October 2022 arXiv papers — page 99

Jeffrey Dominic, Nandita Bhaskhar, Arjun D. Desai, Andrew Schmidt

Although supervised learning has enabled high performance for image segmentation, it requires a large amount of labeled training data, which can be difficult to obtain in the medical imaging field. Self-supervised learning (SSL) methods involving pretext tasks have shown promise in overcoming this requirement by first pretraining models using unlabeled data.

Angie Michaiel, Amy Bernard

Rapid anthropogenic environmental changes, including those due to habitat contamination, degradation, and climate change, have far-reaching effects on biological systems that may outpace animals' adaptive responses (Radchuk et al., 2019). Neurobiological systems mediate interactions between animals and their environments and evolved over millions of years to

S. Desidera, M. Damasso, R. Gratton, S. Benatti

Transiting planets around young stars are key benchmarks for our understanding of planetary systems. One of such candidates was identified around the K dwarf HD 18599 by TESS, labeled as TOI-179. We present the confirmation of the transiting planet and the characterization of the host star and of the TOI-179 system over a broad range of angular separations.

Jicang Cai, Saeed Vahidian, Weijia Wang, Mohsen Joneidi

Meta-learning often referred to as learning-to-learn is a promising notion raised to mimic human learning by exploiting the knowledge of prior tasks but being able to adapt quickly to novel tasks. A plethora of models has emerged in this context and improved the learning efficiency, robustness, etc. The question that arises here is can we emulate other aspec

Jorg Bornschein, Yazhe Li, Marcus Hutter

Minimum Description Length (MDL) provides a framework and an objective for principled model evaluation. It formalizes Occam's Razor and can be applied to data from non-stationary sources. In the prequential formulation of MDL, the objective is to minimize the cumulative next-step log-loss when sequentially going through the data and using previous observatio

Andrew Campbell, Sean O'Rourke

We consider the spectrum of random Laplacian matrices of the form $L_n=A_n-D_n$ where $A_n$ is a real symmetric random matrix and $D_n$ is a diagonal matrix whose entries are equal to the corresponding row sums of $A_n$. If $A_n$ is a Wigner matrix with entries in the domain of attraction of a Gaussian distribution the empirical spectral measure of $L_n$ is

Yuting Tan, Pak Ki Henry Tsang, Vladimir Dobrosavljević, Louk Rademaker

The moir\'e pattern induced by lattice mismatch in transition-metal dichalcogenide heterobilayers causes the formation of flat bands, where interactions dominate the kinetic energy. At fractional fillings of the flat valence band, the long-range electron interactions then induce Wigner-Mott crystals. In this Letter we investigate the nontrivial electronic ph

Samuel Federman, S. Thomas Megeath, John J. Tobin, Patrick D. Sheehan

We present an 870 $\mu$m continuum survey of 300 protostars from the Herschel Orion Protostar Survey using the Atacama Compact Array (ACA). These data measure protostellar flux densities on envelope scales $\leq$8000 au (20") and resolve the structure of envelopes with 1600 au (4") resolution, a factor of 3-5 improvement in angular resolution over existing s

Thomas E. Roth, Samuel T. Elkin

In superconducting circuit quantum information technologies, classical microwave pulses are applied to control and measure the qubit states. Currently, the design of these microwave pulses use simple theoretical or numerical models that do not account for the self-consistent interactions of how the qubit state modifies the applied microwave pulse. In this wo

Hao-sheng Jiang, Jia-li Chen, Chong-qi Zhang

To avoid multicollinearity in regression analysis, Ye and Zhou(2013) proposed $K-$optimality criterion. By far the most popular models for modeling the response of a mixture experiment are the Scheff\'{e} models. However, there have been no reports about constructing $K-$optimal designs for mixture models. The paper constructs $K-$optimal designs for first-o

Adam Bielski, Paolo Favaro

We introduce MOVE, a novel method to segment objects without any form of supervision. MOVE exploits the fact that foreground objects can be shifted locally relative to their initial position and result in realistic (undistorted) new images. This property allows us to train a segmentation model on a dataset of images without annotation and to achieve state of

J. Schober, M. T. Sargent, R. S. Klessen, D. R. G. Schleicher

The infrared-radio correlation (IRRC) of star-forming galaxies can be used to estimate their star formation rate (SFR) based on the radio continuum luminosity at MHz-GHz frequencies. For its application in future deep radio surveys, it is crucial to know whether the IRRC persists at high redshift z. Previous works have reported that the 1.4 GHz IRRC correlat

