November 2020 arXiv papers — page 38

Michelle Victora, Stefan Krastanov, Alexander Sanchez de la Cerda, Steven Willis

We present an approach to purification and entanglement routing on complex quantum network architectures, that is, how a quantum network equipped with imperfect channel fidelities and limited memory storage time can distribute entanglement between users. We explore how network parameters influence the performance of path-finding algorithms necessary for opti

Paz Beniamini, Pawan Kumar, Xiangcheng Ma, Eliot Quataert

We describe three different methods for exploring the hydrogen reionization epoch using fast radio bursts (FRBs) and provide arguments for the existence of FRBs at high redshift (z). The simplest way, observationally, is to determine the maximum dispersion measure (DM$_{\rm max}$) of FRBs for an ensemble that includes bursts during the reionization. The DM$_

Eloisa Poggio, Chervin F. P. Laporte, Kathryn V. Johnston, Elena D'Onghia

Using N-body simulations of the Milky Way interacting with a satellite similar to the Sagittarius dwarf galaxy, we quantitatively analyse the vertical response of the Galactic disc to the satellite's repeated impacts. We approximate the vertical distortion of the Galactic disc as the sum of the first three Fourier azimuthal terms m = 0, 1 and 2, and observe

Josh Borrow, Matthieu Schaller, Richard G. Bower

Smoothed Particle Hydrodynamics (SPH) is a Lagrangian method for solving the fluid equations that is commonplace in astrophysics, prized for its natural adaptivity and stability. The choice of variable to smooth in SPH has been the topic of contention, with smoothed pressure (P-SPH) being introduced to reduce errors at contact discontinuities relative to smo

Stefan Krastanov, Alexander Sanchez de la Cerda, Prineha Narang

The entanglement resource required for quantum information processing comes in a variety of forms, from Bell states to multipartite GHZ states or cluster states. Purifying these resources after their imperfect generation is an indispensable step towards using them in quantum architectures. While this challenge, both in the case of Bell pairs and more general

Hala Lamdouar, Charig Yang, Weidi Xie, Andrew Zisserman

The objective of this paper is to design a computational architecture that discovers camouflaged objects in videos, specifically by exploiting motion information to perform object segmentation. We make the following three contributions: (i) We propose a novel architecture that consists of two essential components for breaking camouflage, namely, a differenti

Timothée Devergne, Andrea Cattaneo, Frédéric Bournaud, Ioanna Koutsouridou

We use simulations to study the growth of a pseudobulge in an isolated thin exponential stellar disc embedded in a static spherical halo. We observe a transition from later to earlier morphological types and an increase in bar prominence for higher disc-to-halo mass ratios, for lower disc-to-halo size ratios, and for lower halo concentrations. We compute bul

Nikolai Erokhovets

In short geometrization conjecture of W.\,Thurston (finally proved by G.~Perelman) says that any oriented $3$-manifold can be canonically partitioned into pieces, which have a geometric structure of one of the eight types. In the seminal paper (1991) M.\,W.\,Davis and T.\,Januszkiewicz introduced a wide class of $n$-dimensional manifolds -- small covers over

Anwesh Bhattacharya, Nehal C. P., Mousumi Das, Abhishek Paswan

We present a novel algorithm to detect double nuclei galaxies (DNG) called GOTHIC (Graph BOosted iterated HIll Climbing) - that detects whether a given image of a galaxy has two or more closely separated nuclei. Our aim is to detect samples of dual or multiple active galactic nuclei (AGN) in galaxies. Although galaxy mergers are common, the detection of dual

J. de Curtó, R. Duvall

Generative Adversarial Networks (GANs) have had tremendous applications in Computer Vision. Yet, in the context of space science and planetary exploration the door is open for major advances. We introduce tools to handle planetary data from the mission Chang'E-4 and present a framework for Neural Style Transfer using Cycle-consistency from rendered images. T

Susmita Das, Shashi M. Kanbur, Radoslaw Smolec, Anupam Bhardwaj

We present a new grid of convective BL Herculis models using the state-of-the-art 1D non-linear radial stellar pulsation tool MESA-RSP. We investigate the impact of metallicity and four sets of different convection parameters on multi-wavelength properties. Non-linear models were computed for periods typical for BL Her stars, i.e. $1 \leq \mathrm{P (days)} \

M. Lezgi, M. Ali-Akbari, M. Asadi

We study holographically the zero and finite temperature behavior of the potential energy and holographic subregion complexity corresponding to a probe meson in a non-conformal model. We observe that in zero and low temperature non-conformality has a decreasing effect on the dimensionless meson potential energy. However, non-conformal corrections increase ab

