June 2020 arXiv papers — page 14

Werner Kirsch, M. Krishna

We compute the density of states for the Cauchy distribution for a large class of random operators and show it is analytic in a strip about the real axis.

Jian Song, Yimin Xiao, Wangjun Yuan

For real symmetric and complex Hermitian Gaussian processes whose values are $d\times d$ matrices, we characterize the conditions under which the probability that at least $k$ eigenvalues collide is positive for $2\le k\le d$, and we obtain the Hausdorff dimension of the set of collision times.

Linshan Jiang, Rui Tan, Arvind Easwaran

The Internet of Things (IoT) will be a main data generation infrastructure for achieving better system intelligence. This paper considers the design and implementation of a practical privacy-preserving collaborative learning scheme, in which a curious learning coordinator trains a better machine learning model based on the data samples contributed by a numbe

Mohammed Mouçouf

We define and give some properties and characterizations of S-rings of Krull type. We also determine, in the case of an independent S-ring of Krull type A, the injective dimension of the quotient category Mod(A)/\mathcal{M}_{0}, where \mathcal{M}_{0} is the thick subcategory of the modules with trivial maps into the codivisorial modules.

Tianbo Gu, Zheng Fang, Allaukik Abhishek, Hao Fu

Internet of Things (IoT) has become the most promising technology for service automation, monitoring, and interconnection, etc. However, the security and privacy issues caused by IoT arouse concerns. Recent research focuses on addressing security issues by looking inside platform and apps. In this work, we creatively change the angle to consider security pro

Tianbo Gu, Allaukik Abhishek, Hao Fu, Huanle Zhang

As a massive number of the Internet of Things (IoT) devices are deployed, the security and privacy issues in IoT arouse more and more attention. The IoT attacks are causing tremendous loss to the IoT networks and even threatening human safety. Compared to traditional networks, IoT networks have unique characteristics, which make the attack detection more cha

Victor V. Batyrev

Mirrors $X^{\vee}$ of quasi-smooth Calabi-Yau hypersurfaces $X$ in weighted projective spaces ${\Bbb P}(w_0, \ldots, w_d)$ can be obtained as Calabi-Yau compactifications of non-degenerate affine toric hypersurfaces defined by Laurent polynomials whose Newton polytope is the lattice simplex spanned by $d+1$ lattice vectors $v_i$ satisfying the relation $\sum

Jinyue Song, Tianbo Gu, Yunjie Ge, Prasant Mohapatra

In recent years, there is an emerging trend that some computing services are moving from cloud to the edge of the networks. Compared to cloud computing, edge computing can provide services with faster response, lower expense, and more security. The massive idle computing resources closing to the edge also enhance the deployment of edge services. Instead of u

Ralph Rudd, Thomas A. McWalter, Joerg Kienitz, Eckhard Platen

Recursive marginal quantization (RMQ) allows the construction of optimal discrete grids for approximating solutions to stochastic differential equations in d-dimensions. Product Markovian quantization (PMQ) reduces this problem to d one-dimensional quantization problems by recursively constructing product quantizers, as opposed to a truly optimal quantizer.

Binghong Chen, Chengtao Li, Hanjun Dai, Le Song

Retrosynthetic planning is a critical task in organic chemistry which identifies a series of reactions that can lead to the synthesis of a target product. The vast number of possible chemical transformations makes the size of the search space very big, and retrosynthetic planning is challenging even for experienced chemists. However, existing methods either

Chang Won Ahn, Jae Hun Jo, Jong Chan Kim, Hamid Ullah

Lead-free double perovskite halides are emerging optoelectronic materials that are alternatives to lead-based perovskite halides. Recently, single-crystalline double perovskite halides were synthesized, and their intriguing functional properties were demonstrated. Despite such pioneering works, lead-free double perovskite halides with better crystallinity ar

Carsten Chong, Robert C. Dalang

We consider a class of parabolic stochastic PDEs on bounded domains $D\subseteq\mathbb{R}^d$ that includes the stochastic heat equation, but with a fractional power $γ$ of the Laplacian. Viewing the solution as a process with values in a scale of fractional Sobolev spaces $H_r$, with $r < γ- d/2$, we study its power variations in $H_r$ along regular partitio

Jake Chandler, Richard Booth

The belief revision literature has largely focussed on the issue of how to revise one&#39;s beliefs in the light of information regarding matters of fact. Here we turn to an important but comparatively neglected issue: How might one extend a revision operator to handle conditionals as input? Our approach to this question of &#39;conditional revision&#39; is

