October 2020 arXiv papers — page 92

Jolanta Zjupa, Björn Malte Schäfer, Oliver Hahn

Accurate measurements of the cosmic shear signal require a separation of the true weak gravitational lensing signal from intrinsic shape correlations of galaxies. These `intrinsic alignments' of galaxies originate from galaxy formation processes and are expected to be correlated with the gravitational field through tidal processes affecting the galaxies,

Tim Hallatt, Eve J Lee

The giant planet occurrence rate rises with orbital period out to at least $\sim$300 days. Large-scale planetary migration through the disk has long been suspected to be the physical origin of this feature, as the timescale of standard Type I migration in a standard solar nebula is longer farther from the star. These calculations also find that typical Jupit

Cecilia Bacchini, Filippo Fraternali, Gabriele Pezzulli, Antonino Marasco

In the last decades, much effort has been put into finding the star formation law which could unequivocally link the gas and the star formation rate (SFR) densities measured on sub-kiloparsec scale in star-forming galaxies. The conventional approach of using the observed surface densities to infer star formation laws has however revealed a major and well-kno

Rahul M. Nandkishore, Wonjune Choi, Yong Baek Kim

We explain how gapped quantum spin liquids, both conventional and 'fractonic', may be unambiguously diagnosed experimentally using the technique of multidimensional coherent spectroscopy. 'Conventional' gapped quantum spin liquids (e.g. $Z_2$ spin liquid) do not have clear signatures in linear response, but do have clear fingerprints in non-l

Nushkia Chamba

A brief narrative on how the effective radius and isophotal diameters were accepted as galaxy size measures is presented. Evidence suggests that these parameters were defined only based on observational premises, independent of any astrophysical theories. An alternative, new physically motivated size definition based on the expected gas density threshold req

Fernanda Roman-Oliveira, Ana L. Chies-Santos, Fabricio Ferrari, Geferson Lucatelli

We explore the morphometric properties of a group of 73 ram pressure stripping candidates in the A901/A902 multi-cluster system, at z$\sim$0.165, to characterise the morphologies and structural evolution of jellyfish galaxies. By employing a quantitative measurement of morphometric indicators with the algorithm \textsc{morfometryka} on Hubble Space Telescope

Saeid Foroughi-Abari, Felix Kling, Yu-Dai Tsai

We identify potentially the world's most sensitive location to search for millicharged particles in the 10 MeV to 100 GeV mass range: the forward region at the LHC. We propose constructing a scintillator-based experiment, FORward MicrOcharge SeArch (FORMOSA) in this location, and estimate the corresponding sensitivity projection. We show that FORMOSA can

Melissa van Beekveld, Sascha Caron, Luc Hendriks, Paul Jackson

The lack of evidence for new physics at the Large Hadron Collider so far has prompted the development of model-independent search techniques. In this study, we compare the anomaly scores of a variety of anomaly detection techniques: an isolation forest, a Gaussian mixture model, a static autoencoder, and a $β$-variational autoencoder (VAE), where we define t

Damien Prieur, Walter Bonani, Karin Popa, Olaf Walter

Intrinsic properties of a compound (e.g., electronic structure, crystallographic structure, optical and magnetic properties) define notably its chemical and physical behavior. In the case of nanomaterials, these fundamental properties depend on the occurrence of quantum mechanical size effects and on the considerable increase of the surface to bulk ratio. He

Ivan Pidchenko, Juliane März, Myrtille O J Y Hunault, Stephen Bauters

The chemical properties of actinide materials are often predefined and described based on the data available for isostructural species. This is the case for potassium plutonyl (PuVI) carbonate, K4PuVIO2(CO3)3(cr), a complex relevant for nuclear technology and the environment, of which the crystallographic and thermodynamic properties of which are still lacki

Tomasz Szandala

With the prevalence of digital cameras, the number of digital images increases quickly, which raises the demand for non-manual image quality assessment. While there are many methods considered useful for detecting blurriness, in this paper we propose and evaluate a new method that uses a deep convolutional neural network, which can determine whether an image

Kyongsik Yun, Changrak Choi, Ryan Alimo, Anthony Davis

State-of-the-art motion planners cannot scale to a large number of systems. Motion planning for multiple agents is an NP (non-deterministic polynomial-time) hard problem, so the computation time increases exponentially with each addition of agents. This computational demand is a major stumbling block to the motion planner's application to future NASA mis

