February 2024 arXiv papers — page 131

Spenser Talkington, Martin Claassen

Quadratic Lindbladians encompass a rich class of dissipative electronic and bosonic quantum systems, which have been predicted to host new and exotic physics. In this study, we develop a Lindblad-Keldysh spectroscopic response formalism for open quantum systems that elucidates their steady-state response properties and dissipative phase transitions via finit

Konstantin Kolokolov, Pavel Pekichev, Karthik Raghunathan

We introduce a novel neural network module that adeptly handles recursive data flow in neural network architectures. At its core, this module employs a self-consistent approach where a set of recursive equations is solved iteratively, halting when the difference between two consecutive iterations falls below a defined threshold. Leveraging this mechanism, we

Arnaud Carayol, Philippe Duchon, Florent Koechlin, Cyril Nicaud

Every language recognized by a non-deterministic finite automaton can be recognized by a deterministic automaton, at the cost of a potential increase of the number of states, which in the worst case can go from $n$ states to $2^n$ states. In this article, we investigate this classical result in a probabilistic setting where we take a deterministic automaton

Wilka Carvalho, Momchil S. Tomov, William de Cothi, Caswell Barry

Adaptive behavior often requires predicting future events. The theory of reinforcement learning prescribes what kinds of predictive representations are useful and how to compute them. This paper integrates these theoretical ideas with work on cognition and neuroscience. We pay special attention to the successor representation (SR) and its generalizations, wh

Lars A. L. Janssen, Rob H. B. Fey, Bart Besselink, Nathan van de Wouw

Complex mechatronic systems are typically composed of interconnected modules, often developed by independent teams. This development process challenges the verification of system specifications before all modules are integrated. To address this challenge, a modular redesign framework is proposed in this paper. Herein, first, allowed changes in the dynamics (

Afshin Shaygani, Kazimierz Adamiak, Mehrdad R. Kermani

A Dielectric Barrier Discharge (DBD) plasma actuator for controlling airflow is proposed. It consists of diverging and converging nozzles, two concentric cylinders and an actuator mounted in-between the two cylinders. The actuator employs electrohydrodynamic (EHD) body force to induce an air jet within the air gap between the two cylinders, effectively creat

Tim Grimbergen, Stefano Schmidt, Chinmay Kalaghatgi, Chris van den Broeck

We introduce a machine learning model designed to rapidly and accurately predict the time domain gravitational wave emission of non-precessing binary black hole coalescences, incorporating the effects of higher order modes of the multipole expansion of the waveform. Expanding on our prior work, we decompose each mode by amplitude and phase and reduce dimensi

Guillermo Garcia-Barrios, Juana M. Gutierrez-Arriola, Nicolas Saenz-Lechon, Victor Jose Osma-Ruiz

An analysis of the relationship between the bandwidth of acoustic signals and the required resolution of steered-response power phase transform (SRP-PHAT) maps used for sound source localization is presented. This relationship does not rely on the far-field assumption, nor does it depend on any specific array topology. The proposed analysis considers the com

Federico Ghimenti, Ludovic Berthier, Frédéric van Wijland

Equilibrium sampling of the configuration space in disordered systems requires algorithms that bypass the glassy slowing down of the physical dynamics. Irreversible Monte Carlo algorithms breaking detailed balance successfully accelerate sampling in some systems. We first implement an irreversible event-chain Monte Carlo algorithm in a model of polydisperse

Ehsan Latif, Gyeong-Geon Lee, Knut Neumann, Tamara Kastorff

The advancement of natural language processing has paved the way for automated scoring systems in various languages, such as German (e.g., German BERT [G-BERT]). Automatically scoring written responses to science questions in German is a complex task and challenging for standard G-BERT as they lack contextual knowledge in the science domain and may be unalig

Elena H. Sánchez, Marianna Vasilakaki, Su Seong Lee, Peter S. Normile

Dense systems of magnetic nanoparticles may exhibit dipolar collective behavior. However, two fundamental questions remain unsolved: i) whether the transition temperature may be affected by the particle anisotropy or it is essentially determined by the intensity of the interparticle dipolar interactions, and ii) what is the minimum ratio of dipole-dipole int

Paolo Abiuso, Pavel Sekatski, John Calsamiglia, Martí Perarnau-Llobet

We consider the estimation of an unknown parameter $\theta$ through a quantum probe at thermal equilibrium. The probe is assumed to be in a Gibbs state according to its Hamiltonian $H_\theta$, which is divided in a parameter-encoding term $H^{\rm P}_\theta$ and an additional, parameter-independent, control $H^{\rm C}$. Given a fixed encoding, we find the max

