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1,601 | A Result of Uniqueness of Solutions of the Shigesada-Kawasaki-Teramoto Equations | We derive the uniqueness of weak solutions to the Shigesada-Kawasaki-Teramoto
(SKT) systems using the adjoint problem argument. Combining with [PT17] we then
derive the well-posedness for the SKT systems in space dimension $d\le 4$
| 0 | 0 | 1 | 0 | 0 | 0 |
1,602 | Mind the Gap: A Well Log Data Analysis | The main task in oil and gas exploration is to gain an understanding of the
distribution and nature of rocks and fluids in the subsurface. Well logs are
records of petro-physical data acquired along a borehole, providing direct
information about what is in the subsurface. The data collected by logging
wells can have significant economic consequences, due to the costs inherent to
drilling wells, and the potential return of oil deposits. In this paper, we
describe preliminary work aimed at building a general framework for well log
prediction.
First, we perform a descriptive and exploratory analysis of the gaps in the
neutron porosity logs of more than a thousand wells in the North Sea. Then, we
generate artificial gaps in the neutron logs that reflect the statistics
collected before. Finally, we compare Artificial Neural Networks, Random
Forests, and three algorithms of Linear Regression in the prediction of missing
gaps on a well-by-well basis.
| 1 | 0 | 0 | 1 | 0 | 0 |
1,603 | Inconsistency of Template Estimation with the Fr{é}chet mean in Quotient Space | We tackle the problem of template estimation when data have been randomly
transformed under an isometric group action in the presence of noise. In order
to estimate the template, one often minimizes the variance when the influence
of the transformations have been removed (computation of the Fr{é}chet mean
in quotient space). The consistency bias is defined as the distance (possibly
zero) between the orbit of the template and the orbit of one element which
minimizes the variance. In this article we establish an asymptotic behavior of
the consistency bias with respect to the noise level. This behavior is linear
with respect to the noise level. As a result the inconsistency is unavoidable
as soon as the noise is large enough. In practice, the template estimation with
a finite sample is often done with an algorithm called max-max. We show the
convergence of this algorithm to an empirical Karcher mean. Finally, our
numerical experiments show that the bias observed in practice cannot be
attributed to the small sample size or to a convergence problem but is indeed
due to the previously studied inconsistency.
| 0 | 0 | 1 | 1 | 0 | 0 |
1,604 | All-optical switching and unidirectional plasmon launching with electron-hole plasma driven silicon nanoantennas | High-index dielectric nanoparticles have become a powerful platform for
modern light science, enabling various fascinating applications, especially in
nonlinear nanophotonics for which they enable special types of optical
nonlinearity, such as electron-hole plasma photoexcitation, which are not
inherent to plasmonic nanostructures. Here, we propose a novel geometry for
highly tunable all-dielectric nanoantennas, consisting of a chain of silicon
nanoparticles excited by an electric dipole source, which allows tuning their
radiation properties via electron-hole plasma photoexcitation. We show that the
slowly guided modes determining the Van Hove singularity of the nanoantenna are
very sensitive to the nanoparticle permittivity, opening up the ability to
utilize this effect for efficient all-optical modulation. We show that by
pumping several boundary nanoparticles with relatively low intensities may
cause dramatic variations in the nanoantenna radiation power patterns and
Purcell factor. We also demonstrate that ultrafast pumping of the designed
nanoantenna allows unidirectional launching of surface plasmon-polaritons, with
interesting implications for modern nonlinear nanophotonics.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,605 | Group chasing tactics: how to catch a faster prey? | We propose a bio-inspired, agent-based approach to describe the natural
phenomenon of group chasing in both two and three dimensions. Using a set of
local interaction rules we created a continuous-space and discrete-time model
with time delay, external noise and limited acceleration. We implemented a
unique collective chasing strategy, optimized its parameters and studied its
properties when chasing a much faster, erratic escaper. We show that collective
chasing strategies can significantly enhance the chasers' success rate. Our
realistic approach handles group chasing within closed, soft boundaries -
contrasting most of those published in the literature with periodic ones -- and
resembles several properties of pursuits observed in nature, such as the
emergent encircling or the escaper's zigzag motion.
| 0 | 1 | 0 | 1 | 0 | 0 |
1,606 | On solving a restricted linear congruence using generalized Ramanujan sums | Consider the linear congruence equation $x_1+\ldots+x_k \equiv b\,(\text{mod
} n)$ for $b,n\in\mathbb{Z}$. By $(a,b)_s$, we mean the largest
$l^s\in\mathbb{N}$ which divides $a$ and $b$ simultaneously. For each $d_j|n$,
define $\mathcal{C}_{j,s} = \{1\leq x\leq n^s | (x,n^s)_s = d^s_j\}$. Bibak et
al. gave a formula using Ramanujan sums for the number of solutions of the
above congruence equation with some gcd restrictions on $x_i$. We generalize
their result with generalized gcd restrictions on $x_i$ by proving that for the
above linear congruence, the number of solutions is
$$\frac{1}{n^s}\sum\limits_{d|n}c_{d,s}(b)\prod\limits_{j=1}^{\tau(n)}\left(c_{\frac{n}{d_j},s}(\frac{n^s}{d^s})\right)^{g_j}$$
where $g_j = |\{x_1,\ldots, x_k\}\cap \mathcal{C}_{j,s}|$ for $j=1,\ldots
\tau(n)$ and $c_{d,s}$ denote the generalized ramanujan sum defined by E.
Cohen.
| 0 | 0 | 1 | 0 | 0 | 0 |
1,607 | Magnetization spin dynamics in a (LuBi)3Fe5O12 (BLIG) epitaxial film | Bismuth substituted lutetium iron garnet (BLIG) films exhibit larger Faraday
rotation, and have a higher Curie temperature than yttrium iron garnet. We have
observed magnetic stripe domains and measured domain widths of 1.4 {\mu}{\mu}m
using Fourier domain polarization microscopy, Faraday rotation experiments
yield a coercive field of 5 Oe. These characterizations form the basis of
micromagnetic simulations that allow us to estimate and compare spin wave
excitations in BLIG films. We observed that these films support thermal magnons
with a precessional frequency of 7 GHz with a line width of 400 MHz. Further,
we studied the dependence of precessional frequency on the externally applied
magnetic field. Brillouin light scattering experiments and precession
frequencies predicted by simulations show similar trend with increasing field.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,608 | Probing for sparse and fast variable selection with model-based boosting | We present a new variable selection method based on model-based gradient
boosting and randomly permuted variables. Model-based boosting is a tool to fit
a statistical model while performing variable selection at the same time. A
drawback of the fitting lies in the need of multiple model fits on slightly
altered data (e.g. cross-validation or bootstrap) to find the optimal number of
boosting iterations and prevent overfitting. In our proposed approach, we
augment the data set with randomly permuted versions of the true variables, so
called shadow variables, and stop the step-wise fitting as soon as such a
variable would be added to the model. This allows variable selection in a
single fit of the model without requiring further parameter tuning. We show
that our probing approach can compete with state-of-the-art selection methods
like stability selection in a high-dimensional classification benchmark and
apply it on gene expression data for the estimation of riboflavin production of
Bacillus subtilis.
| 0 | 0 | 0 | 1 | 0 | 0 |
1,609 | A surface-hopping method for semiclassical calculations of cross sections for radiative association with electronic transitions | A semicalssical method based on surface-hopping techniques is developed to
model the dynamics of radiative association with electronic transitions in
arbitrary polyatomic systems. It can be proven that our method is an extension
of the established semiclassical formula used in the characterization of
diatomic molecule- formation. Our model is tested for diatomic molecules. It
gives the same cross sections as the former semiclassical formula, but contrary
to the former method it allows us to follow the fate of the trajectories after
the emission of a photon. This means that we can characterize the rovibrational
states of the stabilized molecules: using semiclassial quantization we can
obtain quantum state resolved cross sections or emission spectra for the
radiative association process. The calculated semiclassical state resolved
spectra show good agreement with the result of quantum mechanical perturbation
theory. Furthermore our surface-hopping model is not only applicable for the
description of radiative association but it can be use for semiclassical
characterization of any molecular process where spontaneous emission occurs.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,610 | Holographic coherent states from random tensor networks | Random tensor networks provide useful models that incorporate various
important features of holographic duality. A tensor network is usually defined
for a fixed graph geometry specified by the connection of tensors. In this
paper, we generalize the random tensor network approach to allow quantum
superposition of different spatial geometries. We set up a framework in which
all possible bulk spatial geometries, characterized by weighted adjacent
matrices of all possible graphs, are mapped to the boundary Hilbert space and
form an overcomplete basis of the boundary. We name such an overcomplete basis
as holographic coherent states. A generic boundary state can be expanded on
this basis, which describes the state as a superposition of different spatial
geometries in the bulk. We discuss how to define distinct classical geometries
and small fluctuations around them. We show that small fluctuations around
classical geometries define "code subspaces" which are mapped to the boundary
Hilbert space isometrically with quantum error correction properties. In
addition, we also show that the overlap between different geometries is
suppressed exponentially as a function of the geometrical difference between
the two geometries. The geometrical difference is measured in an area law
fashion, which is a manifestation of the holographic nature of the states
considered.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,611 | Persistent Spread Measurement for Big Network Data Based on Register Intersection | Persistent spread measurement is to count the number of distinct elements
that persist in each network flow for predefined time periods. It has many
practical applications, including detecting long-term stealthy network
activities in the background of normal-user activities, such as stealthy DDoS
attack, stealthy network scan, or faked network trend, which cannot be detected
by traditional flow cardinality measurement. With big network data, one
challenge is to measure the persistent spreads of a massive number of flows
without incurring too much memory overhead as such measurement may be performed
at the line speed by network processors with fast but small on-chip memory. We
propose a highly compact Virtual Intersection HyperLogLog (VI-HLL) architecture
for this purpose. It achieves far better memory efficiency than the best prior
work of V-Bitmap, and in the meantime drastically extends the measurement
range. Theoretical analysis and extensive experiments demonstrate that VI-HLL
provides good measurement accuracy even in very tight memory space of less than
1 bit per flow.
| 1 | 0 | 0 | 0 | 0 | 0 |
1,612 | High-dimensional Linear Regression for Dependent Observations with Application to Nowcasting | In the last few years, an extensive literature has been focused on the
$\ell_1$ penalized least squares (Lasso) estimators of high dimensional linear
regression when the number of covariates $p$ is considerably larger than the
sample size $n$. However, there is limited attention paid to the properties of
the estimators when the errors or/and the covariates are serially dependent. In
this study, we investigate the theoretical properties of the Lasso estimators
for linear regression with random design under serially dependent and/or
non-sub-Gaussian errors and covariates. In contrast to the traditional case in
which the errors are i.i.d and have finite exponential moments, we show that
$p$ can at most be a power of $n$ if the errors have only polynomial moments.