P. I. Karpov, G. -Y. Zhu, M. P. Heller, M. Heyl

Recent developments have highlighted the potential of quantum spin models to realize the phenomenology of confinement leading to the formation of bound states such as mesons. In this work we show that Ising chains also provide a platform to realize and probe particle collisions in pristine form with the key advantage that one can not only monitor the asympto

Bradley Meyer, Anh T. Tran

We compute the nonabelian $\mathrm{SL_2}(\mathbb{C})$-character varieties of the rational knots $C(2n+1,2m,2)$ in the Conway notation, where $m$ and $n$ are non-zero integers. By studying real points on these varieties, we determine the left orderability of the fundamental groups of the cyclic branched covers of $C(2n+1,2m,2)$.

Simone Giombi, Jonah Hyman

We study operators in the rank-$j$ totally symmetric representation of $O(N)$ in the critical $O(N)$ model in arbitrary dimension $d$, in the limit of large $N$ and large charge $j$ with $j/N\equiv \hat{j}$ fixed. The scaling dimensions of the operators in this limit may be obtained by a semiclassical saddle point calculation. Using the standard Hubbard-Stra

R. Schick, T. Ziman, M. E. Zhitomirsky

In frustrated magnetic systems with competing interactions fluctuations can lift the residual accidental degeneracy. We argue that the state selection may have different outcomes for quantum and thermal order by disorder. As an example, we consider the semiclassical Heisenberg fcc antiferromagnet with only the nearest-neighbor interactions. Zero-point oscill

Alberto Biella, Marco Schiró

It is well known that by repeatedly measuring a quantum system it is possible to completely freeze its dynamics into a well defined state, a signature of the quantum Zeno effect. Here we show that for a many-body system evolving under competing unitary evolution and variable-strength measurements the onset of the Zeno effect takes the form of a sharp phase t

Dustin G. Mixon, Hans Parshall, Jianzong Pi

Neural collapse is an emergent phenomenon in deep learning that was recently discovered by Papyan, Han and Donoho. We propose a simple "unconstrained features model" in which neural collapse also emerges empirically. By studying this model, we provide some explanation for the emergence of neural collapse in terms of the landscape of empirical risk.

Eklavya Sharma

The harmonic function was first introduced by Lee and Lee (JACM 1985) for analyzing their online bin-packing algorithm. Subsequently, it has been used to obtain approximation algorithms for many different packing problems. Here we slightly generalize the harmonic function and give alternative proofs of its important properties.

D. V. Khveshchenko

This note discusses the attempts of fitting a number of the approximate power-law dependencies observed in the cuprates into one consistent holographic or holographically inspired hydrodynamic framework. Contrary to the expectations, the goal of reproducing as many as possible of the established behaviors of the thermodynamic and transport coefficients appea

J. Davy Kirkpatrick, Christopher R. Gelino, Jacqueline K. Faherty, Aaron M. Meisner

We present final Spitzer trigonometric parallaxes for 361 L, T, and Y dwarfs. We combine these with prior studies to build a list of 525 known L, T, and Y dwarfs within 20 pc of the Sun, 38 of which are presented here for the first time. Using published photometry and spectroscopy as well as our own follow-up, we present an array of color-magnitude and color

G. Krein, T. C. Peixoto

We study the prospects of using femtoscopic low-momentum correlation measurements at the Large Hadron Collider to access properties of the J/psi-nucleon interaction. The QCD multipole expansion in terms of the J/psi chromopolarizability relates the forward scattering amplitude to a key matrix element to the origin of the nucleon mass problem, the average chr

Marta Vanin, Tom Van Acker, Reinhilde D'hulst, Dirk Van Hertem

State estimation plays a key role in the transition from the passive to the active operation of distribution systems, as it allows to monitor these networks and, successively, to perform control actions. However, designing state estimators for distribution systems carries a significant amount of challenges. This is due to the physical complexity of the netwo

Naomi Clare Robertson, David Alonso, Joachim Harnois-Déraps, Omar Darwish

We measure the cross-correlation between galaxy weak lensing data from the Kilo Degree Survey (KiDS-1000, DR4) and cosmic microwave background (CMB) lensing data from the Atacama Cosmology Telescope (ACT, DR4) and the Planck Legacy survey. We use two samples of source galaxies, selected with photometric redshifts, $(0.1<z_{\rm B}<1.2)$ and $(1.2<z_{\rm B}<2)

