May 2022 arXiv papers — page 82

Joydeep Dey, Sudeep Pasricha

In emerging automotive cyber-physical systems (CPS), accurate environmental perception is critical to achieving safety and performance goals. Enabling robust perception for vehicles requires solving multiple complex problems related to sensor selection/ placement, object detection, and sensor fusion. Current methods address these problems in isolation, which

Ying Yuan, Abdusalam Abdukerim, Zihao Bo, Wei Chen

In the two-component Majorana dark matter model, one dark matter particle can scatter off the target nuclei, and turn into a slightly heavier component. In the framework of a simplified model with a vector boson mediator, both the tree-level and loop-level processes contribute to the signal in direct detection experiment. In this paper, we report the search

Yuta Ozawa, Takayuki Nagata, Taku Nonomura

The present study proposed the framework of the spatiotemporal superresolution measurement based on the sparse regression with dimensionality reduction using the proper orthogonal decomposition (POD). The non-time-resolved particle image velocimetry (PIV) and the time-resolved near-field acoustic measurements using microphones were simultaneously performed f

Ryosuke Nishide, Shuji Ishihara

Pattern dynamics on curved surfaces are found everywhere in nature. The geometry of surfaces have been shown to influence dynamics and play a functional role, yet a comprehensive understanding is still elusive. Here, we report for the first time that a static Turing pattern on a flat surface can propagate on a curved surface, as opposed to previous studies,

Riad Sonbol, Ghaida Rebdawi, Nada Ghneim

Natural Language Processing (NLP) is widely used to support the automation of different Requirements Engineering (RE) tasks. Most of the proposed approaches start with various NLP steps that analyze requirements statements, extract their linguistic information, and convert them to easy-to-process representations, such as lists of features or embedding-based

Jiahao Dai, Jing-Wen Yi, Li Chai

In this paper, the fast consensus problem of high-order multi-agent systems under undirected topologies is considered. The direct link between the consensus convergence rate and the control gains is established. An accelerated consensus algorithm based on gradient descent is proposed to optimize the convergence rate. By applying the Routh-Hurwitz stability c

Ziyun Chen, Zhiyi Huang, Dorsa Majdi, Zipeng Yan

Consider Myerson's optimal auction with respect to an inaccurate prior, e.g., estimated from data, which is an underestimation of the true value distribution. Can the auctioneer expect getting at least the optimal revenue w.r.t. the inaccurate prior since the true value distribution is larger? This so-called strong revenue monotonicity is known to be true fo

Philip E. Rosen, Dan Zhang, Jonathan H. Jiang, Leopold Van Ijzendoorn

Deep space exploration offers the most profound opportunity for the expansion of humanity and our understanding of the Universe, but remains extremely challenging. Progress will continue to be paced by uncrewed missions followed up by crewed missions to ever further destinations. Major space powers continue to invest in crewed deep space exploration as an im

Yoshitaka Hatta, Bo-Wen Xiao, Feng Yuan

Inspired by a recent study of Iancu, Mueller and Triantafyllopoulos [1] and earlier papers by Golec-Biernat and Wusthoff [2,3], we propose semi-inclusive diffractive deep inelastic scattering (SIDDIS) to investigate the gluon tomography in the nucleon and nuclei at small-$x$. The relevant diffractive quark and gluon parton distribution functions (DPDF) can b

Lucas Friedrich, Jonas Maziero

With the rapid development of quantum computers, several applications are being proposed for them. Quantum simulations, simulation of chemical reactions, solution of optimization problems and quantum neural networks (QNNs) are some examples. However, problems such as noise, limited number of qubits and circuit depth, and gradient vanishing must be resolved b

Jiajia Zhou, Masao Doi

Using Onsager variational principle, we study the dynamic coupling between the stress and the composition in polymer solution. In the original derivation of the two-fluid model [Doi and Onuki, J. Phys. II France {\bf 2}, 1631 (1992)], the polymer stress was introduced \emph{a priopri}, therefore a constitutive equation is required to close the equations. Bas

Zachary Vendeiro, Joshua Ramette, Alyssa Rudelis, Michelle Chong

Machine learning is emerging as a technology that can enhance physics experiment execution and data analysis. Here, we apply machine learning to accelerate the production of a Bose-Einstein condensate (BEC) of $^{87}\mathrm{Rb}$ atoms by Bayesian optimization of up to 55 control parameters. This approach enables us to prepare BECs of $2.8 \times 10^3$ optica

