November 2022 arXiv papers — page 161

Lara I. Allen, Tamas G. Molnar, Zoltan Dombovari, S. John Hogan

In this paper, we analyse the celebrated Haken-Kelso-Bunz (HKB) model, describing the dynamics of bimanual coordination, in the presence of delay. We study the linear dynamics, stability, nonlinear behaviour and bifurcations of this model by both theoretical and numerical analysis. We calculate in-phase and anti-phase limit cycles as well as quasi-periodic s

Ignacio Magaña Hernandez

Alternative theories of gravity predict up to six distinct polarization modes for gravitational waves. Strong gravitational lensing of gravitational waves allows us to probe the polarization content of these signals by effectively increasing the number of observations from the same astrophysical source. The lensing time delays due to the multiple observed le

Maximilian Rothammer, Dominic T. Meiers, Maximilian Maier, Georg von Freymann

Novel bifunctional cellulose diacetate derivatives were synthesized in order to achieve bio-based photoresists, which can be structured by two-photon absorption via direct laser writing (DLW) without the need to use a photoinitiator. Therefore, cellulose diacetate is functionalized with thiol moieties and olefinic or methacrylic side groups enabling thiol-co

Shafika Showkat Moni, Deepti Gupta

With the ubiquitous advancement in smart medical devices and systems, the potential of Remote Patient Monitoring (RPM) network is evolving in modern healthcare systems. The medical professionals (doctors, nurses, or medical experts) can access vitals and sensitive physiological information about the patients and provide proper treatment to improve the qualit

Tobias Kaiser

Periods are defined as integrals of semialgebraic functions defined over the rationals. Periods form a countable ring not much is known about. Examples are given by taking the antiderivative of a power series which is algebraic over the polynomial ring over the rationals and evaluate it at a rational number. We follow this path and close these algebraic powe

Fabio M. Mele, Johannes Münch

In spatially non-compact minisuperpace models, spatial integrals in the Hamiltonian and symplectic form must be regularised by confining them to a finite volume $V_o$, known as the fiducial cell. As this restriction is unnecessary in the complete theory before homogeneous reduction, the physical significance of the fiducial cell has been largely debated, esp

Yujie Qian, Jinhyuk Lee, Sai Meher Karthik Duddu, Zhuyun Dai

Multi-vector retrieval models improve over single-vector dual encoders on many information retrieval tasks. In this paper, we cast the multi-vector retrieval problem as sparse alignment between query and document tokens. We propose AligneR, a novel multi-vector retrieval model that learns sparsified pairwise alignments between query and document tokens (e.g.

Peng Zhang, Yawen Huang, Bingzhang Hu, Shizheng Wang

Reinforcement Learning (RL)-based control system has received considerable attention in recent decades. However, in many real-world problems, such as Batch Process Control, the environment is uncertain, which requires expensive interaction to acquire the state and reward values. In this paper, we present a cost-efficient framework, such that the RL model can

Daniel Goodair, Dan Crisan

We prove the existence and uniqueness of maximal solutions to the 3D SALT (Stochastic Advection by Lie Transport, [Holm arXiv:1410.8311]) Navier-Stokes Equation in velocity and vorticity form, on the torus and the bounded domain respectively. The current work partners the paper [Goodair et al, arXiv:2209.09137] as an application of the abstract framework pre

Z. Z. Li, Y. F. Niu, G. Colò

"Why is the EoS for tin so soft?" is a longstanding question, which prevents us from determining the nuclear incompressibility $K_\infty$ accurately. To solve this puzzle, a fully self-consistent quasiparticle random phase approximation (QRPA) plus quasiparticle-vibration coupling (QPVC) approach based on Skyrme-Hartree-Fock-Bogoliubov is developed. We show

Chao-Han Huck Yang, Bo Li, Yu Zhang, Nanxin Chen

We propose a quantum kernel learning (QKL) framework to address the inherent data sparsity issues often encountered in training large-scare acoustic models in low-resource scenarios. We project acoustic features based on classical-to-quantum feature encoding. Different from existing quantum convolution techniques, we utilize QKL with features in the quantum

Oussama Messai, Aladine Chetouani

Deep learning-based quality metrics have recently given significant improvement in Image Quality Assessment (IQA). In the field of stereoscopic vision, information is evenly distributed with slight disparity to the left and right eyes. However, due to asymmetric distortion, the objective quality ratings for the left and right images would differ, necessitati