Joshua S. Friedman, Jay Jorgenson, Lejla Smajlovic

Let $M$ be a finite volume, non-compact hyperbolic Riemann surface, possibly with elliptic fixed points, and let $\chi$ denote a finite dimensional unitary representation of the fundamental group of $M$. Let $\Delta$ denote the hyperbolic Laplacian which acts on smooth sections of the flat bundle over $M$ associated to $\chi$. From the spectral theory of $\D

Lorenzo M. Procopio, Francisco Delgado, Marco Enriquez, Nadia Belabas

We uncover new behaviors of the transmission of information by three quantum channels in superposition of causal orders subject to some level of noise. We find that the transmission can exhibit three different behaviors as the level of noise is varied. This multifold behavior can be explained by the different equivalence classes of quantum switch matrices re

Maria Kalantzi, Agoritsa Polyzou, George Karypis

Automated Team Formation is becoming increasingly important for a plethora of applications in open source community projects, remote working platforms, as well as online educational systems. The latter case, in particular, poses significant challenges that are specific to the educational domain. Indeed, teaming students aims to accomplish far more than the s

Rutwik Palaskar, Renu Vyas, Vilas Khedekar, Sangeeta Palaskar

Oral cancer has more than 83% survival rate if detected in its early stages, however, only 29% of cases are currently detected early. Deep learning techniques can detect patterns of oral cancer cells and can aid in its early detection. In this work, we present the first results of neural networks for oral cancer detection using microscopic images. We compare

Joseph A. Vincent, Mac Schwager

We present a method for computing exact reachable sets for deep neural networks with rectified linear unit (ReLU) activation. Our method is well-suited for use in rigorous safety analysis of robotic perception and control systems with deep neural network components. Our algorithm can compute both forward and backward reachable sets for a ReLU network iterate

Usham V. Dias

Co-prime sensing is a sub-Nyquist technique for signal acquisition. Several modifications to the prototype co-prime array have been proposed in the literature. Researchers have also demonstrated low latency estimation. This paper describes the functioning of a Family of Adjustable Pivot Co-Prime Arrays. However, the main focus is to introduce the reader to a

Daria Poliakova

The paper is devoted to explaining the operadic meaning of freehedra, a family of polytopes originally defined to study free loop spaces. We introduce the notion of operadic pairs and algebras over them. Cellular chains on Stasheff associahedra and Stasheff multiplihedra assemble into a two-colored operadic pair that governs $A_\infty$-algebras and $A_\infty

Jing Lu, Kaiyun Wang, Guohua Wu, Bin Zhao

In this paper, we will focus on k-bounded sober spaces and show the existence of a T_0 space X not admitting any k-bounded sobrification. This strengthens a result of Zhao, Lu and Wang, who proved that the canonical k-bounded sobrification does not exist. Our work provides a complete solution to a question of Zhao and Ho, and shows that unlike Sob and BSob,t

Raphael Steiner

A conjecture by Lichiardopol states that for every $k \ge 1$ there exists an integer $g(k)$ such that every digraph of minimum out-degree at least $g(k)$ contains $k$ vertex-disjoint directed cycles of pairwise distinct lengths. Motivated by Lichiardopol's conjecture, we study the existence of vertex-disjoint directed cycles satisfying length constraints in

N. D. Miles, S. Deustua, G. Tancredi

HSTCosmicrays is a python-based pipeline designed to find and characterize cosmic rays found in dark frames (exposures taken with the shutter closed). Dark exposures are obtained routinely by all the Hubble Space Telescope (HST) instruments for calibration. The main processing pipeline runs locally or in the cloud on AWS. To date, we have characterized more

Zhonghao Wang, Kai Wang, Mo Yu, Jinjun Xiong

We study the problem of concept induction in visual reasoning, i.e., identifying concepts and their hierarchical relationships from question-answer pairs associated with images; and achieve an interpretable model via working on the induced symbolic concept space. To this end, we first design a new framework named object-centric compositional attention model

Anthony D. Rhodes, Manan Goel

We provide a high fidelity deep learning algorithm (HyperSeg) for interactive video segmentation tasks using a convolutional network with context-aware skip connections, and compressed, hypercolumn image features combined with a convolutional tessellation procedure. In order to maintain high output fidelity, our model crucially processes and renders all imag

Vitor Fortes Rey, Kamalveer Kaur Garewal, Paul Lukowicz

Human activity recognition (HAR) using wearable sensors has benefited much less from recent advances in Machine Learning than fields such as computer vision and natural language processing. This is to a large extent due to the lack of large scale repositories of labeled training data. In our research we aim to facilitate the use of online videos, which exist