YingQiao Wang

Making accurate motion prediction of surrounding agents such as pedestrians and vehicles is a critical task when robots are trying to perform autonomous navigation tasks. Recent research on multi-modal trajectory prediction, including regression and classification approaches, perform very well at short-term prediction. However, when it comes to long-term pre

Ilia Karpov, Ekaterina Glazkova

The widespread of Online Social Networks and the opportunity to commercialize popular accounts have attracted a large number of automated programs, known as artificial accounts. This paper focuses on the classification of human and fake accounts on the social network, by employing several graph neural networks, to efficiently encode attributes and network gr

Jovana Mitrovic, Brian McWilliams, Jacob Walker, Lars Buesing

Self-supervised learning has emerged as a strategy to reduce the reliance on costly supervised signal by pretraining representations only using unlabeled data. These methods combine heuristic proxy classification tasks with data augmentations and have achieved significant success, but our theoretical understanding of this success remains limited. In this pap

Martin Gauch, Frederik Kratzert, Daniel Klotz, Grey Nearing

Long Short-Term Memory Networks (LSTMs) have been applied to daily discharge prediction with remarkable success. Many practical scenarios, however, require predictions at more granular timescales. For instance, accurate prediction of short but extreme flood peaks can make a life-saving difference, yet such peaks may escape the coarse temporal resolution of d

Janardhan Kulkarni, Stefan Schmid, Paweł Schmidt

Reconfigurable optical topologies are emerging as a promising technology to improve the efficiency of datacenter networks. This paper considers the problem of scheduling opportunistic links in such reconfigurable datacenters. We study the online setting and aim to minimize flow completion times. The problem is a two-tier generalization of classic switch sche

Sourav Nandy, Ferdinand Evers, Soumya Bera

Many-body localization is a fascinating theoretical concept describing the intricate interplay of quantum interference, i.e. localization, with many-body interaction induced dephasing. Numerous computational tests and also several experiments have been put forward to support the basic concept. Typically, averages of time-dependent global observables have bee

Brenden Roberts, Shenghan Jiang, Olexei I. Motrunich

We continue recent efforts to discover examples of deconfined quantum criticality in one-dimensional models. In this work we investigate the transition between a $\mathbb{Z}_3$ ferromagnet and a phase with valence bond solid (VBS) order in a spin chain with $\mathbb{Z}_3\times\mathbb{Z}_3$ global symmetry. We study a model with alternating projective represe

Tina Diao, Samriddhi Singla, Ayan Mukhopadhyay, Ahmed Eldawy

Recent wildfires in the United States have resulted in loss of life and billions of dollars, destroying countless structures and forests. Fighting wildfires is extremely complex. It is difficult to observe the true state of fires due to smoke and risk associated with ground surveillance. There are limited resources to be deployed over a massive area and the

Francesco Bajardi, Salvatore Capozziello

We discuss non-minimally coupled cosmologies involving different geometric invariants. Specifically, actions containing a non-minimally coupled scalar field to gravity described, in turn, by curvature, torsion and Gauss--Bonnet scalars are considered. We show that couplings, potentials and kinetic terms are determined by the existence of Noether symmetries w

Bhusan Chettri, Emmanouil Benetos, Bob L. T. Sturm

The Automatic Speaker Verification Spoofing and Countermeasures Challenges motivate research in protecting speech biometric systems against a variety of different access attacks. The 2017 edition focused on replay spoofing attacks, and involved participants building and training systems on a provided dataset (ASVspoof 2017). More than 60 research papers have

Michael Blondin, Christoph Haase, Philip Offtermatt

Numerous tasks in program analysis and synthesis reduce to deciding reachability in possibly infinite graphs such as those induced by Petri nets. However, the Petri net reachability problem has recently been shown to require non-elementary time, which raises questions about the practical applicability of Petri nets as target models. In this paper, we introdu

Nemanja Draganić, David Munhá Correia, Benny Sudakov

In this note we show that every tournament on $n$ vertices contains the $k$-th power of a directed path of length $n/2^{6k+7}$, which improves upon the recent bound of Scott and Korándi of $n/2^{2^{3k}}$. By doing so, we get an inverse exponential dependence on $k$, which is best possible as Yuster recently showed an upper bound of $kn/{2^{k/2}}$.

