July 2022 arXiv papers — page 49

Quentin Serdel, Christophe Grand, Julien Marzat, Julien Moras

This paper introduces an Online Localisation and Colored Mesh Reconstruction (OLCMR) ROS perception architecture for ground exploration robots aiming to perform robust Simultaneous Localisation And Mapping (SLAM) in challenging unknown environments and provide an associated colored 3D mesh representation in real time. It is intended to be used by a remote hu

Alexei D. Chepelianskii, Satya N. Majumdar, Hendrik Schawe, Emmanuel Trizac

Among random sampling methods, Markov Chain Monte Carlo algorithms are foremost. Using a combination of analytical and numerical approaches, we study their convergence properties towards the steady state, within a random walk Metropolis scheme. Analysing the relaxation properties of some model algorithms sufficiently simple to enable analytic progress, we sh

M. Moscibrodzka

We describe upgrades to a numerical code which computes synchrotron and inverse-Compton emission from relativistic plasma including full polarization. The introduced upgrades concern scattering kernel which is now capable of scattering the polarized and unpolarized photons on non-thermal population of electrons. We describe the scheme to approach this proble

Alexandre Bittar, Philip N. Garner

Using Bayes's theorem, we derive a unit-wise recurrence as well as a backward recursion similar to the forward-backward algorithm. The resulting Bayesian recurrent units can be integrated as recurrent neural networks within deep learning frameworks, while retaining a probabilistic interpretation from the direct correspondence with hidden Markov models. Whils

Mahdi Gilany, Paul Wilson, Amoon Jamzad, Fahimeh Fooladgar

MOTIVATION: Detection of prostate cancer during transrectal ultrasound-guided biopsy is challenging. The highly heterogeneous appearance of cancer, presence of ultrasound artefacts, and noise all contribute to these difficulties. Recent advancements in high-frequency ultrasound imaging - micro-ultrasound - have drastically increased the capability of tissue

Charles-Edouard Bréhier, David Cohen, Giuseppe Giordano

We design and study splitting integrators for the temporal discretization of the stochastic FitzHugh--Nagumo system. This system is a model for signal propagation in nerve cells where the voltage variable is solution of a one-dimensional parabolic PDE with a cubic nonlinearity driven by additive space-time white noise. We first show that the numerical soluti

Péter Vrana

The zero-error capacity of a classical channel is a parameter of its confusability graph, and is equal to the minimum of the values of graph parameters that are additive under the disjoint union, multiplicative under the strong product, monotone under homomorphisms between the complements, and normalized. We show that any such function either has uncountably

Bestami Günay, Sefa Burak Okcu, Hasan Şakir Bilge

In recent years, number of edge computing devices and artificial intelligence applications on them have advanced excessively. In edge computing, decision making processes and computations are moved from servers to edge devices. Hence, cheap and low power devices are required. FPGAs are very low power, inclined to do parallel operations and deeply suitable de

Francesca Bevilacqua, Alessandro Lanza, Monica Pragliola, Fiorella Sgallari

We propose a novel automatic parameter selection strategy for variational imaging problems under Poisson noise corruption. The selection of a suitable regularization parameter, whose value is crucial in order to achieve high quality reconstructions, is known to be a particularly hard task in low photon-count regimes. In this work, we extend the so-called res

Farzam Dadgar-Rad, Mokarram Hossain

Hard-magnetic soft materials (HMSMs) are particulate composites that consist of a soft matrix embedded with particles of high remnant magnetic induction. Since the application of an external magnetic flux induces a body couple in HMSMs, the Cauchy stress tensor in these materials is asymmetric, in general. Therefore, the micropolar continuum theory can be em

Angelika Adensamer, Rita Gsenger, Lukas Daniel Klausner

Algorithmic decision support (ADS) is increasingly used in a whole array of different contexts and structures in various areas of society, influencing many people's lives. Its use raises questions, among others, about accountability, transparency and responsibility. Our article aims to give a brief overview of the central issues connected to ADS, responsibil

Shan Gao, Xihong Wu, Tianshu Qu

The conventional room geometry blind inference techniques with acoustic signals are conducted based on the prior knowledge of the environment, such as the room impulse response (RIR) or the sound source position, which will limit its application under unknown scenarios. To solve this problem, we have proposed a room geometry reconstruction method in this pap

P. Kumar, F. Fabre, A. Durand, T. Clua-Provost

Optically-active spin defects hosted in hexagonal boron nitride (hBN) are promising candidates for the development of a two-dimensional (2D) quantum sensing unit. Here, we demonstrate quantitative magnetic imaging with hBN flakes doped with negatively-charged boron-vacancy (V$_{\rm B}^-$) centers through neutron irradiation. As a proof-of-concept, we image t

