April 2023 arXiv papers — page 83

Qingyue Yang, Hongjing Niu, Pengfei Xia, Wei Zhang

Domain generalization (DG), aiming at models able to work on multiple unseen domains, is a must-have characteristic of general artificial intelligence. DG based on single source domain training data is more challenging due to the lack of comparable information to help identify domain invariant features. In this paper, it is determined that the domain invaria

Stefan Escaida Navarro, Tanguy Navez, Olivier Goury, Luis Molina

In this paper, we introduce a novel open source toolbox for design optimization in Soft Robotics. We consider that design optimization is an important trend in Soft Robotics that is changing the way in which designs will be shared and adopted. We evaluate this toolbox on the example of a cable-driven, sensorized soft finger. For devices like these, that feat

Manash Dey, Subhankar Roy

We propose a unique lepton mixing scheme and its association with an exact hierarchy-philic neutrino mass matrix texture in the light of Type-I+Type-II seesaw mechanism under the framework of $A_4 \times Z_{10}$ discrete flavour symmetry. The proposed model successfully predicts the normal ordering of neutrino masses and the two Majorana phases. Additionally

Benjamin Towle, Ke Zhou

Language models pre-trained on large self-supervised corpora, followed by task-specific fine-tuning has become the dominant paradigm in NLP. These pre-training datasets often have a one-to-many structure--e.g. in dialogue there are many valid responses for a given context. However, only some of these responses will be desirable in our downstream task. This r

Antón F. Faedo, Carlos Hoyos, Javier G. Subils

We study the phase diagram of a strongly coupled confining theory in $2+1$ dimensions, as a function of temperature and baryon chemical potential. The theory has a fully fledged supergravity holographic dual, that we use to predict a line of first order phase transitions separating a confining phase and a deconfined phase. Both phases exhibit a non-zero bary

Mofassir ul Islam Arif, Mohsan Jameel, Lars Schmidt-Thieme

Object detection has seen remarkable progress in recent years with the introduction of Convolutional Neural Networks (CNN). Object detection is a multi-task learning problem where both the position of the objects in the images as well as their classes needs to be correctly identified. The idea here is to maximize the overlap between the ground-truth bounding

Alice Bellettini, Andrea Richaud, Vittorio Penna

We study the motion of superfluid vortices with filled massive cores. Previous point-vortex models already pointed out the impact of the core mass on the vortex dynamical properties, but relied on an assumption that is questionable in many physical systems where the immiscibility condition is barely satisfied: the fact that the massive core always lays at th

Owen D. Miller

Near-field optics is an exciting frontier of photonics and plasmonics. The tandem of strongly localized fields and enhanced emission rates offers significant opportunities for wide-ranging applications, while also creating basic questions: How large can such enhancements be? To what extent do material losses inhibit optimal response? Over what bandwidths can

M. Cristina Câmara, André Guimarães, Jonathan R. Partington

This paper is concerned with paired operators in the context of the Lebesgue Hilbert space on the unit circle and its subspace, the Hardy space. By considering when such operators commute, generalizations of the Brown--Halmos results for Toeplitz operators are derived. Further, the kernels of such operators are described, giving results on invariant and near

Youssef Azouzi

In this paper, we explore an abstraction of uniform integrability in vector lattices and demonstrate its application by providing a positive solution to an open question posed by Kuo, Rodda, and Watson. Specifically, we show that for finite p and with T as a conditionally expectation operator, spaces Lp are sequentially complete. Furthermore, we demonstrate

Felix Ott, Lucas Heublein, David Rügamer, Bernd Bischl

The localization of objects is a crucial task in various applications such as robotics, virtual and augmented reality, and the transportation of goods in warehouses. Recent advances in deep learning have enabled the localization using monocular visual cameras. While structure from motion (SfM) predicts the absolute pose from a point cloud, absolute pose regr

Jonas Schuett, Anka Reuel, Alexis Carlier

Organizations that develop and deploy artificial intelligence (AI) systems need to take measures to reduce the associated risks. In this paper, we examine how AI companies could design an AI ethics board in a way that reduces risks from AI. We identify five high-level design choices: (1) What responsibilities should the board have? (2) What should its legal

Jeffrey D. Rudie, Rachit Saluja, David A. Weiss, Pierre Nedelec

The University of California San Francisco Brain Metastases Stereotactic Radiosurgery (UCSF-BMSR) dataset is a public, clinical, multimodal brain MRI dataset consisting of 560 brain MRIs from 412 patients with expert annotations of 5136 brain metastases. Data consists of registered and skull stripped T1 post-contrast, T1 pre-contrast, FLAIR and subtraction (

