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Adaptive Stimulus Selection in ERP-Based Brain-Computer Interfaces by Maximizing Expected Discrimination Gain | Brain-computer interfaces (BCIs) can provide an alternative means of
communication for individuals with severe neuromuscular limitations. The
P300-based BCI speller relies on eliciting and detecting transient
event-related potentials (ERPs) in electroencephalography (EEG) data, in
response to a user attending to rarely occurring target stimuli amongst a
series of non-target stimuli. However, in most P300 speller implementations,
the stimuli to be presented are randomly selected from a limited set of options
and stimulus selection and presentation are not optimized based on previous
user data. In this work, we propose a data-driven method for stimulus selection
based on the expected discrimination gain metric. The data-driven approach
selects stimuli based on previously observed stimulus responses, with the aim
of choosing a set of stimuli that will provide the most information about the
user's intended target character. Our approach incorporates knowledge of
physiological and system constraints imposed due to real-time BCI
implementation. Simulations were performed to compare our stimulus selection
approach to the row-column paradigm, the conventional stimulus selection method
for P300 spellers. Results from the simulations demonstrated that our adaptive
stimulus selection approach has the potential to significantly improve
performance from the conventional method: up to 34% improvement in accuracy and
43% reduction in the mean number of stimulus presentations required to spell a
character in a 72-character grid. In addition, our greedy approach to stimulus
selection provides the flexibility to accommodate design constraints.
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Bursting dynamics of viscous film without circular symmetry: the effect of confinement | We experimentally investigate the bursting dynamics of confined liquid film
suspended in air and find a viscous dynamics distinctly different from the
non-confined counterpart, due to lack of circular symmetry in the shape of
expanding hole: the novel confined-viscous bursting proceeds at a constant
speed and a rim formed at the bursting tip does not grow. We find a
confined-viscous to confined-inertial crossover, as well as a
nonconfined-inertial to confined-inertial crossover, at which bursting speed
does not change although the circular symmetry in the hole shape breaks
dynamically.
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Low-energy electron-positron collider to search and study (μ^+μ^-) bound state | We discuss a low energy $e^+e^-$ collider for production of the not yet
observed ($\mu^+\mu^-$) bound system (dimuonium). Collider with large crossing
angle for $e^+e^-$ beams intersection produces dimuonium with non-zero
momentum, therefore, its decay point is shifted from the beam collision area
providing effective suppression of the elastic $e^+e^-$ scattering background.
The experimental constraints define subsequent collider specifications. We show
preliminary layout of the accelerator and obtained main parameters. High
luminosity in chosen beam energy range allows to study $\pi^\pm$ and $\eta$
-mesons.
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Følner functions and the generic Word Problem for finitely generated amenable groups | We introduce and investigate different definitions of effective amenability,
in terms of computability of F{\o}lner sets, Reiter functions, and F{\o}lner
functions. As a consequence, we prove that recursively presented amenable
groups have subrecursive F{\o}lner function, answering a question of Gromov,
for the same class of groups we prove that solvability of the Equality Problem
on a generic set (generic EP) is equivalent to solvability of the Word Problem
on the whole group (WP), thus providing the first examples of finitely
presented groups with unsolvable generic EP. In particular, we prove that for
finitely presented groups, solvability of generic WP doesn't imply solvability
of generic EP.
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Differences between Health Related News Articles from Reliable and Unreliable Media | In this study, we examine a collection of health-related news articles
published by reliable and unreliable media outlets. Our analysis shows that
there are structural, topical, and semantic differences in the way reliable and
unreliable media outlets conduct health journalism. We argue that the findings
from this study will be useful for combating health disinformation problem.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Numerical Study of Carr and Lee's Correlation Immunization Strategy for Volatility Derivatives | In their seminal work `Robust Replication of Volatility Derivatives,' Carr
and Lee show how to robustly price and replicate a variety of claims written on
the quadratic variation of a risky asset under the assumption that the asset's
volatility process is independent of the Brownian motion that drives the
asset's price. Additionally, they propose a correlation immunization method
that minimizes the pricing and hedging error that results when the correlation
between the risky asset's price and volatility is nonzero. In this paper, we
perform a number of Monte Carlo experiments to test the effectiveness of Carr
and Lee's immunization strategy. Our results indicate that the correlation
immunization method is an effective means of reducing pricing and hedging
errors that result from nonzero correlation.
| 0 | 0 | 0 | 0 | 0 | 1 |
Functional renormalization group study of parallel double quantum dots: Effects of asymmetric dot-lead couplings | We explore the effects of asymmetry of hopping parameters between double
parallel quantum dots and the leads on the conductance and a possibility of
local magnetic moment formation in this system using functional renormalization
group approach with the counterterm. We demonstrate a possibility of a quantum
phase transition to a local moment regime (so called singular Fermi liquid
(SFL) state) for various types of hopping asymmetries and discuss respective
gate voltage dependences of the conductance. It is shown, that depending on the
type of the asymmetry, the system can demonstrate either a first order quantum
phase transition to SFL state, accompanied by a discontinuous change of the
conductance, similarly to the symmetric case, or the second order quantum phase
transition, in which the conductance is continuous and exhibits Fano-type
asymmetric resonance near the transition point. A semi-analytical explanation
of these different types of conductance behavior is presented.
| 0 | 1 | 0 | 0 | 0 | 0 |
Phenotype-based and Self-learning Inter-individual Sleep Apnea Screening with a Level IV Monitoring System | Purpose: We propose a phenotype-based artificial intelligence system that can
self-learn and is accurate for screening purposes, and test it on a Level IV
monitoring system. Methods: Based on the physiological knowledge, we
hypothesize that the phenotype information will allow us to find subjects from
a well-annotated database that share similar sleep apnea patterns. Therefore,
for a new-arriving subject, we can establish a prediction model from the
existing database that is adaptive to the subject. We test the proposed
algorithm on a database consisting of 62 subjects with the signals recorded
from a Level IV wearable device measuring the thoracic and abdominal movements
and the SpO2. Results: With the leave-one cross validation, the accuracy of the
proposed algorithm to screen subjects with an apnea-hypopnea index greater or
equal to 15 is 93.6%, the positive likelihood ratio is 6.8, and the negative
likelihood ratio is 0.03. Conclusion: The results confirm the hypothesis and
show that the proposed algorithm has great potential to screen patients with
SAS.
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Generative adversarial network-based approach to signal reconstruction from magnitude spectrograms | In this paper, we address the problem of reconstructing a time-domain signal
(or a phase spectrogram) solely from a magnitude spectrogram. Since magnitude
spectrograms do not contain phase information, we must restore or infer phase
information to reconstruct a time-domain signal. One widely used approach for
dealing with the signal reconstruction problem was proposed by Griffin and Lim.
This method usually requires many iterations for the signal reconstruction
process and depending on the inputs, it does not always produce high-quality
audio signals. To overcome these shortcomings, we apply a learning-based
approach to the signal reconstruction problem by modeling the signal
reconstruction process using a deep neural network and training it using the
idea of a generative adversarial network. Experimental evaluations revealed
that our method was able to reconstruct signals faster with higher quality than
the Griffin-Lim method.
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The Braid Shelf | The braids of $B\_\infty$ can be equipped with a selfdistributive operation
$\mathbin{\triangleright}$ enjoying a number of deep properties. This text is a
survey of known properties and open questions involving this structure, its
quotients, and its extensions.
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Ultra-Fast Relaxation, Decoherence and Localization of Photoexcited States in $π$-Conjugated Polymers: A TEBD Study | The exciton relaxation dynamics of photoexcited electronic states in
poly($p$-phenylenevinylene) (PPV) are theoretically investigated within a
coarse-grained model, in which both the exciton and nuclear degrees of freedom
are treated quantum mechanically. The Frenkel-Holstein Hamiltonian is used to
describe the strong exciton-phonon coupling present in the system, while
external damping of the internal nuclear degrees of freedom are accounted for
by a Lindblad master equation. Numerically, the dynamics are computed using the
time evolving block decimation (TEBD) and quantum jump trajectory techniques.
The values of the model parameters physically relevant to polymer systems
naturally lead to a separation of time scales, with the ultra-fast dynamics
corresponding to energy transfer from the exciton to the internal phonon modes
(i.e., the C-C bond oscillations), while the longer time dynamics correspond to
damping of these phonon modes by the external dissipation. Associated with
these time scales, we investigate the following processes that are indicative
of the system relaxing onto the emissive chromophores of the polymer: 1)
Exciton-polaron formation occurs on an ultra-fast time scale, with the
associated exciton-phonon correlations present within half a vibrational time
period of the C-C bond oscillations. 2) Exciton decoherence is driven by the
decay in the vibrational overlaps associated with exciton-polaron formation,
occurring on the same time scale. 3) Exciton density localization is driven by
the external dissipation, arising from `wavefunction collapse' occurring as a
result of the system-environment interactions. Finally, we show how
fluorescence anisotropy measurements can be used to investigate the exciton
decoherence process during the relaxation dynamics.
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Towards Reverse-Engineering Black-Box Neural Networks | Many deployed learned models are black boxes: given input, returns output.
Internal information about the model, such as the architecture, optimisation
procedure, or training data, is not disclosed explicitly as it might contain
proprietary information or make the system more vulnerable. This work shows
that such attributes of neural networks can be exposed from a sequence of
queries. This has multiple implications. On the one hand, our work exposes the
vulnerability of black-box neural networks to different types of attacks -- we
show that the revealed internal information helps generate more effective
adversarial examples against the black box model. On the other hand, this
technique can be used for better protection of private content from automatic
recognition models using adversarial examples. Our paper suggests that it is
actually hard to draw a line between white box and black box models.
| 1 | 0 | 0 | 1 | 0 | 0 |
Learning Dexterous In-Hand Manipulation | We use reinforcement learning (RL) to learn dexterous in-hand manipulation
policies which can perform vision-based object reorientation on a physical
Shadow Dexterous Hand. The training is performed in a simulated environment in
which we randomize many of the physical properties of the system like friction
coefficients and an object's appearance. Our policies transfer to the physical
robot despite being trained entirely in simulation. Our method does not rely on
any human demonstrations, but many behaviors found in human manipulation emerge
naturally, including finger gaiting, multi-finger coordination, and the
controlled use of gravity. Our results were obtained using the same distributed
RL system that was used to train OpenAI Five. We also include a video of our
results: this https URL
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Index transforms with Weber type kernels | New index transforms with Weber type kernels, consisting of products of
Bessel functions of the first and second kind are investigated. Mapping
properties and inversion formulas are established for these transforms in
Lebesgue spaces. The results are applied to solve a boundary value problem on
the wedge for a fourth order partial differential equation.
| 0 | 0 | 1 | 0 | 0 | 0 |
Computationally Efficient Robust Estimation of Sparse Functionals | Many conventional statistical procedures are extremely sensitive to seemingly
minor deviations from modeling assumptions. This problem is exacerbated in
modern high-dimensional settings, where the problem dimension can grow with and
possibly exceed the sample size. We consider the problem of robust estimation
of sparse functionals, and provide a computationally and statistically
efficient algorithm in the high-dimensional setting. Our theory identifies a
unified set of deterministic conditions under which our algorithm guarantees
accurate recovery. By further establishing that these deterministic conditions
hold with high-probability for a wide range of statistical models, our theory
applies to many problems of considerable interest including sparse mean and
covariance estimation; sparse linear regression; and sparse generalized linear
models.
| 1 | 0 | 0 | 1 | 0 | 0 |
Resource Sharing Among mmWave Cellular Service Providers in a Vertically Differentiated Duopoly | With the increasing interest in the use of millimeter wave bands for 5G
cellular systems comes renewed interest in resource sharing. Properties of
millimeter wave bands such as massive bandwidth, highly directional antennas,
high penetration loss, and susceptibility to shadowing, suggest technical
advantages to spectrum and infrastructure sharing in millimeter wave cellular
networks. However, technical advantages do not necessarily translate to
increased profit for service providers, or increased consumer surplus. In this
paper, detailed network simulations are used to better understand the economic
implications of resource sharing in a vertically differentiated duopoly market
for cellular service. The results suggest that resource sharing is less often
profitable for millimeter wave service providers compared to microwave cellular
service providers, and does not necessarily increase consumer surplus.
| 1 | 0 | 0 | 0 | 0 | 0 |
How to Generate Pseudorandom Permutations Over Other Groups: Even-Mansour and Feistel Revisited | Recent results by Alagic and Russell have given some evidence that the
Even-Mansour cipher may be secure against quantum adversaries with quantum
queries, if considered over other groups than $(\mathbb{Z}/2)^n$. This prompts
the question as to whether or not other classical schemes may be generalized to
arbitrary groups and whether classical results still apply to those generalized
schemes.
