August 2022 arXiv papers — page 63

Willie Aboumrad

We develop an operator commutant version of the First Fundamental Theorem of invariant theory for the general linear quantum group $U_q(\mathfrak{gl}_n)$ by using a double centralizer property inside a quantized Clifford algebra. In particular, we show that $U_q(\mathfrak{gl}_m)$ generates the centralizer of the $U_q(\mathfrak{gl}_n)$-action on the tensor pr

Andreas Dedner, Alice Hodson

In this paper we present a framework for the construction and implementation of general virtual element spaces based on projections built from constrained least squares problems. Building on the triples used for finite element spaces, we introduce the concept of a VEM tuple which encodes the necessary building blocks to construct these projections. Using thi

Morihiko Saito

Let $f$ be a convergent power series of $n$ variables having an isolated singularity at 0. For a rational number $\alpha$, setting $(X,0)=({\mathbb C}^n,0)$, we show that the length of the ${\mathcal D}_X$-module ${\mathcal D}_Xf^{-\alpha}$ is given by $\widetilde{\nu}_{\alpha}+r_f\widetilde{\delta}_{\alpha}+1$. Here $r_f$ is the number of local irreducible

Qingjun Jin, Ke Ren, Gang Yang, Rui Yu

Evanescent operators are a special class of operators that vanish in four-dimensional spacetime but are non-zero in $d=4-2\epsilon$ dimensions. In this paper, we continue our systematic study of the evanescent operators in the pure Yang-Mills theory and focus on their two-loop renormalization. We develop an efficient strategy to compute the two-loop divergen

Junjie Dong, Rebecca A. Fischer, Lars Stixrude, Matthew C. Brennan

At the interface of Earth's upper and lower mantle, the post-spinel transition boundary controls the dynamics and morphologies of downwelling slabs and upwelling plumes, and its Clapeyron slope is hence one of the most important constraints on mantle convection. In this study, we reported a new in situ experimental dataset on phase stability in Mg$_{2}$SiO$_

Evan Miller

In this paper, we will introduce the inviscid vortex stretching equation, which is a model equation for the 3D Euler equation where the advection of vorticity is neglected. We will show that there are smooth solutions of this equation which blowup in finite-time, even when restricting to axisymmetric, swirl-free solutions. This provides further evidence of t

G. A. P. Ribeiro, A. Klümper, P. A. Pearce

In this paper we investigate certain fusion relations associated to an integrable vertex model on the square lattice which is invariant under $Sp(4)$ symmetry. We establish a set of functional relations which include a transfer matrix inversion identity. The solution of these relations in the thermodynamic limit allows us to compute the partition function pe

Zhong Zhuang, Taihui Li, Hengkang Wang, Ju Sun

Blind image deblurring (BID) has been extensively studied in computer vision and adjacent fields. Modern methods for BID can be grouped into two categories: single-instance methods that deal with individual instances using statistical inference and numerical optimization, and data-driven methods that train deep-learning models to deblur future instances dire

Hao-Yan Chen, Wei-Min Gu, Jin-Bo Fu, Shan-Shan Weng

Ultraluminous X-ray bursts (hereafter ULXBs) are ultraluminous X-ray flares with a fast rise ($\sim$ one minute) and a slow decay ($\sim$ an hour), which are commonly observed in extragalactic globular clusters. Most ULXBs are observational one-off bursts, whereas five flares from the same source in NGC 5128 were discovered by Irwin et al. (2016). In this Le

Gabriel Coutinho, Pedro Ferreira Baptista, Chris Godsil, Thomás Jung Spier

The adjacency matrix of a graph G is the Hamiltonian for a continuous-time quantum walk on the vertices of G. Although the entries of the adjacency matrix are integers, its eigenvalues are generally irrational and, because of this, the behaviour of the walk is typically not periodic. In consequence we can usually only compute numerical approximations to para

Jiantao Wu, Fabrizio Orlandi, Tarek AlSkaif, Declan O'Sullivan

In a decentralized household energy system comprised of various devices such as home appliances, electric vehicles, and solar panels, end-users are able to dig deeper into the system's details and further achieve energy sustainability if they are presented with data on the electric energy consumption and production at the granularity of the device. However,

Paweł Foralewski, Henryk Hudzik, Paweł Kolwicz

In this paper we introduce the notion of a quasi-modular and we prove that the respective Minkowski functional of the unit quasi-modular ball becomes a quasi-norm. In this way, we refer to and complete the well-known theory related to the notions of a modular and a convex modular that lead to the F-norm and to the norm, respectively. We use the obtained resu

