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Coherent control of flexural vibrations in dual-nanoweb fibers using phase-modulated two-frequency light | Coherent control of the resonant response in spatially extended
optomechanical structures is complicated by the fact that the optical drive is
affected by the back-action from the generated phonons. Here we report a new
approach to coherent control based on stimulated Raman-like scattering, in
which the optical pressure can remain unaffected by the induced vibrations even
in the regime of strong optomechanical interactions. We demonstrate
experimentally coherent control of flexural vibrations simultaneously along the
whole length of a dual-nanoweb fiber, by imprinting steps in the relative phase
between the components of a two-frequency pump signal,the beat frequency being
chosen to match a flexural resonance. Furthermore, sequential switching of the
relative phase at time intervals shorter than the lifetime of the vibrations
reduces their amplitude to a constant value that is fully adjustable by tuning
the phase-modulation depth and switching rate. The results may trigger new
developments in silicon photonics, since such coherent control uniquely
decouples the amplitude of optomechanical oscillations from power-dependent
thermal effects and nonlinear optical loss.
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Static and Dynamic Magnetic Properties of FeMn/Pt Multilayers | Recently we have demonstrated the presence of spin-orbit toque in FeMn/Pt
multilayers which, in combination with the anisotropy field, is able to rotate
its magnetization consecutively from 0o to 360o without any external field.
Here, we report on an investigation of static and dynamic magnetic properties
of FeMn/Pt multilayers using combined techniques of magnetometry, ferromagnetic
resonance, inverse spin Hall effect and spin Hall magnetoresistance
measurements. The FeMn/Pt multilayer was found to exhibit ferromagnetic
properties, and its temperature dependence of saturation magnetization can be
fitted well using a phenomenological model by including a finite distribution
in Curie temperature due to subtle thickness variations across the multilayer
samples. The non-uniformity in static magnetic properties is also manifested in
the ferromagnetic resonance spectra, which typically exhibit a broad resonance
peak. A damping parameter of around 0.106 is derived from the frequency
dependence of ferromagnetic resonance linewidth, which is comparable to the
reported values for other types of Pt-based multilayers. Clear inverse spin
Hall signals and spin Hall magnetoresistance have been observed in all samples
below the Curie temperature, which corroborate the strong spin-orbit torque
effect observed previously.
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Ultraproducts of crossed product von Neumann algebras | We study a relationship between the ultraproduct of a crossed product von
Neumann algebra and the crossed product of an ultraproduct von Neumann algebra.
As an application, the continuous core of an ultraproduct von Neumann algebra
is described.
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Concept Drift Detection and Adaptation with Hierarchical Hypothesis Testing | A fundamental issue for statistical classification models in a streaming
environment is that the joint distribution between predictor and response
variables changes over time (a phenomenon also known as concept drifts), such
that their classification performance deteriorates dramatically. In this paper,
we first present a hierarchical hypothesis testing (HHT) framework that can
detect and also adapt to various concept drift types (e.g., recurrent or
irregular, gradual or abrupt), even in the presence of imbalanced data labels.
A novel concept drift detector, namely Hierarchical Linear Four Rates (HLFR),
is implemented under the HHT framework thereafter. By substituting a
widely-acknowledged retraining scheme with an adaptive training strategy, we
further demonstrate that the concept drift adaptation capability of HLFR can be
significantly boosted. The theoretical analysis on the Type-I and Type-II
errors of HLFR is also performed. Experiments on both simulated and real-world
datasets illustrate that our methods outperform state-of-the-art methods in
terms of detection precision, detection delay as well as the adaptability
across different concept drift types.
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Interval-based Prediction Uncertainty Bound Computation in Learning with Missing Values | The problem of machine learning with missing values is common in many areas.
A simple approach is to first construct a dataset without missing values simply
by discarding instances with missing entries or by imputing a fixed value for
each missing entry, and then train a prediction model with the new dataset. A
drawback of this naive approach is that the uncertainty in the missing entries
is not properly incorporated in the prediction. In order to evaluate prediction
uncertainty, the multiple imputation (MI) approach has been studied, but the
performance of MI is sensitive to the choice of the probabilistic model of the
true values in the missing entries, and the computational cost of MI is high
because multiple models must be trained. In this paper, we propose an
alternative approach called the Interval-based Prediction Uncertainty Bounding
(IPUB) method. The IPUB method represents the uncertainties due to missing
entries as intervals, and efficiently computes the lower and upper bounds of
the prediction results when all possible training sets constructed by imputing
arbitrary values in the intervals are considered. The IPUB method can be
applied to a wide class of convex learning algorithms including penalized
least-squares regression, support vector machine (SVM), and logistic
regression. We demonstrate the advantages of the IPUB method by comparing it
with an existing method in numerical experiment with benchmark datasets.
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Methods for Interpreting and Understanding Deep Neural Networks | This paper provides an entry point to the problem of interpreting a deep
neural network model and explaining its predictions. It is based on a tutorial
given at ICASSP 2017. It introduces some recently proposed techniques of
interpretation, along with theory, tricks and recommendations, to make most
efficient use of these techniques on real data. It also discusses a number of
practical applications.
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Passive Classification of Source Printer using Text-line-level Geometric Distortion Signatures from Scanned Images of Printed Documents | In this digital era, one thing that still holds the convention is a printed
archive. Printed documents find their use in many critical domains such as
contract papers, legal tenders and proof of identity documents. As more
advanced printing, scanning and image editing techniques are becoming
available, forgeries on these legal tenders pose a serious threat. Ability to
easily and reliably identify source printer of a printed document can help a
lot in reducing this menace. During printing procedure, printer hardware
introduces certain distortions in printed characters' locations and shapes
which are invisible to naked eyes. These distortions are referred as geometric
distortions, their profile (or signature) is generally unique for each printer
and can be used for printer classification purpose. This paper proposes a set
of features for characterizing text-line-level geometric distortions, referred
as geometric distortion signatures and presents a novel system to use them for
identification of the origin of a printed document. Detailed experiments
performed on a set of thirteen printers demonstrate that the proposed system
achieves state of the art performance and gives much higher accuracy under
small training size constraint. For four training and six test pages of three
different fonts, the proposed method gives 99\% classification accuracy.
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The duration of load effect in lumber as stochastic degradation | This paper proposes a gamma process for modelling the damage that accumulates
over time in the lumber used in structural engineering applications when stress
is applied. The model separates the stochastic processes representing features
internal to the piece of lumber on the one hand, from those representing
external forces due to applied dead and live loads. The model applies those
external forces through a time-varying population level function designed for
time-varying loads. The application of this type of model, which is standard in
reliability analysis, is novel in this context, which has been dominated by
accumulated damage models (ADMs) over more than half a century. The proposed
model is compared with one of the traditional ADMs. Our statistical results
based on a Bayesian analysis of experimental data highlight the limitations of
using accelerated testing data to assess long-term reliability, as seen in the
wide posterior intervals. This suggests the need for more comprehensive testing
in future applications, or to encode appropriate expert knowledge in the priors
used for Bayesian analysis.
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Partial Bridging of Vaccine Efficacy to New Populations | Suppose one has data from one or more completed vaccine efficacy trials and
wishes to estimate the efficacy in a new setting. Often logistical or ethical
considerations make running another efficacy trial impossible. Fortunately, if
there is a biomarker that is the primary modifier of efficacy, then the
biomarker-conditional efficacy may be identical in the completed trials and the
new setting, or at least informative enough to meaningfully bound this
quantity. Given a sample of this biomarker from the new population, we might
hope we can bridge the results of the completed trials to estimate the vaccine
efficacy in this new population. Unfortunately, even knowing the true
conditional efficacy in the new population fails to identify the marginal
efficacy due to the unknown conditional unvaccinated risk. We define a curve
that partially identifies (lower bounds) the marginal efficacy in the new
population as a function of the population's marginal unvaccinated risk, under
the assumption that one can identify bounds on the conditional unvaccinated
risk in the new population. Interpreting the curve only requires identifying
plausible regions of the marginal unvaccinated risk in the new population. We
present a nonparametric estimator of this curve and develop valid lower
confidence bounds that concentrate at a parametric rate. We use vaccine
terminology throughout, but the results apply to general binary interventions
and bounded outcomes.
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An induced map between rationalized classifying spaces for fibrations | Let $B{ aut}_1X$ be the Dold-Lashof classifying space of orientable
fibrations with fiber $X$. For a rationally weakly trivial map $f:X\to Y$, our
strictly induced map $a_f: (Baut_1X)_0\to (Baut_1Y)_0$ induces a natural map
from a $X_0$-fibration to a $Y_0$-fibration. It is given by a map between the
differential graded Lie algebras of derivations of Sullivan models. We note
some conditions that the map $a_f$ admits a section and note some relations
with the Halperin conjecture. Furthermore we give the obstruction class for a
lifting of a classifying map $h: B\to (Baut_1Y)_0$ and apply it for liftings of
$G$-actions on $Y$ for a compact connected Lie group $G$ as the case of $B=BG$
and evaluating of rational toral ranks as $r_0(Y)\leq r_0(X)$.
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The Statistical Recurrent Unit | Sophisticated gated recurrent neural network architectures like LSTMs and
GRUs have been shown to be highly effective in a myriad of applications. We
develop an un-gated unit, the statistical recurrent unit (SRU), that is able to
learn long term dependencies in data by only keeping moving averages of
statistics. The SRU's architecture is simple, un-gated, and contains a
comparable number of parameters to LSTMs; yet, SRUs perform favorably to more
sophisticated LSTM and GRU alternatives, often outperforming one or both in
various tasks. We show the efficacy of SRUs as compared to LSTMs and GRUs in an
unbiased manner by optimizing respective architectures' hyperparameters in a
Bayesian optimization scheme for both synthetic and real-world tasks.
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Many-body localization in the droplet spectrum of the random XXZ quantum spin chain | We study many-body localization properties of the disordered XXZ spin chain
in the Ising phase. Disorder is introduced via a random magnetic field in the
$z$-direction. We prove a strong form of dynamical exponential clustering for
eigenstates in the droplet spectrum: For any pair of local observables
separated by a distance $\ell$, the sum of the associated correlators over
these states decays exponentially in $\ell$, in expectation. This exponential
clustering persists under the time evolution in the droplet spectrum. Our
result applies to the large disorder regime as well as to the strong Ising
phase at fixed disorder, with bounds independent of the support of the
observables.
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FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices | Deep neural networks show great potential as solutions to many sensing
application problems, but their excessive resource demand slows down execution
time, pausing a serious impediment to deployment on low-end devices. To address
this challenge, recent literature focused on compressing neural network size to
improve performance. We show that changing neural network size does not
proportionally affect performance attributes of interest, such as execution
time. Rather, extreme run-time nonlinearities exist over the network
configuration space. Hence, we propose a novel framework, called FastDeepIoT,
that uncovers the non-linear relation between neural network structure and
execution time, then exploits that understanding to find network configurations
that significantly improve the trade-off between execution time and accuracy on
mobile and embedded devices. FastDeepIoT makes two key contributions. First,
FastDeepIoT automatically learns an accurate and highly interpretable execution
time model for deep neural networks on the target device. This is done without
prior knowledge of either the hardware specifications or the detailed
implementation of the used deep learning library. Second, FastDeepIoT informs a
compression algorithm how to minimize execution time on the profiled device
without impacting accuracy. We evaluate FastDeepIoT using three different
sensing-related tasks on two mobile devices: Nexus 5 and Galaxy Nexus.
