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Title: Control for Schrödinger equation on hyperbolic surfaces,
Abstract: We show that the any nonempty open set on a hyperbolic surface provides
observability and control for the time dependent Schrödinger equation. The
only other manifolds for which this was previously known are flat tori. The
proof is based on the main estimate in Dyatlov-Jin and standard arguments of
control theory. | [
0,
0,
1,
0,
0,
0
] |
Title: Stability for gains from large investors' strategies in M1/J1 topologies,
Abstract: We prove continuity of a controlled SDE solution in Skorokhod's $M_1$ and
$J_1$ topologies and also uniformly, in probability, as a non-linear functional
of the control strategy. The functional comes from a finance problem to model
price impact of a large investor in an illiquid market. We show that
$M_1$-continuity is the key to ensure that proceeds and wealth processes from
(self-financing) càdlàg trading strategies are determined as the
continuous extensions for those from continuous strategies. We demonstrate by
examples how continuity properties are useful to solve different stochastic
control problems on optimal liquidation and to identify asymptotically
realizable proceeds. | [
0,
0,
1,
0,
0,
0
] |
Title: Apparent and Intrinsic Evolution of Active Region Upflows,
Abstract: We analyze the evolution of Fe XII coronal plasma upflows from the edges of
ten active regions (ARs) as they cross the solar disk using the Hinode Extreme
Ultraviolet Imaging Spectrometer (EIS). Confirming the results of Demoulin et
al. (2013, Sol. Phys. 283, 341), we find that for each AR there is an observed
long term evolution of the upflows which is largely due to the solar rotation
progressively changing the viewpoint of dominantly stationary upflows. From
this projection effect, we estimate the unprojected upflow velocity and its
inclination to the local vertical. AR upflows typically fan away from the AR
core by 40 deg. to near vertical for the following polarity. The span of
inclination angles is more spread for the leading polarity with flows angled
from -29 deg. (inclined towards the AR center) to 28 deg. (directed away from
the AR). In addition to the limb-to-limb apparent evolution, we identify an
intrinsic evolution of the upflows due to coronal activity which is AR
dependent. Further, line widths are correlated with Doppler velocities only for
the few ARs having the largest velocities. We conclude that for the line widths
to be affected by the solar rotation, the spatial gradient of the upflow
velocities must be large enough such that the line broadening exceeds the
thermal line width of Fe XII. Finally, we find that upflows occurring in pairs
or multiple pairs is a common feature of ARs observed by Hinode/EIS, with up to
four pairs present in AR 11575. This is important for constraining the upflow
driving mechanism as it implies that the mechanism is not a local one occurring
over a single polarity. AR upflows originating from reconnection along
quasi-separatrix layers (QSLs) between over-pressure AR loops and neighboring
under-pressure loops is consistent with upflows occurring in pairs, unlike
other proposed mechanisms acting locally in one polarity. | [
0,
1,
0,
0,
0,
0
] |
Title: Evolution of protoplanetary disks from their taxonomy in scattered light: Group I vs. Group II,
Abstract: High-resolution imaging reveals a large morphological variety of
protoplanetary disks. To date, no constraints on their global evolution have
been found from this census. An evolutionary classification of disks was
proposed based on their IR spectral energy distribution, with the Group I
sources showing a prominent cold component ascribed to an earlier stage of
evolution than Group II. Disk evolution can be constrained from the comparison
of disks with different properties. A first attempt of disk taxonomy is now
possible thanks to the increasing number of high-resolution images of Herbig
Ae/Be stars becoming available. Near-IR images of six Group II disks in
scattered light were obtained with VLT/NACO in Polarimetric Differential
Imaging, which is the most efficient technique to image the light scattered by
the disk material close to the stars. We compare the stellar/disk properties of
this sample with those of well-studied Group I sources available from the
literature. Three Group II disks are detected. The brightness distribution in
the disk of HD163296 indicates the presence of a persistent ring-like structure
with a possible connection with the CO snowline. A rather compact (less than
100 AU) disk is detected around HD142666 and AK Sco. A taxonomic analysis of 17
Herbig Ae/Be sources reveals that the difference between Group I and Group II
is due to the presence or absence of a large disk cavity (larger than 5 AU).
There is no evidence supporting the evolution from Group I to Group II. Group
II are not evolved version of the Group I. Within the Group II disks, very
different geometries (both self-shadowed and compact) exist. HD163296 could be
the primordial version of a typical Group I. Other Group II, like AK Sco and
HD142666, could be smaller counterpart of Group I unable to open cavities as
large as those of Group I. | [
0,
1,
0,
0,
0,
0
] |
Title: Sandwich semigroups in locally small categories II: Transformations,
Abstract: Fix sets $X$ and $Y$, and write $\mathcal{PT}_{XY}$ for the set of all
partial functions $X\to Y$. Fix a partial function $a:Y\to X$, and define the
operation $\star_a$ on $\mathcal{PT}_{XY}$ by $f\star_ag=fag$ for
$f,g\in\mathcal{PT}_{XY}$. The sandwich semigroup $(\mathcal{PT}_{XY},\star_a)$
is denoted $\mathcal{PT}_{XY}^a$. We apply general results from Part I to
thoroughly describe the structural and combinatorial properties of
$\mathcal{PT}_{XY}^a$, as well as its regular and idempotent-generated
subsemigroups, Reg$(\mathcal{PT}_{XY}^a)$ and $\mathbb E(\mathcal{PT}_{XY}^a)$.
After describing regularity, stability and Green's relations and preorders, we
exhibit Reg$(\mathcal{PT}_{XY}^a)$ as a pullback product of certain regular
subsemigroups of the (non-sandwich) partial transformation semigroups
$\mathcal{PT}_X$ and $\mathcal{PT}_Y$, and as a kind of "inflation" of
$\mathcal{PT}_A$, where $A$ is the image of the sandwich element $a$. We also
calculate the rank (minimal size of a generating set) and, where appropriate,
the idempotent rank (minimal size of an idempotent generating set) of
$\mathcal{PT}_{XY}^a$, Reg$(\mathcal{PT}_{XY}^a)$ and $\mathbb
E(\mathcal{PT}_{XY}^a)$. The same program is also carried out for sandwich
semigroups of totally defined functions and for injective partial functions.
Several corollaries are obtained for various (non-sandwich) semigroups of
(partial) transformations with restricted image, domain and/or kernel. | [
0,
0,
1,
0,
0,
0
] |
Title: Environmental feedback drives cooperation in spatial social dilemmas,
Abstract: Exploiting others is beneficial individually but it could also be detrimental
globally. The reverse is also true: a higher cooperation level may change the
environment in a way that is beneficial for all competitors. To explore the
possible consequence of this feedback we consider a coevolutionary model where
the local cooperation level determines the payoff values of the applied
prisoner's dilemma game. We observe that the coevolutionary rule provides a
significantly higher cooperation level comparing to the traditional setup
independently of the topology of the applied interaction graph. Interestingly,
this cooperation supporting mechanism offers lonely defectors a high surviving
chance for a long period hence the relaxation to the final cooperating state
happens logarithmically slow. As a consequence, the extension of the
traditional evolutionary game by considering interactions with the environment
provides a good opportunity for cooperators, but their reward may arrive with
some delay. | [
0,
0,
0,
0,
1,
0
] |
Title: Learning Hidden Quantum Markov Models,
Abstract: Hidden Quantum Markov Models (HQMMs) can be thought of as quantum
probabilistic graphical models that can model sequential data. We extend
previous work on HQMMs with three contributions: (1) we show how classical
hidden Markov models (HMMs) can be simulated on a quantum circuit, (2) we
reformulate HQMMs by relaxing the constraints for modeling HMMs on quantum
circuits, and (3) we present a learning algorithm to estimate the parameters of
an HQMM from data. While our algorithm requires further optimization to handle
larger datasets, we are able to evaluate our algorithm using several synthetic
datasets. We show that on HQMM generated data, our algorithm learns HQMMs with
the same number of hidden states and predictive accuracy as the true HQMMs,
while HMMs learned with the Baum-Welch algorithm require more states to match
the predictive accuracy. | [
0,
0,
0,
1,
0,
0
] |
Title: Higher cohomology vanishing of line bundles on generalized Springer's resolution,
Abstract: We give a proof of a conjecture raised by Michael Finkelberg and Andrei
Ionov. As a corollary, the coefficients of multivariable version of Kostka
functions introduced by Finkelberg and Ionov are non-negative. | [
0,
0,
1,
0,
0,
0
] |
Title: Coalescing particle systems and applications to nonlinear Fokker-Planck equations,
Abstract: We study a stochastic particle system with a logarithmically-singular
inter-particle interaction potential which allows for inelastic particle
collisions. We relate the squared Bessel process to the evolution of localized
clusters of particles, and develop a numerical method capable of detecting
collisions of many point particles without the use of pairwise computations, or
very refined adaptive timestepping. We show that when the system is in an
appropriate parameter regime, the hydrodynamic limit of the empirical mass
density of the system is a solution to a nonlinear Fokker-Planck equation, such
as the Patlak-Keller-Segel (PKS) model, or its multispecies variant. We then
show that the presented numerical method is well-suited for the simulation of
the formation of finite-time singularities in the PKS, as well as PKS pre- and
post-blow-up dynamics. Additionally, we present numerical evidence that blow-up
with an increasing total second moment in the two species Keller-Segel system
occurs with a linearly increasing second moment in one component, and a
linearly decreasing second moment in the other component. | [
0,
0,
1,
0,
0,
0
] |
Title: Periodic solution for strongly nonlinear oscillators by He's new amplitude-frequency relationship,
Abstract: This paper applies He's new amplitude-frequency relationship recently
established by Ji-Huan He (Int J Appl Comput Math 3 1557-1560, 2017) to study
periodic solutions of strongly nonlinear systems with odd nonlinearities. Some
examples are given to illustrate the effectiveness, ease and convenience of the
method. In general, the results are valid for small as well as large
oscillation amplitude. The method can be easily extended to other nonlinear
systems with odd nonlinearities and can therefore be found widely applicable in
engineering and other science. The method used in this paper can be applied
directly to highly nonlinear problems without any discretization, linearization
or additional requirements. | [
0,
1,
0,
0,
0,
0
] |
Title: A General Algorithm to Calculate the Inverse Principal $p$-th Root of Symmetric Positive Definite Matrices,
Abstract: We address the general mathematical problem of computing the inverse $p$-th
root of a given matrix in an efficient way. A new method to construct iteration
functions that allow calculating arbitrary $p$-th roots and their inverses of
symmetric positive definite matrices is presented. We show that the order of
convergence is at least quadratic and that adaptively adjusting a parameter $q$
always leads to an even faster convergence. In this way, a better performance
than with previously known iteration schemes is achieved. The efficiency of the
iterative functions is demonstrated for various matrices with different
densities, condition numbers and spectral radii. | [
0,
0,
1,
0,
0,
0
] |
Title: Latent Mixture Modeling for Clustered Data,
Abstract: This article proposes a mixture modeling approach to estimating cluster-wise
conditional distributions in clustered (grouped) data. We adapt the
mixture-of-experts model to the latent distributions, and propose a model in
which each cluster-wise density is represented as a mixture of latent experts
with cluster-wise mixing proportions distributed as Dirichlet distribution. The
model parameters are estimated by maximizing the marginal likelihood function
using a newly developed Monte Carlo Expectation-Maximization algorithm. We also
extend the model such that the distribution of cluster-wise mixing proportions
depends on some cluster-level covariates. The finite sample performance of the
proposed model is compared with some existing mixture modeling approaches as
well as linear mixed model through the simulation studies. The proposed model
is also illustrated with the posted land price data in Japan. | [
0,
0,
0,
1,
0,
0
] |
Title: Fast Switching Dual Fabry-Perot-Cavity-based Optical Refractometry for Assessment of Gas Refractivity and Density - Estimates of Its Precision, Accuracy, and Temperature Dependence,
Abstract: Dual Fabry-Perot-Cavity-based Optical Refractometry (DFCB-OR) have been shown
to have excellent potential for characterization of gases, in particular their
refractivity and density. However, its performance has in practice been found
to be limited by drifts. To remedy this, drift-free DFPC-OR (DF-DFCB-OR) has
recently been proposed. Suggested methodologies for realization of a specific
type of DF-DFCB-OR, termed Fast Switching DFCB-OR (FS-DFCB-OR), have been
presented in an accompanying work. This paper scrutinizes the performance and
the limitations of both DF- and FS-DFCB-OR for assessments of refractivity and
gas density, in particular their precision, accuracy, and temperature
dependence. It is shown that both refractivity and gas density can be assessed
by FS-DFCB-OR with a precision in the 10$^{-9}$ range under STP conditions. It
is demonstrated that the absolute accuracy is mainly limited by the accuracy by
which the instantaneous deformation of the cavity or the higher order virial
coefficients can be assessed. It is also shown that the internal accuracy, i.e.
the accuracy by which the system can be characterized with respect to an
internal standard, can be several orders of magnitude better than the absolute.