In addition, the rate of convergence becomes slower due to the serial
dependencies in errors and the covariates. We also consider sign consistency
for model selection via Lasso when there are serial correlations in the errors
or the covariates or both. Adopting the framework of functional dependence
measure, we provide a detailed description on how the rates of convergence and
the selection consistencies of the estimators depend on the dependence measures
and moment conditions of the errors and the covariates. Simulation results show
that Lasso regression can be substantially more powerful than the mixed
frequency data sampling regression (MIDAS) in the presence of irrelevant
variables. We apply the results obtained for the Lasso method to nowcasting
mixing frequency data in which serially correlated errors and a large number of
covariates are common. In real examples, the Lasso procedure outperforms the
MIDAS in both forecasting and nowcasting.
| 0 | 0 | 1 | 1 | 0 | 0 |
1,613 | Dynamic control of the optical emission from GaN/InGaN nanowire quantum dots by surface acoustic waves | The optical emission of InGaN quantum dots embedded in GaN nanowires is
dynamically controlled by a surface acoustic wave (SAW). The emission energy of
both the exciton and biexciton lines is modulated over a 1.5 meV range at ~330
MHz. A small but systematic difference in the exciton and biexciton spectral
modulation reveals a linear change of the biexciton binding energy with the SAW
amplitude. The present results are relevant for the dynamic control of
individual single photon emitters based on nitride semiconductors.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,614 | Robust Tracking and Behavioral Modeling of Movements of Biological Collectives from Ordinary Video Recordings | We propose a novel computational method to extract information about
interactions among individuals with different behavioral states in a biological
collective from ordinary video recordings. Assuming that individuals are acting
as finite state machines, our method first detects discrete behavioral states
of those individuals and then constructs a model of their state transitions,
taking into account the positions and states of other individuals in the
vicinity. We have tested the proposed method through applications to two
real-world biological collectives: termites in an experimental setting and
human pedestrians in a university campus. For each application, a robust
tracking system was developed in-house, utilizing interactive human
intervention (for termite tracking) or online agent-based simulation (for
pedestrian tracking). In both cases, significant interactions were detected
between nearby individuals with different states, demonstrating the
effectiveness of the proposed method.
| 1 | 1 | 0 | 0 | 0 | 0 |
1,615 | Understanding Membership Inferences on Well-Generalized Learning Models | Membership Inference Attack (MIA) determines the presence of a record in a
machine learning model's training data by querying the model. Prior work has
shown that the attack is feasible when the model is overfitted to its training
data or when the adversary controls the training algorithm. However, when the
model is not overfitted and the adversary does not control the training
algorithm, the threat is not well understood. In this paper, we report a study
that discovers overfitting to be a sufficient but not a necessary condition for
an MIA to succeed. More specifically, we demonstrate that even a
well-generalized model contains vulnerable instances subject to a new
generalized MIA (GMIA). In GMIA, we use novel techniques for selecting
vulnerable instances and detecting their subtle influences ignored by
overfitting metrics. Specifically, we successfully identify individual records
with high precision in real-world datasets by querying black-box machine
learning models. Further we show that a vulnerable record can even be
indirectly attacked by querying other related records and existing
generalization techniques are found to be less effective in protecting the
vulnerable instances. Our findings sharpen the understanding of the fundamental
cause of the problem: the unique influences the training instance may have on
the model.
| 0 | 0 | 0 | 1 | 0 | 0 |
1,616 | Identification of Near-Infrared [Se III] and [Kr VI] Emission Lines in Planetary Nebulae | We identify [Se III] 1.0994 micron in the planetary nebula (PN) NGC 5315 and
[Kr VI] 1.2330 micron in three PNe, from spectra obtained with the FIRE
spectrometer on the 6.5-m Baade Telescope. Se and Kr are the two most
widely-detected neutron-capture elements in astrophysical nebulae, and can be
enriched by s-process nucleosynthesis in PN progenitor stars. The detection of
[Se III] 1.0994 micron is particularly valuable when paired with observations
of [Se IV] 2.2858 micron, as it can be used to improve the accuracy of nebular
Se abundance determinations, and allows Se ionization correction factor (ICF)
schemes to be empirically tested for the first time. We present new effective
collision strength calculations for Se^{2+} and Kr^{5+}, which we use to
compute ionic abundances. In NGC 5315, we find that the Se abundance computed
from Se^{3+}/H^+ is lower than that determined with ICFs that incorporate
Se^{2+}/H^+. We compute new Kr ICFs that take Kr^{5+}/H^+ into account, by
fitting correlations found in grids of Cloudy models between Kr ionic fractions
and those of more abundant elements, and use these to derive Kr abundances in
four PNe. Observations of [Se III] and [Kr VI] in a larger sample of PNe, with
a range of excitation levels, are needed to rigorously test the ICF
prescriptions for Se and our new Kr ICFs.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,617 | On reduction of differential inclusions and Lyapunov stability | In this paper, locally Lipschitz regular functions are utilized to identify
and remove infeasible directions from differential inclusions. The resulting
reduced differential inclusion is point-wise smaller (in the sense of set
containment) than the original differential inclusion. The reduced inclusion is
utilized to develop a generalized notion of a derivative in the direction(s) of
a set-valued map for locally Lipschitz candidate Lyapunov functions. The
developed generalized derivative yields less conservative statements of
Lyapunov stability results, invariance-like results, and Matrosov results for
differential inclusions. Illustrative examples are included to demonstrate the
utility of the developed stability theorems.
| 1 | 0 | 0 | 0 | 0 | 0 |
1,618 | Deep Generative Networks For Sequence Prediction | This thesis investigates unsupervised time series representation learning for
sequence prediction problems, i.e. generating nice-looking input samples given
a previous history, for high dimensional input sequences by decoupling the
static input representation from the recurrent sequence representation. We
introduce three models based on Generative Stochastic Networks (GSN) for
unsupervised sequence learning and prediction. Experimental results for these
three models are presented on pixels of sequential handwritten digit (MNIST)
data, videos of low-resolution bouncing balls, and motion capture data. The
main contribution of this thesis is to provide evidence that GSNs are a viable
framework to learn useful representations of complex sequential input data, and
to suggest a new framework for deep generative models to learn complex
sequences by decoupling static input representations from dynamic time
dependency representations.
| 0 | 0 | 0 | 1 | 0 | 0 |
1,619 | Composite Behavioral Modeling for Identity Theft Detection in Online Social Networks | In this work, we aim at building a bridge from poor behavioral data to an
effective, quick-response, and robust behavior model for online identity theft
detection. We concentrate on this issue in online social networks (OSNs) where
users usually have composite behavioral records, consisting of
multi-dimensional low-quality data, e.g., offline check-ins and online user
generated content (UGC). As an insightful result, we find that there is a
complementary effect among different dimensions of records for modeling users'
behavioral patterns. To deeply exploit such a complementary effect, we propose
a joint model to capture both online and offline features of a user's composite
behavior. We evaluate the proposed joint model by comparing with some typical
models on two real-world datasets: Foursquare and Yelp. In the widely-used
setting of theft simulation (simulating thefts via behavioral replacement), the
experimental results show that our model outperforms the existing ones, with
the AUC values $0.956$ in Foursquare and $0.947$ in Yelp, respectively.
Particularly, the recall (True Positive Rate) can reach up to $65.3\%$ in
Foursquare and $72.2\%$ in Yelp with the corresponding disturbance rate (False
Positive Rate) below $1\%$. It is worth mentioning that these performances can
be achieved by examining only one composite behavior (visiting a place and
posting a tip online simultaneously) per authentication, which guarantees the
low response latency of our method. This study would give the cybersecurity
community new insights into whether and how a real-time online identity
authentication can be improved via modeling users' composite behavioral
patterns.
| 1 | 0 | 0 | 0 | 0 | 0 |
1,620 | Asymptotic properties of the set of systoles of arithmetic Riemann surfaces | The purpose this article is to try to understand the mysterious coincidence
between the asymptotic behavior of the volumes of the Moduli Space of closed
hyperbolic surfaces of genus $g$ with respect to the Weil-Petersson metric and
the asymptotic behavior of the number of arithmetic closed hyperbolic surfaces
of genus $g$. If the set of arithmetic surfaces is well distributed then its
image for any interesting function should be well distributed too. We
investigate the distribution of the function systole. We give several results
indicating that the systoles of arithmetic surfaces can not be concentrated,
consequently the same holds for the set of arithmetic surfaces. The proofs are
based in different techniques: combinatorics (obtaining regular graphs with any
girth from results of B. Bollobas and constructions with cages and Ramanujan
graphs), group theory (constructing finite index subgroups of surface groups
from finite index subgroups of free groups using results of G. Baumslag) and
geometric group theory (linking the geometry of graphs with the geometry of
coverings of a surface).
| 0 | 0 | 1 | 0 | 0 | 0 |
1,621 | Nonlinear elliptic equations on Carnot groups | This article concerns a class of elliptic equations on Carnot groups
depending on one real positive parameter and involving a subcritical
nonlinearity (for the critical case we refer to G. Molica Bisci and D.
Repovš, Yamabe-type equations on Carnot groups, Potential Anal. 46:2
(2017), 369-383; arXiv:1705.10100 [math.AP]). As a special case of our results
we prove the existence of at least one nontrivial solution for a subelliptic
equation defined on a smooth and bounded domain $D$ of the Heisenberg group
$\mathbb{H}^n=\mathbb{C}^n\times \mathbb{R}$. The main approach is based on
variational methods.
| 0 | 0 | 1 | 0 | 0 | 0 |
1,622 | Raptor Codes for Higher-Order Modulation Using a Multi-Edge Framework | In this paper, we represent Raptor codes as multi-edge type low-density
parity-check (MET-LDPC) codes, which gives a general framework to design them
for higher-order modulation using MET density evolution. We then propose an
efficient Raptor code design method for higher-order modulation, where we
design distinct degree distributions for distinct bit levels. We consider a
joint decoding scheme based on belief propagation for Raptor codes and also
derive an exact expression for the stability condition. In several examples, we
demonstrate that the higher-order modulated Raptor codes designed using the
multi-edge framework outperform previously reported higher-order modulation
codes in literature.
| 1 | 0 | 1 | 0 | 0 | 0 |
1,623 | A Tidy Data Model for Natural Language Processing using cleanNLP | The package cleanNLP provides a set of fast tools for converting a textual
corpus into a set of normalized tables. The underlying natural language
processing pipeline utilizes Stanford's CoreNLP library, exposing a number of
annotation tasks for text written in English, French, German, and Spanish.
Annotators include tokenization, part of speech tagging, named entity
recognition, entity linking, sentiment analysis, dependency parsing,
coreference resolution, and information extraction.
| 1 | 0 | 0 | 1 | 0 | 0 |
1,624 | Relaxation of the EM Algorithm via Quantum Annealing for Gaussian Mixture Models | We propose a modified expectation-maximization algorithm by introducing the
concept of quantum annealing, which we call the deterministic quantum annealing
expectation-maximization (DQAEM) algorithm. The expectation-maximization (EM)
algorithm is an established algorithm to compute maximum likelihood estimates
and applied to many practical applications. However, it is known that EM
heavily depends on initial values and its estimates are sometimes trapped by
local optima. To solve such a problem, quantum annealing (QA) was proposed as a
novel optimization approach motivated by quantum mechanics. By employing QA, we
then formulate DQAEM and present a theorem that supports its stability.
