October 2020 arXiv papers — page 61

Laura Graves, Vineel Nagisetty, Vijay Ganesh

The Right to be Forgotten is part of the recently enacted General Data Protection Regulation (GDPR) law that affects any data holder that has data on European Union residents. It gives EU residents the ability to request deletion of their personal data, including training records used to train machine learning models. Unfortunately, Deep Neural Network model

Jinle Zhu, Qiang Li, Zilong Liu, Hongyang Chen

Non-orthogonal multiple access (NOMA) and millimeter wave (mmWave) are two key enabling technologies for the fifth-generation (5G) mobile networks and beyond. In this paper, we consider uplink communications with a hybrid beamforming structure and focus on improving the spectral efficiency (SE) and energy efficiency (EE) of mmWave multiple-input multiple-out

Koichiro Niinuma, Itir Onal Ertugrul, Jeffrey F Cohn, László A Jeni

Critical obstacles in training classifiers to detect facial actions are the limited sizes of annotated video databases and the relatively low frequencies of occurrence of many actions. To address these problems, we propose an approach that makes use of facial expression generation. Our approach reconstructs the 3D shape of the face from each video frame, ali

Jorge Deolindo Silva, Raúl Oset Sinha

We study the geometry of surfaces in $\mathbb R^5$ by relating it to the geometry of regular and singular surfaces in $\mathbb R^4$ obtained by orthogonal projections. In particular, we obtain relations between asymptotic directions, which are not second order geometry for surfaces in $\mathbb R^5$ but are in $\mathbb R^4$. We also relate the umbilic curvatu

V. I. Piterbarg, Yu. A. Scherbakova

A sequence of accompanying laws is suggested in the limit theorem of B. V. Gnedenko for maximums of independent random variables belonging to maximum domain of attraction of the Gumbel distribution. It is shown that this sequence gives an exponential power rate of convergence whereas the Gumbel distribution gives only a logarithmic rate. As examples, classes

Huu Le, Christopher Zach, Edward Rosten, Oliver J. Woodford

Non-linear least squares solvers are used across a broad range of offline and real-time model fitting problems. Most improvements of the basic Gauss-Newton algorithm tackle convergence guarantees or leverage the sparsity of the underlying problem structure for computational speedup. With the success of deep learning methods leveraging large datasets, stochas

Frederik Wiehr, Anke Hirsch, Florian Daiber, Antonio Kruger

For mixed-initiative control between cyber-physical systems (CPS) and its users, it is still an open question how machines can safely hand over control to humans. In this work, we propose a concept to provide technological support that uses formal methods from AI -- description logic (DL) and automated planning -- to predict more reliably when a hand-over is

O. I. Hryhorchak

The relations between a quantum wave impedance function and elements of transfer and scattering matrixes for quantum mechanical systems with arbitrary localized form of potential were established. Obtained results allows using the advantages of both methods, namely a transfer matrix technique and a quantum wave impedance approach, for an investigating of nan

Kai Jiang, Xiangyue Liu, Zheng Ju, Xiang Luo

Compared with MS-COCO, the dataset for the competition has a larger proportion of large objects which area is greater than 96x96 pixels. As getting fine boundaries is vitally important for large object segmentation, Mask R-CNN with PointRend is selected as the base segmentation framework to output high-quality object boundaries. Besides, a better engine that

The Pierre Auger Collaboration, A. Aab, P. Abreu, M. Aglietta

Results of a search for ultra-high-energy neutrinos with the Pierre Auger Observatory from the direction of the blazar TXS 0506+056 are presented. They were obtained as part of the follow-up that stemmed from the detection of high-energy neutrinos and gamma rays with IceCube, \textit{Fermi}-LAT, MAGIC, and other detectors of electromagnetic radiation in seve

A. Leviatan

We consider several variants of SU(3) partial dynamical symmetry in relation to quadrupole shapes in nuclei. Explicit construction of Hamiltonians with such property is presented in the framework of the interacting boson model (IBM), including higher order terms, and in its proton-neutron extension (IBM-2). The cases considered include a single prolate-defor

Fangqi Li, Chong Di, Shenghong Li

Selecting the optimal subset from all vertices as seeds to maximize the influence in a social network has been a task of interest. Various methods have been proposed to select the optimal vertices in a static network, however, they are challenged by the dynamics, i.e. the time-dependent variation of the social network structure. Such dynamics hinder the para

