April 2023 arXiv papers — page 16

Z. P. Fu, Z. W. Zhang, K. Lin, D. Wu

The state of burning plasma had been achieved in inertial confinement fusion (ICF), which was regarded as a great milestone for high-gain laser fusion energy. In the burning plasma, alpha particles incident on the cryogenic (warm dense) fuels cannot be simply regarded as single particles, and the new physics brought about by the density effects of alpha part

Kenya Murase

Magnetic particle imaging (MPI) is an imaging method that can visualize magnetic nanoparticles in positive contrast, without radiation exposure. Recently, we proposed an image reconstruction method for projection-based MPI (pMPI), in which the system function was incorporated into the simultaneous algebraic reconstruction technique and the total variation mi

Xinbing Wang, Luoyi Fu, Huquan Kang, Zhouyang Jin

Three influential laws, namely Sarnoff's Law, Metcalfe's Law, and Reed's Law, have been established to describe network value in terms of the number of neighbors, edges, and subgraphs. Here, we highlight the coexistence of these laws in citation networks for the first time, utilizing the Deep-time Digital Earth academic literature. We further introduce a nov

Jaehoon Jeong

We present an efficient algorithm for constructing all the matrix elements of covariant tensor currents of massless particles of arbitrary spins in the covariant formulation. The construction of matrix elements can be taken simply by assembling the basic matrix elements which are derived from the basic three-point vertices. We obtain the selection rules for

Joo Hyung Lee, Wonpyo Park, Nicole Mitchell, Jonathan Pilault

This paper introduces JaxPruner, an open-source JAX-based pruning and sparse training library for machine learning research. JaxPruner aims to accelerate research on sparse neural networks by providing concise implementations of popular pruning and sparse training algorithms with minimal memory and latency overhead. Algorithms implemented in JaxPruner use a

Ella Gale, Oliver Matthews

Despite the huge recent breakthroughs in neural networks (NNs) for artificial intelligence (specifically deep convolutional networks) such NNs do not achieve human-level performance: they can be hacked by images that would fool no human and lack `common sense'. It has been argued that a basis of human-level intelligence is mankind's ability to perform relati

Marat Gilfanov, Giuseppina Fabbiano, Bret Lehmer, Andreas Zezas

X-ray appearance of normal galaxies is mainly determined by X-ray binaries powered by accretion onto a neutron star or a stellar mass black hole. Their populations scale with the star-formation rate and stellar mass of the host galaxy and their X-ray luminosity distributions show a significant split between star-forming and passive galaxies, both facts being

Donald Flynn

This dissertation examines the impact of a drift {\mu} on Brownian Bees, which is a type of branching Brownian motion that retains only the N closest particles to the origin. The selection effect in the 0-drift system ensures that it remains recurrent and close to the origin. The study presents two novel findings that establish a threshold for {\mu}: below t

Alessandro Bombini

In this paper we present the first version of ganX -- generate artificially new XRF, a Python library to generate X-ray fluorescence Macro maps (MA-XRF) from a coloured RGB image. To do that, a Monte Carlo method is used, where each MA-XRF pixel signal is sampled out of an XRF signal probability function. Such probability function is computed using a databas

Bahar Arslan, Samuel D. Relton, Marcel Schweitzer

Matrix functions play an increasingly important role in many areas of scientific computing and engineering disciplines. In such real-world applications, algorithms working in floating-point arithmetic are used for computing matrix functions and additionally, input data might be unreliable, e.g., due to measurement errors. Therefore, it is crucial to understa

Shruthi K, Poornima A. S

In the present medical services, the board, clinical well-being records are as electronic clinical record (EHR/EMR) frameworks. These frameworks store patients' clinical histories in a computerized design. Notwithstanding, a patient's clinical information is gained in a productive and ideal way and is demonstrated to be troublesome through these records. Pow

C. Adam, D. Ciurla, K. Oles, T. Romanczukiewicz

We analyze the perturbative Relativistic Moduli Space approach, where the amplitudes of the Derrick modes are promoted to collective coordinates. In particular, we analyse the possibility to calculate the critical velocity, i.e., the initial velocity of kinks at which single bounce scattering changes into a multi-bounce or annihilation collision, in the resu

Mauro Faccin, Emilien Schultz, Floriana Gargiulo

At the beginning of the COVID-19 pandemic, the urge to find a cure triggered an international race to repurpose known drugs. Chloroquine, and next Hydroxychloroquine, emerged quickly as a promising treatment. While later clinical studies demonstrated its inefficacy and possible dangerous side effects, the drug caused heated and politicized debates at an inte

