallel computers. Trajector

Evan Camrud, Ewan Davies, Alex Karduna, Holden Lee

We study the problem of approximately counting the number of list packings of a graph. The analogous problem for usual vertex coloring and list coloring has attracted a lot of attention. For list packing the setup is similar but we seek a full decomposition of the lists of colors into pairwise-disjoint proper list colorings. In particular, the existence of a

Xiliang Zhu, Chia-Tien Chang, Shayna Gardiner, David Rossouw

Punctuation restoration is a crucial step after Automatic Speech Recognition (ASR) systems to enhance transcript readability and facilitate subsequent NLP tasks. Nevertheless, conventional lexical-based approaches are inadequate for solving the punctuation restoration task in Spanish, where ambiguity can be often found between unpunctuated declaratives and q

Chad Bustard, John Wu

The coarse-grained propagation of Galactic cosmic rays (CRs) is traditionally constrained by phenomenological models of Milky Way CR propagation fit to a variety of direct and indirect observables; however, constraining the fine-grained transport of CRs along individual magnetic field lines -- for instance, diffusive vs streaming transport models -- is an un

Amedeo Giuliani, Rasoul Nikbakht, Giovanni Geraci, Seongjoon Kang

This article proposes a generative neural network architecture for spatially consistent air-to-ground channel modeling. The approach considers the trajectories of uncrewed aerial vehicles along typical urban paths, capturing spatial dependencies within received signal strength (RSS) sequences from multiple cellular base stations (gNBs). Through the incorpora

Diego Tapias, Charles Marteau, Fabián Aguirre-López, Peter Sollich

There are two key paradigms for non-equilibrium dynamics: on the one hand, aging towards an equilibrium state that cannot be reached on reasonable timescales; on the other, external driving that can lead to non-equilibrium steady states. We explore how these two mechanisms interact, by studying the behaviour of trap models, which are paradigmatic description

K. L. Helmes, R. H. Stockbridge, C. Zhu

This paper analyzes single-item continuous-review inventory models with random supplies in which the inventory dynamic between orders is described by a diffusion process, and a long-term average cost criterion is used to evaluate decisions. The class of models have general drift and diffusion coefficients and boundary points that are consistent with the noti

Juan Pedro Tarigo, Cecilia Stari, Cristina Masoller, Arturo C. Marti

The basin entropy is a measure that quantifies, in a system that has two or more attractors, the predictability of a final state, as a function of the initial conditions. While the basin entropy has been demonstrated on a variety of multistable dynamical systems, to the best of our knowledge, it has not yet been tested in systems with a time delay, whose pha

Vignesh V Menon, Prajit T Rajendran, Amritha Premkumar, Benjamin Bross

Conventional per-title encoding schemes strive to optimize encoding resolutions to deliver the utmost perceptual quality for each bitrate ladder representation. Nevertheless, maintaining encoding time within an acceptable threshold is equally imperative in online streaming applications. Furthermore, modern client devices are equipped with the capability for

Abraham Holtermann, Jacquelyn Noronha-Hostler, Anne M. Sickles, Xiaoning Wang

The quark-gluon plasma (QGP) is a high temperature state of matter produced in the collisions of two nuclei at relativistic energies. The properties of this matter at short distance scales are probed using jets with high transverse momentum ($p_T$) resulting from quarks and gluons scattered with large momentum transfer in the earliest stages of the collision

Ekrem Oğuzhan Angüner, Tülün Ergin

The presence of a spectral softening, occurring at 3 PeV energies, seen in the local cosmic-ray energy spectrum provides an evidence that our Galaxy hosts astrophysical objects, known as hadronic PeVatrons, that are capable of accelerating hadrons to PeV energies and above. Recent results from ground-based particle detector array experiments have provided co

Vladimir Petrov, Gage MacLin, Venanzio Cichella

This paper introduces a framework for depth and pitch control of underwater vehicles in near-surface wave conditions. By effectively managing tail, sail plane angles and hover tank operations utilizing a Linear Quadratic Regulator controller and L1 Adaptive Autopilot augmentation, the system ensures balanced control input distribution and significantly atten

