November 2022 arXiv papers — page 37

Anshu Gupta, Kim-Vy Tran, Trevor Mendel, Anishya Harshan

This paper presents deep K-band spectroscopic observations of galaxies at z=3-4 with composite photometric rest-frame Hb+[OIII] equivalent widths EW_0>600A, comparable to the EW of galaxies observed during the epoch of reionisation (EoR, z>6). The typical spectroscopic [OIII] EW_0 and stellar mass of our targets is ~ 700A and log(M_star/M_sun)=8.98. By stack

Luca De Luigi, Damiano Bolognini, Federico Domeniconi, Daniele De Gregorio

In this paper, we propose the first-ever real benchmark thought for evaluating Neural Radiance Fields (NeRFs) and, in general, Neural Rendering (NR) frameworks. We design and implement an effective pipeline for scanning real objects in quantity and effortlessly. Our scan station is built with less than 500$ hardware budget and can collect roughly 4000 images

Guangwei Fu, Néstor Espinoza, David K. Sing, Joshua D. Lothringer

JWST is here. The early release observation program (ERO) provides us with the first look at the scientific data and the spectral capabilities. One of the targets from ERO is HAT-P-18b, an inflated Saturn-mass planet with an equilibrium temperature of $\sim$850K. We present the NIRISS/SOSS transmission spectrum of HAT-P-18b from 0.6 to 2.8$\mu m$ and reveal

Alessandro Lella, Pierluca Carenza, Giuseppe Lucente, Maurizio Giannotti

The parameter space of massive axion-like-particles (ALPs) with $m_a \sim {\mathcal O} (100)$ MeV and coupled with nucleons is largely unexplored. Here, we present new constraints in this parameter region. In doing so, we characterize the supernova emissivity of heavy ALPs from a proto-neutron star, including for the first time mass effects in both nucleon-n

L. Piro, M. Colpi, J. Aird, A. Mangiagli

The European Space Agency is studying two large-class missions bound to operate in the decade of the 30s, and aiming at investigating the most energetic and violent phenomena in the Universe. $Athena$ is poised to study the physical conditions of baryons locked in large-scale structures from the epoch of their formation, as well as to yield an accurate censu

M. Middleton, A. Gurpide, D. J. Walton

An expected signature of the presence of neutron stars in the population of ultraluminous X-ray sources (ULXs) are large scale changes in X-ray luminosity, as systems reach spin equilibrium and a propeller state ensues. We explore the predicted luminosity changes when the disc is locally super-critical, finding that a significant parameter space in dipole fi

Aviv Netanyahu, Tianmin Shu, Joshua Tenenbaum, Pulkit Agrawal

In this work, we consider one-shot imitation learning for object rearrangement tasks, where an AI agent needs to watch a single expert demonstration and learn to perform the same task in different environments. To achieve a strong generalization, the AI agent must infer the spatial goal specification for the task. However, there can be multiple goal specific

Gene Chou, Yuval Bahat, Felix Heide

Probabilistic diffusion models have achieved state-of-the-art results for image synthesis, inpainting, and text-to-image tasks. However, they are still in the early stages of generating complex 3D shapes. This work proposes Diffusion-SDF, a generative model for shape completion, single-view reconstruction, and reconstruction of real-scanned point clouds. We

Sebastian Gerard, Josephine Sullivan

Domain-specific variants of contrastive learning can construct positive pairs from two distinct in-domain images, while traditional methods just augment the same image twice. For example, we can form a positive pair from two satellite images showing the same location at different times. Ideally, this teaches the model to ignore changes caused by seasons, wea

Youmin Zhang, Matteo Poggi, Stefano Mattoccia

We present TemporalStereo, a coarse-to-fine stereo matching network that is highly efficient, and able to effectively exploit the past geometry and context information to boost matching accuracy. Our network leverages sparse cost volume and proves to be effective when a single stereo pair is given. However, its peculiar ability to use spatio-temporal informa

Alexandre Homrich, David Simmons-Duffin, Pedro Vieira

Conformal Regge theory predicts the existence of analytically continued CFT data for complex spin. How could this work when there are so many more operators with large spin compared to small spin? Using planar N=4 SYM as a testground we find a simple physical picture. Operators do organize themselves into analytic families but the continuation of the higher

Sophie Ostmeier, Brian Axelrod, Benjamin F. J. Verhaaren, Soren Christensen

To determine if a convolutional neural network (CNN) deep learning model can accurately segment acute ischemic changes on non-contrast CT compared to neuroradiologists. Non-contrast CT (NCCT) examinations from 232 acute ischemic stroke patients who were enrolled in the DEFUSE 3 trial were included in this study. Three experienced neuroradiologists independen

