July 2022 arXiv papers — page 133

Martina Muratore, Olaf Hartwig, Daniele Vetrugno, Stefano Vitale

We present a study of the use and limits of the Time-Delay Interferometry null channels for in flight estimation of the Laser Interferometer Space Antenna instrumental noise. The paper considers how the two main limiting noise sources, test-mass acceleration noise and interferometric phase measurement noise, propagate through different Time-Delay Interferome

Hervé Le Coroller, Mathias Nowak, Kevin Wagner, Markus Kasper

Keplerian-Stacker is an algorithm able to combine multiple observations acquired at different epochs taking into account the orbital motion of a potential planet present in the images to boost the ultimate detection limit. In 2019, a total of 100 hours of observation were allocated to VLT VISIR-NEAR, a collaboration between ESO and Breakthrough Initiatives,

Manuel Cáceres, Massimo Cairo, Andreas Grigorjew, Shahbaz Khan

Minimum flow decomposition (MFD) is the NP-hard problem of finding a smallest decomposition of a network flow/circulation $X$ on a directed graph $G$ into weighted source-to-sink paths whose superposition equals $X$. We show that, for acyclic graphs, considering the \emph{width} of the graph (the minimum number of paths needed to cover all of its edges) yiel

C. Baumgartner, A. C. Birch, H. Schunker, R. H. Cameron

The twist of the magnetic field above a sunspot is an important quantity in solar physics. For example, magnetic twist plays a role in the initiation of flares and coronal mass ejections (CMEs). Various proxies for the twist above the photosphere have been found using models of uniformly twisted flux tubes, and are routinely computed from single photospheric

Jiahao Huang, Xiaodan Xing, Zhifan Gao, Guang Yang

Fast MRI aims to reconstruct a high fidelity image from partially observed measurements. Exuberant development in fast MRI using deep learning has been witnessed recently. Meanwhile, novel deep learning paradigms, e.g., Transformer based models, are fast-growing in natural language processing and promptly developed for computer vision and medical image analy

FangYiKuang Ding, Yang Li

Due to its significance in the recommendation system and community operations, user migration has garnered the interest of cyber-demography experts from numerous disciplines. However, contemporary research frequently overlooks the theory behind related prediction techniques, such as the Hidden Markov model. By combining the two fundamental processes of "opin

Eduardo Martinez-Enriquez, Maria del Mar Gonzalez, Javier Portilla

We introduce a method for deterministic decoupling of global features and show its applicability to improve data analysis performance, as well as to open new venues for feature transfer. We propose a new formalism that is based on defining transformations on submanifolds, by following trajectories along the features gradients. Through these transformations w

Aurore Archimbaud, Zlatko Drmač, Klaus Nordhausen, Una Radojičić

Invariant Coordinate Selection (ICS) is a multivariate data transformation and a dimension reduction method that can be useful in many different contexts. It can be used for outlier detection or cluster identification, and can be seen as an independent component or a non-Gaussian component analysis method. The usual implementation of ICS is based on a joint

Stephanie Weirich

These lecture notes describe the design of a minimal dependently-typed language called "pi-forall" and walk through the implementation of its type checker. They are based on lectures given at the Oregon Programming Languages Summer School during July 2023.

Hao Li, S. A. Shah, Ajay Ram Srimath Kandada, Carlos Silva

We review our recent quantum stochastic model for spectroscopic lineshapes in the presence of a co-evolving and non-stationary background population of excitations. Starting from a field theory description for interacting bosonic excitons, we derive a reduced model whereby optical excitons are coupled to an incoherent background via scattering as mediated by

Benjamin Doerr, Yassine Ghannane, Marouane Ibn Brahim

While the theoretical analysis of evolutionary algorithms (EAs) has made significant progress for pseudo-Boolean optimization problems in the last 25 years, only sporadic theoretical results exist on how EAs solve permutation-based problems. To overcome the lack of permutation-based benchmark problems, we propose a general way to transfer the classic pseudo-

Masahiro Suzuki, Yutaka Matsuo

Multimodal learning is a framework for building models that make predictions based on different types of modalities. Important challenges in multimodal learning are the inference of shared representations from arbitrary modalities and cross-modal generation via these representations; however, achieving this requires taking the heterogeneous nature of multimo

Gary Leung, Jun Gao, Xiaohui Zeng, Sanja Fidler

Existing transformer-based image backbones typically propagate feature information in one direction from lower to higher-levels. This may not be ideal since the localization ability to delineate accurate object boundaries, is most prominent in the lower, high-resolution feature maps, while the semantics that can disambiguate image signals belonging to one ob

