May 2022 arXiv papers — page 80

Joao Monteiro, Mohamed Osama Ahmed, Hossein Hajimirsadeghi, Greg Mori

We study settings where gradient penalties are used alongside risk minimization with the goal of obtaining predictors satisfying different notions of monotonicity. Specifically, we present two sets of contributions. In the first part of the paper, we show that different choices of penalties define the regions of the input space where the property is observed

Run-Qiu Yang, Li Li, Rong-Gen Cai

We give a conjecture on the lower bound of the ADM mass $M$ by using the null energy condition. The conjecture includes a Penrose-like inequality $3M\geq\kappa\mathcal{A}/(4\pi)+\sqrt{\mathcal{A}/4\pi}$ and the Penrose inequality $ 2M\geq\sqrt{{\mathcal{A}}/{4\pi}}$ with $\mathcal{A}$ the event horizon area and $\kappa$ the surface gravity. Both the conjectu

Tobias Kallehauge

Statistical inference for extreme values of random events is difficult in practice due to low sample sizes and inaccurate models for the studied rare events. If prior knowledge for extreme values is available, Bayesian statistics can be applied to reduce the sample complexity, but this requires a known probability distribution. By working with the quantiles

Mikhail Dubashinskiy

We study horocycle eigenfunctions at Lobachevsky plane. They are functions $u\colon \mathbb H=\mathbb C^+=\{z\in\mathbb C\colon \Im z>0\}\to\mathbb C$ such that $\left(-y^2\left(\frac{\partial^2}{\partial x^2}+\frac{\partial^2}{\partial y^2}\right)+ 2i\tau y\frac{\partial}{\partial x}\right)u(x+iy)=s^2 u(x+iy)$, $x+iy\in\mathbb C^+$, with $\tau,s\in\mathbb R

Changgang Zheng, Zhaoqi Xiong, Thanh T Bui, Siim Kaupmees

The rat race between user-generated data and data-processing systems is currently won by data. The increased use of machine learning leads to further increase in processing requirements, while data volume keeps growing. To win the race, machine learning needs to be applied to the data as it goes through the network. In-network classification of data can redu

Aaqib Bulla, Shahid M Shah

In this paper, we study the performance of an energy efficient wireless communication system, assisted by a finite-element-intelligent reflecting surface (IRS). With no instantaneous channel state information (CSI) at the transmitter, we characterize the system performance in terms of the outage probability (OP) of energy efficiency (EE). Depending upon the

Nicholas F. Boardman, Gail Zasowski, Jeffrey A. Newman, Sebastian F. Sanchez

Gas-phase metallicity gradients in galaxies provide important clues to those galaxies' formation histories. Using SDSS-IV MaNGA data, we previously demonstrated that gas metallicity gradients vary systematically and significantly across the galaxy mass--size plane: at stellar masses beyond approximately $10^{10}$ $\mathrm{M_\odot}$, more extended galaxies di

Karthik GVB, Vivek S. Borkar, Gaurav S. Kasbekar

We consider the problem of scheduling packet transmissions in a wireless network of users while minimizing the energy consumed and the transmission delay. A challenge is that transmissions of users that are close to each other mutually interfere, while users that are far apart can transmit simultaneously without much interference. Each user has a queue of pa

Liu Li, Qiang Ma, Matthew Sinclair, Antonios Makropoulos

Fetal Magnetic Resonance Imaging (MRI) is used in prenatal diagnosis and to assess early brain development. Accurate segmentation of the different brain tissues is a vital step in several brain analysis tasks, such as cortical surface reconstruction and tissue thickness measurements. Fetal MRI scans, however, are prone to motion artifacts that can affect the

Jeremy Siegert

In this paper we show that the asymptotic dimension of an unbounded proper metric space is bounded above by a coarse analog of Ponomarev's cofinal dimension of topological spaces, which we call the coarse cofinal dimension. We also show that asymptotic dimension is bounded below by the cofinal dimension of the Higson corona by existing results of Miyata, Aus

Konstantinos Varvoutas, Anastasios Gounaris, Georgia Kougka, Hajo A. Reijers

The Product Data Model (PDM) is an example of a data-centric approach to modelling information-intensive business processes, which offers exibility and facilitates process optimization. Because the approach is declarative in nature, there may be multiple, alternative execution plans that can produce the desired end product. To generate such plans, several he

Giuseppe D'Onofrio, Pierre Patie, Laura Sacerdote

To overcome some limits of classical neuronal models, we propose a Markovian generalization of the classical model based on Jacobi processes by introducing downwards jumps to describe the activity of a single neuron. The statistical analysis of inter-spike intervals is performed by studying the first-passage times of the proposed Markovian Jacobi process wit