Benjamin Jourdain, William Margheriti

It was shown by the authors that two one-dimensional probability measures in the convex order admit a martingale coupling with respect to which the integral of $\vert x-y\vert$ is smaller than twice their $\mathcal W_1$-distance (Wasserstein distance with index $1$). We showed that replacing $\vert x-y\vert$ and $\mathcal W_1$ respectively with $\vert x-y\ve

D. F. Aranguren-Quintero, E. Ramos, J. Silva-Valencia, M. S. Figueira

We discuss the temperature-dependent thermoelectric transport properties of semiconductor nanostructures comprising a quantum dot coupled to quantum wires: the thermal dependence of the electrical conductance, thermal conductance, and thermopower. We explore the universality of the thermoelectric properties in the temperature range associated with the Kondo

Sagi Levanon, Oshry Markovich, Itamar Gozlan, Ortal Bakhshian

Prediction of stress conditions is important for monitoring plant growth stages, disease detection, and assessment of crop yields. Multi-modal data, acquired from a variety of sensors, offers diverse perspectives and is expected to benefit the prediction process. We present several methods and strategies for abiotic stress prediction in banana plantlets, on

Robert Bredereck, Andrzej Kaczmarczyk, Rolf Niedermeier

Finding an envy-free allocation of indivisible resources to agents is a central task in many multiagent systems. Often, non-trivial envy-free allocations do not exist, and, when they do, finding them can be computationally hard. Classical envy-freeness requires that every agent likes the resources allocated to it at least as much as the resources allocated t

Mauro Salazar, Dario Paccagnan, Andrea Agazzi, W. P. M. H.

When people choose routes minimizing their individual delay, the aggregate congestion can be much higher compared to that experienced by a centrally-imposed routing. Yet centralized routing is incompatible with the presence of self-interested agents. How can we reconcile the two? In this paper we address this question within a repeated game framework and pro

Richard Weinhold, Robert Mieth

The proposed open-source Power Market Tool (POMATO) aims to enable research on interconnected modern and future electricity markets in the context of the physical transmission system and its secure operation. POMATO has been designed to study capacity allocation and congestion management (CACM) policies of European zonal electricity markets, especially flow-

Lee Braine, Keith Haviland, Owen Smith-Jaynes, Andy Vautier

This paper summarises a successful application of functional programming within a commercial environment. We report on experience at Accenture's Financial Services Solution Centre in London with simulating an object-oriented financial system in order to assist analysis and design. The work was part of a large IT project for an international investment bank a

Tracie L. Shing, John S. Preisser, Richard C. Zink

A SAS macro, GEECORR, has been developed for the analysis of correlated binary data based on the Prentice (1988) estimating equations method that extends the Liang and Zeger (1986) generalized estimating equations (GEE) method to include additional estimating equations for the pairwise correlation between binary variates. This extension allows for flexible m

Krzysztof Piotrzkowski, Mariusz Przybycien

In everyday research, it is tacitly assumed that the scattering cross-sections have fixed values for the given particle species, centre-of-mass energy, and particle polarizations. However, this assumption has been called into question after several observations of suppression of high-energy bremsstrahlung. This process will play a major role in experiments a

M. Yu. Kuznetsov, P. G. Tinyakov

We propose a new method for the estimation of ultra-high energy cosmic ray (UHECR) mass composition from a distribution of their arrival directions. The method employs a test statistic (TS) based on a characteristic deflection of UHECR events with respect to the distribution of luminous matter in the local Universe. Making realistic simulations of the mock U

Harry J. D. Miller, M. Hamed Mohammady, Martí Perarnau-Llobet, Giacomo Guarnieri

In thermodynamics, entropy production and work quantify irreversibility and the consumption of useful energy, respectively, when a system is driven out of equilibrium. For quantum systems, these quantities can be identified at the stochastic level by unravelling the system's evolution in terms of quantum jump trajectories. We here derive a general formula fo

Cristina Postigo, Antoni Ginebreda, Maria Vittoria Barbieri, Damia Barcelo

The management of the anthropogenic water cycle must ensure the preservation of the quality and quantity of water resources and their careful allocation to the different uses. Protection of water resources requires the control of pollution sources that may deteriorate them. This is a challenging task in multi-stressed catchments. This work presents an approa

Noam M. D. Kolodner

In this paper we identify different classes of free group extension using core graphs. We show that every free group extension $H\leq K\leq F$ has a base $B$ such that the associated pointed graph morphism $\Gamma_{B}\left(H\right)\to\Gamma_{B}\left(H\right)$ is onto. But if we examine graphs without base points, there is an extension $\left\langle b\right\r

