title
stringlengths 7
239
| abstract
stringlengths 7
2.76k
| cs
int64 0
1
| phy
int64 0
1
| math
int64 0
1
| stat
int64 0
1
| quantitative biology
int64 0
1
| quantitative finance
int64 0
1
|
|---|---|---|---|---|---|---|---|
Stable Self-Assembled Atomic-Switch Networks for Neuromorphic Applications | Nature inspired neuromorphic architectures are being explored as an
alternative to imminent limitations of conventional complementary metal-oxide
semiconductor (CMOS) architectures. Utilization of such architectures for
practical applications like advanced pattern recognition tasks will require
synaptic connections that are both reconfigurable and stable. Here, we report
realization of stable atomic-switch networks (ASN), with inherent complex
connectivity, self-assembled from percolating metal nanoparticles (NPs). The
device conductance reflects the configuration of synapses which can be
modulated via voltage stimulus. By controlling Relative Humidity (RH) and
oxygen partial-pressure during NP deposition we obtain stochastic conductance
switching that is stable over several months. Detailed characterization reveals
signatures of electric-field induced atomic-wire formation within the
tunnel-gaps of the oxidized percolating network. Finally we show that the
synaptic structure can be reconfigured by stimulating at different repetition
rates, which can be utilized as short-term to long-term memory conversion. This
demonstration of stable stochastic switching in ASNs provides a promising route
to hardware implementation of biological neuronal models and, as an example, we
highlight possible applications in Reservoir Computing (RC).
| 1 | 1 | 0 | 0 | 0 | 0 |
On the arithmetic of simple singularities of type E | An ADE Dynkin diagram gives rise to a family of algebraic curves. In this
paper, we use arithmetic invariant theory to study the integral points of the
curves associated to the exceptional diagrams $E_6, E_7$, $E_8$. These curves
are non-hyperelliptic of genus 3 or 4. We prove that a positive proportion of
each family consists of curves with integral points everywhere locally but no
integral points globally.
| 0 | 0 | 1 | 0 | 0 | 0 |
Centralized Network Utility Maximization over Aggregate Flows | We study a network utility maximization (NUM) decomposition in which the set
of flow rates is grouped by source-destination pairs. We develop theorems for
both single-path and multipath cases, which relate an arbitrary NUM problem
involving all flow rates to a simpler problem involving only the aggregate
rates for each source-destination pair. The optimal aggregate flows are then
apportioned among the constituent flows of each pair. This apportionment is
simple for the case of $\alpha$-fair utility functions. We also show how the
decomposition can be implemented with the alternating direction method of
multipliers (ADMM) algorithm.
| 1 | 0 | 1 | 0 | 0 | 0 |
Bottom-up Object Detection by Grouping Extreme and Center Points | With the advent of deep learning, object detection drifted from a bottom-up
to a top-down recognition problem. State of the art algorithms enumerate a
near-exhaustive list of object locations and classify each into: object or not.
In this paper, we show that bottom-up approaches still perform competitively.
We detect four extreme points (top-most, left-most, bottom-most, right-most)
and one center point of objects using a standard keypoint estimation network.
We group the five keypoints into a bounding box if they are geometrically
aligned. Object detection is then a purely appearance-based keypoint estimation
problem, without region classification or implicit feature learning. The
proposed method performs on-par with the state-of-the-art region based
detection methods, with a bounding box AP of 43.2% on COCO test-dev. In
addition, our estimated extreme points directly span a coarse octagonal mask,
with a COCO Mask AP of 18.9%, much better than the Mask AP of vanilla bounding
boxes. Extreme point guided segmentation further improves this to 34.6% Mask
AP.
| 1 | 0 | 0 | 0 | 0 | 0 |
The Different Shapes of the LIS Energy Spectra of Cosmic Ray He and C Nuclei Below ~1 GeV/nuc and The Cosmic Ray He/C Nuclei Ratio vs. Energy -V1 Measurements and LBM Propagation Predictions | This paper examines the cosmic ray He and C nuclei spectra below ~1 GeV/nuc,
as well as the very rapid increase in the He/C ratio below ~100 MeV/nuc,
measured by Voyager 1 beyond the heliopause. Using a simple Leaky Box Model
(LBM) for galactic propagation we have not been able to simultaneously
reproduce the individual He and C nuclei spectra and the large increase in He/C
ratio that is observed at low energies. However, using a truncated LBM with
different truncation parameters for each nucleus that are related to their rate
of energy loss by ionization which is ~Z2/A, these different features can be
matched. This suggests that we are observing the effects of the source
distribution of cosmic rays in the galaxy on the low energy spectra of cosmic
ray nuclei and that there may be a paucity of nearby sources. In this
propagation model we start very specific source spectra for He and C which are
~dj/dP = P-2.24, the same for each nucleus and also for all rigidities. These
source spectra become spectra with spectral indices ~-2.69 at high rigidities
for both charges as a result of a rigidity dependence of the diffusion
coefficient governing the propagation which is taken to be ~P-0.45. This
exponent is determined directly from the B/C ratio measured by AMS-2. These
propagated P-2.69 spectra, when extended to high energies, predict He and C
intensities and a He/C ratio that are within +3-5% of the intensities and ratio
recently measured by AMS-2 in the energy range from 10 to 1000 GeV/nuc.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Nonconvex Splitting Method for Symmetric Nonnegative Matrix Factorization: Convergence Analysis and Optimality | Symmetric nonnegative matrix factorization (SymNMF) has important
applications in data analytics problems such as document clustering, community
detection and image segmentation. In this paper, we propose a novel nonconvex
variable splitting method for solving SymNMF. The proposed algorithm is
guaranteed to converge to the set of Karush-Kuhn-Tucker (KKT) points of the
nonconvex SymNMF problem. Furthermore, it achieves a global sublinear
convergence rate. We also show that the algorithm can be efficiently
implemented in parallel. Further, sufficient conditions are provided which
guarantee the global and local optimality of the obtained solutions. Extensive
numerical results performed on both synthetic and real data sets suggest that
the proposed algorithm converges quickly to a local minimum solution.
| 0 | 0 | 1 | 1 | 0 | 0 |
Galactic Dark Matter Halos and Globular Cluster Populations. III: Extension to Extreme Environments | The total mass M_GCS in the globular cluster (GC) system of a galaxy is
empirically a near-constant fraction of the total mass M_h = M_bary + M_dark of
the galaxy, across a range of 10^5 in galaxy mass. This trend is radically
unlike the strongly nonlinear behavior of total stellar mass M_star versus M_h.
We discuss extensions of this trend to two more extreme situations: (a) entire
clusters of galaxies, and (b) the Ultra-Diffuse Galaxies (UDGs) recently
discovered in Coma and elsewhere. Our calibration of the ratio \eta_M = M_GCS /
M_h from normal galaxies, accounting for new revisions in the adopted
mass-to-light ratio for GCs, now gives \eta_M = 2.9 \times 10^{-5} as the mean
absolute mass fraction. We find that the same ratio appears valid for galaxy
clusters and UDGs. Estimates of \eta_M in the four clusters we examine tend to
be slightly higher than for individual galaxies, butmore data and better
constraints on the mean GC mass in such systems are needed to determine if this
difference is significant. We use the constancy of \eta_M to estimate total
masses for several individual cases; for example, the total mass of the Milky
Way is calculated to be M_h = 1.1 \times 10^{12} M_sun. Physical explanations
for the uniformity of \eta_M are still descriptive, but point to a picture in
which massive, dense star clusters in their formation stages were relatively
immune to the feedback that more strongly influenced lower-density regions
where most stars form.
| 0 | 1 | 0 | 0 | 0 | 0 |
Database of Parliamentary Speeches in Ireland, 1919-2013 | We present a database of parliamentary debates that contains the complete
record of parliamentary speeches from Dáil Éireann, the lower house and
principal chamber of the Irish parliament, from 1919 to 2013. In addition, the
database contains background information on all TDs (Teachta Dála, members of
parliament), such as their party affiliations, constituencies and office
positions. The current version of the database includes close to 4.5 million
speeches from 1,178 TDs. The speeches were downloaded from the official
parliament website and further processed and parsed with a Python script.
Background information on TDs was collected from the member database of the
parliament website. Data on cabinet positions (ministers and junior ministers)
was collected from the official website of the government. A record linkage
algorithm and human coders were used to match TDs and ministers.
| 1 | 0 | 0 | 1 | 0 | 0 |
Reduced Modeling of Unknown Trajectories | This paper deals with model order reduction of parametrical dynamical
systems. We consider the specific setup where the distribution of the system's
trajectories is unknown but the following two sources of information are
available: \textit{(i)} some "rough" prior knowledge on the system's
realisations; \textit{(ii)} a set of "incomplete" observations of the system's
trajectories. We propose a Bayesian methodological framework to build
reduced-order models (ROMs) by exploiting these two sources of information. We
emphasise that complementing the prior knowledge with the collected data
provably enhances the knowledge of the distribution of the system's
trajectories. We then propose an implementation of the proposed methodology
based on Monte-Carlo methods. In this context, we show that standard ROM
learning techniques, such e.g. Proper Orthogonal Decomposition or Dynamic Mode
Decomposition, can be revisited and recast within the probabilistic framework
considered in this paper.~We illustrate the performance of the proposed
approach by numerical results obtained for a standard geophysical model.
| 0 | 0 | 0 | 1 | 0 | 0 |
Pump-Enhanced Continuous-Wave Magnetometry using Nitrogen-Vacancy Ensembles | Ensembles of nitrogen-vacancy centers in diamond are a highly promising
platform for high-sensitivity magnetometry, whose efficacy is often based on
efficiently generating and monitoring magnetic-field dependent infrared
fluorescence. Here we report on an increased sensing efficiency with the use of
a 532-nm resonant confocal cavity and a microwave resonator antenna for
measuring the local magnetic noise density using the intrinsic nitrogen-vacancy
concentration of a chemical-vapor deposited single-crystal diamond. We measure
a near-shot-noise-limited magnetic noise floor of 200 pT/$\sqrt{\text{Hz}}$
spanning a bandwidth up to 159 Hz, and an extracted sensitivity of
approximately 3 nT/$\sqrt{\text{Hz}}$, with further enhancement limited by the
noise floor of the lock-in amplifier and the laser damage threshold of the
optical components. Exploration of the microwave and optical pump-rate
parameter space demonstrates a linewidth-narrowing regime reached by virtue of
using the optical cavity, allowing an enhanced sensitivity to be achieved,
despite an unoptimized collection efficiency of <2 %, and a low
nitrogen-vacancy concentration of about 0.2 ppb.
| 0 | 1 | 0 | 0 | 0 | 0 |
Topological Spin Liquid with Symmetry-Protected Edge States | Topological spin liquids are robust quantum states of matter with long-range
entanglement and possess many exotic properties such as the fractional
statistics of the elementary excitations. Yet these states, short of local
parameters like all topological states, are elusive for conventional
experimental probes. In this work, we combine theoretical analysis and quantum
Monte Carlo numerics on a frustrated spin model which hosts a $\mathbb Z_2$
topological spin liquid ground state, and demonstrate that the presence of
symmetry-protected gapless edge modes is a characteristic feature of the state,
originating from the nontrivial symmetry fractionalization of the elementary
excitations. Experimental observation of these modes on the edge would directly
indicate the existence of the topological spin liquids in the bulk, analogous
to the fact that the observation of Dirac edge states confirmed the existence
of topological insulators.
| 0 | 1 | 0 | 0 | 0 | 0 |
SMT Queries Decomposition and Caching in Semi-Symbolic Model Checking | In semi-symbolic (control-explicit data-symbolic) model checking the
state-space explosion problem is fought by representing sets of states by
first-order formulas over the bit-vector theory. In this model checking
approach, most of the verification time is spent in an SMT solver on deciding
satisfiability of quantified queries, which represent equality of symbolic
states. In this paper, we introduce a new scheme for decomposition of symbolic
states, which can be used to significantly improve the performance of any
semi-symbolic model checker. Using the decomposition, a model checker can issue
much simpler and smaller queries to the solver when compared to the original
case. Some SMT calls may be even avoided completely, as the satisfaction of
some of the simplified formulas can be decided syntactically. Moreover, the
decomposition allows for an efficient caching scheme for quantified formulas.