Mengyuan Wang, Mehran Mesbahi

We examine two configurations for parallel hybrid electric aircraft, one with, and one without, a mechanical connection between the engines and the electric motors. For this two designs, we then review the power allocation problem in the context of aircraft energy management for a 19-seat conceptual Hybrid Electric Aircraft. We then represent the original op

Emmanuel Deruty, Maarten Grachten

Although the use of AI tools in music composition and production is steadily increasing, as witnessed by the newly founded AI song contest, analysis of music produced using these tools is still relatively uncommon as a mean to gain insight in the ways AI tools impact music production. In this paper we present a case study of "Melatonin", a song produced by e

Daniele Agostini, Claudia Fevola, Anna-Laura Sattelberger, Simon Telen

We study vector spaces associated to a family of generalized Euler integrals. Their dimension is given by the Euler characteristic of a very affine variety. Motivated by Feynman integrals from particle physics, this has been investigated using tools from homological algebra and the theory of $D$-modules. We present an overview and uncover new relations betwe

Alan Inglis, Andrew Parnell, Catherine Hurley

Tree-based regression and classification has become a standard tool in modern data science. Bayesian Additive Regression Trees (BART) has in particular gained wide popularity due its flexibility in dealing with interactions and non-linear effects. BART is a Bayesian tree-based machine learning method that can be applied to both regression and classification

Zhi-Qi Cheng, Qi Dai, Siyao Li, Teruko Mitamura

Grounded Situation Recognition (GSR) aims to generate structured semantic summaries of images for "human-like" event understanding. Specifically, GSR task not only detects the salient activity verb (e.g. buying), but also predicts all corresponding semantic roles (e.g. agent and goods). Inspired by object detection and image captioning tasks, existing method

Guang Hao Low

We consider classical shadows of fermion wavefunctions with $\eta$ particles occupying $n$ modes. We prove that all $k$-Reduced Density Matrices (RDMs) may be simultaneously estimated to an average variance of $\epsilon^{2}$ using at most $\binom{\eta}{k}\big(1-\frac{\eta-k}{n}\big)^{k}\frac{1+n}{1+n-k}/\epsilon^{2}$ measurements in random single-particle ba

Yi-Yu Lin, Jie-Chen Jin

We construct a new toy model of the holographic principle, named as holographic qubit threads model, which is an enlightening step towards the issue of spacetime emergence ("it from qubit"). More specifically, we propose for the first time that each bit thread in a locking bit thread configuration is in a "qubit" state, i.e., the quantum superposition state

Richard Li, Carlos Esteves, Ameesh Makadia, Pulkit Agrawal

We present a system for accurately predicting stable orientations for diverse rigid objects. We propose to overcome the critical issue of modelling multimodality in the space of rotations by using a conditional generative model to accurately classify contact surfaces. Our system is capable of operating from noisy and partially-observed pointcloud observation

Neil G. MacLaren, Prosenjit Kundu, Naoki Masuda

Successfully anticipating sudden major changes in complex systems is a practical concern. Such complex systems often form a heterogeneous network, which may show multistage transitions in which some nodes experience a regime shift earlier than others as an environment gradually changes. Here we investigate early warning signals for networked systems undergoi

Annika Bidner, Julia Lindberg, Olof Lindman, Kinga Skorupska

At Public Service Broadcaster SVT in Sweden, background music and sounds in programs have for many years been one of the most common complaints from the viewers. The most sensitive group are people with hearing disabilities, but many others also find background sounds annoying. To address this problem SVT has added Enhanced Speech, a feature with lower backg

T. Banks

We review and clarify ideas proposed many years ago for understanding cosmology in a holographic framework. The basic strategy is to use Jacobson's\cite{ted95} identification of Einstein's equations with the hydrodynamic equations of the "Area = 4 Entropy" law for causal diamonds, to identify a quantum system whose hydrodynamics match those of a given space-

M. Surendran, B. Zhao, G. Ren, S. Singh

Perovskite chalcogenides have emerged as a new class of semiconductors with tunable band gap in the visible-infrared region. High quality thin films are critical to understand the fundamental properties and realize the potential applications based on these materials. We report growth of quasi-epitaxial thin films of quasi one-dimensional (quasi-1D) hexagonal

Xinyi Wang, Chaowei Jiang, Xueshang Feng, Aiying Duan

The relationship between solar eruption and sunspot rotation has been widely reported, and the underlying mechanism requires to be studied. Here we performed a full 3D MHD simulation of data-constrained approach to study the mechanism of flare eruptions in active region (AR) NOAA 10930, which is characterized by continuous sunspot rotation and homologous eru

