March 2020 arXiv papers — page 109

Jarkko Suuronen, Muhammad Emzir, Sari Lasanen, Simo Särkkä

X-ray tomography has applications in various industrial fields such as sawmill industry, oil and gas industry, chemical engineering, and geotechnical engineering. In this article, we study Bayesian methods for the X-ray tomography reconstruction. In Bayesian methods, the inverse problem of tomographic reconstruction is solved with help of a statistical prior

Xinmeng Li, James A. Van Deventer, Soha Hassoun

Antibodies are capable of potently and specifically binding individual antigens and, in some cases, disrupting their functions. The key challenge in generating antibody-based inhibitors is the lack of fundamental information relating sequences of antibodies to their unique properties as inhibitors. We develop a pipeline, Antibody Sequence Analysis Pipeline u

Jose A. Carrillo, Daniel Matthes, Marie-Therese Wolfram

This paper reviews different numerical methods for specific examples of Wasserstein gradient flows: we focus on nonlinear Fokker-Planck equations,but also discuss discretizations of the parabolic-elliptic Keller-Segel model and of the fourth order thin film equation. The methods under review are of Lagrangian nature, that is, the numerical approximations tra

Shangsen Li, Lailong Luo, Deke Guo

Set synchronization is a fundamental task in distributed applications and implementations. Existing methods that synchronize simple sets are mainly based on compact data structures such as Bloom filter and its variants. However, these methods are infeasible to synchronize a pair of multisets which allow an element to appear for multiple times. To this end, i

Philip Heinisch, Katharina Ostaszewski, Hendrik Ranocha

When considering different hardware platforms, not just the time-to-solution can be of importance but also the energy necessary to reach it. This is not only the case with battery powered and mobile devices but also with high-performance parallel cluster systems due to financial and practical limits on power consumption and cooling. Recent developments in ha

Ana Pervan, Todd Murphey

We consider the problem of designing synthetic cells to achieve a complex goal (e.g., mimicking the immune system by seeking invaders) in a complex environment (e.g., the circulatory system), where they might have to change their control policy, communicate with each other, and deal with stochasticity including false positives and negatives---all with minima

Ahmed Abbes, Michel Gros

We give in this note an overview of a recent work leading to a generalization of the Hodge-Tate spectral sequence to morphisms. The latter takes place in Faltings topos, but its construction requires the introduction of a relative variant of this topos which is the main novelty of our work.

Qian Li, San-Yang Liu, Xin-She Yang

All metaheuristic optimization algorithms require some initialization, and the initialization for such optimizers is usually carried out randomly. However, initialization can have some significant influence on the performance of such algorithms. This paper presents a systematic comparison of 22 different initialization methods on the convergence and accuracy

Jonathan E. Ron, Pascale Monzo, Nils Gauthier, Raphael Voituriez

During migration cells exhibit a rich variety of seemingly random migration patterns, which makes unraveling the underlying mechanisms that control cell migration a daunting challenge. For efficient migration cells require a mechanism for polarization, so that traction forces are produced in the direction of motion, while adhesion is released to allow forwar

Zhangsheng Lai, Aik Beng Ng, Liang Ze Wong, Simon See

Reasoning over knowledge graphs is traditionally built upon a hierarchy of languages in the Semantic Web Stack. Starting from the Resource Description Framework (RDF) for knowledge graphs, more advanced constructs have been introduced through various syntax extensions to add reasoning capabilities to knowledge graphs. In this paper, we show how standardized

Fa Chen, Zhendong Zhang, Wei Luo, Xiaofei Yang

Racetrack memory based on magnetic domain walls (DWs) motion exhibits advantages of small volume and high reading speed. When compared to current-induced DW motion, voltage-induced DW motion exhibits lower dissipation. On the other hand, the DW in an antiferromagnet (AFM) moves at a high velocity with weak stray field. In this work, the AFM DW motion induced

Sixuan Zhang, Tonghua Liu, Shuo Cao, Yuting Liu

Quantum entanglement as the one of the most general quantum resources, can be quantified by von Neumann entropy. However, as we know, the von Neumann entropy is only statistical quantity or operator, it therefore has fluctuation. The quantum fluctuation of entanglement (QFE) between Unruh-Dewitt detector modeled by a two-level atom is investigated in a relat

Raphaël Dang-Nhu, Gagandeep Singh, Pavol Bielik, Martin Vechev

We develop an effective generation of adversarial attacks on neural models that output a sequence of probability distributions rather than a sequence of single values. This setting includes the recently proposed deep probabilistic autoregressive forecasting models that estimate the probability distribution of a time series given its past and achieve state-of

Xin-She Yang

Many problems in science and engineering can be formulated as optimization problems, subject to complex nonlinear constraints. The solutions of highly nonlinear problems usually require sophisticated optimization algorithms, and traditional algorithms may struggle to deal with such problems. A current trend is to use nature-inspired algorithms due to their f

Giuseppe Gaeta

In a recent report we have collected some data about the COVID-19 epidemics in Northern Italy; in this follow-up we analyze how these changed after the mild restrictive measures taken by the Government two weeks ago and the large campaign of public awareness developed in the meanwhile.

