October 2022 arXiv papers — page 67

Albert Xu, Jhih-Yi Hsieh, Bhaskar Vundurthy, Eliana Cohen

In deep metric learning, the Triplet Loss has emerged as a popular method to learn many computer vision and natural language processing tasks such as facial recognition, object detection, and visual-semantic embeddings. One issue that plagues the Triplet Loss is network collapse, an undesirable phenomenon where the network projects the embeddings of all data

Peter Frankl, Jian Wang

For positive integers $n>k>t$ let $\binom{[n]}{k}$ denote the collection of all $k$-subsets of the standard $n$-element set $[n]=\{1,\ldots,n\}$. Subsets of $\binom{[n]}{k}$ are called $k$-graphs. A $k$-graph $\mathcal{F}$ is called $t$-intersecting if $|F\cap F'|\geq t$ for all $F,F'\in \mathcal{F}$. One of the central results of extremal set theory is the

Gregory Stock, Juan A. Fraire, Holger Hermanns, Eduardo Cruz

Recent breakthroughs in technology have led to a thriving "new space" culture in low-Earth orbit (LEO) in which performance and cost considerations dominate over resilience and reliability as mission goals. These advances create a manifold of opportunities for new research and business models but come with a number of striking new challenges. In particular,

Fukun Yin, Wen Liu, Zilong Huang, Pei Cheng

Implicit neural 3D representation has achieved impressive results in surface or scene reconstruction and novel view synthesis, which typically uses the coordinate-based multi-layer perceptrons (MLPs) to learn a continuous scene representation. However, existing approaches, such as Neural Radiance Field (NeRF) and its variants, usually require dense input vie

Dimitrios Stoidis, Andrea Cavallaro

People may be unaware of the privacy risks of uploading an image online. In this paper, we present Graph Privacy Advisor, an image privacy classifier that uses scene information and object cardinality as cues to predict whether an image is private. Graph Privacy Advisor simplifies a state-of-the-art graph model and improves its performance by refining the re

Laura Arditti, Martino Trevisan, Luca Vassio, Alberto De Lazzari

Payment platforms have significantly evolved in recent years to keep pace with the proliferation of online and cashless payments. These platforms are increasingly aligned with online social networks, allowing users to interact with each other and transfer small amounts of money in a Peer-to-Peer fashion. This poses new challenges for analysing payment data,

Anna Paola Muntoni, Andrea De Martino

In order to grow in any given environment, bacteria need to collect information about the medium composition and implement suitable growth strategies by adjusting their regulatory and metabolic degrees of freedom. In the standard sense, optimal strategy selection is achieved when bacteria grow at the fastest rate possible in that medium. While this view of o

Leonhard Balduf, Martin Florian, Björn Scheuermann

We explore issues relating to the storage of digital art, based on an empirical investigation into the storage of audiovisual data referenced by non-fungible tokens (NFTs). We identify current trends in NFT data storage and highlight problems with implemented solutions. We particularly focus our investigation on the use of the Interplanetary Filesystem (IPFS

Shaobo Li, Xiaoguang Li, Lifeng Shang, Chengjie Sun

Previous works show that Pre-trained Language Models (PLMs) can capture factual knowledge. However, some analyses reveal that PLMs fail to perform it robustly, e.g., being sensitive to the changes of prompts when extracting factual knowledge. To mitigate this issue, we propose to let PLMs learn the deterministic relationship between the remaining context and

Alexander Kovalenko, Vitaliy Pozdnyakov, Ilya Makarov

Timely detected anomalies in the chemical technological processes, as well as the earliest detection of the cause of the fault, significantly reduce the production cost in the industrial factories. Data on the state of the technological process and the operation of production equipment are received by a large number of different sensors. To better predict th

D. Kumar, A. K. B. Chand, P. R. Massopust

In this paper, a novel class of quantum fractal functions is introduced based on the Meyer-K\"onig-Zeller operator $M_{q,n}$. These quantum Meyer-K\"onig-Zeller (MKZ) fractal functions employ $M_{q,n} f$ as the base function in the iterated function system for $\alpha$-fractal functions. For $f\in C(I)$, $I$ closed in $\mathbb{R}$, it is shown that there exi

Amodio Carleo, Gaetano Lambiase, Ali Övgün

Non-linear electrodynamics (NLED) is a generalization of Maxwell's electrodynamics for strong fields. It could have significant implications for the study of black holes and cosmology and have been extensively studied in the literature, extending from quantum to cosmological contexts. Recently, its application to black holes, inflation and dark energy has ca