FastDeepIoT further reduces the neural network execution time by $48\%$ to
$78\%$ and energy consumption by $37\%$ to $69\%$ compared with the
state-of-the-art compression algorithms.
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Robust MPC for tracking of nonholonomic robots with additive disturbances | In this paper, two robust model predictive control (MPC) schemes are proposed
for tracking control of nonholonomic systems with bounded disturbances:
tube-MPC and nominal robust MPC (NRMPC). In tube-MPC, the control signal
consists of a control action and a nonlinear feedback law based on the
deviation of the actual states from the states of a nominal system. It renders
the actual trajectory within a tube centered along the optimal trajectory of
the nominal system. Recursive feasibility and input-to-state stability are
established and the constraints are ensured by tightening the input domain and
the terminal region. While in NRMPC, an optimal control sequence is obtained by
solving an optimization problem based on the current state, and the first
portion of this sequence is applied to the real system in an open-loop manner
during each sampling period. The state of nominal system model is updated by
the actual state at each step, which provides additional a feedback. By
introducing a robust state constraint and tightening the terminal region,
recursive feasibility and input-to-state stability are guaranteed. Simulation
results demonstrate the effectiveness of both strategies proposed.
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Investigation on the use of Hidden-Markov Models in automatic transcription of music | Hidden Markov Models (HMMs) are a ubiquitous tool to model time series data,
and have been widely used in two main tasks of Automatic Music Transcription
(AMT): note segmentation, i.e. identifying the played notes after a multi-pitch
estimation, and sequential post-processing, i.e. correcting note segmentation
using training data. In this paper, we employ the multi-pitch estimation method
called Probabilistic Latent Component Analysis (PLCA), and develop AMT systems
by integrating different HMM-based modules in this framework. For note
segmentation, we use two different twostate on/o? HMMs, including a
higher-order one for duration modeling. For sequential post-processing, we
focused on a musicological modeling of polyphonic harmonic transitions, using a
first- and second-order HMMs whose states are defined through candidate note
mixtures. These different PLCA plus HMM systems have been evaluated
comparatively on two different instrument repertoires, namely the piano (using
the MAPS database) and the marovany zither. Our results show that the use of
HMMs could bring noticeable improvements to transcription results, depending on
the instrument repertoire.
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Network archaeology: phase transition in the recoverability of network history | Network growth processes can be understood as generative models of the
structure and history of complex networks. This point of view naturally leads
to the problem of network archaeology: Reconstructing all the past states of a
network from its structure---a difficult permutation inference problem. In this
paper, we introduce a Bayesian formulation of network archaeology, with a
generalization of preferential attachment as our generative mechanism. We
develop a sequential importance sampling algorithm to evaluate the posterior
averages of this model, as well as an efficient heuristic that uncovers the
history of a network in linear time. We use these methods to identify and
characterize a phase transition in the quality of the reconstructed history,
when they are applied to artificial networks generated by the model itself.
Despite the existence of a no-recovery phase, we find that non-trivial
inference is possible in a large portion of the parameter space as well as on
empirical data.
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Big Data, Data Science, and Civil Rights | Advances in data analytics bring with them civil rights implications.
Data-driven and algorithmic decision making increasingly determine how
businesses target advertisements to consumers, how police departments monitor
individuals or groups, how banks decide who gets a loan and who does not, how
employers hire, how colleges and universities make admissions and financial aid
decisions, and much more. As data-driven decisions increasingly affect every
corner of our lives, there is an urgent need to ensure they do not become
instruments of discrimination, barriers to equality, threats to social justice,
and sources of unfairness. In this paper, we argue for a concrete research
agenda aimed at addressing these concerns, comprising five areas of emphasis:
(i) Determining if models and modeling procedures exhibit objectionable bias;
(ii) Building awareness of fairness into machine learning methods; (iii)
Improving the transparency and control of data- and model-driven decision
making; (iv) Looking beyond the algorithm(s) for sources of bias and
unfairness-in the myriad human decisions made during the problem formulation
and modeling process; and (v) Supporting the cross-disciplinary scholarship
necessary to do all of that well.
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Global well-posedness for 2-D Boussinesq system with the temperature-dependent viscosity and supercritical dissipation | The present paper is dedicated to the global well-posedness issue for the
Boussinesq system with the temperature-dependent viscosity in $\mathbb{R}^2.$
We aim at extending the work by Abidi and Zhang ( Adv. Math. 2017 (305)
1202--1249 ) to a supercritical dissipation for temperature.
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CO~($J = 1-0$) Observations of a Filamentary Molecular Cloud in the Galactic Region Centered at $l = 150\arcdeg, b = 3.5\arcdeg$ | We present large-field (4.25~$\times$~3.75 deg$^2$) mapping observations
toward the Galactic region centered at $l = 150\arcdeg, b = 3.5\arcdeg$ in the
$J = 1-0$ emission line of CO isotopologues ($^{12}$CO, $^{13}$CO, and
C$^{18}$O), using the 13.7 m millimeter-wavelength telescope of the Purple
Mountain Observatory. Based on the $^{13}$CO observations, we reveal a
filamentary cloud in the Local Arm at a velocity range of $-$0.5 to
6.5~km~s$^{-1}$. This molecular cloud contains 1 main filament and 11
sub-filaments, showing the so-called "ridge-nest" structure. The main filament
and three sub-filaments are also detected in the C$^{18}$O line. The velocity
structures of most identified filaments display continuous distribution with
slight velocity gradients. The measured median excitation temperature, line
width, length, width, and linear mass of the filaments are $\sim$9.28~K,
0.85~km~s$^{-1}$, 7.30~pc, 0.79~pc, and 17.92~$M_\sun$~pc$^{-1}$, respectively,
assuming a distance of 400~pc. We find that the four filaments detected in the
C$^{18}$O line are thermally supercritical, and two of them are in the
virialized state, and thus tend to be gravitationally bound. We identify in
total 146 $^{13}$CO clumps in the cloud, about 77$\%$ of the clumps are
distributed along the filaments. About 56$\%$ of the virialized clumps are
found to be associated with the supercritical filaments. Three young stellar
object (YSO) candidates are also identified in the supercritical filaments,
based on the complementary infrared (IR) data. These results indicate that the
supercritical filaments, especially the virialized filaments, may contain
star-forming activities.
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Leveraging Deep Neural Network Activation Entropy to cope with Unseen Data in Speech Recognition | Unseen data conditions can inflict serious performance degradation on systems
relying on supervised machine learning algorithms. Because data can often be
unseen, and because traditional machine learning algorithms are trained in a
supervised manner, unsupervised adaptation techniques must be used to adapt the
model to the unseen data conditions. However, unsupervised adaptation is often
challenging, as one must generate some hypothesis given a model and then use
that hypothesis to bootstrap the model to the unseen data conditions.
Unfortunately, reliability of such hypotheses is often poor, given the mismatch
between the training and testing datasets. In such cases, a model hypothesis
confidence measure enables performing data selection for the model adaptation.
Underlying this approach is the fact that for unseen data conditions, data
variability is introduced to the model, which the model propagates to its
output decision, impacting decision reliability. In a fully connected network,
this data variability is propagated as distortions from one layer to the next.
This work aims to estimate the propagation of such distortion in the form of
network activation entropy, which is measured over a short- time running window
on the activation from each neuron of a given hidden layer, and these
measurements are then used to compute summary entropy. This work demonstrates
that such an entropy measure can help to select data for unsupervised model
adaptation, resulting in performance gains in speech recognition tasks. Results
from standard benchmark speech recognition tasks show that the proposed
approach can alleviate the performance degradation experienced under unseen
data conditions by iteratively adapting the model to the unseen datas acoustic
condition.
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Asymmetric Preheating | We study the generation of the matter-antimatter asymmetry during bosonic
preheating, focusing on the sources of the asymmetry. If the asymmetry appears
in the multiplication factor of the resonant particle production, the
matter-antimatter ratio will grow during preheating. On the other hand, if the
asymmetry does not grow during preheating, one has to find out another reason.
We consider several scenarios for the asymmetric preheating to distinguish the
sources of the asymmetry. We also discuss a new baryogenesis scenario, in which
the asymmetry is generated without introducing neither loop corrections nor
rotation of a field.
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A multi-device dataset for urban acoustic scene classification | This paper introduces the acoustic scene classification task of DCASE 2018
Challenge and the TUT Urban Acoustic Scenes 2018 dataset provided for the task,
and evaluates the performance of a baseline system in the task. As in previous
years of the challenge, the task is defined for classification of short audio
samples into one of predefined acoustic scene classes, using a supervised,
closed-set classification setup. The newly recorded TUT Urban Acoustic Scenes
2018 dataset consists of ten different acoustic scenes and was recorded in six
large European cities, therefore it has a higher acoustic variability than the
previous datasets used for this task, and in addition to high-quality binaural
recordings, it also includes data recorded with mobile devices. We also present
the baseline system consisting of a convolutional neural network and its
performance in the subtasks using the recommended cross-validation setup.
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Asymptotically preserving particle-in-cell methods for inhomogenous strongly magnetized plasmas | We propose a class of Particle-In-Cell (PIC) methods for the Vlasov-Poisson
system with a strong and inhomogeneous external magnetic field with fixed
direction, where we focus on the motion of particles in the plane orthogonal to
the magnetic field (so-called poloidal directions). In this regime, the time
step can be subject to stability constraints related to the smallness of Larmor
radius and plasma frequency. To avoid this limitation, our approach is based on
first and higher-order semi-implicit numerical schemes already validated on
dissipative systems [3] and for homogeneous magnetic fields [10]. Thus, when
the magnitude of the external magnetic field becomes large, this method
provides a consistent PIC discretization of the guiding-center system taking
into account variations of the magnetic field. We carry out some theoretical
proofs and perform several numerical experiments that establish a solid
validation of the method and its underlying concepts.
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Cluster Failure Revisited: Impact of First Level Design and Data Quality on Cluster False Positive Rates | Methodological research rarely generates a broad interest, yet our work on
the validity of cluster inference methods for functional magnetic resonance
imaging (fMRI) created intense discussion on both the minutia of our approach
and its implications for the discipline. In the present work, we take on
various critiques of our work and further explore the limitations of our
original work. We address issues about the particular event-related designs we
used, considering multiple event types and randomisation of events between
subjects. We consider the lack of validity found with one-sample permutation
(sign flipping) tests, investigating a number of approaches to improve the
false positive control of this widely used procedure. We found that the
combination of a two-sided test and cleaning the data using ICA FIX resulted in
nominal false positive rates for all datasets, meaning that data cleaning is
not only important for resting state fMRI, but also for task fMRI. Finally, we
discuss the implications of our work on the fMRI literature as a whole,
estimating that at least 10% of the fMRI studies have used the most problematic
cluster inference method (P = 0.01 cluster defining threshold), and how
individual studies can be interpreted in light of our findings. These
additional results underscore our original conclusions, on the importance of
data sharing and thorough evaluation of statistical methods on realistic null
data.
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An Integrated Simulator and Dataset that Combines Grasping and Vision for Deep Learning | Deep learning is an established framework for learning hierarchical data
representations. While compute power is in abundance, one of the main
challenges in applying this framework to robotic grasping has been obtaining
the amount of data needed to learn these representations, and structuring the
data to the task at hand. Among contemporary approaches in the literature, we
highlight key properties that have encouraged the use of deep learning
techniques, and in this paper, detail our experience in developing a simulator
for collecting cylindrical precision grasps of a multi-fingered dexterous
robotic hand.