Gobinda Garai, Bankim C. Mandal

In this paper, we propose, analyze and implement efficient time parallel methods for the Cahn-Hilliard (CH) equation. It is of great importance to develop efficient numerical methods for the CH equation, given the range of applicability of the CH equation has. The CH equation generally needs to be simulated for a very long time to get the solution of phase c

Djordje Bogdanović, Marija Dimitrijević Ćirić, Voja Radovanović, Richard J. Szabo

We address the problem of UV/IR mixing in noncommutative quantum field theories from the perspective of braided $L_\infty$-structures and the Batalin-Vilkovisky formalism. We describe the example of braided noncommutative scalar field theory and its quantization using braided homological perturbation theory. The formalism is illustrated through one-loop calc

Linyang Li, Pengyu Wang, Ke Ren, Tianxiang Sun

The extraordinary performance of large language models (LLMs) heightens the importance of detecting whether the context is generated by an AI system. More importantly, while more and more companies and institutions release their LLMs, the origin can be hard to trace. Since LLMs are heading towards the time of AGI, similar to the origin tracing in anthropolog

Yuchen Zhang, Yanda Meng, Yalin Zheng

Atrial fibrillation (AF) is the most common cardiac arrhythmia. Accurate segmentation of the left atrial (LA) and LA scars can provide valuable information to predict treatment outcomes in AF. In this paper, we proposed to automatically segment LA cavity and quantify LA scars with late gadolinium enhancement Magnetic Resonance Imagings (LGE-MRIs). We adopted

Zhi Hou, Baosheng Yu, Dacheng Tao

Human-object interactions (HOIs) are crucial for human-centric scene understanding applications such as human-centric visual generation, AR/VR, and robotics. Since existing methods mainly explore capturing HOIs, rendering HOI remains less investigated. In this paper, we address this challenge in HOI animation from a compositional perspective, i.e., animating

Magdalena Dul, Michal K. Grzeszczyk, Ewelina Nojszewska, Arkadiusz Sitek

This study examines the effect of COVID-19 pandemic and associated lockdowns on access to crucial diagnostic procedures for breast cancer patients, including screenings and treatments. To quantify the impact of the lockdowns on patient outcomes and cost, the study employs a mathematical model of breast cancer progression. The model includes ten different sta

Aniruddha Biswas, Palash Sarkar

We show that the problem of counting the number of $n$-variable unate functions reduces to the problem of counting the number of $n$-variable monotone functions. Using recently obtained results on $n$-variable monotone functions, we obtain counts of $n$-variable unate functions up to $n=9$. We use an enumeration strategy to obtain the number of $n$-variable

Pietro Barbiero, Gabriele Ciravegna, Francesco Giannini, Mateo Espinosa Zarlenga

Deep learning methods are highly accurate, yet their opaque decision process prevents them from earning full human trust. Concept-based models aim to address this issue by learning tasks based on a set of human-understandable concepts. However, state-of-the-art concept-based models rely on high-dimensional concept embedding representations which lack a clear

Chutima Yannawa, Peera Pongkitiwanichakul, David Ruffolo, Piyanate Chuychai

The statistics of the magnetic field line separation provide insight into how a bundle of field lines spreads out and the dispersion of non-thermal particles in a turbulent environment, which underlies various astrophysical phenomena. Its diffusive character depends on the distance along the field line, the initial separation, and the characteristics of the

J. Kurpas, A. D. Schwope, A. M. Pires, F. Haberl

We report the discovery of the isolated neutron star (INS) candidates eRASSU J065715.3+260428 and eRASSU J131716.9-402647 from the Spectrum Roentgen Gamma (SRG) eROSITA All-Sky Survey. Selected for their soft X-ray emission and absence of catalogued counterparts, both objects were recently targeted with the Large Binocular Telescope and the Southern African

Gabriel Tseng, Ruben Cartuyvels, Ivan Zvonkov, Mirali Purohit

Machine learning methods for satellite data have a range of societally relevant applications, but labels used to train models can be difficult or impossible to acquire. Self-supervision is a natural solution in settings with limited labeled data, but current self-supervised models for satellite data fail to take advantage of the characteristics of that data,

Cheng Huang, Xu Zhang, Gaopei Pan, Heqiu Li

The ground states of twisted bilayer graphene (TBG) at chiral and flat-band limit with integer fillings are known from exact solutions, while their dynamical and thermodynamical properties are revealed by unbiased quantum Monte Carlo (QMC) simulations. However, to elucidate experimental observations of correlated metallic, insulating and superconducting stat

