November 2020 arXiv papers — page 19

Hui Wei

The neural mechanism of memory has a very close relation with the problem of representation in artificial intelligence. In this paper a computational model was proposed to simulate the network of neurons in brain and how they process information. The model refers to morphological and electrophysiological characteristics of neural information processing, and

Cheng Cheng

The history of oil and gas well stimulation through hydraulic fracturing is characterized by a pursuit of optimal designs tailored to reservoir properties. However, as with many engineering systems, the impact of variability and uncertainty (in this case of reservoir properties) is not accounted in these deterministic approaches. We propose an expansion of t

Burak Erdogan, William R. Green

We investigate $L^1\to L^\infty$ dispersive estimates for the one dimensional Dirac equation with a potential. In particular, we show that the Dirac evolution satisfies the natural $t^{-\frac12}$ decay rate, which may be improved to $t^{-\frac32}$ at the cost of spatial weights when the thresholds are regular. We classify the structure of threshold obstructi

Matheus R. F. Mendonça, André M. S. Barreto, Artur Ziviani

Network seeding for efficient information diffusion over time-varying graphs~(TVGs) is a challenging task with many real-world applications. There are several ways to model this spatio-temporal influence maximization problem, but the ultimate goal is to determine the best moment for a node to start the diffusion process. In this context, we propose Spatio-Te

Swarnava Ghosh, Kaushik Bhattacharya

We present an accurate and efficient finite-difference formulation and parallel implementation of Kohn-Sham Density (Operator) Functional Theory (DFT) for non periodic systems embedded in a bulk environment. Specifically, employing non-local pseudopotentials, local reformulation of electrostatics, and truncation of the spatial Kohn-Sham Hamiltonian, and the

Tina LY Wu, Anna Murphy, Chao Chen, Dana Kulic

External feedback in the form of visual, auditory and tactile cues has been used to assist patients to overcome mobility challenges. However, these cues can become less effective over time. There is limited research on adapting cues to account for inter and intra-personal variations in cue responsiveness. We propose a cue-provision framework that consists of

Yao-Tong Chen, Lei Du, Yan Zhang, Jin-Hui Wu

We propose a hybrid cavity magnomechanical system to realize and transfer the bipartite entanglements and Einstein-Podolsky-Rosen (EPR) steerings between magnons, photons, and phonons in the regime of stability of the system. As a parity-time-symmetric-like structure exhibiting the natural magnetostrictive magnon-phonon interaction, our passive-active cavity

Jordi Vila-Pérez, Matteo Giacomini, Ruben Sevilla, Antonio Huerta

This work presents the face-centred finite volume (FCFV) paradigm for the simulation of compressible flows. The FCFV method defines the unknowns at the face barycentre and uses a hybridisation procedure to eliminate all the degrees of freedom inside the cells. In addition, Riemann solvers are defined implicitly within the expressions of the numerical fluxes.

Melvyn B. Nathanson

Let $\mathcal{B} = (B_1,\ldots, B_h)$ be an $h$-tuple of sets of positive integers. Let $g_{\mathcal{B} }(n)$ count the number of representations of $n$ in the form $n = b_1\cdots b_h$, where $b_i \in B_i$ for all $i \in \{1,\ldots, h\}$. It is proved that $\liminf_{n\rightarrow \infty} g_{\mathcal{B} }(n) \geq 2$ implies $\limsup_{n\rightarrow \infty} g_{\m

Hui Ouyang

In view of the great performance of circumcentered isometry methods for solving the best approximation problem, in this work we further investigate the locally proper circumcenter mapping and circumcentered method. Various examples of locally proper circumcenter mapping are presented and studied. Inspired by some results on circumcentered-reflection method b

Sina Amini Niaki, Ehsan Haghighat, Trevor Campbell, Anoush Poursartip

We present a Physics-Informed Neural Network (PINN) to simulate the thermochemical evolution of a composite material on a tool undergoing cure in an autoclave. In particular, we solve the governing coupled system of differential equations -- including conductive heat transfer and resin cure kinetics -- by optimizing the parameters of a deep neural network (D

Harry Oviedo, Hugo Lara

In this paper, the spectral algorithm for nonlinear equations (SANE) is adapted to the problem of finding a zero of a given tangent vector field on a Riemannian manifold. The generalized version of SANE uses, in a systematic way, the tangent vector field as a search direction and a continuous real-valued function that adapts this direction and ensures that i

Juno Hwang, Wonseok Hwang, Junghyo Jo

The restricted Boltzmann machine (RBM) is a representative generative model based on the concept of statistical mechanics. In spite of the strong merit of interpretability, unavailability of backpropagation makes it less competitive than other generative models. Here we derive differentiable loss functions for both binary and multinary RBMs. Then we demonstr

Chang Li, Lei Ni, Xiaohua Zhu

By studying a complex Monge-Amp\`ere equation, we present an alternate proof to a recent result of Chu-Lee-Tam concerning the projectivity of a compact K\"ahler manifold $N^n$ with $\Ric_k< 0$ for some integer $k$ with $1<k<n$, and the ampleness of the canonical line bundle $K_N$.

