July 2022 arXiv papers — page 90

Luca Ciambelli, Robert G. Leigh

A universal symmetry algebra organizing the gravitational phase space has been recently found. It corresponds to the subset of diffeomorphisms that become physical at corners -- codimension-$2$ surfaces supporting Noether charges. It applies to both finite distance and asymptotic corners. In this paper, we study this algebra and its representations, via the

Jhony H. Giraldo, Sajid Javed, Naoufel Werghi, Thierry Bouwmans

Moving Object Detection (MOD) is a fundamental step for many computer vision applications. MOD becomes very challenging when a video sequence captured from a static or moving camera suffers from the challenges: camouflage, shadow, dynamic backgrounds, and lighting variations, to name a few. Deep learning methods have been successfully applied to address MOD

Jhony H. Giraldo, Arif Mahmood, Belmar Garcia-Garcia, Dorina Thanou

Graph Signal Processing (GSP) is an emerging research field that extends the concepts of digital signal processing to graphs. GSP has numerous applications in different areas such as sensor networks, machine learning, and image processing. The sampling and reconstruction of static graph signals have played a central role in GSP. However, many real-world grap

Manuel Hohmann

In general relativity, the only dynamical field describing the gravitational interaction of matter, is the metric. It induces the causal structure of spacetime, governs the motion of physical bodies through its Levi-Civita connection, and mediates gravity via the curvature of this connection. While numerous modified theories of gravity retain these principle

Marcos Pellejero-Ibanez, Raul E. Angulo, Matteo Zennaro, Jens Stuecker

We present an emulator that accurately predicts the power spectrum of galaxies in redshift space as a function of cosmological parameters. Our emulator is based on a 2nd-order Lagrangian bias expansion that is displaced to Eulerian space using cosmological $N$-body simulations. Redshift space distortions are then imprinted using the non-linear velocity field

A. Vigan, M. Lopez, M. El Morsy, E. Muslimov

New generation exoplanet imagers on large ground-based telescopes are highly optimised for the detection of young giant exoplanets in the near-infrared, but they are intrinsically limited for their characterisation by the low spectral resolution of their integral field spectrographs ($R<100$). High-dispersion spectroscopy at $R \gg 10^4$ would be a powerful

Stefano Baiguera, Lorenzo Cederle, Silvia Penati

In 2+1 dimensions, we propose a renormalizable non-linear sigma model action which describes the $\mathcal{N}=2$ supersymmetric generalization of Galilean Electrodynamics. We first start with the simplest model obtained by null reduction of the relativistic Abelian $\mathcal{N}=1$ supersymmetric QED in 3+1 dimensions and study its renormalization properties

Stephen R. Kane, Zhexing Li

Planetary rings are often speculated as being a relatively common attribute of giant planets, partly based on their prevalence within the Solar System. However, their formation and sustainability remain a topic of open discussion, and the most massive planet within our planetary system harbors a very modest ring system. Here, we present the results of a N-bo

Ken Kikuchi

We explain when and why symmetries enhance in fermionic rational conformal field theories. In order to achieve the goal, we first clarify invariants under renormalization group flows. In particular, we find the Ocneanu rigidity is not enough to protect some quantities. Concretely, while (double) braidings are subject to the rigidity, they jump at conformal f

Hengxiao Guo, Aaron J. Barth, Shu Wang

We perform a systematic survey of active galactic nucleus (AGN) continuum lags using $\sim$3 day cadence $gri$-band light curves from the Zwicky Transient Facility. We select a sample of 94 type 1 AGN at $z<0.8$ with significant and consistent inter-band lags based on the interpolated cross-correlation function method and the Bayesian method JAVELIN. Within

Rajeev Acharya, Igor Aleiner, Richard Allen, Trond I. Andersen

Practical quantum computing will require error rates that are well below what is achievable with physical qubits. Quantum error correction offers a path to algorithmically-relevant error rates by encoding logical qubits within many physical qubits, where increasing the number of physical qubits enhances protection against physical errors. However, introducin

Diptimoy Ghosh, Amartya Harsh Singh, Farman Ullah

We study the analytic structure of in-in correlation functions in a deSitter background. The aim of this study is to probe the initial conditions for inflation through the features of correlation functions of the field fluctuations, and understand precisely how an in-in correlator responds to particles in the initial state. We emphasize that the choice of va

Raimon Luna, Gabriele Bozzola, Vitor Cardoso, Vasileios Paschalidis

We compute the emission of linear momentum (kicks) by both gravitational and electromagnetic radiation in fully general-relativistic numerical evolutions of quasi-circular charged black hole binaries. We derive analytical expressions for slowly moving bodies and explore numerically a variety of mass ratios and charge-to-mass ratios. We find that for the equa