Amnon Catav, Boyang Fu, Jason Ernst, Sriram Sankararaman

When training a predictive model over medical data, the goal is sometimes to gain insights about a certain disease. In such cases, it is common to use feature importance as a tool to highlight significant factors contributing to that disease. As there are many existing methods for computing feature importance scores, understanding their relative merits is no

Anton Shafarevich

Let $\mathbb{K}$ be an algebraically closed field of characteristic zero and $\mathbb{G}_a$ be the additive group of $\mathbb{K}$. We say that an irreducible algebraic variety $X$ of dimension $n$ over the field $\mathbb{K}$ admits an additive action if there is a regular action of the group $\mathbb{G}_a^n = \mathbb{G}_a \times \ldots \times \mathbb{G}_a$ (

Sebastiano Vascon, Samuel Rota Bulò, Vittorio Murino, Marcello Pelillo

DSLib is an open-source implementation of the Dominant Set (DS) clustering algorithm written entirely in Matlab. The DS method is a graph-based clustering technique rooted in the evolutionary game theory that starts gaining lots of interest in the computer science community. Thanks to its duality with game theory and its strict relation to the notion of maxi

Domenico Zaza, Michele Ciavarella, Giuseppe Zurlo

The accumulation of residual stress during welding and additive manufacturing is an important effect that can significantly anticipate the workpiece failure. In this work we exploit the physical and analytical transparency of a 1.5D model to show that the deposition of thermally expanded material onto an elastic substrate leads to the accumulation of strain

Ying Fu, Miao-Ling Lin, Le Wang, Qiye Liu

Quantum spin liquid (QSL) represents a new class of condensed matter states characterized by the long-range many-body entanglement of topological orders. The most prominent feature of the elusive QSL state is the existence of fractionalized spin excitations. Subject to the strong quantum fluctuations, the spin-1/2 antiferromagnetic system on a kagome lattice

James Zou, Aubrey Johnson, Jeanelle France, Srinidhi Bharadwaj

Image registration is an important preprocessing step in neuroimaging which allows for the matching of anatomical and functional information between modalities and subjects. This can be challenging if there are gross differences in image geometry or in signal intensity, such as in the case of some molecular PET radioligands, where control subjects display re

Hanwook Chung, Vikrant Singh Tomar, Benoit Champagne

In this paper, we introduce a spectral-domain inverse filtering approach for single-channel speech de-reverberation using deep convolutional neural network (CNN). The main goal is to better handle realistic reverberant conditions where the room impulse response (RIR) filter is longer than the short-time Fourier transform (STFT) analysis window. To this end,

Roxana Zeraati, Viola Priesemann, Anna Levina

Self-organized criticality has been proposed to be a universal mechanism for the emergence of scale-free dynamics in many complex systems, and possibly in the brain. While such scale-free patterns were identified experimentally in many different types of neural recordings, the biological principles behind their emergence remained unknown. Utilizing different

Uri Kol

The dual Komar mass generalizes the concept of the NUT parameter and is akin to the magnetic charge in electrodynamics. In asymptotically flat spacetimes it coincides with the dual supertranslation charge. The dual mass vanishes identically on Riemannian manifolds in General Relativity unless conical singularities corresponding to Misner strings are introduc

Shrey Desai, Jiacheng Xu, Greg Durrett

Compressive summarization systems typically rely on a crafted set of syntactic rules to determine what spans of possible summary sentences can be deleted, then learn a model of what to actually delete by optimizing for content selection (ROUGE). In this work, we propose to relax the rigid syntactic constraints on candidate spans and instead leave compression

Jiacheng Xu, Shrey Desai, Greg Durrett

An advantage of seq2seq abstractive summarization models is that they generate text in a free-form manner, but this flexibility makes it difficult to interpret model behavior. In this work, we analyze summarization decoders in both blackbox and whitebox ways by studying on the entropy, or uncertainty, of the model's token-level predictions. For two stron

Mehdi Miah, Justine Pepin, Nicolas Saunier, Guillaume-Alexandre Bilodeau

This paper addresses the problem of selecting appearance features for multiple object tracking (MOT) in urban scenes. Over the years, a large number of features has been used for MOT. However, it is not clear whether some of them are better than others. Commonly used features are color histograms, histograms of oriented gradients, deep features from convolut

Victoria Krakovna, Laurent Orseau, Richard Ngo, Miljan Martic

Designing reward functions is difficult: the designer has to specify what to do (what it means to complete the task) as well as what not to do (side effects that should be avoided while completing the task). To alleviate the burden on the reward designer, we propose an algorithm to automatically generate an auxiliary reward function that penalizes side effec