Esther López, Irene Artacho, Alejandro Datas

A standardized method for measuring thermophotovoltaic (TPV) efficiency has not been yet established, which makes the reported results difficult to compare. Besides, most of the TPV efficiencies reported to date have been obtained using small view factors, i.e., large cell-to-emitter distances, so the impact of the series resistance is usually underestimated

Tobias Schnabel

Various studies in recent years have pointed out large issues in the offline evaluation of recommender systems, making it difficult to assess whether true progress has been made. However, there has been little research into what set of practices should serve as a starting point during experimentation. In this paper, we examine four larger issues in recommend

Felix Härer

The execution of decentralized applications on blockchains is limited today by technical and organizational barriers, including scalability and the high complexity to specify execution correctly for developers as well as for domain experts in organizations. Overcoming these limitations could allow for decentralized coordination beyond data, where distributed

Gaurav Gyawali, Mabrur Ahmed, Eric Aspling, Luke Ellert-Beck

Quantum computers and simulators promise to enable the study of strongly correlated quantum systems. Yet, surprisingly, it is hard for them to compute ground states. They can, however, efficiently compute the dynamics of closed quantum systems. We propose a method to study the quantum thermodynamics of strongly correlated electrons from quantum dynamics. We

Songyan Hou, Parnian Kassraie, Anastasis Kratsios, Andreas Krause

Existing generalization bounds fail to explain crucial factors that drive the generalization of modern neural networks. Since such bounds often hold uniformly over all parameters, they suffer from over-parametrization and fail to account for the strong inductive bias of initialization and stochastic gradient descent. As an alternative, we propose a novel opt

James Townsend, Jan-Willem van de Meent

Lossless compression implementations typically contain two programs, an encoder and a decoder, which are required to be inverse to one another. We observe that a significant class of compression methods, based on asymmetric numeral systems (ANS), have shared structure between the encoder and decoder -- the decoder program is the 'reverse' of the encoder prog

Lutz Rapp

Existing unrepeatered submarine links are increasingly upgraded to the most advanced modulation format currently available for commercial applications. Quite often the use of third-order codirectional Raman amplifiers is necessary. Power fluctuations of the involved high-power pump induces phase shifts in phase modulated signals via the nonlinear Kerr effect

Mohsin Bilal, Robert Jewsbury, Ruoyu Wang, Hammam M. AlGhamdi

Image analysis and machine learning algorithms operating on multi-gigapixel whole-slide images (WSIs) often process a large number of tiles (sub-images) and require aggregating predictions from the tiles in order to predict WSI-level labels. In this paper, we present a review of existing literature on various types of aggregation methods with a view to help

Dingzhu Wen, Xiang Jiao, Peixi Liu, Guangxu Zhu

Departing from the classic paradigm of data-centric designs, the 6G networks for supporting edge AI features task-oriented techniques that focus on effective and efficient execution of AI task. Targeting end-to-end system performance, such techniques are sophisticated as they aim to seamlessly integrate sensing (data acquisition), communication (data transmi

Ethan H. Nguyen, Haichun Yang, Zuhayr Asad, Ruining Deng

Circle representation has recently been introduced as a medical imaging optimized representation for more effective instance object detection on ball-shaped medical objects. With its superior performance on instance detection, it is appealing to extend the circle representation to instance medical object segmentation. In this work, we propose CircleSnake, a

Yi Zhang, Jitao Sang, Junyang Wang

Deep learning models often learn to make predictions that rely on sensitive social attributes like gender and race, which poses significant fairness risks, especially in societal applications, e.g., hiring, banking, and criminal justice. Existing work tackles this issue by minimizing information about social attributes in models for debiasing. However, the h

Austin K. Daniel, Akimasa Miyake

The limited computational power of constant-depth quantum circuits can be boosted by adapting future gates according to the outcomes of mid-circuit measurements. We formulate computation of a variety of Boolean functions in the framework of adaptive measurement-based quantum computation using a cluster state resource and a classical side-processor that can a

Saisab Bhowmik, Bhaskar Ghawri, Youngju Park, Dongkyu Lee

New phases of matter can be stabilized by a combination of diverging electronic density of states, strong interactions, and spin-orbit coupling. Recent experiments in magic-angle twisted bilayer graphene (TBG) have uncovered a wealth of novel phases as a result of interaction-driven spin-valley flavour polarization. In this work, we explore correlated phases

Manuel H. Muñoz-Arias, Ivan H. Deutsch, Pablo M. Poggi

We revisit well-known protocols in quantum metrology using collective spins and propose a unifying picture for optimal state preparation based on a semiclassical description in phase space. We show how this framework allows for quantitative predictions of the timescales required to prepare various metrologically useful states, and that these predictions rema

