October 2020 arXiv papers — page 84

Chad Nester

This paper introduces an approach to constructing ledger structures for cryptocurrency systems with basic category theory. Compositional theories of resource convertibility allow us to express the material history of virtual goods, and ownership is modelled by a free construction. Our notion of ownership admits an intuitive graphical representation through s

Nilanjan Bondyopadhaya, Dibyendu Roy

We study nonequilibrium transport in various open quantum systems whose systems and leads/baths are made of topological superconductors (TSs), semiconductors, and metals. Using quantum Langevin equations and Green's function method, we derive exact expressions for steady-state electrical, thermal, and spin current at the junctions between a system and le

Cheng-Ping Zhang, Jiewen Xiao, Benjamin T. Zhou, Jin-Xin Hu

Recent studies have shown that moir\'{e} flat bands in a twisted bilayer graphene(TBG) can acquire nontrivial Berry curvatures when aligned with hexagonal boron nitride substrate [1, 2], which can be manifested as a correlated Chern insulator near the 3/4 filling [3, 4]. In this work, we show that the large Berry curvatures in the moir\'{e} bands lead to str

Anirban Basu

We consider certain elliptic modular graph functions that arise in the asymptotic expansion around the non--separating node of genus two string invariants that appear in the integrand of the $D^8 R^4$ interaction in the low momentum expansion of the four graviton amplitude in type II superstring theory. These elliptic modular graphs have links given by the G

Cesare Giulio Ardito, Elliot McKernon

We study blocks with an abelian defect group and a cyclic inertial quotient acting freely but not transitively. We prove that when p=2, such blocks are inertial, i.e. basic Morita equivalent to their Brauer correspondent. Together with a result of the second author on Singer cycle actions on homocyclic defect groups, this completes the classification of 2-bl

Han Woong Yoo, Rene Riegler, David Brunner, Stephan Albert

This paper demonstrates a vibration test for a resonant MEMS scanning system in operation to evaluate the vibration immunity for automotive lidar applications. The MEMS mirror has a reinforcement structure on the backside of the mirror, causing vibration coupling by a mismatch between the center of mass and the rotation axis. An analysis of energy variation

Dorothee Frey, Pierre Portal

Peral/Miyachi's celebrated theorem on fixed time $L^{p}$ estimates with loss of derivatives for the wave equation states that the operator $(I-Δ)^{- \fracα{2}}\exp(i \sqrt{-Δ})$ is bounded on $L^{p}(\mathbb{R}^{d})$ if and only if $α\geq s_{p}:=(d-1)|\frac{1}{p}-\frac{1}{2}|$. We extend this result to operators of the form $\mathcal{L} = -\sum \limits _{

Chusei Kiumi, Kei Saito

We study space-inhomogeneous quantum walks (QWs) on the integer lattice which we assign three different coin matrices to the positive part, the negative part, and the origin, respectively. We call them two-phase QWs with one defect. They cover one-defect and two-phase QWs, which have been intensively researched. Localization is one of the most characteristic

Yichen Qian, Zhiyu Tan, Xiuyu Sun, Ming Lin

In recent deep image compression neural networks, the entropy model plays a critical role in estimating the prior distribution of deep image encodings. Existing methods combine hyperprior with local context in the entropy estimation function. This greatly limits their performance due to the absence of a global vision. In this work, we propose a novel Global

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

The Born cross-sections and effective form factors for process $e^+e^-\toΞ^-\barΞ^+$ are measured at eight center-of-mass energies between 2.644 and 3.080 GeV, using a total integrated luminosity of 363.9 pb$^{-1}$ $e^+e^-$ collision data collected with the BESIII detector at BEPCII. After performing a fit to the Born cross-section of $e^+e^-\toΞ^-\barΞ^+$,

Andrew Moore, Jeremy Barnes

The majority of work in targeted sentiment analysis has concentrated on finding better methods to improve the overall results. Within this paper we show that these models are not robust to linguistic phenomena, specifically negation and speculation. In this paper, we propose a multi-task learning method to incorporate information from syntactic and semantic

Naoka Hiraoka, Kelton Whiteaker, Marian Blankenhorn, Yoshiyuki Hayashi

We have developed a much sensitve technique to conduct magnetometry under ultrahigh pressures up to 6.3~GPa, which can detect a weak volume susceptibilities as small as $\sim 10^{-4}$. An opposed-anvil-type high-pressure cell is designed by numerical analysis to give nearly zero magnetic response, in a commercial SQUID magnetometer. We introduced procedures

Wei Huang, Yifan Zhou, Gaojie Jin, Youcheng Sun

This paper presents a formal verification guided approach for a principled design and implementation of robust and resilient learning-enabled systems. We focus on learning-enabled state estimation systems (LE-SESs), which have been widely used in robotics applications to determine the current state (e.g., location, speed, direction, etc.) of a complex system

Hans Havlicek, Karl Svozil

Criteria and constructive methods for the completion of an incomplete basis of, or context in, a four-dimensional Hilbert space by (in)decomposable vectors are given.