Pengyu Liu, Alexander J. Bohn, David S. Doelman, Ben J. Sutlieff

The vector Apodizing Phase Plate (vAPP) is a pupil plane coronagraph that suppresses starlight by forming a dark hole in its point spread function (PSF). The unconventional and non-axisymmetrical PSF arising from the phase modification applied by this coronagraph presents a special challenge to post-processing techniques. We aim to implement a recently devel

Welf Rehberg, Joaquim Ortiz-Haro, Marc Toussaint, Wolfgang Hönig

Quadrotors are agile flying robots that are challenging to control. Considering the full dynamics of quadrotors during motion planning is crucial to achieving good solution quality and small tracking errors during flight. Optimization-based methods scale well with high-dimensional state spaces and can handle dynamic constraints directly, therefore they are o

Kareem T. Elgindy

In this paper, we present a novel pseudospectral (PS) method for solving a new class of initial-value problems (IVPs) of time-dependent one-dimensional fractional partial differential equations (FPDEs) with variable coefficients and periodic solutions. A main ingredient of our work is the use of the recently developed periodic RL/Caputo fractional derivative

Satyabrata Patro, Sumit Kumar, Anubhav Majumdar, Anurag Tripathi

We study the time-dependent flow behavior of gravity-driven free surface granular flows using the discrete element method and continuum modeling. Discrete element method (DEM) simulations of slightly polydisperse disks flowing over a periodic chute with a bumpy base are performed. A simple numerical solution based on a continuum approach with the inertial nu

Federico Benzi, Federica Ferraguti, Cristian Secchi

The technical specification ISO/TS 15066 provides the foundational elements for assessing the safety of collaborative human-robot cells, which are the cornerstone of the modern industrial paradigm. The standard implementation of the ISO/TS 15066 procedure, however, often results in conservative motions of the robot, with consequently low performance of the c

Cornelius Brand, Robert Ganian, Kirill Simonov

Probably Approximately Correct (i.e., PAC) learning is a core concept of sample complexity theory, and efficient PAC learnability is often seen as a natural counterpart to the class P in classical computational complexity. But while the nascent theory of parameterized complexity has allowed us to push beyond the P-NP ``dichotomy'' in classical computational

Jia-Jie Zhu

This paper provides answers to an open problem: given a nonlinear data-driven dynamical system model, e.g., kernel conditional mean embedding (CME) and Koopman operator, how can one propagate the ambiguity sets forward for multiple steps? This problem is the key to solving distributionally robust control and learning-based control of such learned system mode

Eryan Hu, Guohuan Zhao

We consider the linear non-local operator $\mathcal{L}$ denoted by \[ \mathcal{L} u (x) = \int_{\mathbb{R}^d} \left(u(x+z)-u(x)\right) a(x,z)J(z)\,d z. \] Here $a(x,z)$ is bounded and $J(z)$ is the jumping kernel of a L\'evy process, which only has a low-order singularity near the origin and does not allow for standard scaling. The aim of this work is twofol

Joeri De Ro, Lucas Hataishi

We introduce the notion of an action of a discrete or compact quantum group on an operator system, and study equivariant operator system injectivity. We then prove a duality result that relates equivariant injectivity with dual injectivity on associated crossed products. As an application, we give a description of the equivariant injective envelope of the re

Xihua Xu

This paper focuses on the novel scheme to unify both Lagrangian staggered-grid and cell-centered hydrodynamic methods in one dimension. The scheme neither contains empirical parameters nor solves the Riemann problem. It includes two key points: one is the relationship between pressure and velocity, and the other is Newton's second law. The two methods that m

Sheng Chen, Zihao Tang, Dongnan Liu, Ché Fornusek

Precise thigh muscle volumes are crucial to monitor the motor functionality of patients with diseases that may result in various degrees of thigh muscle loss. T1-weighted MRI is the default surrogate to obtain thigh muscle masks due to its contrast between muscle and fat signals. Deep learning approaches have recently been widely used to obtain these masks t

I. V. Voronchikhin, D. V. Kirpichnikov

We discuss the mechanism to produce electron-specific dark matter mediators of spin-0 and spin-2 in the electron fixed target experiments such as NA64 and LDMX. The secondary positrons induced by the electromagnetic shower can produce the mediators via annihilation on atomic electrons. That mechanism, for some selected kinematics, results in the enhanced sen

Mr Thomas J. Cairnes, Mr Christopher J. Ford, Dr Efi Psomopoulou, Professor Nathan Lepora