T. Mitchell Roddenberry, Madeline Navarro, Santiago Segarra

We consider the problem of inferring the unobserved edges of a graph from data supported on its nodes. In line with existing approaches, we propose a convex program for recovering a graph Laplacian that is approximately diagonalizable by a set of eigenvectors obtained from the second-order moment of the observed data. Unlike existing work, we incorporate pri

Ruiyuan Lin, Suya You, Raghuveer Rao, C. -C. Jay Kuo

The construction of a multilayer perceptron (MLP) as a piecewise low-order polynomial approximator using a signal processing approach is presented in this work. The constructed MLP contains one input, one intermediate and one output layers. Its construction includes the specification of neuron numbers and all filter weights. Through the construction, a one-t

Peter Feller, Diana Hubbard

In this short note we construct examples of open books for 3-manifolds that show that arbitrarily high twisting of the monodromy of the open book does not guarantee maximality of the Euler characteristic of the pages among the open books supporting the same contact manifold. We find our examples of open books as the double branched covers of families of clos

Mio Kubota, Kin-ya Oda, Keigo Shimada, Masahide Yamaguchi

We investigate cosmological perturbations of scalar-tensor theories in Palatini formalism. First we introduce an action where the Ricci scalar is conformally coupled to a function of a scalar field and its kinetic term and there is also a k-essence term consisting of the scalar and its kinetic term. This action has three frames that are equivalent to one ano

Daniel Faílde, Daniel Baldomir

Topological edge states are predicted to be responsible for the high efficient thermoelectric response of topological insulators, currently the best thermoelectric materials. However, to explain their figure of merit the coexistence of topological electrons, entropy and phonons can not be considered independently. In a background that puts together electrody

Mahadevan Ganesh, Stuart C Hawkins, Alexandre Tartakovsky, Ramakrishna Tipireddy

Partial differential equations (PDEs) are fundamental for theoretically describing numerous physical processes that are based on some input fields in spatial configurations. Understanding the physical process, in general, requires computational modeling of the PDE. Uncertainty in the computational model manifests through lack of precise knowledge of the inpu

Florian Ziel, Antonia Arsova

This study evaluates the performance of cointegrated vector autoregressive (VAR) models for very short- and short-term wind power forecasting. Preliminary results for a German data set comprising six wind power production time series indicate that taking into account potential cointegrating relations between the individual series can improve forecasts at sho

Hongjun Wang, Guanbin Li, Xiaobai Liu, Liang Lin

Although deep convolutional neural networks (CNNs) have demonstrated remarkable performance on multiple computer vision tasks, researches on adversarial learning have shown that deep models are vulnerable to adversarial examples, which are crafted by adding visually imperceptible perturbations to the input images. Most of the existing adversarial attack meth

Philip Hacker, Emil Wiedemann, Meike Zehlike

Increasingly, scholars seek to integrate legal and technological insights to combat bias in AI systems. In recent years, many different definitions for ensuring non-discrimination in algorithmic decision systems have been put forward. In this paper, we first briefly describe the EU law framework covering cases of algorithmic discrimination. Second, we presen

Vincent Dumont, Verónica Rodríguez Tribaldos, Jonathan Ajo-Franklin, Kesheng Wu

Deep Learning approaches for real, large, and complex scientific data sets can be very challenging to design. In this work, we present a complete search for a finely-tuned and efficiently scaled deep learning classifier to identify usable energy from seismic data acquired using Distributed Acoustic Sensing (DAS). While using only a subset of labeled images d

Matthew Tivnan, Wenying Wang, J. Webster Stayman

Spectral CT has great potential for a variety of clinical applications due to the improved material discrimination with respect to conventional CT. Many clinical and preclinical spectral CT systems have two spectral channels for dual-energy CT using strategies such as split-filtration, dual-layer detectors, or kVp-switching. However, there are emerging clini

Adil Belhaj, Anas El Balali, Wijdane El Hadri, Emilio Torrente-Lujan

We investigate the thermodynamic properties of the Hawking-Page phase transition of AdS black holes. We present evidence for the existence of two universal critical constants associated with the Hawking-Page (HP) and minimum black hole thermodynamical transition points. These constants are defined by C_S =\frac{S_{HP}-S_{min}}{S_{min}} and C_T =\frac{T_{HP}-

Jonas Höchst, Alvar Penning, Patrick Lampe, Bernd Freisleben

Computer systems used in the field of humanitarian technology are often based on general-purpose single-board computers, such as Raspberry Pis. While these systems offer great flexibility for developers and users, configuration and deployment either introduces overhead by executing scripts on multiple devices or requires deeper technical understanding when b

