title stringlengths 7 239 | abstract stringlengths 7 2.76k | cs int64 0 1 | phy int64 0 1 | math int64 0 1 | stat int64 0 1 | quantitative biology int64 0 1 | quantitative finance int64 0 1 |
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Strong and broadly tunable plasmon resonances in thick films of aligned carbon nanotubes | Low-dimensional plasmonic materials can function as high quality terahertz
and infrared antennas at deep subwavelength scales. Despite these antennas'
strong coupling to electromagnetic fields, there is a pressing need to further
strengthen their absorption. We address this problem by fabricating thick films
of align... | 0 | 1 | 0 | 0 | 0 | 0 |
The coordination of centralised and distributed generation | In this paper, we analyse the interaction between centralised carbon emissive
technologies and distributed intermittent non-emissive technologies. A
representative consumer can satisfy his electricity demand by investing in
distributed generation (solar panels) and by buying power from a centralised
firm at a price t... | 0 | 0 | 1 | 0 | 0 | 0 |
Computation of annular capacity by Hamiltonian Floer theory of non-contractible periodic trajectories | The first author introduced a relative symplectic capacity $C$ for a
symplectic manifold $(N,\omega_N)$ and its subset $X$ which measures the
existence of non-contractible periodic trajectories of Hamiltonian isotopies on
the product of $N$ with the annulus $A_R=(R,R)\times\mathbb{R}/\mathbb{Z}$. In
the present paper... | 0 | 0 | 1 | 0 | 0 | 0 |
A New Torsion Pendulum for Gravitational Reference Sensor Technology Development | We report on the design and sensitivity of a new torsion pendulum for
measuring the performance of ultra-precise inertial sensors and for the
development of associated technologies for space-based gravitational wave
observatories and geodesy missions. The apparatus comprises a 1 m-long, 50
um-diameter, tungsten fiber... | 0 | 1 | 0 | 0 | 0 | 0 |
Optical Angular Momentum in Classical Electrodynamics | Invoking Maxwell's classical equations in conjunction with expressions for
the electromagnetic (EM) energy, momentum, force, and torque, we use a few
simple examples to demonstrate the nature of the EM angular momentum. The
energy and the angular momentum of an EM field will be shown to have an
intimate relationship;... | 0 | 1 | 0 | 0 | 0 | 0 |
Efficient variational Bayesian neural network ensembles for outlier detection | In this work we perform outlier detection using ensembles of neural networks
obtained by variational approximation of the posterior in a Bayesian neural
network setting. The variational parameters are obtained by sampling from the
true posterior by gradient descent. We show our outlier detection results are
comparabl... | 1 | 0 | 0 | 1 | 0 | 0 |
Emergent high-spin state above 7 GPa in superconducting FeSe | The local electronic and magnetic properties of superconducting FeSe have
been investigated by K$\beta$ x-ray emission (XES) and simultaneous x-ray
absorption spectroscopy (XAS) at the Fe K-edge at high pressure and low
temperature. Our results indicate a sluggish decrease of the local Fe spin
moment under pressure u... | 0 | 1 | 0 | 0 | 0 | 0 |
Verification in Staged Tile Self-Assembly | We prove the unique assembly and unique shape verification problems,
benchmark measures of self-assembly model power, are
$\mathrm{coNP}^{\mathrm{NP}}$-hard and contained in $\mathrm{PSPACE}$ (and in
$\mathrm{\Pi}^\mathrm{P}_{2s}$ for staged systems with $s$ stages). En route,
we prove that unique shape verification ... | 1 | 0 | 0 | 0 | 0 | 0 |
Combinets: Creativity via Recombination of Neural Networks | One of the defining characteristics of human creativity is the ability to
make conceptual leaps, creating something surprising from typical knowledge. In
comparison, deep neural networks often struggle to handle cases outside of
their training data, which is especially problematic for problems with limited
training d... | 0 | 0 | 0 | 1 | 0 | 0 |
Siamese Network of Deep Fisher-Vector Descriptors for Image Retrieval | This paper addresses the problem of large scale image retrieval, with the aim
of accurately ranking the similarity of a large number of images to a given
query image. To achieve this, we propose a novel Siamese network. This network
consists of two computational strands, each comprising of a CNN component
followed by... | 1 | 0 | 0 | 0 | 0 | 0 |
Gender Disparities in Science? Dropout, Productivity, Collaborations and Success of Male and Female Computer Scientists | Scientific collaborations shape ideas as well as innovations and are both the
substrate for, and the outcome of, academic careers. Recent studies show that
gender inequality is still present in many scientific practices ranging from
hiring to peer-review processes and grant applications. In this work, we
investigate ... | 1 | 1 | 0 | 0 | 0 | 0 |
Estimating a network from multiple noisy realizations | Complex interactions between entities are often represented as edges in a
network. In practice, the network is often constructed from noisy measurements
and inevitably contains some errors. In this paper we consider the problem of
estimating a network from multiple noisy observations where edges of the