Shailesh Lal, Suvajit Majumder, Evgeny Sobko

We provide a novel Neural Network architecture that can: i) output R-matrix for a given quantum integrable spin chain, ii) search for an integrable Hamiltonian and the corresponding R-matrix under assumptions of certain symmetries or other restrictions, iii) explore the space of Hamiltonians around already learned models and reconstruct the family of integra

Il-Seung Jang, Kyu-Hwan Lee, Se-jin Oh

The quantum Grothendieck ring of a certain category of finite-dimensional modules over a quantum loop algebra associated with a complex finite-dimensional simple Lie algebra $\mathfrak{g}$ has a quantum cluster algebra structure of skew-symmetric type. Partly motivated by a search of a ring corresponding to a quantum cluster algebra of {\em skew-symmetrizabl

Gehendra Sharma, Sungkwang Mun, Nayeon Lee, Luke Peterson

Design exploration is an important step in the engineering design process. This involves the search for design/s that meet the specified design criteria and accomplishes the predefined objective/s. In recent years, machine learning-based approaches have been widely used in engineering design problems. This paper showcases Artificial Neural Network (ANN) arch

Sina Abdous, Reza Abdollahzadeh, Mohammad Hossein Rohban

Instance-level graph neural network explainers have proven beneficial for explaining such networks on histopathology images. However, there has been few methods that provide model explanations, which are common patterns among samples within the same class. We envision that graph-based histopathological image analysis can benefit significantly from such expla

Manuj Yadav, Densil Cabrera, Jungsoo Kim, Janina Fels

Open-plan offices (OPOs) have been around for more than half a century now, chronicling the vicissitudes of workplace topography amongst other factors. This paper addresses one such factor - the sound environment in occupied OPOs in relation to several objective workplace parameters, using measurements in contemporary OPOs and comparisons with studies over t

Rakhi R, Geethika Santhosh, Prajwel Joseph, Koshy George

NGC 5291, an early-type galaxy surrounded by a giant HI ring, is believed to be formed from collision with another galaxy. Several star forming complexes and tidal dwarf galaxies are distributed along the collisional ring which are sites of star formation in environments where extreme dynamical effects are involved. Dynamical effects can affect the star form

Michael Sarrazin, Coraline Stasser

We introduce a toy model of baryogenesis where our usual visible Universe is a 3-brane coevolving with a hidden 3-brane in a multidimensional bulk, in an ekpyrotic-like approach. The visible matter and antimatter sectors are coupled with the hidden matter and antimatter sectors, breaking the C/CP invariance and leading to baryogenesis occurring after the qua

Cheng Deng, Jiaxin Ding, Luoyi Fu, Weinan Zhang

The pandemic of COVID-19 has inspired extensive works across different research fields. Existing literature and knowledge platforms on COVID-19 only focus on collecting papers on biology and medicine, neglecting the interdisciplinary efforts, which hurdles knowledge sharing and research collaborations between fields to address the problem. Studying interdisc

Jakub Koncki, Magdalena Zielenkiewicz

We consider the Hilbert scheme of points in the affine complex plane. We find explicit formulas for the Nakajima's creation operators and their K-theoretic counterparts in terms of the Kirwan map. We obtain a description of the action of Nakajima's creation operators on the Chern classes of the tautological bundle.

Aaron Hurst, Daniel E. Lucani, Qi Zhang

Exponential growth in the amount of data generated by the Internet of Things currently pose significant challenges for data communication, storage and analytics and leads to high costs for organisations hoping to leverage their data. Novel techniques are therefore needed to holistically improve the efficiency of data storage and analytics in IoT systems. The

Vipul Arora, Eldon Chung, Zeyong Li, Thomas Tan

Two party differential privacy allows two parties who do not trust each other, to come together and perform a joint analysis on their data whilst maintaining individual-level privacy. We show that any efficient, computationally differentially private protocol that has black-box access to key agreement (and nothing stronger), is also an efficient, information

Manuj Yadav, Jungsoo Kim, Densil Cabrera, Richard de Dear

Within the soundscapes of open-plan offices, irrelevant speech has consistently been reported as the most distracting, and causing performance decrements for workers. Notwithstanding this generalization, the 'babble' created by multiple simultaneously active talkers can sometimes provide beneficial sound masking, but due to spatial release from masking (SRM)