Tu Anh Nguyen, Maureen de Seyssel, Patricia Rozé, Morgane Rivière

We introduce a new unsupervised task, spoken language modeling: the learning of linguistic representations from raw audio signals without any labels, along with the Zero Resource Speech Benchmark 2021: a suite of 4 black-box, zero-shot metrics probing for the quality of the learned models at 4 linguistic levels: phonetics, lexicon, syntax and semantics. We p

K. Mahesh Krishna, P. Sam Johnson

Famous Naimark-Han-Larson dilation theorem for frames in Hilbert spaces states that every frame for a separable Hilbert space $\mathcal{H}$ is image of a Riesz basis under an orthogonal projection from a separable Hilbert space $\mathcal{H}_1$ which contains $\mathcal{H}$ isometrically. In this paper, we derive dilation result for p-approximate Schauder fram

Josua Grawitter, Holger Stark

Droplets move on substrates with a spatio-temporal wettability pattern as generated, for example, on light-switchable surfaces. To study such cases, we implement the boundary-element method to solve the governing Stokes equations for the fluid flow field inside and on the surface of a droplet and supplement it by the Cox-Voinov law for the dynamics of the co

Angel Villar-Corrales, Veniamin I. Morgenshtern

In the last few years, large improvements in image clustering have been driven by the recent advances in deep learning. However, due to the architectural complexity of deep neural networks, there is no mathematical theory that explains the success of deep clustering techniques. In this work we introduce Projected-Scattering Spectral Clustering (PSSC), a stat

Alfred Harwood, Matteo Brunelli, Alessio Serafini

We consider a wide family of optical coherent feedback loops acting on an optomechanical system operating in the linearized regime. We assess the efficacy of such loops in improving key operations, such as cooling, steady-state squeezing and entanglement, as well as optical to mechanical state transfer. We find that mechanical sideband cooling can be enhance

C. -J. Yang, A. Ekström, C. Forssén, G. Hagen

Chiral effective field theory ($\chi$EFT), as originally proposed by Weinberg, promises a theoretical connection between low-energy nuclear interactions and quantum chromodynamics (QCD). However, the important property of renormalization-group (RG) invariance is not fulfilled in current implementations and its consequences for predicting atomic nuclei beyond

Geoffrey S Johnson

A prediction interval covers a future observation from a random process in repeated sampling, and is typically constructed by identifying a pivotal quantity that is also an ancillary statistic. Analogously, a tolerance interval covers a population percentile in repeated sampling and is often based on a pivotal quantity. One approach we consider in non-normal

Jiaxin Jin, Chanwoo Kim

We prove that exponential moments of a fluctuation of the pure transport equation decay pointwisely almost as fast as $t^{-3}$ when the domain is any general strictly convex subset of $\mathbb{R}^3$ with the smooth boundary of the diffuse boundary condition. We prove the theorem by establishing a novel $L^1$-$L^\infty$ framework via stochastic cycles.

Maria Vittoria Barbieri, Luis Simon Monllor-Alcaraz, Cristina Postigo, Miren Lopez de Alda

Water is an essential resource for all living organisms. The continuous and increasing use of pesticides in agricultural and urban activities results in the pollution of water resources and represents an environmental risk. To control and reduce pesticide pollution, reliable multi-residue methods for the detection of these compounds in water are needed. In t

Andres Aramburo Garcia, Kyrylo Bondarenko, Alexey Boyarsky, Dylan Nelson

We study the effects of galaxy formation physics on the magnetization of the intergalactic medium (IGM) using the IllustrisTNG simulations. We demonstrate that large-scale regions affected by the outflows from galaxies and clusters contain magnetic fields that are several orders of magnitude stronger than in unaffected regions with the same electron density.

Dax Enshan Koh, Sabee Grewal

The classical shadows protocol, recently introduced by Huang, Kueng, and Preskill [Nat. Phys. 16, 1050 (2020)], is a quantum-classical protocol to estimate properties of an unknown quantum state. Unlike full quantum state tomography, the protocol can be implemented on near-term quantum hardware and requires few quantum measurements to make many predictions w

Megan Johnson, Jae-Hun Jung

Persistent Homology (PH) is a useful tool to study the underlying structure of a data set. Persistence Diagrams (PDs), which are 2D multisets of points, are a concise summary of the information found by studying the PH of a data set. However, PDs are difficult to incorporate into a typical machine learning workflow. To that end, two main methods for represen

Baichuan Mo, Zhenliang Ma, Haris N. Koutsopoulos, Jinhua Zhao

Transit network simulation models are often used for performance and retrospective analysis of urban rail systems, taking advantage of the availability of extensive automated fare collection (AFC) and automated vehicle location (AVL) data. Important inputs to such models, in addition to origin-destination flows, include passenger path choices and train capac