In this paper, we generalize the Even-Mansour cipher and the Feistel cipher.
We show that Even and Mansour's original notions of secrecy are obtained on a
one-key, group variant of the Even-Mansour cipher. We generalize the result by
Kilian and Rogaway, that the Even-Mansour cipher is pseudorandom, to super
pseudorandomness, also in the one-key, group case. Using a Slide Attack we
match the bound found above. After generalizing the Feistel cipher to arbitrary
groups we resolve an open problem of Patel, Ramzan, and Sundaram by showing
that the $3$-round Feistel cipher over an arbitrary group is not super
pseudorandom. Finally, we generalize a result by Gentry and Ramzan showing that
the Even-Mansour cipher can be implemented using the Feistel cipher as the
public permutation. In this last result, we also consider the one-key case over
a group and generalize their bound.
| 1 | 0 | 1 | 0 | 0 | 0 |
On Weyl's asymptotics and remainder term for the orthogonal and unitary groups | We examine the asymptotics of the spectral counting function of a compact
Riemannian manifold by V.G.~Avakumovic \cite{Avakumovic} and L.~Hörmander
\cite{Hormander-eigen} and show that for the scale of orthogonal and unitary
groups ${\bf SO}(N)$, ${\bf SU}(N)$, ${\bf U}(N)$ and ${\bf Spin}(N)$ it is not
sharp. While for negative sectional curvature improvements are possible and
known, {\it cf.} e.g., J.J.~Duistermaat $\&$ V.~Guillemin \cite{Duist-Guill},
here, we give sharp and contrasting examples in the positive Ricci curvature
case [non-negative for ${\bf U}(N)$]. Furthermore here the improvements are
sharp and quantitative relating to the dimension and {\it rank} of the group.
We discuss the implications of these results on the closely related problem of
closed geodesics and the length spectrum.
| 0 | 0 | 1 | 0 | 0 | 0 |
Virtual plane-wave imaging via Marchenko redatuming | Marchenko redatuming is a novel scheme used to retrieve up- and down-going
Green's functions in an unknown medium. Marchenko equations are based on
reciprocity theorems and are derived on the assumption of the existence of so
called focusing functions, i.e. functions which exhibit time-space focusing
properties once injected in the subsurface. In contrast to interferometry but
similarly to standard migration methods, Marchenko redatuming only requires an
estimate of the direct wave from the virtual source (or to the virtual
receiver), illumination from only one side of the medium, and no physical
sources (or receivers) inside the medium. In this contribution we consider a
different time-focusing condition within the frame of Marchenko redatuming and
show how this can lead to the retrieval of virtual plane-wave responses, thus
allowing multiple-free imaging using only a 1 dimensional sampling of the
targeted model. The potential of the new method is demonstrated on a 2D
synthetic model.
| 0 | 1 | 0 | 0 | 0 | 0 |
Probing the local nature of excitons and plasmons in few-layer MoS2 | Excitons and plasmons are the two most fundamental types of collective
electronic excitations occurring in solids. Traditionally, they have been
studied separately using bulk techniques that probe their average energetic
structure over large spatial regions. However, as the dimensions of materials
and devices continue to shrink, it becomes crucial to understand how these
excitations depend on local variations in the crystal- and chemical structure
on the atomic scale. Here we use monochromated low-loss
scanning-transmission-electron-microscopy electron-energy-loss (LL-STEM-EEL)
spectroscopy, providing the best simultaneous energy and spatial resolution
achieved to-date to unravel the full set of electronic excitations in few-layer
MoS2 nanosheets over a wide energy range. Using first-principles many-body
calculations we confirm the excitonic nature of the peaks at ~2eV and ~3eV in
the experimental EEL spectrum and the plasmonic nature of higher energy-loss
peaks. We also rationalise the non-trivial dependence of the EEL spectrum on
beam and sample geometry such as the number of atomic layers and distance to
steps and edges. Moreover, we show that the excitonic features are dominated by
the long wavelength (q=0) components of the probing field, while the plasmonic
features are sensitive to a much broader range of q-vectors, indicating a
qualitative difference in the spatial character of the two types of collective
excitations. Our work provides a template protocol for mapping the local nature
of electronic excitations that open new possibilities for studying
photo-absorption and energy transfer processes on a nanometer scale.
| 0 | 1 | 0 | 0 | 0 | 0 |
Flavour composition and entropy increase of cosmological neutrinos after decoherence | We investigate the evolution of the flavour composition of the cosmic
neutrino background from neutrino decoupling until today. The decoherence of
neutrino mass states is described by means of Lindblad operators. Decoherence
goes along with the increase of neutrino family entropy, which we obtain as a
function of initial spectral distortions, mixing angles and CP-violation phase.
We also present the expected flavour composition of the cosmic neutrino
background after decoherence is completed. Decoherence is proposed to happen
after the two heaviest neutrino mass states become non-relativistic. We discuss
how the associated increase of entropy could be observed (in principle). The
physics of two- or three-flavour oscillation of cosmological neutrinos
resembles in many aspects two- or three-level systems in atomic clocks, which
were recently proposed by Weinberg for the study of decoherence phenomena.
| 0 | 1 | 0 | 0 | 0 | 0 |
Random non-Abelian G-circulant matrices. Spectrum of random convolution operators on large finite groups | We analyse the limiting behavior of the eigenvalue and singular value
distribution for random convolution operators on large (not necessarily
Abelian) groups, extending the results by M. Meckes for the Abelian case. We
show that for regular sequences of groups the limiting distribution of
eigenvalues (resp. singular values) is a mixture of eigenvalue (resp. singular
value) distributions of Ginibre matrices with the directing measure being
related to the limiting behavior of the Plancherel measure of the sequence of
groups. In particular for the sequence of symmetric groups, the limiting
distributions are just the circular and quarter circular laws, whereas e.g. for
the dihedral groups the limiting distributions have unbounded supports but are
different than in the Abelian case.
We also prove that under additional assumptions on the sequence of groups (in
particular for symmetric groups of increasing order) families of stochastically
independent random projection operators converge in moments to free circular
elements.
Finally, in the Gaussian case we provide Central Limit Theorems for linear
eigenvalue statistics.
| 0 | 0 | 1 | 0 | 0 | 0 |
Fog Robotics for Efficient, Fluent and Robust Human-Robot Interaction | Active communication between robots and humans is essential for effective
human-robot interaction. To accomplish this objective, Cloud Robotics (CR) was
introduced to make robots enhance their capabilities. It enables robots to
perform extensive computations in the cloud by sharing their outcomes. Outcomes
include maps, images, processing power, data, activities, and other robot
resources. But due to the colossal growth of data and traffic, CR suffers from
serious latency issues. Therefore, it is unlikely to scale a large number of
robots particularly in human-robot interaction scenarios, where responsiveness
is paramount. Furthermore, other issues related to security such as privacy
breaches and ransomware attacks can increase. To address these problems, in
this paper, we have envisioned the next generation of social robotic
architectures based on Fog Robotics (FR) that inherits the strengths of Fog
Computing to augment the future social robotic systems. These new architectures
can escalate the dexterity of robots by shoving the data closer to the robot.
Additionally, they can ensure that human-robot interaction is more responsive
by resolving the problems of CR. Moreover, experimental results are further
discussed by considering a scenario of FR and latency as a primary factor
comparing to CR models.
| 1 | 0 | 0 | 0 | 0 | 0 |
An Estimation of the Star Formation Rate in the Perseus Complex | We present the results of our investigation of the star-forming potential in
the Perseus star-forming complex. We build on previous starless core,
protostellar core, and young stellar object (YSO) catalogs from Spitzer,
Herschel, and SCUBA observations in the literature. We place the cores and YSOs
within seven star-forming clumps based on column densities greater than 5x10^21
cm^-2. We calculate the mean density and free-fall time for 69 starless cores
as 5.55x10^-19 gcm^-3 and 0.1 Myr,respectively, and we estimate the star
formation rate for the near future as 150 Msun Myr^-1. According to Bonnor
Ebert stability analysis, we find that majority of starless cores in Perseus
are unstable. Broadly, these cores can collapse to form the next generation of
stars. We found a relation between starless cores and YSOs, where the numbers
of young protostars (Class 0 + Class I) are similar to the numbers of starless
cores. This similarity, which shows a one-to-one relation, suggests that these
starless cores may form the next generation of stars with approximately the
same formation rate as the current generation, as identified by the Class 0 and
Class I protostars. It follows that if such a relation between starless cores
and any YSO stage exists, the SFR values of these two populations must be
nearly constant. In brief, we propose that this one-to-one relation is an
important factor in better understanding the star formation process within a
cloud.
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Deep Neural Networks - A Brief History | Introduction to deep neural networks and their history.
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An advanced active quenching circuit for ultra-fast quantum cryptography | Commercial photon-counting modules based on actively quenched solid-state
avalanche photodiode sensors are used in a wide variety of applications.
Manufacturers characterize their detectors by specifying a small set of
parameters, such as detection efficiency, dead time, dark counts rate,
afterpulsing probability and single-photon arrival-time resolution (jitter).