Nicholas Rober, Michael Everett, Songan Zhang, Jonathan P. How

As neural networks become more integrated into the systems that we depend on for transportation, medicine, and security, it becomes increasingly important that we develop methods to analyze their behavior to ensure that they are safe to use within these contexts. The methods used in this paper seek to certify safety for closed-loop systems with neural networ

Gwangho Choi, Judith Su

Generating a coherent optical frequency comb at an arbitrary wavelength is important for fields such as spectroscopy and optical communications. Dark solitons which are coherent states of optical frequency combs in normal dispersion microresonators can extend the operating wavelength and be excited via intermodal coupling. They have been investigated over th

Shuguang Chen, Leonardo Neves, Thamar Solorio

In this work, we take the named entity recognition task in the English language as a case study and explore style transfer as a data augmentation method to increase the size and diversity of training data in low-resource scenarios. We propose a new method to effectively transform the text from a high-resource domain to a low-resource domain by changing its s

Dae Gwan Lee

We study the approximation properties of pseudo-differential operators with small time-frequency dispersion, meaning that their spreading functions are supported in a small neighborhood of the origin. It is commonly assumed that for such operators $H$, the output $Hf$ can differ only a little from a scalar multiple of the input $f$. However, we disprove this

F. Acernese, M. Agathos, A. Ain, S. Albanesi

Detector characterization and data quality studies -- collectively referred to as {\em DetChar} activities in this article -- are paramount to the scientific exploitation of the joint dataset collected by the LIGO-Virgo-KAGRA global network of ground-based gravitational-wave (GW) detectors. They take place during each phase of the operation of the instrument

F. Acernese, M. Agathos, A. Ain, S. Albanesi

The Advanced Virgo detector has contributed with its data to the rapid growth of the number of detected gravitational-wave (GW) signals in the past few years, alongside the two Advanced LIGO instruments. First during the last month of the Observation Run 2 (O2) in August 2017 (with, most notably, the compact binary mergers GW170814 and GW170817), and then du

Corto Mascle

We study the verification of distributed systems where processes are finite automata with access to a shared pool of locks. We consider objectives that are boolean combinations of local regular constraints. We show that the problem, PSPACE-complete in general, falls in NP with the right assumptions on the system. We use restrictions on the number of locks a

Bracha Laufer-Goldshtein, Adam Fisch, Regina Barzilay, Tommi Jaakkola

Machine learning applications frequently come with multiple diverse objectives and constraints that can change over time. Accordingly, trained models can be tuned with sets of hyper-parameters that affect their predictive behavior (e.g., their run-time efficiency versus error rate). As the number of constraints and hyper-parameter dimensions grow, naively se

J. Wofford, A. Yelikar, H. Gallagher, E. Champion

The Rapid Iterative FiTting (RIFT) parameter inference algorithm provides a framework for efficient, highly-parallelized parameter inference for GW sources. In this paper, we summarize essential algorithm enhancements and operating point choices for the RIFT iterative algorithm, including choices used for analysis of LIGO/Virgo O3 observations. We also descr

Steven Ge, Toshiya Itoh

A recently introduced restricted variant of the multidimensional stable roommate problem is the roommate diversity problem: each agent belongs to one of two types (e.g., red and blue), and the agents' preferences over the coalitions solely depend on the fraction of agents of their own type among their roommates. There are various notions of stability that de

Surya Raghavendran, Brian R. Williams

We present a conjectural description of the space of local operators on a stack of finitely many fivebranes in $M$ theory at the level of the holomorphic twist. Our approach is through the lens of twisted holography and utilizes a description of the minimal twist of eleven-dimensional supergravity. We find that the spaces of local operators are modules for t

Mia Conlon, Domenico Pellegrino, J. K. Slingerland

Kitaev's toric code is constructed using a finite gauge group from gauge theory. Such gauge theories can be generalized with the gauge group generalized to any finite-dimensional semisimple Hopf algebra. This also leads to generalizations of the toric code. Here we consider the simple case where the gauge group is unchanged but furnished with a non-trivial q

Andrés Galindo-Olarte, Juntao Huang, Jennifer K. Ryan, Yingda Cheng

This paper considers the discontinuous Galerkin (DG) methods for solving the Vlasov-Maxwell (VM) system, a fundamental model for collisionless magnetized plasma. The DG methods provide accurate numerical description with conservation and stability properties. However, to resolve the high dimensional probability distribution function, the computational cost i

