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A Decision Tree Based Approach Towards Adaptive Profiling of Distributed Applications | The adoption of the distributed paradigm has allowed applications to increase
their scalability, robustness and fault tolerance, but it has also complicated
their structure, leading to an exponential growth of the applications'
configuration space and increased difficulty in predicting their performance.
In this work, we describe a novel, automated profiling methodology that makes
no assumptions on application structure. Our approach utilizes oblique Decision
Trees in order to recursively partition an application's configuration space in
disjoint regions, choose a set of representative samples from each subregion
according to a defined policy and return a model for the entire space as a
composition of linear models over each subregion. An extensive evaluation over
real-life applications and synthetic performance functions showcases that our
scheme outperforms other state-of-the-art profiling methodologies. It
particularly excels at reflecting abnormalities and discontinuities of the
performance function, allowing the user to influence the sampling policy based
on the modeling accuracy and the space coverage.
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A Tractable Approach to Dynamic Network Dimensioning Based on the Best-cell Configuration | Spatial distributions of other cell interference (OCIF) and interference to
own-cell power ratio (IOPR) with reference to the distance between a mobile and
its serving base station (BS) are modeled for the down-link reception of
cellular systems based on the best-cell configuration instead of the
nearest-cell configuration. This enables a more realistic evaluation of two
competing objectives in network dimensioning: coverage and rate capacity. More
outcomes useful for dynamic network dimensioning are also derived, including
maximum BS transmission power per cell size and the cell density required for
an adequate coverage of a given traffic density.
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Parametric uncertainty in complex environmental models: a cheap emulation approach for models with high-dimensional output | In order to understand underlying processes governing environmental and
physical processes, and predict future outcomes, a complex computer model is
frequently required to simulate these dynamics. However there is inevitably
uncertainty related to the exact parametric form or the values of such
parameters to be used when developing these simulators, with \emph{ranges} of
plausible values prevalent in the literature. Systematic errors introduced by
failing to account for these uncertainties have the potential to have a large
effect on resulting estimates in unknown quantities of interest. Due to the
complexity of these types of models, it is often unfeasible to run large
numbers of training runs that are usually required for full statistical
emulators of the environmental processes. We therefore present a method for
accounting for uncertainties in complex environmental simulators without the
need for very large numbers of training runs and illustrate the method through
an application to the Met Office's atmospheric transport model NAME. We
conclude that there are two principle parameters that are linked with
variability in NAME outputs, namely the free tropospheric turbulence parameter
and particle release height. Our results suggest the former should be
significantly larger than is currently implemented as a default in NAME, whilst
changes in the latter most likely stem from inconsistencies between the model
specified ground height at the observation locations and the true height at
this location. Estimated discrepancies from independent data are consistent
with the discrepancy between modelled and true ground height.
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Prediction and Control with Temporal Segment Models | We introduce a method for learning the dynamics of complex nonlinear systems
based on deep generative models over temporal segments of states and actions.
Unlike dynamics models that operate over individual discrete timesteps, we
learn the distribution over future state trajectories conditioned on past
state, past action, and planned future action trajectories, as well as a latent
prior over action trajectories. Our approach is based on convolutional
autoregressive models and variational autoencoders. It makes stable and
accurate predictions over long horizons for complex, stochastic systems,
effectively expressing uncertainty and modeling the effects of collisions,
sensory noise, and action delays. The learned dynamics model and action prior
can be used for end-to-end, fully differentiable trajectory optimization and
model-based policy optimization, which we use to evaluate the performance and
sample-efficiency of our method.
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Artificial Intelligence and Statistics | Artificial intelligence (AI) is intrinsically data-driven. It calls for the
application of statistical concepts through human-machine collaboration during
generation of data, development of algorithms, and evaluation of results. This
paper discusses how such human-machine collaboration can be approached through
the statistical concepts of population, question of interest,
representativeness of training data, and scrutiny of results (PQRS). The PQRS
workflow provides a conceptual framework for integrating statistical ideas with
human input into AI products and research. These ideas include experimental
design principles of randomization and local control as well as the principle
of stability to gain reproducibility and interpretability of algorithms and
data results. We discuss the use of these principles in the contexts of
self-driving cars, automated medical diagnoses, and examples from the authors'
collaborative research.