To support our theoretical contribution, we show the performance gain of our
model checker SymDIVINE on a set of examples from the Software Verification
Competition.
| 1 | 0 | 0 | 0 | 0 | 0 |
Homotopy dimer algebras and cyclic contractions | Dimer algebras arise from a particular type of quiver gauge theory. However,
part of the input to such a theory is the gauge group, and this choice may
impose additional constraints on the algebra. If the gauge group of a dimer
theory is abelian, then the algebra that arises is not actually the dimer
algebra itself, but a particular quotient we introduce called the 'homotopy
algebra'. We show that a homotopy algebra $\Lambda$ on a torus is a dimer
algebra if and only if it is noetherian, and otherwise $\Lambda$ is the
quotient of a dimer algebra by homotopy relations. Stated in physics terms, a
dimer theory is superconformal if and only if the corresponding dimer and
homotopy algebras coincide. We also give an explicit description of the center
of a homotopy algebra in terms of a special subset of its perfect matchings. In
our proofs we introduce formalized notions of Higgsing and the mesonic chiral
ring from quiver gauge theory.
| 0 | 0 | 1 | 0 | 0 | 0 |
On the trace problem for Triebel--Lizorkin spaces with mixed norms | The subject is traces of Sobolev spaces with mixed Lebesgue norms on
Euclidean space. Specifically, restrictions to the hyperplanes given by the
first and last coordinates are applied to functions belonging to
quasi-homogeneous, mixed-norm Lizorkin--Triebel spaces; Sobolev spaces are
obtained from these as special cases. Spaces admitting traces in the
distribution sense are characterised except for the borderline cases; these are
also covered in case of the first variable. With respect to the first variable
the trace spaces are proved to be mixed-norm Lizorkin--Triebel spaces with a
specific sum exponent. For the last variable they are similarly defined Besov
spaces. The treatment includes continuous right-inverses and higher order
traces. The results rely on a sequence version of Nikolskij's inequality,
Marschall's inequality for pseudo-differential operators (and Fourier
multiplier assertions), as well as dyadic ball criteria.
| 0 | 0 | 1 | 0 | 0 | 0 |
Breaking the curse of dimensionality in regression | Models with many signals, high-dimensional models, often impose structures on
the signal strengths. The common assumption is that only a few signals are
strong and most of the signals are zero or close (collectively) to zero.
However, such a requirement might not be valid in many real-life applications.
In this article, we are interested in conducting large-scale inference in
models that might have signals of mixed strengths. The key challenge is that
the signals that are not under testing might be collectively non-negligible
(although individually small) and cannot be accurately learned. This article
develops a new class of tests that arise from a moment matching formulation. A
virtue of these moment-matching statistics is their ability to borrow strength
across features, adapt to the sparsity size and exert adjustment for testing
growing number of hypothesis. GRoup-level Inference of Parameter, GRIP, test
harvests effective sparsity structures with hypothesis formulation for an
efficient multiple testing procedure. Simulated data showcase that GRIPs error
control is far better than the alternative methods. We develop a minimax
theory, demonstrating optimality of GRIP for a broad range of models, including
those where the model is a mixture of a sparse and high-dimensional dense
signals.
| 0 | 0 | 1 | 1 | 0 | 0 |
DVAE++: Discrete Variational Autoencoders with Overlapping Transformations | Training of discrete latent variable models remains challenging because
passing gradient information through discrete units is difficult. We propose a
new class of smoothing transformations based on a mixture of two overlapping
distributions, and show that the proposed transformation can be used for
training binary latent models with either directed or undirected priors. We
derive a new variational bound to efficiently train with Boltzmann machine
priors. Using this bound, we develop DVAE++, a generative model with a global
discrete prior and a hierarchy of convolutional continuous variables.
Experiments on several benchmarks show that overlapping transformations
outperform other recent continuous relaxations of discrete latent variables
including Gumbel-Softmax (Maddison et al., 2016; Jang et al., 2016), and
discrete variational autoencoders (Rolfe 2016).
| 0 | 0 | 0 | 1 | 0 | 0 |
Bet-hedging against demographic fluctuations | Biological organisms have to cope with stochastic variations in both the
external environment and the internal population dynamics. Theoretical studies
and laboratory experiments suggest that population diversification could be an
effective bet-hedging strategy for adaptation to varying environments. Here we
show that bet-hedging can also be effective against demographic fluctuations
that pose a trade-off between growth and survival for populations even in a
constant environment. A species can maximize its overall abundance in the long
term by diversifying into coexisting subpopulations of both "fast-growing" and
"better-surviving" individuals. Our model generalizes statistical physics
models of birth-death processes to incorporate dispersal, during which new
populations are founded, and can further incorporate variations of local
environments. In this way we unify different bet-hedging strategies against
demographic and environmental variations as a general means of adaptation to
both types of uncertainties in population growth.
| 0 | 1 | 0 | 0 | 0 | 0 |
ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU | To improve the performance of Intensive Care Units (ICUs), the field of
bio-statistics has developed scores which try to predict the likelihood of
negative outcomes. These help evaluate the effectiveness of treatments and
clinical practice, and also help to identify patients with unexpected outcomes.
However, they have been shown by several studies to offer sub-optimal
performance. Alternatively, Deep Learning offers state of the art capabilities
in certain prediction tasks and research suggests deep neural networks are able
to outperform traditional techniques. Nevertheless, a main impediment for the
adoption of Deep Learning in healthcare is its reduced interpretability, for in
this field it is crucial to gain insight on the why of predictions, to assure
that models are actually learning relevant features instead of spurious
correlations. To address this, we propose a deep multi-scale convolutional
architecture trained on the Medical Information Mart for Intensive Care III
(MIMIC-III) for mortality prediction, and the use of concepts from coalitional
game theory to construct visual explanations aimed to show how important these
inputs are deemed by the network. Our results show our model attains state of
the art performance while remaining interpretable. Supporting code can be found
at this https URL.
| 1 | 0 | 0 | 1 | 0 | 0 |
Low-complexity Approaches for MIMO Capacity with Per-antenna Power Constraint | This paper proposes two low-complexity iterative algorithms to compute the
capacity of a single-user multiple-input multiple-output channel with
per-antenna power constraint. The first method results from manipulating the
optimality conditions of the considered problem and applying fixed-point
iteration. In the second approach, we transform the considered problem into a
minimax optimization program using the well-known MAC- BC duality, and then
solve it by a novel alternating optimization method. In both proposed iterative
methods, each iteration involves an optimization problem which can be
efficiently solved by the water-filling algorithm. The proposed iterative
methods are provably convergent. Complexity analysis and extensive numerical
experiments are carried out to demonstrate the superior performance of the
proposed algorithms over an existing approach known as the mode-dropping
algorithm.
| 1 | 0 | 0 | 0 | 0 | 0 |
The VISTA ZYJHKs Photometric System: Calibration from 2MASS | In this paper we describe the routine photometric calibration of data taken
with the VIRCAM instrument on the ESO VISTA telescope. The broadband ZYJHKs
data are directly calibrated from 2MASS point sources visible in every VISTA
image. We present the empirical transformations between the 2MASS and VISTA,
and WFCAM and VISTA, photometric systems for regions of low reddening. We
investigate the long-term performance of VISTA+VIRCAM. An investigation of the
dependence of the photometric calibration on interstellar reddening leads to
these conclusions: (1) For all broadband filters, a linear colour-dependent
correction compensates the gross effects of reddening where $E(B-V)<5.0$. (2)
For $Z$ and $Y$, there is a significantly larger scatter above E(B-V)=5.0, and
insufficient measurements to adequately constrain the relation beyond this
value. (3) The $JHK\!s$ filters can be corrected to a few percent up to
E(B-V)=10.0. We analyse spatial systematics over month-long timescales, both
inter- and intra-detector and show that these are present only at very low
levels in VISTA. We monitor and remove residual detector-to-detector offsets.
We compare the calibration of the main pipeline products: pawprints and tiles.
We show how variable seeing and transparency affect the final calibration
accuracy of VISTA tiles, and discuss a technique, {\it grouting}, for
mitigating these effects. Comparison between repeated reference fields is used
to demonstrate that the VISTA photometry is precise to better than $\simeq2\%$
for the $Y$$J$$H$$Ks$ bands and $3\%$ for the $Z$ bands. Finally we present
empirically determined offsets to transform VISTA magnitudes into a true Vega
system.
| 0 | 1 | 0 | 0 | 0 | 0 |
Spin-filtering in superconducting junction with the manganite interlayer | We report on the electronic transport and the impact of spin-filtering in
mesa-structures made of epitaxial thin films of cuprate superconductor
YBa2Cu3Ox(YBCO) and the manganite LaMnO3 (LMO) interlayer with the Au/Nb
counterelectrode. Ferromagnetic resonance measurements of heterostructure
Au/LMO/YBCO shows ferromagnetic state at temperatures below 150 K as in the
case of reference LMO film grown on the neodymium gallate substrate. The
heights of the tunneling barrier evaluated from resistive characteristics of
mesa-structures at different thickness of interlayer showed an exponential
decrease from 30 mV down to 5 mV with the increase of manganite interlayer
thickness. Temperature dependence of the conductivity of mesa-structures could
be described taking into account the d-wave superconductivity in YBCO and a
spin filtering of the electron transport. Spin filtering is supported also by
measurements of magneto-resistance and the high sensitivity of mesa-structure
conductivity to weak magnetic fields.
| 0 | 1 | 0 | 0 | 0 | 0 |
Intersection of conjugate solvable subgroups in symmetric groups | It is shown that for a solvable subgroup $G$ of an almost simple group $S$
which socle is isomorphic to $A_n$ $ (n\ge5)$ there are $x,y,z,t \in S$ such
that $G \cap G^x \cap G^y \cap G^z \cap G^t =1.$
| 0 | 0 | 1 | 0 | 0 | 0 |
Improving inference of the dynamic biological network underlying aging via network propagation | Gene expression (GE) data capture valuable condition-specific information
("condition" can mean a biological process, disease stage, age, patient, etc.)
However, GE analyses ignore physical interactions between gene products, i.e.,
proteins. Since proteins function by interacting with each other, and since
biological networks (BNs) capture these interactions, BN analyses are
promising. However, current BN data fail to capture condition-specific
information. Recently, GE and BN data have been integrated using network
propagation (NP) to infer condition-specific BNs. However, existing NP-based
studies result in a static condition-specific network, even though cellular
processes are dynamic. A dynamic process of our interest is aging. We use
prominent existing NP methods in a new task of inferring a dynamic rather than
static condition-specific (aging-related) network. Then, we study evolution of
network structure with age - we identify proteins whose network positions
significantly change with age and predict them as new aging-related candidates.
We validate the predictions via e.g., functional enrichment analyses and
literature search. Dynamic network inference via NP yields higher prediction
quality than the only existing method for inferring a dynamic aging-related BN,
which does not use NP.
| 0 | 0 | 0 | 0 | 1 | 0 |
Optimal Weighting for Exam Composition | A problem faced by many instructors is that of designing exams that
accurately assess the abilities of the students. Typically these exams are
prepared several days in advance, and generic question scores are used based on
rough approximation of the question difficulty and length. For example, for a
recent class taught by the author, there were 30 multiple choice questions
worth 3 points, 15 true/false with explanation questions worth 4 points, and 5
analytical exercises worth 10 points. We describe a novel framework where
algorithms from machine learning are used to modify the exam question weights
in order to optimize the exam scores, using the overall class grade as a proxy
for a student's true ability. We show that significant error reduction can be
obtained by our approach over standard weighting schemes, and we make several
new observations regarding the properties of the "good" and "bad" exam
questions that can have impact on the design of improved future evaluation
methods.
| 0 | 0 | 0 | 1 | 0 | 0 |
Generalized orderless pooling performs implicit salient matching | Most recent CNN architectures use average pooling as a final feature encoding
step. In the field of fine-grained recognition, however, recent global
representations like bilinear pooling offer improved performance. In this
paper, we generalize average and bilinear pooling to "alpha-pooling", allowing
for learning the pooling strategy during training. In addition, we present a
novel way to visualize decisions made by these approaches. We identify parts of
training images having the highest influence on the prediction of a given test
image. It allows for justifying decisions to users and also for analyzing the
influence of semantic parts. For example, we can show that the higher capacity
VGG16 model focuses much more on the bird's head than, e.g., the lower-capacity
VGG-M model when recognizing fine-grained bird categories. Both contributions
allow us to analyze the difference when moving between average and bilinear
pooling. In addition, experiments show that our generalized approach can
outperform both across a variety of standard datasets.
| 1 | 0 | 0 | 0 | 0 | 0 |
The Gaia-ESO Survey: Exploring the complex nature and origins of the Galactic bulge populations | Abridged: We used the fourth internal data release of the Gaia-ESO survey to
characterize the bulge chemistry, spatial distribution, kinematics, and to
compare it chemically with the thin and thick disks. The sample consist on
~2500 red clump stars in 11 bulge fields ($-10^\circ\leq l\leq+8^\circ$ and
$-10^\circ\leq b\leq-4^\circ$), and a set of ~6300 disk stars selected for
comparison. The bulge MDF is confirmed to be bimodal across the whole sampled
area, with metal-poor stars dominating at high latitudes. The metal-rich stars
exhibit bar-like kinematics and display a bimodality in their magnitude
distribution, a feature which is tightly associated with the X-shape bulge.
