January 2022 arXiv papers — page 45

Michael A. Altvater, Sheng-Hsiung Hung, Nikhil Tilak, Choong-Jae Won

Proximity effect is a very powerful approach and has been widely applied to induce electron correlations such as: superconductivity, magnetism and spin-orbit effects at the interface of heterostructure quantum materials. However, proximity induced charge density wave (CDW) state has remained elusive. We report the first observation of a novel proximity induc

Qiaoling Ye, Arash A. Amini, Qing Zhou

We consider the task of learning causal structures from data stored on multiple machines, and propose a novel structure learning method called distributed annealing on regularized likelihood score (DARLS) to solve this problem. We model causal structures by a directed acyclic graph that is parameterized with generalized linear models, so that our method is a

Yan Luo, Yongkang Wong, Mohan S. Kankanhalli, Qi Zhao

The learning process of deep learning methods usually updates the model's parameters in multiple iterations. Each iteration can be viewed as the first-order approximation of Taylor's series expansion. The remainder, which consists of higher-order terms, is usually ignored in the learning process for simplicity. This learning scheme empowers various multimedi

Wenfu Xu, Zhiqiang Tan

Consider estimation of average treatment effects with multi-valued treatments using augmented inverse probability weighted (IPW) estimators, depending on outcome regression and propensity score models in high-dimensional settings. These regression models are often fitted by regularized likelihood-based estimation, while ignoring how the fitted functions are

Minjie Cheng, Hongteng Xu

Global pooling is one of the most significant operations in many machine learning models and tasks, whose implementation, however, is often empirical in practice. In this study, we develop a novel and solid global pooling framework through the lens of optimal transport. We demonstrate that most existing global pooling methods are equivalent to solving some s

Devika V S, Dinesh Kumar Sahu, Ravi Kumar Pujala, Surajit Dhara

Nontrivial shape of colloidal particles create complex elastic distortions and topological defects in liquid crystals and play a key role in governing their electrophoretic propulsion through the medium. Here, we report experimental results on defects and electrophoretic transport of anisometric (snowman-shaped) dielectric particles subjected to an alternati

Miryeong Kwon, Donghyun Gouk, Sangwon Lee, Myoungsoo Jung

Graph neural networks (GNNs) process large-scale graphs consisting of a hundred billion edges. In contrast to traditional deep learning, unique behaviors of the emerging GNNs are engaged with a large set of graphs and embedding data on storage, which exhibits complex and irregular preprocessing. We propose a novel deep learning framework on large graphs, Hol

Siyuan Hong, Yanqiu Sun, Yuan Liu, Yishuai Wang

The oxide interface between LaAlO3 and KTaO3(111) can host an electron gas that condenses into superconductivity at low temperatures. In this work, we demonstrate a local and non-volatile control of this electron gas using a biased conducting atomic force microscope tip. By scanning the tip, charges can be accumulated on the surface of LaAlO3, which subseque

Carmen Caprau, Antonia Yeung

We show that the virtual singular braid monoid on $n$ strands embeds in a group $VSG_n$, which we call the virtual singular braid group on $n$ strands. The group $VSG_n$ contains a normal subgroup $VSPG_n$ of virtual singular pure braids. We show that $VSG_n$ is a semi-direct product of $VSPG_n$ and the symmetric group $S_n$. We provide a presentation for $V

Jiasi Weng, Jian Weng, Gui Tang, Anjia Yang

This paper proposes a new approach for privacy-preserving and verifiable convolutional neural network (CNN) testing, enabling a CNN model developer to convince a user of the truthful CNN performance over non-public data from multiple testers, while respecting model privacy. To balance the security and efficiency issues, three new efforts are done by appropri

Kaifei Kang, Helmuth Berger, Kenji Watanabe, Takashi Taniguchi

Proximity-induced superconductivity in a ferromagnet can induce Cooper pairs with a finite center-of-mass momentum. The resultant spatially modulated superconducting order parameter is able to stabilize Josephson junctions (JJs) with pi phase difference in superconductor-ferromagnet heterostructures and realize 'quiet' phase qubits. The emergence of two-dime

Wei Kou, Gang Xie, Xiaopeng Wang, Chengdong Han

Numerous experimental and theoretical investigations have highlighted the power law behavior of the proton structure function $ F_2(x, Q^2) $, particularly the dependence of its power constant on various kinematic variables. In this study, we analyze the proton structure function $ F_2 $ employing the analytical solution of the Balitsky-Kovchegov equation, w