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Shortcut Sequence Tagging | Deep stacked RNNs are usually hard to train. Adding shortcut connections
across different layers is a common way to ease the training of stacked
networks. However, extra shortcuts make the recurrent step more complicated. To
simply the stacked architecture, we propose a framework called shortcut block,
which is a marriage of the gating mechanism and shortcuts, while discarding the
self-connected part in LSTM cell. We present extensive empirical experiments
showing that this design makes training easy and improves generalization. We
propose various shortcut block topologies and compositions to explore its
effectiveness. Based on this architecture, we obtain a 6% relatively
improvement over the state-of-the-art on CCGbank supertagging dataset. We also
get comparable results on POS tagging task.
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Distortions of the Cosmic Microwave Background through cooling lines during the epoch of Reionization | By using N-body hydrodynamical cosmological simulations in which the
chemistry of major metals and molecules is consistently solved for, we study
the interaction of metallic fine-structure lines with the CMB. Our analysis
shows that the collisional induced emissions in the OI 145 $\mu$m and CII 158
$\mu$m lines during reionization introduce a distortion of the CMB spectrum at
low frequencies ($\nu < 300$ GHz) with amplitudes up to $\Delta
I_{\nu}/B_{\nu}(T_{\rm CMB})\sim 10^{-8}$-$10^{-7}$, i.e., at the $\sim 0.1$
percent level of FIRAS upper limits. Shorter wavelength fine-structure
transitions (OI 63 $\mu$m, FeII 26 $\mu$m, and SiII 35 $\mu$m) typically sample
the reionization epoch at higher observing frequencies ($\nu > 400$ GHz). This
corresponds to the Wien tail of the CMB spectrum and the distortion level
induced by those lines may be as high as $\Delta I_{\nu}/B_{\nu}(T_{\rm
CMB})\sim 10^{-4}$. The angular anisotropy produced by these lines should be
more relevant at higher frequencies: while practically negligible at $\nu=145
$GHz, signatures from CII 158 $\mu$m and OI 145 $\mu$m should amount to 1%-5%
of the anisotropy power measured at $l \sim 5000$ and $\nu=220 $GHz by the ACT
and SPT collaborations (after assuming $\Delta \nu_{\rm obs}/\nu_{\rm
obs}\simeq 0.005$ for the line observations). Our simulations show that
anisotropy maps from different lines (e.g., OI 145 $\mu$m and CII 158 $\mu$m)
at the same redshift show a very high degree ($>0.8$) of spatial correlation,
allowing for the use of observations at different frequencies to unveil the
same snapshot of the reionization epoch. Finally, our simulations demonstrate
that line-emission anisotropies extracted in narrow frequency/redshift shells
are practically uncorrelated in frequency space, thus enabling standard methods
for removal of foregrounds that vary smoothly in frequency, just as in HI 21 cm
studies.
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Improving brain computer interface performance by data augmentation with conditional Deep Convolutional Generative Adversarial Networks | One of the big restrictions in brain computer interface field is the very
limited training samples, it is difficult to build a reliable and usable system
with such limited data. Inspired by generative adversarial networks, we propose
a conditional Deep Convolutional Generative Adversarial (cDCGAN) Networks
method to generate more artificial EEG signal automatically for data
augmentation to improve the performance of convolutional neural networks in
brain computer interface field and overcome the small training dataset
problems. We evaluate the proposed cDCGAN method on BCI competition dataset of
motor imagery. The results show that the generated artificial EEG data from
Gaussian noise can learn the features from raw EEG data and has no less than
the classification accuracy of raw EEG data in the testing dataset. Also by
using generated artificial data can effectively improve classification accuracy
at the same model with limited training data.
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From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood | Our goal is to learn a semantic parser that maps natural language utterances
into executable programs when only indirect supervision is available: examples
are labeled with the correct execution result, but not the program itself.
Consequently, we must search the space of programs for those that output the
correct result, while not being misled by spurious programs: incorrect programs
that coincidentally output the correct result. We connect two common learning
paradigms, reinforcement learning (RL) and maximum marginal likelihood (MML),
and then present a new learning algorithm that combines the strengths of both.
The new algorithm guards against spurious programs by combining the systematic
search traditionally employed in MML with the randomized exploration of RL, and
by updating parameters such that probability is spread more evenly across
consistent programs. We apply our learning algorithm to a new neural semantic
parser and show significant gains over existing state-of-the-art results on a
recent context-dependent semantic parsing task.
