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Finite temperature disordered bosons in two dimensions | We study phase transitions in a two dimensional weakly interacting Bose gas
in a random potential at finite temperatures. We identify superfluid, normal
fluid, and insulator phases and construct the phase diagram. At T=0 one has a
tricritical point where the three phases coexist. The truncation of the energy
distribution at the trap barrier, which is a generic phenomenon in cold atom
systems, limits the growth of the localization length and in contrast to the
thermodynamic limit the insulator phase is present at any temperature.
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Morphology and properties evolution upon ring-opening polymerization during extrusion of cyclic butylene terephthalate and graphene-related-materials into thermally conductive nanocomposites | In this work, the study of thermal conductivity before and after in-situ
ring-opening polymerization of cyclic butylene terephthalate into poly
(butylene terephthalate) in presence of graphene-related materials (GRM) is
addressed, to gain insight in the modification of nanocomposites morphology
upon polymerization. Five types of GRM were used: one type of graphite
nanoplatelets, two different grades of reduced graphene oxide (rGO) and the
same rGO grades after thermal annealing for 1 hour at 1700°C under vacuum
to reduce their defectiveness. Polymerization of CBT into pCBT, morphology and
nanoparticle organization were investigated by means of differential scanning
calorimetry, electron microscopy and rheology. Electrical and thermal
properties were investigated by means of volumetric resistivity and bulk
thermal conductivity measurement. In particular, the reduction of nanoflake
aspect ratio during ring-opening polymerization was found to have a detrimental
effect on both electrical and thermal conductivities in nanocomposites.
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Financial density forecasts: A comprehensive comparison of risk-neutral and historical schemes | We investigate the forecasting ability of the most commonly used benchmarks
in financial economics. We approach the usual caveats of probabilistic
forecasts studies -small samples, limited models and non-holistic validations-
by performing a comprehensive comparison of 15 predictive schemes during a time
period of over 21 years. All densities are evaluated in terms of their
statistical consistency, local accuracy and forecasting errors. Using a new
composite indicator, the Integrated Forecast Score (IFS), we show that
risk-neutral densities outperform historical-based predictions in terms of
information content. We find that the Variance Gamma model generates the
highest out-of-sample likelihood of observed prices and the lowest predictive
errors, whereas the ARCH-based GJR-FHS delivers the most consistent forecasts
across the entire density range. In contrast, lognormal densities, the Heston
model or the Breeden-Litzenberger formula yield biased predictions and are
rejected in statistical tests.
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Deep Room Recognition Using Inaudible Echos | Recent years have seen the increasing need of location awareness by mobile
applications. This paper presents a room-level indoor localization approach
based on the measured room's echos in response to a two-millisecond single-tone
inaudible chirp emitted by a smartphone's loudspeaker. Different from other
acoustics-based room recognition systems that record full-spectrum audio for up
to ten seconds, our approach records audio in a narrow inaudible band for 0.1
seconds only to preserve the user's privacy. However, the short-time and
narrowband audio signal carries limited information about the room's
characteristics, presenting challenges to accurate room recognition. This paper
applies deep learning to effectively capture the subtle fingerprints in the
rooms' acoustic responses. Our extensive experiments show that a two-layer
convolutional neural network fed with the spectrogram of the inaudible echos
achieve the best performance, compared with alternative designs using other raw
data formats and deep models. Based on this result, we design a RoomRecognize
cloud service and its mobile client library that enable the mobile application
developers to readily implement the room recognition functionality without
resorting to any existing infrastructures and add-on hardware.
Extensive evaluation shows that RoomRecognize achieves 99.7%, 97.7%, 99%, and
89% accuracy in differentiating 22 and 50 residential/office rooms, 19 spots in
a quiet museum, and 15 spots in a crowded museum, respectively. Compared with
the state-of-the-art approaches based on support vector machine, RoomRecognize
significantly improves the Pareto frontier of recognition accuracy versus
robustness against interfering sounds (e.g., ambient music).
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Linguistic Diversities of Demographic Groups in Twitter | The massive popularity of online social media provides a unique opportunity
for researchers to study the linguistic characteristics and patterns of user's
interactions. In this paper, we provide an in-depth characterization of
language usage across demographic groups in Twitter. In particular, we extract
the gender and race of Twitter users located in the U.S. using advanced image
processing algorithms from Face++. Then, we investigate how demographic groups
(i.e. male/female, Asian/Black/White) differ in terms of linguistic styles and
also their interests. We extract linguistic features from 6 categories
(affective attributes, cognitive attributes, lexical density and awareness,
temporal references, social and personal concerns, and interpersonal focus), in
order to identify the similarities and differences in particular writing set of
attributes. In addition, we extract the absolute ranking difference of top
phrases between demographic groups. As a dimension of diversity, we also use
the topics of interest that we retrieve from each user. Our analysis unveils
clear differences in the writing styles (and the topics of interest) of
different demographic groups, with variation seen across both gender and race
lines. We hope our effort can stimulate the development of new studies related
to demographic information in the online space.
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And That's A Fact: Distinguishing Factual and Emotional Argumentation in Online Dialogue | We investigate the characteristics of factual and emotional argumentation
styles observed in online debates. Using an annotated set of "factual" and
"feeling" debate forum posts, we extract patterns that are highly correlated
with factual and emotional arguments, and then apply a bootstrapping
methodology to find new patterns in a larger pool of unannotated forum posts.
This process automatically produces a large set of patterns representing
linguistic expressions that are highly correlated with factual and emotional
language. Finally, we analyze the most discriminating patterns to better
understand the defining characteristics of factual and emotional arguments.
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A conservative sharp-interface method for compressible multi-material flows | In this paper we develop a conservative sharp-interface method dedicated to
simulating multiple compressible fluids. Numerical treatments for a cut cell
shared by more than two materials are proposed. First, we simplify the
interface interaction inside such a cell with a reduced model to avoid explicit
interface reconstruction and complex flux calculation. Second, conservation is
strictly preserved by an efficient conservation correction procedure for the
cut cell. To improve the robustness, a multi-material scale separation model is
developed to consistently remove non-resolved interface scales. In addition,
the multi-resolution method and local time-stepping scheme are incorporated
into the proposed multi-material method to speed up the high-resolution
simulations. Various numerical test cases, including the multi-material shock
tube problem, inertial confinement fusion implosion, triple-point shock
interaction and shock interaction with multi-material bubbles, show that the
method is suitable for a wide range of complex compressible multi-material
flows.
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Complexity of products: the effect of data regularisation | Among several developments, the field of Economic Complexity (EC) has notably
seen the introduction of two new techniques. One is the Bootstrapped Selective
Predictability Scheme (SPSb), which can provide quantitative forecasts of the
Gross Domestic Product of countries. The other, Hidden Markov Model (HMM)
regularisation, denoises the datasets typically employed in the literature. We
contribute to EC along three different directions. First, we prove the
convergence of the SPSb algorithm to a well-known statistical learning
technique known as Nadaraya-Watson Kernel regression. The latter has
significantly lower time complexity, produces deterministic results, and it is
interchangeable with SPSb for the purpose of making predictions. Second, we
study the effects of HMM regularization on the Product Complexity and logPRODY
metrics, for which a model of time evolution has been recently proposed. We
find confirmation for the original interpretation of the logPRODY model as
describing the change in the global market structure of products with new
insights allowing a new interpretation of the Complexity measure, for which we
propose a modification. Third, we explore new effects of regularisation on the
data. We find that it reduces noise, and observe for the first time that it
increases nestedness in the export network adjacency matrix.
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A review on applications of two-dimensional materials in surface enhanced Raman spectroscopy | Two-dimensional (2D) materials, such as graphene and MoS2, have been
attracting wide interest in surface enhancement Raman spectroscopy. This
perspective gives an overview of recent developments in 2D materials'
application in surface enhanced Raman spectroscopy. This review focuses on the
applications of using bare 2D materials and metal/2D material hybrid substrate
for Raman enhancement. The Raman enhancing mechanism of 2D materials will also
be discussed. The progress covered herein shows great promise for widespread
adoption of 2D materials in SERS application.
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A variety of elastic anomalies in orbital-active nearly-itinerant cobalt vanadate spinel | We perform ultrasound velocity measurements on a single crystal of
nearly-metallic spinel Co$_{1.21}$V$_{1.79}$O$_4$ which exhibits a
ferrimagnetic phase transition at $T_C \sim$ 165 K. The experiments reveal a
variety of elastic anomalies in not only the paramagnetic phase above $T_C$ but
also the ferrimagnetic phase below $T_C$, which should be driven by the
nearly-itinerant character of the orbitally-degenerate V 3$d$ electrons. In the
paramagnetic phase above $T_C$, the elastic moduli exhibit
elastic-mode-dependent unusual temperature variations, suggesting the existence
of a dynamic spin-cluster state. Furthermore, above $T_C$, the sensitive
magnetic-field response of the elastic moduli suggests that, with the negative
magnetoresistance, the magnetic-field-enhanced nearly-itinerant character of
the V 3$d$ electrons emerges from the spin-cluster state. This should be
triggered by the inter-V-site interactions acting on the orbitally-degenerate
3$d$ electrons. In the ferrimagnetic phase below $T_C$, the elastic moduli
exhibit distinct anomalies at $T_1\sim$ 95 K and $T_2\sim$ 50 K, with a sign
change of the magnetoresistance at $T_1$ (positive below $T_1$) and an
enhancement of the positive magnetoresistance below $T_2$, respectively. These
observations below $T_C$ suggest the successive occurrence of an orbital glassy
order at $T_1$ and a structural phase transition at $T_2$, where the rather
localized character of the V 3$d$ electrons evolves below $T_1$ and is further
enhanced below $T_2$.
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Anomaly Detection using One-Class Neural Networks | We propose a one-class neural network (OC-NN) model to detect anomalies in
complex data sets. OC-NN combines the ability of deep networks to extract a
progressively rich representation of data with the one-class objective of
creating a tight envelope around normal data. The OC-NN approach breaks new
ground for the following crucial reason: data representation in the hidden
layer is driven by the OC-NN objective and is thus customized for anomaly
detection. This is a departure from other approaches which use a hybrid
approach of learning deep features using an autoencoder and then feeding the
features into a separate anomaly detection method like one-class SVM (OC-SVM).
The hybrid OC-SVM approach is sub-optimal because it is unable to influence
representational learning in the hidden layers. A comprehensive set of
experiments demonstrate that on complex data sets (like CIFAR and GTSRB), OC-NN
performs on par with state-of-the-art methods and outperformed conventional
shallow methods in some scenarios.