Stewart A. Koppell, Yonatan Israel, Adam J. Bowman, Brannon B. Klopfer

A number of visions for a new generation of dose-efficient electron microscopes have been advanced. These proposals, while inspired by quantum principles, make little contact with the broader field of quantum metrology. We discuss a framework calculating the amount of information carried by each electron. This makes it possible to evaluate the potential effe

D. Choudhury, S. Borkotokey, Rajnish Kumar, Sudipta Sarangi

In cooperative games with transferable utilities, the Shapley value is an extreme case of marginalism while the Equal Division rule is an extreme case of egalitarianism. The Shapley value does not assign anything to the non-productive players and the Equal Division rule does not concern itself to the relative efficiency of the players in generating a resourc

Mahesh Kumar Mulimani, Soling Zimik, Jaya Kumar Alageshan, Rahul Pandit

Spiral waves are ubiquitous spatiotemporal patterns that occur in various excitable systems. In cardiac tissue, the formation of these spiral waves is associated with life-threatening arrhythmias, and, therefore, it is important to study the dynamics of these waves. Tracking the trajectory of a spiral-wave tip can reveal important dynamical features of a spi

Xidong Wang

In this article, a novel prescribed performance adaptive fixed-time backstepping control strategy is investigated for the attitude tracking of a 3-DOF helicopter. First, a new unified barrier function (UBF) is designed to convert the prescribed performance constrained system into an unconstrained one. Then, a fixed-time (FxT) backstepping control framework i

Benny Ren, Ian Barnett

Data collected from wearable devices and smartphones can shed light on an individual's pattern of behavioral and circadian routine. Phone use can be modeled as alternating event process, between the state of active use and the state of being idle. Markov chains and alternating recurrent event models are commonly used to model state transitions in cases such

Xin Wang, Serdar Kadioglu

We introduce a pattern mining framework that operates on semi-structured datasets and exploits the dichotomy between outcomes. Our approach takes advantage of constraint reasoning to find sequential patterns that occur frequently and exhibit desired properties. This allows the creation of novel pattern embeddings that are useful for knowledge extraction and

Limin Xu, Jiahao Mao, Xingyu Gao, Zheng Liu

Hohenberg-Kohn (HK) theorem is a cornerstone of modern electronic structure calculations. For interacting electrons, given that the internal part of the Hamiltonian ($\hat H_{int}$), containing the kinetic energy and Couloumb interaction of electrons, has a fixed form, the theorem states that when the electrons are subject to an external electrostatic field,

Prafulla Oak

We introduce Feynman-like rules to compute quivers for two loops and higher for the coloured planar $\phi^3$ theory for winding number zero. We demonstrate this for a few cases. Then we extend this further to the case of $\phi^n$ theories, for any n. We also construct the quivers for mixed theories. We do this at all loop order.

Yuping Ruan

This paper generalizes D. Burago and S. Ivanov's work on filling volume minimality and boundary rigidity of almost real hyperbolic metrics. We show that regions with metrics close to a negatively curved symmetric metric are strict minimal fillings and hence boundary rigid. This includes perturbations of complex, quaternionic and Cayley hyperbolic metrics.

Vikram Mahamiya, Alok Shukla, Nandini Garg, Brahmananda Chakraborty

We have investigated the hydrogen storage capabilities of scandium decorated holey graphyne, a recently synthesized carbon allotrope, by applying density functional theory and molecular dynamics simulations. We have observed that one unit cell of holey graphyne can adsorb 6 Sc atoms, and each Sc atom can adsorb up to 5 H$_2$ molecules with an average binding

Jun Hua, Min-Huan Chu, Jin-Chen He, Xiangdong Ji

We present the state-of-the-art lattice QCD calculation of the pion and kaon light-cone distribution amplitudes (DAs) using large-momentum effective theory. The calculation is done at three lattice spacings $a\approx\{0.06,0.09,0.12\}$ fm and physical pion and kaon masses, with the meson momenta $P_z = \{1.29,1.72,2.15\}$ GeV. The result is non-perturbativel

Tareq Tayeh, Sulaiman Aburakhia, Ryan Myers, Abdallah Shami

As a substantial amount of multivariate time series data is being produced by the complex systems in Smart Manufacturing, improved anomaly detection frameworks are needed to reduce the operational risks and the monitoring burden placed on the system operators. However, building such frameworks is challenging, as a sufficiently large amount of defective train