Andreas Heuer

A myriad of different data are generated to characterize a soccer match. Here we discuss which performance indicators are particularly helpful to forecast the future results of a team via an estimation of the underlying team strengths with minimum statistical uncertainty. We introduce an appropriate statistical framework and exemplify it for different perfor

Hui Chen, Guiguang Ding, Xudong Liu, Zijia Lin

Enabling bi-directional retrieval of images and texts is important for understanding the correspondence between vision and language. Existing methods leverage the attention mechanism to explore such correspondence in a fine-grained manner. However, most of them consider all semantics equally and thus align them uniformly, regardless of their diverse complexi

Xuan Luo, Hao Sun, Yi-Ling Xie

In this paper we study the single longitudinal spin asymmetry $A_{UL}^{\sin(ϕ_h-ϕ_R)}$ of dihadron production in semi-inclusive deep inelastic scattering (SIDIS) via helicity-dependent dihadron fragmentation function (DiFF), which describes the correlation of the longitudinal polarization of a fragmenting quark with the transverse momenta of the produced had

Shisong Li, Stephan Schlamminger, Qing Wang

Permanent magnets together with yokes to concentrate the magnetic flux into a cylindrical air-gap are widely employed in Kibble balances. These experiments require a uniform magnetic flux density along a vertical path, typically a substantial fraction of the length of the air-gap. Fringe fields that are present at both ends of the air-gap limit the region wh

Y V S Harish, Harit Pandya, Ayush Gaud, Shreya Terupally

Existing deep learning based visual servoing approaches regress the relative camera pose between a pair of images. Therefore, they require a huge amount of training data and sometimes fine-tuning for adaptation to a novel scene. Furthermore, current approaches do not consider underlying geometry of the scene and rely on direct estimation of camera pose. Thus

Yanlin Qian, Jani Käpylä, Joni-Kristian Kämäräinen, Samu Koskinen

Temporal Color Constancy (CC) is a recently proposed approach that challenges the conventional single-frame color constancy. The conventional approach is to use a single frame - shot frame - to estimate the scene illumination color. In temporal CC, multiple frames from the view finder sequence are used to estimate the color. However, there are no realistic l

Matthias Hofer, Siddarth Shivkumar, Bilal El Waly, Sophie Brasselet

Coherent Anti Stokes Raman Scattering (CARS) offers many advantages for nonlinear bio-imaging, thanks to its sub-cellular spatial resolution and unique chemical specificity. Its working principle requires two incident pulsed laser beams with distinct frequencies to be focused in space and time, which focus quality however rapidly deteriorates when propagatin

Shen Gao, Peihao Dong, Zhiwen Pan, Geoffrey Ye Li

For ultra-dense networks with wireless backhaul, caching strategy at small base stations (SBSs), usually with limited storage, is critical to meet massive high data rate requests. Since the content popularity profile varies with time in an unknown way, we exploit reinforcement learning (RL) to design a cooperative caching strategy with maximum-distance separ

K. P. Heeg, A. Kaldun, C. Strohm, C. Ott

Coherent control of quantum dynamics is key to a multitude of fundamental studies and applications alike. In the visible or longer-wavelength domains, near-resonant light fields have become the primary tool to control electron dynamics. Recently, coherent control in the extreme-ultraviolet range was demonstrated, with timing stability of the applied light fi

Zhi-Tian Zou, Ying Li, Qi-Xin Li, Xin Liu

In this work, we study the ($S$, $P$ and $D$)-wave $K^+K^-$ contributions to $B\to KKK$ decays in the perturbative QCD approach at leading order. Within the two-meson wave functions describing the nonperturbative dynamics in the kaon-pair for different waves, we calculate the branching fractions and the direct $CP$ asymmetries of these decay modes in the cor