Andrey Shternshis, Piero Mazzarisi, Stefano Marmi

This paper investigates the degree of efficiency for the Moscow Stock Exchange. A market is called efficient if prices of its assets fully reflect all available information. We show that the degree of market efficiency is significantly low for most of the months from 2012 to 2021. We calculate the degree of market efficiency by (i) filtering out regularities

Fad Sun, Jinwu Ye

In material science, it was established that as the number of particles $ N $ in a material gets more and more, especially in the thermodynamic limit, various macroscopic quantum phenomena such as superconductivity, superfluidity, quantum magnetism, Fractional quantum Hall effects and various quantum or topological phase transitions (QPT) emerge in such non-

Alice Bonino, Rossella Gamba, Patricia Schmidt, Alessandro Nagar

The origin and formation of stellar-mass binary black holes remains an open question that can be addressed by precise measurements of the binary and orbital parameters from their gravitational-wave signal. Such binaries are expected to circularize due to the emission of gravitational waves as they approach merger. However, depending on their formation channe

William Basener

Target detection in hyperspectral imagery is the process of locating pixels from an image which are likely to contain target, typically done by comparing one or more spectra for the desired target material to each pixel in the image. Target identification is the process of target detection incorporating an additional process to identify more specifically the

Ruchi Pandey, Gesesew R. Habtie, Rahul Bandyopadhyay, Ramkrishna Das

We present the evolution of the optical spectra of the 2021 outburst of RS Ophiuchi (RS Oph) over about a month after the outburst. The spectral evolution is similar to the previous outbursts. Early spectra show prominent P Cygni profiles of hydrogen Balmer, \ion{Fe}{ii}, and \ion{He}{i} lines. The emission lines were very broad during the initial days, whic

Pedro Agostini, Tolga Altinoluk, Néstor Armesto, Fabio Dominguez

We study the effects on multigluon production at mid-rapidity in the Color Glass Condensate of the non-eikonal corrections that stem from relaxing the shockwave approximation and giving the target a finite size. We extend previous works performed in the dilute-dilute approximation suitable for proton-proton collisions, to the dilute-dense one applicable to p

H. E. S. S. Collaboration, H. Abdalla, F. Aharonian, F. Ait Benkhali

The central region of the Milky Way is one of the foremost locations to look for dark matter (DM) signatures. We report the first results on a search for DM particle annihilation signals using new observations from an unprecedented gamma-ray survey of the Galactic Center (GC) region, ${\it i.e.}$, the Inner Galaxy Survey, at very high energies ($\gtrsim$ 100

Jin-Ping Zhu, Xiangyu Ivy Wang, Hui Sun, Yuan-Pei Yang

Here we collect three unique bursts, GRBs\,060614, 211211A and 211227A, all characterized by a long-duration main emission (ME) phase and a rebrightening extended emission (EE) phase, to study their observed properties and the potential origin as neutron star-black hole (NSBH) mergers. NS-first-born (BH-first-born) NSBH mergers tend to contain fast-spinning

Spencer A. Thomas

The usage of chemical imaging technologies is becoming a routine accompaniment to traditional methods in pathology. Significant technological advances have developed these next generation techniques to provide rich, spatially resolved, multidimensional chemical images. The rise of digital pathology has significantly enhanced the synergy of these imaging moda

Jarek Duda, Sabina Podlewska

While there is a general focus on predictions of values, mathematically more appropriate is prediction of probability distributions: with additional possibilities like prediction of uncertainty, higher moments and quantiles. For the purpose of the computer-aided drug design field, this article applies Hierarchical Correlation Reconstruction approach, previou

Huaying Wei, Katsuhiko Matsuzaki

We investigate strongly symmetric homeomorphisms of the real line which appear in harmonic analysis aspects of quasiconformal Teichm\"uller theory. An element in this class can be characterized by a property that it can be extended quasiconformally to the upper half-plane so that its complex dilatation induces a vanishing Carleson measure. However, different

Marco Calzá

We study a regular rotating black hole evaporating under the Hawking emission of a single scalar field. The black hole is described by the Kerr-black-bounce metric with a nearly extremal regularizing parameter $\ell=0.99r_+$. We compare the results with a Kerr black hole evaporating under the same conditions. Firstly, we compute the gray-body factors and sho

Wang Yi

With the development of the subway and the pressing demand of environmentally friendly transportation, more and more people travel by subway. In recent decades, the issues about passenger passive safety on the train have received extensive attention. In this research, the head injury of a standing passenger in the subway is investigated. Three MADYMO models

