March 2020 arXiv papers — page 136

Chengyuan Zhang, Jiacheng Zhu, Wenshuo Wang, Junqiang Xi

Interpretation of common-yet-challenging interaction scenarios can benefit well-founded decisions for autonomous vehicles. Previous research achieved this using their prior knowledge of specific scenarios with predefined models, limiting their adaptive capabilities. This paper describes a Bayesian nonparametric approach that leverages continuous (i.e., Gauss

F. Herz, C. Kathmann, S. -A. Biehs

Within the framework of macroscopic quantum electrodynamics and scattering theory, we derive the general expressions for the variance of radiative heat transfer between two arbitrarily shaped objects placed in an arbitrary environment in terms of their T-operators. The such derived expression is valid in the far- and near-field regime of thermal radiation as

Alexander J. Malcolm

In this paper we measure how efficiently a finite simple group $G$ is generated by its elements of order $p$, where $p$ is a fixed prime. This measure, known as the $p$-width of $G$, is the minimal $k\in \mathbb{N}$ such that any $g\in G$ can be written as a product of at most $k$ elements of order $p$. Using primarily character theoretic methods, we sharply

Mirco Colosi, Irvin Aloise, Tiziano Guadagnino, Dominik Schlegel

Nowadays, SLAM (Simultaneous Localization and Mapping) is considered by the Robotics community to be a mature field. Currently, there are many open-source systems that are able to deliver fast and accurate estimation in typical real-world scenarios. Still, all these systems often provide an ad-hoc implementation that entailed to predefined sensor configurati

Aleksandra Orzechowska, Joanna Fiedor, Pawel Kulakowski

Chlorophylls and carotenoids, key components of photosynthetic systems, are proposed for molecular communications at the nanoscale with the mechanism of resonance energy transfer. Both types of pigments are introduced focusing on their exceptional properties like energy harvesting, ultra-low energy consumption during transmissions, picoseconds delays, an abi

Antonio Loquercio, Alexey Dosovitskiy, Davide Scaramuzza

Motivated by the astonishing capabilities of natural intelligent agents and inspired by theories from psychology, this paper explores the idea that perception gets coupled to 3D properties of the world via interaction with the environment. Existing works for depth estimation require either massive amounts of annotated training data or some form of hard-coded

Gao Feifei, Li Zhihui, Liu Chengji, Han Duo

The Quantum Key Distribution protocol can encode a single quantum state and implements an information-theoretically secure key distribution protocol in communication. In the actual QKD experimental system, there are usually two encoding methods which are phase encoding and polarization encoding. Ma et al.[Phase-Matching Quantum Key Distribution, Phys. Rev. X

Dat Quoc Nguyen, Anh Tuan Nguyen

We present PhoBERT with two versions, PhoBERT-base and PhoBERT-large, the first public large-scale monolingual language models pre-trained for Vietnamese. Experimental results show that PhoBERT consistently outperforms the recent best pre-trained multilingual model XLM-R (Conneau et al., 2020) and improves the state-of-the-art in multiple Vietnamese-specific

Tamir Blum, Kazuya Yoshida

Robots can now learn how to make decisions and control themselves, generalizing learned behaviors to unseen scenarios. In particular, AI powered robots show promise in rough environments like the lunar surface, due to the environmental uncertainties. We address this critical generalization aspect for robot locomotion in rough terrain through a training algor

Kelbij Star, Giovanni Stabile, Gianluigi Rozza, Joris Degroote

A Finite-Volume based POD-Galerkin reduced order modeling strategy for steady-state Reynolds averaged Navier--Stokes (RANS) simulation is extended for low-Prandtl number flow. The reduced order model is based on a full order model for which the effects of buoyancy on the flow and heat transfer are characterized by varying the Richardson number. The Reynolds

Arnaud Guillin, Pierre Monmarché

Combining the results of [14] and [10], the trend to equilibrium in large time is studied for a large particle system associated to a Vlasov-Fokker-Planck equation. Under some conditions (that allow non-convex confining potentials) the convergence rate is proven to be independent from the number of particles. From this are derived uniform in time propagation

Manuel Arias Chao, Chetan Kulkarni, Kai Goebel, Olga Fink

Physics-based and data-driven models for remaining useful lifetime (RUL) prediction typically suffer from two major challenges that limit their applicability to complex real-world domains: (1) incompleteness of physics-based models and (2) limited representativeness of the training dataset for data-driven models. Combining the advantages of these two directi