Jiachen Jiang, Luigi C. Gallo, Andrew C. Fabian, Michael L. Parker

We present a detailed analysis of the XMM-Newton observations of five narrow-line Seyfert 1 galaxies (NLS1s). They all show very soft continuum emission in the X-ray band with a photon index of $\Gamma\gtrsim 2.5$. Therefore, they are referred to as `ultra-soft' NLS1s in this paper. By modeling their optical/UV-X-ray spectral energy distribution (SED) with a

Lei Xun, Bashir M. Al-Hashimi, Jonathon Hare, Geoff V. Merrett

Deep neural network (DNN) inference is increasingly being executed on mobile and embedded platforms due to low latency and better privacy. However, efficient deployment on these platforms is challenging due to the intensive computation and memory access. We propose a holistic system design for DNN performance and energy optimisation, combining the trade-off

Jane Breen, Emanuele Crisostomi, Sooyeong Kim

In this paper, we determine a formula for Kemeny's constant for a graph with multiple bridges, in terms of quantities that are inherent to the subgraphs obtained upon removal of all bridges and that can be computed independently. With the formula, we consider several optimization problems for Kemeny's constant for graphs with bridges, and we remark on the co

Naresh Manwani, Mudit Agarwal

In this paper, we present online algorithm called {\it Delaytron} for learning multi class classifiers using delayed bandit feedbacks. The sequence of feedback delays $\{d_t\}_{t=1}^T$ is unknown to the algorithm. At the $t$-th round, the algorithm observes an example $\mathbf{x}_t$ and predicts a label $\tilde{y}_t$ and receives the bandit feedback $\mathbb

Zoltán Kovács, Alexander Thaller

We report on a first experiment about an email based course that connects learning Python basics and introductory probability theory. In the experiment 7 short sequences of homework were sent out to prospective mathematics teachers who did not have any programming background formerly, but already had some minor knowledge on probability theory. The experiment

Zhicheng Yang, Jinghui Qin, Jiaqi Chen, Liang Lin

Recently, deep learning models have made great progress in MWP solving on answer accuracy. However, they are uninterpretable since they mainly rely on shallow heuristics to achieve high performance without understanding and reasoning the grounded math logic. To address this issue and make a step towards interpretable MWP solving, we first construct a high-qu

Calum Robert MacLellan, Feng Dong

Scheduling the batch size to increase is an effective strategy to control gradient noise when training deep neural networks. Current approaches implement scheduling heuristics that neglect structure within the optimization procedure, limiting their flexibility to the training dynamics and capacity to discern the impact of their adaptations on generalization.

Graham A. Niblo, Roger Plymen, Nick Wright

The compact, connected Lie group $E_6$ admits two forms: simply connected and adjoint type. As we previously established, the Baum-Connes isomorphism relates the two Langlands dual forms, giving a duality between the equivariant K-theory of the Weyl group acting on the corresponding maximal tori. Our study of the $A_n$ case showed that this duality persists

Stuart Russell, John F. Rudge, Jessica C. E. Irving, Sanne Cottaar

The Earth's ellipticity of figure has an effect on the travel times of seismic waves over teleseismic distances. Tables of ellipticity corrections and coefficients have been used by seismologists for several decades, however due to the increasing variety and complexity of seismic phases in use, current tables of ellipticity coefficients are now outmoded and

V. M. Braun, K. G. Chetyrkin, A. N. Manashov

Conformal symmetry of QCD is restored at the Wilson-Fisher critical point in noninteger $4-2\epsilon$ space-time dimensions. Correlation functions of multiplicatively renormalizable operators with different anomalous dimensions at the critical point vanish identically. We show that this property allows one to calculate off-diagonal parts of the anomalous dim

Jiachen Jiang, Douglas J. K. Buisson, Thomas Dauser, Andrew C. Fabian

We present an analysis of the NuSTAR and Swift spectra of the black hole candidate MAXI J1813-095 in a failed-transition outburst in 2018. The NuSTAR observations show evidence of reflected emission from the inner region of the accretion disc. By modelling the reflection component in the spectra, we find a disc inner radius of $R_{\rm in}<7$ $r_{\rm g}$. Thi

Martin Raum

The Bernstein-Gelfand tensor product functors are endofunctors of the category of Harish-Chandra modules provided by tensor products with finite dimensional modules. We provide an automorphic analogue of these tensor product functors, implemented by vector-valued automorphic representations that are trivial at all finite places. They naturally explain the ro