However, they usually do not specify the range of conditions over which these
parameters are constant or present a sufficient description of the
characterization process. In this work, we perform a few novel tests on two
commercial detectors and identify an additional set of imperfections that must
be specified to sufficiently characterize their behavior. These include
rate-dependence of the dead time and jitter, detection delay shift, and
"twilighting." We find that these additional non-ideal behaviors can lead to
unexpected effects or strong deterioration of the performance of a system using
these devices. We explain their origin by an in-depth analysis of the active
quenching process. To mitigate the effects of these imperfections, a
custom-built detection system is designed using a novel active quenching
circuit. Its performance is compared against two commercial detectors in a fast
quantum key distribution system with hyper-entangled photons and a random
number generator.
| 1 | 1 | 0 | 0 | 0 | 0 |
Shannon entropy: a study of confined hydrogenic-like atoms | The Shannon entropy in the atomic, molecular and chemical physics context is
presented by using as test cases the hydrogenic-like atoms $H_c$, ${He_c}^+$
and ${Li_c}^{2+}$ confined by an impenetrable spherical box. Novel expressions
for entropic uncertainty relation and Shannon entropies $S_r$ and $S_p$ are
proposed to ensure their physical dimensionless characteristic. The electronic
ground state energy and the quantities $S_r$, $S_p$ and $S_t$ are calculated
for the hydrogenic-like atoms to different confinement radii by using a
variational method. The global behavior of these quantities and different
conjectures are analyzed. The results are compared, when available, with those
previously published.
| 0 | 1 | 0 | 0 | 0 | 0 |
Exploring Halo Substructure with Giant Stars. XV. Discovery of a Connection between the Monoceros Ring and the Triangulum-Andromeda Overdensity? | Thanks to modern sky surveys, over twenty stellar streams and overdensity
structures have been discovered in the halo of the Milky Way. In this paper, we
present an analysis of spectroscopic observations of individual stars from one
such structure, "A13", first identified as an overdensity using the M giant
catalog from the Two Micron All-Sky Survey. Our spectroscopic observations show
that stars identified with A13 have a velocity dispersion of $\lesssim$ 40
$\mathrm{km~s^{-1}}$, implying that it is a genuine coherent structure rather
than a chance super-position of random halo stars. From its position on the
sky, distance ($\sim$15~kpc heliocentric), and kinematical properties, A13 is
likely to be an extension of another low Galactic latitude substructure -- the
Galactic Anticenter Stellar Structure (also known as the Monoceros Ring) --
towards smaller Galactic longitude and farther distance. Furthermore, the
kinematics of A13 also connect it with another structure in the southern
Galactic hemisphere -- the Triangulum-Andromeda overdensity. We discuss these
three connected structures within the context of a previously proposed scenario
that one or all of these features originate from the disk of the Milky Way.
| 0 | 1 | 0 | 0 | 0 | 0 |
Universal fitness dynamics through an adaptive resource utilization model | The fitness of a species determines its abundance and survival in an
ecosystem. At the same time, species take up resources for growth, so their
abundance affects the availability of resources in an ecosystem. We show here
that such species-resource coupling can be used to assign a quantitative metric
for fitness to each species. This fitness metric also allows for the modeling
of drift in species composition, and hence ecosystem evolution through
speciation and adaptation. Our results provide a foundation for an entirely
computational exploration of evolutionary ecosystem dynamics on any length or
time scale. For example, we can evolve ecosystem dynamics even by initiating
dynamics out of a single primordial ancestor and show that there exists a well
defined ecosystem-averaged fitness dynamics that is resilient against resource
shocks.
| 0 | 0 | 0 | 0 | 1 | 0 |
Integral and measure-turnpike properties for infinite-dimensional optimal control systems | We first derive a general integral-turnpike property around a set for
infinite-dimensional non-autonomous optimal control problems with any possible
terminal state constraints, under some appropriate assumptions. Roughly
speaking, the integral-turnpike property means that the time average of the
distance from any optimal trajectory to the turnpike set con- verges to zero,
as the time horizon tends to infinity. Then, we establish the measure-turnpike
property for strictly dissipative optimal control systems, with state and
control constraints. The measure-turnpike property, which is slightly stronger
than the integral-turnpike property, means that any optimal (state and control)
solution remains essentially, along the time frame, close to an optimal
solution of an associated static optimal control problem, except along a subset
of times that is of small relative Lebesgue measure as the time horizon is
large. Next, we prove that strict strong duality, which is a classical notion
in optimization, implies strict dissipativity, and measure-turnpike. Finally,
we conclude the paper with several comments and open problems.
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Optimal rate of convergence in Stratified Boussinesq system | We study the vortex patch problem for $2d-$stratified Navier-Stokes system.
We aim at extending several results obtained in
\cite{ad,danchinpoche,hmidipoche} for standard Euler and Navier-Stokes systems.
We shall deal with smooth initial patches and establish global strong estimates
uniformly with respect to the viscosity in the spirit of \cite{HZ-poche,
Z-poche}. This allows to prove the convergence of the viscous solutions towards
the inviscid one. In the setting of a Rankine vortex, we show that the rate of
convergence for the vortices is optimal in $L^p$ space and is given by $(\mu
t)^{\frac{1}{2p}}$. This generalizes the result of \cite{ad} obtained for $L^2$
space.
| 0 | 0 | 1 | 0 | 0 | 0 |
On indirect noise in multicomponent nozzle flows | A one-dimensional, unsteady nozzle flow is modelled to identify the sources
of indirect noise in multicomponent gases. First, from non-equilibrium
thermodynamics relations, it is shown that a compositional inhomogeneity
advected in an accelerating flow is a source of sound induced by
inhomogeneities in the mixture (i) chemical potentials and (ii) specific heat
capacities. Second, it is shown that the acoustic, entropy and compositional
linear perturbations evolve independently from each other and they become
coupled through mean-flow gradients and/or at the boundaries. Third, the
equations are cast in invariant formulation and a mathematical solution is
found by asymptotic expansion of path-ordered integrals with an infinite radius
of convergence. Finally, the transfer functions are calculated for a supersonic
nozzle with finite spatial extent perturbed by a methane-air compositional
inhomogeneity. The proposed framework will help identify and quantify the
sources of sound in nozzles with relevance, for example, to aeronautical gas
turbines.
| 0 | 1 | 0 | 0 | 0 | 0 |
A note on conditional covariance matrices for elliptical distributions | In this short note we provide an analytical formula for the conditional
covariance matrices of the elliptically distributed random vectors, when the
conditioning is based on the values of any linear combination of the marginal
random variables. We show that one could introduce the univariate invariant
depending solely on the conditioning set, which greatly simplifies the
calculations. As an application, we show that one could define uniquely defined
quantile-based sets on which conditional covariance matrices must be equal to
each other if only the vector is multivariate normal. The similar results are
obtained for conditional correlation matrices of the general elliptic case.
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The Passive Eavesdropper Affects my Channel: Secret-Key Rates under Real-World Conditions (Extended Version) | Channel-reciprocity based key generation (CRKG) has gained significant
importance as it has recently been proposed as a potential lightweight security
solution for IoT devices. However, the impact of the attacker's position in
close range has only rarely been evaluated in practice, posing an open research
problem about the security of real-world realizations. Furthermore, this would
further bridge the gap between theoretical channel models and their
practice-oriented realizations. For security metrics, we utilize
cross-correlation, mutual information, and a lower bound on secret-key
capacity. We design a practical setup of three parties such that the channel
statistics, although based on joint randomness, are always reproducible. We run
experiments to obtain channel states and evaluate the aforementioned metrics
for the impact of an attacker depending on his position. It turns out the
attacker himself affects the outcome, which has not been adequately regarded
yet in standard channel models.
| 1 | 0 | 1 | 0 | 0 | 0 |
On dimensions supporting a rational projective plane | A rational projective plane ($\mathbb{QP}^2$) is a simply connected, smooth,
closed manifold $M$ such that $H^*(M;\mathbb{Q}) \cong
\mathbb{Q}[\alpha]/\langle \alpha^3 \rangle$. An open problem is to classify
the dimensions at which such a manifold exists. The Barge-Sullivan rational
surgery realization theorem provides necessary and sufficient conditions that
include the Hattori-Stong integrality conditions on the Pontryagin numbers. In
this article, we simplify these conditions and combine them with the signature
equation to give a single quadratic residue equation that determines whether a
given dimension supports a $\mathbb{QP}^2$. We then confirm existence of a
$\mathbb{QP}^2$ in two new dimensions and prove several non-existence results
using factorizations of numerators of divided Bernoulli numbers. We also
resolve the existence question in the Spin case, and we discuss existence
results for the more general class of rational projective spaces.
| 0 | 0 | 1 | 0 | 0 | 0 |
Implicit Causal Models for Genome-wide Association Studies | Progress in probabilistic generative models has accelerated, developing
richer models with neural architectures, implicit densities, and with scalable
algorithms for their Bayesian inference. However, there has been limited
progress in models that capture causal relationships, for example, how
individual genetic factors cause major human diseases. In this work, we focus
on two challenges in particular: How do we build richer causal models, which
can capture highly nonlinear relationships and interactions between multiple
causes? How do we adjust for latent confounders, which are variables
influencing both cause and effect and which prevent learning of causal
relationships? To address these challenges, we synthesize ideas from causality
and modern probabilistic modeling. For the first, we describe implicit causal
models, a class of causal models that leverages neural architectures with an
implicit density. For the second, we describe an implicit causal model that
adjusts for confounders by sharing strength across examples. In experiments, we
scale Bayesian inference on up to a billion genetic measurements. We achieve
state of the art accuracy for identifying causal factors: we significantly
outperform existing genetics methods by an absolute difference of 15-45.3%.
| 1 | 0 | 0 | 1 | 0 | 0 |
Vector Quantization as Sparse Least Square Optimization | Vector quantization aims to form new vectors/matrices with shared values
close to the original. It could compress data with acceptable information loss
and could be of great usefulness in areas like Image Processing, Pattern
Recognition, and Machine Learning. In this paper, the problem of vector
quantization is examined from a new perspective, namely sparse least square
optimization. Specifically, inspired by the property of sparsity of Lasso, a
novel quantization algorithm based on $l_1$ least square is proposed and
implemented. Similar schemes with $l_1 + l_2$ combination penalization and
$l_0$ regularization are simultaneously proposed. In addition, to produce
quantization results with given amount of quantized values(instead of
penalization coefficient $\lambda$), this paper proposed an iterative sparse
least square method and a cluster-based least square quantization method. It is
also noticed that the later method is mathematically equivalent to an improved
version of the existed clustering-based quantization algorithm, although the
two algorithms originated from different intuitions. The algorithms proposed
were tested under three scenarios of data and their computational performance,
including information loss, time consumption and the distribution of the value
of sparse vectors were compared and analyzed. The paper offers a new
perspective to probe the area of vector quantization, and the algorithms
proposed could offer better performance especially when the required
post-quantization value amounts are not on a tiny scale.
| 1 | 0 | 0 | 1 | 0 | 0 |
The role of spatial scale in joint optimisations of generation and transmission for European highly renewable scenarios | The effects of the spatial scale on the results of the optimisation of
transmission and generation capacity in Europe are quantified under a 95% CO2
reduction compared to 1990 levels, interpolating between one-node-per-country
solutions and many-nodes-per-country. The trade-offs that come with higher
spatial detail between better exposure of transmission bottlenecks,
exploitation of sites with good renewable resources (particularly wind power)
and computational limitations are discussed. It is shown that solutions with no
grid expansion beyond today's capacities are only around 20% more expensive
than with cost-optimal grid expansion.