Hong-Bin Chen, Hung-Lin Fu, Jun-Yi Guo

A graph is Hamiltonian if it contains a cycle which visits every vertex of the graph exactly once. In this paper, we consider the problem of Hamiltonicity of a graph $G_n$, which will be called the prime difference graph of order $n$, with vertex set $\{1,2,\cdots, n\}$ and edge set $\{uv: |u-v|$ is a prime number$\}$. A recent result, conjectured by Sun and

Fan Hu, Jiaxin Jiang, Peng Yin

The outbreak of COVID-19 caused millions of deaths worldwide, and the number of total infections is still rising. It is necessary to identify some potentially effective drugs that can be used to prevent the development of severe symptoms or even death for those infected. Fortunately, many efforts have been made, and several effective drugs have been identifi

E. Hashorva

For a given stationary max-stable random field $X(t),t\in Z^d$ the corresponding generalised Pickands constant coincides with the classical extremal index $θ$ which always exists. In this contribution we discuss necessary and sufficient conditions for $θ$ to be 0, positive or equal to 1 and also show that $θ$ is equal to the so-called block extremal index. F

E. Bernard, M. Fathi, A. Levitt, G. Stoltz

We propose an approach to obtaining explicit estimates on the resolvent of hypocoercive operators by using Schur complements, rather than from an exponential decay of the evolution semigroup combined with a time integral. We present applications to Langevin-like dynamics and Fokker--Planck equations, as well as the linear Boltzmann equation (which is also th

E. Harikumar, Vishnu Rajagopal

In this paper, we study the quantisation of Dirac field theory in the $κ$-deformed space-time. We adopt a quantisation method that uses only equations of motion for quantising the field. Starting from $κ$-deformed Dirac equation, valid up to first order in the deformation parameter $a$, we derive deformed unequal time anti-commutation relation between deform

Jeffrey M. Ede

Large, carefully partitioned datasets are essential to train neural networks and standardize performance benchmarks. As a result, we have set up new repositories to make our electron microscopy datasets available to the wider community. There are three main datasets containing 19769 scanning transmission electron micrographs, 17266 transmission electron micr

Mark J. Kuiack, Ralph A. M. J. Wijers, Antonia Rowlinson, Aleksandar Shulevski

We report on the detection of extreme giant pulses (GPs) from one of the oldest-known pulsars, the highly variable PSR B0950+08, with the Amsterdam-ASTRON Radio Transient Facility And Analysis Centre (AARTFAAC), a parallel transient detection instrument operating as a subsystem of the LOw Frequency ARray (LOFAR). During processing of our Northern Hemisphere

Nicolas Heist, Sven Hertling, Daniel Ringler, Heiko Paulheim

Knowledge Graphs are an emerging form of knowledge representation. While Google coined the term Knowledge Graph first and promoted it as a means to improve their search results, they are used in many applications today. In a knowledge graph, entities in the real world and/or a business domain (e.g., people, places, or events) are represented as nodes, which

Klas Modin, Milo Viviani

Point-vortex dynamics describe idealized, non-smooth solutions to the incompressible Euler equations on 2-dimensional manifolds. Integrability results for few point-vortices on various domains is a vivid topic, with many results and techniques scattered in the literature. Here we give a unified framework for proving integrability results for $N=2$, $3$, or $

M. Lavagna, V. Talbo, T. Q. Duong, A. Crépieux

We study electrical and thermoelectrical properties for a double quantum dot system. We consider the cases of both single-level and multilevel quantum dots whatever the way they are coupled, either in a series or in a parallel arrangement. The calculations are performed by using the nonequilibrium Green function theory. In the case of a single-level double q

Osama M. Bushnaq, Mustafa A. Kishk, Abdulkadir Çelik, Mohamed-Slim Alouini

Unmanned aerial vehicle (UAV) assisted cellular communication is gaining significant interest recently. Although it offers several advantages over terrestrial communication, UAV communication suffers from two main shortcomings. The typical untethered UAV (uUAV) has a limited battery power supply and therefore limited flying time, and it needs an extra wirele

Sascha Wirges, Ye Yang, Sven Richter, Haohao Hu

We propose an object detector for top-view grid maps which is additionally trained to generate an enriched version of its input. Our goal in the joint model is to improve generalization by regularizing towards structural knowledge in form of a map fused from multiple adjacent range sensor measurements. This training data can be generated in an automatic fash