Moyi Tian, Pablo Moriano

Communities often represent key structural and functional clusters in networks. To preserve such communities, it is important to understand their robustness under network perturbations. Previous work in community robustness analysis has focused on studying changes in the community structure as a response of edge rewiring and node or edge removal. However, th

Caroline Gieser, Henrik Beuther, Dmitry Semenov, Aida Ahmadi

In this study, we investigate how physical properties, such as the density and temperature profiles, evolve on core scales through the evolutionary sequence during high-mass star formation ranging from protostars in cold infrared dark clouds to evolved UCHII regions. We observed 11 high-mass star-forming regions with ALMA at 3 mm wavelengths. Based on the 3

Bart van Marum, Matthia Sabatelli, Hamidreza Kasaei

In this paper we propose a novel bipedal locomotion controller that uses noisy exteroception to traverse a wide variety of terrains. Building on the cutting-edge advancements in attention based belief encoding for quadrupedal locomotion, our work extends these methods to the bipedal domain, resulting in a robust and reliable internal belief of the terrain ah

Riccardo Rende, Federica Gerace, Alessandro Laio, Sebastian Goldt

Transformers are neural networks that revolutionized natural language processing and machine learning. They process sequences of inputs, like words, using a mechanism called self-attention, which is trained via masked language modeling (MLM). In MLM, a word is randomly masked in an input sequence, and the network is trained to predict the missing word. Despi

Igor N. Karnaukhov

The Kondo insulator state (KIS) is among the least understood phase state in condensed matter physics. KIS is the state of the electron liquid in the Kondo lattice at half filling, is studied within the mean field approach. We demonstrate, that $Z_2$-field, which is formed by electrons and local moments, leads to the state of the Kondo insulator in a lattice

Xinwen Fan, Yukang Zhang, Yang Lu, Hanzi Wang

Pedestrian attribute recognition (PAR) has received increasing attention because of its wide application in video surveillance and pedestrian analysis. Extracting robust feature representation is one of the key challenges in this task. The existing methods mainly use the convolutional neural network (CNN) as the backbone network to extract features. However,

R. Jalalzadeh, S. Jalalzadeh, B. Malekolkalami

We investigate the isometrically embedded Bianchi type-V cosmology braneworld model in a $D$-dimensional bulk space. The model provides a fluid of geometric dark energy (GDE) and unification of fundamental forces similar to the Kaluza--Klein (KK) theory. The Planck energy density, the fine structure constant, the muon mass, and the number of extra dimensions

Aram Bahrini, Mohammadsadra Khamoshifar, Hossein Abbasimehr, Robert J. Riggs

Developed by OpenAI, ChatGPT (Conditional Generative Pre-trained Transformer) is an artificial intelligence technology that is fine-tuned using supervised machine learning and reinforcement learning techniques, allowing a computer to generate natural language conversation fully autonomously. ChatGPT is built on the transformer architecture and trained on mil

Matthew E. Caplan

The diffusion coefficients of neutron rich nuclei in crystallizing white dwarf (WD) stars are essential microphysics input for modeling the evolution of the composition profile. Recently, molecular dynamics simulations have been used to compute diffusion coefficients for realistic mixtures of C-O and O-Ne WDs with many trace nuclides that could be important

Amir M. Ben-Amram, Lars Kristiansen, Jakob Grue Simonsen

We study the computational complexity of converting one representation of real numbers into another representation. Typical examples of representations are Cauchy sequences, base-10 expansions, Dedekind cuts and continued fractions.

Yisong Yang

It is shown that nonlinear electrodynamics of the Born--Infeld theory type may be exploited to shed insight into a few fundamental problems in theoretical physics, including rendering electromagnetic asymmetry to energetically exclude magnetic monopoles, achieving finite electromagnetic energy to relegate curvature singularities of charged black holes, and p

João Domingos, João Xavier

This paper considers a distributed detection setup where agents in a network want to detect a time-varying signal embedded in temporally correlated noise. The signal of interest is the impulse response of an ARMA (auto-regressive moving average) filter, and the noise is the output of yet another ARMA filter which is fed white Gaussian noise. For this extende

Vyacheslav M. Silkin, Stefan-Ludwig Drechsler, Dmitry V. Efremov

Despite decades of intensive experimental and theoretical efforts, the physics of cuprate high-temperature superconductors in general, and, in particular, their normal state, is still under debate. Here, we report our investigation of low-energy charge excitations in the normal state. We find that the peculiarities of the electronic band structure at low ene