| 0 | 1 | 0 | 0 | 0 | 0 |
Recovery Guarantees for One-hidden-layer Neural Networks | In this paper, we consider regression problems with one-hidden-layer neural
networks (1NNs). We distill some properties of activation functions that lead
to $\mathit{local~strong~convexity}$ in the neighborhood of the ground-truth
parameters for the 1NN squared-loss objective. Most popular nonlinear
activation functions satisfy the distilled properties, including rectified
linear units (ReLUs), leaky ReLUs, squared ReLUs and sigmoids. For activation
functions that are also smooth, we show $\mathit{local~linear~convergence}$
guarantees of gradient descent under a resampling rule. For homogeneous
activations, we show tensor methods are able to initialize the parameters to
fall into the local strong convexity region. As a result, tensor initialization
followed by gradient descent is guaranteed to recover the ground truth with
sample complexity $ d \cdot \log(1/\epsilon) \cdot \mathrm{poly}(k,\lambda )$
and computational complexity $n\cdot d \cdot \mathrm{poly}(k,\lambda) $ for
smooth homogeneous activations with high probability, where $d$ is the
dimension of the input, $k$ ($k\leq d$) is the number of hidden nodes,
$\lambda$ is a conditioning property of the ground-truth parameter matrix
between the input layer and the hidden layer, $\epsilon$ is the targeted
precision and $n$ is the number of samples. To the best of our knowledge, this
is the first work that provides recovery guarantees for 1NNs with both sample
complexity and computational complexity $\mathit{linear}$ in the input
dimension and $\mathit{logarithmic}$ in the precision.
| 1 | 0 | 0 | 1 | 0 | 0 |
Attention-based Information Fusion using Multi-Encoder-Decoder Recurrent Neural Networks | With the rising number of interconnected devices and sensors, modeling
distributed sensor networks is of increasing interest. Recurrent neural
networks (RNN) are considered particularly well suited for modeling sensory and
streaming data. When predicting future behavior, incorporating information from
neighboring sensor stations is often beneficial. We propose a new RNN based
architecture for context specific information fusion across multiple spatially
distributed sensor stations. Hereby, latent representations of multiple local
models, each modeling one sensor station, are jointed and weighted, according
to their importance for the prediction. The particular importance is assessed
depending on the current context using a separate attention function. We
demonstrate the effectiveness of our model on three different real-world sensor
network datasets.
| 1 | 0 | 0 | 1 | 0 | 0 |
Kharita: Robust Map Inference using Graph Spanners | The widespread availability of GPS information in everyday devices such as
cars, smartphones and smart watches make it possible to collect large amount of
geospatial trajectory information. A particularly important, yet technically
challenging, application of this data is to identify the underlying road
network and keep it updated under various changes. In this paper, we propose
efficient algorithms that can generate accurate maps in both batch and online
settings. Our algorithms utilize techniques from graph spanners so that they
produce maps can effectively handle a wide variety of road and intersection
shapes. We conduct a rigorous evaluation of our algorithms over two real-world
datasets and under a wide variety of performance metrics. Our experiments show
a significant improvement over prior work. In particular, we observe an
increase in Biagioni f-score of up to 20% when compared to the state of the art
while reducing the execution time by an order of magnitude. We also make our
source code open source for reproducibility and enable other researchers to
build on our work.
| 1 | 0 | 0 | 0 | 0 | 0 |
DeepProbe: Information Directed Sequence Understanding and Chatbot Design via Recurrent Neural Networks | Information extraction and user intention identification are central topics
in modern query understanding and recommendation systems. In this paper, we
propose DeepProbe, a generic information-directed interaction framework which
is built around an attention-based sequence to sequence (seq2seq) recurrent
neural network. DeepProbe can rephrase, evaluate, and even actively ask
questions, leveraging the generative ability and likelihood estimation made
possible by seq2seq models. DeepProbe makes decisions based on a derived
uncertainty (entropy) measure conditioned on user inputs, possibly with
multiple rounds of interactions. Three applications, namely a rewritter, a
relevance scorer and a chatbot for ad recommendation, were built around
DeepProbe, with the first two serving as precursory building blocks for the
third. We first use the seq2seq model in DeepProbe to rewrite a user query into
one of standard query form, which is submitted to an ordinary recommendation
system. Secondly, we evaluate DeepProbe's seq2seq model-based relevance
scoring. Finally, we build a chatbot prototype capable of making active user
interactions, which can ask questions that maximize information gain, allowing
for a more efficient user intention idenfication process. We evaluate first two
applications by 1) comparing with baselines by BLEU and AUC, and 2) human judge
evaluation. Both demonstrate significant improvements compared with current
state-of-the-art systems, proving their values as useful tools on their own,
and at the same time laying a good foundation for the ongoing chatbot
application.
| 1 | 0 | 0 | 1 | 0 | 0 |
Dictionary-based Monitoring of Premature Ventricular Contractions: An Ultra-Low-Cost Point-of-Care Service | While cardiovascular diseases (CVDs) are prevalent across economic strata,
the economically disadvantaged population is disproportionately affected due to
the high cost of traditional CVD management. Accordingly, developing an
ultra-low-cost alternative, affordable even to groups at the bottom of the
economic pyramid, has emerged as a societal imperative. Against this backdrop,
we propose an inexpensive yet accurate home-based electrocardiogram(ECG)
monitoring service. Specifically, we seek to provide point-of-care monitoring
of premature ventricular contractions (PVCs), high frequency of which could
indicate the onset of potentially fatal arrhythmia. Note that a traditional
telecardiology system acquires the ECG, transmits it to a professional
diagnostic centre without processing, and nearly achieves the diagnostic
accuracy of a bedside setup, albeit at high bandwidth cost. In this context, we
aim at reducing cost without significantly sacrificing reliability. To this
end, we develop a dictionary-based algorithm that detects with high sensitivity
the anomalous beats only which are then transmitted. We further compress those
transmitted beats using class-specific dictionaries subject to suitable
reconstruction/diagnostic fidelity. Such a scheme would not only reduce the
overall bandwidth requirement, but also localising anomalous beats, thereby
reducing physicians' burden. Finally, using Monte Carlo cross validation on
MIT/BIH arrhythmia database, we evaluate the performance of the proposed
system. In particular, with a sensitivity target of at most one undetected PVC
in one hundred beats, and a percentage root mean squared difference less than
9% (a clinically acceptable level of fidelity), we achieved about 99.15%
reduction in bandwidth cost, equivalent to 118-fold savings over traditional
telecardiology.
| 1 | 0 | 0 | 0 | 0 | 0 |
Temporal Multimodal Fusion for Video Emotion Classification in the Wild | This paper addresses the question of emotion classification. The task
consists in predicting emotion labels (taken among a set of possible labels)
best describing the emotions contained in short video clips. Building on a
standard framework -- lying in describing videos by audio and visual features
used by a supervised classifier to infer the labels -- this paper investigates
several novel directions. First of all, improved face descriptors based on 2D
and 3D Convo-lutional Neural Networks are proposed. Second, the paper explores
several fusion methods, temporal and multimodal, including a novel hierarchical
method combining features and scores. In addition, we carefully reviewed the
different stages of the pipeline and designed a CNN architecture adapted to the
task; this is important as the size of the training set is small compared to
the difficulty of the problem, making generalization difficult. The so-obtained
model ranked 4th at the 2017 Emotion in the Wild challenge with the accuracy of
58.8 %.
| 1 | 0 | 0 | 0 | 0 | 0 |
Bonding charge distribution analysis of molecule by computation of interatomic charge penetration | Charge transfer among individual atoms in a molecule is the key concept in
the modern electronic theory of chemical bonding. In this work, we defined an
atomic region between two atoms by Slater orbital exponents of valence
electrons and suggested a method for analytical calculation of charge
penetration between all atoms in a molecule. Computation of charge penetration
amount is self-consistently performed until each orbital exponent converges to
its certain values respectively. Charge penetration matrix was calculated for
ethylene and MgO, and bonding charge and its distribution were analyzed by
using the charge penetration matrix and the orbital exponents under the bonding
state. These results were compared with those by density function method and
showed that this method is a simple and direct method to obtain bonding charge
distribution of molecule from atomic orbital functions.
| 0 | 1 | 0 | 0 | 0 | 0 |
Shatter functions with polynomial growth rates | We study how a single value of the shatter function of a set system restricts
its asymptotic growth. Along the way, we refute a conjecture of Bondy and
Hajnal which generalizes Sauer's Lemma.
| 1 | 0 | 1 | 0 | 0 | 0 |
On Newstead's Mayer-Vietoris argument in characteristic 2 | Consider the moduli space of framed flat $U(2)$ connections with fixed odd
determinant over a surface. Newstead combined some fundamental facts about this
moduli space with the Mayer-Vietoris sequence to compute its betti numbers over
any field not of characteristic two. We adapt his method in characteristic two
to produce conjectural recursive formulae for the mod two betti numbers of the
framed moduli space which we partially verify. We also discuss the interplay
with the mod two cohomology ring structure of the unframed moduli space.
| 0 | 0 | 1 | 0 | 0 | 0 |
Hunting high and low: Disentangling primordial and late-time non-Gaussianity with cosmic densities in spheres | Non-Gaussianities of dynamical origin are disentangled from primordial ones
using the formalism of large deviation statistics with spherical collapse
dynamics. This is achieved by relying on accurate analytical predictions for
the one-point probability distribution function (PDF) and the two-point
clustering of spherically-averaged cosmic densities (sphere bias). Sphere bias
extends the idea of halo bias to intermediate density environments and voids as
underdense regions. In the presence of primordial non-Gaussianity, sphere bias
displays a strong scale dependence relevant for both high and low density
regions, which is predicted analytically. The statistics of densities in
spheres are built to model primordial non-Gaussianity via an initial skewness
with a scale-dependence that depends on the bispectrum of the underlying model.
The analytical formulas with the measured nonlinear dark matter variance as
input are successfully tested against numerical simulations. For local
non-Gaussianity with a range from $f_{\rm NL}=-100$ to $+100$ they are found to
agree within 2\% or better for densities $\rho\in[0.5,3]$ in spheres of radius
15 Mpc$/h$ down to $z=0.35$. The validity of the large deviation statistics
formalism is thereby established for all observationally relevant local-type
departures from perfectly Gaussian initial conditions. The corresponding
estimators for the amplitude of the nonlinear variance $\sigma_8$ and
primordial skewness $f_{\rm NL}$ are validated using a fiducial joint maximum
likelihood experiment. The influence of observational effects and the prospects
for a future detection of primordial non-Gaussianity from joint one- and
two-point densities-in-spheres statistics are discussed.
| 0 | 1 | 0 | 0 | 0 | 0 |
Measure Theory and Integration By and For the Learner | Measure Theory and Integration is exposed with the clear aim to help
beginning learners to perfectly master its essence. In opposition of a delivery
of the contents in an academic and vertical course, the knowledge is broken
into exercises which are left to the learners for solutions. Hints are present
at any corner to help readers to achieve the solutions. In that way, the
knowledge is constructed by the readers by summarizing the results of one or a
group of exercises.
Each chapter is organized into Summary documents which contain the knowledge,
Discovery documents which give the learner the opportunity to extract the
knowledge himself through exercises and into Solution Documents which offer
detailed answers for the exercises. Exceptionally, a few number of results (A
key lemma related the justification of definition of the integral of a
non-negative function, the Caratheodory's theorem and the Lebesgue-Stieljes
measure on $\mathbb{R}^d$) are presented in appendix documents and given for
reading in small groups.
The full theory is presented in the described way. We highly expect that any
student who goes through the materials, alone or in a small group or under the
supervision of an assistant will gain a very solid knowledge in the subject and
by the way ensure a sound foundation for studying disciplines such as
Probability Theory, Statistics, Functional Analysis, etc.