Nico Catalano, Alessandro Maranelli, Agnese Chiatti, Matteo Matteucci

Semantic segmentation is a key prerequisite to robust image understanding for applications in \acrlong{ai} and Robotics. \acrlong{fss}, in particular, concerns the extension and optimization of traditional segmentation methods in challenging conditions where limited training examples are available. A predominant approach in \acrlong{fss} is to rely on a sing

Martin Ferianc, Hongxiang Fan, Miguel Rodrigues

Ensembles of separate neural networks (NNs) have shown superior accuracy and confidence calibration over single NN across tasks. To improve the hardware efficiency of ensembles of separate NNs, recent methods create ensembles within a single network via adding early exits or considering multi input multi output approaches. However, it is unclear which of the

Huachen Chen, Laura Pertusi, Xiaolei Zhao

Using the algebraic criterion proved by Bandiera, Manetti and Meazzini, we show the formality conjecture for universally gluable objects with linearly reductive automorphism groups in the bounded derived category of a K3 surface. As an application, we prove the formality conjecture for polystable objects in the Kuznetsov components of Gushel--Mukai threefold

Felix Draxler, Stefan Wahl, Christoph Schnörr, Ullrich Köthe

We present a novel theoretical framework for understanding the expressive power of normalizing flows. Despite their prevalence in scientific applications, a comprehensive understanding of flows remains elusive due to their restricted architectures. Existing theorems fall short as they require the use of arbitrarily ill-conditioned neural networks, limiting p

Liam Bernheimer, Hristiana Atanasova, Guy Cohen

Describing the ground states of continuous, real-space quantum many-body systems, like atoms and molecules, is a significant computational challenge with applications throughout the physical sciences. Recent progress was made by variational methods based on machine learning (ML) ansatzes. However, since these approaches are based on energy minimization, ansa

Abhijin Adiga, Yohai Trabelsi, Tanvir Ferdousi, Madhav Marathe

Optimal allocation of agricultural water in the event of droughts is an important global problem. In addressing this problem, many aspects, including the welfare of farmers, the economy, and the environment, must be considered. Under this backdrop, our work focuses on several resource-matching problems accounting for agents with multi-crop portfolios, geogra

Luis Alberto Rabanal Ramirez, Cláudio Márcio de Freitas Silva

In this work, a rectangular microstrip antenna with inset is designed, simulated and optimized. In the optimization process the patch is deformed, it new antenna present a amorphous patch. The optimization process was conducted with Genetic Algorithm (GA), S11 parameters was obtained with full wave Finite-Differences Time-Domain (FDTD-3D), and the initial co

Javier Álvarez-Liébana, M. Dolores Ruiz-Medina

This work adopts a Banach-valued time series framework for component-wise estimation and prediction, from temporal correlated functional data, in presence of exogenous variables. The strong-consistency of the proposed functional estimator and associated plug-in predictor is formulated. The simulation study undertaken illustrates their large-sample size prope

Farid Madani, Maxime Denis, Pascal Szriftgiser, Jean Claude Garreau

Phase transitions are prevalent throughout physics, spanning thermal phenomena like water boiling to magnetic transitions in solids. They encompass cosmological phase transitions in the early universe and the transition into a quark-gluon plasma in high-energy collisions. Quantum phase transitions, particularly intriguing, occur at temperatures near absolute

Jan Hendrik Bruinier, Martin Raum

The notion of formal Siegel modular forms for an arithmetic subgroup $\Gamma$ of the symplectic group of genus $n$ is a generalization of symmetric formal Fourier-Jacobi series. Assuming an upper bound on the affine covering number of the Siegel modular variety associated with $\Gamma$, we prove that all formal Siegel modular forms are given by Fourier-Jacob

Smitha S., Sudheesh K. Kattumannil, Sreedevi E. P

The study on the generating function approach to entropy become popular as it generates several well-known entropy measures discussed in the literature. In this work, we define the weighted cumulative residual entropy generating function (WCREGF) and study its properties. We then introduce the dynamic weighted cumulative residual entropy generating function

Zheng Xiong, Risto Vuorio, Jacob Beck, Matthieu Zimmer

Learning a universal policy across different robot morphologies can significantly improve learning efficiency and enable zero-shot generalization to unseen morphologies. However, learning a highly performant universal policy requires sophisticated architectures like transformers (TF) that have larger memory and computational cost than simpler multi-layer per

Sajad Boked, Bari Maqbool, V. Jithesh, Ranjeev Misra

Since its discovery in 1992, GRS 1915+105 has been among the brightest sources in the X-ray sky. However, in early 2018, it dimmed significantly and has stayed in this faint state ever since. We report on AstroSat and NuSTAR observation of GRS 1915+105 in its unusual low/hard state during 2019 May. We performed time-resolved spectroscopy of the X-ray flares