Lukas Koch

Neutrino cross-section measurements are often presented as unfolded binned distributions in "true" variables. The ill-posedness of the unfolding problem can lead to results with strong anti-correlations and fluctuations between bins, which make comparisons to theoretical models in plots difficult. To alleviate this problem, one can introduce regularisation t

Montserrat Teixidor i Bigas

We show that on a generic curve, a bundle obtained by successive extensions is stable. We compute the dimension of the set of such extensions. We use degeneration methods specializing the curve to a chain of elliptic components

Alexander Huss, Joey Huston, Stephen Jones, Mathieu Pellen

Les Houches activities in 2021 were truncated due to the lack of an in-person component. However, given the rapid progress in the field, and the restart of the LHC, we wanted to continue the bi-yearly tradition of updating the standard model precision wishlist. If nothing else, this will keep us from having even more work to do at Les Houches 2023.

Simon Ohler, Daniel Brady, Winfried Lötzsch, Michael Fleischhauer

Self-Organized Criticality (SOC) is a ubiquitous dynamical phenomenon believed to be responsible for the emergence of universal scale-invariant behavior in many, seemingly unrelated systems, such as forest fires, virus spreading or atomic excitation dynamics. SOC describes the buildup of large-scale and long-range spatio-temporal correlations as a result of

Yong Bai, Yu-Jie Zhang, Peng Zhao, Masashi Sugiyama

The standard supervised learning paradigm works effectively when training data shares the same distribution as the upcoming testing samples. However, this stationary assumption is often violated in real-world applications, especially when testing data appear in an online fashion. In this paper, we formulate and investigate the problem of \emph{online label s

Yue Song, Nicu Sebe, Wei Wang

Inserting an SVD meta-layer into neural networks is prone to make the covariance ill-conditioned, which could harm the model in the training stability and generalization abilities. In this paper, we systematically study how to improve the covariance conditioning by enforcing orthogonality to the Pre-SVD layer. Existing orthogonal treatments on the weights ar

Yao Cheng

Recently Atobe-Oi-Yasuda established the newform theory for irreducible tempered generic representations of unramified ${\rm U}_{2n+1}$ over non-archimedean local fields. In this paper we extend their result to every irreducible generic representations and compute the dimensions of the spaces of oldforms. We also compute the Rankin-Selberg integrals attached

Othmane Belarbi, Aftab Khan, Pietro Carnelli, Theodoros Spyridopoulos

The rapid growth of connected devices has led to the proliferation of novel cyber-security threats known as zero-day attacks. Traditional behaviour-based IDS rely on DNN to detect these attacks. The quality of the dataset used to train the DNN plays a critical role in the detection performance, with underrepresented samples causing poor performances. In this

Colin J. Cotter, Robert C. Kirby, Hunter Morris

We derive a linearized rotating shallow water system modeling tides, which can be discretized by mixed finite elements. Unlike previous models, this model allows for multiple layers stratified by density. Like the single-layer case~\cite{kirby2021preconditioning} a weighted-norm preconditioner gives a (nearly) parameter-robust method for solving the resultin

Xiaoquan Xu, Xinpeng Wen, Xiaoyong Xi

In this paper, we mainly discuss some basic properties of Scott power spaces. For a $T_0$ space $X$, let $\mathsf{K}(X)$ be the poset of all nonempty compact saturated subsets of $X$ endowed with the Smyth order. It is proved that the Scott power space $\Sigma \mathsf{K}(X)$ of a well-filtered space $X$ is still well-filtered, and a $T_0$ space $Y$ is well-f

Francis Fortin, Federico Garcia, Sylvain Chaty

High-Mass X-ray Binaries (HMXBs) are produced after the first supernova event in a massive binary. These objects are intrinsically young, and can suffer from a significant natal kick. As such, the progenitors of HMXBs are likely to have formed away from the current location of the X-ray emitting systems. We aim to find the birthplace of the known HMXBs of ou

Patrick Sattler, Juliane Aulbach, Johannes Zirngibl, Georg Carle

Apple recently published its first Beta of the iCloud Private Relay, a privacy protection service with promises resembling the ones of VPNs. The architecture consists of two layers (ingress and egress), operated by disjoint providers. The service is directly integrated into Apple's operating systems and therefore provides a low entry level barrier for a larg

