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Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering | Existing deep multitask learning (MTL) approaches align layers shared between
tasks in a parallel ordering. Such an organization significantly constricts the
types of shared structure that can be learned. The necessity of parallel
ordering for deep MTL is first tested by comparing it with permuted ordering of
shared layers. The results indicate that a flexible ordering can enable more
effective sharing, thus motivating the development of a soft ordering approach,
which learns how shared layers are applied in different ways for different
tasks. Deep MTL with soft ordering outperforms parallel ordering methods across
a series of domains. These results suggest that the power of deep MTL comes
from learning highly general building blocks that can be assembled to meet the
demands of each task.
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Relational Algebra for In-Database Process Mining | The execution logs that are used for process mining in practice are often
obtained by querying an operational database and storing the result in a flat
file. Consequently, the data processing power of the database system cannot be
used anymore for this information, leading to constrained flexibility in the
definition of mining patterns and limited execution performance in mining large
logs. Enabling process mining directly on a database - instead of via
intermediate storage in a flat file - therefore provides additional flexibility
and efficiency. To help facilitate this ideal of in-database process mining,
this paper formally defines a database operator that extracts the 'directly
follows' relation from an operational database. This operator can both be used
to do in-database process mining and to flexibly evaluate process mining
related queries, such as: "which employee most frequently changes the 'amount'
attribute of a case from one task to the next". We define the operator using
the well-known relational algebra that forms the formal underpinning of
relational databases. We formally prove equivalence properties of the operator
that are useful for query optimization and present time-complexity properties
of the operator. By doing so this paper formally defines the necessary
relational algebraic elements of a 'directly follows' operator, which are
required for implementation of such an operator in a DBMS.
| 1 | 0 | 0 | 0 | 0 | 0 |
Global existence for the nonlinear fractional Schrödinger equation with fractional dissipation | We consider the initial value problem for the fractional nonlinear
Schrödinger equation with a fractional dissipation. Global existence and
scattering are proved depending on the order of the fractional dissipation.
| 0 | 0 | 1 | 0 | 0 | 0 |
Statistical properties of an enstrophy conserving discretisation for the stochastic quasi-geostrophic equation | A framework of variational principles for stochastic fluid dynamics was
presented by Holm (2015), and these stochastic equations were also derived by
Cotter et al. (2017). We present a conforming finite element discretisation for
the stochastic quasi-geostrophic equation that was derived from this framework.
The discretisation preserves the first two moments of potential vorticity, i.e.
the mean potential vorticity and the enstrophy. Following the work of Dubinkina
and Frank (2007), who investigated the statistical mechanics of discretisations
of the deterministic quasi-geostrophic equation, we investigate the statistical
mechanics of our discretisation of the stochastic quasi-geostrophic equation.
We compare the statistical properties of our discretisation with the Gibbs
distribution under assumption of these conserved quantities, finding that there
is agreement between the statistics under a wide range of set-ups.
| 0 | 1 | 0 | 0 | 0 | 0 |
Conditional Optimal Stopping: A Time-Inconsistent Optimization | Inspired by recent work of P.-L. Lions on conditional optimal control, we
introduce a problem of optimal stopping under bounded rationality: the
objective is the expected payoff at the time of stopping, conditioned on
another event. For instance, an agent may care only about states where she is
still alive at the time of stopping, or a company may condition on not being
bankrupt. We observe that conditional optimization is time-inconsistent due to
the dynamic change of the conditioning probability and develop an equilibrium
approach in the spirit of R. H. Strotz' work for sophisticated agents in
discrete time. Equilibria are found to be essentially unique in the case of a
finite time horizon whereas an infinite horizon gives rise to non-uniqueness
and other interesting phenomena. We also introduce a theory which generalizes
the classical Snell envelope approach for optimal stopping by considering a
pair of processes with Snell-type properties.
| 0 | 0 | 0 | 0 | 0 | 1 |
Principles for optimal cooperativity in allosteric materials | Allosteric proteins transmit a mechanical signal induced by binding a ligand.
However, understanding the nature of the information transmitted and the
architectures optimizing such transmission remains a challenge. Here we show
using an {\it in-silico} evolution scheme and theoretical arguments that
architectures optimized to be cooperative, which propagate efficiently energy,
{qualitatively} differ from previously investigated materials optimized to
propagate strain. Although we observe a large diversity of functioning
cooperative architectures (including shear, hinge and twist designs), they all
obey the same principle {of displaying a {\it mechanism}, i.e. an extended
{soft} mode}. We show that its optimal frequency decreases with the spatial
extension $L$ of the system as $L^{-d/2}$, where $d$ is the spatial dimension.
For these optimal designs, cooperativity decays logarithmically with $L$ for
$d=2$ and does not decay for $d=3$. Overall our approach leads to a natural
explanation for several observations in allosteric proteins, and { indicates an
experimental path to test if allosteric proteins lie close to optimality}.
| 0 | 1 | 0 | 0 | 0 | 0 |
Improved electronic structure and magnetic exchange interactions in transition metal oxides | We discuss the application of the Agapito Curtarolo and Buongiorno Nardelli
(ACBN0) pseudo-hybrid Hubbard density functional to several transition metal
oxides. ACBN0 is a fast, accurate and parameter-free alternative to traditional
DFT+$U$ and hybrid exact exchange methods. In ACBN0, the Hubbard energy of
DFT+$U$ is calculated via the direct evaluation of the local Coulomb and
exchange integrals in which the screening of the bare Coulomb potential is
accounted for by a renormalization of the density matrix. We demonstrate the
success of the ACBN0 approach for the electronic properties of a series
technologically relevant mono-oxides (MnO, CoO, NiO, FeO, both at equilibrium
and under pressure). We also present results on two mixed valence compounds,
Co$_3$O$_4$ and Mn$_3$O$_4$. Our results, obtained at the computational cost of
a standard LDA/PBE calculation, are in excellent agreement with hybrid
functionals, the GW approximation and experimental measurements.
| 0 | 1 | 0 | 0 | 0 | 0 |
Test of SensL SiPM coated with NOL-1 wavelength shifter in liquid xenon | A SensL MicroFC-SMT-60035 6x6 mm$^2$ silicon photo-multiplier coated with a
NOL-1 wavelength shifter have been tested in the liquid xenon to detect the
175-nm scintillation light. For comparison, a Hamamatsu vacuum ultraviolet
sensitive MPPC VUV3 3x3 mm$^2$ was tested under the same conditions. The
photodetection efficiency of $13.1 \pm 2.5$% and $6.0 \pm 1.0$%,
correspondingly, is obtained.
| 0 | 1 | 0 | 0 | 0 | 0 |
Neon2: Finding Local Minima via First-Order Oracles | We propose a reduction for non-convex optimization that can (1) turn an
stationary-point finding algorithm into an local-minimum finding one, and (2)
replace the Hessian-vector product computations with only gradient
computations. It works both in the stochastic and the deterministic settings,
without hurting the algorithm's performance.
As applications, our reduction turns Natasha2 into a first-order method
without hurting its performance. It also converts SGD, GD, SCSG, and SVRG into
algorithms finding approximate local minima, outperforming some best known
results.
| 1 | 0 | 0 | 1 | 0 | 0 |
Geometrical Insights for Implicit Generative Modeling | Learning algorithms for implicit generative models can optimize a variety of
criteria that measure how the data distribution differs from the implicit model
distribution, including the Wasserstein distance, the Energy distance, and the
Maximum Mean Discrepancy criterion. A careful look at the geometries induced by
these distances on the space of probability measures reveals interesting
differences. In particular, we can establish surprising approximate global
convergence guarantees for the $1$-Wasserstein distance,even when the
parametric generator has a nonconvex parametrization.
| 1 | 0 | 0 | 1 | 0 | 0 |
Simple Countermeasures to Mitigate the Effect of Pollution Attack in Network Coding Based Peer-to-Peer Live Streaming | Network coding based peer-to-peer streaming represents an effective solution
to aggregate user capacities and to increase system throughput in live
multimedia streaming. Nonetheless, such systems are vulnerable to pollution
attacks where a handful of malicious peers can disrupt the communication by
transmitting just a few bogus packets which are then recombined and relayed by
unaware honest nodes, further spreading the pollution over the network. Whereas
previous research focused on malicious nodes identification schemes and
pollution-resilient coding, in this paper we show pollution countermeasures
which make a standard network coding scheme resilient to pollution attacks.
Thanks to a simple yet effective analytical model of a reference node
collecting packets by malicious and honest neighbors, we demonstrate that i)
packets received earlier are less likely to be polluted and ii) short
generations increase the likelihood to recover a clean generation. Therefore,
we propose a recombination scheme where nodes draw packets to be recombined
according to their age in the input queue, paired with a decoding scheme able
to detect the reception of polluted packets early in the decoding process and
short generations. The effectiveness of our approach is experimentally
evaluated in a real system we developed and deployed on hundreds to thousands
peers. Experimental evidence shows that, thanks to our simple countermeasures,
the effect of a pollution attack is almost canceled and the video quality
experienced by the peers is comparable to pre-attack levels.
| 1 | 0 | 0 | 0 | 0 | 0 |
Small-scale structure and the Lyman-$α$ forest baryon acoustic oscillation feature | The baryon-acoustic oscillation (BAO) feature in the Lyman-$\alpha$ forest is
one of the key probes of the cosmic expansion rate at redshifts z~2.5, well
before dark energy is believed to have become dynamically significant. A key
advantage of the BAO as a standard ruler is that it is a sharp feature and
hence is more robust against broadband systematic effects than other
cosmological probes. However, if the Lyman-$\alpha$ forest transmission is
sensitive to the initial streaming velocity of the baryons relative to the dark
matter, then the BAO peak position can be shifted. Here we investigate this
sensitivity using a suite of hydrodynamic simulations of small regions of the
intergalactic medium with a range of box sizes and physics assumptions; each
simulation starts from initial conditions at the kinematic decoupling era
(z~1059), undergoes a discrete change from neutral gas to ionized gas thermal
evolution at reionization (z~8), and is finally processed into a Lyman-$\alpha$
forest transmitted flux cube. Streaming velocities suppress small-scale
structure, leading to less violent relaxation after reionization. The changes
in the gas distribution and temperature-density relation at low redshift are
more subtle, due to the convergent temperature evolution in the ionized phase.
The change in the BAO scale is estimated to be of the order of 0.12% at z=2.5;
some of the major uncertainties and avenues for future improvement are
discussed. The predicted streaming velocity shift would be a subdominant but
not negligible effect (of order $0.26\sigma$) for the upcoming DESI
Lyman-$\alpha$ forest survey, and exceeds the cosmic variance floor. It is
hoped that this study will motivate additional theoretical work on the
magnitude of the BAO shift, both in the Lyman-$\alpha$ forest and in other
tracers of large-scale structure.