Moumita Bhowmik, Manmeet Singh, Suryachandra Rao, Souvik Paul

Simulation of turbulent flows, especially at the edges of clouds in the atmosphere, is an inherently challenging task. Hitherto, the best possible computational method to perform such experiments is the Direct Numerical Simulation (DNS). DNS involves solving non-linear partial differential equations for fluid flows, also known as Navier-Stokes equations, on

Charles Elbar, Jakub Skrzeczkowski

There has been recently an important interest in deriving rigorously the Cahn-Hilliard equation from the nonlocal equation, also called aggregation equation. So far, only non-degenerate mobilities were treated. Since we are motivated by models for the biomechanics of living tissues, it is useful to include degenerate motilities. In this framework, we present

Qinghua Zhao, Shuai Ma, Yuxuan Lei

External knowledge,e.g., entities and entity descriptions, can help humans understand texts. Many works have been explored to include external knowledge in the pre-trained models. These methods, generally, design pre-training tasks and implicitly introduce knowledge by updating model weights, alternatively, use it straightforwardly together with the original

Niladri Modak, S Ashutosh, Shyamal Guchhait, Sayantan Das

Under the introduction of any interface in its trajectory, an optical beam experiences polarization-dependent deflections in the longitudinal and transverse directions with respect to the plane of incidence. The physics of such optical beam shifts is connected to profound universal wave phenomena governed by the fine interference effects of wave packets and

Spyridon Chapaloglou, Erick Alves, Vincenzo Trovato, Elisabetta Tedeschi

The decarbonization of many heavy power-consuming industries is dependent on the integration of renewable energy sources and energy storage systems in isolated autonomous power systems. The optimal energy management in such schemes becomes harder due to the increased complexity and stability requirements, the rapidly varying operating conditions and uncertai

Fangquan Lin, Wei Jiang, Hanwei Zhang, Cheng Yang

KDD CUP 2022 proposes a time-series forecasting task on spatial dynamic wind power dataset, in which the participants are required to predict the future generation given the historical context factors. The evaluation metrics contain RMSE and MAE. This paper describes the solution of Team 88VIP, which mainly comprises two types of models: a gradient boosting

Dan Abramovich, Bernd Schober

We show how the notion of fantastacks can be used to effectively desingularize binomial varieties defined over algebraically closed fields. In contrast to a desingularization via blow-ups in smooth centers, we drastically reduce the number of steps and the number of charts appearing along the process. Furthermore, we discuss how our considerations extend to

H. Olivares-Pilón, A. M. Escobar-Ruiz, F. Montoya

In this study, the quantum 3-body harmonic system with finite rest length $R$ and zero total angular momentum $L=0$ is explored. It governs the near-equilibrium $S$-states eigenfunctions $\psi(r_{12},r_{13},r_{23})$ of three identical point particles interacting by means of any pairwise confining potential $V(r_{12},r_{13},r_{23})$ that entirely depends on t

Indrajit Sen

Non-normalizable states are difficult to interpret in the orthodox quantum formalism but often occur as solutions to physical constraints in quantum gravity. We argue that pilot-wave theory gives a straightforward physical interpretation of non-normalizable quantum states, as the theory requires only a normalized density of configurations to generate statist

Qian Zhao, Emmanuel J. Candes

Accurate statistical inference in logistic regression models remains a critical challenge when the ratio between the number of parameters and sample size is not negligible. This is because approximations based on either classical asymptotic theory or bootstrap calculations are grossly off the mark. This paper introduces a resized bootstrap method to infer mo

Peter Fettke, Alexander Rombach

AI Planning, Machine Learning and Process Mining have so far developed into separate research fields. At the same time, many interesting concepts and insights have been gained at the intersection of these areas in recent years. For example, the behavior of future processes is now comprehensively predicted with the aid of Machine Learning. For the practical a

Sean MacAvaney, Nicola Tonellotto, Craig Macdonald

Search systems often employ a re-ranking pipeline, wherein documents (or passages) from an initial pool of candidates are assigned new ranking scores. The process enables the use of highly-effective but expensive scoring functions that are not suitable for use directly in structures like inverted indices or approximate nearest neighbour indices. However, re-

Soumya Vasisht, Shushman Choudhury, Nawaf Nazir, Stephen Zoepf

We apply Bayesian Linear Regression to estimate the response rate of drivers to variable message signs at Seattle-Tacoma International Airport, or SeaTac. Our approach uses vehicle speed and flow data measured at the entrances of the arrival and departure-ways of the airport terminal, and sign message data. Depending on the time of day, we estimate that betw