Olivier Mazet, Camille Noûs

We propose in this article a brief description of the work, over almost a decade, resulting from a collaboration between mathematicians and biologists from four different research laboratories, identifiable as the co-authors of the articles whose results are described here, and implicitely co-authors of this article, under the signature of Camille No\^us. Th

Jesus Silva-Rodriguez, Xingpeng Li

This paper proposes a Networked Micro Water-Energy Nexus (NetMicroWEN) capable of co-optimizing and simultaneously supplying water and energy to local consumers in nearby communities. The system manages different water and energy inputs of different communities in a local network to cooperatively meet their demands. This paper considers a centralized network

Meng-Lin Du, Eliecer Hernández, Juan Nieves

We use a $\mathcal{O}(\Lambda_\text{QCD}/m_c)$ heavy quark effective theory scheme, where only $\mathcal{O}(\Lambda_\text{QCD}/m_b)$ and perturbative QCD short distance corrections are neglected, to study the matrix elements of the scalar, pseudoscalar, vector, axial-vector and tensor currents between the $\Lambda_b$ ground state and the odd parity charm $\L

Benjamin Marais, Tony Quertier, Stéphane Morucci

Cybercrime is one of the major digital threats of this century. In particular, ransomware attacks have significantly increased, resulting in global damage costs of tens of billion dollars. In this paper, we train and test different Machine Learning and Deep Learning models for malware detection, malware classification and ransomware detection. We introduce a

Renu Solanki, Monisha Khanna, Shailly Anand, Anita Gulati

Agent based modelling is a computational approach that aims to understand the behaviour of complex systems through simplified interactions of programmable objects in computer memory called agents. Agent based models (ABMs) are predominantly used in fields of biology, ecology, social sciences and economics where the systems of interest often consist of severa

Rahul Kumar Walia

Loop quantum gravity inspired partial polymer quantization in four-dimensional spacetime leads to a globally regular black hole with a single horizon. The polymerized black hole metric is characterized by the minimum length parameter $k$, and mimics the Schwarzschild black hole in the weak-field limit. We present an analytic and numerical investigation of th

B. P. Kay, T. L. Tang, I. A. Tolstukhin, G. B. Roderick

Absolute cross sections for the addition of $s$- and $d$-wave neutrons to $^{14}$C and $^{14}$N have been determined simultaneously via the ($d$,$p$) reaction at 10 MeV/u. The difference between the neutron and proton separation energies, $\Delta S$, is around $-20$ MeV for the $^{14}$C$+$$n$ system and $+8$ MeV for $^{14}$N$+$$n$. The population of the $1s_

Rosanna Milner, Md Asif Jalal, Raymond W. M. Ng, Thomas Hain

For speech emotion datasets, it has been difficult to acquire large quantities of reliable data and acted emotions may be over the top compared to less expressive emotions displayed in everyday life. Lately, larger datasets with natural emotions have been created. Instead of ignoring smaller, acted datasets, this study investigates whether information learnt

G. Rodrigues-da-Silva, L. G. Medeiros

Higher-order theories of gravity are extensions to general relativity (GR) motivated mainly by high-energy physics searching for GR ultraviolet completeness. They are characterized by the inclusion of correction terms in the Einstein-Hilbert action that leads to higher-order field equations. In this paper, we propose investigating inflation due to the GR ext

Yufeng Xin, Shih-Wen Fu, Anirban Mandal, Ryan Tanaka

Most recent network failure diagnosis systems focused on data center networks where complex measurement systems can be deployed to derive routing information and ensure network coverage in order to achieve accurate and fast fault localization. In this paper, we target wide-area networks that support data-intensive distributed applications. We first present a

Zihao Song, Shirantha Welikala, Panos J. Antsaklis, Hai Lin

In this paper, we consider the problem of platooning control with mismatched disturbances using the distributed adaptive backstepping method. The main challenges are: (1) maintaining the compositionality and the distributed nature of the controller, and (2) ensuring the robustness of the controller with respect to general types of disturbances. To address th

Keyuan Zhang, Jiayu Bai, Jialin Liu

Recent years, there has been growing interests in experience-driven procedural level generation. Various metrics have been formulated to model player experience and help generate personalised levels. In this work, we question whether experience metrics can adapt to agents with different personas. We start by reviewing existing metrics for evaluating game lev

Caglar Gulcehre, Srivatsan Srinivasan, Jakub Sygnowski, Georg Ostrovski

Deep neural networks are the most commonly used function approximators in offline reinforcement learning. Prior works have shown that neural nets trained with TD-learning and gradient descent can exhibit implicit regularization that can be characterized by under-parameterization of these networks. Specifically, the rank of the penultimate feature layer, also