Serge Thill, Vicky Charisi, Tony Belpaeme, Ana Paiva

Understanding and modelling children's cognitive processes and their behaviour in the context of their interaction with robots and social artificial intelligence systems is a fundamental prerequisite for meaningful and effective robot interventions. However, children's development involve complex faculties such as exploration, creativity and curiosity which

Vojtěch Chlan, Martin Adamec, Helena Štěpánková, Victor G. Thomas

Behavior of individual molecules of normal and heavy water in beryl single crystals was studied by $^1$H and $^2$H nuclear magnetic resonance spectroscopy. From temperature dependences of the spectra we deduce that type-I water molecules embedded in the beryl voids are oriented quite differently from the view established in the literature. Namely, contrary t

Mohammed Abouzaid, Nathaniel Bottman

Categorical symplectic geometry is the study of a rich collection of invariants of symplectic manifolds, including the Fukaya $A_\infty$-category, Floer cohomology, and symplectic cohomology. Beginning with work of Wehrheim and Woodward in the late 2000s, several authors have developed techniques for functorial manipulation of these invariants. We survey the

Yi Liu, Xuan Zhang, Ying Li, Guixin Liang

Video understanding is an important problem in computer vision. Currently, the well-studied task in this research is human action recognition, where the clips are manually trimmed from the long videos, and a single class of human action is assumed for each clip. However, we may face more complicated scenarios in the industrial applications. For example, in t

M. S. Murga, V . V. Akimkin, D. S. Wiebe

Polycyclic aromatic hydrocarbons (PAHs) and fullerenes play a major role in the physics and chemistry of the interstellar medium. Based on a number of recent experimental and theoretical investigations we developed a model in which PAHs are subject to photo-dissociation (carbon and hydrogen loss) and hydrogenation. We take into account that dehydrogenated PA

Federico Zadra, Alessandro Bravetti, Angel Alejandro García-Chung, Marcello Seri

Leveraging techniques from the literature on geometric numerical integration, we propose a new general method to compute exact expressions for the BCH formula. In its utmost generality, the method consists in embedding the Lie algebra of interest into a subalgebra of the algebra of vector fields on some manifold by means of an isomorphism, so that the BCH fo

A. Banerjee, D. Banerjee, G. Kanwar, A. Mariani

A generalization of Wilsonian lattice gauge theory may be obtained by considering the possible self-adjoint extensions of the electric field operator in the Hamiltonian formalism. In the special case of 3D $\mathrm{U}(1)$ gauge theory these are parametrised by a phase $\theta$, and the ordinary Wilson theory is recovered for $\theta=0$. We consider the case

Marcos V. Conde, Radu Timofte, Yibin Huang, Jingyang Peng

Cameras capture sensor RAW images and transform them into pleasant RGB images, suitable for the human eyes, using their integrated Image Signal Processor (ISP). Numerous low-level vision tasks operate in the RAW domain (e.g. image denoising, white balance) due to its linear relationship with the scene irradiance, wide-range of information at 12bits, and sens

Esther Julien, Krzysztof Postek, Ş. İlker Birbil

Two-stage robust optimization problems constitute one of the hardest optimization problem classes. One of the solution approaches to this class of problems is K-adaptability. This approach simultaneously seeks the best partitioning of the uncertainty set of scenarios into K subsets, and optimizes decisions corresponding to each of these subsets. In general c

Zhiwei Hu, Víctor Gutiérrez-Basulto, Zhiliang Xiang, Ru Li

We investigate the knowledge graph entity typing task which aims at inferring plausible entity types. In this paper, we propose a novel Transformer-based Entity Typing (TET) approach, effectively encoding the content of neighbors of an entity. More precisely, TET is composed of three different mechanisms: a local transformer allowing to infer missing types o

Niklas Beisert, Egor Im

Integrability of the one-dimensional Hubbard model and of the factorised scattering problem encountered on the worldsheet of AdS strings can be expressed in terms of a peculiar quantum algebra. In this article, we derive the classical limit of these algebraic integrable structures based on established results for the exceptional simple Lie superalgebra d(2,1

Jürgen Ulbricht, Minghui Liu

Our current understanding of the Big Bang indicates that the universe was not a dimensionless point at $t=0$ but already had four dimensions. This work, exploring physics beyond the Standard Model, abandons the point-like nature of fundamental particles. It comprises theoretical and experimental parts. Theoretically, we present the Empirical Toy Ansatz about

Motiejus Valiunas

We observe that abelian subgroups of Helly groups are finitely generated, and consequently, soluble subgroups of Helly groups are virtually abelian.