Wang Yi

With the development of the subway and the pressing demand of environmentally friendly transportation, more and more people travel by subway. In recent decades, the issues about passenger passive safety on the train have received extensive attention. In this research, the head injury of a standing passenger in the subway is investigated. Three MADYMO models

Hammond Pearce, Ramesh Karri, Benjamin Tan

Designers use third-party intellectual property (IP) cores and outsource various steps in the integrated circuit (IC) design and manufacturing flow. As a result, security vulnerabilities have been rising. This is forcing IC designers and end users to re-evaluate their trust in ICs. If attackers get hold of an unprotected IC, they can reverse engineer the IC

Dongho Kang, Flavio De Vincenti, Stelian Coros

We present a versatile nonlinear model predictive control (NMPC) formulation for quadrupedal locomotion. Our formulation jointly optimizes a base trajectory and a set of footholds over a finite time horizon based on simplified dynamics models. We leverage second-order sensitivity analysis and a sparse Gauss-Newton (SGN) method to solve the resulting optimal

Carsten H. Chong, Thomas Delerue, Fabian Mies

Consider the sum $Y=B+B(H)$ of a Brownian motion $B$ and an independent fractional Brownian motion $B(H)$ with Hurst parameter $H\in(0,1)$. Even though $B(H)$ is not a semimartingale, it was shown in [\textit{Bernoulli} \textbf{7} (2001) 913--934] that $Y$ is a semimartingale if $H>3/4$. Moreover, $Y$ is locally equivalent to $B$ in this case, so $H$ cannot

Haocong Zheng, Yiwen Pan, Yufan Wang

Every 4d $\mathcal{N} = 2$ SCFT $\mathcal{T}$ corresponds to an associated VOA $\mathbb{V}(\mathcal{T})$, which is in general non-rational with a more involved representation theory. Null states in $\mathbb{V}(\mathcal{T})$ can give rise to non-trivial flavored modular differential equations, which must be satisfied by the refined/flavored character of all t

Namita Bindal, Ravish Kumar Raj, Md Mahadi Rajib, Jayasimha Atulasimha

The development of energy-efficient neuromorphic hardware using spintronic devices based on antiferromagnetic (AFM) skyrmion motion on nanotracks has gained considerable interest. Owing to its properties such as robustness against external magnetic fields, negligible stray fields, and zero net topological charge, AFM skyrmions follow straight trajectories th

Xiaoyan Su, Ying Wang, Guixiang Xu

In this paper, our goal is to establish the Sobolev space associated to the partial harmonic oscillator. Based on its heat kernel estimate, we firstly give the definition of the fractional powers of the partial harmonic oscillator $$\AH=-\partial_{\rho}^2-\Delta_x+|x|^2,$$ and show that its negative powers are well defined on $L^p(\mathbb R^{d+1})$ for $p\in

Kaine A. Bunting, Huw Morgan

Accurate forecasting of the solar wind has grown in importance as society becomes increasingly dependent on technology that is susceptible to space weather events. This work describes an inner boundary condition for ambient solar wind models based on tomography maps of the coronal plasma density gained from coronagraph observations, providing a novel alterna

Peter K. F. Kuhfittig

Modified Newtonian dynamics (MOND) is a hypothesized modification of Newton's law of universal gravitation to account for the flat rotation curves in the outer regions of galaxies, thereby eliminating the need for dark matter. Although a highly successful model, it is not a self-contained physical theory since it is based entirely on observations. It is prop

Cedric Gommes, Thomas Louis, Isabelle Bourgot, Erik Maquoi

In solid tumors, cells constantly interact with the surrounding extracellular matrix. In particular cancer-associated fibroblasts modulate the architecture of the matrix by exerting forces and contracting collagen fibres, creating paths that facilitate cancer cell migration. The characterization of the collagen fibre network and its space and time-dependent

Leonidas Droukas, Zoe Doulgeri, Nikolaos L. Tsakiridis, Dimitra Triantafyllou

This paper presents a comprehensive review of ground agricultural robotic systems and applications with special focus on harvesting that span research and commercial products and results, as well as their enabling technologies. The majority of literature concerns the development of crop detection, field navigation via vision and their related challenges. Hea

Yingdong Hu, Renhao Wang, Kaifeng Zhang, Yang Gao

Establishing visual correspondence across images is a challenging and essential task. Recently, an influx of self-supervised methods have been proposed to better learn representations for visual correspondence. However, we find that these methods often fail to leverage semantic information and over-rely on the matching of low-level features. In contrast, hum