Grégoire Delétang, Anian Ruoss, Jordi Grau-Moya, Tim Genewein

Reliable generalization lies at the heart of safe ML and AI. However, understanding when and how neural networks generalize remains one of the most important unsolved problems in the field. In this work, we conduct an extensive empirical study (20'910 models, 15 tasks) to investigate whether insights from the theory of computation can predict the limits of n

Susumu Inoue, Matteo Cerruti, Kohta Murase, Ruo-Yu Liu

Powerful winds with wide opening angles, likely driven by accretion disks around black holes (BHs), are observed in the majority of active galactic nuclei (AGN) and can play a crucial role in AGN and galaxy evolution. If protons are accelerated in the wind near the BH via diffusive shock acceleration, $pp$ and $p\gamma$ processes generate neutrinos as well a

Beatrice Acciaio, Gudmund Pammer

We provide a short proof of the intriguing characterisation of the convex order given by Wiesel and Zhang.

Chris Adolphsen, Kevin Andre, Deepa Angal-Kalinin, Michaela Arnold

Energy-recovery linacs (ERLs) have been emphasised by the recent (2020) update of the European Strategy for Particle Physics as one of the most promising technologies for the accelerator base of future high-energy physics. The current paper has been written as a base document to support and specify details of the recently published European roadmap for the d

Marla Narazani, Ignacio Sarasua, Sebastian Pölsterl, Aldana Lizarraga

Alzheimer's Disease (AD) is the most common form of dementia and often difficult to diagnose due to the multifactorial etiology of dementia. Recent works on neuroimaging-based computer-aided diagnosis with deep neural networks (DNNs) showed that fusing structural magnetic resonance images (sMRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) l

Nathan Ng, Neha Hulkund, Kyunghyun Cho, Marzyeh Ghassemi

Developing and deploying machine learning models safely depends on the ability to characterize and compare their abilities to generalize to new environments. Although recent work has proposed a variety of methods that can directly predict or theoretically bound the generalization capacity of a model, they rely on strong assumptions such as matching train/tes

Ahmed A. Elkamshishy, Chris H. Greene

Ultracold collisions of neutral atoms and molecules have been of great interest since experimental advances enabled the cooling and trapping of such species. This study is a theoretical investigation of a low-energy collision between an alkali atom and a diatomic molecule, accompanied by absorption of a photon from an external electromagnetic field. The long

Ignacio Sarasua, Sebastian Pölsterl, Christian Wachinger

Modeling temporal changes in subcortical structures is crucial for a better understanding of the progression of Alzheimer's disease (AD). Given their flexibility to adapt to heterogeneous sequence lengths, mesh-based transformer architectures have been proposed in the past for predicting hippocampus deformations across time. However, one of the main limitati

Carlos Vega, Diego Porras, Alejandro González-Tudela

Topological insulators feature a number of topologically protected boundary modes linked to the value of their bulk invariant. While in one-dimensional systems the boundary modes are zero dimensional and localized, in two-dimensional topological insulators the boundary modes are chiral, one-dimensional propagating modes along the edges of the system. Thus, t

Vladimiro Benedetti, Nicolas Perrin

In this paper we study general hyperplane sections of adjoint and coadjoint varieties. We show that these are the only sections of homogeneous varieties such that a maximal torus of the automorphism group of the ambient variety stabilizes them. We then study their geometry, provide formulas for their classical cohomology rings in terms of Schubert classes an

Weiming Hu, Qiang Wang, Li Zhang, Luca Bertinetto

In this paper we introduce SiamMask, a framework to perform both visual object tracking and video object segmentation, in real-time, with the same simple method. We improve the offline training procedure of popular fully-convolutional Siamese approaches by augmenting their losses with a binary segmentation task. Once the offline training is completed, SiamMa

Longkang Li, Baoyuan Wu

Integer programming (IP) is an important and challenging problem. Approximate methods have shown promising performance on both effectiveness and efficiency for solving the IP problem. However, we observed that a large fraction of variables solved by some iterative approximate methods fluctuate around their final converged discrete states in very long iterati

J. M. Franco-Patino, R. González-Jiménez, S. Dolan, M. B. Barbaro

We present a complete comparison of semi-inclusive $\nu_\mu$-$^{12}$C cross-section measurements by T2K and MINER$\nu$A collaborations with the predictions from the SuSAv2-MEC model implemented in the neutrino-nucleus event generator GENIE and an unfactorized approach based on the relativistic distorted wave impulse approximation (RDWIA). Results, that inclu