Ching-Wei Ho, Ping Zhong

Given a sequence of deterministic matrices $A = A_N$ and a sequence of deterministic nonnegative matrices $\Sigma=\Sigma_N$ such that $A\to a$ and $\Sigma\to \sigma$ in $\ast$-distribution for some operators $a$ and $\sigma$ in a finite von Neumann algebra $\mathcal{A}$. Let $U =U_N$ and $V=V_N$ be independent Haar-distributed unitary matrices. We use free p

Attila Simko, Anders Garpebring, Joakim Jonsson, Tufve Nyholm

Concerns about the reproducibility of deep learning research are more prominent than ever, with no clear solution in sight. The relevance of machine learning research can only be improved if we also employ empirical rigor that incorporates reproducibility guidelines, especially so in the medical imaging field. The Medical Imaging with Deep Learning (MIDL) co

Simone Ciarella, Jeanne Trinquier, Martin Weigt, Francesco Zamponi

Several strategies have been recently proposed in order to improve Monte Carlo sampling efficiency using machine learning tools. Here, we challenge these methods by considering a class of problems that are known to be exponentially hard to sample using conventional local Monte Carlo at low enough temperatures. In particular, we study the antiferromagnetic Po

Patrick Bessel, André Niebur, Daniel Kranz, Jannika Lauth

Heterostructured Au/CuS nanocrystals (NCs) exhibit localized surface plasmon resonance (LSPR) centered at two different wavelengths (551 nm and 1051 nm) with a slight broadening compared to respective homostructured Au and CuS NC spectra. By applying ultrafast transient absorption spectroscopy (TAS) we show that a resonant excitation at the respective LSPR m

Andrea Ventura, Wanda Maria Alberico, Roberta Antolini, Silvia Arezzini

This work presents the ASIMOV Prize for scientific publishing, which was launched in Italy in 2016. The prize aims to bring the young generations closer to scientific culture, through the critical reading of popular science books. The books are selected by a committee that includes scientists, professors, Ph.D. and Ph.D. students, writers, journalists and fr

F. Mercier, J. -P. Ebran, E. Khan

Background: The study of additional terms in functionals is relevant to better describe nuclear structure phenomenology. Among these terms, the tensor one is known to impact nuclear structure properties, especially in neutron-rich nuclei. However, its effect has not been studied on the whole nuclear chart yet. Purpose: The impact of terms corresponding to th

Marcos V. Conde, Ivan Aerlic, Simon Jégou

The Google Universal Image Embedding (GUIE) Challenge is one of the first competitions in multi-domain image representations in the wild, covering a wide distribution of objects: landmarks, artwork, food, etc. This is a fundamental computer vision problem with notable applications in image retrieval, search engines and e-commerce. In this work, we explain ou

Gorka Muñoz-Gil, Alexandre Dauphin, Federica A. Beduini, Alejandro Sánchez de Miguel

The analysis of the colour of artificial lights at night has an impact on diverse fields, but current data sources have either limited resolution or scarce availability of images for a specific region. In this work, we propose crowdsourced photos of streetlights as an alternative data source: for this, we designed NightUp Castelldefels, a pilot for a citizen

Fernando Alonso-Fernandez, Julian Fierrez-Aguilar, Javier Ortega-Garcia

Several works related to information fusion for signature verification have been presented. However, few works have focused on sensor fusion and sensor interoperability. In this paper, these two topics are evaluated for signature verification using two different commercial Tablet PCs. An enrolment strategy using signatures from the two Tablet PCs is also pro

Salvador Linares-Mustarós, Maria Àngels Farreras-Noguer, Núria Arimany-Serrat, Germà Coenders

Due to their type of mathematical construction, the use of standard financial ratios in studies analysing the financial health of a group of firms leads to a series of statistical problems that can invalidate the results obtained. These problems are originated by the asymmetry of financial ratios. The present article justifies the use of a new methodology us

Antonio Terpin, Nicolas Lanzetti, Batuhan Yardim, Florian Dörfler

Policy Optimization (PO) algorithms have been proven particularly suited to handle the high-dimensionality of real-world continuous control tasks. In this context, Trust Region Policy Optimization methods represent a popular approach to stabilize the policy updates. These usually rely on the Kullback-Leibler (KL) divergence to limit the change in the policy.