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Low Cost, Open-Source Testbed to Enable Full-Sized Automated Vehicle Research | An open-source vehicle testbed to enable the exploration of automation
technologies for road vehicles is presented. The platform hardware and
software, based on the Robot Operating System (ROS), are detailed. Two methods
are discussed for enabling the remote control of a vehicle (in this case, an
electric 2013 Ford Focus). The first approach used digital filtering of
Controller Area Network (CAN) messages. In the case of the test vehicle, this
approach allowed for the control of acceleration from a tap-point on the CAN
bus and the OBD-II port. The second approach, based on the emulation of the
analog output(s) of a vehicle's accelerator pedal, brake pedal, and steering
torque sensors, is more generally applicable and, in the test vehicle, allowed
for the full control vehicle acceleration, braking, and steering. To
demonstrate the utility of the testbed for vehicle automation research, system
identification was performed on the test vehicle and speed and steering
controllers were designed to allow the vehicle to follow a predetermined path.
The resulting system was shown to be differentially flat, and a high level path
following algorithm was developed using the differentially flat properties and
state feedback. The path following algorithm is experimentally validated on the
automation testbed developed in the paper.
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Sub-sampled Cubic Regularization for Non-convex Optimization | We consider the minimization of non-convex functions that typically arise in
machine learning. Specifically, we focus our attention on a variant of trust
region methods known as cubic regularization. This approach is particularly
attractive because it escapes strict saddle points and it provides stronger
convergence guarantees than first- and second-order as well as classical trust
region methods. However, it suffers from a high computational complexity that
makes it impractical for large-scale learning. Here, we propose a novel method
that uses sub-sampling to lower this computational cost. By the use of
concentration inequalities we provide a sampling scheme that gives sufficiently
accurate gradient and Hessian approximations to retain the strong global and
local convergence guarantees of cubically regularized methods. To the best of
our knowledge this is the first work that gives global convergence guarantees
for a sub-sampled variant of cubic regularization on non-convex functions.
Furthermore, we provide experimental results supporting our theory.
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Emergence of Seismic Metamaterials: Current State and Future Perspectives | Following the advent of electromagnetic metamaterials at the turn of the
century, researchers working in other areas of wave physics have translated
concepts of electromagnetic metamaterials to acoustics, elastodynamics, as well
as to heat, mass and light diffusion processes. In elastodynamics, seismic
metamaterials have emerged in the last decade for soft soils structured at the
meter scale, and have been tested thanks to full-scale experiments on holey
soils five years ago. Born in the soil, seismic metamaterials grow
simultaneously on the field of tuned-resonators buried in the soil, around
building's foundations or near the soil-structure's interface, and on the field
of above-surface resonators. In this perspective article, we quickly recall
some research advances made in all these types of seismic metamaterials and we
further dress an inventory of which material parameters can be achieved and
which cannot, notably from the effective medium theory perspective. We finally
envision perspectives on future developments of large scale auxetic
metamaterials for building's foundations, forests of trees for seismic
protection and metamaterial-like transformed urbanism at the city scale.
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Case studies in network community detection | Community structure describes the organization of a network into subgraphs
that contain a prevalence of edges within each subgraph and relatively few
edges across boundaries between subgraphs. The development of
community-detection methods has occurred across disciplines, with numerous and
varied algorithms proposed to find communities. As we present in this Chapter
via several case studies, community detection is not just an "end game" unto
itself, but rather a step in the analysis of network data which is then useful
for furthering research in the disciplinary domain of interest. These
case-study examples arise from diverse applications, ranging from social and
political science to neuroscience and genetics, and we have chosen them to
demonstrate key aspects of community detection and to highlight that community
detection, in practice, should be directed by the application at hand.
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Multi-particle instability in a spin-imbalanced Fermi gas | Weak attractive interactions in a spin-imbalanced Fermi gas induce a
multi-particle instability, binding multiple fermions together. The maximum
binding energy per particle is achieved when the ratio of the number of up- and
down-spin particles in the instability is equal to the ratio of the up- and
down-spin densities of states in momentum at the Fermi surfaces, to utilize the
variational freedom of all available momentum states. We derive this result
using an analytical approach, and verify it using exact diagonalization. The
multi-particle instability extends the Cooper pairing instability of balanced
Fermi gases to the imbalanced case, and could form the basis of a many-body
state, analogously to the construction of the Bardeen-Cooper-Schrieffer theory
of superconductivity out of Cooper pairs.
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Weighted boundedness of maximal functions and fractional Bergman operators | The aim of this paper is to study two-weight norm inequalities for fractional
maximal functions and fractional Bergman operator defined on the upper-half
space. Namely, we characterize those pairs of weights for which these maximal
operators satisfy strong and weak type inequalities. Our characterizations are
in terms of Sawyer and Békollé-Bonami type conditions. We also obtain a
$\Phi$-bump characterization for these maximal functions, where $\Phi$ is a
Orlicz function. As a consequence, we obtain two-weight norm inequalities for
fractional Bergman operators. Finally, we provide some sharp weighted
inequalities for the fractional maximal functions.
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A Framework for Algorithm Stability | We say that an algorithm is stable if small changes in the input result in
small changes in the output. This kind of algorithm stability is particularly
relevant when analyzing and visualizing time-varying data. Stability in general
plays an important role in a wide variety of areas, such as numerical analysis,
machine learning, and topology, but is poorly understood in the context of
(combinatorial) algorithms. In this paper we present a framework for analyzing
the stability of algorithms. We focus in particular on the tradeoff between the
stability of an algorithm and the quality of the solution it computes. Our
framework allows for three types of stability analysis with increasing degrees
of complexity: event stability, topological stability, and Lipschitz stability.
We demonstrate the use of our stability framework by applying it to kinetic
Euclidean minimum spanning trees.
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A Survey of Question Answering for Math and Science Problem | Turing test was long considered the measure for artificial intelligence. But
with the advances in AI, it has proved to be insufficient measure. We can now
aim to mea- sure machine intelligence like we measure human intelligence. One
of the widely accepted measure of intelligence is standardized math and science
test. In this paper, we explore the progress we have made towards the goal of
making a machine smart enough to pass the standardized test. We see the
challenges and opportunities posed by the domain, and note that we are quite
some ways from actually making a system as smart as a even a middle school
scholar.
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Thermal and structural properties of iron at high pressure by molecular dynamics | We investigate the basic thermal, mechanical and structural properties of
body centred cubic iron ($\alpha$-Fe) at several temperatures and positive
loading by means of Molecular Dynamics simulations in conjunction with the
embedded-atom method potential and its modified counterpart one. Computations
of its thermal properties like average energy and density of atoms, transport
sound velocities at finite temperatures and pressures are detailed studied as
well. Moreover, there are suggestions to obtain hexagonal close- packed
structure ($\varepsilon$-phase) of this metal under positive loading. To
demonstrate that, one can increase sufficiently the pressure of simulated
system at several temperature's ranges; these structural changes depend only on
potential type used. The ensuring structures are studied via the pair radial
distribution functions (PRDF) and precise common- neighbour analysis method
(CNA) as well.
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Interacting Fields and Flows: Magnetic Hot Jupiters | We present Magnetohydrodynamic (MHD) simulations of the magnetic interactions
between a solar type star and short period hot Jupiter exoplanets, using the
publicly available MHD code PLUTO. It has been predicted that emission due to
magnetic interactions such as the electron cyclotron maser instability (ECMI)
will be observable. In our simulations, a planetary outflow, due to UV
evaporation of the exoplanets atmosphere, results in the build-up of
circumplanetary material. We predict the ECMI emission and determine that the
emission is prevented from escaping from the system. This is due to the
evaporated material leading to a high plasma frequency in the vicinity of the
planet, which inhibits the ECMI process.
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From which world is your graph? | Discovering statistical structure from links is a fundamental problem in the
analysis of social networks. Choosing a misspecified model, or equivalently, an
incorrect inference algorithm will result in an invalid analysis or even
falsely uncover patterns that are in fact artifacts of the model. This work
focuses on unifying two of the most widely used link-formation models: the
stochastic blockmodel (SBM) and the small world (or latent space) model (SWM).
Integrating techniques from kernel learning, spectral graph theory, and
nonlinear dimensionality reduction, we develop the first statistically sound
polynomial-time algorithm to discover latent patterns in sparse graphs for both
models. When the network comes from an SBM, the algorithm outputs a block
structure. When it is from an SWM, the algorithm outputs estimates of each
node's latent position.
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Topological and Hodge L-Classes of Singular Covering Spaces and Varieties with Trivial Canonical Class | The signature of closed oriented manifolds is well-known to be multiplicative
under finite covers. This fails for Poincaré complexes as examples of C. T.
C. Wall show. We establish the multiplicativity of the signature, and more
generally, the topological L-class, for closed oriented stratified
pseudomanifolds that can be equipped with a middle-perverse Verdier self-dual
complex of sheaves, determined by Lagrangian sheaves along strata of odd
codimension (so-called L-pseudomanifolds). This class of spaces contains all
Witt spaces and thus all pure-dimensional complex algebraic varieties. We apply
this result in proving the Brasselet-Schürmann-Yokura conjecture for normal
complex projective 3-folds with at most canonical singularities, trivial
canonical class and positive irregularity. The conjecture asserts the equality
of topological and Hodge L-class for compact complex algebraic rational
homology manifolds.
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Identities for the shifted harmonic numbers and binomial coefficients | We develop new closed form representations of sums of (n + {\alpha})th
shifted harmonic numbers and reciprocal binomial coefficients in terms of
{\alpha}th shifted harmonic numbers. Some interesting new consequences and
illustrative examples are considered.
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Mechanism of light energy transport in the avian retina | We studied intermediate filaments (IFs) in the retina of the Pied flycatcher
(Ficedula hypoleuca) in the foveolar zone. Single IFs span Müller cells (MC)
lengthwise; cylindrical bundles of IFs (IFBs) appear inside the cone inner
segment (CIS) at the outer limiting membrane (OLM) level. IFBs adjoin the cone
cytoplasmatic membrane, following lengthwise regularly spaced, forming a
skeleton of the CIS, located above the OLM. IFBs follow along the cone outer
segment (COS), with single IFs separating from the IFB, touching and entering
in-between the light-sensitive disks of the cone membrane. We propose a
mechanism of exciton transfer from the inner retinal surface to the visual
pigments in the photoreceptor cells. This includes excitation transfer in
donor-acceptor systems, from the IF donors to the rhodopsin acceptors, with
theoretic efficiency over 80%. This explains high image contrast in fovea and
foveola in daylight, while the classical mechanism that describes Müller
cells as optical lightguides operates in night vision, with loss of resolution
traded for sensitivity. Our theory receives strong confirmation in morphology
and function of the cones and pigment cells. In daylight the lateral surface of
the photosensor disks is blocked from the (scattered or oblique) light by the
pigment cells. Thus the light energy can only get to the cone via intermediate
filaments that absorb photons in the Müller cell endfeet and conduct excitons
to the cone. Thus, the disks are consumed at their lateral surfaces, moving to
the apex of the cone, with new disks produced below. An alternative hypothesis
of direct light passing through the cone with its organelles and hitting the
lowest disk contradicts morphological evidence, as thus all of the other disks
would have no useful function in daylight vision.