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Scale-dependent perturbations finally detectable by future galaxy surveys and their contribution to cosmological model selection | By means of the present geometrical and dynamical observational data, it is
very hard to establish, from a statistical perspective, a clear preference
among the vast majority of the proposed models for the dynamical dark energy
and/or modified gravity theories alternative with respect to the $\Lambda$CDM
scenario. On the other hand, on scales much smaller than present Hubble scale,
there are possibly detectable differences in the growth of the matter
perturbations for different modes of the perturbations, even in the context of
the $\Lambda$CDM model. Here, we analyze the evolution of the dark matter
perturbations in the context of $\Lambda$CDM and some dynamical dark energy
models involving future cosmological singularities, such as the sudden future
singularity and the finite scale factor singularity. We employ the Newtonian
gauge formulation for the derivation of the perturbation equations for the
growth function, and we abandon both the sub-Hubble approximation and the
slowly varying potential assumption. We apply the Fisher Matrix approach to
three future planned galaxy surveys e.g., DESI, Euclid, and WFirst-2.4. With
the mentioned surveys on hand, only with the dynamical probes, we will achieve
multiple goals: $1.$ the improvement in the accuracy of the determination of
the $f\sigma_{8}$ will give the possibility to discriminate between the
$\Lambda$CDM and the alternative dark energy models even in the
scale-independent approach; $2.$ it will be possible to test the goodness of
the scale-independence finally, and also to quantify the necessity of a scale
dependent approach to the growth of the perturbations, in particular using
surveys which encompass redshift bins with scales $k<0.005\,h$ Mpc$^{-1}$; $3.$
the scale-dependence itself might add much more discriminating power in
general, but further advanced surveys will be needed.
| 0 | 1 | 0 | 0 | 0 | 0 |
InfoCatVAE: Representation Learning with Categorical Variational Autoencoders | This paper describes InfoCatVAE, an extension of the variational autoencoder
that enables unsupervised disentangled representation learning. InfoCatVAE uses
multimodal distributions for the prior and the inference network and then
maximizes the evidence lower bound objective (ELBO). We connect the new ELBO
derived for our model with a natural soft clustering objective which explains
the robustness of our approach. We then adapt the InfoGANs method to our
setting in order to maximize the mutual information between the categorical
code and the generated inputs and obtain an improved model.
| 0 | 0 | 0 | 1 | 0 | 0 |
Quadratic twists of abelian varieties and disparity in Selmer ranks | We study the parity of 2-Selmer ranks in the family of quadratic twists of a
fixed principally polarised abelian variety over a number field. Specifically,
we determine the proportion of twists having odd (resp. even) 2-Selmer rank.
This generalises work of Klagsbrun--Mazur--Rubin for elliptic curves and Yu for
Jacobians of hyperelliptic curves. Several differences in the statistics arise
due to the possibility that the Shafarevich--Tate group (if finite) may have
order twice a square. In particular, the statistics for parities of 2-Selmer
ranks and 2-infinity Selmer ranks need no longer agree and we describe both.
| 0 | 0 | 1 | 0 | 0 | 0 |
From acquaintance to best friend forever: robust and fine-grained inference of social tie strengths | Social networks often provide only a binary perspective on social ties: two
individuals are either connected or not. While sometimes external information
can be used to infer the strength of social ties, access to such information
may be restricted or impractical. Sintos and Tsaparas (KDD 2014) first
suggested to infer the strength of social ties from the topology of the network
alone, by leveraging the Strong Triadic Closure (STC) property. The STC
property states that if person A has strong social ties with persons B and C, B
and C must be connected to each other as well (whether with a weak or strong
tie). Sintos and Tsaparas exploited this to formulate the inference of the
strength of social ties as NP-hard optimization problem, and proposed two
approximation algorithms. We refine and improve upon this landmark paper, by
developing a sequence of linear relaxations of this problem that can be solved
exactly in polynomial time. Usefully, these relaxations infer more fine-grained
levels of tie strength (beyond strong and weak), which also allows to avoid
making arbitrary strong/weak strength assignments when the network topology
provides inconclusive evidence. One of the relaxations simultaneously infers
the presence of a limited number of STC violations. An extensive theoretical
analysis leads to two efficient algorithmic approaches. Finally, our
experimental results elucidate the strengths of the proposed approach, and
sheds new light on the validity of the STC property in practice.
| 1 | 0 | 0 | 0 | 0 | 0 |
Conditional bias robust estimation of the total of curve data by sampling in a finite population: an illustration on electricity load curves | For marketing or power grid management purposes, many studies based on the
analysis of the total electricity consumption curves of groups of customers are
now carried out by electricity companies. Aggregated total or mean load curves
are estimated using individual curves measured at fine time grid and collected
according to some sampling design. Due to the skewness of the distribution of
electricity consumptions, these samples often contain outlying curves which may
have an important impact on the usual estimation procedures. We introduce
several robust estimators of the total consumption curve which are not
sensitive to such outlying curves. These estimators are based on the
conditional bias approach and robust functional methods. We also derive mean
square error estimators of these robust estimators and finally, we evaluate and
compare the performance of the suggested estimators on Irish electricity data.
| 0 | 0 | 0 | 1 | 0 | 0 |
Ulrich bundles on smooth projective varieties of minimal degree | We classify the Ulrich vector bundles of arbitrary rank on smooth projective
varieties of minimal degree. In the process, we prove the stability of the
sheaves of relative differentials on rational scrolls.
| 0 | 0 | 1 | 0 | 0 | 0 |
$k$-shellable simplicial complexes and graphs | In this paper we show that a $k$-shellable simplicial complex is the
expansion of a shellable complex. We prove that the face ring of a pure
$k$-shellable simplicial complex satisfies the Stanley conjecture. In this way,
by applying expansion functor to the face ring of a given pure shellable
complex, we construct a large class of rings satisfying the Stanley conjecture.
Also, by presenting some characterizations of $k$-shellable graphs, we extend
some results due to Castrillón-Cruz, Cruz-Estrada and Van Tuyl-Villareal.
| 0 | 0 | 1 | 0 | 0 | 0 |
The Effect of Phasor Measurement Units on the Accuracy of the Network Estimated Variables | The most commonly used weighted least square state estimator in power
industry is nonlinear and formulated by using conventional measurements such as
line flow and injection measurements. PMUs (Phasor Measurement Units) are
gradually adding them to improve the state estimation process. In this paper
the way of corporation the PMU data to the conventional measurements and a
linear formulation of the state estimation using only PMU measured data are
investigated. Six cases are tested while gradually increasing the number of
PMUs which are added to the measurement set and the effect of PMUs on the
accuracy of variables are illustrated and compared by applying them on IEEE 14,
30 test systems.
| 1 | 0 | 1 | 0 | 0 | 0 |
$ε$-Regularity and Structure of 4-dimensional Shrinking Ricci Solitons | A closed four dimensional manifold cannot possess a non-flat Ricci soliton
metric with arbitrarily small $L^2$-norm of the curvature. In this paper, we
localize this fact in the case of shrinking Ricci solitons by proving an
$\varepsilon$-regularity theorem, thus confirming a conjecture of Cheeger-Tian.
As applications, we will also derive structural results concerning the
degeneration of the metrics on a family of complete non-compact four
dimensional shrinking Ricci solitons without a uniform entropy lower bound. In
the appendix, we provide a detailed account of the equivariant good chopping
theorem when collapsing with locally bounded curvature happens.
| 0 | 0 | 1 | 0 | 0 | 0 |
Cosmological model discrimination with Deep Learning | We demonstrate the potential of Deep Learning methods for measurements of
cosmological parameters from density fields, focusing on the extraction of
non-Gaussian information. We consider weak lensing mass maps as our dataset. We
aim for our method to be able to distinguish between five models, which were
chosen to lie along the $\sigma_8$ - $\Omega_m$ degeneracy, and have nearly the
same two-point statistics. We design and implement a Deep Convolutional Neural
Network (DCNN) which learns the relation between five cosmological models and
the mass maps they generate. We develop a new training strategy which ensures
the good performance of the network for high levels of noise. We compare the
performance of this approach to commonly used non-Gaussian statistics, namely
the skewness and kurtosis of the convergence maps. We find that our
implementation of DCNN outperforms the skewness and kurtosis statistics,
especially for high noise levels. The network maintains the mean discrimination
efficiency greater than $85\%$ even for noise levels corresponding to ground
based lensing observations, while the other statistics perform worse in this
setting, achieving efficiency less than $70\%$. This demonstrates the ability
of CNN-based methods to efficiently break the $\sigma_8$ - $\Omega_m$
degeneracy with weak lensing mass maps alone. We discuss the potential of this
method to be applied to the analysis of real weak lensing data and other
datasets.
| 0 | 1 | 0 | 1 | 0 | 0 |
Deep Memory Networks for Attitude Identification | We consider the task of identifying attitudes towards a given set of entities
from text. Conventionally, this task is decomposed into two separate subtasks:
target detection that identifies whether each entity is mentioned in the text,
either explicitly or implicitly, and polarity classification that classifies
the exact sentiment towards an identified entity (the target) into positive,
negative, or neutral.
Instead, we show that attitude identification can be solved with an
end-to-end machine learning architecture, in which the two subtasks are
interleaved by a deep memory network. In this way, signals produced in target
detection provide clues for polarity classification, and reversely, the
predicted polarity provides feedback to the identification of targets.
Moreover, the treatments for the set of targets also influence each other --
the learned representations may share the same semantics for some targets but
vary for others. The proposed deep memory network, the AttNet, outperforms
methods that do not consider the interactions between the subtasks or those
among the targets, including conventional machine learning methods and the
state-of-the-art deep learning models.
| 1 | 0 | 0 | 0 | 0 | 0 |
Discrete flow posteriors for variational inference in discrete dynamical systems | Each training step for a variational autoencoder (VAE) requires us to sample
from the approximate posterior, so we usually choose simple (e.g. factorised)
approximate posteriors in which sampling is an efficient computation that fully
exploits GPU parallelism. However, such simple approximate posteriors are often
insufficient, as they eliminate statistical dependencies in the posterior.
While it is possible to use normalizing flow approximate posteriors for
continuous latents, some problems have discrete latents and strong statistical
dependencies. The most natural approach to model these dependencies is an
autoregressive distribution, but sampling from such distributions is inherently
sequential and thus slow. We develop a fast, parallel sampling procedure for
autoregressive distributions based on fixed-point iterations which enables
efficient and accurate variational inference in discrete state-space latent
variable dynamical systems. To optimize the variational bound, we considered
two ways to evaluate probabilities: inserting the relaxed samples directly into
the pmf for the discrete distribution, or converting to continuous logistic
latent variables and interpreting the K-step fixed-point iterations as a
normalizing flow. We found that converting to continuous latent variables gave
considerable additional scope for mismatch between the true and approximate
posteriors, which resulted in biased inferences, we thus used the former
approach. Using our fast sampling procedure, we were able to realize the
benefits of correlated posteriors, including accurate uncertainty estimates for
one cell, and accurate connectivity estimates for multiple cells, in an order
of magnitude less time.
| 0 | 0 | 0 | 1 | 1 | 0 |
Audio Super Resolution using Neural Networks | We introduce a new audio processing technique that increases the sampling
rate of signals such as speech or music using deep convolutional neural
networks. Our model is trained on pairs of low and high-quality audio examples;
at test-time, it predicts missing samples within a low-resolution signal in an
interpolation process similar to image super-resolution. Our method is simple
and does not involve specialized audio processing techniques; in our
experiments, it outperforms baselines on standard speech and music benchmarks
at upscaling ratios of 2x, 4x, and 6x. The method has practical applications in
telephony, compression, and text-to-speech generation; it demonstrates the
effectiveness of feed-forward convolutional architectures on an audio
generation task.