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A Recursive Bayesian Approach To Describe Retinal Vasculature Geometry | Demographic studies suggest that changes in the retinal vasculature geometry,
especially in vessel width, are associated with the incidence or progression of
eye-related or systemic diseases. To date, the main information source for
width estimation from fundus images has been the intensity profile between
vessel edges. However, there are many factors affecting the intensity profile:
pathologies, the central light reflex and local illumination levels, to name a
few. In this study, we introduce three information sources for width
estimation. These are the probability profiles of vessel interior, centreline
and edge locations generated by a deep network. The probability profiles
provide direct access to vessel geometry and are used in the likelihood
calculation for a Bayesian method, particle filtering. We also introduce a
geometric model which can handle non-ideal conditions of the probability
profiles. Our experiments conducted on the REVIEW dataset yielded consistent
estimates of vessel width, even in cases when one of the vessel edges is
difficult to identify. Moreover, our results suggest that the method is better
than human observers at locating edges of low contrast vessels.
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Quantum Harmonic Analysis of the Density Matrix: Basics | In this Review we will study rigorously the notion of mixed states and their
density matrices. We mostly give complete proofs. We will also discuss the
quantum-mechanical consequences of possible variations of Planck's constant h.
This Review has been written having in mind two readerships: mathematical
physicists and quantum physicists. The mathematical rigor is maximal, but the
language and notation we use throughout should be familiar to physicists.
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The bromodomain-containing protein Ibd1 links multiple chromatin related protein complexes to highly expressed genes in Tetrahymena thermophila | Background: The chromatin remodelers of the SWI/SNF family are critical
transcriptional regulators. Recognition of lysine acetylation through a
bromodomain (BRD) component is key to SWI/SNF function; in most eukaryotes,
this function is attributed to SNF2/Brg1.
Results: Using affinity purification coupled to mass spectrometry (AP-MS) we
identified members of a SWI/SNF complex (SWI/SNFTt) in Tetrahymena thermophila.
SWI/SNFTt is composed of 11 proteins, Snf5Tt, Swi1Tt, Swi3Tt, Snf12Tt, Brg1Tt,
two proteins with potential chromatin interacting domains and four proteins
without orthologs to SWI/SNF proteins in yeast or mammals. SWI/SNFTt subunits
localize exclusively to the transcriptionally active macronucleus (MAC) during
growth and development, consistent with a role in transcription. While
Tetrahymena Brg1 does not contain a BRD, our AP-MS results identified a
BRD-containing SWI/SNFTt component, Ibd1 that associates with SWI/SNFTt during
growth but not development. AP-MS analysis of epitope-tagged Ibd1 revealed it
to be a subunit of several additional protein complexes, including putative
SWRTt, and SAGATt complexes as well as a putative H3K4-specific histone methyl
transferase complex. Recombinant Ibd1 recognizes acetyl-lysine marks on
histones correlated with active transcription. Consistent with our AP-MS and
histone array data suggesting a role in regulation of gene expression, ChIP-Seq
analysis of Ibd1 indicated that it primarily binds near promoters and within
gene bodies of highly expressed genes during growth.
Conclusions: Our results suggest that through recognizing specific histones
marks, Ibd1 targets active chromatin regions of highly expressed genes in
Tetrahymena where it subsequently might coordinate the recruitment of several
chromatin remodeling complexes to regulate the transcriptional landscape of
vegetatively growing Tetrahymena cells.
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A Bayesian Perspective on Generalization and Stochastic Gradient Descent | We consider two questions at the heart of machine learning; how can we
predict if a minimum will generalize to the test set, and why does stochastic
gradient descent find minima that generalize well? Our work responds to Zhang
et al. (2016), who showed deep neural networks can easily memorize randomly
labeled training data, despite generalizing well on real labels of the same
inputs. We show that the same phenomenon occurs in small linear models. These
observations are explained by the Bayesian evidence, which penalizes sharp
minima but is invariant to model parameterization. We also demonstrate that,
when one holds the learning rate fixed, there is an optimum batch size which
maximizes the test set accuracy. We propose that the noise introduced by small
mini-batches drives the parameters towards minima whose evidence is large.
Interpreting stochastic gradient descent as a stochastic differential equation,
we identify the "noise scale" $g = \epsilon (\frac{N}{B} - 1) \approx \epsilon
N/B$, where $\epsilon$ is the learning rate, $N$ the training set size and $B$
the batch size. Consequently the optimum batch size is proportional to both the
learning rate and the size of the training set, $B_{opt} \propto \epsilon N$.
We verify these predictions empirically.
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Higher Derivative Field Theories: Degeneracy Conditions and Classes | We provide a full analysis of ghost free higher derivative field theories
with coupled degrees of freedom. Assuming the absence of gauge symmetries, we
derive the degeneracy conditions in order to evade the Ostrogradsky ghosts, and
analyze which (non)trivial classes of solutions this allows for. It is shown
explicitly how Lorentz invariance avoids the propagation of "half" degrees of
freedom. Moreover, for a large class of theories, we construct the field
redefinitions and/or (extended) contact transformations that put the theory in
a manifestly first order form. Finally, we identify which class of theories
cannot be brought to first order form by such transformations.
| 0 | 1 | 0 | 0 | 0 | 0 |
3D Morphology Prediction of Progressive Spinal Deformities from Probabilistic Modeling of Discriminant Manifolds | We introduce a novel approach for predicting the progression of adolescent
idiopathic scoliosis from 3D spine models reconstructed from biplanar X-ray
images. Recent progress in machine learning have allowed to improve
classification and prognosis rates, but lack a probabilistic framework to
measure uncertainty in the data. We propose a discriminative probabilistic
manifold embedding where locally linear mappings transform data points from
high-dimensional space to corresponding low-dimensional coordinates. A
discriminant adjacency matrix is constructed to maximize the separation between
progressive and non-progressive groups of patients diagnosed with scoliosis,
while minimizing the distance in latent variables belonging to the same class.
To predict the evolution of deformation, a baseline reconstruction is projected
onto the manifold, from which a spatiotemporal regression model is built from
parallel transport curves inferred from neighboring exemplars. Rate of
progression is modulated from the spine flexibility and curve magnitude of the
3D spine deformation. The method was tested on 745 reconstructions from 133
subjects using longitudinal 3D reconstructions of the spine, with results
demonstrating the discriminatory framework can identify between progressive and
non-progressive of scoliotic patients with a classification rate of 81% and
prediction differences of 2.1$^{o}$ in main curve angulation, outperforming
other manifold learning methods. Our method achieved a higher prediction
accuracy and improved the modeling of spatiotemporal morphological changes in
highly deformed spines compared to other learning methods.
| 1 | 0 | 0 | 1 | 0 | 0 |
The Galaxy's Veil of Excited Hydrogen | Many of the baryons in our Galaxy probably lie outside the well known disk
and bulge components. Despite a wealth of evidence for the presence of some gas
in galactic halos, including absorption line systems in the spectra of quasars,
high velocity neutral hydrogen clouds in our Galaxy halo, line emitting ionised
hydrogen originating from galactic winds in nearby starburst galaxies, and the
X-ray coronas surrounding the most massive galaxies, accounting for the gas in
the halo of any galaxy has been observationally challenging primarily because
of its low density in the expansive halo. The most sensitive measurements come
from detecting absorption by the intervening gas in the spectra of distant
objects such as quasars or distant halo stars, but these have typically been
limited to a few lines of sight to sufficiently bright objects. Massive
spectroscopic surveys of millions of objects provide an alternative approach to
the problem. Here, we present the first evidence for a widely distributed,
neutral, excited hydrogen component of the Galaxy's halo. It is observed as the
slight, (0.779 $\pm$ 0.006)\%, absorption of flux near the rest wavelength of
H$\alpha$ in the combined spectra of hundreds of thousands of galaxy spectra
and is ubiquitous in high latitude lines of sight. This observation provides an
avenue to tracing, both spatially and kinematically, the majority of the gas in
the halo of our Galaxy.
| 0 | 1 | 0 | 0 | 0 | 0 |
Photonic-chip supercontinuum with tailored spectra for precision frequency metrology | Supercontinuum generation using chip-integrated photonic waveguides is a
powerful approach for spectrally broadening pulsed laser sources with very low
pulse energies and compact form factors. When pumped with a mode-locked laser
frequency comb, these waveguides can coherently expand the comb spectrum to
more than an octave in bandwidth to enable self-referenced stabilization.
However, for applications in frequency metrology and precision spectroscopy, it
is desirable to not only support self-referencing, but also to generate
low-noise combs with customizable broadband spectra. In this work, we
demonstrate dispersion-engineered waveguides based on silicon nitride that are
designed to meet these goals and enable precision optical metrology experiments
across large wavelength spans. We perform a clock comparison measurement and
report a clock-limited relative frequency instability of $3.8\times10^{-15}$ at
$\tau = 2$ seconds between a 1550 nm cavity-stabilized reference laser and
NIST's calcium atomic clock laser at 657 nm using a two-octave
waveguide-supercontinuum comb.
| 0 | 1 | 0 | 0 | 0 | 0 |
Endogenizing Epistemic Actions | Through a series of examples, we illustrate some important drawbacks that the
action logic framework suffers from in its ability to represent the dynamics of
information updates. We argue that these problems stem from the fact that the
action model, a central construct designed to encode agents' uncertainty about
actions, is itself effectively common knowledge amongst the agents. In response
to these difficulties, we motivate and propose an alternative semantics that
avoids them by (roughly speaking) endogenizing the action model. We discuss the
relationship to action logic, and provide a sound and complete axiomatization.
| 1 | 0 | 0 | 0 | 0 | 0 |
The Meaning of Memory Safety | We give a rigorous characterization of what it means for a programming
language to be memory safe, capturing the intuition that memory safety supports
local reasoning about state. We formalize this principle in two ways. First, we
show how a small memory-safe language validates a noninterference property: a
program can neither affect nor be affected by unreachable parts of the state.
Second, we extend separation logic, a proof system for heap-manipulating
programs, with a memory-safe variant of its frame rule. The new rule is
stronger because it applies even when parts of the program are buggy or
malicious, but also weaker because it demands a stricter form of separation
between parts of the program state. We also consider a number of pragmatically
motivated variations on memory safety and the reasoning principles they
support. As an application of our characterization, we evaluate the security of
a previously proposed dynamic monitor for memory safety of heap-allocated data.
| 1 | 0 | 0 | 0 | 0 | 0 |
The Flexible Group Spatial Keyword Query | We present a new class of service for location based social networks, called
the Flexible Group Spatial Keyword Query, which enables a group of users to
collectively find a point of interest (POI) that optimizes an aggregate cost
function combining both spatial distances and keyword similarities. In
addition, our query service allows users to consider the tradeoffs between
obtaining a sub-optimal solution for the entire group and obtaining an
optimimized solution but only for a subgroup.