Andrey Voynov, Kfir Aberman, Daniel Cohen-Or

Text-to-Image models have introduced a remarkable leap in the evolution of machine learning, demonstrating high-quality synthesis of images from a given text-prompt. However, these powerful pretrained models still lack control handles that can guide spatial properties of the synthesized images. In this work, we introduce a universal approach to guide a pretr

Daniel Kane, Marc Hodes, Martin Z. Bazant, Toby L. Kirk

We consider laminar, fully-developed, Poiseuille flows of liquid in the Cassie state through diabatic, parallel-plate microchannels symmetrically textured with isoflux ridges. Through the use of matched asymptotic expansions we analytically develop expressions for (apparent hydrodynamic) slip lengths and variously-defined Nusselt numbers. Our small parameter

George Cowperthwaite, Adrian Kent

We compare schemes for testing whether two parties share a two-qubit singlet state. The first, standard, scheme tests Braunstein-Caves (or CHSH) inequalities, comparing the correlations of local measurements drawn from a fixed finite set against the quantum predictions for a singlet. The second, alternative, scheme tests the correlations of local measurement

Samuel M. Corson, Saharon Shelah

Let $\kappa$ be an inaccessible cardinal, $\mathfrak{U}$ be a universal algebra, and $\sim$ be the equivalence relation on $\mathfrak{U}^{\kappa}$ of eventual equality. From mild assumptions on $\kappa$ we give general constructions of $\mathcal{E} \in End(\mathfrak{U}^{\kappa}/\sim)$ satisfying $\mathcal{E} \circ \mathcal{E} = \mathcal{E}$ which do not desc

Xiang Chen, Jiamu Xie, Zixin Wang, Bohui Shen

Censorship, anti-censorship, and self-censorship in an authoritarian regime have been extensively studies, yet the relationship between these intertwined factors is not well understood. In this paper, we report results of a large-scale survey study (N = 526) with Sina Weibo users toward bridging this research gap. Through descriptive statistics, correlation

L. Wang, N. K. Paul, J. Hihath, J. S. Gomez-Diaz

We demonstrate that Cherenkov radiation can be manipulated in terms of operation frequency, bandwidth, and efficiency by simultaneously controlling the properties of drifting electrons and the photonic states supported by their surrounding media. We analytically show that the radiation rate strongly depends on the momentum of the excited photonic state, in t

John P. Agapiou, Alexander Sasha Vezhnevets, Edgar A. Duéñez-Guzmán, Jayd Matyas

Multi-agent artificial intelligence research promises a path to develop intelligent technologies that are more human-like and more human-compatible than those produced by "solipsistic" approaches, which do not consider interactions between agents. Melting Pot is a research tool developed to facilitate work on multi-agent artificial intelligence, and provides

Nan Li, Alexandros Iosifidis, Qi Zhang

This paper studies the computational offloading of CNN inference in device-edge co-inference systems. Inspired by the emerging paradigm semantic communication, we propose a novel autoencoder-based CNN architecture (AECNN), for effective feature extraction at end-device. We design a feature compression module based on the channel attention method in CNN, to c

Kirill Serkh, James Bremer

It is well known that second order homogeneous linear ordinary differential equations with slowly varying coefficients admit slowly varying phase functions. This observation underlies the Liouville-Green method and many other techniques for the asymptotic approximation of the solutions of such equations. It is also the basis of a recently developed numerical

Giulia Vezzani, Dhruva Tirumala, Markus Wulfmeier, Dushyant Rao

The ability to effectively reuse prior knowledge is a key requirement when building general and flexible Reinforcement Learning (RL) agents. Skill reuse is one of the most common approaches, but current methods have considerable limitations.For example, fine-tuning an existing policy frequently fails, as the policy can degrade rapidly early in training. In a

Felix Chalumeau, Thomas Pierrot, Valentin Macé, Arthur Flajolet

A fascinating aspect of nature lies in its ability to produce a collection of organisms that are all high-performing in their niche. Quality-Diversity (QD) methods are evolutionary algorithms inspired by this observation, that obtained great results in many applications, from wing design to robot adaptation. Recently, several works demonstrated that these me

Mark Braverman, Subhash Khot, Dor Minzer

We show that the value of the $n$-fold repeated GHZ game is at most $2^{-\Omega(n)}$, improving upon the polynomial bound established by Holmgren and Raz. Our result is established via a reduction to approximate subgroup type questions from additive combinatorics.