It is concluded that the temperature dependence of FS-DFCB-OR is exceptionally
small, typically in the 10$^{-8}$ to 10$^{-7}$/C range, and primarily caused by
thermal expansion of the FPC-spacer material. Finally, this paper discusses
means on how to design a FS-DFCB-or system for optimal performance and
epitomizes the conclusions of this and our accompanying works regarding both
DF- and FS-DFCB-OR in terms of performance and provides an outlook for both
techniques. Our works can serve as a basis for future realizations of
instrumentation for assessments of gas refractivity and density that can fully
benefit from the extraordinary potential of FPC-OR. | [
0,
1,
0,
0,
0,
0
] |
Title: Lazy Automata Techniques for WS1S,
Abstract: We present a new decision procedure for the logic WS1S. It originates from
the classical approach, which first builds an automaton accepting all models of
a formula and then tests whether its language is empty. The main novelty is to
test the emptiness on the fly, while constructing a symbolic, term-based
representation of the automaton, and prune the constructed state space from
parts irrelevant to the test. The pruning is done by a generalization of two
techniques used in antichain-based language inclusion and universality checking
of finite automata: subsumption and early termination. The richer structure of
the WS1S decision problem allows us, however, to elaborate on these techniques
in novel ways. Our experiments show that the proposed approach can in many
cases significantly outperform the classical decision procedure (implemented in
the MONA tool) as well as recently proposed alternatives. | [
1,
0,
0,
0,
0,
0
] |
Title: Joint distribution of conjugate algebraic numbers: a random polynomial approach,
Abstract: Given a polynomial $q(z):=a_0+a_1z+\dots+a_nz^n$ and a vector of positive
weights $\mathbf{w}=(w_0, w_1,\dots,w_n)$, define the $\mathbf{w}$-weighted
$l_p$-norm of $q$ as $$ l_{p,\mathbf{w}}[q]:=\left(\sum_{k=0}^{n}|w_k
a_k|^p\right)^{1/p},\quad p\in[1,\infty]. $$ Define the $\mathbf{w}$-weighted
$l_p$-norm of an algebraic number to be the $\mathbf{w}$-weighted $l_p$-norm of
its minimal polynomial. For non-negative integers $k,l$ such that $k+2l\leq n$
and a Borel subset $B\subset \mathbb{R}^k\times\mathbb{C}_+^l$ denote by
$\Phi_{p,\mathbf{w},k,l}(Q,B)$ the number of ordered $(k+l)$-tuples in $B$ of
conjugate algebraic numbers of degree $n$ and $\mathbf{w}$-weighted $l_p$-norm
at most $ Q$. We show that $$ \lim_{ Q\to\infty}\frac{\Phi_{p,\mathbf{w},k,l}(
Q,B)}{
Q^{n+1}}=\frac{\mathrm{Vol}_{n+1}(\mathbb{B}_{p,\mathbf{w}}^{n+1})}{2\zeta(n+1)}\int_B
\rho_{p,\mathbf{w},k,l}(\mathbf{x},\mathbf{z})\rm d \mathbf{x}\rm d \mathbf{z},
$$ where $\mathrm{Vol}_{n+1}(\mathbb{B}_{p,\mathbf{w}}^{n+1})$ is the volume of
the unit $\mathbf{w}$-weighted $l_p$-ball and $\rho_{p,\mathbf{w},k,l}$ shall
denote the correlation function of $k$ real and $l$ complex zeros of the random
polynomial $\sum_{k=1}^n \frac{\eta_k}{w_k} z^k$ for i.i.d. random variables
$\eta_k $ with density $c_p e^{|t|^p}$ for $p<\infty$ resp. with constant
density on $[-1,1]$ for $p=\infty$. We give an explicit formula for
$\rho_{p,\mathbf{w},k,l}$ which in the case $k+2l=n$ simplifies to $$
\rho_{p,\mathbf{w},n-2l,l}=\frac{2}{(n+1)\mathrm{Vol}_{n+1}(\mathbb{B}_{p,\mathbf{w}}^{n+1})}\,\frac{\sqrt{|\mathrm{D}[q]|}\phantom{1^n}}{(l_{p,\mathbf{w}}[q])^{n+1}},
$$ where $q$ is the monic polynomial whose zeros are the arguments of the
correlation function $\rho_{p,\mathbf{w},n-2l,l}$ and $\mathrm{D}[q]$ denotes
its discriminant. | [
0,
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1,
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0,
0
] |
Title: Towards a Context-Aware IDE-Based Meta Search Engine for Recommendation about Programming Errors and Exceptions,
Abstract: Study shows that software developers spend about 19% of their time looking
for information in the web during software development and maintenance.
Traditional web search forces them to leave the working environment (e.g., IDE)
and look for information in the web browser. It also does not consider the
context of the problems that the developers search solutions for. The frequent
switching between web browser and the IDE is both time-consuming and
distracting, and the keyword-based traditional web search often does not help
much in problem solving. In this paper, we propose an Eclipse IDE-based web
search solution that exploits the APIs provided by three popular web search
engines-- Google, Yahoo, Bing and a popular programming Q & A site, Stack
Overflow, and captures the content-relevance, context-relevance, popularity and
search engine confidence of each candidate result against the encountered
programming problems. Experiments with 75 programming errors and exceptions
using the proposed approach show that inclusion of different types of context
information associated with a given exception can enhance the recommendation
accuracy of a given exception. Experiments both with two existing approaches
and existing web search engines confirm that our approach can perform better
than them in terms of recall, mean precision and other performance measures
with little computational cost. | [
1,
0,
0,
0,
0,
0
] |
Title: Infrared Flares from M Dwarfs: a Hinderance to Future Transiting Exoplanet Studies,
Abstract: Many current and future exoplanet missions are pushing to infrared (IR)
wavelengths where the flux contrast between the planet and star is more
favorable (Deming et al. 2009), and the impact of stellar magnetic activity is
decreased. Indeed, a recent analysis of starspots and faculae found these forms
of stellar activity do not substantially impact the transit signatures or
science potential for FGKM stars with JWST (Zellem et al. 2017). However, this
is not true in the case of flares, which I demonstrate can be a hinderance to
transit studies in this note. | [
0,
1,
0,
0,
0,
0
] |
Title: Bio-Inspired Multi-Layer Spiking Neural Network Extracts Discriminative Features from Speech Signals,
Abstract: Spiking neural networks (SNNs) enable power-efficient implementations due to
their sparse, spike-based coding scheme. This paper develops a bio-inspired SNN
that uses unsupervised learning to extract discriminative features from speech
signals, which can subsequently be used in a classifier. The architecture
consists of a spiking convolutional/pooling layer followed by a fully connected
spiking layer for feature discovery. The convolutional layer of leaky,
integrate-and-fire (LIF) neurons represents primary acoustic features. The
fully connected layer is equipped with a probabilistic spike-timing-dependent
plasticity learning rule. This layer represents the discriminative features
through probabilistic, LIF neurons. To assess the discriminative power of the
learned features, they are used in a hidden Markov model (HMM) for spoken digit
recognition. The experimental results show performance above 96% that compares
favorably with popular statistical feature extraction methods. Our results
provide a novel demonstration of unsupervised feature acquisition in an SNN. | [
1,
0,
0,
0,
0,
0
] |
Title: Zonotope hit-and-run for efficient sampling from projection DPPs,
Abstract: Determinantal point processes (DPPs) are distributions over sets of items
that model diversity using kernels. Their applications in machine learning
include summary extraction and recommendation systems. Yet, the cost of
sampling from a DPP is prohibitive in large-scale applications, which has
triggered an effort towards efficient approximate samplers. We build a novel
MCMC sampler that combines ideas from combinatorial geometry, linear
programming, and Monte Carlo methods to sample from DPPs with a fixed sample
cardinality, also called projection DPPs. Our sampler leverages the ability of
the hit-and-run MCMC kernel to efficiently move across convex bodies. Previous
theoretical results yield a fast mixing time of our chain when targeting a
distribution that is close to a projection DPP, but not a DPP in general. Our
empirical results demonstrate that this extends to sampling projection DPPs,
i.e., our sampler is more sample-efficient than previous approaches which in
turn translates to faster convergence when dealing with costly-to-evaluate
functions, such as summary extraction in our experiments. | [
1,
0,
0,
1,
0,
0
] |
Title: Natural Time, Nowcasting and the Physics of Earthquakes: Estimation of Seismic Risk to Global Megacities,
Abstract: This paper describes the use of the idea of natural time to propose a new
method for characterizing the seismic risk to the world's major cities at risk
of earthquakes. Rather than focus on forecasting, which is the computation of
probabilities of future events, we define the term seismic nowcasting, which is
the computation of the current state of seismic hazard in a defined geographic
region. | [
0,
1,
0,
0,
0,
0
] |
Title: DONUT: CTC-based Query-by-Example Keyword Spotting,
Abstract: Keyword spotting--or wakeword detection--is an essential feature for
hands-free operation of modern voice-controlled devices. With such devices
becoming ubiquitous, users might want to choose a personalized custom wakeword.
In this work, we present DONUT, a CTC-based algorithm for online
query-by-example keyword spotting that enables custom wakeword detection. The
algorithm works by recording a small number of training examples from the user,
generating a set of label sequence hypotheses from these training examples, and
detecting the wakeword by aggregating the scores of all the hypotheses given a
new audio recording. Our method combines the generalization and
interpretability of CTC-based keyword spotting with the user-adaptation and
convenience of a conventional query-by-example system. DONUT has low
computational requirements and is well-suited for both learning and inference
on embedded systems without requiring private user data to be uploaded to the
cloud. | [
1,
0,
0,
0,
0,
0
] |
Title: Quasi-Frobenius-splitting and lifting of Calabi-Yau varieties in characteristic $p$,
Abstract: Extending the notion of Frobenius-splitting, we prove that every finite
height Calabi-Yau variety defined over an algebraically closed field of
positive characteristic can be lifted to the ring of Witt vectors of length
two. | [
0,
0,
1,
0,
0,
0
] |
Title: Consistency and Asymptotic Normality of Latent Blocks Model Estimators,
Abstract: Latent Block Model (LBM) is a model-based method to cluster simultaneously
the $d$ columns and $n$ rows of a data matrix. Parameter estimation in LBM is a
difficult and multifaceted problem. Although various estimation strategies have
been proposed and are now well understood empirically, theoretical guarantees
about their asymptotic behavior is rather sparse. We show here that under some
mild conditions on the parameter space, and in an asymptotic regime where
$\log(d)/n$ and $\log(n)/d$ tend to $0$ when $n$ and $d$ tend to $+\infty$, (1)
the maximum-likelihood estimate of the complete model (with known labels) is
consistent and (2) the log-likelihood ratios are equivalent under the complete
and observed (with unknown labels) models. This equivalence allows us to
transfer the asymptotic consistency to the maximum likelihood estimate under
the observed model. Moreover, the variational estimator is also consistent. | [
0,
0,
1,
1,
0,
0
] |
Title: High Speed Elephant Flow Detection Under Partial Information,
Abstract: In this paper we introduce a new framework to detect elephant flows at very
high speed rates and under uncertainty. The framework provides exact
mathematical formulas to compute the detection likelihood and introduces a new
flow reconstruction lemma under partial information. These theoretical results
lead to the design of BubbleCache, a new elephant flow detection algorithm
designed to operate near the optimal tradeoff between computational scalability
and accuracy by dynamically tracking the traffic's natural cutoff sampling
rate. We demonstrate on a real world 100 Gbps network that the BubbleCache
algorithm helps reduce the computational cost by a factor of 1000 and the
memory requirements by a factor of 100 while detecting the top flows on the
network with very high probability. | [
1,
0,
0,
0,
0,
0
] |
Title: Scalable k-Means Clustering via Lightweight Coresets,
Abstract: Coresets are compact representations of data sets such that models trained on
a coreset are provably competitive with models trained on the full data set. As
such, they have been successfully used to scale up clustering models to massive
data sets. While existing approaches generally only allow for multiplicative
approximation errors, we propose a novel notion of lightweight coresets that
allows for both multiplicative and additive errors. We provide a single
algorithm to construct lightweight coresets for k-means clustering as well as
soft and hard Bregman clustering. The algorithm is substantially faster than
existing constructions, embarrassingly parallel, and the resulting coresets are
smaller. We further show that the proposed approach naturally generalizes to
statistical k-means clustering and that, compared to existing results, it can
be used to compute smaller summaries for empirical risk minimization. In
extensive experiments, we demonstrate that the proposed algorithm outperforms
existing data summarization strategies in practice. | [
1,
0,
0,
1,
0,
0
] |
Title: Scaling relations in the diffusive infiltration in fractals,
Abstract: In a recent work on fluid infiltration in a Hele-Shaw cell with the
pore-block geometry of Sierpinski carpets (SCs), the area filled by the
invading fluid was shown to scale as F~t^n, with n<1/2, thus providing a
macroscopic realization of anomalous diffusion [Filipovitch et al, Water
Resour. Res. 52 5167 (2016)]. The results agree with simulations of a diffusion
equation with constant pressure at one of the borders of those fractals, but
the exponent n is very different from the anomalous exponent nu=1/D_W of single
particle diffusion in the same fractals (D_W is the random walk dimension).