They overlap with the metal-rich end of the thin disk sequence in the [Mg/Fe]
vs. [Fe/H] plane. Metal-poor bulge stars have a more isotropic hot kinematics
and do not participate in the X-shape bulge. With similar Mg-enhancement
levels, the position of the metal-poor bulge sequence "knee" is observed at
[Fe/H]$_{knee}=-0.37\pm0.09$, being 0.06 dex higher than that of the thick
disk. It suggests a higher SFR for the bulge than for the thick disk. Finally,
we present a chemical evolution model that suitably fits the whole bulge
sequence by assuming a fast ($<1$ Gyr) intense burst of stellar formation at
early epochs. We associate metal-rich stars with the B/P bulge formed from the
secular evolution of the early thin disk. On the other hand, the metal-poor
subpopulation might be the product of an early prompt dissipative collapse
dominated by massive stars. Nevertheless, our results do not allow us to firmly
rule out the possibility that these stars come from the secular evolution of
the early thick disk. This is the first time that an analysis of the bulge MDF
and $\alpha$-abundances has been performed in a large area on the basis of a
homogeneous, fully spectroscopic analysis of high-resolution, high S/N data.
| 0 | 1 | 0 | 0 | 0 | 0 |
On the Complexity of Robust Stable Marriage | Robust Stable Marriage (RSM) is a variant of the classical Stable Marriage
problem, where the robustness of a given stable matching is measured by the
number of modifications required for repairing it in case an unforeseen event
occurs. We focus on the complexity of finding an (a,b)-supermatch. An
(a,b)-supermatch is defined as a stable matching in which if any 'a'
(non-fixed) men/women break up it is possible to find another stable matching
by changing the partners of those 'a' men/women and also the partners of at
most 'b' other couples. In order to show deciding if there exists an
(a,b)-supermatch is NP-Complete, we first introduce a SAT formulation that is
NP-Complete by using Schaefer's Dichotomy Theorem. Then, we show the
equivalence between the SAT formulation and finding a (1,1)-supermatch on a
specific family of instances.
| 1 | 0 | 0 | 0 | 0 | 0 |
Vacuum Friction | We know that in empty space there is no preferred state of rest. This is true
both in special relativity but also in Newtonian mechanics with its associated
Galilean relativity. It comes as something of a surprise, therefore, to
discover the existence a friction force associated with spontaneous emission.
he resolution of this paradox relies on a central idea from special relativity
even though our derivation of it is non-relativistic. We examine the
possibility that the physics underlying this effect might be explored in an ion
trap, via the observation of a superposition of different mass states.
| 0 | 1 | 0 | 0 | 0 | 0 |
Intelligent Personal Assistant with Knowledge Navigation | An Intelligent Personal Agent (IPA) is an agent that has the purpose of
helping the user to gain information through reliable resources with the help
of knowledge navigation techniques and saving time to search the best content.
The agent is also responsible for responding to the chat-based queries with the
help of Conversation Corpus. We will be testing different methods for optimal
query generation. To felicitate the ease of usage of the application, the agent
will be able to accept the input through Text (Keyboard), Voice (Speech
Recognition) and Server (Facebook) and output responses using the same method.
Existing chat bots reply by making changes in the input, but we will give
responses based on multiple SRT files. The model will learn using the human
dialogs dataset and will be able respond human-like. Responses to queries about
famous things (places, people, and words) can be provided using web scraping
which will enable the bot to have knowledge navigation features. The agent will
even learn from its past experiences supporting semi-supervised learning.
| 1 | 0 | 0 | 0 | 0 | 0 |
Private Data System Enabling Self-Sovereign Storage Managed by Executable Choreographies | With the increased use of Internet, governments and large companies store and
share massive amounts of personal data in such a way that leaves no space for
transparency. When a user needs to achieve a simple task like applying for
college or a driving license, he needs to visit a lot of institutions and
organizations, thus leaving a lot of private data in many places. The same
happens when using the Internet. These privacy issues raised by the centralized
architectures along with the recent developments in the area of serverless
applications demand a decentralized private data layer under user control. We
introduce the Private Data System (PDS), a distributed approach which enables
self-sovereign storage and sharing of private data. The system is composed of
nodes spread across the entire Internet managing local key-value databases. The
communication between nodes is achieved through executable choreographies,
which are capable of preventing information leakage when executing across
different organizations with different regulations in place. The user has full
control over his private data and is able to share and revoke access to
organizations at any time. Even more, the updates are propagated instantly to
all the parties which have access to the data thanks to the system design.
Specifically, the processing organizations may retrieve and process the shared
information, but are not allowed under any circumstances to store it on long
term. PDS offers an alternative to systems that aim to ensure self-sovereignty
of specific types of data through blockchain inspired techniques but face
various problems, such as low performance. Both approaches propose a
distributed database, but with different characteristics. While the
blockchain-based systems are built to solve consensus problems, PDS's purpose
is to solve the self-sovereignty aspects raised by the privacy laws, rules and
principles.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Curious Family of Binomial Determinants That Count Rhombus Tilings of a Holey Hexagon | We evaluate a curious determinant, first mentioned by George Andrews in 1980
in the context of descending plane partitions. Our strategy is to combine the
famous Desnanot-Jacobi-Dodgson identity with automated proof techniques. More
precisely, we follow the holonomic ansatz that was proposed by Doron Zeilberger
in 2007. We derive a compact and nice formula for Andrews's determinant, and
use it to solve a challenge problem that we posed in a previous paper. By
noting that Andrews's determinant is a special case of a two-parameter family
of determinants, we find closed forms for several one-parameter subfamilies.
The interest in these determinants arises because they count cyclically
symmetric rhombus tilings of a hexagon with several triangular holes inside.
| 1 | 0 | 0 | 0 | 0 | 0 |
Community Structure Characterization | This entry discusses the problem of describing some communities identified in
a complex network of interest, in a way allowing to interpret them. We suppose
the community structure has already been detected through one of the many
methods proposed in the literature. The question is then to know how to extract
valuable information from this first result, in order to allow human
interpretation. This requires subsequent processing, which we describe in the
rest of this entry.
| 1 | 1 | 0 | 0 | 0 | 0 |
Design of Deep Neural Networks as Add-on Blocks for Improving Impromptu Trajectory Tracking | This paper introduces deep neural networks (DNNs) as add-on blocks to
baseline feedback control systems to enhance tracking performance of arbitrary
desired trajectories. The DNNs are trained to adapt the reference signals to
the feedback control loop. The goal is to achieve a unity map between the
desired and the actual outputs. In previous work, the efficacy of this approach
was demonstrated on quadrotors; on 30 unseen test trajectories, the proposed
DNN approach achieved an average impromptu tracking error reduction of 43% as
compared to the baseline feedback controller. Motivated by these results, this
work aims to provide platform-independent design guidelines for the proposed
DNN-enhanced control architecture. In particular, we provide specific
guidelines for the DNN feature selection, derive conditions for when the
proposed approach is effective, and show in which cases the training efficiency
can be further increased.
| 1 | 0 | 0 | 0 | 0 | 0 |
Application of data science techniques to disentangle X-ray spectral variation of super-massive black holes | We apply three data science techniques, Nonnegative Matrix Factorization
(NMF), Principal Component Analysis (PCA) and Independent Component Analysis
(ICA), to simulated X-ray energy spectra of a particular class of super-massive
black holes. Two competing physical models, one whose variable components are
additive and the other whose variable components are multiplicative, are known
to successfully describe X-ray spectral variation of these super-massive black
holes, within accuracy of the contemporary observation. We hope to utilize
these techniques to compare the viability of the models by probing the
mathematical structure of the observed spectra, while comparing advantages and
disadvantages of each technique. We find that PCA is best to determine the
dimensionality of a dataset, while NMF is better suited for interpreting
spectral components and comparing them in terms of the physical models in
question. ICA is able to reconstruct the parameters responsible for spectral
variation. In addition, we find that the results of these techniques are
sufficiently different that applying them to observed data may be a useful test
in comparing the accuracy of the two spectral models.
| 0 | 1 | 0 | 0 | 0 | 0 |
Strong convergence rates of modified truncated EM method for stochastic differential equations | Motivated by truncated EM method introduced by Mao (2015), a new explicit
numerical method named modified truncated Euler-Maruyama method is developed in
this paper. Strong convergence rates of the given numerical scheme to the exact
solutions to stochastic differential equations are investigated under given
conditions in this paper. Compared with truncated EM method, the given
numerical simulation strongly converges to the exact solution at fixed time $T$
and over a time interval $[0,T]$ under weaker sufficient conditions. Meanwhile,
the convergence rates are also obtained for both cases. Two examples are
provided to support our conclusions.
| 0 | 0 | 1 | 0 | 0 | 0 |
Some aspects of holomorphic mappings: a survey | This expository paper is concerned with the properties of proper holomorphic
mappings between domains in complex affine spaces. We discuss some of the main
geometric methods of this theory, such as the Reflection Principle, the scaling
method, and the Kobayashi-Royden metric. We sketch the proofs of certain
principal results and discuss some recent achievements. Several open problems
are also stated.
| 0 | 0 | 1 | 0 | 0 | 0 |
Local Linear Constraint based Optimization Model for Dual Spectral CT | Dual spectral computed tomography (DSCT) can achieve energy- and
material-selective images, and has a superior distinguishability of some
materials than conventional single spectral computed tomography (SSCT).
However, the decomposition process is illposed, which is sensitive with noise,
thus the quality of decomposed images are usually degraded, especially the
signal-to-noise ratio (SNR) is much lower than single spectra based directly
reconstructions. In this work, we first establish a local linear relationship
between dual spectra based decomposed results and single spectra based directly
reconstructed images. Then, based on this constraint, we propose an
optimization model for DSCT and develop a guided image filtering based
iterative solution method. Both simulated and real experiments are provided to
validate the effectiveness of the proposed approach.
| 0 | 0 | 1 | 0 | 0 | 0 |
Efficient Online Timed Pattern Matching by Automata-Based Skipping | The timed pattern matching problem is an actively studied topic because of
its relevance in monitoring of real-time systems. There one is given a log $w$
and a specification $\mathcal{A}$ (given by a timed word and a timed automaton
in this paper), and one wishes to return the set of intervals for which the log
$w$, when restricted to the interval, satisfies the specification
$\mathcal{A}$. In our previous work we presented an efficient timed pattern
matching algorithm: it adopts a skipping mechanism inspired by the classic
Boyer--Moore (BM) string matching algorithm. In this work we tackle the problem
of online timed pattern matching, towards embedded applications where it is
vital to process a vast amount of incoming data in a timely manner.