Toshihiro Nonaka, Masakiyo Kitazawa, ShinIchi Esumi

We propose a method to remove the contributions of pileup events from higher-order cumulants and moments of event-by-event particle distributions. Assuming that the pileup events are given by the superposition of two independent single-collision events, we show that the true moments in each multiplicity bin can be obtained recursively from lower multiplicity

Dehai Yu, Zheng Chen

Droplet vaporization has a great impact on combustion of liquid fuels and might greatly affects the engine performance. Physical understanding and theoretical interpretation of the droplet vaporization behavior are decisive to constitute appropriate models for spray combustion. In this work, we proposed a fully transient formulation for the droplet vaporizat

Lexing Ying

Natural gradients have been widely used in optimization of loss functionals over probability space, with important examples such as Fisher-Rao gradient descent for Kullback-Leibler divergence, Wasserstein gradient descent for transport-related functionals, and Mahalanobis gradient descent for quadratic loss functionals. This note considers the situation in w

Huilan Zeng, Chen-Song Zhang, Shuo Zhang

In this paper, a piecewise quadratic nonconforming finite element method on rectangular grids for a fourth-order elliptic singular perturbation problem is presented. This proposed method is robustly convergent with respect to the perturbation parameter. Numerical results are presented to verify the theoretical findings. The new method uses piecewise quadrati

Brandon Montemuro, Christopher M. White, Joseph C. Klewicki, Gregory P. Chini

Many exact coherent states (ECS) arising in wall-bounded shear flows have an asymptotic structure at extreme Reynolds number Re in which the effective Reynolds number governing the streak and roll dynamics is O(1). Consequently, these viscous ECS are not suitable candidates for quasi-coherent structures away from the wall that necessarily are inviscid in the

Hai-Bo Li, Mao-Zhi Yang

Based on the mechanism of neutral meson mixing, we predict the branching fraction of the semileptonic decay of $D_s^+\to π^0 \ell^+ ν_\ell$ ($\ell=e$, $μ$) using recently measured branching fraction of $D_s^+\to ηe^+ν_e$ by BESIII experiment . We also give a formula that can describe the neutral meson mixing of $π^0$, $η$, $η&#39;$ and a pseudoscalar gluoniu

Anita Liebenau, Nick Wormald

We provide asymptotic formulae for the numbers of bipartite graphs with given degree sequence, and of loopless digraphs with given in- and out-degree sequences, for a wide range of parameters. Our results cover medium range densities and close the gaps between the results known for the sparse and dense ranges. In the case of bipartite graphs, these results w

Ryota Higurashi, Naomi Tsuji, Yasunobu Uchiyama

The supernova remnant (SNR) RX J1713.7$-$3946 is one of the best-studied accelerators of cosmic rays because of its strong nonthermal X-ray and gamma-ray radiation. We have analyzed accumulated \chandra\ observations with a total exposure time of $\sim$266 ks in the northwest rim of RX J1713.7$-$3946. We detect a substantially large number of point-like sour

Shengfei Lyu, Jiaqi Liu

Convolutional neural network (CNN) and recurrent neural network (RNN) are two popular architectures used in text classification. Traditional methods to combine the strengths of the two networks rely on streamlining them or concatenating features extracted from them. In this paper, we propose a novel method to keep the strengths of the two networks to a great

Shehroze Farooqi, Maaz Musa, Zubair Shafiq, Fareed Zaffar

Online social networks support a vibrant ecosystem of third-party apps that get access to personal information of a large number of users. Despite several recent high-profile incidents, methods to systematically detect data misuse by third-party apps on online social networks are lacking. We propose CanaryTrap to detect misuse of data shared with third-party

Shengfei Lyu, Xing Tian, Yang Li, Bingbing Jiang

The probabilistic classification vector machine (PCVM) synthesizes the advantages of both the support vector machine and the relevant vector machine, delivering a sparse Bayesian solution to classification problems. However, the PCVM is currently only applicable to binary cases. Extending the PCVM to multi-class cases via heuristic voting strategies such as

Long Chen, Lei Tong, Feixiang Zhou, Zheheng Jiang

Underwater image enhancement is such an important vision task due to its significance in marine engineering and aquatic robot. It is usually work as a pre-processing step to improve the performance of high level vision tasks such as underwater object detection. Even though many previous works show the underwater image enhancement algorithms can boost the det

Charles Hadfield, Sergey Bravyi, Rudy Raymond, Antonio Mezzacapo

Obtaining precise estimates of quantum observables is a crucial step of variational quantum algorithms. We consider the problem of estimating expectation values of molecular Hamiltonians, obtained on states prepared on a quantum computer. We propose a novel estimator for this task, which is locally optimised with knowledge of the Hamiltonian and a classical