Marcio C. Araújo Filho, José N. V. Gomes

In this paper, we compute universal estimates of eigenvalues of a coupled system of elliptic differential equations in divergence form on a bounded domain in Euclidean space. As an application, we show an interesting case of rigidity inequalities of the eigenvalues of the Laplacian, more precisely, we consider a countable family of bounded domains in Gaussia

Izaro Fernandez-Iriondo, Antonio Jimenez-Marin, Ibai Diez, Paolo Bonifazi

Brain networks can be defined and explored through their connectivity. Here, we analyzed the relationship between structural connectivity (SC) across 2,514 regions that cover the entire brain and brainstem, and their dynamic functional connectivity (DFC). To do so, we focused on a combination of two metrics: the first assesses the degree of SC-DFC similarity

Shuhao Zhang, Yujia Yang, Seth Siriya, Ye Pu

This paper addresses the trajectory planning for multiple autonomous underwater vehicles (AUVs) in strong waves that can disturb the AUVs' trajectory tracking ability and cause obstacle and inter-vehicle collisions. A novel approach based on Hamilton-Jacobi differential game formulation and model predictive control is proposed to generate safety-guaranteed t

Toshiyuki Tanaka, Kenji Hasegawa

Theoretically modelling the 21-cm signals caused by Population III stars (Pop III stars) is the key to extracting fruitful information on Pop III stars from current and forthcoming 21-cm observations. In this work we develop a new module of Pop III stars in which the escape fractions of ionizing photons and Lyman-Werner (LW) photons, photo-heating by UV radi

Debatri Chattopadhyay, Simon Stevenson, Jarrod R. Hurley, Matthew Bailes

Binaries comprised of a neutron star (NS) and a black hole (BH) have so far eluded observations as pulsars and with gravitational waves (GWs). We model the formation and evolution of these NS+BH binaries - including pulsar evolution - using the binary population synthesis code COMPAS. We predict the presence of a total of 50-2000 binaries containing a pulsar

Yuwen Li

This work presents uniform preconditioners for the discrete Laplace--Beltrami operator on hypersurfaces. In particular, within the framework of fast auxiliary space preconditioning (FASP), we develop efficient and user-friendly multilevel preconditioners for the Laplace--Beltrami type equation discretized by Lagrange, nonconforming linear, and discontinuous

Marcelo M. Cavalcanti, Valeria N. Domingos Cavalcanti, Victor H. Gonzalez Martinez, Türker Özsarı

We study the stabilization and the wellposedness of solutions of the wave equation with subcritical semilinearities and locally distributed nonlinear dissipation. The novelty of this paper is that we deal with the difficulty that the main equation does not have good nonlinear structure amenable to a direct proof of a priori bounds and a desirable observabili

Isabel M. E. Santos-Santos, Azadeh Fattahi, Laura V. Sales, Julio F. Navarro

We use the APOSTLE $\Lambda$CDM cosmological hydrodynamical simulations of the Local Group to study the recent accretion of massive satellites into the halo of Milky Way (MW)-sized galaxies. These systems are selected to be close analogues to the Large Magellanic Cloud (LMC), the most massive satellite of the MW. The simulations allow us to address, in a cos

D. Flores-Alfonso, C. S. Lopez-Monsalvo, M. Maceda

The defining property of every three-dimensional $\varepsilon$-contact manifold is shown to be equivalent to requiring the fulfillment of London's equation in 2+1 electromagnetism. To illustrate this point, we show that every such manifold that is also K-contact and $\eta$-Einstein is a vacuum solution to the most general quadratic-curvature gravity action,

Siva Athreya, Oleg Butkovsky, Khoa Lê, Leonid Mytnik

We study stochastic reaction--diffusion equation $$ \partial_tu_t(x)=\frac12 \partial^2_{xx}u_t(x)+b(u_t(x))+\dot{W}_{t}(x), \quad t>0,\, x\in D $$ where $b$ is a generalized function in the Besov space $\mathcal{B}^\beta_{q,\infty}({\mathbb R})$, $D\subset{\mathbb R}$ and $\dot W$ is a space-time white noise on ${\mathbb R}_+\times D$. We introduce a notion