Lingyu Gao, Debanjan Ghosh, Kevin Gimpel

We propose a type-controlled framework for inquisitive question generation. We annotate an inquisitive question dataset with question types, train question type classifiers, and finetune models for type-controlled question generation. Empirical results demonstrate that we can generate a variety of questions that adhere to specific types while drawing from th

Shanzhuo Zhang, Zhiyuan Yan, Yueyang Huang, Lihang Liu

Accurate ADMET (an abbreviation for "absorption, distribution, metabolism, excretion, and toxicity") predictions can efficiently screen out undesirable drug candidates in the early stage of drug discovery. In recent years, multiple comprehensive ADMET systems that adopt advanced machine learning models have been developed, providing services to estimate mult

Yoichi Watanabe, A. Biswas, K. Rangarajan, G. Rath

Background and Purpose: Radiomics features are used to identify disease types and predict therapy outcomes. However, how the radiomics features are different among different anatomical structures has never been investigated. Hence, we analyzed the radiomics features of 22 anatomical structures in the head and neck area in CT images. Furthermore, we studied w

Xinyu Chen, Renjie Li, Yueyao Yu, Yuanwen Shen

We solve a fundamental challenge in semiconductor IC design: the fast and accurate characterization of nanoscale photonic devices. Much like the fusion between AI and EDA, many efforts have been made to apply DNNs such as convolutional neural networks (CNN) to prototype and characterize next-gen optoelectronic devices commonly found in photonic integrated ci

Edward Taylor, Richard P. Hill, Daniel Letourneau

It has been postulated that the delivery of radiotherapy at ultra-high dose rates ("FLASH") reduces normal tissue toxicities by depleting them of oxygen. The fraction of normal tissue and cancer cells surviving radiotherapy depends on dose and oxygen levels in an exponential manner and even a very small fraction of tissue at low oxygen levels can determine r

Youfei Yu, Min Zhang, Bhramar Mukherjee

Comparative effectiveness research often involves evaluating the differences in the risks of an event of interest between two or more treatments using observational data. Often, the post-treatment outcome of interest is whether the event happens within a pre-specified time window, which leads to a binary outcome. One source of bias for estimating the causal

Zheng Cao, Koichi Hattori, Masaru Hongo, Xu-Guang Huang

We develop a relativistic (quasi-)hydrodynamic framework, dubbed the gyrohydrodynamics, to describe fluid dynamics of many-body systems with spin under strong vorticity based on entropy-current analysis. This framework generalizes the recently-developed spin hydrodynamics to the regime where the spin density is at the leading order in derivatives but suppres

Alessandro Monti, Shane Nicholas, Mohammad Omidyeganeh, Alfredo Pinelli

We have performed high-fidelity simulations of turbulent open-channel flows over submerged rigid canopies made of cylindrical filaments of fixed length $l=0.25H$ ($H$ being the domain depth) mounted on the wall with an angle of inclination $\theta$. The inclination is the free parameter that sets the density of the canopy by varying its frontal area. The den

Qianqian Hou

This paper is concerned with the boundary layer problem on a chemotaxis-Navier-Stokes system modelling boundary layer formation of aerobic bacteria in fluid. Completing the system with physical Robin-type boundary conditions for oxygen, no-flux and Dirichlet boundary conditions for bacteria and fluid velocity, we show that the gradients of its radial solutio

Bassam Bamieh

The Koopman representation is an infinite dimensional linear representation of linear or nonlinear dynamical systems. It represents the dynamics of output maps (aka observables), which are functions on the state space whose evaluation is interpreted as an output. Conceptually simple derivations and commentary on the Koopman representation are given. We empha

Jonathan Hehir, Xiaoyue Niu, Aleksandra Slavkovic

Among community detection methods, spectral clustering enjoys two desirable properties: computational efficiency and theoretical guarantees of consistency. Most studies of spectral clustering consider only the edges of a network as input to the algorithm. Here we consider the problem of performing community detection in the presence of discrete node covariat

Eduardo J. Aguilar, Valmir C. Barbosa

In cluster analysis, a common first step is to scale the data aiming to better partition them into clusters. Even though many different techniques have throughout many years been introduced to this end, it is probably fair to say that the workhorse in this preprocessing phase has been to divide the data by the standard deviation along each dimension. Like di