Basudeb Mondal, Vijay B. Shenoy, Subhro Bhattacharjee

Emergent Dirac fermions provide the starting point to understanding the plethora of novel condensed matter phases. The nature of the associated phases and phase transitions crucially depends on both the emergent symmetries as well as the implementation of the microscopic ones on the low-energy Dirac fermions. Here, we show that $j=3/2$ electrons in spin-orbi

Yaohua Zha, Jinpeng Wang, Tao Dai, Bin Chen

Pre-trained point cloud models have found extensive applications in 3D understanding tasks like object classification and part segmentation. However, the prevailing strategy of full fine-tuning in downstream tasks leads to large per-task storage overhead for model parameters, which limits the efficiency when applying large-scale pre-trained models. Inspired

Ingo Nitschke, Axel Voigt

Observer-invariance is regarded as a minimum requirement for an appropriate definition of time derivatives. We systematically discuss such time derivatives for surface tensor field and provide explicit formulations for material, upper-convected, lower-convected and Jaumann/corotational time derivatives which all lead to different physical implications. We co

Jonas Matthies, Muhammad Burhan Hafez, Mostafa Kotb, Stefan Wermter

Over the last few years, we have not seen any major developments in model-free or model-based learning methods that would make one obsolete relative to the other. In most cases, the used technique is heavily dependent on the use case scenario or other attributes, e.g. the environment. Both approaches have their own advantages, for example, sample efficiency

Supreeth Mysore Venkatesh, Antonio Macaluso, Matthias Klusch

Coalition Structure Generation (CSG) is an NP-Hard problem in which agents are partitioned into mutually exclusive groups to maximize their social welfare. In this work, we propose QuACS, a novel hybrid quantum classical algorithm for Coalition Structure Generation in Induced Subgraph Games (ISGs). Starting from a coalition structure where all the agents bel

Joseph D Hart

The master stability function (MSF) yields the stability of the globally synchronized state of a network of identical oscillators in terms of the eigenvalues of the adjacency matrix. In order to compute the MSF, one must have an accurate model of an uncoupled oscillator, but often such a model does not exist. We present a reservoir computing technique for es

Aida Abiad, Carlos A. Alfaro, Ralihe R. Villagrán

The search for a highly discriminating and easily computable invariant to distinguish graphs remains a challenging research topic. Here we focus on cospectral graphs whose complements are also cospectral (generalized cospectral), and on coinvariant graphs (same Smith normal form) whose complements are also coinvariant (generalized coinvariant). We show a new

Changwei Yuan, Shengxuan Ding, Yin Wang, Jian Feng

Effective placement of emergency rescue resources, particularly with joint suppliers in complex disaster scenarios, is crucial for ensuring the reliability, efficiency, and quality of emergency rescue activities. However, limited research has considered the interaction between different disasters and material classification, which are highly vital to the eme

Jun Liu, Yiming Meng, Maxwell Fitzsimmons, Ruikun Zhou

The search for Lyapunov functions is a crucial task in the analysis of nonlinear systems. In this paper, we present a physics-informed neural network (PINN) approach to learning a Lyapunov function that is nearly maximal for a given stable set. A Lyapunov function is considered nearly maximal if its sub-level sets can be made arbitrarily close to the boundar

Tom Benhamou, Natasha Dobrinen

We develop the theory of cofinal types of ultrafilters over measurable cardinals and establish its connections to Galvin's property. We generalize fundamental results from the countable to the uncountable, but often in surprisingly strengthened forms, and present models with varying structures of the cofinal types of ultrafilters over measurable cardinals.

Jamie Tolan, Hung-I Yang, Ben Nosarzewski, Guillaume Couairon

Vegetation structure mapping is critical for understanding the global carbon cycle and monitoring nature-based approaches to climate adaptation and mitigation. Repeated measurements of these data allow for the observation of deforestation or degradation of existing forests, natural forest regeneration, and the implementation of sustainable agricultural pract

Marios Georgiou, Ioannis Rousochatzakis, Joseph J. Betouras

We investigate the effect of Dzyaloshinskii-Moriya (DM) interactions in the planar pyrochlore (checkerboard) antiferromagnet, one of the paradigmatic models of spin frustration, and establish the classical phase diagram using a combination of analytical and numerical approaches. While anisotropic interactions generally tend to remove the frustration, here we

Seth Hovland, Greg Vinal

In the complex of curves of a closed orientable surface of genus $g,$ $\mathcal{C}(S_g),$ a preferred finite set of geodesics between any two vertices, called \emph{efficient geodesics} introduced by Birman, Margalit, and Menasco in \cite{birman_margalit_menasco_2016}. The main tool used to establish the existence of efficient geodesics was a \emph{dot graph