Sanjana Ramprasad, Kundan Krishna, Zachary C Lipton, Byron C Wallace

Recent work has shown that large language models (LLMs) are capable of generating summaries zero-shot (i.e., without explicit supervision) that, under human assessment, are often comparable or even preferred to manually composed reference summaries. However, this prior work has focussed almost exclusively on evaluating news article summarization. How do zero

Luciano Ristori

We introduce a new pattern recognition algorithm for track finding in High Energy Physics Experiments based on an extension of the Hough Transform to multiple dimensions. A remarkable property of this algorithm is that the execution time is simply proportional to the total number of the hits to be processed, making it particularly attractive for high occupan

Mikel Bober-Irizar, Soumya Banerjee

For half a century, artificial intelligence research has attempted to reproduce the human qualities of abstraction and reasoning - creating computer systems that can learn new concepts from a minimal set of examples, in settings where humans find this easy. While specific neural networks are able to solve an impressive range of problems, broad generalisation

Michael Widom

The In-Sn binary alloy system exhibits several unusual features that challenge crystallographic and thermodynamic expectations. We combine first principles total energy calculation with simple thermodynamic modeling to address two key points. First, we evaluate energies along the Bain path to interpret the discontinuous transition between the phases $\alpha$

Álvaro Suárez, Arturo C. Marti, Kristina Zuza, Jenaro Guisasola

We investigate learning difficulties among second-year students on electromagnetism courses when they apply Amp\`ere-Maxwell's law. Using phenomenography, we analysed written answers from 65 undergraduate physics students to four questions on Amp\`ere's and Amp\`ere-Maxwell's laws. We complemented our research by interviewing twelve students. To design the q

Daniel R. Weisz, Andrew E. Dolphin, Alessandro Savino, Kristen B. W. McQuinn

We present NIRCam and NIRISS modules for DOLPHOT, a widely-used crowded field stellar photometry package. We describe details of the modules including pixel masking, astrometric alignment, star finding, photometry, catalog creation, and artificial star tests (ASTs). We tested these modules using NIRCam and NIRISS images of M92 (a Milky Way globular cluster),

Gerardo Perez-Palomino, Juan E Page

A Fosters equivalent circuit for 2-D Planar Periodic Structures (PPSs) that exhibit an arbitrary geometry is presented for first time in this paper. The proposed 4-port network shows an invariant circuit topology to the PPS geometry and is completely comprised of invariant-frequency lumped elements. The circuit is the simplest in terms of number of elements

Xuefeng Du, Zhen Fang, Ilias Diakonikolas, Yixuan Li

Using unlabeled data to regularize the machine learning models has demonstrated promise for improving safety and reliability in detecting out-of-distribution (OOD) data. Harnessing the power of unlabeled in-the-wild data is non-trivial due to the heterogeneity of both in-distribution (ID) and OOD data. This lack of a clean set of OOD samples poses significan

Felipe Rodrigues Perche-Mahlow, André Felipe-Zanella, William Alberto Cruz-Castañeda, Marcellus Amadeus

In recent years, groundbreaking advancements in Generative Artificial Intelligence (GenAI) have triggered a transformative paradigm shift, significantly influencing various domains. In this work, we specifically explore an integrated approach, leveraging advanced techniques in GenAI and computer vision emphasizing image manipulation. The methodology unfolds

Yash J. Patel, Akash Kundu, Mateusz Ostaszewski, Xavier Bonet-Monroig

The key challenge in the noisy intermediate-scale quantum era is finding useful circuits compatible with current device limitations. Variational quantum algorithms (VQAs) offer a potential solution by fixing the circuit architecture and optimizing individual gate parameters in an external loop. However, parameter optimization can become intractable, and the

Andrew Chacon, Harley Rutherford, Akram Hamato, Munetaka Nitta

Purpose: To compare the accuracy with which different hadronic inelastic physics models across ten Geant4 Monte Carlo simulation toolkit versions can predict positron-emitting fragments produced along the beam path during carbon and oxygen ion therapy. Materials and Methods: Phantoms of polyethylene, gelatin or poly(methyl methacrylate) were irradiated with

Michelle L. Hill, Stephen R. Kane, Paul A. Dalba, Mason MacDougall

The Transiting Exoplanet Survey Satellite (TESS) continues to dramatically increase the number of known transiting exoplanets, and is optimal for monitoring bright stars amenable to radial velocity (RV) and atmospheric follow-up observations. TOI-1386 is a solar-type (G5V) star that was detected via TESS photometry to exhibit transit signatures in three sect

