October 2020 arXiv papers — page 50

Jonathan Atteia, Yunlong Lian, Mark Oliver Goerbig

As a consequence of the approximate spin-valley symmetry in graphene, the ground state of electrons in graphene at charge neutrality is a particular SU(4) quantum-Hall ferromagnet to minimize their exchange energy. If only the Coulomb interaction is taken into account, this ferromagnet can appeal either to the spin degree of freedom or equivalently to the va

Miriam Cabero, Ashish Mahabal, Jess McIver

The rapid release of accurate sky localization for gravitational-wave candidates is crucial for multi-messenger observations. During the third observing run of Advanced LIGO and Advanced Virgo, automated gravitational-wave alerts were publicly released within minutes of detection. Subsequent inspection and analysis resulted in the eventual retraction of a fr

Haotao Wang, Tianlong Chen, Shupeng Gui, Ting-Kuei Hu

Adversarial training and its many variants substantially improve deep network robustness, yet at the cost of compromising standard accuracy. Moreover, the training process is heavy and hence it becomes impractical to thoroughly explore the trade-off between accuracy and robustness. This paper asks this new question: how to quickly calibrate a trained model i

A. D. Dolgov

Recent astronomical data on Black hole observations are reviewed. The arguments in favor that the observed black holes are predominantly primordial (PBH) are presented. The mass spectrum of PBH is best fit to the log-normal one. A model of PBH formation with log-normal spectrum is briefly described.

Hugo Le Baher, Vincent Lemaire, Romain Trinquart

In some industrial applications such as fraud detection, the performance of common supervision techniques may be affected by the poor quality of the available labels : in actual operational use-cases, these labels may be weak in quantity, quality or trustworthiness. We propose a benchmark to evaluate the natural robustness of different algorithms taken from

Kun Fang, Zi-Wen Liu

It has been recently shown that there exist universal fundamental limits to the accuracy and efficiency of the transformation from noisy resource states to pure ones (e.g.,~distillation) in any well-behaved quantum resource theory [Fang/Liu, Phys. Rev. Lett. 125, 060405 (2020)]. Here, we develop a novel and powerful method for analyzing the limitations on qu

Nicholas Monath, Avinava Dubey, Guru Guruganesh, Manzil Zaheer

The applicability of agglomerative clustering, for inferring both hierarchical and flat clustering, is limited by its scalability. Existing scalable hierarchical clustering methods sacrifice quality for speed and often lead to over-merging of clusters. In this paper, we present a scalable, agglomerative method for hierarchical clustering that does not sacrif

SHINE Collaboration, A. Acharya, H. Adhikary, A. Aduszkiewicz

The production cross section of 30.92 GeV/$c$ protons on carbon is measured by the NA61/SHINE spectrometer at the CERN SPS by means of beam attenuation in a copy (replica) of the 90-cm-long target of the T2K neutrino oscillation experiment. The employed method for direct production cross-section estimation minimizes model corrections for elastic and quasi-el

Hao Zheng, Mirella Lapata

Although neural sequence-to-sequence models have been successfully applied to semantic parsing, they fail at compositional generalization, i.e., they are unable to systematically generalize to unseen compositions of seen components. Motivated by traditional semantic parsing where compositionality is explicitly accounted for by symbolic grammars, we propose a

Caspar Hopfmann, Weijie Nie, Nand Lal Sharma, Carmen Weigelt

We present a 1 GHz-clocked, maximally entangled and on-demand photon pair source based on droplet etched GaAs quantum dots using two-photon excitation. By employing these GaP microlensenhanced devices in conjunction with their substantial brightness, raw entanglement fidelities of up to $0.95 \pm 0.01$ and post-selected photon indistinguishabilities of up to

D. Rosado-Belza, J. Falcón-Barroso, J. H. Knapen, A. Bittner

Studying the stellar kinematics of galaxies is a key tool in the reconstruction of their evolution. However, the current measurements of the stellar kinematics are complicated by several factors, including dust extinction and the presence of multiple stellar populations. We use integral field spectroscopic data of four galaxies from the TIMER survey to explo

Connor Armstrong, Joseph A. Farrow, Arthur E. Lipstein

We derive an on-shell diagram recursion for tree-level scattering amplitudes in $\mathcal{N}=7$ supergravity. The diagrams are evaluated in terms of Grassmannian integrals and momentum twistors, generalising previous results of Hodges in momentum twistor space to non-MHV amplitudes. In particular, we recast five and six-point NMHV amplitudes in terms of $\ma