Rute C. Sofia

The architectural semantics of \emph{Information-Centric Networking} bring in interesting features in regards to mobility management: Information-Centric Networking is content-oriented, connection-less, and receiver-driven. Despite such intrinsic advantages, the support for node movement is being based on the principles of IP solutions. IP-based solutions ar

Bryan Debin, Etienne Granet

We define a new variant of exclusion processes in discrete time that has jump probabilities that depend on the last jump performed. In a particular limit for the jump probabilities and in suitable scaling limits for space and time, we compute the time evolution of the particle density starting from an arbitrary initial configuration, with closed boundary con

Javiera Castillo-Navarro, Bertrand Le Saux, Alexandre Boulch, Nicolas Audebert

The development of semi-supervised learning techniques is essential to enhance the generalization capacities of machine learning algorithms. Indeed, raw image data are abundant while labels are scarce, therefore it is crucial to leverage unlabeled inputs to build better models. The availability of large databases have been key for the development of learning

Matthew Tivnan, Wenying Wang, Grace Gang, Eleni Liapi

Quantitative estimation of contrast agent concentration is made possible by spectral CT and material decomposition. There are several approaches to modulate the sensitivity of the imaging system to obtain the different spectral channels required for decomposition. Spectral CT technologies that enable this varied sensitivity include source kV-switching, dual-

Gustaf Halvardsson, Johanna Peterson, César Soto-Valero, Benoit Baudry

The automatic interpretation of sign languages is a challenging task, as it requires the usage of high-level vision and high-level motion processing systems for providing accurate image perception. In this paper, we use Convolutional Neural Networks (CNNs) and transfer learning in order to make computers able to interpret signs of the Swedish Sign Language (

Xiaojian Wang, Jingyuan Wang, Ke Tang

Deep learning models are favored in many research and industry areas and have reached the accuracy of approximating or even surpassing human level. However they've long been considered by researchers as black-box models for their complicated nonlinear property. In this paper, we propose a multi-level decision framework to provide comprehensive interpreta

Matthew Tivnan, Wenying Wang, Steven Tilley, Jeffrey H. Siewerdsen

Spectral CT is an emerging modality that uses a data acquisition scheme with varied spectral responses to provide enhanced material discrimination in addition to the structural information of conventional CT. Existing clinical and preclinical designs with this capability include kV-switching, split-filtration, and dual-layer detector systems to provide two s

Nathanial P Hendler, Renu Malhotra

With the growing numbers of asteroids being discovered, identifying an observationally complete sample is essential for statistical analyses and for informing theoretical models of the dynamical evolution of the solar system. We present an easily implemented method of estimating the empirical observational completeness in absolute magnitude, H_lim, as a func

Chenjie Fan, Dana Mendelson

In this article, we study the log-log blowup dynamics for the mass critical nonlinear Schrödinger equation on $\mathbb{R}^{2}$ under rough but structured random perturbations at $L^{2}(\mathbb{R}^2)$ regularity. In particular, by employing probabilistic methods, we provide a construction of a family of $L^{2}(\mathbb{R}^2)$ regularity solutions which do not

Berta Bescos, Carlos Campos, Juan D. Tardós, José Neira

The assumption of scene rigidity is common in visual SLAM algorithms. However, it limits their applicability in populated real-world environments. Furthermore, most scenarios including autonomous driving, multi-robot collaboration and augmented/virtual reality, require explicit motion information of the surroundings to help with decision making and scene und

Nicolas Delporte

This thesis focuses on renormalization of quantum field theories. Its first part considers three tensor models in three dimensions, a Fermionic quartic with tensors of rank-3 and two Bosonic sextic, of ranks 3 and 5. We rely upon the large-$N$ melonic expansion of tensor models. For the first model, invariant under $U(N)^3$, we obtain the RG flow of the two

Andrew Kinai, Fred Otieno, Nelson Bore, Komminist Weldemariam

Attacks targeting several millions of non-internet based application users are on the rise. These applications such as SMS and USSD typically do not benefit from existing multi-factor authentication methods due to the nature of their interaction interfaces and mode of operations. To address this problem, we propose an approach that augments blockchain with m

T. Encrenaz, T. K. Greathouse, E. Marcq, T. Widemann

Following the announcement of the detection of phosphine (PH$_3$) in the cloud deck of Venus at millimeter wavelengths, we have searched for other possible signatures of this molecule in the infrared range. Since 2012, we have been observing Venus in the thermal infrared at various wavelengths to monitor the behavior of SO$_2$ and H$_2$O at the cloud top. We