The development of robotic grippers is driven by the need to execute particular manual tasks or meet specific objectives in handling operations. Grippers with specific functions vary from being small, accurate and highly controllable such as the surgical tool effectors of the Da Vinci robot (designed to be used as non-invasive grippers controlled by a human

Yulong Huang, Yang Zhang, Qifan Wang, Chenxu Wang

Sequential recommender models typically generate predictions in a single step during testing, without considering additional prediction correction to enhance performance as humans would. To improve the accuracy of these models, some researchers have attempted to simulate human analogical reasoning to correct predictions for testing data by drawing analogies

Annika Lang, Per Ljung, Axel Målqvist

A multiscale method is proposed for a parabolic stochastic partial differential equation with additive noise and highly oscillatory diffusion. The framework is based on the localized orthogonal decomposition (LOD) method and computes a coarse-scale representation of the elliptic operator, enriched by fine-scale information on the diffusion. Optimal order str

Shinnosuke Okawa

We prove a relative version of the fact that semiorthogonal decompositions of the bounded derived category of coherent sheaves are strongly constrained by the base locus of the canonical linear system. As an application we prove that the derived category of minimal surfaces $X$ with $H^1 (X,\mathcal{O}_X) \neq 0$ are semiorthogonally indecomposable.

Federico Piazzon, Enrico Facca, Mario Putti

The $L^1$ optimal transport density $\mu^*$ is the unique $L^\infty$ solution of the Monge-Kantorovich equations. It has been recently characterized also as the unique minimizer of the $L^1$ -transport energy functional E. In the present work we develop and we prove convergence of a numerical approxi- mation scheme for $\mu^*$ . Our approach relies upon the

Mitia Duerinckx, Antoine Gloria

This work relates quantitatively homogenization to Anderson localization for acoustic operators in disordered media. By blending dispersive estimates for homogenized operators and quantitative homogenization of the wave equation, we derive large-scale dispersive estimates for waves in disordered media that we apply to the spreading of low-energy eigenstates.

Ti Wang, Hong Liu, Runwei Ding, Wenhao Li

Despite substantial progress in 3D human pose estimation from a single-view image, prior works rarely explore global and local correlations, leading to insufficient learning of human skeleton representations. To address this issue, we propose a novel Interweaved Graph and Attention Network (IGANet) that allows bidirectional communications between graph convo

Solène Tarride, Martin Maarand, Mélodie Boillet, James McGrath

This paper presents a complete workflow designed for extracting information from Quebec handwritten parish registers. The acts in these documents contain individual and family information highly valuable for genetic, demographic and social studies of the Quebec population. From an image of parish records, our workflow is able to identify the acts and extract

Alessandro Bonetti, Simone Guidetti, Lorenzo Sabattini

This paper proposes two novel path planning algorithms, Roadmap Hybrid A* and Waypoints Hybrid A*, for car-like autonomous vehicles in logistics and industrial contexts with obstacles (e.g., pallets or containers) and narrow corridors. Roadmap Hybrid A* combines Hybrid A* with a graph search algorithm applied to a static roadmap. The former enables obstacle

Dehai Zhao, Zhenchang Xing, Xin Xia, Deheng Ye

Programming screencasts (e.g., video tutorials on Youtube or live coding stream on Twitch) are important knowledge source for developers to learn programming knowledge, especially the workflow of completing a programming task. Nonetheless, the image nature of programming screencasts limits the accessibility of screencast content and the workflow embedded in

Simon Lemaire, Silvano Pitassi

We prove discrete versions of the first and second Weber inequalities on $\boldsymbol{H}(\mathbf{curl})\cap\boldsymbol{H}(\mathrm{div}_{\eta})$-like hybrid spaces spanned by polynomials attached to the faces and to the cells of a polyhedral mesh. The proven hybrid Weber inequalities are optimal in the sense that (i) they are formulated in terms of $\boldsymb

Swarupananda Pradhan, Sudip mandal

The atomic population trapped in uncoupled atomic states is a limiting factor for processes based on laser-atom interaction. The use of repump laser, bi-chromatic field, and vector magnetic field are explored in degenerate as well as non-degenerate atomic system to overcome the limitation. The magnetic resonance of 85Rb atoms under these complementary condit

Yicun Huang, Changfu Zou, Yang Li, Torsten Wik

The concept of integrating physics-based and data-driven approaches has become popular for modeling sustainable energy systems. However, the existing literature mainly focuses on the data-driven surrogates generated to replace physics-based models. These models often trade accuracy for speed but lack the generalizability, adaptability, and interpretability i