Elliott Skomski, Jan Drgona, Aaron Tuor

Recent works exploring deep learning application to dynamical systems modeling have demonstrated that embedding physical priors into neural networks can yield more effective, physically-realistic, and data-efficient models. However, in the absence of complete prior knowledge of a dynamical system's physical characteristics, determining the optimal structure

Rajeev Singh, Gabriel Sophys, Radoslaw Ryblewski

Evolution of spin polarization in the Gubser-expanding conformal perfect-fluid hydrodynamic background is studied. The analysis of the conformal transformation properties of the conservation laws is extended to the case of the angular momentum conservation. The explicit forms of equations of motion for spin components are derived and analysed, and some speci

Seyed Mohsen Hosseini

In machine learning approach to image denoising a network is trained to recover a clean image from a noisy one. In this paper a novel structure is proposed based on training multiple specialized networks as opposed to existing structures that are base on a single network. The proposed model is an alternative for training a very deep network to avoid issues l

Rong Huang

We consider the problem of detecting sparse heterogeneous mixtures in a two-sample setting from a nonparametric perspective, where the effect manifests itself as a positive shift. We suggest a two-sample higher criticism test, and show that it is first-order comparable to the likelihood ratio test for the generalized Guassian mixture models in all sparsity r

Baorui Ma, Zhizhong Han, Yu-Shen Liu, Matthias Zwicker

Reconstructing continuous surfaces from 3D point clouds is a fundamental operation in 3D geometry processing. Several recent state-of-the-art methods address this problem using neural networks to learn signed distance functions (SDFs). In this paper, we introduce \textit{Neural-Pull}, a new approach that is simple and leads to high quality SDFs. Specifically

Zhiyao Xie, Haoxing Ren, Brucek Khailany, Ye Sheng

IR drop is a fundamental constraint required by almost all chip designs. However, its evaluation usually takes a long time that hinders mitigation techniques for fixing its violations. In this work, we develop a fast dynamic IR drop estimation technique, named PowerNet, based on a convolutional neural network (CNN). It can handle both vector-based and vector

Zhiyao Xie, Guan-Qi Fang, Yu-Hung Huang, Haoxing Ren

Design flow parameters are of utmost importance to chip design quality and require a painfully long time to evaluate their effects. In reality, flow parameter tuning is usually performed manually based on designers' experience in an ad hoc manner. In this work, we introduce a machine learning-based automatic parameter tuning methodology that aims to find the

Jan Drgona, Soumya Vasisht, Aaron Tuor, Draguna Vrabie

In this paper, we provide sufficient conditions for dissipativity and local asymptotic stability of discrete-time dynamical systems parametrized by deep neural networks. We leverage the representation of neural networks as pointwise affine maps, thus exposing their local linear operators and making them accessible to classical system analytic and design meth

Zhiyao Xie, Hai Li, Xiaoqing Xu, Jiang Hu

IR drop constraint is a fundamental requirement enforced in almost all chip designs. However, its evaluation takes a long time, and mitigation techniques for fixing violations may require numerous iterations. As such, fast and accurate IR drop prediction becomes critical for reducing design turnaround time. Recently, machine learning (ML) techniques have bee

A. Y. Klimenko

This work proposes a series of quantum experiments that can, at least in principle, allow for examining microscopic mechanisms associated with decoherence. These experiments can be interpreted as a quantum-mechanical version of non-equilibrium mixing between two volumes separated by a thin interface. One of the principal goals of such experiments is in ident

N. Yonemaru, S. Kuroyanagi, G. Hobbs, K. Takahashi

Cosmic strings are potential gravitational wave (GW) sources that can be probed by pulsar timing arrays (PTAs). In this work we develop a detection algorithm for a GW burst from a cusp on a cosmic string, and apply it to Parkes PTA data. We find four events with a false alarm probability less than 1%. However further investigation shows that all of these are

Plamen Stefanov, Samy Tindel

We study the effect of additive noise to the inversion of FIOs associated to a diffeomorphic canonical relation. We use the microlocal defect measures to measure the power spectrum of the noise and analyze how that power spectrum is transformed under the inversion. In particular, we compute the standard deviation of the noise added to the inversion as a func

J. K. Jain

This chapter appears in "Fractional Quantum Hall Effects: New Development," edited by B. I. Halperin and J. K. Jain (World Scientific, 2020). The chapter begins with a primer on composite fermions, and then reviews three directions that have recently been pursued. It reports on theoretical calculations making detailed quantitative predictions for two sets of

