March 2023 arXiv papers — page 31

Michael Regan, Jena D. Hwang, Keisuke Sakaguchi, James Pustejovsky

Making sense of familiar yet new situations typically involves making generalizations about causal schemas, stories that help humans reason about event sequences. Reasoning about events includes identifying cause and effect relations shared across event instances, a process we refer to as causal schema induction. Statistical schema induction systems may leve

Yifei Yin, Chen Guo, Manuel Kaufmann, Juan Jose Zarate

We propose Hi4D, a method and dataset for the automatic analysis of physically close human-human interaction under prolonged contact. Robustly disentangling several in-contact subjects is a challenging task due to occlusions and complex shapes. Hence, existing multi-view systems typically fuse 3D surfaces of close subjects into a single, connected mesh. To a

Benjamin Moseley, Heather Newman, Kirk Pruhs

We consider the online $k$-median clustering problem in which $n$ points arrive online and must be irrevocably assigned to a cluster on arrival. As there are lower bound instances that show that an online algorithm cannot achieve a competitive ratio that is a function of $n$ and $k$, we consider a beyond worst-case analysis model in which the algorithm is pr

Sakshi Choudhary, Sai Aparna Aketi, Gobinda Saha, Kaushik Roy

Training at the edge utilizes continuously evolving data generated at different locations. Privacy concerns prohibit the co-location of this spatially as well as temporally distributed data, deeming it crucial to design training algorithms that enable efficient continual learning over decentralized private data. Decentralized learning allows serverless train

Kunyang Sun, Wei Lin, Haoqin Shi, Zhengming Zhang

Unsupervised domain adaptive object detection is a challenging vision task where object detectors are adapted from a label-rich source domain to an unlabeled target domain. Recent advances prove the efficacy of the adversarial based domain alignment where the adversarial training between the feature extractor and domain discriminator results in domain-invari

Juraj Bodik, Valérie Chavez-Demoulin

Existing approaches to causal discovery often rely on restrictive modeling assumptions that limit their applicability in real-world settings, particularly when data are heavy-tailed or contain a mixture of discrete and continuous variables. Identifiability of causal graphs has been established under several structural models, including linear non-Gaussian mo

Yan Sun, Yifan Yuan, Zeduo Yu, Reese Kuper

The ever-growing demands for memory with larger capacity and higher bandwidth have driven recent innovations on memory expansion and disaggregation technologies based on Compute eXpress Link (CXL). Especially, CXL-based memory expansion technology has recently gained notable attention for its ability not only to economically expand memory capacity and bandwi

Nahuel Soprano-Loto, Matthieu Jonckheere, Pascal Moyal

We consider the problem of sequential matching in a stochastic block model with several classes of nodes and generic compatibility constraints. When the probabilities of connections do not scale with the size of the graph, we show that under the NCOND condition, a simple max-weight type policy allows to attain an asymptotically perfect matching while no sequ

Arash Ghahraman, Gyula Bene

This paper presents the second-order perturbation theory of the Navier-Stokes equations for free surface flows, with the wave amplitude considered as the perturbation parameter. Gravity-capillary surface waves in incompressible viscous fluids are subjected. The results provide a systematic derivation of a nonlinear surface wave equation that fully takes into

Roshan Kaundinya, Omkar Nippanikar, Akash Singh, K. P. Yogendran

We rewrite the worldsheet torus partition function of the Thermal AdS CFT by isolating the boundary parameters. Using this, we show that the spectrum of the Euclidean BTZ black hole and Lorentzian AdS3 can be extracted -- the latter as a zero temperature limit. A similar procedure recovers the Lorentzian BTZ spectrum proposed in an earlier work. We then use

Narges Rashvand, Sanaz Sadat Hosseini, Mona Azarbayjani, Hamed Tabkhi

In urban settings, bus transit stands as a significant mode of public transportation, yet faces hurdles in delivering accurate and reliable arrival times. This discrepancy often culminates in delays and a decline in ridership, particularly in areas with a heavy reliance on bus transit. A prevalent challenge is the mismatch between actual bus arrival times an

Andrew Golightly, Laura E. Wadkin, Sam A. Whitaker, Andrew W. Baggaley

We consider the case of performing Bayesian inference for stochastic epidemic compartment models, using incomplete time course data consisting of incidence counts that are either the number of new infections or removals in time intervals of fixed length. We eschew the most natural Markov jump process representation for reasons of computational efficiency, an

