January 2022 arXiv papers — page 32

Gengchen Mai, Yao Xuan, Wenyun Zuo, Krzysztof Janowicz

Generating learning-friendly representations for points in a 2D space is a fundamental and long-standing problem in machine learning. Recently, multi-scale encoding schemes (such as Space2Vec) were proposed to directly encode any point in 2D space as a high-dimensional vector, and has been successfully applied to various (geo)spatial prediction tasks. Howeve

Alireza Famili, Angelos Stavrou, Haining Wang, Jung-Min

In many scenarios, unmanned aerial vehicles (UAVs), aka drones, need to have the capability of autonomous flying to carry out their mission successfully. In order to allow these autonomous flights, drones need to know their location constantly. Then, based on the current position and the final destination, navigation commands will be generated and drones wil

Sinan Gündoğdu, Stéphane Virally, Marco Scaglia, Denis V. Seletskiy

We propose and analyze a new time-domain method for subcycle metrology of quantum electric fields using a combination of a 3rd order nonlinear optical process and homodyne detection with a local oscillator (LO) field. The new method enables isolation of intrinsically weak quantum noise contribution by subtraction of the shot noise of the LO on a pulse-by-pul

L. M. Robledo

We apply a formalism recently developed to carry out Generator Coordinate Method calculations using a set of Hartree- Fock- Bogoliubov wave functions, where each of the members of the set can be expanded in an arbitrary basis. In this paper it is assumed that the HFB wave functions are expanded in Harmonic Oscillator (HO) bases with different oscillator leng

Jana Wagemaker, Nate Foster, Tobias Kappé, Dexter Kozen

We introduce Concurrent NetKAT (CNetKAT), an extension of NetKAT with operators for specifying and reasoning about concurrency in scenarios where multiple packets interact through state. We provide a model of the language based on partially-ordered multisets (pomsets), which are a well-established mathematical structure for defining the denotational semantic

Albrecht Kurze

Much research has been done around creating multisensory ideation and prototyping tools. We relate our own multisensory tool, the Loaded Dice, to this domain. We briefly explain what sensing and actuating possibilities the Loaded Dice already have (including thermal sensations), and how they are methodically embedded in workshops using an extended interactio

Georgios Piliouras, Fang-Yi Yu

The recent framework of performative prediction is aimed at capturing settings where predictions influence the target/outcome they want to predict. In this paper, we introduce a natural multi-agent version of this framework, where multiple decision makers try to predict the same outcome. We showcase that such competition can result in interesting phenomena b

Michele Pizzochero, Efthimios Kaxiras

Although the unconventional $\pi$-magnetism at the zigzag edges of graphene holds promise for a wide array of applications, whether and to what degree it plays a role in their chemistry remains poorly understood. Here, we investigate the addition of a hydrogen atom $-$ the simplest yet the most experimentally relevant adsorbate $-$ to zigzag graphene nanorib

Jonathan Karp, Alexander Hampel, Andrew J. Millis

We perform a comparative $2 \times 2$ real space cluster DMFT study on minimal models for NdNiO$_2$ and CaCuO$_2$ obtained from downfolding DFT states, using a Nambu formalism that allows for both superconducting and antiferromagnetic order. We produce a phase diagram in temperature and doping. We find that for the nickelate, like the cuprate, the stoichiome

U. Sureshkumar, A. Durkalec, A. Pollo, M. Bilicki

We investigate how different mid-infrared (mid-IR) properties of galaxies trace the environment in which the galaxies are located. For this purpose, we first study the dependence of galaxy clustering on the absolute magnitude at 3.4 $\mu$m and redshift. Then, we look into the environmental dependence of mid-IR luminosities and the galaxy properties derived f

Piero Naldesi, Peter D. Drummond, Vanja Dunjko, Anna Minguzzi

We revisit the proposal of Castin and Weiss [Phys. Rev. Lett. vol. 102, 010403 (2009)] for using the scattering of a quantum matter-wave soliton on a barrier in order to create a coherent superposition state of the soliton being entirely to the left of the barrier and being entirely to the right of the barrier. In that proposal, is was assumed that the scatt

Vyacheslav Klyukhin

This review describes the composition of the Compact Muon Solenoid (CMS) detector and the methodology for modelling the heterogeneous CMS magnetic system, starting with the formulation of the magnetostatics problem for modelling the magnetic flux of the CMS superconducting solenoid enclosed in a steel flux-return yoke. The review includes a section on the ma

Yawen Wang, Daniel Crankshaw, Neeraja J. Yadwadkar, Daniel Berger

Cloud platforms run many software agents on each server node. These agents manage all aspects of node operation, and in some cases frequently collect data and make decisions. Unfortunately, their behavior is typically based on pre-defined static heuristics or offline analysis; they do not leverage on-node machine learning (ML). In this paper, we first charac