Finally, we demonstrate numerical simulations to confirm its efficiency.
| 0 | 1 | 0 | 1 | 0 | 0 |
1,625 | Multiband Superconductivity in the time reversal symmetry broken superconductor Re6Zr | We report point contact Andreev Reflection (PCAR) measurements on a
high-quality single crystal of the non-centrosymmetric superconductor Re6Zr. We
observe that the PCAR spectra can be fitted by taking two isotropic
superconducting gaps with Delta_1 ~ 0.79 meV and Delta_2 ~ 0.22 meV
respectively, suggesting that there are at least two bands which contribute to
superconductivity. Combined with the observation of time reversal symmetry
breaking at the superconducting transition from muon spin relaxation
measurements (Phys. Rev. Lett. 112, 107002 (2014)), our results imply an
unconventional superconducting order in this compound: A multiband singlet
state that breaks time reversal symmetry or a triplet state dominated by
interband pairing.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,626 | Influence of broken-pair excitations on the exact pair wavefunction | Doubly occupied configuration interaction (DOCI), the exact diagonalization
of the Hamiltonian in the paired (seniority zero) sector of the Hilbert space,
is a combinatorial cost wave function that can be very efficiently approximated
by pair coupled cluster doubles (pCCD) at mean-field computational cost. As
such, it is a very interesting candidate as a starting point for building the
full configuration interaction (FCI) ground state eigenfunction belonging to
all (not just paired) seniority sectors. The true seniority zero sector of FCI
(referred to here as FCI${}_0$) includes the effect of coupling between all
seniority sectors rather than just seniority zero, and is, in principle,
different from DOCI. We here study the accuracy with which DOCI approximates
FCI${}_0$. Using a set of small Hubbard lattices, where FCI is possible, we
show that DOCI $\sim$ FCI${}_0$ under weak correlation. However, in the strong
correlation regime, the nature of the FCI${}_0$ wavefunction can change
significantly, rendering DOCI and pCCD a less than ideal starting point for
approximating FCI.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,627 | Dynamical regularities of US equities opening and closing auctions | We first investigate the evolution of opening and closing auctions volumes of
US equities along the years. We then report dynamical properties of pre-auction
periods: the indicative match price is strongly mean-reverting because the
imbalance is; the final auction price reacts to a single auction order
placement or cancellation in markedly different ways in the opening and closing
auctions when computed conditionally on imbalance improving or worsening
events; the indicative price reverts towards the mid price of the regular limit
order book but is not especially bound to the spread.
| 0 | 0 | 0 | 0 | 0 | 1 |
1,628 | Comment on "Laser cooling of $^{173}$Yb for isotope separation and precision hyperfine spectroscopy" | We present measurements of the hyperfine splitting in the Yb-173
$6s6p~^1P_1^{\rm o} (F^{\prime}=3/2,7/2)$ states that disagree significantly
with those measured previously by Das and Natarajan [Phys. Rev. A 76, 062505
(2007)]. We point out inconsistencies in their measurements and suggest that
their error is due to optical pumping and improper determination of the atomic
line center. Our measurements are made using an optical frequency comb. We use
an optical pumping scheme to improve the signal-to-background ratio for the
$F^{\prime}=3/2$ component.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,629 | Training GANs with Optimism | We address the issue of limit cycling behavior in training Generative
Adversarial Networks and propose the use of Optimistic Mirror Decent (OMD) for
training Wasserstein GANs. Recent theoretical results have shown that
optimistic mirror decent (OMD) can enjoy faster regret rates in the context of
zero-sum games. WGANs is exactly a context of solving a zero-sum game with
simultaneous no-regret dynamics. Moreover, we show that optimistic mirror
decent addresses the limit cycling problem in training WGANs. We formally show
that in the case of bi-linear zero-sum games the last iterate of OMD dynamics
converges to an equilibrium, in contrast to GD dynamics which are bound to
cycle. We also portray the huge qualitative difference between GD and OMD
dynamics with toy examples, even when GD is modified with many adaptations
proposed in the recent literature, such as gradient penalty or momentum. We
apply OMD WGAN training to a bioinformatics problem of generating DNA
sequences. We observe that models trained with OMD achieve consistently smaller
KL divergence with respect to the true underlying distribution, than models
trained with GD variants. Finally, we introduce a new algorithm, Optimistic
Adam, which is an optimistic variant of Adam. We apply it to WGAN training on
CIFAR10 and observe improved performance in terms of inception score as
compared to Adam.
| 1 | 0 | 0 | 1 | 0 | 0 |
1,630 | New concepts of inertial measurements with multi-species atom interferometry | In the field of cold atom inertial sensors, we present and analyze innovative
configurations for improving their measurement range and sensitivity,
especially attracting for onboard applications. These configurations rely on
multi-species atom interferometry, involving the simultaneous manipulation of
different atomic species in a unique instrument to deduce inertial
measurements. Using a dual-species atom accelerometer manipulating
simultaneously both isotopes of rubidium, we report a preliminary experimental
realization of original concepts involving the implementation of two atom
interferometers first with different interrogation times and secondly in phase
quadrature. These results open the door to a new generation of atomic sensors
relying on high performance multi-species atom interferometric measurements.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,631 | LAMN in a class of parametric models for null recurrent diffusion | We study statistical models for one-dimensional diffusions which are
recurrent null. A first parameter in the drift is the principal one, and
determines regular varying rates of convergence for the score and the
information process. A finite number of other parameters, of secondary
importance, introduces additional flexibility for the modelization of the
drift, and does not perturb the null recurrent behaviour. Under time-continuous
observation we obtain local asymptotic mixed normality (LAMN), state a local
asymptotic minimax bound, and specify asymptotically optimal estimators.
| 0 | 0 | 1 | 1 | 0 | 0 |
1,632 | A recipe for topological observables of density matrices | Meaningful topological invariants for mixed quantum states are challenging to
identify as there is no unique way to define them, and most choices do not
directly relate to physical observables. Here, we propose a simple pragmatic
approach to construct topological invariants of mixed states while preserving a
connection to physical observables, by continuously deforming known topological
invariants for pure (ground) states. Our approach relies on expectation values
of many-body operators, with no reference to single-particle (e.g., Bloch)
wavefunctions. To illustrate it, we examine extensions to mixed states of
$U(1)$ geometric (Berry) phases and their corresponding topological invariant
(winding or Chern number). We discuss measurement schemes, and provide a
detailed construction of invariants for thermal or more general mixed states of
quantum systems with (at least) $U(1)$ charge-conservation symmetry, such as
quantum Hall insulators.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,633 | Generalization Tower Network: A Novel Deep Neural Network Architecture for Multi-Task Learning | Deep learning (DL) advances state-of-the-art reinforcement learning (RL), by
incorporating deep neural networks in learning representations from the input
to RL. However, the conventional deep neural network architecture is limited in
learning representations for multi-task RL (MT-RL), as multiple tasks can refer
to different kinds of representations. In this paper, we thus propose a novel
deep neural network architecture, namely generalization tower network (GTN),
which can achieve MT-RL within a single learned model. Specifically, the
architecture of GTN is composed of both horizontal and vertical streams. In our
GTN architecture, horizontal streams are used to learn representation shared in
similar tasks. In contrast, the vertical streams are introduced to be more
suitable for handling diverse tasks, which encodes hierarchical shared
knowledge of these tasks. The effectiveness of the introduced vertical stream
is validated by experimental results. Experimental results further verify that
our GTN architecture is able to advance the state-of-the-art MT-RL, via being
tested on 51 Atari games.
| 1 | 0 | 0 | 1 | 0 | 0 |
1,634 | On a class of infinitely differentiable functions in ${\mathbb R}^n$ admitting holomorphic extension in ${\mathbb C}^n$ | A space $G(M, \varPhi)$ of infinitely differentiable functions in ${\mathbb
R}^n$ constructed with a help of a family
$\varPhi=\{\varphi_m\}_{m=1}^{\infty}$ of real-valued functions $\varphi_m
\in~C({\mathbb R}^n)$ and a logarithmically convex sequence $M$ of positive
numbers is considered in the article. In view of conditions on $M$ each
function of $G(M, \varPhi)$ can be extended to an entire function in ${\mathbb
C}^n$. Imposed conditions on $M$ and $\varPhi$ allow to describe the space of
such extensions.
| 0 | 0 | 1 | 0 | 0 | 0 |
1,635 | Spatially resolved, energy-filtered imaging of core level and valence band photoemission of highly p and n doped silicon patterns | An accurate description of spatial variations in the energy levels of
patterned semiconductor substrates on the micron and sub-micron scale as a
function of local doping is an important technological challenge for the
microelectronics industry. Spatially resolved surface analysis by photoelectron
spectromicroscopy can provide an invaluable contribution thanks to the
relatively non-destructive, quantitative analysis. We present results on highly
doped n and p type patterns on, respectively, p and n type silicon substrates.
Using synchrotron radiation and spherical aberration-corrected energy
filtering, we have obtained a spectroscopic image series at the Si 2p core
level and across the valence band. Local band alignments are extracted,
accounting for doping, band bending and surface photovoltage.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,636 | Effects of Degree Correlations in Interdependent Security: Good or Bad? | We study the influence of degree correlations or network mixing in
interdependent security. We model the interdependence in security among agents
using a dependence graph and employ a population game model to capture the
interaction among many agents when they are strategic and have various security
measures they can choose to defend themselves. The overall network security is
measured by what we call the average risk exposure (ARE) from neighbors, which
is proportional to the total (expected) number of attacks in the network.
We first show that there exists a unique pure-strategy Nash equilibrium of a
population game. Then, we prove that as the agents with larger degrees in the
dependence graph see higher risks than those with smaller degrees, the overall
network security deteriorates in that the ARE experienced by agents increases
and there are more attacks in the network. Finally, using this finding, we
demonstrate that the effects of network mixing on ARE depend on the (cost)
effectiveness of security measures available to agents; if the security
measures are not effective, increasing assortativity of dependence graph
results in higher ARE. On the other hand, if the security measures are
effective at fending off the damages and losses from attacks, increasing
assortativity reduces the ARE experienced by agents.
| 1 | 1 | 0 | 0 | 0 | 0 |
1,637 | Towards more Reliable Transfer Learning | Multi-source transfer learning has been proven effective when within-target
labeled data is scarce. Previous work focuses primarily on exploiting domain
similarities and assumes that source domains are richly or at least comparably
labeled. While this strong assumption is never true in practice, this paper
relaxes it and addresses challenges related to sources with diverse labeling
volume and diverse reliability. The first challenge is combining domain
similarity and source reliability by proposing a new transfer learning method
that utilizes both source-target similarities and inter-source relationships.
The second challenge involves pool-based active learning where the oracle is
only available in source domains, resulting in an integrated active transfer
learning framework that incorporates distribution matching and uncertainty
sampling. Extensive experiments on synthetic and two real-world datasets
clearly demonstrate the superiority of our proposed methods over several
baselines including state-of-the-art transfer learning methods.
| 0 | 0 | 0 | 1 | 0 | 0 |
1,638 | Forming short-period Wolf-Rayet X-ray binaries and double black holes through stable mass transfer | We show that black-hole High-Mass X-ray Binaries (HMXBs) with O- or B-type
donor stars and relatively short orbital periods, of order one week to several
months may survive spiral in, to then form Wolf-Rayet (WR) X-ray binaries with
orbital periods of order a day to a few days; while in systems where the
compact star is a neutron star, HMXBs with these orbital periods never survive
spiral-in. We therefore predict that WR X-ray binaries can only harbor black
holes. The reason why black-hole HMXBs with these orbital periods may survive
spiral in is: the combination of a radiative envelope of the donor star, and a
high mass of the compact star. In this case, when the donor begins to overflow
its Roche lobe, the systems are able to spiral in slowly with stable Roche-lobe
overflow, as is shown by the system SS433. In this case the transferred mass is
ejected from the vicinity of the compact star (so-called "isotropic
re-emission" mass loss mode, or "SS433-like mass loss"), leading to gradual
spiral-in. If the mass ratio of donor and black hole is $>3.5$, these systems
will go into CE evolution and are less likely to survive. If they survive, they
produce WR X-ray binaries with orbital periods of a few hours to one day.