Adrian Kent

Experiments have recently been proposed testing whether quantum gravitational interactions generate entanglement between adjacent masses in position superposition states. We propose potentially less challenging experiments that test quantum gravity against theories with classical space-times defined by postulating semi-classical gravity (or classical effects

Peter Szabo, Szabolcs Goger, Jorge Charry, Mohammad Reza Karimpour

Polarizability is a key response property of physical and chemical systems, which has an impact on intermolecular interactions, spectroscopic observables, and vacuum polarization. The calculation of polarizability for quantum systems involves an infinite sum over all excited (bound and continuum) states, concealing the physical interpretation of polarization

Ermerson Araujo, Yuri Lima, Mauricio Poletti

We construct Markov partitions for non-invertible and/or singular nonuniformly hyperbolic systems defined on higher dimensional Riemannian manifolds. The generality of the setup covers classical examples not treated so far, such as geodesic flows in closed manifolds, multidimensional billiard maps, and Viana maps, and includes all the recent results of the l

Jørgen Ellegaard Andersen, Gaëtan Borot, Séverin Charbonnier, Alessandro Giacchetto

For bordered surfaces S, we develop a complete parallel between the geometry of the combinatorial Teichm\"uller space $T_S^{comb}$ equipped with Kontsevich symplectic form $\omega_K$, and then the usual Weil-Petersson geometry of Teichm\"uller space $T_S$. The basis for this is an identification of $T_S^{comb}$ with a space of measured foliations with transv

Adrian Kent

Theories involving localized collapse allow the possibility that classical information could be obtained about quantum states without using POVMS and without allowing superluminal signalling. We can model this by extending quantum theory to include hypothetical devices that read out information about the local quantum state at a given point, defined by consi

Zeqian Li, Jacob Whitehill

We propose a new method for speaker diarization that can handle overlapping speech with 2+ people. Our method is based on compositional embeddings [1]: Like standard speaker embedding methods such as x-vector [2], compositional embedding models contain a function f that separates speech from different speakers. In addition, they include a composition functio

LHCb collaboration, R. Aaij, C. Abellán Beteta, T. Ackernley

The relative branching fractions of $B^+ \to h^+h^{\prime +}h^{\prime -}$ decays, where $h^{(\prime)}$ is a pion or kaon, are measured. The analysis is performed with a data sample, collected with the LHCb detector, corresponding to an integrated luminosity of $3.0 {\rm fb}^{-1}$ of $pp$ collisions. The results obtained improve significantly on previous meas

Joe P. Renaud, Wade G. Henning, Prabal Saxena, Marc Neveu

Using the Andrade-derived Sundberg-Cooper rheology, we apply several improvements to the secular tidal evolution of TRAPPIST-1e and the early history of Pluto-Charon under the simplifying assumption of homogeneous bodies. By including higher-order eccentricity terms (up to and including $e^{20}$), we find divergences from the traditionally used $e^{2}$ trunc

Peter Jorgensen

If $k$ is a field, $A$ a finite dimensional $k$-algebra, then the simple $A$-modules form a simple minded collection in the derived category $\operatorname{D}^b( \operatorname{mod} A )$. Their extension closure is $\operatorname{mod} A$; in particular, it is abelian. This situation is emulated by a general simple minded collection $\mathcal{S}$ in a suitable

Fuli Feng, Weiran Huang, Xiangnan He, Xin Xin

Graph Convolutional Network (GCN) is an emerging technique for information retrieval (IR) applications. While GCN assumes the homophily property of a graph, real-world graphs are never perfect: the local structure of a node may contain discrepancy, e.g., the labels of a node's neighbors could vary. This pushes us to consider the discrepancy of local structur

Bo Feng, Qian Lou, Lei Jiang, Geoffrey C. Fox

Billions of text analysis requests containing private emails, personal text messages, and sensitive online reviews, are processed by recurrent neural networks (RNNs) deployed on public clouds every day. Although prior secure networks combine homomorphic encryption (HE) and garbled circuit (GC) to preserve users' privacy, naively adopting the HE and GC hybrid

Nicholas L. R. Spong, Yuechun Jiao, Oliver D. W. Hughes, Kevin J. Weatherill

We demonstrate a collectively-encoded qubit based on a single Rydberg excitation stored in an ensemble of $N$ entangled atoms. Qubit rotations are performed by applying microwave fields that drive excitations between Rydberg states. Coherent read-out is performed by mapping the excitation into a single photon. Ramsey interferometry is used to probe the coher