Jinhong Deng, Wen Li, Yuhua Chen, Lixin Duan

Cross-domain object detection is challenging, because object detection model is often vulnerable to data variance, especially to the considerable domain shift between two distinctive domains. In this paper, we propose a new Unbiased Mean Teacher (UMT) model for cross-domain object detection. We reveal that there often exists a considerable model bias for the

Alec Angus

Electromagnetic propulsion technology has been thought to provide a potential form of future spacecraft propulsion for some time. In contrast to ion thrusters, which utilize the Coulomb force to accelerate positively charged species, electromagnetic propulsion systems utilize the Lorentz force to accelerate all species in a quasi-neutral state, providing sig

David Tolpin, Yuan Zhou, Hongseok Yang

Probabilistic programs with mixed support (both continuous and discrete latent random variables) commonly appear in many probabilistic programming systems (PPSs). However, the existence of the discrete random variables prohibits many basic gradient-based inference engines, which makes the inference procedure on such models particularly challenging. Existing

Helen Brooks, Christian T Preuss, Peter Skands

In conventional parton showers (including ones based on dipoles/antennae), a given $(\mathrm{Born}+m)$-parton configuration can typically be reached via ${\mathcal O}(m!)$ different "shower histories". In the context of matrix-element-correction and merging procedures, accounting for these histories mandates fairly complex and resource-intensive algo

Ludovica Falsi, Luca Tartara, Fabrizio Di Mei, Mariano Flammini

Nonlinear response in a material increases with its index of refraction as $n^4$. Commonly, $n \sim$ 1 so that diffraction, dispersion, and chromatic walk-off limit nonlinear scattering. Ferroelectric crystals with a periodic 3D polarization structure overcome some of these constraints through versatile Cherenkov and quasi-phase-matching mechanisms. Three-di

Takaaki Yoshino, Daichi Yamaura, Maki Komiya, Masakazu Sugawara

Enhanced manipulation and analysis of bio-particles using light confined in nano-scale dielectric structures has proceeded apace in the last several years. Small mode volumes, along with the lack of a need for bulky optical elements give advantages in sensitivity and scalability relative to conventional optical manipulation. However, manipulation of lipid ve

MyeongAh Cho, Taeoh Kim, Ig-Jae Kim, Kyungjae Lee

Heterogeneous Face Recognition (HFR) is a task that matches faces across two different domains such as visible light (VIS), near-infrared (NIR), or the sketch domain. Due to the lack of databases, HFR methods usually exploit the pre-trained features on a large-scale visual database that contain general facial information. However, these pre-trained features

Yurui Ren, Xiaoming Yu, Junming Chen, Thomas H. Li

Pose-guided person image generation is to transform a source person image to a target pose. This task requires spatial manipulations of source data. However, Convolutional Neural Networks are limited by the lack of ability to spatially transform the inputs. In this paper, we propose a differentiable global-flow local-attention framework to reassemble the inp

Ma-Cheng Yang, Jun-Li Li, Cong-Feng Qiao

The decompositions of separable Werner state, and also isotropic state, are well-known tough issues in quantum information theory, in this work we investigate them in the Bloch vector representation, exploring the symmetric informationally complete positive operator-valued measure (SIC-POVM) in the Hilbert space. We successfully get the decomposition for arb

Ritam Mallick, Shailendra Singh, R Prasad

Fluctuation at the neutron star center gives rise to a small deconfined quark core very close to the star center. The density discontinuity at the quark-hadron boundary initiates a shock wave, which propagates outwards of the star. The shock has enough energy to combust nuclear matter to 2-flavor quark matter in the star. The 2-flavor quark matter is not sta

Jan Wiersig

Recent experiments have demonstrated the feasibility of exploiting spectral singularities in open quantum and wave systems, so-called exceptional points, for sensors with strongly enhanced sensitivity. Here, we study theoretically the influence of classical parametric noise on the performance of such sensors. Within a Lindblad-type formalism for stochastic H

N. N. Orlova, S. I. Bozhko, E. V. Deviatov

We study conductivity of strongly disordered amorphous antimony films under high bias voltages. We observe non-linear current-voltage characteristic, where the conductivity value at zero bias is one of two distinct values, being determined by the sign of previously applied voltage. Relaxation curves demonstrate high stability of these conductivity values on

Hailong Liu, Takatsugu Hirayama, Luis Yoichi Morales, Hiroshi Murase

Although automated driving systems have been used frequently, they are still unpopular in society. To increase the popularity of automated vehicles (AVs), assisting pedestrians to accurately understand the driving intentions and improving their perception of safety when interacting with AVs are considered effective. Therefore, the AV should send information