Several of the well-known WR+O binaries in our Galaxy and the Magellanic
Clouds, with orbital periods in the range between a week and several months,
are expected to evolve into close WR-Black-Hole binaries,which may later
produce close double black holes. The galactic formation rate of double black
holes resulting from such systems is still uncertain, as it depends on several
poorly known factors in this evolutionary picture. It might possibly be as high
as $\sim 10^{-5}$ per year.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,639 | Syzygies of Cohen-Macaulay modules over one dimensional Cohen-Macaulay local rings | We study syzygies of (maximal) Cohen-Macaulay modules over one dimensional
Cohen-Macaulay local rings. We compare these modules to Cohen-Macaulay modules
over the endomorphism ring of the maximal ideal. After this comparison, we give
several characterizations of almost Gorenstein rings in terms of syzygies of
Cohen-Macaulay modules.
| 0 | 0 | 1 | 0 | 0 | 0 |
1,640 | Chirality provides a direct fitness advantage and facilitates intermixing in cellular aggregates | Chirality in shape and motility can evolve rapidly in microbes and cancer
cells. To determine how chirality affects cell fitness, we developed a model of
chiral growth in compact aggregates such as microbial colonies and solid
tumors. Our model recapitulates previous experimental findings and shows that
mutant cells can invade by increasing their chirality or switching their
handedness. The invasion results either in a takeover or stable coexistence
between the mutant and the ancestor depending on their relative chirality. For
large chiralities, the coexistence is accompanied by strong intermixing between
the cells, while spatial segregation occurs otherwise. We show that the
competition within the aggregate is mediated by bulges in regions where the
cells with different chiralities meet. The two-way coupling between aggregate
shape and natural selection is described by the chiral Kardar-Parisi-Zhang
equation coupled to the Burgers' equation with multiplicative noise. We solve
for the key features of this theory to explain the origin of selection on
chirality. Overall, our work suggests that chirality could be an important
ecological trait that mediates competition, invasion, and spatial structure in
cellular populations.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,641 | Learning to Draw Samples with Amortized Stein Variational Gradient Descent | We propose a simple algorithm to train stochastic neural networks to draw
samples from given target distributions for probabilistic inference. Our method
is based on iteratively adjusting the neural network parameters so that the
output changes along a Stein variational gradient direction (Liu & Wang, 2016)
that maximally decreases the KL divergence with the target distribution. Our
method works for any target distribution specified by their unnormalized
density function, and can train any black-box architectures that are
differentiable in terms of the parameters we want to adapt. We demonstrate our
method with a number of applications, including variational autoencoder (VAE)
with expressive encoders to model complex latent space structures, and
hyper-parameter learning of MCMC samplers that allows Bayesian inference to
adaptively improve itself when seeing more data.
| 0 | 0 | 0 | 1 | 0 | 0 |
1,642 | Preconditioned dynamic mode decomposition and mode selection algorithms for large datasets using incremental proper orthogonal decomposition | This note proposes a simple and general framework of dynamic mode
decomposition (DMD) and a mode selection for large datasets. The proposed
framework explicitly introduces a preconditioning step using an incremental
proper orthogonal decomposition to DMD and mode selection algorithms. By
performing the preconditioning step, the DMD and the mode selection can be
performed with low memory consumption and small computational complexity and
can be applied to large datasets. In addition, a simple mode selection
algorithm based on a greedy method is proposed. The proposed framework is
applied to the analysis of a three-dimensional flows around a circular
cylinder.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,643 | A functional perspective on emergent supersymmetry | We investigate the emergence of ${\cal N}=1$ supersymmetry in the long-range
behavior of three-dimensional parity-symmetric Yukawa systems. We discuss a
renormalization approach that manifestly preserves supersymmetry whenever such
symmetry is realized, and use it to prove that supersymmetry-breaking operators
are irrelevant, thus proving that such operators are suppressed in the
infrared. All our findings are illustrated with the aid of the
$\epsilon$-expansion and a functional variant of perturbation theory, but we
provide numerical estimates of critical exponents that are based on the
non-perturbative functional renormalization group.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,644 | Variants of RMSProp and Adagrad with Logarithmic Regret Bounds | Adaptive gradient methods have become recently very popular, in particular as
they have been shown to be useful in the training of deep neural networks. In
this paper we have analyzed RMSProp, originally proposed for the training of
deep neural networks, in the context of online convex optimization and show
$\sqrt{T}$-type regret bounds. Moreover, we propose two variants SC-Adagrad and
SC-RMSProp for which we show logarithmic regret bounds for strongly convex
functions. Finally, we demonstrate in the experiments that these new variants
outperform other adaptive gradient techniques or stochastic gradient descent in
the optimization of strongly convex functions as well as in training of deep
neural networks.
| 1 | 0 | 0 | 1 | 0 | 0 |
1,645 | Dykstra's Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions | We study connections between Dykstra's algorithm for projecting onto an
intersection of convex sets, the augmented Lagrangian method of multipliers or
ADMM, and block coordinate descent. We prove that coordinate descent for a
regularized regression problem, in which the (separable) penalty functions are
seminorms, is exactly equivalent to Dykstra's algorithm applied to the dual
problem. ADMM on the dual problem is also seen to be equivalent, in the special
case of two sets, with one being a linear subspace. These connections, aside
from being interesting in their own right, suggest new ways of analyzing and
extending coordinate descent. For example, from existing convergence theory on
Dykstra's algorithm over polyhedra, we discern that coordinate descent for the
lasso problem converges at an (asymptotically) linear rate. We also develop two
parallel versions of coordinate descent, based on the Dykstra and ADMM
connections.
| 0 | 0 | 1 | 1 | 0 | 0 |
1,646 | A Topological Perspective on Interacting Algebraic Theories | Techniques from higher categories and higher-dimensional rewriting are
becoming increasingly important for understanding the finer, computational
properties of higher algebraic theories that arise, among other fields, in
quantum computation. These theories have often the property of containing
simpler sub-theories, whose interaction is regulated in a limited number of
ways, which reveals a topological substrate when pictured by string diagrams.
By exploring the double nature of computads as presentations of higher
algebraic theories, and combinatorial descriptions of "directed spaces", we
develop a basic language of directed topology for the compositional study of
algebraic theories. We present constructions of computads, all with clear
analogues in standard topology, that capture in great generality such notions
as homomorphisms and actions, and the interactions of monoids and comonoids
that lead to the theory of Frobenius algebras and of bialgebras. After a number
of examples, we describe how a fragment of the ZX calculus can be reconstructed
in this framework.
| 1 | 0 | 1 | 0 | 0 | 0 |
1,647 | Dynamic nested sampling: an improved algorithm for parameter estimation and evidence calculation | We introduce dynamic nested sampling: a generalisation of the nested sampling
algorithm in which the number of "live points" varies to allocate samples more
efficiently. In empirical tests the new method significantly improves
calculation accuracy compared to standard nested sampling with the same number
of samples; this increase in accuracy is equivalent to speeding up the
computation by factors of up to ~72 for parameter estimation and ~7 for
evidence calculations. We also show that the accuracy of both parameter
estimation and evidence calculations can be improved simultaneously. In
addition, unlike in standard nested sampling, more accurate results can be
obtained by continuing the calculation for longer. Popular standard nested
sampling implementations can be easily adapted to perform dynamic nested
sampling, and several dynamic nested sampling software packages are now
publicly available.
| 0 | 1 | 0 | 1 | 0 | 0 |
1,648 | Multistage Adaptive Testing of Sparse Signals | Multistage design has been used in a wide range of scientific fields. By
allocating sensing resources adaptively, one can effectively eliminate null
locations and localize signals with a smaller study budget. We formulate a
decision-theoretic framework for simultaneous multi- stage adaptive testing and
study how to minimize the total number of measurements while meeting
pre-specified constraints on both the false positive rate (FPR) and missed
discovery rate (MDR). The new procedure, which effectively pools information
across individual tests using a simultaneous multistage adaptive ranking and
thresholding (SMART) approach, can achieve precise error rates control and lead
to great savings in total study costs. Numerical studies confirm the
effectiveness of SMART for FPR and MDR control and show that it achieves
substantial power gain over existing methods. The SMART procedure is
demonstrated through the analysis of high-throughput screening data and spatial
imaging data.
| 0 | 0 | 0 | 1 | 0 | 0 |
1,649 | On the commutativity of the powerspace constructions | We investigate powerspace constructions on topological spaces, with a
particular focus on the category of quasi-Polish spaces. We show that the upper
and lower powerspaces commute on all quasi-Polish spaces, and show more
generally that this commutativity is equivalent to the topological property of
consonance. We then investigate powerspace constructions on the open set
lattices of quasi-Polish spaces, and provide a complete characterization of how
the upper and lower powerspaces distribute over the open set lattice
construction.
| 1 | 0 | 1 | 0 | 0 | 0 |
1,650 | Bounds on poloidal kinetic energy in plane layer convection | A numerical method is presented which conveniently computes upper bounds on
heat transport and poloidal energy in plane layer convection for infinite and
finite Prandtl numbers. The bounds obtained for the heat transport coincide
with earlier results. These bounds imply upper bounds for the poloidal energy
which follow directly from the definitions of dissipation and energy. The same
constraints used for computing upper bounds on the heat transport lead to
improved bounds for the poloidal energy.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,651 | Concentration of Multilinear Functions of the Ising Model with Applications to Network Data | We prove near-tight concentration of measure for polynomial functions of the
Ising model under high temperature. For any degree $d$, we show that a
degree-$d$ polynomial of a $n$-spin Ising model exhibits exponential tails that
scale as $\exp(-r^{2/d})$ at radius $r=\tilde{\Omega}_d(n^{d/2})$. Our
concentration radius is optimal up to logarithmic factors for constant $d$,
improving known results by polynomial factors in the number of spins. We
demonstrate the efficacy of polynomial functions as statistics for testing the
strength of interactions in social networks in both synthetic and real world
data.
| 1 | 0 | 1 | 1 | 0 | 0 |
1,652 | Switching Isotropic and Directional Exploration with Parameter Space Noise in Deep Reinforcement Learning | This paper proposes an exploration method for deep reinforcement learning
based on parameter space noise. Recent studies have experimentally shown that
parameter space noise results in better exploration than the commonly used
action space noise. Previous methods devised a way to update the diagonal
covariance matrix of a noise distribution and did not consider the direction of
the noise vector and its correlation. In addition, fast updates of the noise
distribution are required to facilitate policy learning. We propose a method
that deforms the noise distribution according to the accumulated returns and
the noises that have led to the returns. Moreover, this method switches
isotropic exploration and directional exploration in parameter space with
regard to obtained rewards. We validate our exploration strategy in the OpenAI
Gym continuous environments and modified environments with sparse rewards. The
proposed method achieves results that are competitive with a previous method at
baseline tasks. Moreover, our approach exhibits better performance in sparse
reward environments by exploration with the switching strategy.
| 0 | 0 | 0 | 1 | 0 | 0 |
1,653 | Fast and high-accuracy measuring technique for transmittance spectrum in VIS-NIR | In this paper, based on the framework of traditional spectrophotometry, we
put forward a novel fast and high-accuracy technique for measuring
transmittance spectrum in VIS-NIR wave range, its key feature is that during
the measurement procedure, the output wavelength of the grating monochromator
is kept increasing continuously and at the same time, the photoelectric
detectors execute a concurrently continuous data acquisition routine. Initial
experiment result shows that the newly proposed technique could shorten the
time consumed for measuring the transmittance spectrum down to 50% that of the
conventional spectrophotometric method, a relative error of 0.070% and a
repeatability error of 0.042% are generated. Compared with the current mostly
used techniques (spectrophotometry, methods based on multi-channel spectrometer
and strategy using Fourier transform spectrometer) for obtaining transmittance
spectrum in VIS-NIR, the new strategy has at all once the following advantages,
firstly the measuring speed could be greatly quicken, fast measurement of
transmittance spectrum in VIS-NIR is therefore promising, which would find wide
application in dynamic environment, secondly high measuring accuracy
(0.1%-0.3%) is available, and finally the measuring system has high mechanical
stability because the motor of the grating monochromator is rotating
continuously during the measurement.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,654 | The solution to the initial value problem for the ultradiscrete Somos-4 and 5 equations | We propose a method to solve the initial value problem for the ultradiscrete
Somos-4 and Somos-5 equations by expressing terms in the equations as convex
polygons and regarding max-plus algebras as those on polygons.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,655 | Width-tuned magnetic order oscillation on zigzag edges of honeycomb nanoribbons | Quantum confinement and interference often generate exotic properties in
nanostructures. One recent highlight is the experimental indication of a
magnetic phase transition in zigzag-edged graphene nanoribbons at the critical
ribbon width of about 7 nm [G. Z. Magda et al., Nature \textbf{514}, 608
(2014)]. Here we show theoretically that with further increase in the ribbon
width, the magnetic correlation of the two edges can exhibit an intriguing
oscillatory behavior between antiferromagnetic and ferromagnetic, driven by
acquiring the positive coherence between the two edges to lower the free
energy. The oscillation effect is readily tunable in applied magnetic fields.