Saijoscha Heck, Meng Han, Denis Jelovina, Jia-Bao Ji

We present the experimental observation of two-center interference in the ionization time delays of Kr2. Using attosecond electron-ion-coincidence spectroscopy, we simultaneously measure the photoionization delays of krypton monomer and dimer. The relative time delay is found to oscillate as a function of the electron kinetic energy, an effect that is traced

Fernando Alonso-Fernandez, Julian Fierrez-Aguilar, Francisco del-Valle, Javier Ortega-Garcia

On-line signature verification for Tablet PC devices is studied. The on-line signature verification algorithm presented by the authors at the First International Signature Verification Competition (SVC 2004) is adapted to work in Tablet PC environments. An example prototype of securing access and securing document application using this Tablet PC system is a

M. P. Frías, A. Torres, M. D. Ruiz-Medina

This paper introduces a new modeling framework for the statistical analysis of point patterns on a manifold M_{d}, defined by a connected and compact two-point homogeneous space, including the special case of the sphere. The presented approach is based on temporal Cox processes driven by a L^{2}(\mathbb{M}_{d})-valued log-intensity. Different aggregation sch

Paul Mineiro

A confidence sequence (CS) is an anytime-valid sequential inference primitive which produces an adapted sequence of sets for a predictable parameter sequence with a time-uniform coverage guarantee. This work constructs a non-parametric non-asymptotic lower CS for the running average conditional expectation whose slack converges to zero given non-negative rig

Michael Hartisch, Ulf Lorenz

The necessity to deal with uncertain data is a major challenge in decision making. Robust optimization emerged as one of the predominant paradigms to produce solutions that hedge against uncertainty. In order to obtain an even more realistic description of the underlying problem where the decision maker can react to newly disclosed information, multistage mo

Andrei Sipos

Kohlenbach and the author have extracted a rate of metastability for approximate curves associated to continuous pseudocontractive self-mappings in Banach spaces which are uniformly convex and uniformly smooth, whose convergence is due to Reich. In this note, we show that this result may be extended to Reich's original convergence statement involving resolve

Korbinian Kottmann

We perform quantum simulation on classical and quantum computers and set up a machine learning framework in which we can map out phase diagrams of known and unknown quantum many-body systems in an unsupervised fashion. The classical simulations are done with state-of-the-art tensor network methods in one and two spatial dimensions. For one dimensional system

Eduardo Ribeiro, Andreas Uhl, Fernando Alonso-Fernandez

Several recent works have addressed the ability of deep learning to disclose rich, hierarchical and discriminative models for the most diverse purposes. Specifically in the super-resolution field, Convolutional Neural Networks (CNNs) using different deep learning approaches attempt to recover realistic texture and fine grained details from low resolution ima

Martijn Caspers

Let $M_q(H_{\mathbb{R}})$ be the $q$-Gaussian von Neumann algebra associated with a separable infinite dimensional real Hilbert space $H_{\mathbb{R}}$ where $-1 < q < 1$. We show that $M_q(H_{\mathbb{R}}) \not \simeq M_0(H_{\mathbb{R}})$ for $-1 < q \not = 0 < 1$. The C$^\ast$-algebraic counterpart of this result was obtained recently in [BCKW22]. Using idea

Chris N. Self, Sofyan Iblisdir, Gavin K. Brennen, Konstantinos Meichanetzidis

The evaluation of the Jones polynomial at roots of unity is a paradigmatic problem for quantum computers. In this work we present experimental results obtained from existing noisy quantum computers for special cases of this problem, where it is classically tractable. Our approach relies on the reduction of the problem of evaluating the Jones polynomial of a

André Brandenburger, Folker Hoffmann, Alexander Charlish

Reinforcement learning (RL) is already widely applied to applications such as robotics, but it is only sparsely used in sensor management. In this paper, we apply the popular Proximal Policy Optimization (PPO) approach to a multi-agent UAV tracking scenario. While recorded data of real scenarios can accurately reflect the real world, the required amount of d

Lorenzo Valvo, Andrea Carati

In this paper we discuss the results of some Molecular Dynamics simulations of a magnetized One Component Plasma, targeted to estimate the diffusion coefficient $D_{\perp}$ in the plane orthogonal to the magnetic field lines. We find that there exists a threshold with respect to the magnetic field strength $|\vec B|$: for weak magnetic field the diffusion co

Philippe Voinov, Manuel Rigger, Zhendong Su

Software developers frequently refactor code. Often, a single logical refactoring change involves changing multiple related components in a source base such as renaming each occurrence of a variable or function. While many code editors can perform such common and generic refactorings, they do not support more complex refactorings or those that are specific t

Yicheng Hsu, Chenghumg Ma, Mingsian R. Bai

Telepresence aims to create an immersive but virtual experience of the audio and visual scene at the far end for users at the near end. In this contribution, we propose an array-based binaural rendering system that converts the array microphone signals into the head-related transfer function (HRTF) filtered output signals for headphone-rendering. The propose