Sishuo Chen, Wenkai Yang, Zhiyuan Zhang, Xiaohan Bi

Natural language processing (NLP) models are known to be vulnerable to backdoor attacks, which poses a newly arisen threat to NLP models. Prior online backdoor defense methods for NLP models only focus on the anomalies at either the input or output level, still suffering from fragility to adaptive attacks and high computational cost. In this work, we take th

Cecilia Latotzke, Batuhan Balim, Tobias Gemmeke

The biggest challenge for the deployment of Deep Neural Networks (DNNs) close to the generated data on edge devices is their size, i.e., memory footprint and computational complexity. Both are significantly reduced with quantization. With the resulting lower word-length, the energy efficiency of DNNs increases proportionally. However, lower word-length typic

Nicola Scafetta

The Coupled Model Intercomparison Project (phase 6) (CMIP6) global circulation models (GCMs) predict equilibrium climate sensitivity (ECS) values ranging between 1.8 and 5.7 $^\circ$C. To narrow this range, we group 38 GCMs into low, medium and high ECS subgroups and test their accuracy and precision in hindcasting the mean global surface warming observed fr

Ayeon Lee, Young Sun Lee, Young Kwang Kim, Timothy C. Beers

We present a chemodynamical analysis of 11,562 metal-rich, high-eccentricity halo-like main-sequence (MS) stars, which has been referred to as the Splash or Splashed Disk, selected from Sloan Digital Sky Survey (SDSS) and Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). When divided into two groups, a low-[$\alpha$/Fe] population (LAP) and

Huiyin Xue, Nikolaos Aletras

Transformer-based pre-trained language models are vocabulary-dependent, mapping by default each token to its corresponding embedding. This one-to-one mapping results into embedding matrices that occupy a lot of memory (i.e. millions of parameters) and grow linearly with the size of the vocabulary. Previous work on on-device transformers dynamically generate

Krzysztof Olejniczak, Milan Šulc

Detection and recognition of text from scans and other images, commonly denoted as Optical Character Recognition (OCR), is a widely used form of automated document processing with a number of methods available. Yet OCR systems still do not achieve 100% accuracy, requiring human corrections in applications where correct readout is essential. Advances in machi

Bruce T. Tsurutani, James Green, Rajkumar Hajra

On ~1613 UT, 3 February 2022 SpaceX launched 49 Starlink satellites from Cape Kennedy, Florida into ~210 km altitude orbits where they were to receive subsequent boosts. We assume that 9 satellites received subsequent boosts and succeeded in obtaining stable orbits. Several of the Starlink satellites reentered the atmosphere in the early morning hours of 7 F

Daniel Pablos, Alba Soto-Ontoso

Energetic jets that traverse the quark-gluon plasma created in heavy-ion collisions serve as excellent probes to study this new state of deconfined QCD matter. Presently, however, our ability to achieve a crisp theoretical interpretation of the crescent number of jet observables measured in experiments is hampered by the presence of selection biases. The aim

Guozhi Dong, Michael Hintermüller, Kostas Papafitsoros

We propose and analyze a numerical algorithm for solving a class of optimal control problems for learning-informed semilinear partial differential equations. The latter is a class of PDEs with constituents that are in principle unknown and are approximated by nonsmooth ReLU neural networks. We first show that a direct smoothing of the ReLU network with the a

Phillip D. Keathley, Simon V. B. Jensen, Matthew D. Yeung, Mina R. Bionta

Time-domain analysis of harmonic fields with sub-cycle resolution is now experimentally viable due to the emergence of sensitive, on-chip techniques for petahertz-scale optical-field sampling. We demonstrate how such a time-domain, field-resolved analysis uncovers the extreme nonlinear electron dynamics responsible for high-harmonic generation within solids.

Giuseppe Orlando, Tommaso Benacchio, Luca Bonaventura

We present an accurate and efficient solver for atmospheric dynamics simulations that allows for non-conforming mesh refinement. The model equations are the conservative Euler equations for compressible flows. The numerical method is based on an $h-$adaptive Discontinuous Galerkin spatial discretization and on a second order Additive Runge Kutta IMEX method

Pezhman Nasirifard, Hans-Arno Jacobsen

Operating a scalable and reliable server application, such as publish/subscribe (pub/sub) systems, requires tremendous development efforts and resources. The emerging serverless paradigm simplifies the development and deployment of highly available applications by delegating most operational concerns to the cloud providers. The serverless paradigm describes

Anthony Kiely, Eoin O'Connor, Thomás Fogarty, Gabriel T. Landi

The statistics of work done on a quantum system can be quantified by the two-point measurement scheme. We show how the Shannon entropy of the work distribution admits a general upper bound depending on the initial diagonal entropy, and a purely quantum term associated to the relative entropy of coherence. We demonstrate that this approach captures strong sig