original netwo... | 0 | 0 | 1 | 1 | 0 | 0 |
Autonomous drone race: A computationally efficient vision-based navigation and control strategy | Drone racing is becoming a popular sport where human pilots have to control
their drones to fly at high speed through complex environments and pass a
number of gates in a pre-defined sequence. In this paper, we develop an
autonomous system for drones to race fully autonomously using only onboard
resources. Instead of... | 1 | 0 | 0 | 0 | 0 | 0 |
Experimental investigations on nucleation, bubble growth, and micro-explosion characteristics during the combustion of ethanol/Jet A-1 fuel droplets | The combustion characteristics of ethanol/Jet A-1 fuel droplets having three
different proportions of ethanol (10%, 30%, and 50% by vol.) are investigated
in the present study. The large volatility differential between ethanol and Jet
A-1 and the nominal immiscibility of the fuels seem to result in combustion
charact... | 0 | 1 | 0 | 0 | 0 | 0 |
Hypergraph $p$-Laplacian: A Differential Geometry View | The graph Laplacian plays key roles in information processing of relational
data, and has analogies with the Laplacian in differential geometry. In this
paper, we generalize the analogy between graph Laplacian and differential
geometry to the hypergraph setting, and propose a novel hypergraph
$p$-Laplacian. Unlike th... | 1 | 0 | 0 | 1 | 0 | 0 |
Controlling motile disclinations in a thick nematogenic material with an electric field | Manipulating topological disclination networks that arise in a
symmetry-breaking phase transfor- mation in widely varied systems including
anisotropic materials can potentially lead to the design of novel materials
like conductive microwires, self-assembled resonators, and active anisotropic
matter. However, progress... | 0 | 1 | 0 | 0 | 0 | 0 |
How Generative Adversarial Networks and Their Variants Work: An Overview | Generative Adversarial Networks (GAN) have received wide attention in the
machine learning field for their potential to learn high-dimensional, complex
real data distribution. Specifically, they do not rely on any assumptions about
the distribution and can generate real-like samples from latent space in a
simple mann... | 1 | 0 | 0 | 0 | 0 | 0 |
Principal Boundary on Riemannian Manifolds | We revisit the classification problem and focus on nonlinear methods for
classification on manifolds. For multivariate datasets lying on an embedded
nonlinear Riemannian manifold within the higher-dimensional space, our aim is
to acquire a classification boundary between the classes with labels. Motivated
by the prin... | 1 | 0 | 0 | 1 | 0 | 0 |
Exploiting network topology for large-scale inference of nonlinear reaction models | The development of chemical reaction models aids understanding and prediction
in areas ranging from biology to electrochemistry and combustion. A systematic
approach to building reaction network models uses observational data not only
to estimate unknown parameters, but also to learn model structure. Bayesian
inferen... | 1 | 0 | 0 | 1 | 0 | 0 |
Numerical investigations of non-uniqueness for the Navier-Stokes initial value problem in borderline spaces | We consider the Cauchy problem for the incompressible Navier-Stokes equations
in $\mathbb{R}^3$ for a one-parameter family of explicit scale-invariant
axi-symmetric initial data, which is smooth away from the origin and invariant
under the reflection with respect to the $xy$-plane. Working in the class of
axi-symmetr... | 0 | 1 | 1 | 0 | 0 | 0 |
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics | Inspired by the success of deep learning techniques in the physical and
chemical sciences, we apply a modification of an autoencoder type deep neural
network to the task of dimension reduction of molecular dynamics data. We can
show that our time-lagged autoencoder reliably finds low-dimensional embeddings
for high-d... | 1 | 1 | 0 | 1 | 0 | 0 |
Some integrable maps and their Hirota bilinear forms | We introduce a two-parameter family of birational maps, which reduces to a
family previously found by Demskoi, Tran, van der Kamp and Quispel (DTKQ) when
one of the parameters is set to zero. The study of the singularity confinement
pattern for these maps leads to the introduction of a tau function satisfying a
homog... | 0 | 1 | 0 | 0 | 0 | 0 |
Dynamics of higher-order rational solitons for the nonlocal nonlinear Schrodinger equation with the self-induced parity-time-symmetric potential | The integrable nonlocal nonlinear Schrodinger (NNLS) equation with the
self-induced parity-time-symmetric potential [Phys. Rev. Lett. 110 (2013)
064105] is investigated, which is an integrable extension of the standard NLS
equation. Its novel higher-order rational solitons are found using the nonlocal
version of the ... | 0 | 1 | 1 | 0 | 0 | 0 |
N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning | While bigger and deeper neural network architectures continue to advance the
state-of-the-art for many computer vision tasks, real-world adoption of these
networks is impeded by hardware and speed constraints. Conventional model
compression methods attempt to address this problem by modifying the
architecture manuall... | 1 | 0 | 0 | 1 | 0 | 0 |