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The Molecular Structures of Local Arm and Perseus Arm in the Galactic Region of l=[139.75,149.75]$^\circ$, b=[-5.25,5.25]$^\circ$ | Using the Purple Mountain Observatory Delingha (PMODLH) 13.7 m telescope, we
report a 96-square-degree 12CO/13CO/C18O mapping observation toward the
Galactic region of l = [139.75, 149.75]$^\circ$, b = [-5.25, 5.25]$^\circ$. The
molecular structure of the Local Arm and Perseus Arm are presented. Combining
HI data and part of the Outer Arm results, we obtain that the warp structure of
both atomic and molecular gas is obvious, while the flare structure only exists
in atomic gas in this observing region. In addition, five filamentary giant
molecular clouds on the Perseus Arm are identified. Among them, four are newly
identified. Their relations with the Milky Way large-scale structure are
discussed.
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The Theory is Predictive, but is it Complete? An Application to Human Perception of Randomness | When we test a theory using data, it is common to focus on correctness: do
the predictions of the theory match what we see in the data? But we also care
about completeness: how much of the predictable variation in the data is
captured by the theory? This question is difficult to answer, because in
general we do not know how much "predictable variation" there is in the
problem. In this paper, we consider approaches motivated by machine learning
algorithms as a means of constructing a benchmark for the best attainable level
of prediction.
We illustrate our methods on the task of predicting human-generated random
sequences. Relative to an atheoretical machine learning algorithm benchmark, we
find that existing behavioral models explain roughly 15 percent of the
predictable variation in this problem. This fraction is robust across several
variations on the problem. We also consider a version of this approach for
analyzing field data from domains in which human perception and generation of
randomness has been used as a conceptual framework; these include sequential
decision-making and repeated zero-sum games. In these domains, our framework
for testing the completeness of theories provides a way of assessing their
effectiveness over different contexts; we find that despite some differences,
the existing theories are fairly stable across our field domains in their
performance relative to the benchmark. Overall, our results indicate that (i)
there is a significant amount of structure in this problem that existing models
have yet to capture and (ii) there are rich domains in which machine learning
may provide a viable approach to testing completeness.
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Clustering with Statistical Error Control | This paper presents a clustering approach that allows for rigorous
statistical error control similar to a statistical test. We develop estimators
for both the unknown number of clusters and the clusters themselves. The
estimators depend on a tuning parameter alpha which is similar to the
significance level of a statistical hypothesis test. By choosing alpha, one can
control the probability of overestimating the true number of clusters, while
the probability of underestimation is asymptotically negligible. In addition,
the probability that the estimated clusters differ from the true ones is
controlled. In the theoretical part of the paper, formal versions of these
statements on statistical error control are derived in a standard model setting
with convex clusters. A simulation study and two applications to temperature
and gene expression microarray data complement the theoretical analysis.
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DNA Base Pair Mismatches Induce Structural Changes and Alter the Free Energy Landscape of Base Flip | Double-stranded DNA may contain mismatched base pairs beyond the Watson-Crick
pairs guanine-cytosine and adenine-thymine. Such mismatches bear adverse
consequences for human health. We utilize molecular dynamics and metadynamics
computer simulations to study the equilibrium structure and dynamics for both
matched and mismatched base pairs. We discover significant differences between
matched and mismatched pairs in structure, hydrogen bonding, and base flip work
profiles. Mismatched pairs shift further in the plane normal to the DNA strand
and are more likely to exhibit non-canonical structures, including the e-motif.
We discuss potential implications on mismatch repair enzymes' detection of DNA
mismatches.
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Average sampling and average splines on combinatorial graphs | In the setting of a weighted combinatorial finite or infinite countable graph
$G$ we introduce functional Paley-Wiener spaces $PW_{\omega}(L),\>\omega>0,$
defined in terms of the spectral resolution of the combinatorial Laplace
operator $L$ in the space $L_{2}(G)$. It is shown that functions in certain
$PW_{\omega}(L),\>\omega>0,$ are uniquely defined by their averages over some
families of "small" subgraphs which form a cover of $G$. Reconstruction methods
for reconstruction of an $f\in PW_{\omega}(L)$ from appropriate set of its
averages are introduced. One method is using language of Hilbert frames.
Another one is using average variational interpolating splines which are
constructed in the setting of combinatorial graphs.
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Hilbert isometries and maximal deviation preserving maps on JB-algebras | In this paper we characterize the surjective linear variation norm isometries
on JB-algebras. Variation norm isometries are precisely the maps that preserve
the maximal deviation, the quantum analogue of the standard deviation, which
plays an important role in quantum statistics. Consequently, we characterize
the Hilbert's metric isometries on cones in JB-algebras.
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Visual Entailment: A Novel Task for Fine-Grained Image Understanding | Existing visual reasoning datasets such as Visual Question Answering (VQA),
often suffer from biases conditioned on the question, image or answer
distributions. The recently proposed CLEVR dataset addresses these limitations
and requires fine-grained reasoning but the dataset is synthetic and consists
of similar objects and sentence structures across the dataset.
In this paper, we introduce a new inference task, Visual Entailment (VE) -
consisting of image-sentence pairs whereby a premise is defined by an image,
rather than a natural language sentence as in traditional Textual Entailment
tasks. The goal of a trained VE model is to predict whether the image
semantically entails the text. To realize this task, we build a dataset SNLI-VE
based on the Stanford Natural Language Inference corpus and Flickr30k dataset.
We evaluate various existing VQA baselines and build a model called Explainable
Visual Entailment (EVE) system to address the VE task. EVE achieves up to 71%
accuracy and outperforms several other state-of-the-art VQA based models.
Finally, we demonstrate the explainability of EVE through cross-modal attention
visualizations. The SNLI-VE dataset is publicly available at
this https URL necla-ml/SNLI-VE.
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On relations between weak and strong type inequalities for maximal operators on non-doubling metric measure spaces | In this article we characterize all possible cases that may occur in the
relations between the sets of $p$ for which weak type $(p,p)$ and strong type
$(p,p)$ inequalities for the Hardy--Littlewood maximal operators, both centered
and non-centered, hold in the context of general metric measure spaces.
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Random Caching Based Cooperative Transmission in Heterogeneous Wireless Networks | Base station cooperation in heterogeneous wireless networks (HetNets) is a
promising approach to improve the network performance, but it also imposes a
significant challenge on backhaul. On the other hand, caching at small base
stations (SBSs) is considered as an efficient way to reduce backhaul load in
HetNets. In this paper, we jointly consider SBS caching and cooperation in a
downlink largescale HetNet. We propose two SBS cooperative transmission schemes
under random caching at SBSs with the caching distribution as a design
parameter. Using tools from stochastic geometry and adopting appropriate
integral transformations, we first derive a tractable expression for the
successful transmission probability under each scheme. Then, under each scheme,
we consider the successful transmission probability maximization by optimizing
the caching distribution, which is a challenging optimization problem with a
non-convex objective function. By exploring optimality properties and using
optimization techniques, under each scheme, we obtain a local optimal solution
in the general case and global optimal solutions in some special cases.
Compared with some existing caching designs in the literature, e.g., the most
popular caching, the i.i.d. caching and the uniform caching, the optimal random
caching under each scheme achieves better successful transmission probability
performance. The analysis and optimization results provide valuable design
insights for practical HetNets.
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Electronic characteristics of ultrathin SrRuO$_3$ films and their relationship with the metal$-$insulator transition | SrRuO$_3$ (SRO) films are known to exhibit insulating behavior as their
thickness approaches four unit cells. We employ electron energy$-$loss (EEL)
spectroscopy to probe the spatially resolved electronic structures of both
insulating and conducting SRO to correlate them with the metal$-$insulator
transition (MIT). Importantly, the central layer of the ultrathin insulating
film exhibits distinct features from the metallic SRO. Moreover, EEL near edge
spectra adjacent to the SrTiO$_3$ (STO) substrate or to the capping layer are
remarkably similar to those of STO. The site$-$projected density of states
based on density functional theory (DFT) partially reflects the characteristics
of the spectra of these layers. These results may provide important information
on the possible influence of STO on the electronic states of ultrathin SRO.
| 0 | 1 | 0 | 0 | 0 | 0 |
Solution properties of a 3D stochastic Euler fluid equation | We prove local well-posedness in regular spaces and a Beale-Kato-Majda
blow-up criterion for a recently derived stochastic model of the 3D Euler fluid
equation for incompressible flow. This model describes incompressible fluid
motions whose Lagrangian particle paths follow a stochastic process with
cylindrical noise and also satisfy Newton's 2nd Law in every Lagrangian domain.
| 0 | 1 | 1 | 0 | 0 | 0 |
Existence of locally maximally entangled quantum states via geometric invariant theory | We study a question which has natural interpretations in both quantum
mechanics and in geometry. Let $V_1,..., V_n$ be complex vector spaces of
dimension $d_1,...,d_n$ and let $G= SL_{d_1} \times \dots \times SL_{d_n}$.