Benjamin J. Brown

Decoding algorithms are essential to fault-tolerant quantum-computing architectures. In this perspective we explore decoding algorithms for the surface code; a prototypical quantum low-density parity-check code that underlies many of the leading efforts to demonstrate scalable quantum computing. Central to our discussion is the minimum-weight perfect-matchin

Igal Arav, Jerome P. Gauntlett, Matthew M. Roberts, Christopher Rosen

We construct a new class of supersymmetric $AdS_3\times Y_7$ solutions of type IIB supergravity, where $Y_7$ is an $S^5$ fibration over a spindle, which are dual to $d=2$, $\mathcal{N}=(0,2)$ SCFTs. The solutions are constructed in a sub-truncation of $D=5$, $SO(6)$ maximal gauged supergravity and they all lie within the anti-twist class. We show that the ce

Juan Maldacena, Grant N. Remmen

We point out some common qualitative features of the Coon amplitude$\unicode{x2014}$a family of deformations of the Veneziano amplitude with logarithmic Regge trajectories$\unicode{x2014}$and the open string scattering amplitude for strings ending on a D-brane in AdS. Both reduce to the Veneziano amplitude at relatively low energies. Both systems have an acc

Yitian Sun, Tracy R. Slatyer

We show that Dense Neural Networks can be used to accurately model the cooling of high-energy particles in the early universe, in the context of the public code package DarkHistory. DarkHistory self-consistently computes the temperature and ionization history of the early universe in the presence of exotic energy injections, such as might arise from the anni

T. Yontan, H. Cakmak, S. Bilir, T. Banks

We present photometric, astrometric, and kinematic studies of the old open star clusters NGC 1193 and NGC 1798. Both of the clusters are investigated by combining data sets from Gaia EDR3 and CCD UBV observational data. Analysis of the radial distribution of stars through the cluster regions indicates that the cluster limit radii are $r_{\rm lim}=8'$ for bot

Alok Laddha, Siddharth G. Prabhu, Suvrat Raju, Pushkal Shrivastava

We study a novel asymptotic limit of massive scalar fields in nongravitational quantum field theories in four-dimensional flat space. We foliate the spacetime into a set of dS$_3$ slices that are spacelike to, and at a constant proper distance from, an arbitrarily chosen origin, and study the boundary dS$_3$ obtained in the infinite-distance limit. Massive b

Po-Yao Huang, Hu Xu, Juncheng Li, Alexei Baevski

This paper studies a simple extension of image-based Masked Autoencoders (MAE) to self-supervised representation learning from audio spectrograms. Following the Transformer encoder-decoder design in MAE, our Audio-MAE first encodes audio spectrogram patches with a high masking ratio, feeding only the non-masked tokens through encoder layers. The decoder then

Corinna Ulcigrai

We survey some recent advances in the study of (area-preserving) flows on surfaces, in particular on the typical dynamical, ergodic and spectral properties of smooth area-preserving (or locally Hamiltonian) flows, as well as recent breakthroughs on linearization and rigidity questions in higher genus. We focus in particular on the Diophantine-like conditions

Yining Hong, Yilun Du, Chunru Lin, Joshua B. Tenenbaum

In this paper, we address the challenging problem of 3D concept grounding (i.e. segmenting and learning visual concepts) by looking at RGBD images and reasoning about paired questions and answers. Existing visual reasoning approaches typically utilize supervised methods to extract 2D segmentation masks on which concepts are grounded. In contrast, humans are

Pierre Tapie, Alexis M. Prevost, Lorraine Montel, Léa-Laetitia Pontani

We present a simple method to produce giant lipid pseudo-vesicles (vesicles with an oily cap on the top), trapped in an agarose gel. The method can be implemented using only a regular micropipette and relies on the formation of a water/oil/water double droplet in liquid agarose. We characterize the produced vesicle with fluorescence imaging and establish the

Matthijs H. J. de Jong, Adarsh Ganesan, Andrea Cupertino, Simon Gröblacher

Mechanical frequency combs are poised to bring the applications and utility of optical frequency combs into the mechanical domain. So far, their main challenge has been strict requirements on drive frequencies and power, which complicate operation. We demonstrate a straightforward mechanism to create a frequency comb consisting of mechanical overtones (integ

Hongwen Zhang, Yating Tian, Yuxiang Zhang, Mengcheng Li

We present PyMAF-X, a regression-based approach to recovering parametric full-body models from monocular images. This task is very challenging since minor parametric deviation may lead to noticeable misalignment between the estimated mesh and the input image. Moreover, when integrating part-specific estimations into the full-body model, existing solutions te