The materials have been successfully used as such in normal real analysis
classes several times.
| 0 | 0 | 1 | 0 | 0 | 0 |
Target Tracking for Contextual Bandits: Application to Demand Side Management | We propose a contextual-bandit approach for demand side management by
offering price incentives. More precisely, a target mean consumption is set at
each round and the mean consumption is modeled as a complex function of the
distribution of prices sent and of some contextual variables such as the
temperature, weather, and so on. The performance of our strategies is measured
in quadratic losses through a regret criterion. We offer $\sqrt{T}$ upper
bounds on this regret (up to poly-logarithmic terms), for strategies inspired
by standard strategies for contextual bandits (like LinUCB, Li et al., 2010).
Simulations on a real data set gathered by UK Power Networks, in which price
incentives were offered, show that our strategies are effective and may indeed
manage demand response by suitably picking the price levels.
| 1 | 0 | 0 | 1 | 0 | 0 |
Quantizing deep convolutional networks for efficient inference: A whitepaper | We present an overview of techniques for quantizing convolutional neural
networks for inference with integer weights and activations. Per-channel
quantization of weights and per-layer quantization of activations to 8-bits of
precision post-training produces classification accuracies within 2% of
floating point networks for a wide variety of CNN architectures. Model sizes
can be reduced by a factor of 4 by quantizing weights to 8-bits, even when
8-bit arithmetic is not supported. This can be achieved with simple, post
training quantization of weights.We benchmark latencies of quantized networks
on CPUs and DSPs and observe a speedup of 2x-3x for quantized implementations
compared to floating point on CPUs. Speedups of up to 10x are observed on
specialized processors with fixed point SIMD capabilities, like the Qualcomm
QDSPs with HVX.
Quantization-aware training can provide further improvements, reducing the
gap to floating point to 1% at 8-bit precision. Quantization-aware training
also allows for reducing the precision of weights to four bits with accuracy
losses ranging from 2% to 10%, with higher accuracy drop for smaller
networks.We introduce tools in TensorFlow and TensorFlowLite for quantizing
convolutional networks and review best practices for quantization-aware
training to obtain high accuracy with quantized weights and activations. We
recommend that per-channel quantization of weights and per-layer quantization
of activations be the preferred quantization scheme for hardware acceleration
and kernel optimization. We also propose that future processors and hardware
accelerators for optimized inference support precisions of 4, 8 and 16 bits.
| 0 | 0 | 0 | 1 | 0 | 0 |
Angle-dependent electron spin resonance of YbRh$_2$Si$_2$ measured with planar microwave resonators and in-situ rotation | We present a new experimental approach to investigate the magnetic properties
of the anisotropic heavy-fermion system YbRh$_2$Si$_2$ as a function of
crystallographic orientation. Angle-dependent electron spin resonance (ESR)
measurements are performed at a low temperature of 1.6 K and at an ESR
frequency of 4.4 GHz utilizing a superconducting planar microwave resonator in
a $^4$He-cryostat in combination with in-situ sample rotation. The obtained ESR
g-factor of YbRh$_2$Si$_2$ as a function of the crystallographic angle is
consistent with results of previous measurements using conventional ESR
spectrometers at higher frequencies and fields. Perspectives to implement this
experimental approach into a dilution refrigerator and to reach the
magnetically ordered phase of YbRh$_2$Si$_2$ are discussed.
| 0 | 1 | 0 | 0 | 0 | 0 |
Role of Kohn-Sham Kinetic Energy Density in Designing Asymptotically Correct Semilocal Exchange-Correlation Functionals in Two Dimensions | The positive definite Kohn-Sham kinetic energy(KS-KE) density plays crucial
role in designing semilocal meta generalized gradient approximations(meta-GGAs)
for low dimensional quantum systems. It has been rigorously shown that near
nucleus and at the asymptotic region, the KE-KS differ from its von
Weizsäcker(VW) counterpart as contributions from different orbitals (i.e.,
s and p orbitals) play important role. This has been explored using two
dimensional isotropic quantum harmonic oscillator as a test case. Several
meta-GGA ingredients with different physical behaviors are also constructed and
further used to design an accurate semilocal functionals at meta-GGA level. In
the asymptotic region, a new exchange energy functional is constructed using
the meta-GGA ingredients with formally exact properties of the enhancement
factor. Also, it has been shown that exact asymptotic behavior of the exchange
energy density and potential can be attained by choosing accurately the
enhancement factor as a functional of meta-GGA ingredients.
| 0 | 1 | 0 | 0 | 0 | 0 |
BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs | In this paper we describe our attempt at producing a state-of-the-art Twitter
sentiment classifier using Convolutional Neural Networks (CNNs) and Long Short
Term Memory (LSTMs) networks. Our system leverages a large amount of unlabeled
data to pre-train word embeddings. We then use a subset of the unlabeled data
to fine tune the embeddings using distant supervision. The final CNNs and LSTMs
are trained on the SemEval-2017 Twitter dataset where the embeddings are fined
tuned again. To boost performances we ensemble several CNNs and LSTMs together.
Our approach achieved first rank on all of the five English subtasks amongst 40
teams.
| 1 | 0 | 0 | 1 | 0 | 0 |
Protein Mutation Stability Ternary Classification using Neural Networks and Rigidity Analysis | Discerning how a mutation affects the stability of a protein is central to
the study of a wide range of diseases. Machine learning and statistical
analysis techniques can inform how to allocate limited resources to the
considerable time and cost associated with wet lab mutagenesis experiments. In
this work we explore the effectiveness of using a neural network classifier to
predict the change in the stability of a protein due to a mutation. Assessing
the accuracy of our approach is dependent on the use of experimental data about
the effects of mutations performed in vitro. Because the experimental data is
prone to discrepancies when similar experiments have been performed by multiple
laboratories, the use of the data near the juncture of stabilizing and
destabilizing mutations is questionable. We address this later problem via a
systematic approach in which we explore the use of a three-way classification
scheme with stabilizing, destabilizing, and inconclusive labels. For a
systematic search of potential classification cutoff values our classifier
achieved 68 percent accuracy on ternary classification for cutoff values of
-0.6 and 0.7 with a low rate of classifying stabilizing as destabilizing and
vice versa.
| 0 | 0 | 0 | 0 | 1 | 0 |
Starspot activity and superflares on solar-type stars | We analyze the correlation between starspots and superflares on solar-type
stars using observations from the Kepler mission. The analysis shows that the
observed fraction of stars with superflares decreases as the rotation period
increases and as the amplitude of photometric variability associated with
rotation decreases. We found that the fraction of stars with superflares among
the stars showing large-amplitude rotational variations, which are thought to
be the signature of the large starspots, also decreases as the rotation period
increases. The small fraction of superflare stars among the stars with large
starspots in the longer-period regime suggests that some of the stars with
large starspots show a much lower flare activity than the superflare stars with
the same spot area. Assuming simple relations between spot area and lifetime
and between spot temperature and photospheric temperature, we compared the size
distribution of large starspot groups on slowly-rotating solar-type stars with
that of sunspot groups. The size distribution of starspots shows the power-law
distribution and the size distribution of larger sunspots lies on this
power-law line. We also found that frequency-energy distributions for flares
originating from spots with different sizes are the same for solar-type stars
with superflares and the Sun. These results suggest that the magnetic activity
we observe on solar-type stars with superflares and that on the Sun is caused
by the same physical processes.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Contractive Approach to Separable Lyapunov Functions for Monotone Systems | Monotone systems preserve a partial ordering of states along system
trajectories and are often amenable to separable Lyapunov functions that are
either the sum or the maximum of a collection of functions of a scalar
argument. In this paper, we consider constructing separable Lyapunov functions
for monotone systems that are also contractive, that is, the distance between
any pair of trajectories exponentially decreases. The distance is defined in
terms of a possibly state-dependent norm. When this norm is a weighted
one-norm, we obtain conditions which lead to sum-separable Lyapunov functions,
and when this norm is a weighted infinity-norm, symmetric conditions lead to
max-separable Lyapunov functions. In addition, we consider two classes of
Lyapunov functions: the first class is separable along the system's state, and
the second class is separable along components of the system's vector field.
The latter case is advantageous for many practically motivated systems for
which it is difficult to measure the system's state but easier to measure the
system's velocity or rate of change. In addition, we present an algorithm based
on sum-of-squares programming to compute such separable Lyapunov functions. We
provide several examples to demonstrate our results.
| 1 | 0 | 0 | 0 | 0 | 0 |
Isotopic ratios in outbursting comet C/2015 ER61 | Isotopic ratios in comets are critical to understanding the origin of
cometary material and the physical and chemical conditions in the early solar
nebula. Comet C/2015 ER61 (PANSTARRS) underwent an outburst with a total
brightness increase of 2 magnitudes on the night of 2017 April 4. The sharp
increase in brightness offered a rare opportunity to measure the isotopic
ratios of the light elements in the coma of this comet. We obtained two
high-resolution spectra of C/2015 ER61 with UVES/VLT on the nights of 2017
April 13 and 17. At the time of our observations, the comet was fading
gradually following the outburst. We measured the nitrogen and carbon isotopic
ratios from the CN violet (0,0) band and found that $^{12}$C/$^{13}$C=100 $\pm$
15, $^{14}$N/$^{15}$N=130 $\pm$ 15. In addition, we determined the
$^{14}$N/$^{15}$N ratio from four pairs of NH$_2$ isotopolog lines and measured
$^{14}$N/$^{15}$N=140 $\pm$ 28. The measured isotopic ratios of C/2015 ER61 do
not deviate significantly from those of other comets.
| 0 | 1 | 0 | 0 | 0 | 0 |
Muon detector for the COSINE-100 experiment | The COSINE-100 dark matter search experiment has started taking physics data
with the goal of performing an independent measurement of the annual modulation
signal observed by DAMA/LIBRA. A muon detector was constructed by using plastic
scintillator panels in the outermost layer of the shield surrounding the
COSINE-100 detector. It is used to detect cosmic ray muons in order to
understand the impact of the muon annual modulation on dark matter analysis.