M. Farasat Shamir, Mushtaq Ahmad, G. Mustafa, Aisha Rashid

This paper offers novel quintessence compact relativistic spherically symmetrical anisotropic solutions under the recently developed Ricci inverse gravity Amendola et al., 2020), by employing Krori and Barua gravitational potentials, $Ar^2=\nu(r), ~\&~Br^2+C=\mu(r)$ (with A, B, and C being real constants). For this objective, a specific explicit equation of

N. Hatamkhani, R. C. Kraan-Korteweg, S. L. Blyth, K. Said

We present six deep Near-InfraRed (JHK_s) photometric catalogues of galaxies identified in six cluster candidates (VC02, VC04, VC05, VC08, VC10, VC11) within the Vela Supercluster (VSCL) as part of our efforts to learn more about this large supercluster which extends across the zone of avoidance (l=272.5 \pm 20 deg, b= \pm 10 deg, at cz~ 18000 km/s). The obs

Arcelino Bruno Lobato Do Nascimento

William Thurston (1946-2012) gave a combinatorial characterization for generic branched self-coverings of the two-sphere by associating a planar graph to them 10.48550/arXiv.1502.04760. By generalizing the notion of local balancing, the author extends the Thurston result to encompass any branched covering of the two-sphere. As an application, we supply a low

Connor Armstrong, Harry Goodhew, Arthur Lipstein, Jiajie Mei

The tree-level wavefunction coefficient for four gravitons in de Sitter space was recently bootstrapped using the Cosmological Optical Theorem, flat space limit, and Manifestly Local Test \cite{Bonifacio:2022vwa}. Inspired by the double copy for scattering amplitudes, we derive a compact new expression for this quantity starting from the wavefunction coeffic

Ji Detao, Liu Weier

Motivation: Microsatellite instability (MSI) is a cancer biomarker associated with cancer prognosis and chemotherapy sensitivity. Since the discovery of MSI, polymerase chain reaction (PCR)-based testing has been considered the gold standard for MSI detection. However, with the decrease in sequencing costs, software that calculates MSI based on next-generati

Ningyu He, Zhehao Zhao, Jikai Wang, Yubin Hu

Although existing techniques have proposed automated approaches to alleviate the path explosion problem of symbolic execution, users still need to optimize symbolic execution by applying various searching strategies carefully. As existing approaches mainly support only coarse-grained global searching strategies, they cannot efficiently traverse through compl

Alexander Koenig, Maximilian Schambach, Johannes Otterbach

Self-supervised pre-training strategies have recently shown impressive results for training general-purpose feature extraction backbones in computer vision. In combination with the Vision Transformer architecture, the DINO self-distillation technique has interesting emerging properties, such as unsupervised clustering in the latent space and semantic corresp

Emilio Cruciani, Emanuela L. Giacomelli, Jinyeop Lee

Complex networked systems in fields such as physics, biology, and social sciences often involve interactions that extend beyond simple pairwise ones. Hypergraphs serve as powerful modeling tools for describing and analyzing the intricate behaviors of systems with multi-body interactions. Herein, we investigate a discrete-time nonlinear averaging dynamics wit

You-An Lee, Detlef Lohse, Sander G. Huisman

We experimentally investigate the early-stage scalar mixing and transport with solvent exchange in quasi-2D jets. We inject an ethanol/oil mixture upward into quiescent water, forming quasi-2D turbulent buoyant jets and triggering the ouzo effect with initial Reynolds numbers, Re_0=420, 840, and 1680. We study starting jets with continuous injection and puff

T. Gessey-Jones, A. Fialkov, E. de Lera Acedo, W. J. Handley

Cosmic rays generated by supernovae carry away a significant portion of the lifetime energy emission of their parent star, making them a plausible mechanism for heating the early universe intergalactic medium (IGM). Following a review of the existing literature on cosmic ray heating, we develop a flexible model of this heating mechanism for use in 3D semi-nu

Ziyun Wang, Fernando Cladera Ojeda, Anthony Bisulco, Daewon Lee

Event-based sensors have recently drawn increasing interest in robotic perception due to their lower latency, higher dynamic range, and lower bandwidth requirements compared to standard CMOS-based imagers. These properties make them ideal tools for real-time perception tasks in highly dynamic environments. In this work, we demonstrate an application where ev

Thanh-Dat Truong, Chi Nhan Duong, Ashley Dowling, Son Lam Phung

Understanding semantic scene segmentation of urban scenes captured from the Unmanned Aerial Vehicles (UAV) perspective plays a vital role in building a perception model for UAV. With the limitations of large-scale densely labeled data, semantic scene segmentation for UAV views requires a broad understanding of an object from both its top and side views. Adap

