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Title: On central leaves of Hodge-type Shimura varieties with parahoric level structure,
Abstract: Kisin and Pappas constructed integral models of Hodge-type Shimura varieties
with parahoric level structure at $p>2$, such that the formal neighbourhood of
a mod~$p$ point can be interpreted as a deformation space of $p$-divisible
group with some Tate cycles (generalising Faltings' construction). In this
paper, we study the central leaf and the closed Newton stratum in the formal
neighbourhoods of mod~$p$ points of Kisin-Pappas integral models with parahoric
level structure; namely, we obtain the dimension of central leaves and the
almost product structure of Newton strata. In the case of hyperspecial level
strucure (i.e., in the good reduction case), our main results were already
obtained by Hamacher, and the result of this paper holds for ramified groups as
well. | [
0,
0,
1,
0,
0,
0
] |
Title: The sequential loss of allelic diversity,
Abstract: This paper gives a new flavor of what Peter Jagers and his co-authors call
`the path to extinction'. In a neutral population with constant size $N$, we
assume that each individual at time $0$ carries a distinct type, or allele. We
consider the joint dynamics of these $N$ alleles, for example the dynamics of
their respective frequencies and more plainly the nonincreasing process
counting the number of alleles remaining by time $t$. We call this process the
extinction process. We show that in the Moran model, the extinction process is
distributed as the process counting (in backward time) the number of common
ancestors to the whole population, also known as the block counting process of
the $N$-Kingman coalescent. Stimulated by this result, we investigate: (1)
whether it extends to an identity between the frequencies of blocks in the
Kingman coalescent and the frequencies of alleles in the extinction process,
both evaluated at jump times; (2) whether it extends to the general case of
$\Lambda$-Fleming-Viot processes. | [
0,
0,
0,
0,
1,
0
] |
Title: A Parsimonious Dynamical Model for Structural Learning in the Human Brain,
Abstract: The human brain is capable of diverse feats of intelligence. A particularly
salient example is the ability to deduce structure from time-varying auditory
and visual stimuli, enabling humans to master the rules of language and to
build rich expectations of their physical environment. The broad relevance of
this ability for human cognition motivates the need for a first-principles
model explicating putative mechanisms. Here we propose a general framework for
structural learning in the brain, composed of an evolving, high-dimensional
dynamical system driven by external stimuli or internal processes. We
operationalize the scenario in which humans learn the rules that generate a
sequence of stimuli, rather than the exemplar stimuli themselves. We model
external stimuli as seemingly disordered chaotic time series generated by
complex dynamical systems; the underlying structure being deduced is then that
of the corresponding chaotic attractor. This approach allows us to demonstrate
and theoretically explain the emergence of five distinct phenomena reminiscent
of cognitive functions: (i) learning the structure of a chaotic system purely
from time series, (ii) generating new streams of stimuli from a chaotic system,
(iii) switching stream generation among multiple learned chaotic systems,
either spontaneously or in response to external perturbations, (iv) inferring
missing data from sparse observations of the chaotic system, and (v)
deciphering superimposed input from different chaotic systems. Numerically, we
show that these phenomena emerge naturally from a recurrent neural network of
Erdos-Renyi topology in which the synaptic strengths adapt in a Hebbian-like
manner. Broadly, our work blends chaotic theory and artificial neural networks
to answer the long standing question of how neural systems can learn the
structure underlying temporal sequences of stimuli. | [
0,
0,
0,
0,
1,
0
] |
Title: Estimation of mean residual life,
Abstract: Yang (1978) considered an empirical estimate of the mean residual life
function on a fixed finite interval. She proved it to be strongly uniformly
consistent and (when appropriately standardized) weakly convergent to a
Gaussian process. These results are extended to the whole half line, and the
variance of the the limiting process is studied. Also, nonparametric
simultaneous confidence bands for the mean residual life function are obtained
by transforming the limiting process to Brownian motion. | [
0,
0,
1,
1,
0,
0
] |
Title: The Effects of Protostellar Disk Turbulence on CO Emission Lines: A Comparison Study of Disks with Constant CO Abundance vs. Chemically Evolving Disks,
Abstract: Turbulence is the leading candidate for angular momentum transport in
protoplanetary disks and therefore influences disk lifetimes and planet
formation timescales. However, the turbulent properties of protoplanetary disks
are poorly constrained observationally. Recent studies have found turbulent
speeds smaller than what fully-developed MRI would produce (Flaherty et al.
2015, 2017). However, existing studies assumed a constant CO/H2 ratio of 0.0001
in locations where CO is not frozen-out or photo-dissociated. Our previous
studies of evolving disk chemistry indicate that CO is depleted by
incorporation into complex organic molecules well inside the freeze-out radius
of CO. We consider the effects of this chemical depletion on measurements of
turbulence. Simon et al. (2015) suggested that the ratio of the peak line flux
to the flux at line center of the CO J=3-2 transition is a reasonable
diagnostic of turbulence, so we focus on that metric, while adding some
analysis of the more complex effects on spatial distribution. We simulate the
emission lines of CO based on chemical evolution models presented in Yu et al.
(2016), and find that the peak-to-trough ratio changes as a function of time as
CO is destroyed. Specifically, a CO-depleted disk with high turbulent velocity
mimics the peak-to-trough ratios of a non-CO-depleted disk with lower turbulent
velocity. We suggest that disk observers and modelers take into account the
possibility of CO depletion when using line peak-to-trough ratios to constrain
the degree of turbulence in disks. Assuming that CO/H2 = 0.0001 at all disk
radii can lead to underestimates of turbulent speeds in the disk by at least
0.2 km/s. | [
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1,
0,
0,
0,
0
] |
Title: Autonomous Urban Localization and Navigation with Limited Information,
Abstract: Urban environments offer a challenging scenario for autonomous driving.
Globally localizing information, such as a GPS signal, can be unreliable due to
signal shadowing and multipath errors. Detailed a priori maps of the
environment with sufficient information for autonomous navigation typically
require driving the area multiple times to collect large amounts of data,
substantial post-processing on that data to obtain the map, and then
maintaining updates on the map as the environment changes. This paper addresses
the issue of autonomous driving in an urban environment by investigating
algorithms and an architecture to enable fully functional autonomous driving
with limited information. An algorithm to autonomously navigate urban roadways
with little to no reliance on an a priori map or GPS is developed. Localization
is performed with an extended Kalman filter with odometry, compass, and sparse
landmark measurement updates. Navigation is accomplished by a compass-based
navigation control law. Key results from Monte Carlo studies show success rates
of urban navigation under different environmental conditions. Experiments
validate the simulated results and demonstrate that, for given test conditions,
an expected range can be found for a given success rate. | [
1,
0,
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0,
0
] |
Title: Iterative Refinement for $\ell_p$-norm Regression,
Abstract: We give improved algorithms for the $\ell_{p}$-regression problem, $\min_{x}
\|x\|_{p}$ such that $A x=b,$ for all $p \in (1,2) \cup (2,\infty).$ Our
algorithms obtain a high accuracy solution in $\tilde{O}_{p}(m^{\frac{|p-2|}{2p
+ |p-2|}}) \le \tilde{O}_{p}(m^{\frac{1}{3}})$ iterations, where each iteration
requires solving an $m \times m$ linear system, $m$ being the dimension of the
ambient space.
By maintaining an approximate inverse of the linear systems that we solve in
each iteration, we give algorithms for solving $\ell_{p}$-regression to $1 /
\text{poly}(n)$ accuracy that run in time $\tilde{O}_p(m^{\max\{\omega,
7/3\}}),$ where $\omega$ is the matrix multiplication constant. For the current
best value of $\omega > 2.37$, we can thus solve $\ell_{p}$ regression as fast
as $\ell_{2}$ regression, for all constant $p$ bounded away from $1.$
Our algorithms can be combined with fast graph Laplacian linear equation
solvers to give minimum $\ell_{p}$-norm flow / voltage solutions to $1 /
\text{poly}(n)$ accuracy on an undirected graph with $m$ edges in
$\tilde{O}_{p}(m^{1 + \frac{|p-2|}{2p + |p-2|}}) \le
\tilde{O}_{p}(m^{\frac{4}{3}})$ time.
For sparse graphs and for matrices with similar dimensions, our iteration
counts and running times improve on the $p$-norm regression algorithm by
[Bubeck-Cohen-Lee-Li STOC`18] and general-purpose convex optimization
algorithms. At the core of our algorithms is an iterative refinement scheme for
$\ell_{p}$-norms, using the smoothed $\ell_{p}$-norms introduced in the work of
Bubeck et al. Given an initial solution, we construct a problem that seeks to
minimize a quadratically-smoothed $\ell_{p}$ norm over a subspace, such that a
crude solution to this problem allows us to improve the initial solution by a
constant factor, leading to algorithms with fast convergence. | [
1,
0,
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1,
0,
0
] |
Title: Minimal free resolution of the associated graded ring of certain monomial curves,
Abstract: In this article, we give the explicit minimal free resolution of the
associated graded ring of certain affine monomial curves in affine 4-space
based on the standard basis theory. As a result, we give the minimal graded
free resolution and compute the Hilbert function of the tangent cone of these
families. | [
0,
0,
1,
0,
0,
0
] |
Title: Forbidden triads and Creative Success in Jazz: The Miles Davis Factor,
Abstract: This article argues for the importance of forbidden triads - open triads with
high-weight edges - in predicting success in creative fields. Forbidden triads
had been treated as a residual category beyond closed and open triads, yet I
argue that these structures provide opportunities to combine socially evolved
styles in new ways. Using data on the entire history of recorded jazz from 1896
to 2010, I show that observed collaborations have tolerated the openness of
high weight triads more than expected, observed jazz sessions had more
forbidden triads than expected, and the density of forbidden triads contributed
to the success of recording sessions, measured by the number of record releases
of session material. The article also shows that the sessions of Miles Davis
had received an especially high boost from forbidden triads. | [
0,
1,
0,
1,
0,
0
] |
Title: Towards fully automated protein structure elucidation with NMR spectroscopy,
Abstract: Nuclear magnetic resonance (NMR) spectroscopy is one of the leading
techniques for protein studies. The method features a number of properties,
allowing to explain macromolecular interactions mechanistically and resolve
structures with atomic resolution. However, due to laborious data analysis, a
full potential of NMR spectroscopy remains unexploited. Here we present an
approach aiming at automation of two major bottlenecks in the analysis
pipeline, namely, peak picking and chemical shift assignment. Our approach
combines deep learning, non-parametric models and combinatorial optimization,
and is able to detect signals of interest in a multidimensional NMR data with
high accuracy and match them with atoms in medium-length protein sequences,
which is a preliminary step to solve protein spatial structure. | [
0,
0,
0,
1,
0,
0
] |
Title: On families of fibred knots with equal Seifert forms,
Abstract: For every genus $g\geq 2$, we construct an infinite family of strongly
quasipositive fibred knots having the same Seifert form as the torus knot
$T(2,2g+1)$. In particular, their signatures and four-genera are maximal and
their homological monodromies (hence their Alexander module structures) agree.
On the other hand, the geometric stretching factors are pairwise distinct and
the knots are pairwise not ribbon concordant. | [
0,
0,
1,
0,
0,
0
] |
Title: A Comparison of Spatial-based Targeted Disease Containment Strategies using Mobile Phone Data,
Abstract: Epidemic outbreaks are an important healthcare challenge, especially in
developing countries where they represent one of the major causes of mortality.
Approaches that can rapidly target subpopulations for surveillance and control
are critical for enhancing containment processes during epidemics.
Using a real-world dataset from Ivory Coast, this work presents an attempt to
unveil the socio-geographical heterogeneity of disease transmission dynamics.