Geometrically, we ask given $(d_1,...,d_n)$, when is the geometric invariant
theory quotient $\mathbb{P}(V_1 \otimes \dots \otimes V_n)// G$ non-empty? This
is equivalent to the quantum mechanical question of whether the multipart
quantum system with Hilbert space $V_1\otimes \dots \otimes V_n$ has a locally
maximally entangled state, i.e. a state such that the density matrix for each
elementary subsystem is a multiple of the identity. We show that the answer to
this question is yes if and only if $R(d_1,...,d_n)\geqslant 0$ where \[
R(d_1,...,d_n) = \prod_i d_i +\sum_{k=1}^n (-1)^k \sum_{1\leq i_1<\dotsb
<i_k\leq n} (\gcd(d_{i_1},\dotsc ,d_{i_k}) )^{2}. \] We also provide a simple
recursive algorithm which determines the answer to the question, and we compute
the dimension of the resulting quotient in the non-empty cases.
| 0 | 0 | 1 | 0 | 0 | 0 |
Convergence of the Expectation-Maximization Algorithm Through Discrete-Time Lyapunov Stability Theory | In this paper, we propose a dynamical systems perspective of the
Expectation-Maximization (EM) algorithm. More precisely, we can analyze the EM
algorithm as a nonlinear state-space dynamical system. The EM algorithm is
widely adopted for data clustering and density estimation in statistics,
control systems, and machine learning. This algorithm belongs to a large class
of iterative algorithms known as proximal point methods. In particular, we
re-interpret limit points of the EM algorithm and other local maximizers of the
likelihood function it seeks to optimize as equilibria in its dynamical system
representation. Furthermore, we propose to assess its convergence as asymptotic
stability in the sense of Lyapunov. As a consequence, we proceed by leveraging
recent results regarding discrete-time Lyapunov stability theory in order to
establish asymptotic stability (and thus, convergence) in the dynamical system
representation of the EM algorithm.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Non-monotone Alternating Updating Method for A Class of Matrix Factorization Problems | In this paper we consider a general matrix factorization model which covers a
large class of existing models with many applications in areas such as machine
learning and imaging sciences. To solve this possibly nonconvex, nonsmooth and
non-Lipschitz problem, we develop a non-monotone alternating updating method
based on a potential function. Our method essentially updates two blocks of
variables in turn by inexactly minimizing this potential function, and updates
another auxiliary block of variables using an explicit formula. The special
structure of our potential function allows us to take advantage of efficient
computational strategies for non-negative matrix factorization to perform the
alternating minimization over the two blocks of variables. A suitable line
search criterion is also incorporated to improve the numerical performance.
Under some mild conditions, we show that the line search criterion is well
defined, and establish that the sequence generated is bounded and any cluster
point of the sequence is a stationary point. Finally, we conduct some numerical
experiments using real datasets to compare our method with some existing
efficient methods for non-negative matrix factorization and matrix completion.
The numerical results show that our method can outperform these methods for
these specific applications.
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Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions | Error bound conditions (EBC) are properties that characterize the growth of
an objective function when a point is moved away from the optimal set. They
have recently received increasing attention in the field of optimization for
developing optimization algorithms with fast convergence. However, the studies
of EBC in statistical learning are hitherto still limited. The main
contributions of this paper are two-fold. First, we develop fast and
intermediate rates of empirical risk minimization (ERM) under EBC for risk
minimization with Lipschitz continuous, and smooth convex random functions.
Second, we establish fast and intermediate rates of an efficient stochastic
approximation (SA) algorithm for risk minimization with Lipschitz continuous
random functions, which requires only one pass of $n$ samples and adapts to
EBC. For both approaches, the convergence rates span a full spectrum between
$\widetilde O(1/\sqrt{n})$ and $\widetilde O(1/n)$ depending on the power
constant in EBC, and could be even faster than $O(1/n)$ in special cases for
ERM. Moreover, these convergence rates are automatically adaptive without using
any knowledge of EBC. Overall, this work not only strengthens the understanding
of ERM for statistical learning but also brings new fast stochastic algorithms
for solving a broad range of statistical learning problems.
| 0 | 0 | 0 | 1 | 0 | 0 |
Combining the $k$-CNF and XOR Phase-Transitions | The runtime performance of modern SAT solvers on random $k$-CNF formulas is
deeply connected with the 'phase-transition' phenomenon seen empirically in the
satisfiability of random $k$-CNF formulas. Recent universal hashing-based
approaches to sampling and counting crucially depend on the runtime performance
of SAT solvers on formulas expressed as the conjunction of both $k$-CNF and XOR
constraints (known as $k$-CNF-XOR formulas), but the behavior of random
$k$-CNF-XOR formulas is unexplored in prior work. In this paper, we present the
first study of the satisfiability of random $k$-CNF-XOR formulas. We show
empirical evidence of a surprising phase-transition that follows a linear
trade-off between $k$-CNF and XOR constraints. Furthermore, we prove that a
phase-transition for $k$-CNF-XOR formulas exists for $k = 2$ and (when the
number of $k$-CNF constraints is small) for $k > 2$.
| 1 | 0 | 0 | 0 | 0 | 0 |
Advertising and Brand Attitudes: Evidence from 575 Brands over Five Years | Little is known about how different types of advertising affect brand
attitudes. We investigate the relationships between three brand attitude
variables (perceived quality, perceived value and recent satisfaction) and
three types of advertising (national traditional, local traditional and
digital). The data represent ten million brand attitude surveys and $264
billion spent on ads by 575 regular advertisers over a five-year period,
approximately 37% of all ad spend measured between 2008 and 2012. Inclusion of
brand/quarter fixed effects and industry/week fixed effects brings parameter
estimates closer to expectations without major reductions in estimation
precision. The findings indicate that (i) national traditional ads increase
perceived quality, perceived value, and recent satisfaction; (ii) local
traditional ads increase perceived quality and perceived value; (iii) digital
ads increase perceived value; and (iv) competitor ad effects are generally
negative.
| 0 | 0 | 0 | 0 | 0 | 1 |
NMR studies of the topological insulator Bi2Te3 | Te NMR studies were carried out for the bismuth telluride topological
insulator in a wide range from room temperature down to 12.5 K. The
measurements were made on a Bruker Avance 400 pulse spectrometer. The NMR
spectra were collected for the mortar and pestle powder sample and for single
crystalline stacks with orientations c parallel and perpendicular to field. The
activation energy responsible for thermal activation. The spectra for the stack
with c parallel to field showed some particular behavior below 91 K.
| 0 | 1 | 0 | 0 | 0 | 0 |
The Gibbons-Hawking ansatz over a wedge | We discuss the Ricci-flat `model metrics' on $\mathbb{C}^2$ with cone
singularities along the conic $\{zw=1\}$ constructed by Donaldson using the
Gibbons-Hawking ansatz over wedges in $\mathbb{R}^3$. In particular we describe
their asymptotic behavior at infinity and compute their energies.
| 0 | 0 | 1 | 0 | 0 | 0 |
Beacon-referenced Mutual Pursuit in Three Dimensions | Motivated by station-keeping applications in various unmanned settings, this
paper introduces a steering control law for a pair of agents operating in the
vicinity of a fixed beacon in a three-dimensional environment. This feedback
law is a modification of the previously studied three-dimensional constant
bearing (CB) pursuit law, in the sense that it incorporates an additional term
to allocate attention to the beacon. We investigate the behavior of the
closed-loop dynamics for a two agent mutual pursuit system in which each agent
employs the beacon-referenced CB pursuit law with regards to the other agent
and a stationary beacon. Under certain assumptions on the associated control
parameters, we demonstrate that this problem admits circling equilibria wherein
the agents move on circular orbits with a common radius, in planes
perpendicular to a common axis passing through the beacon. As the common radius
and distances from the beacon are determined by choice of parameters in the
feedback law, this approach provides a means to engineer desired formations in
a three-dimensional setting.
| 1 | 0 | 0 | 0 | 0 | 0 |
Increased stability of CuZrAl metallic glasses prepared by physical vapor deposition | We carried out molecular dynamics simulations (MD) using realistic empirical
potentials for the vapor deposition (VD) of CuZrAl glasses. VD glasses have
higher densities and lower potential and inherent structure energies than the
melt-quenched glasses for the same alloys. The optimal substrate temperature
for the deposition process is 0.625$\times T_\mathrm{g}$. In VD metallic
glasses (MGs), the total number of icosahedral like clusters is higher than in
the melt-quenched MGs. Surprisingly, the VD glasses have a lower degree of
chemical mixing than the melt-quenched glasses. The reason for it is that the
melt-quenched MGs can be viewed as frozen liquids, which means that their
chemical order is the same as in the liquid state. In contrast, during the
formation of the VD MGs, the absence of the liquid state results in the
creation of a different chemical order with more Zr-Zr homonuclear bonds
compared with the melt-quenched MGs. In order to obtain MGs from melt-quench
technique with similarly low energies as in the VD process, the cooling rate
during quenching would have to be many orders of magnitude lower than currently
accessible to MD simulations. The method proposed in this manuscript is a more
efficient way to create MGs by using MD simulations.
| 0 | 1 | 0 | 0 | 0 | 0 |
A geometric realization of the $m$-cluster categories of type $\tilde{D_n}$ | We show that a subcategory of the $m$-cluster category of type $\tilde{D_n}$
is isomorphic to a category consisting of arcs in an $(n-2)m$-gon with two
central $(m-1)$-gons inside of it. We show that the mutation of colored quivers
and $m$-cluster-tilting objects is compatible with the flip of an
$(m+2)$-angulation. In the final part of this paper, we detail an example of a
quiver of type $\tilde{D_7}$.
| 0 | 0 | 1 | 0 | 0 | 0 |
The SoLid anti-neutrino detector's readout system | The SoLid collaboration have developed an intelligent readout system to
reduce their 3200 silicon photomultiplier detector's data rate by a factor of
10000 whilst maintaining high efficiency for storing data from anti-neutrino
interactions. The system employs an FPGA-level waveform characterisation to
trigger on neutron signals. Following a trigger, data from a space time region
of interest around the neutron will be read out using the IPbus protocol. In
these proceedings the design of the readout system is explained and results
showing the performance of a prototype version of the system are presented.
| 0 | 1 | 0 | 0 | 0 | 0 |
Data hiding in Fingerprint Minutiae Template for Privacy Protection | In this paper, we propose a novel scheme for data hiding in the fingerprint
minutiae template, which is the most popular in fingerprint recognition
systems. Various strategies are proposed in data embedding in order to maintain
the accuracy of fingerprint recognition as well as the undetectability of data
hiding. In bits replacement based data embedding, we replace the last few bits
of each element of the original minutiae template with the data to be hidden.