| 1 | 0 | 0 | 0 | 0 | 0 |
Thermoelectric power factor enhancement by spin-polarized currents - a nanowire case study | Thermoelectric (TE) measurements have been performed on the workhorses of
today's data storage devices, exhibiting either the giant or the anisotropic
magnetoresistance effect (GMR and AMR). The temperature-dependent (50-300 K)
and magnetic field-dependent (up to 1 T) TE power factor (PF) has been
determined for several Co-Ni alloy nanowires with varying Co:Ni ratios as well
as for Co-Ni/Cu multilayered nanowires with various Cu layer thicknesses, which
were all synthesized via a template-assisted electrodeposition process. A
systematic investigation of the resistivity, as well as the Seebeck
coefficient, is performed for Co-Ni alloy nanowires and Co-Ni/Cu multilayered
nanowires. At room temperature, measured values of TE PFs up to 3.6 mWK-2m-1
for AMR samples and 2.0 mWK-2m-1 for GMR nanowires are obtained. Furthermore,
the TE PF is found to increase by up to 13.1 % for AMR Co-Ni alloy nanowires
and by up to 52 % for GMR Co-Ni/Cu samples in an external applied magnetic
field. The magnetic nanowires exhibit TE PFs that are of the same order of
magnitude as TE PFs of Bi-Sb-Se-Te based thermoelectric materials and,
additionally, give the opportunity to adjust the TE power output to changing
loads and hotspots through external magnetic fields.
| 0 | 1 | 0 | 0 | 0 | 0 |
Risk-Sensitive Cooperative Games for Human-Machine Systems | Autonomous systems can substantially enhance a human's efficiency and
effectiveness in complex environments. Machines, however, are often unable to
observe the preferences of the humans that they serve. Despite the fact that
the human's and machine's objectives are aligned, asymmetric information, along
with heterogeneous sensitivities to risk by the human and machine, make their
joint optimization process a game with strategic interactions. We propose a
framework based on risk-sensitive dynamic games; the human seeks to optimize
her risk-sensitive criterion according to her true preferences, while the
machine seeks to adaptively learn the human's preferences and at the same time
provide a good service to the human. We develop a class of performance measures
for the proposed framework based on the concept of regret. We then evaluate
their dependence on the risk-sensitivity and the degree of uncertainty. We
present applications of our framework to self-driving taxis, and robo-financial
advising.
| 1 | 0 | 0 | 1 | 0 | 0 |
A natural framework for isogeometric fluid-structure interaction based on BEM-shell coupling | The interaction between thin structures and incompressible Newtonian fluids
is ubiquitous both in nature and in industrial applications. In this paper we
present an isogeometric formulation of such problems which exploits a boundary
integral formulation of Stokes equations to model the surrounding flow, and a
non linear Kirchhoff-Love shell theory to model the elastic behaviour of the
structure. We propose three different coupling strategies: a monolithic, fully
implicit coupling, a staggered, elasticity driven coupling, and a novel
semi-implicit coupling, where the effect of the surrounding flow is
incorporated in the non-linear terms of the solid solver through its damping
characteristics. The novel semi-implicit approach is then used to demonstrate
the power and robustness of our method, which fits ideally in the isogeometric
paradigm, by exploiting only the boundary representation (B-Rep) of the thin
structure middle surface.
| 0 | 1 | 1 | 0 | 0 | 0 |
Inertial Effects on the Stress Generation of Active Fluids | Suspensions of self-propelled bodies generate a unique mechanical stress
owing to their motility that impacts their large-scale collective behavior. For
microswimmers suspended in a fluid with negligible particle inertia, we have
shown that the virial `swim stress' is a useful quantity to understand the
rheology and nonequilibrium behaviors of active soft matter systems. For larger
self-propelled organisms like fish, it is unclear how particle inertia impacts
their stress generation and collective movement. Here, we analyze the effects
of finite particle inertia on the mechanical pressure (or stress) generated by
a suspension of self-propelled bodies. We find that swimmers of all scales
generate a unique `swim stress' and `Reynolds stress' that impacts their
collective motion. We discover that particle inertia plays a similar role as
confinement in overdamped active Brownian systems, where the reduced run length
of the swimmers decreases the swim stress and affects the phase behavior.
Although the swim and Reynolds stresses vary individually with the magnitude of
particle inertia, the sum of the two contributions is independent of particle
inertia. This points to an important concept when computing stresses in
computer simulations of nonequilibrium systems---the Reynolds and the virial
stresses must both be calculated to obtain the overall stress generated by a
system.
| 0 | 1 | 0 | 0 | 0 | 0 |
On Gauge Invariance and Covariant Derivatives in Metric Spaces | In this manuscript, we will discuss the construction of covariant derivative
operator in quantum gravity. We will find it is appropriate to use affine
connections more general than metric compatible connections in quantum gravity.
We will demonstrate this using the canonical quantization procedure. This is
valid irrespective of the presence and nature of sources. The standard Palatini
formalism, where metric and affine connections are the independent variables,
is not sufficient to construct a source-free theory of gravity with affine
connections more general than the metric compatible Levi-Civita connections.
This is also valid for minimally coupled interacting theories where sources
only couple with metric by using the metric compatible Levi-Civita connections
exclusively. We will discuss a potential formalism and possible extensions of
the action to introduce nonmetricity in these cases. This is also required to
construct a general interacting quantum theory with dynamical general affine
connections. We will have to use a modified Ricci tensor to state Einstein's
equation in the Palatini formalism. General affine connections can be described
by a third rank tensor with one contravariant and two covariant indices.
Antisymmetric part of this tensor in the lower indices gives torsion with a
half factor. In the Palatini formalism or its generalizations considered here,
symmetric part of this tensor in the lower indices is finite when torsion is
finite. This part can give a massless scalar field in a potential formalism. We
will have to extend the local conservation laws when we use general affine
connections. General affine connections can become significant to solve
cosmological problems.
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A Compressed Sensing Approach for Distribution Matching | In this work, we formulate the fixed-length distribution matching as a
Bayesian inference problem. Our proposed solution is inspired from the
compressed sensing paradigm and the sparse superposition (SS) codes. First, we
introduce sparsity in the binary source via position modulation (PM). We then
present a simple and exact matcher based on Gaussian signal quantization. At
the receiver, the dematcher exploits the sparsity in the source and performs
low-complexity dematching based on generalized approximate message-passing
(GAMP). We show that GAMP dematcher and spatial coupling lead to asymptotically
optimal performance, in the sense that the rate tends to the entropy of the
target distribution with vanishing reconstruction error in a proper limit.
Furthermore, we assess the performance of the dematcher on practical
Hadamard-based operators. A remarkable feature of our proposed solution is the
possibility to: i) perform matching at the symbol level (nonbinary); ii)
perform joint channel coding and matching.
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A simple descriptor and predictor for the stable structures of two-dimensional surface alloys | Predicting the ground state of alloy systems is challenging due to the large
number of possible configurations. We identify an easily computed descriptor
for the stability of binary surface alloys, the effective coordination number
$\mathscr{E}$. We show that $\mathscr{E}(M)$ correlates well with the enthalpy
of mixing, from density functional theory (DFT) calculations on
$M_x$Au$_{1-x}$/Ru [$M$ = Mn or Fe]. At each $x$, the most favored structure
has the highest [lowest] value of $\mathscr{E}(M)$ if the system is
non-magnetic [ferromagnetic]. Importantly, little accuracy is lost upon
replacing $\mathscr{E}(M)$ by $\mathscr{E}^*(M)$, which can be quickly computed
without performing a DFT calculation, possibly offering a simple alternative to
the frequently used cluster expansion method.
| 0 | 1 | 0 | 0 | 0 | 0 |
Fractional integrals and Fourier transforms | This paper gives a short survey of some basic results related to estimates of
fractional integrals and Fourier transforms. It is closely adjoint to our
previous survey papers \cite{K1998} and \cite{K2007}. The main methods used in
the paper are based on nonincreasing rearrangements. We give alternative proofs
of some results.
We observe also that the paper represents the mini-course given by the author
at Barcelona University in October, 2014.
| 0 | 0 | 1 | 0 | 0 | 0 |
Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation | As opposed to manual feature engineering which is tedious and difficult to
scale, network representation learning has attracted a surge of research
interests as it automates the process of feature learning on graphs. The
learned low-dimensional node vector representation is generalizable and eases
the knowledge discovery process on graphs by enabling various off-the-shelf
machine learning tools to be directly applied. Recent research has shown that
the past decade of network embedding approaches either explicitly factorize a
carefully designed matrix to obtain the low-dimensional node vector
representation or are closely related to implicit matrix factorization, with
the fundamental assumption that the factorized node connectivity matrix is
low-rank. Nonetheless, the global low-rank assumption does not necessarily hold
especially when the factorized matrix encodes complex node interactions, and
the resultant single low-rank embedding matrix is insufficient to capture all
the observed connectivity patterns. In this regard, we propose a novel
multi-level network embedding framework BoostNE, which can learn multiple
network embedding representations of different granularity from coarse to fine
without imposing the prevalent global low-rank assumption. The proposed BoostNE
method is also in line with the successful gradient boosting method in ensemble
learning as multiple weak embeddings lead to a stronger and more effective one.
We assess the effectiveness of the proposed BoostNE framework by comparing it
with existing state-of-the-art network embedding methods on various datasets,
and the experimental results corroborate the superiority of the proposed
BoostNE network embedding framework.
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Deviation from the dipole-ice model in the new spinel spin-ice candidate, MgEr$_2$Se$_4$ | In spin ice research, small variations in structure or interactions drive a
multitude of different behaviors, yet the collection of known materials relies
heavily on the `227' pyrochlore structure. Here, we present thermodynamic,
structural and inelastic neutron scattering data on a new spin-ice material,
MgEr$_2$Se$_4$, which contributes to the relatively underexplored family of
rare-earth spinel chalcogenides. X-ray and neutron diffraction confirm a normal
spinel structure, and places Er$^{3+}$ moments on an ideal pyrochlore
sublattice. Measurement of crystal electric field excitations with neutron
inelastic scattering confirms that the moments have perfect Ising character,
and further identifies the ground state Kramers doublet as having
dipole-octupolar form with a significant multipolar character. Heat capacity
and magnetic neutron diffuse scattering have ice-like features, but are
inconsistent with Monte Carlo simulations of the nearest-neighbor and
next-nearest-neighbor dipolar spin-ice (DSI) models. A significant remnant
entropy is observed as T$\rightarrow$0 K, but again falls short of the full
Pauling expectation for DSI, unless significant disorder is added. We show that
these observations are fully in-line with what is recently reported for
CdEr$_2$Se$_4$, and point to the importance of quantum fluctuations in these
materials.
| 0 | 1 | 0 | 0 | 0 | 0 |
Generating Nontrivial Melodies for Music as a Service | We present a hybrid neural network and rule-based system that generates pop
music. Music produced by pure rule-based systems often sounds mechanical. Music
produced by machine learning sounds better, but still lacks hierarchical
temporal structure. We restore temporal hierarchy by augmenting machine
learning with a temporal production grammar, which generates the music's
overall structure and chord progressions. A compatible melody is then generated
by a conditional variational recurrent autoencoder. The autoencoder is trained
with eight-measure segments from a corpus of 10,000 MIDI files, each of which
has had its melody track and chord progressions identified heuristically. The
autoencoder maps melody into a multi-dimensional feature space, conditioned by
the underlying chord progression. A melody is then generated by feeding a
random sample from that space to the autoencoder's decoder, along with the
chord progression generated by the grammar. The autoencoder can make musically
plausible variations on an existing melody, suitable for recurring motifs. It
can also reharmonize a melody to a new chord progression, keeping the rhythm
and contour. The generated music compares favorably with that generated by
other academic and commercial software designed for the music-as-a-service
industry.
| 1 | 0 | 0 | 0 | 0 | 0 |
Vision and Challenges for Knowledge Centric Networking (KCN) | In the creation of a smart future information society, Internet of Things
(IoT) and Content Centric Networking (CCN) break two key barriers for both the
front-end sensing and back-end networking. However, we still observe the
missing piece of the research that dominates the current networking traffic
control and system management, e.g., lacking of the knowledge penetrated into
both sensing and networking to glue them holistically. In this paper, we
envision to leverage emerging machine learning or deep learning techniques to
create aspects of knowledge for facilitating the designs. In particular, we can
extract knowledge from collected data to facilitate reduced data volume,
enhanced system intelligence and interactivity, improved service quality,
communication with better controllability and lower cost. We name such a
knowledge-oriented traffic control and networking management paradigm as the
Knowledge Centric Networking (KCN). This paper presents KCN rationale, KCN
benefits, related works and research opportunities.
| 1 | 0 | 0 | 0 | 0 | 0 |
Extracting Geometry from Quantum Spacetime: Obstacles down the road | Any acceptable quantum gravity theory must allow us to recover the classical
spacetime in the appropriate limit. Moreover, the spacetime geometrical notions
should be intrinsically tied to the behavior of the matter that probes them. We
consider some difficulties that would be confronted in attempting such an
enterprise. The problems we uncover seem to go beyond the technical level to
the point of questioning the overall feasibility of the project. The main issue
is related to the fact that, in the quantum theory, it is impossible to assign
a trajectory to a physical object, and, on the other hand, according to the
basic tenets of the geometrization of gravity, it is precisely the trajectories
of free localized objects that define the spacetime geometry. The insights
gained in this analysis should be relevant to those interested in the quest for
a quantum theory of gravity and might help refocus some of its goals.
| 0 | 1 | 0 | 0 | 0 | 0 |
Data-Driven Estimation of Travel Latency Cost Functions via Inverse Optimization in Multi-Class Transportation Networks | We develop a method to estimate from data travel latency cost functions in
multi-class transportation networks, which accommodate different types of
vehicles with very different characteristics (e.g., cars and trucks).