Michael Schüler, Samuel Beaulieu

Observing signatures of light-induced Floquet topological states in materials has been shown to be very challenging. Angle-resolved photoemission spectroscopy (ARPES) is well suited for the investigation of Floquet physics, as it allows to directly probe the dressed electronic states of driven solids. Depending on the system, scattering and decoherence can p

Stein K. F. Stoter, Thi-Hoa Nguyen, René R. Hiemstra, Dominik Schillinger

In this paper, we propose a variationally consistent technique for decreasing the maximum eigenfrequencies of structural dynamics related finite element formulations. Our approach is based on adding a symmetric positive-definite term to the mass matrix that follows from the integral of the traction jump across element boundaries. The added term is weighted b

Suchin Gururangan, Dallas Card, Sarah K. Dreier, Emily K. Gade

Language models increasingly rely on massive web dumps for diverse text data. However, these sources are rife with undesirable content. As such, resources like Wikipedia, books, and newswire often serve as anchors for automatically selecting web text most suitable for language modeling, a process typically referred to as quality filtering. Using a new datase

Jean-Marc Robert, Pascal Véron

Code-based cryptography is one of the main propositions for the post-quantum cryptographic context, and several protocols of this kind have been submitted on the NIST platform. Among them, BIKE and HQC are part of the five alternate candidates selected in the third round of the NIST standardization process in the KEM category. These two schemes make use of m

Bernardo Araneda

We derive a generalization of the flat space Yang's and Newman's equations for self-dual Yang-Mills fields to (locally) conformally Kahler Riemannian 4-manifolds. The results also apply to Einstein metrics (whose full curvature is not necessarily self-dual). We analyse the possibility of hidden symmetries in the form of Backlund transformations, and we find

Yongqi Tian, Xueyuan Gong, Jialin Tang, Binghua Su

In recent years, with the rapid development of artificial intelligence, image generation based on deep learning has dramatically advanced. Image generation based on Generative Adversarial Networks (GANs) is a promising study. However, since convolutions are limited by spatial-agnostic and channel-specific, features extracted by traditional GANs based on conv

Albrecht Kurze, Andreas Bischof

Human data interaction with sensor data from smart homes can cause some implications when it comes to human sensemaking of this data. With our data-driven method Guess the Data for individual and collective data work we revealed in previous work a number of potential pitfalls when interacting with this type of data. We introduce some of the identified, often

Chunyu Li, Brenden W Hamilton, Tongtong Shen, Lorena Alzate

The shock to detonation transition in heterogeneous plastically bonded explosives is dominated by energy localization into hotspots that arise from the interaction of the shockwave with microstructural features and defects. The complex polycrystalline structure of these materials leads to a network of hotspot that can coalesce into deflagration and detonatio

Atsushi Nitanda, Denny Wu, Taiji Suzuki

As an example of the nonlinear Fokker-Planck equation, the mean field Langevin dynamics recently attracts attention due to its connection to (noisy) gradient descent on infinitely wide neural networks in the mean field regime, and hence the convergence property of the dynamics is of great theoretical interest. In this work, we give a concise and self-contain

Ravi Kant, Gaurav Saha, Arjun Kumar

This paper presents the arrangement of an in-pipe climbing robot that works using a clever differential part to explore complex associations of lines. Standard wheeled/continued in-pipe climbing robots are leaned to slip and take while exploring in pipe turns. The mechanism helps in achieving the first eventual outcome of clearing out slip and drag in the ro

Sebin J. Sebastian, A. Jain, S. M. Yusuf, M. Uhlarz

We set forth the structural and magnetic properties of the frustrated spin-$5/2$ triangle lattice antiferromagnet Na$_3$Fe(PO$_4$)$_2$ examined via x-ray diffraction, magnetization, heat capacity, and neutron diffraction measurements on the polycrystalline sample. No structural distortion was detected from the temperature-dependant x-ray diffraction down to

Giuseppe Brandi, T. Di Matteo

Pricing derivatives goes back to the acclaimed Black and Scholes model. However, such a modeling approach is known not to be able to reproduce some of the financial stylized facts, including the dynamics of volatility. In the mathematical finance community, it has therefore emerged a new paradigm, named rough volatility modeling, that represents the volatili

C. M. Lisse, M. L. Sitko, S. J. Wolk, H. M. Günther

We present 2007 - 2020 SpeX VISNIR spectral monitoring of the highly variable RW Aur A CTTS. We find direct evidence for a highly excited, IR bright, asymmetric, and time variable system. Comparison of the spectral and temporal trends found determines 5 different components: (1) a stable continuum from 0.7 - 1.3 um, with approx color temperature 4000K, produ