Iddo Eliazar, Shlomi Reuveni

Restart has the potential of expediting or impeding the completion times of general random processes. Consequently, the issue of mean-performance takes center stage: quantifying how the application of restart on a process of interest impacts its completion-time's mean. Going beyond the mean, little is known on how restart affects stochasticity measures of th

Javier Pérez, Manuel A. Vargas, Juan A. Cabrera, Juan J. Castillo

This paper presents a vehicle lateral controller based on spiking neural networks capable of replicating the behavior of a model-based controller but with the additional ability to perform online adaptation. By making use of neural plasticity and thanks to reward modulation learning, neural connections are modified to adjust the response according to the com

Achim Andres

The matter-antimatter asymmetry cannot be explained by the Standard Model (SM) of elementary particle physics. According to A. Sakharov, additional sources of $\mathcal{CP}$-Violating phenomena are needed to understand the matter-antimatter asymmetry. Electric Dipole Moments (EDMs) of subatomic elementary particles may provide additional $\mathcal{CP}$ viola

Konstanze R. Hahn, Claudio Melis, Fabio Bernardini, Lorenzo Paulatto

Phonon properties of small Si nanowires in [110] direction have been analyzed using density functional perturbation theory. Several samples with varying diameters ranging from 0.38 to 1.5 nm have been calculated. It is found that the frequency of optical phonons at the zone center tend to decrease with increasing size of the nanowire. Investigation of the ph

Luca Ricolfi, Luca Stabile, Lidia Morawska, Giorgio Buonanno

Background: While increasing the ventilation rate is an important measure to remove inhalable virus-laden respiratory particles and lower the risk of infection, direct validation in schools with population-based studies is far from definitive. Methods: We investigated the strength of association between ventilation and SARS-CoV-2 transmission reported among

Stefan Frei, Alexander Heinlein

In order to make the numerical simulation of atherosclerotic plaque growth feasible, a temporal homogenization approach is employed. The resulting macro-scale problem for the plaque growth can be further accelerated by using parallel time integration schemes, such as the parareal algorithm. However, the parallel scalability is dominated by the computational

Guopeng Li

In this paper, we study the low regularity convergence problem for the intermediate long wave equation (ILW), with respect to the depth parameter $\delta>0$, on the real line and the circle. As a natural bridge between the Korteweg-de Vries (KdV) and the Benjamin-Ono (BO) equations, the ILW equation is of physical interest. We prove that the solutions of ILW

Manel Bosch Aguilera, Alexis Ghermaoui, Rémy Vatré, Raphaël Bouganne

We report on experiments exploring the non-Hermitian dynamics of pairs of two-level atoms tightly confined in an optical lattice and driven by a near-resonant laser. Although spontaneous emission is negligible for the long-lived excited state, two-body dissipation arises from strong inelastic collisions between two atoms in the excited state. We demonstrate

Laura A. Hayes, Sophie Musset, Daniel M üller, S äm Krucker

The Spectrometer/Telescope for Imaging X-rays (STIX) is one of the 10 instruments on-board the scientific payload of ESA's Solar Orbiter mission. STIX provides hard X-ray imaging spectroscopy in the 4-150~keV energy range, observing hard X-ray bremsstrahlung emission from the Sun. These observations provide diagnostics of the hottest thermal plasmas ($>$10~M

Conor McMeel, Panos Parpas

We consider the problem of minimizing a convex function that depends on an uncertain parameter $\theta$. The uncertainty in the objective function means that the optimum, $x^*(\theta)$, is also a function of $\theta$. We propose an efficient method to compute $x^*(\theta)$ and its statistics. We use a chaos expansion of $x^*(\theta)$ along a truncated basis

D. A. Zezyulin, S. A. Kolodny, O. V. Kibis, I. V. Tokatly

We developed a theory of electron scattering by a short-range repulsive potential in a cavity. In the regime of ultrastrong electron coupling to the cavity electromagnetic field, the vacuum fluctuations of the field result in the dynamical stabilization of a quasistationary polariton state confined in the core of the repulsive potential. When the energy of a

Tokio Matsuyama, Lenny Neyt

We consider linear and non-linear Cauchy equations in the context of Sobolev spaces. In particular, we show the global existence of solutions to the Kirchhoff equation with initial data in the Sobolev spaces, a problem that has been open for more than eighty years. Our proof is based on a new uniform estimate for solutions to the linear equation with time-de