Constantine Glen Evans, Jackson O'Brien, Erik Winfree, Arvind Murugan

Inspired by biology's most sophisticated computer, the brain, neural networks constitute a profound reformulation of computational principles. Remarkably, analogous high-dimensional, highly-interconnected computational architectures also arise within information-processing molecular systems inside living cells, such as signal transduction cascades and geneti

Taras Panamarev, Bence Kocsis

We explore the dynamics of stellar discs in the close vicinity of a supermassive black hole (SMBH) by means of direct $N$-body simulations. We show that an isolated nuclear stellar disc exhibits anisotropic mass segregation meaning that massive stars settle to lower orbital inclinations and more circular orbits than the light stars. However, in systems in wh

Alexander Lidiak, Casey Jameson, Zhen Qin, Gongguo Tang

It has been recently shown that a state generated by a one-dimensional noisy quantum computer is well approximated by a matrix product operator with a finite bond dimension independent of the number of qubits. We show that full quantum state tomography can be performed for such a state with a minimal number of measurement settings using a method known as ten

Ioan Alexandru Puiu, Raphael Andreas Hauser

For the case of inflexible demand and considering network constraints, we introduce a Cost Minimisation (CM) based market clearing mechanism, and a model representing the standard Social Welfare Maximisation mechanism used in European Day Ahead Electricity Markets. Since the CM model corresponds to a more challenging optimisation problem, we propose four num

Ana Djurdjevac, Helena Kremp, Nicolas Perkowski

In this paper, we study different scaling rough path limit regimes in space and time for the Langevin dynamics on a quasi-planar fluctuating Helfrich surfaces. The convergence results of the processes were already proven in the work by Duncan, Elliott, Pavliotis and Stuart (2015). We extend this work by proving the convergence of the It\^o and Stratonovich r

Marco Frasca, Anish Ghoshal, Alexey S. Koshelev

We show a theorem proving that a non-local bosonic field upon a covariant interaction with a confining gauge field undergoes the confinement of its degrees of freedom present in the free theory changing completely the physical mass spectrum following Kugo-Ojima criterion. This is applicable to an infinite number of excitations of the bosonic field including

Rebecca Coulson, Natasha Dobrinen, Rehana Patel

This is Part I of a two-part series regarding Ramsey properties of Fraisse structures satisfying a property called SDAP+, which strengthens the Disjoint Amalgamation Property. We prove that every Fraisse structure in a finite relational language with relation symbols of any finite arity satisfying this property is indivisible. Novelties include a new formula

Shenghui Chen, Yigit E. Bayiz, David Fridovich-Keil, Ufuk Topcu

Autonomous agents can adopt socially-aware behaviors to reduce social costs, mimicking the way animals interact in nature and humans in society. We present a new approach to model socially-aware decision-making that includes two key elements: bounded rationality and inter-agent relationships. We capture the interagent relationships by introducing a novel mod

Dengpeng Huang, Sigrid Leyendecker

In this work, a simulation model for the optimal control of dielectric elastomer actuated flexible multibody dynamics systems is presented. The Dielectric Elastomer Actuator (DEA) behaves like a flexible artificial muscles in soft robotics. It is modeled as an electromechanically coupled geometrically exact beam, where the electric charges serve as control v

Mike Cummings, Sergio Da Silva, Jenna Rajchgot, Adam Van Tuyl

The geometric vertex decomposability property for polynomial ideals is an ideal-theoretic generalization of the vertex decomposability property for simplicial complexes. Indeed, a homogeneous geometrically vertex decomposable ideal is radical and Cohen-Macaulay, and is in the Gorenstein liaison class of a complete intersection (glicci). In this paper, we ini

Bineet Ghosh, Masaad Khan, Adithya Ashok, Sandeep Chinchali

Robotic perception models, such as Deep Neural Networks (DNNs), are becoming more computationally intensive and there are several models being trained with accuracy and latency trade-offs. However, modern latency accuracy trade-offs largely report mean accuracy for single-step vision tasks, but there is little work showing which model to invoke for multi-ste

Lu Zeng, Sree Hari Krishnan Parthasarathi, Yuzong Liu, Alex Escott

We propose a novel 2-stage sub 8-bit quantization aware training algorithm for all components of a 250K parameter feedforward, streaming, state-free keyword spotting model. For the 1st-stage, we adapt a recently proposed quantization technique using a non-linear transformation with tanh(.) on dense layer weights. In the 2nd-stage, we use linear quantization