Federico Ghelli Visi, Atau Tanaka

This chapter presents an overview of Interactive Machine Learning (IML) techniques applied to the analysis and design of musical gestures. We go through the main challenges and needs related to capturing, analysing, and applying IML techniques to human bodily gestures with the purpose of performing with sound synthesis systems. We discuss how different algor

James Bartusek, Andrea Coladangelo, Dakshita Khurana, Fermi Ma

We prove that quantum-hard one-way functions imply simulation-secure quantum oblivious transfer (QOT), which is known to suffice for secure computation of arbitrary quantum functionalities. Furthermore, our construction only makes black-box use of the quantum-hard one-way function. Our primary technical contribution is a construction of extractable and equiv

Zhiyang He, Jason Li, Magnus Wahlström

Let $G$ be a graph and $S, T \subseteq V(G)$ be (possibly overlapping) sets of terminals, $|S|=|T|=k$. We are interested in computing a vertex sparsifier for terminal cuts in $G$, i.e., a graph $H$ on a smallest possible number of vertices, where $S \cup T \subseteq V(H)$ and such that for every $A \subseteq S$ and $B \subseteq T$ the size of a minimum $(A,B

David d'Enterria, Constantin Loizides

We review the theoretical and experimental progress in the Glauber model of multiple nucleon and/or parton scatterings, after the last 10--15 years of operation with proton and nuclear beams at the CERN Large Hadron Collider (LHC) and with various light and heavy colliding ions at the BNL Relativistic Heavy Ion Collider (RHIC). The main developments and the

Matthias Wilms, Jordan J. Bannister, Pauline Mouches, M. Ethan MacDonald

Brain aging is a widely studied longitudinal process throughout which the brain undergoes considerable morphological changes and various machine learning approaches have been proposed to analyze it. Within this context, brain age prediction from structural MR images and age-specific brain morphology template generation are two problems that have attracted mu

James Mulligan

We overview recent jet measurements in pp and Pb-Pb collisions with the ALICE detector. ALICE reconstructs jets at midrapidity using the anti-$k_{\mathrm{T}}$ algorithm, including both charged particle jets from the ALICE tracking detectors, and full jets from the combination of the ALICE electromagnetic calorimeter with the tracking system. We focus on incl

Yago Martin, Zhenlong Li, Yue Ge

The study of migrations and mobility has historically been severely limited by the absence of reliable data or the temporal sparsity of the available data. Using geospatial digital trace data, the study of population movements can be much more precisely and dynamically measured. Our research seeks to develop a near real-time (one-day lag) Twitter census that

Yujia Yang, Ye Wang, Chris Manzie, Ye Pu

Controlling a fleet of autonomous underwater vehicles can be challenging due to low bandwidth communication between agents. This paper proposes to address this challenge by optimizing the quantization design of the communications between agents for use in distributed algorithms. The proposed approach considers two stages for the problem of multi-AUV control:

A. A. Sokolik, O. V. Kotov, Yu. E. Lozovik

Confined modes at the edge arbitrarily inclined with respect to optical axes of nonmagnetic anisotropic 2D materials are considered. By developing the exact Wiener-Hopf and approximated Fetter methods we studied edge modes dispersions, field and charge density distributions. The 2D layer is described by the Lorentz-type conductivities in one or both directio

Zhenlong Li, Xiao Huang, Xinyue Ye, Xiaoming Li

Understanding human mobility dynamics among places provides fundamental knowledge regarding their interactive gravity, benefiting a wide range of applications in need of prior knowledge in human spatial interactions. The ongoing COVID-19 pandemic uniquely highlights the need for monitoring and measuring fine-scale human spatial interactions. In response to t

Remco Bouckaert, Mareike Fischer, Kristina Wicke

Recently, the perfect phylogeny model with persistent characters has attracted great attention in the literature. It is based on the assumption that complex traits or characters can only be gained once and lost once in the course of evolution. Here, we consider a generalization of this model, namely Dollo parsimony, that allows for multiple character losses.