Kristian Bredies, Jonathan Chirinos Rodriguez, Emanuele Naldi

In this note, we provide a characterization for the set of extreme points of the Lipschitz unit ball in a specific vectorial setting. While the analysis of the case of real-valued functions is covered extensively in the literature, no information about the vectorial case has been provided up to date. Here, we aim at partially filling this gap by considering

Alexey E. Rastegin

An issue which has attracted increasing attention in contemporary researches are Kirkwood--Dirac quasiprobabilities. List of their use includes many questions of quantum physics. Applications of complex tight frames in quantum information science were recently demonstrated. It is shown in this paper that quasiprobabilities naturally appear in the context of

Borislav Polovnikov, Johannes Scherzer, Subhradeep Misra, Xin Huang

We study experimentally and theoretically the hybridization among intralayer and interlayer moir\'e excitons in a MoSe$_2$/WS$_2$ heterostructure with antiparallel alignment. Using a dual-gate device and cryogenic white light reflectance and narrow-band laser modulation spectroscopy, we subject the moir\'e excitons in the MoSe$_2$/WS$_2$ heterostack to a per

Lin Gao, Mian Li, Binjie Hu, Qing Huang

Van der Waals (vdW) layered materials have drawn tremendous interests due to their unique properties. Atom intercalation in the vdW gap of layered materials can tune their electronic structure and generate unexpected properties. Here we report a chemical-scissor mediated method that enables metal intercalation into transition metal dichalcogenides (TMDCs) in

Byon N. Jayawiguna, Piyabut Burikham

In comparison to the original Tolman VII model, Exact Modified Tolman VII (EMTVII) with one additional parameter can increase the compactness of compact object. When the compactness is in the ultracompact regime, the quasinormal modes~(QNMs) of the trapped mode as well as the gravitational echoes become more viable. Starting with the EMTVII model, we introdu

Louis C. Tiao, Vincent Dutordoir, Victor Picheny

Despite their many desirable properties, Gaussian processes (GPs) are often compared unfavorably to deep neural networks (NNs) for lacking the ability to learn representations. Recent efforts to bridge the gap between GPs and deep NNs have yielded a new class of inter-domain variational GPs in which the inducing variables correspond to hidden units of a feed

Han Xu, Congjun Wu, Yu Wang

We employ the sign-problem-free projector determinant quantum Monte Carlo method to study a microscopic model of SU($N$) fermions with singlet-bond and triplet-current interactions on the square lattice. We find the gapped singlet $p_x$ and gapless triplet $d_{x^2-y^2}$ density wave states in the half-filled $N=4$ model. Specifically, the triplet $d_{x^2-y^2

T. Kołodziej, J. Piętosa, R. Puźniak, A. Wiśniewski

Trimeron lattice excitations in single crystalline magnetite, in the form of the $c$ axis switching (i.e. the reorganization of the lattice caused by external magnetic field) at temperatures below the Verwey temperature $T_V$ are observed by magnetization experiments. These excitations exhibit strong sensitivity to doping (with Zn, Al, and Ti), nonstoichiome

Nicholas Boucher, Luca Pajola, Ilia Shumailov, Ross Anderson

Search engines are vulnerable to attacks against indexing and searching via text encoding manipulation. By imperceptibly perturbing text using uncommon encoded representations, adversaries can control results across search engines for specific search queries. We demonstrate that this attack is successful against two major commercial search engines - Google a

Han Liu, Zhoubing Xu, Riqiang Gao, Hao Li

Deep learning models have demonstrated remarkable success in multi-organ segmentation but typically require large-scale datasets with all organs of interest annotated. However, medical image datasets are often low in sample size and only partially labeled, i.e., only a subset of organs are annotated. Therefore, it is crucial to investigate how to learn a uni

Bahrul Ilham, Yanto Setiawan

In this research, the system was designed to solve problems related to High Availability on FDS (Fraud Detection System) servers that cannot be loaded balanced using the Round Robin method, resulting in changes to ISO 8583 messages. As a result, a method that can be used as High availability to maintain the Availability of the FDS Server without changing the

Amir Taheridehkordi, Martin Schlipf, Zoran Sukurma, Moritz Humer

We implement the phaseless auxiliary field quantum Monte Carlo method using the plane-wave based projector augmented wave method and explore the accuracy and the feasibility of applying our implementation to solids. We use a singular value decomposition to compress the two-body Hamiltonian and thus reduce the computational cost. Consistent correlation energi