Rakesh Arora, Sergey Shmarev

This paper addresses the questions of existence and uniqueness of strong solutions to the homogeneous Dirichlet problem for the double phase equation with operators of variable growth: \[ u_t - div \left(|\nabla u|^{p(z)-2} \nabla u+ a(z) |\nabla u|^{q(z)-2} \nabla u \right) = F(z,u) \quad \text{in $Q_T=Ω\times (0,T)$} \] where $Ω\subset \mathbb{R}^N$, $N \g

Zhoujian Sun, Wei Dong, Jinlong Shi, Zhengxing Huang

Objective: To combine medical knowledge and medical data to interpretably predict the risk of disease. Methods: We formulated the disease prediction task as a random walk along a knowledge graph (KG). Specifically, we build a KG to record relationships between diseases and risk factors according to validated medical knowledge. Then, a mathematical object wal

Keenan Granland, Rhys Newbury, David Ting, Chao Chen

Training of convolutional neural networks for semantic segmentation requires accurate pixel-wise labeling which requires large amounts of human effort. The human-in-the-loop method reduces labeling effort; however, it requires human intervention for each image. This paper describes a general iterative training methodology for semantic segmentation, Automatin

Mathieu Ballandras

We study various trivializations of moment maps. First in the general framework of a reductive group $G$ acting on a smooth affine variety. We prove that the moment map is a locally trivial fibration over a regular locus of the center of the Lie algebra of $H$ a maximal compact subgroup of $G$. The construction relies on Kempf-Ness theory and Morse theory of

Masaaki Fukasawa, Kazuki Matsushita

Generalizing the realized variance, the realized skewness (Neuberger, 2012) and the realized kurtosis (Bae and Lee, 2020), we construct realized cumulants with the so-called aggregation property. They are unbiased statistics of the cumulants of a martingale marginal based on sub-period increments of the martingale and its lower-order conditional cumulant pro

Hasan Yılmaz, Matthias Kühmayer, Chia Wei Hsu, Stefan Rotter

The memory effect in disordered systems is a key physical phenomenon that has been employed for optical imaging, metrology, and communication through opaque media. Under the conventional memory effect, when the incident beam is tilted slightly, the transmitted pattern tilts in the same direction. However, the "memory" is limited in its angular range

Jr-Jiun Lin, Chi-Chun Cheng, Yu-Chuan Cheng, Jih-Chiang Tsai

Statically indeterminate systems are experimentally demonstrated to be in fact dynamical at the microscopic scale. Take the classic ladder-wall problem, for instance. Depending on the Young's modulus of the wall, it may take up to twenty minutes before its weight saturates. This finding is shown to be shared by other statically indeterminate systems, such as

Peng W. Zhang, Francis C. M. Lau, Chiu-W. Sham

In this paper, we propose a new method to design low-density parity-check Hadamard (LDPC-Hadamard) codes, a type of ultimate-Shannon-limit approaching channel codes. The technique is based on applying Hadamard constraints to the check nodes in a generalized protograph-based LDPC code, followed by lifting the generalized protograph. We name the codes formed p

Wei Huang, Xingyu Zhao, Xiaowei Huang

The embedding and extraction of useful knowledge is a recent trend in machine learning applications, e.g., to supplement existing datasets that are small. Whilst, as the increasing use of machine learning models in security-critical applications, the embedding and extraction of malicious knowledge are equivalent to the notorious backdoor attack and its defen

Tran Nguyen Le, Francesco Verdoja, Fares J. Abu-Dakka, Ville Kyrki

Accurately modeling local surface properties of objects is crucial to many robotic applications, from grasping to material recognition. Surface properties like friction are however difficult to estimate, as visual observation of the object does not convey enough information over these properties. In contrast, haptic exploration is time consuming as it only p