Mark Carney

This paper embarks upon exploration of quantum vulnerability analysis. By introducing vulnerability graphs, related to attack graphs, this paper provides background theory and a subsequent method for solving significant cybersecurity problems with quantum computing. The example given is to prioritize patches by expressing the connectivity of various vulnerab

Andrea Bonito, Diane Guignard, Wenyu Lei

We consider the numerical approximation of Gaussian random fields on closed surfaces defined as the solution to a fractional stochastic partial differential equation (SPDE) with additive white noise. The SPDE involves two parameters controlling the smoothness and the correlation length of the Gaussian random field. The proposed numerical method relies on the

Vincent Guedj, Antonio Trusiani

The complex Monge-Amp\`ere operator has been defined for locally bounded plurisubharmonic functions by Bedford-Taylor in the 80's. This definition has been extended to compact complex manifolds, and to various classes of mildly unbounded quasi-plurisubharmonic functions by various authors. As this operator is not continuous for the $L^{1}$-topology, several

Dongdong Lin, Benedetta Tondi, Bin Li, Mauro Barni

Due to the proliferation and widespread use of deep neural networks (DNN), their Intellectual Property Rights (IPR) protection has become increasingly important. This paper presents a novel model watermarking method for an unsupervised image-to-image translation (I2IT) networks, named CycleGAN, which leverage the image translation visual quality and watermar

Louis Hamaide, Theo Torres

We study the classical and quantum black hole information in gravitational waves from a black hole's history. We review the necessary concepts regarding quantum information in many-body systems to motivate information retrieval and content in gravitational waves. We then show the first step in an optimal information retrieval strategy is to search for inform

Martin Knoche, Torben Teepe, Stefan Hörmann, Gerhard Rigoll

Recently, face recognition systems have demonstrated remarkable performances and thus gained a vital role in our daily life. They already surpass human face verification accountability in many scenarios. However, they lack explanations for their predictions. Compared to human operators, typical face recognition network system generate only binary decisions w

Antonia Marcu

Over the past years, the crucial role of data has largely been shadowed by the field's focus on architectures and training procedures. We often cause changes to the data without being aware of their wider implications. In this paper we show that distorting images without accounting for the artefacts introduced leads to biased results when establishing occlus

Ievgen Dubovyk, Janusz Gluza, Gabor Somogyi

We discuss the Mellin-Barnes representation of complex multidimensional integrals. Experiments frontiered by the High-Luminosity Large Hadron Collider at CERN and future collider projects demand the development of computational methods to achieve the theoretical precision required by experimental setups. In this regard, performing higher-order calculations i

Mara Pistellato, Filippo Bergamasco, Tehreem Fatima, Andrea Torsello

Polarisation Filter Array (PFA) cameras allow the analysis of light polarisation state in a simple and cost-effective manner. Such filter arrays work as the Bayer pattern for colour cameras, sharing similar advantages and drawbacks. Among the others, the raw image must be demosaiced considering the local variations of the PFA and the characteristics of the i

Philip Freese, Dietmar Gallistl, Daniel Peterseim, Timo Sprekeler

This paper proposes novel computational multiscale methods for linear second-order elliptic partial differential equations in nondivergence-form with heterogeneous coefficients satisfying a Cordes condition. The construction follows the methodology of localized orthogonal decomposition (LOD) and provides operator-adapted coarse spaces by solving localized ce

Alexandre M. Bayen, Alexander Keimer, Nils Müller

Networks are essential models in many applications such as information technology, chemistry, power systems, transportation, neuroscience, and social sciences. In light of such broad applicability, a general theory of dynamical systems on networks may capture shared concepts, and provide a setting for deriving abstract properties. To this end, we develop a c

Ameya Daigavane, Arthur Kosmala, Miles Cranmer, Tess Smidt

Machine learning has become increasingly popular for efficiently modelling the dynamics of complex physical systems, demonstrating a capability to learn effective models for dynamics which ignore redundant degrees of freedom. Learned simulators typically predict the evolution of the system in a step-by-step manner with numerical integration techniques. Howev

Dominik Scheinert, Babak Sistani Zadeh Aghdam, Soeren Becker, Odej Kao

With increasingly more computation being shifted to the edge of the network, monitoring of critical infrastructures, such as intermediate processing nodes in autonomous driving, is further complicated due to the typically resource-constrained environments. In order to reduce the resource overhead on the network link imposed by monitoring, various methods hav