Baruch Solel

The techniques developed by Popescu, Muhly-Solel and Good for the study of algebras generated by weighted shifts are applied to generalize results of Sarkar and of Bhattacharjee-Eschmeier-Keshari-Sarkar concerning dilations and invariant subspaces for commuting tuples of operators. In that paper the authors prove Beurling-Lax-Halmos type results for commutin

Mainak Dan, Seshadhri Srinivasan, Suresh Sundaram, Arvind Easwaran

This paper presents a novel solution technique for scheduling multi-energy system (MES) in a commercial urban building to perform price-based demand response and reduce energy costs. The MES scheduling problem is formulated as a mixed integer nonlinear program (MINLP), a non-convex NPhard problem with uncertainties due to renewable generation and demand. A m

Jia Chen, Qin Jin

Sequence-level learning objective has been widely used in captioning tasks to achieve the state-of-the-art performance for many models. In this objective, the model is trained by the reward on the quality of its generated captions (sequence-level). In this work, we show the limitation of the current sequence-level learning objective for captioning tasks from

Yingheng Tang, Keisuke Kojima, Toshiaki Koike-Akino, Ye Wang

We propose a novel Conditional Variational Autoencoder (CVAE) model, enhanced with adversarial censoring and active learning, for the generation of 550 nm broad bandwidth (1250 nm to 1800 nm) power splitters with arbitrary splitting ratio. The device footprint is 2.25 x 2.25 μ m2 with a 20 x 20 etched hole combination. It is the first demonstration to apply

Florian Kogelbauer

We prove an explicit, non-local hydrodynamic closure for the linear one-dimensional kinetic equation independent on the size of the relaxation time. We compare this dynamical equation to the local approximations obtained from the Chapman--Enskog expansion for small relaxation times. Our results rely on the spectral theory of Jacobi operators with rank-one pe

A. D. Kapustin, M. V. Ioffe, S. A. Paston

The work is devoted to the search for explicit isometric embeddings of a metric corresponding to the collapse of spherically symmetric matter with the formation of a black hole. Two approaches are considered: in the first, the embedding is constructed for the whole manifold at once; in the second, the idea of a junction of solutions, obtained separately for

Triet H. M. Le, David Hin, Roland Croft, M. Ali Babar

Security is an increasing concern in software development. Developer Question and Answer (Q&A) websites provide a large amount of security discussion. Existing studies have used human-defined rules to mine security discussions, but these works still miss many posts, which may lead to an incomplete analysis of the security practices reported on Q&A websites.

Jianwen Huang

Generalized Maxwell distribution is an extension of the classic Maxwell distribution. In this paper, we concentrate on the joint distributional asymptotics of normalized maxima and minima. Under optimal normalizing constants, asymptotic expansions of joint distribution and density for normalized partial maxima and minima are established. These expansions are

Menglei Zhou, Askar B. Abdikamalov, Dimitry Ayzenberg, Cosimo Bambi

We consider the family of singularity-free rotating black hole solutions in Einstein's conformal gravity found in Bambi, Modesto & Rachwal (2017) and we constrain the value of the conformal parameter $L$ from the analysis of a 30 ks NuSTAR observation of the stellar-mass black hole in GS 1354-645 during its outburst in 2015. Our new constraint is much st

Abdelqader Zaben, Guntars Kitenbergs, Andrejs Cēbers

Ferromagnetic filaments in a rotating magnetic field are studied both numerically and experimentally. The filaments are made from micron-sized ferromagnetic particles linked with DNA strands. It is found that at low frequencies of the rotating field a filament rotates synchronously with the field and beyond a critical frequency it undergoes a transition to a

Qingxia Liu, Gong Cheng, Yuzhong Qu

Entity summarization has been a prominent task over knowledge graphs. While existing methods are mainly unsupervised, we present DeepLENS, a simple yet effective deep learning model where we exploit textual semantics for encoding triples and we score each candidate triple based on its interdependence on other triples. DeepLENS significantly outperformed exis

Jianwen Huang, Xinling Liu, Jianjun Wang, Zhongquan Tan

In this paper, asymptotic expansions of the distributions and densities of powered extremes for Maxwell samples are considered. The results show that the convergence speeds of normalized partial maxima relies on the powered index. Additionally, compared with previous result, the convergence rate of the distribution of powered extreme from Maxwell samples is

Qingxia Liu, Gong Cheng, Kalpa Gunaratna, Yuzhong Qu

Entity summarization is the problem of computing an optimal compact summary for an entity by selecting a size-constrained subset of triples from RDF data. Entity summarization supports a multiplicity of applications and has led to fruitful research. However, there is a lack of evaluation efforts that cover the broad spectrum of existing systems. One reason i