Sihao Huang, Alexander F. Siegenfeld, Andrew Gelman

Democracies employ elections at various scales to select officials at the corresponding levels of administration. The geographical distribution of political opinion, the policy issues delegated to each level, and the multilevel interactions between elections can all greatly impact the makeup of these representative bodies. This perspective is not new: the ad

Leonardo Nagami Coregliano, Fernando Granha Jeronimo, Chris Jones

Determining the maximum size $A_2(n,d)$ of a binary code of blocklength $n$ and distance $d$ remains an elusive open question even when restricted to the important class of linear codes. Recently, two linear programming hierarchies extending Delsarte's LP were independently proposed to upper bound $A_2^{\text{Lin}}(n,d)$ (the analogue of $A_2(n,d)$ for linea

Filipe Kelmer, Keti Tenenblat

We provide superposition formulae for the six cases of B\"acklund transformations corresponding to space-like and time-like surfaces in the 3-dimensional pseudo-Euclidean space. In each case, the surfaces have constant negative or positive Gaussian curvature and they correspond to solutions of one of the following equations: the sine-Gordon, the sinh-Gordon,

Vasista Sai Lodagala, Sreyan Ghosh, S. Umesh

In this paper, we propose a new Self-Supervised Learning (SSL) algorithm called data2vec-aqc, for speech representation learning from unlabeled speech data. Our goal is to improve SSL for speech in domains where both unlabeled and labeled data are limited. Building on the recently introduced data2vec, we introduce additional modules to the data2vec framework

Peipei Tang, Bo Jiang, Chengjing Wang

Given a dissimilarity matrix, the metric nearness problem is to find the nearest matrix of distances that satisfy the triangle inequalities. This problem has wide applications, such as sensor networks, image processing, and so on. But it is of great challenge even to obtain a moderately accurate solution due to the $O(n^{3})$ metric constraints and the nonsm

Alexandre Devillers, Mathieu Lefort

Self-supervised visual representation methods are closing the gap with supervised learning performance. These methods rely on maximizing the similarity between embeddings of related synthetic inputs created through data augmentations. This can be seen as a task that encourages embeddings to leave out factors modified by these augmentations, i.e. to be invari

Caio C. Holanda Ribeiro, Uwe R. Fischer

We consider a quasi-one-dimensional dipolar condensate in an analogue black hole setup. It is shown that the existence of a roton minimum in the condensate dispersion relation leaves deep imprints onto the Hawking radiation spectrum. In particular, the emitted radiation can be either more intense or suppressed, depending on the depth of the roton minimum in

Jan Martin Nordbotten, Endre Joachim Lerheim Mossige

'A basic and basically unsolved problem in fluid dynamics is to determine the evolution of rising bubbles and falling drops of one miscible liquid in another' [1]. Here, we address this important literature gap and present the first theory predicting the velocity, volume and composition of such drops at low Reynolds numbers. For the case where the diffusion

Le Xia, Yao Sun, Chengsi Liang, Daquan Feng

Virtual reality (VR) over wireless is expected to be one of the killer applications in next-generation communication networks. Nevertheless, the huge data volume along with stringent requirements on latency and reliability under limited bandwidth resources makes untethered wireless VR delivery increasingly challenging. Such bottlenecks, therefore, motivate t

Le Xie, Tong Huang, Xiangtian Zheng, Yan Liu

The transition towards carbon-neutral electricity is one of the biggest game changers in addressing climate change since it addresses the dual challenges of removing carbon emissions from the two largest sectors of emitters: electricity and transportation. The transition to a carbon-neutral electric grid poses significant challenges to conventional paradigms

George Samartzis, Nikitas Pittis

We study the necessary and sufficient conditions under which the Mean-Variance Criterion (MVC) is equivalent to the Maximum Expected Utility Criterion (MEUC), for two lotteries. Based on Chamberlain (1983), we conclude that the MVC is equivalent to the Second-order Stochastic Dominance Rule (SSDR) under any symmetric Elliptical distribution. We then discuss

Imogen Towler, Scott Kay, Edoardo Altamura

We use the MACSIS hydrodynamical simulations to estimate the extent of gas clumping in the intracluster medium of massive galaxy clusters and how it affects the hydrostatic mass bias. By comparing the clumping to the azimuthal scatter in the emission measure, an observational proxy, we find that they both increase with radius and are larger in higher-mass an