Assembly and initial performance test of each module have been performed at a
ground laboratory. The installation of the detector in Yangyang Underground
Laboratory (Y2L) was completed in the summer of 2016. Using three months of
data, the muon underground flux was measured to be 328 $\pm$ 1(stat.)$\pm$
10(syst.) muons/m$^2$/day. In this report, the assembly of the muon detector
and the results from the analysis are presented.
| 0 | 1 | 0 | 0 | 0 | 0 |
Levitated optomechanics with a fiber Fabry-Perot interferometer | In recent years quantum phenomena have been experimentally demonstrated on
variety of optomechanical systems ranging from micro-oscillators to photonic
crystals. Since single photon couplings are quite small, most experimental
approaches rely on the realization of high finesse Fabry-Perot cavities in
order to enhance the effective coupling. Here we show that by exploiting a,
long path, low finesse fiber Fabry-Perot interferometer ground state cooling
can be achieved. We model a 100 m long cavity with a finesse of 10 and analyze
the impact of additional noise sources arising from the fiber. As a mechanical
oscillator we consider a levitated microdisk but the same approach could be
applied to other optomechanical systems.
| 0 | 1 | 0 | 0 | 0 | 0 |
Spatial cytoskeleton organization supports targeted intracellular transport | The efficiency of intracellular cargo transport from specific source to
target locations is strongly dependent upon molecular motor-assisted motion
along the cytoskeleton. Radial transport along microtubules and lateral
transport along the filaments of the actin cortex underneath the cell membrane
are characteristic for cells with a centrosome. The interplay between the
specific cytoskeleton organization and the motor performance realizes a
spatially inhomogeneous intermittent search strategy. In order to analyze the
efficiency of such intracellular search strategies we formulate a random
velocity model with intermittent arrest states. We evaluate efficiency in terms
of mean first passage times for three different, frequently encountered
intracellular transport tasks: i) the narrow escape problem, which emerges
during cargo transport to a synapse or other specific region of the cell
membrane, ii) the reaction problem, which considers the binding time of two
particles within the cell, and iii) the reaction-escape problem, which arises
when cargo must be released at a synapse only after pairing with another
particle. Our results indicate that cells are able to realize efficient search
strategies for various intracellular transport tasks economically through a
spatial cytoskeleton organization that involves only a narrow actin cortex
rather than a cell body filled with randomly oriented actin filaments.
| 0 | 1 | 0 | 0 | 0 | 0 |
Backdoor Embedding in Convolutional Neural Network Models via Invisible Perturbation | Deep learning models have consistently outperformed traditional machine
learning models in various classification tasks, including image
classification. As such, they have become increasingly prevalent in many real
world applications including those where security is of great concern. Such
popularity, however, may attract attackers to exploit the vulnerabilities of
the deployed deep learning models and launch attacks against security-sensitive
applications. In this paper, we focus on a specific type of data poisoning
attack, which we refer to as a {\em backdoor injection attack}. The main goal
of the adversary performing such attack is to generate and inject a backdoor
into a deep learning model that can be triggered to recognize certain embedded
patterns with a target label of the attacker's choice. Additionally, a backdoor
injection attack should occur in a stealthy manner, without undermining the
efficacy of the victim model. Specifically, we propose two approaches for
generating a backdoor that is hardly perceptible yet effective in poisoning the
model. We consider two attack settings, with backdoor injection carried out
either before model training or during model updating. We carry out extensive
experimental evaluations under various assumptions on the adversary model, and
demonstrate that such attacks can be effective and achieve a high attack
success rate (above $90\%$) at a small cost of model accuracy loss (below
$1\%$) with a small injection rate (around $1\%$), even under the weakest
assumption wherein the adversary has no knowledge either of the original
training data or the classifier model.
| 0 | 0 | 0 | 1 | 0 | 0 |
Chiral and Topological Orbital Magnetism of Spin Textures | Using a semiclassical Green's function formalism, we discover the emergence
of chiral and topological orbital magnetism in two-dimensional chiral spin
textures by explicitly finding the corrections to the orbital magnetization,
proportional to the powers of the gradients of the texture. We show that in the
absence of spin-orbit coupling, the resulting orbital moment can be understood
as the electronic response to the emergent magnetic field associated with the
real-space Berry curvature. By referring to the Rashba model, we demonstrate
that by tuning the parameters of surface systems the engineering of emergent
orbital magnetism in spin textures can pave the way to novel concepts in
orbitronics.
| 0 | 1 | 0 | 0 | 0 | 0 |
The Mira-Titan Universe II: Matter Power Spectrum Emulation | We introduce a new cosmic emulator for the matter power spectrum covering
eight cosmological parameters. Targeted at optical surveys, the emulator
provides accurate predictions out to a wavenumber k~5/Mpc and redshift z<=2.
Besides covering the standard set of LCDM parameters, massive neutrinos and a
dynamical dark energy of state are included. The emulator is built on a sample
set of 36 cosmological models, carefully chosen to provide accurate predictions
over the wide and large parameter space. For each model, we have performed a
high-resolution simulation, augmented with sixteen medium-resolution
simulations and TimeRG perturbation theory results to provide accurate coverage
of a wide k-range; the dataset generated as part of this project is more than
1.2Pbyte. With the current set of simulated models, we achieve an accuracy of
approximately 4%. Because the sampling approach used here has established
convergence and error-control properties, follow-on results with more than a
hundred cosmological models will soon achieve ~1% accuracy. We compare our
approach with other prediction schemes that are based on halo model ideas and
remapping approaches. The new emulator code is publicly available.
| 0 | 1 | 0 | 0 | 0 | 0 |
k-Anonymously Private Search over Encrypted Data | In this paper we compare the performance of various homomorphic encryption
methods on a private search scheme that can achieve $k$-anonymity privacy. To
make our benchmarking fair, we use open sourced cryptographic libraries which
are written by experts and well scrutinized. We find that Goldwasser-Micali
encryption achieves good enough performance for practical use, whereas fully
homomorphic encryptions are much slower than partial ones like
Goldwasser-Micali and Paillier.
| 1 | 0 | 0 | 0 | 0 | 0 |
Stability of Correction Procedure via Reconstruction With Summation-by-Parts Operators for Burgers' Equation Using a Polynomial Chaos Approach | In this paper, we consider Burgers' equation with uncertain boundary and
initial conditions. The polynomial chaos (PC) approach yields a hyperbolic
system of deterministic equations, which can be solved by several numerical
methods. Here, we apply the correction procedure via reconstruction (CPR) using
summation-by-parts operators. We focus especially on stability, which is proven
for CPR methods and the systems arising from the PC approach. Due to the usage
of split-forms, the major challenge is to construct entropy stable numerical
fluxes. For the first time, such numerical fluxes are constructed for all
systems resulting from the PC approach for Burgers' equation. In numerical
tests, we verify our results and show also the advantage of the given ansatz
using CPR methods. Moreover, one of the simulations, i.e. Burgers' equation
equipped with an initial shock, demonstrates quite fascinating observations.
The behaviour of the numerical solutions from several methods (finite volume,
finite difference, CPR) differ significantly from each other. Through careful
investigations, we conclude that the reason for this is the high sensitivity of
the system to varying dissipation. Furthermore, it should be stressed that the
system is not strictly hyperbolic with genuinely nonlinear or linearly
degenerate fields.
| 0 | 0 | 1 | 0 | 0 | 0 |
Special Solutions of Bi-Riccati Delay-Differential Equations | Delay-differential equations are functional differential equations that
involve shifts and derivatives with respect to a single independent variable.
Some integrability candidates in this class have been identified by various
means. For three of these equations we consider their elliptic and soliton-type
solutions. Using Hirota's bilinear method, we find that two of our equations
possess three-soliton-type solutions.
| 0 | 1 | 1 | 0 | 0 | 0 |
Signatures of the Kondo effect in VSe2 | VSe2 is a transition metal dichaclogenide which has a charge-density wave
transition that has been well studied. We report on a low-temperature upturn in
the resistivity and, at temperatures below this resistivity minimum, an unusual
magnetoresistance which is negative at low fields and positive at higher
fields, in single crystals of VSe2. The negative magnetoresistance has a
parabolic dependence on the magnetic field and shows little angular dependence.
The magnetoresistance at temperatures above the resistivity minimum is always
positive. We interpret these results as signatures of the Kondo effect in VSe2.
An upturn in the susceptibility indicates the presence of interlayer V ions
which can provide the localized magnetic moments required for scattering the
conduction electrons in the Kondo effect. The low-temperature behaviour of the
heat capacity, including a high value of gamma, along with a deviation from a
Curie-Weiss law observed in the low-temperature magnetic susceptibility, are
consistent with the presence of magnetic interactions between the paramagnetic
interlayer V ions and a Kondo screening of these V moments.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Procedural Texture Generation Framework Based on Semantic Descriptions | Procedural textures are normally generated from mathematical models with
parameters carefully selected by experienced users. However, for naive users,
the intuitive way to obtain a desired texture is to provide semantic
descriptions such as "regular," "lacelike," and "repetitive" and then a
procedural model with proper parameters will be automatically suggested to
generate the corresponding textures. By contrast, it is less practical for
users to learn mathematical models and tune parameters based on multiple
examinations of large numbers of generated textures. In this study, we propose
a novel framework that generates procedural textures according to user-defined
semantic descriptions, and we establish a mapping between procedural models and
semantic texture descriptions. First, based on a vocabulary of semantic
attributes collected from psychophysical experiments, a multi-label learning
method is employed to annotate a large number of textures with semantic
attributes to form a semantic procedural texture dataset. Then, we derive a low
dimensional semantic space in which the semantic descriptions can be separated
from one other. Finally, given a set of semantic descriptions, the diverse
properties of the samples in the semantic space can lead the framework to find
an appropriate generation model that uses appropriate parameters to produce a
desired texture. The experimental results show that the proposed framework is
effective and that the generated textures closely correlate with the input
semantic descriptions.
| 1 | 0 | 0 | 0 | 0 | 0 |
Inward Migration of the TRAPPIST-1 Planets as Inferred From Their Water-Rich Compositions | Multiple planet systems provide an ideal laboratory for probing exoplanet
composition, formation history and potential habitability. For the TRAPPIST-1
planets, the planetary radii are well established from transits (Gillon et al.,
2016, Gillon et al., 2017), with reasonable mass estimates coming from transit
timing variations (Gillon et al., 2017, Wang et al., 2017) and dynamical
modeling (Quarles et al., 2017). The low bulk densities of the TRAPPIST-1
planets demand significant volatile content. Here we show using
mass-radius-composition models, that TRAPPIST-1f and g likely contain
substantial ($\geq50$ wt\%) water/ice, with b and c being significantly drier
($\leq15$ wt\%). We propose this gradient of water mass fractions implies
planets f and g formed outside the primordial snow line whereas b and c formed
inside. We find that compared to planets in our solar system that also formed
within the snow line, TRAPPIST-1b and c contain hundreds more oceans worth of
water. We demonstrate the extent and timescale of migration in the TRAPPIST-1
system depends on how rapidly the planets formed and the relative location of
the primordial snow line. This work provides a framework for understanding the
differences between the protoplanetary disks of our solar system versus M
dwarfs. Our results provide key insights into the volatile budgets, timescales
of planet formation, and migration history of likely the most common planetary
host in the Galaxy.
| 0 | 1 | 0 | 0 | 0 | 0 |
Bidirectional Conditional Generative Adversarial Networks | Conditional Generative Adversarial Networks (cGANs) are generative models
that can produce data samples ($x$) conditioned on both latent variables ($z$)
and known auxiliary information ($c$). We propose the Bidirectional cGAN
(BiCoGAN), which effectively disentangles $z$ and $c$ in the generation process
and provides an encoder that learns inverse mappings from $x$ to both $z$ and
$c$, trained jointly with the generator and the discriminator. We present
crucial techniques for training BiCoGANs, which involve an extrinsic factor
loss along with an associated dynamically-tuned importance weight. As compared
to other encoder-based cGANs, BiCoGANs encode $c$ more accurately, and utilize
$z$ and $c$ more effectively and in a more disentangled way to generate
samples.
| 1 | 0 | 0 | 1 | 0 | 0 |
Micromagnetic study of a feasibility of the magnetic anisotropy engineering in nano-structured epitaxial films of (III,Mn)V ferromagnetic semiconductors | The attainability of modification of the apparent magnetic anisotropy in
(III,Mn)V ferromagnetic semiconductors is probed by means of the
finite-elements-based modelling. The most representative case of (Ga,Mn)As and
its in-plane uniaxial anisotropy is investigated. The hysteresis loops of the
continuous films of a ferromagnetic semiconductor as well as films structured
with the elliptic antidots are modelled for various eccentricity, orientation,
and separation of the anti dots. The effect of anti-dots on the magnetic
anisotropy is confirmed but overall is found to be very weak. The subsequent
modelling for (Ga,Mn)As film with the elliptic dots comprising of metallic NiFe
shows much stronger effect, revealing switching of the magnetic moment in the
ferromagnetic semiconductor governed by the switching behavior of the metallic
inclusions.
| 0 | 1 | 0 | 0 | 0 | 0 |
On the exponential large sieve inequality for sparse sequences modulo primes | We complement the argument of M. Z. Garaev (2009) with several other ideas to
obtain a stronger version of the large sieve inequality with sparse exponential
sequences of the form $\lambda^{s_n}$. In particular, we obtain a result which
is non-trivial for monotonically increasing sequences $\cal{S}=\{s_n
\}_{n=1}^{\infty}$ provided $s_n\le n^{2+o(1)}$, whereas the original argument
of M. Z. Garaev requires $s_n \le n^{15/14 +o(1)}$ in the same setting. We also
give an application of our result to arithmetic properties of integers with
almost all digits prescribed.
| 0 | 0 | 1 | 0 | 0 | 0 |
Excess conduction of YBaCuO point contacts between 100 and 200 K | $YBaCuO-Ag$ pressure point contacts with direct conduction are investigated.