Rongjie Huang, Zhou Zhao, Huadai Liu, Jinglin Liu

Denoising diffusion probabilistic models (DDPMs) have recently achieved leading performances in many generative tasks. However, the inherited iterative sampling process costs hinder their applications to text-to-speech deployment. Through the preliminary study on diffusion model parameterization, we find that previous gradient-based TTS models require hundre

Takato Yasuno, Junichiro Fujii, Masazumi Amakata

Urban rivers provide a water environment that influences residential living. River surface monitoring has become crucial for making decisions about where to prioritize cleaning and when to automatically start the cleaning treatment. We focus on the organic mud, or "scum", that accumulates on the river's surface and contributes to the river's odor and has ext

Carlos Francisco Moreno-Garcia, Francesc Serratosa

Image registration is a research field in which images must be compared and aligned independently of the point of view or camera characteristics. In some applications (such as forensic biometrics, satellite photography or outdoor scene identification) classical image registration systems fail due to one of the images compared represents a tiny piece of the o

Hong Chang

For the complex of curves of a closed orientable surface of genus $g$, $\mathcal{C}(S_{g>1})$, the notion of efficient geodesic in was introduced in arXiv:1408.4133. There it was established that there always exists (finitely many) efficient geodesics between any two vertices, $ v_{\alpha} , v_{\beta} \in \mathcal{C}(S_g)$, representing homotopy classes of s

Alar Leibak, Christian Porter, Cong Ling

In this paper, we introduce the notion of unit reducibility for number fields, that is, number fields in which all positive unary forms attain their nonzero minimum at a unit. Furthermore, we investigate the link between unit reducibility and the number of homothety classes of perfect unary forms for a given number field, and prove an open conjecture about t

Alistair Benford, Richard Montgomery

Sumner's universal tournament conjecture states that every $(2n-2)$-vertex tournament should contain a copy of every $n$-vertex oriented tree. If we know the number of leaves of an oriented tree, or its maximum degree, can we guarantee a copy of the tree with fewer vertices in the tournament? Due to work initiated by H\"aggkvist and Thomason (for number of l

Dongwoo T Chung

The wavelet scattering transform (WST) has recently gained attention in the context of large-scale structure studies, being a possible generator of summary statistics encapsulating non-Gaussianities beyond the reach of the conventional power spectrum. This work examines the three-dimensional solid harmonic WST in the context of a three-dimensional line-inten

Jonas Hetz

We complete the determination of the generalised Springer correspondence for connected reductive algebraic groups, by proving a conjecture of Lusztig on the last open cases which occur for groups of type $E_8$.

Sree Hari Krishnan Parthasarathi, Lu Zeng, Christin Jose, Joseph Wang

We propose a novel approach for semi-supervised learning (SSL) designed to overcome distribution shifts between training and real-world data arising in the keyword spotting (KWS) task. Shifts from training data distribution are a key challenge for real-world KWS tasks: when a new model is deployed on device, the gating of the accepted data undergoes a shift

Federico Incardona, Alessandro Costa, Kevin Munari, Pietro Bruno

We present preliminary test results for the correct sizing of the bare metal hardware that will host the database of the Monitoring system (MON) for the Cherenkov Telescope Array (CTA). The MON is the subsystem of the Array Control and Data Acquisition System (ACADA) that is responsible for monitoring and logging the overall CTA array. It acquires and stores

Jiamin Li

Let $I$ be an equidimensional ideal of a ring polynomial $R$ over $\mathbb{C}$ and let $J$ be its generic linkage. We prove that there is a uniform bound of the difference between the F-pure thresholds of $I_p$ and $J_p$ via the generalized Frobenius powers of ideals. This provides evidence that the F-pure threshold of an equidimensional ideal $I$ is less th

Stanley H. Chan

Imaging at a long distance often requires advanced image restoration algorithms to compensate for the distortions caused by atmospheric turbulence. However, unlike many standard restoration problems such as deconvolution, the forward image formation model of the atmospheric turbulence does not have a simple expression. Thanks to the Zernike representation of

Thomas W. Baumgarte, David Hilditch

While the 1+log slicing condition has been extremely successful in numerous numerical relativity simulations, it is also known to develop "gauge-shocks" in some examples. Alternative "shock-avoiding" slicing conditions suggested by Alcubierre prevent these pathologies in those examples, but have not yet been explored and tested very broadly. In this paper we