Rémi Delogne, Vincent Schellekens, Laurent Jacques

Rank-one projections (ROP) of matrices and quadratic random sketching of signals support several data processing and machine learning methods, as well as recent imaging applications, such as phase retrieval or optical processing units. In this paper, we demonstrate how signal estimation can be operated directly through such quadratic sketches--equivalent to

Yiming Fang, Xuejun Liu, Hui Liu

It has been verified that only a small fraction of the neoantigens presented by MHC class I molecules on the cell surface can elicit T cells. The limitation can be attributed to the binding specificity of T cell receptor (TCR) to peptide-MHC complex (pMHC). Computational prediction of T cell binding to neoantigens is an challenging and unresolved task. In th

Vinayak Shantaram Bhat, Sho Watanabe, Florian Kronast, Korbinian Baumgaertl

Aperiodicity and un-conventional rotational symmetries allow quasicrystalline structures to exhibit unprecedented physical and functional properties. In magnetism, artificial ferromagnetic quasicrystals exhibited knee anomalies suggesting reprogrammable magnetic properties via nonstochastic switching. However, the decisive roles of short-range exchange and l

Fujun Hou

We present some conditions for social preference transitivity under the majority rule when the individual preferences include cycles. First, our concern is with the restriction on the preference orderings of individuals except those (called cycle members) whose preferences constitute the cycles, but the considered transitivity is, of course, of the society a

Yasushi Nagano, Koji Hukushima

In Bayesian statistics, horseshoe prior has attracted increasing attention as an approach to the sparse estimation. The estimation accuracy of compressed sensing with the horseshoe prior is evaluated by statistical mechanical method. It is found that there exists a phase transition in signal recoverability in the plane of the number of observations and the n

George Choueiri, Balachandra Suri, Jack Merrin, Maksym Serbyn

Theoretical foundations of chaos have have been predominantly laid out for finite-dimensional dynamical systems, such as the three-body problem in classical mechanics and the Lorenz model in dissipative systems. In contrast, many real-world chaotic phenomena, e.g. weather, arise in systems with many (formally infinite) degrees of freedom, which limits direct

Demian Gholipour Ghalandari, Chris Hokamp, Georgiana Ifrim

Sentence compression reduces the length of text by removing non-essential content while preserving important facts and grammaticality. Unsupervised objective driven methods for sentence compression can be used to create customized models without the need for ground-truth training data, while allowing flexibility in the objective function(s) that are used for

Zhuoyin Dai, Ruoguang Li, Jingran Xu, Yong Zeng

Cell-free massive MIMO and symbiotic radio communication have been recently proposed as the promising beyond fifth-generation (B5G) networking architecture and transmission technology, respectively. To reap the benefits of both, this paper studies cell-free symbiotic radio communication systems, where a number of cell-free access points (APs) cooperatively s

Florian Kofler, Ivan Ezhov, Lucas Fidon, Izabela Horvath

Human ratings are abstract representations of segmentation quality. To approximate human quality ratings on scarce expert data, we train surrogate quality estimation models. We evaluate on a complex multi-class segmentation problem, specifically glioma segmentation, following the BraTS annotation protocol. The training data features quality ratings from 15 e

Holger Grosshans, Simon Jantac

Thus far, Computational Fluid Dynamics (CFD) simulations fail to reliably predict the electrostatic charging of powder during pneumatic conveying. The lack of a predictive tool is one reason for unwanted discharges and growing deposits that make a plant a prime candidate for an explosion. This paper reviews the numerical models' state-of-the-art, limitations

Congpei An, Hao-Ning Wu

Singular and oscillatory functions feature in numerous applications. The high-accuracy approximation of such functions shall greatly help us develop high-order methods for solving applied mathematics problems. This paper demonstrates that hyperinterpolation, a discrete projection method with coefficients obtained by evaluating the $L^2$ orthogonal projection

Lanhai He, Lukáš Tomaník, Sebastian Malerz, Florian Trinter

Solvent interactions, particularly hydration, are vital in chemical and biochemical systems. Model systems unveil microscopic details of such interactions. We uncover a specific hydrogen-bonding motif of the biomolecular building block indole (C$_8$H$_7$N), tryptophan's chromophore, in water: a strong localized $\text{N-H}\cdots\text{OH}_2$ hydrogen bond, al

Jia Gao, Songbo Hou

Denote by $G=(V,E)$ a finite graph. We study a generalized Chern-Simons equation $$ \Delta u=\lambda \mathrm{e}^u(\mathrm{e}^{bu}-1)+4\pi\sum\limits_{j=1}^{N}\delta_{p_j} $$ on $G$, where $\lambda$ and $b$ are positive constants; $N$ is a positive integer; $p_1, p_2, \cdot\cdot\cdot, p_N$ are distinct vertices of $V$ and $\delta_{p_j}$ is the Dirac delta mas