David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang

Distributional shift is one of the major obstacles when transferring machine learning prediction systems from the lab to the real world. To tackle this problem, we assume that variation across training domains is representative of the variation we might encounter at test time, but also that shifts at test time may be more extreme in magnitude. In particular,

Yongle Jiang

We prove that $L(SL_2(\textbf{k}))$ is a maximal Haagerup von Neumann subalgebra in $L(\textbf{k}^2\rtimes SL_2(\textbf{k}))$ for $\textbf{k}=\mathbb{Q}$. Then we show how to modify the proof to handle $\textbf{k}=\mathbb{Z}$. The key step for the proof is a complete description of all intermediate von Neumann subalgebras between $L(SL_2(\textbf{k}))$ and $L

Gaohang Yu, Yi Zhou, Laishui Lv

Higher-order Markov chains play a very important role in many fields, ranging from multilinear PageRank to financial modeling. In this paper, we propose three accelerated higher-order power methods for computing the limiting probability distribution of higher-order Markov chains, namely higher-order power method with momentum and higher-order quadratic extra

D. K. Galloway, J. J. M. in 't Zand, J. Chenevez, H. Wörpel

We present the largest sample of type-I (thermonuclear) X-ray bursts yet assembled, comprising 7083 bursts from 85 bursting sources. The sample is drawn from observations with Xenon-filled proportional counters on the long-duration satellites RXTE, BeppoSAX and INTEGRAL, between 1996 February 8, and 2012 May 3. The burst sources were drawn from a comprehensi

L. L. Kitchatinov, I. S. Potravnov, A. A. Nepomnyashchikh

Rotation periods inferred from the magnetic variability of some Ap stars are incredibly long, exceeding ten years in some cases. An explanation for such slow rotation is lacking. This paper attempts to provide an explanation of the super-slow rotation of the magnetic and thermal patterns of Ap stars in terms of the longitudinal drift of the unstable disturba

Subrato Bharati, Prajoy Podder, M. Rubaiyat Hossain Mondal

Lung disease is common throughout the world. These include chronic obstructive pulmonary disease, pneumonia, asthma, tuberculosis, fibrosis, etc. Timely diagnosis of lung disease is essential. Many image processing and machine learning models have been developed for this purpose. Different forms of existing deep learning techniques including convolutional ne

Jeroen Schillewaert, Koen Struyve, Anne Thomas

We prove a local-to-global result for fixed points of groups acting on affine buildings (possibly non-discrete) of types $\tilde{A}_2$ or $\tilde{C}_2$. In the discrete case, our theorem establishes the corresponding special cases of a conjecture by Marquis.

Lumin Yang, Jiajie Zhuang, Hongbo Fu, Xiangzhi Wei

We introduce SketchGNN, a convolutional graph neural network for semantic segmentation and labeling of freehand vector sketches. We treat an input stroke-based sketch as a graph, with nodes representing the sampled points along input strokes and edges encoding the stroke structure information. To predict the per-node labels, our SketchGNN uses graph convolut

Xiaoxian Tang, Houjie Wang, Ruriko Yoshida

Most data in genome-wide phylogenetic analysis (phylogenomics) is essentially multidimensional, posing a major challenge to human comprehension and computational analysis. Also, we can not directly apply statistical learning models in data science to a set of phylogenetic trees since the space of phylogenetic trees is not Euclidean. In fact, the space of phy

Qijing Huang, Ameer Haj-Ali, William Moses, John Xiang

The performance of the code a compiler generates depends on the order in which it applies the optimization passes. Choosing a good order--often referred to as the phase-ordering problem, is an NP-hard problem. As a result, existing solutions rely on a variety of heuristics. In this paper, we evaluate a new technique to address the phase-ordering problem: dee

Wladimir Lyra, Andrew N. Youdin, Anders Johansen

The New Horizons flyby of the cold classical Kuiper Belt object MU69 showed it to be a contact binary. The existence of other contact binaries in the 1-10km range raises the question of how common these bodies are and how they evolved into contact. Here we consider that the lobes of MU69 formed as a binary in the Solar nebula, and calculate its orbital evolu

Xi Yang, Ding Xia, Taichi Kin, Takeo Igarashi

Medicine is an important application area for deep learning models. Research in this field is a combination of medical expertise and data science knowledge. In this paper, instead of 2D medical images, we introduce an open-access 3D intracranial aneurysm dataset, IntrA, that makes the application of points-based and mesh-based classification and segmentation

Bin Fu

We develop a polynomial method on finite fields to amplify the hardness of spare sets in nondeterministic time complexity classes on a randomized streaming model. One of our results shows that if there exists a $2^{n^{o(1)}}$-sparse set in $NTIME(2^{n^{o(1)}})$ that does not have any randomized streaming algorithm with $n^{o(1)}$ updating time, and $n^{o(1)}

Ryutaroh Matsumoto

We propose and prove an existential theorem for entanglement-assisted asymmetric quantum error correction. Then we demonstrate its superiority over the conventional one.