Chao Pan, Ryan Gabrys, Xujun Liu, Charles Colbourn

Trades, introduced by Hedayat, are two sets of blocks of elements which may be exchanged (traded) without altering the counts of certain subcollections of elements within their constituent blocks. They are of importance in applications where certain combinations of elements dynamically become prohibited from being placed in the same group of elements, since

Yulin Yang, Patrick Geneva, Xingxing Zuo, Guoquan Huang

In this paper, we study in-depth the problem of online self-calibration for robust and accurate visual-inertial state estimation. In particular, we first perform a complete observability analysis for visual-inertial navigation systems (VINS) with full calibration of sensing parameters, including IMU and camera intrinsics and IMU-camera spatial-temporal extri

Xiaoli Liu, Jianqin Yin, Di Guo, Huaping Liu

Early action prediction (EAP) aims to recognize human actions from a part of action execution in ongoing videos, which is an important task for many practical applications. Most prior works treat partial or full videos as a whole, ignoring rich action knowledge hidden in videos, i.e., semantic consistencies among different partial videos. In contrast, we par

Jianfeng Dong, Yabing Wang, Xianke Chen, Xiaoye Qu

This paper aims for the task of text-to-video retrieval, where given a query in the form of a natural-language sentence, it is asked to retrieve videos which are semantically relevant to the given query, from a great number of unlabeled videos. The success of this task depends on cross-modal representation learning that projects both videos and sentences int

Wei Pu, Jun-Jie Huang, Barak Sober, Nathan Daly

In this paper, we focus on X-ray images of paintings with concealed sub-surface designs (e.g., deriving from reuse of the painting support or revision of a composition by the artist), which include contributions from both the surface painting and the concealed features. In particular, we propose a self-supervised deep learning-based image separation approach

Alastair Grant-Stuart

An algebraic quantum field theory (AQFT) may be expressed as a functor from a category of spacetimes to a category of algebras of observables. However, a generic category $\mathsf{C}$ whose objects admit interpretation as spacetimes is not necessarily viable as the domain of an AQFT functor; often, additional constraints on the morphisms of $\mathsf{C}$ must

Minh Tran, Mohammad Soleymani

In this paper, we introduce a pretrained audio-visual Transformer trained on more than 500k utterances from nearly 4000 celebrities from the VoxCeleb2 dataset for human behavior understanding. The model aims to capture and extract useful information from the interactions between human facial and auditory behaviors, with application in emotion recognition. We

Xi Tu, Yingying Guo

In this paper, we present a construction of $u_{0}\in B^{1}_{\infty,1}$ and get the local ill-posedness for the rotation Camassa-Holm equation modelling the equatorial water waves with the weak Coriolis effect by proving the norm inflation.

Yuanyuan Peng, Pengpeng Luan, Hongbin Tu, Xiong Li

Priori knowledge of pulmonary anatomy plays a vital role in diagnosis of lung diseases. In CT images, pulmonary fissure segmentation is a formidable mission due to various of factors. To address the challenge, an useful approach based on ODoS filter and shape features is presented for pulmonary fissure segmentation. Here, we adopt an ODoS filter by merging t

Xi Tu, Zhaoyang Yin, Yingying Guo

This paper is devoted to studying the local well-posedness (existence,uniqueness and continuous dependence) for the generalized Camassa-Holm equations in critial Besov spaces $B^{\frac{1}{p}}_{p,1}$ with $1\leq p<+\infty$, which improves the previous index $s> \max\{\frac{1}{2},\frac{1}{p}\}$ or $s=\frac{1}{p},\ p\in[1,2],\ r=1$ in \cite{linb,tu-yin4}. The m

Mehwish Nasim, Derek Weber, Tobin South, Jonathan Tuke

Contrary to expectations that the increased connectivity offered by the internet and particularly Online Social Networks (OSNs) would result in broad consensus on contentious issues, we instead frequently observe the formation of polarised echo chambers, in which only one side of an argument is entertained. These can progress to filter bubbles, actively filt

Carter Davis, Alexandre Sollaci, James Traina

We show that public firm profit rates fell by half since 1980. Inferred as the residual from the rise of US corporate profit rates in aggregate data, private firm profit rates doubled since 1980. Public firm financial returns matched their fall in profit rates, while public firm representativeness increased from 30% to 60% of the US capital stock. These resu

Audrey J. Wang, Jianyu Deng, David Westbury, Yi Wang

In interfacial science, there is an increasing need to measure flow velocity fields at interfaces with ultrahigh spatial and temporal resolution to study transport phenomena. Although laser-induced fluorescence photobleaching anemometry (LIFPA) has achieved nanoscopic resolution for flow measurement, it requires pre-calibration, which is unavailable for unkn