Kui Jiang, Zhongyuan Wang, Chen Chen, Zheng Wang

Convolutional neural network (CNN) and Transformer have achieved great success in multimedia applications. However, little effort has been made to effectively and efficiently harmonize these two architectures to satisfy image deraining. This paper aims to unify these two architectures to take advantage of their learning merits for image deraining. In particu

Shouan Wang, Xinyu Zhang, GuiPeng Zhang, Yijin Xiong

While camera and LiDAR are widely used in most of the assisted and autonomous driving systems, only a few works have been proposed to associate the temporal synchronization and extrinsic calibration for camera and LiDAR which are dedicated to online sensors data fusion. The temporal and spatial calibration technologies are facing the challenges of lack of re

Duncan V. Mifsud, Zuzana Kaňuchová, Sergio Ioppolo, Péter Herczku

The detection of ozone (O3) in the surface ices of Ganymede, Jupiters largest moon, and of the Saturnian moons Rhea and Dione, has motivated several studies on the route of formation of this species. Previous studies have successfully quantified trends in the production of O3 as a result of the irradiation of pure molecular ices using ultraviolet photons and

Xue-feng Zhan, Qiang Ke, Min-xiang Li, Xue-xiang Xu

State $g^{\hat{n}}\hat{a}^{\dag m}\left\vert \alpha \right\rangle $ and state $\hat{a}^{\dag m}g^{\hat{n}}\left\vert \alpha \right\rangle $ are same to state $\hat{a}^{\dag m}\left\vert g\alpha \right\rangle $, which is called as multi-photon-addition amplified coherent state (MPAACS) by us. Here, $\hat{n}$, $\hat{a}^{\dag }$, $\left\vert \alpha \right\rangl

Ricard Durall, Ammar Ghanim, Mario Fernandez, Norman Ettrich

Seismic data processing involves techniques to deal with undesired effects that occur during acquisition and pre-processing. These effects mainly comprise coherent artefacts such as multiples, non-coherent signals such as electrical noise, and loss of signal information at the receivers that leads to incomplete traces. In the past years, there has been a rem

Mingqiang Gu, Y. H. Bai, G. P. Zhang, Thomas F. George

Microscopic coupling between the electron spin and the lattice vibration is responsible for an array of exotic properties from morphic effects in simple magnets to magnetodielectric coupling in multiferroic spinels and hematites. Traditionally, a single spin-phonon coupling constant is used to characterize how effectively the lattice can affect the spin, but

Tomás Chacón Rebollo, Soledad Fernández-García, David Moreno-Lopez, Isabel Sánchez Muñoz

In this work, we introduce a Variational Multi-Scale (VMS) method for the numerical approximation of parabolic problems, where sub-grid scales are approximated from the eigenpairs of associated elliptic operator. The abstract method is particularized to the one-dimensional advection-diffusion equations, for which the sub-grid components are exactly calculate

Yuetian Weng, Zizheng Pan, Mingfei Han, Xiaojun Chang

The task of action detection aims at deducing both the action category and localization of the start and end moment for each action instance in a long, untrimmed video. While vision Transformers have driven the recent advances in video understanding, it is non-trivial to design an efficient architecture for action detection due to the prohibitively expensive

Haotian Bai, Ruimao Zhang, Jiong Wang, Xiang Wan

Weakly Supervised Object Localization (WSOL), which aims to localize objects by only using image-level labels, has attracted much attention because of its low annotation cost in real applications. Recent studies leverage the advantage of self-attention in visual Transformer for long-range dependency to re-active semantic regions, aiming to avoid partial acti

Edward G. A. Henderson, Dónal M. McSweeney, Andrew F. Green

Abdominal organ segmentation is a difficult and time-consuming task. To reduce the burden on clinical experts, fully-automated methods are highly desirable. Current approaches are dominated by Convolutional Neural Networks (CNNs) however the computational requirements and the need for large data sets limit their application in practice. By implementing a sma

Naftali R. Smith, Pierre Le Doussal, Satya N. Majumdar, Gregory Schehr

We consider an overdamped run-and-tumble particle in two dimensions, with self propulsion in an orientation that stochastically rotates by 90 degrees at a constant rate, clockwise or counter-clockwise with equal probabilities. In addition, the particle is confined by an external harmonic potential of stiffness $\mu$, and possibly diffuses. We find the exact

Kexin Liang, Geng Chai, Zhengwen Cao, Qing Wang

Excess noise is a major obstacle to high-performance continuous-variable quantum key distribution (CVQKD), which is mainly derived from the amplitude attenuation and phase fluctuation of quantum signals caused by channel instability. Here, an excess noise suppression scheme based on equalization is proposed. In this scheme, the distorted signals can be corre