Shalini Roy Koneru, Kamalnath Kadirvel, Hamish Fraser, Yunzhi Wang

Nanoscale multi-phase microstructures observed in multi-principal element alloys (MPEAs) such as $\rm AlMo_{0.5}NbTa_{0.5}TiZr$, $\rm Al_{0.5}NbTa_{0.8}Ti_{1.5}V_{0.2}Zr$, $\rm TiZrNbTa$, $\rm AlCoCrFeNi$ and $\rm Fe_{15}Co_{15}Ni_{20}Mn_{20}Cu_{30}$ that exhibit promising mechanical or functional properties may have evolved through spinodal-mediated phase t

Johannes R. Eskilt, Laura Herold, Eiichiro Komatsu, Kai Murai

Polarization of the cosmic microwave background (CMB) is sensitive to new physics violating parity symmetry, such as the presence of a pseudoscalar "axionlike" field. Such a field may be responsible for early dark energy (EDE), which is active prior to recombination and provides a solution to the so-called Hubble tension. The EDE field coupled to photons in

Junkai Deng, Fei Hou, Xuhui Chen, Wencheng Wang

Recently, building on the foundation of neural radiance field, various techniques have emerged to learn unsigned distance fields (UDF) to reconstruct 3D non-watertight models from multi-view images. Yet, a central challenge in UDF-based volume rendering is formulating a proper way to convert unsigned distance values into volume density, ensuring that the res

Eoin Hurley, François Pirot

We analyse uniformly random proper $k$-colourings of sparse graphs with maximum degree $\Delta$ in the regime $\Delta < k\ln k $. This regime corresponds to the lower side of the shattering threshold for random graph colouring, a paradigmatic example of the shattering threshold for random Constraint Satisfaction Problems. We prove a variety of results about

Pierre Godfard

We prove that level $5$ Witten-Reshetikhin-Turaev $\mathrm{SO}(3)$ quantum representations, also known as the Fibonacci representations, of mapping class groups are locally rigid. More generally, for any prime level $\ell$, we prove that the level $\ell$ $\mathrm{SO}(3)$ quantum representations are locally rigid on all surfaces of genus $g\geq 3$ if and only

Marilena Crupi, Antonino Ficarra

A very well-covered graph is an unmixed graph without isolated vertices such that the height of its edge ideal is half of the number of vertices. We study these graphs by means of Betti splittings and mapping cone constructions. We show that the cover ideals of Cohen-Macaulay very well-covered graphs are splittable. As a consequence, we compute explicitly th

Jian Liang, Ran He, Tieniu Tan

Machine learning methods strive to acquire a robust model during the training process that can effectively generalize to test samples, even in the presence of distribution shifts. However, these methods often suffer from performance degradation due to unknown test distributions. Test-time adaptation (TTA), an emerging paradigm, has the potential to adapt a p

P. Mardesic, G. J. Gutierrez Guillen, D. Sugny

The author of the comment~[arXiv:2302.04190] criticizes our published results in Phys. Rev. Lett. \textbf{125}, 064301 (2020) about the Tennis Racket Effect (TRE). The TRE is a geometric effect which occurs in the free rotation of any asymmetric rigid body. We explain why the criticism of this comment is not valid.

Jing-jun Zhu, Kaipeng Liu, Xi Chen, Stéphane Guérin

Using optimal control, we establish and link the ultimate bounds in time (referred to as quantum speed limit) and energy of two- and three-level quantum nonlinear systems which feature 1:2 resonance. Despite the unreachable complete inversion, by using the Pontryagin maximum principle, we determine the optimal time, pulse area, or energy, for a given arbitra

Robin Scholle, Pietro M. Bonetti, Demetrio Vilardi, Walter Metzner

We present an unbiased mean-field analysis of magnetic and charge orders in the two-dimensional Hubbard model on a square lattice, both at zero and finite temperatures. Unrestricted Hartree-Fock calculations on large finite lattices are complemented by solutions restricted to N\'eel and circular spiral order in the thermodynamic limit. The magnetic states ar

Fabio Paronetto

We want to prove a Harnack type inequality for solutions of strongly degenerate parabolic, or elliptic-parabolic, equations. To do that, we first define a De Giorgi class of order $p = 2$ that contains the solutions of evolution equations of the types $\uprho (x,t) u_t + A u = 0$ and $(\uprho (x,t) u)_t + A u = 0$, where $\uprho > 0$ almost everywhere and $A

Quintino Francesco Lotito, Martina Contisciani, Caterina De Bacco, Leonardo Di Gaetano

From social to biological systems, many real-world systems are characterized by higher-order, non-dyadic interactions. Such systems are conveniently described by hypergraphs, where hyperedges encode interactions among an arbitrary number of units. Here, we present an open-source python library, hypergraphx (HGX), providing a comprehensive collection of algor