E. Bossio, M. Agostini

Nuclear double-beta decays are a unique probe to search for new physics beyond the Standard Model. Still-unknown particles, non-standard interactions, or the violation of fundamental symmetries would affect the decay kinematic, creating detectable and characteristic experimental signatures. In particular, the energy distribution of the electrons emitted in t

Guoqing Zhen, Guoliang Lv, Helei Liu, Akira Dohi

Type I X-ray bursts are rapidly brightening phenomena triggered by thermonuclear burning on accreting layer of a neutron star (NS). The light curves represent the physical properties of NSs and the nuclear reactions on the proton-rich nuclei. The numerical treatments of the accreting NS and physics of the NS interior are not established, which shows uncertai

Miguel Crispim Romão, José Guilherme Milhano, Marco van Leeuwen

We present a survey of a comprehensive set of jet substructure observables commonly used to study the modifications of jets resulting from interactions with the Quark Gluon Plasma in Heavy Ion Collisions. The \jewel{} event generator is used to produce simulated samples of quenched and unquenched jets. Three distinct analyses using Machine Learning technique

Suyash Fulay, Nabeel Gillani, Deb Roy

Identity spans multiple dimensions; however, the relative salience of a dimension of identity can vary markedly from person to person. Furthermore, there is often a difference between one's internal identity (how salient different aspects of one's identity are to oneself) and external identity (how salient different aspects are to the external world). We att

Leilei Cui, Qihan He, Zongyan Lv, Xuexiu Zhong

In the present paper, we study the existence of normalized solutions to the following Kirchhoff type equations \begin{equation*} -\left(a+b\int_{\R^3}|\nabla u|^2\right)\Delta u+V(x)u+\lambda u=g(u)~\hbox{in}~\R^3 \end{equation*} satisfying the normalized constraint $\displaystyle\int_{\R^3}u^2=c$, where $a,b,c>0$ are prescribed constants, and the nonlineari

Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo

The recent breakthroughs in natural language processing for model pretraining on large quantities of data have opened the way for similar foundation models in computer vision. These models could greatly simplify the use of images in any system by producing all-purpose visual features, i.e., features that work across image distributions and tasks without fine

Anna L. H. Hughes, Irene Gonzalez-Hernandez, Sean G. McManus, Kiran Jain

In helioseismic studies, an observational parameter of primary concern is the P-angle, the angle along which lies the solar axis of rotation for a given image. For the six observing sites employed by The Global Oscillation Network Group (GONG), this angle acts additionally as a marker of relative image orientation, allowing concurrent images to be precisely

Ed Bennett, Ho Hsiao, Jong-Wan Lee, Biagio Lucini

We provide the first determination of the mass of the lightest flavor-singlet pseudoscalar and scalar bound states (mesons), in the $\rm{Sp}(4)$ Yang-Mills theory coupled to two flavors of fundamental fermions, using lattice methods. This theory has applications both to composite Higgs and strongly-interacting dark matter scenarios. We find the singlets to h

Damien Pous, Jana Wagemaker

We prove two completeness results for Kleene algebra with tests and a top element, with respect to guarded string languages and binary relations. While the equational theories of those two classes of models coincide over the signature of Kleene algebra, this is no longer the case when we consider an additional constant ``top'' for the full element. Indeed, t

Kai Wang, Yangyao Chen, Qingyang Li, Xiaohu Yang

The star formation and quenching of central galaxies are regulated by the assembly histories of their host halos. In this work, we use the central stellar mass to halo mass ratio as a proxy of halo formation time, and we devise three different models, from the physical hydrodynamical simulation to the empirical statistical model, to demonstrate its robustnes

Enrico Donato, Yasmin Tauqeer Ansari, Cecilia Laschi, Egidio Falotico

Enabling reaching capabilities in highly redundant continuum robot arms is an active area of research. Existing solutions comprise of task-space controllers, whose proper functioning is still limited to laboratory environments. In contrast, this work proposes a novel plant-inspired behaviour-based controller that exploits information obtained from proximity

Revan MacQueen

We give a simple proof of the well-known result that the marginal strategies of a coarse correlated equilibrium form a Nash equilibrium in two-player zero-sum games. A corollary of this fact is that no-external-regret learning algorithms that converge to the set of coarse correlated equilibria will also converge to Nash equilibria in two-player zero-sum game