George Michalopoulos, Helen Chen, Alexander Wong

In most clinical practice settings, there is no rigorous reviewing of the clinical documentation, resulting in inaccurate information captured in the patient medical records. The gold standard in clinical data capturing is achieved via "expert-review", where clinicians can have a dialogue with a domain expert (reviewers) and ask them questions about

Fintan Costello, Paul Watts

Null Hypothesis Significance Testing (NHST) has long been central to the scientific project, guiding theory development and supporting evidence-based intervention and decision-making. Recent years, however, have seen growing awareness of serious problems with NHST as it is typically used, and hence to proposals to limit the use of NHST techniques, to abandon

Amil Merchant, Barret Zoph, Ekin Dogus Cubuk

Data augmentation has emerged as a powerful technique for improving the performance of deep neural networks and led to state-of-the-art results in computer vision. However, state-of-the-art data augmentation strongly distorts training images, leading to a disparity between examples seen during training and inference. In this work, we explore a recently propo

Raouf Kerkouche, Gergely Ács, Claude Castelluccia

Federated Learning enables entities to collaboratively learn a shared prediction model while keeping their training data locally. It prevents data collection and aggregation and, therefore, mitigates the associated privacy risks. However, it still remains vulnerable to various security attacks where malicious participants aim at degrading the generated model

Saien Xie, Brendan D. Faeth, Yanhao Tang, Lizhong Li

Moiré superlattices comprised of stacked two-dimensional materials present a versatile platform for engineering and investigating new emergent quantum states of matter. At present, the vast majority of investigated systems have long moiré wavelengths, but investigating these effects at shorter, incommensurate wavelengths, and at higher energy scales, remains

Yang Deng, Ziyao Xu, Li Zhou, Huanping Liu

The development of artificial intelligent composition has resulted in the increasing popularity of machine-generated pieces, with frequent copyright disputes consequently emerging. There is an insufficient amount of research on the judgement of artificial and machine-generated works; the creation of a method to identify and distinguish these works is of part

Shibo Zhou, Xiaohua Li

Spiking neural network (SNN) is interesting due to its strong bio-plausibility and high energy efficiency. However, its performance is falling far behind conventional deep neural networks (DNNs). In this paper, considering a general class of single-spike temporal-coded integrate-and-fire neurons, we analyze the input-output expressions of both leaky and nonl

Ndeye Coumba Sarr

Un des résultats fondamentaux de la théorie de Bass-Serre est le théorème suivant : un groupe est amalgamé si et seulement si il agit sur un arbre avec comme domaine fondamental un segment. Dans cet article nous donnons un analogue pour les groupes profinis de ce résultat en utilisant la théorie des prographes de Deschamps et Suarez introduite. A fundamental

Rik J. C. van Esch, Shengling Shi, Antoine Bernas, Svitlana Zinger

Several studies claim that listening to Mozart music affects cognition and can be used to treat neurological conditions like epilepsy. Research into this Mozart effect has not addressed how dynamic interactions between brain networks, i.e. effective connectivity, are affected. The Granger-causality analysis is often used to infer effective connectivity. Firs

Soraia F. Paulo, Daniel Medeiros, Pedro Borges, Joaquim Jorge

Immersive Colonography allows medical professionals to navigate inside the intricate tubular geometries of subject-specific 3D colon images using Virtual Reality displays. Typically, camera travel is performed via Fly-Through or Fly-Over techniques that enable semi-automatic traveling through a constrained, well-defined path at user controlled speeds. Howeve

Aditya Rotti, Boris Bolliet, Jens Chluba, Mathieu Remazeilles

The Sunyaev-Zeldovich (SZ) effect provides a powerful cosmological probe, which traditionally is approached independently as cluster number count (CNC) or power spectrum (PS) analysis. Here, we devise a new method for analysing the $y$-map by introducing the survey completeness function, conventionally only used in the CNC analysis, in the $yy$-PS modeling.