K. Yamagami, Y. Fujisawa, M. Pardo-Almanza, B. R. M. Smith

Fe$_5$GeTe$_2$ is a van der Waals (vdW)-coupled unconventional ferromagnetic metal with a high Curie temperature ($T_C$) exceeding 300 K. The formation of an anomalous ground state significantly below $T_C$ has received considerable attention, resulting in increased interest in understanding the spin-polarized electronic state evolution near the Fermi energy

Shijie Bao, Qi'an Guan

In this article, we consider Bergman kernels related to modules at boundary points on Stein manifolds, and obtain a log-subharmonicity property of the Bergman kernels. As applications, we obtain a lower estimate of weighted $L^2$ integrals on Stein manifolds, and reprove an effectiveness result of strong openness property of modules at boundary points on Ste

Shaleeza Sohail, Zongwen Fan, Xin Gu, Fariza Sabrina

In recent years cybersecurity has become a major concern in adaptation of smart applications. Specially, in smart homes where a large number of IoT devices are used having a secure and trusted mechanisms can provide peace of mind for users. Accurate detection of cyber attacks is crucial, however precise identification of the type of attacks plays a huge role

Hirokuni Iiboshi, Daisuke Ozaki

We quantitatively explore the impact of social security reforms in Japan, which is facing rapid aging and the highest government debt among developed countries, using an overlapping generations model with four types of agents distinguished by gender and employment type. We find that introducing social security reforms without extending the retirement age rai

Azarakhsh Keipour, Mohammadreza Mousaei, Maryam Bandari, Stefan Schaal

Deformable linear objects (e.g., cables, ropes, and threads) commonly appear in our everyday lives. However, perception of these objects and the study of physical interaction with them is still a growing area. There have already been successful methods to model and track deformable linear objects. However, the number of methods that can automatically extract

Carmen Galaz-García

For odd $n$ we construct a path $\rho_t\colon \pi_1(S) \to SL(n,\mathbb{R})$ of discrete, faithful and Zariski dense representations of a surface group such that $\rho_t(\pi_1(S)) \subset SL(n,\mathbb{Q})$ for every $t\in \mathbb{Q}$.

Tomohiro Furukawa, Sanefumi Moriyama, Hikaru Sasaki

Duality cascades are a series of duality transformations in field theories, which can be realized as the Hanany-Witten transitions in brane configurations on a circle. In the setup of the ABJM theory and its generalizations, from the physical requirement that duality cascades always end and the final destination depends only on the initial brane configuratio

Raphael Chinchilla, Guosong Yang, Joao P. Hespanha

We address the problem of finding a local solution to a nonconvex-nonconcave minmax optimization using Newton type methods, including interior-point ones. We modify the Hessian matrix of these methods such that, at each step, the modified Newton update direction can be seen as the solution to a quadratic program that locally approximates the minmax problem.

Zhangrong Qin, Wenbo Chen, Chunyan Qin, Xin Xu

Spurious currents, which are often observed near a curved interface in the multiphase simulations by diffuse interface methods, are unphysical phenomena and usually damage the computational accuracy and stability. In this paper, the origination and suppression of spurious currents are investigated by using the multiphase lattice Boltzmann method driven by ch

Xiujuan Zhang, Tian Zhang, Ming-Hui Lu, Yan-Feng Chen

The past decades have witnessed the flourishing of non-Hermitian physics in non-conservative systems, leading to unprecedented phenomena of unidirectional invisibility, enhanced sensitivity and more recently the novel topological features such as bulk Fermi arcs. Among them, growing efforts have been invested to an intriguing phenomenon, known as the non-Her

Jinyuan Liu, Tuo Lin, Tian Chen, Xinlian Zhang

The semiparametric regression models have attracted increasing attention owing to their robustness compared to their parametric counterparts. This paper discusses the efficiency bound for functional response models (FRM), an emerging class of semiparametric regression that serves as a timely solution for research questions involving pairwise observations. Th

Lorenzo Sadun, Thibaud Taillefumier

We recently introduced idealized mean-field models for networks of integrate-and-fire neurons with impulse-like interactions -- the so-called delayed Poissonian mean-field models. Such models are prone to blowups: for a strong enough interaction coupling, the mean-field rate of interaction diverges in finite time with a finite fraction of neurons spiking sim

Woong Gyu La, Lingjie Kong, Sunil Muralidhara, Pratik Nichat

We propose DeepSim, a reinforcement learning environment build toolkit for ROS and Gazebo. It allows machine learning or reinforcement learning researchers to access the robotics domain and create complex and challenging custom tasks in ROS and Gazebo simulation environments. This toolkit provides building blocks of advanced features such as collision detect