Here we use a scaling approach to show that those exponents are related as
n=nu(D_F-D_B), where D_F and D_B are the fractal dimensions of the bulk and of
the border from which diffusing particles come, respectively. This relation is
supported by accurate numerical estimates in two SCs and in two generalized
Menger sponges (MSs), in which we performed simulations of single particle
random walks (RWs) with a rigid impermeable border and of a diffusive
infiltration model in which that border is permanently filled with diffusing
particles. This study includes one MS whose external border is also fractal.
The exponent relation is also consistent with the recent simulational and
experimental results on fluid infiltration in SCs, and explains the approximate
quadratic dependence of n on D_F in these fractals. We also show that the
mean-square displacement of single particle RWs has log-periodic oscillations,
whose periods are similar for fractals with the same scaling factor in the
generator (even with different embedding dimensions), which is consistent with
the discrete scale invariance scenario. The roughness of a diffusion front
defined in the infiltration problem also shows this type of oscillation, which
is enhanced in fractals with narrow channels between large lacunas. | [
0,
1,
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0,
0
] |
Title: Adaptive Sequential MCMC for Combined State and Parameter Estimation,
Abstract: In the case of a linear state space model, we implement an MCMC sampler with
two phases. In the learning phase, a self-tuning sampler is used to learn the
parameter mean and covariance structure. In the estimation phase, the parameter
mean and covariance structure informs the proposed mechanism and is also used
in a delayed-acceptance algorithm. Information on the resulting state of the
system is given by a Gaussian mixture. In on-line mode, the algorithm is
adaptive and uses a sliding window approach to accelerate sampling speed and to
maintain appropriate acceptance rates. We apply the algorithm to joined state
and parameter estimation in the case of irregularly sampled GPS time series
data. | [
0,
0,
0,
1,
0,
0
] |
Title: Uncharted Forest a Technique for Exploratory Data Analysis,
Abstract: Exploratory data analysis is crucial for developing and understanding
classification models from high-dimensional datasets. We explore the utility of
a new unsupervised tree ensemble called uncharted forest for visualizing class
associations, sample-sample associations, class heterogeneity, and
uninformative classes for provenance studies. The uncharted forest algorithm
can be used to partition data using random selections of variables and metrics
based on statistical spread. After each tree is grown, a tally of the samples
that arrive at every terminal node is maintained. Those tallies are stored in
single sample association matrix and a likelihood measure for each sample being
partitioned with one another can be made. That matrix may be readily viewed as
a heat map, and the probabilities can be quantified via new metrics that
account for class or cluster membership. We display the advantages and
limitations of using this technique by applying it to two classification
datasets and three provenance study datasets. Two of the metrics presented in
this paper are also compared with widely used metrics from two algorithms that
have variance-based clustering mechanisms. | [
0,
0,
0,
1,
0,
0
] |
Title: Reconfigurable cluster state generation in specially poled nonlinear waveguide arrays,
Abstract: We present a new approach for generating cluster states on-chip, with the
state encoded in the spatial component of the photonic wavefunction. We show
that for spatial encoding, a change of measurement basis can improve the
practicality of cluster state algorithm implementation, and demonstrate this by
simulating Grover's search algorithm. Our state generation scheme involves
shaping the wavefunction produced by spontaneous parametric down-conversion in
on-chip waveguides using specially tailored nonlinear poling patterns.
Furthermore the form of the cluster state can be reconfigured quickly by
driving different waveguides in the array. | [
0,
1,
0,
0,
0,
0
] |
Title: Solutions of the Helmholtz equation given by solutions of the eikonal equation,
Abstract: We find the form of the refractive index such that a solution, $S$, of the
eikonal equation yields an exact solution, $\exp ({\rm i} k_{0} S)$, of the
corresponding Helmholtz equation. | [
0,
1,
0,
0,
0,
0
] |
Title: The Amplitude-Phase Decomposition for the Magnetotelluric Impedance Tensor,
Abstract: The Phase Tensor (PT) marked a breakthrough in understanding and analysis of
electric galvanic distortion but does not contain any impedance amplitude
information and therefore cannot quantify resistivity without complementary
data. We formulate a complete impedance tensor decomposition into the PT and a
new Amplitude Tensor (AT) that is shown to be complementary and mathematically
independent to the PT. We show that for the special cases of 1D and 2D models,
the geometric AT parameters (strike and skew angles) converge to PT parameters
and the singular values of the AT correspond to the impedance amplitudes of the
transverse electric and transverse magnetic modes. In all cases, we show that
the AT contains both galvanic and inductive amplitudes, the latter of which is
argued to be physically related to the inductive information of the PT. The
geometric parameters of the inductive AT and the PT represent the same geometry
of the subsurface conductivity distribution that is affected by induction
processes, and therefore we hypothesise that geometric PT parameters can be
used to approximate the inductive AT. Then, this hypothesis leads to the
estimation of the galvanic AT which is equal to the galvanic electric
distortion tensor at the lowest measured period. This estimation of the
galvanic distortion departs from the common assumption to consider 1D or 2D
regional structures and can be applied for general 3D subsurfaces. We
demonstrate exemplarily with an explicit formulation how our hypothesis can be
used to recover the galvanic electric anisotropic distortion for 2D
subsurfaces, which was, until now, believed to be indeterminable for 2D data.
Moreover, we illustrate the AT as a mapping tool and we compare it to the PT
with both synthetic and real data examples. Lastly, we argue that the AT can
provide important non-redundant amplitude information to PT inversions. | [
0,
1,
0,
0,
0,
0
] |
Title: Real-time Convolutional Neural Networks for Emotion and Gender Classification,
Abstract: In this paper we propose an implement a general convolutional neural network
(CNN) building framework for designing real-time CNNs. We validate our models
by creating a real-time vision system which accomplishes the tasks of face
detection, gender classification and emotion classification simultaneously in
one blended step using our proposed CNN architecture. After presenting the
details of the training procedure setup we proceed to evaluate on standard
benchmark sets. We report accuracies of 96% in the IMDB gender dataset and 66%
in the FER-2013 emotion dataset. Along with this we also introduced the very
recent real-time enabled guided back-propagation visualization technique.
Guided back-propagation uncovers the dynamics of the weight changes and
evaluates the learned features. We argue that the careful implementation of
modern CNN architectures, the use of the current regularization methods and the
visualization of previously hidden features are necessary in order to reduce
the gap between slow performances and real-time architectures. Our system has
been validated by its deployment on a Care-O-bot 3 robot used during
RoboCup@Home competitions. All our code, demos and pre-trained architectures
have been released under an open-source license in our public repository. | [
1,
0,
0,
0,
0,
0
] |
Title: Realization of an atomically thin mirror using monolayer MoSe2,
Abstract: Advent of new materials such as van der Waals heterostructures, propels new
research directions in condensed matter physics and enables development of
novel devices with unique functionalities. Here, we show experimentally that a
monolayer of MoSe2 embedded in a charge controlled heterostructure can be used
to realize an electrically tunable atomically-thin mirror, that effects 90%
extinction of an incident field that is resonant with its exciton transition.
The corresponding maximum reflection coefficient of 45% is only limited by the
ratio of the radiative decay rate to the linewidth of exciton transition and is
independent of incident light intensity up to 400 Watts/cm2. We demonstrate
that the reflectivity of the mirror can be drastically modified by applying a
gate voltage that modifies the monolayer charge density. Our findings could
find applications ranging from fast programmable spatial light modulators to
suspended ultra-light mirrors for optomechanical devices. | [
0,
1,
0,
0,
0,
0
] |
Title: Equivariant mirror symmetry for the weighted projective line,
Abstract: In this paper, we establish equivariant mirror symmetry for the weighted
projective line. This extends the results by B. Fang, C.C. Liu and Z. Zong,
where the projective line was considered [{\it Geometry \& Topology}
24:2049-2092, 2017]. More precisely, we prove the equivalence of the
$R$-matrices for A-model and B-model at large radius limit, and establish
isomorphism for $R$-matrices for general radius. We further demonstrate that
the graph sum of higher genus cases are the same for both models, hence
establish equivariant mirror symmetry for the weighted projective line. | [
0,
0,
1,
0,
0,
0
] |
Title: Current-Voltage Characteristics of Weyl Semimetal Semiconducting Devices, Veselago Lenses and Hyperbolic Dirac Phase,
Abstract: The current-voltage characteristics of a new range of devices built around
Weyl semimetals has been predicted using the Landauer formalism. The potential
step and barrier have been reconsidered for a three-dimensional Weyl
semimetals, with analogies to the two-dimensional material graphene and to
optics. With the use of our results we also show how a Veselago lens can be
made from Weyl semimetals, e.g. from NbAs and NbP. Such a lens may have many
practical applications and can be used as a probing tip in a scanning tunneling
microscope (STM). The ballistic character of Weyl fermion transport inside the
semimetal tip, combined with the ideal focusing of the Weyl fermions (by
Veselago lens) on the surface of the tip may create a very narrow electron beam
from the tip to the surface of the studied material. With a Weyl semimetal
probing tip the resolution of the present STMs can be improved significantly,
and one may image not only individual atoms but also individual electron
orbitals or chemical bonding and therewith to resolve the long-term issue of
chemical and hydrogen bond formation. We show that applying a pressure to the
Weyl semimental, having no centre of spacial inversion one may model matter at
extreme conditions such as those arising in the vicinity of a black hole. As
the materials Cd3As2 and Na3Bi show an asymmetry in their Dirac cones, a
scaling factor was used to model this asymmetry. The scaling factor created
additional regions of no propagation and condensed the appearance of
resonances. We argue that under an external pressure there may arise a
topological phase transition in Weyl semimetals, where the electron transport
changes character and becomes anisotropic. There a hyperbolic Dirac phases
occurs where there is a strong light absorption and photo-current generation. | [
0,
1,
0,
0,
0,
0
] |
Title: Safer Classification by Synthesis,
Abstract: The discriminative approach to classification using deep neural networks has
become the de-facto standard in various fields. Complementing recent
reservations about safety against adversarial examples, we show that
conventional discriminative methods can easily be fooled to provide incorrect
labels with very high confidence to out of distribution examples. We posit that
a generative approach is the natural remedy for this problem, and propose a
method for classification using generative models. At training time, we learn a
generative model for each class, while at test time, given an example to
classify, we query each generator for its most similar generation, and select
the class corresponding to the most similar one. Our approach is general and
can be used with expressive models such as GANs and VAEs. At test time, our
method accurately "knows when it does not know," and provides resilience to out
of distribution examples while maintaining competitive performance for standard
examples. | [
1,
0,
0,
1,
0,
0
] |
Title: A geometrical analysis of global stability in trained feedback networks,
Abstract: Recurrent neural networks have been extensively studied in the context of
neuroscience and machine learning due to their ability to implement complex
computations. While substantial progress in designing effective learning
algorithms has been achieved in the last years, a full understanding of trained
recurrent networks is still lacking. Specifically, the mechanisms that allow
computations to emerge from the underlying recurrent dynamics are largely
unknown. Here we focus on a simple, yet underexplored computational setup: a
feedback architecture trained to associate a stationary output to a stationary
input. As a starting point, we derive an approximate analytical description of
global dynamics in trained networks which assumes uncorrelated connectivity
weights in the feedback and in the random bulk. The resulting mean-field theory
suggests that the task admits several classes of solutions, which imply
different stability properties. Different classes are characterized in terms of
the geometrical arrangement of the readout with respect to the input vectors,
defined in the high-dimensional space spanned by the network population. We
find that such approximate theoretical approach can be used to understand how
standard training techniques implement the input-output task in finite-size
feedback networks. In particular, our simplified description captures the local
and the global stability properties of the target solution, and thus predicts
training performance. | [
0,
0,
0,
0,
1,
0
] |
Title: First observation of Ce volume collapse in CeN,
Abstract: On the occasion of the 80th anniversary of the first observation of Ce volume
collapse in CeN a remembrance of the implications of that transcendent event is
presented, along with a review of the knowledge of Ce physical properties
available at that time. Coincident anniversary corresponds to the first
proposal for Ce as a mix valence element, motivating to briefly review how the
valence instability of Ce was investigated since that time. | [
0,
1,
0,
0,
0,
0
] |
Title: Focusing on a Probability Element: Parameter Selection of Message Importance Measure in Big Data,
Abstract: Message importance measure (MIM) is applicable to characterize the importance
of information in the scenario of big data, similar to entropy in information
theory. In fact, MIM with a variable parameter can make an effect on the
characterization of distribution. Furthermore, by choosing an appropriate
parameter of MIM, it is possible to emphasize the message importance of a
certain probability element in a distribution. Therefore, parametric MIM can
play a vital role in anomaly detection of big data by focusing on probability
of an anomalous event. In this paper, we propose a parameter selection method
of MIM focusing on a probability element and then present its major properties.