Marcus Riesmeier, Frank Woittennek

Late-lumping feedback design for infinite-dimensional linear systems with unbounded input operators is considered. The proposed scheme is suitable for the approximation of backstepping and flatness-based designs and relies on a decomposition of the feedback into a bounded and an unbounded part. Approximation applies to the bounded part only, while the unboun

Sanja Rukavina, Vladimir D. Tonchev

In this paper we analyze possible actions of an automorphism of order six on a $2$-$(70, 24, 8)$ design, and give a complete classification for the action of the cyclic automorphism group of order six $G= \langle \rho \rangle \cong Z_6 \cong Z_2 \times Z_3$ where $\rho^3$ fixes exactly $14$ points (blocks) and $\rho^2$ fixes $4$ points (blocks). Up to isomor

Kosio Beshkov, Jonas Verhellen, Mikkel Elle Lepperød

Artificial and biological agents cannon learn given completely random and unstructured data. The structure of data is encoded in the metric relationships between data points. In the context of neural networks, neuronal activity within a layer forms a representation reflecting the transformation that the layer implements on its inputs. In order to utilize the

Panu Lahti

In a complete metric space equipped with a doubling measure and supporting a $(1,1)$-Poincar\'e inequality, we show that every set satisfying a suitable capacitary density condition is removable for Newton-Sobolev functions.

Matteo Vaghi, Augusto Luis Ballardini, Simone Fontana, Domenico Giorgio Sorrenti

Camera localization, i.e., camera pose regression, represents an important task in computer vision since it has many practical applications such as in the context of intelligent vehicles and their localization. Having reliable estimates of the regression uncertainties is also important, as it would allow us to catch dangerous localization failures. In the li

Mattie Tesfaldet, Derek Nowrouzezahrai, Christopher Pal

Recent extensions of Cellular Automata (CA) have incorporated key ideas from modern deep learning, dramatically extending their capabilities and catalyzing a new family of Neural Cellular Automata (NCA) techniques. Inspired by Transformer-based architectures, our work presents a new class of $\textit{attention-based}$ NCAs formed using a spatially localized$

Ava Polzin, Raffaella Margutti, Deanne Coppejans, Katie Auchettl

We present a detailed compilation and analysis of the X-ray phase space of low- to intermediate-redshift ($ 0\le z \le 1$) transients that consolidates observed light curves (and theory where necessary) for a large variety of classes of transient/variable phenomena in the 0.3--10 keV energy band. We include gamma-ray burst afterglows, supernovae, supernova s

Giannis Delimpaltadakis, Morteza Lahijanian, Manuel Mazo, Luca Laurenti

Interval Markov Decision Processes (IMDPs) are finite-state uncertain Markov models, where the transition probabilities belong to intervals. Recently, there has been a surge of research on employing IMDPs as abstractions of stochastic systems for control synthesis. However, due to the absence of algorithms for synthesis over IMDPs with continuous action-spac

Kathinka Gerlinger, Bastian Pfau, Martin Hennecke, Lisa-Marie Kern

We present time-resolved scanning x-ray microscopy measurements with picosecond photo-excitation via a tailored infrared pump laser at a scanning transmission x-ray microscope. Specifically, we image the laser-induced demagnetization and remagnetization of thin ferrimagnetic GdFe films proceeding on a few nanoseconds time scale. Controlling the heat load on

Ruming Zhang

In the author's previous paper (Zhang et al. 2022), exponential convergence was proved for the perfectly matched layers (PML) approximation of scattering problems with periodic surfaces in 2D. However, due to the overlapping of singularities, an exceptional case, i.e., when the wave number is a half integer, has to be excluded in the proof. However, numerica

Nenghan Wan, Xiaomao Lin, Roger A. Pielke, Xubin Zeng

Global responses of the hydrological cycle to climate change have been widely studied but uncertainties of temperature responses to lower-tropospheric water vapor still remain. Here, we investigate the trends in global total precipitable water (TPW) and surface temperature from 1958 to 2021 using improved ERA5 and JRA-55 reanalysis datasets and further valid

Johannes Hertrich, Manuel Gräf, Robert Beinert, Gabriele Steidl

The aim of this paper is twofold. Based on the geometric Wasserstein tangent space, we first introduce Wasserstein steepest descent flows. These are locally absolutely continuous curves in the Wasserstein space whose tangent vectors point into a steepest descent direction of a given functional. This allows the use of Euler forward schemes instead of Jordan--

Yu Gui, Rohan Hore, Zhimei Ren, Rina Foygel Barber

This paper introduces an assumption-lean method that constructs valid and efficient lower predictive bounds (LPBs) for survival times with censored data. We build on recent work by Cand\`es et al. (2021), whose approach first subsets the data to discard any data points with early censoring times, and then uses a reweighting technique (namely, weighted confor