Cristian Ramirez-Atencia, David Camacho

Over the last decade, developments in unmanned aerial vehicles (UAVs) has greatly increased, and they are being used in many fields including surveillance, crisis management or automated mission planning. This last field implies the search of plans for missions with multiple tasks, UAVs and ground control stations; and the optimization of several objectives,

Maciej Ulas

In this note we consider the title Diophantine equation from both theoretical as well as experimental point of view. In particular, we prove that for $k=4, 6$ and each choice of the signs our equation has infinitely many co-prime positive integer solutions. For $k=5, 7$ and all choices of the signs we computed all co-prime positive integer solutions $(x, y,

Cristodor Ionescu

We notice the connection between almost Cohen-Macaulay rings and the Cohen-Macaulay defect. We introduce a Serre-type condition for modules, that is connected to the Cohen-Macaulay defect in the same way that the condition $(S_n)$ is connected to Cohen-Macaulay modules.

Matthew Thiesse, Stephen T. Wilson, Jack Fannon, Matthew Malek

The PocketWATCH facility is a unique multi-purpose test bed designed to replicate the conditions of large water Cherenkov detectors. Housed at the University of Sheffield, the facility consists of a light-tight 2000L ultrapure water tank with purification and temperature control systems. Water temperature, resistivity, and UV attenuation in the tank are moni

André Luiz Corrêa Vianna Filho, Francisco Guillén-González

In the present review we focus on the chemotaxis-consumption model $\partial_t u - \Delta u = - \nabla \cdot (u \nabla v)$ and $\partial_t v - \Delta v = - u^s v$ in $(0,T) \times \Omega$, for any fixed $s \geq 1$, endowed with isolated boundary conditions and nonnegative initial conditions, where $(u,v)$ model cell density and chemical signal concentration.

Neslihan Suzen, Evgeny M. Mirkes, Damian Roland, Jeremy Levesley

Electronic patient records (EPRs) produce a wealth of data but contain significant missing information. Understanding and handling this missing data is an important part of clinical data analysis and if left unaddressed could result in bias in analysis and distortion in critical conclusions. Missing data may be linked to health care professional practice pat

Manish Prajapat, Johannes Köhler, Matteo Turchetta, Andreas Krause

Safely exploring environments with a-priori unknown constraints is a fundamental challenge that restricts the autonomy of robots. While safety is paramount, guarantees on sufficient exploration are also crucial for ensuring autonomous task completion. To address these challenges, we propose a novel safe guaranteed exploration framework using optimal control,

Claudio Bonanno, Alessandro Nada, Davide Vadacchino

Motivated by the recently-established connection between Jarzynski's equality and the theoretical framework of Stochastic Normalizing Flows, we investigate a protocol relying on out-of-equilibrium lattice Monte Carlo simulations to mitigate the infamous computational problem of topological freezing. We test our proposal on $2d$ $\mathrm{CP}^{N-1}$ models and

Arslan Akram

Many people are interested in ChatGPT since it has become a prominent AIGC model that provides high-quality responses in various contexts, such as software development and maintenance. Misuse of ChatGPT might cause significant issues, particularly in public safety and education, despite its immense potential. The majority of researchers choose to publish the

Amir Ziai, Aneesh Vartakavi

High-quality and consistent annotations are fundamental to the successful development of robust machine learning models. Traditional data annotation methods are resource-intensive and inefficient, often leading to a reliance on third-party annotators who are not the domain experts. Hard samples, which are usually the most informative for model training, tend

Brian Yang, Huangyuan Su, Nikolaos Gkanatsios, Tsung-Wei Ke

Diffusion models excel at modeling complex and multimodal trajectory distributions for decision-making and control. Reward-gradient guided denoising has been recently proposed to generate trajectories that maximize both a differentiable reward function and the likelihood under the data distribution captured by a diffusion model. Reward-gradient guided denois

Paolo Benincasa, Francisco Vazão

We provide a general analysis of the asymptotic behaviour of perturbative contributions to observables in arbitrary power-law FRW cosmologies, indistinctly the Bunch-Davies wavefunction and cosmological correlators. We consider a large class of scalar toy models, including conformally-coupled and massless scalars in arbitrary dimensions, that admits a first

Gregory Coppola

This paper introduces the Quantified Boolean Bayesian Network (QBBN), which provides a unified view of logical and probabilistic reasoning. The QBBN is meant to address a central problem with the Large Language Model (LLM), which has become extremely popular in Information Retrieval, which is that the LLM hallucinates. A Bayesian Network, by construction, ca