In addition, we discuss the parameter selection with prior probability, and
investigate the availability in a statistical processing model of big data for
anomaly detection problem. | [
1,
0,
1,
0,
0,
0
] |
Title: Deep Multi-camera People Detection,
Abstract: This paper addresses the problem of multi-view people occupancy map
estimation. Existing solutions for this problem either operate per-view, or
rely on a background subtraction pre-processing. Both approaches lessen the
detection performance as scenes become more crowded. The former does not
exploit joint information, whereas the latter deals with ambiguous input due to
the foreground blobs becoming more and more interconnected as the number of
targets increases.
Although deep learning algorithms have proven to excel on remarkably numerous
computer vision tasks, such a method has not been applied yet to this problem.
In large part this is due to the lack of large-scale multi-camera data-set.
The core of our method is an architecture which makes use of monocular
pedestrian data-set, available at larger scale then the multi-view ones,
applies parallel processing to the multiple video streams, and jointly utilises
it. Our end-to-end deep learning method outperforms existing methods by large
margins on the commonly used PETS 2009 data-set. Furthermore, we make publicly
available a new three-camera HD data-set. Our source code and trained models
will be made available under an open-source license. | [
1,
0,
0,
0,
0,
0
] |
Title: SMAGEXP: a galaxy tool suite for transcriptomics data meta-analysis,
Abstract: Bakground: With the proliferation of available microarray and high throughput
sequencing experiments in the public domain, the use of meta-analysis methods
increases. In these experiments, where the sample size is often limited,
meta-analysis offers the possibility to considerably enhance the statistical
power and give more accurate results. For those purposes, it combines either
effect sizes or results of single studies in a appropriate manner. R packages
metaMA and metaRNASeq perform meta-analysis on microarray and NGS data,
respectively. They are not interchangeable as they rely on statistical modeling
specific to each technology.
Results: SMAGEXP (Statistical Meta-Analysis for Gene EXPression) integrates
metaMA and metaRNAseq packages into Galaxy. We aim to propose a unified way to
carry out meta-analysis of gene expression data, while taking care of their
specificities. We have developed this tool suite to analyse microarray data
from Gene Expression Omnibus (GEO) database or custom data from affymetrix
microarrays. These data are then combined to carry out meta-analysis using
metaMA package. SMAGEXP also offers to combine raw read counts from Next
Generation Sequencing (NGS) experiments using DESeq2 and metaRNASeq package. In
both cases, key values, independent from the technology type, are reported to
judge the quality of the meta-analysis. These tools are available on the Galaxy
main tool shed. Source code, help and installation instructions are available
on github.
Conclusion: The use of Galaxy offers an easy-to-use gene expression
meta-analysis tool suite based on the metaMA and metaRNASeq packages. | [
0,
0,
0,
1,
1,
0
] |
Title: Person Following by Autonomous Robots: A Categorical Overview,
Abstract: A wide range of human-robot collaborative applications in industry, search
and rescue operations, healthcare, and social interactions require an
autonomous robot to follow its human companion. Different operating mediums and
applications pose diverse challenges by adding constraints on the choice of
sensors, the degree of autonomy, and dynamics of the person following robot.
Researchers have addressed these challenges in many ways and contributed to the
development of a large body of literature. This paper provides a comprehensive
overview of the literature by categorizing different aspects of
person-following by autonomous robots. Also, the corresponding operational
challenges are identified based on various design choices for ground,
underwater, and aerial scenarios. In addition, state-of-the-art methods for
perception, planning, control, and interaction are elaborately discussed, and
their feasibilities are evaluated in terms of standard operational and
performance metrics. Furthermore, several prospective application areas are
identified, and open problems are highlighted for future research. | [
1,
0,
0,
0,
0,
0
] |
Title: Structures, phase transitions, and magnetic properties of Co3Si from first-principles calculations,
Abstract: Co3Si was recently reported to exhibit remarkable magnetic properties in the
nanoparticle form [Appl. Phys. Lett. 108, 152406 (2016)], yet better
understanding of this material is to be promoted. Here we report a study on the
crystal structures of Co3Si using adaptive genetic algorithm, and discuss its
electronic and magnetic properties from first-principles calculations. Several
competing phases of Co3Si have been revealed from our calculations. We show
that the hexagonal Co3Si structure reported in experiments has lower energy in
non-magnetic state than ferromagnetic state at zero temperature. The
ferromagnetic state of the hexagonal structure is dynamically unstable with
imaginary phonon modes and transforms to a new orthorhombic structure, which is
confirmed by our structure searches to have the lowest energy for both Co3Si
and Co3Ge. Magnetic properties of the experimental hexagonal structure and the
lowest-energy structures obtained from our structure searches are investigated
in detail. | [
0,
1,
0,
0,
0,
0
] |
Title: Steady-state analysis of single exponential vacation in a $PH/MSP/1/\infty$ queue using roots,
Abstract: We consider an infinite-buffer single-server queue where inter-arrival times
are phase-type ($PH$), the service is provided according to Markovian service
process $(MSP)$, and the server may take single, exponentially distributed
vacations when the queue is empty. The proposed analysis is based on roots of
the associated characteristic equation of the vector-generating function (VGF)
of system-length distribution at a pre-arrival epoch. Also, we obtain the
steady-state system-length distribution at an arbitrary epoch along with some
important performance measures such as the mean number of customers in the
system and the mean system sojourn time of a customer. Later, we have
established heavy- and light-traffic approximations as well as an approximation
for the tail probabilities at pre-arrival epoch based on one root of the
characteristic equation. At the end, we present numerical results in the form
of tables to show the effect of model parameters on the performance measures. | [
1,
0,
1,
0,
0,
0
] |
Title: Introducing AIC model averaging in ecological niche modeling: a single-algorithm multi-model strategy to account for uncertainty in suitability predictions,
Abstract: Aim: The Akaike information Criterion (AIC) is widely used science to make
predictions about complex phenomena based on an entire set of models weighted
by Akaike weights. This approach (AIC model averaging; hereafter AvgAICc) is
often preferable than alternatives based on the selection of a single model.
Surprisingly, AvgAICc has not yet been introduced in ecological niche modeling
(ENM). We aimed to introduce AvgAICc in the context of ENM to serve both as an
optimality criterion in analyses that tune-up model parameters and as a
multi-model prediction strategy.
Innovation: Results from the AvgAICc approach differed from those of
alternative approaches with respect to model complexity, contribution of
environmental variables, and predicted amount and geographic location of
suitable conditions for the focal species. Two theoretical properties of the
AvgAICc approach might justify that future studies will prefer its use over
alternative approaches: (1) it is not limited to make predictions based on a
single model, but it also uses secondary models that might have important
predictive power absent in a given single model favored by alternative
optimality criteria; (2) it balances goodness of fit and model accuracy, this
being of critical importance in applications of ENM that require model
transference.
Main conclusions: Our introduction of the AvgAICc approach in ENM; its
theoretical properties, which are expected to confer advantages over
alternatives approaches; and the differences we found when comparing the
AvgAICc approach with alternative ones; should eventually lead to a wider use
of the AvgAICc approach. Our work should also promote further methodological
research comparing properties of the AvgAICc approach with respect to those of
alternative procedures. | [
0,
0,
0,
0,
1,
0
] |
Title: Modeling polypharmacy side effects with graph convolutional networks,
Abstract: The use of drug combinations, termed polypharmacy, is common to treat
patients with complex diseases and co-existing conditions. However, a major
consequence of polypharmacy is a much higher risk of adverse side effects for
the patient. Polypharmacy side effects emerge because of drug-drug
interactions, in which activity of one drug may change if taken with another
drug. The knowledge of drug interactions is limited because these complex
relationships are rare, and are usually not observed in relatively small
clinical testing. Discovering polypharmacy side effects thus remains an
important challenge with significant implications for patient mortality. Here,
we present Decagon, an approach for modeling polypharmacy side effects. The
approach constructs a multimodal graph of protein-protein interactions,
drug-protein target interactions, and the polypharmacy side effects, which are
represented as drug-drug interactions, where each side effect is an edge of a
different type. Decagon is developed specifically to handle such multimodal
graphs with a large number of edge types. Our approach develops a new graph
convolutional neural network for multirelational link prediction in multimodal
networks. Decagon predicts the exact side effect, if any, through which a given
drug combination manifests clinically. Decagon accurately predicts polypharmacy
side effects, outperforming baselines by up to 69%. We find that it
automatically learns representations of side effects indicative of
co-occurrence of polypharmacy in patients. Furthermore, Decagon models
particularly well side effects with a strong molecular basis, while on
predominantly non-molecular side effects, it achieves good performance because
of effective sharing of model parameters across edge types. Decagon creates
opportunities to use large pharmacogenomic and patient data to flag and
prioritize side effects for follow-up analysis. | [
0,
0,
0,
1,
1,
0
] |
Title: Musical intervals under 12-note equal temperament: a geometrical interpretation,
Abstract: Musical intervals in multiple of semitones under 12-note equal temperament,
or more specifically pitch-class subsets of assigned cardinality ($n$-chords)
are conceived as positive integer points within an Euclidean $n$-space. The
number of distinct $n$-chords is inferred from combinatorics with the extension
to $n=0$, involving an Euclidean 0-space. The number of repeating $n$-chords,
or points which are turned into themselves during a circular permutation,
$T_n$, of their coordinates, is inferred from algebraic considerations.