Tianjun Li, Junle Pei, Xiangwei Yin, Bin Zhu

We revisit the Simple Extension of the Standard Model (SESM), which can account for various tentative new physics anomalies and dark matter. The B physics anomalies, muon anomalous magnetic moment, and dark matter can be explained already in SESM. In this work, we study the unitarity constraint on this model and comment the parameter space on the edge of uni

Hai-Jing Xu, Song-lin Zhao

In this paper, we develop a Cauchy matrix reduction technique that enables us to obtain solutions for the reduced local and nonlocal complex equations from the Cauchy matrix solutions of the original before-reduction systems. Specifically, by imposing local and nonlocal complex reductions on some Ablowitz-Kaup-Newell-Segur-type equations, we study some local

Olivera Latković, Atila Čeki

We analyze multicolor light curves of six totally eclipsing, short-period W UMa binaries and derive, for the first time, their orbital and stellar parameters. The mass ratios are established robustly through an automated q-search procedure that performs a heuristic survey of the parameter space. Five stars belong to the W and one to the A subtype. The mass r

Jens Biele, Matthias Grott, Michael E. Zolensky, Artur Benisek

We provide detailed background, theoretical and practical, on the specific heat cp of minerals and mixtures thereof, 'astro-materials', as well as background information on common minerals and other relevant solid substances found on the surfaces of solar system bodies. Furthermore, we demonstrate how to use specific heat and composition data for lunar sampl

Antonio Pifferi, Massimo Miniati, Andrea Farina, Sanathana Konugolu Venkata Sekar

Non-invasive in vivo sensing of the lung with light would help diagnose and monitor pulmonary disorders (caused by e.g. COVID-19, emphysema, immature lung tissue in infants). We investigated the possibility to probe the lung with time domain diffuse optics, taking advantage of the increased depth (few cm) reached by photons detected after a long (few ns) pro

Ádám Wolf, Stefan Romeder-Finger, Károly Széll, Péter Galambos

The Laboratory Automation Plug & Play (LAPP) framework is an over-arching reference architecture concept for the integration of robots in life science laboratories. The plug & play nature lies in the fact that manual configuration is not required, including the teaching of the robots. In this paper a digital twin (DT) based concept is proposed that outlines

Florian Kofler, Suprosanna Shit, Ivan Ezhov, Lucas Fidon

Deep convolutional neural networks (CNN) have proven to be remarkably effective in semantic segmentation tasks. Most popular loss functions were introduced targeting improved volumetric scores, such as the Dice coefficient (DSC). By design, DSC can tackle class imbalance, however, it does not recognize instance imbalance within a class. As a result, a large

Davide Ghion, Mattia Zorzi

We consider the problem of distributed Kalman filtering for sensor networks in the case there is a limit in data transmission and there is model uncertainty. More precisely, we propose a distributed filtering strategy with event-triggered communication in which the state estimators are computed according to the least favorable model. The latter belongs to a

Baosheng Zhang, Xiaoguang Ma, Hongjun Ma, Chunbo Luo

Most feature-based stereo visual odometry (SVO) approaches estimate the motion of mobile robots by matching and tracking point features along a sequence of stereo images. However, in dynamic scenes mainly comprising moving pedestrians, vehicles, etc., there are insufficient robust static point features to enable accurate motion estimation, causing failures w

Daishi Kiyohara

This paper extends Bombieri and Pila's estimate of lattice points on curves to arbitrary finite sets by incorporating considerations of minimal separation and the doubling constant. We derive the estimate by establishing the $\ell^2$ decoupling inequality for non-degenerate curves in $\mathbb{R}^n$. Additionally, we review the curve-lifting method introduced

Wei Liu, Aigeng Yang, Hao Sun

We study the scenarios where a strongly first-order electroweak phase transition (EWPT) is triggered by a light singlet scalar, which has feeble interactions to the Higgs. Since the singlet scalar is light and has weak couplings, it can decay at a macroscopic distance away from the collision point. Therefore, it can be regarded as a long-lived particles (LLP

Dinghui Wang, Huaiqiang Wang, Dingyu Xing, Haijun Zhang

Layered van der Waals (vdW) topological materials, especially the recently discovered MnBi$_2$Te$_4$-family magnetic topological insulators (TIs), have aroused great attention. However, there has been a serious debate about whether the surface states are gapped or gapless for antiferromagnetic (AFM) TI MnBi$_2$Te$_4$, which is crucial to the prospect of vari

