May 2022 arXiv papers — page 60

Elena Apresyan, Gor Sarkissian, Vyacheslav P. Spiridonov

We study properties of a parafermionic generalization of the hyperbolic hypergeometric function appearing as the most important part in the fusion matrix for Liouville field theory and the Racah-Wigner symbols for the Faddeev modular double. We show that this generalized hypergeometric function is a limiting form of the rarefied elliptic hypergeometric funct

Elena Arcari, Andrea Iannelli, Andrea Carron, Melanie N. Zeilinger

The performance of model-based control techniques strongly depends on the quality of the employed dynamics model. If strong guarantees are desired, it is therefore common to robustly treat all possible sources of uncertainty, such as model inaccuracies or external disturbances. This, however, can result in overly conservative control strategies. In this pape

Florian Bernlochner, Matteo Fael, Kevin Olschewsky, Eric Persson

We present the first determination of $V_{cb}$ from inclusive $B\to X_c \ell \bar\nu_\ell$ using moments of the dilepton invariant mass, $q^2$. These moments are reparametrization invariant quantities and depend on a reduced set of non-perturbative parameters. This reduced opens a new path to extract these parameters up to $1/m_b^4$ purely from data and ther

Ohad Vilk, Michael Assaf, Baruch Meerson

We study stationary fluctuations in two models involving $N$ Brownian particles undergoing stochastic resetting to the origin in 1d. We start with the basic reset model where the particles reset independently (model A). Then we introduce nonlocal interparticle correlations by postulating that only the particle farthest from the origin is reset (model B). At

Pourya Shamsolmoali, Masoumeh Zareapoor, Eric Granger, Huiyu Zhou

Skin lesion detection in dermoscopic images is essential in the accurate and early diagnosis of skin cancer by a computerized apparatus. Current skin lesion segmentation approaches show poor performance in challenging circumstances such as indistinct lesion boundaries, low contrast between the lesion and the surrounding area, or heterogeneous background that

Andres Karjus, Mar Canet Solà, Tillmann Ohm, Sebastian E. Ahnert

The quantification of visual aesthetics and complexity have a long history, the latter previously operationalized via the application of compression algorithms. Here we generalize and extend the compression approach beyond simple complexity measures to quantify algorithmic distance in historical and contemporary visual media. The proposed "ensemble" approach

Stefano Biagi, Marco Bramanti

We consider degenerate Kolmogorov-Fokker-Planck operators $$ \mathcal{L}u=\sum_{i,j=1}^{q}a_{ij}(x,t)\partial_{x_{i}x_{j}}^{2}u+\sum_{k,j=1}^{N}b_{jk}x_{k}\partial_{x_{j}}u-\partial_{t}u,\qquad (x,t)\in\mathbb{R}^{N+1},N\geq q\geq1 $$ such that the corresponding model operator having constant $a_{ij}$ is hypoelliptic, translation invariant w.r.t. a Lie group

Mikkel Bennedsen, Eric Hillebrand, Jingying Zhou Lykke

This paper estimates the two-component energy balance model as a linear state space system (EBM-SS model) using historical data. It is a joint model for the temperature in the mixed layer, the temperature in the deep ocean layer, and radiative forcing. The EBM-SS model allows for the modeling of non-stationarity in forcing, the incorporation of multiple data

Moritz Böhle, Mario Fritz, Bernt Schiele

We present a new direction for increasing the interpretability of deep neural networks (DNNs) by promoting weight-input alignment during training. For this, we propose to replace the linear transforms in DNNs by our B-cos transform. As we show, a sequence (network) of such transforms induces a single linear transform that faithfully summarises the full model

Ria Patel, Brandan Roachell, Silvina Caino-Lores, Ross Ketron

This paper presents an interdisciplinary effort aiming to develop and share sustainable knowledge necessary to analyze, understand, and use published scientific results to advance reproducibility in multi-messenger astrophysics. Specifically, we target the breakthrough work associated with the generation of the first image of a black hole, called M87. The im

Hugo Bohy, Ahmad Hammoudeh, Antoine Maiorca, Stéphane Dupont

The development of virtual agents has enabled human-avatar interactions to become increasingly rich and varied. Moreover, an expressive virtual agent i.e. that mimics the natural expression of emotions, enhances social interaction between a user (human) and an agent (intelligent machine). The set of non-verbal behaviors of a virtual character is, therefore,

Jianping Xu, Tao Zu, Yi-Cheng Hsu, Xiaoli Wang

Purpose: To develop a model-based deep neural network for high-quality image reconstruction of undersampled multi-coil chemical exchange saturation transfer (CEST) data. Theory and Methods: Inspired by the variational network, the CEST image reconstruction equation is unrolled into a deep neural network (CEST-VN) with a k-space data-sharing block that takes