Finally, the total number of $n$-chords and the number of $T_n$ set classes are
determined. Palindrome and pseudo palindrome $n$-chords are defined and
included among repeating $n$-chords, with regard to an equivalence relation,
$T_n/T_nI$, where reflection is added to circular permutation. To this respect,
the number of $T_n$ set classes is inferred concerning palindrome and pseudo
palindrome $n$-chords and the remaining $n$-chords. The above results are
reproduced within the framework of a geometrical interpretation, where positive
integer points related to $n$-chords of cardinality, $n$, belong to a regular
inclined $n$-hedron, $\Psi_{12}^n$, the vertexes lying on the coordinate axes
of a Cartesian orthogonal reference frame at a distance, $x_i=12$, $1\le i\le
n$, from the origin. Considering $\Psi_{12}^n$ as special cases of lattice
polytopes, the number of related nonnegative integer points is also determined
for completeness. A comparison is performed with the results inferred from
group theory. | [
0,
0,
1,
0,
0,
0
] |
Title: Optimal Control of Partially Observable Piecewise Deterministic Markov Processes,
Abstract: In this paper we consider a control problem for a Partially Observable
Piecewise Deterministic Markov Process of the following type: After the jump of
the process the controller receives a noisy signal about the state and the aim
is to control the process continuously in time in such a way that the expected
discounted cost of the system is minimized. We solve this optimization problem
by reducing it to a discrete-time Markov Decision Process. This includes the
derivation of a filter for the unobservable state. Imposing sufficient
continuity and compactness assumptions we are able to prove the existence of
optimal policies and show that the value function satisfies a fixed point
equation. A generic application is given to illustrate the results. | [
0,
0,
1,
0,
0,
0
] |
Title: Moment-based parameter estimation in binomial random intersection graph models,
Abstract: Binomial random intersection graphs can be used as parsimonious statistical
models of large and sparse networks, with one parameter for the average degree
and another for transitivity, the tendency of neighbours of a node to be
connected. This paper discusses the estimation of these parameters from a
single observed instance of the graph, using moment estimators based on
observed degrees and frequencies of 2-stars and triangles. The observed data
set is assumed to be a subgraph induced by a set of $n_0$ nodes sampled from
the full set of $n$ nodes. We prove the consistency of the proposed estimators
by showing that the relative estimation error is small with high probability
for $n_0 \gg n^{2/3} \gg 1$. As a byproduct, our analysis confirms that the
empirical transitivity coefficient of the graph is with high probability close
to the theoretical clustering coefficient of the model. | [
1,
0,
1,
1,
0,
0
] |
Title: Efficient Compression and Indexing of Trajectories,
Abstract: We present a new compressed representation of free trajectories of moving
objects. It combines a partial-sums-based structure that retrieves in constant
time the position of the object at any instant, with a hierarchical
minimum-bounding-boxes representation that allows determining if the object is
seen in a certain rectangular area during a time period. Combined with spatial
snapshots at regular intervals, the representation is shown to outperform
classical ones by orders of magnitude in space, and also to outperform previous
compressed representations in time performance, when using the same amount of
space. | [
1,
0,
0,
0,
0,
0
] |
Title: Denoising Linear Models with Permuted Data,
Abstract: The multivariate linear regression model with shuffled data and additive
Gaussian noise arises in various correspondence estimation and matching
problems. Focusing on the denoising aspect of this problem, we provide a
characterization the minimax error rate that is sharp up to logarithmic
factors. We also analyze the performance of two versions of a computationally
efficient estimator, and establish their consistency for a large range of input
parameters. Finally, we provide an exact algorithm for the noiseless problem
and demonstrate its performance on an image point-cloud matching task. Our
analysis also extends to datasets with outliers. | [
0,
0,
1,
1,
0,
0
] |
Title: Clustering Spectrum of scale-free networks,
Abstract: Real-world networks often have power-law degrees and scale-free properties
such as ultra-small distances and ultra-fast information spreading. In this
paper, we study a third universal property: three-point correlations that
suppress the creation of triangles and signal the presence of hierarchy. We
quantify this property in terms of $\bar c(k)$, the probability that two
neighbors of a degree-$k$ node are neighbors themselves. We investigate how the
clustering spectrum $k\mapsto\bar c(k)$ scales with $k$ in the hidden variable
model and show that $c(k)$ follows a {\it universal curve} that consists of
three $k$-ranges where $\bar c(k)$ remains flat, starts declining, and
eventually settles on a power law $\bar c(k)\sim k^{-\alpha}$ with $\alpha$
depending on the power law of the degree distribution. We test these results
against ten contemporary real-world networks and explain analytically why the
universal curve properties only reveal themselves in large networks. | [
1,
1,
0,
0,
0,
0
] |
Title: Weighted $L_{p,q}$-estimates for higher order elliptic and parabolic systems with BMO coefficients on Reifenberg flat domains,
Abstract: We prove weighted $L_{p,q}$-estimates for divergence type higher order
elliptic and parabolic systems with irregular coefficients on Reifenberg flat
domains. In particular, in the parabolic case the coefficients do not have any
regularity assumptions in the time variable. As functions of the spatial
variables, the leading coefficients are permitted to have small mean
oscillations. The weights are in the class of Muckenhoupt weights $A_p$. We
also prove the solvability in weighted Sobolev spaces for the systems in the
whole space, on a half space, and on bounded Reifenberg flat domains. | [
0,
0,
1,
0,
0,
0
] |
Title: Linear Spectral Estimators and an Application to Phase Retrieval,
Abstract: Phase retrieval refers to the problem of recovering real- or complex-valued
vectors from magnitude measurements. The best-known algorithms for this problem
are iterative in nature and rely on so-called spectral initializers that
provide accurate initialization vectors. We propose a novel class of estimators
suitable for general nonlinear measurement systems, called linear spectral
estimators (LSPEs), which can be used to compute accurate initialization
vectors for phase retrieval problems. The proposed LSPEs not only provide
accurate initialization vectors for noisy phase retrieval systems with
structured or random measurement matrices, but also enable the derivation of
sharp and nonasymptotic mean-squared error bounds. We demonstrate the efficacy
of LSPEs on synthetic and real-world phase retrieval problems, and show that
our estimators significantly outperform existing methods for structured
measurement systems that arise in practice. | [
0,
0,
0,
1,
0,
0
] |
Title: A geometric perspective on the method of descent,
Abstract: We derive a representation formula for the tensorial wave equation $\Box_\bg
\phi^I=F^I$ in globally hyperbolic Lorentzian spacetimes $(\M^{2+1}, \bg)$ by
giving a geometric formulation of the method of descent which is applicable for
any dimension. | [
0,
0,
1,
0,
0,
0
] |
Title: Detecting Changes in Hidden Markov Models,
Abstract: We consider the problem of sequential detection of a change in the
statistical behavior of a hidden Markov model. By adopting a worst-case
analysis with respect to the time of change and by taking into account the data
that can be accessed by the change-imposing mechanism we offer alternative
formulations of the problem. For each formulation we derive the optimum
Shewhart test that maximizes the worst-case detection probability while
guaranteeing infrequent false alarms. | [
0,
0,
1,
1,
0,
0
] |
Title: Towards an Empirical Study of Affine Types for Isolated Actors in Scala,
Abstract: LaCasa is a type system and programming model to enforce the object
capability discipline in Scala, and to provide affine types. One important
application of LaCasa's type system is software isolation of concurrent
processes. Isolation is important for several reasons including security and
data-race freedom. Moreover, LaCasa's affine references enable efficient,
by-reference message passing while guaranteeing a "deep-copy" semantics. This
deep-copy semantics enables programmers to seamlessly port concurrent programs
running on a single machine to distributed programs running on large-scale
clusters of machines.
This paper presents an integration of LaCasa with actors in Scala,
specifically, the Akka actor-based middleware, one of the most widely-used
actor systems in industry. The goal of this integration is to statically ensure
the isolation of Akka actors. Importantly, we present the results of an
empirical study investigating the effort required to use LaCasa's type system
in existing open-source Akka-based systems and applications. | [
1,
0,
0,
0,
0,
0
] |
Title: FPGA Architecture for Deep Learning and its application to Planetary Robotics,
Abstract: Autonomous control systems onboard planetary rovers and spacecraft benefit
from having cognitive capabilities like learning so that they can adapt to
unexpected situations in-situ. Q-learning is a form of reinforcement learning
and it has been efficient in solving certain class of learning problems.
However, embedded systems onboard planetary rovers and spacecraft rarely
implement learning algorithms due to the constraints faced in the field, like
processing power, chip size, convergence rate and costs due to the need for
radiation hardening. These challenges present a compelling need for a portable,
low-power, area efficient hardware accelerator to make learning algorithms
practical onboard space hardware. This paper presents a FPGA implementation of
Q-learning with Artificial Neural Networks (ANN). This method matches the
massive parallelism inherent in neural network software with the fine-grain
parallelism of an FPGA hardware thereby dramatically reducing processing time.
Mars Science Laboratory currently uses Xilinx-Space-grade Virtex FPGA devices
for image processing, pyrotechnic operation control and obstacle avoidance. We
simulate and program our architecture on a Xilinx Virtex 7 FPGA. The
architectural implementation for a single neuron Q-learning and a more complex
Multilayer Perception (MLP) Q-learning accelerator has been demonstrated. The
results show up to a 43-fold speed up by Virtex 7 FPGAs compared to a
conventional Intel i5 2.3 GHz CPU. Finally, we simulate the proposed
architecture using the Symphony simulator and compiler from Xilinx, and
evaluate the performance and power consumption. | [
1,
1,
0,
0,
0,
0
] |
Title: Exact Diffusion for Distributed Optimization and Learning --- Part II: Convergence Analysis,
Abstract: Part I of this work [2] developed the exact diffusion algorithm to remove the
bias that is characteristic of distributed solutions for deterministic
optimization problems. The algorithm was shown to be applicable to a larger set
of combination policies than earlier approaches in the literature. In
particular, the combination matrices are not required to be doubly stochastic,
which impose stringent conditions on the graph topology and communications
protocol. In this Part II, we examine the convergence and stability properties
of exact diffusion in some detail and establish its linear convergence rate. We
also show that it has a wider stability range than the EXTRA consensus
solution, meaning that it is stable for a wider range of step-sizes and can,
therefore, attain faster convergence rates. Analytical examples and numerical
simulations illustrate the theoretical findings. | [
0,
0,
1,
0,
0,
0
] |
Title: Approximating meta-heuristics with homotopic recurrent neural networks,
Abstract: Much combinatorial optimisation problems constitute a non-polynomial (NP)
hard optimisation problem, i.e., they can not be solved in polynomial time. One
such problem is finding the shortest route between two nodes on a graph.
Meta-heuristic algorithms such as $A^{*}$ along with mixed-integer programming
(MIP) methods are often employed for these problems. Our work demonstrates that
it is possible to approximate solutions generated by a meta-heuristic algorithm
using a deep recurrent neural network. We compare different methodologies based
on reinforcement learning (RL) and recurrent neural networks (RNN) to gauge
their respective quality of approximation. We show the viability of recurrent
neural network solutions on a graph that has over 300 nodes and argue that a
sequence-to-sequence network rather than other recurrent networks has improved
approximation quality. Additionally, we argue that homotopy continuation --
that increases chances of hitting an extremum -- further improves the estimate
generated by a vanilla RNN. | [
1,
0,
0,
1,
0,
0
] |
Title: Theory of Compact Hausdorff Shape,
Abstract: In this paper, we aim to establish a new shape theory, compact Hausdorff
shape (CH-shape) for general Hausdorff spaces. We use the "internal" method and
direct system approach on the homotopy category of compact Hausdorff spaces.
Such a construction can preserve most good properties of H-shape given by Rubin
and Sanders. Most importantly, we can moreover develop the entire homology
theory for CH-shape, including the exactness, dual to the consequence of
Mardešić and Segal. | [
0,
0,
1,
0,
0,
0
] |
Title: Heating and cooling of coronal loops with turbulent suppression of parallel heat conduction,
Abstract: Using the "enthalpy-based thermal evolution of loops" (EBTEL) model, we
investigate the hydrodynamics of the plasma in a flaring coronal loop in which
heat conduction is limited by turbulent scattering of the electrons that
transport the thermal heat flux. The EBTEL equations are solved analytically in
each of the two (conduction-dominated and radiation-dominated) cooling phases.