Shrijita Bhattacharya, Tapabrata Maiti

Despite the popularism of Bayesian neural networks in recent years, its use is somewhat limited in complex and big data situations due to the computational cost associated with full posterior evaluations. Variational Bayes (VB) provides a useful alternative to circumvent the computational cost and time complexity associated with the generation of samples fro

Yueying Cao, Xianlin Zheng, Simone De Camillis, Bingyang Shi

Lanthanide-based upconversion nanoparticles (UCNPs) generally require high power laser excitation. Here we report wide-field upconversion microscopy at single-nanoparticle sensitivity using incoherent excitation of a 970-nm light-emitting diode (LED). We show that due to its broad emission spectrum, LED excitation is about 3 times less effective for UCNPs an

Shahzad, Eun-Sung Jung

The progress of the network and device technologies enables any device to be connected to the Internet thereby forming an Internet of Things (IoT) architecture. Internet-related activities based on the IoT services can inevitably generate a lot of real-time data which leads a big data phenomenon. To efficiently handle such big IoT data, software-defined netw

Dan-Bo Zhang, Zhan-Hao Yuan, Tao Yin

Variational quantum eigensolver(VQE) typically minimizes energy with hybrid quantum-classical optimization, which aims to find the ground state. Here, we propose a VQE by minimizing energy variance, which is called as variance-VQE(VVQE). The VVQE can be viewed as an self-verifying eigensolver for arbitrary eigenstate by designing, since an eigenstate for a H

Shali Jiang, Daniel R. Jiang, Maximilian Balandat, Brian Karrer

Bayesian optimization is a sequential decision making framework for optimizing expensive-to-evaluate black-box functions. Computing a full lookahead policy amounts to solving a highly intractable stochastic dynamic program. Myopic approaches, such as expected improvement, are often adopted in practice, but they ignore the long-term impact of the immediate de

Nelson Duran, Wagner J. Favaro

The current review focuses on important aspects of pyroptosis, such as morphological features of inflammasomes, as well as on knowledge about coronavirus infection mechanisms and the association between SARS-CoV-2 infection and cell pyroptosis. The application of immunomodulation therapies to Covid-19 patients is the most promising treatment under investigat

Quansen Jiu, Yanqing Wang, Wei Wei

In this paper, it is shown that there does not exist a non-trivial Leray&#39;s backward self-similar solution to the 3D Navier-Stokes equations with profiles in Morrey spaces $\dot{\mathcal{M}}^{q,1}(\mathbb{R}^{3})$ provided $3/2<q<6$, or in $\dot{\mathcal{M}}^{q,l}(\mathbb{R}^{3})$ provided $6\leq q<\infty$ and $2<l\leq q$. This generalizes the correspondi

Seung Hun Lee, Bohm-Jung Yang

We propose a route to achieve odd-parity spin-triplet superconductivity in metallic collinear antiferromagnets with inversion symmetry. Owing to the existence of hidden antiunitary symmetry, which we call the effective time-reversal symmetry (eTRS), the Fermi surfaces of ordinary antiferromagnetic metals are generally spin-degenerate, and spin-singlet pairin

Oliver Knill

Positive curvature and bosons Compact positive curvature Riemannian manifolds M with symmetry group G allow Conner-Kobayashi reductions M to N, where N is the fixed point set of the symmetry G. The set N is a union of smaller-dimensional totally geodesic positive curvature manifolds each with even co-dimension. By Berger, N is not empty. By Lefschetz, M and

Sheng-Jie Liu, Haowen Luo, Qian Shi

Although deep learning has achieved great success in image classification tasks, its performance is subject to the quantity and quality of training samples. For classification of polarimetric synthetic aperture radar (PolSAR) images, it is nearly impossible to annotate the images from visual interpretation. Therefore, it is urgent for remote sensing scientis

Songtian S. Zhang, Jia-Xin Yin, Muhammad Ikhlas, Hung-Ju Tien

We use scanning tunneling microscopy/spectroscopy (STM/S) to elucidate the atomically resolved electronic structure in strongly correlated topological kagome magnet Mn$_3$Sn. In stark contrast to its broad single-particle electronic structure, we observe a pronounced resonance with a Fano line shape at the Fermi level resembling the many-body Kondo resonance

Alexandros Papangelis, Stefan Ultes

As conversational agents become integral parts of many aspects of our lives, current approaches are reaching bottlenecks of performance that require increasing amounts of data or increasingly powerful models. It is also becoming clear that such agents are here to stay and accompany us for long periods of time. If we are, therefore, to design agents that can

Pilar Coloma, Patrick Huber, Jonathan M. Link

The XENON1T experiment recently reported an excess of events at low electron recoil energies, which may be due to interactions of solar neutrinos inside the detector via a large neutrino magnetic moment. We point out that a $^{51}$Cr neutrino source placed close to the detector can directly test this hypothesis.