Giulia Cusin, Gianmassimo Tasinato

One of the guaranteed features of the stochastic gravitational wave background (SGWB) is the presence of Doppler anisotropies induced by the motion of the detector with respect to the rest frame of the SGWB source. We point out that kinematic effects can be amplified if the SGWB is characterised by large tilts in its spectrum as a function of frequency, or b

Alexander Nesterov, Dmitry Umerenkov

Medical entity extraction (EE) is a standard procedure used as a first stage in medical texts processing. Usually Medical EE is a two-step process: named entity recognition (NER) and named entity normalization (NEN). We propose a novel method of doing medical EE from electronic health records (EHR) as a single-step multi-label classification task by fine-tun

Roman Travkin, Ruotao Yang

This is a successive paper of arXiv:1909.11492. We prove an equivalence between the category of finite-dimensional representations of degenerate supergroup $\underline{GL}(M|N)$ and the category of $(GL_M(O) \ltimes U_{M, N}(F), \chi_{M, N})$-equivariant D-modules on $Gr_{N}$. We also prove that we can realize the category of finite-dimensional representatio

Natalia P. Bondarenko

In this paper, we develop two approaches to investigation of inverse spectral problems for a new class of nonlocal operators on metric graphs. The Laplace differential operator is considered on a star-shaped graph with nonlocal integral matching condition. This operator is adjoint to the functional-differential operator with frozen argument at the central ve

Thuan Nguyen, Boyang Lyu, Prakash Ishwar, Matthias Scheutz

Invariance-principle-based methods such as Invariant Risk Minimization (IRM), have recently emerged as promising approaches for Domain Generalization (DG). Despite promising theory, such approaches fail in common classification tasks due to the mixing of true invariant features and spurious invariant features. To address this, we propose a framework based on

Lyle Regenwetter, Colin Weaver, Faez Ahmed

This paper demonstrates how Automated Machine Learning (AutoML) methods can be used as effective surrogate models in engineering design problems. To do so, we consider the challenging problem of structurally-performant bicycle frame design and demonstrate across-the-board dominance by AutoML in regression and classification surrogate modeling tasks. We also

Ankit Mahajan, Joonho Lee, Sandeep Sharma

We present efficient algorithms for using selected configuration interaction (sCI) trial wave functions in phaseless auxiliary field quantum Monte Carlo (ph-AFQMC). These advancements, geared towards optimizing computational performance for longer CI expansions, allow us to use up to a million configurations feasibly in the trial state for ph-AFQMC. In one e

B. M. Hegelich, L. Labun, O. Z. Labun, G. Torrieri

A model detector undergoing constant, infinite-duration acceleration converges to an equilibrium state described by the Hawking-Unruh temperature $T_a=(a/2\pi)(\hbar/c)$. To relate this prediction to experimental observables, a point-like charged particle, such as an electron, is considered in place of the model detector. Instead of the detector's internal d

Niki Myrto Mavraki, Harry Schmidt

We prove that Zhang's dynamical Bogomolov conjecture holds uniformly along $1$-parameter families of rational split maps and curves. This provides dynamical analogues of recent results of Dimitrov-Gao-Habegger and K\"uhne. In fact, we prove a stronger Bogomolov-type result valid for families of split maps in the spirit of the relative Bogomolov conjecture. W

Marcin Pitera, Thorsten Schmidt

While the estimation of risk is an important question in the daily business of banking and insurance, many existing plug-in estimation procedures suffer from an unnecessary bias. This often leads to the underestimation of risk and negatively impacts backtesting results, especially in small sample cases. In this article we show that the link between estimatio

Laurens Bliek, Paulo da Costa, Reza Refaei Afshar, Yingqian Zhang

This paper reports on the first international competition on AI for the traveling salesman problem (TSP) at the International Joint Conference on Artificial Intelligence 2021 (IJCAI-21). The TSP is one of the classical combinatorial optimization problems, with many variants inspired by real-world applications. This first competition asked the participants to

André Großardt

I review the arguments most often raised against a fundamental coupling of classical spacetime to quantum matter. I show that an experiment by Page and Geilker does not exclude such a semiclassical theory but mandates an inclusion of an objective mechanism for wave function collapse. In this regard, I present a classification of semiclassical models defined

Rob Shooter, Emma Ross, Agustinus Ribal, Ian R. Young

The joint extremal spatial dependence of wind speed and significant wave height in the North East Atlantic is quantified using Metop satellite scatterometer and hindcast observations for the period 2007-2018, and a multivariate spatial conditional extremes (MSCE) model, ultimately motivated by the work of Heffernan and Tawn (2004). The analysis involves (a)