Leveraging our earlier work on inverse variational inequalities, we develop a
data-driven approach to estimate the travel latency cost functions. Extensive
numerical experiments using benchmark networks, ranging from moderate-sized to
large-sized, demonstrate the effectiveness and efficiency of our approach.
| 1 | 0 | 1 | 0 | 0 | 0 |
Autoencoder Based Sample Selection for Self-Taught Learning | Self-taught learning is a technique that uses a large number of unlabeled
data as source samples to improve the task performance on target samples.
Compared with other transfer learning techniques, self-taught learning can be
applied to a broader set of scenarios due to the loose restrictions on source
data. However, knowledge transferred from source samples that are not
sufficiently related to the target domain may negatively influence the target
learner, which is referred to as negative transfer. In this paper, we propose a
metric for the relevance between a source sample and target samples. To be more
specific, both source and target samples are reconstructed through a
single-layer autoencoder with a linear relationship between source samples and
target samples simultaneously enforced. An l_{2,1}-norm sparsity constraint is
imposed on the transformation matrix to identify source samples relevant to the
target domain. Source domain samples that are deemed relevant are assigned
pseudo-labels reflecting their relevance to target domain samples, and are
combined with target samples in order to provide an expanded training set for
classifier training. Local data structures are also preserved during source
sample selection through spectral graph analysis. Promising results in
extensive experiments show the advantages of the proposed approach.
| 0 | 0 | 0 | 1 | 0 | 0 |
Guiding Chemical Synthesis: Computational Prediction of the Regioselectivity of CH Functionalization | We will develop a computational method (RegioSQM) for predicting the
regioselectivity of CH functionalization reactions that can be used by
synthetic chemists who are not experts in computational chemistry through a
simple web interface (regiosqm.org). CH functionalization, i.e. replacing the
hydrogen atom in a CH bond with another atom or molecule, is arguably single
most promising technique for increasing the efficiency of chemical synthesis,
but there are no generally applicable predictive tools that predict which CH
bond is most reactive. RegioSQM uses semiempirical quantum chemistry methods to
predict relative stabilities of reaction intermediates which correlates with
reaction rate and our goal is to determine which quantum method and
intermediate give the best result for each reaction type. Finally, we will
experimentally demonstrate how RegioSQM can be used to make the chemical
synthesis part of drug discovery more efficient.
| 0 | 1 | 0 | 0 | 0 | 0 |
Potential-Function Proofs for First-Order Methods | This note discusses proofs for convergence of first-order methods based on
simple potential-function arguments. We cover methods like gradient descent
(for both smooth and non-smooth settings), mirror descent, and some accelerated
variants.
| 1 | 0 | 0 | 0 | 0 | 0 |
Multidimensional $p$-adic continued fraction algorithms | We give a new class of multidimensional $p$-adic continued fraction
algorithms. We propose an algorithm in the class for which we can expect that
multidimensional $p$-adic version of Lagrange's Theorem holds.
| 0 | 0 | 1 | 0 | 0 | 0 |
Shutting down or powering up a (U)LIRG? Merger components in distinctly different evolutionary states in IRAS 19115-2124 (The Bird) | We present new SINFONI near-infrared integral field unit (IFU) spectroscopy
and SALT optical long-slit spectroscopy characterising the history of a nearby
merging luminous infrared galaxy, dubbed the Bird (IRAS19115-2114). The NIR
line-ratio maps of the IFU data-cubes and stellar population fitting of the
SALT spectra now allow dating of the star formation (SF) over the triple system
uncovered from our previous adaptive optics data. The distinct components
separate very clearly in a line-ratio diagnostic diagram. An off-nuclear pure
starburst dominates the current SF of the Bird with 60-70% of the total, with a
4-7 Myr age, and signs of a fairly constant long-term star formation of the
underlying stellar population. The most massive nucleus, in contrast, is
quenched with a starburst age of >40 Myr and shows hints of budding AGN
activity. The secondary massive nucleus is at an intermediate stage. The two
major components have a population of older stars, consistent with a starburst
triggered 1 Gyr ago in a first encounter. The simplest explanation of the
history is that of a triple merger, where the strongly star forming component
has joined later. We detect multiple gas flows in different phases. The Bird
offers an opportunity to witness multiple stages of galaxy evolution in the
same system; triggering as well as quenching of SF, and the early appearance of
AGN activity. It also serves as a cautionary note on interpretations of
observations with lower spatial resolution and/or without infrared data. At
high-redshift the system would look like a clumpy starburst with crucial pieces
of its puzzle hidden, in danger of misinterpretations.
| 0 | 1 | 0 | 0 | 0 | 0 |
Asymptotics to all orders of the Hurwitz zeta function | We present several formulae for the large-$t$ asymptotics of the modified
Hurwitz zeta function $\zeta_1(x,s),x>0,s=\sigma+it,0<\sigma\leq1,t>0,$ which
are valid to all orders. In the case of $x=0$, these formulae reduce to the
asymptotic expressions recently obtained for the Riemann zeta function, which
include the classical results of Siegel as a particular case.
| 0 | 0 | 1 | 0 | 0 | 0 |
Distributed Stochastic Approximation with Local Projections | We propose a distributed version of a stochastic approximation scheme
constrained to remain in the intersection of a finite family of convex sets.
The projection to the intersection of these sets is also computed in a
distributed manner and a `nonlinear gossip' mechanism is employed to blend the
projection iterations with the stochastic approximation using multiple time
scales
| 1 | 0 | 0 | 0 | 0 | 0 |
Expected Policy Gradients | We propose expected policy gradients (EPG), which unify stochastic policy
gradients (SPG) and deterministic policy gradients (DPG) for reinforcement
learning. Inspired by expected sarsa, EPG integrates across the action when
estimating the gradient, instead of relying only on the action in the sampled
trajectory. We establish a new general policy gradient theorem, of which the
stochastic and deterministic policy gradient theorems are special cases. We
also prove that EPG reduces the variance of the gradient estimates without
requiring deterministic policies and, for the Gaussian case, with no
computational overhead. Finally, we show that it is optimal in a certain sense
to explore with a Gaussian policy such that the covariance is proportional to
the exponential of the scaled Hessian of the critic with respect to the
actions. We present empirical results confirming that this new form of
exploration substantially outperforms DPG with the Ornstein-Uhlenbeck heuristic
in four challenging MuJoCo domains.
| 1 | 0 | 0 | 1 | 0 | 0 |
A new Hysteretic Nonlinear Energy Sink (HNES) | The behavior of a new Hysteretic Nonlinear Energy Sink (HNES) coupled to a
linear primary oscillator is investigated in shock mitigation. Apart from a
small mass and a nonlinear elastic spring of the Duffing oscillator, the HNES
is also comprised of a purely hysteretic and a linear elastic spring of
potentially negative stiffness, connected in parallel. The Bouc-Wen model is
used to describe the force produced by both the purely hysteretic and linear
elastic springs. Coupling the primary oscillator with the HNES three nonlinear
equations of motion are derived, in terms of the two displacements and the
dimensionless hysteretic variable, which are integrated numerically using the
analog equation method. The performance of the HNES is examined by quantifying
the percentage of the initially induced energy in the primary system that is
passively transferred and dissipated by the HNES. Remarkable results are
achieved for a wide range of initial input energies. The great performance of
the HNES is mostly evidenced when the linear spring stiffness takes on negative
values.
| 0 | 1 | 0 | 0 | 0 | 0 |
Ultra-Low Noise Amplifier Design for Magnetic Resonance Imaging systems | This paper demonstrates designing and developing of an Ultra-Low Noise
Amplifier which should potentially increase the sensitivity of the existing
Magnetic Resonance Imaging (MRI) systems. The Design of the LNA is fabricated
and characterized including matching and input high power protection circuits.
The estimate improvement of SNR of the LNA in comparison to room temperature
operation is taken here. The Cascode amplifier topology is chosen to be
investigated for high performance Low Noise amplifier design and for the
fabrication. The fabricated PCB layout of the Cascode LNA is tested by using
measurement instruments Spectrum Analyser and Vector Network analyzer. The
measurements of fabricated PCB layout of the Cascode LNA at room temperature
had the following performance, the operation frequency is 32 MHz, the noise
figure is 0.45 dB at source impedance 50 {\Omega}, the gain is 11.6 dB, the
output return loss is 21.1 dB, and the input return loss 0.12 dB and it is
unconditionally stable for up to 6 GHz band. The goal of the research is
achieved where the Cascode LNA had improvement of SNR.
| 0 | 1 | 0 | 0 | 0 | 0 |
Virtual Astronaut for Scientific Visualization - A Prototype for Santa Maria Crater on Mars | To support scientific visualization of multiple-mission data from Mars, the
Virtual Astronaut (VA) creates an interactive virtual 3D environment built on
the Unity3D Game Engine. A prototype study was conducted based on orbital and
Opportunity Rover data covering Santa Maria Crater in Meridiani Planum on Mars.
The VA at Santa Maria provides dynamic visual representations of the imaging,
compositional, and mineralogical information. The VA lets one navigate through
the scene and provides geomorphic and geologic contexts for the rover
operations. User interactions include in-situ observations visualization,
feature measurement, and an animation control of rover drives. This paper
covers our approach and implementation of the VA system. A brief summary of the
prototype system functions and user feedback is also covered. Based on external
review and comments by the science community, the prototype at Santa Maria has
proven the VA to be an effective tool for virtual geovisual analysis.
| 1 | 1 | 0 | 0 | 0 | 0 |
Self-Supervised Generalisation with Meta Auxiliary Learning | Learning with auxiliary tasks has been shown to improve the generalisation of
a primary task. However, this comes at the cost of manually-labelling
additional tasks which may, or may not, be useful for the primary task. We
propose a new method which automatically learns labels for an auxiliary task,
such that any supervised learning task can be improved without requiring access
to additional data. The approach is to train two neural networks: a
label-generation network to predict the auxiliary labels, and a multi-task
network to train the primary task alongside the auxiliary task. The loss for
the label-generation network incorporates the multi-task network's performance,
and so this interaction between the two networks can be seen as a form of meta
learning. We show that our proposed method, Meta AuXiliary Learning (MAXL),
outperforms single-task learning on 7 image datasets by a significant margin,
without requiring additional auxiliary labels. We also show that MAXL
outperforms several other baselines for generating auxiliary labels, and is
even competitive when compared with human-defined auxiliary labels. The
self-supervised nature of our method leads to a promising new direction towards
automated generalisation. The source code is available at
\url{this https URL}.
| 1 | 0 | 0 | 1 | 0 | 0 |
Measuring High-Energy Spectra with HAWC | The High-Altitude Water-Cherenkov (HAWC) experiment is a TeV $\gamma$-ray
observatory located \unit[4100]{m} above sea level on the Sierra Negra mountain
in Puebla, Mexico. The detector consists of 300 water-filled tanks, each
instrumented with 4 photomultiplier tubes that utilize the water-Cherenkov
technique to detect atmospheric air showers produced by cosmic $\gamma$ rays.