The excess (relative to the normal state) conductivity mainly caused by
fluctuational pairing of electrons above $T_c$ is measured in the temperature
interval 100-200~$K$. The superconductivity above 120~$K$ is found to be of the
two-dimensional type. The obtained preliminary results indicate the presence of
small amount of an unknown phase with $T'_c\gtrsim 200~K$ in $YBaCuO$.
| 0 | 1 | 0 | 0 | 0 | 0 |
Quivers with additive labelings: classification and algebraic entropy | We show that Zamolodchikov dynamics of a recurrent quiver has zero algebraic
entropy only if the quiver has a weakly subadditive labeling, and conjecture
the converse. By assigning a pair of generalized Cartan matrices of affine type
to each quiver with an additive labeling, we completely classify such quivers,
obtaining $40$ infinite families and $13$ exceptional quivers. This completes
the program of classifying Zamolodchikov periodic and integrable quivers.
| 0 | 0 | 1 | 0 | 0 | 0 |
Harnessing bistability for directional propulsion of untethered, soft robots | In most macro-scale robotics systems , propulsion and controls are enabled
through a physical tether or complex on-board electronics and batteries. A
tether simplifies the design process but limits the range of motion of the
robot, while on-board controls and power supplies are heavy and complicate the
design process. Here we present a simple design principle for an untethered,
entirely soft, swimming robot with the ability to achieve preprogrammed,
directional propulsion without a battery or on-board electronics. Locomotion is
achieved by employing actuators that harness the large displacements of
bistable elements, triggered by surrounding temperature changes. Powered by
shape memory polymer (SMP) muscles, the bistable elements in turn actuates the
robot's fins. Our robots are fabricated entirely using a commercially available
3D printer with no post-processing. As a proof-of-concept, we demonstrate the
ability to program a vessel, which can autonomously deliver a cargo and
navigate back to the deployment point.
| 1 | 0 | 0 | 0 | 0 | 0 |
Cs nDJ Rydberg-atom macrodimers formed by long-range multipole interaction | Long-range macrodimers formed by D-state cesium Rydberg atoms are studied in
experiments and in calculations. Cesium 62DJ-62DJ Rydberg-atom macrodimers,
bonded via long-range multipole interaction, are prepared by two-color
photo-association in a cesium atom trap. The first color (pulse A) resonantly
excites seed Rydberg atoms, while the second (pulse B, detuned by the molecular
binding energy) resonantly excites the Rydberg-atom macrodimer states below the
62DJ pair asymptotes. The Rydberg-atom molecules are measured by extraction of
auto-ionization products and Rydberg-atom electric-field ionization, and ion
detection. Molecular spectra are compared with calculations of adiabatic
molecular potentials. The lifetime of the molecules is obtained from
exponential fits to the dependence of the molecular signal on the detection
delay time; lifetimes of about 6 us are found.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Brief Review of Galactic Winds | Galactic winds from star-forming galaxies play at key role in the evolution
of galaxies and the inter-galactic medium. They transport metals out of
galaxies, chemically-enriching the inter-galactic medium and modifying the
chemical evolution of galaxies. They affect the surrounding inter-stellar and
circum-galactic media, thereby influencing the growth of galaxies through gas
accretion and star-formation. In this contribution we first summarize the
physical mechanisms by which the momentum and energy output from a population
of massive stars and associated supernovae can drive galactic winds. We use the
proto-typical example of M82 to illustrate the multiphase nature of galactic
winds. We then describe how the basic properties of galactic winds are derived
from the data, and summarize how the properties of galactic winds vary
systematically with the properties of the galaxies that launch them. We
conclude with a brief discussion of the broad implications of galactic winds.
| 0 | 1 | 0 | 0 | 0 | 0 |
Transfer Learning for Speech Recognition on a Budget | End-to-end training of automated speech recognition (ASR) systems requires
massive data and compute resources. We explore transfer learning based on model
adaptation as an approach for training ASR models under constrained GPU memory,
throughput and training data. We conduct several systematic experiments
adapting a Wav2Letter convolutional neural network originally trained for
English ASR to the German language. We show that this technique allows faster
training on consumer-grade resources while requiring less training data in
order to achieve the same accuracy, thereby lowering the cost of training ASR
models in other languages. Model introspection revealed that small adaptations
to the network's weights were sufficient for good performance, especially for
inner layers.
| 1 | 0 | 0 | 1 | 0 | 0 |
Information-Propogation-Enhanced Neural Machine Translation by Relation Model | Even though sequence-to-sequence neural machine translation (NMT) model have
achieved state-of-art performance in the recent fewer years, but it is widely
concerned that the recurrent neural network (RNN) units are very hard to
capture the long-distance state information, which means RNN can hardly find
the feature with long term dependency as the sequence becomes longer.
Similarly, convolutional neural network (CNN) is introduced into NMT for
speeding recently, however, CNN focus on capturing the local feature of the
sequence; To relieve this issue, we incorporate a relation network into the
standard encoder-decoder framework to enhance information-propogation in neural
network, ensuring that the information of the source sentence can flow into the
decoder adequately. Experiments show that proposed framework outperforms the
statistical MT model and the state-of-art NMT model significantly on two data
sets with different scales.
| 1 | 0 | 0 | 0 | 0 | 0 |
Neural Machine Translation and Sequence-to-sequence Models: A Tutorial | This tutorial introduces a new and powerful set of techniques variously
called "neural machine translation" or "neural sequence-to-sequence models".
These techniques have been used in a number of tasks regarding the handling of
human language, and can be a powerful tool in the toolbox of anyone who wants
to model sequential data of some sort. The tutorial assumes that the reader
knows the basics of math and programming, but does not assume any particular
experience with neural networks or natural language processing. It attempts to
explain the intuition behind the various methods covered, then delves into them
with enough mathematical detail to understand them concretely, and culiminates
with a suggestion for an implementation exercise, where readers can test that
they understood the content in practice.
| 1 | 0 | 0 | 1 | 0 | 0 |
Uncountable realtime probabilistic classes | We investigate the minimum cases for realtime probabilistic machines that can
define uncountably many languages with bounded error. We show that logarithmic
space is enough for realtime PTMs on unary languages. On binary case, we follow
the same result for double logarithmic space, which is tight. When replacing
the worktape with some limited memories, we can follow uncountable results on
unary languages for two counters.
| 1 | 0 | 0 | 0 | 0 | 0 |
A combinatorial model for the path fibration | We introduce the abstract notion of a necklical set in order to describe a
functorial combinatorial model of the path fibration over the geometric
realization of a path connected simplicial set. In particular, to any path
connected simplicial set $X$ we associate a necklical set
$\widehat{\mathbf{\Omega}}X$ such that its geometric realization
$|\widehat{\mathbf{\Omega}}X|$, a space built out of gluing cubical cells, is
homotopy equivalent to the based loop space on $|X|$ and the differential
graded module of chains $C_*(\widehat{\mathbf{\Omega}}X)$ is a differential
graded associative algebra generalizing Adams' cobar construction.
| 0 | 0 | 1 | 0 | 0 | 0 |
A Systematic Evaluation of Static API-Misuse Detectors | Application Programming Interfaces (APIs) often have usage constraints, such
as restrictions on call order or call conditions. API misuses, i.e., violations
of these constraints, may lead to software crashes, bugs, and vulnerabilities.
Though researchers developed many API-misuse detectors over the last two
decades, recent studies show that API misuses are still prevalent. Therefore,
we need to understand the capabilities and limitations of existing detectors in
order to advance the state of the art. In this paper, we present the first-ever
qualitative and quantitative evaluation that compares static API-misuse
detectors along the same dimensions, and with original author validation. To
accomplish this, we develop MUC, a classification of API misuses, and
MUBenchPipe, an automated benchmark for detector comparison, on top of our
misuse dataset, MUBench. Our results show that the capabilities of existing
detectors vary greatly and that existing detectors, though capable of detecting
misuses, suffer from extremely low precision and recall. A systematic
root-cause analysis reveals that, most importantly, detectors need to go beyond
the naive assumption that a deviation from the most-frequent usage corresponds
to a misuse and need to obtain additional usage examples to train their models.
We present possible directions towards more-powerful API-misuse detectors.
| 1 | 0 | 0 | 0 | 0 | 0 |
Human life is unlimited - but short | Does the human lifespan have an impenetrable biological upper limit which
ultimately will stop further increase in life lengths? This question is
important for understanding aging, and for society, and has led to intense
controversies. Demographic data for humans has been interpreted as showing
existence of a limit, or even as an indication of a decreasing limit, but also
as evidence that a limit does not exist. This paper studies what can be
inferred from data about human mortality at extreme age. We show that in
western countries and Japan and after age 110 the probability of dying is about
47% per year. Hence there is no finite upper limit to the human lifespan.
Still, given the present stage of biotechnology, it is unlikely that during the
next 25 years anyone will live longer than 128 years in these countries. Data,
remarkably, shows no difference in mortality after age 110 between sexes,
between ages, or between different lifestyles or genetic backgrounds. These
results, and the analysis methods developed in this paper, can help testing
biological theories of ageing and aid confirmation of success of efforts to
find a cure for ageing.
| 0 | 0 | 0 | 1 | 0 | 0 |
Weakly- and Semi-Supervised Object Detection with Expectation-Maximization Algorithm | Object detection when provided image-level labels instead of instance-level
labels (i.e., bounding boxes) during training is an important problem in
computer vision, since large scale image datasets with instance-level labels
are extremely costly to obtain. In this paper, we address this challenging
problem by developing an Expectation-Maximization (EM) based object detection
method using deep convolutional neural networks (CNNs). Our method is
applicable to both the weakly-supervised and semi-supervised settings.