Francesco Michele Ventrella, Nimish Pujara, Guido Boffetta, Massimo Cencini

Many species of phytoplankton migrate vertically near the surface of the ocean, either in search of light or nutrients. These motile organisms are affected by ocean waves at the surface. We derive a set of wave-averaged equations to describe the motion of spheroidal microswimmers. We include several possible effects, such as gyrotaxis, settling, and wind-dri

Yu Tian, Zhi-Xiang Li

Time-delay interferometry (TDI) is a crucial technology for space-based gravitational wave detectors. Previous studies have identified the optimal TDI configuration for the first-generation. In this research, we used an Algebraic approach theory to describe the TDI space and employed a method to maximize the signal-to-noise ratio (SNR) to derive the optimal

Zhen-Qing Chen, Eryan Hu, Guohuan Zhao

Let $d \geq 2$, $\alpha \in (0,2)$, and $X$ be the rectilinear $\alpha$-stable process on $\mathbb{R}^d$. We first present a geometric characterization of an open subset $D\subset \mathbb{R}^d$ so that the part process $X^D$ of $X$ in $D$ is irreducible. We then study the properties of the transition density functions of $X^D$, including the strict positivit

Ignacio Guerra, Monica Musso

In an inviscid and incompressible fluid in dimension 3, we prove the existence of several helical filaments, or vortex helices, collapsing into each others.

Christian A. Schroth, Stefan Vlaski, Abdelhak M. Zoubir

Distributed learning paradigms, such as federated or decentralized learning, allow a collection of agents to solve global learning and optimization problems through limited local interactions. Most such strategies rely on a mixture of local adaptation and aggregation steps, either among peers or at a central fusion center. Classically, aggregation in distrib

Robert McRae

We show that if $\mathcal{U}$ and $\mathcal{V}$ are locally finite abelian categories of modules for vertex operator algebras $U$ and $V$, respectively, then the Deligne tensor product of $\mathcal{U}$ and $\mathcal{V}$ can be realized as a certain category $\mathcal{D}(\mathcal{U},\mathcal{V})$ of modules for the tensor product vertex operator algebra $U\ot

Muthumanimaran Vetrivelan, Abhisek Panda, Sai Vinjanampathy

Thermodynamically consistent measurements can either preserve statistics (unbiased) or preserve marginal states (non-invasive) but not both. Here we show the existence of metrological tasks which unequally favor each of the aforementioned measurement types. We consider two different metrology tasks, namely weak value amplification technique and repeated metr

Gobinda Garai, Bankim C. Mandal

In this paper, we design, analyze and implement efficient time parallel method for a class of fourth order time-dependent partial differential equations (PDEs), namely biharmonic heat equation, linearized Cahn-Hilliard (CH) equation and the nonlinear CH equation. We use diagonalization technique on all-at-once system to develop efficient iterative time paral

Kåre Fridell, Ryuichiro Kitano, Ryoto Takai

We discuss sensitivities to lepton flavor violating (and conserving) interactions at future muon colliders, especially at $\mu^+\mu^+$ colliders. Compared with the searches for rare decays of $\mu$ and $\tau$, we find that the TeV-scale future colliders have better sensitivities depending on the pattern of hierarchy in the flavor mixings. As an example, we s

Annika Frommholz, Fabian Seipel, Sebastian Lapuschkin, Wojciech Samek

Deep neural networks are a promising tool for Audio Event Classification. In contrast to other data like natural images, there are many sensible and non-obvious representations for audio data, which could serve as input to these models. Due to their black-box nature, the effect of different input representations has so far mostly been investigated by measuri

Jungho Moon, Sungsam Kang, Jin Hee Hong, Seokchan Yoon

Second-harmonic generation (SHG) microscopy provides label-free imaging of biological tissues with unique contrast mechanisms, but its resolution is limited by the diffraction limit. Here, we present the first experimental demonstration of super-resolution quantitative phase imaging of the SHG field based on synthetic aperture Fourier holographic microscopy.

Aksel Biørn-Hansen

Sustainability has over the past two decades emerged as a key concern in human-computer interaction, with a much critiqued focus on quantification and eco-feedback. This approach fits within a modernist framing of sustainability, treating the environment (and our impact on it) as an externality, reducing it to a set of simple metrics. While data about the cl

Lorenzo Pichierri, Guido Carnevale, Lorenzo Sforni, Andrea Testa

In this paper, we propose a distributed algorithm to control a team of cooperating robots aiming to protect a target from a set of intruders. Specifically, we model the strategy of the defending team by means of an online optimization problem inspired by the emerging distributed aggregative framework. In particular, each defending robot determines its own po