Dan Betea, Anton Nazarov, Travis Scrimshaw

We study large random partitions boxed into a rectangle and coming from skew Howe duality, or alternatively from dual Schur measures. As the sides of the rectangle go to infinity, we obtain: 1) limit shape results for the profiles generalizing the Vershik--Kerov--Logan--Shepp curve; and 2) universal edge asymptotic results for the first parts in the form of

Azam Rabiee, Alok Goel, Johnson D'Souza, Saurabh Khanwalkar

Online learning platforms provide learning materials and answers to students' academic questions by experts, peers, or systems. This paper explores question-type identification as a step in content understanding for an online learning platform. The aim of the question-type identifier is to categorize question types based on their structure and complexity, us

Maximilian Schaefer

Using simulations between pairs of $\epsilon$-greedy q-learners with one-period memory, this article demonstrates that the potential function of the stochastic replicator dynamics (Foster and Young, 1990) allows it to predict the emergence of error-proof cooperative strategies from the underlying parameters of the repeated prisoner's dilemma. The observed co

Yilun Wu, Federico Paredes-Vallés, Guido C. H. E. de Croon

Inspired by frame-based methods, state-of-the-art event-based optical flow networks rely on the explicit construction of correlation volumes, which are expensive to compute and store, rendering them unsuitable for robotic applications with limited compute and energy budget. Moreover, correlation volumes scale poorly with resolution, prohibiting them from est

Jerome Sieber, Andrea Zanelli, Antoine P. Leeman, Samir Bennani

Tube-based model predictive control (MPC) methods bound deviations from a nominal trajectory due to uncertainties in order to ensure constraint satisfaction. While techniques that compute the tubes online reduce conservativeness and increase performance, they suffer from high and potentially prohibitive computational complexity. This paper presents an asynch

Ali Harakeh, Jordan Hu, Naiqing Guan, Steven L. Waslander

Estimating the uncertainty in deep neural network predictions is crucial for many real-world applications. A common approach to model uncertainty is to choose a parametric distribution and fit the data to it using maximum likelihood estimation. The chosen parametric form can be a poor fit to the data-generating distribution, resulting in unreliable uncertain

Hoang Phan, Lam Tran, Quyen Tran, Ngoc N. Tran

Multi-task learning (MTL) trains deep neural networks to optimize several objectives simultaneously using a shared backbone, which leads to reduced computational costs, improved data efficiency, and enhanced performance through cross-task knowledge sharing. Although recent gradient manipulation techniques aim to find a common descent direction that benefits

Wei Xie, Weijing Li

Entanglement properties of random multipartite quantum states which are invariant under global SU($d$) action are investigated. The random states live in the tensor power of an irreducible representation of SU($d$). We calculate and analyze the expectation and fluctuation of the second-order R\'enyi entanglement measure of the random invariant and near-invar

Carlos Ortega-Taberner, Maria Hermanns

Recent years have seen a growing interest in topological phases beyond the standard paradigm of gapped, isolated systems. One recent direction is to explore topological features in non-hermitian systems that are commonly used as effective descriptions of open systems. Another direction explores the fate of topology at critical points, where the bulk gap coll

Wayne Paul Martis, Sachit Rao

In this paper, the collision avoidance problem for non-holonomic robots moving at constant linear speeds in the 2-D plane is considered. The maneuvers to avoid collisions are designed using dynamic vortex potential fields (PFs) and their negative gradients; this formulation leads to a reciprocal behaviour between the robots, denoted as being cooperative. The

Daniel Richtmann, Nils Meyer, Tilo Wettig

We describe our implementation of a multigrid solver for Wilson-clover fermions, which increases parallelism by solving for multiple right-hand sides (MRHS) simultaneously. The solver is based on Grid and thus runs on all computing architectures supported by the Grid framework. We present detailed benchmarks of the relevant kernels, such as hopping and clove

Arjun Choudhry, Inder Khatri, Arkajyoti Chakraborty, Dinesh Kumar Vishwakarma

Recent works on fake news detection have shown the efficacy of using emotions as a feature or emotions-based features for improved performance. However, the impact of these emotion-guided features for fake news detection in cross-domain settings, where we face the problem of domain shift, is still largely unexplored. In this work, we evaluate the impact of e

Jon-Ivar Skullerud, Gert Aarts, Chris Allton, M. Naeem Anwar

We present the most recent results from the FASTSUM collaboration for hadron properties at high temperature. This includes the temperature dependence of the light and charmed meson and baryon spectrum, as well as properties of heavy quarkonia. The results are obtained using anisotropic lattices with a fixed scale approach. We also present the status of our n