Wei Zhang, Yu Bao

Deep Neural Networks (DNNs) have already become a crucial computational approach to revealing the spatial patterns in the human brain; however, there are three major shortcomings in utilizing DNNs to detect the spatial patterns in functional Magnetic Resonance Signals: 1). It is a fully connected architecture that increases the complexity of network structur

Nima Noii, Amirreza Khodadadian, Fadi Aldakheel

A probabilistic approach to phase-field brittle and ductile fracture with random material and geometric properties is proposed within this work. In the macroscopic failure mechanics, materials properties and exactness of spatial quantities (of different phases in the geometrical domain) are assumed to be homogeneous and deterministic. This is unlike the lowe

Yun Zheng, Jing Wang, Judith Irwin, Q. Daniel Wang

We study the vertical distribution of the highly inclined galaxies from the Continuum Halos in Nearby Galaxies - an EVLA Survey (CHANG-ES). We explore the feasibility of photometrically deriving the HI disk scale-heights from the moment-0 images of the relatively edge-on galaxies with inclination >80 deg, by quantifying the systematic broadening effects and

G. P. Vasilyev, N. V. Peskov, T. M. Lysak

A new finite-difference model of heat transfer inside a shallow coaxial ground heat exchanger and in the surrounding layered soil is presented, taking into account the freezing of ground moisture. Three modes of heat exchanger operation are numerically simulated: stationary mode, transient mode and controlled mode. In the stationary mode, estimates of the se

Lipei Du, Ulrich Heinz

We explore the response to energetic partons of a baryon-charged medium produced in low energy heavy-ion collisions in which the partonic energy loss rate is expected to depend on both temperature and baryon chemical potential. The energy and momentum deposited by the partons are described by dynamical sources added to hydrodynamic equations of motion. We st

Lingjia Huang, Quoc-Hung Nguyen, Yiran Xu

In this paper, we study the asymptotic stability of Penrose-stable equilibria among solutions of the screened Vlasov-Poisson system in $\mathbb{R}^d$ with $d\geq 3$ that was first established by Bedrossian, Masmoudi, and Mouhot in \cite{JBedrossian2018} with smooth initial data. More precisely, we prove the sharp decay estimates for the density of the pertur

Yachun Li, Peng Qu, Zirong Zeng, Deng Zhang

We study the 3D hyperdissipative Navier-Stokes equations on the torus, where the viscosity exponent $\alpha$ can be larger than the Lions exponent $5/4$. It is well-known that, due to Lions [55], for any $L^2$ divergence-free initial data, there exist unique smooth Leray-Hopf solutions when $\alpha \geq 5/4$. We prove that even in this high dissipative regim

Jillur Rahman Saurav, Mohammad Sadegh Nasr, Paul Koomey, Michael Robben

Here we present a structural similarity index measure (SSIM) guided conditional Generative Adversarial Network (cGAN) that generatively performs image-to-image (i2i) synthesis to generate photo-accurate protein channels in multiplexed spatial proteomics images. This approach can be utilized to accurately generate missing spatial proteomics channels that were

Charles Monnoyer de Galland, Renato Vizuete, Julien M. Hendrickx, Elena Panteley

We propose a method for analyzing the distributed random coordinate descent algorithm for solving separable resource allocation problems in the context of an open multiagent system, where agents can be replaced during the process. In particular, we characterize the evolution of the distance to the minimizer in expectation by following a time-varying optimiza

Daniel Diaz-Anichtchenko, Daniel Errandonea

We report a density-functional theory study of the structural and electronic properties of Cd2V2O7 under high-pressure conditions. The calculations have been performed by using first-principle calculations with the CRYSTAL program. The occurrence of two structural phase transitions, at 0.3 and 10.9 GPa, is proposed. The crystal structure of the different hig

Xinrui Zhang, Qin Wang, Rujia Li, Qi Wang

As a fundamental technology of decentralized finance (DeFi), blockchain's ability to maintain a distributed fair ledger is threatened by manipulation of block/transaction order. In this paper, we propose a frontrunning block attack against the Clique-based Proof of Authority (PoA) algorithms. Our attack can frontrun blocks from honest in-turn sealers by brea

Luca Mattia Rolla, Alessandro Giovannelli

This paper analyses the forecasting performance of a new class of factor models with martingale difference errors (FMMDE) recently introduced by Lee and Shao (2018). The FMMDE makes it possible to retrieve a transformation of the original series so that the resulting variables can be partitioned according to whether they are conditionally mean-independent wi