By employing a spatially explicit meta-population epidemic model derived from
mobile phone Call Detail Records (CDRs), we investigate how the differences in
mobility patterns may affect the course of a realistic infectious disease
outbreak. We consider different existing measures of the spatial dimension of
human mobility and interactions, and we analyse their relevance in identifying
the highest risk sub-population of individuals, as the best candidates for
isolation countermeasures. The approaches presented in this paper provide
further evidence that mobile phone data can be effectively exploited to
facilitate our understanding of individuals' spatial behaviour and its
relationship with the risk of infectious diseases' contagion. In particular, we
show that CDRs-based indicators of individuals' spatial activities and
interactions hold promise for gaining insight of contagion heterogeneity and
thus for developing containment strategies to support decision-making during
country-level pandemics. | [
1,
1,
0,
0,
0,
0
] |
Title: Attosecond Streaking in the Water Window: A New Regime of Attosecond Pulse Characterization,
Abstract: We report on the first streaking measurement of water-window attosecond
pulses generated via high harmonic generation, driven by sub-2-cycle,
CEP-stable, 1850 nm laser pulses. Both the central photon energy and the energy
bandwidth far exceed what has been demonstrated thus far, warranting the
investigation of the attosecond streaking technique for the soft X-ray regime
and the limits of the FROGCRAB retrieval algorithm under such conditions. We
also discuss the problem of attochirp compensation and issues regarding much
lower photo-ionization cross sections compared with the XUV in addition to the
fact that several shells of target gases are accessed simultaneously. Based on
our investigation, we caution that the vastly different conditions in the soft
X-ray regime warrant a diligent examination of the fidelity of the measurement
and the retrieval procedure. | [
0,
1,
0,
0,
0,
0
] |
Title: Interplay of Fluorescence and Phosphorescence in Organic Biluminescent Emitters,
Abstract: Biluminescent organic emitters show simultaneous fluorescence and
phosphorescence at room temperature. So far, the optimization of the room
temperature phosphorescence (RTP) in these materials has drawn the attention of
research. However, the continuous wave operation of these emitters will
consequently turn them into systems with vastly imbalanced singlet and triplet
populations, which is due to the respective excited state lifetimes. This study
reports on the exciton dynamics of the biluminophore NPB
(N,N-di(1-naphthyl)-N,N-diphenyl-(1,1-biphenyl)-4,4-diamine). In the extreme
case, the singlet and triplet exciton lifetimes stretch from 3 ns to 300 ms,
respectively. Through sample engineering and oxygen quenching experiments, the
triplet exciton density can be controlled over several orders of magnitude
allowing to studying exciton interactions between singlet and triplet
manifolds. The results show, that singlet-triplet annihilation reduces the
overall biluminescence efficiency already at moderate excitation levels.
Additionally, the presented system represents an illustrative role model to
study excitonic effects in organic materials. | [
0,
1,
0,
0,
0,
0
] |
Title: Planning Hybrid Driving-Stepping Locomotion on Multiple Levels of Abstraction,
Abstract: Navigating in search and rescue environments is challenging, since a variety
of terrains has to be considered. Hybrid driving-stepping locomotion, as
provided by our robot Momaro, is a promising approach. Similar to other
locomotion methods, it incorporates many degrees of freedom---offering high
flexibility but making planning computationally expensive for larger
environments.
We propose a navigation planning method, which unifies different levels of
representation in a single planner. In the vicinity of the robot, it provides
plans with a fine resolution and a high robot state dimensionality. With
increasing distance from the robot, plans become coarser and the robot state
dimensionality decreases. We compensate this loss of information by enriching
coarser representations with additional semantics. Experiments show that the
proposed planner provides plans for large, challenging scenarios in feasible
time. | [
1,
0,
0,
0,
0,
0
] |
Title: Blackbody Radiation in Classical Physics: A Historical Perspective,
Abstract: We point out that current textbooks of modern physics are a century
out-of-date in their treatment of blackbody radiation within classical physics.
Relativistic classical electrodynamics including classical electromagnetic
zero-point radiation gives the Planck spectrum with zero-point radiation as the
blackbody radiation spectrum. In contrast, nonrelativistic mechanics cannot
support the idea of zero-point energy; therefore if nonrelativistic classical
statistical mechanics or nonrelativistic mechanical scatterers are invoked for
radiation equilibrium, one arrives at only the low-frequency Rayleigh-Jeans
part of the spectrum which involves no zero-point energy, and does not include
the high-frequency part of the spectrum involving relativistically-invariant
classical zero-point radiation. Here we first discuss the correct understanding
of blackbody radiation within relativistic classical physics, and then we
review the historical treatment. Finally, we point out how the presence of
Lorentz-invariant classical zero-point radiation and the use of relativistic
particle interactions transform the previous historical arguments so as now to
give the Planck spectrum including classical zero-point radiation. Within
relativistic classical electromagnetic theory, Planck's constant h appears as
the scale of source-free zero-point radiation. | [
0,
1,
0,
0,
0,
0
] |
Title: The effect of stellar and AGN feedback on the low redshift Lyman-$α$ forest in the Sherwood simulation suite,
Abstract: We study the effect of different feedback prescriptions on the properties of
the low redshift ($z\leq1.6$) Ly$\alpha$ forest using a selection of
hydrodynamical simulations drawn from the Sherwood simulation suite. The
simulations incorporate stellar feedback, AGN feedback and a simplified scheme
for efficiently modelling the low column density Ly$\alpha$ forest. We confirm
a discrepancy remains between Cosmic Origins Spectrograph (COS) observations of
the Ly$\alpha$ forest column density distribution function (CDDF) at $z \simeq
0.1$ for high column density systems ($N_{\rm HI}>10^{14}\rm\,cm^{-2}$), as
well as Ly$\alpha$ velocity widths that are too narrow compared to the COS
data. Stellar or AGN feedback -- as currently implemented in our simulations --
have only a small effect on the CDDF and velocity width distribution. We
conclude that resolving the discrepancy between the COS data and simulations
requires an increase in the temperature of overdense gas with $\Delta=4$--$40$,
either through additional He$\,\rm \scriptstyle II\ $ photo-heating at $z>2$ or
fine-tuned feedback that ejects overdense gas into the IGM at just the right
temperature for it to still contribute significantly to the Ly$\alpha$ forest.
Alternatively a larger, currently unresolved turbulent component to the line
width could resolve the discrepancy. | [
0,
1,
0,
0,
0,
0
] |
Title: Exploring a search for long-duration transient gravitational waves associated with magnetar bursts,
Abstract: Soft gamma repeaters and anomalous X-ray pulsars are thought to be magnetars,
neutron stars with strong magnetic fields of order $\mathord{\sim}
10^{13}$--$10^{15} \, \mathrm{gauss}$. These objects emit intermittent bursts
of hard X-rays and soft gamma rays. Quasiperiodic oscillations in the X-ray
tails of giant flares imply the existence of neutron star oscillation modes
which could emit gravitational waves powered by the magnetar's magnetic energy
reservoir. We describe a method to search for transient gravitational-wave
signals associated with magnetar bursts with durations of 10s to 1000s of
seconds. The sensitivity of this method is estimated by adding simulated
waveforms to data from the sixth science run of Laser Interferometer
Gravitational-wave Observatory (LIGO). We find a search sensitivity in terms of
the root sum square strain amplitude of $h_{\mathrm{rss}} = 1.3 \times 10^{-21}
\, \mathrm{Hz}^{-1/2}$ for a half sine-Gaussian waveform with a central
frequency $f_0 = 150 \, \mathrm{Hz}$ and a characteristic time $\tau = 400 \,
\mathrm{s}$. This corresponds to a gravitational wave energy of
$E_{\mathrm{GW}} = 4.3 \times 10^{46} \, \mathrm{erg}$, the same order of
magnitude as the 2004 giant flare which had an estimated electromagnetic energy
of $E_{\mathrm{EM}} = \mathord{\sim} 1.7 \times 10^{46} (d/ 8.7 \,
\mathrm{kpc})^2 \, \mathrm{erg}$, where $d$ is the distance to SGR 1806-20. We
present an extrapolation of these results to Advanced LIGO, estimating a
sensitivity to a gravitational wave energy of $E_{\mathrm{GW}} = 3.2 \times
10^{43} \, \mathrm{erg}$ for a magnetar at a distance of $1.6 \, \mathrm{kpc}$.
These results suggest this search method can probe significantly below the
energy budgets for magnetar burst emission mechanisms such as crust cracking
and hydrodynamic deformation. | [
0,
1,
0,
0,
0,
0
] |
Title: Coupling between a charge density wave and magnetism in an Heusler material,
Abstract: The Prototypical magnetic memory shape alloy Ni$_2$MnGa undergoes various
phase transitions as a function of temperature, pressure, and doping. In the
low-temperature phases below 260 K, an incommensurate structural modulation
occurs along the [110] direction which is thought to arise from softening of a
phonon mode. It is not at present clear how this phenomenon is related, if at
all, to the magnetic memory effect. Here we report time-resolved measurements
which track both the structural and magnetic components of the phase transition
from the modulated cubic phase as it is brought into the high-symmetry phase.
The results suggest that the photoinduced demagnetization modifies the Fermi
surface in regions that couple strongly to the periodicity of the structural
modulation through the nesting vector. The amplitude of the periodic lattice
distortion, however, appears to be less affected by the demagnetizaton. | [
0,
1,
0,
0,
0,
0
] |
Title: Best-Effort FPGA Programming: A Few Steps Can Go a Long Way,
Abstract: FPGA-based heterogeneous architectures provide programmers with the ability
to customize their hardware accelerators for flexible acceleration of many
workloads. Nonetheless, such advantages come at the cost of sacrificing
programmability. FPGA vendors and researchers attempt to improve the
programmability through high-level synthesis (HLS) technologies that can
directly generate hardware circuits from high-level language descriptions.
However, reading through recent publications on FPGA designs using HLS, one
often gets the impression that FPGA programming is still hard in that it leaves
programmers to explore a very large design space with many possible
combinations of HLS optimization strategies.
In this paper we make two important observations and contributions. First, we
demonstrate a rather surprising result: FPGA programming can be made easy by
following a simple best-effort guideline of five refinement steps using HLS. We
show that for a broad class of accelerator benchmarks from MachSuite, the
proposed best-effort guideline improves the FPGA accelerator performance by
42-29,030x. Compared to the baseline CPU performance, the FPGA accelerator
performance is improved from an average 292.5x slowdown to an average 34.4x
speedup. Moreover, we show that the refinement steps in the best-effort
guideline, consisting of explicit data caching, customized pipelining,
processing element duplication, computation/communication overlapping and
scratchpad reorganization, correspond well to the best practice guidelines for
multicore CPU programming. Although our best-effort guideline may not always
lead to the optimal solution, it substantially simplifies the FPGA programming
effort, and will greatly support the wide adoption of FPGA-based acceleration
by the software programming community. | [
1,
0,
0,
0,
0,
0
] |
Title: Distributional Adversarial Networks,
Abstract: We propose a framework for adversarial training that relies on a sample
rather than a single sample point as the fundamental unit of discrimination.
Inspired by discrepancy measures and two-sample tests between probability
distributions, we propose two such distributional adversaries that operate and
predict on samples, and show how they can be easily implemented on top of
existing models. Various experimental results show that generators trained with
our distributional adversaries are much more stable and are remarkably less
prone to mode collapse than traditional models trained with pointwise
prediction discriminators. The application of our framework to domain
adaptation also results in considerable improvement over recent
state-of-the-art. | [
1,
0,
0,
0,
0,
0
] |
Title: Achieving non-discrimination in prediction,
Abstract: Discrimination-aware classification is receiving an increasing attention in
data science fields. The pre-process methods for constructing a
discrimination-free classifier first remove discrimination from the training
data, and then learn the classifier from the cleaned data. However, they lack a
theoretical guarantee for the potential discrimination when the classifier is
deployed for prediction. In this paper, we fill this gap by mathematically
bounding the probability of the discrimination in prediction being within a
given interval in terms of the training data and classifier. We adopt the
causal model for modeling the data generation mechanism, and formally defining
discrimination in population, in a dataset, and in prediction. We obtain two
important theoretical results: (1) the discrimination in prediction can still
exist even if the discrimination in the training data is completely removed;
and (2) not all pre-process methods can ensure non-discrimination in prediction
even though they can achieve non-discrimination in the modified training data.