Yuhma Asano, Goro Ishiki, Takaki Matsumoto, Shinji Shimasaki

We consider a double scaling limit of the plane wave matrix model (PWMM), in which the gravity dual geometry of PWMM reduces to a class of spherical NS5-brane solutions. We identify the form of the scaling limit for the dual geometry of PWMM around a general vacuum and then translate the limit into the field theoretic language. We also show that the limit in

Quentin Meeus, Marie-Francine Moens, Hugo Van hamme

Most spoken language understanding systems use a pipeline approach composed of an automatic speech recognition interface and a natural language understanding module. This approach forces hard decisions when converting continuous inputs into discrete language symbols. Instead, we propose a representation model to encode speech in rich bidirectional encodings

Mateusz Olko, Michał Zając, Aleksandra Nowak, Nino Scherrer

Inferring causal structure from data is a challenging task of fundamental importance in science. Observational data are often insufficient to identify a system's causal structure uniquely. While conducting interventions (i.e., experiments) can improve the identifiability, such samples are usually challenging and expensive to obtain. Hence, experimental desig

Pierre Mendy, Babacar M. Ndiaye, Diaraf Seck, Idrissa Ly

By considering the Wade Formula, we propose a model to study the evolution of the oil price per barrel. Our model shows that the policy of diversification of the energy is to be supported. This model is proposed to see how it is possible to control parameters so that the oil price should decrease.

Jian Wang, Yefan Wang

We present analytic results of the two-loop master integrals for hadronic $tW$ production that contain two massive propagators. For the planar integral family, we succeed in constructing the canonical basis, so the results are written in terms of multiple polylogarithms. The master integrals in the non-planar integral families have been calculated by Taylor

Gabriel Mahuas, Olivier Marre, Thierry Mora, Ulisse Ferrari

Neural networks encode information through their collective spiking activity in response to external stimuli. This population response is noisy and strongly correlated, with complex interplay between correlations induced by the stimulus, and correlations caused by shared noise. Understanding how these correlations affect information transmission has so far b

Sarthak Duary, Eliot Hijano, Milan Patra

Asymptotic Fock spaces lead to IR divergences in S-matrices. The issue can be traced back to the assumption of asymptotic decoupling, and its relaxation leads to Faddeev-Kulish states and an IR-finite S-matrix. In this paper we initiate the exploration of these states in the context of the flat limit of AdS/CFT. We construct asymptotic states as flat limits

Ewa L. Lokas

Among the many processes involved in galaxy evolution, those of bar formation, quenching, and feedback from an active galactic nucleus (AGN) seem to be connected, however, the nature of these relations remains unclear. In this work, we aim to elucidate them by studying the formation of a barred galaxy in a major merger of two disks in the IllustrisTNG simula

Oskar van der Wal, Dominik Bachmann, Alina Leidinger, Leendert van Maanen

As Large Language Models and Natural Language Processing (NLP) technology rapidly develop and spread into daily life, it becomes crucial to anticipate how their use could harm people. One problem that has received a lot of attention in recent years is that this technology has displayed harmful biases, from generating derogatory stereotypes to producing dispa

Cuneyt Gurcan Akcora, Murat Kantarcioglu, Yulia R. Gel, Baris Coskunuzer

Topological data analysis (TDA) delivers invaluable and complementary information on the intrinsic properties of data inaccessible to conventional methods. However, high computational costs remain the primary roadblock hindering the successful application of TDA in real-world studies, particularly with machine learning on large complex networks. Indeed, most

Philipp Ritzinger, Karel Vyborny

Resistance of certain (conductive and otherwise isotropic) ferromagnets turns out to exhibit anisotropy with respect to the direction of magnetisation: R$_\parallel$ different from R$_\perp$ with reference to the electric current direction. This century-old phenomenon is reviewed both from the perspective of materials and physical mechanisms involved. More r

Jacob Bedrossian

In these short, rather informal, expository notes I review the current state of the field regarding the mathematics of Landau damping, based on lectures given at the CIRM Research School on Kinetic Theory, November 14--18, 2022. These notes are mainly on Vlasov-Poisson in $(x,v) \in \mathbb T^d \times \mathbb R^d$ however a brief discussion of the important

Tommaso Pegolotti, Bastian Seifert, Markus Püschel

M\"obius inversion of functions on partially ordered sets (posets) $\mathcal{P}$ is a classical tool in combinatorics. For finite posets it consists of two, mutually inverse, linear transformations called zeta and M\"obius transform, respectively. In this paper we provide novel fast algorithms for both that require $O(nk)$ time and space, where $n = |\mathca