Specifically, we start with the Franek-Jennings-Smyth (FJS) string matching
algorithm---a recent variant of the BM algorithm---and extend it to timed
pattern matching. Our experiments indicate the efficiency of our FJS-type
algorithm in online and offline timed pattern matching.
| 1 | 0 | 0 | 0 | 0 | 0 |
APO Time Resolved Color Photometry of Highly-Elongated Interstellar Object 1I/'Oumuamua | We report on $g$, $r$ and $i$ band observations of the Interstellar Object
'Oumuamua (1I) taken on 2017 October 29 from 04:28 to 08:40 UTC by the Apache
Point Observatory (APO) 3.5m telescope's ARCTIC camera. We find that 1I's
colors are $g-r=0.41\pm0.24$ and $r-i=0.23\pm0.25$, consistent with the visible
spectra of Masiero (2017), Ye et al. (2017) and Fitzsimmons et al. (2017), and
most comparable to the population of Solar System C/D asteroids, Trojans, or
comets. We find no evidence of any cometary activity at a heliocentric distance
of 1.46 au, approximately 1.5 months after 1I's closest approach distance to
the Sun. Significant brightness variability was seen in the $r$ observations,
with the object becoming notably brighter towards the end of the run. By
combining our APO photometric time series data with the Discovery Channel
Telescope (DCT) data of Knight et al. (2017), taken 20 h later on 2017 October
30, we construct an almost complete light curve with a most probable lightcurve
period of $P \simeq 4~{\rm h}$. Our results imply a double peaked rotation
period of 8.1 $\pm$ 0.02 h, with a peak-to-peak amplitude of 1.5 - 2.1 mags.
Assuming that 1I's shape can be approximated by an ellipsoid, the amplitude
constraint implies that 1I has an axial ratio of 3.5 to 10.3, which is
strikingly elongated. Assuming that 1I is rotating above its critical break up
limit, our results are compatible with 1I having having modest cohesive
strength and may have obtained its elongated shape during a tidal disruption
event before being ejected from its home system. Astrometry useful for
constraining 1I's orbit was also obtained and published in Weaver et al.
(2017).
| 0 | 1 | 0 | 0 | 0 | 0 |
Variable selection in discriminant analysis for mixed variables and several groups | We propose a method for variable selection in discriminant analysis with
mixed categorical and continuous variables. This method is based on a criterion
that permits to reduce the variable selection problem to a problem of
estimating suitable permutation and dimensionality. Then, estimators for these
parameters are proposed and the resulting method for selecting variables is
shown to be consistent. A simulation study that permits to study several
poperties of the proposed approach and to compare it with an existing method is
given.
| 0 | 0 | 1 | 1 | 0 | 0 |
AdaGAN: Boosting Generative Models | Generative Adversarial Networks (GAN) (Goodfellow et al., 2014) are an
effective method for training generative models of complex data such as natural
images. However, they are notoriously hard to train and can suffer from the
problem of missing modes where the model is not able to produce examples in
certain regions of the space. We propose an iterative procedure, called AdaGAN,
where at every step we add a new component into a mixture model by running a
GAN algorithm on a reweighted sample. This is inspired by boosting algorithms,
where many potentially weak individual predictors are greedily aggregated to
form a strong composite predictor. We prove that such an incremental procedure
leads to convergence to the true distribution in a finite number of steps if
each step is optimal, and convergence at an exponential rate otherwise. We also
illustrate experimentally that this procedure addresses the problem of missing
modes.
| 1 | 0 | 0 | 1 | 0 | 0 |
A Classification-Based Study of Covariate Shift in GAN Distributions | A basic, and still largely unanswered, question in the context of Generative
Adversarial Networks (GANs) is whether they are truly able to capture all the
fundamental characteristics of the distributions they are trained on. In
particular, evaluating the diversity of GAN distributions is challenging and
existing methods provide only a partial understanding of this issue. In this
paper, we develop quantitative and scalable tools for assessing the diversity
of GAN distributions. Specifically, we take a classification-based perspective
and view loss of diversity as a form of covariate shift introduced by GANs. We
examine two specific forms of such shift: mode collapse and boundary
distortion. In contrast to prior work, our methods need only minimal human
supervision and can be readily applied to state-of-the-art GANs on large,
canonical datasets. Examining popular GANs using our tools indicates that these
GANs have significant problems in reproducing the more distributional
properties of their training dataset.
| 1 | 0 | 0 | 1 | 0 | 0 |
Sequence-to-Sequence Models Can Directly Translate Foreign Speech | We present a recurrent encoder-decoder deep neural network architecture that
directly translates speech in one language into text in another. The model does
not explicitly transcribe the speech into text in the source language, nor does
it require supervision from the ground truth source language transcription
during training. We apply a slightly modified sequence-to-sequence with
attention architecture that has previously been used for speech recognition and
show that it can be repurposed for this more complex task, illustrating the
power of attention-based models. A single model trained end-to-end obtains
state-of-the-art performance on the Fisher Callhome Spanish-English speech
translation task, outperforming a cascade of independently trained
sequence-to-sequence speech recognition and machine translation models by 1.8
BLEU points on the Fisher test set. In addition, we find that making use of the
training data in both languages by multi-task training sequence-to-sequence
speech translation and recognition models with a shared encoder network can
improve performance by a further 1.4 BLEU points.
| 1 | 0 | 0 | 1 | 0 | 0 |
Ensemble Methods for Personalized E-Commerce Search Challenge at CIKM Cup 2016 | Personalized search has been a hot research topic for many years and has been
widely used in e-commerce. This paper describes our solution to tackle the
challenge of personalized e-commerce search at CIKM Cup 2016. The goal of this
competition is to predict search relevance and re-rank the result items in SERP
according to the personalized search, browsing and purchasing preferences.
Based on a detailed analysis of the provided data, we extract three different
types of features, i.e., statistic features, query-item features and session
features. Different models are used on these features, including logistic
regression, gradient boosted decision trees, rank svm and a novel deep match
model. With the blending of multiple models, a stacking ensemble model is built
to integrate the output of individual models and produce a more accurate
prediction result. Based on these efforts, our solution won the champion of the
competition on all the evaluation metrics.
| 1 | 0 | 0 | 0 | 0 | 0 |
Comparison of Sobol' sequences in financial applications | Sobol' sequences are widely used for quasi-Monte Carlo methods that arise in
financial applications. Sobol' sequences have parameter values called direction
numbers, which are freely chosen by the user, so there are several
implementations of Sobol' sequence generators. The aim of this paper is to
provide a comparative study of (non-commercial) high-dimensional Sobol'
sequences by calculating financial models. Additionally, we implement the
Niederreiter sequence (in base 2) with a slight modification, that is, we
reorder the rows of the generating matrices, and analyze and compare it with
the Sobol' sequences.
| 1 | 0 | 0 | 1 | 0 | 0 |
A wavelet integral collocation method for nonlinear boundary value problems in Physics | A high order wavelet integral collocation method (WICM) is developed for
general nonlinear boundary value problems in physics. This method is
established based on Coiflet approximation of multiple integrals of interval
bounded functions combined with an accurate and adjustable boundary extension
technique. The convergence order of this approximation has been proven to be N
as long as the Coiflet with N-1 vanishing moment is adopted, which can be any
positive even integers. Before the conventional collocation method is applied
to the general problems, the original differential equation is changed into its
equivalent form by denoting derivatives of the unknown function as new
functions and constructing relations between the low and high order
derivatives. For the linear cases, error analysis has proven that the proposed
WICM is order N, and condition numbers of relevant matrices are almost
independent of the number of collocation points. Numerical examples of a wide
range of nonlinear differential equations in physics demonstrate that accuracy
of the proposed WICM is even greater than N, and most interestingly, such
accuracy is independent of the order of the differential equation to be solved.
Comparison to existing numerical methods further justifies the accuracy and
efficiency of the proposed method.
| 0 | 0 | 1 | 0 | 0 | 0 |
Recommendations of the LHC Dark Matter Working Group: Comparing LHC searches for heavy mediators of dark matter production in visible and invisible decay channels | Weakly-coupled TeV-scale particles may mediate the interactions between
normal matter and dark matter. If so, the LHC would produce dark matter through
these mediators, leading to the familiar "mono-X" search signatures, but the
mediators would also produce signals without missing momentum via the same
vertices involved in their production. This document from the LHC Dark Matter
Working Group suggests how to compare searches for these two types of signals
in case of vector and axial-vector mediators, based on a workshop that took
place on September 19/20, 2016 and subsequent discussions. These suggestions
include how to extend the spin-1 mediated simplified models already in
widespread use to include lepton couplings. This document also provides
analytic calculations of the relic density in the simplified models and reports
an issue that arose when ATLAS and CMS first began to use preliminary numerical
calculations of the dark matter relic density in these models.
| 0 | 1 | 0 | 0 | 0 | 0 |
Generalization of the concepts of seniority number and ionicity | We present generalized versions of the concepts of seniority number and
ionicity. These generalized numbers count respectively the partially occupied
and fully occupied shells for any partition of the orbital space into shells.
The Hermitian operators whose eigenspaces correspond to wave functions of
definite generalized seniority or ionicity values are introduced. The
generalized seniority numbers (GSNs) afford to establish refined hierarchies of
configuration interaction (CI) spaces within those of fixed ordinary seniority.
Such a hierarchy is illustrated on the buckminsterfullerene molecule.
| 0 | 1 | 0 | 0 | 0 | 0 |
Rise of the HaCRS: Augmenting Autonomous Cyber Reasoning Systems with Human Assistance | As the size and complexity of software systems increase, the number and
sophistication of software security flaws increase as well. The analysis of
these flaws began as a manual approach, but it soon became apparent that tools
were necessary to assist human experts in this task, resulting in a number of
techniques and approaches that automated aspects of the vulnerability analysis
process.
Recently, DARPA carried out the Cyber Grand Challenge, a competition among
autonomous vulnerability analysis systems designed to push the tool-assisted
human-centered paradigm into the territory of complete automation. However,
when the autonomous systems were pitted against human experts it became clear
that certain tasks, albeit simple, could not be carried out by an autonomous
system, as they require an understanding of the logic of the application under
analysis.
Based on this observation, we propose a shift in the vulnerability analysis
paradigm, from tool-assisted human-centered to human-assisted tool-centered. In
this paradigm, the automated system orchestrates the vulnerability analysis
process, and leverages humans (with different levels of expertise) to perform
well-defined sub-tasks, whose results are integrated in the analysis. As a
result, it is possible to scale the analysis to a larger number of programs,
and, at the same time, optimize the use of expensive human resources.
In this paper, we detail our design for a human-assisted automated
vulnerability analysis system, describe its implementation atop an open-sourced
autonomous vulnerability analysis system that participated in the Cyber Grand
Challenge, and evaluate and discuss the significant improvements that
non-expert human assistance can offer to automated analysis approaches.
| 1 | 0 | 0 | 0 | 0 | 0 |
Block-Diagonal and LT Codes for Distributed Computing With Straggling Servers | We propose two coded schemes for the distributed computing problem of
multiplying a matrix by a set of vectors. The first scheme is based on
partitioning the matrix into submatrices and applying maximum distance
separable (MDS) codes to each submatrix. For this scheme, we prove that up to a
given number of partitions the communication load and the computational delay
(not including the encoding and decoding delay) are identical to those of the
scheme recently proposed by Li et al., based on a single, long MDS code.