Spectral analysis of stationary random bivariate signals | A novel approach towards the spectral analysis of stationary random bivariate
signals is proposed. Using the Quaternion Fourier Transform, we introduce a
quaternion-valued spectral representation of random bivariate signals seen as
complex-valued sequences. This makes possible the definition of a scalar
quaternion-va... | 0 | 0 | 0 | 1 | 0 | 0 |
Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition | We propose a method (TT-GP) for approximate inference in Gaussian Process
(GP) models. We build on previous scalable GP research including stochastic
variational inference based on inducing inputs, kernel interpolation, and
structure exploiting algebra. The key idea of our method is to use Tensor Train
decomposition ... | 1 | 0 | 0 | 1 | 0 | 0 |
Some preliminary results on the set of principal congruences of a finite lattice | In the second edition of the congruence lattice book, Problem 22.1 asks for a
characterization of subsets $Q$ of a finite distributive lattice $D$ such that
there is a finite lattice $L$ whose congruence lattice is isomorphic to $D$ and
under this isomorphism $Q$ corresponds the the principal congruences of $L$. In
t... | 0 | 0 | 1 | 0 | 0 | 0 |
Extracting 3D Vascular Structures from Microscopy Images using Convolutional Recurrent Networks | Vasculature is known to be of key biological significance, especially in the
study of cancer. As such, considerable effort has been focused on the automated
measurement and analysis of vasculature in medical and pre-clinical images. In
tumors in particular, the vascular networks may be extremely irregular and the
app... | 1 | 0 | 0 | 0 | 0 | 0 |
Search for Interstellar LiH in the Milky Way | We report the results of a sensitive search for the 443.952902 GHz $J=1-0$
transition of the LiH molecule toward two interstellar clouds in the Milky Way,
W49N and Sgr B2 (Main), that has been carried out using the Atacama Pathfinder
Experiment (APEX) telescope. The results obtained toward W49N place an upper
limit o... | 0 | 1 | 0 | 0 | 0 | 0 |
Neural Probabilistic Model for Non-projective MST Parsing | In this paper, we propose a probabilistic parsing model, which defines a
proper conditional probability distribution over non-projective dependency
trees for a given sentence, using neural representations as inputs. The neural
network architecture is based on bi-directional LSTM-CNNs which benefits from
both word- an... | 1 | 0 | 0 | 1 | 0 | 0 |
Modularity of complex networks models | Modularity is designed to measure the strength of division of a network into
clusters (known also as communities). Networks with high modularity have dense
connections between the vertices within clusters but sparse connections between
vertices of different clusters. As a result, modularity is often used in
optimizat... | 0 | 0 | 1 | 0 | 0 | 0 |
BOLD5000: A public fMRI dataset of 5000 images | Vision science, particularly machine vision, has been revolutionized by
introducing large-scale image datasets and statistical learning approaches.
Yet, human neuroimaging studies of visual perception still rely on small
numbers of images (around 100) due to time-constrained experimental procedures.
To apply statisti... | 0 | 0 | 0 | 0 | 1 | 0 |
Prospects for gravitational wave astronomy with next generation large-scale pulsar timing arrays | Next generation radio telescopes, namely the Five-hundred-meter Aperture
Spherical Telescope (FAST) and the Square Kilometer Array (SKA), will
revolutionize the pulsar timing arrays (PTAs) based gravitational wave (GW)
searches. We review some of the characteristics of FAST and SKA, and the
resulting PTAs, that are p... | 0 | 1 | 0 | 0 | 0 | 0 |
On Identifiability of Nonnegative Matrix Factorization | In this letter, we propose a new identification criterion that guarantees the
recovery of the low-rank latent factors in the nonnegative matrix factorization
(NMF) model, under mild conditions. Specifically, using the proposed criterion,
it suffices to identify the latent factors if the rows of one factor are
\emph{s... | 1 | 0 | 0 | 1 | 0 | 0 |
Optimal Non-blocking Decentralized Supervisory Control Using G-Control Consistency | Supervisory control synthesis encounters with computational complexity. This
can be reduced by decentralized supervisory control approach. In this paper, we
define intrinsic control consistency for a pair of states of the plant.
G-control consistency (GCC) is another concept which is defined for a natural
projection ... | 1 | 0 | 0 | 0 | 0 | 0 |
Fair mixing: the case of dichotomous preferences | Agents vote to choose a fair mixture of public outcomes; each agent likes or
dislikes each outcome. We discuss three outstanding voting rules. The
Conditional Utilitarian rule, a variant of the random dictator, is
Strategyproof and guarantees to any group of like-minded agents an influence
proportional to its size. I... | 1 | 0 | 0 | 0 | 0 | 0 |
Inverse system characterizations of the (hereditarily) just infinite property in profinite groups | We give criteria on an inverse system of finite groups that ensure the limit
is just infinite or hereditarily just infinite. More significantly, these
criteria are 'universal' in that all (hereditarily) just infinite profinite
groups arise as limits of the specified form.