Wenting Xu, Zhenghua Xu, Junyang Chen, Chang Qi

To reduce doctors' workload, deep-learning-based automatic medical report generation has recently attracted more and more research efforts, where deep convolutional neural networks (CNNs) are employed to encode the input images, and recurrent neural networks (RNNs) are used to decode the visual features into medical reports automatically. However, these stat

Weipeng Zhuo, Ziqi Zhao, Ka Ho Chiu, Shiju Li

We study the problem of floor identification for radiofrequency (RF) signal samples obtained in a crowdsourced manner, where the signal samples are highly heterogeneous and most samples lack their floor labels. We propose GRAFICS, a graph embedding-based floor identification system. GRAFICS first builds a highly versatile bipartite graph model, having APs on

Lukas Bödeker, Eliana Fiorelli, Markus Müller

Quantum neural networks form one pillar of the emergent field of quantum machine learning. Here, quantum generalisations of classical networks realizing associative memories - capable of retrieving patterns, or memories, from corrupted initial states - have been proposed. It is a challenging open problem to analyze quantum associative memories with an extens

Alexander Terenin, David R. Burt, Artem Artemev, Seth Flaxman

Gaussian processes are frequently deployed as part of larger machine learning and decision-making systems, for instance in geospatial modeling, Bayesian optimization, or in latent Gaussian models. Within a system, the Gaussian process model needs to perform in a stable and reliable manner to ensure it interacts correctly with other parts of the system. In th

Hugo Bloem, Andrew Curtis, Daniel Tetzlaff

Geological process models simulate a range of dynamic processes to evolve a base topography into a final 2D cross-section or 3D geological scenario. In principle, process parameters may be updated to better align with observed geophysical or geological data; however, many realisations of process models that embody different conceptual models may provide simi

Eusebio Gardella, Hannes Thiel

We say that an algebra is zero-product balanced if $ab\otimes c$ and $a\otimes bc$ agree modulo tensors of elements with zero-product. This is closely related to but more general than the notion of a zero-product determined algebra of Bre\v{s}ar, Gra\v{s}i\v{c} and Ortega. Every surjective, zero-product preserving map from a zero-product balanced algebra is

Kay Hansel, Julen Urain, Jan Peters, Georgia Chalvatzaki

Motion generation in cluttered, dense, and dynamic environments is a central topic in robotics, rendered as a multi-objective decision-making problem. Current approaches trade-off between safety and performance. On the one hand, reactive policies guarantee fast response to environmental changes at the risk of suboptimal behavior. On the other hand, planning-

Shengjia Chen, Nikunj Gupta, Woodward B. Galbraith, Valay Shah

Rheumatoid arthritis (RA) is an autoimmune condition caused when patients' immune system mistakenly targets their own tissue. Machine learning (ML) has the potential to identify patterns in patient electronic health records (EHR) to forecast the best clinical treatment to improve patient outcomes. This study introduced a Drug Response Prediction (DRP) framew

Weipeng Zhuo, Ka Ho Chiu, Jierun Chen, Jiajie Tan

In applications such as elderly care, dementia anti-wandering and pandemic control, it is important to ensure that people are within a predefined area for their safety and well-being. We propose GEM, a practical, semi-supervised Geofencing system with network EMbedding, which is based only on ambient radio frequency (RF) signals. GEM models measured RF signa

Ioannis Chatzigeorgiou, Dmitry Savostyanov

We consider a transmitter that encodes data packets using network coding and broadcasts coded packets. A receiver employing network decoding recovers the data packets if a sufficient number of error-free coded packets are gathered. The receiver does not abandon its efforts to recover the data packets if network decoding is unsuccessful; instead, it employs s

Johann Huber, Oumar Sane, Alex Coninx, Faiz Ben Amar

Robotics grasping refers to the task of making a robotic system pick an object by applying forces and torques on its surface. Despite the recent advances in data-driven approaches, grasping remains an unsolved problem. Most of the works on this task are relying on priors and heavy constraints to avoid the exploration problem. Novelty Search (NS) refers to ev

Amir Rasouli, Iuliia Kotseruba

Predicting pedestrian behavior is a crucial task for intelligent driving systems. Accurate predictions require a deep understanding of various contextual elements that potentially impact the way pedestrians behave. To address this challenge, we propose a novel framework that relies on different data modalities to predict future trajectories and crossing acti