Maria Vittoria Barbieri, Cristina Postigo, Simon Monllor-Alcaraz, Damia Barcelo

The occurrence of polar pesticides in sediments has not been extensively investigated because of their relatively poor hydrophobicity and apparently less persistence in the environment. However, their continuous release into the aquatic systems calls for the evaluation of their potential accumulation in sediments and the role of this matrix as a potential so

Saeed S. Jahromi, Hadi Yarloo, Roman Orus

We study the 3D Kitaev and Kitaev-Heisenberg models respectively on the hyperhoneycomb and hyperoctagon lattices, both at zero and finite-temperature, in the thermodynamic limit. Our analysis relies on advanced tensor network (TN) simulations based on graph Projected Entangled-Pair States (gPEPS). We map out the TN phase diagrams of the models and characteri

J. P. Brooks, D. J. Edwards, C. E. Larson, N. Van Cleemput

We propose the use of a conjecturing machine that suggests feature relationships in the form of bounds involving nonlinear terms for numerical features and boolean expressions for categorical features. The proposed Conjecturing framework recovers known nonlinear and boolean relationships among features from data. In both settings, true underlying relationshi

Salvino Ciccariello

An algebraic approximation, of order $K$, of a polyhedron correlation function (CF) can be obtained from $\gamma\pp(r)$, its chord-length distribution (CLD), considering first, within the subinterval $[D_{i-1},\, D_i]$ of the full range of distances, a polynomial in the two variables $(r-D_{i-1})^{1/2}$ and $(D_{i}-r)^{1/2}$ such that its expansions around $

S. Azzoni, M. H. Abitbol, D. Alonso, A. Gough

The characterization and modeling of polarized foregrounds has become a critical issue in the quest for primordial $B$-modes. A typical method to proceed is to factorize and parametrize the spectral properties of foregrounds and their scale dependence (i.e. assuming that foreground spectra are well described everywhere by their sky average). Since in reality

Yi Xue, David Ren, Laura Waller

Fluorescence microscopy is a powerful tool for imaging biological samples with molecular specificity. In contrast, phase microscopy provides label-free measurement of the sample's refractive index (RI), which is an intrinsic optical property that quantitatively relates to cell morphology, mass, and stiffness. Conventional imaging techniques measure either th

Yukako Fujishiro, Naoya Kanazawa, Ryosuke Kurihara, Hiroaki Ishizuka

The electrical Hall effect can be significantly enhanced through the interplay of the conduction electrons with magnetism, which is known as the anomalous Hall effect (AHE). Whereas the mechanism related to band topology has been intensively studied towards energy efficient electronics, those related to electron scattering have received limited attention. He

Saeid Asgari Taghanaki, Jieliang Luo, Ran Zhang, Ye Wang

The 3D deep learning community has seen significant strides in pointcloud processing over the last few years. However, the datasets on which deep models have been trained have largely remained the same. Most datasets comprise clean, clutter-free pointclouds canonicalized for pose. Models trained on these datasets fail in uninterpretible and unintuitive ways

Emmett L. Wyman, Yakun Xi, Steve Zelditch

Let $\{e_j\}$ be an orthonormal basis of Laplace eigenfunctions of a compact Riemannian manifold $(M,g)$. Let $H \subset M$ be a submanifold and let $\{\psi_k\}$ be an orthonormal basis of Laplace eigenfunctions of $H$ with the induced metric. We obtain joint asymptotics for the Fourier coefficients \[ \langle \gamma_H e_j, \psi_k \rangle_{L^2(H)} = \int_H e

Eric C. Kerrigan, Yuanbo Nie, Omar Faqir, Caroline H. Kennedy

A variety of optimal control, estimation, system identification and design problems can be formulated as functional optimization problems with differential equality and inequality constraints. Since these problems are infinite-dimensional and often do not have a known analytical solution, one has to resort to numerical methods to compute an approximate solut

Yuri Belousov, Roberto Grimaudo, Antonino Messina, Agostino Migliore

We propose a method to describe the evolution of two spins coupled by hyperfine interaction in an external time-dependent magnetic field. We apply the approach to the case of hyperfine interaction with axial symmetry, which can be solved exactly in a constant, appropriately oriented magnetic field. In order to treat the nonstationary dynamical problem, we mo

Diego Sáez-Chillón Gómez

A general $f(\mathcal{R})$ gravitational theory is considered within the Palatini formalism. By applying the variational principle and the usual conditions on the boundary, we show explicitly that a surface term remains such that as in their metric-compatible counterparts, an additional surface term has to be added in the gravitational action, which plays a