Sambuddha Chattopadhyay, Christian J. Eckhardt, Dante M. Kennes, Michael A. Sentef

Inspired by the striking discovery of metastable superconductivity in $\mathrm{K}_3\mathrm{C}_{60}$ at 100K, far above $T_{\textrm{c}}=20K$, we discuss possible mechanisms for long-lived, photo-induced superconductivity. Starting from a model of optically-driven Raman phonons coupled to inter-band electronic transitions, we develop a microscopic mechanism fo

Patrick Rockenschaub, Adam Hilbert, Tabea Kossen, Falk von Dincklage

Deep learning (DL) can aid doctors in detecting worsening patient states early, affording them time to react and prevent bad outcomes. While DL-based early warning models usually work well in the hospitals they were trained for, they tend to be less reliable when applied at new hospitals. This makes it difficult to deploy them at scale. Using carefully harmo

Emmanuel Zambrini Cruzeiro, Christine De Mol, Serge Massar, Stefano Pironio

We present quantum-inspired algorithms for classification tasks inspired by the problem of quantum state discrimination. By construction, these algorithms can perform multiclass classification, prevent overfitting, and generate probability outputs. While they could be implemented on a quantum computer, we focus here on classical implementations of such algor

Fengjiao Zou, Jennifer Ogle, Weimin Jin, Patrick Gerard

Interacting with pedestrians is challenging for Autonomous vehicles (AVs). This study evaluates how AV operations /associated signaling and roadway infrastructure affect pedestrian behavior in virtual reality. AVs were designed with different operations and signal indications, including negotiating with no signal, negotiating with a yellow signal, and yellow

Loukas Vlahos, Heinz Isliker

We review the current literature on the formation of Coherent Structures (CoSs) in strongly turbulent 3D magnetized plasmas. CoSs (Current Sheets (CS), magnetic filaments, large amplitude magnetic disturbances, vortices, and shocklets) appear intermittently inside a turbulent plasma and are collectively the locus of magnetic energy transfer (dissipation) int

Suman Adhya, Debarshi Kumar Sanyal

Topic modeling is a dominant method for exploring document collections on the web and in digital libraries. Recent approaches to topic modeling use pretrained contextualized language models and variational autoencoders. However, large neural topic models have a considerable memory footprint. In this paper, we propose a knowledge distillation framework to com

Denis Blessing, Onur Celik, Xiaogang Jia, Moritz Reuss

Imitation learning uses data for training policies to solve complex tasks. However, when the training data is collected from human demonstrators, it often leads to multimodal distributions because of the variability in human actions. Most imitation learning methods rely on a maximum likelihood (ML) objective to learn a parameterized policy, but this can resu

Valeriia Liakh, Manuel Luna, Elena Khomenko

Several energetic disturbances have been identified as triggers of the large-amplitude oscillations (LAOs) in prominences. However, the mechanisms for LAOs excitation are not well understood. We aim to study these mechanisms, performing time-dependent numerical simulations in 2.5D and 2D setups using magnetohydrodynamic (MHD) code MANCHA3D. Two types of dist

Elia Bruè, Aaron Naber, Daniele Semola

It was conjectured by Milnor in 1968 that the fundamental group of a complete manifold with nonnegative Ricci curvature is finitely generated. The main result of this paper is a counterexample, which provides an example $M^7$ with ${\rm Ric}\geq 0$ such that $\pi_1(M)=\mathbb{Q}/\mathbb{Z}$ is infinitely generated. There are several new points behind the res

Peter Davies

Beeping models are models for networks of weak devices, such as sensor networks or biological networks. In these networks, nodes are allowed to communicate only via emitting beeps: unary pulses of energy. Listening nodes only the capability of {\it carrier sensing}: they can only distinguish between the presence or absence of a beep, but receive no other inf

Lucas Chesnel, Sergei A. Nazarov, Jari Taskinen

We investigate the spectrum of a Laplace operator with mixed boundary conditions in an unbounded chamfered quarter of layer. This problem arises in the study of the spectrum of the Dirichlet Laplacian in thick polyhedral domains having some symmetries such as the so-called Fichera layer. The geometry we consider depends on two parameters gathered in some vec

P. O. Kazinski, P. S. Korolev, G. Yu. Lazarenko, V. A. Ryakin

The theory of multiplexing electromagnetic signals by means of twisted photons generated by a uniform circular array (UCA) is developed in the case when the receiving antenna represents an array of elements located on a circular arc. The radiating elements are characterized by certain current distributions and are not points, in general. The polarization of

Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, Lucas Beyer

We propose a simple pairwise Sigmoid loss for Language-Image Pre-training (SigLIP). Unlike standard contrastive learning with softmax normalization, the sigmoid loss operates solely on image-text pairs and does not require a global view of the pairwise similarities for normalization. The sigmoid loss simultaneously allows further scaling up the batch size, w

Michał Zając, Kamil Deja, Anna Kuzina, Jakub M. Tomczak

Diffusion models have achieved remarkable success in generating high-quality images thanks to their novel training procedures applied to unprecedented amounts of data. However, training a diffusion model from scratch is computationally expensive. This highlights the need to investigate the possibility of training these models iteratively, reusing computation

Angana Bhattacharya, Bhagwat S. Chouhan, Bhairov K. Bhowmik, Rajan Singh

The toroidal dipole excitation is an important field for metamaterial research because of their low-loss attribute. In this study, we demonstrate numerically and experimentally, a unique polarization independent terahertz metamaterial that modulates a broad resonance into a sharp mode by coupling the inherent toroidal dipole excitation to the lattice mode of

ATLAS Collaboration

Differential and double-differential distributions of kinematic variables of leptons from decays of top-quark pairs ($t\bar{t}$) are measured using the full LHC Run 2 data sample collected with the ATLAS detector. The data were collected at a $pp$ collision energy of $\sqrt{s}=13$ TeV and correspond to an integrated luminosity of 140 fb$^{-1}$. The measureme

Travis Greene, Sofie Goethals, David Martens, Galit Shmueli

Algorithms used by organizations increasingly wield power in society as they decide the allocation of key resources and basic goods. In order to promote fairer, juster, and more transparent uses of such decision-making power, explainable artificial intelligence (XAI) aims to provide insights into the logic of algorithmic decision-making. Despite much researc

Max Weissenbacher

We study the massless Vlasov equation on the exterior of the subextremal and extremal Reissner-Nordstr\"om spacetimes. We prove that moments decay at an exponential rate in the subextremal case and at a polynomial rate in the extremal case. This polynomial rate is shown to be sharp along the event horizon. In the extremal case we show that transversal deriva

Matthias Ruf, Mathias Schäffner

We study stochastic homogenization for convex integral functionals $$u\mapsto \int_D W(\omega,\tfrac{x}\varepsilon,\nabla u)\,\mathrm{d}x,\quad\mbox{where}\quad u:D\subset \mathbb{R}^d\to\mathbb{R}^m,$$ defined on Sobolev spaces. Assuming only stochastic integrability of the map $\omega\mapsto W(\omega,0,\xi)$, we prove homogenization results under two diffe

I. Mattich, J. Sendra, H. Galinski, G. Isapour

Magnetically assembled superparamagnetic colloids have been exploited as fluid mixers, swimmers and delivery systems in several microscale applications. The encapsulation of such colloids in droplets may open new opportunities to build magnetically controlled displays and optical components. Here, we study the assembly of superparamagnetic colloids inside dr

Emanuele Vivoli, Luca Bossi, Marco Bertini, Pierluigi Falorni

Holographic imaging is a technique that uses microwave energy to create a three-dimensional image of an object or scene. This technology has potential applications in land mine detection, as the long-wavelength microwave energy can penetrate the ground and create an image of hidden objects without the need for direct physical contact. However, the inversion

Yifu Zhang, Xinggang Wang, Xiaoqing Ye, Wei Zhang

Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects across video frames. Detection boxes serve as the basis of both 2D and 3D MOT. The inevitable changing of detection scores leads to object missing after tracking. We propose a hierarchical data association strategy to mine the true objects in low-score detection boxes, wh

Daoqiang Liu

The main purpose of this short note is to derive some generalizations of the long neck principle and give a spectral width inequality of geodesic collar neighborhoods. Our results are obtained via the spinorial Callias operator approach. An important step is to introduce the relative Gromov-Lawson pair on a compact manifold with boundary, relative to a backg

Nicolò Leone, Stefano Azzini, Sonia Mazzucchi, Valter Moretti

Single-photon entanglement is a peculiar type of entanglement in which two or more degrees of freedom of a single photon are correlated quantum-mechanically. Here, we demonstrate a photonic integrated chip (PIC) able to generate and manipulate single-photon path-entangled states, using a commercial red LED as light source. A Bell test, in the Clauser, Horne,

Arnaud Klipfel, Nitish Sontakke, Ren Liu, Sehoon Ha

Learning various motor skills for quadrupedal robots is a challenging problem that requires careful design of task-specific mathematical models or reward descriptions. In this work, we propose to learn a single capable policy using deep reinforcement learning by imitating a large number of reference motions, including walking, turning, pacing, jumping, sitti