G. N. Zeminiani

The $\Upsilon$ and $\eta_b$ as well as $B^*$ meson mass shifts (scalar potentials) are estimated for the first time in symmetric nuclear matter. The main interest is, whether or not the strengths of the bottomonium-nuclear matter and charmonium-nuclear matter interactions are similar or very different, in the range of a few tens of MeV at the nuclear matter

Shuman Du, Xiong Shen, Wenhai Liang, Peng Wang

A multistage smoothing multistep pulse compressor (MPC) based on a single-pass single-grating-pair (SSGP) main compressor is proposed to simplify the entire petawatt (PW) compressor. Only one grating pair with relatively long distance is used to generate the same amount of spectral dispersion in the main compressor compared with a four-grating main compresso

Biyuan Liu, Huaixin Chen, Zhixi Wang

The Siamese network is becoming the mainstream in change detection of remote sensing images (RSI). However, in recent years, the development of more complicated structure, module and training processe has resulted in the cumbersome model, which hampers their application in large-scale RSI processing. To this end, this paper proposes an extremely lightweight

Donald E. Taylor

Matrix generators for the general and special linear groups, the symplectic groups and the general and special unitary groups over finite fields. For the most part the generators have been obtained by translating Steinberg's generators for groups of Lie type into matrix form.

Ying Zhou, Jingping Xu, Shuangyuan Xie, Yaping Yang

We propose a scheme to generate entanglement between two cavity modes and squeeze magnon mode in a magnon-cavity QED system, where the two microwave cavity modes are coupled with a massive yttrium iron garnet (YIG) sphere through magnetic dipole interaction. The nonlinearity used in our system originates from a squeezed driving via parametric down-conversion

Esmaeel Zanganeh, Polina Kapitanova, Andrey Sayanskiy, Sergey Kosulnikov

Controlling the electromagnetic properties of materials beyond those achievable with natural substances has become a reality with the advent of metamaterials, artificially designed materials offering a wide range of unusual physical phenomena. The extreme properties that metamaterials provide can protect optical and electromagnetic systems from surrounding o

Soheyla Amirian, Thiab R. Taha, Khaled Rasheed, Hamid R. Arabnia

Physical computing infrastructure, data gathering, and algorithms have recently had significant advances to extract information from images and videos. The growth has been especially outstanding in image captioning and video captioning. However, most of the advancements in video captioning still take place in short videos. In this research, we caption longer

Soheyla Amirian, Thiab R. Taha, Khaled Rasheed, Hamid R. Arabnia

Generative Adversarial Networks (GANs) are machine learning methods that are used in many important and novel applications. For example, in imaging science, GANs are effectively utilized in generating image datasets, photographs of human faces, image and video captioning, image-to-image translation, text-to-image translation, video prediction, and 3D object

Alene K. Rhea, Kelsey Markey, Lauren D'Arinzo, Hilke Schellmann

Automated hiring systems are among the fastest-developing of all high-stakes AI systems. Among these are algorithmic personality tests that use insights from psychometric testing, and promise to surface personality traits indicative of future success based on job seekers' resumes or social media profiles. We interrogate the validity of such systems using sta

Henk Barendregt, Antonino Raffone

Consciousness will be introduced axiomatically, inspired by Buddhist insight meditation and psychology, logic in computer science, and cognitive neuroscience, as consisting of a stream of $configurations$ that is $compound$, $discrete$, and (non-deterministically) $computable$. Within this context the notions of self, concentration, mindfulness, and various

Hao Wang, Yurij Salmaniw

The inclusion of cognitive processes, such as perception, learning and memory, are inevitable in mechanistic animal movement modelling. Cognition is the unique feature that distinguishes animal movement from mere particle movement in chemistry or physics. Hence, it is essential to incorporate such knowledge-based processes into animal movement models. Here,

Juncheng Dong, Suya Wu, Mohammadreza Sultani, Vahid Tarokh

Recently Reinforcement Learning (RL) has been applied as an anti-adversarial remedy in wireless communication networks. However, studying the RL-based approaches from the adversary's perspective has received little attention. Additionally, RL-based approaches in an anti-adversary or adversarial paradigm mostly consider single-channel communication (either ch

D. Pauli, L. M. Oskinova, W. -R. Hamann, V. Ramachandran

Massive stars at low metallicity are among the main feedback agents in the early Universe and in present-day star forming galaxies. When in binaries, these stars are potential progenitors of gravitational-wave events. Knowledge of stellar masses is a prerequisite to understanding evolution and feedback of low-metallicity massive stars. Using abundant spectro