Ke Xu, Nestor O. Perez-Arancibia

The vision of creating entirely-soft robots capable of performing complex tasks will be accomplished only when the controllers required for autonomous operation can be fully implemented on soft components. Despite recent advances in compliant fluidic circuitry for mechanical signal processing, the applicability of this technology for soft robot control has b

Adnan Rashid, Umair Siddique, Sofiene Tahar

Due to major breakthroughs in software and engineering technologies, embedded systems are increasingly being utilized in areas ranging from aerospace and next-generation transportation systems, to smart grid and smart cities, to health care systems, and broadly speaking to what is known as Cyber-Physical Systems (CPS). A CPS is primarily composed of several

Joo-Kyung Kim, Young-Bum Kim

In large-scale domain classification, an utterance can be handled by multiple domains with overlapped capabilities. However, only a limited number of ground-truth domains are provided for each training utterance in practice while knowing as many as correct target labels is helpful for improving the model performance. In this paper, given one ground-truth dom

Jhanani Selvakumar, Efstathios Bakolas

In this paper, we address a pursuit-evasion game involving multiple players by utilizing tools and techniques from reinforcement learning and matrix game theory. In particular, we consider the problem of steering an evader to a goal destination while avoiding capture by multiple pursuers, which is a high-dimensional and computationally intractable problem in

Kei Kase, Chris Paxton, Hammad Mazhar, Tetsuya Ogata

While robots can learn models to solve many manipulation tasks from raw visual input, they cannot usually use these models to solve new problems. On the other hand, symbolic planning methods such as STRIPS have long been able to solve new problems given only a domain definition and a symbolic goal, but these approaches often struggle on the real world roboti

Quoc-Hung Nguyen, Nguyen Cong Phuc

We characterize the existence of solutions to the quasilinear Riccati type equation \begin{eqnarray*} \left\{ \begin{array}{rcl} -{\rm div}\,\mathcal{A}(x, \nabla u)&=& |\nabla u|^q + σ\quad \text{in} ~Ω, \\ u&=&0 \quad \text{on}~ \partial Ω, \end{array}\right. \end{eqnarray*} with a distributional or measure datum $σ$. Here ${\rm div}\,\mathcal{A}(x, \nabla

Marcin Waniek, Gururaghav Raman, Bedoor AlShebli, Jimmy Chih-Hsien Peng

Disinformation continues to attract attention due to its increasing threat to society. Nevertheless, a disinformation-based attack on critical infrastructure has never been studied to date. Here, we consider traffic networks and focus on fake information that manipulates drivers' decisions to create congestion. We study the optimization problem faced by

Jieyu Lin, Kristina Dzeparoska, Sai Qian Zhang, Alberto Leon-Garcia

In cooperative multi-agent reinforcement learning (c-MARL), agents learn to cooperatively take actions as a team to maximize a total team reward. We analyze the robustness of c-MARL to adversaries capable of attacking one of the agents on a team. Through the ability to manipulate this agent's observations, the adversary seeks to decrease the total team r

H. Masuda, H. Sakai, H. Takahashi, Y. Yamasaki

Field-dependent magnetic structure of a layered Dirac material EuMnBi$_2$ was investigated in detail by the single crystal neutron diffraction and the resonant x-ray magnetic diffraction techniques. On the basis of the reflection conditions in the antiferromagnetic phase at zero field, the Eu moments were found to be ordered ferromagnetically within the $ab$

X. -J. Hao, R. -Y. Yuan, J. -J. Jin, Y. Guo

Using the transfer matrix method, spin- and valley-dependent electron transport properties modulated by the velocity barrier were studied in the normal/ferromagnetic/normal monolayer MoS$_{2}$ quantum structure. Based on Snell's Law in optics, we define the velocity barrier as $ξ=v_{2}/v_{1}$ by changing the Fermi velocity of the intermediate ferromagnet

Yu-Siang Wang, Yen-Ling Kuo, Boris Katz

We demonstrate how we can practically incorporate multi-step future information into a decoder of maximum likelihood sequence models. We propose a "k-step look-ahead" module to consider the likelihood information of a rollout up to k steps. Unlike other approaches that need to train another value network to evaluate the rollouts, we can directly appl

Zhanshan Ma

We comparatively analyzed the spatiotemporal fluctuations of the 2019-novel coronavirus (COVID-19) and SARS outbreaks to understand their epidemiological characteristics. Methodologically, we introduced TPL (Taylor power law) to characterize their spatiotemporal heterogeneity/stability and Hubbell (2001) unified neutral theory of biodiversity (UNTB) [specifi