Construction of HAWC was completed in March of 2015. The experiment's wide
instantaneous field of view (\unit[2]{sr}) and high duty cycle (> 95\%) make it
a powerful survey instrument sensitive to pulsars, supernova remnants, and
other $\gamma$-ray sources. The mechanisms of particle acceleration at these
sources can be studied by analyzing their high-energy spectra. To this end, we
have developed an event-by-event energy-reconstruction algorithm using an
artificial neural network to estimate energies of primary $\gamma$ rays at
HAWC. We will present the details of this technique and its performance as well
as the current progress toward using it to measure energy spectra of
$\gamma$-ray sources.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Study on Arbitrarily Varying Channels with Causal Side Information at the Encoder | In this work, we study two models of arbitrarily varying channels, when
causal side information is available at the encoder in a causal manner. First,
we study the arbitrarily varying channel (AVC) with input and state
constraints, when the encoder has state information in a causal manner. Lower
and upper bounds on the random code capacity are developed. A lower bound on
the deterministic code capacity is established in the case of a
message-averaged input constraint. In the setting where a state constraint is
imposed on the jammer, while the user is under no constraints, the random code
bounds coincide, and the random code capacity is determined. Furthermore, for
this scenario, a generalized non-symmetrizability condition is stated, under
which the deterministic code capacity coincides with the random code capacity.
A second model considered in our work is the arbitrarily varying degraded
broadcast channel with causal side information at the encoder (without
constraints). We establish inner and outer bounds on both the random code
capacity region and the deterministic code capacity region. The capacity region
is then determined for a class of channels satisfying a condition on the mutual
informations between the strategy variables and the channel outputs. As an
example, we show that the condition holds for the arbitrarily varying binary
symmetric broadcast channel, and we find the corresponding capacity region.
| 1 | 0 | 1 | 0 | 0 | 0 |
On the Three Properties of Stationary Populations and knotting with Non-Stationary Populations | We propose three properties that are related to the stationary population
identity (SPI) of population biology by connecting it with stationary
populations and non-stationary populations which are approaching stationarity.
These properties provide deeper insights into cohort formation in real-world
populations and the length of the duration for which stationary and
non-stationary conditions hold. The new concepts are based on the time gap
between the occurrence of stationary and non-stationary populations within the
SPI framework that we refer to as Oscillatory SPI and the Amplitude of SPI.
| 0 | 0 | 0 | 0 | 1 | 0 |
Generating and designing DNA with deep generative models | We propose generative neural network methods to generate DNA sequences and
tune them to have desired properties. We present three approaches: creating
synthetic DNA sequences using a generative adversarial network; a DNA-based
variant of the activation maximization ("deep dream") design method; and a
joint procedure which combines these two approaches together. We show that
these tools capture important structures of the data and, when applied to
designing probes for protein binding microarrays, allow us to generate new
sequences whose properties are estimated to be superior to those found in the
training data. We believe that these results open the door for applying deep
generative models to advance genomics research.
| 1 | 0 | 0 | 1 | 0 | 0 |
Radon background in liquid xenon detectors | The radioactive daughters isotope of 222Rn are one of the highest risk
contaminants in liquid xenon detectors aiming for a small signal rate. The
noble gas is permanently emanated from the detector surfaces and mixed with the
xenon target. Because of its long half-life 222Rn is homogeneously distributed
in the target and its subsequent decays can mimic signal events. Since no
shielding is possible this background source can be the dominant one in future
large scale experiments. This article provides an overview of strategies used
to mitigate this source of background by means of material selection and
on-line radon removal techniques.
| 0 | 1 | 0 | 0 | 0 | 0 |
Minimax Regret Bounds for Reinforcement Learning | We consider the problem of provably optimal exploration in reinforcement
learning for finite horizon MDPs. We show that an optimistic modification to
value iteration achieves a regret bound of $\tilde{O}( \sqrt{HSAT} +
H^2S^2A+H\sqrt{T})$ where $H$ is the time horizon, $S$ the number of states,
$A$ the number of actions and $T$ the number of time-steps. This result
improves over the best previous known bound $\tilde{O}(HS \sqrt{AT})$ achieved
by the UCRL2 algorithm of Jaksch et al., 2010. The key significance of our new
results is that when $T\geq H^3S^3A$ and $SA\geq H$, it leads to a regret of
$\tilde{O}(\sqrt{HSAT})$ that matches the established lower bound of
$\Omega(\sqrt{HSAT})$ up to a logarithmic factor. Our analysis contains two key
insights. We use careful application of concentration inequalities to the
optimal value function as a whole, rather than to the transitions probabilities
(to improve scaling in $S$), and we define Bernstein-based "exploration
bonuses" that use the empirical variance of the estimated values at the next
states (to improve scaling in $H$).
| 1 | 0 | 0 | 1 | 0 | 0 |
Asymptotic Theory for the Maximum of an Increasing Sequence of Parametric Functions | \cite{HillMotegi2017} present a new general asymptotic theory for the maximum
of a random array $\{\mathcal{X}_{n}(i)$ $:$ $1$ $\leq $ $i$ $\leq $
$\mathcal{L}\}_{n\geq 1}$, where each $\mathcal{X}_{n}(i)$ is assumed to
converge in probability as $n$ $\rightarrow $ $\infty $. The array dimension
$\mathcal{L}$ is allowed to increase with the sample size $n$. Existing extreme
value theory arguments focus on observed data $\mathcal{X}_{n}(i)$, and require
a well defined limit law for $\max_{1\leq i\leq
\mathcal{L}}|\mathcal{X}_{n}(i)|$ by restricting dependence across $i$. The
high dimensional central limit theory literature presumes approximability by a
Gaussian law, and also restricts attention to observed data.
\cite{HillMotegi2017} do not require $\max_{1\leq i\leq
\mathcal{L}_{n}}|\mathcal{X}_{n}(i)|$ to have a well defined limit nor be
approximable by a Gaussian random variable, and we do not make any assumptions
about dependence across $i$. We apply the theory to filtered data when the
variable of interest $\mathcal{X}_{n}(i,\theta _{0})$ is not observed, but its
sample counterpart $\mathcal{X}_{n}(i,\hat{\theta}_{n})$ is observed where
$\hat{\theta}_{n}$ estimates $\theta _{0}$. The main results are illustrated by
looking at unit root tests for a high dimensional random variable, and a
residuals white noise test.
| 0 | 0 | 1 | 1 | 0 | 0 |
Resilient Active Information Gathering with Mobile Robots | Applications of safety, security, and rescue in robotics, such as multi-robot
target tracking, involve the execution of information acquisition tasks by
teams of mobile robots. However, in failure-prone or adversarial environments,
robots get attacked, their communication channels get jammed, and their sensors
may fail, resulting in the withdrawal of robots from the collective task, and
consequently the inability of the remaining active robots to coordinate with
each other. As a result, traditional design paradigms become insufficient and,
in contrast, resilient designs against system-wide failures and attacks become
important. In general, resilient design problems are hard, and even though they
often involve objective functions that are monotone or submodular, scalable
approximation algorithms for their solution have been hitherto unknown. In this
paper, we provide the first algorithm, enabling the following capabilities:
minimal communication, i.e., the algorithm is executed by the robots based only
on minimal communication between them; system-wide resiliency, i.e., the
algorithm is valid for any number of denial-of-service attacks and failures;
and provable approximation performance, i.e., the algorithm ensures for all
monotone (and not necessarily submodular) objective functions a solution that
is finitely close to the optimal. We quantify our algorithm's approximation
performance using a notion of curvature for monotone set functions. We support
our theoretical analyses with simulated and real-world experiments, by
considering an active information gathering scenario, namely, multi-robot
target tracking.
| 1 | 0 | 0 | 1 | 0 | 0 |
Optical properties of a four-layer waveguiding nanocomposite structure in near-IR regime | The theoretical study of the optical properties of TE- and TM- modes in a
four-layer structure composed of the magneto-optical yttrium iron garnet
guiding layer on a dielectric substrate covered by planar nanocomposite guiding
multilayer is presented. The dispersion equation is obtained taking into
account the bigyrotropic properties of yttrium-iron garnet, and an original
algorithm for the guided modes identification is proposed. The dispersion
spectra are analyzed and the energy flux distributions across the structure are
calculated. The fourfold difference between the partial power fluxes within the
waveguide layers is achieved in the wavelength range of 200 nm.
| 0 | 1 | 0 | 0 | 0 | 0 |
High Dimensional Structured Superposition Models | High dimensional superposition models characterize observations using
parameters which can be written as a sum of multiple component parameters, each
with its own structure, e.g., sum of low rank and sparse matrices, sum of
sparse and rotated sparse vectors, etc. In this paper, we consider general
superposition models which allow sum of any number of component parameters, and
each component structure can be characterized by any norm. We present a simple
estimator for such models, give a geometric condition under which the
components can be accurately estimated, characterize sample complexity of the
estimator, and give high probability non-asymptotic bounds on the componentwise
estimation error. We use tools from empirical processes and generic chaining
for the statistical analysis, and our results, which substantially generalize
prior work on superposition models, are in terms of Gaussian widths of suitable
sets.
| 1 | 0 | 0 | 1 | 0 | 0 |
Source Forager: A Search Engine for Similar Source Code | Developers spend a significant amount of time searching for code: e.g., to
understand how to complete, correct, or adapt their own code for a new context.
Unfortunately, the state of the art in code search has not evolved much beyond
text search over tokenized source. Code has much richer structure and semantics
than normal text, and this property can be exploited to specialize the
code-search process for better querying, searching, and ranking of code-search
results.
We present a new code-search engine named Source Forager. Given a query in
the form of a C/C++ function, Source Forager searches a pre-populated code
database for similar C/C++ functions. Source Forager preprocesses the database
to extract a variety of simple code features that capture different aspects of
code. A search returns the $k$ functions in the database that are most similar
to the query, based on the various extracted code features.
We tested the usefulness of Source Forager using a variety of code-search
queries from two domains. Our experiments show that the ranked results returned
by Source Forager are accurate, and that query-relevant functions can be
reliably retrieved even when searching through a large code database that
contains very few query-relevant functions.