Apostolos A. Christou, Galin Borisov, Aldo Dell'Oro, Alberto Cellino

We investigate the mineralogy of L5 Martian Trojan asteroids via reflectance spectroscopy, in particular (101429) 1998 $\mbox{VF}_{31}$, the only L5 Trojan that does not belong to the Eureka family (Christou, 2013). We find that this asteroid most likely belongs to the Bus-Demeo S-complex, in agreement with Rivkin et al. (2007) and obtain good spectral match

Yat Tin Chow, Fuqun Han, Jun Zou

We propose a novel direct sampling method (DSM) for the effective and stable inversion of the Radon transform. The DSM is based on a generalization of the important almost orthogonality property in classical DSMs to fractional order Sobolev duality products and to a new family of probing functions. The fractional order duality product proves to be able to gr

V. Bouillaut, S. Lepot, S. Aumaître, B. Gallet

We report on the transition between two regimes of heat transport in a radiatively driven convection experiment, where a fluid gets heated up within a tunable heating length $\ell$ in the vicinity of the bottom of the tank. The first regime is similar to the one observed in standard Rayleigh-Bénard experiments, the Nusselt number $Nu$ being related to the Ra

Muhammad Wildan Abdul Hakim, Syarifah Rosita Dewi, Yurio Windiatmoko, Umar Abdul Aziz

VRP (Vehicle Routing Problem) is an NP hard problem, and it has attracted a lot of research interest. In contexts where vehicles have limited carrying capacity, such as volume and weight but needed to deliver items at various locations. Initially before creating a route, each vehicle needs a group of delivery points that are not exceeding their maximum capac

Jaewoong Choi, Sion Jang, Jaeyoung Kim, Jiho Lee

In this study, we address the challenges in developing a deep learning-based automatic patent citation recommendation system. Although deep learning-based recommendation systems have exhibited outstanding performance in various domains (such as movies, products, and paper citations), their validity in patent citations has not been investigated, owing to the

Mari Napari, Tahmida N. Huq, David J. Meeth, Mikko J. Heikkilä

High-performance p-type oxide thin film transistors (TFTs) have great potential for many semiconductor applications. However, these devices typically suffer from low hole mobility and high off-state currents. We fabricated p-type TFTs with a phase-pure polycrystalline Cu2O semiconductor channel grown by atomic layer deposition (ALD). The TFT switching charac

Ved Datar, Harish Seshadri, Jian Song

In this short note we prove that a Kahler manifold with lower Ricci curvature bound and almost maximal volume is Gromov-Hausdorff close to the projective space with the Fubini-Study metric. This is done by combining the recent results of Kewei Zhang and Yuchen Liu on holomorphic rigidity of such Kahler manifolds with the structure theorem of Tian-Wang for al

Aibek Makazhanov, Sharon Goldwater, Adam Lopez

We present LemMED, a character-level encoder-decoder for contextual morphological analysis (combined lemmatization and tagging). LemMED extends and is named after two other attention-based models, namely Lematus, a contextual lemmatizer, and MED, a morphological (re)inflection model. Our approach does not require training separate lemmatization and tagging m

Nikolai Leonenko, Vitalii Makogin, Mehmet Siddik Cadirci

We introduce some new classes of unimodal rotational invariant directional distributions, which generalize von Mises-Fisher distribution. We propose three types of distributions, one of which represents axial data. For each new type we provide formulae and short computational study of parameter estimators by the method of moments and the method of maximum li

Aaqib Saeed, David Grangier, Neil Zeghidour

We introduce COLA, a self-supervised pre-training approach for learning a general-purpose representation of audio. Our approach is based on contrastive learning: it learns a representation which assigns high similarity to audio segments extracted from the same recording while assigning lower similarity to segments from different recordings. We build on top o

Jin Zhang, Xiaowei Liu

In convergence analysis of finite element methods for singularly perturbed reaction--diffusion problems, balanced norms have been successfully introduced to replace standard energy norms so that layers can be captured. In this article, we focus on the convergence analysis in a balanced norm on Bakhvalov-type rectangular meshes. In order to achieve our goal,

Cong Li, Guangyang Dai, Yongqing Cai, Yang Wang

Super-high resolution laser-based angle-resolved photoemission measurements are carried out on LiFeAs superconductor to investigate its electron dynamics. Three energy scales at $\sim$20 meV, $\sim$34 meV and $\sim$55 meV are revealed for the first time in the electron self-energy both in the superconducting state and normal state. The $\sim$20 meV and $\sim