Nana Obayashi, Andrea Vicari, Kai Junge, Kamran Shakir

Aquatic creatures exhibit remarkable adaptations of their body to efficiently interact with the surrounding fluid. The tight coupling between their morphology, motion, and the environment are highly complex but serves as a valuable example when creating biomimetic structures in soft robotic swimmers. We focus on the use of asymmetry in structures to aid thru

Christopher J. Banks, Ewan Colman, Anthony Wood, Thomas Doherty

We used a spatially explicit agent-based model of SARS-CoV-2 transmission combined with spatially fine-grained COVID-19 observation data from Public Health Scotland to investigate the initial rise of the Omicron (BA.1) variant of concern. We evaluated plausible scenarios for transmission rate advantage and vaccine immune escape relative to the Delta variant

Quentin Meeus, Marie-Francine Moens, Hugo Van hamme

We explore the benefits that multitask learning offer to speech processing as we train models on dual objectives with automatic speech recognition and intent classification or sentiment classification. Our models, although being of modest size, show improvements over models trained end-to-end on intent classification. We compare different settings to find th

Jinfeng Xu, Xianzhi Li, Yuan Tang, Qiao Yu

Semantic scene completion (SSC) aims to complete a partial 3D scene and predict its semantics simultaneously. Most existing works adopt the voxel representations, thus suffering from the growth of memory and computation cost as the voxel resolution increases. Though a few works attempt to solve SSC from the perspective of 3D point clouds, they have not fully

Wenjing Chen, Zexi Wang

In this paper, we consider the normalized ground state solution for the following biharmonic Choquard type problem \begin{align*} \begin{split} \left\{ \begin{array}{ll} \Delta^2u-\beta\Delta u=\lambda u+(I_\mu*F(u))f(u), \quad\mbox{in}\ \ \mathbb{R}^4, \displaystyle\int_{\mathbb{R}^4}|u|^2dx=c^2,\quad u\in H^2(\mathbb{R}^4), \end{array} \right. \end{split}

Hiroaki Karuo, Julien Korinman

We compute the Azumaya loci of Kauffman-bracket skein algebras of closed surfaces at odd roots of unity and provide partial results for open surfaces as well. As applications, we give an alternative definition of the projective representations of the Torelli groups derived from non-semisimple TQFTs and we strengthen a result by Frohman-Kania Bartoszynska-L\^

Jihoi Kim

We analyse the energy supercritical semilinear wave equation $$\Phi_{tt}-\Delta\Phi-|\Phi|^{p-1}\Phi=0$$ in $\mathbb R^d$ space. We first prove in a suitable regime of parameters the existence of a countable family of self similar profiles which bifurcate from the soliton solution. We then prove the non radial finite codimensional stability of these profiles

Xiaolong He, Youngsoo Choi, William D. Fries, Jonathan L. Belof

A parametric adaptive greedy Latent Space Dynamics Identification (gLaSDI) framework is developed for accurate, efficient, and certified data-driven physics-informed greedy auto-encoder simulators of high-dimensional nonlinear dynamical systems. In the proposed framework, an auto-encoder and dynamics identification models are trained interactively to discove

Cristina Manuel, Juan M. Torres-Rincon

We derive the total helicity conservation law for a perfect electromagnetic relativistic fluid. As the conservation equation contains the derivative of the magnetic helicity, it can be reshaped as having the same form as the chiral anomaly equation if the fluid is isentropic. We also take the non-relativistic limit of the helicity conservation law, and check

Michiel Van Beirendonck, Jan-Pieter D'Anvers, Furkan Turan, Ingrid Verbauwhede

Fully Homomorphic Encryption is a technique that allows computation on encrypted data. It has the potential to change privacy considerations in the cloud, but computational and memory overheads are preventing its adoption. TFHE is a promising Torus-based FHE scheme that relies on bootstrapping, the noise-removal tool invoked after each encrypted logical/arit

Finnian Gray, David Kubiznak, T. Rick Perche, Jaime Redondo-Yuste

We revisit the motion of massless particles with anyonic spin in the horizon of Kerr--Newman geometry. As recently shown, such particles can move within the horizon of the black hole due to the coupling of charges associated with a 2-parametric central extension of the 2-dimensional Carroll group to the magnetic field generated by the black hole -- the so ca