We believe that Source Forager is a first step towards much-needed tools that
provide a better code-search experience.
| 1 | 0 | 0 | 0 | 0 | 0 |
Crossmatching variable objects with the Gaia data | Tens of millions of new variable objects are expected to be identified in
over a billion time series from the Gaia mission. Crossmatching known variable
sources with those from Gaia is crucial to incorporate current knowledge,
understand how these objects appear in the Gaia data, train supervised
classifiers to recognise known classes, and validate the results of the
Variability Processing and Analysis Coordination Unit (CU7) within the Gaia
Data Analysis and Processing Consortium (DPAC). The method employed by CU7 to
crossmatch variables for the first Gaia data release includes a binary
classifier to take into account positional uncertainties, proper motion,
targeted variability signals, and artefacts present in the early calibration of
the Gaia data. Crossmatching with a classifier makes it possible to automate
all those decisions which are typically made during visual inspection. The
classifier can be trained with objects characterized by a variety of attributes
to ensure similarity in multiple dimensions (astrometry, photometry,
time-series features), with no need for a-priori transformations to compare
different photometric bands, or of predictive models of the motion of objects
to compare positions. Other advantages as well as some disadvantages of the
method are discussed. Implementation steps from the training to the assessment
of the crossmatch classifier and selection of results are described.
| 0 | 1 | 0 | 0 | 0 | 0 |
A New Test of Multivariate Nonlinear Causality | The multivariate nonlinear Granger causality developed by Bai et al. (2010)
plays an important role in detecting the dynamic interrelationships between two
groups of variables. Following the idea of Hiemstra-Jones (HJ) test proposed by
Hiemstra and Jones (1994), they attempt to establish a central limit theorem
(CLT) of their test statistic by applying the asymptotical property of
multivariate $U$-statistic. However, Bai et al. (2016) revisit the HJ test and
find that the test statistic given by HJ is NOT a function of $U$-statistics
which implies that the CLT neither proposed by Hiemstra and Jones (1994) nor
the one extended by Bai et al. (2010) is valid for statistical inference. In
this paper, we re-estimate the probabilities and reestablish the CLT of the new
test statistic. Numerical simulation shows that our new estimates are
consistent and our new test performs decent size and power.
| 0 | 0 | 0 | 1 | 0 | 0 |
Nonlinear dynamics of polar regions in paraelectric phase of (Ba1-x,Srx)TiO3 ceramics | The dynamic dielectric nonlinearity of barium strontium titanate
(Ba1-x,Srx)TiO3 ceramics is investigated in their paraelectric phase. With the
goal to contribute to the identification of the mechanisms that govern the
dielectric nonlinearity in this family, we analyze the amplitude and the phase
angles of the first and the third harmonics of polarization. Our study shows
that an interpretation of the field-dependent polarization in paraelectric
(Ba1-x,Srx)TiO3 ceramics in terms of the Rayleigh-type dynamics is inadequate
for our samples and that their nonlinear response rather resembles that
observed in canonical relaxor Pb(Mg1/3Nb2/3)O3.
| 0 | 1 | 0 | 0 | 0 | 0 |
Nonlinear Modal Decoupling Based Power System Transient Stability Analysis | Nonlinear modal decoupling (NMD) was recently proposed to nonlinearly
transform a multi-oscillator system into a number of decoupled oscillators
which together behave the same as the original system in an extended
neighborhood of the equilibrium. Each oscillator has just one degree of freedom
and hence can easily be analyzed to infer the stability of the original system
associated with one electromechanical mode. As the first attempt of applying
the NMD methodology to realistic power system models, this paper proposes an
NMD-based transient stability analysis approach. For a multi-machine power
system, the approach first derives decoupled nonlinear oscillators by a
coordinates transformation, and then applies Lyapunov stability analysis to
oscillators to assess the stability of the original system. Nonlinear modal
interaction is also considered. The approach can be efficiently applied to a
large-scale power grid by conducting NMD regarding only selected modes. Case
studies on a 3-machine 9-bus system and an NPCC 48-machine 140-bus system show
the potentials of the approach in transient stability analysis for
multi-machine systems.
| 1 | 0 | 0 | 0 | 0 | 0 |
KELT-18b: Puffy Planet, Hot Host, Probably Perturbed | We report the discovery of KELT-18b, a transiting hot Jupiter in a 2.87d
orbit around the bright (V=10.1), hot, F4V star BD+60 1538 (TYC 3865-1173-1).
We present follow-up photometry, spectroscopy, and adaptive optics imaging that
allow a detailed characterization of the system. Our preferred model fits yield
a host stellar temperature of 6670+/-120 K and a mass of 1.524+/-0.069 Msun,
situating it as one of only a handful of known transiting planets with hosts
that are as hot, massive, and bright. The planet has a mass of 1.18+/-0.11
Mjup, a radius of 1.57+/-0.04 Rjup, and a density of 0.377+/-0.040 g/cm^3,
making it one of the most inflated planets known around a hot star. We argue
that KELT-18b's high temperature and low surface gravity, which yield an
estimated ~600 km atmospheric scale height, combined with its hot, bright host
make it an excellent candidate for observations aimed at atmospheric
characterization. We also present evidence for a bound stellar companion at a
projected separation of ~1100 AU, and speculate that it may have contributed to
the strong misalignment we suspect between KELT-18's spin axis and its planet's
orbital axis. The inferior conjunction time is 2457542.524998 +/-0.000416
(BJD_TDB) and the orbital period is 2.8717510 +/- 0.0000029 days. We encourage
Rossiter-McLaughlin measurements in the near future to confirm the suspected
spin-orbit misalignment of this system.
| 0 | 1 | 0 | 0 | 0 | 0 |
BAMBI: An R package for Fitting Bivariate Angular Mixture Models | Statistical analyses of directional or angular data have applications in a
variety of fields, such as geology, meteorology and bioinformatics. There is
substantial literature on descriptive and inferential techniques for univariate
angular data, with the bivariate (or more generally, multivariate) cases
receiving more attention in recent years. However, there is a lack of software
implementing inferential techniques in practice, especially in the bivariate
situation. In this article, we introduce *BAMBI*, an R package for analyzing
bivariate (and univariate) angular data. We implement random generation,
density evaluation, and computation of summary measures for three bivariate
(viz., bivariate wrapped normal, von Mises sine and von Mises cosine) and two
univariate (viz., univariate wrapped normal and von Mises) angular
distributions. The major contribution of BAMBI to statistical computing is in
providing Bayesian methods for modeling angular data using finite mixtures of
these distributions. We also provide functions for visual and numerical
diagnostics and Bayesian inference for the fitted models. In this article, we
first provide a brief review of the distributions and techniques used in BAMBI,
then describe the capabilities of the package, and finally conclude with
demonstrations of mixture model fitting using BAMBI on the two real datasets
included in the package, one univariate and one bivariate.
| 0 | 0 | 0 | 1 | 0 | 0 |
Finite Sample Analysis of Two-Timescale Stochastic Approximation with Applications to Reinforcement Learning | Two-timescale Stochastic Approximation (SA) algorithms are widely used in
Reinforcement Learning (RL). Their iterates have two parts that are updated
using distinct stepsizes. In this work, we develop a novel recipe for their
finite sample analysis. Using this, we provide a concentration bound, which is
the first such result for a two-timescale SA. The type of bound we obtain is
known as `lock-in probability'. We also introduce a new projection scheme, in
which the time between successive projections increases exponentially. This
scheme allows one to elegantly transform a lock-in probability into a
convergence rate result for projected two-timescale SA. From this latter
result, we then extract key insights on stepsize selection. As an application,
we finally obtain convergence rates for the projected two-timescale RL
algorithms GTD(0), GTD2, and TDC.
| 1 | 0 | 0 | 0 | 0 | 0 |
Existence and uniqueness of solutions to Y-systems and TBA equations | We consider Y-system functional equations of the form $$
Y_n(x+i)Y_n(x-i)=\prod_{m=1}^N (1+Y_m(x))^{G_{nm}}$$ and the corresponding
nonlinear integral equations of the Thermodynamic Bethe Ansatz. We prove an
existence and uniqueness result for solutions of these equations, subject to
appropriate conditions on the analytical properties of the $Y_n$, in particular
the absence of zeros in a strip around the real axis. The matrix $G_{nm}$ must
have non-negative real entries, and be irreducible and diagonalisable over
$\mathbb{R}$ with spectral radius less than 2. This includes the adjacency
matrices of finite Dynkin diagrams, but covers much more as we do not require
$G_{nm}$ to be integers. Our results specialise to the constant Y-system,
proving existence and uniqueness of a strictly positive solution in that case.
| 0 | 1 | 0 | 0 | 0 | 0 |
Normalization of Neural Networks using Analytic Variance Propagation | We address the problem of estimating statistics of hidden units in a neural
network using a method of analytic moment propagation. These statistics are
useful for approximate whitening of the inputs in front of saturating
non-linearities such as a sigmoid function. This is important for
initialization of training and for reducing the accumulated scale and bias
dependencies (compensating covariate shift), which presumably eases the
learning. In batch normalization, which is currently a very widely applied
technique, sample estimates of statistics of hidden units over a batch are
used. The proposed estimation uses an analytic propagation of mean and variance
of the training set through the network. The result depends on the network
structure and its current weights but not on the specific batch input. The
estimates are suitable for initialization and normalization, efficient to
compute and independent of the batch size. The experimental verification well
supports these claims. However, the method does not share the generalization
properties of BN, to which our experiments give some additional insight.
| 0 | 0 | 0 | 1 | 0 | 0 |
Ferrimagnetism in the Spin-1/2 Heisenberg Antiferromagnet on a Distorted Triangular Lattice | The ground state of the spin-$1/2$ Heisenberg antiferromagnet on a distorted
triangular lattice is studied using a numerical-diagonalization method. The
network of interactions is the $\sqrt{3}\times\sqrt{3}$ type; the interactions
are continuously controlled between the undistorted triangular lattice and the
dice lattice. We find new states between the nonmagnetic 120-degree-structured
state of the undistorted triangular case and the up-up-down state of the dice
case. The intermediate states show spontaneous magnetizations that are smaller
than one third of the saturated magntization corresponding to the up-up-down
state.
| 0 | 1 | 0 | 0 | 0 | 0 |
Delta sets for symmetric numerical semigroups with embedding dimension three | This work extends the results known for the Delta sets of non-symmetric
numerical semigroups with embedding dimension three to the symmetric case.
Thus, we have a fast algorithm to compute the Delta set of any embedding
dimension three numerical semigroup. Also, as a consequence of these resutls,
the sets that can be realized as Delta sets of numerical semigroups of
embedding dimension three are fully characterized.
| 0 | 0 | 1 | 0 | 0 | 0 |
Riemann-Hilbert problems for the resolved conifold | We study the Riemann-Hilbert problems associated to the Donaldson-Thomas
theory of the resolved conifold. We give explicit solutions in terms of the
Barnes double and triple sine functions. We show that the corresponding tau
function is a non-perturbative partition function, in the sense that its
asymptotic expansion coincides with the topological string partition function.
| 0 | 0 | 1 | 0 | 0 | 0 |
On the Power of Over-parametrization in Neural Networks with Quadratic Activation | We provide new theoretical insights on why over-parametrization is effective
in learning neural networks. For a $k$ hidden node shallow network with
quadratic activation and $n$ training data points, we show as long as $ k \ge
\sqrt{2n}$, over-parametrization enables local search algorithms to find a
\emph{globally} optimal solution for general smooth and convex loss functions.