G. P. Zhang, Y. H. Bai, M. S. Si, Thomas F. George

All-optical spin switching (AOS) represents a new frontier in magnetic storage technology -- spin manipulation without a magnetic field, -- but its underlying working principle is not well understood. Many AOS ferrimagnets such as GdFeCo are amorphous and renders the high-level first-principles study unfeasible. The crystalline half-metallic Heusler Mn$_2$Ru

Guohao Shen, Yuling Jiao, Yuanyuan Lin, Joel L. Horowitz

We propose a penalized nonparametric approach to estimating the quantile regression process (QRP) in a nonseparable model using rectifier quadratic unit (ReQU) activated deep neural networks and introduce a novel penalty function to enforce non-crossing of quantile regression curves. We establish the non-asymptotic excess risk bounds for the estimated QRP an

Arnon Turetzky, Tzvi Michelson, Yossi Adi, Shmuel Peleg

Convolutional neural networks contain strong priors for generating natural looking images [1]. These priors enable image denoising, super resolution, and inpainting in an unsupervised manner. Previous attempts to demonstrate similar ideas in audio, namely deep audio priors, (i) use hand picked architectures such as harmonic convolutions, (ii) only work with

Navdeep Rana, M. S. Mrudul, Daniil Kartashov, Misha Ivanov

High-harmonic spectroscopy of solids is a powerful tool, which provides access to both electronic structure and ultrafast electronic response of solids, from their band structure and density of states, to phase transitions, including the emergence of the topological edge states, to the PetaHertz electronic response. However, in spite of these successes, high

Kasper J. Kusmierek, Sahand Mahmoodian, Martin Cordier, Jakob Hinney

Waveguide QED with cold atoms provides a potent platform for the study of non-equilibrium, many-body, and open-system quantum dynamics. Even with weak coupling and strong photon loss, the collective enhancement of light-atom interactions leads to strong correlations of photons arising in transmission, as shown in recent experiments. Here we apply an improved

Xian He, Shuai Zhang, Shanlin Yang, Bo Ouyang

Previous soft tissue manipulation studies assumed that the grasping point was known and the target deformation can be achieved. During the operation, the constraints are supposed to be constant, and there is no obstacles around the soft tissue. To go beyond these assumptions, a deep reinforcement learning framework with prior knowledge is proposed for soft t

Hussam Al Daas, Grey Ballard, Laura Grigori, Suraj Kumar

Multiple Tensor-Times-Matrix (Multi-TTM) is a key computation in algorithms for computing and operating with the Tucker tensor decomposition, which is frequently used in multidimensional data analysis. We establish communication lower bounds that determine how much data movement is required to perform the Multi-TTM computation in parallel. The crux of the pr

Guolei Sun, Yun Liu, Hao Tang, Ajad Chhatkuli

The essence of video semantic segmentation (VSS) is how to leverage temporal information for prediction. Previous efforts are mainly devoted to developing new techniques to calculate the cross-frame affinities such as optical flow and attention. Instead, this paper contributes from a different angle by mining relations among cross-frame affinities, upon whic

Jiangbei Yue, Dinesh Manocha, He Wang

Trajectory prediction has been widely pursued in many fields, and many model-based and model-free methods have been explored. The former include rule-based, geometric or optimization-based models, and the latter are mainly comprised of deep learning approaches. In this paper, we propose a new method combining both methodologies based on a new Neural Differen

Yeying Jin, Aashish Sharma, Robby T. Tan

Shadow removal from a single image is generally still an open problem. Most existing learning-based methods use supervised learning and require a large number of paired images (shadow and corresponding non-shadow images) for training. A recent unsupervised method, Mask-ShadowGAN~\cite{Hu19}, addresses this limitation. However, it requires a binary mask to re

Jinrong Yang, Songtao Liu, Zeming Li, Xiaoping Li

The perceptive models of autonomous driving require fast inference within a low latency for safety. While existing works ignore the inevitable environmental changes after processing, streaming perception jointly evaluates the latency and accuracy into a single metric for video online perception, guiding the previous works to search trade-offs between accurac

Yuhong Jin, Lei Hou, Ming Du, Yushu Chen

Traditional supervised bearing fault diagnosis methods rely on massive labelled data, yet annotations may be very time-consuming or infeasible. The fault diagnosis approach that utilizes limited labelled data is becoming increasingly popular. In this paper, a Wavelet Transform (WT) and self-supervised learning-based bearing fault diagnosis framework is propo