This strategy can be further improved using an optimized bits replacement based
data embedding, which is able to minimize the impact of data hiding on the
performance of fingerprint recognition. The third strategy is an order
preserving mechanism which is proposed to reduce the detectability of data
hiding. By using such a mechanism, it would be difficult for the attacker to
differentiate the minutiae template with hidden data from the original minutiae
templates. The experimental results show that the proposed data hiding scheme
achieves sufficient capacity for hiding common personal data, where the
accuracy of fingerprint recognition is acceptable after the data hiding.
| 1 | 0 | 0 | 0 | 0 | 0 |
On asymptotic normality of certain linear rank statistics | We consider asymptotic normality of linear rank statistics under various
randomization rules met in clinical trials and designed for patients'
allocation into treatment and placebo arms. Exposition relies on some general
limit theorem due to McLeish (1974) which appears to be well suited for the
problem considered and may be employed for other similar rules undis- cussed in
the paper. Examples of applications include well known results as well as
several new ones.
| 0 | 0 | 1 | 1 | 0 | 0 |
Computations with p-adic numbers | This document contains the notes of a lecture I gave at the "Journées
Nationales du Calcul Formel" (JNCF) on January 2017. The aim of the lecture was
to discuss low-level algorithmics for p-adic numbers. It is divided into two
main parts: first, we present various implementations of p-adic numbers and
compare them and second, we introduce a general framework for studying
precision issues and apply it in several concrete situations.
| 1 | 0 | 1 | 0 | 0 | 0 |
A novel procedure for the identification of chaos in complex biological systems | We demonstrate the presence of chaos in stochastic simulations that are
widely used to study biodiversity in nature. The investigation deals with a set
of three distinct species that evolve according to the standard rules of
mobility, reproduction and predation, with predation following the cyclic rules
of the popular rock, paper and scissors game. The study uncovers the
possibility to distinguish between time evolutions that start from slightly
different initial states, guided by the Hamming distance which heuristically
unveils the chaotic behavior. The finding opens up a quantitative approach that
relates the correlation length to the average density of maxima of a typical
species, and an ensemble of stochastic simulations is implemented to support
the procedure. The main result of the work shows how a single and simple
experimental realization that counts the density of maxima associated with the
chaotic evolution of the species serves to infer its correlation length. We use
the result to investigate others distinct complex systems, one dealing with a
set of differential equations that can be used to model a diversity of natural
and artificial chaotic systems, and another one, focusing on the ocean water
level.
| 0 | 1 | 0 | 0 | 0 | 0 |
DeepStory: Video Story QA by Deep Embedded Memory Networks | Question-answering (QA) on video contents is a significant challenge for
achieving human-level intelligence as it involves both vision and language in
real-world settings. Here we demonstrate the possibility of an AI agent
performing video story QA by learning from a large amount of cartoon videos. We
develop a video-story learning model, i.e. Deep Embedded Memory Networks
(DEMN), to reconstruct stories from a joint scene-dialogue video stream using a
latent embedding space of observed data. The video stories are stored in a
long-term memory component. For a given question, an LSTM-based attention model
uses the long-term memory to recall the best question-story-answer triplet by
focusing on specific words containing key information. We trained the DEMN on a
novel QA dataset of children's cartoon video series, Pororo. The dataset
contains 16,066 scene-dialogue pairs of 20.5-hour videos, 27,328 fine-grained
sentences for scene description, and 8,913 story-related QA pairs. Our
experimental results show that the DEMN outperforms other QA models. This is
mainly due to 1) the reconstruction of video stories in a scene-dialogue
combined form that utilize the latent embedding and 2) attention. DEMN also
achieved state-of-the-art results on the MovieQA benchmark.
| 1 | 0 | 0 | 0 | 0 | 0 |
Optimisation de la QoS dans un r{é}seau de radio cognitive en utilisant la m{é}taheuristique SFLA (Shuffled Frog Leaping Algorithm) | This work proposes a study of quality of service (QoS) in cognitive radio
networks. This study is based on a stochastic optimization method called
shuffled frog leaping algorithm (SFLA). The interest of the SFLA algorithm is
to guarantee a better solution in a multi-carrier context in order to satisfy
the requirements of the secondary user (SU).
| 1 | 0 | 0 | 0 | 0 | 0 |
Some remarks on Kuratowski partitions | We introduce the notion of $K$-ideals associated with Kuratowski partitions
and we prove that each $\kappa$-complete ideal on a measurable cardinal
$\kappa$ can be represented as a $K$-ideal. Moreover, we show some results
concerning precipitous and Fréchet ideals.
| 0 | 0 | 1 | 0 | 0 | 0 |
Boosted nonparametric hazards with time-dependent covariates | Given functional data samples from a survival process with time dependent
covariates, we propose a practical boosting procedure for estimating its hazard
function nonparametrically. The estimator is consistent if the model is
correctly specified; alternatively an oracle inequality can be demonstrated for
tree-based models. To avoid overfitting, boosting employs several
regularization devices. One of them is step-size restriction, but the rationale
for this is somewhat mysterious from the viewpoint of consistency. Our
convergence bounds bring some clarity to this issue by revealing that step-size
restriction is a mechanism for preventing the curvature of the risk from
derailing convergence. We use our boosting procedure to shed new light on a
question from the operations literature concerning the effect of workload on
service rates in an emergency department.
| 0 | 0 | 0 | 1 | 0 | 0 |
$J^+$-like invariants of periodic orbits of the second kind in the restricted three body problem | We determine three invariants: Arnold's $J^+$-invariant as well as
$\mathcal{J}_1$ and $\mathcal{J}_2$ invariants, which were introduced by
Cieliebak-Frauenfelder-van Koert, of periodic orbits of the second kind near
the heavier primary in the restricted three-body problem, provided that the
mass ratio is sufficiently small.
| 0 | 0 | 1 | 0 | 0 | 0 |
Nonlinear Calderón-Zygmund inequalities for maps | Being motivated by the problem of deducing $L^p$-bounds on the second
fundamental form of an isometric immersion from $L^p$-bounds on its mean
curvature vector field, we prove a (nonlinear) Calderón-Zygmund inequality
for maps between complete (possibly noncompact) Riemannian manifolds.
| 0 | 0 | 1 | 0 | 0 | 0 |
Comparing multiple networks using the Co-expression Differential Network Analysis (CoDiNA) | Biomedical sciences are increasingly recognising the relevance of gene
co-expression-networks for analysing complex-systems, phenotypes or diseases.
When the goal is investigating complex-phenotypes under varying conditions, it
comes naturally to employ comparative network methods. While approaches for
comparing two networks exist, this is not the case for multiple networks. Here
we present a method for the systematic comparison of an unlimited number of
networks: Co-expression Differential Network Analysis (CoDiNA) for detecting
links and nodes that are common, specific or different to the networks.
Applying CoDiNA to a neurogenesis study identified genes for neuron
differentiation. Experimentally overexpressing one candidate resulted in
significant disturbance in the underlying neurogenesis' gene regulatory
network. We compared data from adults and children with active tuberculosis to
test for signatures of HIV. We also identified common and distinct network
features for particular cancer types with CoDiNA. These studies show that
CoDiNA successfully detects genes associated with the diseases.
| 0 | 0 | 0 | 1 | 1 | 0 |
Classical and Quantum Factors of Channels | Given a classical channel, a stochastic map from inputs to outputs, can we
replace the input with a simple intermediate variable that still yields the
correct conditional output distribution? We examine two cases: first, when the
intermediate variable is classical; second, when the intermediate variable is
quantum. We show that the quantum variable's size is generically smaller than
the classical, according to two different measures---cardinality and entropy.
We demonstrate optimality conditions for a special case. We end with several
related results: a proposal for extending the special case, a demonstration of
the impact of quantum phases, and a case study concerning pure versus mixed
states.
| 0 | 0 | 0 | 1 | 0 | 0 |
Learning Intrinsic Sparse Structures within Long Short-Term Memory | Model compression is significant for the wide adoption of Recurrent Neural
Networks (RNNs) in both user devices possessing limited resources and business
clusters requiring quick responses to large-scale service requests. This work
aims to learn structurally-sparse Long Short-Term Memory (LSTM) by reducing the
sizes of basic structures within LSTM units, including input updates, gates,
hidden states, cell states and outputs. Independently reducing the sizes of
basic structures can result in inconsistent dimensions among them, and
consequently, end up with invalid LSTM units. To overcome the problem, we
propose Intrinsic Sparse Structures (ISS) in LSTMs. Removing a component of ISS
will simultaneously decrease the sizes of all basic structures by one and
thereby always maintain the dimension consistency. By learning ISS within LSTM
units, the obtained LSTMs remain regular while having much smaller basic
structures. Based on group Lasso regularization, our method achieves 10.59x
speedup without losing any perplexity of a language modeling of Penn TreeBank
dataset. It is also successfully evaluated through a compact model with only
2.69M weights for machine Question Answering of SQuAD dataset. Our approach is
successfully extended to non- LSTM RNNs, like Recurrent Highway Networks
(RHNs). Our source code is publicly available at
this https URL
| 1 | 0 | 0 | 0 | 0 | 0 |
Electron Acceleration Mechanisms in Thunderstorms | Thunderstorms produce strong electric fields over regions on the order of
kilometer. The corresponding electric potential differences are on the order of
100 MV. Secondary cosmic rays reaching these regions may be significantly
accelerated and even amplified in relativistic runaway avalanche processes.
These phenomena lead to enhancements of the high-energy background radiation
observed by detectors on the ground and on board aircraft. Moreover, intense
submillisecond gamma-ray bursts named terrestrial gamma-ray flashes (TGFs)
produced in thunderstorms are detected from low Earth orbit satellites. When
passing through the atmosphere, these gamma-rays are recognized to produce
secondary relativistic electrons and positrons rapidly trapped in the
geomagnetic field and injected into the near-Earth space environment. In the
present work, we attempt to give an overview of the current state of research
on high-energy phenomena associated with thunderstorms.
| 0 | 1 | 0 | 0 | 0 | 0 |
Consistent estimation in Cox proportional hazards model with measurement errors and unbounded parameter set | Cox proportional hazards model with measurement error is investigated. In
Kukush et al. (2011) [Journal of Statistical Research 45, 77-94] and Chimisov
and Kukush (2014) [Modern Stochastics: Theory and Applications 1, 13-32]
asymptotic properties of simultaneous estimator $\lambda_n(\cdot)$, $\beta_n$
were studied for baseline hazard rate $\lambda(\cdot)$ and regression parameter
$\beta$, at that the parameter set $\Theta=\Theta_{\lambda}\times
\Theta_{\beta}$ was assumed bounded. In the present paper, the set
$\Theta_{\lambda}$ is unbounded from above and not separated away from $0$. We
construct the estimator in two steps: first we derive a strongly consistent
estimator and then modify it to provide its asymptotic normality.
| 0 | 0 | 1 | 1 | 0 | 0 |
Comparative Efficiency of Altruism and Egoism as Voting Strategies in Stochastic Environment | In this paper, we study the efficiency of egoistic and altruistic strategies
within the model of social dynamics determined by voting in a stochastic
environment (the ViSE model) using two criteria: maximizing the average capital
increment and minimizing the number of bankrupt participants. The proposals are
generated stochastically; three families of the corresponding distributions are
considered: normal distributions, symmetrized Pareto distributions, and
Student's $t$-distributions. It is found that the "pit of losses" paradox
described earlier does not occur in the case of heavy-tailed distributions. The
egoistic strategy better protects agents from extinction in aggressive
environments than the altruistic ones, however, the efficiency of altruism is
higher in more favorable environments. A comparison of altruistic strategies
with each other shows that in aggressive environments, everyone should be
supported to minimize extinction, while under more favorable conditions, it is
more efficient to support the weakest participants. Studying the dynamics of
participants' capitals we identify situations where the two considered criteria
contradict each other. At the next stage of the study, combined voting
strategies and societies involving participants with selfish and altruistic
strategies will be explored.