Based on the results, we develop a two-phase framework for constructing a
discrimination-free classifier with a theoretical guarantee. The experiments
demonstrate the theoretical results and show the effectiveness of our two-phase
framework. | [
1,
0,
0,
1,
0,
0
] |
Title: Undersampled dynamic X-ray tomography with dimension reduction Kalman filter,
Abstract: In this paper, we consider prior-based dimension reduction Kalman filter for
undersampled dynamic X-ray tomography. With this method, the X-ray
reconstructions are parameterized by a low-dimensional basis. Thus, the
proposed method is a) computationally very light; and b) extremely robust as
all the computations can be done explicitly. With real and simulated
measurement data, we show that the method provides accurate reconstructions
even with very limited number of angular directions. | [
0,
0,
0,
1,
0,
0
] |
Title: Extended Sammon Projection and Wavelet Kernel Extreme Learning Machine for Gait-Based Legitimate User Identification on Smartphones,
Abstract: Smartphones have ubiquitously integrated into our home and work environments,
however, users normally rely on explicit but inefficient identification
processes in a controlled environment. Therefore, when a device is stolen, a
thief can have access to the owner's personal information and services against
the stored password/s. As a result of this potential scenario, this work
demonstrates the possibilities of legitimate user identification in a
semi-controlled environment through the built-in smartphones motion dynamics
captured by two different sensors. This is a two-fold process: sub-activity
recognition followed by user/impostor identification. Prior to the
identification; Extended Sammon Projection (ESP) method is used to reduce the
redundancy among the features. To validate the proposed system, we first
collected data from four users walking with their device freely placed in one
of their pants pockets. Through extensive experimentation, we demonstrate that
together time and frequency domain features optimized by ESP to train the
wavelet kernel based extreme learning machine classifier is an effective system
to identify the legitimate user or an impostor with \(97\%\) accuracy. | [
1,
0,
0,
0,
0,
0
] |
Title: On the free path length distribution for linear motion in an n-dimensional box,
Abstract: We consider the distribution of free path lengths, or the distance between
consecutive bounces of random particles, in an n-dimensional rectangular box.
If each particle travels a distance R, then, as R tends to infinity the free
path lengths coincides with the distribution of the length of the intersection
of a random line with the box (for a natural ensemble of random lines) and we
give an explicit formula (piecewise real analytic) for the probability density
function in dimension two and three.
In dimension two we also consider a closely related model where each particle
is allowed to bounce N times, as N tends to infinity, and give an explicit
(again piecewise real analytic) formula for its probability density function.
Further, in both models we can recover the side lengths of the box from the
location of the discontinuities of the probability density functions. | [
0,
0,
1,
0,
0,
0
] |
Title: Spin-Orbit Misalignments of Three Jovian Planets via Doppler Tomography,
Abstract: We present measurements of the spin-orbit misalignments of the hot Jupiters
HAT-P-41 b and WASP-79 b, and the aligned warm Jupiter Kepler-448 b. We
obtained these measurements with Doppler tomography, where we spectroscopically
resolve the line profile perturbation during the transit due to the
Rossiter-McLaughlin effect. We analyze time series spectra obtained during
portions of five transits of HAT-P-41 b, and find a value of the spin-orbit
misalignment of $\lambda = -22.1_{-6.0}^{+0.8 \circ}$. We reanalyze the radial
velocity Rossiter-McLaughlin data on WASP-79 b obtained by Addison et al.
(2013) using Doppler tomographic methodology. We measure
$\lambda=-99.1_{-3.9}^{+4.1\circ}$, consistent with but more precise than the
value found by Addison et al. (2013). For Kepler-448 b we perform a joint fit
to the Kepler light curve, Doppler tomographic data, and a radial velocity
dataset from Lillo-Box et al. (2015). We find an approximately aligned orbit
($\lambda=-7.1^{+4.2 \circ}_{-2.8}$), in modest disagreement with the value
found by Bourrier et al. (2015). Through analysis of the Kepler light curve we
measure a stellar rotation period of $P_{\mathrm{rot}}=1.27 \pm 0.11$ days, and
use this to argue that the full three-dimensional spin-orbit misalignment is
small, $\psi\sim0^{\circ}$. | [
0,
1,
0,
0,
0,
0
] |
Title: Nauticle: a general-purpose particle-based simulation tool,
Abstract: Nauticle is a general-purpose simulation tool for the flexible and highly
configurable application of particle-based methods of either discrete or
continuum phenomena. It is presented that Nauticle has three distinct layers
for users and developers, then the top two layers are discussed in detail. The
paper introduces the Symbolic Form Language (SFL) of Nauticle, which
facilitates the formulation of user-defined numerical models at the top level
in text-based configuration files and provides simple application examples of
use. On the other hand, at the intermediate level, it is shown that the SFL can
be intuitively extended with new particle methods without tedious recoding or
even the knowledge of the bottom level. Finally, the efficiency of the code is
also tested through a performance benchmark. | [
1,
1,
0,
0,
0,
0
] |
Title: Microwave SQUID Multiplexer demonstration for Cosmic Microwave Background Imagers,
Abstract: Key performance characteristics are demonstrated for the microwave SQUID
multiplexer ($\mu$MUX) coupled to transition edge sensor (TES) bolometers that
have been optimized for cosmic microwave background (CMB) observations. In a
64-channel demonstration, we show that the $\mu$MUX produces a white, input
referred current noise level of 29~pA$/\sqrt{\mathrm{Hz}}$ at -77~dB microwave
probe tone power, which is well below expected fundamental detector and photon
noise sources for a ground-based CMB-optimized bolometer. Operated with
negligible photon loading, we measure 98~pA$/\sqrt{\mathrm{Hz}}$ in the
TES-coupled channels biased at 65% of the sensor normal resistance. This noise
level is consistent with that predicted from bolometer thermal fluctuation
(i.e., phonon) noise. Furthermore, the power spectral density exhibits a white
spectrum at low frequencies ($\sim$~100~mHz), which enables CMB mapping on
large angular scales that constrain the physics of inflation. Additionally, we
report cross-talk measurements that indicate a level below 0.3%, which is less
than the level of cross-talk from multiplexed readout systems in deployed CMB
imagers. These measurements demonstrate the $\mu$MUX as a viable readout
technique for future CMB imaging instruments. | [
0,
1,
0,
0,
0,
0
] |
Title: Preserving Differential Privacy Between Features in Distributed Estimation,
Abstract: Privacy is crucial in many applications of machine learning. Legal, ethical
and societal issues restrict the sharing of sensitive data making it difficult
to learn from datasets that are partitioned between many parties. One important
instance of such a distributed setting arises when information about each
record in the dataset is held by different data owners (the design matrix is
"vertically-partitioned").
In this setting few approaches exist for private data sharing for the
purposes of statistical estimation and the classical setup of differential
privacy with a "trusted curator" preparing the data does not apply. We work
with the notion of $(\epsilon,\delta)$-distributed differential privacy which
extends single-party differential privacy to the distributed,
vertically-partitioned case. We propose PriDE, a scalable framework for
distributed estimation where each party communicates perturbed random
projections of their locally held features ensuring
$(\epsilon,\delta)$-distributed differential privacy is preserved. For
$\ell_2$-penalized supervised learning problems PriDE has bounded estimation
error compared with the optimal estimates obtained without privacy constraints
in the non-distributed setting. We confirm this empirically on real world and
synthetic datasets. | [
1,
0,
0,
1,
0,
0
] |
Title: Parallel implementation of a vehicle rail dynamical model for multi-core systems,
Abstract: This research presents a model of a complex dynamic object running on a
multi-core system. Discretization and numerical integration for multibody
models of vehicle rail elements in the vertical longitudinal plane fluctuations
is considered. The implemented model and solution of the motion differential
equations allow estimating the basic processes occurring in the system with
various external influences. Hence the developed programming model can be used
for performing analysis and comparing new vehicle designs.
Keywords-dynamic model; multi-core system; SMP system; rolling stock. | [
1,
0,
0,
0,
0,
0
] |
Title: A Python Calculator for Supernova Remnant Evolution,
Abstract: A freely available Python code for modelling SNR evolution has been created.
This software is intended for two purposes: to understand SNR evolution; and to
use in modelling observations of SNR for obtaining good estimates of SNR
properties. It includes all phases for the standard path of evolution for
spherically symmetric SNRs. In addition, alternate evolutionary models are
available, including evolution in a cloudy ISM, the fractional energy loss
model, and evolution in a hot low-density ISM. The graphical interface takes in
various parameters and produces outputs such as shock radius and velocity vs.
time, SNR surface brightness profile and spectrum. Some interesting properties
of SNR evolution are demonstrated using the program. | [
0,
1,
0,
0,
0,
0
] |
Title: Über die Präzision interprozeduraler Analysen,
Abstract: In this work, we examine two approaches to interprocedural data-flow analysis
of Sharir and Pnueli in terms of precision: the functional and the call-string
approach. In doing so, not only the theoretical best, but all solutions are
regarded which occur when using abstract interpretation or widening
additionally. It turns out that the solutions of both approaches coincide. This
property is preserved when using abstract interpretation; in the case of
widening, a comparison of the results is not always possible. | [
1,
0,
0,
0,
0,
0
] |
Title: Cancellation theorem for Grothendieck-Witt-correspondences and Witt-correspondences,
Abstract: The cancellation theorem for Grothendieck-Witt-correspondences and
Witt-correspondences between smooth varieties over an infinite prefect field
$k$, $char k \neq 2$, is proved, the isomorphism
$$Hom_{\mathbf{DM}^\mathrm{GW}_\mathrm{eff}}(A^\bullet,B^\bullet) \simeq
Hom_{\mathbf{DM}^\mathrm{GW}_\mathrm{eff}}(A^\bullet(1),B^\bullet(1)),$$ for
$A^\bullet,B^\bullet\in \mathbf{DM}^\mathrm{GW}_\mathrm{eff}(k)$ in the
category of effective Grothendieck-Witt-motives constructed in
\cite{AD_DMGWeff} is obtained (and similarly for Witt-motives).
This implies that the canonical functor $\Sigma_{\mathbb G_m^{\wedge
1}}^\infty\colon \mathbf{DM}^\mathrm{GW}_\mathrm{eff}(k)\to
\mathbf{DM}^\mathrm{GW}(k)$ is fully faithful, where
$\mathbf{DM}^\mathrm{GW}(k)$ is the category of non-effective GW-motives
(defined by stabilization of $\mathbf{DM}^\mathrm{GW}_\mathrm{eff}(k)$ along
$\mathbb G_m^{\wedge 1}$) and yields the main property of motives of smooth
varieties in the category $\mathbf{DM}^\mathrm{GW}(k)$: $$
Hom_{\mathbf{DM}^\mathrm{GW}(k)}(M^{GW}(X), \Sigma_{\mathbb G_m^{\wedge
1}}^\infty\mathcal F[i]) \simeq H^i_{Nis}(X,\mathcal F) ,$$ for any smooth
variety $X$ and homotopy invariant sheave with GW-transfers $\mathcal F$ (and
similarly for $\mathbf{DM}^\mathrm{W}(k)$). | [
0,
0,
1,
0,
0,
0
] |
Title: Jumping across biomedical contexts using compressive data fusion,
Abstract: Motivation: The rapid growth of diverse biological data allows us to consider
interactions between a variety of objects, such as genes, chemicals, molecular
signatures, diseases, pathways and environmental exposures. Often, any pair of
objects--such as a gene and a disease--can be related in different ways, for
example, directly via gene-disease associations or indirectly via functional
annotations, chemicals and pathways. Different ways of relating these objects
carry different semantic meanings. However, traditional methods disregard these
semantics and thus cannot fully exploit their value in data modeling.
Results: We present Medusa, an approach to detect size-k modules of objects
that, taken together, appear most significant to another set of objects. Medusa
operates on large-scale collections of heterogeneous data sets and explicitly
distinguishes between diverse data semantics. It advances research along two
dimensions: it builds on collective matrix factorization to derive different
semantics, and it formulates the growing of the modules as a submodular
optimization program. Medusa is flexible in choosing or combining semantic
meanings and provides theoretical guarantees about detection quality. In a
systematic study on 310 complex diseases, we show the effectiveness of Medusa
in associating genes with diseases and detecting disease modules. We
demonstrate that in predicting gene-disease associations Medusa compares
favorably to methods that ignore diverse semantic meanings. We find that the
utility of different semantics depends on disease categories and that, overall,
Medusa recovers disease modules more accurately when combining different
semantics. | [
1,
0,
0,
1,
0,
0
] |
Title: Memories of a Theoretical Physicist,
Abstract: While I was dealing with a brain injury and finding it difficult to work, two
friends (Derek Westen, a friend of the KITP, and Steve Shenker, with whom I was
recently collaborating), suggested that a new direction might be good. Steve in
particular regarded me as a good writer and suggested that I try that. I
quickly took to Steve's suggestion. Having only two bodies of knowledge, myself
and physics, I decided to write an autobiography about my development as a
theoretical physicist.