Ann-Kathrin Raab, Marcus Seidel, Chen Guo, Ivan Sytcevich

The output of a 200 kHz, 34 W, 300 fs Yb amplifier is compressed to 31 fs with > 88 % efficiency to reach a peak power of 2.5 GW, which to date is a record for a single-stage bulk multi-pass cell. Despite operation 80 times above the critical power for self-focusing in bulk material, the setup demonstrates excellent preservation of the input beam quality. Ex

Andrew M. Saxe, Shagun Sodhani, Sam Lewallen

Our theoretical understanding of deep learning has not kept pace with its empirical success. While network architecture is known to be critical, we do not yet understand its effect on learned representations and network behavior, or how this architecture should reflect task structure.In this work, we begin to address this gap by introducing the Gated Deep Li

Ö. F. Dayi, S. E. Gürleyen

We study the three-dimensional transport theory of massive spin-1/2 fermions resulting from the vorticity dependent quantum kinetic equation. This quantum kinetic equation has been introduced to take account of noninertial properties of rotating coordinate frames. We show that it is the appropriate relativistic kinetic equation which provides the vorticity d

Alessandro Giuliani, Bruno Renzi, Fabio Toninelli

We study a model of fully-packed dimer configurations (or perfect matchings) on a bipartite periodic graph that is two-dimensional but not planar. The graph is obtained from $\mathbb Z^2$ via the addition of an extensive number of extra edges that break planarity (but not bipartiteness). We prove that, if the weight $\lambda$ of the non-planar edges is small

Zhixiang Hu, An Liu, Yubo Wan, Tony Xiao Han

Multiband fusion enhances WiFi sensing by jointly utilizing signals from multiple non-contiguous frequency bands. However, in the multi-band WiFi sensing signal model, there are many local optimums in the associated likelihood function due to the existence of high frequency component and phase distortion factors, posing challenges for high-accuracy parameter

Giuseppina Barletta, Elisabetta Tornatore

We establish some existence and regularity results to the Dirichlet problem, for a class of quasilinear elliptic equations involving a partial differential operator, depending on the gradient of the solution. Our results are formulated in the Orlicz Sobolev spaces and under general growth conditions on the convection term. The sub and supersolutions method i

Yikang Ding, Qingtian Zhu, Xiangyue Liu, Wentao Yuan

Supervised multi-view stereo (MVS) methods have achieved remarkable progress in terms of reconstruction quality, but suffer from the challenge of collecting large-scale ground-truth depth. In this paper, we propose a novel self-supervised training pipeline for MVS based on knowledge distillation, termed KD-MVS, which mainly consists of self-supervised teache

Yecine Megdiche, Fabian Huch, Lukas Stevens

In interactive theorem proving, formalization quality is a key factor for maintainability and re-usability of developments and can also impact proof-checking performance. Commonly, anti-patterns that cause quality issues are known to experienced users. However, in many theorem prover systems, there are no automatic tools to check for their presence and make

Una Radojicic, Niko Lietzen, Klaus Nordhausen, Joni Virta

Tensor-valued data benefits greatly from dimension reduction as the reduction in size is exponential in the number of modes. To achieve maximal reduction without loss in information, our objective in this work is to give an automated procedure for the optimal selection of the reduced dimensionality. Our approach combines a recently proposed data augmentation

Zhiyu Huang, Haochen Liu, Jingda Wu, Chen Lv

Predicting the future states of surrounding traffic participants and planning a safe, smooth, and socially compliant trajectory accordingly is crucial for autonomous vehicles. There are two major issues with the current autonomous driving system: the prediction module is often separated from the planning module and the cost function for planning is hard to s

Siheon Ryee, Sangkook Choi, Myung Joon Han

We propose a way to identify strongly Hund-correlated materials by unveiling a key signature of Hund correlations at the two-particle level. The defining feature is the {\it sign} of the response of the {\it frozen spin ratio} (the long-time local spin-spin correlation function divided by the instantaneous value) under variation of electron density. The unde

Emil V. Denning, Andreas Knorr, Florian Katsch, Marten Richter

Nonlinear optical effects such as four-wave mixing and generation of squeezed light are ubiquitous in optical devices and light sources. For new devices operating at low optical power, the resonant nonlinearity arising from the two-photon sensitive bound biexciton in a semiconductor microcavity is an interesting prospective platform. Due to the particularly

Alain Connes, Caterina Consani

In this paper we consider two spectral realizations of the zeros of the Riemann zeta function. The first one involves all non-trivial (non-real) zeros and is expressed in terms of a Laplacian intimately related to the prolate wave operator. The second spectral realization affects only the critical zeros and it is cast in terms of sheaf cohomology. The novelt