Benjamin Colburn, Luis G. Sanchez Giraldo, Kan Li, Jose C. Principe

Unlike the conventional kernel adaptive filtering (KAF) approach of using a fixed kernel to define the Reproducing Kernel Hilbert Space (RKHS), this paper embeds the statistics of the input data in the kernel definition, obtaining a closed-form solution for nonlinear adaptive filtering. We call this solution the Functional Wiener Filter (FWF), and it is form

Greg Weiler

Kontsevich's formula for rational plane curves is a recursive relation for the number $N_d$ of degree $d$ rational curves in $\mathbb{P}^2$ passing through $3d-1$ general points. We provide two proofs of this recursion: the first more direct and combinatoric, the second more abstract. In order to achieve this, we introduce several moduli spaces, such as the

Wu Lin, Felix Dangel, Runa Eschenhagen, Juhan Bae

Adaptive gradient optimizers like Adam(W) are the default training algorithms for many deep learning architectures, such as transformers. Their diagonal preconditioner is based on the gradient outer product which is incorporated into the parameter update via a square root. While these methods are often motivated as approximate second-order methods, the squar

Sergio Calvo-Ordonez, Matthieu Meunier, Francesco Piatti, Yuantao Shi

In this paper, we present Partially Stochastic Infinitely Deep Bayesian Neural Networks, a novel family of architectures that integrates partial stochasticity into the framework of infinitely deep neural networks. Our new class of architectures is designed to improve the computational efficiency of existing architectures at training and inference time. To do

Xingpeng Sun, Haoming Meng, Souradip Chakraborty, Amrit Singh Bedi

While LLMs excel in processing text in these human conversations, they struggle with the nuances of verbal instructions in scenarios like social navigation, where ambiguity and uncertainty can erode trust in robotic and other AI systems. We can address this shortcoming by moving beyond text and additionally focusing on the paralinguistic features of these au

Anna Cetera, Ali Rabiee, Sima Ghafoori, Reza Abiri

There have been different reports of developing Brain-Computer Interface (BCI) platforms to investigate the noninvasive electroencephalography (EEG) signals associated with plan-to-grasp tasks in humans. However, these reports were unable to clearly show evidence of emerging neural activity from the planning (observation) phase - dominated by the vision cort

Qixiang Ma, Antoine Łucas, Huazhong Shu, Adrien Kaladji

Deep-learning-based automated segmentation of vascular structures in preoperative CT scans contributes to computer-assisted diagnosis and intervention procedure in vascular diseases. While CT angiography (CTA) is the common standard, non-contrast CT imaging is significant as a contrast-risk-free alternative, avoiding complications associated with contrast ag

Edward Valachovic

This research introduces a novel approach to resampling periodically correlated (PC) time series using bandpass filters for frequency separation called the Variable Bandpass Periodic Block Bootstrap (VBPBB) and then examines the significant advantages of this new method. While bootstrapping allows estimation of a statistic's sampling distribution by resampli

Alejandro H. Córsico, Leandro G. Althaus

Breathing pulses are mixing episodes that could develop during the core-helium burning phase of low- and intermediate-mass stars. The occurrence of breathing pulses is expected to bear consequences on the formation and evolution of white dwarfs, particularly on the core chemical structure, which can be probed by asteroseismology. We aim to explore the conseq

Bastien Mallein, Sanjay Ramassamy, Arvind Singh

The infinite-bin model is a one-dimensional particle system on $\mathbb{Z}$ introduced by Foss and Konstantopoulos in relation with last passage percolation on complete directed acyclic graphs. In this model, at each integer time, a particle is selected at random according to its rank, and produces a child at the location immediately to its right. In this ar

Mallku Soldevila, Rodrigo Ribeiro, Beta Ziliani

We propose the first steps in the development of a tool to automate the translation of Redex models into a (hopefully) semantically equivalent model in Coq, and to provide tactics to help in the certification of fundamental properties of such models. The work is heavily based on a model of Redex's semantics developed by Klein et al. By means of a simple gene