Comparison of the results with typical observed cooling times in solar flares
shows that the turbulent mean free-path $\lambda_T$ lies in a range
corresponding to a regime in which classical (collision-dominated) conduction
plays at most a limited role. We also consider the magnitude and duration of
the heat input that is necessary to account for the enhanced values of
temperature and density at the beginning of the cooling phase and for the
observed cooling times. We find through numerical modeling that in order to
produce a peak temperature $\simeq 1.5 \times 10^7$~K and a 200~s cooling time
consistent with observations, the flare heating profile must extend over a
significant period of time; in particular, its lingering role must be taken
into consideration in any description of the cooling phase. Comparison with
observationally-inferred values of post-flare loop temperatures, densities, and
cooling times thus leads to useful constraints on both the magnitude and
duration of the magnetic energy release in the loop, as well as on the value of
the turbulent mean free-path $\lambda_T$. | [
0,
1,
0,
0,
0,
0
] |
Title: How to avoid the curse of dimensionality: scalability of particle filters with and without importance weights,
Abstract: Particle filters are a popular and flexible class of numerical algorithms to
solve a large class of nonlinear filtering problems. However, standard particle
filters with importance weights have been shown to require a sample size that
increases exponentially with the dimension D of the state space in order to
achieve a certain performance, which precludes their use in very
high-dimensional filtering problems. Here, we focus on the dynamic aspect of
this curse of dimensionality (COD) in continuous time filtering, which is
caused by the degeneracy of importance weights over time. We show that the
degeneracy occurs on a time-scale that decreases with increasing D. In order to
soften the effects of weight degeneracy, most particle filters use particle
resampling and improved proposal functions for the particle motion. We explain
why neither of the two can prevent the COD in general. In order to address this
fundamental problem, we investigate an existing filtering algorithm based on
optimal feedback control that sidesteps the use of importance weights. We use
numerical experiments to show that this Feedback Particle Filter (FPF) by Yang
et al. (2013) does not exhibit a COD. | [
0,
0,
1,
1,
0,
0
] |
Title: Recommendation with k-anonymized Ratings,
Abstract: Recommender systems are widely used to predict personalized preferences of
goods or services using users' past activities, such as item ratings or
purchase histories. If collections of such personal activities were made
publicly available, they could be used to personalize a diverse range of
services, including targeted advertisement or recommendations. However, there
would be an accompanying risk of privacy violations. The pioneering work of
Narayanan et al.\ demonstrated that even if the identifiers are eliminated, the
public release of user ratings can allow for the identification of users by
those who have only a small amount of data on the users' past ratings.
In this paper, we assume the following setting. A collector collects user
ratings, then anonymizes and distributes them. A recommender constructs a
recommender system based on the anonymized ratings provided by the collector.
Based on this setting, we exhaustively list the models of recommender systems
that use anonymized ratings. For each model, we then present an item-based
collaborative filtering algorithm for making recommendations based on
anonymized ratings. Our experimental results show that an item-based
collaborative filtering based on anonymized ratings can perform better than
collaborative filterings based on 5--10 non-anonymized ratings. This surprising
result indicates that, in some settings, privacy protection does not
necessarily reduce the usefulness of recommendations. From the experimental
analysis of this counterintuitive result, we observed that the sparsity of the
ratings can be reduced by anonymization and the variance of the prediction can
be reduced if $k$, the anonymization parameter, is appropriately tuned. In this
way, the predictive performance of recommendations based on anonymized ratings
can be improved in some settings. | [
1,
0,
0,
1,
0,
0
] |
Title: Ro-vibrational states of H$_2^+$. Variational calculations,
Abstract: The nonrelativistic variational calculation of a complete set of
ro-vibrational states in the H$_2^+$ molecular ion supported by the ground
$1s\sigma$ adiabatic potential is presented. It includes both bound states and
resonances located above the $n=1$ threshold. In the latter case we also
evaluate a predissociation width of a state wherever it is significant.
Relativistic and radiative corrections are discussed and effective adiabatic
potentials of these corrections are included as supplementary files. | [
0,
1,
0,
0,
0,
0
] |
Title: Control of automated guided vehicles without collision by quantum annealer and digital devices,
Abstract: We formulate an optimization problem to control a large number of automated
guided vehicles in a plant without collision. The formulation consists of
binary variables. A quadratic cost function over these variables enables us to
utilize certain solvers on digital computers and recently developed
purpose-specific hardware such as D-Wave 2000Q and the Fujitsu digital
annealer. In the present study, we consider an actual plant in Japan, in which
vehicles run, and assess efficiency of our formulation for optimizing the
vehicles via several solvers. We confirm that our formulation can be a powerful
approach for performing smooth control while avoiding collisions between
vehicles, as compared to a conventional method. In addition, comparative
experiments performed using several solvers reveal that D-Wave 2000Q can be
useful as a rapid solver for generating a plan for controlling the vehicles in
a short time although it deals only with a small number of vehicles, while a
digital computer can rapidly solve the corresponding optimization problem even
with a large number of binary variables. | [
1,
0,
0,
0,
0,
0
] |
Title: Meta-learning: searching in the model space,
Abstract: There is no free lunch, no single learning algorithm that will outperform
other algorithms on all data. In practice different approaches are tried and
the best algorithm selected. An alternative solution is to build new algorithms
on demand by creating a framework that accommodates many algorithms. The best
combination of parameters and procedures is searched here in the space of all
possible models belonging to the framework of Similarity-Based Methods (SBMs).
Such meta-learning approach gives a chance to find the best method in all
cases. Issues related to the meta-learning and first tests of this approach are
presented. | [
0,
0,
0,
1,
0,
0
] |
Title: GIFT: Guided and Interpretable Factorization for Tensors - An Application to Large-Scale Multi-platform Cancer Analysis,
Abstract: Given multi-platform genome data with prior knowledge of functional gene
sets, how can we extract interpretable latent relationships between patients
and genes? More specifically, how can we devise a tensor factorization method
which produces an interpretable gene factor matrix based on gene set
information while maintaining the decomposition quality and speed? We propose
GIFT, a Guided and Interpretable Factorization for Tensors. GIFT provides
interpretable factor matrices by encoding prior knowledge as a regularization
term in its objective function. Experiment results demonstrate that GIFT
produces interpretable factorizations with high scalability and accuracy, while
other methods lack interpretability. We apply GIFT to the PanCan12 dataset, and
GIFT reveals significant relations between cancers, gene sets, and genes, such
as influential gene sets for specific cancer (e.g., interferon-gamma response
gene set for ovarian cancer) or relations between cancers and genes (e.g., BRCA
cancer - APOA1 gene and OV, UCEC cancers - BST2 gene). | [
1,
0,
0,
0,
1,
0
] |
Title: Remarks to the article: New Light on the Invention of the Achromatic Telescope Objective,
Abstract: The article analysis was carried out within the confines of the replication
project of the telescope, which was used by Mikhail Lomonosov at observation
the transit of Venus in 1761. At that time he discovered the Venusian
atmosphere. It is known that Lomonosov used Dollond 4.5 feet long achromatic
telescope. The investigation revealed significant faults in the description of
the approximation method, which most likely was used by J. Dollond & Son during
manufacturing of the early achromatic lenses. | [
0,
1,
0,
0,
0,
0
] |
Title: The spin-Brauer diagram algebra,
Abstract: We investigate the spin-Brauer diagram algebra, denoted ${\bf SB}_n(\delta)$,
that arises from studying an analogous form of Schur-Weyl duality for the
action of the pin group on ${\bf V}^{\otimes n} \otimes \Delta$. Here ${\bf V}$
is the standard $N$-dimensional complex representation of ${\bf Pin}(N)$ and
$\Delta$ is the spin representation. When $\delta = N$ is a positive integer,
we define a surjective map ${\bf SB}_n(N) \twoheadrightarrow {\rm End}_{{\bf
Pin}(N)}({\bf V}^{\otimes n} \otimes \Delta)$ and show it is an isomorphism for
$N \geq 2n$. We show ${\bf SB}_n(\delta)$ is a cellular algebra and use
cellularity to characterize its irreducible representations. | [
0,
0,
1,
0,
0,
0
] |
Title: New Braided $T$-Categories over Hopf (co)quasigroups,
Abstract: Let $H$ be a Hopf quasigroup with bijective antipode and let $Aut_{HQG}(H)$
be the set of all Hopf quasigroup automorphisms of $H$. We introduce a category
${_{H}\mathcal{YDQ}^{H}}(\alpha,\beta)$ with $\alpha,\beta\in Aut_{HQG}(H)$ and
construct a braided $T$-category $\mathcal{YDQ}(H)$ having all the categories
${_{H}\mathcal{YDQ}^{H}}(\alpha,\beta)$ as components. | [
0,
0,
1,
0,
0,
0
] |
Title: Adaptive Questionnaires for Direct Identification of Optimal Product Design,
Abstract: We consider the problem of identifying the most profitable product design
from a finite set of candidates under unknown consumer preference. A standard
approach to this problem follows a two-step strategy: First, estimate the
preference of the consumer population, represented as a point in part-worth
space, using an adaptive discrete-choice questionnaire. Second, integrate the
estimated part-worth vector with engineering feasibility and cost models to
determine the optimal design. In this work, we (1) demonstrate that accurate
preference estimation is neither necessary nor sufficient for identifying the
optimal design, (2) introduce a novel adaptive questionnaire that leverages
knowledge about engineering feasibility and manufacturing costs to directly
determine the optimal design, and (3) interpret product design in terms of a
nonlinear segmentation of part-worth space, and use this interpretation to
illuminate the intrinsic difficulty of optimal design in the presence of noisy
questionnaire responses. We establish the superiority of the proposed approach
using a well-documented optimal product design task. This study demonstrates
how the identification of optimal product design can be accelerated by
integrating marketing and manufacturing knowledge into the adaptive
questionnaire. | [
1,
0,
0,
1,
0,
0
] |
Title: Gate Tunable Magneto-resistance of Ultra-Thin WTe2 Devices,
Abstract: In this work, the magneto-resistance (MR) of ultra-thin WTe2/BN
heterostructures far away from electron-hole equilibrium is measured. The
change of MR of such devices is found to be determined largely by a single
tunable parameter, i.e. the amount of imbalance between electrons and holes. We
also found that the magnetoresistive behavior of ultra-thin WTe2 devices is
well-captured by a two-fluid model. According to the model, the change of MR
could be as large as 400,000%, the largest potential change of MR among all
materials known, if the ultra-thin samples are tuned to neutrality when
preserving the mobility of 167,000 cm2V-1s-1 observed in bulk samples. Our
findings show the prospects of ultra-thin WTe2 as a variable magnetoresistance
material in future applications such as magnetic field sensors, information
storage and extraction devices, and galvanic isolators. The results also
provide important insight into the electronic structure and the origin of the
large MR in ultra-thin WTe2 samples. | [
0,
1,
0,
0,
0,
0
] |
Title: Hölder regularity of the 2D dual semigeostrophic equations via analysis of linearized Monge-Ampère equations,
Abstract: We obtain the Hölder regularity of time derivative of solutions to the dual
semigeostrophic equations in two dimensions when the initial potential density
is bounded away from zero and infinity. Our main tool is an interior Hölder
estimate in two dimensions for an inhomogeneous linearized Monge-Ampère
equation with right hand side being the divergence of a bounded vector field.
As a further application of our Hölder estimate, we prove the Hölder
regularity of the polar factorization for time-dependent maps in two dimensions
with densities bounded away from zero and infinity. Our applications improve
previous work by G. Loeper who considered the cases of densities sufficiently
close to a positive constant. | [
0,
0,
1,
0,
0,
0
] |
Title: The Top 10 Topics in Machine Learning Revisited: A Quantitative Meta-Study,
Abstract: Which topics of machine learning are most commonly addressed in research?
This question was initially answered in 2007 by doing a qualitative survey
among distinguished researchers. In our study, we revisit this question from a
quantitative perspective. Concretely, we collect 54K abstracts of papers
published between 2007 and 2016 in leading machine learning journals and
conferences. We then use machine learning in order to determine the top 10
topics in machine learning. We not only include models, but provide a holistic
view across optimization, data, features, etc. This quantitative approach
allows reducing the bias of surveys. It reveals new and up-to-date insights
into what the 10 most prolific topics in machine learning research are. This
allows researchers to identify popular topics as well as new and rising topics
for their research. | [
1,
0,
0,
0,
0,
0
] |
Title: Random taste heterogeneity in discrete choice models: Flexible nonparametric finite mixture distributions,
Abstract: This study proposes a mixed logit model with multivariate nonparametric
finite mixture distributions. The support of the distribution is specified as a
high-dimensional grid over the coefficient space, with equal or unequal
intervals between successive points along the same dimension; the location of
each point on the grid and the probability mass at that point are model
parameters that need to be estimated. The framework does not require the
analyst to specify the shape of the distribution prior to model estimation, but
can approximate any multivariate probability distribution function to any
arbitrary degree of accuracy. The grid with unequal intervals, in particular,
offers greater flexibility than existing multivariate nonparametric
specifications, while requiring the estimation of a small number of additional
parameters. An expectation maximization algorithm is developed for the
estimation of these models. Multiple synthetic datasets and a case study on
travel mode choice behavior are used to demonstrate the value of the model
framework and estimation algorithm. Compared to extant models that incorporate
random taste heterogeneity through continuous mixture distributions, the
proposed model provides better out-of-sample predictive ability. Findings
reveal significant differences in willingness to pay measures between the
proposed model and extant specifications. The case study further demonstrates
the ability of the proposed model to endogenously recover patterns of attribute
non-attendance and choice set formation. | [
0,
0,
0,
1,
0,
0
] |
Title: Microscopic mechanism of tunable band gap in potassium doped few-layer black phosphorus,
Abstract: Tuning band gaps in two-dimensional (2D) materials is of great interest in
the fundamental and practical aspects of contemporary material sciences.