Charles Yuan, Michael Carbin

Emerging quantum algorithms for problems such as element distinctness, subset sum, and closest pair demonstrate computational advantages by relying on abstract data structures. Practically realizing such an algorithm as a program for a quantum computer requires an efficient implementation of the data structure whose operations correspond to unitary operators

Zhicheng Cao, Kaituo Zhang, Liaojun Pang, Heng Zhao

Face-based age estimation has attracted enormous attention due to wide applications to public security surveillance, human-computer interaction, etc. With vigorous development of deep learning, age estimation based on deep neural network has become the mainstream practice. However, seeking a more suitable problem paradigm for age change characteristics, desi

Daniel Contreras, Sébastien Martineau, Vincent Tassion

Schramm's Locality Conjecture asserts that the value of the critical percolation parameter $p_c$ of a graph satisfying $p_c<1$ depends only on its local structure. In this note, we prove this conjecture in the particular case of transitive graphs with polynomial growth. Our proof relies on two recent works about such graphs, namely supercritical sharpness of

Chen Zhao, Feng Mi, Xintao Wu, Kai Jiang

The fairness-aware online learning framework has arisen as a powerful tool for the continual lifelong learning setting. The goal for the learner is to sequentially learn new tasks where they come one after another over time and the learner ensures the statistic parity of the new coming task across different protected sub-populations (e.g. race and gender). A

Audun D. Myers, Max M. Chumley, Firas A. Khasawneh, Elizabeth Munch

This work is dedicated to the topological analysis of complex transitional networks for dynamic state detection. Transitional networks are formed from time series data and they leverage graph theory tools to reveal information about the underlying dynamic system. However, traditional tools can fail to summarize the complex topology present in such graphs. In

Sunder Sethuraman, Jianfei Xue

We consider the space-time scaling limit of the particle mass in zero-range particle systems on a $1$D discrete torus $\mathbb{Z}/N\mathbb{Z}$ with a finite number of defects. We focus on two classes of increasing jump rates $g$, when $g(n)\sim n^\alpha$, for $0<\alpha\leq 1$, and when $g$ is a bounded function. In such a model, a particle at a regular site

David Hanson, Alishba Imran, Gerardo Morales, Vytas Krisciunas

Open Arms is a novel open-source platform of realistic human-like robotic hands and arms hardware with 28 Degree-of-Freedom (DoF), designed to extend the capabilities and accessibility of humanoid robotic grasping and manipulation. The Open Arms framework includes an open SDK and development environment, simulation tools, and application development tools to

Taesoo Song, Joerg Aichelin, Elena Bratkovskaya

We investigate the hidden strange $\phi$ meson production in heavy-ion collisions from subthreshold ($E_{kin}\approx 1$ A GeV) to relativistic ($E_{kin}\approx 21$ A TeV) energies as well as its coupling to the open strange mesons (kaons, antikaons) and their productions. Our study is based on the off-shell microscopic Parton-Hadron-String Dynamics (PHSD) tr

Lun Ai, Johannes Langer, Stephen H. Muggleton, Ute Schmid

The topic of comprehensibility of machine-learned theories has recently drawn increasing attention. Inductive Logic Programming (ILP) uses logic programming to derive logic theories from small data based on abduction and induction techniques. Learned theories are represented in the form of rules as declarative descriptions of obtained knowledge. In earlier w

Yue Cao, XiaoJiang Zhou, Jiaqi Feng, Peihao Huang

Rich user behavior data has been proven to be of great value for Click-Through Rate (CTR) prediction applications, especially in industrial recommender, search, or advertising systems. However, it's non-trivial for real-world systems to make full use of long-term user behaviors due to the strict requirements of online serving time. Most previous works adopt

Emanuele Rodaro, Jan Philipp Wächter

We give a survey on results regarding self-similar and automaton presentations of free groups and semigroups and related products. Furthermore, we discuss open problems and results with respect to algebraic decision problems in this area.