Marco Radaelli, Joseph A. Smiga, Gabriel T. Landi, Felix C. Binder

We consider the estimation of parameters encoded in the measurement record of a continuously monitored quantum system in the jump unraveling, corresponding to a single-shot scenario, where information is continuously gathered. Here, it is generally difficult to assess the precision of the estimation procedure via the Fisher Information due to intricate tempo

Katsuki Aoki, Andrea Cristofoli

We investigate the relationships between classical observables in cosmology and the classical limit of quantum scattering amplitudes. We first look at the relation between Bogoliubov transformations and the notion of classical limit. Then, we compute the cosmological redshift for a particle in a cosmological background and the emitted gravitational waveform

Roland Gruber, Steffen Rüger, Thomas Wittenberg

Objective: We propose a new approach for volumetric instance segmentation in X-ray Computed Tomography (CT) data for Non-Destructive Testing (NDT) by combining the Segment Anything Model (SAM) with tile-based Flood Filling Networks (FFN). Our work evaluates the performance of SAM on volumetric NDT data-sets and demonstrates its effectiveness to segment insta

Eduard Feireisl, Elisabetta Rocca, Giulio Schimperna

We consider the Oberbeck--Boussinesq approximation driven by an inhomogeneous temperature distribution on the boundary of a bounded fluid domain. The relevant boundary conditions are perturbed by a non--local term arising in the incompressible limit of the Navier--Stokes--Fourier system. The long time behaviour of the resulting initial/boundary value problem

Florian Lourens, Detlef Rogalla, Ellen Suhr, Alfred Ludwig

A Li-Al-O thin film materials library, deposited by inert magnetron sputtering and post-deposition annealing in O2 atmosphere, was used to study the effects of different annealing temperatures (300 to 850{\deg}C) and durations (1 min to 7 h) on crystallinity and composition of the films. XPS depth profiling revealed inhomogeneous compositional depth profiles

Michael Y. Fatemi, Wesley A. Suttle, Brian M. Sadler

Deceptive path planning (DPP) is the problem of designing a path that hides its true goal from an outside observer. Existing methods for DPP rely on unrealistic assumptions, such as global state observability and perfect model knowledge, and are typically problem-specific, meaning that even minor changes to a previously solved problem can force expensive com

Francois Béguin, Bin Yu

The purpose of this paper is to prove that, for every $n\in \mathbb N$, there exists a closed hyperbolic $3$-manifold $M$ which carries at least $n$ non-$\mathbb R$-covered Anosov flows, that are pairwise orbitally inequivalent. Due to a recent result by Fenley, such Anosov flows are quasi-geodesic. Hence, we get the existence of hyperbolic $3$-manifolds car

Moritz Blumenthal, Chiara Fantinato, Christina Unterberg-Buchwald, Markus Haltmeier

Purpose: To develop a neural network architecture for improved calibrationless reconstruction of radial data when no ground truth is available for training. Methods: NLINV-Net is a model-based neural network architecture that directly estimates images and coil sensitivities from (radial) k-space data via non-linear inversion (NLINV). Combined with a training

Haonan Yuan, Qingyun Sun, Xingcheng Fu, Cheng Ji

Dynamic Graphs widely exist in the real world, which carry complicated spatial and temporal feature patterns, challenging their representation learning. Dynamic Graph Neural Networks (DGNNs) have shown impressive predictive abilities by exploiting the intrinsic dynamics. However, DGNNs exhibit limited robustness, prone to adversarial attacks. This paper pres

Marek Šuppa, Daniel Skala, Daniela Jašš, Samuel Sučík

This study details our approach for the CASE 2024 Shared Task on Climate Activism Stance and Hate Event Detection, focusing on Hate Speech Detection, Hate Speech Target Identification, and Stance Detection as classification challenges. We explored the capability of Large Language Models (LLMs), particularly GPT-4, in zero- or few-shot settings enhanced by re

Yannick Feld, Alexander K. Hartmann, Alessandro Torcini

A regime of coexistence of asynchronous and clustered dynamics is analyzed for globally coupled homogeneous and heterogeneous inhibitory networks of quadratic integrate-and-fire (QIF) neurons subject to Gaussian noise. The analysis is based on accurate extensive simulations and complemented by a mean-field description in terms of low-dimensional next generat

Miguel Escudero

Cosmological structure formation simulations of ultralight axion-like dark matter have shown that an axion star forms at the center of every dark matter halo in the Universe. These axion stars would then form in large numbers during the dark ages, $z \lesssim 70$. Axion stars would represent the densest axion environments in the Universe, and as such they ca