Nils Bruin, Daniel Lewis

We implement two-cover descent for plane quartics over Q with all 28 bitangents rational and show that on a significant collection of test cases, it resolves the existence of rational points. We also review a classical description of the relevant moduli space and use it to generate examples. We observe that local obstructions are quite rare for such curves,

Katsuki Aoki, Shinji Mukohyama

The idea of gauge theories of gravity predicts that there should exist not only the massless graviton but also massive particles carrying the gravitational force. We study the cosmology in a quadratic gravity with dynamical torsion where gravity may be interpreted as a gauge force associated with the Poincaré group. In addition to the massless spin-2 gravito

Christopher Shriver

The $f$-invariant is an isomorphism invariant of free-group measure-preserving actions introduced by Lewis Bowen in [arXiv:0802.4294], where it was used to show that two finite-entropy Bernoulli shifts over a finitely generated free group can be isomorphic only if their base measures have the same Shannon entropy. In [arXiv:0902.0174] Bowen showed that the $

Ahmad Wisnu Mulyadi, Eunji Jun, Heung-Il Suk

Electronic health records (EHR) consist of longitudinal clinical observations portrayed with sparsity, irregularity, and high-dimensionality, which become major obstacles in drawing reliable downstream clinical outcomes. Although there exist great numbers of imputation methods to tackle these issues, most of them ignore correlated features, temporal dynamics

Shuang Qiu, Xiaohan Wei, Zhuoran Yang, Jieping Ye

We consider online learning for episodic stochastically constrained Markov decision processes (CMDPs), which plays a central role in ensuring the safety of reinforcement learning. Here the loss function can vary arbitrarily across the episodes, and both the loss received and the budget consumption are revealed at the end of each episode. Previous works solve

Hongjie Wang, Yang Zhao, Chaojian Li, Yue Wang

The excellent performance of modern deep neural networks (DNNs) comes at an often prohibitive training cost, limiting the rapid development of DNN innovations and raising various environmental concerns. To reduce the dominant data movement cost of training, process in-memory (PIM) has emerged as a promising solution as it alleviates the need to access DNN we

Ryan Kaveh, Justin Doong, Andy Zhou, Carolyn Schwendeman

In the past few years it has been demonstrated that electroencephalography (EEG) can be recorded from inside the ear (in-ear EEG). To open the door to low-profile earpieces as wearable brain-computer interfaces (BCIs), this work presents a practical in-ear EEG device based on multiple dry electrodes, a user-generic design, and a lightweight wireless interfac

Michael Pinelis, David Ruppert

We find economically and statistically significant gains when using machine learning for portfolio allocation between the market index and risk-free asset. Optimal portfolio rules for time-varying expected returns and volatility are implemented with two Random Forest models. One model is employed in forecasting the sign probabilities of the excess return wit

Zhen-hua Zhao

In this paper, we consider the imbedding of the popular and well-motivated trimaximal mixing and $μ$-$τ$ reflection symmetry (which can help us shape the forms of the neutrino mass matrix) in the minimal seesaw model (which contains much fewer parameters than the general seesaw model) with two TeV-scale right-handed neutrinos (for realizing a low-scale seesa

Wei Jin, Yaxin Li, Han Xu, Yiqi Wang

Deep neural networks (DNNs) have achieved significant performance in various tasks. However, recent studies have shown that DNNs can be easily fooled by small perturbation on the input, called adversarial attacks. As the extensions of DNNs to graphs, Graph Neural Networks (GNNs) have been demonstrated to inherit this vulnerability. Adversary can mislead GNNs

Mucong Ding, Constantinos Daskalakis, Soheil Feizi

Generative Adversarial Networks (GANs) are modern methods to learn the underlying distribution of a data set. GANs have been widely used in sample synthesis, de-noising, domain transfer, etc. GANs, however, are designed in a model-free fashion where no additional information about the underlying distribution is available. In many applications, however, pract