Advait Sarkar

Explaining the behaviour of intelligent systems will get increasingly and perhaps intractably challenging as models grow in size and complexity. We may not be able to expect an explanation for every prediction made by a brain-scale model, nor can we expect explanations to remain objective or apolitical. Our functionalist understanding of these models is of l

Ion Simaciu, Zoltan Borsos, Gheorghe Dumitrescu

In this paper, we deduce the expression of the gravito-acoustic force between two oscillating bubbles using the hypothesis that this type of force is a force of scattering-absorption of the energy of excitatory waves. The expression of the gravito-acoustic force at resonance highlights the dependence of this force on the product of the virtual masses of the

Petra Berenbrink, Amin Coja-Oghlan, Oliver Gebhard, Max Hahn-Klimroth

We study the Consensus problem among $n$ agents, defined as follows. Initially, each agent holds one of two possible opinions. The goal is to reach a consensus configuration in which every agent shares the same opinion. To this end, agents randomly sample other agents and update their opinion according to a simple update function depending on the sampled opi

Amilson R. Fritsch, Shangjie Guo, Sophia M. Koh, I. B. Spielman

We establish a dataset of over $1.6\times10^4$ experimental images of Bose--Einstein condensates containing solitonic excitations to enable machine learning (ML) for many-body physics research. About $33~\%$ of this dataset has manually assigned and carefully curated labels. The remainder is automatically labeled using SolDet -- an implementation of a physic

Barbara Schütt, Marc Heinrich, Sonja Marahrens, J. Marius Zöllner

Verification and validation are major challenges for developing automated driving systems. A concept that gets more and more recognized for testing in automated driving is scenario-based testing. However, it introduces the problem of what scenarios are relevant for testing and which are not. This work aims to find relevant, interesting, or critical parameter

Jani Jokela

A mixed lattice is a lattice-type structure consisting of a set with two partial orderings, and generalizing the notion of a lattice. Mixed lattice theory has previously been studied in various algebraic structures, such as groups and semigroups, while the more general notion of a mixed lattice remains unexplored. In this paper, we study the fundamental prop

Dennis Hendriks, Arjan van der Meer, Wytse Oortwijn

Software-intensive systems constantly evolve. To prevent software changes from unintentionally introducing costly system defects, it is important to understand their impact to reduce risk. However, it is in practice nearly impossible to foresee the full impact of software changes when dealing with huge industrial systems with many configurations and usage sc

Naveen Sisodia, Johan Pelloux-Prayer, Liliana D. Buda-Prejbeanu, Lorena Anghel

Magnetic skyrmions are promising candidates for logic-in-memory applications, intrinsically merging high density non-volatile data storage with computing capabilities, owing to their nanoscale size, fast motion, and mutual repulsions. However, concepts proposed so far suffer from reliability issues as well as inefficient conversion of magnetic information to

Mohammad Hossein Amani, Simone Bombari, Marco Mondelli, Rattana Pukdee

In this paper, we study the compression of a target two-layer neural network with N nodes into a compressed network with M<N nodes. More precisely, we consider the setting in which the weights of the target network are i.i.d. sub-Gaussian, and we minimize the population L_2 loss between the outputs of the target and of the compressed network, under the assum

Cristian J. Vaca-Rubio, Dariush Salami, Petar Popovski, Elisabeth de Carvalho

Since electromagnetic signals are omnipresent, Radio Frequency (RF)-sensing has the potential to become a universal sensing mechanism with applications in localization, smart-home, retail, gesture recognition, intrusion detection, etc. Two emerging technologies in RF-sensing, namely sensing through Large Intelligent Surfaces (LISs) and mmWave Frequency-Modul

Naveen Sisodia, Johan Pelloux-Prayer, Liliana D. Buda-Prejbeanu, Lorena Anghel

Magnetic skyrmions are promising candidates as elementary nanoscale bits in logic-in-memory devices, intrinsically merging high density memory and computing capabilities. Here we exploit the dynamics of skyrmions interacting with anisotropy energy barriers patterned by ion irradiation to design programmable logic gates. Using micromagnetic simulations with e

Song Jin Ri

We introduce a two-variable refinement $\hat{Z}_a(q,t)$ of plumbed 3-manifold invariants $\hat{Z}_a(q)$, which were previously defined for weakly negative definite plumbed 3-manifolds. We also provide a number of explicit examples in which we argue the recovering process to obtain $\hat{Z}_a(q)$ from $\hat{Z}_a(q,t)$ by taking a limit $ t\rightarrow 1 $. For