This is a corrected and revised version of th... | 0 | 0 | 1 | 0 | 0 | 0 |
p-FP: Extraction, Classification, and Prediction of Website Fingerprints with Deep Learning | Recent advances in learning Deep Neural Network (DNN) architectures have
received a great deal of attention due to their ability to outperform
state-of-the-art classifiers across a wide range of applications, with little
or no feature engineering. In this paper, we broadly study the applicability of
deep learning to ... | 1 | 0 | 0 | 1 | 0 | 0 |
Equal confidence weighted expectation value estimates | In this article the issues are discussed with the Bayesian approach,
least-square fits, and most-likely fits. Trying to counter these issues, a
method, based on weighted confidence, is proposed for estimating probabilities
and other observables. This method sums over different model parameter
combinations but does no... | 0 | 0 | 1 | 1 | 0 | 0 |
Protein Folding and Machine Learning: Fundamentals | In spite of decades of research, much remains to be discovered about folding:
the detailed structure of the initial (unfolded) state, vestigial folding
instructions remaining only in the unfolded state, the interaction of the
molecule with the solvent, instantaneous power at each point within the
molecule during fold... | 0 | 0 | 0 | 0 | 1 | 0 |
Discrete configuration spaces of squares and hexagons | We consider generalizations of the familiar fifteen-piece sliding puzzle on
the 4 by 4 square grid. On larger grids with more pieces and more holes,
asymptotically how fast can we move the puzzle into the solved state? We also
give a variation with sliding hexagons. The square puzzles and the hexagon
puzzles are both... | 1 | 0 | 1 | 0 | 0 | 0 |
On the non commutative Iwasawa main conjecture for abelian varieties over function fields | We establish the Iwasawa main conjecture for semi-stable abelian varieties
over a function field of characteristic $p$ under certain restrictive
assumptions. Namely we consider $p$-torsion free $p$-adic Lie extensions of the
base field which contain the constant $\mathbb Z_p$-extension and are
everywhere unramified. ... | 0 | 0 | 1 | 0 | 0 | 0 |
Absorption and Emission Probabilities of Electrons in Electric and Magnetic Fields for FEL | We consider induced emission of ultrarelativistic electrons in strong
electric (magnetic) fields that are uniform along the direction of the electron
motion and are not uniform in the transverse direction. The stimulated
absorption and emission probabilities are found in such system.
| 0 | 1 | 0 | 0 | 0 | 0 |
Design, Development and Evaluation of a UAV to Study Air Quality in Qatar | Measuring gases for air quality monitoring is a challenging task that claims
a lot of time of observation and large numbers of sensors. The aim of this
project is to develop a partially autonomous unmanned aerial vehicle (UAV)
equipped with sensors, in order to monitor and collect air quality real time
data in design... | 1 | 0 | 0 | 0 | 0 | 0 |
Gaussian Process bandits with adaptive discretization | In this paper, the problem of maximizing a black-box function $f:\mathcal{X}
\to \mathbb{R}$ is studied in the Bayesian framework with a Gaussian Process
(GP) prior. In particular, a new algorithm for this problem is proposed, and
high probability bounds on its simple and cumulative regret are established.
The query ... | 1 | 0 | 0 | 1 | 0 | 0 |
Conditional Time Series Forecasting with Convolutional Neural Networks | We present a method for conditional time series forecasting based on an
adaptation of the recent deep convolutional WaveNet architecture. The proposed
network contains stacks of dilated convolutions that allow it to access a broad
range of history when forecasting, a ReLU activation function and conditioning
is perfo... | 0 | 0 | 0 | 1 | 0 | 0 |
Automated Assistants to Identify and Prompt Action on Visual News Bias | Bias is a common problem in today's media, appearing frequently in text and
in visual imagery. Users on social media websites such as Twitter need better
methods for identifying bias. Additionally, activists --those who are motivated
to effect change related to some topic, need better methods to identify and
countera... | 1 | 0 | 0 | 0 | 0 | 0 |
A Matched Filter Technique For Slow Radio Transient Detection And First Demonstration With The Murchison Widefield Array | Many astronomical sources produce transient phenomena at radio frequencies,
but the transient sky at low frequencies (<300 MHz) remains relatively
unexplored. Blind surveys with new widefield radio instruments are setting
increasingly stringent limits on the transient surface density on various
timescales. Although m... | 0 | 1 | 0 | 0 | 0 | 0 |
Shape and Energy Consistent Pseudopotentials for Correlated Electron systems | A method is developed for generating pseudopotentials for use in
correlated-electron calculations. The paradigms of shape and energy consistency
are combined and defined in terms of correlated-electron wave-functions. The
resulting energy consistent correlated electron pseudopotentials (eCEPPs) are
constructed for H,... | 0 | 1 | 0 | 0 | 0 | 0 |
Bayes model selection | We offer a general Bayes theoretic framework to tackle the model selection
problem under a two-step prior design: the first-step prior serves to assess
the model selection uncertainty, and the second-step prior quantifies the prior
belief on the strength of the signals within the model chosen from the first
step.