We propose algorithms to process three variants of the query: (i) the group
nearest neighbor with keywords query, which finds a POI that optimizes the
aggregate cost function for the whole group of size n, (ii) the subgroup
nearest neighbor with keywords query, which finds the optimal subgroup and a
POI that optimizes the aggregate cost function for a given subgroup size m (m
<= n), and (iii) the multiple subgroup nearest neighbor with keywords query,
which finds optimal subgroups and corresponding POIs for each of the subgroup
sizes in the range [m, n]. We design query processing algorithms based on
branch-and-bound and best-first paradigms. Finally, we provide theoretical
bounds and conduct extensive experiments with two real datasets which verify
the effectiveness and efficiency of the proposed algorithms.
| 1 | 0 | 0 | 0 | 0 | 0 |
Undersampled windowed exponentials and their applications | We characterize the completeness and frame/basis property of a union of
under-sampled windowed exponentials of the form $$ {\mathcal F}(g): =\{e^{2\pi
i n x}: n\ge 0\}\cup \{g(x)e^{2\pi i nx}: n<0\} $$ for $L^2[-1/2,1/2]$ by the
spectra of the Toeplitz operators with symbol $g$. Using this characterization,
we classify all real-valued functions $g$ such that ${\mathcal F}(g)$ is
complete or forms a frame/basis. Conversely, we use the classical
Kadec-1/4-theorem in non-harmonic Fourier series to determine all $\xi$ such
that the Toeplitz operators with symbol $e^{2\pi i \xi x}$ is injective or
invertible. These results demonstrate an elegant interaction between frame
theory of windowed exponentials and Toeplitz operators. Finally, as an
application, we use our results to answer some open questions in dynamical
sampling, phase retrieval and derivative samplings on $\ell^2({\mathbb Z})$ and
Paley-Wiener spaces of bandlimited functions.
| 0 | 0 | 1 | 0 | 0 | 0 |
Globally Optimal Gradient Descent for a ConvNet with Gaussian Inputs | Deep learning models are often successfully trained using gradient descent,
despite the worst case hardness of the underlying non-convex optimization
problem. The key question is then under what conditions can one prove that
optimization will succeed. Here we provide a strong result of this kind. We
consider a neural net with one hidden layer and a convolutional structure with
no overlap and a ReLU activation function. For this architecture we show that
learning is NP-complete in the general case, but that when the input
distribution is Gaussian, gradient descent converges to the global optimum in
polynomial time. To the best of our knowledge, this is the first global
optimality guarantee of gradient descent on a convolutional neural network with
ReLU activations.
| 0 | 0 | 1 | 1 | 0 | 0 |
Detection of irregular QRS complexes using Hermite Transform and Support Vector Machine | Computer based recognition and detection of abnormalities in ECG signals is
proposed. For this purpose, the Support Vector Machines (SVM) are combined with
the advantages of Hermite transform representation. SVM represent a special
type of classification techniques commonly used in medical applications.
Automatic classification of ECG could make the work of cardiologic departments
faster and more efficient. It would also reduce the number of false diagnosis
and, as a result, save lives. The working principle of the SVM is based on
translating the data into a high dimensional feature space and separating it
using a linear classificator. In order to provide an optimal representation for
SVM application, the Hermite transform domain is used. This domain is proved to
be suitable because of the similarity of the QRS complex with Hermite basis
functions. The maximal signal information is obtained using a small set of
features that are used for detection of irregular QRS complexes. The aim of the
paper is to show that these features can be employed for automatic ECG signal
analysis.
| 1 | 0 | 0 | 0 | 0 | 0 |
On the Lipschitz equivalence of self-affine sets | Let $A$ be an expanding $d\times d$ matrix with integer entries and
${\mathcal D}\subset {\mathbb Z}^d$ be a finite digit set. Then the pair $(A,
{\mathcal D})$ defines a unique integral self-affine set $K=A^{-1}(K+{\mathcal
D})$. In this paper, by replacing the Euclidean norm with a pseudo-norm $w$ in
terms of $A$, we construct a hyperbolic graph on $(A, {\mathcal D})$ and show
that $K$ can be identified with the hyperbolic boundary. Moreover, if $(A,
{\mathcal D})$ safisfies the open set condition, we also prove that two totally
disconnected integral self-affine sets are Lipschitz equivalent if an only if
they have the same $w$-Hausdorff dimension, that is, their digit sets have
equal cardinality. We extends some well-known results in the self-similar sets
to the self-affine sets.
| 0 | 0 | 1 | 0 | 0 | 0 |
The Gross-Pitaevskii equations of a static and spherically symmetric condensate of gravitons | In this paper we consider the Dvali and Gómez assumption that the end state
of a gravitational collapse is a Bose-Einstein condensate of gravitons. We then
construct the two Gross-Pitaevskii equations for a static and spherically
symmetric configuration of the condensate. These two equations correspond to
the constrained minimisation of the gravitational Hamiltonian with respect to
the redshift and the Newtonian potential, per given number of gravitons. We
find that the effective geometry of the condensate is the one of a gravastar (a
DeSitter star) with a sub-Planckian cosmological constant, for masses larger
than the Planck scale. Thus, a condensate corresponding to a semiclassical
black hole, is always quantum and weakly coupled. Finally, we obtain that the
boundary of our gravastar, although it is not the location of a horizon,
corresponds to the Schwarzschild radius.
| 0 | 1 | 0 | 0 | 0 | 0 |
Stability and instability in saddle point dynamics - Part I | We consider the problem of convergence to a saddle point of a concave-convex
function via gradient dynamics. Since first introduced by Arrow, Hurwicz and
Uzawa in [1] such dynamics have been extensively used in diverse areas, there
are, however, features that render their analysis non trivial. These include
the lack of convergence guarantees when the function considered is not strictly
concave-convex and also the non-smoothness of subgradient dynamics. Our aim in
this two part paper is to provide an explicit characterization to the
asymptotic behaviour of general gradient and subgradient dynamics applied to a
general concave-convex function. We show that despite the nonlinearity and
non-smoothness of these dynamics their $\omega$-limit set is comprised of
trajectories that solve only explicit linear ODEs that are characterized within
the paper.
More precisely, in Part I an exact characterization is provided to the
asymptotic behaviour of unconstrained gradient dynamics. We also show that when
convergence to a saddle point is not guaranteed then the system behaviour can
be problematic, with arbitrarily small noise leading to an unbounded variance.
In Part II we consider a general class of subgradient dynamics that restrict
trajectories in an arbitrary convex domain, and show that their limiting
trajectories are solutions of subgradient dynamics on only affine subspaces.
The latter is a smooth class of dynamics with an asymptotic behaviour exactly
characterized in Part I, as solutions to explicit linear ODEs. These results
are used to formulate corresponding convergence criteria and are demonstrated
with several examples and applications presented in Part II.
| 1 | 0 | 1 | 0 | 0 | 0 |
Continual Prediction of Notification Attendance with Classical and Deep Network Approaches | We investigate to what extent mobile use patterns can predict -- at the
moment it is posted -- whether a notification will be clicked within the next
10 minutes. We use a data set containing the detailed mobile phone usage logs
of 279 users, who over the course of 5 weeks received 446,268 notifications
from a variety of apps. Besides using classical gradient-boosted trees, we
demonstrate how to make continual predictions using a recurrent neural network
(RNN). The two approaches achieve a similar AUC of ca. 0.7 on unseen users,
with a possible operation point of 50% sensitivity and 80% specificity
considering all notification types (an increase of 40% with respect to a
probabilistic baseline). These results enable automatic, intelligent handling
of mobile phone notifications without the need for user feedback or
personalization. Furthermore, they showcase how forego feature-extraction by
using RNNs for continual predictions directly on mobile usage logs. To the best
of our knowledge, this is the first work that leverages mobile sensor data for
continual, context-aware predictions of interruptibility using deep neural
networks.
| 1 | 0 | 0 | 0 | 0 | 0 |
Extensions of Operators, Liftings of Monads and Distributive Laws | In a previous study, the algebraic formulation of the First Fundamental
Theorem of Calculus (FFTC) is shown to allow extensions of differential and
Rota-Baxter operators on the one hand, and to give rise to liftings of monads
and comonads, and mixed distributive laws on the other. Generalizing the FFTC,
we consider in this paper a class of constraints between a differential
operator and a Rota-Baxter operator. For a given constraint, we show that the
existences of extensions of differential and Rota-Baxter operators, of liftings
of monads and comonads, and of mixed distributive laws are equivalent. We
further give a classification of the constraints satisfying these equivalent
conditions.
| 0 | 0 | 1 | 0 | 0 | 0 |
Cascaded Incremental Nonlinear Dynamic Inversion Control for MAV Disturbance Rejection | Micro Aerial Vehicles (MAVs) are limited in their operation outdoors near
obstacles by their ability to withstand wind gusts. Currently widespread
position control methods such as Proportional Integral Derivative control do
not perform well under the influence of gusts. Incremental Nonlinear Dynamic
Inversion (INDI) is a sensor-based control technique that can control nonlinear
systems subject to disturbances. It was developed for the attitude control of
manned aircraft or MAVs. In this paper we generalize this method to the outer
loop control of MAVs under severe gust loads. Significant improvements over a
traditional Proportional Integral Derivative (PID) controller are demonstrated
in an experiment where the quadrotor flies in and out of a windtunnel exhaust
at 10 m/s. The control method does not rely on frequent position updates, as is
demonstrated in an outside experiment using a standard GPS module. Finally, we
investigate the effect of using a linearization to calculate thrust vector
increments, compared to a nonlinear calculation. The method requires little
modeling and is computationally efficient.
| 1 | 0 | 0 | 0 | 0 | 0 |
Numerical Evaluation of Elliptic Functions, Elliptic Integrals and Modular Forms | We describe algorithms to compute elliptic functions and their relatives
(Jacobi theta functions, modular forms, elliptic integrals, and the
arithmetic-geometric mean) numerically to arbitrary precision with rigorous
error bounds for arbitrary complex variables. Implementations in ball
arithmetic are available in the open source Arb library. We discuss the
algorithms from a concrete implementation point of view, with focus on
performance at tens to thousands of digits of precision.
| 1 | 0 | 0 | 0 | 0 | 0 |
In situ Electric Field Skyrmion Creation in Magnetoelectric Cu$_2$OSeO$_3$ | Magnetic skyrmions are localized nanometric spin textures with quantized
winding numbers as the topological invariant. Rapidly increasing attention has
been paid to the investigations of skyrmions since their experimental discovery
in 2009, due both to the fundamental properties and the promising potential in
spintronics based applications. However, controlled creation of skyrmions
remains a pivotal challenge towards technological applications. Here, we report
that skyrmions can be created locally by electric field in the magnetoelectric
helimagnet Cu$\mathsf{_2}$OSeO$\mathsf{_3}$. Using Lorentz transmission
electron microscopy, we successfully write skyrmions in situ from a helical
spin background. Our discovery is highly coveted since it implies that
skyrmionics can be integrated into contemporary field effect transistor based
electronic technology, where very low energy dissipation can be achieved, and
hence realizes a large step forward to its practical applications.
| 0 | 1 | 0 | 0 | 0 | 0 |
An online sequence-to-sequence model for noisy speech recognition | Generative models have long been the dominant approach for speech
recognition. The success of these models however relies on the use of
sophisticated recipes and complicated machinery that is not easily accessible
to non-practitioners. Recent innovations in Deep Learning have given rise to an
alternative - discriminative models called Sequence-to-Sequence models, that
can almost match the accuracy of state of the art generative models. While
these models are easy to train as they can be trained end-to-end in a single
step, they have a practical limitation that they can only be used for offline
recognition. This is because the models require that the entirety of the input
sequence be available at the beginning of inference, an assumption that is not
valid for instantaneous speech recognition. To address this problem, online
sequence-to-sequence models were recently introduced. These models are able to
start producing outputs as data arrives, and the model feels confident enough
to output partial transcripts. These models, like sequence-to-sequence are
causal - the output produced by the model until any time, $t$, affects the
features that are computed subsequently. This makes the model inherently more
powerful than generative models that are unable to change features that are
computed from the data. This paper highlights two main contributions - an
improvement to online sequence-to-sequence model training, and its application
to noisy settings with mixed speech from two speakers.