Biao Zhang, Peter Wonka

We propose a novel 3d shape representation for 3d shape reconstruction from a single image. Rather than predicting a shape directly, we train a network to generate a training set which will be fed into another learning algorithm to define the shape. The nested optimization problem can be modeled by bi-level optimization. Specifically, the algorithms for bi-l

Vera Melinda Galfi, Valerio Lucarini

Extreme events provide relevant insights into the dynamics of climate and their understanding is key for mitigating the impact of climate variability and climate change. By applying large deviation theory to a state-of-the-art Earth system model, we define the climatology of persistent heatwaves and cold spells in key target geographical regions by estimatin

Ugur Tirnakli, Constantino Tsallis, Nihat Ay

The standard Large Deviation Theory (LDT) is mathematically illustrated by the Boltzmann-Gibbs factor which describes the thermal equilibrium of short-range-interacting many-body Hamiltonian systems, the velocity distribution of which is Maxwellian. It is generically applicable to systems satisfying the Central Limit Theorem (CLT). When we focus instead on s

Mark Berger, Jakub Zavrel, Paul Groth

Expert search aims to find and rank experts based on a user's query. In academia, retrieving experts is an efficient way to navigate through a large amount of academic knowledge. Here, we study how different distributed representations of academic papers (i.e. embeddings) impact academic expert retrieval. We use the Microsoft Academic Graph dataset and exper

Laurent Bétermin

Certain types of neurons, called "grid cells", have been shown to fire on a triangular grid when an animal is navigating on a two-dimensional environment, whereas recent studies suggest that the face-centred-cubic (FCC) lattice is the good candidate for the same phenomenon in three dimensions. The goal of this paper is to give new evidences of these

Bernd Illing, Jean Ventura, Guillaume Bellec, Wulfram Gerstner

Learning in the brain is poorly understood and learning rules that respect biological constraints, yet yield deep hierarchical representations, are still unknown. Here, we propose a learning rule that takes inspiration from neuroscience and recent advances in self-supervised deep learning. Learning minimizes a simple layer-specific loss function and does not

Hannes Saffrich, Peter Thiemann

Imperative session types provide an imperative interface to session-typed communication. In such an interface, channel references are first-class objects with operations that change the typestate of the channel. Compared to functional session type APIs, the program structure is simpler at the surface, but typestate is required to model the current state of c

Benjamin Midtvedt, Saga Helgadottir, Aykut Argun, Jesús Pineda

Video microscopy has a long history of providing insights and breakthroughs for a broad range of disciplines, from physics to biology. Image analysis to extract quantitative information from video microscopy data has traditionally relied on algorithmic approaches, which are often difficult to implement, time consuming, and computationally expensive. Recently

Demetrio Lacava, Giampiero M. Gallo, Edoardo Otranto

Taking the European Central Bank unconventional policies as a reference, we suggest a class of Multiplicative Error Models (MEM) taylored to analyze the impact such policies have on stock market volatility. The new set of models, called MEM with Asymmetry and Policy effects (MAP), keeps the base volatility dynamics separate from a component reproducing polic

Fan Bao, Kun Xu, Chongxuan Li, Lanqing Hong

The learning and evaluation of energy-based latent variable models (EBLVMs) without any structural assumptions are highly challenging, because the true posteriors and the partition functions in such models are generally intractable. This paper presents variational estimates of the score function and its gradient with respect to the model parameters in a gene

Cristian Barrera-Hinojosa, Baojiu Li, Marco Bruni, Jian-hua He

We investigate the transverse modes of the gravitational and velocity fields in $Λ$CDM, based on a high-resolution simulation performed using the adaptive-mesh refinement general-relativistic $N$-body code GRAMSES. We study the generation of vorticity in the dark matter velocity field at low redshift, providing fits to the shape and evolution of its power sp

Radoslav Fulek, Balázs Keszegh

A $0$-$1$ matrix $M$ is saturating for a $0$-$1$ matrix $P$ if $M$ does not contain a submatrix that can be turned into $P$ by changing some $1$ entries to $0$ entries, and changing an arbitrary $0$ to $1$ in $M$ introduces such a submatrix in $M$. In saturation problems for $0$-$1$ matrices we are interested in estimating the minimum number of $1$ entries i

David S. Berman, Kwangeon Kim, Kanghoon Lee

We construct the classical double copy formalism for M-theory. This extends the current state of the art by including the three form potential of eleven dimensional supergravity along with the metric. The key for this extension is to construct a Kerr-Schild type Ansatz for exceptional field theory. This Kerr-Schild Ansatz then allows us to find the solutions