Pasquale Bosso, Luciano Petruzziello, Fabian Wagner, Fabrizio Illuminati

Different approaches to quantum gravity converge in predicting the existence of a minimal scale of length. This raises the fundamental question as to whether and how an intrinsic limit to spatial resolution can affect quantum mechanical observables associated to internal degrees of freedom. We answer this question in general terms by showing that the spin op

Amit Singh Ubhi, Leonid Prokhorov, Sam Cooper, Chiara Di Fronzo

We demonstrate the control scheme of an active platform with a six degree of freedom (6D) seismometer. The inertial sensor simultaneously measures translational and tilt degrees of freedom of the platform and does not require any additional sensors for the stabilisation. We show that a feedforward cancellation scheme can efficiently decouple tilt-to-horizont

Sudarshan Ananth, Nipun Bhave, S. I. Aadharsh Raj

The first truly non-MHV interaction vertices in the light-cone formulation of pure gravity appear at order 6. From a closed form expression, for gravitation in the light-cone gauge, we extract and present all 6-point interaction vertices. We invoke symmetry arguments to explain the structure of these vertices. Symmetry considerations also allow us to place c

Ilnura Usmanova, Yarden As, Maryam Kamgarpour, Andreas Krause

Optimizing noisy functions online, when evaluating the objective requires experiments on a deployed system, is a crucial task arising in manufacturing, robotics and many others. Often, constraints on safe inputs are unknown ahead of time, and we only obtain noisy information, indicating how close we are to violating the constraints. Yet, safety must be guara

Zhixing Li, Hong Guo, Yi Mao

Atomic hydrogen (H I) gas, mostly residing in dark matter halos after cosmic reionization, is the fuel for star formation. Its relation with properties of host halo is the key to understand the cosmic H I distribution. In this work, we propose a flexible, empirical model of H I-halo relation. In this model, while the H I mass depends primarily on the mass of

Yujin Lu, Alessandro Del Buono, Tianhang Xiao, Alessandro Iafrati

The water landing of an amphibious aircraft is a complicated problem that can lead to uncomfortable riding situation and structural damage due to large vertical accelerations and the consequent dynamic responses. The problem herein is investigated by solving unsteady incompressible Reynolds-averaged Navier-Stokes equations with a standard k-omega turbulence

Alessandro Maraio, Alex Hall, Andy Taylor

Headline constraints on cosmological parameters from current weak lensing surveys are derived from two-point statistics that are known to be statistically sub-optimal, even in the case of Gaussian fields. We study the performance of a new fast implementation of the Quadratic Maximum Likelihood (QML) estimator, optimal for Gaussian fields, to test the perform

S. R. Bhavanam, Sumohana S. Channappayya, P. K. Srijith, Shantanu Desai

Cosmic Ray (CR) hits are the major contaminants in astronomical imaging and spectroscopic observations involving solid-state detectors. Correctly identifying and masking them is a crucial part of the image processing pipeline, since it may otherwise lead to spurious detections. For this purpose, we have developed and tested a novel Deep Learning based framew

Harsh Narola, Soumen Roy, Anand S. Sengupta

Accurate waveform models describing the complete evolution of compact binaries are crucial for the maximum likelihood detection framework, testing the predictions of General Relativity (GR) and investigating the possibility of an alternative theory of gravity. Deviations from GR could manifest in subtle variations of the numerical value of the GW signal's po

Fatih Cagatay Akyon, Erdem Akagunduz, Sinan Onur Altinuc, Alptekin Temizel

Drone detection has become an essential task in object detection as drone costs have decreased and drone technology has improved. It is, however, difficult to detect distant drones when there is weak contrast, long range, and low visibility. In this work, we propose several sequence classification architectures to reduce the detected false-positive ratio of

Hung Ba Tran, Yu-ichiro Matsushita

The mechanism of spin reorientation in Nd$_{2}$Fe$_{14}$B, which is a host crystal of a well-known neodymium permanent magnet, is studied by combining first-principles calculations and Monte Carlo simulations. The spin reorientation is thought to be derived from crystal field effects and gets less attention because of the undesirable property for hard magnet

Gurucharan Mohanta, Ketan M. Patel

A framework based on a class of abelian gauge symmetries is proposed in which the masses of only the third generation quarks and leptons arise at the tree level. The fermions of the first and second families receive their masses through radiative corrections induced by the new gauge bosons in the loops. It is shown that the class of abelian symmetries which