Kai Zhang, Yaokang Zhu, Jun Wang, Jie Zhang

Graph neural networks are promising architecture for learning and inference with graph-structured data. Yet difficulties in modelling the ``parts&#39;&#39; and their ``interactions&#39;&#39; still persist in terms of graph classification, where graph-level representations are usually obtained by squeezing the whole graph into a single vector through graph po

Kenneth Marino, Rob Fergus, Arthur Szlam, Abhinav Gupta

This paper formulates hypothesis verification as an RL problem. Specifically, we aim to build an agent that, given a hypothesis about the dynamics of the world, can take actions to generate observations which can help predict whether the hypothesis is true or false. Existing RL algorithms fail to solve this task, even for simple environments. In order to tra

Yuze Wang, Charalampos Konstantinou, Kenichi Soga, Jason T. DeJong

Microbial-Induced Calcium carbonate (CaCO3) Precipitation (MICP) has been extensively studied for soil improvement in geotechnical engineering. The properties of calcium carbonate crystals such as size and quantity affect the strength of MICP-treated soil. This study demonstrates how the data from micro-scale microfluidic experiments that examine the effects

James Ren Hou Lee, Linda Wang, Alexander Wong

While recent advances in deep learning have led to significant improvements in facial expression classification (FEC), a major challenge that remains a bottleneck for the widespread deployment of such systems is their high architectural and computational complexities. This is especially challenging given the operational requirements of various FEC applicatio

Rui Aguiar, Nikka Mofid, Hyunji Alex Nam

There has been a current trend in reinforcement learning for healthcare literature, where in order to prepare clinical datasets, researchers will carry forward the last results of the non-administered test known as the last-observation-carried-forward (LOCF) value to fill in gaps, assuming that it is still an accurate indicator of the patient&#39;s current s

Y. Xiao, M. O. Borgh, L. S. Weiss, A. Blinova

We experimentally and theoretically explore the creation and time evolution of vortex lines in the polar magnetic phase of a trapped spin-1 $^{87}$Rb Bose-Einstein condensate. A process of phase-imprinting a nonsingular vortex, its decay into a pair of singular spinor vortices, and a rapid exchange of magnetic phases creates a pair of three-dimensional, sing

Ali-akbar Agha-mohammadi, Eric Heiden, Karol Hausman, Gaurav S. Sukhatme

Representing the environment is a fundamental task in enabling robots to act autonomously in unknown environments. In this work, we present confidence-rich mapping (CRM), a new algorithm for spatial grid-based mapping of the 3D environment. CRM augments the occupancy level at each voxel by its confidence value. By explicitly storing and evolving confidence v

Keisuke Kawano, Takuro Kutsuna, Satoshi Koide

Multiple sequences alignment (MSA) is a traditional and challenging task for time-series analyses. The MSA problem is formulated as a discrete optimization problem and is typically solved by dynamic programming. However, the computational complexity increases exponentially with respect to the number of input sequences. In this paper, we propose neural time w

Tristan C. Collins, Bin Guo, Freid Tong

We study the degenerations of asymptotically conical Ricci-flat Kähler metrics as the Kähler class degenerates to a semi-positive class. We show that under appropriate assumptions, the Ricci-flat Kähler metrics converge to a incomplete smooth Ricci-flat Kähler metric away from a compact subvariety. As a consequence, we construct singular Calabi-Yau metrics w

Steffen Lewitzka

We introduce the concept of access-based intuitionistic knowledge which relies on the intuition that agent $i$ knows $φ$ if $i$ has found access to a proof of $φ$. Basic principles are distribution and factivity of knowledge as well as $\squareφ\rightarrow K_iφ$ and $K_i(φ\veeψ) \rightarrow (K_iφ\vee K_iψ)$, where $\squareφ$ reads `$φ$ is proved&#39;. The fo

Wenshan Hong, Lu Liu, Chang Liu, Xiaoyan Ma

Sr$_2$CuTeO$_6$ is a square-lattice Néel antiferromagnet with superexchange between first-neighbor $S=1/2$ Cu spins mediated by plaquette centered Te ions. Substituting Te by W, the affected impurity plaquettes have predominantly second-neighbor interactions, thus causing local magnetic frustration. Here we report a study of Sr$_2$CuTe$_{1-x}$W$_x$O$_6$ usin