Jiancong Huang, Juan Rojas, Matthieu Zimmer, Hongmin Wu

Policy optimization in reinforcement learning requires the selection of numerous hyperparameters across different environments. Fixing them incorrectly may negatively impact optimization performance leading notably to insufficient or redundant learning. Insufficient learning (due to convergence to local optima) results in under-performing policies whilst red

Johannes Bjerva, Elizabeth Salesky, Sabrina J. Mielke, Aditi Chaudhary

Typological knowledge bases (KBs) such as WALS (Dryer and Haspelmath, 2013) contain information about linguistic properties of the world's languages. They have been shown to be useful for downstream applications, including cross-lingual transfer learning and linguistic probing. A major drawback hampering broader adoption of typological KBs is that they a

Chilong Lin

In this article, we present a very general but not ultimate solution of CPV problem in the standard model. Our study starts from a naturally Hermitian ${\bf M^2}\equiv M^q \cdot M^{q\dagger}$ rather than the previously assumed Hermitian $M^q$. The only assumption employed here is that the real part and imaginary part of $\bf M^2$ can be, respectively, diagon

Cristiano Saltori, Stéphane Lathuiliére, Nicu Sebe, Elisa Ricci

3D object detectors based only on LiDAR point clouds hold the state-of-the-art on modern street-view benchmarks. However, LiDAR-based detectors poorly generalize across domains due to domain shift. In the case of LiDAR, in fact, domain shift is not only due to changes in the environment and in the object appearances, as for visual data from RGB cameras, but

Nandan Thakur, Nils Reimers, Johannes Daxenberger, Iryna Gurevych

There are two approaches for pairwise sentence scoring: Cross-encoders, which perform full-attention over the input pair, and Bi-encoders, which map each input independently to a dense vector space. While cross-encoders often achieve higher performance, they are too slow for many practical use cases. Bi-encoders, on the other hand, require substantial traini

Takao Murakami, Kenta Takahashi

LDP (Local Differential Privacy) has recently attracted much attention as a metric of data privacy that prevents the inference of personal data from obfuscated data in the local model. However, there are scenarios in which the adversary wants to perform re-identification attacks to link the obfuscated data to users in this model. LDP can cause excessive obfu

Oscar Hernan Madrid Padilla, Wesley Tansey, Yanzhen Chen

Quantile regression is the task of estimating a specified percentile response, such as the median, from a collection of known covariates. We study quantile regression with rectified linear unit (ReLU) neural networks as the chosen model class. We derive an upper bound on the expected mean squared error of a ReLU network used to estimate any quantile conditio

Chad Nester

We identify the algebraic structure of the material histories generated by concurrent processes. Specifically, we extend existing categorical theories of resource convertibility to capture concurrent interaction. Our formalism admits an intuitive graphical presentation via string diagrams for proarrow equipments. We also consider certain induced categories o

Roy Overbeek, Jörg Endrullis, Aloïs Rosset

We extend the powerful Pullback-Pushout (PBPO) approach for graph rewriting with strong matching. Our approach, called \pbpostrong, exerts more control over the embedding of the pattern in the host graph, which is important for a large class of graph rewrite systems. In addition, we show that \pbpostrong is well-suited for rewriting labeled graphs and certai

Krzysztof Domino, Mátyás Koniorczyk, Krzysztof Krawiec, Konrad Jałowiecki

In this work, we consider a practical railway dispatching problem: delay and conflict management on a single-track railway line. We examine the issue of train dispatching consequences caused by the arrival of an already delayed train to the segment being considered. This problem is computationally hard and often needs to be solved timely. Here, we introduce

Caucher Birkar

We study the non-klt locus of singularities of pairs. We show that given a pair $(X,B)$ and a projective morphism $X\to Z$ with connected fibres such that $-(K_X+B)$ is nef over $Z$, the non-klt locus of $(X,B)$ has at most two connected components near each fibre of $X\to Z$. This was conjectured by Hacon and Han. In a different direction we answer a questi

Dan Alistarh, Janne H. Korhonen

We consider a standard distributed optimisation setting where $N$ machines, each holding a $d$-dimensional function $f_i$, aim to jointly minimise the sum of the functions $\sum_{i = 1}^N f_i (x)$. This problem arises naturally in large-scale distributed optimisation, where a standard solution is to apply variants of (stochastic) gradient descent. We focus o