Lyle Ramshaw

Consider equilateral pentagons $V_1,\ldots,V_5$ in the Euclidean plane. When we identify pentagons that differ by translation, rotation, and magnification, the moduli space of possible shapes that we get is an oft-studied polygon space: a 2-manifold $E_5$ known topologically to be a quadruple torus (genus 4). We study $E_5$ geometrically, our goal being a co

David Nicolas Nenning, Armin Rainer, Gerhard Schindl

The Borel map takes a smooth function to its infinite jet of derivatives (at zero). We study the restriction of this map to ultradifferentiable classes of Beurling type in a very general setting which encompasses the classical Denjoy-Carleman and Braun-Meise-Taylor classes. More precisely, we characterize when the Borel image of one class covers the sequence

Suha Shreim, Francesco Ferrante, Christophe Prieur

Boundary feedback control design is studied for 1D hyperbolic systems with an in-domain disturbance and a boundary feedback controller under the effect of actuator saturation. Nonlinear semigroup theory is used to prove well-posedness of mild solution pairs to the closed-loop system. Sufficient conditions in the form of dissipation functional inequalities ar

Graeme A Stewart, Peter Elmer, Elizabeth Sexton-Kennedy

The HEP Software Foundation was founded in 2014 to tackle common problems of software development and sustainability for high-energy physics. In this paper we outline the motivation for the founding of the organisation and give a brief history of its development. We describe how the organisation functions today and what challenges remain to be faced in the f

Tue Herlau

Reinforcement learning systems will to a greater and greater extent make decisions that significantly impact the well-being of humans, and it is therefore essential that these systems make decisions that conform to our expectations of morally good behavior. The morally good is often defined in causal terms, as in whether one's actions have in fact caused a p

Bin Wang, Yaolin Jiang

The aim of this paper is to formulate and analyze numerical discretizations of charged-particle dynamics (CPD) in a strong nonuniform magnetic field. A strategy is firstly performed for the two dimensional CPD to construct the semi-discretization and full-discretization which have optimal accuracy. This accuracy is improved in the position and in the velocit

Gianluca Occhetta, Eleonora A. Romano, Luis E. Solá Conde, Jarosław A. Wiśniewski

In this paper we study varieties admitting torus actions as geometric realizations of birational transformations. We present an explicit construction of these geometric realizations for a particular class of birational transformations, and study some of their geometric properties, such as their Mori, Nef and Movable cones.

Aurélien Morel, Yakumo Kunimoto, Alex Coninx, Stéphane Doncieux

Grasping a particular object may require a dedicated grasping movement that may also be specific to the robot end-effector. No generic and autonomous method does exist to generate these movements without making hypotheses on the robot or on the object. Learning methods could help to autonomously discover relevant grasping movements, but they face an importan

Madjid Hadjal, Encarni Medina-López, Jinchang Ren, Alejandro Gallego

Chlorophyll-a (Chl) retrieval from ocean colour remote sensing is problematic for relatively turbid coastal waters due to the impact of non-algal materials on atmospheric correction and standard Chl algorithm performance. Artificial neural networks (NNs) provide an alternative approach for retrieval of Chl from space and results in northwest European shelf s

Hoil Lee, Fadhel Ayed, Paul Jung, Juho Lee

This article studies the infinite-width limit of deep feedforward neural networks whose weights are dependent, and modelled via a mixture of Gaussian distributions. Each hidden node of the network is assigned a nonnegative random variable that controls the variance of the outgoing weights of that node. We make minimal assumptions on these per-node random var

Xiazhi Hao, S. Y. Lou

The existence of decompositions of the nonlinear integrable systems not only permits us to establish so-called linear superposition solutions but also to derive new nonlinear integrable coupled systems. Restricting our attention to the single component decompositions of the potential BKP hierarchy, we obtain that suitable linear superpositions of some decomp

Bin Wang, Zhen Miao, Yaolin Jiang

In this paper, we are concerned with two-scale integrators for the non-relativistic Klein--Gordon (NRKG) equation with a dimensionless parameter $0<\varepsilon\ll 1$, which is inversely proportional to the speed of light. The highly oscillatory property in time of this model corresponds to the parameter $\varepsilon$ and the equation {in the form of $\partia

Fedor Moiseev, Zhe Dong, Enrique Alfonseca, Martin Jaggi

Large language models (LLMs) have demonstrated human-level performance on a vast spectrum of natural language tasks. However, it is largely unexplored whether they can better internalize knowledge from a structured data, such as a knowledge graph, or from text. In this work, we propose a method to infuse structured knowledge into LLMs, by directly training T

Parth Chavan

Inspired by a famous formula of Ramanujan for odd zeta values, we prove an analogous formula involving the Hurwitz zeta function. We introduce a new integral kernel related to the Hurwitz zeta function, generalizing the integral kernel associated to Ramanujan's identity. We also derive several infinite families of identities analogous to Ramanujan's formula.