This is not written for any particular audience, but just to give myself a
goal. It will probably have too much physics for a nontechnical reader, and too
little for a physicist, but perhaps there with be different things for each.
Parts may be tedious. But it is somewhat unique, I think, a blow-by-blow
history of where I started and where I got to.
Probably the target audience is theoretical physicists, especially young
ones, who may enjoy comparing my struggles with their own. Some disclaimers:
This is based on my own memories, jogged by the arXiv and Inspire. There will
surely be errors and omissions. And note the title: this is about my memories,
which will be different for other people. Also, it would not be possible for me
to mention all the authors whose work might intersect mine, so this should not
be treated as a reference work. | [
0,
1,
0,
0,
0,
0
] |
Title: Best-Choice Edge Grafting for Efficient Structure Learning of Markov Random Fields,
Abstract: Incremental methods for structure learning of pairwise Markov random fields
(MRFs), such as grafting, improve scalability by avoiding inference over the
entire feature space in each optimization step. Instead, inference is performed
over an incrementally grown active set of features. In this paper, we address
key computational bottlenecks that current incremental techniques still suffer
by introducing best-choice edge grafting, an incremental, structured method
that activates edges as groups of features in a streaming setting. The method
uses a reservoir of edges that satisfy an activation condition, approximating
the search for the optimal edge to activate. It also reorganizes the search
space using search-history and structure heuristics. Experiments show a
significant speedup for structure learning and a controllable trade-off between
the speed and quality of learning. | [
1,
0,
0,
1,
0,
0
] |
Title: Cavity-enhanced transport of charge,
Abstract: We theoretically investigate charge transport through electronic bands of a
mesoscopic one-dimensional system, where inter-band transitions are coupled to
a confined cavity mode, initially prepared close to its vacuum. This coupling
leads to light-matter hybridization where the dressed fermionic bands interact
via absorption and emission of dressed cavity-photons. Using a self-consistent
non-equilibrium Green's function method, we compute electronic transmissions
and cavity photon spectra and demonstrate how light-matter coupling can lead to
an enhancement of charge conductivity in the steady-state. We find that
depending on cavity loss rate, electronic bandwidth, and coupling strength, the
dynamics involves either an individual or a collective response of Bloch
states, and explain how this affects the current enhancement. We show that the
charge conductivity enhancement can reach orders of magnitudes under
experimentally relevant conditions. | [
0,
1,
0,
0,
0,
0
] |
Title: Entropy Production Rate is Maximized in Non-Contractile Actomyosin,
Abstract: The actin cytoskeleton is an active semi-flexible polymer network whose
non-equilibrium properties coordinate both stable and contractile behaviors to
maintain or change cell shape. While myosin motors drive the actin cytoskeleton
out-of-equilibrium, the role of myosin-driven active stresses in the
accumulation and dissipation of mechanical energy is unclear. To investigate
this, we synthesize an actomyosin material in vitro whose active stress content
can tune the network from stable to contractile. Each increment in activity
determines a characteristic spectrum of actin filament fluctuations which is
used to calculate the total mechanical work and the production of entropy in
the material. We find that the balance of work and entropy does not increase
monotonically and, surprisingly, the entropy production rate is maximized in
the non-contractile, stable state. Our study provides evidence that the origins
of system entropy production and activity-dependent dissipation arise from
disorder in the molecular interactions between actin and myosin | [
0,
0,
0,
0,
1,
0
] |
Title: Intrinsic resolving power of XUV diffraction gratings measured with Fizeau interferometry,
Abstract: We introduce a method for using Fizeau interferometry to measure the
intrinsic resolving power of a diffraction grating. This method is more
accurate than traditional techniques based on a long-trace profiler (LTP),
since it is sensitive to long-distance phase errors not revealed by a d-spacing
map. We demonstrate 50,400 resolving power for a mechanically ruled XUV grating
from Inprentus, Inc. | [
0,
1,
0,
0,
0,
0
] |
Title: Random matrix approach for primal-dual portfolio optimization problems,
Abstract: In this paper, we revisit the portfolio optimization problems of the
minimization/maximization of investment risk under constraints of budget and
investment concentration (primal problem) and the maximization/minimization of
investment concentration under constraints of budget and investment risk (dual
problem) for the case that the variances of the return rates of the assets are
identical. We analyze both optimization problems by using the Lagrange
multiplier method and the random matrix approach. Thereafter, we compare the
results obtained from our proposed approach with the results obtained in
previous work. Moreover, we use numerical experiments to validate the results
obtained from the replica approach and the random matrix approach as methods
for analyzing both the primal and dual portfolio optimization problems. | [
1,
1,
0,
0,
0,
0
] |
Title: Insight into the modeling of seismic waves for detection of underground cavities,
Abstract: Motivated by the need to detect an underground cavity within the procedure of
an On-Site-Inspection (OSI), of the Comprehensive Nuclear Test Ban Treaty
Organization, the aim of this paper is to present results on the comparison of
our numerical simulations with an analytic solution. The accurate numerical
modeling can facilitate the development of proper analysis techniques to detect
the remnants of an underground nuclear test. The larger goal is to help set a
rigorous scientific base of OSI and to contribute to bringing the Treaty into
force. For our 3D numerical simulations, we use the discontinuous Galerkin
Spectral Element Code SPEED jointly developed at MOX (The Laboratory for
Modeling and Scientific Computing, Department of Mathematics) and at DICA
(Department of Civil and Environmental Engineering) of the Politecnico di
Milano. | [
0,
1,
0,
0,
0,
0
] |
Title: How to centralize and normalize quandle extensions,
Abstract: We show that quandle coverings in the sense of Eisermann form a (regular
epi)-reflective subcategory of the category of surjective quandle
homomorphisms, both by using arguments coming from categorical Galois theory
and by constructing concretely a centralization congruence. Moreover, we show
that a similar result holds for normal quandle extensions. | [
0,
0,
1,
0,
0,
0
] |
Title: Geometric Fluctuation Theorem,
Abstract: We derive an extended fluctuation theorem for a geometric pumping in a
spin-boson system under a periodic control of environmental temperatures by
using a Markovian quantum master equation. We perform the Monte-Carlo
simulation and obtain the current distribution, the average current and the
fluctuation. Using the extended fluctuation theorem we try to explain the
results of our simulation. The fluctuation theorem leads to the fluctuation
dissipation relations but the absence of the conventional reciprocal relation. | [
0,
1,
0,
0,
0,
0
] |
Title: Unsupervised Domain Adaptation Based on Source-guided Discrepancy,
Abstract: Unsupervised domain adaptation is the problem setting where data generating
distributions in the source and target domains are different, and labels in the
target domain are unavailable. One important question in unsupervised domain
adaptation is how to measure the difference between the source and target
domains. A previously proposed discrepancy that does not use the source domain
labels requires high computational cost to estimate and may lead to a loose
generalization error bound in the target domain. To mitigate these problems, we
propose a novel discrepancy called source-guided discrepancy (S-disc), which
exploits labels in the source domain. As a consequence, S-disc can be computed
efficiently with a finite sample convergence guarantee. In addition, we show
that S-disc can provide a tighter generalization error bound than the one based
on an existing discrepancy. Finally, we report experimental results that
demonstrate the advantages of S-disc over the existing discrepancies. | [
0,
0,
0,
1,
0,
0
] |
Title: On structured surfaces with defects: geometry, strain incompatibility, internal stress, and natural shapes,
Abstract: Given a distribution of defects on a structured surface, such as those
represented by 2-dimensional crystalline materials, liquid crystalline
surfaces, and thin sandwiched shells, what is the resulting stress field and
the deformed shape? Motivated by this concern, we first classify, and quantify,
the translational, rotational, and metrical defects allowable over a broad
class of structured surfaces. With an appropriate notion of strain, the defect
densities are then shown to appear as sources of strain incompatibility. The
strain incompatibility relations, with appropriate kinematical assumptions on
the decomposition of strain into elastic and plastic parts, and the stress
equilibrium relations, with a suitable choice of material response, provide the
necessary equations for determining both the internal stress field and the
deformed shape. We demonstrate this by applying our theory to Kirchhoff-Love
shells with a kinematics which allows for small in-surface strains but
moderately large rotations. | [
0,
1,
1,
0,
0,
0
] |
Title: Fatiguing STDP: Learning from Spike-Timing Codes in the Presence of Rate Codes,
Abstract: Spiking neural networks (SNNs) could play a key role in unsupervised machine
learning applications, by virtue of strengths related to learning from the fine
temporal structure of event-based signals. However, some spike-timing-related
strengths of SNNs are hindered by the sensitivity of spike-timing-dependent
plasticity (STDP) rules to input spike rates, as fine temporal correlations may
be obstructed by coarser correlations between firing rates. In this article, we
propose a spike-timing-dependent learning rule that allows a neuron to learn
from the temporally-coded information despite the presence of rate codes. Our
long-term plasticity rule makes use of short-term synaptic fatigue dynamics. We
show analytically that, in contrast to conventional STDP rules, our fatiguing
STDP (FSTDP) helps learn the temporal code, and we derive the necessary
conditions to optimize the learning process. We showcase the effectiveness of
FSTDP in learning spike-timing correlations among processes of different rates
in synthetic data. Finally, we use FSTDP to detect correlations in real-world
weather data from the United States in an experimental realization of the
algorithm that uses a neuromorphic hardware platform comprising phase-change
memristive devices. Taken together, our analyses and demonstrations suggest
that FSTDP paves the way for the exploitation of the spike-based strengths of
SNNs in real-world applications. | [
1,
0,
0,
1,
0,
0
] |
Title: Coresets for Vector Summarization with Applications to Network Graphs,
Abstract: We provide a deterministic data summarization algorithm that approximates the
mean $\bar{p}=\frac{1}{n}\sum_{p\in P} p$ of a set $P$ of $n$ vectors in
$\REAL^d$, by a weighted mean $\tilde{p}$ of a \emph{subset} of $O(1/\eps)$
vectors, i.e., independent of both $n$ and $d$. We prove that the squared
Euclidean distance between $\bar{p}$ and $\tilde{p}$ is at most $\eps$
multiplied by the variance of $P$. We use this algorithm to maintain an
approximated sum of vectors from an unbounded stream, using memory that is
independent of $d$, and logarithmic in the $n$ vectors seen so far. Our main
application is to extract and represent in a compact way friend groups and
activity summaries of users from underlying data exchanges. For example, in the
case of mobile networks, we can use GPS traces to identify meetings, in the
case of social networks, we can use information exchange to identify friend
groups. Our algorithm provably identifies the {\it Heavy Hitter} entries in a
proximity (adjacency) matrix. The Heavy Hitters can be used to extract and
represent in a compact way friend groups and activity summaries of users from
underlying data exchanges. We evaluate the algorithm on several large data
sets. | [
1,
0,
0,
0,
0,
0
] |
Title: Two-dimensional matter-wave solitons and vortices in competing cubic-quintic nonlinear lattices,
Abstract: The nonlinear lattice---a new and nonlinear class of periodic
potentials---was recently introduced to generate various nonlinear localized
modes. Several attempts failed to stabilize two-dimensional (2D) solitons
against their intrinsic critical collapse in Kerr media. Here, we provide a
possibility for supporting 2D matter-wave solitons and vortices in an extended
setting---the cubic and quintic model---by introducing another nonlinear
lattice whose period is controllable and can be different from its cubic
counterpart, to its quintic nonlinearity, therefore making a fully `nonlinear
quasi-crystal'.