Shuo Yang, Wei Yu, Ying Zheng, Hongxun Yao

Merchandise categories inherently form a semantic hierarchy with different levels of concept abstraction, especially for fine-grained categories. This hierarchy encodes rich correlations among various categories across different levels, which can effectively regularize the semantic space and thus make predictions less ambiguous. However, previous studies of

M. Ablikim, M. N. Achasov, P. Adlarson, S. Ahmed

The cross sections of the process $e^{+}e^{-} \to ηJ/ψ$ at center-of-mass energies ($\sqrt{s}$) between 3.81 and 4.60 GeV are measured with high precision by using data samples collected with the BESIII detector operating at the BEPCII storage ring. Three structures are observed by analyzing the lineshape of the measured cross sections, and a maximum-likelih

Yang Feng, Futang Peng, Xu Zhang, Wei Zhu

Deep image embedding provides a way to measure the semantic similarity of two images. It plays a central role in many applications such as image search, face verification, and zero-shot learning. It is desirable to have a universal deep embedding model applicable to various domains of images. However, existing methods mainly rely on training specialist embed

Hammad A. Ayyubi, Yi Yao, Ajay Divakaran

Neural Ordinary Differential Equations (NODEs) have proven to be a powerful modeling tool for approximating (interpolation) and forecasting (extrapolation) irregularly sampled time series data. However, their performance degrades substantially when applied to real-world data, especially long-term data with complex behaviors (e.g., long-term trend across year

Yu-Hui Chen, Ronnie R. Tamming, Kai Chen, Zhepeng Zhang

Bandgap control is of central importance for semiconductor technologies. The traditional means of control is to dope the lattice chemically, electrically or optically with charge carriers. Here, we demonstrate for the first time a widely tunable bandgap (renormalisation up to 650 meV at room-temperature) in two-dimensional (2D) semiconductors by coherently d

Peter Benner, Davide Palitta

The nonsymmetric T-Riccati equation is a quadratic matrix equation where the linear part corresponds to the so-called T-Sylvester or T-Lyapunov operator that has previously been studied in the literature. It has applications in macroeconomics and policy dynamics. So far, it presents an unexplored problem in numerical analysis, and both, theoretical results a

Yi Yang, Yuxuan Guo, Xiangyu Chang

Many real-world classification problems come with costs which can vary for different types of misclassification. It is thus important to develop cost-sensitive classifiers which minimize the total misclassification cost. Although binary cost-sensitive classifiers have been well-studied, solving multicategory classification problems is still challenging. A po

Behzad Ghazanfari, Fatemeh Afghah, MohammadTaghi Hajiaghayi

This paper proposes inverse feature learning as a novel supervised feature learning technique that learns a set of high-level features for classification based on an error representation approach. The key contribution of this method is to learn the representation of error as high-level features, while current representation learning methods interpret error b

Bo Liu, Mengya Shen

Geometry and topology of decision regions are closely related with classification performance and robustness against adversarial attacks. In this paper, we use differential geometry to theoretically explore the geometrical and topological properties of decision regions produced by deep neural networks (DNNs). The goal is to obtain some geometrical and topolo

Xingyu Zhao, Alec Banks, James Sharp, Valentin Robu

Increasingly sophisticated mathematical modelling processes from Machine Learning are being used to analyse complex data. However, the performance and explainability of these models within practical critical systems requires a rigorous and continuous verification of their safe utilisation. Working towards addressing this challenge, this paper presents a prin

Jakob Runge

The paper introduces a novel conditional independence (CI) based method for linear and nonlinear, lagged and contemporaneous causal discovery from observational time series in the causally sufficient case. Existing CI-based methods such as the PC algorithm and also common methods from other frameworks suffer from low recall and partially inflated false posit

Marine De Clerck, Oleg Evnin

We consider identical quantum bosons with weak contact interactions in a two-dimensional isotropic harmonic trap, and focus on states at the Lowest Landau Level (LLL). At linear order in the coupling parameter $g$, we exploit the rich algebraic structure of the problem to give an explicit construction of a large family of quantum states with energies of the

Jinseok Choi, Gilwon Lee, Ahmed Alkhateeb, Alan Gatherer

Employing low-resolution analog-to-digital converters (ADCs) for millimeter wave receivers with large antenna arrays provides opportunity to efficiently reduce power consumption of the receiver. Reducing ADC resolution, however, results in performance degradation due to non-negligible quantization error. In addition, the large number of radio frequency (RF)