These novel properties suggest new experimental manifestation of the edge
magnetic orders in graphene nanoribbons, and enhance the hopes of graphene-like
spintronic nanodevices functioning at room temperature.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,656 | Fast and accurate approximation of the full conditional for gamma shape parameters | The gamma distribution arises frequently in Bayesian models, but there is not
an easy-to-use conjugate prior for the shape parameter of a gamma. This
inconvenience is usually dealt with by using either Metropolis-Hastings moves,
rejection sampling methods, or numerical integration. However, in models with a
large number of shape parameters, these existing methods are slower or more
complicated than one would like, making them burdensome in practice. It turns
out that the full conditional distribution of the gamma shape parameter is well
approximated by a gamma distribution, even for small sample sizes, when the
prior on the shape parameter is also a gamma distribution. This article
introduces a quick and easy algorithm for finding a gamma distribution that
approximates the full conditional distribution of the shape parameter. We
empirically demonstrate the speed and accuracy of the approximation across a
wide range of conditions. If exactness is required, the approximation can be
used as a proposal distribution for Metropolis-Hastings.
| 0 | 0 | 0 | 1 | 0 | 0 |
1,657 | Two variants of the Froiduire-Pin Algorithm for finite semigroups | In this paper, we present two algorithms based on the Froidure-Pin Algorithm
for computing the structure of a finite semigroup from a generating set. As was
the case with the original algorithm of Froidure and Pin, the algorithms
presented here produce the left and right Cayley graphs, a confluent
terminating rewriting system, and a reduced word of the rewriting system for
every element of the semigroup.
If $U$ is any semigroup, and $A$ is a subset of $U$, then we denote by
$\langle A\rangle$ the least subsemigroup of $U$ containing $A$. If $B$ is any
other subset of $U$, then, roughly speaking, the first algorithm we present
describes how to use any information about $\langle A\rangle$, that has been
found using the Froidure-Pin Algorithm, to compute the semigroup $\langle A\cup
B\rangle$. More precisely, we describe the data structure for a finite
semigroup $S$ given by Froidure and Pin, and how to obtain such a data
structure for $\langle A\cup B\rangle$ from that for $\langle A\rangle$. The
second algorithm is a lock-free concurrent version of the Froidure-Pin
Algorithm.
| 1 | 0 | 1 | 0 | 0 | 0 |
1,658 | Holon Wigner Crystal in a Lightly Doped Kagome Quantum Spin Liquid | We address the problem of a lightly doped spin-liquid through a large-scale
density-matrix renormalization group (DMRG) study of the $t$-$J$ model on a
Kagome lattice with a small but non-zero concentration, $\delta$, of doped
holes. It is now widely accepted that the undoped ($\delta=0$) spin 1/2
Heisenberg antiferromagnet has a spin-liquid groundstate. Theoretical arguments
have been presented that light doping of such a spin-liquid could give rise to
a high temperature superconductor or an exotic topological Fermi liquid metal
(FL$^\ast$). Instead, we infer that the doped holes form an insulating
charge-density wave state with one doped-hole per unit cell - i.e. a Wigner
crystal (WC). Spin correlations remain short-ranged, as in the spin-liquid
parent state, from which we infer that the state is a crystal of spinless
holons (WC$^\ast$), rather than of holes. Our results may be relevant to Kagome
lattice Herbertsmithite $\rm ZnCu_3(OH)_6Cl_2$ upon doping.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,659 | Accelerated Block Coordinate Proximal Gradients with Applications in High Dimensional Statistics | Nonconvex optimization problems arise in different research fields and arouse
lots of attention in signal processing, statistics and machine learning. In
this work, we explore the accelerated proximal gradient method and some of its
variants which have been shown to converge under nonconvex context recently. We
show that a novel variant proposed here, which exploits adaptive momentum and
block coordinate update with specific update rules, further improves the
performance of a broad class of nonconvex problems. In applications to sparse
linear regression with regularizations like Lasso, grouped Lasso, capped
$\ell_1$ and SCAP, the proposed scheme enjoys provable local linear
convergence, with experimental justification.
| 1 | 0 | 0 | 1 | 0 | 0 |
1,660 | Phase retrieval using alternating minimization in a batch setting | This paper considers the problem of phase retrieval, where the goal is to
recover a signal $z\in C^n$ from the observations $y_i=|a_i^* z|$,
$i=1,2,\cdots,m$. While many algorithms have been proposed, the alternating
minimization algorithm has been one of the most commonly used methods, and it
is very simple to implement. Current work has proved that when the observation
vectors $\{a_i\}_{i=1}^m$ are sampled from a complex Gaussian distribution
$N(0, I)$, it recovers the underlying signal with a good initialization when
$m=O(n)$, or with random initialization when $m=O(n^2)$, and it conjectured
that random initialization succeeds with $m=O(n)$. This work proposes a
modified alternating minimization method in a batch setting, and proves that
when $m=O(n\log^{3}n)$, the proposed algorithm with random initialization
recovers the underlying signal with high probability. The proof is based on the
observation that after each iteration of alternating minimization, with high
probability, the angle between the estimated signal and the underlying signal
is reduced.
| 0 | 0 | 1 | 1 | 0 | 0 |
1,661 | Inverse statistical problems: from the inverse Ising problem to data science | Inverse problems in statistical physics are motivated by the challenges of
`big data' in different fields, in particular high-throughput experiments in
biology. In inverse problems, the usual procedure of statistical physics needs
to be reversed: Instead of calculating observables on the basis of model
parameters, we seek to infer parameters of a model based on observations. In
this review, we focus on the inverse Ising problem and closely related
problems, namely how to infer the coupling strengths between spins given
observed spin correlations, magnetisations, or other data. We review
applications of the inverse Ising problem, including the reconstruction of
neural connections, protein structure determination, and the inference of gene
regulatory networks. For the inverse Ising problem in equilibrium, a number of
controlled and uncontrolled approximate solutions have been developed in the
statistical mechanics community. A particularly strong method,
pseudolikelihood, stems from statistics. We also review the inverse Ising
problem in the non-equilibrium case, where the model parameters must be
reconstructed based on non-equilibrium statistics.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,662 | Static structure of chameleon dark Matter as an explanation of dwarf spheroidal galactic core | We propose a novel mechanism which explains cored dark matter density profile
in recently observed dark matter rich dwarf spheroidal galaxies. In our
scenario, dark matter particle mass decreases gradually as function of distance
towards the center of a dwarf galaxy due to its interaction with a chameleon
scalar. At closer distance towards galactic center the strength of attractive
scalar fifth force becomes much stronger than gravity and is balanced by the
Fermi pressure of dark matter cloud, thus an equilibrium static configuration
of dark matter halo is obtained. Like the case of soliton star or fermion
Q-star, the stability of the dark matter halo is obtained as the scalar
achieves a static profile and reaches an asymptotic value away from the
galactic center. For simple scalar-dark matter interaction and quadratic scalar
self interaction potential, we show that dark matter behaves exactly like cold
dark matter (CDM) beyond few $\rm{kpc}$ away from galactic center but at closer
distance it becomes lighter and fermi pressure cannot be ignored anymore. Using
Thomas-Fermi approximation, we numerically solve the radial static profile of
the scalar field, fermion mass and dark matter energy density as a function of
distance. We find that for fifth force mediated by an ultra light scalar, it is
possible to obtain a flattened dark matter density profile towards galactic
center. In our scenario, the fifth force can be neglected at distance $ r \geq
1\, \rm{kpc}$ from galactic center and dark matter can be simply treated as
heavy non-relativistic particles beyond this distance, thus reproducing the
success of CDM at large scales.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,663 | Multi-Stakeholder Recommendation: Applications and Challenges | Recommender systems have been successfully applied to assist decision making
by producing a list of item recommendations tailored to user preferences.
Traditional recommender systems only focus on optimizing the utility of the end
users who are the receiver of the recommendations. By contrast,
multi-stakeholder recommendation attempts to generate recommendations that
satisfy the needs of both the end users and other parties or stakeholders. This
paper provides an overview and discussion about the multi-stakeholder
recommendations from the perspective of practical applications, available data
sets, corresponding research challenges and potential solutions.
| 1 | 0 | 0 | 0 | 0 | 0 |
1,664 | Unbalancing Sets and an Almost Quadratic Lower Bound for Syntactically Multilinear Arithmetic Circuits | We prove a lower bound of $\Omega(n^2/\log^2 n)$ on the size of any
syntactically multilinear arithmetic circuit computing some explicit
multilinear polynomial $f(x_1, \ldots, x_n)$. Our approach expands and improves
upon a result of Raz, Shpilka and Yehudayoff ([RSY08]), who proved a lower
bound of $\Omega(n^{4/3}/\log^2 n)$ for the same polynomial. Our improvement
follows from an asymptotically optimal lower bound for a generalized version of
Galvin's problem in extremal set theory.