Muhammad Umer Anwaar, Zhiwei Han, Shyam Arumugaswamy, Rayyan Ahmad Khan

In graph neural networks (GNNs), message passing iteratively aggregates nodes' information from their direct neighbors while neglecting the sequential nature of multi-hop node connections. Such sequential node connections e.g., metapaths, capture critical insights for downstream tasks. Concretely, in recommender systems (RSs), disregarding these insights lea

Matthew Henderson, Ivan Vulić

We propose ConVEx (Conversational Value Extractor), an efficient pretraining and fine-tuning neural approach for slot-labeling dialog tasks. Instead of relying on more general pretraining objectives from prior work (e.g., language modeling, response selection), ConVEx's pretraining objective, a novel pairwise cloze task using Reddit data, is well aligned wit

Nur Ahmed, Muntasir Wahed

Increasingly, modern Artificial Intelligence (AI) research has become more computationally intensive. However, a growing concern is that due to unequal access to computing power, only certain firms and elite universities have advantages in modern AI research. Using a novel dataset of 171394 papers from 57 prestigious computer science conferences, we document

Shin'ichi Nojiri, Sergei D. Odintsov, Valerio Faraoni

We construct models of Einstein and $f(R)$ gravity with two scalar fields, which admit analytical solutions describing time-varying dynamical black holes. Their thermodynamics is investigated in the adiabatic approximation. In addition to the Misner-Sharp-Hernandez quasilocal mass, we provide time-dependent thermodynamical quantities, including the Hawking t

Simon H. Tindemans, Goran Strbac

Thermostatically controlled loads such as refrigerators are exceptionally suitable as a flexible demand resource. This paper derives a decentralised load control algorithm for refrigerators. It is adapted from an existing continuous time control approach, with the aim to achieve low computational complexity and an ability to handle discrete time steps of var

Anil Kumar Yadav

In the present study, we comment on Brans-Dicke scalar field cosmological model in Lyra's geometry [Maurya \& Zia, Phys. Rev. D \textbf{100}, 023503 (2019)]. In this comment, we investigate that there is no acceleration in the model proposed by the authors of Phys. Rev. D \textbf{100}, 023503 (2019). Therefore, despite the claims to the contrary the Brans-Di

Yejia Chen, Zhiguo Lü, Yiying Yan, Hang Zheng

We present an interesting dynamical temporal localization of a strongly driven two-level system (TLS), a plateau with quantized oscillation, by an analytical and transparent method, the counter-rotating-hybridized rotating-wave (CHRW) method. This approach, which is based on unitary transformations with a single parameter, treats the rotating and counter-rot

Fangyu Liu, Ehsan Shareghi, Zaiqiao Meng, Marco Basaldella

Despite the widespread success of self-supervised learning via masked language models (MLM), accurately capturing fine-grained semantic relationships in the biomedical domain remains a challenge. This is of paramount importance for entity-level tasks such as entity linking where the ability to model entity relations (especially synonymy) is pivotal. To addre

Dirk Kreimer

We consider a coaction which exists for any bridge-free graph. It is based on the cubical chain complex associated to any such graph by considering two boundary operations: shrinking edges or removing them. Only if the number of spanning trees of a graph $G$ equals its number of internal edges we find that the graphical coaction $\Delta^G$ constructed here a

Matthew A. Fisher, Tui Nolan, Matthew M. Graham, Dennis Prangle

Measure transport underpins several recent algorithms for posterior approximation in the Bayesian context, wherein a transport map is sought to minimise the Kullback--Leibler divergence (KLD) from the posterior to the approximation. The KLD is a strong mode of convergence, requiring absolute continuity of measures and placing restrictions on which transport

Soham Bhattacharya, Monoranjan Guchait, Aravind H. Vijay

Machine learning techniques are used for treating jets as images to explore the performance of boosted top quark tagging. Tagging performances are studied in both hadronic and leptonic channels of top quark decay, employing a convolutional neural network (CNN) based technique along with boosted decision trees (BDT). This computer vision approach is also appl

Chengping He, Xue Ming, Qing Li, Xiyu Zhu

Recently, superconductivity at about 9-15K was discovered in Nd$_{1-x}$Sr$_x$NiO$_2$ infinite-layer thin films, which has stimulated enormous interests in related rare-earth nickelates. Usually, the first step to synthesize this 112 phase is to fabricate the RNiO$_3$ phase, however, it was reported that the 113 phase is very difficult to be synthesized succe

Shuxiao Chen, Hangfeng He, Weijie J. Su

As a popular approach to modeling the dynamics of training overparametrized neural networks (NNs), the neural tangent kernels (NTK) are known to fall behind real-world NNs in generalization ability. This performance gap is in part due to the \textit{label agnostic} nature of the NTK, which renders the resulting kernel not as \textit{locally elastic} as NNs~\