Andrea Valassi, Taylor Childers, Laurence Field, Stefan Hageböck

Event generators simulate particle interactions using Monte Carlo techniques, providing the primary connection between experiment and theory in experimental high energy physics. These software packages, which are the first step in the simulation worflow of collider experiments, represent approximately 5 to 20% of the annual WLCG usage for the ATLAS and CMS e

Chao Zhang, Yungui Gong, Dicong Liang, Bin Wang

We study eccentric orbits of the Schwarzschild spacetime for extreme mass ratio system (EMRI) in modified gravity theories with additional scalar fields. Due to the additional energy and angular momentum carried away by the scalar field, the orbit of the EMRI in modified gravity decays faster than that in general relativity. The time that it takes the eccent

Saeid Alikhani, Nima Ghanbari, Hassan Zaherifar

Let $G=(V(G),E(G))$ be a simple graph. A set $D\subseteq V(G)$ is a strong dominating set of $G$, if for every vertex $x\in V(G)\setminus D$ there is a vertex $y\in D$ with $xy\in E(G)$ and $deg(x)\leq deg(y)$. The strong domination number $\gamma_{st}(G)$ is defined as the minimum cardinality of a strong dominating set. In this paper, we examine the effects

Tanya Liyaqat, Tanvir Ahmad, Chandni Saxena

Detecting probable Drug Target Interaction (DTI) is a critical task in drug discovery. Conventional DTI studies are expensive, labor-intensive, and take a lot of time, hence there are significant reasons to construct useful computational techniques that may successfully anticipate possible DTIs. Although certain methods have been developed for this cause, nu

Lei Wang, Rongfei Fan

This paper investigates an Internet of Things (IoT) system in which multiple devices are observing some object's physical parameters and then offloading their observations back to the BS in time with opportunistic channel access. Specifically, each device accesses the common channel through contention with a certain probability firstly and then the winner ev

Tzu-Chao Hung, Roberto Robles, Brian Kiraly, Julian H. Strik

Understanding the fundamental mechanisms of optoelectronic excitation and relaxation pathways on the single-molecule level has only recently been started by combining scanning tunneling microscopy (STM) and spectroscopy (STS) with STM-induced luminescence (STML). In this paper, we investigate cationic and anionic fluorescence of individual zinc phthalocyanin

Daniel Chen, Sebastian Ohlig

We explore new connections between complete non-ambiguous trees (CNATs) and permutations. We give a bijection between tree-like tableaux and a specific subset of CNATs. This map is used to establish and solve a recurrence relation for the number of tree-like tableaux of a fixed size without occupied corners, proving a conjecture by Laborde-Zubieta. We end by

Laila Loudiki, Mustapha Kchikech, El Hassan Essaky

The diameter of a graph is the maximum distance among all pairs of vertices. Thus a graph $G$ has diameter $d$ if any two vertices are at distance at most $d$ and there are two vertices at distance $d$. We are interested in studying the diameter of circulant graphs $C_n(1,s)$, i.e., graphs with the set $\{0,1,\ldots, n-1\}$ of integers as vertex set and in w

Peng Zhang, Ben Liu, Jingjing Pan

An iteratively reweighted least squares (IRLS) method is proposed for estimating polyserial and polychoric correlation coefficients in this paper. It iteratively calculates the slopes in a series of weighted linear regression models fitting on conditional expected values. For polyserial correlation coefficient, conditional expectations of the latent predicto

Zahra Babaiee, Lucas Liebenwein, Ramin Hasani, Daniela Rus

Filter pruning of a CNN is typically achieved by applying discrete masks on the CNN's filter weights or activation maps, post-training. Here, we present a new filter-importance-scoring concept named pruning by active attention manipulation (PAAM), that sparsifies the CNN's set of filters through a particular attention mechanism, during-training. PAAM learns

Michael Sucker, Peter Ochs

We apply the PAC-Bayes theory to the setting of learning-to-optimize. To the best of our knowledge, we present the first framework to learn optimization algorithms with provable generalization guarantees (PAC-bounds) and explicit trade-off between a high probability of convergence and a high convergence speed. Even in the limit case, where convergence is gua

N. Dupont, F. Arrouas, L. Gabardos, N. Ombredane

We apply quantum optimal control to shape the phase-space distribution of Bose-Einstein condensates in a one-dimensional optical lattice. By a time-dependent modulation of the lattice position, determined from optimal control theory, we prepare, in the phase space of each lattice site, translated and squeezed Gaussian states, and superpositions of Gaussian s

Henrique Donâncio, Laurent Vercouter, Harald Roclawski

Deep Reinforcement Learning (DRL) has demonstrated impressive results in domains such as games and robotics, where task formulations are well-defined. However, few DRL benchmarks are grounded in complex, real-world environments, where safety constraints, partial observability, and the need for hand-engineered task representations pose significant challenges.