Matthew Kent Myers, Nick Wright, Stephen McGough, Nicholas Martin

Due to the rapid temporal and fine-grained nature of complex human assembly atomic actions, traditional action segmentation approaches requiring the spatial (and often temporal) down sampling of video frames often loose vital fine-grained spatial and temporal information required for accurate classification within the manufacturing domain. In order to fully

Karma Tenzin, Arunesh Roy, Homayoun Jafari, Bruno Banas

Studies of structure-property relationships in spintronics are essential for the design of materials that can fill specific roles in devices. For example, materials with low symmetry allow unconventional configurations of charge-to-spin conversion which can be used to generate efficient spin-orbit torques. Here, we explore the relationship between crystal sy

Davide Evangelista, James Nagy, Elena Morotti, Elena Loli Piccolomini

The solution of linear inverse problems arising, for example, in signal and image processing is a challenging problem since the ill-conditioning amplifies, in the solution, the noise present in the data. Recently introduced algorithms based on deep learning overwhelm the more traditional model-based approaches in performance, but they typically suffer from i

Karim Benakli, Cassiano A. Daniel, Wenqi Ke

We derive fully explicit equations of motion, and the associated set of constraints, describing the propagation in a flat space-time of a charged spin-3/2 massive state in a constant electromagnetic background. For this purpose, we provide the Lagrangian for the physical fermionic fields in the first massive level of the open superstring. We first write a co

Katarzyna Gas, Maciej Sawicki

Solid state wafers are indispensable components in material science as substrates for epitaxial homo- or hetero-structures or carriers for two-dimensional materials. However, a reliable determination of magnetic properties of nanomaterials in volume magnetometry is frequently affected by unexpectedly rich magnetism of these substrates, including significant

Karim Benakli, Cassiano A. Daniel, Wenqi Ke

Minimal coupling leads to problems such as loss of causality if one wants to describe charged particles of spin greater than one propagating in a constant electromagnetic background. Regge trajectories in string theory contain such states, so their study may allow us to investigate possible avenues to remedy the pathologies. We present here two explicit form

Ben Young

A fundamental result in the study of graph homomorphisms is Lov\'asz's theorem that two graphs are isomorphic if and only if they admit the same number of homomorphisms from every graph. A line of work extending Lov\'asz's result to more general types of graphs was recently capped by Cai and Govorov, who showed that it holds for graphs with vertex and edge w

Kazuma Kobayashi, Dinesh Kumar, Syed Bahauddin Alam

In response to the urgent need to establish AI/ML-integrated Digital Twin (DT) technology within next-generation nuclear systems, advancements in modeling methods and simulation codes are necessary. The increased complexity of models demands significant computational resources to quantify their uncertainties. To address this challenge, a data-driven non-intr

José Carlos Ureña Marcos, Benno Liebchen

Gaining control over the motion of active particles is crucial for applications ranging from targeted cargo delivery to nanomedicine. While much progress has been made recently to control active motion based on external forces, flows or gradients in concentration or light intensity, which all have a well-defined direction or bias, little is known about how t

Cheng Li, Siyang Gao, Jianzhong Du

We consider performing simulation experiments in the presence of covariates. Here, covariates refer to some input information other than system designs to the simulation model that can also affect the system performance. To make decisions, decision makers need to know the covariate values of the problem. Traditionally in simulation-based decision making, sim

Anju Maurya, Rashmi Singh, Shantanu Rastogi

Computational study of polycyclic aromatic hydrocarbons (PAHs) with phenyl side group substituted at different positions is reported. The infrared spectral variations due to the position of phenyl substitution, ionization state and the size of the molecules are discussed and possible contribution of phenyl-PAHs to the mid-infrared emission features from astr

Maximilian Zipfl, Barbara Schütt, J. Marius Zöllner, Eric Sax

A major challenge in the safety assessment of automated vehicles is to ensure that risk for all traffic participants is as low as possible. A concept that is becoming increasingly popular for testing in automated driving is scenario-based testing. It is founded on the assumption that most time on the road can be seen as uncritical and in mainly critical situ

Federico Benzi, Cristian Secchi

During physical human robot collaboration, it is important to be able to implement a time-varying interactive behaviour while ensuring robust stability. Admittance control and passivity theory can be exploited for achieving these objectives. Nevertheless, when the admittance dynamics is time-varying, it can happen that, for ensuring a passive and stable beha

David Schubert, Pritha Gupta, Marcel Wever

In anomaly detection, a prominent task is to induce a model to identify anomalies learned solely based on normal data. Generally, one is interested in finding an anomaly detector that correctly identifies anomalies, i.e., data points that do not belong to the normal class, without raising too many false alarms. Which anomaly detector is best suited depends o