| 1 | 0 | 0 | 1 | 0 | 0 |
Existence of infinite Viterbi path for pairwise Markov models | For hidden Markov models one of the most popular estimates of the hidden
chain is the Viterbi path -- the path maximising the posterior probability. We
consider a more general setting, called the pairwise Markov model, where the
joint process consisting of finite-state hidden regime and observation process
is assumed to be a Markov chain. We prove that under some conditions it is
possible to extend the Viterbi path to infinity for almost every observation
sequence which in turn enables to define an infinite Viterbi decoding of the
observation process, called the Viterbi process. This is done by constructing a
block of observations, called a barrier, which ensures that the Viterbi path
goes trough a given state whenever this block occurs in the observation
sequence.
| 0 | 0 | 1 | 1 | 0 | 0 |
Coding for Segmented Edit Channels | This paper considers insertion and deletion channels with the additional
assumption that the channel input sequence is implicitly divided into segments
such that at most one edit can occur within a segment. No segment markers are
available in the received sequence. We propose code constructions for the
segmented deletion, segmented insertion, and segmented insertion-deletion
channels based on subsets of Varshamov-Tenengolts codes chosen with
pre-determined prefixes and/or suffixes. The proposed codes, constructed for
any finite alphabet, are zero-error and can be decoded segment-by-segment. We
also derive an upper bound on the rate of any zero-error code for the segmented
edit channel, in terms of the segment length. This upper bound shows that the
rate scaling of the proposed codes as the segment length increases is the same
as that of the maximal code.
| 1 | 0 | 0 | 0 | 0 | 0 |
Charge compensation at the interface between the polar NaCl(111) surface and a NaCl aqueous solution | Periodic supercell models of electric double layers formed at the interface
between a charged surface and an electrolyte are subject to serious finite size
errors and require certain adjustments in the treatment of the long-range
electrostatic interactions. In a previous publication (C. Zhang, M. Sprik,
Phys. Rev. B 94, 245309 (2016)) we have shown how this can be achieved using
finite field methods. The test system was the familiar simple point charge
model of a NaCl aqueous solution confined between two oppositely charged walls.
Here this method is extended to the interface between the (111) polar surface
of a NaCl crystal and a high concentration NaCl aqueous solution. The crystal
is kept completely rigid and the compensating charge screening the polarization
can only be provided by the electrolyte. We verify that the excess electrolyte
ionic charge at the interface conforms to the Tasker 1/2 rule for compensating
charge in the theory of polar rocksalt (111) surfaces. The interface can be
viewed as an electric double layer with a net charge. We define a generalized
Helmholtz capacitance $C_\text{H}$ which can be computed by varying the applied
electric field. We find $C_\text{H} = 8.23 \, \mu \mathrm{Fcm}^{-2}$, which
should be compared to the $4.23 \, \mu \mathrm{Fcm}^{-2}$ for the (100)
non-polar surface of the same NaCl crystal. This is rationalized by the
observation that compensating ions shed their first solvation shell adsorbing
as contact ions pairs on the polar surface.
| 0 | 1 | 0 | 0 | 0 | 0 |
CrowdTone: Crowd-powered tone feedback and improvement system for emails | In this paper, we present CrowdTone, a system designed to help people set the
appropriate tone in their email communication. CrowdTone utilizes the context
and content of an email message to identify and set the appropriate tone
through a consensus-building process executed by crowd workers. We evaluated
CrowdTone with 22 participants, who provided a total of 29 emails that they had
received in the past, and ran them through CrowdTone. Participants and
professional writers assessed the quality of improvements finding a substantial
increase in the percentage of emails deemed "appropriate" or "very appropriate"
- from 25% to more than 90% by recipients, and from 45% to 90% by professional
writers. Additionally, the recipients' feedback indicated that more than 90% of
the CrowdTone processed emails showed improvement.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Deterministic and Generalized Framework for Unsupervised Learning with Restricted Boltzmann Machines | Restricted Boltzmann machines (RBMs) are energy-based neural-networks which
are commonly used as the building blocks for deep architectures neural
architectures. In this work, we derive a deterministic framework for the
training, evaluation, and use of RBMs based upon the Thouless-Anderson-Palmer
(TAP) mean-field approximation of widely-connected systems with weak
interactions coming from spin-glass theory. While the TAP approach has been
extensively studied for fully-visible binary spin systems, our construction is
generalized to latent-variable models, as well as to arbitrarily distributed
real-valued spin systems with bounded support. In our numerical experiments, we
demonstrate the effective deterministic training of our proposed models and are
able to show interesting features of unsupervised learning which could not be
directly observed with sampling. Additionally, we demonstrate how to utilize
our TAP-based framework for leveraging trained RBMs as joint priors in
denoising problems.
| 1 | 1 | 0 | 1 | 0 | 0 |
Integrability conditions for Compound Random Measures | Compound random measures (CoRM's) are a flexible and tractable framework for
vectors of completely random measure. In this paper, we provide conditions to
guarantee the existence of a CoRM. Furthermore, we prove some interesting
properties of CoRM's when exponential scores and regularly varying Lévy
intensities are considered.
| 0 | 0 | 1 | 1 | 0 | 0 |
Tunable low energy Ps beam for the anti-hydrogen free fall and for testing gravity with a Mach-Zehnder interferometer | The test of gravitational force on antimatter in the field of the matter
gravitational field, produced by earth, can be done by a free fall experiment
which involves only General Relativity, and with a Mach-Zehnder interferometer
which involves Quantum Mechanics. This article presents a new method to produce
a tunable low energy (Ps ) beam suitable for trapping the (Hbar + ) ion in a
free fall experiment, and suitable for a gravity Mach-Zehnder interferometer
with (Ps). The low energy (Ps) beam is tunable in the [10 eV, 100 eV] range.
| 0 | 1 | 0 | 0 | 0 | 0 |
An analysis of incorporating an external language model into a sequence-to-sequence model | Attention-based sequence-to-sequence models for automatic speech recognition
jointly train an acoustic model, language model, and alignment mechanism. Thus,
the language model component is only trained on transcribed audio-text pairs.
This leads to the use of shallow fusion with an external language model at
inference time. Shallow fusion refers to log-linear interpolation with a
separately trained language model at each step of the beam search. In this
work, we investigate the behavior of shallow fusion across a range of
conditions: different types of language models, different decoding units, and
different tasks. On Google Voice Search, we demonstrate that the use of shallow
fusion with a neural LM with wordpieces yields a 9.1% relative word error rate
reduction (WERR) over our competitive attention-based sequence-to-sequence
model, obviating the need for second-pass rescoring.
| 1 | 0 | 0 | 0 | 0 | 0 |
Multi-Generator Generative Adversarial Nets | We propose a new approach to train the Generative Adversarial Nets (GANs)
with a mixture of generators to overcome the mode collapsing problem. The main
intuition is to employ multiple generators, instead of using a single one as in
the original GAN. The idea is simple, yet proven to be extremely effective at
covering diverse data modes, easily overcoming the mode collapse and delivering
state-of-the-art results. A minimax formulation is able to establish among a
classifier, a discriminator, and a set of generators in a similar spirit with
GAN. Generators create samples that are intended to come from the same
distribution as the training data, whilst the discriminator determines whether
samples are true data or generated by generators, and the classifier specifies
which generator a sample comes from. The distinguishing feature is that
internal samples are created from multiple generators, and then one of them
will be randomly selected as final output similar to the mechanism of a
probabilistic mixture model. We term our method Mixture GAN (MGAN). We develop
theoretical analysis to prove that, at the equilibrium, the Jensen-Shannon
divergence (JSD) between the mixture of generators' distributions and the
empirical data distribution is minimal, whilst the JSD among generators'
distributions is maximal, hence effectively avoiding the mode collapse. By
utilizing parameter sharing, our proposed model adds minimal computational cost
to the standard GAN, and thus can also efficiently scale to large-scale
datasets. We conduct extensive experiments on synthetic 2D data and natural
image databases (CIFAR-10, STL-10 and ImageNet) to demonstrate the superior
performance of our MGAN in achieving state-of-the-art Inception scores over
latest baselines, generating diverse and appealing recognizable objects at
different resolutions, and specializing in capturing different types of objects
by generators.
| 1 | 0 | 0 | 1 | 0 | 0 |
Neural Style Transfer: A Review | The seminal work of Gatys et al. demonstrated the power of Convolutional
Neural Networks (CNNs) in creating artistic imagery by separating and
recombining image content and style. This process of using CNNs to render a
content image in different styles is referred to as Neural Style Transfer
(NST). Since then, NST has become a trending topic both in academic literature
and industrial applications. It is receiving increasing attention and a variety
of approaches are proposed to either improve or extend the original NST
algorithm. In this paper, we aim to provide a comprehensive overview of the
current progress towards NST. We first propose a taxonomy of current algorithms
in the field of NST. Then, we present several evaluation methods and compare
different NST algorithms both qualitatively and quantitatively. The review
concludes with a discussion of various applications of NST and open problems
for future research. A list of papers discussed in this review, corresponding
codes, pre-trained models and more comparison results are publicly available at
this https URL.
| 1 | 0 | 0 | 1 | 0 | 0 |
Distribution of water in the G327.3-0.6 massive star-forming region | We aim at characterizing the large-scale distribution of H2O in G327.3-0.6, a
massive star-forming region made of individual objects in different
evolutionary phases. We investigate variations of H2O abundance as function of
evolution. We present Herschel continuum maps at 89 and 179 $\mu$m of the whole
region and an APEX map at 350 {\mu}m of the IRDC. New spectral HIFI maps toward
the IRDC region covering low-energy H2O lines at 987 and 1113 GHz are also
presented and combined with HIFI pointed observations of the G327 hot core. We
infer the physical properties of the gas through optical depth analysis and
radiative transfer modeling. The continuum emission at 89 and 179 {\mu}m
follows the thermal continuum emission at longer wavelengths, with a peak at
the position of the hot core, a secondary peak in the Hii region, and an
arch-like layer of hot gas west of the Hii region. The same morphology is
observed in the 1113 GHz line, in absorption toward all dust condensations.
Optical depths of ~80 and 15 are estimated and correspond to column densities
of 10^15 and 2 10^14 cm-2, for the hot core and IRDC position. These values
indicate an H2O to H2 ratio of 3 10^-8 toward the hot core; the abundance of
H2O does not change along the IRDC with values of some 10^-8. Infall (over ~
20") is detected toward the hot core position with a rate of 1-1.3 10^-2 M_sun
/yr, high enough to overcome the radiation pressure due to the stellar
luminosity. The source structure of the hot core region is complex, with a cold
outer gas envelope in expansion, situated between the outflow and the observer,
extending over 0.32 pc. The outflow is seen face-on and centered away from the
hot core. The distribution of H2O along the IRDC is roughly constant with an
abundance peak in the more evolved object. These water abundances are in
agreement with previous studies in other massive objects and chemical models.
| 0 | 1 | 0 | 0 | 0 | 0 |
Straightening rule for an $m'$-truncated polynomial ring | We consider a certain quotient of a polynomial ring categorified by both the
isomorphic Green rings of the symmetric groups and Schur algebras generated by
the signed Young permutation modules and mixed powers respectively. They have
bases parametrised by pairs of partitions whose second partitions are multiples
of the odd prime $p$ the characteristic of the underlying field. We provide an
explicit formula rewriting a signed Young permutation module (respectively,
mixed power) in terms of signed Young permutation modules (respectively, mixed
powers) labelled by those pairs of partitions. As a result, for each partition
$\lambda$, we discovered the number of compositions $\delta$ such that $\delta$
can be rearranged to $\lambda$ and whose partial sums of $\delta$ are not
divisible by $p$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Effective Blog Pages Extractor for Better UGC Accessing | Blog is becoming an increasingly popular media for information publishing.