B. Roter, N. Ninkovic, S. V. Dordevic

In this work we used unsupervised machine learning methods in order to find possible clustering structures in superconducting materials data sets. We used the SuperCon database, as well as our own data sets complied from literature, in order to explore how machine learning algorithms groups superconductors. Both conventional clustering methods like k-means,

Juan Pinto-Ríos, Felipe Calderón, Ariel Leiva, Gabriel Hermosilla

A deep reinforcement learning approach is applied, for the first time, to solve the routing, modulation, spectrum and core allocation (RMSCA) problem in dynamic multicore fiber elastic optical networks (MCF-EONs). To do so, a new environment - compatible with OpenAI's Gym - was designed and implemented to emulate the operation of MCF-EONs. The new environmen

Jon Andre Ottesen, Matthan W. A. Caan, Inge Rasmus Groote, Atle Bjørnerud

Objective: To improve accelerated MRI reconstruction through a densely connected cascading deep learning reconstruction framework. Materials and Methods: A cascading deep learning reconstruction framework (baseline model) was modified by applying three architectural modifications: Input-level dense connections between cascade inputs and outputs, an improved

Siqi Yang, Mingzhe Xie, Yao Fu, Zihan Zhao

We propose the boost asymmetry of the diboson productions in pp collisions as a new experimental observable, which can provide unique information on the proton structure. The boost asymmetry rises as the difference in the kinematics of the two bosons, that are coupled to the two different quark and antiquark initial states, respectively, and thus reflects di

Patrícia Martinková, František Bartoš, Marek Brabec

Inter-rater reliability (IRR), which is a prerequisite of high-quality ratings and assessments, may be affected by contextual variables such as the rater's or ratee's gender, major, or experience. Identification of such heterogeneity sources in IRR is important for implementation of policies with the potential to decrease measurement error and to increase IR

Francisco López, Lars Karlsson, Paolo Bientinesi

Expressions that involve matrices and vectors, known as linear algebra expressions, are commonly evaluated through a sequence of invocations to highly optimised kernels provided in libraries such as BLAS and LAPACK. A sequence of kernels represents an algorithm, and in general, because of associativity, algebraic identities, and multiple kernels, one express

Chris Hamilton, Roman R. Rafikov

The dozens of compact object mergers detected by LIGO/Virgo raise a key theoretical question: how do initially wide binaries shrink sufficiently quickly that they are able to merge via gravitational wave (GW) radiation within a Hubble time? One promising class of answers involves secular driving of binary eccentricity by some external tidal perturbation. Thi

Marco Bertola, Alan Groot, Arno B. J. Kuijlaars

Critical measures in the complex plane are saddle points for the logarithmic energy with external field. Their local and global structure was described by Martinez-Finkelshtein and Rakhmanov. In this paper we start the development of a theory of critical measures on higher genus Riemann surfaces, where the logarithmic energy is replaced by the energy with re

Steven Jöns, Claus-Dieter Munz

In this paper, we consider Riemann solvers with phase transition effects based on the Euler-Fourier equation system. One exact and two approximate solutions of the two-phase Riemann problem are obtained by modelling the phase transition process via the theory of classical irreversible thermodynamics. Closure is obtained by appropriate Onsager coefficients fo

Agus Gunawan, Muhammad Adi Nugroho, Se Jin Park

In recent years, a ton of research has been conducted on real image denoising tasks. However, the efforts are more focused on improving real image denoising through creating a better network architecture. We explore a different direction where we propose to improve real image denoising performance through a better learning strategy that can enable test-time

Ameer Jaber

Let $R$ be a commutative ring with unity $(1\not=0)$ and let $\mathfrak{J}(R)$ be the set of all ideals of $R$. Let $\phi:\mathfrak{J}(R)\rightarrow\mathfrak{J}(R)\cup\{\emptyset\}$ be a reduction function of ideals of $R$ and let $\delta:\mathfrak{J}(R)\rightarrow\mathfrak{J}(R)$ be an expansion function of ideals of $R$. We recall that a proper ideal $I$ o

John Nay

Climate adaptation could yield significant benefits. However, the uncertainty of which future climate scenarios will occur decreases the feasibility of proactively adapting. Climate adaptation projects could be underwritten by benefits paid for in the climate scenarios that each adaptation project is designed to address because other entities would like to h

Tu Bui, Ning Yu, John Collomosse

Rapid advances in Generative Adversarial Networks (GANs) raise new challenges for image attribution; detecting whether an image is synthetic and, if so, determining which GAN architecture created it. Uniquely, we present a solution to this task capable of 1) matching images invariant to their semantic content; 2) robust to benign transformations (changes in