Feisi Fu, Panagiota Kiourti, Wenchao Li

We present a novel methodology for neural network backdoor attacks. Unlike existing training-time attacks where the Trojaned network would respond to the Trojan trigger after training, our approach inserts a Trojan that will remain dormant until it is activated. The activation is realized through a specific perturbation to the network's weight parameters onl

Maryam Toloubidokhti, Nilesh Kumar, Zhiyuan Li, Prashnna K. Gyawali

Prior knowledge about the imaging physics provides a mechanistic forward operator that plays an important role in image reconstruction, although myriad sources of possible errors in the operator could negatively impact the reconstruction solutions. In this work, we propose to embed the traditional mechanistic forward operator inside a neural function, and fo

Artem Reshetnikov, Maria-Cristina Marinescu, Joaquim More Lopez

Large datasets that were made publicly available to the research community over the last 20 years have been a key enabling factor for the advances in deep learning algorithms for NLP or computer vision. These datasets are generally pairs of aligned image / manually annotated metadata, where images are photographs of everyday life. Scholarly and historical co

Xiong Hui, Zijun He, Yangjie Liu

In this work, we present the design of cycloidal waveguides from a gradient refractive index (GRIN) medium in analogy to the fastest descending problem in classical mechanics. Light rays propagate along cycloids in this medium, of which the refractive index can be determined from relating to the descending speed under gravity force. It can be used as GRIN le

Douglas A. Creager, Hendrik van Antwerpen

We present stack graphs, an extension of Visser et al.'s scope graphs framework. Stack graphs power Precise Code Navigation at GitHub, allowing users to navigate name binding references both within and across repositories. Like scope graphs, stack graphs encode the name binding information about a program in a graph structure, in which paths represent valid

Tong Xu, Yiming Li, Yong Jiang, Shu-Tao Xia

Deep neural networks (DNNs) are vulnerable to backdoor attacks. The backdoor adversaries intend to maliciously control the predictions of attacked DNNs by injecting hidden backdoors that can be activated by adversary-specified trigger patterns during the training process. One recent research revealed that most of the existing attacks failed in the real physi

Felix Kreuk, Yaniv Taigman, Adam Polyak, Jade Copet

In this work, we study the task of Audio Language Modeling, in which we aim at learning probabilistic models for audio that can be used for generation and completion. We use a state-of-the-art perceptually-guided audio compression model, to encode audio to discrete representations. Next, we train a transformer-based causal language model using these represen

Ruming Zhang

The perfectly matched layer (PML) is a very popular tool in the truncation of wave scattering in unbounded domains. In Chandler-Wilde & Monk et al. 2009, the author proposed a conjecture that for scattering problems with rough surfaces, the PML converges exponentially with respect to the PML parameter in any compact subset. In the author's previous paper (Zh

Rom Gutman, Ehud Karavani, Yishai Shimoni

Theoretical guarantees for causal inference using propensity scores are partly based on the scores behaving like conditional probabilities. However, scores between zero and one, especially when outputted by flexible statistical estimators, do not necessarily behave like probabilities. We perform a simulation study to assess the error in estimating the averag

Yizhou Zhao, Hua Sun

A collection of $K$ random variables are called $(K,n)$-MDS if any $n$ of the $K$ variables are independent and determine all remaining variables. In the MDS variable generation problem, $K$ users wish to generate variables that are $(K,n)$-MDS using a randomness variable owned by each user. We show that to generate $1$ bit of $(K,n)$-MDS variables for each

Segun Goh, Andreas M. Menzel, René Wittmann, Hartmut Löwen

Magnetic gels are composite materials, consisting of a polymer matrix and embedded magnetic particles. Those are mechanically coupled to each other, giving rise to the magnetostrictive effects as well as to a controllable overall elasticity responsive to external magnetic fields. Due to their inherent composite and thereby multiscale nature, a theoretical fr

Qinfeng Li, Changyou Wang

Let $\mu>0$ be a fixed constant, and we prove that minimizers to the following energy functional \begin{align*} E_f(u,\Omega):=\int_{\Omega}|\nabla u|^2+\mu P(\Omega) \end{align*}exist among pairs $(\Omega,u)$ such that $\Omega$ is an $M$-uniform domain with finite perimeter and fixed volume, and $u \in H^1(\Omega,\mathbb{S}^2)$ with $u =\nu_{\Omega}$, the m