Mohammed Abouzaid, Luís Diogo

We study the compact monotone Fukaya category of $T^*S^n$, for $n\geq 2$, and show that it is split-generated by two classes of objects: the zero-section $S^n$ (equipped with suitable bounding cochains) and a 1-parameter family of monotone Lagrangian tori $(S^1\times S^{n-1})_\tau$, with monotonicity constants $\tau>0$ (equipped with rank 1 unitary local sys

Kevin Raina

Monitoring the progression of lesions is important for clinical response. Summary statistics such as lesion volume are objective and easy to interpret, which can help clinicians assess lesion growth or decay. CNNs are commonly used in medical image segmentation for their ability to produce useful features within large contexts and their associated efficient

Nicholas Roberts, Davis Liang, Graham Neubig, Zachary C. Lipton

Neural Machine Translation (NMT) systems are typically evaluated using automated metrics that assess the agreement between generated translations and ground truth candidates. To improve systems with respect to these metrics, NLP researchers employ a variety of heuristic techniques, including searching for the conditional mode (vs. sampling) and incorporating

Adiel Statman, Liat Rozenberg, Dan Feldman

In projective clustering we are given a set of n points in $R^d$ and wish to cluster them to a set $S$ of $k$ linear subspaces in $R^d$ according to some given distance function. An $\eps$-coreset for this problem is a weighted (scaled) subset of the input points such that for every such possible $S$ the sum of these distances is approximated up to a factor

Priyank Pathak

Research into the task of re-identification (ReID) is picking up momentum in computer vision for its many use cases and zero-shot learning nature. This paper proposes a computationally efficient fine-grained ReID model, FGReID, which is among the first models to unify image and video ReID while keeping the number of training parameters minimal. FGReID takes

Subhojit Biswas, Diganta Mukherjee, Indranil SenGupta

This paper proposes swaps on two important new measures of generalized variance, namely the maximum eigenvalue and trace of the covariance matrix of the assets involved. We price these generalized variance swaps for Barndorff-Nielsen and Shephard model used in financial markets. We consider multiple assets in the portfolio for theoretical purpose and demonst

Jeffrey Kuan, Mark Landry, Andrew Lin, Andrew Park

We construct a two-class asymmetric interacting particle system with $U_q(so_6)$ or $U_q(so_8)$ symmetry, in which up to two particles may occupy a site if the two particles have different class. The particles exhibit a drift, but there is no preference given between first-class and second-class particles. The quantum group symmetry leads to reversible measu

Leen Decin

The chemical enrichment of the Universe; the mass spectrum of planetary nebulae, white dwarfs and gravitational wave progenitors; the frequency distribution of Type I and II supernovae; the fate of exoplanets ... a multitude of phenomena which is highly regulated by the amounts of mass that stars expel through a powerful wind. For more than half a century, t

Tobias Pulls

Tor recently integrated a circuit padding framework for creating padding machines: defenses that work by defining state machines that inject dummy traffic to protect against traffic analysis attacks like Website Fingerprinting (WF) attacks. In this paper, we explore the design of effective and efficient padding machines to defend against WF attacks. Through

Slađana Babić, Christophe Ley, Lorenzo Ricci, David Veredas

Economic and financial crises are characterised by unusually large events. These tail events co-move because of linear and/or nonlinear dependencies. We introduce TailCoR, a metric that combines (and disentangles) these linear and non-linear dependencies. TailCoR between two variables is based on the tail inter quantile range of a simple projection. It is di

Nham Le, Tuan Lai, Trung Bui, Doo Soon Kim

With the renaissance of deep learning, neural networks have achieved promising results on many natural language understanding (NLU) tasks. Even though the source codes of many neural network models are publicly available, there is still a large gap from open-sourced models to solving real-world problems in enterprises. Therefore, to fill this gap, we introdu

Fan Zhang, Stefaan Poedts, Andrea Lani, Błażej Kuźma

To study acoustic wave propagation and the corresponding energy deposition in partially ionized plasmas, we use a two-fluid computational model that treats neutrals and charged particles (electrons and ions) as two separate fluids. This two-fluid model takes into account the ion-neutral collisions, ionization, and recombination, allowing us to investigate bo

Edward C. Burks, Dustin A. Gilbert, Peyton D. Murray, Chad Flores

Free-standing, interconnected metallic nanowire networks with density as low as 40 mg/cm^{3} have been achieved over cm-scale areas, using electrodeposition into polycarbonate membranes that have been ion-tracked at multiple angles. Networks of interconnected magnetic nanowires further provide an exciting platform to explore 3-dimensional nanomagnetism, wher

Tianhong Dai, Hengyan Liu, Anil Anthony Bharath

Episodic self-imitation learning, a novel self-imitation algorithm with a trajectory selection module and an adaptive loss function, is proposed to speed up reinforcement learning. Compared to the original self-imitation learning algorithm, which samples good state-action pairs from the experience replay buffer, our agent leverages entire episodes with hinds