Ilias Aouaj, Vincent Padois, Alessandro Saccon

Accurate post-impact velocity predictions are essential in developing impact-aware manipulation strategies for robots, where contacts are intentionally established at non-zero speed mimicking human manipulation abilities in dynamic grasping and pushing of objects. Starting from the recorded dynamic response of a 7DOF torque-controlled robot that intentionall

Yuge Zhang, Quanlu Zhang, Yaming Yang

Weight sharing, as an approach to speed up architecture performance estimation has received wide attention. Instead of training each architecture separately, weight sharing builds a supernet that assembles all the architectures as its submodels. However, there has been debate over whether the NAS process actually benefits from weight sharing, due to the gap

Hiroki Tanabe, Ellen H. Fukuda, Nobuo Yamashita

Many descent algorithms for multiobjective optimization have been developed in the last two decades. Tanabe et al. (Comput Optim Appl 72(2):339--361, 2019) proposed a proximal gradient method for multiobjective optimization, which can solve multiobjective problems, whose objective function is the sum of a continuously differentiable function and a closed, pr

Zheng Liu, Fu Zhang

A local Bundle Adjustment (BA) on a sliding window of keyframes has been widely used in visual SLAM and proved to be very effective in lowering the drift. But in lidar SLAM, BA method is hardly used because the sparse feature points (e.g., edge and plane) make the exact point matching impossible. In this paper, we formulate the lidar BA as minimizing the dis

Seonghoon Choi, Jiří Vaníček

Choosing an appropriate representation of the molecular Hamiltonian is one of the challenges faced by simulations of the nonadiabatic quantum dynamics around a conical intersection. The adiabatic, exact quasidiabatic, and strictly diabatic representations are exact and unitary transforms of each other, whereas the approximate quasidiabatic Hamiltonian ignore

Yanghoon Kim, Seungpil Won, Seunghyun Yoon, Kyomin Jung

Applying generative adversarial networks (GANs) to text-related tasks is challenging due to the discrete nature of language. One line of research resolves this issue by employing reinforcement learning (RL) and optimizing the next-word sampling policy directly in a discrete action space. Such methods compute the rewards from complete sentences and avoid erro

Gerard Besson, Sylvestre Gallot

The purpose of this report is to acknowledge the influence of M. Gromov's vision of geometry on our own works. It is two-fold: in the first part we aim at describing some results, in dimension 3, around the question: which open 3-manifolds carry a complete Riemannian metric of positive or non negative scalar curvature? In the second part we look for weak

He Wang, Jin Wang

We quantify the quantum correlations between two accelerated detectors coupled to a scalar field in a cavity. It has been realized that an accelerated detector will experience a thermal bath, which is termed the Unruh effect. We examine the similarities and differences for quantum correlations regarding either temperature or acceleration. As the acceleration

Xiaotong Chen, Kaizhi Zheng, Zhen Zeng, Cameron Kisailus

In order to enable robust operation in unstructured environments, robots should be able to generalize manipulation actions to novel object instances. For example, to pour and serve a drink, a robot should be able to recognize novel containers which afford the task. Most importantly, robots should be able to manipulate these novel containers to fulfill the ta

Elham Daneshmand, Majid Khadiv, Felix Grimminger, Ludovic Righetti

In this paper, we present a novel two-level variable Horizon Model Predictive Control (VH-MPC) framework for bipedal locomotion. In this framework, the higher level computes the landing location and timing (horizon length) of the swing foot to stabilize the unstable part of the center of mass (CoM) dynamics, using feedback from the CoM states. The lower leve

Boyan Wan, Mishal Sohail

Copy mechanism allows sequence-to-sequence models to choose words from the input and put them directly into the output, which is finding increasing use in abstractive summarization. However, since there is no explicit delimiter in Chinese sentences, most existing models for Chinese abstractive summarization can only perform character copy, resulting in ineff

Wei Xu, Fu Zhang

This paper presents a computationally efficient and robust LiDAR-inertial odometry framework. We fuse LiDAR feature points with IMU data using a tightly-coupled iterated extended Kalman filter to allow robust navigation in fast-motion, noisy or cluttered environments where degeneration occurs. To lower the computation load in the presence of large number of

Philippe Robert, Gaetan Vignoud

In neuroscience, learning and memory are usually associated to long-term changes of neuronal connectivity. In this context, synaptic plasticity refers to the set of mechanisms driving the dynamics of neuronal connections, called {\em synapses} and represented by a scalar value, the synaptic weight. Spike-Timing Dependent Plasticity (STDP) is a biologically-b