Juan Heredia, Christian Schlette, Mikkel Baun Kjærgaard

Worldwide most factories aim for low-cost and fast production ignoring resources and energy consumption. But, high revenues have been accompanied by environmental degradation. The United Nations reacted to the ecological problem and proposed the Sustainable Development Goals, and one of them is Sustainable Production (Goal 12). In addition, the participation

Héctor Olivero, Denis Talay

In this work we address the problem of detecting whether a sampled probability distribution of a random variable $V$ has infinite first moment. This issue is notably important when the sample results from complex numerical simulation methods. For example, such a situation occurs when one simulates stochastic particle systems with complex and singular McKean-

Martín Ochoa, Jorge Toro-Pozo, David Basin

Digital authentication is a mature field, offering a range of solutions with rigorous mathematical guarantees. Nevertheless, paper documents, where cryptographic techniques are not directly applicable, are still widely utilized due to usability and legal reasons. We propose a novel approach to authenticating paper documents using smartphones by taking short

Yiming Zhu, Hongyu Liu, Yibing Song, ziyang Yuan

Free-form text prompts allow users to describe their intentions during image manipulation conveniently. Based on the visual latent space of StyleGAN[21] and text embedding space of CLIP[34], studies focus on how to map these two latent spaces for text-driven attribute manipulations. Currently, the latent mapping between these two spaces is empirically design

Robert Hovden, Yi Jiang, Huolin L. Xin, Lena F. Kourkoutis

The discrete Fourier transform is among the most routine tools used in high-resolution scanning / transmission electron microscopy (S/TEM). However, when calculating a Fourier transform, periodic boundary conditions are imposed and sharp discontinuities between the edges of an image cause a cross patterned artifact along the reciprocal space axes. This artif

Shriram Chennakesavalu, David J. Toomer, Grant M. Rotskoff

Coarse-grained models are a core computational tool in theoretical chemistry and biophysics. A judicious choice of a coarse-grained model can yield physical insight by isolating the essential degrees of freedom that dictate the thermodynamic properties of a complex, condensed-phase system. The reduced complexity of the model typically leads to lower computat

Zhuoqing Song, Weijian Li, Kexin Jin, Lei Shi

Decentralized optimization is an emerging paradigm in distributed learning in which agents achieve network-wide solutions by peer-to-peer communication without the central server. Since communication tends to be slower than computation, when each agent communicates with only a few neighboring agents per iteration, they can complete iterations faster than wit

Ram C. Dhuley

Cryocooler conduction cooled devices can experience significant cooldown time due to lower available cooling capacity compares to convection cooled devices. Therefore, the cooldown time is an important design parameter for conduction cooled devices. This article introduces a framework developed in Python for calculating the cooldown profiles and cooldown tim

Alexander New, Benjamin Eng, Andrea C. Timm, Andrew S. Gearhart

In this work, we assess the ability of physics-informed neural networks (PINNs) to solve increasingly-complex coupled ordinary differential equations (ODEs). We focus on a pair of benchmarks: discretized partial differential equations and harmonic oscillators, each of which has a tunable parameter that controls its complexity. Even by varying network archite

Gianmarco Roggiolani, Matteo Sodano, Tiziano Guadagnino, Federico Magistri

Plant phenotyping is a central task in agriculture, as it describes plants' growth stage, development, and other relevant quantities. Robots can help automate this process by accurately estimating plant traits such as the number of leaves, leaf area, and the plant size. In this paper, we address the problem of joint semantic, plant instance, and leaf instanc

Debapratim Banerjee

In the current paper we consider a Wigner matrix and consider an analytic function of polynomial growth on a set containing the support of the semicircular law in its interior. We prove that the linear spectral statistics corresponding to the function and the point process at the edge of the Wigner matrix are asymptotically independent when the entries of th

Jakob Stubenrauch, Christian Keup, Anno C. Kurth, Moritz Helias

We analytically determine the number and distribution of fixed points in a canonical model of a chaotic neural network. This distribution reveals that fixed points and dynamics are confined to separate shells in phase space. Furthermore, the distribution enables us to determine the eigenvalue spectra of the Jacobian at the fixed points. Despite the radial se

Aloni Cohen, Adam Smith, Marika Swanberg, Prashant Nalini Vasudevan

Recent digital rights frameworks give users the right to delete their data from systems that store and process their personal information (e.g., the "right to be forgotten" in the GDPR). How should deletion be formalized in complex systems that interact with many users and store derivative information? We argue that prior approaches fall short. Definitions o