Irina Cristali, Victor Veitch

We address the problem of using observational data to estimate peer contagion effects, the influence of treatments applied to individuals in a network on the outcomes of their neighbors. A main challenge to such estimation is that homophily - the tendency of connected units to share similar latent traits - acts as an unobserved confounder for contagion effec

Kordag Mehmet Kilic, Jin Sima, Jehoshua Bruck

Neural gates compute functions based on weighted sums of the input variables. The expressive power of neural gates (number of distinct functions it can compute) depends on the weight sizes and, in general, large weights (exponential in the number of inputs) are required. Studying the trade-offs among the weight sizes, circuit size and depth is a well-studied

Kagan Yanik, Bibek Bhandari, Sreenath K. Manikandan, Andrew N. Jordan

We discuss the thermodynamic aspects of a single qubit based device, powered by weak quantum measurements, and feedback controlled by a quantum Maxwell's demon. We discuss both discrete and time-continuous operation of the measurement based device at finite temperature of the reservoir. In the discrete example where a demon acquires information via discrete

B. Chaouchi, M. Kostic, D. Velinov

In this paper, we analyze metrical approximations of functions $F :\Lambda times X \rightarrow Y$ by trigonometric polynomials and $\rho$-periodic type functions, where $\emptyset \neq \Lambda \subseteq {\mathbb R}^{n},$ $X$ and $Y $are complex Banach spaces, and $\rho$ is a general binary relation on $Y .$ Besides the classical concept, we analyze Stepanov,

Mingrui Zhang, Peng Ding

Mediation analysis assesses the extent to which the exposure affects the outcome indirectly through a mediator and the extent to which it operates directly through other pathways. The popular Baron-Kenny approach estimates the indirect and direct effects of the exposure on the outcome based on linear regressions. However, when the exposure and the mediator a

Neetha Jambigi, Thomas Bach, Felix Schabernack, Michael Felderer

Capturing customer workloads of database systems to replay these workloads during internal testing can be beneficial for software quality assurance. However, we experienced that such replays can produce a large amount of false positive alerts that make the results unreliable or time consuming to analyze. Therefore, we design a machine learning based approach

Jacob Miller, Stephen Kobourov, Vahan Huroyan

Hyperbolic geometry offers a natural focus + context for data visualization and has been shown to underlie real-world complex networks. However, current hyperbolic network visualization approaches are limited to special types of networks and do not scale to large datasets. With this in mind, we designed, implemented, and analyzed three methods for hyperbolic

B. A. Tay

We study a linear chain of oscillators with inhomogeneity in their interactions with phonon bath. In a previous work on the Markovian master equation of the system, we investigated a model in which the difference in the site-phonon coupling between adjacent oscillators is the same throughout the chain. Here we look into another model in which the oscillators

Deng Wang

Recently, the Event Horizon Telescope collaboration has reported the first image of the supermassive black hole Sagittarius in the Galactic Center. We attempt to test the validity of the no-hair theorem of black holes using this new shadow observation. Considering the Einstein-Maxwell-klein-Gordon theory with a minimally-coupled scalar field, we find that ou

Shaohui Liu, Chengyang Wu, Hao Zhu

Solving the optimal power flow (OPF) problem is a fundamental task to ensure the system efficiency and reliability in real-time electricity grid operations. We develop a new topology-informed graph neural network (GNN) approach for predicting the optimal solutions of real-time ac-OPF problem. To incorporate grid topology to the NN model, the proposed GNN-for

Rahnuma Islam Nishat, Venkatesh Srinivasan, Sue Whitesides

A \emph{simple} $s,t$ path $P$ in a rectangular grid graph $\mathbb{G}$ is a Hamiltonian path from the top-left corner $s$ to the bottom-right corner $t$ such that each \emph{internal} subpath of $P$ with both endpoints $a$ and $b$ on the boundary of $\mathbb{G}$ has the minimum number of bends needed to travel from $a$ to $b$ (i.e., $0$, $1$, or $2$ bends,

Khalid Alnajjar, Mika Hämäläinen

The goal of the paper is to predict answers to questions given a passage of Qur'an. The answers are always found in the passage, so the task of the model is to predict where an answer starts and where it ends. As the initial data set is rather small for training, we make use of multilingual BERT so that we can augment the training data by using data availabl