Ying Zhang, Xinan Wang, Jianhui Wang, Yingchen Zhang

This paper develops a model-free volt-VAR optimization (VVO) algorithm via multi-agent deep reinforcement learning (MADRL) in unbalanced distribution systems. This method is novel since we cast the VVO problem in unbalanced distribution networks to an intelligent deep Q-network (DQN) framework, which avoids solving a specific optimization model directly when

Efstathios Bakolas, Alexandros Tsolovikos

In this work, we consider the problem of steering the first two moments of the uncertain state of a discrete time nonlinear stochastic system to prescribed goal quantities at a given final time. In principle, the latter problem can be formulated as a density tracking problem, which seeks for a feedback policy that will keep the probability density function o

Michael Bowman, Jiucai Zhang, Xiaoli Zhang

Shared control in teleoperation for providing robot assistance to accomplish object manipulation, called telemanipulation, is a new promising yet challenging problem. This has unique challenges--on top of teleoperation challenges in general--due to difficulties of physical discrepancy between human hands and robot hands as well as the fine motion constraints

Shahryar Rahnamayan, Seyed Jalaleddin Mousavirad

Many real-world problems are categorized as large-scale problems, and metaheuristic algorithms as an alternative method to solve large-scale problem; they need the evaluation of many candidate solutions to tackle them prior to their convergence, which is not affordable for practical applications since the most of them are computationally expensive. In other

Ahmed Salem, Rui Wen, Michael Backes, Shiqing Ma

Machine learning (ML) has made tremendous progress during the past decade and is being adopted in various critical real-world applications. However, recent research has shown that ML models are vulnerable to multiple security and privacy attacks. In particular, backdoor attacks against ML models have recently raised a lot of awareness. A successful backdoor

Cao Vien Phung, Anna Engelmann, Admela Jukan

The terahertz (THz) frequency band (0.3-10THz) has the advantage of large available bandwidth and is a candidate to satisfy the ever increasing mobile traffic in wireless communications. However, the THz channels are often absorbed by molecules in the atmosphere, which can decrease the signal quality resulting in high bit error rate of received data. In this

Daomin Cao, Peng Luo, Shuangjie Peng

In this paper we are concerned with the well-known Brezis-Nirenberg problem \begin{equation*} \begin{cases} -Δu= u^{\frac{N+2}{N-2}}+\varepsilon u, &{\text{in}~Ω},\\ u>0, &{\text{in}~Ω},\\ u=0, &{\text{on}~\partial Ω}. \end{cases} \end{equation*} The existence of multi-peak solutions to the above problem for small $\varepsilon>0$ was obtained by Musso and Pi

Huan Lei, Lei Wu, Weinan E

We introduce a machine-learning-based framework for constructing continuum non-Newtonian fluid dynamics model directly from a micro-scale description. Dumbbell polymer solutions are used as examples to demonstrate the essential ideas. To faithfully retain molecular fidelity, we establish a micro-macro correspondence via a set of encoders for the micro-scale

Fatih Ilhan, Suleyman Serdar Kozat

We investigate spatio-temporal event analysis using point processes. Inferring the dynamics of event sequences spatiotemporally has many practical applications including crime prediction, social media analysis, and traffic forecasting. In particular, we focus on spatio-temporal Hawkes processes that are commonly used due to their capability to capture excita

Yuxin Xiao, Adit Krishnan, Hari Sundaram

Some social networks provide explicit mechanisms to allocate social rewards such as reputation based on user activity, while the mechanism is more opaque in other networks. Nonetheless, there are always individuals who obtain greater rewards and reputation than their peers. An intuitive yet important question to ask is whether these successful users employ s

Tianlang Chen, Jiajun Deng, Jiebo Luo

Existing image-text matching approaches typically leverage triplet loss with online hard negatives to train the model. For each image or text anchor in a training mini-batch, the model is trained to distinguish between a positive and the most confusing negative of the anchor mined from the mini-batch (i.e. online hard negative). This strategy improves the mo

Yudong Chen, Tengyao Wang, Richard J. Samworth

We introduce a new method for high-dimensional, online changepoint detection in settings where a $p$-variate Gaussian data stream may undergo a change in mean. The procedure works by performing likelihood ratio tests against simple alternatives of different scales in each coordinate, and then aggregating test statistics across scales and coordinates. The alg