Mali Jin, Nikolaos Aletras

Complaining is a speech act extensively used by humans to communicate a negative inconsistency between reality and expectations. Previous work on automatically identifying complaints in social media has focused on using feature-based and task-specific neural network models. Adapting state-of-the-art pre-trained neural language models and their combinations w

Frederick Osei-Tutu Agyapong-Fordjour, Seok Joon Yun, Hyung-Jin Kim, Wooseon Choi

Among transition metal dichalcogenides (TMdCs) as alternatives for Pt-based catalysts, metallic-TMdCs catalysts have highly reactive basal-plane but are unstable. Meanwhile, chemically stable semiconducting-TMdCs show limiting catalytic activity due to their inactive basal-plane. Here, we propose metallic vanadium sulfide (VSn) nanodispersed in a semiconduct

Jonáš Kulhánek, Erik Derner, Robert Babuška

Visual navigation is essential for many applications in robotics, from manipulation, through mobile robotics to automated driving. Deep reinforcement learning (DRL) provides an elegant map-free approach integrating image processing, localization, and planning in one module, which can be trained and therefore optimized for a given environment. However, to dat

Conor Wildy, Hermine Landt, Martin J. Ward, Bozena Czerny

The nucleus of the active galaxy NGC 5548 was the target of two intensive spectroscopic monitoring campaigns at X-ray, ultraviolet (UV), and optical frequencies in 2013/14. These campaigns detected the presence of a massive obscuration event. In 2016/17, Landt et al. conducted a near-IR spectroscopic monitoring campaign on NGC 5548 and discovered He i 1.08 μ

Anand Panchbhai, Tommaso Soru, Edgard Marx

A booming amount of information is continuously added to the Internet as structured and unstructured data, feeding knowledge bases such as DBpedia and Wikidata with billions of statements describing millions of entities. The aim of Question Answering systems is to allow lay users to access such data using natural language without needing to write formal quer

Yiping Guo, Howard D. Bondell

Compared to the conditional mean as a simple point estimator, the conditional density function is more informative to describe the distributions with multi-modality, asymmetry or heteroskedasticity. In this paper, we propose a novel parametric conditional density estimation method by showing the connection between the general density and the likelihood funct

Yingyao Wang, Junwei Bao, Guangyi Liu, Youzheng Wu

This paper aims to enhance the few-shot relation classification especially for sentences that jointly describe multiple relations. Due to the fact that some relations usually keep high co-occurrence in the same context, previous few-shot relation classifiers struggle to distinguish them with few annotated instances. To alleviate the above relation confusion

Duncan Lee, Kitty Meeks

Spatial correlation in areal unit count data is typically modelled by a set of random effects that are assigned a conditional autoregressive (CAR) prior distribution. The spatial correlation structure implied by this model depends on a binary neighbourhood matrix, where two random effects are assumed to be partially autocorrelated if their areal units share

Wilfried Buchmuller, Emilian Dudas, Yoshiyuki Tatsuta

Intersecting D-brane models and their T-dual magnetic compactifications provide an attractive framework for particle physics, allowing for chiral fermions and supersymmetry breaking. Generically, magnetic compactifications have tachyons that are usually removed by Wilson lines. However, quantum corrections prevent local minima for Wilson lines. We therefore

V. A. Marsakov, V. V. Koval', M. L. Gozha

The differences in the relationships between the physical parameters and the chemical-element abundances in accreted globular star clusters and those formed inside the Galaxy have been investigated. The information on the supposed formation sites of the clusters based on the Gaia~DR2 data is borrowed from the literature. Those sources estimate the probabilit

Frank Neumann, Mojgan Pourhassan, Carsten Witt

In the last decade remarkable progress has been made in development of suitable proof techniques for analysing randomised search heuristics. The theoretical investigation of these algorithms on classes of functions is essential to the understanding of the underlying stochastic process. Linear functions have been traditionally studied in this area resulting i

Iztok Fister, Iztok Fister

The paper presents a novel software framework for Association Rule Mining named uARMSolver. The framework is written fully in C++ and runs on all platforms. It allows users to preprocess their data in a transaction database, to make discretization of data, to search for association rules and to guide a presentation/visualization of the best rules found using

Ezra Tampubolon, Holger Boche

Competitive non-cooperative online decision-making agents whose actions increase congestion of scarce resources constitute a model for widespread modern large-scale applications. To ensure sustainable resource behavior, we introduce a novel method to steer the agents toward a stable population state, fulfilling the given coupled resource constraints. The pro