Konstantinos Papatryfonos, Louis Vervoort, André Nachbin, Matthieu Labousse

Since its discovery in 2005, the hydrodynamic pilot-wave system has provided a concrete macroscopic realization of wave-particle duality and concomitant classical analogs of a growing list of quantum effects. The question naturally arises as to whether this system might support statistical states that violate Bell's inequality, and so yield a classical analo

Carol Marie Webster, Panagiotis Pantidos, DeNapoli Clarke, Jiannis K. Pachos

Break-in' Point, a 2012 arts and science performance and community engagement research initiative, was presented in the spring and fall semesters at the University of Leeds in the United Kingdom at Stage@Leeds. The outcome of a collaboration between dance artist A3 and theoretical physicist A2, under the direction of performance researcher A1, Break-in' Poin

Jennifer Johnson-Leung, Brooks Roberts, Ralf Schmidt

We introduce the family of stable Klingen congruence subgroups of GSp(4). We use these subgroups to study both local paramodular vectors and Siegel modular forms of degree $2$ with paramodular level. In the first part, when $F$ is a nonarchimedean local field of characteristic zero and $(\pi,V)$ is an irreducible, admissible representation of GSp(4,F) with t

Imon Banerjee, Jean Honorio

We study the meta-learning for support (i.e. the set of non-zero entries) recovery in high-dimensional Principal Component Analysis. We reduce the sufficient sample complexity in a novel task with the information that is learned from auxiliary tasks. We assume each task to be a different random Principal Component (PC) matrix with a possibly different suppor

Yasmine Amhis, Marzia Bordone, Méril Reboud

We perform an analysis of $\Lambda_b\to\Lambda(1520)$ local form factors. We use dispersive techniques to provide a model-independent parametrisation of the form factors that can be used in the whole kinematic region. We use lattice QCD data to constrain the free parameters in the form factors expansion, which is further constrained by endpoint relations, di

J. P. Wittmer, A. N. Semenov, J. Baschnagel

Focusing on non-ergodic macroscopic systems we reconsider the variances of time averages time-series. The total variance (direct average over all time-series) is known to be the sum of an internal variance (fluctuations within the meta-basins) and an external variance (fluctuations between meta-basins). It is shown that whenever the time-averaged observable

Ekaterina Krymova, Guillaume Obozinski, Michael Schenk, Loic Coyle

In the Large Hadron Collider, the beam losses are continuously measured for machine protection. By design, most of the particle losses occur in the collimation system, where the particles with high oscillation amplitudes or large momentum error are scraped from the beams. The level of particle losses typically is optimized manually by changing multiple contr

Yuanqin He, Yan Kang, Xinyuan Zhao, Jiahuan Luo

Vertical federated learning (VFL), a variant of Federated Learning (FL), has recently drawn increasing attention as the VFL matches the enterprises' demands of leveraging more valuable features to achieve better model performance. However, conventional VFL methods may run into data deficiency as they exploit only aligned and labeled samples (belonging to dif

Soumen Pachal, Avinash Achar

Missing data scenarios are very common in ML applications in general and time-series/sequence applications are no exceptions. This paper pertains to a novel Recurrent Neural Network (RNN) based solution for sequence prediction under missing data. Our method is distinct from all existing approaches. It tries to encode the missingness patterns in the data dire

Janis Postels, Martin Danelljan, Luc Van Gool, Federico Tombari

Normalizing Flows (NFs) are flexible explicit generative models that have been shown to accurately model complex real-world data distributions. However, their invertibility constraint imposes limitations on data distributions that reside on lower dimensional manifolds embedded in higher dimensional space. Practically, this shortcoming is often bypassed by ad

Andras Suto

In this third paper of a series that started with arXiv:2106.10032 [math-ph] and continued with arXiv:2108.02659 [math-ph] we show that in $d\geq 3$ dimensions at low temperatures or high densities bosons interacting via pair potentials that are both positive and positive type form permutation cycles whose length diverges proportionally with the number of pa

P. Kosec, E. Kara, A. C. Fabian, F. Furst

Hercules X-1 is a nearly edge-on accreting X-ray pulsar with a warped accretion disk, precessing with a period of about 35 days. The disk precession allows for unique and changing sightlines towards the X-ray source. To investigate the accretion flow at a variety of sightlines, we obtained a large observational campaign on Her X-1 with XMM-Newton (380 ks exp