We ... | 0 | 0 | 1 | 1 | 0 | 0 |
Phase Congruency Parameter Optimization for Enhanced Detection of Image Features for both Natural and Medical Applications | Following the presentation and proof of the hypothesis that image features
are particularly perceived at points where the Fourier components are maximally
in phase, the concept of phase congruency (PC) is introduced. Subsequently, a
two-dimensional multi-scale phase congruency (2D-MSPC) is developed, which has
been a... | 1 | 0 | 1 | 0 | 0 | 0 |
Community structure detection and evaluation during the pre- and post-ictal hippocampal depth recordings | Detecting and evaluating regions of brain under various circumstances is one
of the most interesting topics in computational neuroscience. However, the
majority of the studies on detecting communities of a functional connectivity
network of the brain is done on networks obtained from coherency attributes,
and not fro... | 1 | 0 | 0 | 0 | 1 | 0 |
Shapley effects for sensitivity analysis with correlated inputs: comparisons with Sobol' indices, numerical estimation and applications | The global sensitivity analysis of a numerical model aims to quantify, by
means of sensitivity indices estimate, the contributions of each uncertain
input variable to the model output uncertainty. The so-called Sobol' indices,
which are based on the functional variance analysis, present a difficult
interpretation in ... | 0 | 0 | 1 | 1 | 0 | 0 |
Ridesourcing Car Detection by Transfer Learning | Ridesourcing platforms like Uber and Didi are getting more and more popular
around the world. However, unauthorized ridesourcing activities taking
advantages of the sharing economy can greatly impair the healthy development of
this emerging industry. As the first step to regulate on-demand ride services
and eliminate... | 1 | 0 | 0 | 1 | 0 | 0 |
A Semantic Cross-Species Derived Data Management Application | Managing dynamic information in large multi-site, multi-species, and
multi-discipline consortia is a challenging task for data management
applications. Often in academic research studies the goals for informatics
teams are to build applications that provide extract-transform-load (ETL)
functionality to archive and ca... | 1 | 0 | 0 | 0 | 0 | 0 |
Retrosynthetic reaction prediction using neural sequence-to-sequence models | We describe a fully data driven model that learns to perform a retrosynthetic
reaction prediction task, which is treated as a sequence-to-sequence mapping
problem. The end-to-end trained model has an encoder-decoder architecture that
consists of two recurrent neural networks, which has previously shown great
success ... | 1 | 0 | 0 | 1 | 0 | 0 |
Redshift, metallicity and size of two extended dwarf Irregular galaxies. A link between dwarf Irregulars and Ultra Diffuse Galaxies? | We present the results of the spectroscopic and photometric follow-up of two
field galaxies that were selected as possible stellar counterparts of local
high velocity clouds. Our analysis shows that the two systems are distant (D>20
Mpc) dwarf irregular galaxies unrelated to the local HI clouds. However, the
newly de... | 0 | 1 | 0 | 0 | 0 | 0 |
Collapsing hyperkähler manifolds | Given a projective hyperkahler manifold with a holomorphic Lagrangian
fibration, we prove that hyperkahler metrics with volume of the torus fibers
shrinking to zero collapse in the Gromov-Hausdorff sense (and smoothly away
from the singular fibers) to a compact metric space which is a half-dimensional
special Kahler ... | 0 | 0 | 1 | 0 | 0 | 0 |
Fast amortized inference of neural activity from calcium imaging data with variational autoencoders | Calcium imaging permits optical measurement of neural activity. Since
intracellular calcium concentration is an indirect measurement of neural
activity, computational tools are necessary to infer the true underlying
spiking activity from fluorescence measurements. Bayesian model inversion can
be used to solve this pr... | 1 | 0 | 0 | 1 | 0 | 0 |
Hidden multiparticle excitation in weakly interacting Bose-Einstein Condensate | We investigate multiparticle excitation effect on a collective density
excitation as well as a single-particle excitation in a weakly interacting
Bose--Einstein condensate (BEC). We find that although the weakly interacting
BEC offers weak multiparticle excitation spectrum at low temperatures, this
multiparticle exci... | 0 | 1 | 0 | 0 | 0 | 0 |
Hausdorff Measure: Lost in Translation | In the present article we describe how one can define Hausdorff measure
allowing empty elements in coverings, and using infinite countable coverings
only. In addition, we discuss how the use of different nonequivalent
interpretations of the notion "countable set", that is typical for classical
and modern mathematics,... | 0 | 0 | 1 | 0 | 0 | 0 |
Orthogonalized ALS: A Theoretically Principled Tensor Decomposition Algorithm for Practical Use | The popular Alternating Least Squares (ALS) algorithm for tensor
decomposition is efficient and easy to implement, but often converges to poor
local optima---particularly when the weights of the factors are non-uniform. We
propose a modification of the ALS approach that is as efficient as standard
ALS, but provably r... | 1 | 0 | 0 | 1 | 0 | 0 |