Beixiong Zheng, Changsheng You, Rui Zhang

To achieve the full passive beamforming gains of intelligent reflecting surface (IRS), accurate channel state information (CSI) is indispensable but practically challenging to acquire, due to the excessive amount of channel parameters to be estimated which increases with the number of IRS reflecting elements as well as that of IRS-served users. To tackle thi

Piyush Jain, Sean C P Coogan, Sriram Ganapathi Subramanian, Mark Crowley

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science an

Yusuke Koda, Jihong Park, Mehdi Bennis, Koji Yamamoto

The goal of this study is to improve the accuracy of millimeter wave received power prediction by utilizing camera images and radio frequency (RF) signals, while gathering image inputs in a communication-efficient and privacy-preserving manner. To this end, we propose a distributed multimodal machine learning (ML) framework, coined multimodal split learning

Hemanshu Kaul, Jeffrey A. Mudrock

We initiate the study of applying the Combinatorial Nullstellensatz to the DP-coloring of graphs even though, as is well-known, the Alon-Tarsi theorem does not apply to DP-coloring. We define the notion of good covers of prime order which allows us to apply the Combinatorial Nullstellensatz to DP-coloring. We apply these tools to DP-coloring of the cones of

Miika Hannula, Juha Kontinen, Jan Van den Bussche, Jonni Virtema

We introduce a novel variant of BSS machines called Separate Branching BSS machines (S-BSS in short) and develop a Fagin-type logical characterisation for languages decidable in non-deterministic polynomial time by S-BSS machines. We show that NP on S-BSS machines is strictly included in NP on BSS machines and that every NP language on S-BSS machines is a co

Gang Bao, Yiwen Lin, Xiang Xu

This paper develops an efficient numerical method for the inverse scattering problem of a time-harmonic plane wave incident on a perfectly reflecting random periodic structure. The method is based on a novel combination of the Monte Carlo technique for sampling the probability space, a continuation method with respect to the wavenumber, and the Karhunen-Lo$\

Avik Roy, Nikiforos Nikiforou, Nuno Castro, Timothy Andeen

Vector-like Quarks (VLQs) are potential signatures of physics beyond the Standard Model at the TeV energy scale and major efforts have been put forward at both ATLAS and CMS experiments in search of these particles. In order to make these search results more relatable in the context of most plausible theories of VLQs, it is deemed important to present the an

Orad Reshef, Igor Aharonovich, Andrea Armani, Sylvain Gigan

On January 13th 2020, the inaugural Photonics Online Meetup (POM) brought together more than 1100 researchers to discuss the latest advances in photonics. Or rather, it didn't, because the meeting was completely delocalized with the speakers, organizers, and attendees scattered across six continents and hundreds of locations, connected via a video-confer

Hengyi Cai, Hongshen Chen, Cheng Zhang, Yonghao Song

Current state-of-the-art neural dialogue systems are mainly data-driven and are trained on human-generated responses. However, due to the subjectivity and open-ended nature of human conversations, the complexity of training dialogues varies greatly. The noise and uneven complexity of query-response pairs impede the learning efficiency and effects of the neur

Jin Liu, Shunping Ji

A great deal of research has demonstrated recently that multi-view stereo (MVS) matching can be solved with deep learning methods. However, these efforts were focused on close-range objects and only a very few of the deep learning-based methods were specifically designed for large-scale 3D urban reconstruction due to the lack of multi-view aerial image bench

Jianchun Chu

Guan-Ren-Wang established the curvature estimate of convex hypersurface satisfying the Weingarten curvature equation $σ_{k}(κ(X)) = f(X,ν(X))$. In this note, we give a simple proof of this result.

Toshiyuki Katsura, Katsuyuki Takashima

Castryck, Decru, and Smith used superspecial genus-2 curves and their Richelot isogeny graph for basing genus-2 isogeny cryptography, and recently, Costello and Smith devised an improved isogeny path-finding algorithm in the genus-2 setting. In order to establish a firm ground for the cryptographic construction and analysis, we give a new characterization of

Weijun Xie, Jie Zhang, Shabbir Ahmed

This paper studies data-driven distributionally robust bottleneck combinatorial problems (DRBCP) with stochastic costs, where the probability distribution of the cost vector is contained in a ball of distributions centered at the empirical distribution specified by the Wasserstein distance. We study two distinct versions of DRBCP from different applications:

Alexey S. Koshelev, K. Sravan Kumar, Anupam Mazumdar, Alexei A. Starobinsky

In this paper we will study $R^2$-like inflation in a non-local modification of gravity which contains quadratic in Ricci scalar and Weyl tensor terms with analytic infinite derivative form-factors in the action. It is known that the inflationary solution of the local $R+R^2$ gravity remains a particular exact solution in this model. It was shown earlier tha