Binh Nguyen, Sarah Jenkins, Sergiu Ruta, Richard Evans

We have computationally studied the properties of higher-order magnetic anisotropy constants in an L10/A1-FePt coreshell system which is characterized by a strong second-order 2-ion Fe-Pt anisotropy component. We show that the coreshell structure induces an unexpected fourth-order anisotropy constant K2 the magnitude of which varies non-monotonically with th

E. Lanina, A. Sleptsov

We introduce a novel symmetry for quantum 6j-symbols, which we call the tug-the-hook symmetry. Unlike other known symmetries, it is applicable for any representations, including ones with multiplicities. We provide several evidences in favour of the tug-the-hook symmetry. First, this symmetry follows from the eigenvalue conjecture. Second, it is shown by sev

Amir Zeldes, Nick Howell, Noam Ordan, Yifat Ben Moshe

Foundational Hebrew NLP tasks such as segmentation, tagging and parsing, have relied to date on various versions of the Hebrew Treebank (HTB, Sima'an et al. 2001). However, the data in HTB, a single-source newswire corpus, is now over 30 years old, and does not cover many aspects of contemporary Hebrew on the web. This paper presents a new, freely available

Halie M. Rando, Ronan Lordan, Likhitha Kolla, Elizabeth Sell

In the 21st century, several emergent viruses have posed a global threat. Each pathogen has emphasized the value of rapid and scalable vaccine development programs. The ongoing SARS-CoV-2 pandemic has made the importance of such efforts especially clear. New biotechnological advances in vaccinology allow for recent advances that provide only the nucleic acid

Piotr Dulian, Adam Sawicki

For a Haar random set $\mathcal{S}\subset U(d)$ of quantum gates we consider the uniform measure $\nu_\mathcal{S}$ whose support is given by $\mathcal{S}$. The measure $\nu_\mathcal{S}$ can be regarded as a $\delta(\nu_\mathcal{S},t)$-approximate $t$-design, $t\in\mathbb{Z}_+$. We propose a random matrix model that aims to describe the probability distributi

Vincenzo Perri, Lisi Qarkaxhija, Albin Zehe, Andreas Hotho

Natural Language Processing and Machine Learning have considerably advanced Computational Literary Studies. Similarly, the construction of co-occurrence networks of literary characters, and their analysis using methods from social network analysis and network science, have provided insights into the micro- and macro-level structure of literary texts. Combini

Marcin Jurek, Catherine A. Calder, Corwin Zigler

We formulate a statistical flight-pause model for human mobility, represented by a collection of random objects, called motions, appropriate for mobile phone tracking (MPT) data. We develop the statistical machinery for parameter inference and trajectory imputation under various forms of missing data. We show that common assumptions about the missing data me

Moritz Lichter

At the core of the quest for a logic for PTime is a mismatch between algorithms making arbitrary choices and isomorphism-invariant logics. One approach to overcome this problem is witnessed symmetric choice. It allows for choices from definable orbits which are certified by definable witnessing automorphisms. We consider the extension of fixed-point logic wi

Chen Shao, Zhou Chen, Pascal Friederich

Graph neural networks are widely used to learn global representations of graphs, which are then used for regression or classification tasks. Typically, the graphs in such data sets are connected, i.e. each training sample consists of a single internally connected graph associated with a global label. However, there is a wide variety of yet unconsidered but a

Pierre Alderic Bagnoud, Corentin Bodart

The complete growth series of a finitely generated group is given by $\sum_{n\ge 0} A_ns^n$, where $A_n$ is the sum of elements of length $n$ in the group semiring. We study the $\mathbb NG$-rationality and $\mathbb NG$-algebraicity of such series. We show that having dead ends of arbitrarily large depths is an obstruction to $\mathbb NG$-rationality. In the

Chris D. A. Blair, Sofia Zhidkova

We study a solution generating technique in supergravity which can be viewed as a generalised version of U-duality, taking solutions of type IIA supergravity on a 6-sphere with RR flux to new solutions of 11-dimensional supergravity. The new solutions are characterised by an underlying 6-algebra structure. Our construction provides an 11-dimensional uplift o

Eliana Fiorelli, Stefano Gherardini, Stefano Marcantoni

In this work, we study the stochastic entropy production in open quantum systems whose time evolution is described by a class of non-unital quantum maps. In particular, as in [Phys. Rev. E 92, 032129 (2015)], we consider Kraus operators that can be related to a nonequilibrium potential. This class accounts for both thermalization and equilibration to a non-t