Karin Baur, Diana Bergerova, Jenni Voon, Lejie Xu

A triangulation of a polygon is a subdivision of it into triangles, using diagonals between its vertices. Two different triangulations of a polygon can be related by a sequence of flips: a flip replaces a diagonal by the unique other diagonal in the quadrilateral it defines. In this paper, we study coloured triangulations and coloured flips. In this more gen

M. Gloria Fiestras-Janeiro, Ignacio García-Jurado, Ana Meca, Manuel A. Mosquera

In this paper we introduce and analyse, from a game theoretical perspective, several multi-agent or multi-item continuous review inventory models in which the buyers are exempted from ordering costs if the price of their orders is greater than or equal to a certain amount. For all models we obtain the optimal ordering policy. We first analyse a simple model

Yukun Huang, Yixin Liu, Raghuveer Thirukovalluru, Arman Cohan

To enhance Large Language Models' (LLMs) reliability, calibration is essential -- the model's assessed confidence scores should align with the actual likelihood of its responses being correct. However, current confidence elicitation methods and calibration metrics typically rely on a binary true/false assessment of response correctness. This approach does no

Florian Kirchschlager, Lars Mattsson, Frederick A. Gent

Dust in the interstellar medium (ISM) is critical to the absorption and intensity of emission profiles used widely in astronomical observations, and necessary for star and planet formation. Supernovae (SNe) both produce and destroy ISM dust. In particular the destruction rate is difficult to assess. Theory and prior simulations of dust processing by SNe in a

Federico Grazzini, Joshua Dorrington, Christian M. Grams, George C. Craig

The accurate prediction of intense precipitation events is one of the main objectives of operational weather services. This task is even more relevant nowadays, with the rapid progression of global warming which intensifies these events. Numerical weather prediction models have improved continuously over time, providing uncertainty estimation with dynamical

Loïc Philoxene, Vu Hung Dao, Raymond Frésard

Elucidating the impact of strong electronic interactions on the collective excitations of metallic systems has been of longstanding interest, mainly due to the inadequacy of the random phase approximation (RPA) in the strongly correlated regime. Here, we adopt our newly developed radial Kotliar and Ruckenstein slave boson representation to analyze the charge

Austin J. King, Benjamin C. Bromley, Preston W. Harris, Scott J. Kenyon

Light echoes of debris disks around active stars can reveal disk structure and composition even when disks are not spatially resolved. Unfortunately, distinguishing reflected light from quiescent starlight and unexpected post-peak flare structure is challenging, especially for edge-on geometries where the time delay between observed flare photons and light s

Naveed Ejaz, Fakhra Kashif, Salimur Choudhury

In recent years, the rising use of social media has propelled automated cyberbullying detection into a prominent research domain. However, challenges persist due to the absence of a standardized definition and universally accepted datasets. Many researchers now view cyberbullying as a facet of cyberaggression, encompassing factors like repetition, peer relat

Dalila Sánchez-Escobedo, Xiao Lin, Josep R. Casas, Montse Pardàs

Semantic segmentation and depth estimation are two important tasks in the area of image processing. Traditionally, these two tasks are addressed in an independent manner. However, for those applications where geometric and semantic information is required, such as robotics or autonomous navigation,depth or semantic segmentation alone are not sufficient. In t

Sushmita Gupta, M. S. Ramanujan, Peter Strulo

Single-elimination (SE) tournaments are a popular format used in competitive environments and decision making. Algorithms for SE tournament manipulation have been an active topic of research in recent years. In this paper, we initiate the algorithmic study of a novel variant of SE tournament manipulation that aims to model the fact that certain matchups are

Evan D. Cook, Marc-Antoine Lavoie, Steven L. Waslander

Out-of-distribution (OOD) detection is a critical task for safe deployment of learning systems in the open world setting. In this work, we investigate the use of feature density estimation via normalizing flows for OOD detection and present a fully unsupervised approach which requires no exposure to OOD data, avoiding researcher bias in OOD sample selection.