Ze-Hao Wu, Hua-Cheng Zhou, Bao-Zhu Guo, Feiqi Deng

In this paper, the convergence and noise-tolerant performance of a tracking differentiator in the presence of multiple stochastic disturbances are investigated for the first time. We consider a quite general case where the input signal is corrupted by additive colored noise, and the tracking differentiator itself is disturbed by additive colored noise and wh

Man-Kwun Chiu, Stefan Felsner, Manfred Scheucher, Felix Schröder

Felsner, Hurtado, Noy and Streinu (2000) conjectured that arrangement graphs of simple great-circle arrangements have chromatic number at most $3$. Motivated by this conjecture, we study the colorability of arrangement graphs for different classes of arrangements of (pseudo-)circles. In this paper the conjecture is verified for $\triangle$-saturated pseudoci

Sameer Khurana, Antoine Laurent, James Glass

We propose the SAMU-XLSR: Semantically-Aligned Multimodal Utterance-level Cross-Lingual Speech Representation learning framework. Unlike previous works on speech representation learning, which learns multilingual contextual speech embedding at the resolution of an acoustic frame (10-20ms), this work focuses on learning multimodal (speech-text) multilingual s

Jyotirmoy Mukherjee

We study the properties of pseudo entropy, a new generalization of entanglement entropy, in free Maxwell field theory in $d = 4$ dimension. We prepare excited states by the different components of the field strengths located at different Euclidean times acting on the vacuum. We compute the difference between the pseudo R\'{e}nyi entropy and the R\'{e}nyi ent

Tue Herlau

The past two decades have seen a growing interest in combining causal information, commonly represented using causal graphs, with machine learning models. Probability trees provide a simple yet powerful alternative representation of causal information. They enable both computation of intervention and counterfactuals, and are strictly more general, since they

Anton Bochkarev, Yury Lysogorskiy, Christoph Ortner, Gábor Csányi

Traditionally, interatomic potentials assume local bond formation supplemented by long-range electrostatic interactions when necessary. This ignores intermediate range multi-atom interactions that arise from the relaxation of the electronic structure. Here, we present the multilayer atomic cluster expansion (ml-ACE) that includes collective, semi-local multi

Dachao Lin, Zhihua Zhang

In this short note, we give the convergence analysis of the policy in the recent famous policy mirror descent (PMD). We mainly consider the unregularized setting following [11] with generalized Bregman divergence. The difference is that we directly give the convergence rates of policy under generalized Bregman divergence. Our results are inspired by the conv

R. Aruna Devi, K. Anitha

Rough membership function defines the measurement of relationship between conditional and decision attribute from an Information system. In this paper we propose a new method to construct rough graph through rough membership function $\omega_{G}^F(f)$. Rough graph identifies the pattern between the objects with imprecise and uncertain information. We explore

Nathanaël Fijalkow, Cristian Riveros, James Worrell

Probabilistic automata are an extension of nondeterministic finite automata in which transitions are annotated with probabilities. Despite its simplicity, this model is very expressive and many of the associated algorithmic questions are undecidable. In this work we focus on the emptiness problem (and its variant the value problem), which asks whether a give

Matt L. Sampson, James R. Beattie, Mark R. Krumholz, Roland M. Crocker

Cosmic rays (CRs) are a dynamically important component of the interstellar medium (ISM) of galaxies. The $\sim$GeV CRs that carry most CR energy and pressure are likely confined by self-generated turbulence, leading them to stream along magnetic field lines at the ion Alfv\'en speed. However, the consequences of self-confinement for CR propagation on galaxy

Iván León, Diego Pazó

The dynamics of ensembles of phase oscillators are usually described considering their infinite-size limit. In practice, however, this limit is fully accessible only if the Ott-Antonsen theory can be applied, and the heterogeneity is distributed following a rational function. In this work we demonstrate the usefulness of a moment-based scheme to reproduce th

David Krejcirik, Vladimir Lotoreichik

The spectrum of the Dirichlet Laplacian on any quasi-conical open set coincides with the non-negative semi-axis. We show that there is a connected quasi-conical open set such that the respective Dirichlet Laplacian has a positive (embedded) eigenvalue. This open set is constructed as the tower of cubes of growing size connected by windows of vanishing size.