Garvita Agarwal, Lauren Hay, Ia Iashvili, Benjamin Mannix

A framework is presented to extract and understand decision-making information from a deep neural network (DNN) classifier of jet substructure tagging techniques. The general method studied is to provide expert variables that augment inputs ("eXpert AUGmented" variables, or XAUG variables), then apply layerwise relevance propagation (LRP) to networks both wi

Medha Subramanian, Jan Viebahn, Simon H. Tindemans, Benjamin Donnot

System operators are faced with increasingly volatile operating conditions. In order to manage system reliability in a cost-effective manner, control room operators are turning to computerised decision support tools based on AI and machine learning. Specifically, Reinforcement Learning (RL) is a promising technique to train agents that suggest grid control a

Jane X. Wang

Meta-learning, or learning to learn, has gained renewed interest in recent years within the artificial intelligence community. However, meta-learning is incredibly prevalent within nature, has deep roots in cognitive science and psychology, and is currently studied in various forms within neuroscience. The aim of this review is to recast previous lines of re

S. Sharma, Arushi, K. Motla, J. Beare

We present a comprehensive study on superconducting properties of Re$_7$B$_3$ and Re$_3$B through specific heat, magnetic susceptibility, resistivity, and transverse and zero-field muon spin rotation/relaxation ($\mu$SR) experiments on polycrystalline samples. Re$_7$B$_3$ (T$_C$ = 3.2~K) is a non-centrosymmetric type-II ($\kappa$ $\approx$ 9.27) superconduct

Ilaria Incaviglia, Andreas Frutiger, Yves Blickenstorfer, Fridolin Treindl

In recent years, cell-based assays have been frequently used in molecular interaction analysis. Cell-based assays complement traditional biochemical and biophysical methods, as they allow for molecular interaction analysis, mode of action studies and even drug screening processes to be performed under physiologically relevant conditions. In most cellular ass

Doug Shi-Dong, Siva Nadarajah

Aerodynamic shape optimization (ASO) involves finding an optimal surface while constraining a set of nonlinear partial differential equations (PDE). The conventional approaches use quasi-Newton methods operating in the reduced-space, where the PDE constraints are eliminated at each design step by decoupling the flow solver from the optimizer. Conversely, the

Vrej Zarikian

Let $j:Y \to X$ be a continuous surjection of compact metric spaces. Whyburn proved that $j$ is irreducible, meaning that $j(F) \subsetneq X$ for any proper closed subset $F \subsetneq Y$, if and only if $j$ is almost one-to-one, in the sense that \[ \overline{\{y \in Y: j^{-1}(j(y)) = y\}} = Y. \] In this note we prove the following generalization: There ex

Athira Menon, Norbert Langer, Selma E. de Mink, Stephen Justham

The majority of close massive binary stars with initial periods of a few days experience a contact phase, in which both stars overflow their Roche lobes simultaneously. We perform the first dedicated study of the evolution of massive contact binaries and provide a comprehensive prediction of their observed properties. We compute 2790 detailed binary models f

Guillermo Ballesteros, Marcos A. G. Garcia, Mathias Pierre

We investigate the power spectrum of Non-Cold Dark Matter (NCDM) produced in a state out of thermal equilibrium. We consider dark matter production from the decay of scalar condensates (inflaton, moduli), the decay of thermalized and non-thermalized particles, and from thermal and non-thermal freeze-in. For each case, we compute the NCDM phase space distribu

Tatyana Shcherbina

This paper adapts the recently developed rigorous application of the supersymmetric transfer matrix approach for the 1d band matrices to the case of the orthogonal symmetry. We consider $N\times N$ block band matrices consisting of $W\times W$ random Gaussian blocks (parametrized by $j,k \in\Lambda=[1,n]\cap \mathbb{Z}$, $N=nW$) with a fixed entry's variance

Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar

Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slow

Chunkai Zhang, Zilin Du, Yuting Yang, Wensheng Gan

Utility mining has emerged as an important and interesting topic owing to its wide application and considerable popularity. However, conventional utility mining methods have a bias toward items that have longer on-shelf time as they have a greater chance to generate a high utility. To eliminate the bias, the problem of on-shelf utility mining (OSUM) is intro

Chunkai Zhang, Zilin Du, Wensheng Gan, Philip S. Yu

High-utility sequential pattern mining (HUSPM) has recently emerged as a focus of intense research interest. The main task of HUSPM is to find all subsequences, within a quantitative sequential database, that have high utility with respect to a user-defined minimum utility threshold. However, it is difficult to specify the minimum utility threshold, especial