Zeyd Boukhers, Arnim Bleier, Yeliz Ucer Yediel, Mio Hienstorfer-Heitmann

Data privacy and ownership are significant in social data science, raising legal and ethical concerns. Sharing and analyzing data is difficult when different parties own different parts of it. An approach to this challenge is to apply de-identification or anonymization techniques to the data before collecting it for analysis. However, this can reduce data ut

Mathias Bersweiler, Yojiro Oba, Evelyn Pratami Sinaga, Inma Peral

Taking advantage of recent progress in neutron instrumentation and in the understanding of magnetic-field-dependent small-angle neutron scattering, here, we study the three-dimensional magnetization distribution within an isotropic Nd-Fe-B bulk magnet. The magnetic neutron scattering cross section of this system features the so-called spike anisotropy, which

Facundo N. Airaudo, Rainald Löhner, Roland Wüchner, Harbir Antil

An adjoint-based procedure to determine weaknesses, or, more generally the material properties of structures is developed and tested. Given a series of force and deformation/strain measurements, the material properties are obtained by minimizing the weighted differences between the measured and computed values. Several examples with truss, plain strain and v

Juan Fernández-Sánchez, Wolfgang Trutschnig

Working with shuffles we establish a close link between Kendall's tau, the so-called length measure, and the surface area of bivariate copulas and derive some consequences. While it is well-known that Spearman's rho of a bivariate copula A is a rescaled version of the volume of the area under the graph of A, in this contribution we show that the other famous

CRESST Collaboration, G. Angloher, S. Banik, G. Benato

New-generation direct searches for low mass dark matter feature detection thresholds at energies well below 100 eV, much lower than the energies of commonly used X-ray calibration sources. This requires new calibration sources with sub-keV energies. When searching for nuclear recoil signals, the calibration source should ideally cause mono-energetic nuclear

Alexandre Voute, Alexander Dörfler, Laurent Wiesenfeld, Olivier Dulieu

Rate constants for the charge transfer reaction between N${}_{2}$H${}^{+}$ and Rb in the mK regime are measured in an ion-atom hybrid trap and are found to be lower than the Langevin capture limit. Multireference ab initio computation of the potential energy surfaces involved in the reaction reveals that the low-temperature charge transfer is hindered by sho

Qihui Yang, Joan Saldaña, Caterina Scoglio

The Markovian approach, which assumes exponentially distributed interinfection times, is dominant in epidemic modeling. However, this assumption is unrealistic as an individual's infectiousness depends on its viral load and varies over time. In this paper, we present a Susceptible-Infected-Recovered-Vaccinated-Susceptible epidemic model incorporating non-Mar

Zhao An, Pedro Soubelet, Yaroslav Zhumagulov, Michael Zopf

The electron-hole exchange interaction is a fundamental mechanism that drives valley depolarization via intervalley exciton hopping in semiconductor multi-valley systems. Here, we report polarization-resolved photoluminescence spectroscopy of neutral excitons and negatively charged trions in monolayer MoSe$_2$ and WSe$_2$ under biaxial strain. We observe a m

Rupam Samanta, Somadutta Bhatta, Jiangyong Jia, Matthew Luzum

A collision between two atomic nuclei accelerated at a speed close to that of light creates a dense system of quarks and gluons. Interactions among them are so strong that they behave collectively like a droplet of fluid of ten-femtometer size, which expands into the vacuum and eventually fragments into thousands of particles. We report a new manifestation o

Man Liu, Feng Li, Chunjie Zhang, Yunchao Wei

Generalized Zero-Shot Learning (GZSL) identifies unseen categories by knowledge transferred from the seen domain, relying on the intrinsic interactions between visual and semantic information. Prior works mainly localize regions corresponding to the sharing attributes. When various visual appearances correspond to the same attribute, the sharing attributes i

Steven Dahdah, James Richard Forbes

This paper proposes a method to identify a Koopman model of a feedback-controlled system given a known controller. The Koopman operator allows a nonlinear system to be rewritten as an infinite-dimensional linear system by viewing it in terms of an infinite set of lifting functions. A finite-dimensional approximation of the Koopman operator can be identified

Nicholas Monath, Manzil Zaheer, Kelsey Allen, Andrew McCallum

Dual encoder models are ubiquitous in modern classification and retrieval. Crucial for training such dual encoders is an accurate estimation of gradients from the partition function of the softmax over the large output space; this requires finding negative targets that contribute most significantly ("hard negatives"). Since dual encoder model parameters chan