Marc Finzi, Roberto Bondesan, Max Welling

Continuous input signals like images and time series that are irregularly sampled or have missing values are challenging for existing deep learning methods. Coherently defined feature representations must depend on the values in unobserved regions of the input. Drawing from the work in probabilistic numerics, we propose Probabilistic Numeric Convolutional Ne

Peiran Yin, Xiaohui Luo, Liang Zhang, Shaochun Lin

Physical systems with gain and loss can be described by a non-Hermitian Hamiltonian, which is degenerated at the exceptional points (EPs). Many new and unexpected features have been explored in the non-Hermitian systems with a great deal of recent interest. One of the most fascinating features is that, chiral state conversion appears when one EP is encircled

Milad Moradi, Matthias Samwald

Deep neural networks and other intricate Artificial Intelligence (AI) models have reached high levels of accuracy on many biomedical natural language processing tasks. However, their applicability in real-world use cases may be limited due to their vague inner working and decision logic. A post-hoc explanation method can approximate the behavior of a black-b

James Pavur, Ivan Martinovic

As the space industry approaches a period of rapid change, securing both emerging and legacy satellite missions will become vital. However, space technology has been largely overlooked by the systems security community. This systematization of knowledge paper seeks to understand why this is the case and to offer a starting point for technical security resear

Marcus Michelen, Julian Sahasrabudhe

Let $f = \sum_{k=0}^n \varepsilon_k z^k$ be a random polynomial, where $\varepsilon_0,\ldots ,\varepsilon_n$ are iid standard Gaussian random variables, and let $ζ_1,\ldots,ζ_n$ denote the roots of $f$. We show that the point process determined by the magnitude of the roots $\{ 1-|ζ_1|,\ldots, 1-|ζ_n| \}$ tends to a Poisson point process at the scale $n^{-2}

Lucas Smaira, João Carreira, Eric Noland, Ellen Clancy

We describe the 2020 edition of the DeepMind Kinetics human action dataset, which replenishes and extends the Kinetics-700 dataset. In this new version, there are at least 700 video clips from different YouTube videos for each of the 700 classes. This paper details the changes introduced for this new release of the dataset and includes a comprehensive set of

Tian-Le Zhao, Ye-Fei Yuan, Rajiv Kumar

We study the small scale magnetic reconnection above the radiative inefficient accretion flow around massive black hole via 2D magnetohydrodynamics (MHD) numerical simulation, in order to model the blob formation and ejection from the accretion flow around Sgr A*. The connection of both the newly emerging magnetic field and the pre-existing magnetic field is

Jack W Shepherd, Sarah Lecinski, Jasmine Wragg, Sviatlana Shashkova

The physical and chemical environment inside cells is of fundamental importance to all life but has traditionally been difficult to determine on a subcellular basis. Here we combine cutting-edge genomically integrated FRET biosensing to readout localized molecular crowding in single live yeast cells. Confocal microscopy allows us to build subcellular crowdin

Zhiyuan Jiang

In a heterogeneous unreliable multiaccess network, wherein terminals share a common wireless channel with distinctive error probabilities, existing works have showed that a persistent round-robin (RR-P) scheduling policy (i.e., greedy policy) can be arbitrarily worse than the optimum in terms of Age of Information (AoI) under standard Automatic Repeat reQues

Befekadu G. Gebraselase, Bjarne E. Helvik, Yuming Jiang

Blockchain has been considered as an important technique to enable secure management of virtual network functions and network slices. To understand such capabilities of a blockchain, e.g. transaction confirmation time, demands a thorough study on the transaction characteristics of the blockchain. This paper presents a comprehensive study on the transaction c

Marc Hovemann

We study the Besov-Morrey spaces $ \mathcal{N}^{s}_{u,p,q}(\mathbb{R}^{d}) $ and show that under certain conditions on the parameters these spaces can be characterized in terms of higher-order differences. Furthermore we prove that some of the mentioned conditions are also necessary.