Shaifali Parashar, Yuxuan Long, Mathieu Salzmann, Pascal Fua

A recent trend in Non-Rigid Structure-from-Motion (NRSfM) is to express local, differential constraints between pairs of images, from which the surface normal at any point can be obtained by solving a system of polynomial equations. The systems of equations derived in previous work, however, are of high degree, having up to five real solutions, thus requirin

Zichang Liu, Li Chou, Anshumali Shrivastava

On-device localization and tracking are increasingly crucial for various applications. Along with a rapidly growing amount of location data, machine learning (ML) techniques are becoming widely adopted. A key reason is that ML inference is significantly more energy-efficient than GPS query at comparable accuracy, and GPS signals can become extremely unreliab

Jiafan He, Dongruo Zhou, Quanquan Gu

Reinforcement learning (RL) with linear function approximation has received increasing attention recently. However, existing work has focused on obtaining $\sqrt{T}$-type regret bound, where $T$ is the number of interactions with the MDP. In this paper, we show that logarithmic regret is attainable under two recently proposed linear MDP assumptions provided

Carl Lian

We extend the theory of tautological classes on moduli spaces of stable curves to the more general setting of moduli spaces of admissible Galois covers of curves, introducing the so-called H-tautological ring. The main new feature is the existence of restriction-corestriction morphisms remembering intermediate quotients of Galois covers, which are a rich sou

Xianrui Zheng, Yulan Liu, Deniz Gunceler, Daniel Willett

Today, many state-of-the-art automatic speech recognition (ASR) systems apply all-neural models that map audio to word sequences trained end-to-end along one global optimisation criterion in a fully data driven fashion. These models allow high precision ASR for domains and words represented in the training material but have difficulties recognising words tha

M. Van de Sande, C. Walsh, T. J. Millar

To explain the properties of dust in the interstellar medium (ISM), the presence of a refractory organic mantle is necessary. The outflows of AGB stars are among the main contributors of stellar dust to the ISM. We present the first study of the refractory organic contribution of AGB stars to the ISM. Based on laboratory experiments, we included a new reacti

Andreas Kreuml, Olaf Mordhorst

This note treats several problems for the fractional perimeter or $s$-perimeter on the sphere. The spherical fractional isoperimetric inequality is established. It turns out that the equality cases are exactly the spherical caps. Furthermore, the convergence of fractional perimeters to the surface area as $s \nearrow 1$ is proven. It is shown that their limi

Chi-Kang Chang

Let $f:X\rightarrow Y$ be an algebraic fibre space between normal projective varieties and $F$ be a general fibre of $f$. We prove an Iitaka-type inequality $\kappa(X,-K_X)\leq \kappa(F,-K_F)+\kappa(Y,-K_Y)$ under some mild conditions. We also obtain some more results relates the positivity of $-K_X$ and $-K_Y$.

Johanna Teske, Sharon Xuesong Wang, Angie Wolfgang, Tianjun Gan

$Kepler$ revealed that roughly one-third of Sun-like stars host planets orbiting within 100 days and between the size of Earth and Neptune. How do these planets form, what are they made of, and do they represent a continuous population or multiple populations? To help address these questions, we began the Magellan-TESS Survey (MTS), which uses Magellan II/PF

Martin Kolarik, Radim Burget, Kamil Riha

The widespread use of Batch Normalization has enabled training deeper neural networks with more stable and faster results. However, the Batch Normalization works best using large batch size during training and as the state-of-the-art segmentation convolutional neural network architectures are very memory demanding, large batch size is often impossible to ach

José A. Perusquía, Jim E. Griffin, Cristiano Villa

$n$-gram profiles have been successfully and widely used to analyse long sequences of potentially differing lengths for clustering or classification. Mainly, machine learning algorithms have been used for this purpose but, despite their predictive performance, these methods cannot discover hidden structures or provide a full probabilistic representation of t

Martin Kolarik, Radim Burget, Carlos M. Travieso-Gonzalez, Jan Kocica

We present a novel approach of 2D to 3D transfer learning based on mapping pre-trained 2D convolutional neural network weights into planar 3D kernels. The method is validated by the proposed planar 3D res-u-net network with encoder transferred from the 2D VGG-16, which is applied for a single-stage unbalanced 3D image data segmentation. In particular, we eva

C. Pereira, J. D. Rodrigues, J. T. Mendonça, H. Terças

Soliton hydrodynamics is an appealing tool to describe strong turbulence in low-dimensional systems. Strong turbulence in quasi-one dimensional spuerfluids, such as Bose-Einstein condensates, involves the dynamics of dark solitons and, therefore, the description of a statistical ensemble of dark-solitons, i.e. soliton gases, is necessary. In this work, we pr