| 1 | 0 | 0 | 0 | 0 | 0 |
1,665 | Saturated absorption competition microscopy | We introduce the concept of saturated absorption competition (SAC) microscopy
as a means of providing sub-diffraction spatial resolution in fluorescence
imaging. Unlike the post-competition process between stimulated and spontaneous
emission that is used in stimulated emission depletion (STED) microscopy, SAC
microscopy breaks the diffraction limit by emphasizing a pre-competition
process that occurs in the fluorescence absorption stage in a manner that
shares similarities with ground-state depletion (GSD) microscopy. Moreover,
unlike both STED and GSD microscopy, SAC microscopy offers a reduction in
complexity and cost by utilizing only a single continuous-wave laser diode and
an illumination intensity that is ~ 20x smaller than that used in STED. Our
approach can be physically implemented in a confocal microscope by dividing the
input laser source into a time-modulated primary excitation beam and a
doughnut-shaped saturation beam, and subsequently employing a homodyne
detection scheme to select the modulated fluorescence signal. Herein, we
provide both a physico-chemical model of SAC and experimentally demonstrate by
way of a proof-of-concept experiment a transverse spatial resolution of
~lambda/6.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,666 | Topological and non inertial effects on the interbank light absorption | In this work, we investigate the combined influence of the nontrivial
topology introduced by a disclination and non inertial effects due to rotation,
in the energy levels and the wave functions of a noninteracting electron gas
confined to a two-dimensional pseudoharmonic quantum dot, under the influence
of an external uniform magnetic field. The exact solutions for energy
eigenvalues and wave functions are computed as functions of the applied
magnetic field strength, the disclination topological charge, magnetic quantum
number and the rotation speed of the sample. We investigate the modifications
on the light interband absorption coefficient and absorption threshold
frequency. We observe novel features in the system, including a range of
magnetic field without corresponding absorption phenomena, which is due to a
tripartite term of the Hamiltonian, involving magnetic field, the topological
charge of the defect and the rotation frequency.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,667 | Evolution of antiferromagnetic domains in the all-in-all-out ordered pyrochlore Nd$_2$Zr$_2$O$_7$ | We report the observation of magnetic domains in the exotic,
antiferromagnetically ordered all-in-all-out state of Nd$_2$Zr$_2$O$_7$,
induced by spin canting. The all-in-all-out state can be realized by Ising-like
spins on a pyrochlore lattice and is established in Nd$_2$Zr$_2$O$_7$ below
0.31 K for external magnetic fields up to 0.14 T. Two different spin
arrangements can fulfill this configuration which leads to the possibility of
magnetic domains. The all-in-all-out domain structure can be controlled by an
external magnetic field applied parallel to the [111] direction. This is a
result of different spin canting mechanism for the two all-in-all-out
configurations for such a direction of the magnetic field. The change of the
domain structure is observed through a hysteresis in the magnetic
susceptibility. No hysteresis occurs, however, in case the external magnetic
field is applied along [100].
| 0 | 1 | 0 | 0 | 0 | 0 |
1,668 | Phylogenetic networks that are their own fold-ups | Phylogenetic networks are becoming of increasing interest to evolutionary
biologists due to their ability to capture complex non-treelike evolutionary
processes. From a combinatorial point of view, such networks are certain types
of rooted directed acyclic graphs whose leaves are labelled by, for example,
species. A number of mathematically interesting classes of phylogenetic
networks are known. These include the biologically relevant class of stable
phylogenetic networks whose members are defined via certain fold-up and un-fold
operations that link them with concepts arising within the theory of, for
example, graph fibrations. Despite this exciting link, the structural
complexity of stable phylogenetic networks is still relatively poorly
understood. Employing the popular tree-based, reticulation-visible, and
tree-child properties which allow one to gauge this complexity in one way or
another, we provide novel characterizations for when a stable phylogenetic
network satisfies either one of these three properties.
| 0 | 0 | 0 | 0 | 1 | 0 |
1,669 | Hyperbolic inverse mean curvature flow | In this paper, we prove the short-time existence of hyperbolic inverse (mean)
curvature flow (with or without the specified forcing term) under the
assumption that the initial compact smooth hypersurface of $\mathbb{R}^{n+1}$
($n\geqslant2$) is mean convex and star-shaped. Several interesting examples
and some hyperbolic evolution equations for geometric quantities of the
evolving hypersurfaces have been shown. Besides, under different assumptions
for the initial velocity, we can get the expansion and the convergence results
of a hyperbolic inverse mean curvature flow in the plane $\mathbb{R}^2$, whose
evolving curves move normally.
| 0 | 0 | 1 | 0 | 0 | 0 |
1,670 | Theoretically Principled Trade-off between Robustness and Accuracy | We identify a trade-off between robustness and accuracy that serves as a
guiding principle in the design of defenses against adversarial examples.
Although the problem has been widely studied empirically, much remains unknown
concerning the theory underlying this trade-off. In this work, we quantify the
trade-off in terms of the gap between the risk for adversarial examples and the
risk for non-adversarial examples. The challenge is to provide tight bounds on
this quantity in terms of a surrogate loss. We give an optimal upper bound on
this quantity in terms of classification-calibrated loss, which matches the
lower bound in the worst case. Inspired by our theoretical analysis, we also
design a new defense method, TRADES, to trade adversarial robustness off
against accuracy. Our proposed algorithm performs well experimentally in
real-world datasets. The methodology is the foundation of our entry to the
NeurIPS 2018 Adversarial Vision Challenge in which we won the 1st place out of
1,995 submissions in the robust model track, surpassing the runner-up approach
by $11.41\%$ in terms of mean $\ell_2$ perturbation distance.
| 1 | 0 | 0 | 1 | 0 | 0 |
1,671 | A new charge reconstruction algorithm for the DAMPE silicon microstrip detector | The DArk Matter Particle Explorer (DAMPE) is one of the four satellites
within the Strategic Pioneer Research Program in Space Science of the Chinese
Academy of Science (CAS). The Silicon-Tungsten Tracker (STK), which is composed
of 768 singled-sided silicon microstrip detectors, is one of the four
subdetectors in DAMPE, providing track reconstruction and charge identification
for relativistic charged particles. The charge response of DAMPE silicon
microstrip detectors is complicated, depending on the incident angle and impact
position. A new charge reconstruction algorithm for the DAMPE silicon
microstrip detector is introduced in this paper. This algorithm can correct the
complicated charge response, and was proved applicable by the ion test beam.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,672 | The Structure Transfer Machine Theory and Applications | Representation learning is a fundamental but challenging problem, especially
when the distribution of data is unknown. We propose a new representation
learning method, termed Structure Transfer Machine (STM), which enables feature
learning process to converge at the representation expectation in a
probabilistic way. We theoretically show that such an expected value of the
representation (mean) is achievable if the manifold structure can be
transferred from the data space to the feature space. The resulting structure
regularization term, named manifold loss, is incorporated into the loss
function of the typical deep learning pipeline. The STM architecture is
constructed to enforce the learned deep representation to satisfy the intrinsic
manifold structure from the data, which results in robust features that suit
various application scenarios, such as digit recognition, image classification
and object tracking. Compared to state-of-the-art CNN architectures, we achieve
the better results on several commonly used benchmarks\footnote{The source code
is available. this https URL }.
| 0 | 0 | 0 | 1 | 0 | 0 |
1,673 | Inverse Fractional Knapsack Problem with Profits and Costs Modification | We address in this paper the problem of modifying both profits and costs of a
fractional knapsack problem optimally such that a prespecified solution becomes
an optimal solution with prespect to new parameters. This problem is called the
inverse fractional knapsack problem. Concerning the $l_1$-norm, we first prove
that the problem is NP-hard. The problem can be however solved in quadratic
time if we only modify profit parameters. Additionally, we develop a
quadratic-time algorithm that solves the inverse fractional knapsack problem
under $l_\infty$-norm.
| 1 | 0 | 1 | 0 | 0 | 0 |
1,674 | Large dimensional analysis of general margin based classification methods | Margin-based classifiers have been popular in both machine learning and
statistics for classification problems. Since a large number of classifiers are
available, one natural question is which type of classifiers should be used
given a particular classification task. We aim to answering this question by
investigating the asymptotic performance of a family of large-margin
classifiers in situations where the data dimension $p$ and the sample $n$ are
both large. This family covers a broad range of classifiers including support
vector machine, distance weighted discrimination, penalized logistic
regression, and large-margin unified machine as special cases. The asymptotic
results are described by a set of nonlinear equations and we observe a close
match of them with Monte Carlo simulation on finite data samples. Our
analytical studies shed new light on how to select the best classifier among
various classification methods as well as on how to choose the optimal tuning
parameters for a given method.
| 1 | 0 | 0 | 1 | 0 | 0 |
1,675 | Complete reducibility, Kulshammer's question, conjugacy classes: a D_4 example | Let $k$ be a nonperfect separably closed field. Let $G$ be a connected
reductive algebraic group defined over $k$. We study rationality problems for
Serre's notion of complete reducibility of subgroups of $G$. In particular, we
present a new example of subgroup $H$ of $G$ of type $D_4$ in characteristic
$2$ such that $H$ is $G$-completely reducible but not $G$-completely reducible
over $k$ (or vice versa). This is new: all known such examples are for $G$ of
exceptional type. We also find a new counterexample for Külshammer's question
on representations of finite groups for $G$ of type $D_4$. A problem concerning
the number of conjugacy classes is also considered. The notion of nonseparable
subgroups plays a crucial role in all our constructions.
| 0 | 0 | 1 | 0 | 0 | 0 |
1,676 | Covariance structure associated with an equality between two general ridge estimators | In a general linear model, this paper derives a necessary and sufficient
condition under which two general ridge estimators coincide with each other.
The condition is given as a structure of the dispersion matrix of the error
term. Since the class of estimators considered here contains linear unbiased
estimators such as the ordinary least squares estimator and the best linear
unbiased estimator, our result can be viewed as a generalization of the
well-known theorems on the equality between these two estimators, which have
been fully studied in the literature. Two related problems are also considered:
equality between two residual sums of squares, and classification of dispersion
matrices by a perturbation approach.
| 0 | 0 | 1 | 1 | 0 | 0 |
1,677 | Three natural subgroups of the Brauer-Picard group of a Hopf algebra with applications | In this article we construct three explicit natural subgroups of the
Brauer-Picard group of the category of representations of a finite-dimensional
Hopf algebra. In examples the Brauer Picard group decomposes into an ordered
product of these subgroups, somewhat similar to a Bruhat decomposition.
Our construction returns for any Hopf algebra three types of braided
autoequivalences and correspondingly three families of invertible bimodule
categories. This gives examples of so-called (2-)Morita equivalences and
defects in topological field theories. We have a closer look at the case of
quantum groups and Nichols algebras and give interesting applications. Finally,
we briefly discuss the three families of group-theoretic extensions.
| 0 | 0 | 1 | 0 | 0 | 0 |
1,678 | UAV Visual Teach and Repeat Using Only Semantic Object Features | We demonstrate the use of semantic object detections as robust features for
Visual Teach and Repeat (VTR). Recent CNN-based object detectors are able to
reliably detect objects of tens or hundreds of categories in a video at frame
rates. We show that such detections are repeatable enough to use as landmarks
for VTR, without any low-level image features. Since object detections are
highly invariant to lighting and surface appearance changes, our VTR can cope
with global lighting changes and local movements of the landmark objects. In
the teaching phase, we build a series of compact scene descriptors: a list of
detected object labels and their image-plane locations. In the repeating phase,
we use Seq-SLAM-like relocalization to identify the most similar learned scene,
then use a motion control algorithm based on the funnel lane theory to navigate
the robot along the previously piloted trajectory. We evaluate the method on a
commodity UAV, examining the robustness of the algorithm to new viewpoints,
lighting conditions, and movements of landmark objects. The results suggest
that semantic object features could be useful due to their invariance to
superficial appearance changes compared to low-level image features.