Pavel Tonkaev, Yuri Kivshar

Future technologies underpinning high-performance optical communications, ultrafast computations and compact biosensing will rely on densely packed reconfigurable optical circuitry based on nanophotonics. For many years, plasmonics was considered as the only available platform for nanoscale optics, but the recently emerged novel field of Mie resonant metapho

Jing-Chen Xu, Peng-Xin Gao, Xiang-Jun Shi

Rotation is a significant characteristic of the Sun and other stars, and it plays an important role in understanding their dynamo actions and magnetic activities. In this study, the rotation of the solar chromospheric activity is investigated from a global point of view with an over 40 yr Mg II index. We determined the time-varying rotational period lengths

M. Batkova, I. Batko, E. Bauer, R. T. Khan

We report about influence of external pressure on electrical resistivity of EuB5.99C0.01, the compound believed to be intrinsically inhomogeneous due to fluctuation of carbon content. Our results show that the low-temperature resistivity maximum shifts to lower temperature with applied pressure, opposite to the behavior reported for stoichiometric EuB6. The

Wubo Li, Dongwei Jiang, Wei Zou, Xiangang Li

Audio Visual Scene-aware Dialog (AVSD) is a task to generate responses when discussing about a given video. The previous state-of-the-art model shows superior performance for this task using Transformer-based architecture. However, there remain some limitations in learning better representation of modalities. Inspired by Neural Machine Translation (NMT), we

M. Baťková, I. Baťko, M. Mihalik

We report electron tunneling spectroscopy studies on single crystalline FeSi sample performed for the case of homogeneous tunnel junction contacts and for the case of counter electrodes made from Pt-Rh alloy. Our results reveal that while the tunneling spectroscopy in the configuration with Pt-Rh tip is preferably sensitive to the d-partial density of states

Katharina Dort

The physics aims at the proposed future high-energy linear $e^+e^-$ collider CLIC pose challenging demands on the performance of the detector system. In particular, the vertex and tracking detectors have to combine a spatial resolution of a few micrometres and a low material budget with a time-stamping accuracy of a few nanoseconds. For the vertex detector,

Soumya Suvra Ghosal, Deepak P, Anna Jurek-Loughrey

Disinformation is often presented in long textual articles, especially when it relates to domains such as health, often seen in relation to COVID-19. These articles are typically observed to have a number of trustworthy sentences among which core disinformation sentences are scattered. In this paper, we propose a novel unsupervised task of identifying senten

Xinyu Zuo, Yubo Chen, Kang Liu, Jun Zhao

Modern models of event causality detection (ECD) are mainly based on supervised learning from small hand-labeled corpora. However, hand-labeled training data is expensive to produce, low coverage of causal expressions and limited in size, which makes supervised methods hard to detect causal relations between events. To solve this data lacking problem, we inv

Gianluca Savaia, Youngil Sohn, Simone Formentin, Giulio Panzani

The End-of-Line (EoL) calibration of semi-active suspension systems for road vehicles is usually a critical and expensive task, needing a team of vehicle and control experts as well as many hours of professional driving. In this paper, we propose a purely data-based tuning method enabling the automatic calibration of the parameters of a proprietary suspensio

Raul M. Alonso, Adrian Escapa, Ana Sotres, Antonio Moran

The aim of this study is to evaluate the integration of microbial electrochemical technologies (MET) with anaerobic digestion (AD) to overcome AD limitations caused by propionate accumulation. The study focuses on understanding to what extent the inoculum impacts on the behaviour of the integrated systems (AD-MET) from the perspective of propionate degradati

Alessio Porretta, Luca Rossi

We analyze a mean-field game model proposed by economists R.E. Lucas and B. Moll (2014) to describe economic systems where production is based on knowledge growth and diffusion. This model reduces to a PDE system where a backward Hamilton-Jacobi-Bellman equation is coupled with a forward KPP-type equation with nonlocal reaction term. We study the existence o

Daan Leermakers, Boris Skoric

Unclonable Encryption, introduced by Gottesman in 2003, is a quantum protocol that guarantees the secrecy of a successfully transferred classical message even when all keys leak at a later time. We propose an Unclonable Encryption protocol with the additional property that the sender's key material is allowed to leak even in the case of an unsuccessful r

Michele Perlangeli, Simone Peli, Davide Soranzio, Denny Puntel

Time-resolved optical spectroscopy (TR-OS) has emerged as a fundamental spectroscopic tool for probing complex materials, to both investigate ground-state-related properties and trigger phase transitions among different states with peculiar electronic and lattice structures. We describe a versatile approach to perform polarization-resolved TR-OS measurements

Jun Zheng, Guchuan Zhu, Meng Li

This chapter presents the development and the analysis of a scheme for aggregate power tracking control of heterogeneous populations of thermostatically controlled loads (TCLs) based on partial differential equations (PDEs) control theory and techniques. By employing a thermostat--based deadband control with forced switching in the operation of individual TC