However, due to the use of shorter MDS codes, our scheme yields a significantly
lower overall computational delay when the delay incurred by encoding and
decoding is also considered. We further propose a second coded scheme based on
Luby Transform (LT) codes under inactivation decoding. Interestingly, LT codes
may reduce the delay over the partitioned scheme at the expense of an increased
communication load. We also consider distributed computing under a deadline and
show numerically that the proposed schemes outperform other schemes in the
literature, with the LT code-based scheme yielding the best performance for the
scenarios considered.
| 1 | 0 | 0 | 0 | 0 | 0 |
Gevrey estimates for one dimensional parabolic invariant manifolds of non-hyperbolic fixed points | We study the Gevrey character of a natural parameterization of one
dimensional invariant manifolds associated to a parabolic direction of fixed
points of analytic maps, that is, a direction associated with an eigenvalue
equal to $1$. We show that, under general hypotheses, these invariant manifolds
are Gevrey with type related to some explicit constants. We provide examples of
the optimality of our results as well as some applications to celestial
mechanics, namely, the Sitnikov problem and the restricted planar three body
problem.
| 0 | 0 | 1 | 0 | 0 | 0 |
Improving Factor-Based Quantitative Investing by Forecasting Company Fundamentals | On a periodic basis, publicly traded companies are required to report
fundamentals: financial data such as revenue, operating income, debt, among
others. These data points provide some insight into the financial health of a
company. Academic research has identified some factors, i.e. computed features
of the reported data, that are known through retrospective analysis to
outperform the market average. Two popular factors are the book value
normalized by market capitalization (book-to-market) and the operating income
normalized by the enterprise value (EBIT/EV). In this paper: we first show
through simulation that if we could (clairvoyantly) select stocks using factors
calculated on future fundamentals (via oracle), then our portfolios would far
outperform a standard factor approach. Motivated by this analysis, we train
deep neural networks to forecast future fundamentals based on a trailing
5-years window. Quantitative analysis demonstrates a significant improvement in
MSE over a naive strategy. Moreover, in retrospective analysis using an
industry-grade stock portfolio simulator (backtester), we show an improvement
in compounded annual return to 17.1% (MLP) vs 14.4% for a standard factor
model.
| 1 | 0 | 0 | 1 | 0 | 0 |
Magnetic Flux Tailoring through Lenz Lenses in Toroidal Diamond Indenter Cells: A New Pathway to High Pressure Nuclear Magnetic Resonance | A new pathway to nuclear magnetic resonance spectroscopy in high pressure
diamond anvil cells is introduced, using inductively coupled broadband passive
electro-magnetic lenses to locally amplify the magnetic flux at the isolated
sample, leading to an increase in sensitivity. The lenses are adopted for the
geometrical restrictions imposed by a toroidal diamond indenter cell, and yield
high signal-to-noise ratios at pressures as high as 72 GPa, at initial sample
volumes of only 230 pl. The corresponding levels of detection, LODt, are found
to be up to four orders of magnitude lower compared to formerly used solenoidal
micro-coils in diamond anvil cells, as shown by Proton-NMR measurements on
paraffin oil. This approach opens up the field of ultra-high pressure sciences
for one of the most versatile spectroscopic methods available in a pressure
range unprecedended up to now.
| 0 | 1 | 0 | 0 | 0 | 0 |
Spatial risk measures induced by powers of max-stable random fields | A meticulous assessment of the risk of extreme environmental events is of
great necessity for populations, civil authorities as well as the
insurance/reinsurance industry. Koch (2017, 2018) introduced a concept of
spatial risk measure and a related set of axioms which are well-suited to
analyse and quantify the risk due to events having a spatial extent, precisely
such as natural disasters. In this paper, we first carry out a detailed study
of the correlation (and covariance) structure of powers of the Smith and
Brown-Resnick max-stable random fields. Then, using the latter results, we
thoroughly investigate spatial risk measures associated with variance and
induced by powers of max-stable random fields. In addition, we show that
spatial risk measures associated with several classical risk measures and
induced by such cost fields satisfy (at least) part of the previously mentioned
axioms under appropriate conditions on the max-stable fields. Considering such
cost fields is particularly relevant when studying the impact of extreme wind
speeds on buildings and infrastructure.
| 0 | 0 | 0 | 0 | 0 | 1 |
Fluctuations in 1D stochastic homogenization of pseudo-elliptic equations with long-range dependent potentials | This paper deals with the homogenization problem of one-dimensional
pseudo-elliptic equations with a rapidly varying random potential. The main
purpose is to characterize the homogenization error (random fluctuations),
i.e., the difference between the random solution and the homogenized solution,
which strongly depends on the autocovariance property of the underlying random
potential. It is well known that when the random potential has short-range
dependence, the rescaled homogenization error converges in distribution to a
stochastic integral with respect to standard Brownian motion. Here, we are
interested in potentials with long-range dependence and we prove convergence to
stochastic integrals with respect to Hermite process.
| 0 | 0 | 1 | 0 | 0 | 0 |
Runge-Kutta-Gegenbauer methods for advection-diffusion problems | In this paper, Runge-Kutta-Gegenbauer (RKG) stability polynomials of
arbitrarily high order of accuracy are introduced in closed form. The stability
domain of RKG polynomials extends in the the real direction with the square of
polynomial degree, and in the imaginary direction as an increasing function of
Gegenbauer parameter. Consequently, the polynomials are naturally suited to the
construction of high order stabilized Runge-Kutta (SRK) methods for systems of
PDEs of mixed hyperbolic-parabolic type.
We present SRK methods composed of $L$ ordered forward Euler stages, with
complex-valued stepsizes derived from the roots of RKG stability polynomials of
degree $L$. Internal stability is maintained at large stage number through an
ordering algorithm which limits internal amplification factors to $10 L^2$.
Test results for mildly stiff nonlinear advection-diffusion-reaction problems
with moderate ($\lesssim 1$) mesh Péclet numbers are provided at second,
fourth, and sixth orders, with nonlinear reaction terms treated by complex
splitting techniques above second order.
| 0 | 1 | 0 | 0 | 0 | 0 |
Tensor network method for reversible classical computation | We develop a tensor network technique that can solve universal reversible
classical computational problems, formulated as vertex models on a square
lattice [Nat. Commun. 8, 15303 (2017)]. By encoding the truth table of each
vertex constraint in a tensor, the total number of solutions compatible with
partial inputs/outputs at the boundary can be represented as the full
contraction of a tensor network. We introduce an iterative
compression-decimation (ICD) scheme that performs this contraction efficiently.
The ICD algorithm first propagates local constraints to longer ranges via
repeated contraction-decomposition sweeps over all lattice bonds, thus
achieving compression on a given length scale. It then decimates the lattice
via coarse-graining tensor contractions. Repeated iterations of these two steps
gradually collapse the tensor network and ultimately yield the exact tensor
trace for large systems, without the need for manual control of tensor
dimensions. Our protocol allows us to obtain the exact number of solutions for
computations where a naive enumeration would take astronomically long times.
| 1 | 1 | 0 | 0 | 0 | 0 |
Model Predictions for Time-Resolved Transport Measurements Made near the Superfluid Critical Points of Cold Atoms and $K_3C_{60}$ Films | Recent advances in ultrafast measurement in cold atoms, as well as pump-probe
spectroscopy of $K_3 C_{60}$ films, have opened the possibility of rapidly
quenching systems of interacting fermions to, and across, a finite temperature
superfluid transition. However, determining that a transient state has
approached a second-order critical point is difficult, as standard equilibrium
techniques are inapplicable. We show that the approach to the superfluid
critical point in a transient state may be detected via time-resolved transport
measurements, such as the optical conductivity. We leverage the fact that
quenching to the vicinity of the critical point produces a highly time
dependent density of superfluid fluctuations, which affect the conductivity in
two ways. First, by inelastic scattering between the fermions and the
fluctuations, and second by direct conduction through the fluctuations, with
the latter providing a lower resistance current carrying channel. The
competition between these two effects leads to nonmonotonic behavior in the
time- resolved optical conductivity, providing a signature of the critical
transient state.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Multimodal Corpus of Expert Gaze and Behavior during Phonetic Segmentation Tasks | Phonetic segmentation is the process of splitting speech into distinct
phonetic units. Human experts routinely perform this task manually by analyzing
auditory and visual cues using analysis software, which is an extremely
time-consuming process. Methods exist for automatic segmentation, but these are
not always accurate enough. In order to improve automatic segmentation, we need
to model it as close to the manual segmentation as possible. This corpus is an
effort to capture the human segmentation behavior by recording experts
performing a segmentation task. We believe that this data will enable us to
highlight the important aspects of manual segmentation, which can be used in
automatic segmentation to improve its accuracy.
| 1 | 0 | 0 | 0 | 0 | 0 |
Gradient Estimators for Implicit Models | Implicit models, which allow for the generation of samples but not for
point-wise evaluation of probabilities, are omnipresent in real-world problems
tackled by machine learning and a hot topic of current research. Some examples
include data simulators that are widely used in engineering and scientific
research, generative adversarial networks (GANs) for image synthesis, and
hot-off-the-press approximate inference techniques relying on implicit
distributions. The majority of existing approaches to learning implicit models
rely on approximating the intractable distribution or optimisation objective
for gradient-based optimisation, which is liable to produce inaccurate updates
and thus poor models. This paper alleviates the need for such approximations by
proposing the Stein gradient estimator, which directly estimates the score
function of the implicitly defined distribution. The efficacy of the proposed
estimator is empirically demonstrated by examples that include meta-learning
for approximate inference, and entropy regularised GANs that provide improved
sample diversity.
| 1 | 0 | 0 | 1 | 0 | 0 |
Token Economics in Energy Systems: Concept, Functionality and Applications | Traditional centralized energy systems have the disadvantages of difficult
management and insufficient incentives. Blockchain is an emerging technology,
which can be utilized in energy systems to enhance their management and
control. Integrating token economy and blockchain technology, token economic
systems in energy possess the characteristics of strong incentives and low
cost, facilitating integrating renewable energy and demand side management, and
providing guarantees for improving energy efficiency and reducing emission.
This article describes the concept and functionality of token economics, and
then analyzes the feasibility of applying token economics in the energy
systems, and finally discuss the applications of token economics with an
example in integrated energy systems.
| 0 | 0 | 0 | 0 | 0 | 1 |
ACtuAL: Actor-Critic Under Adversarial Learning | Generative Adversarial Networks (GANs) are a powerful framework for deep
generative modeling. Posed as a two-player minimax problem, GANs are typically
trained end-to-end on real-valued data and can be used to train a generator of
high-dimensional and realistic images. However, a major limitation of GANs is
that training relies on passing gradients from the discriminator through the
generator via back-propagation. This makes it fundamentally difficult to train
GANs with discrete data, as generation in this case typically involves a
non-differentiable function. These difficulties extend to the reinforcement
learning setting when the action space is composed of discrete decisions. We
address these issues by reframing the GAN framework so that the generator is no
longer trained using gradients through the discriminator, but is instead
trained using a learned critic in the actor-critic framework with a Temporal
Difference (TD) objective. This is a natural fit for sequence modeling and we
use it to achieve improvements on language modeling tasks over the standard
Teacher-Forcing methods.
| 1 | 0 | 0 | 1 | 0 | 0 |
Influence des mécanismes dissociés de ludifications sur l'apprentissage en support numérique de la lecture en classe primaire | The introduction of serious games as pedagogical supports in the field of
education is a process gaining in popularity amongst the teaching community.
This article creates a link between the integration of new pedagogical
solutions in first-year primary class and the fundamental research on the
motivation of the players/learners, detailing an experiment based on a game
specifically developed, named QCM. QCM considers the learning worksheets issued
from the Freinet pedagogy using various gameplay mechanisms. The main
contribution of QCM in relation to more traditional games is the dissociation
of immersion mechanisms, in order to improve the understanding of the user
experience. This game also contains a system of gameplay metrics, the analysis
of which shows a relative increase in the motivation of students using QCM
instead of paper worksheets, while revealing large differences in students
behavior in conjunction with the mechanisms of gamification employed. Keywords
: Serious games, learning analytics, gamification, flow.
| 1 | 0 | 0 | 0 | 0 | 0 |
On the Fine-grained Complexity of One-Dimensional Dynamic Programming | In this paper, we investigate the complexity of one-dimensional dynamic
programming, or more specifically, of the Least-Weight Subsequence (LWS)
problem: Given a sequence of $n$ data items together with weights for every
pair of the items, the task is to determine a subsequence $S$ minimizing the
total weight of the pairs adjacent in $S$. A large number of natural problems
can be formulated as LWS problems, yielding obvious $O(n^2)$-time solutions.
In many interesting instances, the $O(n^2)$-many weights can be succinctly
represented. Yet except for near-linear time algorithms for some specific
special cases, little is known about when an LWS instantiation admits a
subquadratic-time algorithm and when it does not. In particular, no lower
bounds for LWS instantiations have been known before. In an attempt to remedy
this situation, we provide a general approach to study the fine-grained
complexity of succinct instantiations of the LWS problem. In particular, given
an LWS instantiation we identify a highly parallel core problem that is
subquadratically equivalent. This provides either an explanation for the
apparent hardness of the problem or an avenue to find improved algorithms as
the case may be.
More specifically, we prove subquadratic equivalences between the following
pairs (an LWS instantiation and the corresponding core problem) of problems: a
low-rank version of LWS and minimum inner product, finding the longest chain of
nested boxes and vector domination, and a coin change problem which is closely
related to the knapsack problem and (min,+)-convolution. Using these
equivalences and known SETH-hardness results for some of the core problems, we
deduce tight conditional lower bounds for the corresponding LWS instantiations.