Thayer Alshaabi, David R. Dewhurst, Joshua R. Minot, Michael V. Arnold

Working from a dataset of 118 billion messages running from the start of 2009 to the end of 2019, we identify and explore the relative daily use of over 150 languages on Twitter. We find that eight languages comprise 80% of all tweets, with English, Japanese, Spanish, and Portuguese being the most dominant. To quantify social spreading in each language over

Juntao Yu, Massimo Poesio

We propose a multi task learning-based neural model for resolving bridging references tackling two key challenges. The first challenge is the lack of large corpora annotated with bridging references. To address this, we use multi-task learning to help bridging reference resolution with coreference resolution. We show that substantial improvements of up to 8

David Kalaj

We prove the following result. If $f$ is a harmonic quasiconformal mapping between two Jordan domains $D$ and $\Omega$ having $\mathscr{C}^1$ boundaries, then the function $f$ is globally H\"older continuous for every $\alpha<1$ but it is not Lipschitz in general. This extends and improves a classical theorem of S. Warschawski for conformal mappings.

Hiêp Hàn, Marcos Kiwi, Matías Pavez-Signé

Words are sequences of letters over a finite alphabet. We study two intimately related topics for this object: quasi-randomness and limit theory. With respect to the first topic we investigate the notion of uniform distribution of letters over intervals, and in the spirit of the famous Chung--Graham--Wilson theorem for graphs we provide a list of word proper

Dan Abramovich, Michael Temkin, Jarosław Włodarczyk

In characteristic zero, we construct relative principalization of ideals for logarithmically regular morphisms of logarithmic schemes, and use it to construct logarithmically regular desingularization of morphisms. These constructions are relatively canonical and even functorial with respect to logarithmically regular morphisms and arbitrary base changes. Re

Brian A. Powell

This work explores the extent to which LSB embedding can be made secure against structural steganalysis through a modification of cover image statistics prior to message embedding. Natural images possess symmetries that are expressed through approximately equal cardinalities of certain sets of $k$-tuples of consecutive pixels. LSB embedding disturbs this bal

Oguzhan Karaahmetoglu, Suleyman Serdar Kozat

We study the spatio-temporal prediction problem, which has attracted the attention of many researchers due to its critical real-life applications. In particular, we introduce a novel approach to this problem. Our approach is based on the Hawkes process, which is a non-stationary and self-exciting point process. We extend the formulations of a standard point

David Benisty, Eduardo Guendelman, Emil Nissimov, Svetlana Pacheva

First, we propose a scale-invariant modified gravity interacting with a neutral scalar &#34;inflaton&#34; and a Higgs-like SU(2)xU(1) iso-doublet scalar field based on the formalism of non-Riemannian (metric-independent) spacetime volume-elements. This model describes in the physical Einstein frame a quintessential inflationary scenario driven by the &#34;in

Oliver Roche-Newton, Audie Warren

An arc is a subset of $\mathbb F_q^2$ which does not contain any collinear triples. Let $A(q,k)$ denote the number of arcs in $\mathbb F_q^2$ with cardinality $k$. This paper is primarily concerned with estimating the size of $A(q,k)$ when $k$ is relatively large, namely $k=q^t$ for some $t>0$. Trivial estimates tell us that \[ {q \choose k} \leq A(q,k) \leq

Sayan Banerjee, Amarjit Budhiraja, Amber L. Puha

We study a single server queue operating under the shortest remaining processing time (SRPT) scheduling policy; that is, the server preemptively serves the job with the shortest remaining processing time first. In this work we are interested in studying the asymptotic behavior of suitably scaled measure-valued state descriptors that describe the evolution of

Tiago Cortinhal, George Tzelepis, Eren Erdal Aksoy

In this paper, we introduce SalsaNext for the uncertainty-aware semantic segmentation of a full 3D LiDAR point cloud in real-time. SalsaNext is the next version of SalsaNet [1] which has an encoder-decoder architecture where the encoder unit has a set of ResNet blocks and the decoder part combines upsampled features from the residual blocks. In contrast to S

Vjekoslav Kovač, Mario Stipčić

In this note we introduce a sequence of bilinear operators that unify ergodic averages and backward martingales in a nontrivial way. We establish its convergence in a range of $L^p$-norms and leave its a.s. convergence as an open problem. This problem shares some similarities with a well-known unresolved conjecture on a.s. convergence of double ergodic avera

Karolos K. Korkas

We propose a new technique for consistent estimation of the number and locations of the change-points in the structure of an irregularly spaced time series. The core of the segmentation procedure is the Ensemble Binary Segmentation method (EBS), a technique in which a large number of multiple change-point detection tasks using the Binary Segmentation (BS) me