Guillaume Barraquand, Pierre Le Doussal

We consider the partition function $Z_{\ell}(\vec x,0\vert \vec y,t)$ of $\ell$ non-intersecting continuous directed polymers of length $t$ in dimension $1+1$, in a white noise environment, starting from positions $\vec x$ and terminating at positions $\vec y$. When $\ell=1$, it is well known that for fixed $x$, the field $\log Z_1(x,0\vert y,t)$ solves the

David G. Harris, N. S. Narayanaswamy

We describe a new algorithm for vertex cover with runtime $O^*(1.25284^k)$, where $k$ is the size of the desired solution and $O^*$ hides polynomial factors in the input size. This improves over previous runtime of $O^*(1.2738^k)$ due to Chen, Kanj, & Xia (2010) standing for more than a decade. The key to our algorithm is to use a potential function which si

Marco Schlichting

We improve, by a factor of 2, known homology stability ranges for the integral homology of symplectic groups over commutative local rings with infinite residue field and show that the obstruction to further stability is bounded below by Milnor-Witt K-theory. In particular our stability range is optimal in many cases.

Polina Binder, Meghan Lawler, LaShadric Grady, Neil Carlson

DNA encoded libraries (DELs) are used for rapid large-scale screening of small molecules against a protein target. These combinatorial libraries are built through several cycles of chemistry and DNA ligation, producing large sets of DNA-tagged molecules. Training machine learning models on DEL data has been shown to be effective at predicting molecules of in

Alban Pouplard, Peichun Amy Tsai

Viscous fingering (VF) is an interfacial instability that occurs in a narrow confinement or porous medium when a less-viscous fluid pushes a more viscous one, producing finger-like patterns. Controlling the VF instability is essential to enhance the efficiency of various technological applications. However, the control of VF instability has been challenging

Udayan Khurana, Kavitha Srinivas, Horst Samulowitz

Data Scientists leverage common sense reasoning and domain knowledge to understand and enrich data for building predictive models. In recent years, we have witnessed a surge in tools and techniques for {\em automated machine learning}. While data scientists can employ various such tools to help with model building, many other aspects such as {\em feature eng

Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong

We present a detailed study of estimation errors in terms of surrogate loss estimation errors. We refer to such guarantees as $\mathscr{H}$-consistency estimation error bounds, since they account for the hypothesis set $\mathscr{H}$ adopted. These guarantees are significantly stronger than $\mathscr{H}$-calibration or $\mathscr{H}$-consistency. They are also

Jennifer Volk, Alex Wynn, Timothy Sherwood, Georgios Tzimpragos

Recent advances in logic schemes and fabrication processes have renewed interest in using superconductor electronics for energy-efficient computing and quantum control processors. However, scalable superconducting memory still poses a challenge. To address this issue, we present an alternative to approaches that solely emphasize storage cell miniaturization

Christopher Cebra, Alexander Strang

Competitive systems can exhibit both hierarchical (transitive) and cyclic (intransitive) structures. Despite theoretical interest in cyclic competition, which offers richer dynamics, and occupies a larger subset of the space of possible competitive systems, most real-world systems are predominantly transitive. Why? Here, we introduce a generic mechanism whic

Alëna Aksënova, Zhehuai Chen, Chung-Cheng Chiu, Daan van Esch

Building inclusive speech recognition systems is a crucial step towards developing technologies that speakers of all language varieties can use. Therefore, ASR systems must work for everybody independently of the way they speak. To accomplish this goal, there should be available data sets representing language varieties, and also an understanding of model co

Maciej Zamorski, Michał Stypułkowski, Konrad Karanowski, Tomasz Trzciński

Contemporary deep neural networks offer state-of-the-art results when applied to visual reasoning, e.g., in the context of 3D point cloud data. Point clouds are important datatype for precise modeling of three-dimensional environments, but effective processing of this type of data proves to be challenging. In the world of large, heavily-parameterized network

Tara Safavi, Doug Downey, Tom Hope

Knowledge graph (KG) link prediction is a fundamental task in artificial intelligence, with applications in natural language processing, information retrieval, and biomedicine. Recently, promising results have been achieved by leveraging cross-modal information in KGs, using ensembles that combine knowledge graph embeddings (KGEs) and contextual language mod

Digvijay Boob, Qi Deng, Guanghui Lan

We present a new feasible proximal gradient method for constrained optimization where both the objective and constraint functions are given by the summation of a smooth, possibly nonconvex function and a convex simple function. The algorithm converts the original problem into a sequence of convex subproblems. Formulating those subproblems requires the evalua