| 1 | 0 | 0 | 0 | 0 | 0 |
1,679 | Towards a Deep Reinforcement Learning Approach for Tower Line Wars | There have been numerous breakthroughs with reinforcement learning in the
recent years, perhaps most notably on Deep Reinforcement Learning successfully
playing and winning relatively advanced computer games. There is undoubtedly an
anticipation that Deep Reinforcement Learning will play a major role when the
first AI masters the complicated game plays needed to beat a professional
Real-Time Strategy game player. For this to be possible, there needs to be a
game environment that targets and fosters AI research, and specifically Deep
Reinforcement Learning. Some game environments already exist, however, these
are either overly simplistic such as Atari 2600 or complex such as Starcraft II
from Blizzard Entertainment. We propose a game environment in between Atari
2600 and Starcraft II, particularly targeting Deep Reinforcement Learning
algorithm research. The environment is a variant of Tower Line Wars from
Warcraft III, Blizzard Entertainment. Further, as a proof of concept that the
environment can harbor Deep Reinforcement algorithms, we propose and apply a
Deep Q-Reinforcement architecture. The architecture simplifies the state space
so that it is applicable to Q-learning, and in turn improves performance
compared to current state-of-the-art methods. Our experiments show that the
proposed architecture can learn to play the environment well, and score 33%
better than standard Deep Q-learning which in turn proves the usefulness of the
game environment.
| 1 | 0 | 0 | 0 | 0 | 0 |
1,680 | A Case for an Atmosphere on Super-Earth 55 Cancri e | One of the primary questions when characterizing Earth-sized and
super-Earth-sized exoplanets is whether they have a substantial atmosphere like
Earth and Venus or a bare-rock surface like Mercury. Phase curves of the
planets in thermal emission provide clues to this question, because a
substantial atmosphere would transport heat more efficiently than a bare-rock
surface. Analyzing phase curve photometric data around secondary eclipse has
previously been used to study energy transport in the atmospheres of hot
Jupiters. Here we use phase curve, Spitzer time-series photometry to study the
thermal emission properties of the super-Earth exoplanet 55 Cancri e. We
utilize a semi-analytical framework to fit a physical model to the infrared
photometric data at 4.5 micron. The model uses parameters of planetary
properties including Bond albedo, heat redistribution efficiency (i.e., ratio
between radiative timescale and advective timescale of the atmosphere), and
atmospheric greenhouse factor. The phase curve of 55 Cancri e is dominated by
thermal emission with an eastward-shifted hot spot. We determine the heat
redistribution efficiency to be ~1.47, which implies that the advective
timescale is on the same order as the radiative timescale. This requirement
cannot be met by the bare-rock planet scenario because heat transport by
currents of molten lava would be too slow. The phase curve thus favors the
scenario with a substantial atmosphere. Our constraints on the heat
redistribution efficiency translate to an atmospheric pressure of ~1.4 bar. The
Spitzer 4.5-micron band is thus a window into the deep atmosphere of the planet
55 Cancri e.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,681 | From LiDAR to Underground Maps via 5G - Business Models Enabling a System-of-Systems Approach to Mapping the Kankberg Mine | With ever-increasing productivity targets in mining operations, there is a
growing interest in mining automation. The PIMM project addresses the
fundamental challenge of network communication by constructing a pilot 5G
network in the underground mine Kankberg. In this report, we discuss how such a
5G network could constitute the essential infrastructure to organize existing
systems in Kankberg into a system-of-systems (SoS). In this report, we analyze
a scenario in which LiDAR equipped vehicles operating in the mine are connected
to existing mine mapping and positioning solutions. The approach is motivated
by the approaching era of remote controlled, or even autonomous, vehicles in
mining operations. The proposed SoS could ensure continuously updated maps of
Kankberg, rendered in unprecedented detail, supporting both productivity and
safety in the underground mine. We present four different SoS solutions from an
organizational point of view, discussing how development and operations of the
constituent systems could be distributed among Boliden and external
stakeholders, e.g., the vehicle suppliers, the hauling company, and the
developers of the mapping software. The four scenarios are compared from both
technical and business perspectives, and based on trade-off discussions and
SWOT analyses. We conclude our report by recommending continued research along
two future paths, namely a closer cooperation with the vehicle suppliers, and
further feasibility studies regarding establishing a Kankberg software
ecosystem.
| 1 | 0 | 0 | 0 | 0 | 0 |
1,682 | The number of realizations of a Laman graph | Laman graphs model planar frameworks that are rigid for a general choice of
distances between the vertices. There are finitely many ways, up to isometries,
to realize a Laman graph in the plane. Such realizations can be seen as
solutions of systems of quadratic equations prescribing the distances between
pairs of points. Using ideas from algebraic and tropical geometry, we provide a
recursive formula for the number of complex solutions of such systems.
| 1 | 0 | 1 | 0 | 0 | 0 |
1,683 | UV Detector based on InAlN/GaN-on-Si HEMT Stack with Photo-to-Dark Current Ratio > 107 | We demonstrate an InAlN/GaN-on-Si HEMT based UV detector with photo to dark
current ratio > 107. Ti/Al/Ni/Au metal stack was evaporated and rapid thermal
annealed for Ohmic contacts to the 2D electron gas (2DEG) at the InAlN/GaN
interface while the channel + barrier was recess etched to a depth of 20 nm to
pinch-off the 2DEG between Source-Drain pads. Spectral responsivity (SR) of 34
A/W at 367 nm was measured at 5 V in conjunction with very high photo to dark
current ratio of > 10^7. The photo to dark current ratio at a fixed bias was
found to be decreasing with increase in recess length of the PD. The fabricated
devices were found to exhibit a UV-to-visible rejection ratio of >103 with a
low dark current < 32 pA at 5 V. Transient measurements showed rise and fall
times in the range of 3-4 ms. The gain mechanism was investigated and carrier
lifetimes were estimated which matched well with those reported elsewhere.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,684 | Deep Reinforcement Learning for Inquiry Dialog Policies with Logical Formula Embeddings | This paper is the first attempt to learn the policy of an inquiry dialog
system (IDS) by using deep reinforcement learning (DRL). Most IDS frameworks
represent dialog states and dialog acts with logical formulae. In order to make
learning inquiry dialog policies more effective, we introduce a logical formula
embedding framework based on a recursive neural network. The results of
experiments to evaluate the effect of 1) the DRL and 2) the logical formula
embedding framework show that the combination of the two are as effective or
even better than existing rule-based methods for inquiry dialog policies.
| 1 | 0 | 0 | 0 | 0 | 0 |
1,685 | Proof of Time's Arrow with Perfectly Chaotic Superdiffusion | The problem of Time's Arrow is rigorously solved in a certain microscopic
system associated with a Hamiltonian using only information about the
microscopic system. This microscopic system obeys an equation with time
reversal symmetry. In detail, we prove that a symplectic map with time reversal
symmetry is an Anosov diffeomorphism. This result guarantees that any initial
density function defined except for a zero volume set converges to the unique
invariant density (uniform distribution) in the sense of mixing. In addition,
we discover that there is a mathematical structure which connects Time's Arrow
(Anosov diffeomorphism) with superdiffusion in our system. In particular, the
mechanism of this superdiffusion in our system is different from those
previously found.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,686 | Using Phone Sensors and an Artificial Neural Network to Detect Gait Changes During Drinking Episodes in the Natural Environment | Phone sensors could be useful in assessing changes in gait that occur with
alcohol consumption. This study determined (1) feasibility of collecting
gait-related data during drinking occasions in the natural environment, and (2)
how gait-related features measured by phone sensors relate to estimated blood
alcohol concentration (eBAC). Ten young adult heavy drinkers were prompted to
complete a 5-step gait task every hour from 8pm to 12am over four consecutive
weekends. We collected 3-xis accelerometer, gyroscope, and magnetometer data
from phone sensors, and computed 24 gait-related features using a sliding
window technique. eBAC levels were calculated at each time point based on
Ecological Momentary Assessment (EMA) of alcohol use. We used an artificial
neural network model to analyze associations between sensor features and eBACs
in training (70% of the data) and validation and test (30% of the data)
datasets. We analyzed 128 data points where both eBAC and gait-related sensor
data was captured, either when not drinking (n=60), while eBAC was ascending
(n=55) or eBAC was descending (n=13). 21 data points were captured at times
when the eBAC was greater than the legal limit (0.08 mg/dl). Using a Bayesian
regularized neural network, gait-related phone sensor features showed a high
correlation with eBAC (Pearson's r > 0.9), and >95% of estimated eBAC would
fall between -0.012 and +0.012 of actual eBAC. It is feasible to collect
gait-related data from smartphone sensors during drinking occasions in the
natural environment. Sensor-based features can be used to infer gait changes
associated with elevated blood alcohol content.
| 1 | 0 | 0 | 1 | 0 | 0 |
1,687 | A Lattice Model of Charge-Pattern-Dependent Polyampholyte Phase Separation | In view of recent intense experimental and theoretical interests in the
biophysics of liquid-liquid phase separation (LLPS) of intrinsically disordered
proteins (IDPs), heteropolymer models with chain molecules configured as
self-avoiding walks on the simple cubic lattice are constructed to study how
phase behaviors depend on the sequence of monomers along the chains. To address
pertinent general principles, we focus primarily on two fully charged
50-monomer sequences with significantly different charge patterns. Each monomer
in our models occupies a single lattice site and all monomers interact via a
screened pairwise Coulomb potential. Phase diagrams are obtained by extensive
Monte Carlo sampling performed at multiple temperatures on ensembles of 300
chains in boxes of sizes ranging from $52\times 52\times 52$ to $246\times
246\times 246$ to simulate a large number of different systems with the overall
polymer volume fraction $\phi$ in each system varying from $0.001$ to $0.1$.
Phase separation in the model systems is characterized by the emergence of a
large cluster connected by inter-monomer nearest-neighbor lattice contacts and
by large fluctuations in local polymer density. The simulated critical
temperatures, $T_{\rm cr}$, of phase separation for the two sequences differ
significantly, whereby the sequence with a more "blocky" charge pattern
exhibits a substantially higher propensity to phase separate. The trend is
consistent with our sequence-specific random-phase-approximation (RPA) polymer
theory, but the variation of the simulated $T_{\rm cr}$ with a previously
proposed "sequence charge decoration" pattern parameter is milder than that
predicted by RPA. Ramifications of our findings for the development of
analytical theory and simulation protocols of IDP LLPS are discussed.
| 0 | 0 | 0 | 0 | 1 | 0 |
1,688 | "Noiseless" thermal noise measurement of atomic force microscopy cantilevers | When measuring quadratic values representative of random fluctuations, such
as the thermal noise of Atomic Force Microscopy (AFM) cantilevers, the
background measurement noise cannot be averaged to zero. We present a signal
processing method that allows to get rid of this limitation using the
ubiquitous optical beam deflection sensor of standard AFMs. We demonstrate a
two orders of magnitude enhancement of the signal to noise ratio in our
experiment, allowing the calibration of stiff cantilevers or easy
identification of higher order modes from thermal noise measurements.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,689 | A Forward Model at Purkinje Cell Synapses Facilitates Cerebellar Anticipatory Control | How does our motor system solve the problem of anticipatory control in spite
of a wide spectrum of response dynamics from different musculo-skeletal
systems, transport delays as well as response latencies throughout the central
nervous system? To a great extent, our highly-skilled motor responses are a
result of a reactive feedback system, originating in the brain-stem and spinal
cord, combined with a feed-forward anticipatory system, that is adaptively
fine-tuned by sensory experience and originates in the cerebellum. Based on
that interaction we design the counterfactual predictive control (CFPC)
architecture, an anticipatory adaptive motor control scheme in which a
feed-forward module, based on the cerebellum, steers an error feedback
controller with counterfactual error signals. Those are signals that trigger
reactions as actual errors would, but that do not code for any current or
forthcoming errors. In order to determine the optimal learning strategy, we
derive a novel learning rule for the feed-forward module that involves an
eligibility trace and operates at the synaptic level. In particular, our
eligibility trace provides a mechanism beyond co-incidence detection in that it
convolves a history of prior synaptic inputs with error signals. In the context
of cerebellar physiology, this solution implies that Purkinje cell synapses
should generate eligibility traces using a forward model of the system being
controlled. From an engineering perspective, CFPC provides a general-purpose
anticipatory control architecture equipped with a learning rule that exploits
the full dynamics of the closed-loop system.