Further, despite that the number of parameters may exceed the sample size,
using theory of Rademacher complexity, we show with weight decay, the solution
also generalizes well if the data is sampled from a regular distribution such
as Gaussian. To prove when $k\ge \sqrt{2n}$, the loss function has benign
landscape properties, we adopt an idea from smoothed analysis, which may have
other applications in studying loss surfaces of neural networks.
| 0 | 0 | 0 | 1 | 0 | 0 |
Multi-Label Learning with Label Enhancement | The task of multi-label learning is to predict a set of relevant labels for
the unseen instance. Traditional multi-label learning algorithms treat each
class label as a logical indicator of whether the corresponding label is
relevant or irrelevant to the instance, i.e., +1 represents relevant to the
instance and -1 represents irrelevant to the instance. Such label represented
by -1 or +1 is called logical label. Logical label cannot reflect different
label importance. However, for real-world multi-label learning problems, the
importance of each possible label is generally different. For the real
applications, it is difficult to obtain the label importance information
directly. Thus we need a method to reconstruct the essential label importance
from the logical multilabel data. To solve this problem, we assume that each
multi-label instance is described by a vector of latent real-valued labels,
which can reflect the importance of the corresponding labels. Such label is
called numerical label. The process of reconstructing the numerical labels from
the logical multi-label data via utilizing the logical label information and
the topological structure in the feature space is called Label Enhancement. In
this paper, we propose a novel multi-label learning framework called LEMLL,
i.e., Label Enhanced Multi-Label Learning, which incorporates regression of the
numerical labels and label enhancement into a unified framework. Extensive
comparative studies validate that the performance of multi-label learning can
be improved significantly with label enhancement and LEMLL can effectively
reconstruct latent label importance information from logical multi-label data.
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Unsure When to Stop? Ask Your Semantic Neighbors | In iterative supervised learning algorithms it is common to reach a point in
the search where no further induction seems to be possible with the available
data. If the search is continued beyond this point, the risk of overfitting
increases significantly. Following the recent developments in inductive
semantic stochastic methods, this paper studies the feasibility of using
information gathered from the semantic neighborhood to decide when to stop the
search. Two semantic stopping criteria are proposed and experimentally assessed
in Geometric Semantic Genetic Programming (GSGP) and in the Semantic Learning
Machine (SLM) algorithm (the equivalent algorithm for neural networks). The
experiments are performed on real-world high-dimensional regression datasets.
The results show that the proposed semantic stopping criteria are able to
detect stopping points that result in a competitive generalization for both
GSGP and SLM. This approach also yields computationally efficient algorithms as
it allows the evolution of neural networks in less than 3 seconds on average,
and of GP trees in at most 10 seconds. The usage of the proposed semantic
stopping criteria in conjunction with the computation of optimal
mutation/learning steps also results in small trees and neural networks.
| 1 | 0 | 0 | 1 | 0 | 0 |
Deep Generative Learning via Variational Gradient Flow | We propose a general framework to learn deep generative models via
\textbf{V}ariational \textbf{Gr}adient Fl\textbf{ow} (VGrow) on probability
spaces. The evolving distribution that asymptotically converges to the target
distribution is governed by a vector field, which is the negative gradient of
the first variation of the $f$-divergence between them. We prove that the
evolving distribution coincides with the pushforward distribution through the
infinitesimal time composition of residual maps that are perturbations of the
identity map along the vector field. The vector field depends on the density
ratio of the pushforward distribution and the target distribution, which can be
consistently learned from a binary classification problem. Connections of our
proposed VGrow method with other popular methods, such as VAE, GAN and
flow-based methods, have been established in this framework, gaining new
insights of deep generative learning. We also evaluated several commonly used
divergences, including Kullback-Leibler, Jensen-Shannon, Jeffrey divergences as
well as our newly discovered `logD' divergence which serves as the objective
function of the logD-trick GAN. Experimental results on benchmark datasets
demonstrate that VGrow can generate high-fidelity images in a stable and
efficient manner, achieving competitive performance with state-of-the-art GANs.
| 1 | 0 | 0 | 1 | 0 | 0 |
Warming trend in cold season of the Yangtze River Delta and its correlation with Siberian high | Based on the meteorological data from 1960 to 2010, we investigated the
temperature variation in the Yangtze River Delta (YRD) by using Mann-Kendall
nonparametric test and explored the correlation between the temperature in the
cold season and the Siberian high intensity (SHI) by using correlation analysis
method. The main results are that (a) the temperature in YRD increased
remarkably during the study period, (b) the warming trend in the cold season
made the higher contribution to annual warming, and (c) there was a significant
negative correlation between the temperature in the cold season and the SHI.
| 0 | 0 | 0 | 1 | 0 | 0 |
Modeling and Quantifying the Forces Driving Online Video Popularity Evolution | Video popularity is an essential reference for optimizing resource allocation
and video recommendation in online video services. However, there is still no
convincing model that can accurately depict a video's popularity evolution. In
this paper, we propose a dynamic popularity model by modeling the video
information diffusion process driven by various forms of recommendation.
Through fitting the model with real traces collected from a practical system,
we can quantify the strengths of the recommendation forces. Such quantification
can lead to characterizing video popularity patterns, user behaviors and
recommendation strategies, which is illustrated by a case study of TV episodes.
| 1 | 0 | 0 | 0 | 0 | 0 |
Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games | Many artificial intelligence (AI) applications often require multiple
intelligent agents to work in a collaborative effort. Efficient learning for
intra-agent communication and coordination is an indispensable step towards
general AI. In this paper, we take StarCraft combat game as a case study, where
the task is to coordinate multiple agents as a team to defeat their enemies. To
maintain a scalable yet effective communication protocol, we introduce a
Multiagent Bidirectionally-Coordinated Network (BiCNet ['bIknet]) with a
vectorised extension of actor-critic formulation. We show that BiCNet can
handle different types of combats with arbitrary numbers of AI agents for both
sides. Our analysis demonstrates that without any supervisions such as human
demonstrations or labelled data, BiCNet could learn various types of advanced
coordination strategies that have been commonly used by experienced game
players. In our experiments, we evaluate our approach against multiple
baselines under different scenarios; it shows state-of-the-art performance, and
possesses potential values for large-scale real-world applications.
| 1 | 0 | 0 | 0 | 0 | 0 |
Measurement of the Lorentz-FitzGerald Body Contraction | A complete foundational discussion of acceleration in context of Special
Relativity is presented. Acceleration allows the measurement of a
Lorentz-FitzGerald body contraction created. It is argued that in the back
scattering of a probing laser beam from a relativistic flying electron cloud
mirror generated by an ultra-intense laser pulse, a first measurement of a
Lorentz-FitzGerald body contraction is feasible.
| 0 | 1 | 0 | 0 | 0 | 0 |
Information Directed Sampling for Stochastic Bandits with Graph Feedback | We consider stochastic multi-armed bandit problems with graph feedback, where
the decision maker is allowed to observe the neighboring actions of the chosen
action. We allow the graph structure to vary with time and consider both
deterministic and Erdős-Rényi random graph models. For such a graph
feedback model, we first present a novel analysis of Thompson sampling that
leads to tighter performance bound than existing work. Next, we propose new
Information Directed Sampling based policies that are graph-aware in their
decision making. Under the deterministic graph case, we establish a Bayesian
regret bound for the proposed policies that scales with the clique cover number
of the graph instead of the number of actions. Under the random graph case, we
provide a Bayesian regret bound for the proposed policies that scales with the
ratio of the number of actions over the expected number of observations per
iteration. To the best of our knowledge, this is the first analytical result
for stochastic bandits with random graph feedback. Finally, using numerical
evaluations, we demonstrate that our proposed IDS policies outperform existing
approaches, including adaptions of upper confidence bound, $\epsilon$-greedy
and Exp3 algorithms.
| 1 | 0 | 0 | 1 | 0 | 0 |
Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning | Supervised object detection and semantic segmentation require object or even
pixel level annotations. When there exist image level labels only, it is
challenging for weakly supervised algorithms to achieve accurate predictions.
The accuracy achieved by top weakly supervised algorithms is still
significantly lower than their fully supervised counterparts. In this paper, we
propose a novel weakly supervised curriculum learning pipeline for multi-label
object recognition, detection and semantic segmentation. In this pipeline, we
first obtain intermediate object localization and pixel labeling results for
the training images, and then use such results to train task-specific deep
networks in a fully supervised manner. The entire process consists of four
stages, including object localization in the training images, filtering and
fusing object instances, pixel labeling for the training images, and
task-specific network training. To obtain clean object instances in the
training images, we propose a novel algorithm for filtering, fusing and
classifying object instances collected from multiple solution mechanisms. In
this algorithm, we incorporate both metric learning and density-based
clustering to filter detected object instances. Experiments show that our
weakly supervised pipeline achieves state-of-the-art results in multi-label
image classification as well as weakly supervised object detection and very
competitive results in weakly supervised semantic segmentation on MS-COCO,
PASCAL VOC 2007 and PASCAL VOC 2012.
| 0 | 0 | 0 | 1 | 0 | 0 |
Hausdorff operators on holomorphic Hardy spaces and applications | The aim of this paper is to characterize the nonnegative functions $\varphi$
defined on $(0,\infty)$ for which the Hausdorff operator
$$\mathscr H_\varphi f(z)= \int_0^\infty
f\left(\frac{z}{t}\right)\frac{\varphi(t)}{t}dt$$ is bounded on the Hardy
spaces of the upper half-plane $\mathcal H_a^p(\mathbb C_+)$, $p\in[1,\infty]$.
The corresponding operator norms and their applications are also given.
| 0 | 0 | 1 | 0 | 0 | 0 |
Three-dimensional color code thresholds via statistical-mechanical mapping | Three-dimensional (3D) color codes have advantages for fault-tolerant quantum
computing, such as protected quantum gates with relatively low overhead and
robustness against imperfect measurement of error syndromes. Here we
investigate the storage threshold error rates for bit-flip and phase-flip noise
in the 3D color code on the body-centererd cubic lattice, assuming perfect
syndrome measurements. In particular, by exploiting a connection between error
correction and statistical mechanics, we estimate the threshold for 1D
string-like and 2D sheet-like logical operators to be $p^{(1)}_\mathrm{3DCC}
\simeq 1.9\%$ and $p^{(2)}_\mathrm{3DCC} \simeq 27.6\%$. We obtain these
results by using parallel tempering Monte Carlo simulations to study the
disorder-temperature phase diagrams of two new 3D statistical-mechanical
models: the 4- and 6-body random coupling Ising models.
| 0 | 1 | 0 | 0 | 0 | 0 |
Does Your Phone Know Your Touch? | This paper explores supervised techniques for continuous anomaly detection
from biometric touch screen data. A capacitive sensor array used to mimic a
touch screen as used to collect touch and swipe gestures from participants. The
gestures are recorded over fixed segments of time, with position and force
measured for each gesture. Support Vector Machine, Logistic Regression, and
Gaussian mixture models were tested to learn individual touch patterns. Test
results showed true negative and true positive scores of over 95% accuracy for
all gesture types, with logistic regression models far outperforming the other
methods. A more expansive and varied data collection over longer periods of
time is needed to determine pragmatic usage of these results.
| 0 | 0 | 0 | 1 | 0 | 0 |
Nucleus: A Pilot Project | Early in 2016, an environmental scan was conducted by the Research Library
Data Working Group for three purposes:
1.) Perform a survey of the data management landscape at Los Alamos National
Laboratory in order to identify local gaps in data management services.
2.) Conduct an environmental scan of external institutions to benchmark
budgets, infrastructure, and personnel dedicated to data management.
3.) Draft a research data infrastructure model that aligns with the current
workflow and classification restrictions at Los Alamos National Laboratory.