Hongyu Zhou, Vasileios Tzoumas

We present safe control of partially-observed linear time-varying systems in the presence of unknown and unpredictable process and measurement noise. We introduce a control algorithm that minimizes dynamic regret, i.e., that minimizes the suboptimality against an optimal clairvoyant controller that knows the unpredictable future a priori. Specifically, our a

Yavdat Il'yasov, Edcarlos D. Silva, Maxwell L. Silva

A minimax variational principle for saddle-point solutions with prescribed energy levels is introduced. The approach is based on the development of the linking theorem to the energy level nonlinear generalized Rayleigh quotients. An application to indefinite elliptic Dirichlet problems is presented. Among the consequences, the existence of solutions with zer

Sambatra Andrianomena, Sultan Hassan

We investigate how the constraints on cosmological and astrophysical parameters ($\Omega_{\rm m}$, $\sigma_{8}$, $A_{\rm SN1}$, $A_{\rm SN2}$) vary when exploiting information from multiple fields in cosmology. We make use of a convolutional neural network to retrieve the salient features from different combinations of field maps from IllustrisTNG in the CAM

Frank Röttger, Thomas Kahle, Rainer Schwabe

In discrete choice experiments, the information matrix depends on the model parameters. Therefore designing optimally informative experiments for arbitrary initial parameters often yields highly nonlinear optimization problems and makes optimal design infeasible. To overcome such challenges, we connect design theory for discrete choice experiments with Lapla

Vladimir Vovk, Ruodu Wang

The notion of an e-value has been recently proposed as a possible alternative to critical regions and p-values in statistical hypothesis testing. In this paper we consider testing the nonparametric hypothesis of symmetry, introduce analogues for e-values of three popular nonparametric tests, define an analogue for e-values of Pitman's asymptotic relative eff

Nathan Kirk, Florian Pausinger

For $m, d \in \mathbb{N}$, a jittered sample of $N=m^d$ points can be constructed by partitioning $[0,1]^d$ into $m^d$ axis-aligned equivolume boxes and placing one point independently and uniformly at random inside each box. We utilise a formula for the expected $\mathcal{L}_2-$discrepancy of stratified samples stemming from general equivolume partitions of

Tianchen Hu, Bo Su, Liyu Shi, Zixiao Wang

The topological Weyl semimetals with peculiar band structure exhibit novel nonlinear optical enhancement phenomena even for light at optical wavelengths. While many intriguing nonlinear optical effects were constantly uncovered in type-I semimetals, few experimental works focused on basic nonlinear optical properties in type-II Weyl semimetals. Here we perfo

Shirshendu Ganguly, Milind Hegde

We develop a new probabilistic and geometric method to obtain several sharp results pertaining to the upper tail behavior of continuum Gibbs measures on infinite ensembles of random continuous curves, also known as line ensembles, satisfying some natural assumptions. The arguments make crucial use of Brownian resampling invariance properties and correlation

Andreas Athenodorou, Claudio Bonanno, Claudio Bonati, Giuseppe Clemente

We compute the topological susceptibility of $N_f=2+1$ QCD with physical quark masses in the high-temperature phase, using numerical simulations of the theory discretized on a space-time lattice. More precisely we estimate the topological susceptibility for five temperatures in the range from $\sim200$ MeV up to $\sim600$ MeV, adopting the spectral projector

Giorgos Prionistis, Costas Vournas

The increasing penetration of Distributed Energy Resources (DER) in the distribution network creates new challenges in the operation of both the transmission and the distribution network. However, the controllability of the converter interfaced devices (CIG), also unveils opportunities for flexible operation and provision of ancillary services with or withou

Ben Chugg, Nicolas Rothbacher, Alex Feng, Xiaoqi Long

This paper introduces a new, highly consequential setting for the use of computer vision for environmental sustainability. Concentrated Animal Feeding Operations (CAFOs) (aka intensive livestock farms or "factory farms") produce significant manure and pollution. Dumping manure in the winter months poses significant environmental risks and violates environmen

Biruk Asmare Muse, Kawser Wazed Nafi, Foutse Khomh, Giuliano Antoniol

Data-intensive systems handle variable, high volume, and high-velocity data generated by human and digital devices. Like traditional software, data-intensive systems are prone to technical debts introduced to cope-up with the pressure of time and resource constraints on developers. Data-access is a critical component of data-intensive systems as it determine

Zhijie Fan, Gia-Wei Chern

We present a new quantum molecular dynamics (MD) method where the electronic structure and atomic forces are solved by a real-space dynamical mean-field theory (DMFT). Contrary to most quantum MD methods that are based on effective single-particle wave functions, the DMFT approach is able to describe correlation-induced Mott metal-insulator transitions and t