Ryan Marcus

Traditional query optimizers are designed to be fast and stateless: each query is quickly optimized using approximate statistics, sent off to the execution engine, and promptly forgotten. Recent work on learned query optimization have shown that it is possible for a query optimizer to "learn from its mistakes," correcting erroneous query plans the next time

Thomas Dufils, Lisanne Knijff, Yunqi Shao, Chao Zhang

Electrochemical energy storage always involves the capacitive process. The prevailing electrode model used in the molecular simulation of polarizable electrode-electrolyte systems is the Siepmann-Sprik model developed for perfect metal electrodes. This model has been recently extended to study the metallicity in the electrode by including the Thomas-Fermi sc

Antonín Klíč, Václav Janiš, Filip Kadlec

We present an improved model for studying the interactions between dipole moments of water molecules confined in beryl crystals, inspired by recent NMR experiments. Our model is based on a local crystal potential with dihexagonal symmetry for the rotations of water dipole moments, leading to deflection from the $ab$ hexagonal crystallographic plane. This pot

Chao Zhang

We use a newly developed quantum Monte Carlo method to investigate the impact of finite dispersion of the optical phonon mode on the properties of the bond Su-Schrieffer-Heeger polaron in two dimensions. We compare the properties of the bond polaron, such as effective mass, ground state energy, and Z factor, with and without positive phonon bandwidth. Our fi

R. Peres

The DARWIN project aims to build and operate a next-generation observatory for dark matter and neutrino physics, featuring a time projection chamber with a proposed active target of 40 t of liquid xenon. As an R&D facility to test fundamental components of the future detector, Xenoscope, a full-scale vertical demonstrator with ~400 kg of liquid xenon and up

Djénabou Bayo, Andreas Honecker, Rudolf A. Römer

We study the two-dimensional site-percolation model on a square lattice. In this paradigmatic model, sites are randomly occupied with probability $p$; a second-order phase transition from a non-percolating to a fully percolating phase appears at occupation density $p_c$, called percolation threshold. Through supervised deep learning approaches like classific

Álvaro Álvarez-Domínguez, Luis J. Garay, Mercedes Martín-Benito, Rita B. Neves

States of low energy in cosmology minimise the energy density when smeared in a chosen time interval. We extend such construction to generic homogeneous (possibly anisotropic) particle creation settings. Focusing on the Schwinger effect, we study the role played by the support of the smearing function and identify the vacua obtained in the limiting cases of

Yuhyun Ji, Sang Mok Park, Semin Kwon, Jung Woo Leem

Hyperspectral imaging acquires data in both the spatial and frequency domains to offer abundant physical or biological information. However, conventional hyperspectral imaging has intrinsic limitations of bulky instruments, slow data acquisition rate, and spatiospectral tradeoff. Here we introduce hyperspectral learning for snapshot hyperspectral imaging in

Florentin Bieder, Julia Wolleb, Alicia Durrer, Robin Sandkühler

Denoising diffusion models have recently achieved state-of-the-art performance in many image-generation tasks. They do, however, require a large amount of computational resources. This limits their application to medical tasks, where we often deal with large 3D volumes, like high-resolution three-dimensional data. In this work, we present a number of differe

Athanassios Tzouvaras

This paper is a sequel to \cite{Tz10}, where a local version of ZFC, LZFC, was introduced and examined and transitive models of ZFC with properties that resemble large cardinal properties, namely Mahlo and $\Pi_1^1$-indescribable models, were considered. By analogy we refer to such models as "large models", and the properties in question as "large model prop

Yurong You, Cheng Perng Phoo, Katie Z Luo, Travis Zhang

For a self-driving car to operate reliably, its perceptual system must generalize to the end-user's environment -- ideally without additional annotation efforts. One potential solution is to leverage unlabeled data (e.g., unlabeled LiDAR point clouds) collected from the end-users' environments (i.e. target domain) to adapt the system to the difference betwee

Yong Cheng

In this work, we aim at understanding incompleteness in an abstract way via metamathematical properties of formal theories. We systematically examine the relationships between the following twelve important metamathematical properties of arithmetical theories: Rosser, EI (Effectively inseparable), RI (Recursively inseparable), TP (Turing persistent), EHU (es

Wenhao Qiu, Sichao Fu, Jingyi Zhang, Chengxiang Lei

Few-shot class-incremental learning (FSCIL) has recently attracted extensive attention in various areas. Existing FSCIL methods highly depend on the robustness of the feature backbone pre-trained on base classes. In recent years, different Transformer variants have obtained significant processes in the feature representation learning of massive fields. Never