Patrice Le Calvez

We introduce the notion of abstract angle at a couple of points defined by two radial foliations of the closed annulus. We use this notion to give unified proofs of some classical results on area preserving positive twist maps of the annulus by using the Lifting Theorem and the Intermediate Value Theorem.

Yu Zhao, Jianguo Wei, Zhichao Lin, Yueheng Sun

Image-to-text tasks, such as open-ended image captioning and controllable image description, have received extensive attention for decades. Here, we further advance this line of work by presenting Visual Spatial Description (VSD), a new perspective for image-to-text toward spatial semantics. Given an image and two objects inside it, VSD aims to produce one d

Y. C. Lee, H. B. Kim, H. L. Kim, S. K. Kim

We developed a simple small-scale experiment to measure the beta decay spectrum of $^{3}$H. The aim of this research is to investigate the presence of sterile neutrinos in the keV region. Tritium nuclei were embedded in a 1$\times$1$\times$1 cm$^3$ LiF crystal from the $^6$Li(n,$\alpha$)$^3$H reaction. The energy of the beta electrons absorbed in the LiF cry

Nikolaos D. Bagis

We prove certain type symmetric inequalities in $\textbf{R}^{2}$ and $\textbf{R}^3$, that ocur in many problems of analysis. These inequalities are generalizations of the Jensen's inequality from one variable to two and three variables

Jack Jewson, Li Li, Laura Battaglia, Stephen Hansen

We consider two applications where we study how dependence structure between many variables is linked to external network data. We first study the interplay between social media connectedness and the co-evolution of the COVID-19 pandemic across USA counties. We next study study how the dependence between stock market returns across firms relates to similarit

Yun Qin, Fei Zhu, Bo Xi

DNA has immense potential as an emerging data storage medium. The principle of DNA storage is the conversion and flow of digital information between binary code stream, quaternary base, and actual DNA fragments. This process will inevitably introduce errors, posing challenges to accurate data recovery. Sequence reconstruction consists of inferring the DNA re

Ana Carolina Silva, Manuel Barbosa, Mario Florido

This paper presents a proof system for reasoning about execution time bounds for a core imperative programming language. Proof systems are defined for three different scenarios: approximations of the worst-case execution time, exact time reasoning, and less pessimistic execution time estimation using amortized analysis. We define a Hoare logic for the three

Christoph Schultheiss, Peter Bühlmann

We consider likelihood score-based methods for causal discovery in structural causal models. In particular, we focus on Gaussian scoring and analyze the effect of model misspecification in terms of non-Gaussian error distribution. We present a surprising negative result for Gaussian likelihood scoring in combination with nonparametric regression methods.

A. J. Maas, E. Ilin, M. Oshagh, E. Pallé

Although high energetic radiation from flares is a potential threat to exoplanet atmospheres and may lead to surface sterilization, it might also provide the extra energy for low-mass stars needed to trigger and sustain prebiotic chemistry. We investigate two flares on TRAPPIST-1, an ultra-cool dwarf star that hosts seven exoplanets of which three lie within

Gabriel Lord, Andreas Petersson

Efficient simulation of stochastic partial differential equations (SPDE) on general domains requires noise discretization. This paper employs piecewise linear interpolation of noise in a fully discrete finite element approximation of a semilinear stochastic reaction-advection-diffusion equation on a convex polyhedral domain. The Gaussian noise is white in ti

David H. W. Glass, Anne E. Sansom, Timothy A. Davis, Cristina C. Popescu

The content and distribution of cool interstellar medium (ISM, <30K) can indicate the evolutionary mechanisms that transform late type to early type galaxies (ETGs). To investigate this, ALMA observations of 12CO[2-1] line emission were obtained for five dusty ETGs from a complete sample in low-density environments. Four of the ETGs have massive (approximate

Cangxiong Chen, Neill D. F. Campbell

Recent works have demonstrated that it is possible to reconstruct training images and their labels from gradients of an image-classification model when its architecture is known. Unfortunately, there is still an incomplete theoretical understanding of the efficacy and failure of these gradient-leakage attacks. In this paper, we propose a novel framework to a