Jun-Muk Hwang

We study a cone structure ${\mathcal C} \subset {\mathbb P} D$ on a holomorphic contact manifold $(M, D \subset T_M)$ such that each fiber ${\mathcal C}_x \subset {\mathbb P} D_x$ is isomorphic to a Legendrian submanifold of fixed isomorphism type. By characterizing subadjoint varieties among Legendrian submanifolds in terms of contact prolongations, we prov

Yuzhuo Wang, Chengzhi Zhang

In the era of big data, the advancement, improvement, and application of algorithms in academic research have played an important role in promoting the development of different disciplines. Academic papers in various disciplines, especially computer science, contain a large number of algorithms. Identifying the algorithms from the full-text content of papers

Benjamin Jourdain, Gilles Pagès

We establish for dual quantization the counterpart of Kieffer's uniqueness result for compactly supported one dimensional probability distributions having a $\log$-concave density (also called strongly unimodal): for such distributions, $L^r$-optimal dual quantizers are unique at each level $N$, the optimal grid being the unique critical point of the qua

Kaixin Wang, Bingyi Kang, Jie Shao, Jiashi Feng

Deep reinforcement learning (RL) agents trained in a limited set of environments tend to suffer overfitting and fail to generalize to unseen testing environments. To improve their generalizability, data augmentation approaches (e.g. cutout and random convolution) are previously explored to increase the data diversity. However, we find these approaches only l

Zhao Jinman, Shawn Zhong, Xiaomin Zhang, Yingyu Liang

We look into the task of \emph{generalizing} word embeddings: given a set of pre-trained word vectors over a finite vocabulary, the goal is to predict embedding vectors for out-of-vocabulary words, \emph{without} extra contextual information. We rely solely on the spellings of words and propose a model, along with an efficient algorithm, that simultaneously

Luca Lach, Robert Haschke, Francesco Ferro, Jordi Pagès

Despite many advances in service robotics, successful and secure object manipulation on mobile platforms is still a challenge. In order to come closer to human grasping performance, it is natural to provide robots with the same capability that humans have: the sense of touch. This abstract presents novel, tactile-equipped end-effectors for the service robot

Edoardo Ballico, Claudio Fontanari

Let $X \subset \mathbb{P}^r$ be smooth and irreducible and for $k \ge 0$ let $ν_k(X)$ (resp., $δ_k(X)$) be the $k$-th contact (resp., the $k$-th secant) defect of $X$. For all $k \ge 0$ we have the inequality $ν_k(X) \ge δ_k(X)$ and in the case $k=1$ we characterize projective varieties $X$ for which equality holds, $\dim \mathrm{Sing}(X) \le δ_1(X) -1$ and

Zhongzheng Ren, Zhiding Yu, Xiaodong Yang, Ming-Yu Liu

Existing work on object detection often relies on a single form of annotation: the model is trained using either accurate yet costly bounding boxes or cheaper but less expressive image-level tags. However, real-world annotations are often diverse in form, which challenges these existing works. In this paper, we present UFO$^2$, a unified object detection fra

Alexander Refsum Jensenius, Michael J. Lyons

This paper provides an overview of the process of editing the forthcoming anthology "A NIME Reader - Fifteen Years of New Interfaces for Musical Expression." The selection process is presented, and we reflect on some of the trends we have observed in re-discovering the collection of more than 1200 NIME papers published throughout the 15-year long his

Itai Gat, Idan Schwartz, Alexander Schwing, Tamir Hazan

Many recent datasets contain a variety of different data modalities, for instance, image, question, and answer data in visual question answering (VQA). When training deep net classifiers on those multi-modal datasets, the modalities get exploited at different scales, i.e., some modalities can more easily contribute to the classification results than others.

Arthur M. Jacobs, Annette Kinder

The Gutenberg Literary English Corpus (GLEC) provides a rich source of textual data for research in digital humanities, computational linguistics or neurocognitive poetics. However, so far only a small subcorpus, the Gutenberg English Poetry Corpus, has been submitted to quantitative text analyses providing predictions for scientific studies of literature. H

Idriss Mazari, Domenec Ruiz-Balet

This work is concerned with two optimisation problems that we tackle from a qualitative perspective. The first one deals with quantitative inequalities for spectral optimisation problems for Schrödinger operators in general domains, the second one deals with the turnpike property for optimal bilinear control problems. In the first part of this article, we pr

Gianmarco Chinello

Let $(W,S)$ be a Coxeter system of finite rank and let $J,K\subset S$. We study the rationality of the Poincaré series of the set of representatives of minimal length of $(W_J,W_K)$-double cosets of $W$: we conclude that it depends mostly on the rationality of the Poincaré series of the normalizers of finite parabolic subgroups of $W$. For affine Weyl groups