Francisco C. Alcaraz, Rodrigo A. Pimenta, Jesko Sirker

A new family of free fermionic quantum spin chains with multispin interactions was recently introduced. Here we show that it is possible to build standard quantum Ising chains -- but with inhomogeneous couplings -- which have the same spectra as the novel spin chains with multispin interactions. The Ising models are obtained by associating an antisymmetric t

Mark C. H. de Jong, Bennert H. M. Smit, Mariëlle J. Meijer, Juriaan Lucassen

In this paper, we show that magnetic skyrmion nucleation can be controlled using Ga+ ion irradiation, which manipulates the magnetic interface effects (in particular the magnetic anisotropy and Dzyaloshinskii-Moriya interaction) that govern the stability and energy cost of skyrmions in thin film systems. We systematically and quantitatively investigated what

Yiling Zhang

We study distributionally robust chance-constrained programs (DRCCPs) with individual chance constraints under a Wasserstein ambiguity. The DRCCPs treat the risk tolerances associated with the distributionally robust chance constraints (DRCCs) as decision variables to trade off between the system cost and risk of violations by penalizing the risk tolerances

Daniel Rodriguez Duque, Erica E. M. Moodie, David A. Stephens

In this work, we examine recently developed methods for Bayesian inference of optimal dynamic treatment regimes (DTRs). DTRs are a set of treatment decision rules aimed at tailoring patient care to patient-specific characteristics, thereby falling within the realm of precision medicine. In this field, researchers seek to tailor therapy with the intention of

Erick Carvajal Barboza, Mahesh Ketkar, Michael Kishinevsky, Paul Gratz

The validation process for microprocessors is a very complex task that consumes substantial engineering time during the design process. Bugs that degrade overall system performance, without affecting its functional correctness, are particularly difficult to debug given the lack of a golden reference for bug-free performance. This work introduces two automate

Jurriaan Rot, Thorsten Wißmann

We provide a categorical notion called uncertain bisimilarity, which allows to reason about bisimilarity in combination with a lack of knowledge about the involved systems. Such uncertainty arises naturally in automata learning algorithms, where one investigates whether two observed behaviours come from the same internal state of a black-box system that can

Javier Carrón Duque, Domenico Marinucci

This survey is devoted to recent developments in the statistical analysis of spherical data, with a view to applications in Cosmology. We will start from a brief discussion of Cosmological questions and motivations, arguing that most Cosmological observables are spherical random fields. Then, we will introduce some mathematical background on spherical random

Dmitry A. Pasechnyuk, Alexander Gornov

This paper proposes novel algorithm for non-convex multimodal constrained optimisation problems. It is based on sequential solving restrictions of problem to sections of feasible set by random subspaces (in general, manifolds) of low dimensionality. This approach varies in a way to draw subspaces, dimensionality of subspaces, and method to solve restricted p

Sabine Frittella, Daniil Kozhemiachenko, Bart Verheij

We explore presumptive reasoning in the paraconsistent case. Specifically, we provide semantics for non-trivial reasoning with presumptive arguments with contradictory assumptions or conclusions. We adapt the case models proposed by Verheij and define the paraconsistent analogues of the three types of validity defined therein: coherent, presumptively valid,

Christian Jung, Claudia Redenbach

Tessellations are an important tool to model the microstructure of cellular and polycrystalline materials. Classical tessellation models include the Voronoi diagram and Laguerre tessellation whose cells are polyhedra. Due to the convexity of their cells, those models may be too restrictive to describe data that includes possibly anisotropic grains with curve

Sounak Mondal, Zhibo Yang, Seoyoung Ahn, Dimitris Samaras

Predicting human gaze is important in Human-Computer Interaction (HCI). However, to practically serve HCI applications, gaze prediction models must be scalable, fast, and accurate in their spatial and temporal gaze predictions. Recent scanpath prediction models focus on goal-directed attention (search). Such models are limited in their application due to a c

Benedikt Andritsch, Lars Watermann, Stefan Koch, Markus Reichhartinger

In this paper a novel discrete-time realization of the super-twisting controller is proposed. The closed-loop system is proven to converge to an invariant set around the origin in finite time. Furthermore, the steady-state error is shown to be independent of the controller gains. It only depends on the sampling time and the unknown disturbance. The proposed

Arti Sahu Gangopadhyay, Ranadip Gangopadhyay, Hemangi Madhusudan Shah, Bankteshwar Tiwari