Meng Zhang, Ahmed Arafa, Jianwei Huang, H. Vincent Poor

We introduce the concept of {\it fresh data trading}, in which a destination user requests, and pays for, fresh data updates from a source provider, and data freshness is captured by the {\it age of information} (AoI) metric. Keeping data fresh relies on frequent data updates by the source, which motivates the source to {\it price fresh data}. In this work,

Haris Aziz, Ethan Brown

The Hylland-Zeckhauser (HZ) rule is a well-known rule for random assignment of items. The complexity of the rule has received renewed interest recently with Vazirani and Yannakakis (2020) proposing a strongly polynomial-time algorithm for the rule under bi-valued utilities, and making several general insights. We study the rule under the case of agents havin

Alexander J. Buser, Tanmoy Bhattacharya, Lukasz Cincio, Rajan Gupta

Recently, Singh and Chandrasekharan showed that fixed points of the non-linear O(3) sigma model can be reproduced near a quantum phase transition of a spin model with just two qubits per lattice site. In a paper by the NuQS collaboration, the proposal is made to simulate such field theories on a quantum computer using the universal properties of a similar mo

Noureddine Snanou

Recall that a $p$-group of order $p^ {n} >p^ {3} $ is of maximal class, if its nilpotency class is $n-1$. In this paper, we study the $p$-groups of maximal class. Furthermore, we introduce a subgroup of a $p$-group of maximal class called the fundamental subgroup. This group plays a fundamental role in the development of the general theory of $p$-groups of m

Sulman Khan

World of Warcraft is a massively multiplayer online video game released on November 23, 2004, by Blizzard Entertainment. In contrast with traditional games only having a single upfront fee to play, WoW also has a monthly subscription to play the game. With customer subscriptions in mind, we can apply the use of churn prediction to not only predict whether a

Anthony D. Forbes

A pentagonal geometry PENT($k$, $r$) is a partial linear space, where every line, or block, is incident with $k$ points, every point is incident with $r$ lines, and for each point $x$, there is a line incident with precisely those points that are not collinear with $x$. An opposite line pair in a pentagonal geometry consists of two parallel lines such that e

Tao Luo, Haizhao Yang

The problem of solving partial differential equations (PDEs) can be formulated into a least-squares minimization problem, where neural networks are used to parametrize PDE solutions. A global minimizer corresponds to a neural network that solves the given PDE. In this paper, we show that the gradient descent method can identify a global minimizer of the leas

Petr Plechac, Thomas N. Haider

Poetic traditions across languages evolved differently, but we find that certain semantic topics occur in several of them, albeit sometimes with temporal delay, or with diverging trajectories over time. We apply Latent Dirichlet Allocation (LDA) to poetry corpora of four languages, i.e. German (52k poems), English (85k poems), Russian (18k poems), and Czech

S. Ahmed, Mostafa Z. Chowdhury, S. R. Sabuj, M. I. Alam

The mobile relaying technique is a critical enhancing technology in wireless communications due to a higher chance of supporting the remote user from the base station (BS) with better quality of service. This paper investigates energy-efficient (EE) mobile relaying networks, mounted on an unmanned aerial vehicle (UAV), while the unknown adversaries try to in

Truman Tapia, Muhammad Zahid Mughal, Clara Rojas

In this work we study the scalar power spectrum and the spectral index for the Starobinsky inflationary model using the phase integral method up-to third-order of approximation. We show that the semiclassical methods reproduce the scalar power spectrum for the Starobinsky model with a good accuracy, and the value of the spectral index compares favorably with

Liem Nguyen

Let $F$ be a finite field, $μ$ be a fixed additive character and $s$ be an integer coprime to $|F^\times|$. For any $a\in F$, the corresponding Weil sum is defined to be $W_{F,s}(a)=\displaystyle\sum_{x \in F} μ(x^s-ax)$. The Weil spectrum counts distinct values of the Weil sum as $a$ runs through the invertible elements in the finite field. The value of the

Brian Cook, Neil Lyall, Akos Magyar

We establish $L^{p_1}\times\cdots\times L^{p_k}\to L^r$ and $\ell^{p_1}\times\cdots\times \ell^{p_k}\to \ell^r$ type bounds for multilinear maximal operators associated to averages over isometric copies of a given non-degenerate $k$-simplex in both the continuous and discrete settings. These provide natural extensions of $L^p\to L^p$ and $\ell^p\to \ell^p$ b