J. Marques Oliveira, B. Sicardy, A. R. Gomes-Júnior, J. L. Ortiz

A stellar occultation by Neptune's main satellite, Triton, was observed on 5 October 2017 from Europe, North Africa, and the USA. We derived 90 light curves from this event, 42 of which yielded a central flash detection. We aimed at constraining Triton's atmospheric structure and the seasonal variations of its atmospheric pressure since the Voyager 2 epoch (

Alexandre Moly, Thomas Costecalde, Felix Martel, Christelle Larzabal

Brain-computer interfaces (BCIs) still face many challenges to step out of laboratories to be used in real-life applications. A key one persists in the high performance control of diverse effectors for complex tasks, using chronic and safe recorders. This control must be robust over time and of high decoding performance without continuous recalibration of th

Daniel Kigli, Ariel Shamir, Shai Avidan

How many labeled pixels are needed to segment an image, without any prior knowledge? We conduct an experiment to answer this question. In our experiment, an Oracle is using Active Learning to train a network from scratch. The Oracle has access to the entire label map of the image, but the goal is to reveal as little pixel labels to the network as possible. W

Canzhe Zhao, Yanjie Ze, Jing Dong, Baoxiang Wang

Temporal difference (TD) learning is a widely used method to evaluate policies in reinforcement learning. While many TD learning methods have been developed in recent years, little attention has been paid to preserving privacy and most of the existing approaches might face the concerns of data privacy from users. To enable complex representative abilities of

Johann Maddi, Christophe Coste, Michel Saint Jean

We study a conservative system of two nonlinear coupled oscillators. The eigenmodes of the system are thus nonlinearly coupled, and one of them may induce a parametric amplification of the other, called an autoparametric resonance of the system. The parametric amplification implies two time scales, a fast one for the forcing and a slow one for the forced mod

Dominic Magirr, José L. Jiménez

Delayed separation of survival curves is a common occurrence in confirmatory studies in immuno-oncology. Many novel statistical methods that aim to efficiently capture potential long-term survival improvements have been proposed in recent years. However, the vast majority do not consider stratification, which is a major limitation considering that most (if n

Jiwon Kim, Kwangrok Ryoo, Gyuseong Lee, Seokju Cho

Semi-supervised learning (SSL) has recently proven to be an effective paradigm for leveraging a huge amount of unlabeled data while mitigating the reliance on large labeled data. Conventional methods focused on extracting a pseudo label from individual unlabeled data sample and thus they mostly struggled to handle inaccurate or noisy pseudo labels, which deg

Giacomo Maletto, Luca Roversi

Reversible Primitive Permutations (RPP) are recursively defined functions designed to model Reversible Computation. We illustrate a proof, fully developed with the proof-assistant Lean, certifying that: "RPP can encode every Primitive Recursive Function". Our reworking of the original proof of that statement is conceptually simpler, fixes some bugs, suggests

Lixi Zhou, Jiaqing Chen, Amitabh Das, Hong Min

There are significant benefits to serve deep learning models from relational databases. First, features extracted from databases do not need to be transferred to any decoupled deep learning systems for inferences, and thus the system management overhead can be significantly reduced. Second, in a relational database, data management along the storage hierarch

David A. Vargas, Robert D. Falgout, Stefanie Günther, Jacob B. Schroder

As CPU clock speeds have stagnated, and high performance computers continue to have ever higher core counts, increased parallelism is needed to take advantage of these new architectures. Traditional serial time-marching schemes are a significant bottleneck, as many types of simulations require large numbers of time-steps which must be computed sequentially.

Joydev Halder, Suman Kumar Tumuluri

In this article, a numerical scheme to find approximate solutions to the McKendrick-Von Foerster equation with diffusion (M-V-D) is presented. The main difficulty in employing the standard analysis to study the properties of this scheme is due to presence of nonlinear and nonlocal term in the Robin boundary condition in the M-V-D. To overcome this, we use th

Dmitriy Serdyuk, Otavio Braga, Olivier Siohan

Audio-visual automatic speech recognition (AV-ASR) extends speech recognition by introducing the video modality as an additional source of information. In this work, the information contained in the motion of the speaker's mouth is used to augment the audio features. The video modality is traditionally processed with a 3D convolutional neural network (e.g. 3

M. T. Beltrán, V. M. Rivilla, R. Cesaroni, D. Galli

Context. To better understand the formation of high-mass stars, it is fundamental to investigate how matter accretes onto young massive stars, how it is ejected, and how all this differs from the low-mass case. The massive protocluster G31.41+0.31 is the ideal target to study all these processes because observations at millimeter and centimeter wavelengths h