Extensive experiments on PASCAL VOC 2007 benchmark show that (1) in the weakly
supervised setting, our method provides significant detection performance
improvement over current state-of-the-art methods, (2) having access to a small
number of strongly (instance-level) annotated images, our method can almost
match the performace of the fully supervised Fast RCNN. We share our source
code at this https URL.
| 1 | 0 | 0 | 0 | 0 | 0 |
Coverage Analysis in Millimeter Wave Cellular Networks with Reflections | The coverage probability of a user in a mmwave system depends on the
availability of line-of-sight paths or reflected paths from any base station.
Many prior works modelled blockages using random shape theory and analyzed the
SIR distribution with and without interference. While, it is intuitive that the
reflected paths do not significantly contribute to the coverage (because of
longer path lengths), there are no works which provide a model and study the
coverage with reflections. In this paper, we model and analyze the impact of
reflectors using stochastic geometry. We observe that the reflectors have very
little impact on the coverage probability.
| 1 | 0 | 0 | 1 | 0 | 0 |
Polar Coding for the Binary Erasure Channel with Deletions | We study the application of polar codes in deletion channels by analyzing the
cascade of a binary erasure channel (BEC) and a deletion channel. We show how
polar codes can be used effectively on a BEC with a single deletion, and
propose a list decoding algorithm with a cyclic redundancy check for this case.
The decoding complexity is $O(N^2\log N)$, where $N$ is the blocklength of the
code. An important contribution is an optimization of the amount of redundancy
added to minimize the overall error probability. Our theoretical results are
corroborated by numerical simulations which show that the list size can be
reduced to one and the original message can be recovered with high probability
as the length of the code grows.
| 1 | 0 | 1 | 0 | 0 | 0 |
Efficient Structured Surrogate Loss and Regularization in Structured Prediction | In this dissertation, we focus on several important problems in structured
prediction. In structured prediction, the label has a rich intrinsic
substructure, and the loss varies with respect to the predicted label and the
true label pair. Structured SVM is an extension of binary SVM to adapt to such
structured tasks.
In the first part of the dissertation, we study the surrogate losses and its
efficient methods. To minimize the empirical risk, a surrogate loss which upper
bounds the loss, is used as a proxy to minimize the actual loss. Since the
objective function is written in terms of the surrogate loss, the choice of the
surrogate loss is important, and the performance depends on it. Another issue
regarding the surrogate loss is the efficiency of the argmax label inference
for the surrogate loss. Efficient inference is necessary for the optimization
since it is often the most time-consuming step. We present a new class of
surrogate losses named bi-criteria surrogate loss, which is a generalization of
the popular surrogate losses. We first investigate an efficient method for a
slack rescaling formulation as a starting point utilizing decomposability of
the model. Then, we extend the algorithm to the bi-criteria surrogate loss,
which is very efficient and also shows performance improvements.
In the second part of the dissertation, another important issue of
regularization is studied. Specifically, we investigate a problem of
regularization in hierarchical classification when a structural imbalance
exists in the label structure. We present a method to normalize the structure,
as well as a new norm, namely shared Frobenius norm. It is suitable for
hierarchical classification that adapts to the data in addition to the label
structure.
| 0 | 0 | 0 | 1 | 0 | 0 |
Optimizing the Coherence of Composite Networks | We consider how to connect a set of disjoint networks to optimize the
performance of the resulting composite network. We quantify this performance by
the coherence of the composite network, which is defined by an $H_2$ norm of
the system. Two dynamics are considered: noisy consensus dynamics with and
without stubborn agents. For noisy consensus dynamics without stubborn agents,
we derive analytical expressions for the coherence of composite networks in
terms of the coherence of the individual networks and the structure of their
interconnections. We also identify optimal interconnection topologies and give
bounds on coherence for general composite graphs. For noisy consensus dynamics
with stubborn agents, we develop a non-combinatorial algorithm that identifies
connecting edges such that the composite network coherence closely approximates
the performance of the optimal composite graph.
| 1 | 0 | 1 | 0 | 0 | 0 |
Deep Learning based Estimation of Weaving Target Maneuvers | In target tracking, the estimation of an unknown weaving target frequency is
crucial for improving the miss distance. The estimation process is commonly
carried out in a Kalman framework. The objective of this paper is to examine
the potential of using neural networks in target tracking applications. To that
end, we propose estimating the weaving frequency using deep neural networks,
instead of classical Kalman framework based estimation. Particularly, we focus
on the case where a set of possible constant target frequencies is known.
Several neural network architectures, requiring low computational resources
were designed to estimate the unknown frequency out of the known set of
frequencies. The proposed approach performance is compared with the multiple
model adaptive estimation algorithm. Simulation results show that in the
examined scenarios, deep neural network outperforms multiple model adaptive
estimation in terms of accuracy and the amount of required measurements to
convergence.
| 0 | 0 | 0 | 1 | 0 | 0 |
On tangent cones to length minimizers in Carnot-Carathéodory spaces | We give a detailed proof of some facts about the blow-up of horizontal curves
in Carnot-Carathéodory spaces.
| 0 | 0 | 1 | 0 | 0 | 0 |
Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer | In many machine learning applications, there are multiple decision-makers
involved, both automated and human. The interaction between these agents often
goes unaddressed in algorithmic development. In this work, we explore a simple
version of this interaction with a two-stage framework containing an automated
model and an external decision-maker. The model can choose to say "Pass", and
pass the decision downstream, as explored in rejection learning. We extend this
concept by proposing "learning to defer", which generalizes rejection learning
by considering the effect of other agents in the decision-making process. We
propose a learning algorithm which accounts for potential biases held by
external decision-makers in a system. Experiments demonstrate that learning to
defer can make systems not only more accurate but also less biased. Even when
working with inconsistent or biased users, we show that deferring models still
greatly improve the accuracy and/or fairness of the entire system.
| 1 | 0 | 0 | 1 | 0 | 0 |
Approximate and Stochastic Greedy Optimization | We consider two greedy algorithms for minimizing a convex function in a
bounded convex set: an algorithm by Jones [1992] and the Frank-Wolfe (FW)
algorithm. We first consider approximate versions of these algorithms. For
smooth convex functions, we give sufficient conditions for convergence, a
unified analysis for the well-known convergence rate of O(1/k) together with a
result showing that this rate is the best obtainable from the proof technique,
and an equivalence result for the two algorithms. We also consider approximate
stochastic greedy algorithms for minimizing expectations. We show that
replacing the full gradient by a single stochastic gradient can fail even on
smooth convex functions. We give a convergent approximate stochastic Jones
algorithm and a convergent approximate stochastic FW algorithm for smooth
convex functions. In addition, we give a convergent approximate stochastic FW
algorithm for nonsmooth convex functions. Convergence rates for these
algorithms are given and proved.
| 1 | 0 | 1 | 0 | 0 | 0 |
Phase diagram of the triangular-lattice Potts antiferromagnet | We study the phase diagram of the triangular-lattice $Q$-state Potts model in
the real $(Q,v)$-plane, where $v=e^J-1$ is the temperature variable. Our first
goal is to provide an obviously missing feature of this diagram: the position
of the antiferromagnetic critical curve. This curve turns out to possess a
bifurcation point with two branches emerging from it, entailing important
consequences for the global phase diagram. We have obtained accurate numerical
estimates for the position of this curve by combining the transfer-matrix
approach for strip graphs with toroidal boundary conditions and the recent
method of critical polynomials. The second goal of this work is to study the
corresponding $A_{p-1}$ RSOS model on the torus, for integer $p=4,5,\ldots,8$.
We clarify its relation to the corresponding Potts model, in particular
concerning the role of boundary conditions. For certain values of $p$, we
identify several new critical points and regimes for the RSOS model and we
initiate the study of the flows between the corresponding field theories.
| 0 | 1 | 0 | 0 | 0 | 0 |
Devam vs. Tamam: 2018 Turkish Elections | On June 24, 2018, Turkey held a historical election, transforming its
parliamentary system to a presidential one. One of the main questions for
Turkish voters was whether to start this new political era with reelecting its
long-time political leader Recep Tayyip Erdogan or not. In this paper, we
analyzed 108M tweets posted in the two months leading to the election to
understand the groups that supported or opposed Erdogan's reelection. We
examined the most distinguishing hashtags and retweeted accounts for both
groups. Our findings indicate strong polarization between both groups as they
differ in terms of ideology, news sources they follow, and preferred TV
entertainment.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning | Insertion is a challenging haptic and visual control problem with significant
practical value for manufacturing. Existing approaches in the model-based
robotics community can be highly effective when task geometry is known, but are
complex and cumbersome to implement, and must be tailored to each individual
problem by a qualified engineer. Within the learning community there is a long
history of insertion research, but existing approaches are typically either too
sample-inefficient to run on real robots, or assume access to high-level object
features, e.g. socket pose. In this paper we show that relatively minor
modifications to an off-the-shelf Deep-RL algorithm (DDPG), combined with a
small number of human demonstrations, allows the robot to quickly learn to
solve these tasks efficiently and robustly. Our approach requires no modeling
or simulation, no parameterized search or alignment behaviors, no vision system
aside from raw images, and no reward shaping. We evaluate our approach on a
narrow-clearance peg-insertion task and a deformable clip-insertion task, both
of which include variability in the socket position. Our results show that
these tasks can be solved reliably on the real robot in less than 10 minutes of
interaction time, and that the resulting policies are robust to variance in the
socket position and orientation.
| 1 | 0 | 0 | 0 | 0 | 0 |
Off-axis electron holography of magnetic nanostructures: magnetic behavior of Mn rich nanoprecipitates in (Mn,Ga)As system | The Lorentz off-axis electron holography technique is applied to study the
magnetic nature of Mn rich nanoprecipitates in (Mn,Ga)As system. The
effectiveness of this technique is demonstrated in detection of the magnetic
field even for small nanocrystals having an average size down to 20 nm.
| 0 | 1 | 0 | 0 | 0 | 0 |
Predicting interactions between individuals with structural and dynamical information | Capturing both the structural and temporal aspects of interactions is crucial
for many real world datasets like contact between individuals. Using the link
stream formalism to capture the dynamic of the systems, we tackle the issue of
activity prediction in link streams, that is to say predicting the number of
links occurring during a given period of time and we present a protocol that
takes advantage of the temporal and structural information contained in the
link stream. Using a supervised learning method, we are able to model the
dynamic of our system to improve the prediction. We investigate the behavior of
our algorithm and crucial elements affecting the prediction. By introducing
different categories of pair of nodes, we are able to improve the quality as
well as increase the diversity of our prediction.
| 1 | 0 | 0 | 0 | 0 | 0 |
Projected Shadowing-based Data Assimilation | In this article we develop algorithms for data assimilation based upon a
computational time dependent stable/unstable splitting. Our particular method
is based upon shadowing refinement and synchronization techniques and is
motivated by work on Assimilation in the Unstable Subspace (AUS) and
Pseudo-orbit Data Assimilation (PDA). The algorithm utilizes time dependent
projections onto the non-stable subspace determined by employing computational
techniques for Lyapunov exponents/vectors. The method is extended to parameter
estimation without changing the problem dynamics and we address techniques for
adapting the method when (as is commonly the case) observations are not
available in the full model state space. We use a combination of analysis and
numerical experiments (with the Lorenz 63 and Lorenz 96 models) to illustrate
the efficacy of the techniques and show that the results compare favorably with
other variational techniques.
| 0 | 1 | 0 | 0 | 0 | 0 |
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