Alfio Di Mauro, Moritz Scherer, Davide Rossi, Luca Benini

Small-size unmanned aerial vehicles (UAV) have the potential to dramatically increase safety and reduce cost in applications like critical infrastructure maintenance and post-disaster search and rescue. Many scenarios require UAVs to shrink toward nano and pico-size form factors. The key open challenge to achieve true autonomy on Nano-UAVs is to run complex

Lena Ji, Mattie Ji

We study real double covers of $\mathbb P^1\times\mathbb P^2$ branched over a $(2,2)$-divisor, which have the structure of a conic bundle threefold with smooth quartic discriminant curve via the second projection. In each isotopy class of smooth plane quartics, we construct examples where the total space of the conic bundle is rational. For five of the six i

Panagiotis Charalampopoulos, Tomasz Kociumaka, Jakub Radoszewski, Solon P. Pissis

We consider approximate circular pattern matching (CPM, in short) under the Hamming and edit distance, in which we are given a length-$n$ text $T$, a length-$m$ pattern $P$, and a threshold $k>0$, and we are to report all starting positions of fragments of $T$ (called occurrences) that are at distance at most $k$ from some cyclic rotation of $P$. In the deci

Zangwei Zheng, Xiangyu Yue, Kai Wang, Yang You

Though vision transformers (ViTs) have exhibited impressive ability for representation learning, we empirically find that they cannot generalize well to unseen domains with previous domain generalization algorithms. In this paper, we propose a novel approach DoPrompt based on prompt learning to embed the knowledge of source domains in domain prompts for targ

Ian Pratt-Hartmann

Take any word over some alphabet. If it is non-empty, go to any position and print out the letter being scanned. Now repeat the following any number of times (possibly zero): either stay at the current letter, or move one letter leftwards (if possible) or move one letter rightwards (if possible); then print out the letter being scanned. In effect, we are goi

Matteo Zambra, Dorian Cazau, Nicolas Farrugia, Alexandre Gensse

Wind speed retrieval at sea surface is of primary importance for scientific and operational applications. Besides weather models, in-situ measurements and remote sensing technologies, especially satellite sensors, provide complementary means to monitor wind speed. As sea surface winds produce sounds that propagate underwater, underwater acoustics recordings

Guoman He, Hanjun Zhang

In this paper, we study the quasi-stationary behavior of the one-dimensional diffusion process with a regular or exit boundary at 0 and an entrance boundary at $\infty$. By using the Doob's $h$-transform, we show that the conditional distribution of the process converges to its unique quasi-stationary distribution exponentially fast in the total variatio

Jaroslav Oľha, Terézia Slanináková, Martin Gendiar, Matej Antol

Despite the constant evolution of similarity searching research, it continues to face the same challenges stemming from the complexity of the data, such as the curse of dimensionality and computationally expensive distance functions. Various machine learning techniques have proven capable of replacing elaborate mathematical models with combinations of simple

Denis Defrère, Azzurra Bigioli, Colin Dandumont, Germain Garreau

Hi-5 is the L'-band (3.5-4.0 $\mu$m) high-contrast imager of Asgard, an instrument suite in preparation for the visitor focus of the VLTI. The system is optimized for high-contrast and high-sensitivity imaging within the diffraction limit of a single UT/AT telescope. It is designed as a double-Bracewell nulling instrument producing spectrally-dispersed (R=20

Yaniv Eliezer, Ulrich Ruhrmair, Nils Wisiol, Stefan Bittner

Nonlinear optics is a rapidly growing field that has found a wide range of applications. A major limitation, however, is the demand of high power, especially for high-order nonlinearities. Here, by reconfiguring a multiple-scattering system, we introduce 'structural nonlinearity' via a nonlinear mapping between the scattering potential and the output light.

Eslam Badr, Francesc Bars

We construct the first examples of what we call fake ES-irreducible components; Definition 2.8. In our way to do so, we classify the automorphism groups of smooth plane sextics that only have automorphisms of order 3 or less; Theorems 2.1, 2.4 and 2.5, Corollaries 2.9 and 2.11.