Vinícius da Silva, Tiago Novello, Guilherme Schardong, Luiz Schirmer

We introduce a novel approach for rendering static and dynamic 3D neural signed distance functions (SDF) in real-time. We rely on nested neighborhoods of zero-level sets of neural SDFs, and mappings between them. This framework supports animations and achieves real-time performance without the use of spatial data-structures. It consists of three uncoupled al

Gonçalo Raposo, Rui Ribeiro, Bruno Martins, Luísa Coheur

In conversational question answering, systems must correctly interpret the interconnected interactions and generate knowledgeable answers, which may require the retrieval of relevant information from a background repository. Recent approaches to this problem leverage neural language models, although different alternatives can be considered in terms of module

Yunling Zheng, Carson Hu, Guang Lin, Meng Yue

We propose GLassoformer, a novel and efficient transformer architecture leveraging group Lasso regularization to reduce the number of queries of the standard self-attention mechanism. Due to the sparsified queries, GLassoformer is more computationally efficient than the standard transformers. On the power grid post-fault voltage prediction task, GLassoformer

Maliha Arif, Calvin Yong, Abhijit Mahalanobis

Even though convolutional neural networks can classify objects in images very accurately, it is well known that the attention of the network may not always be on the semantically important regions of the scene. It has been observed that networks often learn background textures which are not relevant to the object of interest. In turn this makes the networks

J. Manuel García-Islas

Loop quantum gravity is a physical theory which aims at unifying general relativity and quantum mechanics. It takes general relativity very seriously and modifies it via a quantisation. General relativity describes gravity in terms of geometry. Therefore, quantising such theory must be equivalent to quantising geometry and that is what loop quantum gravity d

V. A. Berezin, I. D. Ivanova

Using the principle of least action, the motion equations for a singular hypersurface of arbitrary type in quadratic gravity are derived. Equations containing the "external pressure" and the "external flow" components of the surface energy-momentum tensor together with the Lichnerowicz conditions serve to find the hypersurface itself, while the remaining one

Gil Bor, Travis Willse

We derive the equations of chains for path geometries on surfaces by solving the equivalence problem of a related structure: sub-Riemannian geometry of signature $(1,1)$ on a contact 3-manifold. This approach is significantly simpler than the standard method of solving the full equivalence problem for path geometry. We then use these equations to give a char

Xiangguo Liu, Chao Huang, Yixuan Wang, Bowen Zheng

Neural networks have shown great promises in planning, control, and general decision making for learning-enabled cyber-physical systems (LE-CPSs), especially in improving performance under complex scenarios. However, it is very challenging to formally analyze the behavior of neural network based planners for ensuring system safety, which significantly impede

Ying Wang, Chiuman Ho, Wenju Xu, Ziwei Xuan

It is critical to obtain high resolution features with long range dependency for dense prediction tasks such as semantic segmentation. To generate high-resolution output of size $H\times W$ from a low-resolution feature map of size $h\times w$ ($hw\ll HW$), a naive dense transformer incurs an intractable complexity of $\mathcal{O}(hwHW)$, limiting its applic

Przemysław Małkiewicz, Patrick Peter, S. D. P. Vitenti

We consider a simple cosmological model consisting of an empty Bianchi I Universe, whose Hamiltonian we deparametrise to provide a natural clock variable. The model thus effectively describes an isotropic universe with an induced clock given by the shear. Quantising this model, we obtain various different possible bouncing trajectories (semiquantum expectati

Yotam Gafni, Moshe Tennenholtz

The quality of learning generally improves with the scale and diversity of data. Companies and institutions can therefore benefit from building models over shared data. Many cloud and blockchain platforms, as well as government initiatives, are interested in providing this type of service. These cooperative efforts face a challenge, which we call ``exclusivi

Albion Lawrence, Jörn Callies

Along-track wavenumber spectral densities of sea surface height (SSH) are estimated from Jason-2 altimetry data as a function of spatial location and calendar month, to understand the seasonality of meso- and submesoscale balanced dynamics across the global ocean. Regions with significant mode-1 and mode-2 baroclinic tides are rejected, restricting the analy

Paul Festor, Ali Shafti, Alex Harston, Michey Li

Eye movements have long been studied as a window into the attentional mechanisms of the human brain and made accessible as novelty style human-machine interfaces. However, not everything that we gaze upon, is something we want to interact with; this is known as the Midas Touch problem for gaze interfaces. To overcome the Midas Touch problem, present interfac