Francesco Canella, Edoardo Suerra, Dario Giannotti, Gianluca Galzerano

We report on the dependence of the frequency-to-intensity noise conversion in the locking of an ultrafast laser against a high-finesse optical resonator from the Carrier Envelope Offset (CEO) frequency. By a proper combination of the cavity finesse and laser CEO frequency it is possible to optimize the signal-to-noise ratio of the laser intensity trapped int

Harald Rueß, Simon Burton

Ttraditional safety engineering is coming to a turning point moving from deterministic, non-evolving systems operating in well-defined contexts to increasingly autonomous and learning-enabled AI systems which are acting in largely unpredictable operating contexts. We outline some of underlying challenges of safe AI and suggest a rigorous engineering framewor

A. J. Fallon, E. R. Moan, E. A. Larson, C. A. Sackett

We report a precision measurement of a tune-out wavelength for 87Rb using circularly polarized light. A tune-out wavelength characterizes a zero in the electric polarizability of the atom. For circularly polarized light, the total polarizability depends on both the scalar and vector polarizability components. This shifts the location of the tune-out waveleng

Sheng Zhong, Nima Fazeli, Dmitry Berenson

Retrieving an object from cluttered spaces suchas cupboards, refrigerators, or bins requires tracking objects with limited or no visual sensing. In these scenarios, contact feedback is necessary to estimate the pose of the objects, yet the objects are movable while their shapes and number may be unknown, making the association of contacts with objects extrem

Brahim Dridi, Rached Jaidane

We deal with a weighted biharmonic problem in the unit ball of $\mathbb{R}^{4}$. The non-linearity is assumed to have critical exponential growth in view of Adam's type inequalities. The weight $w(x)$ is of logarithm type and the potential $V$ is a positive continuous function on $\overline{B}$. It is proved that there is a nontrivial positive weak solution

A. R. Balasubramanian, Lucie Guillou, Chana Weil-Kennedy

Reconfigurable broadcast networks (RBN) are a model of distributed computation in which agents can broadcast messages to other agents using some underlying communication topology which can change arbitrarily over the course of executions. In this paper, we conduct parameterized analysis of RBN. We consider cubes,(infinite) sets of configurations in the form

Vacit Oguz Yazici, Longlong Yu, Arnau Ramisa, Luis Herranz

Computer vision has established a foothold in the online fashion retail industry. Main product detection is a crucial step of vision-based fashion product feed parsing pipelines, focused in identifying the bounding boxes that contain the product being sold in the gallery of images of the product page. The current state-of-the-art approach does not leverage t

Amal Alqahtani, Efsun Sarioglu Kay, Sardar Hamidian, Michael Compton

Schizophrenia is one of the most disabling mental health conditions to live with. Approximately one percent of the population has schizophrenia which makes it fairly common, and it affects many people and their families. Patients with schizophrenia suffer different symptoms: formal thought disorder (FTD), delusions, and emotional flatness. In this paper, we

Mitesh Kumar Behera, Rukmani Mohanta

In this paper, we investigate the implication of modular $\Gamma^{\prime}_5 \simeq A^{\prime}_5$ symmetry on neutrino oscillation phenomenology in the linear seesaw framework. In order to achieve the well defined mass structure for the light active neutrinos as dictated by the linear seesaw mechanism, we introduce six heavy fermion fields along with a pair o

Ashot Aleksian, Pierre Del Moral, Aline Kurtzmann, Julian Tugaut

We study the exit-time from a domain of a self-interacting diffusion, where the Brownian motion is replaced by $\sigma B_t$ for a constant $\sigma$. The first part of this work consists in showing that the rate of convergence (of the occupation measure of the self-interacting process toward some explicit Gibbs measure) previously obtained in \cite{kk-ejp} fo

Ahmad Jafar Arifi, Daiki Suenaga, Atsushi Hosaka, Yongseok Oh

Decay properties of multi-strangeness $\Xi$ and $\Omega$ baryon resonances are investigated within the constituent quark model by including relativistic corrections. The strong decay widths of various $\Xi$ and $\Omega$ resonances are computed and tested for internal quark configurations to pin down their spin-parity ($J^P$) quantum numbers and internal stru

Luis F. Abanto-Leon, Matthias Hollick, Bruno Clerckx, Gek Hong Sim

This paper investigates rate-splitting (RS) precoding for non-orthogonal unicast and multicast (NOUM) transmissions using fully-digital and hybrid precoders. We study the nonconvex weighted sum-rate (WSR) maximization problem subject to a multicast requirement. We propose FALCON, an approach based on sequential parametric optimization, to solve the aforement