Ahmed M. Nor

Millimeter wave (mmWave) will play a significant role as a 5G candidate in facing the growing demand of enormous data rate in the near future. The conventional mmWave standard, IEEE 802.11ad, considers establishing only one mmWave link in wireless local area network (WLAN) to provide multi Gbps data rate. But, mmWave has a tenuous channel which hinders it fr

Andrew R. McCluskey, Andrew J. Caruana, Christy J. Kinane, Alexander J. Armstrong

Driven by the availability of modern software and hardware, Bayesian analysis is becoming more popular in neutron and X-ray reflectometry analysis. The understandability and replicability of these analyses may be harmed by inconsistencies in how the probability distributions central to Bayesian methods are represented in the literature. Herein, we provide ad

K. C. Leptos, M. Chioccioli, S. Furlan, A. I. Pesci

A fundamental issue in biology is the nature of evolutionary transitions from unicellular to multicellular organisms. Volvocine algae are models for this transition, as they span from the unicellular biflagellate Chlamydomonas to multicellular species of Volvox with up to 50,000 Chlamydomonas-like cells on the surface of a spherical extracellular matrix. The

Yudong Han, Jianhua Yin, Jianlong Wu, Yinwei Wei

Visual Question Answering (VQA) is fundamentally compositional in nature, and many questions are simply answered by decomposing them into modular sub-problems. The recent proposed Neural Module Network (NMN) employ this strategy to question answering, whereas heavily rest with off-the-shelf layout parser or additional expert policy regarding the network arch

Yuriy Golovaty

We study the Schr\"{o}dinger operators on a non-compact star graph with the Coulomb-type potentials having singularities at the vertex. The convergence of regularized Hamiltonians $H_\varepsilon$ with cut-off Coulomb potentials coupled with $(\alpha \delta+\beta\delta')$-like ones is investigated.The 1D Coulomb potential and the $\delta'$-potential are very

Jiazhi Guan, Hang Zhou, Mingming Gong, Errui Ding

Despite encouraging progress in deepfake detection, generalization to unseen forgery types remains a significant challenge due to the limited forgery clues explored during training. In contrast, we notice a common phenomenon in deepfake: fake video creation inevitably disrupts the statistical regularity in original videos. Inspired by this observation, we pr

Rafael Accácio Nogueira, Romain Bourdais, Hervé Guéguen

In distributed predictive control structures, communication among agents is required to achieve a consensus and approach an optimal global behavior. Such negotiation mechanisms are sensitive to attacks on these exchanges. This paper proposes a monitoring scheme that detects and mitigates these attacks' effects in a resource allocation framework. The performa

Meng Cao, Ji Jiang, Long Chen, Yuexian Zou

We investigate the problem of video Referring Expression Comprehension (REC), which aims to localize the referent objects described in the sentence to visual regions in the video frames. Despite the recent progress, existing methods suffer from two problems: 1) inconsistent localization results across video frames; 2) confusion between the referent and conte

J. M. J. van Leeuwen

The Uhlenbeck-Ford model for soft repulsion, which has only a repulsive interaction, is extended by inclusion of an attraction. This extension still allows an analytical evaluation of the virial coefficients. The integrals over the graph contributions are reduced to a combinatorial problem. We have calculated the virial coefficients to order 6 in the density

Yuzhen Zhang, Wentong Wang, Weizhi Guo, Pei Lv

A profound understanding of inter-agent relationships and motion behaviors is important to achieve high-quality planning when navigating in complex scenarios, especially at urban traffic intersections. We present a trajectory prediction approach with respect to traffic lights, D2-TPred, which uses a spatial dynamic interaction graph (SDG) and a behavior depe

Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan

The task of generating code solutions for a given programming problem can benefit from the use of pre-trained language models such as Codex, which can produce multiple diverse samples. However, a major challenge for this task is to select the most appropriate solution from the multiple samples generated by the pre-trained language models. A natural way to ev

Si Duc Quang

Let $A$ be an annular end of a complete minimal surface $S$ in $\mathbb R^m$ and let $V$ be a $k$-dimension projective subvariety of $\mathbb P^n(\mathbb C)\ (n=m-1)$. Let $g$ be the generalized Gauss map of $S$ into $V\subset\mathbb P^n(\mathbb C)$. In this paper, we establish a modified defect relation of $g$ on the annular end $A$ for $q$ hypersurfaces $\

Wentao Yuan, Qingtian Zhu, Xiangyue Liu, Yikang Ding

Recently, Implicit Neural Representations (INRs) parameterized by neural networks have emerged as a powerful and promising tool to represent different kinds of signals due to its continuous, differentiable properties, showing superiorities to classical discretized representations. However, the training of neural networks for INRs only utilizes input-output p