| 1 | 0 | 0 | 0 | 0 | 0 |
Learning End-to-end Autonomous Driving using Guided Auxiliary Supervision | Learning to drive faithfully in highly stochastic urban settings remains an
open problem. To that end, we propose a Multi-task Learning from Demonstration
(MT-LfD) framework which uses supervised auxiliary task prediction to guide the
main task of predicting the driving commands. Our framework involves an
end-to-end trainable network for imitating the expert demonstrator's driving
commands. The network intermediately predicts visual affordances and action
primitives through direct supervision which provide the aforementioned
auxiliary supervised guidance. We demonstrate that such joint learning and
supervised guidance facilitates hierarchical task decomposition, assisting the
agent to learn faster, achieve better driving performance and increases
transparency of the otherwise black-box end-to-end network. We run our
experiments to validate the MT-LfD framework in CARLA, an open-source urban
driving simulator. We introduce multiple non-player agents in CARLA and induce
temporal noise in them for realistic stochasticity.
| 1 | 0 | 0 | 1 | 0 | 0 |
Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability | Anomaly detection (AD) task corresponds to identifying the true anomalies
from a given set of data instances. AD algorithms score the data instances and
produce a ranked list of candidate anomalies, which are then analyzed by a
human to discover the true anomalies. However, this process can be laborious
for the human analyst when the number of false-positives is very high.
Therefore, in many real-world AD applications including computer security and
fraud prevention, the anomaly detector must be configurable by the human
analyst to minimize the effort on false positives.
In this paper, we study the problem of active learning to automatically tune
ensemble of anomaly detectors to maximize the number of true anomalies
discovered. We make four main contributions towards this goal. First, we
present an important insight that explains the practical successes of AD
ensembles and how ensembles are naturally suited for active learning. Second,
we present several algorithms for active learning with tree-based AD ensembles.
These algorithms help us to improve the diversity of discovered anomalies,
generate rule sets for improved interpretability of anomalous instances, and
adapt to streaming data settings in a principled manner. Third, we present a
novel algorithm called GLocalized Anomaly Detection (GLAD) for active learning
with generic AD ensembles. GLAD allows end-users to retain the use of simple
and understandable global anomaly detectors by automatically learning their
local relevance to specific data instances using label feedback. Fourth, we
present extensive experiments to evaluate our insights and algorithms. Our
results show that in addition to discovering significantly more anomalies than
state-of-the-art unsupervised baselines, our active learning algorithms under
the streaming-data setup are competitive with the batch setup.
| 1 | 0 | 0 | 1 | 0 | 0 |
Accuracy of parameterized proton range models; a comparison | An accurate calculation of proton ranges in phantoms or detector geometries
is crucial for decision making in proton therapy and proton imaging. To this
end, several parameterizations of the range-energy relationship exist, with
different levels of complexity and accuracy. In this study we compare the
accuracy four different parameterizations models: Two analytical models derived
from the Bethe equation, and two different interpolation schemes applied to
range-energy tables. In conclusion, a spline interpolation scheme yields the
highest reproduction accuracy, while the shape of the energy loss-curve is best
reproduced with the differentiated Bragg-Kleeman equation.
| 0 | 1 | 0 | 0 | 0 | 0 |
On the connectivity of level sets of automorphisms of free groups, with applications to decision problems | We show that the level sets of automorphisms of free groups with respect to
the Lipschitz metric are connected as subsets of Culler-Vogtmann space. In fact
we prove our result in a more general setting of deformation spaces. As
applications, we give metric solutions of the conjugacy problem for irreducible
automorphisms and the detection of reducibility. We additionally prove
technical results that may be of independent interest --- such as the fact that
the set of displacements is well ordered.
| 0 | 0 | 1 | 0 | 0 | 0 |
Learning a Code: Machine Learning for Approximate Non-Linear Coded Computation | Machine learning algorithms are typically run on large scale, distributed
compute infrastructure that routinely face a number of unavailabilities such as
failures and temporary slowdowns. Adding redundant computations using
coding-theoretic tools called "codes" is an emerging technique to alleviate the
adverse effects of such unavailabilities. A code consists of an encoding
function that proactively introduces redundant computation and a decoding
function that reconstructs unavailable outputs using the available ones. Past
work focuses on using codes to provide resilience for linear computations and
specific iterative optimization algorithms. However, computations performed for
a variety of applications including inference on state-of-the-art machine
learning algorithms, such as neural networks, typically fall outside this
realm. In this paper, we propose taking a learning-based approach to designing
codes that can handle non-linear computations. We present carefully designed
neural network architectures and a training methodology for learning encoding
and decoding functions that produce approximate reconstructions of unavailable
computation results. We present extensive experimental results demonstrating
the effectiveness of the proposed approach: we show that the our learned codes
can accurately reconstruct $64 - 98\%$ of the unavailable predictions from
neural-network based image classifiers on the MNIST, Fashion-MNIST, and
CIFAR-10 datasets. To the best of our knowledge, this work proposes the first
learning-based approach for designing codes, and also presents the first
coding-theoretic solution that can provide resilience for any non-linear
(differentiable) computation. Our results show that learning can be an
effective technique for designing codes, and that learned codes are a highly
promising approach for bringing the benefits of coding to non-linear
computations.
| 0 | 0 | 0 | 1 | 0 | 0 |
Detection via simultaneous trajectory estimation and long time integration | In this work, we consider the detection of manoeuvring small objects with
radars. Such objects induce low signal to noise ratio (SNR) reflections in the
received signal. We consider both co-located and separated transmitter/receiver
pairs, i.e., mono-static and bi-static configurations, respectively, as well as
multi-static settings involving both types. We propose a detection approach
which is capable of coherently integrating these reflections within a coherent
processing interval (CPI) in all these configurations and continuing
integration for an arbitrarily long time across consecutive CPIs. We estimate
the complex value of the reflection coefficients for integration while
simultaneously estimating the object trajectory. Compounded with this is the
estimation of the unknown time reference shift of the separated transmitters
necessary for coherent processing. Detection is made by using the resulting
integration value in a Neyman-Pearson test against a constant false alarm rate
threshold. We demonstrate the efficacy of our approach in a simulation example
with a very low SNR object which cannot be detected with conventional
techniques.
| 1 | 0 | 0 | 1 | 0 | 0 |
Dust Growth and Magnetic Fields: from Cores to Disks (even down to Planets) | The recent rapid progress in observations of circumstellar disks and
extrasolar planets has reinforced the importance of understanding an intimate
coupling between star and planet formation. Under such a circumstance, it may
be invaluable to attempt to specify when and how planet formation begins in
star-forming regions and to identify what physical processes/quantities are the
most significant to make a link between star and planet formation. To this end,
we have recently developed a couple of projects. These include an observational
project about dust growth in Class 0 YSOs and a theoretical modeling project of
the HL Tauri disk. For the first project, we utilize the archive data of radio
interferometric observations, and examine whether dust growth, a first step of
planet formation, occurs in Class 0 YSOs. We find that while our observational
results can be reproduced by the presence of large ($\sim$ mm) dust grains for
some of YSOs under the single-component modified blackbody formalism, an
interpretation of no dust growth would be possible when a more detailed model
is used. For the second project, we consider an origin of the disk
configuration around HL Tauri, focusing on magnetic fields. We find that
magnetically induced disk winds may play an important role in the HL Tauri
disk. The combination of these attempts may enable us to move towards a
comprehensive understanding of how star and planet formation are intimately
coupled with each other.
| 0 | 1 | 0 | 0 | 0 | 0 |
Economic Design of Memory-Type Control Charts: The Fallacy of the Formula Proposed by Lorenzen and Vance (1986) | The memory-type control charts, such as EWMA and CUSUM, are powerful tools
for detecting small quality changes in univariate and multivariate processes.
Many papers on economic design of these control charts use the formula proposed
by Lorenzen and Vance (1986) [Lorenzen, T. J., & Vance, L. C. (1986). The
economic design of control charts: A unified approach. Technometrics, 28(1),
3-10, DOI: 10.2307/1269598]. This paper shows that this formula is not correct
for memory-type control charts and its values can significantly deviate from
the original values even if the ARL values used in this formula are accurately
computed. Consequently, the use of this formula can result in charts that are
not economically optimal. The formula is corrected for memory-type control
charts, but unfortunately the modified formula is not a helpful tool from a
computational perspective. We show that simulation-based optimization is a
possible alternative method.
| 1 | 0 | 0 | 1 | 0 | 0 |
Error estimates for Riemann sums of some singular functions | In this short note, we obtain error estimates for Riemann sums of some
singular functions.
| 0 | 0 | 1 | 0 | 0 | 0 |
The Relation Between Fundamental Constants and Particle Physics Parameters | The observed constraints on the variability of the proton to electron mass
ratio $\mu$ and the fine structure constant $\alpha$ are used to establish
constraints on the variability of the Quantum Chromodynamic Scale and a
combination of the Higgs Vacuum Expectation Value and the Yukawa couplings.
Further model dependent assumptions provide constraints on the Higgs VEV and
the Yukawa couplings separately. A primary conclusion is that limits on the
variability of dimensionless fundamental constants such as $\mu$ and $\alpha$
provide important constraints on the parameter space of new physics and
cosmologies.
| 0 | 1 | 0 | 0 | 0 | 0 |
Real-Time Object Pose Estimation with Pose Interpreter Networks | In this work, we introduce pose interpreter networks for 6-DoF object pose
estimation. In contrast to other CNN-based approaches to pose estimation that
require expensively annotated object pose data, our pose interpreter network is
trained entirely on synthetic pose data. We use object masks as an intermediate
representation to bridge real and synthetic. We show that when combined with a
segmentation model trained on RGB images, our synthetically trained pose
interpreter network is able to generalize to real data. Our end-to-end system
for object pose estimation runs in real-time (20 Hz) on live RGB data, without
using depth information or ICP refinement.
| 1 | 0 | 0 | 0 | 0 | 0 |
Regularization, sparse recovery, and median-of-means tournaments | A regularized risk minimization procedure for regression function estimation
is introduced that achieves near optimal accuracy and confidence under general
conditions, including heavy-tailed predictor and response variables. The
procedure is based on median-of-means tournaments, introduced by the authors in
[8]. It is shown that the new procedure outperforms standard regularized
empirical risk minimization procedures such as lasso or slope in heavy-tailed
problems.
| 0 | 0 | 1 | 1 | 0 | 0 |
A Restaurant Process Mixture Model for Connectivity Based Parcellation of the Cortex | One of the primary objectives of human brain mapping is the division of the
cortical surface into functionally distinct regions, i.e. parcellation. While
it is generally agreed that at macro-scale different regions of the cortex have
different functions, the exact number and configuration of these regions is not
known. Methods for the discovery of these regions are thus important,
particularly as the volume of available information grows. Towards this end, we
present a parcellation method based on a Bayesian non-parametric mixture model
of cortical connectivity.
| 1 | 0 | 0 | 1 | 0 | 0 |
On Unifying Deep Generative Models | Deep generative models have achieved impressive success in recent years.
Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as
emerging families for generative model learning, have largely been considered
as two distinct paradigms and received extensive independent studies
respectively. This paper aims to establish formal connections between GANs and
VAEs through a new formulation of them. We interpret sample generation in GANs
as performing posterior inference, and show that GANs and VAEs involve
minimizing KL divergences of respective posterior and inference distributions
with opposite directions, extending the two learning phases of classic
wake-sleep algorithm, respectively. The unified view provides a powerful tool
to analyze a diverse set of existing model variants, and enables to transfer
techniques across research lines in a principled way. For example, we apply the
importance weighting method in VAE literatures for improved GAN learning, and
enhance VAEs with an adversarial mechanism that leverages generated samples.
Experiments show generality and effectiveness of the transferred techniques.
| 1 | 0 | 0 | 1 | 0 | 0 |
DeepVisage: Making face recognition simple yet with powerful generalization skills | Face recognition (FR) methods report significant performance by adopting the
convolutional neural network (CNN) based learning methods. Although CNNs are
mostly trained by optimizing the softmax loss, the recent trend shows an
improvement of accuracy with different strategies, such as task-specific CNN
learning with different loss functions, fine-tuning on target dataset, metric
learning and concatenating features from multiple CNNs. Incorporating these
tasks obviously requires additional efforts. Moreover, it demotivates the
discovery of efficient CNN models for FR which are trained only with identity
labels. We focus on this fact and propose an easily trainable and single CNN
based FR method. Our CNN model exploits the residual learning framework.
Additionally, it uses normalized features to compute the loss. Our extensive
experiments show excellent generalization on different datasets. We obtain very
competitive and state-of-the-art results on the LFW, IJB-A, YouTube faces and
CACD datasets.
| 1 | 0 | 0 | 0 | 0 | 0 |
The Erdős-Ginzburg-Ziv constant and progression-free subsets | Ellenberg and Gijswijt gave recently a new exponential upper bound for the
size of three-term arithmetic progression free sets in $({\mathbb Z_p})^n$,
where $p$ is a prime. Petrov summarized their method and generalized their
result to linear forms.
In this short note we use Petrov's result to give new exponential upper
bounds for the Erdős-Ginzburg-Ziv constant of finite Abelian groups of high
rank. Our main results depend on a conjecture about Property D.
| 0 | 0 | 1 | 0 | 0 | 0 |
The Differing Relationships Between Size, Mass, Metallicity and Core Velocity Dispersion of Central and Satellite Galaxies | We study the role of environment in the evolution of central and satellite
galaxies with the Sloan Digital Sky Survey. We begin by studying the size-mass
relation, replicating previous studies, which showed no difference between the
sizes of centrals and satellites at fixed stellar mass, before turning our
attention to the size-core velocity dispersion ($\sigma_0$) and mass-$\sigma_0$
relations. By comparing the median size and mass of the galaxies at fixed
velocity dispersion we find that the central galaxies are consistently larger
and more massive than their satellite counterparts in the quiescent population.
In the star forming population we find there is no difference in size and only
a small difference in mass. To analyse why these difference may be present we
investigate the radial mass profiles and stellar metallicity of the galaxies.
We find that in the cores of the galaxies there is no difference in mass
surface density between centrals and satellites, but there is a large
difference at larger radii. We also find almost no difference between the
stellar metallicity of centrals and satellites when they are separated into
star forming and quiescent groups. Under the assumption that $\sigma_0$ is
invariant to environmental processes, our results imply that central galaxies
are likely being increased in mass and size by processes such as minor mergers,
particularly at high $\sigma_0$, while satellites are being slightly reduced in
mass and size by tidal stripping and harassment, particularly at low
$\sigma_0$, all of which predominantly affect the outer regions of the
galaxies.
| 0 | 1 | 0 | 0 | 0 | 0 |
Configuration Space Singularities of The Delta Manipulator | We investigate the configuration space of the Delta-Manipulator, identify 24
points in the configuration space, where the Jacobian of the Constraint
Equations looses rank and show, that these are not manifold points of the Real
Algebraic Set, which is defined by the Constraint Equations.
| 1 | 0 | 0 | 0 | 0 | 0 |
Constrained Optimisation of Rational Functions for Accelerating Subspace Iteration | Earlier this decade, the so-called FEAST algorithm was released for computing
the eigenvalues of a matrix in a given interval. Previously, rational filter
functions have been examined as a parameter of FEAST. In this thesis, we expand
on existing work with the following contributions: (i) Obtaining
well-performing rational filter functions via standard minimisation algorithms,
(ii) Obtaining constrained rational filter functions efficiently, and (iii)
Improving existing rational filter functions algorithmically. Using our new
rational filter functions, FEAST requires up to one quarter fewer iterations on
average compared to state-of-art rational filter functions.
| 1 | 0 | 0 | 0 | 0 | 0 |
Sets of lengths in atomic unit-cancellative finitely presented monoids | For an element $a$ of a monoid $H$, its set of lengths $\mathsf L (a) \subset
\mathbb N$ is the set of all positive integers $k$ for which there is a
factorization $a=u_1 \cdot \ldots \cdot u_k$ into $k$ atoms. We study the
system $\mathcal L (H) = \{\mathsf L (a) \mid a \in H \}$ with a focus on the
unions $\mathcal U_k (H) \subset \mathbb N$ which are the unions of all sets of
lengths containing a given $k \in \mathbb N$. The Structure Theorem for Unions
-- stating that for all sufficiently large $k$, the sets $\mathcal U_k (H)$ are
almost arithmetical progressions with the same difference and global bound --
has found much attention for commutative monoids and domains. We show that it
holds true for the not necessarily commutative monoids in the title satisfying
suitable algebraic finiteness conditions. Furthermore, we give an explicit
description of the system of sets of lengths of monoids $B_{n} = \langle a,b
\mid ba=b^{n} \rangle$ for $n \in \N_{\ge 2}$. Based on this description, we
show that the monoids $B_n$ are not transfer Krull, which implies that their
systems $\mathcal L (B_n)$ are distinct from systems of sets of lengths of
commutative Krull monoids and others.
| 0 | 0 | 1 | 0 | 0 | 0 |
Spin-orbit effective fields in Pt/GdFeCo bilayers | In the increasing interests on spin-orbit torque (SOT) with various magnetic
materials, we investigated SOT in rare earth-transition metal ferrimagnetic
alloys. The harmonic Hall measurements were performed in Pt/GdFeCo bilayers to
quantify the effective fields resulting from the SOT. It is found that the
damping-like torque rapidly increases near the magnetization compensation
temperature TM of the GdFeCo, which is attributed to the reduction of the net
magnetic moment.
| 0 | 1 | 0 | 0 | 0 | 0 |
Bridging Static and Dynamic Program Analysis using Fuzzy Logic | Static program analysis is used to summarize properties over all dynamic
executions. In a unifying approach based on 3-valued logic properties are
either assigned a definite value or unknown. But in summarizing a set of
executions, a property is more accurately represented as being biased towards
true, or towards false. Compilers use program analysis to determine benefit of
an optimization. Since benefit (e.g., performance) is justified based on the
common case understanding bias is essential in guiding the compiler.
Furthermore, successful optimization also relies on understanding the quality
of the information, i.e. the plausibility of the bias. If the quality of the
static information is too low to form a decision we would like a mechanism that
improves dynamically.
We consider the problem of building such a reasoning framework and present
the fuzzy data-flow analysis. Our approach generalize previous work that use
3-valued logic. We derive fuzzy extensions of data-flow analyses used by the
lazy code motion optimization and unveil opportunities previous work would not
detect due to limited expressiveness. Furthermore we show how the results of
our analysis can be used in an adaptive classifier that improve as the
application executes.
| 1 | 0 | 0 | 0 | 0 | 0 |
The Formation of Heliospheric Arcs of Slow Solar Wind | A major challenge in solar and heliospheric physics is understanding how
highly localized regions, far smaller than 1 degree at the Sun, are the source
of solar-wind structures spanning more than 20 degrees near Earth. The Sun's
atmosphere is divided into magnetically open regions, coronal holes, where
solar-wind plasma streams out freely and fills the solar system, and closed
regions, where the plasma is confined to coronal loops. The boundary between
these regions extends outward as the heliospheric current sheet (HCS).
Measurements of plasma composition imply that the solar wind near the HCS, the
so-called slow solar wind, originates in closed regions, presumably by the
processes of field-line opening or interchange reconnection. Mysteriously,
however, slow wind is also often seen far from the HCS. We use high-resolution,
three-dimensional magnetohydrodynamic simulations to calculate the dynamics of
a coronal hole whose geometry includes a narrow corridor flanked by closed
field and which is driven by supergranule-like flows at the coronal-hole
boundary. We find that these dynamics result in the formation of giant arcs of
closed-field plasma that extend far from the HCS and span tens of degrees in
latitude and longitude at Earth, accounting for the slow solar wind
observations.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Construction of Infinitely Many Solutions to the Strominger System | In this paper we construct explicit smooth solutions to the Strominger system
on generalized Calabi-Gray manifolds, which are compact non-Kähler Calabi-Yau
3-folds with infinitely many distinct topological types and sets of Hodge
numbers.
| 0 | 0 | 1 | 0 | 0 | 0 |
A clustering algorithm for multivariate data streams with correlated components | Common clustering algorithms require multiple scans of all the data to
achieve convergence, and this is prohibitive when large databases, with data
arriving in streams, must be processed. Some algorithms to extend the popular
K-means method to the analysis of streaming data are present in literature
since 1998 (Bradley et al. in Scaling clustering algorithms to large databases.