Sahin Buyukdagli

The comprehensive depiction of the many-body effects governing nanoconfined electrolytes is an essential step for the conception of nanofluidic devices with optimized performance. By incorporating self-consistently multivalent charges into the Poisson-Boltzmann equation dressed by a background monovalent salt, we investigate the impact of strong-coupling ele

Wolfgang Roth, Günther Schindler, Holger Fröning, Franz Pernkopf

We present two methods to reduce the complexity of Bayesian network (BN) classifiers. First, we introduce quantization-aware training using the straight-through gradient estimator to quantize the parameters of BNs to few bits. Second, we extend a recently proposed differentiable tree-augmented naive Bayes (TAN) structure learning approach by also considering

Stephen Muirhead, Alejandro Rivera, Hugo Vanneuville, Laurin Köhler-Schindler

We develop techniques to study the phase transition for planar Gaussian percolation models that are not (necessarily) positively correlated. These models lack the property of positive associations (also known as the `FKG inequality'), and hence many classical arguments in percolation theory do not apply. More precisely, we consider a smooth stationary centre

James Lu, Brendan Bender, Jin Y. Jin, Yuanfang Guan

The longitudinal analysis of patient response time course following doses of therapeutics is currently performed using Pharmacokinetic/Pharmacodynamic (PK/PD) methodologies, which requires significant human experience and expertise in the modeling of dynamical systems. By utilizing recent advancements in deep learning, we show that the governing differential

Michael Y. Hua, Jesson D. Hutchinson, George E. McKenzie, Shaun D. Clarke

The Feynman-alpha method is a neutron noise technique that is used to estimate the prompt neutron period of fissile assemblies. The method and quantity are of widespread interest including in applications such as nuclear criticality safety, safeguards and nonproliferation, and stockpile stewardship; the prompt neutron period may also be used to infer the $k_

Siddarth Kannan, Shiyue Li, Stefano Serpente, Claudia He Yun

Given integers $g \geq 0$, $n \geq 1$, and a vector $w \in (\mathbb{Q} \cap (0, 1])^n$ such that ${2g - 2 + \sum w_i > 0}$, we study the topology of the moduli space $\Delta_{g, w}$ of $w$-stable tropical curves of genus $g$ with volume 1. The space $\Delta_{g, w}$ is the dual complex of the divisor of singular curves in Hassett's moduli space of $w$-stable

Ricard Riba

In this paper we prove that the Rohlin invariant is the unique invariant inducing a homomorphism on the Torelli group. Using this result we generalize the construction of invariants of homology $3$-spheres from families of trivial 2-cocycles on the Torelli group given by Pitsch to include invariants with values on an abelian group with $2$-torsion.

Aran Nayebi, Sanjana Srivastava, Surya Ganguli, Daniel L. K. Yamins

The brain modifies its synaptic strengths during learning in order to better adapt to its environment. However, the underlying plasticity rules that govern learning are unknown. Many proposals have been suggested, including Hebbian mechanisms, explicit error backpropagation, and a variety of alternatives. It is an open question as to what specific experiment

Tim Santens

It is well-known that the Hasse principle holds for quadric hypersurfaces. The Hasse principle fails for integral points on smooth quadric hypersurfaces of dimension 2 but the failure can be completely explained by the Brauer-Manin obstruction. We investigate how often the family of quadric hypersurfaces $ax^2 + by^2 +cz^2 = n$ has a Brauer-Manin obstruction

Tobias Reinhard, Bart Jacobs

Programs for multiprocessor machines commonly perform busy waiting for synchronization. We propose the first separation logic for modularly verifying termination of such programs under fair scheduling. Our logic requires the proof author to associate a ghost signal with each busy-waiting loop and allows such loops to iterate while their corresponding signal

Jan-Christoph Schlage-Puchta

We consider the integral $\int_0^\infty\left(\frac{\sin x}{x}\right)^n\;dx$ as a function of the positive integer $n$. We show that there exists an asymptotic series in $\frac{1}{n}$ and compute the first terms of this series together with an explicit error bound.