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GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data | Object Transfiguration replaces an object in an image with another object
from a second image. For example it can perform tasks like "putting exactly
those eyeglasses from image A on the nose of the person in image B". Usage of
exemplar images allows more precise specification of desired modifications and
improves the diversity of conditional image generation. However, previous
methods that rely on feature space operations, require paired data and/or
appearance models for training or disentangling objects from background. In
this work, we propose a model that can learn object transfiguration from two
unpaired sets of images: one set containing images that "have" that kind of
object, and the other set being the opposite, with the mild constraint that the
objects be located approximately at the same place. For example, the training
data can be one set of reference face images that have eyeglasses, and another
set of images that have not, both of which spatially aligned by face landmarks.
Despite the weak 0/1 labels, our model can learn an "eyeglasses" subspace that
contain multiple representatives of different types of glasses. Consequently,
we can perform fine-grained control of generated images, like swapping the
glasses in two images by swapping the projected components in the "eyeglasses"
subspace, to create novel images of people wearing eyeglasses.
Overall, our deterministic generative model learns disentangled attribute
subspaces from weakly labeled data by adversarial training. Experiments on
CelebA and Multi-PIE datasets validate the effectiveness of the proposed model
on real world data, in generating images with specified eyeglasses, smiling,
hair styles, and lighting conditions etc. The code is available online.
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Flow-free Video Object Segmentation | Segmenting foreground object from a video is a challenging task because of
the large deformations of the objects, occlusions, and background clutter. In
this paper, we propose a frame-by-frame but computationally efficient approach
for video object segmentation by clustering visually similar generic object
segments throughout the video. Our algorithm segments various object instances
appearing in the video and then perform clustering in order to group visually
similar segments into one cluster. Since the object that needs to be segmented
appears in most part of the video, we can retrieve the foreground segments from
the cluster having maximum number of segments, thus filtering out noisy
segments that do not represent any object. We then apply a track and fill
approach in order to localize the objects in the frames where the object
segmentation framework fails to segment any object. Our algorithm performs
comparably to the recent automatic methods for video object segmentation when
benchmarked on DAVIS dataset while being computationally much faster.
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Finding Modes by Probabilistic Hypergraphs Shifting | In this paper, we develop a novel paradigm, namely hypergraph shift, to find
robust graph modes by probabilistic voting strategy, which are semantically
sound besides the self-cohesiveness requirement in forming graph modes. Unlike
the existing techniques to seek graph modes by shifting vertices based on
pair-wise edges (i.e, an edge with $2$ ends), our paradigm is based on shifting
high-order edges (hyperedges) to deliver graph modes. Specifically, we convert
the problem of seeking graph modes as the problem of seeking maximizers of a
novel objective function with the aim to generate good graph modes based on
sifting edges in hypergraphs. As a result, the generated graph modes based on
dense subhypergraphs may more accurately capture the object semantics besides
the self-cohesiveness requirement. We also formally prove that our technique is
always convergent. Extensive empirical studies on synthetic and real world data
sets are conducted on clustering and graph matching. They demonstrate that our
techniques significantly outperform the existing techniques.
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Bounding the size of an almost-equidistant set in Euclidean space | A set of points in d-dimensional Euclidean space is almost equidistant if
among any three points of the set, some two are at distance 1. We show that an
almost-equidistant set in $\mathbb{R}^d$ has cardinality $O(d^{4/3})$.
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Equipping weak equivalences with algebraic structure | We investigate the extent to which the weak equivalences in a model category
can be equipped with algebraic structure. We prove, for instance, that there
exists a monad T such that a morphism of topological spaces admits T-algebra
structure if and only it is a weak homotopy equivalence. Likewise for
quasi-isomorphisms and many other examples. The basic trick is to consider
injectivity in arrow categories. Using algebraic injectivity and cone
injectivity we obtain general results about the extent to which the weak
equivalences in a combinatorial model category can be equipped with algebraic
structure.
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An Experimental Study of the Treewidth of Real-World Graph Data (Extended Version) | Treewidth is a parameter that measures how tree-like a relational instance
is, and whether it can reasonably be decomposed into a tree. Many computation
tasks are known to be tractable on databases of small treewidth, but computing
the treewidth of a given instance is intractable. This article is the first
large-scale experimental study of treewidth and tree decompositions of
real-world database instances (25 datasets from 8 different domains, with sizes
ranging from a few thousand to a few million vertices). The goal is to
determine which data, if any, can benefit of the wealth of algorithms for
databases of small treewidth. For each dataset, we obtain upper and lower bound
estimations of their treewidth, and study the properties of their tree
decompositions. We show in particular that, even when treewidth is high, using
partial tree decompositions can result in data structures that can assist
algorithms.