A. A. Grib, Yu. V. Pavlov

The number of superheavy particles with the mass of the Grand Unification scale with trans-Planckian energy created at the epoch of superheavy particle creation from vacuum by the gravitation of the expanding Universe is calculated. In later collisions of these particles gravitational radiation is radiated playing the role of bremsstrahlung for gravity. The

Jun-ya Gotoh, Michael Jong Kim, Andrew E. B. Lim

We introduce the notion of Worst-Case Sensitivity, defined as the worst-case rate of increase in the expected cost of a Distributionally Robust Optimization (DRO) model when the size of the uncertainty set vanishes. We show that worst-case sensitivity is a Generalized Measure of Deviation and that a large class of DRO models are essentially mean-(worst-case)

Noboru Ito, Yusuke Takimura

Every knot projection is simplified to the trivial spherical curve not increasing double points by using deformations of types 1, 2, and 3 which are analogies of Reidemeister moves of types 1, 2, and 3 on knot diagrams. We introduce RII number of a knot projection that is the minimum number of deformations of negative type 2 among such sequences. By definiti

Darine Al Mniny, Salman Ghazal

Seymour's Second Neighborhood Conjecture (SNC) asserts that every oriented graph has a vertex whose first out-neighborhood is at most as large as its second out-neighborhood. In this paper, we prove that if $G$ is a graph containing no induced $C_4$, $\overline{C_4}$, $S_3$, chair and $\overline{chair}$, then every oriented graph missing $G$ satisfies th

Weizhen Qi, Yeyun Gong, Yu Yan, Jian Jiao

In a sponsored search engine, generative retrieval models are recently proposed to mine relevant advertisement keywords for users' input queries. Generative retrieval models generate outputs token by token on a path of the target library prefix tree (Trie), which guarantees all of the generated outputs are legal and covered by the target library. In actu

Dan Su, Jiqiang Liu, Sencun Zhu, Xiaoyang Wang

The home voice assistants such as Amazon Alexa have become increasingly popular due to many interesting voice-activated services provided through special applications called skills. These skills, though useful, have also introduced new security and privacy challenges. Prior work has verified that Alexa is vulnerable to multiple types of voice attacks, but th

Darine Al Mniny, Salman Ghazal

A $(2+1)$-bispindle $B(k_1,k_2;k_3)$ is the union of two $xy$-dipaths of respective lengths $k_1$ and $k_2$, and one $yx$-dipath of length $k_3$, all these dipaths being pairwise internally disjoint. Recently, Cohen et al. conjectured that, for every positive integers $k_1, k_2, k_3$, there is an integer $g(k_1, k_2, k_3)$ such that every strongly connected

Xinneng Yang, Yan Wu, Junqiao Zhao, Feilin Liu

Semantic segmentation has achieved remarkable results with high computational cost and a large number of parameters. However, real-world applications require efficient inference speed on embedded devices. Most previous works address the challenge by reducing depth, width and layer capacity of network, which leads to poor performance. In this paper, we introd

Deng Zhou

In natural and laboratory magnetized plasmas, an equilibrium electric field may exist perpendicular to the background magnetic field. In such a situation all the plasma species experience a common drift and the unperturbed distribution functions have a common shift in velocity space. In this work the susceptibility tensor is first derived in the laboratory f

Jia-Wei Yan, Ci-Siang Lin, Fu-En Yang, Yu-Jhe Li

Learning interpretable and interpolatable latent representations has been an emerging research direction, allowing researchers to understand and utilize the derived latent space for further applications such as visual synthesis or recognition. While most existing approaches derive an interpolatable latent space and induces smooth transition in image appearan

Boris Bačić, Jason Zhang

The primary focus of this paper is to produce a proof of concept for extracting drowsiness information from videos to help elderly living on their own. To quantify yawning, eyelid and head movement over time, we extracted 3000 images from captured videos for training and testing of deep learning models integrated with OpenCV library. The achieved classificat

Tokuji Araya, Ryo Takahashi

Let $Λ$ be a left and right noetherian ring. First, for $m,n\in\mathbb{N}\cup\{\infty\}$, we give equivalent conditions for a given $Λ$-module to be $n$-torsionfree and have $m$-torsionfree transpose. Using them, we investigate totally reflexive modules and reducing Gorenstein dimension. Next, we introduce homological invariants for $Λ$-modules which we call