Mansur Arief, Zhiyuan Huang, Guru Koushik Senthil Kumar, Yuanlu Bai

Evaluating the reliability of intelligent physical systems against rare safety-critical events poses a huge testing burden for real-world applications. Simulation provides a useful platform to evaluate the extremal risks of these systems before their deployments. Importance Sampling (IS), while proven to be powerful for rare-event simulation, faces challenge

Bowen Tan, Zichao Yang, Maruan AI-Shedivat, Eric P. Xing

Large-scale language models (LMs) pretrained on massive corpora of text, such as GPT-2, are powerful open-domain text generators. However, as our systematic examination reveals, it is still challenging for such models to generate coherent long passages of text (e.g., 1000 tokens), especially when the models are fine-tuned to the target domain on a small corp

IA Grant Wilson, Shivangi Sharma, Joseph Day, Noah Godfrey

Here we present a method to combine half-hourly publicly available electrical generation and interconnector operational data for Great Britain to create a timeseries that approximates its electrical demand. We term the calculated electrical demand ESPENI that is an acronym for Elexon Sum Plus Embedded Net Imports. The method adds value to the original data b

Zhe Xu, Bo Wu, Aditya Ojha, Daniel Neider

Despite the fact that deep reinforcement learning (RL) has surpassed human-level performances in various tasks, it still has several fundamental challenges. First, most RL methods require intensive data from the exploration of the environment to achieve satisfactory performance. Second, the use of neural networks in RL renders it hard to interpret the intern

Sadegh R Alam, Tianfang Li, Pengpeng Zhang, Si-Yuan Zhang

Automated segmentation of esophagus is critical in image guided/adaptive radiotherapy of lung cancer to minimize radiation-induced toxicities such as acute esophagitis. We developed a semantic physics-based data augmentation method for segmenting esophagus in both planning CT (pCT) and cone-beam CT (CBCT) using 3D convolutional neural networks. 191 cases wit

Sonja Otten, Ruslan Krenzler, Hans Daduna, Karsten Kruse

We consider exponential single server queues with state-dependent arrival and service rates which evolve under influences of external environments. The transitions of the queues are influenced by the environment&#39;s state and the movements of the environment depend on the status of the queues (bi-directional interaction). The environment is constructed in

Gary F. Hatke, Monica Montanari, Swaroop Appadwedula, Michael Wentz

The process of contact tracing to reduce the spread of highly infectious and life-threatening diseases has traditionally been a primarily manual process managed by public health entities. This process becomes challenged when faced with a pandemic of the proportions of SARS-CoV2. Digital contact tracing has been proposed as way to augment manual contact traci

Hanchao Yu, Xiao Chen, Humphrey Shi, Terrence Chen

Cardiac motion estimation plays a key role in MRI cardiac feature tracking and function assessment such as myocardium strain. In this paper, we propose Motion Pyramid Networks, a novel deep learning-based approach for accurate and efficient cardiac motion estimation. We predict and fuse a pyramid of motion fields from multiple scales of feature representatio

Ryan-Rhys Griffiths, Jake L. Greenfield, Aditya R. Thawani, Arian R. Jamasb

Photoswitchable molecules display two or more isomeric forms that may be accessed using light. Separating the electronic absorption bands of these isomers is key to selectively addressing a specific isomer and achieving high photostationary states whilst overall red-shifting the absorption bands serves to limit material damage due to UV-exposure and increase

Lionel Blondé, Pablo Strasser, Alexandros Kalousis

Despite the recent success of reinforcement learning in various domains, these approaches remain, for the most part, deterringly sensitive to hyper-parameters and are often riddled with essential engineering feats allowing their success. We consider the case of off-policy generative adversarial imitation learning, and perform an in-depth review, qualitative

Myeong Lee, Seongkyu Lee, Seonghoon Kim, Noseong Park

The COVID-19 pandemic has brought both tangible and intangible damage to our society. Many researchers studied about its societal impacts in the countries that had implemented strong social distancing measures such as stay-at-home orders. Among them, human mobility has been studied extensively due to its importance in flattening the curve. However, mobility

Govind Menon

This paper describes the electrodynamics of a null and force-free field in completely geometric terms. As was previously established in \cite{Menon_FF20}, solutions to force-free electrodynamics are governed by the existence of certain special types of foliations of spacetime. Here the nature of the foliations in a coordinate-free formalism in the null case

Anton Bernshteyn

We present a deterministic distributed algorithm in the LOCAL model that finds a proper $(Δ+ 1)$-edge-coloring of an $n$-vertex graph of maximum degree $Δ$ in $\mathrm{poly}(Δ, \log n)$ rounds. This is the first nontrivial distributed edge-coloring algorithm that uses only $Δ+1$ colors (matching the bound given by Vizing&#39;s theorem). Our approach is inspi