Sanjaya Lohani, Joseph M. Lukens, Ryan T. Glasser, Thomas A. Searles

We propose a series of data-centric heuristics for improving the performance of machine learning systems when applied to problems in quantum information science. In particular, we consider how systematic engineering of training sets can significantly enhance the accuracy of pre-trained neural networks used for quantum state reconstruction without altering th

Bao-An Li, Bao-Jun Cai, Lie-Wen Chen, Wen-Jie Xie

The isospin dependence of in-medium nuclear effective interactions is a fundamental issue in nuclear physics and has broad ramifications in astrophysics. Its uncertainties, especially the difference of neutron-proton interactions in the isosinglet and isotriplet channels, affect significantly the density and momentum dependence of the isovector single-nucleo

Attila Juhos

X-ray computed tomographic infrastructures are medical imaging modalities that rely on the acquisition of rays crossing examined objects while measuring their intensity decrease. Physical measurements are post-processed by mathematical reconstruction algorithms that may offer weaker or top-notch consistency guarantees on the computed volumetric field. Superi

CMS Collaboration

Using a data sample of $\sqrt{s} =$ 13 TeV proton-proton collisions collected by the CMS experiment at the LHC in 2017 and 2018 with an integrated luminosity of 103 fb$^{-1}$, the B$^0$ $\to$ $\psi$(2S)K$^0_\mathrm{S}$ and B$^0_\mathrm{S}$ $\to$ $\psi$(2S)K$^0_\mathrm{S}\pi^+\pi^-$ decays are observed with significances exceeding 5 standard deviations. The r

Sahraoui Dhelim, Liming Chen, Huansheng Ning, Chris Nugent

Death by suicide is the seventh leading death cause worldwide. The recent advancement in Artificial Intelligence (AI), specifically AI applications in image and voice processing, has created a promising opportunity to revolutionize suicide risk assessment. Subsequently, we have witnessed fast-growing literature of research that applies AI to extract audiovis

Benjamin Steinberg

Gasch\"utz (1954) proved that a finite group $G$ has a faithful irreducible complex representation if and only if its socle is generated by a single element as a normal subgroup; this result extends to arbitrary fields of characteristic $p$ so long as $G$ has no nontrivial normal $p$-subgroup. \v{Z}mud$'$ (1956) showed that the minimum number of irreducible

Vojtech Havlicek

Neural Network Quantum States (NQS) represent quantum wavefunctions by artificial neural networks. Here we study the wavefunction access provided by NQS defined in [Science, \textbf{355}, 6325, pp. 602-606 (2017)] and relate it to results from distribution testing. This leads to improved distribution testing algorithms for such NQS. It also motivates an inde

K. K. Krishnan Namboodiri, B. Sundar Rajan

The multi-access variant of the coded caching problem with $N$ files, $K$ users and $K$ caches, where each user has access to $L$ neighbouring caches in a cyclic wrap-around manner, is considered. A cut-set based lower bound on the optimal rate-memory trade-off of the multi-access coded caching (MACC) scheme is derived. Furthermore, an improved lower bound o

Pawel S. Jung, Yana V. Izdebskaya, Vladlen G. Shvedov, Demetrios N. Christodoulides

We study the propagation dynamics of bright optical vortex solitons in nematic liquid crystals with a nonlocal reorientational nonlinear response. We investigate the role of optical birefringence on the stability of these solitons. In agreement with recent experimental observations, we show that birefringence-induced astigmatism can eventually destabilize th

Jussi Heikkilä, Timo Ali-Vehmas, Julius Rissanen

We analyze the link between standardization and economic growth by systematically reviewing leading economics journals, leading economic growth researchers' articles, and economic growth-related books. We make the following observations: 1) No article has analyzed the link between standardization and economic growth in top5 economics journals between 1996 an

Imène Trigui, Damoon Shahbaztabar, Wessam Ajib, Wei-Ping Zhu

Statistical characterization of the signal-to-noise ratio (SNR) of reconfigurable intelligent surface (RIS)-assistedcommunications in the presence of phase noise is an important open issue. In this letter, we exploit the concept of copula modeling to capture the non-standard dependence features that appear due to the presence of discrete phase noise. In part