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A yield-cost tradeoff governs Escherichia coli's decision between fermentation and respiration in carbon-limited growth | Many microbial systems are known to actively reshape their proteomes in
response to changes in growth conditions induced e.g. by nutritional stress or
antibiotics. Part of the re-allocation accounts for the fact that, as the
growth rate is limited by targeting specific metabolic activities, cells simply
respond by fine-tuning their proteome to invest more resources into the
limiting activity (i.e. by synthesizing more proteins devoted to it). However,
this is often accompanied by an overall re-organization of metabolism, aimed at
improving the growth yield under limitation by re-wiring resource through
different pathways. While both effects impact proteome composition, the latter
underlies a more complex systemic response to stress. By focusing on E. coli's
`acetate switch', we use mathematical modeling and a re-analysis of empirical
data to show that the transition from a predominantly fermentative to a
predominantly respirative metabolism in carbon-limited growth results from the
trade-off between maximizing the growth yield and minimizing its costs in terms
of required the proteome share. In particular, E. coli's metabolic phenotypes
appear to be Pareto-optimal for these objective functions over a broad range of
dilutions.
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Towards a Unified Taxonomy of Biclustering Methods | Being an unsupervised machine learning and data mining technique,
biclustering and its multimodal extensions are becoming popular tools for
analysing object-attribute data in different domains. Apart from conventional
clustering techniques, biclustering is searching for homogeneous groups of
objects while keeping their common description, e.g., in binary setting, their
shared attributes. In bioinformatics, biclustering is used to find genes, which
are active in a subset of situations, thus being candidates for biomarkers.
However, the authors of those biclustering techniques that are popular in gene
expression analysis, may overlook the existing methods. For instance, BiMax
algorithm is aimed at finding biclusters, which are well-known for decades as
formal concepts. Moreover, even if bioinformatics classify the biclustering
methods according to reasonable domain-driven criteria, their classification
taxonomies may be different from survey to survey and not full as well. So, in
this paper we propose to use concept lattices as a tool for taxonomy building
(in the biclustering domain) and attribute exploration as means for
cross-domain taxonomy completion.
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Global center stable manifold for the defocusing energy critical wave equation with potential | In this paper we consider the defocusing energy critical wave equation with a
trapping potential in dimension $3$. We prove that the set of initial data for
which solutions scatter to an unstable excited state $(\phi, 0)$ forms a finite
co-dimensional path connected $C^1$ manifold in the energy space. This manifold
is a global and unique center-stable manifold associated with $(\phi,0)$. It is
constructed in a first step locally around any solution scattering to $\phi$,
which might be very far away from $\phi$ in the $\dot{H}^1\times
L^2(\mathbb{R}^3)$ norm. In a second crucial step a no-return property is
proved for any solution which starts near, but not on the local manifolds. This
ensures that the local manifolds form a global one. Scattering to an unstable
steady state is therefore a non-generic behavior, in a strong topological sense
in the energy space. This extends our previous result [18] to the nonradial
case. The new ingredients here are (i) application of the reversed Strichartz
estimate from [3] to construct a local center stable manifold near any solution
that scatters to $(\phi, 0)$. This is needed since the endpoint of the standard
Strichartz estimates fails nonradially. (ii) The nonradial channel of energy
estimate introduced by Duyckaerts-Kenig-Merle [14], which is used to show that
solutions that start off but near the local manifolds associated with $\phi$
emit some amount of energy into the far field in excess of the amount of energy
beyond that of the steady state $\phi$.
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Real-time convolutional networks for sonar image classification in low-power embedded systems | Deep Neural Networks have impressive classification performance, but this
comes at the expense of significant computational resources at inference time.
Autonomous Underwater Vehicles use low-power embedded systems for sonar image
perception, and cannot execute large neural networks in real-time. We propose
the use of max-pooling aggressively, and we demonstrate it with a Fire-based
module and a new Tiny module that includes max-pooling in each module. By
stacking them we build networks that achieve the same accuracy as bigger ones,
while reducing the number of parameters and considerably increasing
computational performance. Our networks can classify a 96x96 sonar image with
98.8 - 99.7 accuracy on only 41 to 61 milliseconds on a Raspberry Pi 2, which
corresponds to speedups of 28.6 - 19.7.