Cristino de Souza, Rhys Newbury, Akansel Cosgun, Pedro Castillo

Pursuit-evasion is the problem of capturing mobile targets with one or more pursuers. We use deep reinforcement learning for pursuing an omni-directional target with multiple, homogeneous agents that are subject to unicycle kinematic constraints. We use shared experience to train a policy for a given number of pursuers that is executed independently by each

Yingqi Qu, Yuchen Ding, Jing Liu, Kai Liu

In open-domain question answering, dense passage retrieval has become a new paradigm to retrieve relevant passages for finding answers. Typically, the dual-encoder architecture is adopted to learn dense representations of questions and passages for semantic matching. However, it is difficult to effectively train a dual-encoder due to the challenges including

Yuang Shi, Chen Zu, Mei Hong, Luping Zhou

With the increasing amounts of high-dimensional heterogeneous data to be processed, multi-modality feature selection has become an important research direction in medical image analysis. Traditional methods usually depict the data structure using fixed and predefined similarity matrix for each modality separately, without considering the potential relationsh

Sunghyun Park, Kangyeol Kim, Junsoo Lee, Jaegul Choo

Video generation models often operate under the assumption of fixed frame rates, which leads to suboptimal performance when it comes to handling flexible frame rates (e.g., increasing the frame rate of the more dynamic portion of the video as well as handling missing video frames). To resolve the restricted nature of existing video generation models' abi

Mehul Damani, Zhiyao Luo, Emerson Wenzel, Guillaume Sartoretti

Multi-agent path finding (MAPF) is an indispensable component of large-scale robot deployments in numerous domains ranging from airport management to warehouse automation. In particular, this work addresses lifelong MAPF (LMAPF) - an online variant of the problem where agents are immediately assigned a new goal upon reaching their current one - in dense and

Alexander Shevtsov, Maria Oikonomidou, Despoina Antonakaki, Polyvios Pratikakis

The presidential elections in the United States on 3 November 2020 have caused extensive discussions on social media. A part of the content on US elections is organic, coming from users discussing their opinions of the candidates, political positions, or relevant content presented on television. Another significant part of the content generated originates fr

Junfang Gong, Shengwen Li, Xinyue Ye, Qiong Peng

High-speed rail (HSR) has become an important mode of inter-city transportation between large cities. Inter-city interaction facilitated by HSR tends to play a more prominent role in promoting urban and regional economic integration and development. Quantifying the impact of HSR's interaction on cities and people is therefore crucial for long-term urban

Ali Rida Khalifeh, Raul Jimenez

We study the interaction, in general curved spacetime, between a spinor and a scalar field describing dark energy; the so-called DE$_ν$ model in curved space. The dominant term is the dimension 5 operator, which results in different energy shifts for the neutrino states: an Aharonov-Bohm-like effect. We study the phenomenology of this term and make observati

Yuanyu Wan, Lijun Zhang

To efficiently solve online problems with complicated constraints, projection-free algorithms including online frank-wolfe (OFW) and its variants have received significant interest recently. However, in the general case, existing efficient projection-free algorithms only achieved the regret bound of $O(T^{3/4})$, which is worse than the regret of projection-

Xuanhong Chen, Xirui Yan, Naiyuan Liu, Ting Qiu

Though significant progress has been made in artistic style transfer, semantic information is usually difficult to be preserved in a fine-grained locally consistent manner by most existing methods, especially when multiple artists styles are required to transfer within one single model. To circumvent this issue, we propose a Stroke Control Multi-Artist Style

Haruki Nishimura, Negar Mehr, Adrien Gaidon, Mac Schwager

Successful robotic operation in stochastic environments relies on accurate characterization of the underlying probability distributions, yet this is often imperfect due to limited knowledge. This work presents a control algorithm that is capable of handling such distributional mismatches. Specifically, we propose a novel nonlinear MPC for distributionally ro

Cheng-Yu Kuo, Andreas Schaarschmidt, Yunduan Cui, Tamim Asfour

This letter presents contact-safe Model-based Reinforcement Learning (MBRL) for robot applications that achieves contact-safe behaviors in the learning process. In typical MBRL, we cannot expect the data-driven model to generate accurate and reliable policies to the intended robotic tasks during the learning process due to sample scarcity. Operating these un

Seongsin Kim, Ungki Lee, Ikjin Lee, Namwoo Kang

In optimization of a shared autonomous electric vehicle (SAEV) system, idle vehicle relocation strategies are important to reduce operation costs and customers' wait time. However, for an on-demand service, continuous optimization for idle vehicle relocation is computationally expensive, and thus, not effective. This study proposes a deep learning-based algo