K. Grigoras, N. Yurttagül, J. -P. Kaikkonen, E. T. Mannila

We fabricate and characterize superconducting through-silicon vias and electrodes suitable for superconducting quantum processors. We measure internal quality factors of a million for test resonators excited at single-photon levels, on chips with superconducting vias used to stitch ground planes on the front and back sides of the chips. This resonator perfor

Antonio Tejero-de-Pablos, Hiroaki Yamane, Yusuke Kurose, Junichi Iho

The presence of plaques in the coronary arteries is a major risk to the patients' life. In particular, non-calcified plaques pose a great challenge, as they are harder to detect and more likely to rupture than calcified plaques. While current deep learning techniques allow precise segmentation of real-life images, the performance in medical images is still l

Guha Balakrishnan, Raghudeep Gadde, Aleix Martinez, Pietro Perona

We present a method for finding paths in a deep generative model's latent space that can maximally vary one set of image features while holding others constant. Crucially, unlike past traversal approaches, ours can manipulate multidimensional features of an image such as facial identity and pixels within a specified region. Our method is principled and conce

Marjorie McShane, Ivan Leon

This paper describes recent progress on natural language generation (NLG) for language-endowed intelligent agents (LEIAs) developed within the OntoAgent cognitive architecture. The approach draws heavily from past work on natural language understanding in this paradigm: it uses the same knowledge bases, theory of computational linguistics, agent architecture

Mohammad Bagher Heydari

In this paper, we analytically study Surface Plasmon Polaritons (SPPs) in graphene-based cylindrical structures with gyroelectric layers. In the general waveguide, each graphene layer is surrounded by two various gyro-electric materials. New closed-form relations are derived for the field contributions of SPP waves. As a special case, a gyro-electric cylindr

Weiyan Lin, Yang Feng, Yongchao Wang, Zichen Lian

The beauty of quantum Hall (QH) effect is the metrological precision of Hall resistance quantization that originates from the topological edge states. Understanding the factors that lead to quantization breakdown not only provides important insights on the nature of the topological protection of these edge states, but is beneficial for device applications in

Chengshuai Yang, Shiyu Zhang, Xin Yuan

Snapshot compressive imaging (SCI) can record the 3D information by a 2D measurement and from this 2D measurement to reconstruct the original 3D information by reconstruction algorithm. As we can see, the reconstruction algorithm plays a vital role in SCI. Recently, deep learning algorithm show its outstanding ability, outperforming the traditional algorithm

Felix Brandt, Patrick Lederer, René Romen

Social decision schemes (SDSs) map the preferences of a group of voters over some set of $m$ alternatives to a probability distribution over the alternatives. A seminal characterization of strategyproof SDSs by Gibbard implies that there are no strategyproof Condorcet extensions and that only random dictatorships satisfy ex post efficiency and strategyproofn

F. Billard, R. Sharma, E. Hertz, O. Faucher

We present a novel self-referenced method for the complete temporal characterization (phase and amplitude) of ultrashort optical laser pulses. The technique, called TIme-Gated Electric field Reconstruction (TIGER), measures a second-order nonlinear signal (namely, second harmonic generation or two-photon absorption) produced by four time-delayed replicas of

Y. Kawazura

The pseudospectral method is a highly accurate numerical scheme suitable for turbulence simulations. We have developed an open-source pseudospectral code, \textsc{\textsf{Calliope}}, which adopts the P3DFFT library \citep{Pekurovsky2012} to perform a fast Fourier transform with the two-dimensional (pencil) decomposition of numerical grids. \textsc{\textsf{Ca

Stefano Montaldo, Cezar Oniciuc, Andrea Ratto

The flat torus ${\mathbb T}={\mathbb S}^1\left (\frac{1}{2} \right ) \times {\mathbb S}^1\left (\frac{1}{2} \right )$ admits a proper biharmonic isometric immersion into the unit $4$-dimensional sphere ${\mathbb S}^4$ given by $\Phi=i \circ \varphi$, where $\varphi:{\mathbb T} \to {\mathbb S}^3(\frac{1}{\sqrt 2})$ is the minimal Clifford torus and $i:{\mathb

Austin Barringer, Hans-Christian Herbig, Daniel Herden, Saad Khalid

We compute univariate and multigraded Hilbert series of invariants and covariants of representations of the circle and orthogonal group $\operatorname{O}_2$. The multigradings considered include the maximal grading associated to the decomposition of the representation into irreducibles as well as the bigrading associated to a cotangent-lifted representation,