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Mathematics in Caging of Robotics | It is a crucial problem in robotics field to cage an object using robots like
multifingered hand. However the problem what is the caging for general
geometrical objects and robots has not been well-described in mathematics
though there were many rigorous studies on the methods how to cage an object by
certain robots. In this article, we investigate the caging problem more
mathematically and describe the problem in terms of recursion of the simple
euclidean moves. Using the description, we show that the caging has the degree
of difficulty which is closely related to a combinatorial problem and a wire
puzzle. It implies that in order to capture an object by caging, from a
practical viewpoint the difficulty plays an important role.
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Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces | Graph models are relevant in many fields, such as distributed computing,
intelligent tutoring systems or social network analysis. In many cases, such
models need to take changes in the graph structure into account, i.e. a varying
number of nodes or edges. Predicting such changes within graphs can be expected
to yield important insight with respect to the underlying dynamics, e.g. with
respect to user behaviour. However, predictive techniques in the past have
almost exclusively focused on single edges or nodes. In this contribution, we
attempt to predict the future state of a graph as a whole. We propose to phrase
time series prediction as a regression problem and apply dissimilarity- or
kernel-based regression techniques, such as 1-nearest neighbor, kernel
regression and Gaussian process regression, which can be applied to graphs via
graph kernels. The output of the regression is a point embedded in a
pseudo-Euclidean space, which can be analyzed using subsequent dissimilarity-
or kernel-based processing methods. We discuss strategies to speed up Gaussian
Processes regression from cubic to linear time and evaluate our approach on two
well-established theoretical models of graph evolution as well as two real data
sets from the domain of intelligent tutoring systems. We find that simple
regression methods, such as kernel regression, are sufficient to capture the
dynamics in the theoretical models, but that Gaussian process regression
significantly improves the prediction error for real-world data.
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Geometric comparison of phylogenetic trees with different leaf sets | The metric space of phylogenetic trees defined by Billera, Holmes, and
Vogtmann, which we refer to as BHV space, provides a natural geometric setting
for describing collections of trees on the same set of taxa. However, it is
sometimes necessary to analyze collections of trees on non-identical taxa sets
(i.e., with different numbers of leaves), and in this context it is not evident
how to apply BHV space. Davidson et al. recently approached this problem by
describing a combinatorial algorithm extending tree topologies to regions in
higher dimensional tree spaces, so that one can quickly compute which
topologies contain a given tree as partial data. In this paper, we refine and
adapt their algorithm to work for metric trees to give a full characterization
of the subspace of extensions of a subtree. We describe how to apply our
algorithm to define and search a space of possible supertrees and, for a
collection of tree fragments with different leaf sets, to measure their
compatibility.
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Automatic Detection of Cyberbullying in Social Media Text | While social media offer great communication opportunities, they also
increase the vulnerability of young people to threatening situations online.
Recent studies report that cyberbullying constitutes a growing problem among
youngsters. Successful prevention depends on the adequate detection of
potentially harmful messages and the information overload on the Web requires
intelligent systems to identify potential risks automatically. The focus of
this paper is on automatic cyberbullying detection in social media text by
modelling posts written by bullies, victims, and bystanders of online bullying.
We describe the collection and fine-grained annotation of a training corpus for
English and Dutch and perform a series of binary classification experiments to
determine the feasibility of automatic cyberbullying detection. We make use of
linear support vector machines exploiting a rich feature set and investigate
which information sources contribute the most for this particular task.
Experiments on a holdout test set reveal promising results for the detection of
cyberbullying-related posts. After optimisation of the hyperparameters, the
classifier yields an F1-score of 64% and 61% for English and Dutch
respectively, and considerably outperforms baseline systems based on keywords
and word unigrams.
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Holographic Entanglement Entropy in Cyclic Cosmology | We discuss a cyclic cosmology in which the visible universe, or introverse,
is all that is accessible to an observer while the extroverse represents the
total spacetime originating from the time when the dark energy began to
dominate. It is argued that entanglement entropy of the introverse is the more
appropriate quantity to render infinitely cyclic, rather than the entropy of
the total universe. Since vanishing entanglement entropy implies disconnected
spacetimes, at the turnaround when the introverse entropy is zero the
disconnected extroverse can be jettisoned with impunity.