Cansu Alakus, Denis Larocque, Sebastien Jacquemont, Fanny Barlaam

Investigating the relationships between two sets of variables helps to understand their interactions and can be done with canonical correlation analysis (CCA). However, the correlation between the two sets can sometimes depend on a third set of covariates, often subject-related ones such as age, gender, or other clinical measures. In this case, applying CCA

Kaidi Hu, Josefina Toran, Ester Lopez-Garcia, Maria Vittoria Barbieri

The occurrence of the extensively used herbicide diuron in the environment poses a severe threat to the ecosystem and human health. Four different ligninolytic fungi were studied as biodegradation candidates for the removal of diuron. Among them, T. versicolor was the most effective species, degrading rapidly not only diuron (83%) but also the major metaboli

Benjamin Charnay, Doriann Blain, Bruno Bézard, Jérémy Leconte

Hubble (HST) spectroscopic transit observations of the temperate sub-Neptune K2-18b were interpreted as the presence of water vapour with potential water clouds. 1D modelling studies also predict the formation of water clouds at some conditions. However, such models cannot predict the cloud cover, driven by atmospheric dynamics and thermal contrasts, and thu

Philipp Kratzer, Simon Bihlmaier, Niteesh Balachandra Midlagajni, Rohit Prakash

As robots become more present in open human environments, it will become crucial for robotic systems to understand and predict human motion. Such capabilities depend heavily on the quality and availability of motion capture data. However, existing datasets of full-body motion rarely include 1) long sequences of manipulation tasks, 2) the 3D model of the work

Federico Castillo, Jeremy L. Martin, Jose A. Samper

We study pure ordered simplicial complexes (i.e., simplicial complexes with a linear order on their ground sets) from the Hopf-theoretic point of view. We define a \textit{Hopf class} to be a family of pure ordered simplicial complexes that give rise to a Hopf monoid under join and deletion/contraction. The prototypical Hopf class is the family of ordered ma

Boudewijn van Dongen, Johannes De Smedt, Claudio Di Ciccio, Jan Mendling

Mixed-paradigm process models integrate strengths of procedural and declarative representations like Petri nets and Declare. They are specifically interesting for process mining because they allow capturing complex behaviour in a compact way. A key research challenge for the proliferation of mixed-paradigm models for process mining is the lack of correspondi

Mihailo Stojnic

In this paper we revisit one of the classical statistical problems, the so-called sparse maximum-likelihood (ML) linear regression. As a way of attacking this type of regression, we present a novel CLuP mechanism that to a degree relies on the \bl{\textbf{Random Duality Theory (RDT)}} based algorithmic machinery that we recently introduced in \cite{Stojniccl

Baptiste Morin

Using work of Antieau and Bhatt-Morrow-Scholze, we define a filtration on topological Hochschild homology and its variants $TP$ and $TC^-$ of quasi-lci rings with bounded torsion, which recovers the BMS-filtration after $p$-adic completion. Then we compute the graded pieces of this filtration in terms of Hodge completed derived de Rham cohomology relative to

Jana Björn, Agnieszka Kałamajska

We study compactness and boundedness of embeddings from Sobolev type spaces on metric spaces into $L^q$ spaces with respect to another measure. The considered Sobolev spaces can be of fractional order and some statements allow also nondoubling measures. Our results are formulated in a general form, using sequences of covering families and local Poincar\'e ty

M. Jesus Garcia-Galan, Luis Simon Monllor-Alcaraz, Cristina Postigo, Enrica Uggetti

The present study evaluated the capacity of a semi-closed, tubular horizontal photobioreactor (PBR) to remove pesticides from agricultural run-off. The study was carried out in July to study its efficiency under the best conditions (highest solar irradiation). A total of 51 pesticides, including 10 transformation products, were selected and investigated base

Xuhong Wang, Ding Lyu, Mengjian Li, Yang Xia

Limited by the time complexity of querying k-hop neighbors in a graph database, most graph algorithms cannot be deployed online and execute millisecond-level inference. This problem dramatically limits the potential of applying graph algorithms in certain areas, such as financial fraud detection. Therefore, we propose Asynchronous Propagation Attention Netwo

B. Breizman, G. Stupakov, G. Vekstein

We consider weakly ionized plasma where frequent elastic scattering of electrons on neutrals change the individual acts and the rate of electron-electron collisions significantly. In this case, the kinetics of electron thermalization is very different from that in fully ionized plasma. The colliding electrons diffuse because of fast scattering on neutrals. W