Jiayang Ao, Qiuhong Ke, Krista A. Ehinger

Existing computer vision systems can compete with humans in understanding the visible parts of objects, but still fall far short of humans when it comes to depicting the invisible parts of partially occluded objects. Image amodal completion aims to equip computers with human-like amodal completion functions to understand an intact object despite it being par

Tjark Heitmann, Jonas Richter, Fengping Jin, Kristel Michielsen

We provide a detailed comparison between the dynamics of high-temperature spatiotemporal correlation functions in quantum and classical spin models. In the quantum case, our large-scale numerics are based on the concept of quantum typicality, which exploits the fact that random pure quantum states can faithfully approximate ensemble averages, allowing the si

Qingguo Hong, YounJu Lee, Jinchao Xu

In this paper, we revisit Korn's inequality for the piecewise $H^1$ space based on general polygonal or polyhedral decompositions of the domain. Our Korn's inequality is expressed with minimal jump terms. These minimal jump terms are identified by characterizing the restriction of rigid body mode to edge/face of the partitions. Such minimal jump conditions a

Ahmed Ghorbel, Ahmed Aldahdooh, Shadi Albarqouni, Wassim Hamidouche

The quality of patient care associated with diagnostic radiology is proportionate to a physician workload. Segmentation is a fundamental limiting precursor to both diagnostic and therapeutic procedures. Advances in machine learning (ML) aim to increase diagnostic efficiency by replacing a single application with generalized algorithms. The goal of unsupervis

Shaogang Ren, Guanhua Fang, Ping Li

Best subset selection is considered the `gold standard' for many sparse learning problems. A variety of optimization techniques have been proposed to attack this non-convex and NP-hard problem. In this paper, we investigate the dual forms of a family of $\ell_0$-regularized problems. An efficient primal-dual method has been developed based on the primal and

Evripidis Bampis, Bruno Escoffier, Paul Youssef

We focus on an online 2-stage problem, motivated by the following situation: consider a system where students shall be assigned to universities. There is a first round where some students apply, and a first (stable) matching $M_1$ has to be computed. However, some students may decide to leave the system (change their plan, go to a foreign university, or to s

Nicolas Matentzoglu, Damien Goutte-Gattat, Shawn Zheng Kai Tan, James P. Balhoff

Similar to managing software packages, managing the ontology life cycle involves multiple complex workflows such as preparing releases, continuous quality control checking, and dependency management. To manage these processes, a diverse set of tools is required, from command line utilities to powerful ontology engineering environments such as ROBOT. Particul

Sang Keun Choe, Willie Neiswanger, Pengtao Xie, Eric Xing

Gradient-based multilevel optimization (MLO) has gained attention as a framework for studying numerous problems, ranging from hyperparameter optimization and meta-learning to neural architecture search and reinforcement learning. However, gradients in MLO, which are obtained by composing best-response Jacobians via the chain rule, are notoriously difficult t

A. Yu. Loginov

The scattering of massless fermions on a one-dimensional Q-ball is studied both analytically and numerically in the background field approximation. The wave functions of the fermionic scattering states are found in analytical form. General expressions are derived for the transmission and reflection coefficients and the corresponding $S$-matrix elements. Gene

Joan Giner-Miguelez, Abel Gómez, Jordi Cabot

Datasets play a central role in the training and evaluation of machine learning (ML) models. But they are also the root cause of many undesired model behaviors, such as biased predictions. To overcome this situation, the ML community is proposing a data-centric cultural shift where data issues are given the attention they deserve, and more standard practices

Aimeric Malter

In this paper we study a particular mirror construction to the complete intersection of two cubics in $\mathbb{P}^5$, due to Libgober and Teitelbaum. Using variations of geometric invariant theory and methods of Favero and Kelly, we prove a derived equivalence of this mirror to the Batyrev-Borisov mirror of the complete intersection.