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Adversarial Training for Disease Prediction from Electronic Health Records with Missing Data | Electronic health records (EHRs) have contributed to the computerization of
patient records and can thus be used not only for efficient and systematic
medical services, but also for research on biomedical data science. However,
there are many missing values in EHRs when provided in matrix form, which is an
important issue in many biomedical EHR applications. In this paper, we propose
a two-stage framework that includes missing data imputation and disease
prediction to address the missing data problem in EHRs. We compared the disease
prediction performance of generative adversarial networks (GANs) and
conventional learning algorithms in combination with missing data prediction
methods. As a result, we obtained a level of accuracy of 0.9777, sensitivity of
0.9521, specificity of 0.9925, area under the receiver operating characteristic
curve (AUC-ROC) of 0.9889, and F-score of 0.9688 with a stacked autoencoder as
the missing data prediction method and an auxiliary classifier GAN (AC-GAN) as
the disease prediction method. The comparison results show that a combination
of a stacked autoencoder and an AC-GAN significantly outperforms other existing
approaches. Our results suggest that the proposed framework is more robust for
disease prediction from EHRs with missing data.
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Hydrodynamic stability in the presence of a stochastic forcing:a case study in convection | We investigate the stability of a statistically stationary conductive state
for Rayleigh-Bénard convection between stress-free plates that arises due to
a bulk stochastic internal heating. This setup may be seen as a generalization
to a stochastic setting of the seminal 1916 study of Lord Rayleigh. Our results
indicate that stochastic forcing at small magnitude has a stabilizing effect,
while strong stochastic forcing has a destabilizing effect. The methodology put
forth in this article, which combines rigorous analysis with careful
computation, also provides an approach to hydrodynamic stability for a variety
of systems subject to a large scale stochastic forcing.
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Asset Price Bubbles: An Option-based Indicator | We construct a statistical indicator for the detection of short-term asset
price bubbles based on the information content of bid and ask market quotes for
plain vanilla put and call options. Our construction makes use of the
martingale theory of asset price bubbles and the fact that such scenarios where
the price for an asset exceeds its fundamental value can in principle be
detected by analysis of the asymptotic behavior of the implied volatility
surface. For extrapolating this implied volatility, we choose the SABR model,
mainly because of its decent fit to real option market quotes for a broad range
of maturities and its ease of calibration. As main theoretical result, we show
that under lognormal SABR dynamics, we can compute a simple yet powerful
closed-form martingale defect indicator by solving an ill-posed inverse
calibration problem. In order to cope with the ill-posedness and to quantify
the uncertainty which is inherent to such an indicator, we adopt a Bayesian
statistical parameter estimation perspective. We probe the resulting posterior
densities with a combination of optimization and adaptive Markov chain Monte
Carlo methods, thus providing a full-blown uncertainty estimation of all the
underlying parameters and the martingale defect indicator. Finally, we provide
real-market tests of the proposed option-based indicator with focus on tech
stocks due to increasing concerns about a tech bubble 2.0.
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Interleaving and Gromov-Hausdorff distance | One of the central notions to emerge from the study of persistent homology is
that of interleaving distance. It has found recent applications in symplectic
and contact geometry, sheaf theory, computational geometry, and phylogenetics.
Here we present a general study of this topic. We define interleaving of
functors with common codomain as solutions to an extension problem. In order to
define interleaving distance in this setting we are led to categorical
generalizations of Hausdorff distance, Gromov-Hausdorff distance, and the space
of metric spaces. We obtain comparisons with previous notions of interleaving
via the study of future equivalences. As an application we recover a definition
of shift equivalences of discrete dynamical systems.
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Attainable Knowledge | The article investigates an evidence-based semantics for epistemic logics in
which pieces of evidence are interpreted as equivalence relations on the
epistemic worlds. It is shown that the properties of knowledge obtained from
potentially infinitely many pieces of evidence are described by modal logic S5.
At the same time, the properties of knowledge obtained from only a finite
number of pieces of evidence are described by modal logic S4. The main
technical result is a sound and complete bi-modal logical system that describes
properties of these two modalities and their interplay.
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Linear complementarity problems on extended second order cones | In this paper, we study the linear complementarity problems on extended
second order cones. We convert a linear complementarity problem on an extended
second order cone into a mixed complementarity problem on the non-negative
orthant. We state necessary and sufficient conditions for a point to be a
solution of the converted problem. We also present solution strategies for this
problem, such as the Newton method and Levenberg-Marquardt algorithm. Finally,
we present some numerical examples.