Tae-Young Jeong, Soungmin Bae, Seong-Yeon Lee, Suyong Jung

Although single-layer transition-metal dichalcogenides with novel valley functionalities are promising candidate to realize valleytronic devices, the essential understanding of valley depolarization mechanisms is still incomplete. Based on pump-probe experiments performed for MoSe2 and WSe2 monolayers and corroborating analysis from density functional calcul

Chun-Fu Chen, Rameswar Panda, Kandan Ramakrishnan, Rogerio Feris

In recent years, a number of approaches based on 2D or 3D convolutional neural networks (CNN) have emerged for video action recognition, achieving state-of-the-art results on several large-scale benchmark datasets. In this paper, we carry out in-depth comparative analysis to better understand the differences between these approaches and the progress made by

David E. Jessop, Séverine Moune, Roberto Moretti, Dominique Gibert

Particularly in the presence of a hydrothermal system, many volcanoes output large quantities of heat through the transport of water from deep within the edifice to the surface. Thus, heat flux is a prime tool for evaluating volcanic activity and unrest. We review the volcanic unrest at La Soufri\`ere de Guadeloupe (French West Indies) using an airborne ther

Jonas Latt, Christophe Coreixas, Joël Beny

We present a novel, hardware-agnostic implementation strategy for lattice Boltzmann (LB) simulations, which yields massive performance on homogeneous and heterogeneous many-core platforms. Based solely on C++17 Parallel Algorithms, our approach does not rely on any language extensions, external libraries, vendor-specific code annotations, or pre-compilation

Fan Yang, Hongyang R. Zhang, Sen Wu, Christopher Ré

The problem of learning one task using samples from another task is central to transfer learning. In this paper, we focus on answering the following question: when does combining the samples from two related tasks perform better than learning with one target task alone? This question is motivated by an empirical phenomenon known as negative transfer, which h

Nithin S. Ramesan, François Baccelli

This paper considers the time evolution of a queue that is embedded in a Poisson point process of moving wireless interferers. The queue is driven by an external arrival process and is subject to a time-varying service process that is a function of the SINR that it sees. Static configurations of interferers result in an infinite queue workload with positive

Nontawat Charoenphakdee, Zhenghang Cui, Yivan Zhang, Masashi Sugiyama

The goal of classification with rejection is to avoid risky misclassification in error-critical applications such as medical diagnosis and product inspection. In this paper, based on the relationship between classification with rejection and cost-sensitive classification, we propose a novel method of classification with rejection by learning an ensemble of c

Tatiana Likhomanenko, Qiantong Xu, Vineel Pratap, Paden Tomasello

Is pushing numbers on a single benchmark valuable in automatic speech recognition? Research results in acoustic modeling are typically evaluated based on performance on a single dataset. While the research community has coalesced around various benchmarks, we set out to understand generalization performance in acoustic modeling across datasets - in particula

Gareth W. Young, David Murphy, Jeffrey Weeter

We present the findings of a pilot-study that analysed the role of haptic feedback in a musical context. To examine the role of haptics in Digital Musical Instrument (DMI) design an experiment was formulated to measure the users' perception of device usability across four separate feedback stages: fully haptic (force and tactile combined), constant force

Yicong He, George K. Atia

The goal of tensor completion is to recover a tensor from a subset of its entries, often by exploiting its low-rank property. Among several useful definitions of tensor rank, the low-tubal-rank was shown to give a valuable characterization of the inherent low-rank structure of a tensor. While some low-tubal-rank tensor completion algorithms with favorable pe

Akbar Karimi, Leonardo Rossi, Andrea Prati

Aspect-Based Sentiment Analysis (ABSA) studies the consumer opinion on the market products. It involves examining the type of sentiments as well as sentiment targets expressed in product reviews. Analyzing the language used in a review is a difficult task that requires a deep understanding of the language. In recent years, deep language models, such as BERT

Venky Krishnan, Rakesh, Soumen Senapati

We prove stability for a formally determined inverse problem for a hyperbolic PDE where the coefficients depend on space and time variables. The hyperbolic operator has constant wave speed and we study the recovery of zeroth order and first order coefficients and the space dimension can be one or higher. We use a modification of the Bukhgeim-Klibanov method

Federico Landini, Ondřej Glembek, Pavel Matějka, Johan Rohdin

This paper describes the system developed by the BUT team for the fourth track of the VoxCeleb Speaker Recognition Challenge, focusing on diarization on the VoxConverse dataset. The system consists of signal pre-processing, voice activity detection, speaker embedding extraction, an initial agglomerative hierarchical clustering followed by diarization using a

Minxi He

The preheating process in the mixed Higgs-$ R^2 $ model has been investigated in depth recently, but the analysis of perturbative reheating is still missing. In this paper, we discuss the effect of perturbative decay during (p)reheating in this model. It is shown that perturbative decay can play an important role throughout the whole reheating process. Depen