Benedikte Wallace, Charles P. Martin, Jim Torresen, Kristian Nymoen

Sound and movement are closely coupled, particularly in dance. Certain audio features have been found to affect the way we move to music. Is this relationship between sound and movement something which can be modelled using machine learning? This work presents initial experiments wherein high-level audio features calculated from a set of music pieces are inc

Stefano Bellucci, Fabio Cardone, Fabio Pistella

After a brief digression on the current landscape of theoretical physics and on some open questions pertaining to coherence with experimental results, still to be settled, it is shown that the properties of the Deformed Minkowski space lead to a plurality of potential physical phenomena that should occur, provided that the resulting formalisms can be conside

Sahil Verma, Zhendong Su

We present ShapeFlow, a dynamic abstract interpreter for TensorFlow which quickly catches tensor shape incompatibility errors, one of the most common bugs in deep learning code. ShapeFlow shares the same APIs as TensorFlow but only captures and emits tensor shapes, its abstract domain. ShapeFlow constructs a custom shape computational graph, similar to the c

Wilmer Ricciotti, James Cheney

Language-integrated query is a powerful programming construct allowing database queries and ordinary program code to interoperate seamlessly and safely. Language-integrated query techniques rely on classical results about the nested relational calculus, stating that its queries can be algorithmically translated to SQL, as long as their result type is a flat

Carlos Leandro Gomes Batista, Tania Basso, Fátima Mattiello-Francisco, Regina Moraes

CubeSat-based nanosatellites are composed of COTS components and rely on its structure and standardized interfaces. A challenge in the nanosatellites context is to adapt the V\&V (Verification and Validation) process to answer to the increase importance of the embedded software, to reduce the artefacts to be delivered aiming at cutting cost and time and stil

Jack Edmonds, Fatemeh Tehranipoor

In today's digital age, the ease of data collection, transfer, and storage continue to shape modern society and the ways we interact with our world. The advantages are numerous, but there is also an increased risk of information unintentionally falling into the wrong hands. Finding methods of protecting sensitive information at the hardware level is of utmos

Vladislav Kargin

A meander system is a union of two arc systems that represent non-crossing pairings of the set $[2n] = \{1, \ldots, 2n\}$ in the upper and lower half-plane. In this paper, we consider random meander systems. We show that for a class of random meander systems, -- for simply-generated meander systems, -- the number of cycles in a system of size $n$ grows linea

João J. Xarez

A pretorsion theory for the category of all categories is presented. The associated prekernels and precokernels are calculated for every functor.

Mayumi Kubota, Julius G. Garcia

This study investigated perceptions and patterns of Facebook use among Filipino and Japanese undergraduate students and the relationship of these factors to creating and maintaining students social capital, international posture, and willingness to communicate. The survey of undergraduate students was conducted online and 483 valid responses were obtained. D

Haroldo C. D. Lima Junior, Carolina L. Benone, Luís C. B. Crispino

We investigate the absorption cross section of planar scalar massless waves impinging on spherically symmetric black holes which are solutions of the novel 4D Einstein-Gauss-Bonnet theory of gravity. Besides the mass of the black hole, the solution depends also on the Gauss-Bonnet constant coupling. Using the partial waves approach, we show that the absorpti

Bob Stienen, Rob Verheyen

We explore the use of autoregressive flows, a type of generative model with tractable likelihood, as a means of efficient generation of physical particle collider events. The usual maximum likelihood loss function is supplemented by an event weight, allowing for inference from event samples with variable, and even negative event weights. To illustrate the ef

Julius G. Garcia, Connie C. Aunario, Go Frendi Gunawan

Creating test with numerous items in Moodle can be tedious and less intuitive compared to conventional method. This study aims to determine the Markdown to Moodle performance in easing the test construction process and explain the underlying factors of the behavioral intention to use the application. Markdown to Moodle is an application that allows users to

Caroline Piaulet, Björn Benneke, Ryan A. Rubenzahl, Andrew W. Howard

With a mass in the Neptune regime and a radius of Jupiter, WASP-107b presents a challenge to planet formation theories. Meanwhile, the planet's low surface gravity and the star's brightness also make it one of the most favorable targets for atmospheric characterization. Here, we present the results of an extensive 4-year Keck/HIRES radial-velocity (RV) follo

Michael Kreshchuk, Shaoyang Jia, William M. Kirby, Gary Goldstein

The analogy between quantum chemistry and light-front quantum field theory, first noted by Kenneth G. Wilson, serves as motivation to develop light-front quantum simulation of quantum field theory. We demonstrate how calculations of hadron structure can be performed on Noisy Intermediate-Scale Quantum devices within the Basis Light-Front Quantization framewo