Julián Haddad, Monika Ludwig

Sharp affine fractional Sobolev inequalities for functions on $\mathbb R^n$ are established. For each $0<s<1$, the new inequalities are significantly stronger than (and directly imply) the sharp fractional Sobolev inequalities of Almgren and Lieb. In the limit as $s\to 1^-$, the new inequalities imply the sharp affine Sobolev inequality of Gaoyong Zhang. As

Josiah Park, Carlos Saltijeral, Ming Zhong

We provide a new numerical procedure for constructing low coherence matrices, Trust-Region Stochastic Tuning for Matrix Incoherence (TRSTMI) and detail the results of experiments with a CPU/GPU parallelized implementation of this method. These trials suggest the superiority of this approach over other existing methods when the size of the matrix is large. We

Federico Incardona, Alessandro Costa, Kevin Munari, Salvatore Gambadoro

The ASTRI Mini-Array is a project for the Cherenkov astronomy in the TeV energy range. ASTRI Mini-Array consists of nine Imaging Atmospheric Cherenkov telescopes located at the Teide Observatory (Canarias Islands). Large volumes of monitoring and logging data result from the operation of a large-scale astrophysical observatory. In the last few years, several

Caio Kalil Lauand, Sean Meyn

This paper concerns quasi-stochastic approximation (QSA) to solve root finding problems commonly found in applications to optimization and reinforcement learning. The general constant gain algorithm may be expressed as the time-inhomogeneous ODE $ \frac{d}{dt}\Theta_t=\alpha f_t (\Theta_t)$, with state process $\Theta$ evolving on $\mathbb{R}^d$. Theory is b

Silvio Bove, Gisella Croce, Giovanni Pisante

In this note we prove the existence of a set $E_0\subset\mathbb{R}^2$, different from a ball, which minimizes, among the convex sets that satisfy a suitable interior cone condition, the ratio \begin{equation} \label{eq:0} \frac{D(E)}{\lambda_\mathcal{H}^2(E)}, \end{equation} where $D$ is the isoperimetric deficit and $\lambda_\mathcal{H}$ the deviation from

Pedro Agostinho, David Dias, Luís Veiga

The InterPlanetary File System (IPFS) is a hypermedia distribution protocol enabling the creation of completely distributed applications. One of the most efficient and effective ways to distribute information is through notifications, with a producer of content (publisher) sharing content with other interested parts (subscribers). IPFS already implements top

Meniar Haddad, Wafa Djobbi

In this paper, we study the Babenko-Bechner-type inequality for the Fourier Weinstein transform associated with the Weinstein operator. We use this inequality to establish a new version of Young's type inequality.

W. Barreto, A. Herrera-Aguilar, R. da Rocha

A family of deformed models of the sine-Gordon-type can be generated by twisting the sine-Gordon model. As a particular case, the 3-sine-Gordon model is here addressed, whose differential configurational entropy and the differential configurational complexity of three topological sectors are discussed, using two complementary approaches. Stability aspects ar

Aurko Roy, Rohan Anil, Guangda Lai, Benjamin Lee

Transformer models have recently emerged as one of the foundational models in natural language processing, and as a byproduct, there is significant recent interest and investment in scaling these models. However, the training and inference costs of these large Transformer language models are prohibitive, thus necessitating more research in identifying more e

Faye Jackson, Misheel Otgonbayar

For integers $k,t \geq 2$, and $1\leq r \leq t$ let $D_k^\times(r,t;n)$ be the number of parts among all $k$-indivisible partitions of $n$ (i.e., partitions where all parts are not divisible by $k$) of $n$ that are congruent to $r$ modulo $t$. Using Wright's circle method, we derive an asymptotic for $D_k^\times(r,t;n)$ as $n \to \infty$ when $k,t$ are copri

Gabriel Cardoso, Sergey Samsonov, Achille Thin, Eric Moulines

Importance Sampling (IS) is a method for approximating expectations under a target distribution using independent samples from a proposal distribution and the associated importance weights. In many applications, the target distribution is known only up to a normalization constant, in which case self-normalized IS (SNIS) can be used. While the use of self-nor

Tianyou Pei, Wei Kang, Nan Liu

In this paper, we study the problem of the 1-of-2 string oblivious transfer (OT) between Alice and Bob in the presence of a passive eavesdropper Eve. The eavesdropper Eve is not allowed to get any information about the private data of Alice or Bob. When Alice and Bob are honest-but-curious users, we propose a protocol that satisfies $1$-private (neither Alic

Vincent Roulet, Siddhartha Srinivasa, Maryam Fazel, Zaid Harchaoui

Iterative optimization algorithms depend on access to information about the objective function. In a differentiable programming framework, this information, such as gradients, can be automatically derived from the computational graph. We explore how nonlinear control algorithms, often employing linear and/or quadratic approximations, can be effectively cast