Christopher S. Jackson

Measuring instruments, especially ones that observe continually over time, have a reality to them that is independent of the states that stimulate their senses. This is the Principle of Instrument Autonomy. Although the mathematical concept of an instrument implicitly embodies this principle, the conventional analysis of continual observation has become over

Ambika Shorny, Hardy Schauffert, James C. Stewart, Sajid Ali

Single photon emitters in two-dimensional (2D) hexagonal boron nitride (hBN) are promising solid-state quantum emitters for photonic applications and quantum networks. Despite their favorable properties, much is still unknown about their characteristics and their atomic origin. We focus on two different kinds of hBN samples that particularly lend themselves

Jeffrey Bergfalk, Martino Lupini, Aristotelis Panagiotopoulos

This is the second installment in a series of papers applying descriptive set theoretic techniques to both analyze and enrich classical functors from homological algebra and algebraic topology. In it, we show that the \v{C}ech cohomology functors $\check{\mathrm{H}}^n$ on the category of locally compact separable metric spaces each factor into (i) what we te

We-Fu Chang

We construct a novel flavor-dependent gauged lepton number $U(1)_\ell$ model for the hierarchical charged lepton masses and the observed $(g-2)_{e,\mu}$. Only tau participates in the tree-level Standard Model ( SM ) Yukawa interaction. At the same time, the masses of electron and muon are light due to radiative generation and(or) the heavy-mediator-suppresse

Naoya Takahashi, Mayank Kumar Singh, Yuki Mitsufuji

Recent progress in deep generative models has improved the quality of voice conversion in the speech domain. However, high-quality singing voice conversion (SVC) of unseen singers remains challenging due to the wider variety of musical expressions in pitch, loudness, and pronunciation. Moreover, singing voices are often recorded with reverb and accompaniment

Sai Raam Venkataraman, S. Balasubramanian, R. Raghunatha Sarma

Transformation-robustness is an important feature for machine learning models that perform image classification. Many methods aim to bestow this property to models by the use of data augmentation strategies, while more formal guarantees are obtained via the use of equivariant models. We recognise that compositional, or part-whole structure is also an importa

Tien-Cuong Bui, Van-Duc Le, Wen-syan Li, Sang Kyun Cha

Graph Neural Networks (GNNs) have become increasingly ubiquitous in numerous applications and systems, necessitating explanations of their predictions, especially when making critical decisions. However, explaining GNNs is challenging due to the complexity of graph data and model execution. Despite additional computational costs, post-hoc explanation approac

Constantin Payerne, Calum Murray, Céline Combet, Cyrille Doux

The abundance of galaxy clusters is a sensitive probe to the amplitude of matter density fluctuations, the total amount of matter in the Universe as well as its expansion history. Inferring correct values and accurate uncertainties of cosmological parameters requires accurate knowledge of cluster abundance statistics, encoded in the likelihood function. In t

Sai Raam Venkataraman, S. Balasubramanian, R. Raghunatha Sarma

Geometric transformations of the training data as well as the test data present challenges to the use of deep neural networks to vision-based learning tasks. In order to address this issue, we present a deep neural network model that exhibits the desirable property of transformation-robustness. Our model, termed RobustCaps, uses group-equivariant convolution

Simone Mayer, Dominik Müller, Frank Kramer

AUCMEDI is a Python-based framework for medical image classification. In this paper, we evaluate the capabilities of AUCMEDI, by applying it to multiple datasets. Datasets were specifically chosen to cover a variety of medical disciplines and imaging modalities. We designed a simple pipeline using Jupyter notebooks and applied it to all datasets. Results sho

Alessio Porretta

We consider Fokker-Planck equations in the whole Euclidean space, driven by Levy processes, under the action of confining drifts, as in the classical Ornstein-Ulhenbeck model. We introduce a new PDE method to get exponential or sub-exponential decay rates, as time goes to infinity, of zero average solutions, under some diffusivity condition on the Levy proce

Rui Zhou, Wenye Zhu, Xiaofei Li

This work proposes a new learning target based on reverberation time shortening (RTS) for speech dereverberation. The learning target for dereverberation is usually set as the direct-path speech or optionally with some early reflections. This type of target suddenly truncates the reverberation, and thus it may not be suitable for network training. The propos

Ismaël Septembre, Julia S. Meyer, Dmitry D. Solnyshkov, Guillaume Malpuech

We study theoretically analog multi-terminal Josephson junctions formed by gapped superfluids created upon resonant pumping of cavity exciton-polaritons. We study the $p$-like bands of a 5-terminal junction in the 4D parameter space created by the superfluid phases acting as quasi-momenta. We find 4/6 Weyl points in 3D subspaces with preserved/broken time-re