Julio Michael Stern, Carlos Alberto de Braganca Pereira, Marcelo de Souza Lauretto, Luis Gustavo Esteves

This article gives a conceptual review of the e-value, ev(H|X) -- the epistemic value of hypothesis H given observations X. This statistical significance measure was developed in order to allow logically coherent and consistent tests of hypotheses, including sharp or precise hypotheses, via the Full Bayesian Significance Test (FBST). Arguments of analysis al

Yuriy Akimov

Understanding of light-matter interaction is a keystone in mastering classical and quantum optics. This paper gives an overview of the fundamental principles used in these two fields for description of light-matter interaction. By exploring the simplest type of matter composed of charge-free particles bearing magnetic moments only, differences in the fundame

Andrés del Pino, Mattia Libralato, Roeland P. van der Marel, Paul Bennet

We present GaiaHub, a publicly available tool that combines $Gaia$ measurements with $Hubble$ $Space$ $Telescope$ ($HST$) archival images to derive proper motions (PMs). It increases the scientific impact of both observatories beyond their individual capabilities. $Gaia$ provides PMs across the whole sky, but the limited mirror size and time baseline restric

Alban Pouplard, Peichun Amy Tsai

Being a major limiting factor for the efficiency of various technologies, such as Enhanced Oil Recovery, the viscous fingering (or Saffman--Taylor) instability has been extensively studied, especially for simple Newtonian fluids. Here, we experimentally and theoretically demonstrate a vital control of inhibiting the viscous fingering instability for complex

Randy F Fela, Andréas Pastor, Patrick Le Callet, Nick Zacharov

To open up new possibilities to assess the multimodal perceptual quality of omnidirectional media formats, we proposed a novel open source 360 audiovisual (AV) quality dataset. The dataset consists of high-quality 360 video clips in equirectangular (ERP) format and higher-order ambisonic (4th order) along with the subjective scores. Three subjective quality

Shahin Honari, Saeed Farajollahi, Tao Lu

Thin large microdisks, that are key for dense spectral microcomb generation at visible to UV wavelengths, face challenges in fabrication. One of the most difficult issues is the buckling effect that significantly reduces the cavity optical quality factor. This work introduces a novel rib disk structure that significantly mitigates the buckling effects. Using

Erik Brockbank, Haoliang Wang, Justin Yang, Suvir Mirchandani

How do people build up trust with artificial agents? Here, we study a key component of interpersonal trust: people's ability to evaluate the competence of another agent across repeated interactions. Prior work has largely focused on appraisal of simple, static skills; in contrast, we probe competence evaluations in a rich setting with agents that learn over

Alban Pouplard, Peichun Amy Tsai

The process of one fluid pushing another is universally common while involving complex interfacial instabilities. Particularly, occurring in a myriad of natural and industrial processes, wavy fingering patterns frequently emerge when a less viscous fluid pushes another more viscous one, such as water invading oil, in a porous medium. Such finger-shaped inter

Satoru Hayami

The instability toward a magnetic skyrmion crystal in centrosymmetric trilayer magnets is investigated based on a spin model with layer-dependent Dzayloshinskii-Moriya interaction. We find various types of skyrmion crystal phases with different skyrmion numbers in a low-temperature phase diagram by performing the simulated annealing. In addition to the N\'ee

Sheng-Lun Xie, Ce Cai, Shao-Lin Xiong, Yun-Wei Yu

Since FRB 200428 has been found to be associated with an X-ray burst from the Galactic magnetar SGR J1935+2154, it is interesting to explore whether the magnetar bursts also follow the similar active periodic behavior as some repeating FRBs. Previous studies show that there is possible period about 230 day in SGR J1935+2154 bursts. Here, we collected an upda

Neelanjan Bhowmik, Jack W. Barker, Yona Falinie A. Gaus, Toby P. Breckon

Lossy image compression strategies allow for more efficient storage and transmission of data by encoding data to a reduced form. This is essential enable training with larger datasets on less storage-equipped environments. However, such compression can cause severe decline in performance of deep Convolution Neural Network (CNN) architectures even when mild c

Koel Dutta Chowdhury, Rricha Jalota, Cristina España-Bonet, Josef van Genabith

Cross-lingual natural language processing relies on translation, either by humans or machines, at different levels, from translating training data to translating test sets. However, compared to original texts in the same language, translations possess distinct qualities referred to as translationese. Previous research has shown that these translation artifac