Deheng Wei, Chongpu Zhai, Dorian A. H. Hanaor, Yixiang Gan

Normal contact behaviour between non adhesive fractal rough particles is studied using a finite element method (FEM). A series of spherical grain surfaces with distinguished roughness features are generated by means of Spherical Harmonics. These surfaces are described by two roughness descriptors, namely, relative roughness (Rr) and fractal dimension (FD). T

Avinash Khare, Avadh Saxena

We show that a number of nonlocal nonlinear equations including the Ablowitz-Musslimani and the Yang variant of the nonlocal nonlinear Schr\"od-inger (NLS) equation, nonlocal modified Korteweg de Vries (mKdV) equation as well as the nonlocal Hirota equation admit novel kinklike and pulselike superposed periodic solutions. Further, we show that the nonlocal m

Alessandro Epasto, Hossein Esfandiari, Vahab Mirrokni, Andres Munoz Medina

When working with user data providing well-defined privacy guarantees is paramount. In this work, we aim to manipulate and share an entire sparse dataset with a third party privately. In fact, differential privacy has emerged as the gold standard of privacy, however, when it comes to sharing sparse datasets, e.g. sparse networks, as one of our main results,

Saiteja Utpala, Bharath K. Sriperumbudur

We consider shrinkage estimation of higher order Hilbert space valued Bochner integrals in a non-parametric setting. We propose estimators that shrink the $U$-statistic estimator of the Bochner integral towards a pre-specified target element in the Hilbert space. Depending on the degeneracy of the kernel of the $U$-statistic, we construct consistent shrinkag

Tommaso Aldinucci, Enrico Civitelli, Leonardo di Gangi, Alessandro Sestini

Focusing on Random Forests, we propose a multi-armed contextual bandit recommendation framework for feature-based selection of a single shallow tree of the learned ensemble. The trained system, which works on top of the Random Forest, dynamically identifies a base predictor that is responsible for providing the final output. In this way, we obtain local inte

Y. Li, D. Phelan, F. Ye, H. Zheng

The unusual anisotropy of the spin glass transition in the pseudobrookite system Fe$_2$TiO$_5$ has been interpreted as arising from an induced, van der Waals-like, interaction among magnetic clusters. Here we present susceptibility ($\chi$) and specific heat data (C) for Fe2TiO5 diluted with non-magnetic Ga, (Fe$_{1-p}$Ga$_p$)$_2$TiO$_5$, for disorder parame

Eric Ahlqvist, Magnus Carlson

We give formulas for 3-fold Massey products in the \'etale cohomology of the ring of integers of a number field and use these to find the first known examples of imaginary quadratic fields with class group of $p$-rank two possessing an infinite $p$-class field tower, where $p$ is an odd prime. Furthermore, a necessary and sufficient condition, in terms of cl

Hao Xing, Yifan Cao, Maximilian Biber, Mingchuan Zhou

Supervised learning depth estimation methods can achieve good performance when trained on high-quality ground-truth, like LiDAR data. However, LiDAR can only generate sparse 3D maps which causes losing information. Obtaining high-quality ground-truth depth data per pixel is difficult to acquire. In order to overcome this limitation, we propose a novel approa

Giuseppe Negro

We consider the sharp Strichartz estimate for the wave equation on $\mathbb R^{1+5}$ in the energy space, due to Bez and Rogers. We show that it can be refined by adding a term proportional to the distance from the set of maximisers, in the spirit of the classical sharpened Sobolev estimate of Bianchi and Egnell.

Alberto García Arroba Parrilla, Dan Stowell

One hour before sunrise, one can experience the dawn chorus where birds from different species sing together. In this scenario, high levels of polyphony, as in the number of overlapping sound sources, are prone to happen resulting in a complex acoustic outcome. Sound Event Detection (SED) tasks analyze acoustic scenarios in order to identify the occurring ev

Michael Lau

We study completely integrable systems attached to Takiff algebras $\mathfrak{g}_N$, extending open Toda systems of split simple Lie algebras $\mathfrak{g}$. With respect to Darboux coordinates on coadjoint orbits $\mathcal{O}$, the potentials of the hamiltonians are products of polynomial and exponential functions. General solutions for equations of motion

Giacomo Sala, Pietro Gambardella

The orbital Hall effect provides an alternative means to the spin Hall effect to convert a charge current into a flow of angular momentum. Recently, compelling signatures of orbital Hall effects have been identified in 3d transition metals. Here, we report a systematic study of the generation, transmission, and conversion of orbital currents in heterostructu