Besides the main content, most of blog pages nowadays also contain noisy
information such as advertisements etc. Removing these unrelated elements can
improves user experience, but also can better adapt the content to various
devices such as mobile phones. Though template-based extractors are highly
accurate, they may incur expensive cost in that a large number of template need
to be developed and they will fail once the template is updated. To address
these issues, we present a novel template-independent content extractor for
blog pages. First, we convert a blog page into a DOM-Tree, where all elements
including the title and body blocks in a page correspond to subtrees. Then we
construct subtree candidate set for the title and the body blocks respectively,
and extract both spatial and content features for elements contained in the
subtree. SVM classifiers for the title and the body blocks are trained using
these features. Finally, the classifiers are used to extract the main content
from blog pages. We test our extractor on 2,250 blog pages crawled from nine
blog sites with obviously different styles and templates. Experimental results
verify the effectiveness of our extractor.
| 1 | 0 | 0 | 0 | 0 | 0 |
The Principle of Similitude in Biology: From Allometry to the Formulation of Dimensionally Homogenous `Laws' | Meaningful laws of nature must be independent of the units employed to
measure the variables. The principle of similitude (Rayleigh 1915) or
dimensional homogeneity, states that only commensurable quantities (ones having
the same dimension) may be compared, therefore, meaningful laws of nature must
be homogeneous equations in their various units of measurement, a result which
was formalized in the $\rm \Pi$ theorem (Vaschy 1892; Buckingham 1914).
However, most relations in allometry do not satisfy this basic requirement,
including the `3/4 Law' (Kleiber 1932) that relates the basal metabolic rate
and body mass, which it is sometimes claimed to be the most fundamental
biological rate (Brown et al. 2004) and the closest to a law in life sciences
(West \& Brown 2004). Using the $\rm \Pi$ theorem, here we show that it is
possible to construct a unique homogeneous equation for the metabolic rates, in
agreement with data in the literature. We find that the variations in the
dependence of the metabolic rates on body mass are secondary, coming from
variations in the allometric dependence of the heart frequencies. This includes
not only different classes of animals (mammals, birds, invertebrates) but also
different exercise conditions (basal and maximal). Our results demonstrate that
most of the differences found in the allometric exponents (White et al. 2007)
are due to compare incommensurable quantities and that our dimensionally
homogenous formula, unify these differences into a single formulation. We
discuss the ecological implications of this new formulation in the context of
the Malthusian's, Fenchel's and the total energy consumed in a lifespan
relations.
| 0 | 1 | 0 | 0 | 0 | 0 |
New insight into the dynamics of rhodopsin photoisomerization from one-dimensional quantum-classical modeling | Characterization of the primary events involved in the $cis-trans$
photoisomerization of the rhodopsin retinal chromophore was approximated by a
minimum one-dimensional quantum-classical model. The developed mathematical
model is identical to that obtained using conventional quantum-classical
approaches, and multiparametric quantum-chemical or molecular dynamics (MD)
computations were not required. The quantum subsystem of the model includes
three electronic states for rhodopsin: (i) the ground state, (ii) the excited
state, and (iii) the primary photoproduct in the ground state. The resultant
model is in perfect agreement with experimental data in terms of the quantum
yield, the time required to reach the conical intersection and to complete the
quantum evolution, the range of the characteristic low frequencies active
within the primary events of the $11-cis$ retinal isomerization, and the
coherent character of the photoreaction. An effective redistribution of excess
energy between the vibration modes of rhodopsin was revealed by analysis of the
dissipation process. The results confirm the validity of the minimal model,
despite its one-dimensional character. The fundamental nature of the
photoreaction was therefore demonstrated using a minimum mathematical model for
the first time.
| 0 | 1 | 0 | 0 | 0 | 0 |
High-order schemes for the Euler equations in cylindrical/spherical coordinates | We consider implementations of high-order finite difference Weighted
Essentially Non-Oscillatory (WENO) schemes for the Euler equations in
cylindrical and spherical coordinate systems with radial dependence only. The
main concern of this work lies in ensuring both high-order accuracy and
conservation. Three different spatial discretizations are assessed: one that is
shown to be high-order accurate but not conservative, one conservative but not
high-order accurate, and a new approach that is both high-order accurate and
conservative. For cylindrical and spherical coordinates, we present convergence
results for the advection equation and the Euler equations with an acoustics
problem; we then use the Sod shock tube and the Sedov point-blast problems in
cylindrical coordinates to verify our analysis and implementations.
| 0 | 1 | 1 | 0 | 0 | 0 |
Suppressing correlations in massively parallel simulations of lattice models | For lattice Monte Carlo simulations parallelization is crucial to make
studies of large systems and long simulation time feasible, while sequential
simulations remain the gold-standard for correlation-free dynamics. Here,
various domain decomposition schemes are compared, concluding with one which
delivers virtually correlation-free simulations on GPU Extensive simulations of
the octahedron model for $2+1$ dimensional Karda--Parisi--Zhang surface growth,
which is very sensitive to correlation in the site-selection dynamics, were
performed to show self-consistency of the parallel runs and agreement with the
sequential algorithm. We present a GPU implementation providing a speedup of
about $30\times$ over a parallel CPU implementation on a single socket and at
least $180\times$ with respect to the sequential reference.
| 0 | 1 | 0 | 0 | 0 | 0 |
New bounds on the strength of some restrictions of Hindman's Theorem | We prove upper and lower bounds on the effective content and logical strength
for a variety of natural restrictions of Hindman's Finite Sums Theorem. For
example, we show that Hindman's Theorem for sums of length at most 2 and 4
colors implies $\mathsf{ACA}_0$. An emerging {\em leitmotiv} is that the known
lower bounds for Hindman's Theorem and for its restriction to sums of at most 2
elements are already valid for a number of restricted versions which have
simple proofs and better computability- and proof-theoretic upper bounds than
the known upper bound for the full version of the theorem. We highlight the
role of a sparsity-like condition on the solution set, which we call apartness.
| 0 | 0 | 1 | 0 | 0 | 0 |
3D Face Morphable Models "In-the-Wild" | 3D Morphable Models (3DMMs) are powerful statistical models of 3D facial
shape and texture, and among the state-of-the-art methods for reconstructing
facial shape from single images. With the advent of new 3D sensors, many 3D
facial datasets have been collected containing both neutral as well as
expressive faces. However, all datasets are captured under controlled
conditions. Thus, even though powerful 3D facial shape models can be learnt
from such data, it is difficult to build statistical texture models that are
sufficient to reconstruct faces captured in unconstrained conditions
("in-the-wild"). In this paper, we propose the first, to the best of our
knowledge, "in-the-wild" 3DMM by combining a powerful statistical model of
facial shape, which describes both identity and expression, with an
"in-the-wild" texture model. We show that the employment of such an
"in-the-wild" texture model greatly simplifies the fitting procedure, because
there is no need to optimize with regards to the illumination parameters.
Furthermore, we propose a new fast algorithm for fitting the 3DMM in arbitrary
images. Finally, we have captured the first 3D facial database with relatively
unconstrained conditions and report quantitative evaluations with
state-of-the-art performance. Complementary qualitative reconstruction results
are demonstrated on standard "in-the-wild" facial databases. An open source
implementation of our technique is released as part of the Menpo Project.
| 1 | 0 | 0 | 0 | 0 | 0 |
Image Segmentation to Distinguish Between Overlapping Human Chromosomes | In medicine, visualizing chromosomes is important for medical diagnostics,
drug development, and biomedical research. Unfortunately, chromosomes often
overlap and it is necessary to identify and distinguish between the overlapping
chromosomes. A segmentation solution that is fast and automated will enable
scaling of cost effective medicine and biomedical research. We apply neural
network-based image segmentation to the problem of distinguishing between
partially overlapping DNA chromosomes. A convolutional neural network is
customized for this problem. The results achieved intersection over union (IOU)
scores of 94.7% for the overlapping region and 88-94% on the non-overlapping
chromosome regions.
| 1 | 0 | 0 | 1 | 0 | 0 |
Implementing Large-Scale Agile Frameworks: Challenges and Recommendations | Based on 13 agile transformation cases over 15 years, this article identifies
nine challenges associated with implementing SAFe, Scrum-at-Scale, Spotify,
LeSS, Nexus, and other mixed or customised large-scale agile frameworks. These
challenges should be considered by organizations aspiring to pursue a
large-scale agile strategy. This article also provides recommendations for
practitioners and agile researchers.
| 1 | 0 | 0 | 0 | 0 | 0 |
On The Limiting Distributions of the Total Height On Families of Trees | A symbolic-computational algorithm, fully implemented in Maple, is described,
that computes explicit expressions for generating functions that enable the
efficient computations of the expectation, variance, and higher moments, of the
random variable `sum of distances to the root', defined on any given family of
rooted ordered trees (defined by degree restrictions). Taking limits, we
confirm, via elementary methods, the fact, due to David Aldous, and expanded by
Svante Janson and others, that the limiting (scaled) distributions are all the
same, and coincide with the limiting distribution of the same random variable,
when it is defined on labeled rooted trees.
| 0 | 0 | 1 | 0 | 0 | 0 |
HESS J1826$-$130: A Very Hard $γ$-Ray Spectrum Source in the Galactic Plane | HESS J1826$-$130 is an unidentified hard spectrum source discovered by
H.E.S.S. along the Galactic plane, the spectral index being $\Gamma$ = 1.6 with
an exponential cut-off at about 12 TeV. While the source does not have a clear
counterpart at longer wavelengths, the very hard spectrum emission at TeV
energies implies that electrons or protons accelerated up to several hundreds
of TeV are responsible for the emission. In the hadronic case, the VHE emission
can be produced by runaway cosmic-rays colliding with the dense molecular
clouds spatially coincident with the H.E.S.S. source.
| 0 | 1 | 0 | 0 | 0 | 0 |
Laser opacity in underdense preplasma of solid targets due to quantum electrodynamics effects | We investigate how next-generation laser pulses at 10 PW $-$ 200 PW interact
with a solid target in the presence of a relativistically underdense preplasma
produced by amplified spontaneous emission (ASE). Laser hole boring and
relativistic transparency are strongly restrained due to the generation of
electron-positron pairs and $\gamma$-ray photons via quantum electrodynamics
(QED) processes. A pair plasma with a density above the initial preplasma
density is formed, counteracting the electron-free channel produced by the hole
boring. This pair-dominated plasma can block the laser transport and trigger an
avalanche-like QED cascade, efficiently transfering the laser energy to
photons. This renders a 1-$\rm\mu m$-scalelength, underdense preplasma
completely opaque to laser pulses at this power level. The QED-induced opacity
therefore sets much higher contrast requirements for such pulse in solid-target
experiments than expected by classical plasma physics. Our simulations show for
example, that proton acceleration from the rear of a solid with a preplasma
would be strongly impaired.