Zezu Liang, Yuan Liu, Tat-Ming Lok, Kaibin Huang

Mobile edge computing (MEC) is a promising technology for enhancing the computation capacities and features of mobile users by offloading complex computation tasks to the edge servers. However, mobility poses great challenges on delivering reliable MEC service required for latency-critical applications. First, mobility management has to tackle the dynamics o

Andrea Ciccotelli, Hanaa Abbas, Roberto Di Pietro

The exploding rate of data publishing in our networked society has magnified the risk of sensitive information leakage and misuse, pushing the need to secure multimedia content from unintended exposure to potentially untrusted third parties. Data sanitization -- the process of securing multimedia by removing or obfuscating sensitive information such as perso

Hanyin Wang, Meghan R. Hutch, Yikuan Li, Adrienne S. Kline

Over 12 billion doses of COVID-19 vaccines have been administered at the time of writing. However, public perceptions of vaccines have been complex. We analyzed COVID-19 vaccine-related tweets to understand the evolving perceptions of COVID-19 vaccines. We finetuned a deep learning classifier using a state-of-the-art model, XLNet, to detect each tweet's sent

Michael Kölle, Lennart Rietdorf, Kyrill Schmid

In multi-agent reinforcement learning systems, the actions of one agent can have a negative impact on the rewards of other agents. One way to combat this problem is to let agents trade their rewards amongst each other. Motivated by this, this work applies a trading approach to a simulated scheduling environment, where the agents are responsible for the assig

Rodrigo Alonso, Mia West

Functional methods and a derivative expansion are employed for laying out a procedure to compute the effective action to any loop order, for scalar fields parametrising an arbitrary Riemannian manifold, while maintaining explicit field-space covariance. In this process, the geometric generalization of the LSZ reduction formula is presented. These results are

A. Sauer, J. Z. Bernád

Quantitative characterization of different entanglement detection criteria for bipartite systems is presented. We review the implication sequence of these criteria and then numerically estimate volume ratios between criteria non-violating quantum states and all quantum states. The numerical approach is based on the hit-and-run algorithm, which is applied to

Francesco Fassò, Nicola Sansonetto

We study some aspects of the dynamics of the nonholonomic system formed by a heavy homogeneous ball constrained to roll without sliding on a steadily rotating surface of revolution. First, in the case in which the figure axis of the surface is vertical (and hence the system is $\textrm{SO(3)}\times\textrm{SO(2)}$-symmetric) and the surface has a (nondegenera

Mircea Mustata, Sebastian Olano

Suppose that $X$ is a smooth complex algebraic variety of dimension $\geq 3$ and $f$ defines a hypersurface $Z$ in $X$, with a unique singular point $P$. Bitoun and Schedler conjectured that the ${\mathcal D}$-module generated by $\tfrac{1}{f}$ has length equal to $g_P(Z)+2$, where $g_{P}(Z)$ is the reduced genus of $Z$ at $P$. We prove that this length is a

Morgan T. Chidester, Ebraheem Farag, F. X. Timmes

We seek signatures of the current experimental $^{12}$C$(\alpha,\gamma)^{16}$O reaction rate probability distribution function in the pulsation periods of carbon-oxygen white dwarf models. We find that adiabatic g-modes trapped by the interior carbon-rich layer offer potentially useful signatures of this reaction rate probability distribution function. Probi

Pablo F. Castro, Pedro D'Argenio, Luciano Putruele, Ramiro Demasi

We introduce a formal notion of masking fault-tolerance between probabilistic transition systems based on a variant of probabilistic bisimulation (named masking simulation). We also provide the corresponding probabilistic game characterization. Even though these games could be infinite, we propose a symbolic way of representing them, such that it can be deci

Marco Caroccia, Giorgio Saracco

Given an open, bounded, planar set $\Omega$, we consider its $p$-Cheeger sets and its isoperimetric sets. We study the set-valued map $\mathfrak{V}:[\frac12,+\infty)\rightarrow\mathcal{P}((0,|\Omega|])$ associating to each $p$ the set of volumes of $p$-Cheeger sets. We show that whenever $\Omega$ satisfies some geometric structural assumptions (convex sets a

Zhi-Qing Zhang, Yan-Chao Zhao, Zhi-Lin Guan, Zhi-Jie Sun

In this work we study the quasi-two-body decays $B\to K^*\gamma\to K\pi\gamma$ in the perturbative QCD (PQCD) approach. The two-meson distribution amplitudes (DAs) are introduced to describe the final state interactions of the $K\pi$ pair, which involve the time-like form factors and the Gegenbauer polynomials. We calculate the CP averaged branching ratios f

Jingjie Shang, Kunchang Li, Kaibin Tian, Haisheng Su

Distracted driving causes thousands of deaths per year, and how to apply deep-learning methods to prevent these tragedies has become a crucial problem. In Track3 of the 6th AI City Challenge, researchers provide a high-quality video dataset with densely action annotations. Due to the small data scale and unclear action boundary, the dataset presents a unique