A. Ciniero, G. Fatti, M. Marsili, D. Dini

If polytetrafluoroethylene (PTFE), commonly known as Teflon, is put into contact and rubbed against another material, almost surely it will be more effective than its counterpart in collecting negative charges. This simple, basic property is captured by the so called triboelectric series, where PTFE ranks extremely high, and that qualitatively orders materia

Kristof Takacs, Alex Mason, Luis Eduardo Cordova-Lopez, Marta Alexy

Ensuring the safety of the equipment, its environment and most importantly, the operator during robot operations is of paramount importance. Robots and complex robotic systems are appearing in more and more industrial and professional service applications. However, while mechanical components and control systems are advancing rapidly, the legislation backgro

Inga Ivanova

Communication of information in complex systems can be considered as major driver of systems evolution. What matters is not the communicated information by itself but rather the meaning that is supplied to the information. However informational exchange in a system of heterogenious agents, which code and decode information with different meaning processing s

Yuhan Chen, Max Q. -H. Meng, Li Liu

This paper proposes a new approach to achieve direct visual servoing (DVS) based on discrete orthogonal moments (DOMs). DVS is performed in such a way that the extraction of geometric primitives, matching, and tracking steps in the conventional feature-based visual servoing pipeline can be bypassed. Although DVS enables highly precise positioning, it suffers

Yaxin Wang, Siman Yang

Locally repairable codes (LRCs) have recently been widely used in distributed storage systems and the LRCs with $(r,\delta)$-locality ($(r,\delta)$-LRCs) attracted a lot of interest for tolerating multiple erasures. Ge et al. constructed $(r,\delta)$-LRCs with unbounded code length and optimal minimum distance when $\delta+1 \leq d \leq 2\delta$ from the par

Stefan Wagner

We apply ourselves to the noncommutative geometry of frame bundles by showing that each C$^*$-algebraic noncommutative principal $\mathrm{SO}(n)$-bundle is, up to isomorphism, uniquely determined by its associated noncommutative vector bundle with respect to the standard representation of $\mathrm{SO}(n)$. For this, we provide a construction procedure, via u

Tomonari Meguro, Akihiro Ozawa, Koji Kobayashi, Kentaro Nomura

The effective tight-binding model with compensated ferrimagnetic inverse-Heusler lattice Ti$_{2}$MnAl, candidate material of magnetic Weyl semimetal, is proposed. The energy spectrum near the Fermi level, the configurations of the Weyl points, and the anomalous Hall conductivity are calculated. We found that the orbital magnetization is finite, while the tot

Naoki Fukushima, Kei-Ichi Kondo

We reconsider the restoration of the residual gauge symmetry (RGS) due to topological effects as a possible criterion for color confinement. Although the RGS is ``spontaneously broken'' in the perturbative vacuum, it must be restored in the true confining vacuum of QCD, provided that color confinement phase is a disordered phase where all of symmetries are u

Eduard Vilalta

We study sufficient conditions under which a nowhere scattered C*-algebra $A$ has a nowhere scattered multiplier algebra $\mathcal{M}(A)$, that is, we study when $\mathcal{M}(A)$ has no nonzero, elementary ideal-quotients. In particular, we prove that a $\sigma$-unital C*-algebra $A$ of finite nuclear dimension, or real rank zero, or stable rank one with $k$

Defeng Xie, Ruichen Wang, Jian Ma, Chen Chen

We introduce a new generative system called Edit Everything, which can take image and text inputs and produce image outputs. Edit Everything allows users to edit images using simple text instructions. Our system designs prompts to guide the visual module in generating requested images. Experiments demonstrate that Edit Everything facilitates the implementati

Mijeong Kim, Hyunjoon Lee, Bohyung Han

Although 3D-aware GANs based on neural radiance fields have achieved competitive performance, their applicability is still limited to objects or scenes with the ground-truths or prediction models for clearly defined canonical camera poses. To extend the scope of applicable datasets, we propose a novel 3D-aware GAN optimization technique through contrastive l

Rui Wang, Quan Liu, Sheng Dai, Chao-Ming Liu

Defect engineering is promising to tailor the physical properties of two-dimensional (2D) semiconductors for function-oriented electronics and optoelectronics. Compared with the extensively studied 2D binary materials, the origin of defects and their influence on physical properties of 2D ternary semiconductors have not been clarified. In this work, we thoro

Evangelos Tsagkournis, Dimitris Panagopoulos, Giannis Petousakis, Grigoris Nikolaou