| 0 | 1 | 0 | 0 | 0 | 0 |
Consequentialist conditional cooperation in social dilemmas with imperfect information | Social dilemmas, where mutual cooperation can lead to high payoffs but
participants face incentives to cheat, are ubiquitous in multi-agent
interaction. We wish to construct agents that cooperate with pure cooperators,
avoid exploitation by pure defectors, and incentivize cooperation from the
rest. However, often the actions taken by a partner are (partially) unobserved
or the consequences of individual actions are hard to predict. We show that in
a large class of games good strategies can be constructed by conditioning one's
behavior solely on outcomes (ie. one's past rewards). We call this
consequentialist conditional cooperation. We show how to construct such
strategies using deep reinforcement learning techniques and demonstrate, both
analytically and experimentally, that they are effective in social dilemmas
beyond simple matrix games. We also show the limitations of relying purely on
consequences and discuss the need for understanding both the consequences of
and the intentions behind an action.
| 1 | 0 | 0 | 0 | 0 | 0 |
Casimir free energy of dielectric films: Classical limit, low-temperature behavior and control | The Casimir free energy of dielectric films, both free-standing in vacuum and
deposited on metallic or dielectric plates, is investigated. It is shown that
the values of the free energy depend considerably on whether the calculation
approach used neglects or takes into account the dc conductivity of film
material. We demonstrate that there are the material-dependent and universal
classical limits in the former and latter cases, respectively. The analytic
behavior of the Casimir free energy and entropy for a free-standing dielectric
film at low temperature in found. According to our results, the Casimir entropy
goes to zero when the temperature vanishes if the calculation approach with
neglected dc conductivity of a film is employed. If the dc conductivity is
taken into account, the Casimir entropy takes the positive value at zero
temperature, depending on the parameters of a film, i.e., the Nernst heat
theorem is violated. By considering the Casimir free energy of silica and
sapphire films deposited on a Au plate in the framework of two calculation
approaches, we argue that physically correct values are obtained by
disregarding the role of dc conductivity. A comparison with the well known
results for the configuration of two parallel plates is made. Finally, we
compute the Casimir free energy of silica, sapphire and Ge films deposited on
high-resistivity Si plates of different thicknesses and demonstrate that it can
be positive, negative and equal to zero. Possible applications of the obtained
results to thin films used in microelectronics are discussed.
| 0 | 1 | 0 | 0 | 0 | 0 |
Discriminant chronicles mining: Application to care pathways analytics | Pharmaco-epidemiology (PE) is the study of uses and effects of drugs in well
defined populations. As medico-administrative databases cover a large part of
the population, they have become very interesting to carry PE studies. Such
databases provide longitudinal care pathways in real condition containing
timestamped care events, especially drug deliveries. Temporal pattern mining
becomes a strategic choice to gain valuable insights about drug uses. In this
paper we propose DCM, a new discriminant temporal pattern mining algorithm. It
extracts chronicle patterns that occur more in a studied population than in a
control population. We present results on the identification of possible
associations between hospitalizations for seizure and anti-epileptic drug
switches in care pathway of epileptic patients.
| 1 | 0 | 0 | 0 | 0 | 0 |
Adversarial Symmetric Variational Autoencoder | A new form of variational autoencoder (VAE) is developed, in which the joint
distribution of data and codes is considered in two (symmetric) forms: ($i$)
from observed data fed through the encoder to yield codes, and ($ii$) from
latent codes drawn from a simple prior and propagated through the decoder to
manifest data. Lower bounds are learned for marginal log-likelihood fits
observed data and latent codes. When learning with the variational bound, one
seeks to minimize the symmetric Kullback-Leibler divergence of joint density
functions from ($i$) and ($ii$), while simultaneously seeking to maximize the
two marginal log-likelihoods. To facilitate learning, a new form of adversarial
training is developed. An extensive set of experiments is performed, in which
we demonstrate state-of-the-art data reconstruction and generation on several
image benchmark datasets.
| 1 | 0 | 0 | 0 | 0 | 0 |
The singular locus of hypersurface sections containing a closed subscheme over finite fields | We prove that there exist hypersurfaces that contain a given closed subscheme
$Z$ of the projective space over a finite field and intersect a given smooth
scheme $X$ off of $Z$ smoothly, if the intersection $V = Z \cap X$ is smooth.
Furthermore, we can give a bound on the dimension of the singular locus of the
hypersurface section and prescribe finitely many local conditions on the
hypersurface. This is an analogue of a Bertini theorem of Bloch over finite
fields and is proved using Poonen's closed point sieve. We also show a similar
theorem for the case where $V$ is not smooth.
| 0 | 0 | 1 | 0 | 0 | 0 |
A Statistical Perspective on Inverse and Inverse Regression Problems | Inverse problems, where in broad sense the task is to learn from the noisy
response about some unknown function, usually represented as the argument of
some known functional form, has received wide attention in the general
scientific disciplines. How- ever, in mainstream statistics such inverse
problem paradigm does not seem to be as popular. In this article we provide a
brief overview of such problems from a statistical, particularly Bayesian,
perspective.
We also compare and contrast the above class of problems with the perhaps
more statistically familiar inverse regression problems, arguing that this
class of problems contains the traditional class of inverse problems. In course
of our review we point out that the statistical literature is very scarce with
respect to both the inverse paradigms, and substantial research work is still
necessary to develop the fields.
| 0 | 0 | 1 | 1 | 0 | 0 |
Model reduction for transport-dominated problems via online adaptive bases and adaptive sampling | This work presents a model reduction approach for problems with coherent
structures that propagate over time such as convection-dominated flows and
wave-type phenomena. Traditional model reduction methods have difficulties with
these transport-dominated problems because propagating coherent structures
typically introduce high-dimensional features that require high-dimensional
approximation spaces. The approach proposed in this work exploits the locality
in space and time of propagating coherent structures to derive efficient
reduced models. First, full-model solutions are approximated locally in time
via local reduced spaces that are adapted with basis updates during time
stepping. The basis updates are derived from querying the full model at a few
selected spatial coordinates. Second, the locality in space of the coherent
structures is exploited via an adaptive sampling scheme that selects at which
components to query the full model for computing the basis updates. Our
analysis shows that, in probability, the more local the coherent structure is
in space, the fewer full-model samples are required to adapt the reduced basis
with the proposed adaptive sampling scheme. Numerical results on benchmark
examples with interacting wave-type structures and time-varying transport
speeds and on a model combustor of a single-element rocket engine demonstrate
the wide applicability of our approach and the significant runtime speedups
compared to full models and traditional reduced models.
| 1 | 0 | 0 | 0 | 0 | 0 |
Weighted parallel SGD for distributed unbalanced-workload training system | Stochastic gradient descent (SGD) is a popular stochastic optimization method
in machine learning. Traditional parallel SGD algorithms, e.g., SimuParallel
SGD, often require all nodes to have the same performance or to consume equal
quantities of data. However, these requirements are difficult to satisfy when
the parallel SGD algorithms run in a heterogeneous computing environment;
low-performance nodes will exert a negative influence on the final result. In
this paper, we propose an algorithm called weighted parallel SGD (WP-SGD).
WP-SGD combines weighted model parameters from different nodes in the system to
produce the final output. WP-SGD makes use of the reduction in standard
deviation to compensate for the loss from the inconsistency in performance of
nodes in the cluster, which means that WP-SGD does not require that all nodes
consume equal quantities of data. We also analyze the theoretical feasibility
of running two other parallel SGD algorithms combined with WP-SGD in a
heterogeneous environment. The experimental results show that WP-SGD
significantly outperforms the traditional parallel SGD algorithms on
distributed training systems with an unbalanced workload.
| 1 | 0 | 0 | 1 | 0 | 0 |
Neural Lander: Stable Drone Landing Control using Learned Dynamics | Precise trajectory control near ground is difficult for multi-rotor drones,
due to the complex ground effects caused by interactions between multi-rotor
airflow and the environment. Conventional control methods often fail to
properly account for these complex effects and fall short in accomplishing
smooth landing. In this paper, we present a novel deep-learning-based robust
nonlinear controller (Neural-Lander) that improves control performance of a
quadrotor during landing. Our approach blends together a nominal dynamics model
coupled with a Deep Neural Network (DNN) that learns the high-order
interactions. We employ a novel application of spectral normalization to
constrain the DNN to have bounded Lipschitz behavior. Leveraging this Lipschitz
property, we design a nonlinear feedback linearization controller using the
learned model and prove system stability with disturbance rejection. To the
best of our knowledge, this is the first DNN-based nonlinear feedback
controller with stability guarantees that can utilize arbitrarily large neural
nets. Experimental results demonstrate that the proposed controller
significantly outperforms a baseline linear proportional-derivative (PD)
controller in both 1D and 3D landing cases. In particular, we show that
compared to the PD controller, Neural-Lander can decrease error in z direction
from 0.13m to zero, and mitigate average x and y drifts by 90% and 34%
respectively, in 1D landing. Meanwhile, Neural-Lander can decrease z error from
0.12m to zero, in 3D landing. We also empirically show that the DNN generalizes
well to new test inputs outside the training domain.
| 1 | 0 | 0 | 0 | 0 | 0 |
Finite-Time Stabilization of Longitudinal Control for Autonomous Vehicles via a Model-Free Approach | This communication presents a longitudinal model-free control approach for
computing the wheel torque command to be applied on a vehicle. This setting
enables us to overcome the problem of unknown vehicle parameters for generating
a suitable control law. An important parameter in this control setting is made
time-varying for ensuring finite-time stability. Several convincing computer
simulations are displayed and discussed. Overshoots become therefore smaller.
The driving comfort is increased and the robustness to time-delays is improved.
| 1 | 0 | 1 | 0 | 0 | 0 |
Review of Geraint F. Lewis and Luke A. Barnes, A Fortunate Universe: Life in a Finely Tuned Cosmos | This new book by cosmologists Geraint F. Lewis and Luke A. Barnes is another
entry in the long list of cosmology-centered physics books intended for a large
audience. While many such books aim at advancing a novel scientific theory, A
Fortunate Universe has no such scientific pretense. Its goals are to assert
that the universe is fine-tuned for life, to defend that this fact can
reasonably motivate further scientific inquiry as to why it is so, and to show
that the multiverse and intelligent design hypotheses are reasonable proposals
to explain this fine-tuning. This book's potential contribution, therefore,
lies in how convincingly and efficiently it can make that case.
| 0 | 1 | 0 | 0 | 0 | 0 |
Introducing SPAIN (SParse Audion INpainter) | A novel sparsity-based algorithm for audio inpainting is proposed by
translating the SPADE algorithm by Kitić et. al.---the state-of-the-art for
audio declipping---into the task of audio inpainting. SPAIN (SParse Audio
INpainter) comes in synthesis and analysis variants. Experiments show that both
A-SPAIN and S-SPAIN outperform other sparsity-based inpainting algorithms and
that A-SPAIN performs on a par with the state-of-the-art method based on linear
prediction.
| 1 | 0 | 0 | 0 | 0 | 0 |
Palindromic Decompositions with Gaps and Errors | Identifying palindromes in sequences has been an interesting line of research
in combinatorics on words and also in computational biology, after the
discovery of the relation of palindromes in the DNA sequence with the HIV
virus. Efficient algorithms for the factorization of sequences into palindromes
and maximal palindromes have been devised in recent years. We extend these
studies by allowing gaps in decompositions and errors in palindromes, and also
imposing a lower bound to the length of acceptable palindromes.