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On the Bernstein-Von Mises Theorem for High Dimensional Nonlinear Bayesian Inverse Problems | We prove a Bernstein-von Mises theorem for a general class of high
dimensional nonlinear Bayesian inverse problems in the vanishing noise limit.
We propose a sufficient condition on the growth rate of the number of unknown
parameters under which the posterior distribution is asymptotically normal.
This growth condition is expressed explicitly in terms of the model dimension,
the degree of ill-posedness of the inverse problem and the noise parameter. The
theoretical results are applied to a Bayesian estimation of the medium
parameter in an elliptic problem.
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Dynamic Word Embeddings for Evolving Semantic Discovery | Word evolution refers to the changing meanings and associations of words
throughout time, as a byproduct of human language evolution. By studying word
evolution, we can infer social trends and language constructs over different
periods of human history. However, traditional techniques such as word
representation learning do not adequately capture the evolving language
structure and vocabulary. In this paper, we develop a dynamic statistical model
to learn time-aware word vector representation. We propose a model that
simultaneously learns time-aware embeddings and solves the resulting "alignment
problem". This model is trained on a crawled NYTimes dataset. Additionally, we
develop multiple intuitive evaluation strategies of temporal word embeddings.
Our qualitative and quantitative tests indicate that our method not only
reliably captures this evolution over time, but also consistently outperforms
state-of-the-art temporal embedding approaches on both semantic accuracy and
alignment quality.
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Constructing Words with High Distinct Square Densities | Fraenkel and Simpson showed that the number of distinct squares in a word of
length n is bounded from above by 2n, since at most two distinct squares have
their rightmost, or last, occurrence begin at each position. Improvements by
Ilie to $2n-\Theta(\log n)$ and by Deza et al. to 11n/6 rely on the study of
combinatorics of FS-double-squares, when the maximum number of two last
occurrences of squares begin. In this paper, we first study how to maximize
runs of FS-double-squares in the prefix of a word. We show that for a given
positive integer m, the minimum length of a word beginning with m
FS-double-squares, whose lengths are equal, is 7m+3. We construct such a word
and analyze its distinct-square-sequence as well as its
distinct-square-density. We then generalize our construction. We also construct
words with high distinct-square-densities that approach 5/6.
| 1 | 0 | 0 | 0 | 0 | 0 |
Annihilators of Koszul Homologies and Almost Complete Intersections | In this article, we propound a question on the annihilator of Koszul
homologies of a system of parameters of an almost complete intersection $R$.
The question can be stated in terms of the acyclicity of certain (finite)
residual approximation complexes whose $0$-th homologies are the residue field
of $R$. We show that our question has an affirmative answer for certain almost
complete intersection rings with small multiplicities, as well as for the
$1$-th Koszul homology of any almost complete intersection. The statement about
the $1$-th Koszul homology is shown to be equivalent to the Monomial Conjecture
and thus follows from its validity.
| 0 | 0 | 1 | 0 | 0 | 0 |
Analyzing Knowledge Transfer in Deep Q-Networks for Autonomously Handling Multiple Intersections | We analyze how the knowledge to autonomously handle one type of intersection,
represented as a Deep Q-Network, translates to other types of intersections
(tasks). We view intersection handling as a deep reinforcement learning
problem, which approximates the state action Q function as a deep neural
network. Using a traffic simulator, we show that directly copying a network
trained for one type of intersection to another type of intersection decreases
the success rate. We also show that when a network that is pre-trained on Task
A and then is fine-tuned on a Task B, the resulting network not only performs
better on the Task B than an network exclusively trained on Task A, but also
retained knowledge on the Task A. Finally, we examine a lifelong learning
setting, where we train a single network on five different types of
intersections sequentially and show that the resulting network exhibited
catastrophic forgetting of knowledge on previous tasks. This result suggests a
need for a long-term memory component to preserve knowledge.
| 1 | 0 | 0 | 0 | 0 | 0 |
From modelling of systems with constraints to generalized geometry and back to numerics | In this note we describe how some objects from generalized geometry appear in
the qualitative analysis and numerical simulation of mechanical systems. In
particular we discuss double vector bundles and Dirac structures. It turns out
that those objects can be naturally associated to systems with constraints --
we recall the mathematical construction in the context of so called implicit
Lagrangian systems. We explain how they can be used to produce new numerical
methods, that we call Dirac integrators.