S. Rusconi, E. Akhmatskaya, D. Sokolovski, N. Ballard

The stochastic simulation algorithm (SSA) and the corresponding Monte Carlo (MC) method are among the most common approaches for studying stochastic processes. They rely on knowledge of interevent probability density functions (PDFs) and on information about dependencies between all possible events. Analytical representations of a PDF are difficult to specif

Tor Lattimore

Bandit convex optimisation is a fundamental framework for studying zeroth-order convex optimisation. This book covers the many tools used for this problem, including cutting plane methods, interior point methods, continuous exponential weights, gradient descent and online Newton step. The nuances between the many assumptions and setups are explained. Althoug

M. Luo, C. Riconda, I. Pusztai, A. Grassi

Autoresonant phase-locking of the plasma wakefield to the beat frequency of two driving lasers offers advantages over conventional wakefield acceleration methods, since it requires less demanding laser parameters and is robust to variations in the target plasma density. Here, we investigate the kinetic and nonlinear processes that come into play during autor

Francesco Alessio, Paolo Di Vecchia

We introduce a classical version of the loop corrected soft graviton theorem and we use it to compute the universal part of the one-loop (2PM) waveform up to sub-subleading order in the energy $\omega$ of the emitted graviton for spinless black-hole scattering. In particular, we compute the action of the soft operators on the classically resummed four-point

Michael S. Yao, Yimeng Zeng, Hamsa Bastani, Jacob Gardner

Offline model-based optimization seeks to optimize against a learned surrogate model without querying the true oracle objective function during optimization. Such tasks are commonly encountered in protein design, robotics, and clinical medicine where evaluating the oracle function is prohibitively expensive. However, inaccurate surrogate model predictions ar

Sophia Schwarz, Tanja Pilz, Olaf Wysocki, Ludwig Hoegner

Point clouds and high-resolution 3D data have become increasingly important in various fields, including surveying, construction, and virtual reality. However, simply having this data is not enough; to extract useful information, semantic labeling is crucial. In this context, we propose a method to transfer annotations from a labeled to an unlabeled point cl

Muhammad Uzair Zahid, Aysen Degerli, Fahad Sohrab, Serkan Kiranyaz

Early detection of myocardial infarction (MI), a critical condition arising from coronary artery disease (CAD), is vital to prevent further myocardial damage. This study introduces a novel method for early MI detection using a one-class classification (OCC) algorithm in echocardiography. Our study overcomes the challenge of limited echocardiography data avai

Kaiqu Liang, Zixu Zhang, Jaime Fernández Fisac

Large language models (LLMs) exhibit advanced reasoning skills, enabling robots to comprehend natural language instructions and strategically plan high-level actions through proper grounding. However, LLM hallucination may result in robots confidently executing plans that are misaligned with user goals or even unsafe in critical scenarios. Additionally, inhe

Celine Degrande, Rogerio Rosenfeld, Andres Vasquez

We study the effects of four-heavy-quark operators in the production of top quarks in the framework of the Standard Model Effective Field Theory (SMEFT) at the LHC. In particular, we compute for the first time the total contribution of the four-top-quark operator which enters only at the one-loop level in the top-quark pair production process. Analytical res

Kristian Ranestad, Rainer Sinn, Simon Telen

We consider semi-algebraic subsets of the Grassmannian of lines in three-space called tree amplituhedra. These arise in the study of scattering amplitudes from particle physics. Our main result states that tree amplituhedra in ${\rm Gr}(2,4)$ are positive geometries. The numerator of their canonical form plays the role of the adjoint in Wachspress geometry,

Younghan Bae, Martijn Kool, Hyeonjun Park

This is the second part in a series of papers on counting surfaces on Calabi-Yau 4-folds. In this paper, we introduce $K$-theoretic $\mathrm{DT}, \mathrm{PT}_0, \mathrm{PT}_1$ invariants and conjecture a $\mathrm{DT}$-$\mathrm{PT}_0$ correspondence. For certain tautological insertions, we derive Lefschetz principles in both the compact and toric case allowin

Ben Anson, Edward Milsom, Laurence Aitchison

A common theoretical approach to understanding neural networks is to take an infinite-width limit, at which point the outputs become Gaussian process (GP) distributed. This is known as a neural network Gaussian process (NNGP). However, the NNGP kernel is fixed and tunable only through a small number of hyperparameters, thus eliminating the possibility of rep

George A. Gontcharov, Charles J. Bonatto, Olga S. Ryutina, Sergey S. Savchenko

We fit various colour-magnitude diagrams (CMDs) of the Galactic globular clusters NGC\,6397 and NGC\,6809 (M55) by isochrones from the Dartmouth Stellar Evolution Database (DSED) and Bag of Stellar Tracks and Isochrones (BaSTI) for $\alpha$-enhanced [$\alpha$/Fe]$=+0.4$. For the CMDs, we use data sets from {\it HST}, {\it Gaia}, VISTA, and other sources util

Navdeep Singh Dhindsa

Many of the exciting features of the Standard Model of the elementary particles are inherently non-perturbative. A theoretical understanding of many physics aspects beyond the Standard Model of elementary particles also requires a non-perturbative framework. One such framework involves discretizing quantum field theories on a spacetime lattice. We can use th