Kai Diethelm

For the numerical solution of Dirichlet-type boundary value problems associated to nonlinear fractional differential equations of order $\alpha \in (1,2)$ that use Caputo derivatives, we suggest to employ shooting methods. In particular, we demonstrate that the so-called proportional secting technique for selecting the required initial values leads to numeri

Fahim Mohammad, Lakshmi Arunachalam, Samanway Sadhu, Boudewijn Aasman

This study proposes the use of Machine Learning models to predict the early onset of sepsis using deidentified clinical data from Montefiore Medical Center in Bronx, NY, USA. A supervised learning approach was adopted, wherein an XGBoost model was trained utilizing 80\% of the train dataset, encompassing 107 features (including the original and derived featu

Gianluigi Lopardo, Frederic Precioso, Damien Garreau

Attention-based architectures, in particular transformers, are at the heart of a technological revolution. Interestingly, in addition to helping obtain state-of-the-art results on a wide range of applications, the attention mechanism intrinsically provides meaningful insights on the internal behavior of the model. Can these insights be used as explanations?

Ashley Shin, Qiao Jin, James Anibal, Zhiyong Lu

Searching for a related article based on a reference article is an integral part of scientific research. PubMed, like many academic search engines, has a "similar articles" feature that recommends articles relevant to the current article viewed by a user. Explaining recommended items can be of great utility to users, particularly in the literature search pro

Zeeshan Patel, Karim El-Refai, Jonathan Pei, Tianle Li

Automated long-form story generation typically employs long-context large language models (LLMs) for one-shot creation, which can produce cohesive but not necessarily engaging content. We introduce Storytelling With Action Guidance (SWAG), a novel approach to storytelling with LLMs. Our approach frames story writing as a search problem through a two-model fe

Yavar Kian, Marián Slodička, Éric Soccorsi, Karel Van Bockstal

This contribution considers the time-fractional subdiffusion with a time-dependent variable-order fractional operator of order $\beta(t)$. It is assumed that $\beta(t)$ is a piecewise constant function with a finite number of jumps. A proof technique based on the Fourier method and results from constant-order fractional subdiffusion equations has been design

Sejoon Oh, Berk Ustun, Julian McAuley, Srijan Kumar

Modern recommender systems may output considerably different recommendations due to small perturbations in the training data. Changes in the data from a single user will alter the recommendations as well as the recommendations of other users. In applications like healthcare, housing, and finance, this sensitivity can have adverse effects on user experience.

Herbert Woisetschläger, Alexander Erben, Bill Marino, Shiqiang Wang

The age of AI regulation is upon us, with the European Union Artificial Intelligence Act (AI Act) leading the way. Our key inquiry is how this will affect Federated Learning (FL), whose starting point of prioritizing data privacy while performing ML fundamentally differs from that of centralized learning. We believe the AI Act and future regulations could be

Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain

Prompt engineering has emerged as an indispensable technique for extending the capabilities of large language models (LLMs) and vision-language models (VLMs). This approach leverages task-specific instructions, known as prompts, to enhance model efficacy without modifying the core model parameters. Rather than updating the model parameters, prompts allow sea

Nathaniel Hudson, J. Gregory Pauloski, Matt Baughman, Alok Kamatar

Deep learning methods are transforming research, enabling new techniques, and ultimately leading to new discoveries. As the demand for more capable AI models continues to grow, we are now entering an era of Trillion Parameter Models (TPM), or models with more than a trillion parameters -- such as Huawei's PanGu-$\Sigma$. We describe a vision for the ecosyste

Samuel Garcin, James Doran, Shangmin Guo, Christopher G. Lucas

Autonomous agents trained using deep reinforcement learning (RL) often lack the ability to successfully generalise to new environments, even when these environments share characteristics with the ones they have encountered during training. In this work, we investigate how the sampling of individual environment instances, or levels, affects the zero-shot gene

Matthew A. Chan, Maria J. Molina, Christopher A. Metzler

Estimating and disentangling epistemic uncertainty, uncertainty that is reducible with more training data, and aleatoric uncertainty, uncertainty that is inherent to the task at hand, is critically important when applying machine learning to high-stakes applications such as medical imaging and weather forecasting. Conditional diffusion models' breakthrough a