Paul J. Green, Lina Pulgarin-Duque, Scott F. Anderson, Chelsea L. MacLeod

Active galactic nuclei (AGN) can vary significantly in their rest-frame optical/UV continuum emission, and with strong associated changes in broad line emission, on much shorter timescales than predicted by standard models of accretion disks around supermassive black holes. Most such ``changing-look'' or "changing-state" AGN -- and at higher luminosities, ch

Aviral Prakash, Kenneth E. Jansen, John A. Evans

Structural subgrid stress models for large eddy simulation often allow for backscatter of energy from unresolved to resolved turbulent scales, but excessive model backscatter can eventually result in numerical instability. A commonly employed strategy to overcome this issue is to set predicted subgrid stresses to zero in regions of model backscatter. This cl

Farzad Mahfouzi, Nicholas Kioussis

Analogous to the Spin-Hall Effect (SHE), {\it ab initio} electronic structure calculations reveal that acoustic phonons can induce charge (spin) current flowing along (normal to) its propagation direction. Using Floquet approach we have calculated the elastodynamical-induced charge and spin pumping in bulk Pt and demonstrate that: (i) While the longitudinal

Amil Dravid, Florian Schiffers, Yunan Wu, Oliver Cossairt

Generative Adversarial Networks (GANs) have shown promise in augmenting datasets and boosting convolutional neural networks' (CNN) performance on image classification tasks. But they introduce more hyperparameters to tune as well as the need for additional time and computational power to train supplementary to the CNN. In this work, we examine the potential

Zhiting Hu, Li Erran Li

Controllable text generation concerns two fundamental tasks of wide applications, namely generating text of given attributes (i.e., attribute-conditional generation), and minimally editing existing text to possess desired attributes (i.e., text attribute transfer). Extensive prior work has largely studied the two problems separately, and developed different

Cody Rivera, Sheng Di, Jiannan Tian, Xiaodong Yu

More and more HPC applications require fast and effective compression techniques to handle large volumes of data in storage and transmission. Not only do these applications need to compress the data effectively during simulation, but they also need to perform decompression efficiently for post hoc analysis. SZ is an error-bounded lossy compressor for scienti

Yekaterina Epshteyn, Chang Liu, Chun Liu, Masashi Mizuno

Noise or fluctuations play an important role in the modeling and understanding of the behavior of various complex systems in nature. Fokker-Planck equations are powerful mathematical tool to study behavior of such systems subjected to fluctuations. In this paper we establish local well-posedness result of a new nonlinear Fokker-Planck equation. Such equation

Geraldo Botelho, Luis Alberto Garcia

First we give a counterexample showing that recent results on separate order continuity of Arens extensions of multilinear operators cannot be improved to get separate order continuity on the product of the whole of the biduals. Then we establish conditions on the operators and/or on the underlying Riesz spaces/Banach lattices so that the extensions are orde

Simon J. Ward, Tengfei Cao, Xiang Zhou, Catie Chang

Biosensors are an essential tool for medical diagnostics, environmental monitoring and food safety. Typically, biosensors are designed to detect specific analytes through functionalization with the appropriate capture agents. However, the use of capture agents limits the number of analytes that can be simultaneously detected and reduces the robustness of the

Raphael Steiner

In 2001, Woodall conjectured that for every pair of integers $s,t \ge 1$, all graphs without a $K_{s,t}$-minor are $(s+t-1)$-choosable. In this note we refute this conjecture in a strong form: We prove that for every choice of constants $\varepsilon>0$ and $C \ge 1$ there exists $N=N(\varepsilon,C) \in \mathbb{N}$ such that for all integers $s,t $ with $N \l

Julia R. Cordero, Thomas R. Groechel, Maja J. Matarić

Study reproducibility and generalizability of results to broadly inclusive populations is crucial in any research. Previous meta-analyses in HRI have focused on the consistency of reported information from papers in various categories. However, members of the HRI community have noted that much of the information needed for reproducible and generalizable stud

Xu Han, Han Gao, Tobias Pfaff, Jian-Xun Wang

Graph-based next-step prediction models have recently been very successful in modeling complex high-dimensional physical systems on irregular meshes. However, due to their short temporal attention span, these models suffer from error accumulation and drift. In this paper, we propose a new method that captures long-term dependencies through a transformer-styl

Xiangguo Liu, Ruochen Jiao, Bowen Zheng, Dave Liang

Neural network-based driving planners have shown great promises in improving task performance of autonomous driving. However, it is critical and yet very challenging to ensure the safety of systems with neural network based components, especially in dense and highly interactive traffic environments. In this work, we propose a safety-driven interactive planni