A variational approximation based on Gaussian ansatz is developed for the
fundamental solitons and in particular, their stability exactly follows the
inverted \textit{Vakhitov-Kolokolov} stability criterion, whereas the vortex
solitons are only studied by means of numerical methods. Stability regions for
two types of localized mode---the fundamental and vortex solitons---are
provided. A noteworthy feature of the localized solutions is that the vortex
solitons are stable only when the period of the quintic nonlinear lattice is
the same as the cubic one or when the quintic nonlinearity is constant, while
the stable fundamental solitons can be created under looser conditions. Our
physical setting (cubic-quintic model) is in the framework of the
Gross-Pitaevskii equation (GPE) or nonlinear Schrödinger equation, the
predicted localized modes thus may be implemented in Bose-Einstein condensates
and nonlinear optical media with tunable cubic and quintic nonlinearities. | [
0,
1,
0,
0,
0,
0
] |
Title: NimbRo-OP2X: Adult-sized Open-source 3D Printed Humanoid Robot,
Abstract: Humanoid robotics research depends on capable robot platforms, but recently
developed advanced platforms are often not available to other research groups,
expensive, dangerous to operate, or closed-source. The lack of available
platforms forces researchers to work with smaller robots, which have less
strict dynamic constraints or with simulations, which lack many real-world
effects. We developed NimbRo-OP2X to address this need. At a height of 135 cm
our robot is large enough to interact in a human environment. Its low weight of
only 19 kg makes the operation of the robot safe and easy, as no special
operational equipment is necessary. Our robot is equipped with a fast onboard
computer and a GPU to accelerate parallel computations. We extend our already
open-source software by a deep-learning based vision system and gait parameter
optimisation. The NimbRo-OP2X was evaluated during RoboCup 2018 in Montréal,
Canada, where it won all possible awards in the Humanoid AdultSize class. | [
1,
0,
0,
0,
0,
0
] |
Title: Approximate Steepest Coordinate Descent,
Abstract: We propose a new selection rule for the coordinate selection in coordinate
descent methods for huge-scale optimization. The efficiency of this novel
scheme is provably better than the efficiency of uniformly random selection,
and can reach the efficiency of steepest coordinate descent (SCD), enabling an
acceleration of a factor of up to $n$, the number of coordinates. In many
practical applications, our scheme can be implemented at no extra cost and
computational efficiency very close to the faster uniform selection. Numerical
experiments with Lasso and Ridge regression show promising improvements, in
line with our theoretical guarantees. | [
1,
0,
1,
0,
0,
0
] |
Title: Learning Latent Representations for Speech Generation and Transformation,
Abstract: An ability to model a generative process and learn a latent representation
for speech in an unsupervised fashion will be crucial to process vast
quantities of unlabelled speech data. Recently, deep probabilistic generative
models such as Variational Autoencoders (VAEs) have achieved tremendous success
in modeling natural images. In this paper, we apply a convolutional VAE to
model the generative process of natural speech. We derive latent space
arithmetic operations to disentangle learned latent representations. We
demonstrate the capability of our model to modify the phonetic content or the
speaker identity for speech segments using the derived operations, without the
need for parallel supervisory data. | [
1,
0,
0,
1,
0,
0
] |
Title: On the generalized nonlinear Camassa-Holm equation,
Abstract: In this paper, a generalized nonlinear Camassa-Holm equation with time- and
space-dependent coefficients is considered. We show that the control of the
higher order dispersive term is possible by using an adequate weight function
to define the energy. The existence and uniqueness of solutions are obtained
via a Picard iterative method. | [
0,
0,
1,
0,
0,
0
] |
Title: Early MFCC And HPCP Fusion for Robust Cover Song Identification,
Abstract: While most schemes for automatic cover song identification have focused on
note-based features such as HPCP and chord profiles, a few recent papers
surprisingly showed that local self-similarities of MFCC-based features also
have classification power for this task. Since MFCC and HPCP capture
complementary information, we design an unsupervised algorithm that combines
normalized, beat-synchronous blocks of these features using cross-similarity
fusion before attempting to locally align a pair of songs. As an added bonus,
our scheme naturally incorporates structural information in each song to fill
in alignment gaps where both feature sets fail. We show a striking jump in
performance over MFCC and HPCP alone, achieving a state of the art mean
reciprocal rank of 0.87 on the Covers80 dataset. We also introduce a new
medium-sized hand designed benchmark dataset called "Covers 1000," which
consists of 395 cliques of cover songs for a total of 1000 songs, and we show
that our algorithm achieves an MRR of 0.9 on this dataset for the first
correctly identified song in a clique. We provide the precomputed HPCP and MFCC
features, as well as beat intervals, for all songs in the Covers 1000 dataset
for use in further research. | [
1,
0,
0,
0,
0,
0
] |
Title: P-Governance Technology: Using Big Data for Political Party Management,
Abstract: Information and Communication Technology (ICT) has been playing a pivotal
role since the last decade in developing countries that brings citizen services
to the doorsteps and connecting people. With this aspiration ICT has introduced
several technologies of citizen services towards all categories of people. The
purpose of this study is to examine the Governance technology perspectives for
political party, emphasizing on the basic critical steps through which it could
be operationalized. We call it P-Governance. P-Governance shows technologies to
ensure governance, management, interaction communication in a political party
by improving decision making processes using big data. P-Governance challenges
the competence perspective to apply itself more assiduously to
operationalization, including the need to choose and give definition to one or
more units of analysis (of which the routine is a promising candidate). This
paper is to focus on research challenges posed by competence to which
P-Governance can and should respond include different strategy issues faced by
particular sections. Both the qualitative as well as quantitative research
approaches were conducted. The standard of citizen services, choice &
consultation, courtesy & consultation, entrance & information, and value for
money have found the positive relation with citizen's satisfaction. This study
results how can be technology make important roles on political movements in
developing countries using big data. | [
1,
0,
0,
0,
0,
0
] |
Title: Analogues of the $p^n$th Hilbert symbol in characteristic $p$ (updated),
Abstract: The $p$th degree Hilbert symbol $(\cdot,\cdot )_p:K^\times/K^{\times p}\times
K^\times/K^{\times p}\to{}_p{\rm Br}(K)$ from characteristic $\neq p$ has two
analogues in characteristic $p$, $$[\cdot,\cdot )_p:K/\wp (K)\times
K^\times/K^{\times p}\to{}_p{\rm Br}(K),$$ where $\wp$ is the Artin-Schreier
map $x\mapsto x^p-x$, and $$((\cdot,\cdot ))_p:K/K^p\times K/K^p\to{}_p{\rm
Br}(K).$$
The symbol $[\cdot,\cdot )_p$ generalizes to an analogue of $(\cdot,\cdot
)_{p^n}$ via the Witt vectors, $$[\cdot,\cdot )_{p^n}:W_n(K)/\wp (W_n(K))\times
K^\times/K^{\times p^n}\to{}_{p^n}{\rm Br}(K).$$
Here $W_n(K)$ is the truncation of length $n$ of the ring of $p$-typical Witt
wectors, i.e. $W_{\{1,p,\ldots,p^{n-1}\}}(K)$.
In this paper we construct similar generalizations for $((\cdot,\cdot ))_p$.
Our construction involves Witt vectors and Weyl algebras. In the process we
obtain a new kind of Weyl algebras in characteristic $p$, with many interesting
properties.
The symbols we introduce, $((\cdot,\cdot ))_{p^n}$ and, more generally,
$((\cdot,\cdot ))_{p^m,p^n}$, which here are defined in terms of central simple
algebras, coincide with the homonymous symbols we introduced in
[arXiv:1711.00980] in terms of the symbols $[\cdot,\cdot )_{p^n}$. This will be
proved in a future paper. In the present paper we only introduce the symbols
and we prove that they have the same properties with the symbols from
[arXiv:1711.00980]. These properies are enough to obtain the representation
theorem for ${}_{p^n}{\rm Br}(K)$ from [arXiv:1711.00980], Theorem 4.10. | [
0,
0,
1,
0,
0,
0
] |
Title: GAMER-2: a GPU-accelerated adaptive mesh refinement code -- accuracy, performance, and scalability,
Abstract: We present GAMER-2, a GPU-accelerated adaptive mesh refinement (AMR) code for
astrophysics. It provides a rich set of features, including adaptive
time-stepping, several hydrodynamic schemes, magnetohydrodynamics,
self-gravity, particles, star formation, chemistry and radiative processes with
GRACKLE, data analysis with yt, and memory pool for efficient object
allocation. GAMER-2 is fully bitwise reproducible. For the performance
optimization, it adopts hybrid OpenMP/MPI/GPU parallelization and utilizes
overlapping CPU computation, GPU computation, and CPU-GPU communication. Load
balancing is achieved using a Hilbert space-filling curve on a level-by-level
basis without the need to duplicate the entire AMR hierarchy on each MPI
process. To provide convincing demonstrations of the accuracy and performance
of GAMER-2, we directly compare with Enzo on isolated disk galaxy simulations
and with FLASH on galaxy cluster merger simulations. We show that the physical
results obtained by different codes are in very good agreement, and GAMER-2
outperforms Enzo and FLASH by nearly one and two orders of magnitude,
respectively, on the Blue Waters supercomputers using $1-256$ nodes. More
importantly, GAMER-2 exhibits similar or even better parallel scalability
compared to the other two codes. We also demonstrate good weak and strong
scaling using up to 4096 GPUs and 65,536 CPU cores, and achieve a uniform
resolution as high as $10{,}240^3$ cells. Furthermore, GAMER-2 can be adopted
as an AMR+GPUs framework and has been extensively used for the wave dark matter
($\psi$DM) simulations. GAMER-2 is open source (available at
this https URL) and new contributions are welcome. | [
0,
1,
0,
0,
0,
0
] |
Title: Distributed sub-optimal resource allocation over weight-balanced graph via singular perturbation,
Abstract: In this paper, we consider distributed optimization design for resource
allocation problems over weight-balanced graphs. With the help of singular
perturbation analysis, we propose a simple sub-optimal continuous-time
optimization algorithm. Moreover, we prove the existence and uniqueness of the
algorithm equilibrium, and then show the convergence with an exponential rate.
Finally, we verify the sub-optimality of the algorithm, which can approach the
optimal solution as an adjustable parameter tends to zero. | [
0,
0,
1,
0,
0,
0
] |
Title: Decentralized P2P Energy Trading under Network Constraints in a Low-Voltage Network,
Abstract: The increasing uptake of distributed energy resources (DERs) in distribution
systems and the rapid advance of technology have established new scenarios in
the operation of low-voltage networks. In particular, recent trends in
cryptocurrencies and blockchain have led to a proliferation of peer-to-peer
(P2P) energy trading schemes, which allow the exchange of energy between the
neighbors without any intervention of a conventional intermediary in the
transactions. Nevertheless, far too little attention has been paid to the
technical constraints of the network under this scenario. A major challenge to
implementing P2P energy trading is that of ensuring that network constraints
are not violated during the energy exchange. This paper proposes a methodology
based on sensitivity analysis to assess the impact of P2P transactions on the
network and to guarantee an exchange of energy that does not violate network
constraints. The proposed method is tested on a typical UK low-voltage network.