Pavel Kuriščák, Pedro Rossa, Horácio Fernandes, João Nuno Silva

Remote Controlled laboratories is a teaching and learning tool that increasingly becomes fundamental in the teaching and learning processes at all the levels. A study of available systems highlights a series of limitations on the used programming languages, overall architecture and network communication patterns that, that hinder these systems to be further

ATLAS Collaboration

Constraints on the Higgs boson self-coupling are set by combining double-Higgs boson analyses in the $b\bar{b}b\bar{b}$, $b\bar{b}\tau^+\tau^-$ and $b\bar{b} \gamma \gamma$ decay channels with single-Higgs boson analyses targeting the $\gamma \gamma$, $ZZ^*$, $WW^*$, $\tau^+ \tau^-$ and $b\bar{b}$ decay channels. The data used in these analyses were recorded

T. Lévèque, C. Fallet, J. Lefebve, A. Piquereau

A strong potential gain for space applications is expected from the anticipated performances of inertial sensors based on cold atom interferometry (CAI) that measure the acceleration of freely falling independent atoms by manipulating them with laser light. In this context, CNES and its partners initiated a phase 0 study, called CARIOQA, in order to develop

Jong-whi Lee, Jinhong Jung

How can we augment a dynamic graph for improving the performance of dynamic graph neural networks? Graph augmentation has been widely utilized to boost the learning performance of GNN-based models. However, most existing approaches only enhance spatial structure within an input static graph by transforming the graph, and do not consider dynamics caused by ti

Hayoung Seong, Junseon Kim, Won-Yong Shin, Howon Lee

Massive Internet of Things (IoT) networks have a wide range of applications, including but not limited to the rapid delivery of emergency and disaster messages. Although various benchmark algorithms have been developed to date for message delivery in such applications, they pose several practical challenges such as insufficient network coverage and/or highly

Shengbing Deng, Junwei Yu

In this paper, we study the existence of normalized solutions to the following nonlinear Choquard equation with exponential growth \begin{align*} \left\{ \begin{aligned} &-\Delta u+\lambda u=(I_{\alpha}\ast F(u))f(u), \quad \quad \hbox{in }\mathbb{R}^{2},\\ &\int_{\mathbb{R}^{2}}|u|^{2}dx=a^{2}, \end{aligned} \right. \end{align*} where $a>0$ is prescribed, $

Tonatiuh Matos

The Scalar Field Dark Matter (SFDM) model, also called Fuzzy, Wave, Bose-Einstein, Ultra-light Dark Matter, has received a lot of attention because it has been able to provide simpler and more natural explanations for various features of galaxies, such as the number of satellite galaxies and the cusp-core problem. We recently showed that this model is able t

Ying Wang, Valeria Lauter, Olga Maximova, Shiva T. Konakanchi

Spatial confinement of electronic topological surface states (TSS) in topological insulators poses a formidable challenge because TSS are protected by time-reversal symmetry. In previous works formation of a gap in the electronic spectrum of TSS has been successfully demonstrated in topological insulator/magnetic material heterostructures, where ferromagneti

N. I. Shvetsov-Shilovski, M. Lein

We investigate the application of deep learning to the retrieval of the internuclear distance in the two-dimensional H$_2^{+}$ molecule from the momentum distribution of photoelectrons produced by strong-field ionization. We study the effect of the carrier-envelope phase on the prediction of the internuclear distance with a convolutional neural network. We a

Sheng Yang, Yiming Li, Yong Jiang, Shu-Tao Xia

Recent studies have demonstrated that deep neural networks (DNNs) are vulnerable to backdoor attacks during the training process. Specifically, the adversaries intend to embed hidden backdoors in DNNs so that malicious model predictions can be activated through pre-defined trigger patterns. In this paper, we explore the backdoor mechanism from the angle of t

Mason R. Calbert, Ryan E. Dougherty

A \emph{covering array} is an $N \times k$ array of elements from a $v$-ary alphabet such that every $N \times t$ subarray contains all $v^t$ tuples from the alphabet of size $t$ at least $\lambda$ times; this is denoted as $\CA_\lambda(N; t, k, v)$. Covering arrays have applications in the testing of large-scale complex systems; in systems that are nondeter

Mikhail Danilov

There are several experimental indications of sterile neutrinos with a mass in the 1 eV ballpark and many experiments are trying to clarify the situation. During 6 years the DANSS experiment collected more than 6 million Inverse Beta Decay (IBD) events and measured the background level during 4 reactor-off periods. Data were collected at 3 distances (10.9 m,