Domain Generalization by Marginal Transfer Learning | Domain generalization is the problem of assigning class labels to an
unlabeled test data set, given several labeled training data sets drawn from
similar distributions. This problem arises in several applications where data
distributions fluctuate because of biological, technical, or other sources of
variation. We de... | 0 | 0 | 0 | 1 | 0 | 0 |
(Non-)formality of the extended Swiss Cheese operads | We study two colored operads of configurations of little $n$-disks in a unit
$n$-disk, with the centers of the small disks of one color restricted to an
$m$-plane, $m<n$. We compute the rational homotopy type of these \emph{extended
Swiss Cheese operads} and show how they are connected to the rational homotopy
types ... | 0 | 0 | 1 | 0 | 0 | 0 |
Pricing options and computing implied volatilities using neural networks | This paper proposes a data-driven approach, by means of an Artificial Neural
Network (ANN), to value financial options and to calculate implied volatilities
with the aim of accelerating the corresponding numerical methods. With ANNs
being universal function approximators, this method trains an optimized ANN on
a data... | 1 | 0 | 0 | 0 | 0 | 1 |
Effect of magnetization on the tunneling anomaly in compressible quantum Hall states | Tunneling of electrons into a two-dimensional electron system is known to
exhibit an anomaly at low bias, in which the tunneling conductance vanishes due
to a many-body interaction effect. Recent experiments have measured this
anomaly between two copies of the half-filled Landau level as a function of
in-plane magnet... | 0 | 1 | 0 | 0 | 0 | 0 |
Learning to Acquire Information | We consider the problem of diagnosis where a set of simple observations are
used to infer a potentially complex hidden hypothesis. Finding the optimal
subset of observations is intractable in general, thus we focus on the problem
of active diagnosis, where the agent selects the next most-informative
observation based... | 1 | 0 | 0 | 1 | 0 | 0 |
How hard is it to cross the room? -- Training (Recurrent) Neural Networks to steer a UAV | This work explores the feasibility of steering a drone with a (recurrent)
neural network, based on input from a forward looking camera, in the context of
a high-level navigation task. We set up a generic framework for training a
network to perform navigation tasks based on imitation learning. It can be
applied to bot... | 1 | 0 | 0 | 0 | 0 | 0 |
Range-efficient consistent sampling and locality-sensitive hashing for polygons | Locality-sensitive hashing (LSH) is a fundamental technique for similarity
search and similarity estimation in high-dimensional spaces. The basic idea is
that similar objects should produce hash collisions with probability
significantly larger than objects with low similarity. We consider LSH for
objects that can be ... | 1 | 0 | 0 | 0 | 0 | 0 |
Decoupled Greedy Learning of CNNs | A commonly cited inefficiency of neural network training by back-propagation
is the update locking problem: each layer must wait for the signal to propagate
through the network before updating. We consider and analyze a training
procedure, Decoupled Greedy Learning (DGL), that addresses this problem more
effectively ... | 1 | 0 | 0 | 1 | 0 | 0 |
Discrete time Pontryagin maximum principle for optimal control problems under state-action-frequency constraints | We establish a Pontryagin maximum principle for discrete time optimal control
problems under the following three types of constraints: a) constraints on the
states pointwise in time, b) constraints on the control actions pointwise in
time, and c) constraints on the frequency spectrum of the optimal control
trajectori... | 1 | 0 | 1 | 0 | 0 | 0 |
Quantitative evaluation of an active Chemotaxis model in Discrete time | A system of $N$ particles in a chemical medium in $\mathbb{R}^{d}$ is studied
in a discrete time setting. Underlying interacting particle system in
continuous time can be expressed as \begin{eqnarray} dX_{i}(t)
&=&[-(I-A)X_{i}(t) + \bigtriangledown h(t,X_{i}(t))]dt + dW_{i}(t), \,\,
X_{i}(0)=x_{i}\in \mathbb{R}^{d}\,... | 0 | 0 | 1 | 0 | 0 | 0 |
Deep Learning the Ising Model Near Criticality | It is well established that neural networks with deep architectures perform
better than shallow networks for many tasks in machine learning. In statistical
physics, while there has been recent interest in representing physical data
with generative modelling, the focus has been on shallow neural networks. A
natural qu... | 1 | 1 | 0 | 1 | 0 | 0 |
A supervised approach to time scale detection in dynamic networks | For any stream of time-stamped edges that form a dynamic network, an
important choice is the aggregation granularity that an analyst uses to bin the
data. Picking such a windowing of the data is often done by hand, or left up to
the technology that is collecting the data. However, the choice can make a big
difference... | 1 | 0 | 0 | 0 | 0 | 0 |
Binarized octree generation for Cartesian adaptive mesh refinement around immersed geometries | We revisit the generation of balanced octrees for adaptive mesh refinement
(AMR) of Cartesian domains with immersed complex geometries. In a recent short