The results show that our method ensures that energy is exchanged between users
under the P2P scheme without violating the network constraints, and that users
can still capture the economic benefits of the P2P architecture. | [
1,
0,
0,
0,
0,
0
] |
Title: Fictitious GAN: Training GANs with Historical Models,
Abstract: Generative adversarial networks (GANs) are powerful tools for learning
generative models. In practice, the training may suffer from lack of
convergence. GANs are commonly viewed as a two-player zero-sum game between two
neural networks. Here, we leverage this game theoretic view to study the
convergence behavior of the training process. Inspired by the fictitious play
learning process, a novel training method, referred to as Fictitious GAN, is
introduced. Fictitious GAN trains the deep neural networks using a mixture of
historical models. Specifically, the discriminator (resp. generator) is updated
according to the best-response to the mixture outputs from a sequence of
previously trained generators (resp. discriminators). It is shown that
Fictitious GAN can effectively resolve some convergence issues that cannot be
resolved by the standard training approach. It is proved that asymptotically
the average of the generator outputs has the same distribution as the data
samples. | [
0,
0,
0,
1,
0,
0
] |
Title: The Wavefunction of the Collapsing Bose-Einstein Condensate,
Abstract: Bose-Einstein condensates with tunable interatomic interactions have been
studied intensely in recent experiments. The investigation of the collapse of a
condensate following a sudden change in the nature of the interaction from
repulsive to attractive has led to the observation of a remnant condensate that
did not undergo further collapse. We suggest that this high-density remnant is
in fact the absolute minimum of the energy, if the attractive atomic
interactions are nonlocal, and is therefore inherently stable. We show that a
variational trial function consisting of a superposition of two distinct
gaussians is an accurate representation of the wavefunction of the ground state
of the conventional local Gross-Pitaevskii field equation for an attractive
condensate and gives correctly the points of emergence of instability. We then
use such a superposition of two gaussians as a variational trial function in
order to calculate the minima of the energy when it includes a nonlocal
interaction term. We use experimental data in order to study the long range of
the nonlocal interaction, showing that they agree very well with a
dimensionally derived expression for this range. | [
0,
1,
0,
0,
0,
0
] |
Title: Not-So-Random Features,
Abstract: We propose a principled method for kernel learning, which relies on a
Fourier-analytic characterization of translation-invariant or
rotation-invariant kernels. Our method produces a sequence of feature maps,
iteratively refining the SVM margin. We provide rigorous guarantees for
optimality and generalization, interpreting our algorithm as online
equilibrium-finding dynamics in a certain two-player min-max game. Evaluations
on synthetic and real-world datasets demonstrate scalability and consistent
improvements over related random features-based methods. | [
1,
0,
0,
1,
0,
0
] |
Title: CLEAR: Coverage-based Limiting-cell Experiment Analysis for RNA-seq,
Abstract: Direct cDNA preamplification protocols developed for single-cell RNA-seq
(scRNA-seq) have enabled transcriptome profiling of rare cells without having
to pool multiple samples or to perform RNA extraction. We term this approach
limiting-cell RNA-seq (lcRNA-seq). Unlike scRNA-seq, which focuses on
'cell-atlasing', lcRNA-seq focuses on identifying differentially expressed
genes (DEGs) between experimental groups. This requires accounting for systems
noise which can obscure biological differences. We present CLEAR, a workflow
that identifies robust transcripts in lcRNA-seq data for between-group
comparisons. To develop CLEAR, we compared DEGs from RNA extracted from
FACS-derived CD5+ and CD5- cells from a single chronic lymphocytic leukemia
patient diluted to input RNA levels of 10-, 100- and 1,000pg. Data quality at
ultralow input levels are known to be noisy. When using CLEAR transcripts vs.
using all available transcripts, downstream analyses reveal more shared DEGs,
improved Principal Component Analysis separation of cell type, and increased
similarity between results across different input RNA amounts. CLEAR was
applied to two publicly available ultralow input RNA-seq data and an in-house
murine neural cell lcRNA-seq dataset. CLEAR provides a novel way to visualize
the public datasets while validates cell phenotype markers for astrocytes,
neural stem and progenitor cells. | [
0,
0,
0,
0,
1,
0
] |
Title: Reducing Storage of Global Wind Ensembles with Stochastic Generators,
Abstract: Wind has the potential to make a significant contribution to future energy
resources. Locating the sources of this renewable energy on a global scale is
however extremely challenging, given the difficulty to store very large data
sets generated by modern computer models. We propose a statistical model that
aims at reproducing the data-generating mechanism of an ensemble of runs via a
Stochastic Generator (SG) of global annual wind data. We introduce an
evolutionary spectrum approach with spatially varying parameters based on
large-scale geographical descriptors such as altitude to better account for
different regimes across the Earth's orography. We consider a multi-step
conditional likelihood approach to estimate the parameters that explicitly
accounts for nonstationary features while also balancing memory storage and
distributed computation. We apply the proposed model to more than 18 million
points of yearly global wind speed. The proposed SG requires orders of
magnitude less storage for generating surrogate ensemble members from wind than
does creating additional wind fields from the climate model, even if an
effective lossy data compression algorithm is applied to the simulation output. | [
0,
0,
0,
1,
0,
0
] |
Title: Continual One-Shot Learning of Hidden Spike-Patterns with Neural Network Simulation Expansion and STDP Convergence Predictions,
Abstract: This paper presents a constructive algorithm that achieves successful
one-shot learning of hidden spike-patterns in a competitive detection task. It
has previously been shown (Masquelier et al., 2008) that spike-timing-dependent
plasticity (STDP) and lateral inhibition can result in neurons competitively
tuned to repeating spike-patterns concealed in high rates of overall
presynaptic activity. One-shot construction of neurons with synapse weights
calculated as estimates of converged STDP outcomes results in immediate
selective detection of hidden spike-patterns. The capability of continual
learning is demonstrated through the successful one-shot detection of new sets
of spike-patterns introduced after long intervals in the simulation time.
Simulation expansion (Lightheart et al., 2013) has been proposed as an approach
to the development of constructive algorithms that are compatible with
simulations of biological neural networks. A simulation of a biological neural
network may have orders of magnitude fewer neurons and connections than the
related biological neural systems; therefore, simulated neural networks can be
assumed to be a subset of a larger neural system. The constructive algorithm is
developed using simulation expansion concepts to perform an operation
equivalent to the exchange of neurons between the simulation and the larger
hypothetical neural system. The dynamic selection of neurons to simulate within
a larger neural system (hypothetical or stored in memory) may be a starting
point for a wide range of developments and applications in machine learning and
the simulation of biology. | [
0,
0,
0,
1,
0,
0
] |
Title: Interpretable High-Dimensional Inference Via Score Projection with an Application in Neuroimaging,
Abstract: In the fields of neuroimaging and genetics, a key goal is testing the
association of a single outcome with a very high-dimensional imaging or genetic
variable. Often, summary measures of the high-dimensional variable are created
to sequentially test and localize the association with the outcome. In some
cases, the results for summary measures are significant, but subsequent tests
used to localize differences are underpowered and do not identify regions
associated with the outcome. Here, we propose a generalization of Rao's score
test based on projecting the score statistic onto a linear subspace of a
high-dimensional parameter space. In addition, we provide methods to localize
signal in the high-dimensional space by projecting the scores to the subspace
where the score test was performed. This allows for inference in the
high-dimensional space to be performed on the same degrees of freedom as the
score test, effectively reducing the number of comparisons. Simulation results
demonstrate the test has competitive power relative to others commonly used. We
illustrate the method by analyzing a subset of the Alzheimer's Disease
Neuroimaging Initiative dataset. Results suggest cortical thinning of the
frontal and temporal lobes may be a useful biological marker of Alzheimer's
risk. | [
0,
0,
1,
1,
0,
0
] |
Title: Drawing Big Graphs using Spectral Sparsification,
Abstract: Spectral sparsification is a general technique developed by Spielman et al.
to reduce the number of edges in a graph while retaining its structural
properties. We investigate the use of spectral sparsification to produce good
visual representations of big graphs. We evaluate spectral sparsification
approaches on real-world and synthetic graphs. We show that spectral
sparsifiers are more effective than random edge sampling. Our results lead to
guidelines for using spectral sparsification in big graph visualization. | [
1,
0,
0,
0,
0,
0
] |
Title: Finding polynomial loop invariants for probabilistic programs,
Abstract: Quantitative loop invariants are an essential element in the verification of
probabilistic programs. Recently, multivariate Lagrange interpolation has been
applied to synthesizing polynomial invariants. In this paper, we propose an
alternative approach. First, we fix a polynomial template as a candidate of a
loop invariant. Using Stengle's Positivstellensatz and a transformation to a
sum-of-squares problem, we find sufficient conditions on the coefficients.
Then, we solve a semidefinite programming feasibility problem to synthesize the
loop invariants. If the semidefinite program is unfeasible, we backtrack after
increasing the degree of the template. Our approach is semi-complete in the
sense that it will always lead us to a feasible solution if one exists and
numerical errors are small. Experimental results show the efficiency of our
approach. | [
1,
0,
0,
0,
0,
0
] |
Title: Multifrequency Excitation and Detection Scheme in Apertureless Scattering Near Field Scanning Optical Microscopy,
Abstract: We theoretically and experimentally demonstrate a multifrequency excitation
and detection scheme in apertureless near field optical microscopy, that
exceeds current state of the art sensitivity and background suppression. By
exciting the AFM tip at its two first flexural modes, and demodulating the
detected signal at the harmonics of their sum, we extract a near field signal
with a twofold improved sensitivity and deep sub-wavelength resolution,
reaching $\lambda/230$. Furthermore, the method offers rich control over
experimental degrees of freedom, expanding the parameter space for achieving
complete optical background suppression. This approach breaks the ground for
non-interferometric complete phase and amplitude retrieval of the near field
signal, and is suitable for any multimodal excitation and higher harmonic
demodulation. | [
0,
1,
0,
0,
0,
0
] |
Title: Global entropy solutions to the compressible Euler equations in the isentropic nozzle flow for large data: Application of the modified Godunov scheme and the generalized invariant regions,
Abstract: We study the motion of isentropic gas in nozzles. This is a major subject in
fluid dynamics. In fact, the nozzle is utilized to increase the thrust of
rocket engines. Moreover, the nozzle flow is closely related to astrophysics.
These phenomena are governed by the compressible Euler equation, which is one
of crucial equations in inhomogeneous conservation laws.
In this paper, we consider its unsteady flow and devote to proving the global
existence and stability of solutions to the Cauchy problem for the general
nozzle. The theorem has been proved in (Tsuge in Arch. Ration. Mech. Anal.