Cristian Camilo Beltran-Hernandez, Damien Petit, Ixchel G. Ramirez-Alpizar, Takayuki Nishi

Reinforcement Learning (RL) methods have been proven successful in solving manipulation tasks autonomously. However, RL is still not widely adopted on real robotic systems because working with real hardware entails additional challenges, especially when using rigid position-controlled manipulators. These challenges include the need for a robust controller to

Mahak Goindani, Jennifer Neville

Social Reinforcement Learning methods, which model agents in large networks, are useful for fake news mitigation, personalized teaching/healthcare, and viral marketing, but it is challenging to incorporate inter-agent dependencies into the models effectively due to network size and sparse interaction data. Previous social RL approaches either ignore agents d

N. D. Khanh, T. Nakajima, X. Z. Yu, S. Gao

Magnetic skyrmions are topologically stable spin swirls with particle-like character and potentially suitable for the design of high-density information bits. While most known skyrmion systems arise in noncentrosymmetric systems with Dzyaloshinskii-Moriya interaction, also centrosymmetric magnets with a triangular lattice can give rise to skyrmion formation,

Fengming Dong, Sherry H. F. Yan

A {\it tiered graph} $G=(V,E)$ with $m $ tiers is a simple graph with $V\subseteq \brk{n}$, where $\brk{n}=\{1,2,\cdots,n\}$, and with a surjective map $t$ from $V$ to $\brk{m}$ such that if $v$ is a vertex adjacent to $v'$ in $G$ with $v>v'$, then $t(v) >t(v')$. For any ordered partition $p=(p_1,p_2,\cdots,p_m)$ of $n$, let $\sett_p$ denote the

Vu The Khoi, Masakazu Teragaito, Anh T. Tran

A slope $r$ is called a left orderable slope of a knot $K \subset S^3$ if the 3-manifold obtained by $r$-surgery along $K$ has left orderable fundamental group. Consider two-bridge knots $C(2m, \pm 2n)$ and $C(2m+1, -2n)$ in the Conway notation, where $m \ge 1$ and $n \ge 2$ are integers. By using \textit{continuous} families of hyperbolic $\mathrm{SL}_2(\ma

R. Lecaros, J. López-Ríos, J. H. Ortega, S. Zamorano

In this article we deal with a class of geometric inverse problem for bottom detection by one single measurement on the free surface in water--waves. We found upper and lower bounds for the size of the region enclosed between two different bottoms, in terms of Neumann and/or Dirichlet data on the free surface. Starting from the general water--waves system in

Xingtong Liu, Yiping Zheng, Benjamin Killeen, Masaru Ishii

High-quality 3D reconstructions from endoscopy video play an important role in many clinical applications, including surgical navigation where they enable direct video-CT registration. While many methods exist for general multi-view 3D reconstruction, these methods often fail to deliver satisfactory performance on endoscopic video. Part of the reason is that

Jad Rahme, Samy Jelassi, Joan Bruna, S. Matthew Weinberg

Designing an incentive compatible auction that maximizes expected revenue is a central problem in Auction Design. Theoretical approaches to the problem have hit some limits in the past decades and analytical solutions are known for only a few simple settings. Computational approaches to the problem through the use of LPs have their own set of limitations. Bu

Ashia Wilson, Maximilian Kasy, Lester Mackey

Cross-validation (CV) is a popular approach for assessing and selecting predictive models. However, when the number of folds is large, CV suffers from a need to repeatedly refit a learning procedure on a large number of training datasets. Recent work in empirical risk minimization (ERM) approximates the expensive refitting with a single Newton step warm-star

Moji Ghadimi, Elizabeth M. Bridge, Jordan Scarabel, Steven Connell

Here we present a cost effective multi-channel opto-mechanical switch and software PID system for locking multiple lasers to a single channel commercial wavemeter. The switch is based on a rotating cylinder that selectively transmits one laser beam at a time to the wavemeter, the wavelength is read by the computer and an error signal is output to the lasers

Linyan Lu, Zhaohui Yang, Mingzhe Chen, Zelin Zang

In this paper, a machine learning based deployment framework of unmanned aerial vehicles (UAVs) is studied. In the considered model, UAVs are deployed as flying base stations (BS) to offload heavy traffic from ground BSs. Due to time-varying traffic distribution, a long short-term memory (LSTM) based prediction algorithm is introduced to predict the future c