Lucas S. Maia, Martín Rocamora, Luiz W. P. Biscainho, Magdalena Fuentes

Beat and downbeat tracking models have improved significantly in recent years with the introduction of deep learning methods. However, despite these improvements, several challenges remain. Particularly, the adaptation of available models to underrepresented music traditions in MIR is usually synonymous with collecting and annotating large amounts of data, w

Norita Kawanaka, Kazunori Kohri

We investigate the effect of conductive heating of the gas surrounding a geometrically thick accretion disk on the growth of a black hole at high redshift. If a black hole is accreting the surrounding gas at a super-Eddington rate, the X-ray radiation from the vicinity of the black hole would be highly anisotropic due to the self-shielding of a geometrically

Kiran Busch, Alexander Rochlitzer, Diana Sola, Henrik Leopold

GPT-3 and several other language models (LMs) can effectively address various natural language processing (NLP) tasks, including machine translation and text summarization. Recently, they have also been successfully employed in the business process management (BPM) domain, e.g., for predictive process monitoring and process extraction from text. This, howeve

Grgur Palle, Clifford Hicks, Roser Valentí, Zhenhai Hu

Strontium ruthenate Sr$_2$RuO$_4$ is an unconventional superconductor whose pairing symmetry has not been fully clarified, despite more than two decades of intensive research. Recent NMR Knight shift experiments have rekindled the Sr$_2$RuO$_4$ pairing debate by giving strong evidence against all odd-parity pairing states, including chiral $p$-wave pairing t

Christopher Lazda

In this article, I define triangulated categories of constructible isocrystals on varieties over a perfect field of positive characteristic, in which Le Stum's abelian category of constructible isocrystals sits as the heart of a natural t-structure. I then prove a Riemann-Hilbert correspondence, showing that, for objects admitting some (unspecified) Frobeniu

Simone Biondini, Nora Brambilla, Gramos Qerimi, Antonio Vairo

In this conference paper, we consider effective field theories of non-relativistic dark matter particles interacting with a light force mediator in the early expanding universe. We present a general framework, where to account in a systematic way for the relevant processes that may affect the dynamics during thermal freeze-out. In the temperature regime wher

Christian Glaser

The ARIANNA in-ice radio detector explores the detection of UHE neutrinos with shallow detector stations on the Ross Ice Shelf and the South Pole. Here, we present recent results that lay the foundation for future large-scale experiments. We show a limit on the UHE neutrino flux derived from ARIANNA data, measurements of the more abundant air showers, result

Jorge Carrasco Muriel, Nicholas Cowie, Marjan Mansouvar, Teddy Groves

Summary: Shu is a visualization tool that integrates diverse data types into a metabolic map, with a focus on supporting multiple conditions and visualizing distributions. The goal is to provide a unified platform for handling the growing volume of multi-omics data, leveraging the metabolic maps developed by the metabolic modeling community. Additionally, sh

Trever Schirmer, Nils Japke, Sofia Greten, Tobias Pfandzelter

Function-as-a-Service is a popular cloud programming model that supports developers by abstracting away most operational concerns with automatic deployment and scaling of applications. Due to the high level of abstraction, developers rely on the cloud platform to offer a consistent service level, as decreased performance leads to higher latency and higher co

Ron Fulbright

Previous study of cellular automata and random Boolean networks has shown emergent behavior occurring at the edge of chaos where the randomness (disorder) of internal connections is set to an intermediate critical value. The value at which maximal emergent behavior occurs has been observed to be inversely related to the total number of interconnected element

Gabriella Pangelinan, K. S. Krishnapriya, Vitor Albiero, Grace Bezold

In recent years, media reports have called out bias and racism in face recognition technology. We review experimental results exploring several speculated causes for asymmetric cross-demographic performance. We consider accuracy differences as represented by variations in non-mated (impostor) and / or mated (genuine) distributions for 1-to-1 face matching. P

CMS Collaboration

A search for pair production of the supersymmetric partner of the top quark, the top squark, in proton-proton collisions at $\sqrt{s}$ = 13 TeV is presented in final states containing at least one hadronically decaying tau lepton and large missing transverse momentum. This final state is highly sensitive to scenarios of supersymmetry in which the decay of th

Sarad Venugopalan, Heiko Aydt

A decentralised organisation (DO) is a multi-stakeholder institution where decision making is assigned to various levels of the organisation. Decentralised stakeholders play an important role in the governance of a decentralised organisation. The ability to measure DO stability will help monitor the health of the organisation and acts as an early warning sys