Thomas Froech, Olaf Wysocki, Ludwig Hoegner, Uwe Stilla

In the reconstruction of fa\c{c}ade elements, the identification of specific object types remains challenging and is often circumvented by rectangularity assumptions or the use of bounding boxes. We propose a new approach for the reconstruction of 3D fa\c{c}ade details. We combine MLS point clouds and a pre-defined 3D model library using a BoW concept, which

Maximilian Engelhardt, Sebastian Giehl, Michael Schubert, Alexander Ihlow

The upcoming 3GPP global mobile communication standard 6G strives to push the technological limits of radio frequency (RF) communication even further than its predecessors: Sum data rates beyond 100 Gbit/s, RF bandwidths above 1 GHz per link, and sub-millisecond latency necessitate very high performance development tools. We propose a new SDR firmware and so

David Jaramillo Duque

6-dimensional superconformal field theories are exotic and fascinating. They emerge from compactifications of F-theory on Calabi-Yau elliptic fibrations, which grants them a rich array of dualities with various other formulations of string and M-theory. In this thesis, we consider extended families of elliptic fibrations, giving rise to 6d theories connected

Aakanksha Gubbala, Daniel P. Arnold, Anika Jena, Stephanie Anujarerat

We study the dynamic structure of lipid domain inclusions embedded within a phase-separated reconstituted lipid bilayer in contact with a swarming flow of gliding filamentous actin. Passive circular domains transition into highly-deformed morphologies that continuously elongate, rotate, and pinch off into smaller fragments, leading to a dynamic steady state

Ali Behcet Alpat, Giovanni Bartolini, Sarah Bollanti, Paolo Di Lazzaro

We describe an experimental setup developed aiming to irradiate samples under UV radiation for accelerated test for solar effects according to the relevant ECSS-ESA standards. This facility has been already used for projects belonging to large space programs (Cosmic Vision, Artes) for simulations up to 3500 equivalent sun hours. In particular, we detail the

Wenjun Fan, Zhihui Du, Max Smith-Creasey, David Fernández

Honeypots are designed to trap the attacker with the purpose of investigating its malicious behavior. Owing to the increasing variety and sophistication of cyber attacks, how to capture high-quality attack data has become a challenge in the context of honeypot area. All-round honeypots, which mean significant improvement in sensibility, countermeasure and st

Benjamin Fuller, Rashmi Pai, Alexander Russell

Risk-limiting audits (RLAs) are techniques for verifying the outcomes of large elections. While they provide rigorous guarantees of correctness, widespread adoption has been impeded by both efficiency concerns and the fact they offer statistical, rather than absolute, conclusions. We attend to both of these difficulties, defining new families of audits that

Toke Vibel, Mikkel Berg Christensen, Mick Althoff Kristensen, Jeppe Juhl Thuesen

The accurate determination of atom numbers is an ubiquitous problem in the field of ultracold atoms. For modest atom numbers, absolute calibration techniques are available, however, for large numbers and high densities, the available techniques neglect many-body scattering processes. Here, a spatial calibration technique for time-of-flight absorption images

Stanisław Kurzyna, Bartosz Niewelt, Mateusz Mazelanik, Wojciech Wasilewski

Collective Rydberg excitations provide promising applications ranging from quantum information processing, and quantum computing to ultra-sensitive electrometry. However, their short lifetime is an immense obstacle in real-life scenarios. The state-of-the-art methods of prolonging the lifetime were mainly implemented for ground-state quantum memories and wou

Wenhao Zheng, Liaoyaqi Wang, Dongshen Peng, Hongxia Xu

The clinical trial is a pivotal and costly process, often spanning multiple years and requiring substantial financial resources. Therefore, the development of clinical trial outcome prediction models aims to exclude drugs likely to fail and holds the potential for significant cost savings. Recent data-driven attempts leverage deep learning methods to integra

I. D. Martínez-Casanueva, D. González-Sanchez, L. Bellido, D. Fernández

Network telemetry based on data models is expected to become the standard mechanism for collecting operational data from network devices efficiently. But the wide variety of standard and proprietary data models along with the different implementations of telemetry protocols offered by network vendors, become a barrier when monitoring heterogeneous network in

Xiayang Fan, Xin Wang, Yuan Sun

Recently, the method of off-resonant modulated driving (ORMD) with a special category of synthetic analytical pulses has improved the experimental performance of two- and multi-qubit gates and aroused many interests for further investigations. It particularly offers a helpful tool to the cold atom qubit platform and works well with the Rydberg dipole-dipole