note [Hasbestan and Senocak, J. Comput. Phys. vol. 351:473-477 (2017)], we
showed that the data-locality of the Z-order curve in hashed linear octree
generation met... | 1 | 1 | 0 | 0 | 0 | 0 |
Adversarial Examples: Opportunities and Challenges | With the advent of the era of artificial intelligence(AI), deep neural
networks (DNNs) have shown huge superiority over human in image recognition,
speech processing, autonomous vehicles and medical diagnosis. However, recent
studies indicate that DNNs are vulnerable to adversarial examples (AEs) which
are designed b... | 0 | 0 | 0 | 1 | 0 | 0 |
Decoupling Learning Rules from Representations | In the artificial intelligence field, learning often corresponds to changing
the parameters of a parameterized function. A learning rule is an algorithm or
mathematical expression that specifies precisely how the parameters should be
changed. When creating an artificial intelligence system, we must make two
decisions... | 1 | 0 | 0 | 1 | 0 | 0 |
Schur P-positivity and involution Stanley symmetric functions | The involution Stanley symmetric functions $\hat{F}_y$ are the stable limits
of the analogues of Schubert polynomials for the orbits of the orthogonal group
in the flag variety. These symmetric functions are also generating functions
for involution words, and are indexed by the involutions in the symmetric
group. By ... | 0 | 0 | 1 | 0 | 0 | 0 |
A Riemannian gossip approach to subspace learning on Grassmann manifold | In this paper, we focus on subspace learning problems on the Grassmann
manifold. Interesting applications in this setting include low-rank matrix
completion and low-dimensional multivariate regression, among others. Motivated
by privacy concerns, we aim to solve such problems in a decentralized setting
where multiple... | 1 | 0 | 1 | 0 | 0 | 0 |
Space-Valued Diagrams, Type-Theoretically (Extended Abstract) | Topologists are sometimes interested in space-valued diagrams over a given
index category, but it is tricky to say what such a diagram even is if we look
for a notion that is stable under equivalence. The same happens in (homotopy)
type theory, where it is known only for special cases how one can define a type
of typ... | 1 | 0 | 1 | 0 | 0 | 0 |
Properties of cyanobacterial UV-absorbing pigments suggest their evolution was driven by optimizing photon dissipation rather than photoprotection | An ancient repertoire of UV absorbing pigments which survive today in the
phylogenetically oldest extant photosynthetic organisms the cyanobacteria point
to a direction in evolutionary adaptation of the pigments and their associated
biota from largely UVC absorbing pigments in the Archean to pigments covering
ever mo... | 0 | 1 | 0 | 0 | 0 | 0 |
Output Impedance Diffusion into Lossy Power Lines | Output impedances are inherent elements of power sources in the electrical
grids. In this paper, we give an answer to the following question: What is the
effect of output impedances on the inductivity of the power network? To address
this question, we propose a measure to evaluate the inductivity of a power
grid, and... | 1 | 0 | 0 | 0 | 0 | 0 |
Enhancing the significance of gravitational wave bursts through signal classification | The quest to observe gravitational waves challenges our ability to
discriminate signals from detector noise. This issue is especially relevant for
transient gravitational waves searches with a robust eyes wide open approach,
the so called all- sky burst searches. Here we show how signal classification
methods inspire... | 0 | 1 | 0 | 0 | 0 | 0 |
Model-based Clustering with Sparse Covariance Matrices | Finite Gaussian mixture models are widely used for model-based clustering of
continuous data. Nevertheless, since the number of model parameters scales
quadratically with the number of variables, these models can be easily
over-parameterized. For this reason, parsimonious models have been developed
via covariance mat... | 0 | 0 | 0 | 1 | 0 | 0 |
An Assessment of Data Transfer Performance for Large-Scale Climate Data Analysis and Recommendations for the Data Infrastructure for CMIP6 | We document the data transfer workflow, data transfer performance, and other
aspects of staging approximately 56 terabytes of climate model output data from
the distributed Coupled Model Intercomparison Project (CMIP5) archive to the
National Energy Research Supercomputing Center (NERSC) at the Lawrence Berkeley
Nati... | 1 | 1 | 0 | 0 | 0 | 0 |
Generalization for Adaptively-chosen Estimators via Stable Median | Datasets are often reused to perform multiple statistical analyses in an
adaptive way, in which each analysis may depend on the outcomes of previous
analyses on the same dataset. Standard statistical guarantees do not account
for these dependencies and little is known about how to provably avoid
overfitting and false... | 1 | 0 | 0 | 1 | 0 | 0 |
Automated Problem Identification: Regression vs Classification via Evolutionary Deep Networks | Regression or classification? This is perhaps the most basic question faced
when tackling a new supervised learning problem. We present an Evolutionary
Deep Learning (EDL) algorithm that automatically solves this by identifying the
question type with high accuracy, along with a proposed deep architecture.