Akio Tomiya, Satoshi Terasaki

GomalizingFlow.jl: is a package to generate configurations for quantum field theory on the lattice using the flow based sampling algorithm in Julia programming language. This software serves two main purposes: to accelerate research of lattice QCD with machine learning with easy prototyping, and to provide an independent implementation to an existing public

M. L. Pereira Junior, W. F. da Cunha, W. F. Giozza, R. T. de Sousa Junior

Several 2D carbon-based materials have been computationally designed in the last years due to the success achieved by graphene. Here, we propose a new 2D all-sp$^2$ carbon allotrope, named Irida-Graphene (IG), using a bottom-up approach. IG is composed of fused rings containing 3-6-8 carbon atoms. We employed density functional theory calculations and reacti

Yike Guo, Qifeng Liu, Jie Chen, Wei Xue

This report presents a comprehensive view of our vision on the development path of the human-machine symbiotic art creation. We propose a classification of the creative system with a hierarchy of 5 classes, showing the pathway of creativity evolving from a mimic-human artist (Turing Artists) to a Machine artist in its own right. We begin with an overview of

Zongrui Li, Qian Zheng, Feishi Wang, Boxin Shi

Uncalibrated photometric stereo (UPS) is challenging due to the inherent ambiguity brought by unknown light. Existing solutions alleviate the ambiguity by either explicitly associating reflectance to light conditions or resolving light conditions in a supervised manner. This paper establishes an implicit relation between light clues and light estimation and

Hao-Wei Chen, Ting-Hsuan Liao, Hsuan-Kung Yang, Chun-Yi Lee

This paper introduces pixel-wise prediction based visual odometry (PWVO), which is a dense prediction task that evaluates the values of translation and rotation for every pixel in its input observations. PWVO employs uncertainty estimation to identify the noisy regions in the input observations, and adopts a selection mechanism to integrate pixel-wise predic

Heming Yang, Ke Yang, Erhan Zhang

Celebrity Endorsement is one of the most significant strategies in brand communication. Nowadays, more and more companies try to build a vivid characteristic for themselves. Therefore, their brand identity communications should accord with some characteristics as humans and regulations. However, the previous works mostly stop by assumptions, instead of propo

Gagandeep Singh

The cost of moving data between the memory units and the compute units is a major contributor to the execution time and energy consumption of modern workloads in computing systems. At the same time, we are witnessing an enormous amount of data being generated across multiple application domains. These trends suggest a need for a paradigm shift towards a data

EJ Janse van Rensburg

The distribution of monomers along a linear polymer grafted on a hard wall is modelled by determining the probability distribution of occupied vertices of Dyck and ballot path models of adsorbing linear polymers. For example, the probability that a Dyck path passes through the lattice site with coordinates $(\lfloor \epsilon n \rfloor,\lfloor \delta \sqrt{n}

Domagoj Majstorovic, Simon Hoffmann, Florian Pfab, Andreas Schimpe

In parallel with the advancement of Automated Driving (AD) functions, teleoperation has grown in popularity over recent years. By enabling remote operation of automated vehicles, teleoperation can be established as a reliable fallback solution for operational design domain limits and edge cases of AD functions. Over the years, a variety of different teleoper

Xi Yang, Mark Harfouche, Kevin C. Zhou, Lucas Kreiss

We present a new Multimodal Fiber Array Snapshot Technique (M-FAST), based on an array of 96 compact cameras placed behind a primary objective lens and a fiber bundle array. which is capable of large-area, high-resolution, multi-channel video acquisition. The proposed design provides two key improvements to prior cascaded imaging system approaches: a novel o

Anssi Manninen, Meghdoot Mozumder, Tanja Tarvainen, Andreas Hauptmann

Diffuse optical tomography (DOT) is a severely ill-posed nonlinear inverse problem that seeks to estimate optical parameters from boundary measurements. In the Bayesian framework, the ill-posedness is diminished by incorporating {\em a priori} information of the optical parameters via the prior distribution. In case the target is sparse or sharp-edged, the c

S. Nepal, G. Guiglion, R. S. de Jong, M. Valentini

With its origin coming from several sources (Big Bang, stars, cosmic rays) and given its strong depletion during its stellar lifetime, the lithium element is of great interest as its chemical evolution in the Milky Way is not well understood at present. To help constrain stellar and galactic chemical evolution models, numerous and precise lithium abundances

Udayan Banerjee, Bhabani Shankar Dey, Indra Narayan Kar, Subir Kumar Saha

In this paper an artificial delay based impedance controller is proposed for robotic manipulators with uncertainty in dynamics. The control law unites the time delayed estimation (TDE) framework with a second order switching controller of super twisting algorithm (STA) type via a novel generalized filtered tracking error (GFTE). While time delayed estimation

M. Ambrosch, G. Guiglion, Š. Mikolaitis, C. Chiappini

We aim to prepare the machine-learning ground for the next generation of spectroscopic surveys, such as 4MOST and WEAVE. Our goal is to show that convolutional neural networks can predict accurate stellar labels from relevant spectral features in a physically meaningful way. We built a neural network and trained it on GIRAFFE spectra with associated stellar