Attila László Nagy, Goitom Simret Kidane, Tamás Turányi, János Tóth

A novel stochastic optimization method called MAC was suggested. The method is based on the calculation of the objective function at several random points and then an empirical expected value and an empirical covariance matrix are calculated. The empirical expected value is proven to converge to the optimum value of the problem. The MAC algorithm was encoded

Changzheng Li, Changjian Su, Rui Xiong

In this paper, we introduce quantum Demazure--Lusztig operators acting by ring automorphisms on the equivariant quantum cohomology of the Springer resolution. Our main application is a presentation of the torus-equivariant quantum cohomology in terms of generators and relations. We provide explicit descriptions for the classical types. We also recover Kim's

Alicja Dutkiewicz, Thomas E. O'Brien, Thomas Schuster

We study the problem of learning the Hamiltonian of a many-body quantum system from experimental data. We show that the rate of learning depends on the amount of control available during the experiment. We consider three control models: one where time evolution can be augmented with instantaneous quantum operations, one where the Hamiltonian itself can be au

Micah Bowles, Hongming Tang, Eleni Vardoulaki, Emma L. Alexander

We present a novel natural language processing (NLP) approach to deriving plain English descriptors for science cases otherwise restricted by obfuscating technical terminology. We address the limitations of common radio galaxy morphology classifications by applying this approach. We experimentally derive a set of semantic tags for the Radio Galaxy Zoo EMU (E

Hao-Jui Kuan, Arthur G. Suvorov, Kostas D. Kokkotas

Gamma-ray bursts resulting from binary neutron-star mergers are sometimes preceded by precursor flares. These harbingers may be ignited by quasi-normal modes, excited by orbital resonances, shattering the stellar crust of one of the inspiralling stars up to $\gtrsim10$ seconds before coalescence. In the rare case that a system displays two precursors, succes

Mehdi Cherti, Alexander Czernik, Stefan Kesselheim, Frederic Effenberger

Solar activity is one of the main drivers of variability in our solar system and the key source of space weather phenomena that affect Earth and near Earth space. The extensive record of high resolution extreme ultraviolet (EUV) observations from the Solar Dynamics Observatory (SDO) offers an unprecedented, very large dataset of solar images. In this work, w

Harold van Heukelum, Ruud Binnekamp, Rogier Wolfert

Current systems design optimisation methodologies are one-sided, as these ignore the socio-technical integration between stakeholder preferences ('what a human wants') and the capability of technical assets ('what a system can deliver'). Moreover, classical multi-objective optimisation methods contain fundamental mathematical flaws. Also, the often-used clas

Julie P. Vidal, Lola Danet, Patrice Péran, Jérémie Pariente

Accurate segmentation of thalamic nuclei, crucial for understanding their role in healthy cognition and in pathologies, is challenging to achieve on standard T1-weighted (T1w) magnetic resonance imaging (MRI) due to poor image contrast. White-matter-nulled (WMn) MRI sequences improve intrathalamic contrast but are not part of clinical protocols or extant dat

Edward Lo, Ningyu He, Yuejie Shi, Jiajia Xu

Recently, the first feature-rich NTFS implementation, NTFS3, has been upstreamed to Linux. Although ensuring the security of NTFS3 is essential for the future of Linux, it remains unclear, however, whether the most recent version of NTFS for Linux contains 0-day vulnerabilities. To this end, we implemented Papora, the first effective fuzzer for NTFS3. We hav

Andrea Canciani, Claudio Felicioli, Andrea Lisi, Fabio Severino

We propose a new approach, termed Hybrid DLT, to address a broad range of industrial use cases where certain properties of both private and public DLTs are valuable, while other properties may be unnecessary or detrimental. The Hybrid DLT approach involves a system where private ledgers, with limited data block dissemination, are collaboratively created by n

Sergey D. Prosnyak, Daniel E. Maison, Leonid V. Skripnikov

Recently, upper bounds on the static time-reversal ($\mathcal{T}$) and spatial parity ($\mathcal{P}$)-violating electron electric dipole moment ($e$EDM) and dimensionless constant characterizing the strength of the $\mathcal{T}$,$\mathcal{P}$-violating scalar-pseudoscalar nucleus-electron interaction have been updated in the JILA experiment using the HfF$^+$

Yash Satsangi, Paniz Behboudian

A key challenge for a reinforcement learning (RL) agent is to incorporate external/expert1 advice in its learning. The desired goals of an algorithm that can shape the learning of an RL agent with external advice include (a) maintaining policy invariance; (b) accelerating the learning of the agent; and (c) learning from arbitrary advice [3]. To address this