We also establish the (min,+)-convolution-hardness of the knapsack problem.
| 1 | 0 | 0 | 0 | 0 | 0 |
Topological quantum paramagnet in a quantum spin ladder | It has recently been found that bosonic excitations of ordered media, such as
phonons or spinons, can exhibit topologically nontrivial band structures. Of
particular interest are magnon and triplon excitations in quantum magnets, as
they can easily be manipulated by an applied field. Here we study triplon
excitations in an S=1/2 quantum spin ladder and show that they exhibit
nontrivial topology, even in the quantum-disordered paramagnetic phase. Our
analysis reveals that the paramagnetic phase actually consists of two separate
regions with topologically distinct triplon excitations. We demonstrate that
the topological transition between these two regions can be tuned by an
external magnetic field. The winding number that characterizes the topology of
the triplons is derived and evaluated. By the bulk-boundary correspondence, we
find that the non-zero winding number implies the presence of localized triplon
end states. Experimental signatures and possible physical realizations of the
topological paramagnetic phase are discussed.
| 0 | 1 | 0 | 0 | 0 | 0 |
Magic wavelengths of Ca$^{+}$ ion for linearly and circularly polarized light | The dynamic dipole polarizabilities of the low-lying states of Ca$^{+}$ for
linearly and circularly polarized light are calculated by using relativistic
configuration interaction plus core polarization (RCICP) approach. The magic
wavelengths, at which the two levels of the transitions have the same ac Stark
shifts, for $4s$-$4p_{j,m}$ and $4s$-$3d_{j,m}$ magnetic sublevels transitions
are determined. The present magic wavelengths for linearly polarized light
agree with the available results excellently. The polarizability for the
circularly polarized light has the scalar, vector and tensor components. The
dynamic polarizability is different for each of magnetic sublevels of the
atomic state. Additional magic wavelengths have been found for the circularly
polarized light. We recommend that the measurement of the magic wavelength near
850 nm for $4s-4p_{\frac32,m=\pm\frac32,\pm\frac12}$ could be able to determine
the oscillator strength ratio of $f_{4p_{\frac32} \to 3d_{\frac32}}$ and
$f_{4p_{\frac32} \to 3d_{\frac52}}$.
| 0 | 1 | 0 | 0 | 0 | 0 |
Network Slicing for 5G with SDN/NFV: Concepts, Architectures and Challenges | The fifth generation of mobile communications is anticipated to open up
innovation opportunities for new industries such as vertical markets. However,
these verticals originate myriad use cases with diverging requirements that
future 5G networks have to efficiently support. Network slicing may be a
natural solution to simultaneously accommodate over a common network
infrastructure the wide range of services that vertical-specific use cases will
demand. In this article, we present the network slicing concept, with a
particular focus on its application to 5G systems. We start by summarizing the
key aspects that enable the realization of so-called network slices. Then, we
give a brief overview on the SDN architecture proposed by the ONF and show that
it provides tools to support slicing. We argue that although such architecture
paves the way for network slicing implementation, it lacks some essential
capabilities that can be supplied by NFV. Hence, we analyze a proposal from the
ETSI to incorporate the capabilities of SDN into the NFV architecture.
Additionally, we present an example scenario that combines SDN and NFV
technologies to address the realization of network slices. Finally, we
summarize the open research issues with the purpose of motivating new advances
in this field.
| 1 | 0 | 0 | 0 | 0 | 0 |
Design of $n$- and $p$-type oxide thermoelectrics in LaNiO$_3$/SrTiO$_3(001)$ superlattices exploiting interface polarity | We investigate the structural, electronic, transport, and thermoelectric
properties of LaNiO$_3$/SrTiO$_3(001)$ superlattices containing either
exclusively $n$- or $p$-type interfaces or coupled interfaces of opposite
polarity by using density functional theory calculations with an on-site
Coulomb repulsion term. The results show that significant octahedral tilts are
induced in the SrTiO$_3$ part of the superlattice. Moreover, the La-Sr
distances and Ni-O out-of-plane bond lengths at the interfaces exhibit a
distinct variation by about $7\,\%$ with the sign of the electrostatic doping.
In contrast to the much studied LaAlO$_3$/SrTiO$_3$ system, the charge mismatch
at the interfaces is exclusively accommodated within the LaNiO$_3$ layers,
whereas the interface polarity leads to a band offset and to the formation of
an electric field within the coupled superlattice. Features of the electronic
structure indicate an orbital-selective quantization of quantum well states.
The potential- and confinement-induced multiband splitting results in complex
cylindrical Fermi surfaces with a tendency towards nesting that depends on the
interface polarity. The analysis of the thermoelectric response reveals a
particularly large positive Seebeck coefficient ($135~\mu$V/K) and a high
figure of merit ($0.35$) for room-temperature cross-plane transport in the
$p$-type superlattice that is attributed to the participation of the SrTiO$_3$
valence band. Superlattices with either $n$- or $p$-type interfaces show
cross-plane Seebeck coefficients of opposite sign and thus emerge as a platform
to construct an oxide-based thermoelectric generator with structurally and
electronically compatible $n$- and $p$-type oxide thermoelectrics.
| 0 | 1 | 0 | 0 | 0 | 0 |
Localized heat perturbation in harmonic 1D crystals. Solutions for an equation of anomalous heat conduction | In this work exact solutions for the equation that describes anomalous heat
propagation in 1D harmonic lattices are obtained. Rectangular, triangular, and
sawtooth initial perturbations of the temperature field are considered. The
solution for an initially rectangular temperature profile is investigated in
detail. It is shown that the decay of the solution near the wavefront is
proportional to $1/ \sqrt{t}$. In the center of the perturbation zone the decay
is proportional to $1/t$. Thus the solution decays slower near the wavefront,
leaving clearly visible peaks that can be detected experimentally.
| 0 | 1 | 0 | 0 | 0 | 0 |
Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction | Recent research in psycholinguistics has provided increasing evidence that
humans predict upcoming content. Prediction also affects perception and might
be a key to robustness in human language processing. In this paper, we
investigate the factors that affect human prediction by building a
computational model that can predict upcoming discourse referents based on
linguistic knowledge alone vs. linguistic knowledge jointly with common-sense
knowledge in the form of scripts. We find that script knowledge significantly
improves model estimates of human predictions. In a second study, we test the
highly controversial hypothesis that predictability influences referring
expression type but do not find evidence for such an effect.
| 1 | 0 | 0 | 1 | 0 | 0 |
Constraints on kinematic parameters at $z\ne0$ | The standard cosmographic approach consists in performing a series expansion
of a cosmological observable around $z=0$ and then using the data to constrain
the cosmographic (or kinematic) parameters at present time. Such a procedure
works well if applied to redshift ranges inside the $z$-series convergence
radius ($z<1$), but can be problematic if we want to cover redshift intervals
that fall outside the $z-$series convergence radius. This problem can be
circumvented if we work with the $y-$redshift, $y=z/(1+z)$, or the scale
factor, $a=1/(1+z)=1-y$, for example. In this paper, we use the scale factor
$a$ as the variable of expansion. We expand the luminosity distance and the
Hubble parameter around an arbitrary $\tilde{a}$ and use the Supernovae Ia (SNe
Ia) and the Hubble parameter data to estimate $H$, $q$, $j$ and $s$ at $z\ne0$
($\tilde{a}\neq1$). We show that the last relevant term for both expansions is
the third. Since the third order expansion of $d_L(z)$ has one parameter less
than the third order expansion of $H(z)$, we also consider, for completeness, a
fourth order expansion of $d_L(z)$. For the third order expansions, the results
obtained from both SNe Ia and $H(z)$ data are incompatible with the
$\Lambda$CDM model at $2\sigma$ confidence level, but also incompatible with
each other. When the fourth order expansion of $d_L(z)$ is taken into account,
the results obtained from SNe Ia data are compatible with the $\Lambda$CDM
model at $2\sigma$ confidence level, but still remains incompatible with
results obtained from $H(z)$ data. These conflicting results may indicate a
tension between the current SNe Ia and $H(z)$ data sets.
| 0 | 1 | 0 | 0 | 0 | 0 |
Joint Syntacto-Discourse Parsing and the Syntacto-Discourse Treebank | Discourse parsing has long been treated as a stand-alone problem independent
from constituency or dependency parsing. Most attempts at this problem are
pipelined rather than end-to-end, sophisticated, and not self-contained: they
assume gold-standard text segmentations (Elementary Discourse Units), and use
external parsers for syntactic features. In this paper we propose the first
end-to-end discourse parser that jointly parses in both syntax and discourse
levels, as well as the first syntacto-discourse treebank by integrating the
Penn Treebank with the RST Treebank. Built upon our recent span-based
constituency parser, this joint syntacto-discourse parser requires no
preprocessing whatsoever (such as segmentation or feature extraction), achieves
the state-of-the-art end-to-end discourse parsing accuracy.
| 1 | 0 | 0 | 0 | 0 | 0 |
Combinatorial Multi-armed Bandit with Probabilistically Triggered Arms: A Case with Bounded Regret | In this paper, we study the combinatorial multi-armed bandit problem (CMAB)
with probabilistically triggered arms (PTAs). Under the assumption that the arm
triggering probabilities (ATPs) are positive for all arms, we prove that a
class of upper confidence bound (UCB) policies, named Combinatorial UCB with
exploration rate $\kappa$ (CUCB-$\kappa$), and Combinatorial Thompson Sampling
(CTS), which estimates the expected states of the arms via Thompson sampling,
achieve bounded regret. In addition, we prove that CUCB-$0$ and CTS incur
$O(\sqrt{T})$ gap-independent regret. These results improve the results in
previous works, which show $O(\log T)$ gap-dependent and $O(\sqrt{T\log T})$
gap-independent regrets, respectively, under no assumptions on the ATPs. Then,
we numerically evaluate the performance of CUCB-$\kappa$ and CTS in a
real-world movie recommendation problem, where the actions correspond to
recommending a set of movies, the arms correspond to the edges between the
movies and the users, and the goal is to maximize the total number of users
that are attracted by at least one movie. Our numerical results complement our
theoretical findings on bounded regret. Apart from this problem, our results
also directly apply to the online influence maximization (OIM) problem studied
in numerous prior works.
| 1 | 0 | 0 | 1 | 0 | 0 |
Generative Adversarial Residual Pairwise Networks for One Shot Learning | Deep neural networks achieve unprecedented performance levels over many tasks
and scale well with large quantities of data, but performance in the low-data
regime and tasks like one shot learning still lags behind. While recent work
suggests many hypotheses from better optimization to more complicated network
structures, in this work we hypothesize that having a learnable and more
expressive similarity objective is an essential missing component. Towards
overcoming that, we propose a network design inspired by deep residual networks
that allows the efficient computation of this more expressive pairwise
similarity objective. Further, we argue that regularization is key in learning
with small amounts of data, and propose an additional generator network based
on the Generative Adversarial Networks where the discriminator is our residual
pairwise network. This provides a strong regularizer by leveraging the
generated data samples. The proposed model can generate plausible variations of
exemplars over unseen classes and outperforms strong discriminative baselines
for few shot classification tasks. Notably, our residual pairwise network
design outperforms previous state-of-theart on the challenging mini-Imagenet
dataset for one shot learning by getting over 55% accuracy for the 5-way
classification task over unseen classes.
| 1 | 0 | 0 | 0 | 0 | 0 |
Evaporation of dilute droplets in a turbulent jet: clustering and entrainment effects | Droplet evaporation in turbulent sprays involves unsteady, multiscale and
multiphase processes which make its comprehension and model capabilities still
limited. The present work aims to investigate droplet vaporization dynamics
within a turbulent spatial developing jet in dilute, non-reacting conditions.
We address the problem using a Direct Numerical Simulation of jet laden with
acetone droplets using an hybrid Eulerian/Lagrangian approach based on the
point droplet approximation. A detailed statistical analysis of both phases is
presented. In particular, we show how crucial is the preferential sampling of
the vapour phase induced by the inhomogeneous localization of the droplets
through the flow. The preferential segregation of droplets develops suddenly
downstream the inlet both within the turbulent core and in the mixing layer.