In applications that involve human-robot interaction (HRI), human-robot teaming (HRT), and cooperative human-machine systems, the inference of the human partner's intent is of critical importance. This paper presents a method for the inference of the human operator's navigational intent, in the context of mobile robots that provide full or partial (e.g., sha

Muhammad Rizwan Khan, Harshan Reddy Gopidi, Oleksandr I. Malyi

Traditional solid-state physics has long correlated the optical properties of materials with their electronic structures. However, recent discoveries of intrinsic gapped metals have challenged this classical view. Gapped metals possess electronic properties distinct from both metals and insulators, with a large concentration of free carriers without any inte

Steffen Jaap Skotvoll Bakker, Jonas Martin, E. Ruben van Beesten, Ingvild Synnøve Brynildsen

National freight transport models are valuable tools for assessing the impact of various policies and investments on achieving decarbonization targets under different future scenarios. However, these models struggle to address several critical elements necessary for strategic planning, such as the development and adoption of new fuel technologies over time,

Dapeng Yao, Shuichi Murakami

Chiral phonons with atomic rotations converted into electron spins result in a change of spin magnetizations in crystals. In this paper, we investigate a new conversion of chiral phonons into magnons both in ferromagnets and antiferromagnets by spin models with exchange and Dzyaloshinskii-Moriya interactions. The atomic rotations in chiral phonons are treate

Muzi Hong, Kohei Kamada, Jun'ichi Yokoyama

The electroweak sphaleron process breaks the baryon number conservation within the realms of the Standard Model of particle physics (SM). Recently, it is pointed out that its decoupling may provide the out-of-equilibrium condition required for baryogenesis. In this paper, we study such a scenario taking into account the baryon-number wash-out effect of the s

Thanh-Tung Nguyen, Viktor Schlegel, Abhinav Kashyap, Stefan Winkler

Clinical notes are assigned ICD codes - sets of codes for diagnoses and procedures. In the recent years, predictive machine learning models have been built for automatic ICD coding. However, there is a lack of widely accepted benchmarks for automated ICD coding models based on large-scale public EHR data. This paper proposes a public benchmark suite for ICD-

Nils Damaschke, Volker Kühn, Holger Nobach

Signal processing of uniformly spaced data from stationary stochastic processes with missing samples is investigated. Besides randomly and independently occurring outliers also correlated data gaps are investigated. Non-parametric estimators for the mean value, the signal variance, the autocovariance and cross-covariance functions and the corresponding power

Luiz Augusto G. da Silva, Luis Antonio B. Kowada, Maria Emília M. T. Walter

The Transposition Distance Problem (TDP) is a classical problem in genome rearrangements which seeks to determine the minimum number of transpositions needed to transform a linear chromosome into another represented by the permutations $\pi$ and $\sigma$, respectively. This paper focuses on the equivalent problem of Sorting By Transpositions (SBT), where $\s

Sehyun Kwon, Joo Young Choi, Ernest K. Ryu

In many computer vision applications, images are acquired with arbitrary or random rotations and translations, and in such setups, it is desirable to obtain semantic representations disentangled from the image orientation. Examples of such applications include semiconductor wafer defect inspection, plankton microscope images, and inference on single-particle

Dmytro Kalpakchi, Johan Boye

We present SweCTRL-Mini, a large Swedish language model that can be used for inference and fine-tuning on a single consumer-grade GPU. The model is based on the CTRL architecture by Keskar, McCann, Varshney, Xiong, and Socher (2019), which means that users of the SweCTRL-Mini model can control the genre of the generated text by inserting special tokens in th

Nadya Gurevich, David Kazhdan

Let $G$ be an even orthogonal quasi-split group defined over a local non-archimedean field $F$. We describe the subspace of smooth vectors of the minimal representation of $G(F),$ realized on the space of square-integrable functions on a cone. Our main tool is the Fourier transform on the cone, for which we give an explicit formula.

Prachi Mohanty, Sourav Marik, R. P. Singh

This paper presents structural, detailed magnetic, and exchange bias studies in polycrystalline Ba$_{2}$ScRuO$_{6}$ synthesized at ambient pressure. In contrast to its strontium analogue, this material crystallizes in a 6L hexagonal structure with the space group P$\overline{3}$m1. The Rietveld refinement using the room-temperature powder X-ray diffraction p

Brian Kenji Iwana, Akihiro Kusuda

Transformers are popular neural network models that use layers of self-attention and fully-connected nodes with embedded tokens. Vision Transformers (ViT) adapt transformers for image recognition tasks. In order to do this, the images are split into patches and used as tokens. One issue with ViT is the lack of inductive bias toward image structures. Because