Recently, black phosphorus (BP) consisting of stacked layers of phosphorene was
experimentally observed to show a widely tunable band gap by means of the
deposition of potassium (K) atoms on the surface, thereby allowing great
flexibility in design and optimization of electronic and optoelectronic
devices. Here, based on the density-functional theory calculations, we
demonstrates that the donated electrons from K dopants are mostly localized at
the topmost BP layer and such a surface charging efficiently screens the K ion
potential. It is found that, as the K doping increases, the extreme surface
charging and its screening of K atoms shift the conduction bands down in
energy, i.e., towards higher binding energy, because they have more charge near
the surface, while it has little influence on the valence bands having more
charge in the deeper layers. This result provides a different explanation for
the observed tunable band gap compared to the previously proposed giant Stark
effect where a vertical electric field from the positively ionized K overlayer
to the negatively charged BP layers shifts the conduction band minimum
${\Gamma}_{\rm 1c}$ (valence band minimum ${\Gamma}_{\rm 8v}$) downwards
(upwards). The present prediction of ${\Gamma}_{\rm 1c}$ and ${\Gamma}_{\rm
8v}$ as a function of the K doping reproduces well the widely tunable band gap,
anisotropic Dirac semimetal state, and band-inverted semimetal state, as
observed by angle-resolved photoemission spectroscopy experiment. Our findings
shed new light on a route for tunable band gap engineering of 2D materials
through the surface doping of alkali metals. | [
0,
1,
0,
0,
0,
0
] |
Title: Graphene oxide nanosheets disrupt lipid composition, Ca2+ homeostasis and synaptic transmission in primary cortical neurons,
Abstract: Graphene has the potential to make a very significant impact on society, with
important applications in the biomedical field. The possibility to engineer
graphene-based medical devices at the neuronal interface is of particular
interest, making it imperative to determine the biocompatibility of graphene
materials with neuronal cells. Here we conducted a comprehensive analysis of
the effects of chronic and acute exposure of rat primary cortical neurons to
few-layers pristine graphene (GR) and monolayer graphene oxide (GO) flakes. By
combining a range of cell biology, microscopy, electrophysiology and omics
approaches we characterized the graphene neuron interaction from the first
steps of membrane contact and internalization to the long-term effects on cell
viability, synaptic transmission and cell metabolism. GR/GO flakes are found in
contact with the neuronal membrane, free in the cytoplasm and internalized
through the endolysosomal pathway, with no significant impact on neuron
viability. However, GO exposure selectively caused the inhibition of excitatory
transmission, paralleled by a reduction in the number of excitatory synaptic
contacts, and a concomitant enhancement of the inhibitory activity. This was
accompanied by induction of autophagy, altered Ca2+ dynamics and by a
downregulation of some of the main players in the regulation of Ca2+
homeostasis in both excitatory and inhibitory neurons. Our results show that,
although graphene exposure does not impact on neuron viability, it does
nevertheless have important effects on neuronal transmission and network
functionality, thus warranting caution when planning to employ this material
for neuro-biological applications. | [
0,
0,
0,
0,
1,
0
] |
Title: Observation of a Modulational Instability in Bose-Einstein condensates,
Abstract: We observe the breakup dynamics of an elongated cloud of condensed $^{85}$Rb
atoms placed in an optical waveguide. The number of localized spatial
components observed in the breakup is compared with the number of solitons
predicted by a plane-wave stability analysis of the nonpolynomial nonlinear
Schrödinger equation, an effective one-dimensional approximation of the
Gross-Pitaevskii equation for cigar-shaped condensates. It is shown that the
numbers predicted from the fastest growing sidebands are consistent with the
experimental data, suggesting that modulational instability is the key
underlying physical mechanism driving the breakup. | [
0,
1,
0,
0,
0,
0
] |
Title: Dynamics of the scenery flow and conical density theorems,
Abstract: Conical density theorems are used in the geometric measure theory to derive
geometric information from given metric information. The idea is to examine how
a measure is distributed in small balls. Finding conditions that guarantee the
measure to be effectively spread out in different directions is a classical
question going back to Besicovitch (1938) and Marstrand (1954). Classically,
conical density theorems deal with the distribution of the Hausdorff measure.
The process of taking blow-ups of a measure around a point induces a natural
dynamical system called the scenery flow. Relying on this dynamics makes it
possible to apply ergodic-theoretical methods to understand the statistical
behavior of tangent measures. This approach was initiated by Furstenberg (1970,
2008) and greatly developed by Hochman (2010). The scenery flow is a
well-suited tool to address problems concerning conical densities.
In this survey, we demonstrate how to develop the ergodic-theoretical
machinery around the scenery flow and use it to study conical density theorems. | [
0,
0,
1,
0,
0,
0
] |
Title: Enhanced mixing in giant impact simulations with a new Lagrangian method,
Abstract: Giant impacts (GIs) are common in the late stage of planet formation. The
Smoothed Particle Hydrodynamics (SPH) method is widely used for simulating the
outcome of such violent collisions, one prominent example being the formation
of the Moon. However, a decade of numerical studies in various areas of
computational astrophysics has shown that the standard formulation of SPH
suffers from several shortcomings such as artificial surface tension and its
tendency to promptly damp turbulent motions on scales much larger than the
physical dissipation scale, both resulting in the suppression of mixing. In
order to quantify how severe these limitations are when modeling GIs we carried
out a comparison of simulations with identical initial conditions performed
with the standard SPH as well as with the novel Lagrangian Meshless Finite Mass
(MFM) method in the GIZMO code. We confirm the lack of mixing between the
impactor and target when SPH is employed, while MFM is capable of driving
vigorous sub-sonic turbulence and leads to significant mixing between the two
bodies. Modern SPH variants with artificial conductivity, a different
formulation of the hydro force or reduced artificial viscosity, do not improve
mixing as significantly. Angular momentum is conserved similarly well in both
methods, but MFM does not suffer from spurious transport induced by artificial
viscosity, resulting in a slightly higher angular momentum of the proto-lunar
disk. Furthermore, SPH initial conditions exhibit an unphysical density
discontinuity at the core-mantle boundary which is easily removed in MFM. | [
0,
1,
0,
0,
0,
0
] |
Title: New Reinforcement Learning Using a Chaotic Neural Network for Emergence of "Thinking" - "Exploration" Grows into "Thinking" through Learning -,
Abstract: Expectation for the emergence of higher functions is getting larger in the
framework of end-to-end reinforcement learning using a recurrent neural
network. However, the emergence of "thinking" that is a typical higher function
is difficult to realize because "thinking" needs non fixed-point, flow-type
attractors with both convergence and transition dynamics. Furthermore, in order
to introduce "inspiration" or "discovery" in "thinking", not completely random
but unexpected transition should be also required.
By analogy to "chaotic itinerancy", we have hypothesized that "exploration"
grows into "thinking" through learning by forming flow-type attractors on
chaotic random-like dynamics. It is expected that if rational dynamics are
learned in a chaotic neural network (ChNN), coexistence of rational state
transition, inspiration-like state transition and also random-like exploration
for unknown situation can be realized.
Based on the above idea, we have proposed new reinforcement learning using a
ChNN as an actor. The positioning of exploration is completely different from
the conventional one. The chaotic dynamics inside the ChNN produces exploration
factors by itself. Since external random numbers for stochastic action
selection are not used, exploration factors cannot be isolated from the output.
Therefore, the learning method is also completely different from the
conventional one.
At each non-feedback connection, one variable named causality trace takes in
and maintains the input through the connection according to the change in its
output. Using the trace and TD error, the weight is updated.
In this paper, as the result of a recent simple task to see whether the new
learning works or not, it is shown that a robot with two wheels and two visual
sensors reaches a target while avoiding an obstacle after learning though there
are still many rooms for improvement. | [
1,
0,
0,
0,
0,
0
] |
Title: Tensorizing Generative Adversarial Nets,
Abstract: Generative Adversarial Network (GAN) and its variants exhibit
state-of-the-art performance in the class of generative models. To capture
higher-dimensional distributions, the common learning procedure requires high
computational complexity and a large number of parameters. The problem of
employing such massive framework arises when deploying it on a platform with
limited computational power such as mobile phones. In this paper, we present a
new generative adversarial framework by representing each layer as a tensor
structure connected by multilinear operations, aiming to reduce the number of
model parameters by a large factor while preserving the generative performance
and sample quality. To learn the model, we employ an efficient algorithm which
alternatively optimizes both discriminator and generator. Experimental outcomes
demonstrate that our model can achieve high compression rate for model
parameters up to $35$ times when compared to the original GAN for MNIST
dataset. | [
1,
0,
0,
1,
0,
0
] |
Title: Design of a Time Delay Reservoir Using Stochastic Logic: A Feasibility Study,
Abstract: This paper presents a stochastic logic time delay reservoir design. The
reservoir is analyzed using a number of metrics, such as kernel quality,
generalization rank, performance on simple benchmarks, and is also compared to
a deterministic design. A novel re-seeding method is introduced to reduce the
adverse effects of stochastic noise, which may also be implemented in other
stochastic logic reservoir computing designs, such as echo state networks.
Benchmark results indicate that the proposed design performs well on
noise-tolerant classification problems, but more work needs to be done to
improve the stochastic logic time delay reservoir's robustness for regression
problems. | [
1,
0,
0,
1,
0,
0
] |
Title: Unsupervised Body Part Regression via Spatially Self-ordering Convolutional Neural Networks,
Abstract: Automatic body part recognition for CT slices can benefit various medical
image applications. Recent deep learning methods demonstrate promising
performance, with the requirement of large amounts of labeled images for
training. The intrinsic structural or superior-inferior slice ordering
information in CT volumes is not fully exploited. In this paper, we propose a
convolutional neural network (CNN) based Unsupervised Body part Regression
(UBR) algorithm to address this problem. A novel unsupervised learning method
and two inter-sample CNN loss functions are presented. Distinct from previous
work, UBR builds a coordinate system for the human body and outputs a
continuous score for each axial slice, representing the normalized position of
the body part in the slice. The training process of UBR resembles a
self-organization process: slice scores are learned from inter-slice
relationships. The training samples are unlabeled CT volumes that are abundant,
thus no extra annotation effort is needed. UBR is simple, fast, and accurate.