Wei Zhang, Yu Bao

In this work, to efficiently help escape the stationary and saddle points, we propose, analyze, and generalize a stochastic strategy performed as an operator for a first-order gradient descent algorithm in order to increase the target accuracy and reduce time consumption. Unlike existing algorithms, the proposed stochastic the strategy does not require any b

Jianzhe Liu, Yichen Zhang, Antonio J. Conejo, Feng Qiu

DC microgrids have promising applications in renewable integration due to their better energy efficiency when connecting DC components. However, they might be unstable since many loads in a DC microgrid are regulated as constant power loads (CPLs) that have a destabilizing negative impedance effect. As a result, the state trajectory displacement caused by ab

Fernando Lorén, Gian L. Paravicini-Bagliani, Sudipta Saha, Jérôme Gautier

We revisit spin-orbit coupling in a plasmonic Berry metasurface comprised of rotated nanoapertures, which is known to imprint a robust far-field polarization response. We present a scattering formalism that shows how that spin-momentum locking emerges from the geometry of the unit cell without requiring global rotation symmetries. We find and confirm with Mu

F. A. Gallego, A. Montes

The symmetric regularized long wave system (SRLW) is a model for the weakly nonlinear ion acoustic and space-charge waves, which was introduced by C. Seyler and D. Fenstermacher. In this paper, we investigated the wellposedness and controllability properties of the generalized symmetric regularized long wave system (g-SRLW) in different structures (periodic

Nitin Jonathan Myers, Yanki Aslan, Geethu Joseph

Phased arrays in near-field communication allow the transmitter to focus wireless signals in a small region around the receiver. Proper focusing is achieved by carefully tuning the phase shifts and the polarization of the signals transmitted from the phased array. In this paper, we study the impact of polarization on near-field focusing and investigate the u

Masato Hisakado, Kodai Hattori, Shintaro Mori

In this study, we apply a multidimensional self-exciting negative binomial distribution (SE-NBD) process to default portfolios with 13 sectors. The SE-NBD process is a Poisson process with a gamma-distributed intensity function. We extend the SE-NBD process to a multidimensional process. Using the multidimensional SE-NBD process (MD-SE-NBD), we can estimate

Felix Christian Bauer, Gregor Lenz, Saeid Haghighatshoar, Sadique Sheik

Spiking Neural Networks (SNNs) are gaining significant traction in machine learning tasks where energy-efficiency is of utmost importance. Training such networks using the state-of-the-art back-propagation through time (BPTT) is, however, very time-consuming. Previous work by Shrestha and Orchard [2018] employs an efficient GPU-accelerated back-propagation a

Chaolong Jiang, Xu Qian, Songhe Song, Chenxuan Zheng

In this paper, we are concerned with arbitrarily high-order momentum-preserving and energy-preserving schemes for solving the generalized Rosenau-type equation, respectively. The derivation of the momentum-preserving schemes is made within the symplectic Runge-Kutta method, coupled with the standard Fourier pseudo-spectral method in space. Then, combined wit

Yichun Zhao, Mingquan Wei, Jiang Zhou

The purpose of this paper is to introduce and investigate some basic properties of mixed homogeneous Herz-Hardy spaces $H\dot{K}_{\vec{p}}^{\alpha, q}(\mathbb{R}^n)$ and mixed non-homogeneous Herz-Hardy spaces $HK_{\vec{p}}^{\alpha, q}(\mathbb{R}^n)$. Furthermore, we establish the atom and molecular decompositions for $H\dot{K}_{\vec{p}}^{\alpha, q}(\mathbb{

Valerio De Luca, Alex Kehagias, Antonio Riotto

The Standard Model Higgs potential becomes unstable at large Higgs field values where its quartic coupling becomes negative. While the tunneling lifetime of our current electroweak vacuum is comfortably longer than the age of the universe, quantum fluctuations during inflation might push the Higgs over the barrier, forming patches which might be lethal for o

Feng Li, Jianyi Zhou, Yinzhu Li, Dan Hao

In recent years, many test case prioritization (TCP) techniques have been proposed to speed up the process of fault detection. However, little work has taken the efficiency problem of these techniques into account. In this paper, we target the Greedy Additional (GA) algorithm, which has been widely recognized to be effective but less efficient, and try to im

Zenan Zhu, Seyed Mostafa Rezayat Sorkhabadi, Yan Gu, Wenlong Zhang

This paper introduces a new invariant extended Kalman filter design that produces real-time state estimates and rapid error convergence for the estimation of the human body movement even in the presence of sensor misalignment and initial state estimation errors. The filter fuses the data returned by an inertial measurement unit (IMU) attached to the body (e.