This report is a summary of those activities and the draft for a pilot data
management project.
| 1 | 0 | 0 | 0 | 0 | 0 |
Non Volatile MoS$_{2}$ Field Effect Transistors Directly Gated By Single Crystalline Epitaxial Ferroelectric | We demonstrate non-volatile, n-type, back-gated, MoS$_{2}$ transistors,
placed directly on an epitaxial grown, single crystalline,
PbZr$_{0.2}$Ti$_{0.8}$O$_{3}$ (PZT) ferroelectric. The transistors show decent
ON current (19 ${\mu}A/{\mu}$m), high on-off ratio (10$^{7}$), and a
subthreshold swing of (SS ~ 92 mV/dec) with a 100 nm thick PZT layer as the
back gate oxide. Importantly, the ferroelectric polarization can directly
control the channel charge, showing a clear anti-clockwise hysteresis. We have
selfconsistently confirmed the switching of the ferroelectric and corresponding
change in channel current from a direct time-dependent measurement. Our results
demonstrate that it is possible to obtain transistor operation directly on
polar surfaces and therefore it should be possible to integrate 2D electronics
with single crystalline functional oxides.
| 0 | 1 | 0 | 0 | 0 | 0 |
Fast and Accurate Sparse Coding of Visual Stimuli with a Simple, Ultra-Low-Energy Spiking Architecture | Memristive crossbars have become a popular means for realizing unsupervised
and supervised learning techniques. In previous neuromorphic architectures with
leaky integrate-and-fire neurons, the crossbar itself has been separated from
the neuron capacitors to preserve mathematical rigor. In this work, we sought
to simplify the design, creating a fast circuit that consumed significantly
lower power at a minimal cost of accuracy. We also showed that connecting the
neurons directly to the crossbar resulted in a more efficient sparse coding
architecture, and alleviated the need to pre-normalize receptive fields. This
work provides derivations for the design of such a network, named the Simple
Spiking Locally Competitive Algorithm, or SSLCA, as well as CMOS designs and
results on the CIFAR and MNIST datasets. Compared to a non-spiking model which
scored 33% on CIFAR-10 with a single-layer classifier, this hardware scored 32%
accuracy. When used with a state-of-the-art deep learning classifier, the
non-spiking model achieved 82% and our simplified, spiking model achieved 80%,
while compressing the input data by 92%. Compared to a previously proposed
spiking model, our proposed hardware consumed 99% less energy to do the same
work at 21x the throughput. Accuracy held out with online learning to a write
variance of 3%, suitable for the often-reported 4-bit resolution required for
neuromorphic algorithms; with offline learning to a write variance of 27%; and
with read variance to 40%. The proposed architecture's excellent accuracy,
throughput, and significantly lower energy usage demonstrate the utility of our
innovations.
| 1 | 0 | 0 | 0 | 0 | 0 |
Astronomy of Cholanaikkan tribe of Kerala | Cholanaikkans are a diminishing tribe of India. With a population of less
than 200 members, this tribe living in the reserved forests about 80 km from
Kozhikode, it is one of the most isolated tribes. A programme of the Government
of Kerala brings some of them to Kozhikode once a year. We studied various
aspects of the tribe during such a visit in 2016. We report their science and
technology.
| 0 | 1 | 0 | 0 | 0 | 0 |
Integral Equations and Machine Learning | As both light transport simulation and reinforcement learning are ruled by
the same Fredholm integral equation of the second kind, reinforcement learning
techniques may be used for photorealistic image synthesis: Efficiency may be
dramatically improved by guiding light transport paths by an approximate
solution of the integral equation that is learned during rendering. In the
light of the recent advances in reinforcement learning for playing games, we
investigate the representation of an approximate solution of an integral
equation by artificial neural networks and derive a loss function for that
purpose. The resulting Monte Carlo and quasi-Monte Carlo methods train neural
networks with standard information instead of linear information and naturally
are able to generate an arbitrary number of training samples. The methods are
demonstrated for applications in light transport simulation.
| 1 | 0 | 0 | 0 | 0 | 0 |
Experiments on bright field and dark field high energy electron imaging with thick target material | Using a high energy electron beam for the imaging of high density matter with
both high spatial-temporal and areal density resolution under extreme states of
temperature and pressure is one of the critical challenges in high energy
density physics . When a charged particle beam passes through an opaque target,
the beam will be scattered with a distribution that depends on the thickness of
the material. By collecting the scattered beam either near or off axis,
so-called bright field or dark field images can be obtained. Here we report on
an electron radiography experiment using 45 MeV electrons from an S-band
photo-injector, where scattered electrons, after interacting with a sample, are
collected and imaged by a quadrupole imaging system. We achieved a few
micrometers (about 4 micrometers) spatial resolution and about 10 micrometers
thickness resolution for a silicon target of 300-600 micron thickness. With
addition of dark field images that are captured by selecting electrons with
large scattering angle, we show that more useful information in determining
external details such as outlines, boundaries and defects can be obtained.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Non-linear Approach to Space Dimension Perception by a Naive Agent | Developmental Robotics offers a new approach to numerous AI features that are
often taken as granted. Traditionally, perception is supposed to be an inherent
capacity of the agent. Moreover, it largely relies on models built by the
system's designer. A new approach is to consider perception as an
experimentally acquired ability that is learned exclusively through the
analysis of the agent's sensorimotor flow. Previous works, based on
H.Poincaré's intuitions and the sensorimotor contingencies theory, allow a
simulated agent to extract the dimension of geometrical space in which it is
immersed without any a priori knowledge. Those results are limited to
infinitesimal movement's amplitude of the system. In this paper, a non-linear
dimension estimation method is proposed to push back this limitation.
| 1 | 0 | 0 | 0 | 0 | 0 |
Foolbox: A Python toolbox to benchmark the robustness of machine learning models | Even todays most advanced machine learning models are easily fooled by almost
imperceptible perturbations of their inputs. Foolbox is a new Python package to
generate such adversarial perturbations and to quantify and compare the
robustness of machine learning models. It is build around the idea that the
most comparable robustness measure is the minimum perturbation needed to craft
an adversarial example. To this end, Foolbox provides reference implementations
of most published adversarial attack methods alongside some new ones, all of
which perform internal hyperparameter tuning to find the minimum adversarial
perturbation. Additionally, Foolbox interfaces with most popular deep learning
frameworks such as PyTorch, Keras, TensorFlow, Theano and MXNet and allows
different adversarial criteria such as targeted misclassification and top-k
misclassification as well as different distance measures. The code is licensed
under the MIT license and is openly available at
this https URL . The most up-to-date documentation can be
found at this http URL .
| 1 | 0 | 0 | 1 | 0 | 0 |
Two-dimensional boron on Pb (110) surface | We simulate boron on Pb(110) surface by using ab initio evolutionary
methodology. Interestingly, the two-dimensional (2D) Dirac Pmmn boron can be
formed because of good lattice matching. Unexpectedly, by increasing the
thickness of 2D boron, a three-bonded graphene-like structure (P2_1/c boron)
was revealed to possess double anisotropic Dirac cones. It is 20 meV/atom lower
in energy than the Pmmn structure, indicating the most stable 2D boron with
particular Dirac cones. The puckered structure of P2_1/c boron results in the
peculiar Dirac cones, as well as substantial mechanical anisotropy. The
calculated Young's modulus is 320 GPa.nm along zigzag direction, which is
comparable with graphene.
| 0 | 1 | 0 | 0 | 0 | 0 |
SOLAR: Deep Structured Latent Representations for Model-Based Reinforcement Learning | Model-based reinforcement learning (RL) methods can be broadly categorized as
global model methods, which depend on learning models that provide sensible
predictions in a wide range of states, or local model methods, which
iteratively refit simple models that are used for policy improvement. While
predicting future states that will result from the current actions is
difficult, local model methods only attempt to understand system dynamics in
the neighborhood of the current policy, making it possible to produce local
improvements without ever learning to predict accurately far into the future.
The main idea in this paper is that we can learn representations that make it
easy to retrospectively infer simple dynamics given the data from the current
policy, thus enabling local models to be used for policy learning in complex
systems. To that end, we focus on learning representations with probabilistic
graphical model (PGM) structure, which allows us to devise an efficient local
model method that infers dynamics from real-world rollouts with the PGM as a
global prior. We compare our method to other model-based and model-free RL
methods on a suite of robotics tasks, including manipulation tasks on a real
Sawyer robotic arm directly from camera images. Videos of our results are
available at this https URL
| 1 | 0 | 0 | 1 | 0 | 0 |
Robust and Fast Decoding of High-Capacity Color QR Codes for Mobile Applications | The use of color in QR codes brings extra data capacity, but also inflicts
tremendous challenges on the decoding process due to chromatic distortion,
cross-channel color interference and illumination variation. Particularly, we
further discover a new type of chromatic distortion in high-density color QR
codes, cross-module color interference, caused by the high density which also
makes the geometric distortion correction more challenging. To address these
problems, we propose two approaches, namely, LSVM-CMI and QDA-CMI, which
jointly model these different types of chromatic distortion. Extended from SVM
and QDA, respectively, both LSVM-CMI and QDA-CMI optimize over a particular
objective function to learn a color classifier. Furthermore, a robust geometric
transformation method and several pipeline refinements are proposed to boost
the decoding performance for mobile applications. We put forth and implement a
framework for high-capacity color QR codes equipped with our methods, called
HiQ. To evaluate the performance of HiQ, we collect a challenging large-scale
color QR code dataset, CUHK-CQRC, which consists of 5390 high-density color QR
code samples. The comparison with the baseline method [2] on CUHK-CQRC shows
that HiQ at least outperforms [2] by 188% in decoding success rate and 60% in
bit error rate. Our implementation of HiQ in iOS and Android also demonstrates
the effectiveness of our framework in real-world applications.
| 1 | 0 | 0 | 0 | 0 | 0 |
The quest for H$_3^+$ at Neptune: deep burn observations with NASA IRTF iSHELL | Emission from the molecular ion H$_3^+$ is a powerful diagnostic of the upper
atmosphere of Jupiter, Saturn, and Uranus, but it remains undetected at
Neptune. In search of this emission, we present near-infrared spectral
observations of Neptune between 3.93 and 4.00 $\mu$m taken with the newly
commissioned iSHELL instrument on the NASA Infrared Telescope Facility in
Hawaii, obtained 17-20 August 2017. We spent 15.4 h integrating across the disk
of the planet, yet were unable to unambiguously identify any H$_3^+$ line
emissions. Assuming a temperature of 550 K, we derive an upper limit on the
column integrated density of $1.0^{+1.2}_{-0.8}\times10^{13}$ m$^{-2}$, which
is an improvement of 30\% on the best previous observational constraint. This
result means that models are over-estimating the density by at least a factor
of 5, highlighting the need for renewed modelling efforts. A potential solution
is strong vertical mixing of polyatomic neutral species from Neptune's upper
stratosphere to the thermosphere, reacting with H$_3^+$, thus greatly reducing
the column integrated H$_3^+$ densities. This upper limit also provide
constraints on future attempts at detecting H$_3^+$ using the James Webb Space
Telescope.
| 0 | 1 | 0 | 0 | 0 | 0 |
Unreasonable Effectivness of Deep Learning | We show how well known rules of back propagation arise from a weighted
combination of finite automata. By redefining a finite automata as a predictor
we combine the set of all $k$-state finite automata using a weighted majority
algorithm. This aggregated prediction algorithm can be simplified using
symmetry, and we prove the equivalence of an algorithm that does this. We
demonstrate that this algorithm is equivalent to a form of a back propagation
acting in a completely connected $k$-node neural network. Thus the use of the
weighted majority algorithm allows a bound on the general performance of deep
learning approaches to prediction via known results from online statistics. The
presented framework opens more detailed questions about network topology; it is
a bridge to the well studied techniques of semigroup theory and applying these
techniques to answer what specific network topologies are capable of
predicting. This informs both the design of artificial networks and the
exploration of neuroscience models.
| 0 | 0 | 0 | 1 | 0 | 0 |
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