Quantitative and qualitative experiments validate its effectiveness. In
addition, we show two applications of UBR in network initialization and anomaly
detection. | [
1,
0,
0,
0,
0,
0
] |
Title: Titanium dioxide hole-blocking layer in ultra-thin-film crystalline silicon solar cells,
Abstract: One of the remaining obstacles to approaching the theoretical efficiency
limit of crystalline silicon (c-Si) solar cells is the exceedingly high
interface recombination loss for minority carriers at the Ohmic contacts. In
ultra-thin-film c-Si solar cells, this contact recombination loss is far more
severe than for traditional thick cells due to the smaller volume and higher
minority carrier concentration of the former. This paper presents a novel
design of an electron passing (Ohmic) contact to n-type Si that is
hole-blocking with significantly reduced hole recombination. This contact is
formed by depositing a thin titanium dioxide (TiO2) layer to form a silicon
metal-insulator-semiconductor (MIS) contact. A 2 {\mu}m thick Si cell with this
TiO2 MIS contact achieved an open circuit voltage (Voc) of 645 mV, which is 10
mV higher than that of an ultra-thin cell with a metal contact. This MIS
contact demonstrates a new path for ultra-thin-film c-Si solar cells to achieve
high efficiencies as high as traditional thick cells, and enables the
fabrication of high-efficiency c-Si solar cells at a lower cost. | [
0,
1,
0,
0,
0,
0
] |
Title: Specification tests in semiparametric transformation models - a multiplier bootstrap approach,
Abstract: We consider semiparametric transformation models, where after pre-estimation
of a parametric transformation of the response the data are modeled by means of
nonparametric regression. We suggest subsequent procedures for testing
lack-of-fit of the regression function and for significance of covariables,
which - in contrast to procedures from the literature - are asymptotically not
influenced by the pre-estimation of the transformation. The test statistics are
asymptotically pivotal and have the same asymptotic distribution as in
regression models without transformation. We show validity of a multiplier
bootstrap procedure which is easier to implement and much less computationally
demanding than bootstrap procedures based on the transformation model. In a
simulation study we demonstrate the superior performance of the procedure in
comparison with the competitors from the literature. | [
0,
0,
1,
1,
0,
0
] |
Title: Driver Drowsiness Estimation from EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR),
Abstract: One big challenge that hinders the transition of brain-computer interfaces
(BCIs) from laboratory settings to real-life applications is the availability
of high-performance and robust learning algorithms that can effectively handle
individual differences, i.e., algorithms that can be applied to a new subject
with zero or very little subject-specific calibration data. Transfer learning
and domain adaptation have been extensively used for this purpose. However,
most previous works focused on classification problems. This paper considers an
important regression problem in BCI, namely, online driver drowsiness
estimation from EEG signals. By integrating fuzzy sets with domain adaptation,
we propose a novel online weighted adaptation regularization for regression
(OwARR) algorithm to reduce the amount of subject-specific calibration data,
and also a source domain selection (SDS) approach to save about half of the
computational cost of OwARR. Using a simulated driving dataset with 15
subjects, we show that OwARR and OwARR-SDS can achieve significantly smaller
estimation errors than several other approaches. We also provide comprehensive
analyses on the robustness of OwARR and OwARR-SDS. | [
1,
0,
0,
0,
0,
0
] |
Title: The complex social network of surnames: A comparison between Brazil and Portugal,
Abstract: We present a study of social networks based on the analysis of Brazilian and
Portuguese family names (surnames). We construct networks whose nodes are names
of families and whose edges represent parental relations between two families.
From these networks we extract the connectivity distribution, clustering
coefficient, shortest path and centrality. We find that the connectivity
distribution follows an approximate power law. We associate the number of hubs,
centrality and entropy to the degree of miscegenation in the societies in both
countries. Our results show that Portuguese society has a higher miscegenation
degree than Brazilian society. All networks analyzed lead to approximate
inverse square power laws in the degree distribution. We conclude that the
thermodynamic limit is reached for small networks (3 or 4 thousand nodes). The
assortative mixing of all networks is negative, showing that the more connected
vertices are connected to vertices with lower connectivity. Finally, the
network of surnames presents some small world characteristics. | [
1,
1,
0,
0,
0,
0
] |
Title: Electrical characterization of structured platinum diselenide devices,
Abstract: Platinum diselenide (PtSe2) is an exciting new member of the two-dimensional
(2D) transition metal dichalcogenide (TMD) family. it has a semimetal to
semiconductor transition when approaching monolayer thickness and has already
shown significant potential for use in device applications. Notably, PtSe2 can
be grown at low temperature making it potentially suitable for industrial
usage. Here, we address thickness dependent transport properties and
investigate electrical contacts to PtSe2, a crucial and universal element of
TMD-based electronic devices. PtSe2 films have been synthesized at various
thicknesses and structured to allow contact engineering and the accurate
extraction of electrical properties. Contact resistivity and sheet resistance
extracted from transmission line method (TLM) measurements are compared for
different contact metals and different PtSe2 film thicknesses. Furthermore, the
transition from semimetal to semiconductor in PtSe2 has been indirectly
verified by electrical characterization of field-effect devices. Finally, the
influence of edge contacts at the metal - PtSe2 interface has been studied by
nanostructuring the contact area using electron beam lithography. By increasing
the edge contact length, the contact resistivity was improved by up to 70%
compared to devices with conventional top contacts. The results presented here
represent crucial steps towards realizing high-performance nanoelectronic
devices based on group-10 TMDs. | [
0,
1,
0,
0,
0,
0
] |
Title: Finger Grip Force Estimation from Video using Two Stream Approach,
Abstract: Estimation of a hand grip force is essential for the understanding of force
pattern during the execution of assembly or disassembly operations. Human
demonstration of a correct way of doing an operation is a powerful source of
information which can be used for guided robot teaching. Typically to assess
this problem instrumented approach is used, which requires hand or object
mounted devices and poses an inconvenience for an operator or limits the scope
of addressable objects. The work demonstrates that contact force may be
estimated using a noninvasive contactless method with the help of vision system
alone. We propose a two-stream approach for video processing, which utilizes
both spatial information of each frame and dynamic information of frame change.
In this work, image processing and machine learning techniques are used along
with dense optical flow for frame change tracking and Kalman filter is used for
stream fusion. Our studies show that the proposed method can successfully
estimate contact grip force with RMSE < 10% of sensor range (RMSE $\approx 0.2$
N), the performances of each stream and overall method performance are
reported. The proposed method has a wide range of applications, including robot
teaching through demonstration, haptic force feedback, and validation of human-
performed operations. | [
1,
0,
0,
0,
0,
0
] |
Title: Markov Properties for Graphical Models with Cycles and Latent Variables,
Abstract: We investigate probabilistic graphical models that allow for both cycles and
latent variables. For this we introduce directed graphs with hyperedges
(HEDGes), generalizing and combining both marginalized directed acyclic graphs
(mDAGs) that can model latent (dependent) variables, and directed mixed graphs
(DMGs) that can model cycles. We define and analyse several different Markov
properties that relate the graphical structure of a HEDG with a probability
distribution on a corresponding product space over the set of nodes, for
example factorization properties, structural equations properties,
ordered/local/global Markov properties, and marginal versions of these. The
various Markov properties for HEDGes are in general not equivalent to each
other when cycles or hyperedges are present, in contrast with the simpler case
of directed acyclic graphical (DAG) models (also known as Bayesian networks).
We show how the Markov properties for HEDGes - and thus the corresponding
graphical Markov models - are logically related to each other. | [
0,
0,
1,
1,
0,
0
] |
Title: A Fast Algorithm for Solving Henderson's Mixed Model Equation,
Abstract: This article investigates a fast and stable method to solve Henderson's mixed
model equation. The proposed algorithm is stable in that it avoids inverting a
matrix of a large dimension and hence is free from the curse of dimensionality.
This tactic is enabled through row operations performed on the design matrix. | [
0,
0,
0,
1,
0,
0
] |
Title: Regularity and stability results for the level set flow via the mean curvature flow with surgery,
Abstract: In this article we us the mean curvature flow with surgery to derive
regularity estimates going past Brakke regularity for the level set flow. We
also show a stability result for the plane under the level set flow. | [
0,
0,
1,
0,
0,
0
] |
Title: Non-canonical Conformal Attractors for Single Field Inflation,
Abstract: We extend the idea of conformal attractors in inflation to non-canonical
sectors by developing a non-canonical conformally invariant theory from two
different approaches. In the first approach, namely, ${\cal N}=1$ supergravity,
the construction is more or less phenomenological, where the non-canonical
kinetic sector is derived from a particular form of the K$\ddot{a}$hler
potential respecting shift symmetry. In the second approach i.e.,
superconformal theory, we derive the form of the Lagrangian from a
superconformal action and it turns out to be exactly of the same form as in the
first approach. Conformal breaking of these theories results in a new class of
non-canonical models which can govern inflation with modulated shape of the
T-models. We further employ this framework to explore inflationary
phenomenology with a representative example and show how the form of the
K$\ddot{a}$hler potential can possibly be constrained in non-canonical models
using the latest confidence contour in the $n_s-r$ plane given by Planck. | [
0,
1,
0,
0,
0,
0
] |
Title: Learning model-based planning from scratch,
Abstract: Conventional wisdom holds that model-based planning is a powerful approach to
sequential decision-making. It is often very challenging in practice, however,
because while a model can be used to evaluate a plan, it does not prescribe how
to construct a plan. Here we introduce the "Imagination-based Planner", the
first model-based, sequential decision-making agent that can learn to
construct, evaluate, and execute plans. Before any action, it can perform a
variable number of imagination steps, which involve proposing an imagined
action and evaluating it with its model-based imagination. All imagined actions
and outcomes are aggregated, iteratively, into a "plan context" which
conditions future real and imagined actions. The agent can even decide how to
imagine: testing out alternative imagined actions, chaining sequences of
actions together, or building a more complex "imagination tree" by navigating
flexibly among the previously imagined states using a learned policy. And our
agent can learn to plan economically, jointly optimizing for external rewards
and computational costs associated with using its imagination. We show that our
architecture can learn to solve a challenging continuous control problem, and
also learn elaborate planning strategies in a discrete maze-solving task. Our
work opens a new direction toward learning the components of a model-based
planning system and how to use them. | [
1,
0,
0,
1,
0,
0
] |
Title: A Simple Reservoir Model of Working Memory with Real Values,
Abstract: The prefrontal cortex is known to be involved in many high-level cognitive
functions, in particular, working memory. Here, we study to what extent a group
of randomly connected units (namely an Echo State Network, ESN) can store and
maintain (as output) an arbitrary real value from a streamed input, i.e. can
act as a sustained working memory unit. Furthermore, we explore to what extent
such an architecture can take advantage of the stored value in order to produce
non-linear computations. Comparison between different architectures (with and
without feedback, with and without a working memory unit) shows that an
explicit memory improves the performances. | [
0,
0,
0,
0,
1,
0
] |
Title: Searching for axion stars and Q-balls with a terrestrial magnetometer network,
Abstract: Light (pseudo-)scalar fields are promising candidates to be the dark matter
in the Universe. Under certain initial conditions in the early Universe and/or
with certain types of self-interactions, they can form compact dark-matter
objects such as axion stars or Q-balls. Direct encounters with such objects can
be searched for by using a global network of atomic magnetometers. It is shown
that for a range of masses and radii not ruled out by existing observations,
the terrestrial encounter rate with axion stars or Q-balls can be sufficiently
high (at least once per year) for a detection. Furthermore, it is shown that a
global network of atomic magnetometers is sufficiently sensitive to
pseudoscalar couplings to atomic spins so that a transit through an axion star
or Q-ball could be detected over a broad range of unexplored parameter space. | [
0,
1,
0,
0,
0,
0
] |
Title: Magnetized strange quark model with Big Rip singularity in $f(R,T)$ gravity,
Abstract: LRS (Locally Rotationally symmetric) Bianchi type-I magnetized strange quark
matter cosmological model have been studied based on $f(R,T)$ gravity. The
exact solutions of the field equations are derived with linearly time varying
deceleration parameter which is consistent with observational data (from SNIa,
BAO and CMB) of standard cosmology. It is observed that the model start with
big bang and ends with a Big Rip. The transition of deceleration parameter from
decelerating phase to accelerating phase with respect to redshift obtained in
our model fits with the recent observational data obtained by Farook et al. in
2017. The well known Hubble parameter $H(z)$ and distance modulus $\mu(z)$ are
discussed with redshift. | [
0,
1,
0,
0,
0,
0
] |
Title: NPC: Neighbors Progressive Competition Algorithm for Classification of Imbalanced Data Sets,
Abstract: Learning from many real-world datasets is limited by a problem called the
class imbalance problem. A dataset is imbalanced when one class (the majority
class) has significantly more samples than the other class (the minority
class). Such datasets cause typical machine learning algorithms to perform
poorly on the classification task. To overcome this issue, this paper proposes
a new approach Neighbors Progressive Competition (NPC) for classification of
imbalanced datasets. Whilst the proposed algorithm is inspired by weighted
k-Nearest Neighbor (k-NN) algorithms, it has major differences from them.
Unlike k- NN, NPC does not limit its decision criteria to a preset number of
nearest neighbors. In contrast, NPC considers progressively more neighbors of
the query sample in its decision making until the sum of grades for one class
is much higher than the other classes. Furthermore, NPC uses a novel method for
grading the training samples to compensate for the imbalance issue. The grades
are calculated using both local and global information. In brief, the
contribution of this paper is an entirely new classifier for handling the
imbalance issue effectively without any manually-set parameters or any need for
expert knowledge. Experimental results compare the proposed approach with five
representative algorithms applied to fifteen imbalanced datasets and illustrate
this algorithms effectiveness. | [
1,
0,
0,
1,
0,
0
] |
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