Javier Del Ser, Alejandro Barredo-Arrieta, Natalia Díaz-Rodríguez, Francisco Herrera

There is a broad consensus on the importance of deep learning models in tasks involving complex data. Often, an adequate understanding of these models is required when focusing on the transparency of decisions in human-critical applications. Besides other explainability techniques, trustworthiness can be achieved by using counterfactuals, like the way a huma

Oliver Russell, Wei Sun

The long-standing Gaussian product inequality (GPI) conjecture states that $E [\prod_{j=1}^{n}|X_j|^{\alpha_j}]\geq\prod_{j=1}^{n}E[|X_j|^{\alpha_j}]$ for any centered Gaussian random vector $(X_1,\dots,X_n)$ and any non-negative real numbers $\alpha_j$, $j=1,\ldots,{n}$. In this note, we prove a novel "opposite GPI" for centered bivariate Gaussian random va

Armand Coudray

We study the peeling for the wave equation on the Vaidya spacetime following the approach developed by Mason and Nicolas in Mason-Nicolas 2009. The idea is to encode the regularity at null infinity of the rescaled field, characterised by Sobolev-type norms, in terms of corresponding function spaces of initial data. All function spaces are obtained from energ

Xiang Li, Yu Wang

In optical lattices attractive ultracold fermions with three hyperfine-spin components (colors) can form three fermionic configurations depending on interactions: unbound fermion, on-site trion and off-site trion, leading to the coexistence of multiple Fermi species in the ordered phase, which manifests that the attractive three-color fermions are unique fro

Cristina Gena, Alberto Lillo, Claudio Mattutino, Enrico Mosca

In this paper we present an educational robot called Wolly, designed to engage children in an affective and social interaction. Indeed, we are now focusing on its role as an educational and affective robot capable of being controlled by coding instructions and at the same time interacting verbally and affectively with children by recognizing their emotions a

Marta Misiaszek-Schreyner

This doctoral dissertation deals with the subject of single-photon technology applications. Particular emphasis was placed on the use of single-photon sources in quantum communication, metrology, and further development of quantum computers. At the beginning of this thesis, the development history of cryptography is described, starting from steganography and

Adam Hibberd

This paper explicates the concept of an Intermediate Point (IP), its incorporation as a node along an interplanetary trajectory, and how it permits the determination and optimization of trajectories to interstellar objects (ISOs). IPs can be used to model Solar Oberth Manoeuvres (SOM) as well as Vinfnity Leveraging Manoeuvres (VLM). The SOM has been establis

P. Curone, A. F. Izquierdo, L. Testi, G. Lodato

(Abridged) Exoplanetary research has provided us with exciting discoveries of planets around very low-mass (VLM) stars (e.g., TRAPPIST-1 and Proxima Centauri). However, current theoretical models strive to explain planet formation in these conditions and do not predict the development of giant planets. Recent high-resolution observations from ALMA of the dis

Rui Yang, Jie Wang, Zijie Geng, Mingxuan Ye

Generalization across different environments with the same tasks is critical for successful applications of visual reinforcement learning (RL) in real scenarios. However, visual distractions -- which are common in real scenes -- from high-dimensional observations can be hurtful to the learned representations in visual RL, thus degrading the performance of ge

Simone Bombari, Mohammad Hossein Amani, Marco Mondelli

The Neural Tangent Kernel (NTK) has emerged as a powerful tool to provide memorization, optimization and generalization guarantees in deep neural networks. A line of work has studied the NTK spectrum for two-layer and deep networks with at least a layer with $\Omega(N)$ neurons, $N$ being the number of training samples. Furthermore, there is increasing evide

Samet Ok, Axel Schwope

We aim to study the temporal and spectral behaviour of \vum from the optical to the X-ray regimes. We used archival \xmmn and \tes observations obtained in 2017 and 2019 to perform a spectral and timing analysis of the highly variable polar. The light curves of both satellites, TESS and XMM-Newton, reveal a double-humped pattern modulated with the periodicit

Pavel Záviška, Pavel Rajmic

We develop the analysis (cosparse) variant of the popular audio declipping algorithm of Siedenburg et al. (2014). Furthermore, we extend both the old and the new variants by the possibility of weighting the time-frequency coefficients. We examine the audio reconstruction performance of several combinations of weights and shrinkage operators. The weights are

Emma Brambila, Rodrigo Gómez, Riza Fazili, Markus Gräfe

The distribution of entanglement via satellite links will drastically extend the reach of quantum networks. Highly efficient entangled photon sources are an essential requirement towards overcoming high channel loss and achieving practical transmission rates in long-distance satellite downlinks. Here we report on an ultrabright entangled photon source that i

LHCb collaboration

This article presents the first measurement of the differential $Z$-boson production cross-section in the forward region using proton-lead collisions with the LHCb detector. The dataset was collected at a nucleon-nucleon centre-of-mass energy of $\sqrt{s_\mathrm{NN}}=8.16\,\mathrm{TeV}$ in 2016, corresponding to an integrated luminosity of $30.8\,\mathrm{nb}