Two distinct mechanisms have been found to drive these phenomena, the inertial
small-scale clustering in the jet core and the intermittent dynamics of
droplets across the turbulent/non-turbulent interface in the mixing layer where
dry air entrainment occurs. These phenomenologies strongly affect the overall
vaporization process and lead to a spectacular widening of droplets size and
vaporization rate distributions in the downstream evolution of the turbulent
spray.
| 0 | 1 | 0 | 0 | 0 | 0 |
A priori Hölder and Lipschitz regularity for generalized $p$-harmonious functions in metric measure spaces | Let $(\mathbb{X} , d, \mu )$ be a proper metric measure space and let $\Omega
\subset \mathbb{X}$ be a bounded domain. For each $x\in \Omega$, we choose a
radius $0< \varrho (x) \leq \mathrm{dist}(x, \partial \Omega ) $ and let $B_x$
be the closed ball centered at $x$ with radius $\varrho (x)$. If $\alpha \in
\mathbb{R}$, consider the following operator in $C( \overline{\Omega} )$, $$
\mathcal{T}_{\alpha}u(x)=\frac{\alpha}{2}\left(\sup_{B_x } u+\inf_{B_x }
u\right)+(1-\alpha)\,\frac{1}{\mu(B_x)}\int_{B_x}\hspace{-0.1cm} u\ d\mu. $$
Under appropriate assumptions on $\alpha$, $\mathbb{X}$, $\mu$ and the radius
function $\varrho$ we show that solutions $u\in C( \overline{\Omega} )$ of the
functional equation $\mathcal{T}_{\alpha}u = u$ satisfy a local Hölder or
Lipschitz condition in $\Omega$. The motivation comes from the so called
$p$-harmonious functions in euclidean domains.
| 0 | 0 | 1 | 0 | 0 | 0 |
Simultaneous tracking of spin angle and amplitude beyond classical limits | We show how simultaneous, back-action evading tracking of non-commuting
observables can be achieved in a widely-used sensing technology, atomic
interferometry. Using high-dynamic-range dynamically-decoupled quantum
non-demolition (QND) measurements on a precessing atomic spin ensemble, we
track the collective spin angle and amplitude with negligible effects from back
action, giving steady-state tracking sensitivity 2.9 dB beyond the standard
quantum limit and 7.0 dB beyond Poisson statistics.
| 0 | 1 | 0 | 0 | 0 | 0 |
Charge exchange in galaxy clusters | Though theoretically expected, the charge exchange emission from galaxy
clusters has not yet been confidently detected. Accumulating hints were
reported recently, including a rather marginal detection with the Hitomi data
of the Perseus cluster. As suggested in Gu et al. (2015), a detection of charge
exchange line emission from galaxy clusters would not only impact the
interpretation of the newly-discovered 3.5 keV line, but also open up a new
research topic on the interaction between hot and cold matter in clusters. We
aim to perform the most systematic search for the O VIII charge exchange line
in cluster spectra using the RGS on board XMM. We introduce a sample of 21
clusters observed with the RGS. The dominating thermal plasma emission is
modeled and subtracted with a two-temperature CIE component, and the residuals
are stacked for the line search. The systematic uncertainties in the fits are
quantified by refitting the spectra with a varying continuum and line
broadening. By the residual stacking, we do find a hint of a line-like feature
at 14.82 A, the characteristic wavelength expected for oxygen charge exchange.
This feature has a marginal significance of 2.8 sigma, and the average
equivalent width is 2.5E-4 keV. We further demonstrate that the putative
feature can be hardly affected by the systematic errors from continuum
modelling and instrumental effects, or the atomic uncertainties of the
neighbouring thermal lines. Assuming a realistic temperature and abundance
pattern, the physical model implied by the possible oxygen line agrees well
with the theoretical model proposed previously to explain the reported 3.5 keV
line. If the charge exchange source indeed exists, we would expect that the
oxygen abundance is potentially overestimated by 8-22% in previous X-ray
measurements which assumed pure thermal lines.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Grazing Gaussian Beam | We consider Friedlander's wave equation in two space dimensions in the
half-space x > 0 with the boundary condition u(x,y,t)=0 when x=0. For a
Gaussian beam w(x,y,t;k) concentrated on a ray path that is tangent to x=0 at
(x,y,t)=(0,0,0) we calculate the "reflected" wave z(x,y,t;k) in t > 0 such that
w(x,y,t;k)+z(x,y,t;k) satisfies Friedlander's wave equation and vanishes on
x=0. These computations are done to leading order in k on the ray path. The
interaction of beams with boundaries has been studied for non-tangential beams
and for beams gliding along the boundary. We find that the amplitude of the
solution on the central ray for large k after leaving the boundary is very
nearly one half of that of the incoming beam.
| 0 | 0 | 1 | 0 | 0 | 0 |
Reconfigurable Manipulator Simulation for Robotics and Multimodal Machine Learning Application: Aaria | This paper represents a systematic way for generation of Aaria, a simulated
model for serial manipulators for the purpose of kinematic or dynamic analysis
with a vast variety of structures based on Simulink SimMechanics. The proposed
model can receive configuration parameters, for instance in accordance with
modified Denavit-Hartenberg convention, or trajectories for its base or joints
for structures with 1 to 6 degrees of freedom (DOF). The manipulator is
equipped with artificial joint sensors as well as simulated Inertial
Measurement Units (IMUs) on each link. The simulation output can be positions,
velocities, torques, in the joint space or IMU outputs; angular velocity,
linear acceleration, tool coordinates with respect to the inertial frame. This
simulation model is a source of a dataset for virtual multimodal sensory data
for automation of robot modeling and control designed for machine learning and
deep learning approaches based on big data.
| 1 | 0 | 0 | 0 | 0 | 0 |
Group actions and a multi-parameter Falconer distance problem | In this paper we study the following multi-parameter variant of the
celebrated Falconer distance problem. Given ${\textbf{d}}=(d_1,d_2, \dots,
d_{\ell})\in \mathbb{N}^{\ell}$ with $d_1+d_2+\dots+d_{\ell}=d$ and $E
\subseteq \mathbb{R}^d$, we define $$ \Delta_{\textbf{d}}(E) = \left\{
\left(|x^{(1)}-y^{(1)}|,\ldots,|x^{(\ell)}-y^{(\ell)}|\right) : x,y \in E
\right\} \subseteq \mathbb{R}^{\ell}, $$ where for $x\in \mathbb{R}^d$ we write
$x=\left( x^{(1)},\dots, x^{(\ell)} \right)$ with $x^{(i)} \in
\mathbb{R}^{d_i}$.
We ask how large does the Hausdorff dimension of $E$ need to be to ensure
that the $\ell$-dimensional Lebesgue measure of $\Delta_{\textbf{d}}(E)$ is
positive? We prove that if $2 \leq d_i$ for $1 \leq i \leq \ell$, then the
conclusion holds provided $$ \dim(E)>d-\frac{\min d_i}{2}+\frac{1}{3}.$$ We
also note that, by previous constructions, the conclusion does not in general
hold if $$\dim(E)<d-\frac{\min d_i}{2}.$$ A group action derivation of a
suitable Mattila integral plays an important role in the argument.
| 0 | 0 | 1 | 0 | 0 | 0 |
Interacting fermions on the half-line: boundary counterterms and boundary corrections | Recent years witnessed an extensive development of the theory of the critical
point in two-dimensional statistical systems, which allowed to prove {\it
existence} and {\it conformal invariance} of the {\it scaling limit} for
two-dimensional Ising model and dimers in planar graphs. Unfortunately, we are
still far from a full understanding of the subject: so far, exact solutions at
the lattice level, in particular determinant structure and exact discrete
holomorphicity, play a cucial role in the rigorous control of the scaling
limit. The few results about not-integrable (interacting) systems at
criticality are still unable to deal with {\it finite domains} and {\it
boundary corrections}, which are of course crucial for getting informations
about conformal covariance. In this thesis, we address the question of adapting
constructive Renormalization Group methods to non-integrable critical systems
in $d= 1+1$ dimensions. We study a system of interacting spinless fermions on a
one-dimensional semi-infinite lattice, which can be considered as a prototype
of the Luttinger universality class with Dirichlet Boundary Conditions. We
develop a convergent renormalized expression for the thermodynamic observables
in the presence of a quadratic {\it boundary defect} counterterm, polynomially
localized at the boundary. In particular, we get explicit bounds on the
boundary corrections to the specific ground state energy.
| 0 | 1 | 1 | 0 | 0 | 0 |
Online and Distributed Robust Regressions under Adversarial Data Corruption | In today's era of big data, robust least-squares regression becomes a more
challenging problem when considering the adversarial corruption along with
explosive growth of datasets. Traditional robust methods can handle the noise
but suffer from several challenges when applied in huge dataset including 1)
computational infeasibility of handling an entire dataset at once, 2) existence
of heterogeneously distributed corruption, and 3) difficulty in corruption
estimation when data cannot be entirely loaded. This paper proposes online and
distributed robust regression approaches, both of which can concurrently
address all the above challenges. Specifically, the distributed algorithm
optimizes the regression coefficients of each data block via heuristic hard
thresholding and combines all the estimates in a distributed robust
consolidation. Furthermore, an online version of the distributed algorithm is
proposed to incrementally update the existing estimates with new incoming data.
We also prove that our algorithms benefit from strong robustness guarantees in
terms of regression coefficient recovery with a constant upper bound on the
error of state-of-the-art batch methods. Extensive experiments on synthetic and
real datasets demonstrate that our approaches are superior to those of existing
methods in effectiveness, with competitive efficiency.
| 1 | 0 | 0 | 1 | 0 | 0 |
Global well-posedness for the Schrödinger map problem with small Besov norm | In this paper we prove a global result for the Schrödinger map problem with
initial data with small Besov norm at critical regularity.
| 0 | 0 | 1 | 0 | 0 | 0 |
Stable explicit schemes for simulation of nonlinear moisture transfer in porous materials | Implicit schemes have been extensively used in building physics to compute
the solution of moisture diffusion problems in porous materials for improving
stability conditions. Nevertheless, these schemes require important
sub-iterations when treating non-linear problems. To overcome this
disadvantage, this paper explores the use of improved explicit schemes, such as
Dufort-Frankel, Crank-Nicolson and hyperbolisation approaches. A first case
study has been considered with the hypothesis of linear transfer. The
Dufort-Frankel, Crank-Nicolson and hyperbolisation schemes were compared to the
classical Euler explicit scheme and to a reference solution. Results have shown
that the hyperbolisation scheme has a stability condition higher than the
standard Courant-Friedrichs-Lewy (CFL) condition. The error of this schemes
depends on the parameter \tau representing the hyperbolicity magnitude added
into the equation. The Dufort-Frankel scheme has the advantages of being
unconditionally stable and is preferable for non-linear transfer, which is the
second case study. Results have shown the error is proportional to O(\Delta t).