Alberto Testoni, Raquel Fernández

Clarification questions are an essential dialogue tool to signal misunderstanding, ambiguities, and under-specification in language use. While humans are able to resolve uncertainty by asking questions since childhood, modern dialogue systems struggle to generate effective questions. To make progress in this direction, in this work we take a collaborative di

D Nunez, F J Nunez Cornu, F de J Escalona-Alcazar, D Cordoba

Structural and tectonic features in the Pacific Coast of Mexico generate a high level of seismic activity in the Jalisco block (JB) region, making it one of the most attractive areas of the world for geophysical investigations. The Rivera North America contact zone has been the object of different tectonic studies in recent years framed within the TsuJal pro

Sudipto Chowdhury, Divay Garg

This article examines the Dirichlet boundary control problem governed by the Poisson equation, where the control variables are square integrable functions defined on the boundary of a two dimensional bounded, convex, polygonal domain. It employs an ultra weak formulation and utilizes Crouzeix-Raviart finite elements to discretize the state variable, while em

Yue Tan, Olaf Wysocki, Ludwig Hoegner, Uwe Stilla

3D building models with facade details are playing an important role in many applications now. Classifying point clouds at facade-level is key to create such digital replicas of the real world. However, few studies have focused on such detailed classification with deep neural networks. We propose a method fusing geometric features with deep learning networks

Beatrice Nettuno, Davide Toffenetti, Christoph Metzl, Linus Weigand

The general epidemic process (GEP), also known as susceptible-infected-recovered model (SIR), describes how an epidemic spreads within a population of susceptible individuals who acquire permanent immunization upon recovery. This model exhibits a second-order absorbing state phase transition, commonly studied assuming immobile healthy individuals. We investi

Cristian Ramirez-Atencia, Gema Bello-Orgaz, Maria D R-Moreno, David Camacho

Due to recent booming of UAVs technologies, these are being used in many fields involving complex tasks. Some of them involve a high risk to the vehicle driver, such as fire monitoring and rescue tasks, which make UAVs excellent for avoiding human risks. Mission Planning for UAVs is the process of planning the locations and actions (loading/dropping a load,

Jasmina Gajcin, Ivana Dusparic

Understanding how failure occurs and how it can be prevented in reinforcement learning (RL) is necessary to enable debugging, maintain user trust, and develop personalized policies. Counterfactual reasoning has often been used to assign blame and understand failure by searching for the closest possible world in which the failure is avoided. However, current

Maximilian Raff, C. David Remy

In autonomous differential equations where a single first integral is present, periodic orbits are well-known to belong to one-parameter families, parameterized by the first integral's values. This paper shows that this characteristic extends to a broader class of conservative hybrid dynamical systems (cHDSs). We study periodic orbits of a cHDS, introducing

Anna Madison, Ellen Novoseller, Vinicius G. Goecks, Benjamin T. Files

Future warfare will require Command and Control (C2) personnel to make decisions at shrinking timescales in complex and potentially ill-defined situations. Given the need for robust decision-making processes and decision-support tools, integration of artificial and human intelligence holds the potential to revolutionize the C2 operations process to ensure ad

Keyuan Zhang, Zhongdong Liu, Nakjung Choi, Bo Ji

In this paper, we study the two-level ski-rental problem,where a user needs to fulfill a sequence of demands for multiple items by choosing one of the three payment options: paying for the on-demand usage (i.e., rent), buying individual items (i.e., single purchase), and buying all the items (i.e., combo purchase). Without knowing future demands, the user ai

Kaleb McDowell, Ellen Novoseller, Anna Madison, Vinicius G. Goecks

Future warfare will require Command and Control (C2) decision-making to occur in more complex, fast-paced, ill-structured, and demanding conditions. C2 will be further complicated by operational challenges such as Denied, Degraded, Intermittent, and Limited (DDIL) communications and the need to account for many data streams, potentially across multiple domai

Lei Zan, Charles K. Assaad, Emilie Devijver, Eric Gaussier

This paper introduces a new structural causal model tailored for representing threshold-based IT systems and presents a new algorithm designed to rapidly detect root causes of anomalies in such systems. When root causes are not causally related, the method is proven to be correct; while an extension is proposed based on the intervention of an agent to relax

Haoyue Sheng, Linrui Ma, Jean-Francois Samson, Dianbo Liu

Background: Chest X-ray imaging-based abnormality localization, essential in diagnosing various diseases, faces significant clinical challenges due to complex interpretations and the growing workload of radiologists. While recent advances in deep learning offer promising solutions, there is still a critical issue of domain inconsistency in cross-domain trans