Rei Chemke, Yi Ming, Janni Yuval

The strength of mid-latitude storm tracks shapes weather and climate phenomena in the extra-tropics, as these storm tracks control the daily to multi-decadal variability of precipitation, temperature and winds. By the end of this century, winter mid-latitude storms are projected to intensify in the Southern Hemisphere, with large consequences over the entire

Sabya Sachi Chatterjee, Antonio Palazzo

The current data of the two long-baseline accelerator experiments NO$\nu$A and T2K, shows a tension at more than 90$\%$ C.L. for 2 degrees of freedom, in the determination of the standard CP-phase $\delta_{\mathrm {CP}}$ in case of neutrino normal ordering (NO). NO$\nu$A measures the value close to $\delta_{\mathrm {CP}} \sim 0.8 \pi$, while T2K prefers the

Víctor Navarro-Fernández, André Schlichting

We study the implicit upwind finite volume scheme for numerically approximating the advection-diffusion equation with a vector field in the low regularity DiPerna-Lions setting. That is, we are concerned with advecting velocity fields that are spatially Sobolev regular and data that are merely integrable. We study the implicit upwind finite volume scheme for

Sven Koehler, Lalith Sharan, Julian Kuhm, Arman Ghanaat

Cardiac Magnetic Resonance (CMR) images are widely used for cardiac diagnosis and ventricular assessment. Extracting specific landmarks like the right ventricular insertion points is of importance for spatial alignment and 3D modeling. The automatic detection of such landmarks has been tackled by multiple groups using Deep Learning, but relatively little att

Matteo Cartiglia, Arianna Rubino, Shyam Narayanan, Charlotte Frenkel

The stringent memory and power constraints required in edge-computing sensory-processing applications have made event-driven neuromorphic systems a promising technology. On-chip online learning provides such systems the ability to learn the statistics of the incoming data and to adapt to their changes. Implementing online learning on event driven-neuromorphi

Ira Globus-Harris, Michael Kearns, Aaron Roth

We propose and analyze an algorithmic framework for "bias bounties": events in which external participants are invited to propose improvements to a trained model, akin to bug bounty events in software and security. Our framework allows participants to submit arbitrary subgroup improvements, which are then algorithmically incorporated into an updated model. O

Naqib Zarin, Dirk van Bokkem, Justin Segond, Stefanie Roos

Existing centralised market places such as Ebay enable companies to gather large amounts of personal data that can be used to manipulate users. Furthermore, users can frequently perform fraud without severe consequence. Reputation systems only solve this problem partially as malicious users can re-join the network with a new identity if their reputation is t

Nicolas Tempelmeier, Elena Demidova

OpenStreetMap (OSM), a collaborative, crowdsourced Web map, is a unique source of openly available worldwide map data, increasingly adopted in Web applications. Vandalism detection is a critical task to support trust and maintain OSM transparency. This task is remarkably challenging due to the large scale of the dataset, the sheer number of contributors, var

Carlos Mas Arabí, Nicolas Englebert, Pedro Parra-Rivas, Simon-Pierre Gorza

The generation of stable short optical pulses in mode-locked lasers is of tremendous importance for many applications. Mode-locking is a broad concept that encompasses different processes enabling short pulse formation. It typically requires an intracavity mechanism that discriminates between single and collective mode lasing, which can be complex and someti

Csongor Beke, Gergely Kál Csáji, Péter Csikvári, Sára Pituk

In this short note we show that a system $M=(E,r)$ with a ground set $E$ of size $m$ and (rank) function $r: 2^E\to \mathbb{Z}_{\geq 0}$ satisfying $r(S)\leq \min(r(E),|S|)$ for every set $S\subseteq E$, the Tutte polynomial $$T_M(x,y):=\sum_{S\subseteq E}(x-1)^{r(E)-r(S)}(y-1)^{|S|-r(S)},$$ written as $T_M(x,y)=\sum_{i,j}t_{ij}x^iy^j$, satisfies that for an

Veronika Baumann, Flavio Del Santo

In a recent paper, S. Gao has claimed that, under the assumption that the initial state of the universe is a pure quantum state, only the many worlds interpretation can account for the observed arrow of time. We show that his argument is untenable and that if endorsed it potentially leads to undermine the search for a scientific explanation of certain phenom

Nihat Gökhan Gögüş, Ozan Günyüz, Özcan Yazıcı

The primary objective of this paper is to study core sets in the setting of m-subharmonic functions on the class of (non-compact) Kahler manifolds. Core sets are investigated in different aspects by considering various classes of plurisubharmonic functions. One of the crucial concepts in studying the structure of this kind of sets is the pseudoconcavity. In