Si-Ying Wang, Qinghong Yang, Fu-Lin Zhang

Master equations under appropriate assumptions are efficient tools for the study of open quantum systems. For many-body systems, subsystems of which locally couple to thermal baths and weakly interact with each other, the local approach provides a more convenient description than the global approach. However, these local master equations are believed to gene

Dalibor Perkovic, Hrvoje Stefancic

Galaxy rotation curves are considered to be convincing evidence for dark matter or some dynamically equivalent alternative mechanism. Starting only from the rotation curve data, we present a model independent approach of testing a general hypothesis that dark matter has the properties of a barotropic fluid. It is shown how the speed of sound squared can be e

Tao Yang, Shenglong Zhou, Yuwang Wang, Yan Lu

Deep neural networks often suffer the data distribution shift between training and testing, and the batch statistics are observed to reflect the shift. In this paper, targeting of alleviating distribution shift in test time, we revisit the batch normalization (BN) in the training process and reveals two key insights benefiting test-time optimization: $(i)$ p

Marco Merafina, Matteo Teodori

We improve the standard theory of collisional stellar systems by considering the presence of a continuous mass distribution. The calculus of the diffusion coefficients is generalized and a new expression of the Fokker-Planck equation is found for multi-mass systems. A King-like distribution function, which validates the basic assumptions of most multi-mass m

Dong Yan, Bastien F. G. Aymon, Pedro M. Reis

We develop a reduced-order model for thin plates made of hard magnetorheological elastomers (hard-MREs), which are materials composed of hard-magnetic particles embedded in a polymeric matrix. First, we propose a new magnetic potential, as an alternative to an existing torque-based 3D continuum theory of hard-MREs, obtained by reformulating the remnant magne

John P. Lalor, Hong Guo

Algorithmic interpretability is necessary to build trust, ensure fairness, and track accountability. However, there is no existing formal measurement method for algorithmic interpretability. In this work, we build upon programming language theory and cognitive load theory to develop a framework for measuring algorithmic interpretability. The proposed measure

Alyssa M. Taylor-LaPole, Mitchel J. Colebank, Justin D. Weigand, Mette S. Olufsen

Patients with hypoplastic left heart syndrome (HLHS) are born with an underdeveloped left heart. They typically receive a sequence of surgeries that result in a single ventricle physiology called the Fontan circulation. While these patients usually survive into early adulthood, they are at risk for medical complications, partially due to their lower than nor

José Luis Romero, Michael Speckbacher

We investigate the retrieval of a binary time-frequency mask from a few observations of filtered white ambient noise. Confirming household wisdom in acoustic modeling, we show that this is possible by inspecting the average spectrogram of ambient noise. Specifically, we show that the lower quantile of the average of $\mathcal{O}(\log(|\Omega|/\varepsilon))$

Javier de la Nuez González

We prove that given a planar embedding of a graph in the sphere the expansion of the graph structure by predicates encoding separation of vertices by simple cycles of the graph is dp-minimal.

Michael Hobley, Victor Prisacariu

Current class-agnostic counting methods can generalise to unseen classes but usually require reference images to define the type of object to be counted, as well as instance annotations during training. Reference-less class-agnostic counting is an emerging field that identifies counting as, at its core, a repetition-recognition task. Such methods facilitate

Ilya Tcenov, Guy Gilboa

Recent advances in depth sensing technologies allow fast electronic maneuvering of the laser beam, as opposed to fixed mechanical rotations. This will enable future sensors, in principle, to vary in real-time the sampling pattern. We examine here the abstract problem of whether adapting the sampling pattern for a given frame can reduce the reconstruction err

Francesc Wilhelmi, Elia Guerra, Paolo Dini

Federated learning (FL), thanks in part to the emergence of the edge computing paradigm, is expected to enable true real-time applications in production environments. However, its original dependence on a central server for orchestration raises several concerns in terms of security, privacy, and scalability. To solve some of these worries, blockchain technol

Christophe Hurlin, Christophe Pérignon, Sébastien Saurin

In credit markets, screening algorithms aim to discriminate between good-type and bad-type borrowers. However, when doing so, they can also discriminate between individuals sharing a protected attribute (e.g. gender, age, racial origin) and the rest of the population. This can be unintentional and originate from the training dataset or from the model itself.