Iván Díaz

Recent approaches to causal inference have focused on causal effects defined as contrasts between the distribution of counterfactual outcomes under hypothetical interventions on the nodes of a graphical model. In this article we develop theory for causal effects defined with respect to a different type of intervention, one which alters the information propag

Nhat Ho, Tongzheng Ren, Sujay Sanghavi, Purnamrita Sarkar

Using gradient descent (GD) with fixed or decaying step-size is a standard practice in unconstrained optimization problems. However, when the loss function is only locally convex, such a step-size schedule artificially slows GD down as it cannot explore the flat curvature of the loss function. To overcome that issue, we propose to exponentially increase the

João P. G. Ramos, Paolo Tilli

For some special window functions $\psi_{\beta} \in H^2(\mathbb{C}^+),$ we prove that, over all sets $\Delta \subset \mathbb{C}^+$ of fixed hyperbolic measure $\nu(\Delta),$ the ones over which the Wavelet transform $W_{\overline{\psi_{\beta}}}$ with window $\overline{\psi_{\beta}}$ concentrates optimally are exactly the discs with respect to the pseudohyper

L. Wells, T. Müller, R. M. Stevenson, J. Skiba-Szymanska

Quantum networks have the potential to transform secure communication via quantum key distribution and enable novel concepts in distributed quantum computing and sensing. Coherent quantum light generation at telecom wavelengths is fundamental for fibre-based network implementations, but Fourier-limited emission and subnatural linewidth photons have so far on

H. Schatz, A. D. Becerril Reyes, A. Best, E. F. Brown

Nuclear Astrophysics is a field at the intersection of nuclear physics and astrophysics, which seeks to understand the nuclear engines of astronomical objects and the origin of the chemical elements. This white paper summarizes progress and status of the field, the new open questions that have emerged, and the tremendous scientific opportunities that have op

Emanuele Galiffi, Shixiong Yin, Andrea Alù

The advent of novel nonlinear materials has stirred unprecedented interest in exploring the use of temporal inhomogeneities to achieve novel forms of wave control, amidst the greater vision of engineering metamaterials across both space and time. When the properties of an unbounded medium are abruptly switched in time, propagating waves are efficiently conve

Qing Zou, Mathews Jacob

In this work, we proposed a continuous-acquisition strategy using a gradient echo (GRE) inversion recovery sequence based on spiral trajectories to simultaneously obtain the $T_1$ mapping and CINE imaging. The acquisition is using a free-breathing and ungated fashion. An approach based on variational auto-encoder(VAE) is used for the motion estimation from t

Ho-Chun Lin, Zeyu Wang, Chia Wei Hsu

Full-wave simulations are indispensable for nanophotonics and electromagnetics but are severely constrained on large systems, especially multi-channel ones such as disordered media, aperiodic metasurfaces, and densely packed photonic circuits where each input requires a large-scale simulation. Here we bypass the computationally demanding solution of Maxwell'

Chen Wang, Danfei Xu, Li Fei-Fei

The ability to plan for multi-step manipulation tasks in unseen situations is crucial for future home robots. But collecting sufficient experience data for end-to-end learning is often infeasible in the real world, as deploying robots in many environments can be prohibitively expensive. On the other hand, large-scale scene understanding datasets contain dive

Xhek Turkeshi, Lorenzo Piroli, Marco Schirò

We investigate entanglement dynamics in continuously monitored open quantum systems featuring current-carrying non-equilibrium states. We focus on a prototypical one-dimensional model of boundary-driven non-interacting fermions with monitoring of the local density, whose average Lindblad dynamics features a well-studied ballistic to diffusive crossover in tr

Weikang Qiao, Licheng Guo, Zhenman Fang, Mau-Chung Frank Chang

The emergence of high-bandwidth memory (HBM) brings new opportunities to boost the performance of sorting acceleration on FPGAs, which was conventionally bounded by the available off-chip memory bandwidth. However, it is nontrivial for designers to fully utilize this immense bandwidth. First, the existing sorter designs cannot be directly scaled at the incre

Weinan E, Jiequn Han, Jihao Long

Solving complex optimal control problems have confronted computational challenges for a long time. Recent advances in machine learning have provided us with new opportunities to address these challenges. This paper takes model predictive control, a popular optimal control method, as the primary example to survey recent progress that leverages machine learnin