Typically, ... | 1 | 0 | 0 | 1 | 0 | 0 |
Attribution of extreme rainfall in Southeast China during May 2015 | Anthropogenic climate change increased the probability that a short-duration,
intense rainfall event would occur in parts of southeast China. This type of
event occurred in May 2015, causing serious flooding.
| 0 | 1 | 0 | 0 | 0 | 0 |
The Kellogg property and boundary regularity for p-harmonic functions with respect to the Mazurkiewicz boundary and other compactifications | In this paper boundary regularity for p-harmonic functions is studied with
respect to the Mazurkiewicz boundary and other compactifications. In
particular, the Kellogg property (which says that the set of irregular boundary
points has capacity zero) is obtained for a large class of compactifications,
but also two exa... | 0 | 0 | 1 | 0 | 0 | 0 |
Nonparametric Inference via Bootstrapping the Debiased Estimator | In this paper, we propose to construct confidence bands by bootstrapping the
debiased kernel density estimator (for density estimation) and the debiased
local polynomial regression estimator (for regression analysis). The idea of
using a debiased estimator was first introduced in Calonico et al. (2015),
where they co... | 0 | 0 | 1 | 1 | 0 | 0 |
Solving constraint-satisfaction problems with distributed neocortical-like neuronal networks | Finding actions that satisfy the constraints imposed by both external inputs
and internal representations is central to decision making. We demonstrate that
some important classes of constraint satisfaction problems (CSPs) can be solved
by networks composed of homogeneous cooperative-competitive modules that have
con... | 0 | 0 | 0 | 0 | 1 | 0 |
Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks | A major challenge in brain tumor treatment planning and quantitative
evaluation is determination of the tumor extent. The noninvasive magnetic
resonance imaging (MRI) technique has emerged as a front-line diagnostic tool
for brain tumors without ionizing radiation. Manual segmentation of brain tumor
extent from 3D MR... | 1 | 0 | 0 | 0 | 0 | 0 |
Asymptotic Blind-spot Analysis of Localization Networks under Correlated Blocking using a Poisson Line Process | In a localization network, the line-of-sight between anchors (transceivers)
and targets may be blocked due to the presence of obstacles in the environment.
Due to the non-zero size of the obstacles, the blocking is typically correlated
across both anchor and target locations, with the extent of correlation
increasing... | 1 | 0 | 0 | 0 | 0 | 0 |
The relation between galaxy morphology and colour in the EAGLE simulation | We investigate the relation between kinematic morphology, intrinsic colour
and stellar mass of galaxies in the EAGLE cosmological hydrodynamical
simulation. We calculate the intrinsic u-r colours and measure the fraction of
kinetic energy invested in ordered corotation of 3562 galaxies at z=0 with
stellar masses larg... | 0 | 1 | 0 | 0 | 0 | 0 |
An alternative to continuous univariate distributions supported on a bounded interval: The BMT distribution | In this paper, we introduce the BMT distribution as an unimodal alternative
to continuous univariate distributions supported on a bounded interval. The
ideas behind the mathematical formulation of this new distribution come from
computer aid geometric design, specifically from Bezier curves. First, we
review general ... | 0 | 0 | 1 | 1 | 0 | 0 |
Deep Object Centric Policies for Autonomous Driving | While learning visuomotor skills in an end-to-end manner is appealing, deep
neural networks are often uninterpretable and fail in surprising ways. For
robotics tasks, such as autonomous driving, models that explicitly represent
objects may be more robust to new scenes and provide intuitive visualizations.
We describe... | 1 | 0 | 0 | 0 | 0 | 0 |
A Search for Laser Emission with Megawatt Thresholds from 5600 FGKM Stars | We searched high resolution spectra of 5600 nearby stars for emission lines
that are both inconsistent with a natural origin and unresolved spatially, as
would be expected from extraterrestrial optical lasers. The spectra were
obtained with the Keck 10-meter telescope, including light coming from within
0.5 arcsec of... | 0 | 1 | 0 | 0 | 0 | 0 |
Learning Large Scale Ordinary Differential Equation Systems | Learning large scale nonlinear ordinary differential equation (ODE) systems
from data is known to be computationally and statistically challenging. We
present a framework together with the adaptive integral matching (AIM)
algorithm for learning polynomial or rational ODE systems with a sparse network
structure. The f... | 0 | 0 | 1 | 1 | 0 | 0 |
Linear Time Clustering for High Dimensional Mixtures of Gaussian Clouds | Clustering mixtures of Gaussian distributions is a fundamental and
challenging problem that is ubiquitous in various high-dimensional data
processing tasks. While state-of-the-art work on learning Gaussian mixture
models has focused primarily on improving separation bounds and their
generalization to arbitrary classe... | 1 | 0 | 0 | 0 | 0 | 0 |
Estimation of Relationship between Stimulation Current and Force Exerted during Isometric Contraction | In this study, we developed a method to estimate the relationship between
stimulation current and volatility during isometric contraction. In functional
electrical stimulation (FES), joints are driven by applying voltage to muscles.
This technology has been used for a long time in the field of rehabilitation,
and rec... | 0 | 0 | 0 | 0 | 1 | 0 |
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