Romar dela Cruz, Sascha Kurz

Minimal codewords have applications in decoding linear codes and in cryptography. We study the maximum number of minimal codewords in binary linear codes of a given length and dimension. Improved lower and upper bounds on the maximum number are presented. We determine the exact values for the case of linear codes of dimension $k$ and length $k+2$ and for sma

Charles K. Chui, Qingtang Jiang, Lin Li, Jian Lu

The synchrosqueezing transform (SST) has been developed as a powerful EMD-like tool for instantaneous frequency (IF) estimation and component separation of non-stationary multicomponent signals. Recently, a direct method of the time-frequency approach, called signal separation operation (SSO), was introduced to solving the problem of multicomponent signal se

Srinivasan Iyer, Sewon Min, Yashar Mehdad, Wen-tau Yih

State-of-the-art Machine Reading Comprehension (MRC) models for Open-domain Question Answering (QA) are typically trained for span selection using distantly supervised positive examples and heuristically retrieved negative examples. This training scheme possibly explains empirical observations that these models achieve a high recall amongst their top few pre

Adam Gowty, Daniel Horsley

An $(n,k)$-Sperner partition system is a set of partitions of some $n$-set such that each partition has $k$ nonempty parts and no part in any partition is a subset of a part in a different partition. The maximum number of partitions in an $(n,k)$-Sperner partition system is denoted $\mathrm{SP}(n,k)$. In this paper we introduce a new construction for Sperner

Bill Yuchen Lin, Ying Sheng, Nguyen Vo, Sandeep Tata

Extracting structured data from HTML documents is a long-studied problem with a broad range of applications like augmenting knowledge bases, supporting faceted search, and providing domain-specific experiences for key verticals like shopping and movies. Previous approaches have either required a small number of examples for each target site or relied on care

Ran Xu, Chen-lin Zhang, Pengcheng Wang, Jayoung Lee

Advanced video analytic systems, including scene classification and object detection, have seen widespread success in various domains such as smart cities and autonomous transportation. With an ever-growing number of powerful client devices, there is incentive to move these heavy video analytics workloads from the cloud to mobile devices to achieve low laten

Ramachandran Anantharaman, Virendra Sule

Given a discrete dynamical system defined by a map in a vector space over a finite field called Finite State Systems (FSS), a dual linear system over the space of functions on the state space is constructed using the dual map. This system constitutes the well known Koopman linear system framework of dynamical systems, hence called the Koopman linear system (

Yuchen He

In this paper, we propose a novel approach for underwater image color correction based on a Tikhonov type optimization model in the CIELAB color space. It presents a new variational interpretation of the complementary adaptation theory in psychophysics, which establishes the connection between colorimetric notions and color constancy of the human visual syst

Jiaying Zhou, Xun Xian, Na Li, Jie Ding

The rapid development in data collecting devices and computation platforms produces an emerging number of agents, each equipped with a unique data modality over a particular population of subjects. While the predictive performance of an agent may be enhanced by transmitting other data to it, this is often unrealistic due to intractable transmission costs and

Ye He, Chao Zhu, Xu-Cheng Yin

State-of-the-art pedestrian detectors have achieved significant progress on non-occluded pedestrians, yet they are still struggling under heavy occlusions. The recent occlusion handling strategy of popular two-stage approaches is to build a two-branch architecture with the help of additional visible body annotations. Nonetheless, these methods still have som

Yakui Weng, Xing'ao Li, Shuai Dong

To efficiently manipulate magnetism is a key physical issue for modern condensed matter physics, which is also crucial for magnetic functional applications. Most previous relevant studies rely on the tuning of spin texture, while the spin orientation is often negligible. As an exception, spin-orbit coupled $J_{\rm eff}$ states of $4d$/$5d$ electrons provide

Akshita Gupta, Inseok Hwang

Model-based reinforcement learning (RL) has emerged as a promising tool for developing controllers for real world systems (e.g., robotics, autonomous driving, etc.). However, real systems often have constraints imposed on their state space which must be satisfied to ensure the safety of the system and its environment. Developing a verification tool for RL al

Shirley Rojas-Salazar, Erin M. Schliep, Christopher K. Wikle, Matthew Hawkey

Data collected by wearable devices in sports provide valuable information about an athlete's behavior such as their activity, performance, and ability. These time series data can be studied with approaches such as hidden Markov and semi-Markov models (HMM and HSMM) for varied purposes including activity recognition and event detection. HSMMs extend the H