Serafim Grubas, Anton Duchkov, Georgy Loginov

The concept of physics-informed neural networks has become a useful tool for solving differential equations due to its flexibility. There are a few approaches using this concept to solve the eikonal equation which describes the first-arrival traveltimes of acoustic and elastic waves in smooth heterogeneous velocity models. However, the challenge of the eikon

Brianna Cantrall, Solomon Quinn, Emory F. Bunn

Broadband time-ordered data obtained from telescopes with a wavelength-dependent, asymmetric beam pattern can be used to extract maps at multiple wavelengths from a single scan. This technique is especially useful when collecting data on cosmic phenomena such as the Cosmic Microwave Background (CMB) radiation, as it provides the ability to separate the CMB s

Karl Daningburg, Richard O'Shaughnessy

Gravitational wave science is dependent upon expensive numerical simulations, which provide the foundational understanding of binary merger radiation needed to interpret observations of massive binary black holes. The high cost of these simulations limits large-scale campaigns to explore the binary black hole parameter space. Surrogate models have been devel

Guangbin Ren, Xin Zhao

The Cayley-Dickson algebra has long been a challenge due to the lack of an explicit multiplication table. Despite being constructible through inductive construction, its explicit structure has remained elusive until now. In this article, we propose a solution to this long-standing problem by revealing the Cayley-Dickson algebra as a twisted group algebra wit

F. Lorenz, M. Günther

We present a novel approach how logic programming for expert systems can be declared directly in an object-oriented language.

Hans Boehringer, Gayoung Chon

It is important to map the large-scale matter distribution in the local Universe for cosmological studies, such as the tracing of the large-scale peculiar velocity flow, the characterisation of the environment for different astronomical objects, and for precision measurements of cosmological parameters. We used X-ray luminous clusters to map this matter dist

Mathilde Bateson, Hervé Lombaert, Ismail Ben Ayed

Supervised learning is well-known to fail at generalization under distribution shifts. In typical clinical settings, the source data is inaccessible and the target distribution is represented with a handful of samples: adaptation can only happen at test time on a few or even a single subject(s). We investigate test-time single-subject adaptation for segmenta

Keyang Zhou, Bharat Lal Bhatnagar, Jan Eric Lenssen, Gerard Pons-Moll

We present TOCH, a method for refining incorrect 3D hand-object interaction sequences using a data prior. Existing hand trackers, especially those that rely on very few cameras, often produce visually unrealistic results with hand-object intersection or missing contacts. Although correcting such errors requires reasoning about temporal aspects of interaction

V. Petrosian, J. Singal, S. Mutchnick

We explore whether an independent determination of the distance-redshift relation, and hence cosmological model parameters, can be obtained from the apparent correlations between two different waveband luminosities or fluxes, as has been claimed in recent works using the X-ray and ultraviolet luminosities and fluxes of quasars. We show that such an independe

Simone Assali, Sebastian Koelling, Zeinab Abboud, Jérôme Nicolas

Ge/SiGe multi-quantum well heterostructures are highly sought-after for silicon-integrated optoelectronic devices operating in the broad range of the electromagnetic spectrum covering infrared to terahertz wavelengths. However, the epitaxial growth of these heterostructures at a thickness of a few microns has been a challenging task due the lattice mismatch

Boro Sitnikovski

We present a simple programming language based on G\"odel numbering and prime factorization, enhanced with explicit, scoped loops, allowing for easy program composition. Further, we will present a theorem prover that allows expressing and working with formal systems. The theorem prover is simple as it relies merely on a substitution rule and set equality to

Peter Cameron, Maciej Dunajski, Paul Tod

We show that conformal geodesics on a Riemannian manifold cannot spiral: there does not exist a conformal geodesic which becomes trapped in every neighbourhood of a point.

Felix Wittwer, Nicholas K. Sauter, Derek Mendez, Billy K. Poon

The upcoming exascale computing systems Frontier and Aurora will draw much of their computing power from GPU accelerators. The hardware for these systems will be provided by AMD and Intel, respectively, each supporting their own GPU programming model. The challenge for applications that harness one of these exascale systems will be to avoid lock-in and to pr

R. Gupta, F. Cosco, R. S. Malik, X. Chen

Using element-specific measurements of the ultrafast demagnetization of Ru/Fe$_{65}$Co$_{35}$ heterostructures, we show that Ru can exhibit a significant magnetic contrast (3% asymmetry) resulting from ultrafast spin currents emanating from the demagnetization process of the FeCo layer. We use this magnetic contrast to investigate how superdiffusive spin cur