Thiago Porcino, Derek Reilly, Esteban Clua, Daniela Trevisan

Head-mounted displays (HMDs) are popular immersive tools in general, not limited to entertainment but also for education, military, and serious games for health. While these displays have strong popularity, they still have user experience issues, triggering possible symptoms of discomfort to users. This condition is known as cybersickness (CS) and is one of

Mengshun Hu, Kui Jiang, Zhixiang Nie, Zheng Wang

Spatial-Temporal Video Super-Resolution (ST-VSR) technology generates high-quality videos with higher resolution and higher frame rates. Existing advanced methods accomplish ST-VSR tasks through the association of Spatial and Temporal video super-resolution (S-VSR and T-VSR). These methods require two alignments and fusions in S-VSR and T-VSR, which is obvio

Johannes Fischer

We show, that for a morphism of schemes from X to Y, that is a finite modification in finitely many closed points, a cohomological Brauer class on Y is represented by an Azumaya algebra if its pullback to X is represented by an Azumaya algebra. Part of the proof uses an extension of a result by Ferrand, on pinching of finite locally free sheaves, to Azumaya

Yaodong Yu, Alexander Wei, Sai Praneeth Karimireddy, Yi Ma

State-of-the-art federated learning methods can perform far worse than their centralized counterparts when clients have dissimilar data distributions. For neural networks, even when centralized SGD easily finds a solution that is simultaneously performant for all clients, current federated optimization methods fail to converge to a comparable solution. We sh

Ethan N. Epperly, Joel A. Tropp

Randomized matrix algorithms have become workhorse tools in scientific computing and machine learning. To use these algorithms safely in applications, they should be coupled with posterior error estimates to assess the quality of the output. To meet this need, this paper proposes two diagnostics: a leave-one-out error estimator for randomized low-rank approx

João Tiago, David Dias, Luís Veiga

The InterPlanetary File System (IPFS) is an hyper-media distribution protocol, addressed by content and identities. It aims to make the web faster, safer, and more open. The JavaScript implementation of IPFS runs on the browser, benefiting from the mass adoption potential that it yields. Startrail takes advantage of the IPFS ecosystem and strives to further

Masum Murshid, Farook Rahaman, Mehedi Kalam

In this paper, we compute quasi-normal modes of ABG black holes (which has a non-linear electrodynamical source) using the WKB methods and AIM. A comparison between the spectrum of QNMs calculated by both methods is made. We analyse how the spectrum of QNMs depends on the black hole parameters, multipole number and overtone number and establish that the ABG

Andrew Gillette, Brendan Keith, Socratis Petrides

In this work, we revisit the marking decisions made in the standard adaptive finite element method (AFEM). Experience shows that a na\"{i}ve marking policy leads to inefficient use of computational resources for adaptive mesh refinement (AMR). Consequently, using AFEM in practice often involves ad-hoc or time-consuming offline parameter tuning to set appropr

Chloé Chopin, Simon De Wergifosse, Nicolas Marchal, Pascal Van Velthem

Due to their memristive properties nanowire networks are very promising for neuromorphic computing applications. Indeed, the resistance of such systems can evolve with the input voltage or current as it confers a synaptic behaviour to the device. Here, we propose a network of silver nanowires (Ag-NWs) which are grown in a nanopourous membrane with interconne

M. Aker, D. Batzler, A. Beglarian, J. Behrens

In this work we present a keV-scale sterile-neutrino search with the first tritium data of the KATRIN experiment, acquired in the commissioning run in 2018. KATRIN performs a spectroscopic measurement of the tritium $\beta$-decay spectrum with the main goal of directly determining the effective electron anti-neutrino mass. During this commissioning phase a l

Bruno Klaus de Aquino Afonso, Lilian Berton

In order to promote the use of machine learning in 5G, the International Telecommunication Union (ITU) proposed in 2021 the second edition of the ITU AI/ML in 5G challenge, with over 1600 participants from 82 countries. This work details the second place solution overall, which is also the winning solution of the Graph Neural Networking Challenge 2021. We ta

Grant Duffy, Shoa L. Clarke, Matthew Christensen, Bryan He

Deep learning has been shown to accurately assess 'hidden' phenotypes and predict biomarkers from medical imaging beyond traditional clinician interpretation of medical imaging. Given the black box nature of artificial intelligence (AI) models, caution should be exercised in applying models to healthcare as prediction tasks might be short-cut by differences