Fernando Romero-López, Akaki Rusetsky, Nikolas Schlage, Carsten Urbach

We present a general method for deriving the energy shift of an interacting system of $N$ spinless particles in a finite volume. To this end, we use the nonrelativistic effective field theory (NREFT), and match the pertinent low-energy constants to the scattering amplitudes. Relativistic corrections are explicitly included up to a given order in the $1/L$ ex

Yukuan Yang, Fangyun Wei, Miaojing Shi, Guoqi Li

Few-shot learning has recently emerged as a new challenge in the deep learning field: unlike conventional methods that train the deep neural networks (DNNs) with a large number of labeled data, it asks for the generalization of DNNs on new classes with few annotated samples. Recent advances in few-shot learning mainly focus on image classification while in t

Dan Zhao, Hancheng Lu, Yazheng Wang, Huan Sun

Intelligent reflect surface (IRS) is a potential technology to build programmable wireless environment in future communication systems. In this paper, we consider an IRS-assisted multi-base station (multi-BS) multi-user millimeter wave (mmWave) downlink communication system, exploiting IRS to extend mmWave signal coverage to blind spots. Considering the impa

Yingheng Wang, Yaosen Min, Xin Chen, Ji Wu

Drug-drug interaction(DDI) prediction is an important task in the medical health machine learning community. This study presents a new method, multi-view graph contrastive representation learning for drug-drug interaction prediction, MIRACLE for brevity, to capture inter-view molecule structure and intra-view interactions between molecules simultaneously. MI

B. Blankleider, J. L. Wray, A. N. Kvinikhidze

We present a simple description of pion-nucleon ($\pi N$) scattering taking into account the full complexity of pion absorption and creation on the nucleon. To do this we solve Dyson-Schwinger equations within the framework of Time-Ordered Perturbation Theory. This enables us to construct partial wave separable $ \pi N$ t matrices that can be useful in model

Haoming Zhang, Chen Wei, Mingqi Zhao, Haiyan Wu

The recorded electroencephalography (EEG) signals are usually contaminated by many artifacts. In recent years, deep learning models have been used for denoising of electroencephalography (EEG) data and provided comparable performance with that of traditional techniques. However, the performance of the existing networks in electromyograph (EMG) artifact remov

Terrence Alsup, Benjamin Peherstorfer

Multi-fidelity methods leverage low-cost surrogate models to speed up computations and make occasional recourse to expensive high-fidelity models to establish accuracy guarantees. Because surrogate and high-fidelity models are used together, poor predictions by surrogate models can be compensated with frequent recourse to high-fidelity models. Thus, there is

Martin Zimmermann

We present an exponential-time algorithm approximating the minimal lookahead necessary to win an $\omega$-regular delay game.

Armando Pezo, Bruno Focassio, Gabriel R. Schleder, Marcio Costa

The robustness of topological materials against disorder and defects is presumed but has not been demonstrated explicitly in realistic systems. In this work, we use state-of-the-art density functional theory and recursive nonequilibrium Green's functions methods to study the effect of disorder in the electronic transport of long nanoribbons, up to 157 nm, as

Jinxu Liu, Wei Gao, Zhanyi Hu

Unlike loose coupling approaches and the EKF-based approaches in the literature, we propose an optimization-based visual-inertial SLAM tightly coupled with raw Global Navigation Satellite System (GNSS) measurements, a first attempt of this kind in the literature to our knowledge. More specifically, reprojection error, IMU pre-integration error and raw GNSS m

Rahool Kumar Barman, Genevieve Belanger, Rohini M. Godbole

In this article we review the case for a light ($< m_{h_{125}}/2$) neutralino and sneutrino being a viable Dark Matter (DM) candidate in Supersymmetry(SUSY). To that end we recapitulate, very briefly, three issues related to the DM which impact the discussions : calculation of DM relic density, detection of the DM in Direct and Indirect experiments and creat

F. Müller, O. Javakhishvili, D. Shergelashvili, I. Keshelashvili

A calorimetric polarimeter based on inorganic LYSO scintillators is described. It has been designed for use in a storage ring to search for electric dipole moments (EDM) of charged particles such as the proton and deuteron. Its development and first use was on the Cooler Synchrotron (COSY) at the Forschungszentrum J\"ulich with 0.97 GeV/c polarized deuterons

D. E. Maison, V. V. Flambaum, N. R. Hutzler, L. V. Skripnikov

Recently the YbOH molecule has been suggested as a candidate to search for the electron electric dipole moment (eEDM) which violates spatial parity (P) and time-reversal (T) symmetries [I. Kozyryev and N. R. Hutzler, Phys. Rev. Lett. 119, 133002 (2017)]. In the present paper we show that the same system can be used to measure coupling constants of the intera