Timothy Budd

We consider maps on genus-$g$ surfaces with $n$ (labeled) faces of prescribed even degrees. It is known since work of Norbury that, if one disallows vertices of degree one, the enumeration of such maps is related to the counting of lattice point in the moduli space of genus-$g$ curves with $n$ labeled points and is given by a symmetric polynomial $N_{g,n}(\e

Andrew C. Kirby, Dimitri J. Mavriplis

A discontinuous Galerkin method for the discretization of the compressible Euler equations, the governing equations of inviscid fluid dynamics, on Cartesian meshes is developed for use of Graphical Processing Units via OCCA, a unified approach to performance portability on multi-threaded hardware architectures. A 30x time-to-solution speedup over CPU-only im

Jonathan Kress, Konrad Schöbel, Andreas Vollmer

We show that the definition of a second order superintegrable system on a (pseudo-)Riemannian manifold gives rise to a conformally invariant notion of superintegrability. Conformal equivalence is the natural extension of the well-known Stäckel transform, which in turn originates from the classical Maupertuis-Jacobi principle. We extend our recently developed

Chun-Hung Liu

A graph $G$ contains another graph $H$ as an immersion if $H$ can be obtained from a subgraph of $G$ by splitting off edges and removing isolated vertices. There is an obvious necessary degree condition for the immersion containment: if $G$ contains $H$ as an immersion, then for every integer $k$, the number of vertices of degree at least $k$ in $G$ is at le

Yuankai Huo, Jinzheng Cai, Chi-Tung Cheng, Ashwin Raju

Non-invasive radiological-based lesion characterization and identification, e.g., to differentiate cancer subtypes, has long been a major aim to enhance oncological diagnosis and treatment procedures. Here we study a specific population of human subjects, with the hope of reducing the need for invasive surgical biopsies of liver cancer patients, which can ca

Pierre Germain, Fabio Pusateri

This paper proposes a fairly general new point of view on the question of asymptotic stability of (topological) solitons. Our approach is based on the use of the distorted Fourier transform at the nonlinear level; it does not rely on Strichartz or virial estimates and is therefore able to treat low power nonlinearities (hence also non-localized solitons) and

Renaud Gauthier

Given a spectral Deligne-Mumford stack $X$, we define a perception of $X$ to be a collection of a certain class of morphisms $Y \rightarrow X$. For the class of affine morphisms in SpDM, we show that from QCoh($X$) on can extract the affine perception $\text{Aff}_X$ of $X$ on the one hand, and a subcategory of an $\infty$-category of representations $\text{R

Mats J. Stensrud, James M. Robins, Aaron Sarvet, Eric J. Tchetgen Tchetgen

Researchers are often interested in treatment effects on outcomes that are only defined conditional on a post-treatment event status. For example, in a study of the effect of different cancer treatments on quality of life at end of follow-up, the quality of life of individuals who die during the study is undefined. In these settings, a naive contrast of outc

Anirban Chakraborty, Debasis Ganguly, Annalina Caputo, Gareth J. F. Jones

An automated contextual suggestion algorithm is likely to recommend contextually appropriate and personalized &#39;points-of-interest&#39; (POIs) to a user, if it can extract information from the user&#39;s preference history (exploitation) and effectively blend it with the user&#39;s current contextual information (exploration) to predict a POI&#39;s &#39;a

Bjorn K. Berntson, Ian Marquette, Willard Miller,

We revise a method by Kalnins, Kress and Miller (2010) for constructing a canonical form for symmetry operators of arbitrary order for the Schrödinger eigenvalue equation $HΨ\equiv (Δ_2 +V)Ψ=EΨ$ on any 2D Riemannian manifold, real or complex, that admits a separation of variables in some orthogonal coordinate system. We apply the method, as an example, to re

Kyriakos Vattis, Isabelle S. Goldstein, Savvas M. Koushiappas

On June 20, 2020, the LIGO-Virgo collaboration announced the discovery of GW190814, a gravitational wave event originating from a binary system merger between a black hole of mass $M_1 = 23.2^{+1.1} _ {-1.0}M_\odot$ and an unidentified object with a mass of $M_2 = 2.59^{+0.08} _ {-0.09}M_\odot$. This second object would be either the heaviest neutron star or