Alperen Kantarcı, Ferda Ofli, Muhammad Imran, Hazım Kemal Ekenel

In December 2019, a novel coronavirus (COVID-19) spread so quickly around the world that many countries had to set mandatory face mask rules in public areas to reduce the transmission of the virus. To monitor public adherence, researchers aimed to rapidly develop efficient systems that can detect faces with masks automatically. However, the lack of represent

A. Bailey, M. Vincenzi, D. Scolnic, J. -C. Cuillandre

The $Euclid$ mission will provide first-of-its-kind coverage in the near-infrared over deep (three fields, $\sim$10-20 square degrees each) and wide ($\sim$10000 square degrees) fields. While the survey is not designed to discover transients, the deep fields will have repeated observations over a two-week span, followed by a gap of roughly six months. In thi

Abhishek Vivekanandan, Niels Maier, J. Marius Zoellner

Environmental perception obtained via object detectors have no predictable safety layer encoded into their model schema, which creates the question of trustworthiness about the system's prediction. As can be seen from recent adversarial attacks, most of the current object detection networks are vulnerable to input tampering, which in the real world could com

Sudeep Pasricha

This article reviews the landscape of ethical challenges of integrating artificial intelligence (AI) into smart healthcare products, including medical electronic devices. Differences between traditional ethics in the medical domain and emerging ethical challenges with AI-driven healthcare are presented, particularly as they relate to transparency, bias, priv

Zhiyong Su, Chao Chu, Long Chen, Yong Li

Objective geometry quality assessment of point clouds is essential to evaluate the performance of a wide range of point cloud-based solutions, such as denoising, simplification, reconstruction, and watermarking. Existing point cloud quality assessment (PCQA) methods dedicate to assigning absolute quality scores to distorted point clouds. Their performance is

Johannes Voigt, Holger Jäkel, Laurent Schmalen

Recursive projection aggregation (RPA) decoding as introduced in [1] is a novel decoding algorithm which performs close to the maximum likelihood decoder for short-length Reed-Muller codes. Recently, an extension to RPA decoding, called sparse multi-decoder RPA (SRPA), has been proposed [2]. The SRPA approach makes use of multiple pruned RPA decoders to lowe

Yung-Ting Lee, Po-Tuan Chen, Zheng-Hong Li, Jyun-Yu Wu

The formation of a charge density wave (CDW) in two-dimensional (2D) materials caused by Peierls instability is a controversial topic. This study investigates the extensively debated role of Fermi surface nesting in causing the CDW state in 2H-NbSe$_{2}$ materials. Four NbSe$_{2}$ structures (i.e., normal, stripe, filled, and hollow structures) are identifie

Katherine M. Collins, Umang Bhatt, Weiyang Liu, Vihari Piratla

Aligning model representations to humans has been found to improve robustness and generalization. However, such methods often focus on standard observational data. Synthetic data is proliferating and powering many advances in machine learning; yet, it is not always clear whether synthetic labels are perceptually aligned to humans -- rendering it likely model

Lukas Muttenthaler, Jonas Dippel, Lorenz Linhardt, Robert A. Vandermeulen

Today's computer vision models achieve human or near-human level performance across a wide variety of vision tasks. However, their architectures, data, and learning algorithms differ in numerous ways from those that give rise to human vision. In this paper, we investigate the factors that affect the alignment between the representations learned by neural net

Mingqi Li, Fei Ding, Dan Zhang, Long Cheng

Pre-trained multilingual language models play an important role in cross-lingual natural language understanding tasks. However, existing methods did not focus on learning the semantic structure of representation, and thus could not optimize their performance. In this paper, we propose Multi-level Multilingual Knowledge Distillation (MMKD), a novel method for

Takuya Fujimura, Tomoki Toda

Deep neural network (DNN)-based speech enhancement usually uses a clean speech as a training target. However, it is hard to collect large amounts of clean speech because the recording is very costly. In other words, the performance of current speech enhancement has been limited by the amount of training data. To relax this limitation, Noisy-target Training (

ALICE Collaboration

Two-particle correlations with ${\rm K}^{0}_{\rm{S}}$, $\Lambda$/$\bar{\Lambda}$, and charged hadrons as trigger particles in the transverse momentum range $8 < p_\mathrm{T,trig}<16$ GeV/$c$, and associated charged particles within $1 < p_\mathrm{T,assoc}<8$ GeV/$c$, are studied at mid-rapidity in pp and central Pb-Pb collisions at a centre-of-mass energy pe