Marco Molinaro, James Dempsey

The IVOA works towards standardising interoperability and curation of data and service holdings of the global astrophysical community. Within the IVOA, the Data Access Layer (DAL) Working Group's goal is to provide technical standards for accessing data collections and catalogues; filtering data holdings based on their metadata; and retrieving the ones in sc

Kolyan Ray, Botond Szabo, Gabriel Clara

Variational Bayes (VB) is a popular scalable alternative to Markov chain Monte Carlo for Bayesian inference. We study a mean-field spike and slab VB approximation of widely used Bayesian model selection priors in sparse high-dimensional logistic regression. We provide non-asymptotic theoretical guarantees for the VB posterior in both $\ell_2$ and prediction

Łukasz Bożyk, Andrzej Grzesik, Bartłomiej Kielak

We consider the problem of determining the inducibility (maximum possible asymptotic density of induced copies) of oriented graphs on four vertices. We provide exact values for more than half of the graphs, and very close lower and upper bounds for all the remaining ones. It occurs that, for some graphs, the structure of extremal constructions maximizing den

Ido Greenberg, Shie Mannor

In many RL applications, once training ends, it is vital to detect any deterioration in the agent performance as soon as possible. Furthermore, it often has to be done without modifying the policy and under minimal assumptions regarding the environment. In this paper, we address this problem by focusing directly on the rewards and testing for degradation. We

Slobodan Djukanović, Yash Patel, Jiři Matas, Tuomas Virtanen

This paper addresses acoustic vehicle counting using one-channel audio. We predict the pass-by instants of vehicles from local minima of clipped vehicle-to-microphone distance. This distance is predicted from audio using a two-stage (coarse-fine) regression, with both stages realised via neural networks (NNs). Experiments show that the NN-based distance regr

Kai-Min Chung, Serge Fehr, Yu-Hsuan Huang, Tai-Ning Liao

We revisit the so-called compressed oracle technique, introduced by Zhandry for analyzing quantum algorithms in the quantum random oracle model (QROM). To start off with, we offer a concise exposition of the technique, which easily extends to the parallel-query QROM, where in each query-round the considered algorithm may make several queries to the QROM in p

Renyu Wang, Ruilin Tong, Yu Ting Yeung, Xiao Chen

This paper describes system setup of our submission to speaker diarisation track (Track 4) of VoxCeleb Speaker Recognition Challenge 2020. Our diarisation system consists of a well-trained neural network based speech enhancement model as pre-processing front-end of input speech signals. We replace conventional energy-based voice activity detection (VAD) with

Shumpei Uno, Yohichi Suzuki, Keigo Hisanaga, Rudy Raymond

In this paper, we propose a quantum amplitude estimation method that uses a modified Grover operator and quadratically improves the estimation accuracy in the ideal case, as in the conventional one using the standard Grover operator. Under the depolarizing noise, the proposed method can outperform the conventional one in the sense that it can in principle ac

Yunqiu Xu, Meng Fang, Ling Chen, Yali Du

We study reinforcement learning (RL) for text-based games, which are interactive simulations in the context of natural language. While different methods have been developed to represent the environment information and language actions, existing RL agents are not empowered with any reasoning capabilities to deal with textual games. In this work, we aim to con

Wenzhong Yan, Di Jin, Zhidi Lin, Feng Yin

Graph neural networks (GNNs) are popular to use for classifying structured data in the context of machine learning. But surprisingly, they are rarely applied to regression problems. In this work, we adopt GNN for a classic but challenging nonlinear regression problem, namely the network localization. Our main findings are in order. First, GNN is potentially

Jacopo Lupi, Cristina Puzzarini, Vincenzo Barone

A gas-phase formation route is proposed for the recently detected propargylimine molecule. In analogy to other imines, such as cyanomethanimine, the addition of a reactive radical (C$_2$H in the present case) to methanimine (CH$_2$NH}) leads to reaction channels open also in the harsh conditions of the interstellar medium. Three possible isomers can be forme

Eleonora Grassucci, Danilo Comminiello, Aurelio Uncini

Deep probabilistic generative models have achieved incredible success in many fields of application. Among such models, variational autoencoders (VAEs) have proved their ability in modeling a generative process by learning a latent representation of the input. In this paper, we propose a novel VAE defined in the quaternion domain, which exploits the properti

Mingjie Chen, Yanpei Shi, Thomas Hain

Many-to-many voice conversion with non-parallel training data has seen significant progress in recent years. StarGAN-based models have been interests of voice conversion. However, most of the StarGAN-based methods only focused on voice conversion experiments for the situations where the number of speakers was small, and the amount of training data was large.