Adriano Verna, Giovanni Stefani, Francesco Offi, Tatsuo Gejo

An experimental and computational investigation of the space-charge effects occurring in ultrafast photoelectron spectroscopy from the gas phase is presented. The target sample CF$_3$I is excited by ultrashort (100 fs) far-ultraviolet radiation pulses produced by a free-electron laser. The modification of the energy distribution of the photoelectrons, i.e. t

Ferdinando Fioretto, Pascal Van Hentenryck, Keyu Zhu

This paper is motivated by applications of a Census Bureau interested in releasing aggregate socio-economic data about a large population without revealing sensitive information about any individual. The released information can be the number of individuals living alone, the number of cars they own, or their salary brackets. Recent events have identified som

Haipeng An, Daneng Yang

We show that the current sensitivities of direct detection experiments have already reached the interesting parameter space of freeze-in dark matter models if the dark sector is in the inelastic dark matter framework and the excited dark matter state is cosmologically stable. Using results recently presented by the XENON1T experiment, we present constraints

B. Leimkuhler, A. Sharma, M. V. Tretyakov

A simple-to-implement weak-sense numerical method to approximate reflected stochastic differential equations (RSDEs) is proposed and analysed. It is proved that the method has the first order of weak convergence. Together with the Monte Carlo technique, it can be used to numerically solve linear parabolic and elliptic PDEs with Robin boundary condition. One

Nurislam Tursynbek, Aleksandr Petiushko, Ivan Oseledets

The brittleness of deep image classifiers to small adversarial input perturbations has been extensively studied in the last several years. However, the main objective of existing perturbations is primarily limited to change the correctly predicted Top-1 class by an incorrect one, which does not intend to change the Top-k prediction. In many digital real-worl

O. Seri-Livni, C. Saguy, F. Horani, E. Lifshitz

Graphene oxide (GO) raised substantial interest in the last two decades thanks to its unique properties beyond those of pristine graphene, including electronic energy band-gap, hydrophilic behavior and numerous anchoring sites required for functionalization. In addition, GO was found to be a cheap mass-production source for the formation of the pristine grap

X. Jessie Jeng, Yifei Hu

We consider the scenario where important signals are not strong enough to be separable from a large amount of noise. Such weak signals commonly exist in large-scale data analysis and play vital roles in many biomedical applications. Existing methods however are mostly underpowered for such weak signals. We address the challenge from the perspective of false

Bernd Fritzke

We introduce the breathing k-means algorithm, which on average significantly improves solutions obtained by the widely-known greedy k-means++ algorithm, the default method for k-means clustering in the scikit-learn package. The improvements are achieved through a novel ``breathing&#39;&#39; technique, that cyclically increases and decreases the number of cen

L. N. Gonçalves, J. C. Fernandes, A. Ferraz, A. G. Silva

A detailed analysis of three pendular motion models is presented. Inertial effects, self-oscillation, and memory, together with non-constant moment of inertia, hysteresis and negative damping are shown to be required for the comprehensive description of the free pendulum oscillatory regime. The effects of very high initial amplitudes, friction in the roller

Dave Epstein, Carl Vondrick

We introduce a framework that predicts the goals behind observable human action in video. Motivated by evidence in developmental psychology, we leverage video of unintentional action to learn video representations of goals without direct supervision. Our approach models videos as contextual trajectories that represent both low-level motion and high-level act

Nikolai Bunzmann, Benjamin Krugmann, Sebastian Weissenseel, Liudmila Kudriashova

Organic light emitting diodes (OLEDs) based on thermally activated delayed fluorescence (TADF) utilize molecular systems with a small energy splitting between singlet and triplet states. This can either be realized in intramolecular charge transfer states of molecules with near-orthogonal donor and acceptor moieties or in intermolecular exciplex states forme

Tomas Neustupa

We deal with a steady Stokes-type problem, associated with a flow of a Newtonian incompressible fluid through a spatially periodic profile cascade. The used mathematical model is based on the reduction to one spatial period, represented by a bounded 2D domain Omega. The corresponding Stokes-type problem is formulated by means of the Stokes equation, equation

Alicia Mergenthaler, Taha Yasseri

Sex work, or the exchange of sexual services for money or goods, is ubiquitous across eras and cultures. However, the practice of selling sex is often hidden due to stigma and the varying legal status of sex work. Online platforms that sex workers use to advertise services have become an increasingly important means of studying a market that is largely hidde

Waïss Azizian, Marc Lelarge

Various classes of Graph Neural Networks (GNN) have been proposed and shown to be successful in a wide range of applications with graph structured data. In this paper, we propose a theoretical framework able to compare the expressive power of these GNN architectures. The current universality theorems only apply to intractable classes of GNNs. Here, we prove