Shotaro Yamasaki, Tomonori Totani, Kenta Kiuchi

The light curve of the fast radio burst (FRB) 181112 is resolved into four successive pulses, and the time interval ($\sim0.8$ ms) between the first and third pulses coincides with that between the second and fourth pulses, which can be interpreted as a neutron star (NS) spinning at a period of about $0.8$ ms. Although this period is shorter than the most ra

N. Quang Hung, N. Dinh Dang, L. Tan Phuc, N. Ngoc Anh

A fully microscopic model for the description of nuclear level density (NLD) in spherical nuclei is proposed. The model is derived by combining the partition function of the exact pairing solution plus the independent-particle model at finite temperature (EP+IPM) with that obtained by using the collective vibrational states calculated from the self-consisten

Yinuo Guo, Zeqi Lin, Jian-Guang Lou, Dongmei Zhang

We formalize human language understanding as a structured prediction task where the output is a partially ordered set (poset). Current encoder-decoder architectures do not take the poset structure of semantics into account properly, thus suffering from poor compositional generalization ability. In this paper, we propose a novel hierarchical poset decoding pa

Rui Wang, Zhen Zhang, Lie-Wen Chen, Yu-Gang Ma

We review the recent progress on studying the nuclear collective dynamics by solving the Boltzmann-Uehling-Uhlenbeck (BUU) equation with the lattice Hamiltonian method treating the collision term by the full-ensemble stochastic collision approach. This lattice BUU (LBUU) method has recently been developed and implemented in a GPU parallel computing technique

Weishi Wang, Shafiq Joty, Steven C. H. Hoi

While participants in a multi-party multi-turn conversation simultaneously engage in multiple conversation topics, existing response selection methods are developed mainly focusing on a two-party single-conversation scenario. Hence, the prolongation and transition of conversation topics are ignored by current methods. In this work, we frame response selectio

Ernst Roos, Ruud Brekelmans, Wouter van Eekelen, Dick den Hertog

Chebyshev's inequality provides an upper bound on the tail probability of a random variable based on its mean and variance. While tight, the inequality has been criticized for only being attained by pathological distributions that abuse the unboundedness of the underlying support and are not considered realistic in many applications. We provide alternati

Huai-Hang Song, Wei-Min Wang, Jia-Qi Wang, Yu-Tong Li

It is shown by multi-dimensional particle-in-cell simulations that intense secondary whistler waves with special vortex-like field topology can be excited by a relativistic laser pulse in the highly magnetized, near-critical density plasma. Such whistler waves with lower frequencies obliquely propagate on both sides of the laser propagation axis. The energy

Arnu Pretorius, Scott Cameron, Elan van Biljon, Tom Makkink

Multi-agent reinforcement learning has recently shown great promise as an approach to networked system control. Arguably, one of the most difficult and important tasks for which large scale networked system control is applicable is common-pool resource management. Crucial common-pool resources include arable land, fresh water, wetlands, wildlife, fish stock,

Giovanni Manfredi, Jean-Louis Rouet, Bruce N. Miller, Gabriel Chardin

The presence of complex hierarchical gravitational structures is one of the main features of the observed universe. Here, structure formation is studied both for the standard ($Λ\rm CDM$) cosmological model and for the Dirac-Milne universe, a matter-antimatter symmetric universe that was {\gm recently} proposed as an alternative "coasting" cosmologic

Haijun Yang, Liuhua Mu, Lei Zhang, Zixin Wang

We found that the AYFFF self-assemblies in the chloride solution of some divalent cations ($Zn^{2+}, Mg^{2+},$ and $Cu^{2+}$) display super strong paramagnetism, which may approach the mass susceptibility of ferromagnetism. We attribute the observed super strong paramagnetism to the existence of the aromatic rings, which interact with the cations through cat

Shubhanker Banerjee, Arun Jayapal, Sajeetha Thavareesan

Social media has penetrated into multilingual societies, however most of them use English to be a preferred language for communication. So it looks natural for them to mix their cultural language with English during conversations resulting in abundance of multilingual data, call this code-mixed data, available in todays' world.Downstream NLP tasks using

Benjamin Dodson

In this paper we prove a global spacetime bound for the quintic, nonlinear wave equation in three dimensions. This bound depends on the $L_{t}^{\infty} L_{x}^{2}$ and $L_{t}^{\infty} \dot{H}^{2}$ norms of the solution to the quintic problem.