Yannick Bidel, Nassim Zahzam, Alexandre Bresson, Cédric Blanchard

We report an airborne gravity survey with an absolute gravimeter based on atom interferometry and two relative gravimeters: a classical LaCoste\&Romberg (L\&R) and a novel iMAR strap-down Inertial Measurement Unit (IMU). We estimated measurement errors for the quantum gravimeter ranging from 0.6 to 1.3 mGal depending on the flight conditions and the filterin

Xudong Shen, Tianhui Tan, Tuan Q. Phan, Jussi Keppo

This paper investigates gender discrimination and its underlying drivers on a prominent Chinese online peer-to-peer (P2P) lending platform. While existing studies on P2P lending focus on disparate treatment (DT), DT narrowly recognizes direct discrimination and overlooks indirect and proxy discrimination, providing an incomplete picture. In this work, we mea

Kun Yuan, Xinmeng Huang, Yiming Chen, Xiaohan Zhang

Decentralized optimization is effective to save communication in large-scale machine learning. Although numerous algorithms have been proposed with theoretical guarantees and empirical successes, the performance limits in decentralized optimization, especially the influence of network topology and its associated weight matrix on the optimal convergence rate,

Pingyi Chen, Chenglu Zhu, Zhongyi Shui, Jiatong Cai

The success of supervised deep learning models in medical image segmentation relies on detailed annotations. However, labor-intensive manual labeling is costly and inefficient, especially in dense object segmentation. To this end, we propose a self-supervised learning based approach with a Prior Self-activation Module (PSM) that generates self-activation map

C. J. Cotter, J. Shipton

We present a compatible finite element discretisation for the vertical slice compressible Euler equations, at next-to-lowest order (i.e., the pressure space is bilinear discontinuous functions). The equations are numerically integrated in time using a fully implicit timestepping scheme which is solved using monolithic GMRES preconditioned by a linesmoother.

Moritz Hauck, Axel Målqvist

Spatial network models are used as a simplified discrete representation in a wide range of applications, e.g., flow in blood vessels, elasticity of fiber based materials, and pore network models of porous materials. Nevertheless, the resulting linear systems are typically large and poorly conditioned and their numerical solution is challenging. This paper pr

Nina Gantert, Achim Klenke

We consider a specific random graph which serves as a disordered medium for a particle performing biased random walk. Take a two-sided infinite horizontal ladder and pick a random spanning tree with a certain edge weight $c$ for the (vertical) rungs. Now take a random walk on that spanning tree with a bias $\beta>1$ to the right. In contrast to other random

Veronica P. Simonsen, Bodil Holst, Ingve Simonsen

In classical binary holography, a target pattern located at infinity is generated by the diffraction of a plane wave passing through a binary mask with holes of the same size, placed at specific positions of a rectangular grid. Fresnel binary atom holography was recently proposed as a means for achieving nanometer-resolution mask-based lithography with metas

Frank Qiu

In this paper, we interpret disentanglement as the discovery of local charts of the data manifold and trace how this definition naturally leads to an equivalent condition for disentanglement: commutativity between factors of variation. We study the impact of this manifold framework to two classes of problems: learning matrix exponential operators and compres

Francesca Ronchini, Samuele Cornell, Romain Serizel, Nicolas Turpault

The aim of the Detection and Classification of Acoustic Scenes and Events Challenge Task 4 is to evaluate systems for the detection of sound events in domestic environments using an heterogeneous dataset. The systems need to be able to correctly detect the sound events present in a recorded audio clip, as well as localize the events in time. This year's task

R. Reda, L. Giovannelli, T. Alberti

The solar activity displays variability and periodic behaviours over a wide range of timescales, with the presence of a most prominent cycle with a mean length of 11 years. Such variability is transported within the heliosphere by solar wind, radiation and other processes, affecting the properties of the interplanetary medium. The presence of solar activity-

Sandro Bettin, Sary Drappeau

We study functions $f$ on $\mathbb Q$ which statisfy a ``quantum modularity'' relation of the shape $$ f(x+1)=f(x), \qquad f(x) - |x|^{-k} f(-1/x) = h(x) $$ where $h:\mathbb R_{\neq 0} \to \mathbb C$ is a function satisfying various regularity conditions. We study the case $\Re(k)\neq 0$. We prove the existence of a limiting function $f^*$ which extends cont

Xiangkun Liu, Shuo Yuan, Chuzhong Pan, Tianyu Zhang

We perform weak lensing tomographic peak studies using the first-year shear data from Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) survey. The effective area used in our analyses after field selection, mask and boundary exclusions is $\sim 58 \deg^2$. The source galaxies are divided into low- and high-redshift bins with $0.2\le z_p\le0.85$ and $0.85\