Antonio E. Russo, William M. Kirby, Kenneth M. Rudinger, Andrew D. Baczewski

We present an extension to the robust phase estimation protocol, which can identify incorrect results that would otherwise lie outside the expected statistical range. Robust phase estimation is increasingly a method of choice for applications such as estimating the effective process parameters of noisy hardware, but its robustness is dependent on the noise s

Andrés Felipe Ramos Padilla, Lingyu Wang, Sylvia Ploeckinger, F. F. S. van der Tak

Atomic fine structure lines have been detected in the local Universe and at high redshifts over the past decades. The [C II] emission line at 158 $\mu$m is an important observable as it provides constraints on the interstellar medium (ISM) cooling processes. We develop a physically motivated framework to simulate the production of far-infrared line emission

J. W. Moffat

The formation, accretion and growth of supermassive black holes in the early universe are investigated. The accretion rate ${\dot M}$ is calculated using the Bondi accretion rate onto black holes. Starting with initial seed black holes with masses $M_{\rm BH}\sim 10^2-10^3M_{\odot}$, the Bondi accretion rate can evolve into a supermassive black hole with mas

Sameer Khurana, Niko Moritz, Takaaki Hori, Jonathan Le Roux

The performance of automatic speech recognition (ASR) systems typically degrades significantly when the training and test data domains are mismatched. In this paper, we show that self-training (ST) combined with an uncertainty-based pseudo-label filtering approach can be effectively used for domain adaptation. We propose DUST, a dropout-based uncertainty-dri

Guglielmo Panelli, Ali Moradmand, Brandon Griffin, Kyle Swanson

We report experimental results on three-dimensional momentum imaging measurements of anions generated via dissociative electron attachment to gaseous formamide. From the momentum images, we analyze the angular and kinetic energy distributions for NH$_2^{-}$, O$^{-}$, and H$^{-}$ fragments and discuss the possible electron attachment and dissociation mechanis

Richard S. J. Tol

The paper presents the professor-student network of Nobel laureates in economics. 74 of the 79 Nobelists belong to one family tree. The remaining 5 belong to 3 separate trees. There are 350 men in the graph, and 4 women. Karl Knies is the central-most professor, followed by Wassily Leontief. No classical and few neo-classical economists have left notable des

Tihomir Valchev

The paper is dedicated to a system of matrix nonlinear evolution equations related to a Hermitian symmetric space of the type $\mathbf{A.III}$. The system under consideration extends the $1+1$ dimensional Heisenberg ferromagnet equation in the sense that its Lax pair has a form rather similar to the pair of the original Heisenberg ferromagnet model. We shall

Hansi Zeng, Qingyao Ai

Using reviews to learn user and item representations is important for recommender system. Current review based methods can be divided into two categories: (1) the Convolution Neural Network (CNN) based models that extract n-gram features from user/item reviews; (2) the Recurrent Neural Network (RNN) based models that learn global contextual representations f

Paolo Antonelli, Lars Eric Hientzsch, Pierangelo Marcati

Hydrodynamic systems for quantum fluids are systems for compressible fluid flows for which quantum effects are macroscopically relevant. We discuss how the presence of the dispersive tensor describing the quantum effects alters the acoustic dispersion at the example of the Quantum Hydrodynamic system (QHD). For the QHD system the dispersion relation is given

Ilka Agricola, Giulia Dileo, Leander Stecker

We show that every $3$-$(\alpha,\delta)$-Sasaki manifold of dimension $4n + 3$ admits a locally defined Riemannian submersion over a quaternionic K\"ahler manifold of scalar curvature $16n(n+2)\alpha\delta$. In the non-degenerate case ($\delta\neq 0$) we describe all homogeneous $3$-$(\alpha,\delta)$-Sasaki manifolds fibering over symmetric Wolf spaces (case

Jeetendra Kumar Tiwari, Birendra Kumar, Harish Chandr Chauhan, Subhasis Ghosh

The tri-layer La$_{3-3x}$Sr$_{1+3x}$Mn$_{3}$O$_{10}$ manganites of Ruddlesden-Popper (RP) series are naturally arranged layered structure with alternate stacking of m-MnO$_2$ (m = 3) planes and rock-salt type block layers (La, Sr)$_2$O$_2$ along c-axis. The dimensionality of the RP series manganites depends on the number of perovskite layers and significantl