A modified Crank-Nicolson scheme has been proposed in order to avoid
sub-iterations to treat the non-linearities at each time step. The main
advantages of the Dufort-Frankel scheme are (i) to be twice faster than the
Crank-Nicolson approach; (ii) to compute explicitly the solution at each time
step; (iii) to be unconditionally stable and (iv) easier to parallelise on
high-performance computer systems. Although the approach is unconditionally
stable, the choice of the time discretisation $\Delta t$ remains an important
issue to accurately represent the physical phenomena.
| 1 | 1 | 0 | 0 | 0 | 0 |
Congruence lattices of finite diagram monoids | We give a complete description of the congruence lattices of the following
finite diagram monoids: the partition monoid, the planar partition monoid, the
Brauer monoid, the Jones monoid (also known as the Temperley-Lieb monoid), the
Motzkin monoid, and the partial Brauer monoid. All the congruences under
discussion arise as special instances of a new construction, involving an ideal
I, a retraction I->M onto the minimal ideal, a congruence on M, and a normal
subgroup of a maximal subgroup outside I.
| 0 | 0 | 1 | 0 | 0 | 0 |
Weighted blowup correspondence of orbifold Gromov--Witten invariants and applications | Let $\sf X$ be a symplectic orbifold groupoid with $\sf S$ being a symplectic
sub-orbifold groupoid, and $\sf X_{\mathfrak a}$ be the weight-$\mathfrak a$
blowup of $\sf X$ along $\sf S$ with $\sf Z$ being the corresponding
exceptional divisor. We show that there is a weighted blowup correspondence
between some certain absolute orbifold Gromov--Witten invariants of $\sf X$
relative to $\sf S$ and some certain relative orbifold Gromov--Witten
invariants of the pair $(\sf X_{\mathfrak a}|Z)$. As an application, we prove
that the symplectic uniruledness of symplectic orbifold groupoids is a weighted
blowup invariant.
| 0 | 0 | 1 | 0 | 0 | 0 |
Constraints on Quenching of $z\lesssim2$ Massive Galaxies from the Evolution of the average Sizes of Star-Forming and Quenched Populations in COSMOS | We use $>$9400 $\log(m/M_{\odot})>10$ quiescent and star-forming galaxies at
$z\lesssim2$ in COSMOS/UltraVISTA to study the average size evolution of these
systems, with focus on the rare, ultra-massive population at
$\log(m/M_{\odot})>11.4$. The large 2-square degree survey area delivers a
sample of $\sim400$ such ultra-massive systems. Accurate sizes are derived
using a calibration based on high-resolution images from the Hubble Space
Telescope. We find that, at these very high masses, the size evolution of
star-forming and quiescent galaxies is almost indistinguishable in terms of
normalization and power-law slope. We use this result to investigate possible
pathways of quenching massive $m>M^*$ galaxies at $z<2$. We consistently model
the size evolution of quiescent galaxies from the star-forming population by
assuming different simple models for the suppression of star-formation. These
models include an instantaneous and delayed quenching without altering the
structure of galaxies and a central starburst followed by compaction. We find
that instantaneous quenching reproduces well the observed mass-size relation of
massive galaxies at $z>1$. Our starburst$+$compaction model followed by
individual growth of the galaxies by minor mergers is preferred over other
models without structural change for $\log(m/M_{\odot})>11.0$ galaxies at
$z>0.5$. None of our models is able to meet the observations at $m>M^*$ and
$z<1$ with out significant contribution of post-quenching growth of individual
galaxies via mergers. We conclude that quenching is a fast process in galaxies
with $ m \ge 10^{11} M_\odot$, and that major mergers likely play a major role
in the final steps of their evolution.
| 0 | 1 | 0 | 0 | 0 | 0 |
Improving LBP and its variants using anisotropic diffusion | The main purpose of this paper is to propose a new preprocessing step in
order to improve local feature descriptors and texture classification.
Preprocessing is implemented by using transformations which help highlight
salient features that play a significant role in texture recognition. We
evaluate and compare four different competing methods: three different
anisotropic diffusion methods including the classical anisotropic Perona-Malik
diffusion and two subsequent regularizations of it and the application of a
Gaussian kernel, which is the classical multiscale approach in texture
analysis. The combination of the transformed images and the original ones are
analyzed. The results show that the use of the preprocessing step does lead to
improved texture recognition.
| 1 | 0 | 0 | 0 | 0 | 0 |
Dimension-free PAC-Bayesian bounds for matrices, vectors, and linear least squares regression | This paper is focused on dimension-free PAC-Bayesian bounds, under weak
polynomial moment assumptions, allowing for heavy tailed sample distributions.
It covers the estimation of the mean of a vector or a matrix, with applications
to least squares linear regression. Special efforts are devoted to the
estimation of Gram matrices, due to their prominent role in high-dimension data
analysis.
| 0 | 0 | 1 | 1 | 0 | 0 |
Configuration Spaces and Robot Motion Planning Algorithms | The paper surveys topological problems relevant to the motion planning
problem of robotics and includes some new results and constructions. First we
analyse the notion of topological complexity of configuration spaces which is
responsible for discontinuities in algorithms for robot navigation. Then we
present explicit motion planning algorithms for coordinated collision free
control of many particles moving in Euclidean spaces or on graphs. These
algorithms are optimal in the sense that they have minimal number of regions of
continuity. Moreover, we describe in full detail the topology of configuration
spaces of two particles on a tree and use it to construct some top-dimensional
cohomology classes in configuration spaces of n particles on a tree.
| 0 | 0 | 1 | 0 | 0 | 0 |
Effect Summaries for Thread-Modular Analysis | We propose a novel guess-and-check principle to increase the efficiency of
thread-modular verification of lock-free data structures. We build on a
heuristic that guesses candidates for stateless effect summaries of programs by
searching the code for instances of a copy-and-check programming idiom common
in lock-free data structures. These candidate summaries are used to compute the
interference among threads in linear time. Since a candidate summary need not
be a sound effect summary, we show how to fully automatically check whether the
precision of candidate summaries is sufficient. We can thus perform sound
verification despite relying on an unsound heuristic. We have implemented our
approach and found it up to two orders of magnitude faster than existing ones.
| 1 | 0 | 0 | 0 | 0 | 0 |
Theory of Correlated Pairs of Electrons Oscillating in Resonant Quantum States to Reach the Critical Temperature in a Metal | The formation of Correlated Electron Pairs Oscillating around the Fermi level
in Resonant Quantum States (CEPO-RQS), when a metal is cooled to its critical
temperature T=Tc, is studied. The necessary conditions for the existence of
CEPO-RQS are analyzed. The participation of electron-electron interaction
screened by an electron dielectric constant of the form proposed by Thomas
Fermi is considered and a physical meaning for the electron-phonon-electron
interaction in the formation of the CEPO-RQS is given. The internal state of
the CEPO-RQS is characterized by taking into account the internal Hamiltonian,
obtaining a general equation that represents its binding energy and depends
only on temperature and critical temperature. A parameter is also defined that
contains the properties that qualitatively characterizes the nature of a
material to form the CEPO-RQS.
| 0 | 1 | 0 | 0 | 0 | 0 |
Feldman-Katok pseudometric and the GIKN construction of nonhyperbolic ergodic measures | The GIKN construction was introduced by Gorodetski, Ilyashenko, Kleptsyn, and
Nalsky in [Functional Analysis and its Applications, 39 (2005), 21--30]. It
gives a nonhyperbolic ergodic measure which is a weak$^*$ limit of a special
sequence of measures supported on periodic orbits. This method was later
adapted by numerous authors and provided examples of nonhyperbolic invariant
measures in various settings. We prove that the result of the GIKN construction
is always a loosely Kronecker measure in the sense of Ornstein, Rudolph, and
Weiss (equivalently, standard measure in the sense of Katok, another name is
loosely Bernoulli measure with zero entropy). For a proof we introduce and
study the Feldman-Katok pseudometric $\bar{F_{K}}$. The pseudodistance
$\bar{F_{K}}$ is a topological counterpart of the $\bar f$ metric for
finite-state stationary stochastic processes introduced by Feldman and,
independently, by Katok, later developed by Ornstein, Rudolph, and Weiss. We
show that every measure given by the GIKN construction is the
$\bar{F_{K}}$-limit of a sequence of periodic measures. On the other hand we
prove that a measure which is the $\bar{F_{K}}$-limit of a sequence of ergodic
measures is ergodic and its entropy is smaller or equal than the lower limit of
entropies of measures in the sequence. Furthermore we demonstrate that
$\bar{F_{K}}$-Cauchy sequence of periodic measures tends in the weak$^*$
topology either to a periodic measure or to a loosely Kronecker measure.
| 0 | 0 | 1 | 0 | 0 | 0 |
Hamiltonian Monte-Carlo for Orthogonal Matrices | We consider the problem of sampling from posterior distributions for Bayesian
models where some parameters are restricted to be orthogonal matrices. Such
matrices are sometimes used in neural networks models for reasons of
regularization and stabilization of training procedures, and also can
parameterize matrices of bounded rank, positive-definite matrices and others.
In \citet{byrne2013geodesic} authors have already considered sampling from
distributions over manifolds using exact geodesic flows in a scheme similar to
Hamiltonian Monte Carlo (HMC). We propose new sampling scheme for a set of
orthogonal matrices that is based on the same approach, uses ideas of
Riemannian optimization and does not require exact computation of geodesic
flows. The method is theoretically justified by proof of symplecticity for the
proposed iteration. In experiments we show that the new scheme is comparable or
faster in time per iteration and more sample-efficient comparing to
conventional HMC with explicit orthogonal parameterization and Geodesic
Monte-Carlo. We also provide promising results of Bayesian ensembling for
orthogonal neural networks and low-rank matrix factorization.
| 1 | 0 | 0 | 1 | 0 | 0 |
Rapid Mixing Swendsen-Wang Sampler for Stochastic Partitioned Attractive Models | The Gibbs sampler is a particularly popular Markov chain used for learning
and inference problems in Graphical Models (GMs). These tasks are
computationally intractable in general, and the Gibbs sampler often suffers
from slow mixing. In this paper, we study the Swendsen-Wang dynamics which is a
more sophisticated Markov chain designed to overcome bottlenecks that impede
the Gibbs sampler. We prove O(\log n) mixing time for attractive binary
pairwise GMs (i.e., ferromagnetic Ising models) on stochastic partitioned
graphs having n vertices, under some mild conditions, including low temperature
regions where the Gibbs sampler provably mixes exponentially slow. Our
experiments also confirm that the Swendsen-Wang sampler significantly
outperforms the Gibbs sampler when they are used for learning parameters of
attractive GMs.
| 1 | 0 | 0 | 1 | 0 | 0 |
Towards effective research recommender systems for repositories | In this paper, we argue why and how the integration of recommender systems
for research can enhance the functionality and user experience in repositories.
We present the latest technical innovations in the CORE Recommender, which
provides research article recommendations across the global network of
repositories and journals. The CORE Recommender has been recently redeveloped
and released into production in the CORE system and has also been deployed in
several third-party repositories. We explain the design choices of this unique
system and the evaluation processes we have in place to continue raising the
quality of the provided recommendations. By drawing on our experience, we
discuss the main challenges in offering a state-of-the-art recommender solution
for repositories. We highlight two of the key limitations of the current
repository infrastructure with respect to developing research recommender
systems: 1) the lack of a standardised protocol and capabilities for exposing
anonymised user-interaction logs, which represent critically important input
data for recommender systems based on collaborative filtering and 2) the lack
of a voluntary global sign-on capability in repositories, which would enable
the creation of personalised recommendation and notification solutions based on
past user interactions.
| 1 | 0 | 0 | 0 | 0 | 0 |
Existence and a priori estimates of solutions for quasilinear singular elliptic systems with variable exponents | This article sets forth results on the existence, a priori estimates and
boundedness of positive solutions of a singular quasilinear systems of elliptic
equations involving variable exponents. The approach is based on Schauder's
fixed point Theorem. A Moser iteration procedure is also obtained for singular
cooperative systems involving variable exponents establishing a priori
estimates and boundedness of solutions.
| 0 | 0 | 1 | 0 | 0 | 0 |
On the sectional curvature along central configurations | In this paper we characterize planar central configurations in terms of a
sectional curvature value of the Jacobi-Maupertuis metric. This
characterization works for the $N$-body problem with general masses and any
$1/r^{\alpha}$ potential with $\alpha> 0$. We also observe dynamical
consequences of these curvature values for relative equilibrium solutions.
These curvature methods work well for strong forces ($\alpha \ge 2$).
| 0 | 0 | 1 | 0 | 0 | 0 |
Interferometric confirmation of "water fountain" candidates | Water fountain stars (WFs) are evolved objects with water masers tracing
high-velocity jets (up to several hundreds of km s$^{-1}$). They could
represent one of the first manifestations of collimated mass-loss in evolved
objects and thus, be a key to understanding the shaping mechanisms of planetary
nebulae. Only 13 objects had been confirmed so far as WFs with interferometer
observations. We present new observations with the Australia Telescope Compact
Array and archival observations with the Very Large Array of four objects that
are considered to be WF candidates, mainly based on single-dish observations.
We confirm IRAS 17291-2147 and IRAS 18596+0315 (OH 37.1-0.8) as bona fide
members of the WF class, with high-velocity water maser emission consistent
with tracing bipolar jets. We argue that IRAS 15544-5332 has been wrongly
considered as a WF in previous works, since we see no evidence in our data nor
in the literature that this object harbours high-velocity water maser emission.
In the case of IRAS 19067+0811, we did not detect any water maser emission, so
its confirmation as a WF is still pending. With the result of this work, there
are 15 objects that can be considered confirmed WFs. We speculate that there is
no significant physical difference between WFs and obscured post-AGB stars in
general. The absence of high-velocity water maser emission in some obscured
post-AGB stars could be attributed to a variability or orientation effect.
| 0 | 1 | 0 | 0 | 0 | 0 |
Subsets and Splits
No saved queries yet
Save your SQL queries to embed, download, and access them later. Queries will appear here once saved.