John P. Boyd

For millenia, sailors have used the empirical rule that the elevation angle of Polaris, the North Star, as measured by sextant, quadrant or astrolabe, is approximately equal to latitude. Here, we show using elementary trigonometry that Empirical Law 1 can be converted from a heuristic to a theorem. A second ancient empirical law is that the distance in kilom

Zheng Liu, Yaoming Zhuang, Pengrun Jia, Chengdong Wu

For aquaculture resource evaluation and ecological environment monitoring, automatic detection and identification of marine organisms is critical. However, due to the low quality of underwater images and the characteristics of underwater biological, a lack of abundant features may impede traditional hand-designed feature extraction approaches or CNN-based ob

Kuanhao Jiang, Rajarshi Mukherjee, Subhabrata Sen, Pragya Sur

Estimation of the average treatment effect (ATE) is a central problem in causal inference. In recent times, inference for the ATE in the presence of high-dimensional covariates has been extensively studied. Among the diverse approaches that have been proposed, augmented inverse probability weighting (AIPW) with cross-fitting has emerged a popular choice in p

Aviad Panahi, Shay Zucker, Gisella Clementini, Marc Audard

Context: The space telescope Gaia is dedicated mainly to performing high-precision astrometry, but also spectroscopy and epoch photometry which can be used to study various types of photometric variability. One such variability type is exoplanetary transits. The photometric data accumulated so far have finally matured enough to allow the detection of some ex

Xianping Li

In this paper, combined with the degradation and monotonicity of parameters of Hamacher operation and the advantages of the correlation between input parameters of Heronian operators, IVq-ROFHHMWA and IVq-ROFHHMGA operators, which are the fusion of Hamacher and Heronian, are proposed, and their properties are studied. Finally, a group decision-making method

Xinghao Wang, Peizhe Jia, Rui-Rui Du, L. N. Pfeiffer

Viscous fluid in an ultrahigh-mobility two-dimensional electron gas (2DEG) in GaAs/AlGaAs quantum wells is systematically studied through measurements of negative magnetoresistance (NMR) and photoresistance under microwave radiation, and the data are analyzed according to recent theoretical work by e.g., Alekseev, Physical Review Letters 117,166601 (2016). S

Jing Lin, Xiaowan Hu, Yuanhao Cai, Haoqian Wang

How to properly model the inter-frame relation within the video sequence is an important but unsolved challenge for video restoration (VR). In this work, we propose an unsupervised flow-aligned sequence-to-sequence model (S2SVR) to address this problem. On the one hand, the sequence-to-sequence model, which has proven capable of sequence modeling in the fiel

Yury Elkin

This thesis consists of two topics related to computational geometry and one topic related to topological data analysis (TDA), which combines fields of computational geometry and algebraic topology for analyzing data. The first part studies the classical problem of finding k nearest neighbors to m query points in a larger set of n reference points in any met

Antonio Pontin, Hayden Fu, Marko Toroš, Tania S. Monteiro

We report on strong cooling and orientational control of all translational and angular degrees of freedom of a nanoparticle levitated in an optical trap in high vacuum. The motional cooling and control of all six degrees of freedom of a nanoparticle levitated by an optical tweezer is accomplished using coherent elliptic scattering within a high finesse optic

Ronald Cardenas, Matthias Galle, Shay B. Cohen

Extractive summaries are usually presented as lists of sentences with no expected cohesion between them and with plenty of redundant information if not accounted for. In this paper, we investigate the trade-offs incurred when aiming to control for inter-sentential cohesion and redundancy in extracted summaries, and their impact on their informativeness. As c

Long-Jing Yin, Ling-Hui Tong, Yue-Ying Zhou, Yang Zhang

Low-energy moir\'e flat bands in magic-angle twisted bilayer graphene (tBG) have demonstrated incredible potentials to exhibit rich exotic quantum phenomena. Theoretically, the moir\'e flat bands of tBG are based on the extended structures, i.e., the moir\'e patterns with periodic boundary conditions. However, a fundamental question of whether the flat bands

Conor Heins, Lancelot Da Costa

In this commentary, we respond to a technical analysis of the Free Energy Principle (hereafter: FEP) presented in "How particular is the physics of the Free Energy Principle" by Aguilera et al. In the target article, the authors analyzed certain sparsely coupled stochastic differential equations whose non-equilibrium steady-state densities are claimed--in pr

Nicholas W. Barendregt, Emily G. Webb, Zachary P. Kilpatrick

Optimal designs minimize the number of experimental runs (samples) needed to accurately estimate model parameters, resulting in algorithms that, for instance, efficiently minimize parameter estimate variance. Governed by knowledge of past observations, adaptive approaches adjust sampling constraints online as model parameter estimates are refined, continuall

Hai-Ming Xu, Lingqiao Liu, Ehsan Abbasnejad

Semi-supervised learning is a promising way to reduce the annotation cost for text-classification. Combining with pre-trained language models (PLMs), e.g., BERT, recent semi-supervised learning methods achieved impressive performance. In this work, we further investigate the marriage between semi-supervised learning and a pre-trained language model. Unlike e