Weikang Lin, Tsutomu Yanagida

The $B-L$ gauge symmetry motivated from the successful generation of the seesaw mechanism and leptogenesis. We show that if the $B-L$ gauge boson constitutes a small fraction of the dark matter (DM) it can explain the Galactic $511$ keV emission via the decay into an electron-positron pair. We find the model parameter space that is consistent with the seesaw

Victor Peterson

Choosing pessimism makes one cynical to and necessitates their destruction of information.

Mamiya Kawaguchi, Mei Huang

The quark anomalous magnetic moment (AMM) is dynamically generated through the spontaneous chiral symmetry breaking. It has been revealed that even though its exact form is still unknown, the quark AMM is essential to explore quark matter properties and QCD phase structure under external magnetic fields. In this study, we take three different forms of the qu

Mario Fuest, Shahin Heydari, Petr Knobloch, Johannes Lankeit

In this paper, we study a cancer invasion model both theoretically and numerically. The model is a nonstationary, nonlinear system of three coupled partial differential equations modeling the motion of cancer cells, degradation of the extracellular matrix, and certain enzymes. We first establish existence of global classical solutions in both two- and three-

Tianxiang Gou

In this paper, we consider blowup of solutions to the Cauchy problem for the following biharmonic nonlinear Schr\"odinger equation (NLS), $$ \textnormal{i} \, \partial_t u=\Delta^2 u-\mu \Delta u-|u|^{2 \sigma} u \quad \text{in} \,\, \R \times \R^d, $$ where $d \geq 1$, $\mu \in \R$ and $0<\sigma<\infty$ if $1 \leq d \leq 4$ and $0<\sigma<4/(d-4)$ if $d \geq

Lorenzo Cerrone, Athul Vijayan, Tejasvinee Mody, Kay Schneitz

Classifying all cells in an organ is a relevant and difficult problem from plant developmental biology. We here abstract the problem into a new benchmark for node classification in a geo-referenced graph. Solving it requires learning the spatial layout of the organ including symmetries. To allow the convenient testing of new geometrical learning methods, the

Ge Tang, Xiao-Yong Yang, Ying Yan, Jie Lu

We implemented arbitrary single qubit gates of geometric quantum computing for a three-level system in a single-shot manner. The evolution time of the gate has been minimized by considering the shortest trajectory of the state on the Bloch sphere. The duration of gates grows from zero with the rotation angle $\gamma$, and the tested T gate time can be reduce

Benjamin Dequêne

Gentle algebras form a class of finite-dimensional algebras introduced by I. Assem and A. Skowro\'{n}ski in the 1980s. Modules over such an algebra can be described by string and band combinatorics in the associated gentle quiver from the work of M.C.R. Butler and C.M. Ringel. Any module can be naturally associated to a quiver representation. A nilpotent end

A. Nuñez-Castiñeyra, I. A. Grenier, F. Bournaud, Y. Dubois

Dynamically, cosmic rays with energies above about one GeV/nucleon may be important agents of galaxy evolution. Their pressures compare with the thermal and magnetic ones impacting galactic gas accretion, fountains and galactic outflows, and alter the mass cycling between the gas phases, its efficiency depends on the properties of CR transport in the differe

Fabrizio Colombo, Antonino De Martino, Stefano Pinton, Irene Sabadini

The spectral theory on the S-spectrum was introduced to give an appropriate mathematical setting to quaternionic quantum mechanics, but it was soon realized that there were different applications of this theory, for example, to fractional heat diffusion and to the spectral theory for the Dirac operator on manifolds. In this seminal paper we introduce the har

Jaewon Yoo, Jongwan Ko, Cristiano G. Sabiu, Jihye Shin

In a galaxy cluster, the relative spatial distributions of dark matter, member galaxies, gas, and intracluster light (ICL) may connote their mutual interactions over the cluster evolution. However, it is a challenging problem to provide a quantitative measure for the shape matching between two multi-dimensional scalar distributions. We present a novel method

Aneta Wojnar

The last findings on stellar and substellar objects in modified gravity are presented, allowing a reader to quickly jump into this topic. Early stellar evolution of low-mass stars, cooling models of brown dwarfs and giant gaseous exoplanets as well as internal structure of terrestrial planets are discussed. Moreover, possible test of models of gravity with t

Prabhav Singh, Ridam Srivastava, K. P. S. Rana, Vineet Kumar

Fake News Detection (FND) is an essential field in natural language processing that aims to identify and check the truthfulness of major claims in a news article to decide the news veracity. FND finds its uses in preventing social, political and national damage caused due to misrepresentation of facts which may harm a certain section of society. Further, wit