Norah Alshahrani, Saied Alshahrani, Esma Wali, Jeanna Matthews

Text classification systems have been proven vulnerable to adversarial text examples, modified versions of the original text examples that are often unnoticed by human eyes, yet can force text classification models to alter their classification. Often, research works quantifying the impact of adversarial text attacks have been applied only to models trained

Xiao Jiang, Grace J. Gang, J. Webster Stayman

In this work, we introduce a new deep learning approach based on diffusion posterior sampling (DPS) to perform material decomposition from spectral CT measurements. This approach combines sophisticated prior knowledge from unsupervised training with a rigorous physical model of the measurements. A faster and more stable variant is proposed that uses a jumpst

Brandon Alberts

We prove significant power savings for the error term when counting abelian extensions of number fields (as well as the twisted version of these results for nontrivial Galois modules). In some cases over $\mathbb{Q}$, these results reveal lower order terms following the same structure as the main term that were not previously known. Assuming the generalized

Anli Ji, Berkay Aydin

Over the past few decades, many applications of physics-based simulations and data-driven techniques (including machine learning and deep learning) have emerged to analyze and predict solar flares. These approaches are pivotal in understanding the dynamics of solar flares, primarily aiming to forecast these events and minimize potential risks they may pose t

Xiaodan Xing, Huiyu Zhou, Yingying Fang, Guang Yang

AI-generated medical images are gaining growing popularity due to their potential to address the data scarcity challenge in the real world. However, the issue of accurate identification of these synthetic images, particularly when they exhibit remarkable realism with their real copies, remains a concern. To mitigate this challenge, image generators such as D

Subhajit Dandapat, Abhimanyu Susobhanan, Lankeswar Dey, A. Gopakumar

Burst with memory events are potential transient gravitational wave sources for the maturing pulsar timing array (PTA) efforts. We provide a computationally efficient prescription to model pulsar timing residuals induced by supermassive black hole pairs in general relativistic hyperbolic trajectories employing a Keplerian-type parametric solution. Injection

John Green, Terry Harris, Kaiyi Huang, Arian Nadjimzadah

This manuscript is intended as an accompaniment to Guth's "A restriction estimate using polynomial partitioning". We begin by summarizing the core ideas of the proof, elaborating the history and development of the techniques therein. From there, we provide supplementary details on some of the standard methods and more technical arguments which may be unfamil

Mahdi Saleh, Michael Sommersperger, Nassir Navab, Federico Tombari

In robotics, it's crucial to understand object deformation during tactile interactions. A precise understanding of deformation can elevate robotic simulations and have broad implications across different industries. We introduce a method using Physics-Encoded Graph Neural Networks (GNNs) for such predictions. Similar to robotic grasping and manipulation scen

Mohammad Mehdi Bagheri-Mohagheghi, Behnam Pourhassan, Emmanuel Saridakis, Salvatore Capozziello

In this work, we investigate the dissociation energy of the North (N) and South (S) poles of a quantum magnetic particle, incorporated within both classical and quantum mechanical perspectives. A simple model of a harmonic oscillator is employed to estimate the dissociation energy of the N-S poles, as well as the corresponding breakdown temperature and inter

Daniel Uvaydov, Milin Zhang, Clifton Paul Robinson, Salvatore D'Oro

Spectrum has become an extremely scarce and congested resource. As a consequence, spectrum sensing enables the coexistence of different wireless technologies in shared spectrum bands. Most existing work requires spectrograms to classify signals. Ultimately, this implies that images need to be continuously created from I/Q samples, thus creating unacceptable

Pratik K. Biswas

Record Linkage is the process of identifying and unifying records from various independent data sources. Existing strategies, which can be either deterministic or probabilistic, often fail to link records satisfactorily under uncertainty. This paper describes an indigenously (locally) developed fuzzy linkage method, based on fuzzy set techniques, which can e

Wojciech Bizoń, Ulrich Haisch, Luca Rottoli, Zach Gillis

We study inclusive double-Higgs boson production at the LHC and at the HL-LHC including variations of the trilinear and of the quartic Higgs boson self-couplings at next-to-leading order (NLO) in QCD with full top quark mass dependence. Our results include the two-loop contributions to the $gg \rightarrow HH$ amplitudes that involve a modified $h_4$ vertex c