Xu Zhang, Donghai Ji

The Umehara algebra is studied with motivation on the problem of the non-existence of common complex submanifolds. In this paper, we prove some new results in Umehara algebra and obtain some applications. In particular, if a complex manifolds admits a holomorphic polynomial isometric immersion to one indefinite complex space form, then it cannot admits a hol

Xiangming Meng, Yoshiyuki Kabashima

We consider the general problem of recovering a high-dimensional signal from noisy quantized measurements. Quantization, especially coarse quantization such as 1-bit sign measurements, leads to severe information loss and thus a good prior knowledge of the unknown signal is helpful for accurate recovery. Motivated by the power of score-based generative model

A. G. Mal'shukov

A domain wall (DW) which moves parallel to a magnetically compensated interface between an antiferromagnetic insulator (AFMI) and a two-dimensional (2D) metal can pump spin polarization into the metal. It is assumed that localized spins of a collinear AFMI interact with itinerant electrons through their exchange interaction on the interface. We employed the

Joshua Forer, Dávid Hvizdoš, Xianwu Jiang, Mehdi Ayouz

The theoretical approach developed here treats uniformly the direct and indirect mechanisms of dissociative recombination (DR) in a diatomic ion. The present theory is based on electron scattering calculations performed at several internuclear distances in the molecule. It is easy to implement becaus there is no need to separately evaluate couplings and the

Dominic T. Meiers, Maximilian Rothammer, Maximilian Maier, Cordt Zollfrank

The great flexibility of direct laser writing arises from the possibility to fabricate precise three-dimensional structures on very small scales as well as the broad range of applicable materials. However, there is still a vast number of promising materials which are currently inaccessible requiring the continuous development of novel photoresists. Here, a n

Gianmarco Gatti, Julia Issing, Louk Rademaker, Florian Margot

The recent observation of correlated phases in transition metal dichalcogenide moir\'e systems at integer and fractional filling promises new insight into metal-insulator transitions and the unusual states of matter that can emerge near such transitions. Here, we combine real- and momentum-space mapping techniques to study moir\'e superlattice effects in 57.

Alberto Quintana, Abigail A. Firme, Christopher J. Jensen, Dongxing Zheng

Magneto-ionics has emerged as a promising approach to manipulate magnetic properties, not only by drastically reducing power consumption associated with electric current based devices but also by enabling novel functionalities. To date, magneto-ionics have been mostly explored in oxygen-based systems, while there is a surge of interests in alternative ionic

Rucha Deshpande, Ashish Avachat, Frank J. Brooks, Mark A. Anastasio

Quantitative phase retrieval (QPR) in propagation-based x-ray phase contrast imaging of heterogeneous and structurally complicated objects is challenging under laboratory conditions due to partial spatial coherence and polychromaticity. A learning-based method (LBM) provides a non-linear approach to this problem while not being constrained by restrictive ass

Raja Yehia, Simon Neves, Eleni Diamanti, Iordanis Kerenidis

We present the architecture and analyze the applications of a metropolitan-scale quantum network that requires only limited hardware resources for end users. Using NetSquid, a quantum network simulation tool based on discrete events, we assess the performance of several quantum network protocols involving two or more users in various configurations in terms

Tsun-An Hsieh, Chao-Han Huck Yang, Pin-Yu Chen, Sabato Marco Siniscalchi

This study addresses the speech enhancement (SE) task within the causal inference paradigm by modeling the noise presence as an intervention. Based on the potential outcome framework, the proposed causal inference-based speech enhancement (CISE) separates clean and noisy frames in an intervened noisy speech using a noise detector and assigns both sets of fra

Chengcai Shen, John C. Raymond, Nicholas A. Murphy

Non-equilibrium ionization (NEI) is essentially required for astrophysical plasma diagnostics once the plasma status departs from ionization equilibrium assumptions. In this work, we perform fast NEI calculations combined with magnetohydrodynamic (MHD) simulations and analyze the ionization properties of a Petschek-type magnetic reconnection current sheet du

Ying Hu, Remi Moreau, Falei Wang

The present paper is devoted to the study of backward stochastic differential equations with mean reflection formulated by Briand et al. [7]. We investigate the solvability of a generalized mean reflected BSDE, whose driver also depends on the distribution of the solution term $Y$. Using a fixed-point argument, BMO martingale theory and the $\theta$-method,

Alessandro Torrielli

This is the extended write-up of a series of lectures on the duality between the Sine-Gordon model and the Thirring model. Prepared for the London Theory Institute (LonTI) - Fall 2022: a PhD-level mini-course, with exercises and a guide to the literature.