Nicolas Berkouk

The sheaf-function correspondence identifies the group of constructible functions on a real analytic manifold $M$ with the Grothendieck group of constructible sheaves on $M$. When $M$ is a finite dimensional real vector space, Kashiwara-Schapira have recently introduced the convolution distance between sheaves of $k$-vector spaces on $M$. In this paper, we c

Melvyn B. Nathanson

Extending the classical result that the roots of a polynomial with coefficients in $\mathbf{C}$ are continuous functions of the coefficients of the polynomial, nonstandard analysis is used to prove that if $\mathcal{F} = \{f_{\lambda} :\lambda \in \Lambda\}$ is a set of polynomials in $\mathbf{C}[t_1,\ldots, t_n]$ and if $^*\mathcal{G} = \{g_{\lambda} :\lamb

Yang Zheng, Tolga Birdal, Fei Xia, Yanchao Yang

We propose a novel method to reliably estimate the pose of a camera given a sequence of images acquired in extreme environments such as deep seas or extraterrestrial terrains. Data acquired under these challenging conditions are corrupted by textureless surfaces, image degradation, and presence of repetitive and highly ambiguous structures. When naively depl

Tianyu Huang, Bowen Dong, Jiaying Lin, Xiaohui Liu

Mirror detection aims to identify the mirror regions in the given input image. Existing works mainly focus on integrating the semantic features and structural features to mine specific relations between mirror and non-mirror regions, or introducing mirror properties like depth or chirality to help analyze the existence of mirrors. In this work, we observe th

Sanaz Mohammadjafari, Mucahit Cevik, Ayse Basar

Generative Adversarial Networks (GANs) have gained significant attention in several computer vision tasks for generating high-quality synthetic data. Various medical applications including diagnostic imaging and radiation therapy can benefit greatly from synthetic data generation due to data scarcity in the domain. However, medical image data is typically ke

Yu-Ting Chen

We prove a Feynman-Kac-type formula for the relative motion of the two-body delta-Bose gas in two dimensions. The multiplicative functional is not exponential, and the process is a skew-product diffusion uniquely extended in law, in the sense of Erickson [30], from ${\rm BES}(0,\beta{\downarrow})$ of Donati-Martin and Yor [27] as the radial part. We give two

Alberto Gomez, Mihaela Porumb, Angela Mumith, Thierry Judge

We propose a new method to automatically contour the left ventricle on 2D echocardiographic images. Unlike most existing segmentation methods, which are based on predicting segmentation masks, we focus at predicting the endocardial contour and the key landmark points within this contour (basal points and apex). This provides a representation that is closer t

Alexander Rogachev, Fedor Ratnikov

High energy physics experiments essentially rely on simulated data for physics analyses. However, running detailed simulation models requires a tremendous amount of computation resources. New approaches to speed up detector simulation are therefore needed. The generation of calorimeter responses is often the most expensive component of the simulation chain f

George Kountouris, Jesper Mørk, Emil Vosmar Denning, Philip Trøst Kristensen

We present a design for an optical dielectric bowtie cavity which features deep sub-wavelength confinement of light. The cavity is derived via simplification of a complex geometry identified through inverse design by topology optimization, and it successfully retains the extreme properties of the original structure, including an effective mode volume of $V_\

Ernesto Aranda-Escolástico, Leonardo J. Colombo, María Guinaldo, Antonio Visioli

We study the problem of periodic event-triggered control of interconnected port-Hamiltonian systems subject to time-varying delays in their communication. In particular, we design a threshold parameter for the event-triggering condition, a sampling period, and a maximum allowable delay such that interconnected port-Hamiltonian control systems with periodic e

M. Aker, D. Batzler, A. Beglarian, J. Behrens

Some extensions of the Standard Model of Particle Physics allow for Lorentz invariance and Charge-Parity-Time (CPT)-invariance violations. In the neutrino sector strong constraints have been set by neutrino-oscillation and time-of-flight experiments. However, some Lorentz-invariance-violating parameters are not accessible via these probes. In this work, we f

Francesco Di Fiore, Laura Mainini

Bayesian optimization is a popular framework for the optimization of black box functions. Multifidelity methods allows to accelerate Bayesian optimization by exploiting low-fidelity representations of expensive objective functions. Popular multifidelity Bayesian strategies rely on sampling policies that account for the immediate reward obtained evaluating th

Shen Zheng, Jinqian Pan, Changjie Lu, Gaurav Gupta

Point cloud analysis is challenging due to the irregularity of the point cloud data structure. Existing works typically employ the ad-hoc sampling-grouping operation of PointNet++, followed by sophisticated local and/or global feature extractors for leveraging the 3D geometry of the point cloud. Unfortunately, the sampling-grouping operations do not address