Jiaming Liang, Yongxin Chen

We study sampling problems associated with non-convex potentials that meanwhile lack smoothness. In particular, we consider target distributions that satisfy either logarithmic-Sobolev inequality or Poincar\'e inequality. Rather than smooth, the potentials are assumed to be semi-smooth or the summation of multiple semi-smooth functions. We develop a sampling

Takaaki Azakami, Hiroshi Kera, Kazuhiko Kawamoto

We propose an evolutionary computation method for an adversarial attack on the length and thickness of parts of legged robots by deep reinforcement learning. This attack changes the robot body shape and interferes with walking-we call the attacked body as adversarial body shape. The evolutionary computation method searches adversarial body shape by minimizin

Christoffer Riis, Francisco Antunes, Frederik Boe Hüttel, Carlos Lima Azevedo

The bias-variance trade-off is a well-known problem in machine learning that only gets more pronounced the less available data there is. In active learning, where labeled data is scarce or difficult to obtain, neglecting this trade-off can cause inefficient and non-optimal querying, leading to unnecessary data labeling. In this paper, we focus on active lear

Philipp D Astolfo, Xing Wang, Xunshan Liu, Marcin Kisiel

Crystalline nanoporous molecular networks are assembled on the Ag(111) surface, where the pores confine electrons originating from the surface state of the metal. Depending on the pore sizes and their coupling, an antibonding level is shifted upwards by 0.1 to 0.3 eV as measured by scanning tunneling microscopy. On molecular sites, a down-shifted bonding sta

Shane Gilroy, Darragh Mullins, Edward Jones, Ashkan Parsi

Accurate detection and classification of vulnerable road users is a safety critical requirement for the deployment of autonomous vehicles in heterogeneous traffic. Although similar in physical appearance to pedestrians, e-scooter riders follow distinctly different characteristics of movement and can reach speeds of up to 45kmph. The challenge of detecting e-

Zhixiong Han, Yaru Hao, Li Dong, Yutao Sun

In-context learning of GPT-like models has been recognized as fragile across different hand-crafted templates, and demonstration permutations. In this work, we propose prototypical calibration to adaptively learn a more robust decision boundary for zero- and few-shot classification, instead of greedy decoding. Concretely, our method first adopts Gaussian mix

Jonas Meinel, Minsik Kwon, Durga Dasari, Hitoshi Sumiya

Nanoscale nuclear magnetic resonance (NMR) signals can be measured through hyperfine interaction to paramagnetic electron sensor spins. A heterodyne approach is widely used to overcome the electron spin lifetime limit in spectral resolution. It uses a series of modified Hahn echo pulse sequences applied coherently with precession signal resulting in a subsam

Myeongseok Kang, Youngmin Jeon, Donghyun You

An artificial neural-network-based subgrid-scale model using the resolved stress, which is capable of predicting untrained decaying isotropic turbulence, is developed. Providing the grid-scale strain-rate tensor alone as input leads the model to predict a subgrid-scale stress tensor aligns with the strain-rate tensor, and the model performs similar to the dy

M. Berry, David Limberg, M. E. Lee-Trimble, Ryan Hayward

Linkages are mechanical devices constructed from rigid bars and freely rotating joints studied both for their utility in engineering and as mathematical idealizations in a number of physical systems. Recently, there has been a resurgence of interest in designing linkages to perform certain tasks from the physics community. We describe a method to design the

Edwin H. Salazar-Jurado, Ruber Hernández-García, Karina Vilches-Ponce, Ricardo J. Barrientos

With the recent success of computer vision and deep learning, remarkable progress has been achieved on automatic personal recognition using vein biometrics. However, collecting large-scale real-world training data for palm vein recognition has turned out to be challenging, mainly due to the noise and irregular variations included at the time of acquisition.

Weizhi Wang, Li Dong, Hao Cheng, Haoyu Song

Human language is grounded on multimodal knowledge including visual knowledge like colors, sizes, and shapes. However, current large-scale pre-trained language models rely on text-only self-supervised training with massive text data, which precludes them from utilizing relevant visual information when necessary. To address this, we propose a novel pre-traini

Dmitry E. Pelinovsky, Michael Plum

Black solitons are identical in the nonlinear Schr\"{o}dinger (NLS) equation with intensity-dependent dispersion and the cubic defocusing NLS equation. We prove that the intensity-dependent dispersion introduces new properties in the stability analysis of the black soliton. First, the spectral stability problem possesses only isolated eigenvalues on the imag