209:365-400 (2013)). However, this result is limited to small data. Our aim in
the present paper is to remove this restriction, that is, we consider large
data. Although the subject is important in Mathematics, Physics and
engineering, it remained open for a long time. The problem seems to lie in a
bounded estimate of approximate solutions, because we have only method to
investigate the behavior with respect to the time variable. To solve this, we
first introduce a generalized invariant region. Compared with the existing
ones, its upper and lower bounds are extended constants to functions of the
space variable. However, we cannot apply the new invariant region to the
traditional difference method. Therefore, we invent the modified Godunov
scheme. The approximate solutions consist of some functions corresponding to
the upper and lower bounds of the invariant regions. These methods enable us to
investigate the behavior of approximate solutions with respect to the space
variable. The ideas are also applicable to other nonlinear problems involving
similar difficulties. | [
0,
0,
1,
0,
0,
0
] |
Title: Gravitational instabilities in a protosolar-like disc II: continuum emission and mass estimates,
Abstract: Gravitational instabilities (GIs) are most likely a fundamental process
during the early stages of protoplanetary disc formation. Recently, there have
been detections of spiral features in young, embedded objects that appear
consistent with GI-driven structure. It is crucial to perform hydrodynamic and
radiative transfer simulations of gravitationally unstable discs in order to
assess the validity of GIs in such objects, and constrain optimal targets for
future observations. We utilise the radiative transfer code LIME to produce
continuum emission maps of a $0.17\,\mathrm{M}_{\odot}$ self-gravitating
protosolar-like disc. We note the limitations of using LIME as is and explore
methods to improve upon the default gridding. We use CASA to produce synthetic
observations of 270 continuum emission maps generated across different
frequencies, inclinations and dust opacities. We find that the spiral structure
of our protosolar-like disc model is distinguishable across the majority of our
parameter space after 1 hour of observation, and is especially prominent at
230$\,$GHz due to the favourable combination of angular resolution and
sensitivity. Disc mass derived from the observations is sensitive to the
assumed dust opacities and temperatures, and therefore can be underestimated by
a factor of at least 30 at 850$\,$GHz and 2.5 at 90$\,$GHz. As a result, this
effect could retrospectively validate GIs in discs previously thought not
massive enough to be gravitationally unstable, which could have a significant
impact on the understanding of the formation and evolution of protoplanetary
discs. | [
0,
1,
0,
0,
0,
0
] |
Title: Information Processing by Networks of Quantum Decision Makers,
Abstract: We suggest a model of a multi-agent society of decision makers taking
decisions being based on two criteria, one is the utility of the prospects and
the other is the attractiveness of the considered prospects. The model is the
generalization of quantum decision theory, developed earlier for single
decision makers realizing one-step decisions, in two principal aspects. First,
several decision makers are considered simultaneously, who interact with each
other through information exchange. Second, a multistep procedure is treated,
when the agents exchange information many times. Several decision makers
exchanging information and forming their judgement, using quantum rules, form a
kind of a quantum information network, where collective decisions develop in
time as a result of information exchange. In addition to characterizing
collective decisions that arise in human societies, such networks can describe
dynamical processes occurring in artificial quantum intelligence composed of
several parts or in a cluster of quantum computers. The practical usage of the
theory is illustrated on the dynamic disjunction effect for which three
quantitative predictions are made: (i) the probabilistic behavior of decision
makers at the initial stage of the process is described; (ii) the decrease of
the difference between the initial prospect probabilities and the related
utility factors is proved; (iii) the existence of a common consensus after
multiple exchange of information is predicted. The predicted numerical values
are in very good agreement with empirical data. | [
1,
0,
0,
0,
0,
0
] |
Title: From Strings to Sets,
Abstract: A complete proof is given of relative interpretability of Adjunctive Set
Theory with Extensionality in an elementary concatenation theory. | [
0,
0,
1,
0,
0,
0
] |
Title: Evolutionary game of coalition building under external pressure,
Abstract: We study the fragmentation-coagulation (or merging and splitting)
evolutionary control model as introduced recently by one of the authors, where
$N$ small players can form coalitions to resist to the pressure exerted by the
principal. It is a Markov chain in continuous time and the players have a
common reward to optimize. We study the behavior as $N$ grows and show that the
problem converges to a (one player) deterministic optimization problem in
continuous time, in the infinite dimensional state space. | [
0,
0,
1,
0,
0,
0
] |
Title: Graphene-based electron transport layers in perovskite solar cells: a step-up for an efficient carrier collection,
Abstract: The electron transport layer (ETL) plays a fundamental role in perovskite
solar cells. Recently, graphene-based ETLs have been proved to be good
candidate for scalable fabrication processes and to achieve higher carrier
injection with respect to most commonly used ETLs. In this work we
experimentally study the effects of different graphene-based ETLs in sensitized
MAPI solar cells. By means of time-integrated and picosecond time-resolved
photoluminescence techniques, the carrier recombination dynamics in MAPI films
embedded in different ETLs is investigated. Using graphene doped mesoporous
TiO2 (G+mTiO2) with the addition of a lithium-neutralized graphene oxide
(GO-Li) interlayer as ETL, we find that the carrier collection efficiency is
increased by about a factor two with respect to standard mTiO2. Taking
advantage of the absorption coefficient dispersion, we probe the MAPI layer
morphology, along the thickness, finding that the MAPI embedded in the ETL
composed by G+mTiO2 plus GO-Li brings to a very good crystalline quality of the
MAPI layer with a trap density about one order of magnitude lower than that
found with the other ETLs. In addition, this ETL freezes MAPI at the tetragonal
phase, regardless of the temperature. Graphene-based ETLs can open the way to
significant improvement of perovskite solar cells. | [
0,
1,
0,
0,
0,
0
] |
Title: Learning and Transferring IDs Representation in E-commerce,
Abstract: Many machine intelligence techniques are developed in E-commerce and one of
the most essential components is the representation of IDs, including user ID,
item ID, product ID, store ID, brand ID, category ID etc. The classical
encoding based methods (like one-hot encoding) are inefficient in that it
suffers sparsity problems due to its high dimension, and it cannot reflect the
relationships among IDs, either homogeneous or heterogeneous ones. In this
paper, we propose an embedding based framework to learn and transfer the
representation of IDs. As the implicit feedbacks of users, a tremendous amount
of item ID sequences can be easily collected from the interactive sessions. By
jointly using these informative sequences and the structural connections among
IDs, all types of IDs can be embedded into one low-dimensional semantic space.
Subsequently, the learned representations are utilized and transferred in four
scenarios: (i) measuring the similarity between items, (ii) transferring from
seen items to unseen items, (iii) transferring across different domains, (iv)
transferring across different tasks. We deploy and evaluate the proposed
approach in Hema App and the results validate its effectiveness. | [
1,
0,
0,
1,
0,
0
] |
Title: Differentiable Supervector Extraction for Encoding Speaker and Phrase Information in Text Dependent Speaker Verification,
Abstract: In this paper, we propose a new differentiable neural network alignment
mechanism for text-dependent speaker verification which uses alignment models
to produce a supervector representation of an utterance. Unlike previous works
with similar approaches, we do not extract the embedding of an utterance from
the mean reduction of the temporal dimension. Our system replaces the mean by a
phrase alignment model to keep the temporal structure of each phrase which is
relevant in this application since the phonetic information is part of the
identity in the verification task. Moreover, we can apply a convolutional
neural network as front-end, and thanks to the alignment process being
differentiable, we can train the whole network to produce a supervector for
each utterance which will be discriminative with respect to the speaker and the
phrase simultaneously. As we show, this choice has the advantage that the
supervector encodes the phrase and speaker information providing good
performance in text-dependent speaker verification tasks. In this work, the
process of verification is performed using a basic similarity metric, due to
simplicity, compared to other more elaborate models that are commonly used. The
new model using alignment to produce supervectors was tested on the
RSR2015-Part I database for text-dependent speaker verification, providing
competitive results compared to similar size networks using the mean to extract
embeddings. | [
1,
0,
0,
0,
0,
0
] |
Title: Partial-wave Coulomb t-matrices for like-charged particles at ground-state energy,
Abstract: We study a special case at which the analytical solution of the
Lippmann-Schwinger integral equation for the partial wave two-body Coulomb
transition matrix for likely charged particles at negative energy is possible.
With the use of the Fock's method of the stereographic projection of the
momentum space onto the four-dimensional unit sphere, the analytical
expressions for s-, p- and d-wave partial Coulomb transition matrices for
repulsively interacting particles at bound-state energy have been derived. | [
0,
1,
0,
0,
0,
0
] |
Title: The Cross-section of a Spherical Double Cone,
Abstract: We show that the poset of $SL(n)$-orbit closures in the product of two
partial flag varieties is a lattice if the action of $SL(n)$ is spherical. | [
0,
0,
1,
0,
0,
0
] |
Title: SESA: Supervised Explicit Semantic Analysis,
Abstract: In recent years supervised representation learning has provided state of the
art or close to the state of the art results in semantic analysis tasks
including ranking and information retrieval. The core idea is to learn how to
embed items into a latent space such that they optimize a supervised objective
in that latent space. The dimensions of the latent space have no clear
semantics, and this reduces the interpretability of the system. For example, in
personalization models, it is hard to explain why a particular item is ranked
high for a given user profile. We propose a novel model of representation
learning called Supervised Explicit Semantic Analysis (SESA) that is trained in
a supervised fashion to embed items to a set of dimensions with explicit
semantics. The model learns to compare two objects by representing them in this
explicit space, where each dimension corresponds to a concept from a knowledge
base. This work extends Explicit Semantic Analysis (ESA) with a supervised
model for ranking problems. We apply this model to the task of Job-Profile
relevance in LinkedIn in which a set of skills defines our explicit dimensions
of the space. Every profile and job are encoded to this set of skills their
similarity is calculated in this space. We use RNNs to embed text input into
this space. In addition to interpretability, our model makes use of the
web-scale collaborative skills data that is provided by users for each LinkedIn
profile. Our model provides state of the art result while it remains
interpretable. | [
1,
0,
0,
0,
0,
0
] |
Title: A Network of Networks Approach to Interconnected Power Grids,
Abstract: We present two different approaches to model power grids as interconnected
networks of networks. Both models are derived from a model for spatially
embedded mono-layer networks and are generalised to handle an arbitrary number
of network layers. The two approaches are distinguished by their use case. The
static glue stick construction model yields a multi-layer network from a
predefined layer interconnection scheme, i.e. different layers are attached
with transformer edges. It is especially suited to construct multi-layer power
grids with a specified number of nodes in and transformers between layers. We
contrast it with a genuine growth model which we label interconnected layer
growth model. | [
0,
1,
0,
0,
0,
0
] |
Title: Marked points on translation surfaces,
Abstract: We show that all GL(2,R) equivariant point markings over orbit closures of
translation surfaces arise from branched covering constructions and periodic
points, completely classify such point markings over strata of quadratic
differentials, and give applications to the finite blocking problem. | [
0,
0,
1,
0,
0,
0
] |
Title: Multitask Learning for Fundamental Frequency Estimation in Music,
Abstract: Fundamental frequency (f0) estimation from polyphonic music includes the
tasks of multiple-f0, melody, vocal, and bass line estimation. Historically
these problems have been approached separately, and only recently, using
learning-based approaches. We present a multitask deep learning architecture
that jointly estimates outputs for various tasks including multiple-f0, melody,
vocal and bass line estimation, and is trained using a large,
semi-automatically annotated dataset. We show that the multitask model
outperforms its single-task counterparts, and explore the effect of various
design decisions in our approach, and show that it performs better or at least
competitively when compared against strong baseline methods. | [
1,
0,
0,
1,
0,
0
] |
Title: Planning with Multiple Biases,
Abstract: Recent work has considered theoretical models for the behavior of agents with
specific behavioral biases: rather than making decisions that optimize a given
payoff function, the agent behaves inefficiently because its decisions suffer
from an underlying bias. These approaches have generally considered an agent
who experiences a single behavioral bias, studying the effect of this bias on
the outcome.
In general, however, decision-making can and will be affected by multiple
biases operating at the same time. How do multiple biases interact to produce
the overall outcome? Here we consider decisions in the presence of a pair of
biases exhibiting an intuitively natural interaction: present bias -- the
tendency to value costs incurred in the present too highly -- and sunk-cost
bias -- the tendency to incorporate costs experienced in the past into one's
plans for the future.
We propose a theoretical model for planning with this pair of biases, and we
show how certain natural behavioral phenomena can arise in our model only when
agents exhibit both biases. As part of our model we differentiate between
agents that are aware of their biases (sophisticated) and agents that are
unaware of them (naive). Interestingly, we show that the interaction between
the two biases is quite complex: in some cases, they mitigate each other's
effects while in other cases they might amplify each other. We obtain a number
of further results as well, including the fact that the planning problem in our
model for an agent experiencing and aware of both biases is computationally
hard in general, though tractable under more relaxed assumptions. | [
1,
1,
0,
0,
0,
0
] |
Title: Zhu reduction for Jacobi $n$-point functions and applications,
Abstract: We establish precise Zhu reduction formulas for Jacobi $n$-point functions
which show the absence of any possible poles arising in these formulas. We then
exploit this to produce results concerning the structure of strongly regular
vertex operator algebras, and also to motivate new differential operators
acting on Jacobi forms. Finally, we apply the reduction formulas to the Fermion
model in order to create polynomials of quasi-Jacobi forms which are Jacobi
forms. | [
0,
0,
1,
0,
0,
0
] |
Title: Achieving rental harmony with a secretive roommate,
Abstract: Given the subjective preferences of n roommates in an n-bedroom apartment,
one can use Sperner's lemma to find a division of the rent such that each
roommate is content with a distinct room. At the given price distribution, no
roommate has a strictly stronger preference for a different room. We give a new
elementary proof that the subjective preferences of only n-1 of the roommates
actually suffice to achieve this envy-free rent division. Our proof, in
particular, yields an algorithm to find such a fair division of rent. The
techniques also give generalizations of Sperner's lemma including a new proof
of a conjecture of the third author. | [
0,
0,
1,
0,
0,
0
] |
Title: A Channel-Based Perspective on Conjugate Priors,
Abstract: A desired closure property in Bayesian probability is that an updated
posterior distribution be in the same class of distributions --- say Gaussians
--- as the prior distribution. When the updating takes place via a statistical
model, one calls the class of prior distributions the `conjugate priors' of the
model. This paper gives (1) an abstract formulation of this notion of conjugate
prior, using channels, in a graphical language, (2) a simple abstract proof
that such conjugate priors yield Bayesian inversions, and (3) a logical
description of conjugate priors that highlights the required closure of the
priors under updating. The theory is illustrated with several standard
examples, also covering multiple updating. | [
1,
0,
0,
0,
0,
0
] |
Title: A FEL Based on a Superlattice,
Abstract: The motion and photon emission of electrons in a superlattice may be
described as in an undulator. Therefore, there is a close analogy between
ballistic electrons in a superlattice and electrons in a free electron laser
(FEL). Touching upon this analogy the intensity of photon emission in the IR
region and the gain are calculated. It is shown that the amplification can be
significant, reaching tens of percent. | [
0,
1,
0,
0,
0,
0
] |
Title: Least Squares Polynomial Chaos Expansion: A Review of Sampling Strategies,
Abstract: As non-institutive polynomial chaos expansion (PCE) techniques have gained
growing popularity among researchers, we here provide a comprehensive review of
major sampling strategies for the least squares based PCE. Traditional sampling
methods, such as Monte Carlo, Latin hypercube, quasi-Monte Carlo, optimal
design of experiments (ODE), Gaussian quadratures, as well as more recent
techniques, such as coherence-optimal and randomized quadratures are discussed.