It is known that a simply connected Riemann surface satisfies the isoperimetric equality if and only if it has constant Gaussian curvature. In this article, we show that Randers Poincar\'e disc satisfies the isoperimetric equality with respect to different volume forms. These metrics do not necessarily have constant (negative) flag curvature. Thus we show th

Andrew Miller

The aim of this paper is twofold. First, we prove $L^p$ estimates for a regularized Green's function in three dimensions. We then establish new estimates for the discrete Green's function and obtain some positivity results. In particular, we prove that the discrete Green's functions with singularity in the interior of the domain cannot be bounded uniformly w

Ryo Hachiuma, Fumiaki Sato, Taiki Sekii

This paper simultaneously addresses three limitations associated with conventional skeleton-based action recognition; skeleton detection and tracking errors, poor variety of the targeted actions, as well as person-wise and frame-wise action recognition. A point cloud deep-learning paradigm is introduced to the action recognition, and a unified framework alon

Vittorio Pippi, Silvia Cascianelli, Rita Cucchiara

Generating synthetic images of handwritten text in a writer-specific style is a challenging task, especially in the case of unseen styles and new words, and even more when these latter contain characters that are rarely encountered during training. While emulating a writer's style has been recently addressed by generative models, the generalization towards r

Peter Kubaščík, Andrej Farkaš, Kamil Olejník, Tinkara Troha

Antiferromagnetic CuMnAs thin films have attracted attention since the discovery of the manipulation of their magnetic structure via electrical, optical, and terahertz pulses of electric fields, enabling convenient approaches to the switching between magnetoresistive states of the film for the information storage. However, the magnetic structure and, thus, t

Junchao Wang, Zhiqi Huang, Lu Huang

Much of the research in supernova cosmology is based on an assumption that the peak luminosity of type Ia supernovae (SNe Ia), after a standardization process, is independent of the galactic environment. A series of recent studies suggested that there is a significant correlation between the standardized luminosity and the progenitor age of SNe Ia. The corre

Rixin Zhou, Jiafu Wei, Qian Zhang, Ruihua Qi

The archaeological dating of bronze dings has played a critical role in the study of ancient Chinese history. Current archaeology depends on trained experts to carry out bronze dating, which is time-consuming and labor-intensive. For such dating, in this study, we propose a learning-based approach to integrate advanced deep learning techniques and archaeolog

Alex Jones, Isaac Caswell, Ishank Saxena, Orhan Firat

Neural machine translation (NMT) has progressed rapidly over the past several years, and modern models are able to achieve relatively high quality using only monolingual text data, an approach dubbed Unsupervised Machine Translation (UNMT). However, these models still struggle in a variety of ways, including aspects of translation that for a human are the ea

Athanassios Tzouvaras

We examine what happens if we replace ZFC with a localistic/relativistic system, LZFC, whose central new axiom, denoted by $Loc({\rm ZFC})$, says that every set belongs to a transitive model of ZFC. LZFC consists of $Loc({\rm ZFC})$ plus some elementary axioms forming Basic Set Theory (BST). Some theoretical reasons for this shift of view are given. All $\Pi

Nathanael L. Baisa

In this paper, we propose a multi-task representation learning framework to jointly estimate the identity, gender and age of individuals from their hand images for the purpose of criminal investigations since the hand images are often the only available information in cases of serious crime such as sexual abuse. We investigate different up-to-date deep learn

Yuxue Cao, Wenbo Zhao, Shengjie Wang, Xiang Zheng

Symmetric bi-manual manipulation is an essential skill in on-orbit operations due to its potent load capacity. Previous works have applied compliant control to maintain the stability of manipulations. However, traditional methods have viewed motion planning and compliant control as two separate modules, which can lead to conflicts with the simultaneous chang

Sujiphat Janaun, Pichet Vanichchapongjaroen

We derive sufficient conditions for theories consisting of multiple vector fields, which could also couple to external fields, to be multi-field generalised Proca theories. The conditions are derived by demanding that the theories have the required structure of constraints, giving the correct number of degrees of freedom. The Faddeev-Jackiw constraint analys

Danny Weyns, Jesper Andersson

Engineering long-running computing systems that achieve their goals under ever-changing conditions pose significant challenges. Self-adaptation has shown to be a viable approach to dealing with changing conditions. Yet, the capabilities of a self-adaptive system are constrained by its operational design domain (ODD), i.e., the conditions for which the system

Alice Barbara Tumpach, Peter Kán

Temporal alignment is an inherent task in most applications dealing with videos: action recognition, motion transfer, virtual trainers, rehabilitation, etc. In this paper we dive into the understanding of this task from a geometric point of view: in particular, we show that the basic properties that are expected from a temporal alignment procedure imply that