David Darrow

In a previous work, we showed that the 2D, extended-source internal DLA (IDLA) of Levine and Peres is $δ^{3/5}$-close to its scaling limit, if $δ$ is the lattice size. In this paper, we investigate the scaling limits of the fluctuations themselves. Namely, we show that two naturally defined error functions, which measure the "lateness" of lattice poi

Topojit Debnath, Bishwajit Debnath, Roger K. Lake

The high breakdown current densities and resilience to scaling of the metallic transition metal trichalcogenides TaSe3 and ZrTe3 make them of interest for possible interconnect applications, and it motivates this study of their thermal conductivities and phonon properties. These crystals consist of planes of strongly bonded one-dimensional chains more weakly

Anshul Shah, Shlok Mishra, Ankan Bansal, Jun-Cheng Chen

Recent progress on action recognition has mainly focused on RGB and optical flow features. In this paper, we approach the problem of joint-based action recognition. Unlike other modalities, constellation of joints and their motion generate models with succinct human motion information for activity recognition. We present a new model for joint-based action re

Thomas G. Fai, Youngmin Park

Organelle size control is a fundamental question in biology that demonstrates the fascinating ability of cells to maintain homeostasis within their highly variable environments. Theoretical models describing cellular dynamics have the potential to help elucidate the principles underlying size control. Here, we perform a detailed study of the active disassemb

Jonathan E. King, David Ryan Koes

Despite recent advancements in deep learning methods for protein structure prediction and representation, little focus has been directed at the simultaneous inclusion and prediction of protein backbone and sidechain structure information. We present SidechainNet, a new dataset that directly extends the ProteinNet dataset. SidechainNet includes angle and atom

Quanling Deng, Victor Calo

We introduce a boundary penalization technique to improve the spectral approximation of isogeometric analysis (IGA). The technique removes the outliers appearing in the high-frequency region of the approximate spectrum when using the $C^{p-1}, p$-th ($p\ge3$) order isogeometric elements. We focus on the classical Laplacian (Dirichlet) eigenvalue problem in 1

Rakshitha Godahewa, Christoph Bergmeir, Geoffrey I. Webb, Pablo Montero-Manso

Many businesses and industries require accurate forecasts for weekly time series nowadays. However, the forecasting literature does not currently provide easy-to-use, automatic, reproducible and accurate approaches dedicated to this task. We propose a forecasting method in this domain to fill this gap, leveraging state-of-the-art forecasting techniques, such

Shayan Monadjemi, Sunwoo Ha, Quan Nguyen, Henry Chai

Recent advances in visual analytics have enabled us to learn from user interactions and uncover analytic goals. These innovations set the foundation for actively guiding users during data exploration. Providing such guidance will become more critical as datasets grow in size and complexity, precluding exhaustive investigation. Meanwhile, the machine learning

Hualin Zhan, Richard Sandberg, Fan Feng, Qinghua Liang

Confined ion transport is involved in nanoporous ionic systems. However, it is challenging to mechanistically predict its electrical characteristics for rational system design and performance evaluation using electrical circuit model due to the gap between the circuit theory and the underlying physical chemistry. Here we demonstrate that machine learning can

Braxton Osting, Dong Wang, Yiming Xu, Dominique Zosso

Archetypal analysis is an unsupervised learning method that uses a convex polytope to summarize multivariate data. For fixed $k$, the method finds a convex polytope with $k$ vertices, called archetype points, such that the polytope is contained in the convex hull of the data and the mean squared distance between the data and the polytope is minimal. In this

Xiaoying Pang, Sunil Thulasidasan, Larry Rybarcyk

We describe an approach to learning optimal control policies for a large, linear particle accelerator using deep reinforcement learning coupled with a high-fidelity physics engine. The framework consists of an AI controller that uses deep neural nets for state and action-space representation and learns optimal policies using reward signals that are provided

Zheng Tracy Ke, Jun S. Liu, Yucong Ma

The knockoff filter is a recent false discovery rate (FDR) control method for high-dimensional linear models. We point out that knockoff has three key components: ranking algorithm, augmented design, and symmetric statistic, and each component admits multiple choices. By considering various combinations of the three components, we obtain a collection of vari