Angel Romero, Shreedhar Govil, Gonca Yilmaz, Yunlong Song

Recently, Model Predictive Contouring Control (MPCC) has arisen as the state-of-the-art approach for model-based agile flight. MPCC benefits from great flexibility in trading-off between progress maximization and path following at runtime without relying on globally optimized trajectories. However, finding the optimal set of tuning parameters for MPCC is cha

K. Sakai, T. Nishimoto, S. Isayama, S. Matsukiyo

We theoretically and numerically investigate the ion-acoustic features of collective Thomson scattering (CTS) in two-stream plasmas. When the electron distribution functions of two (stationary and moving) components overlap with each other at the phase velocities corresponding to the two resonant peaks of the ion-acoustic feature, the theoretical spectrum sh

Takahiko Matsubara

In the previous paper [arXiv:2210.10435], the nonlinear perturbation theory of cosmological density field is generalized to include the tensor-valued bias of astronomical objects, such as spins and shapes of galaxies and any other tensors of arbitrary ranks which are associated with objects that we can observe. We apply this newly developed method to explici

Adrià Mallorquí, Agustín Zaballos, Alan Briones, Guiomar Corral

The SHETLAND-NET research project aims to develop an Internet of Things (IoT) telemetry service in Antarctica by interconnecting Wireless Sensor Networks (WSN) through Near Vertical Incidence Skywave radio links (NVIS) that build a Long Fat Network (LFN). This architecture presents some typical properties of the so-called challenging networks, which require

Yu Hamada, Ryuichiro Kitano, Ryutaro Matsudo, Hiromasa Takaura

Expected precisions of measurements of the elastic scattering cross sections are estimated for $\mu^+ \mu^+$ and $\mu^+ e^-$ colliders, which are recently proposed as future realistic possibilities ($\mu$TRISTAN). Comparing with contributions from possible new physics represented by higher dimensional operators, we find that the measurements at a TeV energy

Xiaoyi Chen, Baisong Xin, Shengfang Zhai, Shiqing Ma

This paper finds that contrastive learning can produce superior sentence embeddings for pre-trained models but is also vulnerable to backdoor attacks. We present the first backdoor attack framework, BadCSE, for state-of-the-art sentence embeddings under supervised and unsupervised learning settings. The attack manipulates the construction of positive and neg

Daniel Irving Bernstein, Sean Dewar, Steven J. Gortler, Anthony Nixon

The maximum likelihood threshold (MLT) of a graph $G$ is the minimum number of samples to almost surely guarantee existence of the maximum likelihood estimate in the corresponding Gaussian graphical model. Recently a new characterization of the MLT in terms of rigidity-theoretic properties of $G$ was proved \cite{Betal}. This characterization was then used t

Zeying Zhang, Weikang Wu, Gui-Bin Liu, Zhi-Ming Yu

We present a systematic classification of emergent particles in all 528 magnetic layer groups and 394 magnetic rod groups, which describe two-dimensional and one-dimensional crystals respectively. Our approach is via constructing a correspondence between a given magnetic layer/rod group and one of the magnetic space group, such that all irreducible represent

Yonglong Li, Christoph Hirche, Marco Tomamichel

We consider the sequential quantum channel discrimination problem using adaptive and non-adaptive strategies. In this setting the number of uses of the underlying quantum channel is not fixed but a random variable that is either bounded in expectation or with high probability. We show that both types of error probabilities decrease to zero exponentially fast

Zeyue Xue, Jianming Liang, Guanglu Song, Zhuofan Zong

Training a large-scale deep neural network in a large-scale dataset is challenging and time-consuming. The recent breakthrough of large-batch optimization is a promising way to tackle this challenge. However, although the current advanced algorithms such as LARS and LAMB succeed in classification models, the complicated pipelines of dense visual predictions

Decmend Fang-Jie Ling, Tetsuya Hashimoto, Shotaro Yamasaki, Tomotsugu Goto

Fast radio bursts (FRBs) are intense bursts of radio emission with durations of milliseconds. Although researchers have found them happening frequently all over the sky, they are still in the dark to understand what causes the phenomena because the existing radio observatories have encountered certain challenges during the discovery of FRB progenitors. The c

Eleonora Denich, Laura Grazia Dolce, Paolo Novati

This paper introduces a very fast method for the computation of the resolvent of fractional powers of operators. The analysis is kept in the continuous setting of (potentially unbounded) self adjoint positive operators in Hilbert spaces. The method is based on the Gauss-Laguerre rule, exploiting a particular integral representation of the resolvent. We provi