Sviatlana Kniahnitskaya, Michele Ortiz, Olivia Ramirez, Katharine Shultis

Motivated by the question of computing the probability of successful power domination by placing k monitors uniformly at random, in this paper we give a recursive formula to count the number of power domination sets of size k in a labeled complete m-ary tree. As a corollary we show that the desired probability can be computed in exponential with linear expon

Madhu R. Kamble, Jose Patino, Maria A. Zuluaga, Massimiliano Todisco

The current outbreak of a coronavirus, has quickly escalated to become a serious global problem that has now been declared a Public Health Emergency of International Concern by the World Health Organization. Infectious diseases know no borders, so when it comes to controlling outbreaks, timing is absolutely essential. It is so important to detect threats as

Saurabh Goswami, Rajagopalan A. N

We propose an adversarial attack for facial class-specific Single Image Super-Resolution (SISR) methods. Existing attacks, such as the Fast Gradient Sign Method (FGSM) or the Projected Gradient Descent (PGD) method, are either fast but ineffective, or effective but prohibitively slow on these networks. By closely inspecting the surface that the MSE loss, use

Brendan K. Beare

This article clarifies the relationship between pricing kernel monotonicity and the existence of opportunities for stochastic arbitrage in a complete and frictionless market of derivative securities written on a market portfolio. The relationship depends on whether the payoff distribution of the market portfolio satisfies a technical condition called adequac

Zhe Lin, Xiaojun Wan

Zero-shot paraphrase generation has drawn much attention as the large-scale high-quality paraphrase corpus is limited. Back-translation, also known as the pivot-based method, is typical to this end. Several works leverage different information as "pivot" such as language, semantic representation and so on. In this paper, we explore using visual information s

Ahmad Al-Badawi

In this paper, we investigated the Dirac and Klein-Gordan equations, as well as the greybody factor for a Schwarzschild black hole (SBH) immersed in quintessence and associated with a cloud of strings. Primarily, we study the Dirac equation using a null tetrad in the Newman- Penrose (NP) formalism. Next, we separate the Dirac equation into radial and angular

Chaofan Sun, Ken Seng Tan, Wei Wei

The replacement closeout convention has drawn more and more attention since the 2008 financial crisis. Compared with the conventional risk-free closeout, the replacement closeout convention incorporates the creditworthiness of the counterparty and thus providing a more accurate estimate of the Mark-to-market value of a financial claim. In contrast to the ris

Brennan Gebotys, Alexander Wong, David A. Clausi

Natural policy gradient methods are popular reinforcement learning methods that improve the stability of policy gradient methods by utilizing second-order approximations to precondition the gradient with the inverse of the Fisher-information matrix. However, to the best of the authors' knowledge, there has not been a study that has investigated the effects o

H. M. Nasir, Khadija Al Hasani

We present and investigate a new type of implicit fractional linear multistep method of order two for fractional initial value problems. The method is obtained from the second order super convergence of the Gr\"unwald-Letnikov approximation of the fractional derivative at a non-integer shift point. The proposed method is of order two consistency and coincide

Quentin Vigneron, Boudewijn F. Roukema

We study the properties of the Newtonian gravitational potential in a spherical Universe for different topologies. For this, we use the non-Euclidean Newtonian theory developed in Vigneron [2022, Class. & Quantum Gravity, 39, 155006] describing Newtonian gravitation in a spherical or hyperbolic Universe. The potential is calculated for a point mass in all th

Minbo Ma, Peng Xie, Fei Teng, Tianrui Li

Weather Forecasting is an attractive challengeable task due to its influence on human life and complexity in atmospheric motion. Supported by massive historical observed time series data, the task is suitable for data-driven approaches, especially deep neural networks. Recently, the Graph Neural Networks (GNNs) based methods have achieved excellent performan

Bianca Gouthier, Daniele Gouthier

The game "Spot It!" is played with a deck of cards in which every pair of cards has exactly one matching symbol and the aim is to be the fastest at finding the match. It is known that finite projective planes correspond to decks in which every card contains $n$ symbols and every symbol appears on $n$ cards. In this paper we relax the hypothesis on the number

P. Herrero-Gómez, J. P. Calupitan, M. Ilyn, A. Berdonces-Layunta

If neutrinos are their own antiparticles, the otherwise-forbidden nuclear reaction known as neutrinoless double beta decay ($\beta\beta 0\nu$) can occur, with a characteristic lifetime which is expected to be very long, making the suppression of backgrounds a daunting task. It has been shown that detecting (``tagging'') the Ba$^{+2}$ dication produced in the