S. Cliff Liu, Robert E. Tarjan, Peilin Zhong

We present an $O(\log d + \log\log_{m/n} n)$-time randomized PRAM algorithm for computing the connected components of an $n$-vertex, $m$-edge undirected graph with maximum component diameter $d$. The algorithm runs on an ARBITRARY CRCW (concurrent-read, concurrent-write with arbitrary write resolution) PRAM using $O(m)$ processors. The time bound holds with

Graziella Scandurra, Janusz Smulko, Laszlo B. Kish

We discuss some of the fundamental practical limitations of the Fluctuation-Enhanced Sensing of odors and gases. We address resolution, measurement speed, reproducibility, memory and other problems such as humidity. Various techniques and ideas are presented to overcome these problems. Circuit solutions are also discussed.

J. F. F. Bulmer, J. A. Jones, I. A. Walmsley

Electro-optic modulators within Mach--Zehnder interferometers are a common construction for optical switches in integrated photonics. A challenge faced when operating at high switching speeds is that noise from the electronic drive signals will effect switching performance. Inspired by the Mach--Zehnder lattice switching devices of Van Campenhout et al. [Opt

Ioannis Mavromatis, Andrea Tassi, Robert J. Piechocki, Mahesh Sooriyabandara

Automated Vehicles are an integral part of Intelligent Transportation Systems (ITSs) and are expected to play a crucial role in the future mobility services. This paper investigates two classes of self-driving vehicles: (i) Level 4&5 Automated Vehicles (AVs) that rely solely on their on-board sensors for environmental perception tasks, and (ii) Connected and

Muammar El Khatib, Wibe A de Jong

ML4Chem is an open-source machine learning library for chemistry and materials science. It provides an extendable platform to develop and deploy machine learning models and pipelines and is targeted to the non-expert and expert users. ML4Chem follows user-experience design and offers the needed tools to go from data preparation to inference. Here we introduc

Jinghui Wang, Hesham A. Rakha

The objective of this paper is to study the effect of travel time information on day-to-day driver route choice behavior. A real-world experimental study is designed to have participants repeatedly choose between two alternative routes for five origin-destination pairs over multiple days after providing them with dynamically updated travel time information (

Minh H. Vu, Tommy Löfstedt, Tufve Nyholm, Raphael Sznitman

Deep learning methods have proven extremely effective at performing a variety of medical image analysis tasks. With their potential use in clinical routine, their lack of transparency has however been one of their few weak points, raising concerns regarding their behavior and failure modes. While most research to infer model behavior has focused on indirect

Mai Gehrke, David Pengelley

We discuss parallels between students and teachers in the process of pedagogical reform. Reform aims for students to develop their own process for becoming independent learners, and to gain personal ownership. Likewise teachers can develop their own personally owned reform process if they have the encouragement and freedom to take individual initiative. We a

Stephanie Houde, Vera Liao, Jacquelyn Martino, Michael Muller

Generative AI is a class of machine learning technology that learns to generate new data from training data. While deep fakes and media-and art-related generative AI breakthroughs have recently caught people's attention and imagination, the overall area is in its infancy for business use. Further, little is known about generative AI's potential for m

Yitong Li, Dianqi Li, Sushant Prakash, Peng Wang

This work shows how to improve and interpret the commonly used dual encoder model for response suggestion in dialogue. We present an attentive dual encoder model that includes an attention mechanism on top of the extracted word-level features from two encoders, one for context and one for label respectively. To improve the interpretability in the dual encode

Prakhar Thapak, Prodip Hore

Recent advancements in attention mechanisms have replaced recurrent neural networks and its variants for machine translation tasks. Transformer using attention mechanism solely achieved state-of-the-art results in sequence modeling. Neural machine translation based on the attention mechanism is parallelizable and addresses the problem of handling long-range

Chu Guo, Dario Poletti

For a real function, automatic differentiation is such a standard algorithm used to efficiently compute its gradient, that it is integrated in various neural network frameworks. However, despite the recent advances in using complex functions in machine learning and the well-established usefulness of automatic differentiation, the support of automatic differe

S. Tang, B. King, H. Hu

We detail a method to produce a GeV-photon source with polarisation purity exceeding $96\%$ and $89\%$ for linear and circular polarisation respectively and with a brilliance of the order of $10^{21}\,\textrm{photons}/(\textrm{s}~\textrm{mm}^2~\textrm{mrad}^2~0.1\%~\textrm{BW})$. Using currently available multi-GeV electron bunches and laser pulses of modera