Lito E. Bocanegra-Rodríguez, To Fu Ma, Paulo N. Seminario-Huertas, Marcio Antonio Jorge Silva

This paper is concerned with longtime dynamics of semilinear Lamé systems $$ \partial^2_t u - μΔu - (λ+ μ) \nabla {\rm div} u + α\partial_t u + f(u) = 0, $$ defined in bounded domains of $\mathbb{R}^3$ with Dirichlet boundary condition. Firstly, we establish the existence of finite dimensional global attractors subjected to critical forcings $f(u)$. Writing

Nabiha Asghar, Ivan Kobyzev, Jesse Hoey, Pascal Poupart

State-of-the-art neural dialogue systems excel at syntactic and semantic modelling of language, but often have a hard time establishing emotional alignment with the human interactant during a conversation. In this work, we bring Affect Control Theory (ACT), a socio-mathematical model of emotions for human-human interactions, to the neural dialogue generation

Zining Wang, Di Feng, Yiyang Zhou, Lars Rosenbaum

The availability of real-world datasets is the prerequisite for developing object detection methods for autonomous driving. While ambiguity exists in object labels due to error-prone annotation process or sensor observation noises, current object detection datasets only provide deterministic annotations without considering their uncertainty. This precludes a

Oksana Busel, Oksana Gorobets, Yuri Gorobets

A degree of sublattice noncompensation of antiferromagnet (DSNA) can play a crucial role in the designing of a diagonal-like and curvilinear geometry at interfaces of antiferromagnet (AFM) with other magnetic media because of the exchange interaction both between AFM sublattices and with neighboring material. We present a conceptually advanced theory which u

Hoda Abbasizanjani, Oliver Kullmann

Conjunctive normal forms where every clause has length at most two are called 2-CNFs. We study minimally unsatisfiable 2-CNFs (2-MUs), that is, unsatisfiable 2-CNFs where removing any clause destroys unsatisfiability, and obtain their full classification up to isomorphism. The main tool is the implication digraph: we show that for 2-MUs these digraphs are "w

James D. Brunner, Nicholas Chia

Dynamic flux balance analysis uses a quasi-steady state assumption to calculate an organism&#39;s metabolic activity at each time-step of a dynamic simulation, using the well-known technique of flux balance analysis. For microbial communities, this calculation is especially costly and involves solving a linear constrained optimization problem for each member

Filippo Pagani, Augustin Chevallier, Sam Power, Thomas House

Markov chain Monte Carlo (MCMC) is a key algorithm in computational statistics, and as datasets grow larger and models grow more complex, many popular MCMC algorithms become too computationally expensive to be practical. Recent progress has been made on this problem through development of MCMC algorithms based on Piecewise Deterministic Markov Processes (PDM

Simona Paiano, Renato Falomo, Paolo Padovani, Paolo Giommi

The BL Lac object 4FGLJ0955.1+3551 has been suggested as a possible source of ultra energetic neutrinos detected by the Icecube observatory. The target was observed in January 2020 at the Large Binocular Telescope. Our spectroscopy (4100-8500 Ang) yields a firm redshift z = 0.557 as deduced by the absorption lines of the host galaxy. The upper limit of the m

Kiril Solovey

This is a chapter in the Encyclopedia of Robotics. It is devoted to the study of complexity of complete (or exact) algorithms for robot motion planning. The term ``complete&#39;&#39; indicates that an approach is guaranteed to find the correct solution (a motion path or trajectory in our setting), or to report that none exists otherwise (in case that for ins

Davor Dragičević, Yeor Hafouta

The purpose of this paper is twofold. In one direction, we extend the spectral method for random piecewise expanding and hyperbolic dynamics developed by the first author \textit{et al}. to establish quenched versions of the large deviation principle, central limit theorem and the local central limit theorem for \emph{vector-valued} observables. We stress th

Lucas Mentch, Siyu Zhou

As the size, complexity, and availability of data continues to grow, scientists are increasingly relying upon black-box learning algorithms that can often provide accurate predictions with minimal a priori model specifications. Tools like random forests have an established track record of off-the-shelf success and even offer various strategies for analyzing

Xin Chen, Yutong Nie, Na Li

Residential loads have great potential to enhance the efficiency and reliability of electricity systems via demand response (DR) programs. One major challenge in residential DR is to handle the unknown and uncertain customer behaviors. Previous works use learning techniques to predict customer DR behaviors, while the influence of time-varying environmental f