Sumanth Dathathri, Krishnamurthy Dvijotham, Alexey Kurakin, Aditi Raghunathan

Convex relaxations have emerged as a promising approach for verifying desirable properties of neural networks like robustness to adversarial perturbations. Widely used Linear Programming (LP) relaxations only work well when networks are trained to facilitate verification. This precludes applications that involve verification-agnostic networks, i.e., networks

Yaiza Aragonés-Soria, Johan Åberg, Chae-Yeun Park, Michael J. Kastoryano

We show that the norm squared amplitudes with respect to a local orthonormal basis (the classical restriction) of finite quantum systems on one-dimensional lattices can be exponentially well approximated by Gibbs states of local Hamiltonians (i.e., are quasi-locally Gibbsian) if the classical conditional mutual information (CMI) of any connected tripartition

Matias Vera, Leonardo Rey Vega, Pablo Piantanida

Overfitting data is a well-known phenomenon related with the generation of a model that mimics too closely (or exactly) a particular instance of data, and may therefore fail to predict future observations reliably. In practice, this behaviour is controlled by various--sometimes heuristics--regularization techniques, which are motivated by developing upper bo

Dmitry I. Ignatov, Gennady V. Khvorykh, Andrey V. Khrunin, Stefan Nikolić

Missing genotypes can affect the efficacy of machine learning approaches to identify the risk genetic variants of common diseases and traits. The problem occurs when genotypic data are collected from different experiments with different DNA microarrays, each being characterised by its pattern of uncalled (missing) genotypes. This can prevent the machine lear

A. F. Krutov, V. E. Troitsky

We extend our relativistic theory of electroweak properties of composite systems to describe simultaneously the gravitational form factors of hadrons. The approach is based on a version of the instant-form relativistic quantum mechanics and makes use of the modified impulse approximation. We exploit the general method of the relativistic invariant parametriz

Kostantinos Papadamou, Savvas Zannettou, Jeremy Blackburn, Emiliano De Cristofaro

The role played by YouTube's recommendation algorithm in unwittingly promoting misinformation and conspiracy theories is not entirely understood. Yet, this can have dire real-world consequences, especially when pseudoscientific content is promoted to users at critical times, such as the COVID-19 pandemic. In this paper, we set out to characterize and detect

Chenkai Yu, Guanya Shi, Soon-Jo Chung, Yisong Yue

This paper studies the impact of imperfect information in online control with adversarial disturbances. In particular, we consider both delayed state feedback and inexact predictions of future disturbances. We introduce a greedy, myopic policy that yields a constant competitive ratio against the offline optimal policy. We also analyze the fundamental limits

I. N. Pashchenko, A. V. Plavin, A. M. Kutkin, Y. Y. Kovalev

The Blandford and K\"{o}nigl model of AGN jets predicts that the position of the apparent opaque jet base - the core - changes with frequency. This effect is observed with radio interferometry and is widely used to infer parameters and structure of the innermost jet regions. The position of the radio core is typically estimated by fitting a Gaussian template

Arslan Chaudhry, Naeemullah Khan, Puneet K. Dokania, Philip H. S. Torr

In continual learning (CL), a learner is faced with a sequence of tasks, arriving one after the other, and the goal is to remember all the tasks once the continual learning experience is finished. The prior art in CL uses episodic memory, parameter regularization or extensible network structures to reduce interference among tasks, but in the end, all the app

Alberto Natali, Mario Coutino, Elvin Isufi, Geert Leus

Signal processing and machine learning algorithms for data supported over graphs, require the knowledge of the graph topology. Unless this information is given by the physics of the problem (e.g., water supply networks, power grids), the topology has to be learned from data. Topology identification is a challenging task, as the problem is often ill-posed, an

Antonio M. García-García, Victor Godet

We study a two-site Sachdev-Ye-Kitaev (SYK) model with complex couplings, and identify a low temperature transition to a gapped phase characterized by a constant in temperature free energy. This transition is observed without introducing a coupling between the two sites, and only appears after ensemble average over the complex couplings. We propose a gravity

Woosung Choi, Minseok Kim, Jaehwa Chung, Soonyoung Jung

Recent deep-learning approaches have shown that Frequency Transformation (FT) blocks can significantly improve spectrogram-based single-source separation models by capturing frequency patterns. The goal of this paper is to extend the FT block to fit the multi-source task. We propose the Latent Source Attentive Frequency Transformation (LaSAFT) block to captu