Federico Benzi, Cristian Mancus, Cristian Secchi

Human-robot collaborative tasks foresee interactions between humans and robots with various degrees of complexity. Specifically, for tasks which involve physical contact among the agents, challenges arise in the modelling and control of such interaction. In this paper we propose a control architecture capable of ensuring a flexible and robustly stable physic

Pierre-Louis Curien, Muriel Livernet, Gabriel Saadia

We construct a cubical analogue of the rigidification functor from quasi-categories to simplicial categories present in the work of Joyal and Lurie. We define a functor from the category of cubical sets of Doherty-Kapulkin-Lindsey-Sattler to the category of (small) simplicial categories. We show that this rigidification functor establishes a Quillen equivale

Bela Bajnok, Peter Pal Pach

Let $G$ be a finite abelian group. A nonempty subset $A$ in $G$ is called a basis of order $h$ if $hA=G$; when $hA \neq G$, it is called a nonbasis of order $h$. Our interest is in all possible sizes of $hA$ when $A$ is a nonbasis of order $h$ in $G$ of maximum size; we provide the complete answer when $h=2$ or $h=3$.

R. J. Smethurst, R. S. Beckmann, B. D. Simmons, A. Coil

Recent observational and theoretical studies have suggested that supermassive black holes (SMBHs) grow mostly through non-merger (`secular') processes. Since galaxy mergers lead to dynamical bulge growth, the only way to observationally isolate non-merger growth is to study galaxies with low bulge-to-total mass ratio (e.g. B/T < 10%). However, bulge growth c

Jinfeng Zhao, Chenwen Yang, Weitao Yuan, Danmei Zhang

Distinct from the phononic valley pseudo-spin, the real physical spin of elastic waves adds a novel tool-kit capable of envisaging the valley-spin physics of topological valley phononic crystals from a local viewpoint. Here, we report the observation of local elastic valley spin as well as the hidden elastic spin-valley locking mechanism overlooked before. W

Eleonora Svanberg

Newtons second law, Schrodingers equation and Maxwells equations are all theories composed of at most second-time derivatives. Indeed, it is not often we need to take the time derivative of the acceleration. So why are we not seeing more higher-order derivative theories? Although several studies present higher derivatives usefulness in quadratic gravity and

Seung Ho Park, Young Su Moon, Nam Ik Cho

Single-image super-resolution (SISR) networks trained with perceptual and adversarial losses provide high-contrast outputs compared to those of networks trained with distortion-oriented losses, such as L1 or L2. However, it has been shown that using a single perceptual loss is insufficient for accurately restoring locally varying diverse shapes in images, of

Bela Bajnok, Connor Berson, Hoang Anh Just

Let $G$ be a finite abelian group and $s$ be a positive integer. A subset $A$ of $G$ is called a {\em perfect $s$-basis of $G$} if each element of $G$ can be written uniquely as the sum of at most $s$ (not-necessarily-distinct) elements of $A$; similarly, we say that $A$ is a {\em perfect restricted $s$-basis of $G$} if each element of $G$ can be written uni

Thomas Blanchet

I introduce a new way of decomposing the evolution of the wealth distribution using a simple continuous time stochastic model, which separates the effects of mobility, savings, labor income, rates of return, demography, inheritance, and assortative mating. Based on two results from stochastic calculus, I show that this decomposition is nonparametrically iden

Lei Wang, Jin-Wen Kang, Qing Zhang, Shuwan Shen

We investigate the medium modifications of momentum splitting fraction and groomed jet radius with both dynamical grooming and soft drop algorithms in heavy-ion collisions. In the calculation, the partonic spectrum of initial hard scattering in p+p collisions is provided by the event generator PYTHIA 8, and the energy loss of fast parton traversing in a hot/

David López-Idiáquez, Céline Teplitsky, Arnaud Grégoire, Amélie Fargevieille

Climate change has been shown to affect fitness-related traits in a wide range of taxa; for instance, warming leads to phenological advancements in many plant and animal species. The influence of climate change on social and secondary sexual traits, that are associated with fitness due to their role as quality signals, is however unknown. Here, we use more t

Ingrid von Glehn, James S. Spencer, David Pfau

We present a novel neural network architecture using self-attention, the Wavefunction Transformer (Psiformer), which can be used as an approximation (or Ansatz) for solving the many-electron Schr\"odinger equation, the fundamental equation for quantum chemistry and material science. This equation can be solved from first principles, requiring no external tra