Michał Bogdan, Andrea Montessori, Adriano Tiribocchi, Fabio Bonaccorso

We report new dynamical modes in confined soft granular flows, such as stochastic jetting and dripping, with no counterpart in continuum viscous fluids. The new modes emerge as a result of the propagation of the chaotic behaviour of individual grains -- here, monodisperse emulsion droplets to the level of the entire system as the emulsion is focused into a n

Eric Lanfer, Thomas Hänel, Roland van Rijswijk-Deij, Nils Aschenbruck

Due to the COVID 19 pandemic, smartphone-based proximity tracing systems became of utmost interest. Many of these systems use BLE signals to estimate the distance between two persons. The quality of this method depends on many factors and, therefore, does not always deliver accurate results. In this paper, we present a multi-channel approach to improve proxi

Martijn Caspers, Bas Janssens, Amudhan Krishnaswamy-Usha, Lukas Miaskiwskyi

Let $\Gamma < G$ be a discrete subgroup of a locally compact unimodular group $G$. Let $m\in C_b(G)$ be a $p$-multiplier on $G$ with $1 \leq p < \infty$ and let $T_{m}: L_p(\widehat{G}) \rightarrow L_p(\widehat{G})$ be the corresponding Fourier multiplier. Similarly, let $T_{m \vert_\Gamma}: L_p(\widehat{\Gamma}) \rightarrow L_p(\widehat{\Gamma})$ be the Fou

Tomas Pippia, Valentin Preda, Samir Bennani, Tamas Keviczky

Satellite constellation missions, consisting of a large number of spacecraft, are increasingly being launched or planned. Such missions require novel control approaches, in particular for what concerns orbital phasing maneuvers. In this context, we consider the problem of reconfiguration of a satellite constellation in a circular formation. In our scenario,

Tianxi Ji, Changqing Luo, Lixing Yu, Qianlong Wang

Computation offloading is indispensable for mobile edge computing (MEC). It uses edge resources to enable intensive computations and save energy for resource-constrained devices. Existing works generally impose strong assumptions on radio channels and network queue sizes. However, practical MEC systems are subject to various uncertainties rendering these ass

Wei Guo, Jing-Mei Qiu

In this paper, we propose a conservative low rank tensor method to approximate nonlinear Vlasov solutions. The low rank approach is based on our earlier work (arxiv: 2106.08834). It takes advantage of the fact that the differential operators in the Vlasov equation are tensor friendly, based on which we propose to dynamically and adaptively build up low rank

K. C. W. Li, F. D. Smit, P. Adsley, R. Neveling

Knowledge of the low-lying monopole strength in $\mathrm{^{12}C}$ $-$ the Hoyle state in particular $-$ is crucial for our understanding of both the astrophysically important $3\alpha$ reaction and of $\alpha$-particle clustering. Multiple theoretical models have predicted a breathing mode of the Hoyle State at $E_{x} \approx 9$ MeV, corresponding to a radia

Ali Ismail, Mariette Awad

In the aftermath of disasters, building damage maps are obtained using change detection to plan rescue operations. Current convolutional neural network approaches do not consider the similarities between neighboring buildings for predicting the damage. We present a novel graph-based building damage detection solution to capture these relationships. Our propo

Kiyoon Kim, Shreyank N Gowda, Oisin Mac Aodha, Laura Sevilla-Lara

We address the problem of capturing temporal information for video classification in 2D networks, without increasing their computational cost. Existing approaches focus on modifying the architecture of 2D networks (e.g. by including filters in the temporal dimension to turn them into 3D networks, or using optical flow, etc.), which increases computation cost

Bojan Jerbić, Marko Švaco, Filip Šuligoj, Bojan Šekoranja

Design usually relies on human ingenuity, but the past decade has seen the field's toolbox expanding to Artificial Intelligence (AI) and its adjacent methods, making room for hybrid, algorithmic creations. This article aims to substantiate the concept of interspecies collaboration - that of natural and artificial intelligence - in the active co-creation of a

Doganay Sirintuna, Alberto Giammarino, Arash Ajoudani

In this work, we introduce an adaptive control framework for human-robot collaborative transportation of objects with unknown deformation behaviour. The proposed framework takes as input the haptic information transmitted through the object, and the kinematic information of the human body obtained from a motion capture system to create reactive whole-body mo

Henrique Guerreiro, João Guerra

The rBergomi model under the physical measure consists of modeling the log-variance as a truncated Brownian semi-stationary process. Then, a deterministic change of measure is applied. The rBergomi model is able to reproduce observed market SP500 smiles with few parameters, but by virtue of the deterministic change of measure, produces flat VIX smiles, in co