We also propose a hybrid sampling method, dubbed alphabetic-coherence-optimal,
that employs the so-called alphabetic optimality criteria used in the context
of ODE in conjunction with coherence-optimal samples. A comparison between the
empirical performance of the selected sampling methods applied to three
numerical examples, including high-order PCE's, high-dimensional problems, and
low oversampling ratios, is presented to provide a road map for practitioners
seeking the most suitable sampling technique for a problem at hand. We observed
that the alphabetic-coherence-optimal technique outperforms other sampling
methods, specially when high-order ODE are employed and/or the oversampling
ratio is low. | [
0,
0,
0,
1,
0,
0
] |
Title: Efficient Dense Labeling of Human Activity Sequences from Wearables using Fully Convolutional Networks,
Abstract: Recognizing human activities in a sequence is a challenging area of research
in ubiquitous computing. Most approaches use a fixed size sliding window over
consecutive samples to extract features---either handcrafted or learned
features---and predict a single label for all samples in the window. Two key
problems emanate from this approach: i) the samples in one window may not
always share the same label. Consequently, using one label for all samples
within a window inevitably lead to loss of information; ii) the testing phase
is constrained by the window size selected during training while the best
window size is difficult to tune in practice. We propose an efficient algorithm
that can predict the label of each sample, which we call dense labeling, in a
sequence of human activities of arbitrary length using a fully convolutional
network. In particular, our approach overcomes the problems posed by the
sliding window step. Additionally, our algorithm learns both the features and
classifier automatically. We release a new daily activity dataset based on a
wearable sensor with hospitalized patients. We conduct extensive experiments
and demonstrate that our proposed approach is able to outperform the
state-of-the-arts in terms of classification and label misalignment measures on
three challenging datasets: Opportunity, Hand Gesture, and our new dataset. | [
1,
0,
0,
0,
0,
0
] |
Title: Open Vocabulary Scene Parsing,
Abstract: Recognizing arbitrary objects in the wild has been a challenging problem due
to the limitations of existing classification models and datasets. In this
paper, we propose a new task that aims at parsing scenes with a large and open
vocabulary, and several evaluation metrics are explored for this problem. Our
proposed approach to this problem is a joint image pixel and word concept
embeddings framework, where word concepts are connected by semantic relations.
We validate the open vocabulary prediction ability of our framework on ADE20K
dataset which covers a wide variety of scenes and objects. We further explore
the trained joint embedding space to show its interpretability. | [
1,
0,
0,
0,
0,
0
] |
Title: NetSpam: a Network-based Spam Detection Framework for Reviews in Online Social Media,
Abstract: Nowadays, a big part of people rely on available content in social media in
their decisions (e.g. reviews and feedback on a topic or product). The
possibility that anybody can leave a review provide a golden opportunity for
spammers to write spam reviews about products and services for different
interests. Identifying these spammers and the spam content is a hot topic of
research and although a considerable number of studies have been done recently
toward this end, but so far the methodologies put forth still barely detect
spam reviews, and none of them show the importance of each extracted feature
type. In this study, we propose a novel framework, named NetSpam, which
utilizes spam features for modeling review datasets as heterogeneous
information networks to map spam detection procedure into a classification
problem in such networks. Using the importance of spam features help us to
obtain better results in terms of different metrics experimented on real-world
review datasets from Yelp and Amazon websites. The results show that NetSpam
outperforms the existing methods and among four categories of features;
including review-behavioral, user-behavioral, reviewlinguistic,
user-linguistic, the first type of features performs better than the other
categories. | [
1,
1,
0,
0,
0,
0
] |
Title: Proving Non-Deterministic Computations in Agda,
Abstract: We investigate proving properties of Curry programs using Agda. First, we
address the functional correctness of Curry functions that, apart from some
syntactic and semantic differences, are in the intersection of the two
languages. Second, we use Agda to model non-deterministic functions with two
distinct and competitive approaches incorporating the non-determinism. The
first approach eliminates non-determinism by considering the set of all
non-deterministic values produced by an application. The second approach
encodes every non-deterministic choice that the application could perform. We
consider our initial experiment a success. Although proving properties of
programs is a notoriously difficult task, the functional logic paradigm does
not seem to add any significant layer of difficulty or complexity to the task. | [
1,
0,
0,
0,
0,
0
] |
Title: Contextual Explanation Networks,
Abstract: Modern learning algorithms excel at producing accurate but complex models of
the data. However, deploying such models in the real-world requires extra care:
we must ensure their reliability, robustness, and absence of undesired biases.
This motivates the development of models that are equally accurate but can be
also easily inspected and assessed beyond their predictive performance. To this
end, we introduce contextual explanation networks (CENs)---a class of
architectures that learn to predict by generating and utilizing intermediate,
simplified probabilistic models. Specifically, CENs generate parameters for
intermediate graphical models which are further used for prediction and play
the role of explanations. Contrary to the existing post-hoc model-explanation
tools, CENs learn to predict and to explain jointly. Our approach offers two
major advantages: (i) for each prediction, valid, instance-specific
explanations are generated with no computational overhead and (ii) prediction
via explanation acts as a regularizer and boosts performance in low-resource
settings. We analyze the proposed framework theoretically and experimentally.
Our results on image and text classification and survival analysis tasks
demonstrate that CENs are not only competitive with the state-of-the-art
methods but also offer additional insights behind each prediction, that are
valuable for decision support. We also show that while post-hoc methods may
produce misleading explanations in certain cases, CENs are always consistent
and allow to detect such cases systematically. | [
1,
0,
0,
1,
0,
0
] |
Title: Inference for Differential Equation Models using Relaxation via Dynamical Systems,
Abstract: Statistical regression models whose mean functions are represented by
ordinary differential equations (ODEs) can be used to describe phenomenons
dynamical in nature, which are abundant in areas such as biology, climatology
and genetics. The estimation of parameters of ODE based models is essential for
understanding its dynamics, but the lack of an analytical solution of the ODE
makes the parameter estimation challenging. The aim of this paper is to propose
a general and fast framework of statistical inference for ODE based models by
relaxation of the underlying ODE system. Relaxation is achieved by a properly
chosen numerical procedure, such as the Runge-Kutta, and by introducing
additive Gaussian noises with small variances. Consequently, filtering methods
can be applied to obtain the posterior distribution of the parameters in the
Bayesian framework. The main advantage of the proposed method is computation
speed. In a simulation study, the proposed method was at least 14 times faster
than the other methods. Theoretical results which guarantee the convergence of
the posterior of the approximated dynamical system to the posterior of true
model are presented. Explicit expressions are given that relate the order and
the mesh size of the Runge-Kutta procedure to the rate of convergence of the
approximated posterior as a function of sample size. | [
0,
0,
0,
1,
0,
0
] |
Title: Imaging the Schwarzschild-radius-scale Structure of M87 with the Event Horizon Telescope using Sparse Modeling,
Abstract: We propose a new imaging technique for radio and optical/infrared
interferometry. The proposed technique reconstructs the image from the
visibility amplitude and closure phase, which are standard data products of
short-millimeter very long baseline interferometers such as the Event Horizon
Telescope (EHT) and optical/infrared interferometers, by utilizing two
regularization functions: the $\ell_1$-norm and total variation (TV) of the
brightness distribution. In the proposed method, optimal regularization
parameters, which represent the sparseness and effective spatial resolution of
the image, are derived from data themselves using cross validation (CV). As an
application of this technique, we present simulated observations of M87 with
the EHT based on four physically motivated models. We confirm that $\ell_1$+TV
regularization can achieve an optimal resolution of $\sim 20-30$% of the
diffraction limit $\lambda/D_{\rm max}$, which is the nominal spatial
resolution of a radio interferometer. With the proposed technique, the EHT can
robustly and reasonably achieve super-resolution sufficient to clearly resolve
the black hole shadow. These results make it promising for the EHT to provide
an unprecedented view of the event-horizon-scale structure in the vicinity of
the super-massive black hole in M87 and also the Galactic center Sgr A*. | [
0,
1,
0,
0,
0,
0
] |
Title: The process of purely event-driven programs,
Abstract: Using process algebra, this paper describes the formalisation of the
process/semantics behind the purely event-driven programming language. | [
1,
0,
0,
0,
0,
0
] |
Title: There's more to the multimedia effect than meets the eye: is seeing pictures believing?,
Abstract: Textbooks in applied mathematics often use graphs to explain the meaning of
formulae, even though their benefit is still not fully explored. To test
processes underlying this assumed multimedia effect we collected performance
scores, eye movements, and think-aloud protocols from students solving problems
in vector calculus with and without graphs. Results showed no overall
multimedia effect, but instead an effect to confirm statements that were
accompanied by graphs, irrespective of whether these statements were true or
false. Eye movement and verbal data shed light on this surprising finding.
Students looked proportionally less at the text and the problem statement when
a graph was present. Moreover, they experienced more mental effort with the
graph, as indicated by more silent pauses in thinking aloud. Hence, students
actively processed the graphs. This, however, was not sufficient. Further
analysis revealed that the more students looked at the statement, the better
they performed. Thus, in the multimedia condition the graph drew students'
attention and cognitive capacities away from focusing on the statement. A good
alternative strategy in the multimedia condition was to frequently look between
graph and problem statement, and thus to integrate their information. In
conclusion, graphs influence where students look and what they process, and may
even mislead them into believing accompanying information. Thus, teachers and
textbook designers should be very critical on when to use graphs and carefully
consider how the graphs are integrated with other parts of the problem. | [
0,
1,
1,
0,
0,
0
] |
Title: Methods of Enumerating Two Vertex Maps of Arbitrary Genus,
Abstract: This paper provides an alternate proof to parts of the Goulden-Slofstra
formula for enumerating two vertex maps by genus, which is an extension of the
famous Harer-Zagier formula that computes the Euler characteristic of the
moduli space of curves. This paper also shows a further simplification to the
Goulden-Slofstra formula. Portions of this alternate proof will be used in a
subsequent paper, where it forms a basis for a more general result that applies
for a certain class of maps with an arbitrary number of vertices. | [
0,
0,
1,
0,
0,
0
] |
Title: Light emission by accelerated electric, toroidal and anapole dipolar sources,
Abstract: Emission of electromagnetic radiation by accelerated particles with electric,
toroidal and anapole dipole moments is analyzed. It is shown that ellipticity
of the emitted light can be used to differentiate between electric and toroidal
dipole sources, and that anapoles, elementary neutral non-radiating
configurations, which consist of electric and toroidal dipoles, can emit light
under uniform acceleration. The existence of non-radiating configurations in
electrodynamics implies that it is impossible to fully determine the internal
makeup of the emitter given only the distribution of the emitted light. Here we
demonstrate that there is a loop-hole in this `inverse source problem'. Our
results imply that there may be a whole range of new phenomena to be discovered
by studying the electromagnetic response of matter under acceleration. | [
0,
1,
0,
0,
0,
0
] |
Title: Transição de fase no sistema de Hénon-Heiles (Phase transition in the Henon-Heiles system),
Abstract: The Henon-Heiles system was originally proposed to describe the dynamical
behavior of galaxies, but this system has been widely applied in dynamical
systems by exhibit great details in phase space. This work presents the
formalism to describe Henon-Heiles system and a qualitative approach of
dynamics behavior. The growth of chaotic region in phase space is observed by
Poincare Surface of Section when the total energy increases. Island of
regularity remain around stable points and relevants phenomena appear, such as
sticky. | [
0,
1,
0,
0,
0,
0
] |
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