We first present an algorithm for obtaining a palindromic decomposition of a
string of length n with the minimal total gap length in time O(n log n * g) and
space O(n g), where g is the number of allowed gaps in the decomposition. We
then consider a decomposition of the string in maximal \delta-palindromes (i.e.
palindromes with \delta errors under the edit or Hamming distance) and g
allowed gaps. We present an algorithm to obtain such a decomposition with the
minimal total gap length in time O(n (g + \delta)) and space O(n g).
| 1 | 0 | 0 | 0 | 0 | 0 |
End-of-Use Core Triage in Extreme Scenarios Based on a Threshold Approach | Remanufacturing is a significant factor in securing sustainability through a
circular economy. Sorting plays a significant role in remanufacturing
pre-processing inspections. Its significance can increase when remanufacturing
facilities encounter extreme situations, such as abnormally huge core arrivals.
Our main objective in this work is switching from less efficient to a more
efficient model and to characterize extreme behavior of core arrival in
remanufacturing and applying the developed model to triage cores. Central
tendency core flow models are not sufficient to handle extreme situations,
however, complementary Extreme Value (EV) approaches have shown to improve
model efficiency. Extreme core flows to remanufacturing facilities are rare but
still likely and can adversely affect remanufacturing business operations. In
this investigation, extreme end-of-use core flow is modelled by a threshold
approach using the Generalized Pareto Distribution (GPD). It is shown that GPD
has better performance than its maxima-block GEV counterpart from practical and
data efficiency perspectives. The model is validated by a synthesized big
dataset, tested by sophisticated statistical Anderson Darling (AD) test, and is
applied to a case of extreme flow to a valve shop in order to predict
probability of over-capacity arrivals that is critical in remanufacturing
business management. Finally, the GPD model combined with triage strategies is
used to initiate investigations into the efficacy of different triage methods
in remanufacturing operations.
| 0 | 0 | 0 | 1 | 0 | 0 |
A temperature-dependent implicit-solvent model of polyethylene glycol in aqueous solution | A temperature (T)-dependent coarse-grained (CG) Hamiltonian of polyethylene
glycol/oxide (PEG/PEO) in aqueous solution is reported to be used in
implicit-solvent material models in a wide temperature (i.e., solvent quality)
range. The T-dependent nonbonded CG interactions are derived from a combined
"bottom-up" and "top-down" approach. The pair potentials calculated from
atomistic replica-exchange molecular dynamics simulations in combination with
the iterative Boltzmann inversion are post-refined by benchmarking to
experimental data of the radius of gyration. For better handling and a fully
continuous transferability in T-space, the pair potentials are conveniently
truncated and mapped to an analytic formula with three structural parameters
expressed as explicit continuous functions of T. It is then demonstrated that
this model without further adjustments successfully reproduces other
experimentally known key thermodynamic properties of semi-dilute PEG solutions
such as the full equation of state (i.e., T-dependent osmotic pressure) for
various chain lengths as well as their cloud point (or collapse) temperature.
| 0 | 1 | 0 | 0 | 0 | 0 |
Directional Statistics and Filtering Using libDirectional | In this paper, we present libDirectional, a MATLAB library for directional
statistics and directional estimation. It supports a variety of commonly used
distributions on the unit circle, such as the von Mises, wrapped normal, and
wrapped Cauchy distributions. Furthermore, various distributions on
higher-dimensional manifolds such as the unit hypersphere and the hypertorus
are available. Based on these distributions, several recursive filtering
algorithms in libDirectional allow estimation on these manifolds. The
functionality is implemented in a clear, well-documented, and object-oriented
structure that is both easy to use and easy to extend.
| 1 | 0 | 0 | 1 | 0 | 0 |
A solution of the dark energy and its coincidence problem based on local antigravity sources without fine-tuning or new scales | A novel idea is proposed for a natural solution of the dark energy and its
cosmic coincidence problem. The existence of local antigravity sources,
associated with astrophysical matter configurations distributed throughout the
universe, can lead to a recent cosmic acceleration effect. Various physical
theories can be compatible with this idea, but here, in order to test our
proposal, we focus on quantum originated spherically symmetric metrics matched
with the cosmological evolution through the simplest Swiss cheese model. In the
context of asymptotically safe gravity, we have explained the observed amount
of dark energy using Newton's constant, the galaxy or cluster length scales,
and dimensionless order one parameters predicted by the theory, without
fine-tuning or extra unproven energy scales. The interior modified
Schwarzschild-de Sitter metric allows us to approximately interpret this result
as that the standard cosmological constant is a composite quantity made of the
above parameters, instead of a fundamental one.
| 0 | 1 | 0 | 0 | 0 | 0 |
Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples | Self-paced learning and hard example mining re-weight training instances to
improve learning accuracy. This paper presents two improved alternatives based
on lightweight estimates of sample uncertainty in stochastic gradient descent
(SGD): the variance in predicted probability of the correct class across
iterations of mini-batch SGD, and the proximity of the correct class
probability to the decision threshold. Extensive experimental results on six
datasets show that our methods reliably improve accuracy in various network
architectures, including additional gains on top of other popular training
techniques, such as residual learning, momentum, ADAM, batch normalization,
dropout, and distillation.
| 0 | 0 | 0 | 1 | 0 | 0 |
Optical signature of Weyl electronic structures in tantalum pnictides Ta$Pn$ ($Pn=$ P, As) | To investigate the electronic structure of Weyl semimetals Ta$Pn$ ($Pn=$P,
As), optical conductivity [$\sigma(\omega)$] spectra are measured over a wide
range of photon energies and temperatures, and these measured values are
compared with band calculations. Two significant structures can be observed: a
bending structure at $\hbar\omega\sim$85 meV in TaAs, and peaks at
$\hbar\omega\sim$ 50 meV (TaP) and $\sim$30 meV (TaAs). The bending structure
can be explained by the interband transition between saddle points connecting a
set of $W_2$ Weyl points. The temperature dependence of the peak intensity can
be fitted by assuming the interband transition between saddle points connecting
a set of $W_1$ Weyl points. Owing to the different temperature dependence of
the Drude weight in both materials, it is found that the Weyl points of TaAs
are located near the Fermi level, whereas those of TaP are further away.
| 0 | 1 | 0 | 0 | 0 | 0 |
A study of cyber security in hospitality industry- threats and countermeasures: case study in Reno, Nevada | The purpose of this study is to analyze cyber security and security practices
of electronic information and network system, network threats, and techniques
to prevent the cyber attacks in hotels. Helping the information technology
directors and chief information officers (CIO) is the aim of this study to
advance policy for security of electronic information in hotels and suggesting
some techniques and tools to secure the computer networks. This research is
completely qualitative while the case study and interviews have done in 5
random hotels in Reno, Nevada, United States of America. The interview has done
with 50 hotel guests, 10 front desk employees, 3 IT manager and 2 assistant of
General manager. The results show that hotels' cyber security is very low and
hotels are very vulnerable in this regard and at the end, the implications and
contribution of the study is mentioned.
| 1 | 0 | 0 | 0 | 0 | 0 |
Weak Fraisse categories | We develop the theory of weak Fraisse categories, where the crucial concept
is the weak amalgamation property, discovered relatively recently in model
theory. We show that, in a suitable framework, every weak Fraisse category has
its unique limit, a special object in a bigger category, characterized by
certain variant of injectivity. This significantly extends the known theory of
Fraisse limits.
| 0 | 0 | 1 | 0 | 0 | 0 |
Multiple Stakeholders in Music Recommender Systems | Music recommendation services collectively spin billions of songs for
millions of listeners on a daily basis. Users can typically listen to a variety
of songs tailored to their personal tastes and preferences. Music is not the
only type of content encountered in these services, however. Advertisements are
generally interspersed throughout the music stream to generate revenue for the
business. Additional content may include artist messaging, ticketing, sports,
news and weather. In this paper, we discuss issues that arise when multiple
content providers are stakeholders in the recommendation process. These
stakeholders each have their own objectives and must work in concert to sustain
a healthy music recommendation service.
| 1 | 0 | 0 | 0 | 0 | 0 |
Large time behavior of solution to nonlinear Dirac equation in $1+1$ dimensions | This paper studies the large time behavior of solution for a class of
nonlinear massless Dirac equations in $R^{1+1}$. It is shown that the solution
will tend to travelling wave solution when time tends to infinity.
| 0 | 0 | 1 | 0 | 0 | 0 |
Compositions of Functions and Permutations Specified by Minimal Reaction Systems | This paper studies mathematical properties of reaction systems that was
introduced by Enrenfeucht and Rozenberg as computational models inspired by
biochemical reaction in the living cells. In particular, we continue the study
on the generative power of functions specified by minimal reaction systems
under composition initiated by Salomaa. Allowing degenerate reaction systems,
functions specified by minimal reaction systems over a quarternary alphabet
that are permutations generate the alternating group on the power set of the
background set.
| 1 | 0 | 0 | 0 | 0 | 0 |
The center problem for the Lotka reactions with generalized mass-action kinetics | Chemical reaction networks with generalized mass-action kinetics lead to
power-law dynamical systems. As a simple example, we consider the Lotka
reactions and the resulting planar ODE. We characterize the parameters
(positive coefficients and real exponents) for which the unique positive
equilibrium is a center.
| 0 | 0 | 1 | 0 | 0 | 0 |
Recovery of Missing Samples Using Sparse Approximation via a Convex Similarity Measure | In this paper, we study the missing sample recovery problem using methods
based on sparse approximation. In this regard, we investigate the algorithms
used for solving the inverse problem associated with the restoration of missed
samples of image signal. This problem is also known as inpainting in the
context of image processing and for this purpose, we suggest an iterative
sparse recovery algorithm based on constrained $l_1$-norm minimization with a
new fidelity metric. The proposed metric called Convex SIMilarity (CSIM) index,
is a simplified version of the Structural SIMilarity (SSIM) index, which is
convex and error-sensitive. The optimization problem incorporating this
criterion, is then solved via Alternating Direction Method of Multipliers
(ADMM). Simulation results show the efficiency of the proposed method for
missing sample recovery of 1D patch vectors and inpainting of 2D image signals.
| 1 | 0 | 0 | 1 | 0 | 0 |
Skin cancer reorganization and classification with deep neural network | As one kind of skin cancer, melanoma is very dangerous. Dermoscopy based
early detection and recarbonization strategy is critical for melanoma therapy.
However, well-trained dermatologists dominant the diagnostic accuracy. In order
to solve this problem, many effort focus on developing automatic image analysis
systems. Here we report a novel strategy based on deep learning technique, and
achieve very high skin lesion segmentation and melanoma diagnosis accuracy: 1)
we build a segmentation neural network (skin_segnn), which achieved very high
lesion boundary detection accuracy; 2) We build another very deep neural
network based on Google inception v3 network (skin_recnn) and its well-trained
weight. The novel designed transfer learning based deep neural network
skin_inceptions_v3_nn helps to achieve a high prediction accuracy.
| 1 | 0 | 0 | 0 | 0 | 0 |
Rescaled extrapolation for vector-valued functions | We extend Rubio de Francia's extrapolation theorem for functions valued in
UMD Banach function spaces, leading to short proofs of some new and known
results. In particular we prove Littlewood-Paley-Rubio de Francia-type
estimates and boundedness of variational Carleson operators for Banach function
spaces with UMD concavifications.
| 0 | 0 | 1 | 0 | 0 | 0 |
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