| 1 | 0 | 1 | 0 | 0 | 0 |
1,690 | De Rham and twisted cohomology of Oeljeklaus-Toma manifolds | Oeljeklaus-Toma (OT) manifolds are complex non-Kähler manifolds whose
construction arises from specific number fields. In this note, we compute their
de Rham cohomology in terms of invariants associated to the background number
field. This is done by two distinct approaches, one using invariant cohomology
and the other one using the Leray-Serre spectral sequence. In addition, we
compute also their Morse-Novikov cohomology. As an application, we show that
the low degree Chern classes of any complex vector bundle on an OT manifold
vanish in the real cohomology. Other applications concern the OT manifolds
admitting locally conformally Kähler (LCK) metrics: we show that there is
only one possible Lee class of an LCK metric, and we determine all the possible
Morse-Novikov classes of an LCK metric, which implies the nondegeneracy of
certain Lefschetz maps in cohomology.
| 0 | 0 | 1 | 0 | 0 | 0 |
1,691 | Episode-Based Active Learning with Bayesian Neural Networks | We investigate different strategies for active learning with Bayesian deep
neural networks. We focus our analysis on scenarios where new, unlabeled data
is obtained episodically, such as commonly encountered in mobile robotics
applications. An evaluation of different strategies for acquisition, updating,
and final training on the CIFAR-10 dataset shows that incremental network
updates with final training on the accumulated acquisition set are essential
for best performance, while limiting the amount of required human labeling
labor.
| 1 | 0 | 0 | 1 | 0 | 0 |
1,692 | Origin of X-ray and gamma-ray emission from the Galactic central region | We study a possible connection between different non-thermal emissions from
the inner few parsecs of the Galaxy. We analyze the origin of the gamma-ray
source 2FGL J1745.6-2858 (or 3FGL J1745.6-2859c) in the Galactic Center and the
diffuse hard X-ray component recently found by NuSTAR, as well as the radio
emission and processes of hydrogen ionization from this area. We assume that a
source in the GC injected energetic particles with power-law spectrum into the
surrounding medium in the past or continues to inject until now. The energetic
particles may be protons, electrons or a combination of both. These particles
diffuse to the surrounding medium and interact with gas, magnetic field and
background photons to produce non-thermal emissions. We study the spectral and
spatial features of the hard X-ray emission and gamma-ray emission by the
particles from the central source. Our goal is to examine whether the hard
X-ray and gamma-ray emissions have a common origin. Our estimations show that
in the case of pure hadronic models the expected flux of hard X-ray emission is
too low. Despite protons can produce a non-zero contribution in gamma-ray
emission, it is unlikely that they and their secondary electrons can make a
significant contribution in hard X-ray flux. In the case of pure leptonic
models it is possible to reproduce both X-ray and gamma-ray emissions for both
transient and continuous supply models. However, in the case of continuous
supply model the ionization rate of molecular hydrogen may significantly exceed
the observed value.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,693 | Freeness and The Partial Transposes of Wishart Random Matrices | We show that the partial transposes of complex Wishart random matrices are
asymptotically free. We also investigate regimes where the number of blocks is
fixed but the size of the blocks increases. This gives a example where the
partial transpose produces freeness at the operator level. Finally we
investigate the case of real Wishart matrices.
| 0 | 0 | 1 | 0 | 0 | 0 |
1,694 | Coset space construction for the conformal group. I. Unbroken phase | The technique for constructing conformally invariant theories within the
coset space construction is developed. It reproduces all consequences of the
conformal invariance and Lagrangians of widely-known conformal field theories.
The method of induced representations, which plays the key role in the
construction, allows to reveal a special role of the "Nambu-Goldstone fields"
for special conformal transformations. Namely, their dependence on the
coordinates turns out to be fixed by the symmetries. This results in the
appearance of the constraints on possible forms of Lagrangians, which ensure
that discrete symmetries are indeed symmetries of the theory.
| 0 | 0 | 1 | 0 | 0 | 0 |
1,695 | Fixed points of polarity type operators | A well-known result says that the Euclidean unit ball is the unique fixed
point of the polarity operator. This result implies that if, in $\mathbb{R}^n$,
the unit ball of some norm is equal to the unit ball of the dual norm, then the
norm must be Euclidean. Motivated by these results and by relatively recent
results in convex analysis and convex geometry regarding various properties of
order reversing operators, we consider, in a real Hilbert space setting, a more
general fixed point equation in which the polarity operator is composed with a
continuous invertible linear operator. We show that if the linear operator is
positive definite, then the considered equation is uniquely solvable by an
ellipsoid. Otherwise, the equation can have several (possibly infinitely many)
solutions or no solution at all. Our analysis yields a few by-products of
possible independent interest, among them results related to coercive bilinear
forms (essentially a quantitative convex analytic converse to the celebrated
Lax-Milgram theorem from partial differential equations) and a characterization
of real Hilbertian spaces.
| 0 | 0 | 1 | 0 | 0 | 0 |
1,696 | Multiple Improvements of Multiple Imputation Likelihood Ratio Tests | Multiple imputation (MI) inference handles missing data by first properly
imputing the missing values $m$ times, and then combining the $m$ analysis
results from applying a complete-data procedure to each of the completed
datasets. However, the existing method for combining likelihood ratio tests has
multiple defects: (i) the combined test statistic can be negative in practice
when the reference null distribution is a standard $F$ distribution; (ii) it is
not invariant to re-parametrization; (iii) it fails to ensure monotonic power
due to its use of an inconsistent estimator of the fraction of missing
information (FMI) under the alternative hypothesis; and (iv) it requires
non-trivial access to the likelihood ratio test statistic as a function of
estimated parameters instead of datasets. This paper shows, via both
theoretical derivations and empirical investigations, that essentially all of
these problems can be straightforwardly addressed if we are willing to perform
an additional likelihood ratio test by stacking the $m$ completed datasets as
one big completed dataset. A particularly intriguing finding is that the FMI
itself can be estimated consistently by a likelihood ratio statistic for
testing whether the $m$ completed datasets produced by MI can be regarded
effectively as samples coming from a common model. Practical guidelines are
provided based on an extensive comparison of existing MI tests.
| 0 | 0 | 1 | 1 | 0 | 0 |
1,697 | Estimating the unseen from multiple populations | Given samples from a distribution, how many new elements should we expect to
find if we continue sampling this distribution? This is an important and
actively studied problem, with many applications ranging from unseen species
estimation to genomics. We generalize this extrapolation and related unseen
estimation problems to the multiple population setting, where population $j$
has an unknown distribution $D_j$ from which we observe $n_j$ samples. We
derive an optimal estimator for the total number of elements we expect to find
among new samples across the populations. Surprisingly, we prove that our
estimator's accuracy is independent of the number of populations. We also
develop an efficient optimization algorithm to solve the more general problem
of estimating multi-population frequency distributions. We validate our methods
and theory through extensive experiments. Finally, on a real dataset of human
genomes across multiple ancestries, we demonstrate how our approach for unseen
estimation can enable cohort designs that can discover interesting mutations
with greater efficiency.
| 1 | 0 | 0 | 1 | 0 | 0 |
1,698 | Continued Fractions and $q$-Series Generating Functions for the Generalized Sum-of-Divisors Functions | We construct new continued fraction expansions of Jacobi-type J-fractions in
$z$ whose power series expansions generate the ratio of the $q$-Pochhamer
symbols, $(a; q)_n / (b; q)_n$, for all integers $n \geq 0$ and where $a,b,q
\in \mathbb{C}$ are non-zero and defined such that $|q| < 1$ and $|b/a| < |z| <
1$. If we set the parameters $(a, b) := (q, q^2)$ in these generalized series
expansions, then we have a corresponding J-fraction enumerating the sequence of
terms $(1-q) / (1-q^{n+1})$ over all integers $n \geq 0$. Thus we are able to
define new $q$-series expansions which correspond to the Lambert series
generating the divisor function, $d(n)$, when we set $z \mapsto q$ in our new
J-fraction expansions. By repeated differentiation with respect to $z$, we also
use these generating functions to formulate new $q$-series expansions of the
generating functions for the sums-of-divisors functions, $\sigma_{\alpha}(n)$,
when $\alpha \in \mathbb{Z}^{+}$. To expand the new $q$-series generating
functions for these special arithmetic functions we define a generalized
classes of so-termed Stirling-number-like "$q$-coefficients", or Stirling
$q$-coefficients, whose properties, relations to elementary symmetric
polynomials, and relations to the convergents to our infinite J-fractions are
also explored within the results proved in the article.
| 0 | 0 | 1 | 0 | 0 | 0 |
1,699 | Implications of a wavelength dependent PSF for weak lensing measurements | The convolution of galaxy images by the point-spread function (PSF) is the
dominant source of bias for weak gravitational lensing studies, and an accurate
estimate of the PSF is required to obtain unbiased shape measurements. The PSF
estimate for a galaxy depends on its spectral energy distribution (SED),
because the instrumental PSF is generally a function of the wavelength. In this
paper we explore various approaches to determine the resulting `effective' PSF
using broad-band data. Considering the Euclid mission as a reference, we find
that standard SED template fitting methods result in biases that depend on
source redshift, although this may be remedied if the algorithms can be
optimised for this purpose. Using a machine-learning algorithm we show that, at
least in principle, the required accuracy can be achieved with the current
survey parameters. It is also possible to account for the correlations between
photometric redshift and PSF estimates that arise from the use of the same
photometry. We explore the impact of errors in photometric calibration, errors
in the assumed wavelength dependence of the PSF model and limitations of the
adopted template libraries. Our results indicate that the required accuracy for
Euclid can be achieved using the data that are planned to determine photometric
redshifts.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,700 | Improving TSP tours using dynamic programming over tree decomposition | Given a traveling salesman problem (TSP) tour $H$ in graph $G$ a $k$-move is
an operation which removes $k$ edges from $H$, and adds $k$ edges of $G$ so
that a new tour $H'$ is formed. The popular $k$-OPT heuristics for TSP finds a
local optimum by starting from an arbitrary tour $H$ and then improving it by a
sequence of $k$-moves.
Until 2016, the only known algorithm to find an improving $k$-move for a
given tour was the naive solution in time $O(n^k)$. At ICALP'16 de Berg,
Buchin, Jansen and Woeginger showed an $O(n^{\lfloor 2/3k \rfloor+1})$-time
algorithm.
We show an algorithm which runs in $O(n^{(1/4+\epsilon_k)k})$ time, where
$\lim \epsilon_k = 0$. We are able to show that it improves over the state of
the art for every $k=5,\ldots,10$. For the most practically relevant case $k=5$
we provide a slightly refined algorithm running in $O(n^{3.4})$ time. We also
show that for the $k=4$ case, improving over the $O(n^3)$-time algorithm of de
Berg et al. would be a major breakthrough: an $O(n^{3-\epsilon})$-time
algorithm for any $\epsilon>0$ would imply an $O(n^{3-\delta})$-time algorithm
for the ALL PAIRS SHORTEST PATHS problem, for some $\delta>0$.
| 1 | 0 | 0 | 0 | 0 | 0 |
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