Lucas Rafael Stefanel Gris, Arnaldo Candido Junior, Vinícius G. dos Santos, Bruno A. Papa Dias

The NURC Project that started in 1969 to study the cultured linguistic urban norm spoken in five Brazilian capitals, was responsible for compiling a large corpus for each capital. The digitized NURC/SP comprises 375 inquiries in 334 hours of recordings taken in S\~ao Paulo capital. Although 47 inquiries have transcripts, there was no alignment between the au

Nima Rahrakhshan, Matthias Kerzel, Philipp Allgeuer, Nicolas Duczek

The act of reaching for an object is a fundamental yet complex skill for a robotic agent, requiring a high degree of visuomotor control and coordination. In consideration of dynamic environments, a robot capable of autonomously adapting to novel situations is desired. In this paper, a developmental robotics approach is used to autonomously learn visuomotor c

Bernhard Stankewitz

Increasingly high-dimensional data sets require that estimation methods do not only satisfy statistical guarantees but also remain computationally feasible. In this context, we consider $ L^{2} $-boosting via orthogonal matching pursuit in a high-dimensional linear model and analyze a data-driven early stopping time $ \tau $ of the algorithm, which is sequen

Maryam Paparimoghadamborazjani, Amin Kazemi

Solar energy is one of the most dependable renewable energy technologies, as it is feasible almost everywhere globally. However, improving the efficiency of a solar PV system remains a significant challenge. To enhance the robustness of the solar system, this paper proposes a trained convolutional neural network (CNN) based fault detection scheme to divide t

Patric de Gentile-Williams

The dynamics of the Restricted 3 Body Problem in the Post Newtonian context have been, and continue to be, studied extensively and a number of characteristics such as ejections of bodies from the system, precession of orbits, chaotic trajectories and collisions are investigated and classified. In this paper, I examine the extent to which these characteristic

Shengli Jiang, Shiyi Qin, Joshua L. Pulsipher, Victor M. Zavala

We discuss basic concepts of convolutional neural networks (CNNs) and outline uses in manufacturing. We begin by discussing how different types of data objects commonly encountered in manufacturing (e.g., time series, images, micrographs, videos, spectra, molecular structures) can be represented in a flexible manner using tensors and graphs. We then discuss

Louis Annabi, Alexandre Pitti, Mathias Quoy

In this article, we propose a variational inference formulation of auto-associative memories, allowing us to combine perceptual inference and memory retrieval into the same mathematical framework. In this formulation, the prior probability distribution onto latent representations is made memory dependent, thus pulling the inference process towards previously

Anna Haensch, Natasa Dragovic, Christoph Börgers, Bruce Boghosian

We introduce a geospatial bounded confidence model with mega-influencers, inspired by Hegselmann and Krause. The inclusion of geography gives rise to large-scale geospatial patterns evolving out of random initial data; that is, spatial clusters of like-minded agents emerge regardless of initialization. Mega-influencers and stochasticity amplify this effect,

Julien Trevisan

We study the error of the number of points of a lattice $L$ that belong to a rectangle, centred at $0$, whose axes are parallel to the coordinate axes, dilated by a factor $t$ and then translated by a vector $X \in \mathbb{R}^{2}$. When we consider the second order moment of the error relatively to $X \in \mathbb{R}^{2}/L$, one shows that, when $t$ is random

Stefano Catani, Simone Devoto, Massimiliano Grazzini, Stefan Kallweit

The associated production of a Higgs boson with a top-antitop quark pair is a crucial process at the LHC since it allows for a direct measurement of the top-quark Yukawa coupling. We present the computation of the radiative corrections to this process at the next-to-next-to-leading order (NNLO) in QCD perturbation theory. This is the very first computation f

Ruiyuan Kang, Panos Liatsis, Dimitrios C. Kyritsis

The current success of machine learning on image-based combustion monitoring is based on massive data, which is costly even impossible for industrial applications. To address this conflict, we introduce few-shot learning in order to achieve combustion monitoring and classification for the first time. Two algorithms, Siamese Network coupled with k Nearest Nei

Dmytro Kolisnyk, Gernot Schaller

A single qutrit with transitions selectively driven by weakly-coupled reservoirs can implement one of the world's smallest refrigerators. We analyze the performance of $N$ such fridges that are collectively coupled to the reservoirs. We observe a quantum boost, manifest in a quadratic scaling of the steady-state cooling current with $N$. As $N$ grows further