Luka Rimanic, Cedric Renggli, Bo Li, Ce Zhang

The k-Nearest Neighbors (kNN) classifier is a fundamental non-parametric machine learning algorithm. However, it is well known that it suffers from the curse of dimensionality, which is why in practice one often applies a kNN classifier on top of a (pre-trained) feature transformation. From a theoretical perspective, most, if not all theoretical results aime

Ranjit Jhala, Niki Vazou

Refinement types enrich a language's type system with logical predicates that circumscribe the set of values described by the type, thereby providing software developers a tunable knob with which to inform the type system about what invariants and correctness properties should be checked on their code. In this article, we distill the ideas developed in t

Yutaka Akagi

Using the method of noncommutative geometry, we define a topological invariant in disordered bosonic Bogoliubov-de Gennes systems, which possess a unique mathematical property---non-Hermiticity. To demonstrate the validity of the definition, we investigate a disordered artificial spin ice model in two dimensions numerically. In the clean limit, we clarify th

Phillip Keung, Julian Salazar, Yichao Lu, Noah A. Smith

We describe an unsupervised method to create pseudo-parallel corpora for machine translation (MT) from unaligned text. We use multilingual BERT to create source and target sentence embeddings for nearest-neighbor search and adapt the model via self-training. We validate our technique by extracting parallel sentence pairs on the BUCC 2017 bitext mining task a

Ephrem Admasu Yekun, Alem Haddush Fitwi, S. Karpaga Selvi, Anubhav Kumar

Nowadays, wind power is considered as one of the most widely used renewable energy applications due to its efficient energy use and low pollution. In order to maintain high integration of wind power into the electricity market, efficient models for wind speed forecasting are in high demand. The non-stationary and nonlinear characteristics of wind speed, howe

Fabio De Gaspari, Dorjan Hitaj, Giulio Pagnotta, Lorenzo De Carli

Reliable identification of encrypted file fragments is a requirement for several security applications, including ransomware detection, digital forensics, and traffic analysis. A popular approach consists of estimating high entropy as a proxy for randomness. However, many modern content types (e.g. office documents, media files, etc.) are highly compressed f

James A. Brofos, Roy R. Lederman

Markov chain Monte Carlo (MCMC) algorithms offer various strategies for sampling; the Hamiltonian Monte Carlo (HMC) family of samplers are MCMC algorithms which often exhibit improved mixing properties. The recently introduced magnetic HMC, a generalization of HMC motivated by the physics of particles influenced by magnetic field forces, has been demonstrate

Martin Keller-Ressel

We introduce Hyperbolic Prototype Learning, a type of supervised learning, where class labels are represented by ideal points (points at infinity) in hyperbolic space. Learning is achieved by minimizing the 'penalized Busemann loss', a new loss function based on the Busemann function of hyperbolic geometry. We discuss several theoretical features of

Thiem Hoang, Le Ngoc Tram, Hyeseung Lee, Pham Ngoc Diep

Dust polarization induced by aligned grains is widely used to study magnetic fields in various environments, including star-forming regions. However, the question of to what optical depth grain alignment still exists in a dense molecular cloud (MC) is unclear. In this paper, we aim to achieve analytical formulae for the minimum size of aligned grains ($a_{\r

V. Shlyannikov, E. Martínez-Pañeda, A. Tumanov, A. Tartygasheva

The crack tip mechanics of strain gradient plasticity solids is investigated analytically and numerically. A first-order mechanism-based strain gradient (MSG) plasticity theory based on Taylor's dislocation model is adopted and implemented in the commercial finite element package ANSYS by means of a user subroutine. Two boundary value problems are consid

James Richardson

The theory of derivators provides a convenient abstract setting for computing with homotopy limits and colimits. In enriched homotopy theory, the analogues of homotopy (co)limits are weighted homotopy (co)limits. In this thesis, we develop a theory of derivators and, more generally, prederivators enriched over a monoidal derivator E. In parallel to the unenr

Yiting Xia, Yiwei Jiang, Tao Ye

Music classification between music made by AI or human composers can be done by deep learning networks. We first transformed music samples in midi format to natural language sequences, then classified these samples by mLSTM (multiplicative Long Short Term Memory) + logistic regression. The accuracy of the result evaluated by 10-fold cross validation can reac

Vinod Muthusamy, Merve Unuvar, Hagen Völzer, Justin D. Weisz

Robotic process automation (RPA) and its next evolutionary stage, intelligent process automation, promise to drive improvements in efficiencies and process outcomes. However, how can business leaders evaluate how to integrate intelligent automation into business processes? What is an appropriate division of labor between humans and machines? How should combi

Iris Agresti, Beatrice Polacchi, Davide Poderini, Emanuele Polino

Self-testing is a method of quantum state and measurement estimation that does not rely on assumptions about the inner working of the used devices. Its experimental realization has been limited to sources producing single quantum states so far. In this work, we experimentally implement two significant building blocks of a quantum network involving two indepe