Alice C. Schwarze, Sara M. Ichinaga, Bingni W. Brunton

A major challenge for causal inference from time-series data is the trade-off between computational feasibility and accuracy. Motivated by process motifs for lagged covariance in an autoregressive model with slow mean-reversion, we propose to infer networks of causal relations via pairwise edge measure (PEMs) that one can easily compute from lagged correlati

Cem Ates Musluoglu, Alexander Bertrand

In this paper, we describe a general algorithmic framework for solving linear signal or feature fusion optimization problems in a distributed setting, for example in a wireless sensor network (WSN). These problems require linearly combining the observed signals (or features thereof) collected at the various sensor nodes to satisfy a pre-defined optimization

Leo Pauly, Michele Lynn Jamrozik, Miguel Ortiz Del Castillo, Olivia Borgue

The use of Deep Learning (DL) algorithms has improved the performance of vision-based space applications in recent years. However, generating large amounts of annotated data for training these DL algorithms has proven challenging. While synthetically generated images can be used, the DL models trained on synthetic data are often susceptible to performance de

Akanksha Agrawal, Henning Fernau, Philipp Kindermann, Kevin Mann

A graph $G$ is well-covered if every minimal vertex cover of $G$ is minimum, and a graph $G$ is well-dominated if every minimal dominating set of $G$ is minimum. Studies on well-covered graphs were initiated in [Plummer, JCT 1970], and well-dominated graphs were first introduced in [Finbow, Hartnell and Nowakow, AC 1988]. Well-dominated graphs are well-cover

Pham Thanh Tâm

Let $R=C[[t]]$ be the ring of power series over an algebraically closed field $C$ of characteristic zero. We show that each connection on a finite flat $R((x))$-module is the sum of a regular singular connection and a diagonalizable $R((x))$-linear endomorphism when it admits a Turrittin-Levelt-Jordan form over $R((x))$. This decomposition is compatible with

Lukas Gehrig, Daniel Steiner, Eduard Vorobyov, Manuel Güdel

The spin evolution of young protostars, surrounded by an accretion disk, still poses problems for observations and theoretical models. In recent studies, the importance of the magnetic star-disk interaction for stellar spin evolution has been elaborated. The accretion disk in these studies, however, is only represented by a simplified model and important fea

Erin E. Fleck, Berit H. Goodge, Matthew R. Barone, Hari P. Nair

The Ruddlesden-Popper ($A_{n+1}B_{n}\text{O}_{3n+1}$) compounds are a highly tunable class of materials whose functional properties can be dramatically impacted by their structural phase $n$. The negligible energetic differences associated with forming a sample with a single value of $n$ versus a mixture of $n$ makes the growth of these materials difficult t

Nicolas Sadoune, Giuliano Giudici, Ke Liu, Lode Pollet

Experimental progress in qubit manufacturing calls for the development of new theoretical tools to analyze quantum data. We show how an unsupervised machine-learning technique can be used to understand short-range entangled many-qubit systems using data of local measurements. The method successfully constructs the phase diagram of a cluster-state model and d

Catia Grimani, Mattia Villani, Michele Fabi, Andrea Cesarini

Cubic gold-platinum free-falling test masses (TMs) constitute the mirrors of future LISA and LISA-like interferometers for low-frequency gravitational wave detection in space. High-energy particles of Galactic and solar origin charge the TMs and thus induce spurious electrostatic and magnetic forces that limit the sensitivity of these interferometers. Prelau

Manli Zhu, Qianhui Men, Edmond S. L. Ho, Howard Leung

Musculoskeletal and neurological disorders are the most common causes of walking problems among older people, and they often lead to diminished quality of life. Analyzing walking motion data manually requires trained professionals and the evaluations may not always be objective. To facilitate early diagnosis, recent deep learning-based methods have shown pro

Minseok Kim, Jinoh Oh, Jaeyoung Do, Sungjin Lee

Graph neural networks (GNNs) have achieved remarkable success in recommender systems by representing users and items based on their historical interactions. However, little attention was paid to GNN's vulnerability to exposure bias: users are exposed to a limited number of items so that a system only learns a biased view of user preference to result in subop

Sebastian Neef, Nils Wisiol

The SSH protocol is commonly used to access remote systems on the Internet, as it provides an encrypted and authenticated channel for communication. If upon establishing a new connection, the presented server key is unknown to the client, the user is asked to verify the key fingerprint manually, which is prone to errors and often blindly trusted. The SSH sta