Preetum Nakkiran, Behnam Neyshabur, Hanie Sedghi

We propose a new framework for reasoning about generalization in deep learning. The core idea is to couple the Real World, where optimizers take stochastic gradient steps on the empirical loss, to an Ideal World, where optimizers take steps on the population loss. This leads to an alternate decomposition of test error into: (1) the Ideal World test error plu

Roya Sabbagh Novin, Amir Yazdani, Andrew Merryweather, Tucker Hermans

Planning under uncertainty is a crucial capability for autonomous systems to operate reliably in uncertain and dynamic environments. The concern of safety becomes even more critical in healthcare settings where robots interact with human patients. In this paper, we propose a novel risk-aware planning framework to minimize the risk of falls by providing a pat

Sumanth Chennupati, Venkatraman Narayanan, Ganesh Sistu, Senthil Yogamani

Panoptic Segmentation aims to provide an understanding of background (stuff) and instances of objects (things) at a pixel level. It combines the separate tasks of semantic segmentation (pixel level classification) and instance segmentation to build a single unified scene understanding task. Typically, panoptic segmentation is derived by combining semantic an

You Li, Zhuowen Lin

Attempts to use generative models for music generation have been common in recent years, and some of them have achieved good results. Pieces generated by some of these models are almost indistinguishable from those being composed by human composers. However, the research on the evaluation system for machine-generated music is still at a relatively early stag

Teng Long, Qing-Shan Jia

The transition to the zero-carbon power system is underway accelerating recently. Hydrogen energy and electric vehicles (EVs) are promising solutions on the supply and demand sides. This paper presents a novel architecture that includes hydrogen production stations (HPSs), fast charging stations (FCSs), and commercial EVs. The proposed architecture jointly o

Y. A. Antipov, A. Y. Zemlyanova

Reconstruction of conformal mappings from canonical slit domains onto multiply-connected physical domains with a free boundary is of interest in many different models arising in fluid mechanics. In the present paper, an exact formula for the conformal map from the exterior of two slits onto the doubly connected flow domain is obtained when a fluid flows in a

Akio Ishida, Y. -K. Martin Peng, Wenjun Liu

A non-relativistic multi-fluid plasma axisymmetric equilibrium model was developed recently to account for the presence of an energetic electron fluid in addition to thermal electron and ion fluids. The equilibrium formulation of a multi-fluid plasma with relativistic energetic electrons is developed and reported in this paper. Relativistic effects in a flui

Sanjay Kumar Sonbhadra, Sonali Agarwal, P. Nagabhushan

The infection of respiratory coronavirus disease 2019 (COVID-19) starts with the upper respiratory tract and as the virus grows, the infection can progress to lungs and develop pneumonia. The conventional way of COVID-19 diagnosis is reverse transcription polymerase chain reaction (RT-PCR), which is less sensitive during early stages; especially if the patie

Lingbing Guo, Zhuo Chen, Jiaoyan Chen, Yichi Zhang

Graph neural network (GNN)-based methods have demonstrated remarkable performance in various knowledge graph (KG) tasks. However, most existing approaches rely on observing all entities during training, posing a challenge in real-world knowledge graphs where new entities emerge frequently. To address this limitation, we introduce Decentralized Attention Netw

Emese Lazar, Shuyuan Qi, Radu Tunaru

Measuring model risk is required by regulators on financial and insurance markets. We separate model risk into parameter estimation risk and model specification risk, and we propose expected shortfall type model risk measures applied to Levy jump models and affine jump-diffusion models. We investigate the impact of parameter estimation risk and model specifi

Björn Lütjens, Brandon Leshchinskiy, Christian Requena-Mesa, Farrukh Chishtie

As climate change increases the intensity of natural disasters, society needs better tools for adaptation. Floods, for example, are the most frequent natural disaster, but during hurricanes the area is largely covered by clouds and emergency managers must rely on nonintuitive flood visualizations for mission planning. To assist these emergency managers, we h

Takashi Odagaki

Exploiting the SIQR model for COVID-19, I show that the wavy infection curve in Japan is the result of fluctuation of policy on isolation measure imposed by the government and obeyed by citizens. Assuming the infection coefficient be a two-valued function of the number of daily confirmed new cases, I show that when the removal rate of infected individuals is

Xuesu Xiao, Bo Liu, Peter Stone

Learning from Hallucination (LfH) is a recent machine learning paradigm for autonomous navigation, which uses training data collected in completely safe environments and adds numerous imaginary obstacles to make the environment densely constrained, to learn navigation planners that produce feasible navigation even in highly constrained (more dangerous) space