In: KDD. p. 9-15, 1998; O'Callaghan et al. in Streaming-data algorithms for
high-quality clustering. In: Proceedings of IEEE international conference on
data engineering. p. 685, 2001), based on the memorization and recursive update
of a small number of summary statistics, but they either don't take into
account the specific variability of the clusters, or assume that the random
vectors which are processed and grouped have uncorrelated components.
Unfortunately this is not the case in many practical situations. We here
propose a new algorithm to process data streams, with data having correlated
components and coming from clusters with different covariance matrices. Such
covariance matrices are estimated via an optimal double shrinkage method, which
provides positive definite estimates even in presence of a few data points, or
of data having components with small variance. This is needed to invert the
matrices and compute the Mahalanobis distances that we use for the data
assignment to the clusters. We also estimate the total number of clusters from
the data.
| 0 | 0 | 1 | 1 | 0 | 0 |
Semantic Interpolation in Implicit Models | In implicit models, one often interpolates between sampled points in latent
space. As we show in this paper, care needs to be taken to match-up the
distributional assumptions on code vectors with the geometry of the
interpolating paths. Otherwise, typical assumptions about the quality and
semantics of in-between points may not be justified. Based on our analysis we
propose to modify the prior code distribution to put significantly more
probability mass closer to the origin. As a result, linear interpolation paths
are not only shortest paths, but they are also guaranteed to pass through
high-density regions, irrespective of the dimensionality of the latent space.
Experiments on standard benchmark image datasets demonstrate clear visual
improvements in the quality of the generated samples and exhibit more
meaningful interpolation paths.
| 1 | 0 | 0 | 1 | 0 | 0 |
Error analysis for global minima of semilinear optimal control problems | In [1] we consider an optimal control problem subject to a semilinear
elliptic PDE together with its variational discretization, where we provide a
condition which allows to decide whether a solution of the necessary first
order conditions is a global minimum. This condition can be explicitly
evaluated at the discrete level. Furthermore, we prove that if the above
condition holds uniformly with respect to the discretization parameter the
sequence of discrete solutions converges to a global solution of the
corresponding limit problem. With the present work we complement our
investigations of [1] in that we prove an error estimate for those discrete
global solutions. Numerical experiments confirm our analytical findings.
| 0 | 0 | 1 | 0 | 0 | 0 |
Random dynamics of two-dimensional stochastic second grade fluids | In this paper, we consider a stochastic model of incompressible non-Newtonian
fluids of second grade on a bounded domain of $\mathbb{R}^2$ with
multiplicative noise. We first show that the solutions to the stochastic
equations of second grade fluids generate a continuous random dynamical system.
Second, we investigate the Fréchet differentiability of the random
dynamical system. Finally, we establish the asymptotic compactness of the
random dynamical system, and the existence of random attractors for the random
dynamical system, we also obtain the upper semi-continuity of the perturbed
random attractors when the noise intensity approaches zero.
| 0 | 0 | 1 | 0 | 0 | 0 |
Lago Distributed Network Of Data Repositories | We describe a set of tools, services and strategies of the Latin American
Giant Observatory (LAGO) data repository network, to implement Data
Accessibility, Reproducibility and Trustworthiness.
| 1 | 0 | 0 | 0 | 0 | 0 |
Hyperfunctions, the Duistermaat-Heckman theorem, and Loop Groups | In this article we investigate the Duistermaat-Heckman theorem using the
theory of hyperfunctions. In applications involving Hamiltonian torus actions
on infinite dimensional manifolds, this more general theory seems to be
necessary in order to accomodate the existence of the infinite order
differential operators which arise from the isotropy representations on the
tangent spaces to fixed points. We will quickly review of the theory of
hyperfunctions and their Fourier transforms. We will then apply this theory to
construct a hyperfunction analogue of the Duistermaat-Heckman distribution. Our
main goal will be to study the Duistermaat-Heckman hyperfunction of $\Omega
SU(2)$, but in getting to this goal we will also characterize the singular
locus of the moment map for the Hamiltonian action of $T\times S^1$ on $\Omega
G$. The main goal of this paper is to present a Duistermaat-Heckman
hyperfunction arising from a Hamiltonian action on an infinite dimensional
manifold.
| 0 | 0 | 1 | 0 | 0 | 0 |
When is selfish routing bad? The price of anarchy in light and heavy traffic | This paper examines the behavior of the price of anarchy as a function of the
traffic inflow in nonatomic congestion games with multiple origin-destination
(O/D) pairs. Empirical studies in real-world networks show that the price of
anarchy is close to 1 in both light and heavy traffic, thus raising the
question: can these observations be justified theoretically? We first show that
this is not always the case: the price of anarchy may remain a positive
distance away from 1 for all values of the traffic inflow, even in simple
three-link networks with a single O/D pair and smooth, convex costs. On the
other hand, for a large class of cost functions (including all polynomials),
the price of anarchy does converge to 1 in both heavy and light traffic,
irrespective of the network topology and the number of O/D pairs in the
network. We also examine the rate of convergence of the price of anarchy, and
we show that it follows a power law whose degree can be computed explicitly
when the network's cost functions are polynomials.
| 1 | 0 | 1 | 0 | 0 | 0 |
Roadmap for the international, accelerator-based neutrino programme | In line with its terms of reference the ICFA Neutrino Panel has developed a
roadmapfor the international, accelerator-based neutrino programme. A "roadmap
discussion document" was presented in May 2016 taking into account the
peer-group-consultation described in the Panel's initial report. The "roadmap
discussion document" was used to solicit feedback from the neutrino
community---and more broadly, the particle- and astroparticle-physics
communities---and the various stakeholders in the programme. The roadmap, the
conclusions and recommendations presented in this document take into account
the comments received following the publication of the roadmap discussion
document.
With its roadmap the Panel documents the approved objectives and milestones
of the experiments that are presently in operation or under construction.
Approval, construction and exploitation milestones are presented for
experiments that are being considered for approval. The timetable proposed by
the proponents is presented for experiments that are not yet being considered
formally for approval. Based on this information, the evolution of the
precision with which the critical parameters governinger the neutrino are known
has been evaluated. Branch or decision points have been identified based on the
anticipated evolution in precision. The branch or decision points have in turn
been used to identify desirable timelines for the neutrino-nucleus cross
section and hadro-production measurements that are required to maximise the
integrated scientific output of the programme. The branch points have also been
used to identify the timeline for the R&D required to take the programme beyond
the horizon of the next generation of experiments. The theory and phenomenology
programme, including nuclear theory, required to ensure that maximum benefit is
derived from the experimental programme is also discussed.
| 0 | 1 | 0 | 0 | 0 | 0 |
Parallel Implementation of Lossy Data Compression for Temporal Data Sets | Many scientific data sets contain temporal dimensions. These are the data
storing information at the same spatial location but different time stamps.
Some of the biggest temporal datasets are produced by parallel computing
applications such as simulations of climate change and fluid dynamics. Temporal
datasets can be very large and cost a huge amount of time to transfer among
storage locations. Using data compression techniques, files can be transferred
faster and save storage space. NUMARCK is a lossy data compression algorithm
for temporal data sets that can learn emerging distributions of element-wise
change ratios along the temporal dimension and encodes them into an index table
to be concisely represented. This paper presents a parallel implementation of
NUMARCK. Evaluated with six data sets obtained from climate and astrophysics
simulations, parallel NUMARCK achieved scalable speedups of up to 8788 when
running 12800 MPI processes on a parallel computer. We also compare the
compression ratios against two lossy data compression algorithms, ISABELA and
ZFP. The results show that NUMARCK achieved higher compression ratio than
ISABELA and ZFP.
| 1 | 0 | 0 | 0 | 0 | 0 |
What we really want to find by Sentiment Analysis: The Relationship between Computational Models and Psychological State | As the first step to model emotional state of a person, we build sentiment
analysis models with existing deep neural network algorithms and compare the
models with psychological measurements to enlighten the relationship. In the
experiments, we first examined psychological state of 64 participants and asked
them to summarize the story of a book, Chronicle of a Death Foretold (Marquez,
1981). Secondly, we trained models using crawled 365,802 movie review data;
then we evaluated participants' summaries using the pretrained model as a
concept of transfer learning. With the background that emotion affects on
memories, we investigated the relationship between the evaluation score of the
summaries from computational models and the examined psychological
measurements. The result shows that although CNN performed the best among other
deep neural network algorithms (LSTM, GRU), its results are not related to the
psychological state. Rather, GRU shows more explainable results depending on
the psychological state. The contribution of this paper can be summarized as
follows: (1) we enlighten the relationship between computational models and
psychological measurements. (2) we suggest this framework as objective methods
to evaluate the emotion; the real sentiment analysis of a person.
| 1 | 0 | 0 | 0 | 0 | 0 |
Survey of multifidelity methods in uncertainty propagation, inference, and optimization | In many situations across computational science and engineering, multiple
computational models are available that describe a system of interest. These
different models have varying evaluation costs and varying fidelities.
Typically, a computationally expensive high-fidelity model describes the system
with the accuracy required by the current application at hand, while
lower-fidelity models are less accurate but computationally cheaper than the
high-fidelity model. Outer-loop applications, such as optimization, inference,
and uncertainty quantification, require multiple model evaluations at many
different inputs, which often leads to computational demands that exceed
available resources if only the high-fidelity model is used. This work surveys
multifidelity methods that accelerate the solution of outer-loop applications
by combining high-fidelity and low-fidelity model evaluations, where the
low-fidelity evaluations arise from an explicit low-fidelity model (e.g., a
simplified physics approximation, a reduced model, a data-fit surrogate, etc.)
that approximates the same output quantity as the high-fidelity model. The
overall premise of these multifidelity methods is that low-fidelity models are
leveraged for speedup while the high-fidelity model is kept in the loop to
establish accuracy and/or convergence guarantees. We categorize multifidelity
methods according to three classes of strategies: adaptation, fusion, and
filtering. The paper reviews multifidelity methods in the outer-loop contexts
of uncertainty propagation, inference, and optimization.
| 1 | 0 | 0 | 1 | 0 | 0 |
Nebular spectroscopy: A guide on H II regions and planetary nebulae | We present a tutorial on the determination of the physical conditions and
chemical abundances in gaseous nebulae. We also include a brief review of
recent results on the study of gaseous nebulae, their relevance for the study
of stellar evolution, galactic chemical evolution, and the evolution of the
universe. One of the most important problems in abundance determinations is the
existence of a discrepancy between the abundances determined with collisionally
excited lines and those determined by recombination lines, this is called the
ADF (abundance discrepancy factor) problem; we review results related to this
problem. Finally, we discuss possible reasons for the large t$^2$ values
observed in gaseous nebulae.
| 0 | 1 | 0 | 0 | 0 | 0 |
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