On a test example of a simple pendulum in a gravity field we compare the
Dirac integrators with classical explicit and implicit methods, we pay special
attention to conservation of constrains. Then, on a more advanced example of
the Ziegler column we show that the choice of numerical methods can indeed
affect the conclusions of qualitative analysis of the dynamics of mechanical
systems. We also tell why we think that Dirac integrators are appropriate for
this kind of systems by explaining the relation with the notions of geometric
degree of non-conservativity and kinematic structural stability.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Deeper Look at Experience Replay | Recently experience replay is widely used in various deep reinforcement
learning (RL) algorithms, in this paper we rethink the utility of experience
replay. It introduces a new hyper-parameter, the memory buffer size, which
needs carefully tuning. However unfortunately the importance of this new
hyper-parameter has been underestimated in the community for a long time. In
this paper we did a systematic empirical study of experience replay under
various function representations. We showcase that a large replay buffer can
significantly hurt the performance. Moreover, we propose a simple O(1) method
to remedy the negative influence of a large replay buffer. We showcase its
utility in both simple grid world and challenging domains like Atari games.
| 1 | 0 | 0 | 0 | 0 | 0 |
Dirac operators with $W^{1,\infty}$-potential under codimension one collapse | We study the behavior of the spectrum of the Dirac operator together with a
symmetric $W^{1, \infty}$-potential on spin manifolds under a collapse of
codimension one with bounded sectional curvature and diameter. If there is an
induced spin structure on the limit space $N$ then there are convergent
eigenvalues which converge to the spectrum of a first order differential
operator $D$ on $N$ together with a symmetric $W^{1,\infty}$-potential. If $N$
is orientable and the dimension of the limit space is even then $D$ is the
Dirac operator $D^N$ on $N$ and if the dimension of the limit space is odd,
then $D = D^N \oplus -D^N$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Tunable Superconducting Qubits with Flux-Independent Coherence | We have studied the impact of low-frequency magnetic flux noise upon
superconducting transmon qubits with various levels of tunability. We find that
qubits with weaker tunability exhibit dephasing that is less sensitive to flux
noise. This insight was used to fabricate qubits where dephasing due to flux
noise was suppressed below other dephasing sources, leading to flux-independent
dephasing times T2* ~ 15 us over a tunable range of ~340 MHz. Such tunable
qubits have the potential to create high-fidelity, fault-tolerant qubit gates
and fundamentally improve scalability for a quantum processor.
| 0 | 1 | 0 | 0 | 0 | 0 |
Kulish-Sklyanin type models: integrability and reductions | We start with a Riemann-Hilbert problem (RHP) related to a BD.I-type
symmetric spaces $SO(2r+1)/S(O(2r-2s +1)\otimes O(2s))$, $s\geq 1$. We consider
two Riemann-Hilbert problems: the first formulated on the real axis
$\mathbb{R}$ in the complex $\lambda$-plane; the second one is formulated on
$\mathbb{R} \oplus i\mathbb{R}$. The first RHP for $s=1$ allows one to solve
the Kulish-Sklyanin (KS) model; the second RHP is relevant for a new type of KS
model. An important example for nontrivial deep reductions of KS model is
given. Its effect on the scattering matrix is formulated. In particular we
obtain new 2-component NLS equations. Finally, using the Wronskian relations we
demonstrate that the inverse scattering method for KS models may be understood
as a generalized Fourier transforms. Thus we have a tool to derive all their
fundamental properties, including the hierarchy of equations and the hierarchy
of their Hamiltonian structures.
| 0 | 1 | 0 | 0 | 0 | 0 |
Takiff algebras with polynomial rings of symmetric invariants | Extending results of Rais-Tauvel, Macedo-Savage, and Arakawa-Premet, we prove
that under mild restrictions on the Lie algebra $\mathfrak q$ having the
polynomial ring of symmetric invariants, the m-th Takiff algebra of $\mathfrak
q$, $\mathfrak q\langle m\rangle$, also has a polynomial ring of symmetric
invariants.
| 0 | 0 | 1 | 0 | 0 | 0 |
Can We Prove Time Protection? | Timing channels are a significant and growing security threat in computer
systems, with no established solution. We have recently argued that the OS must
provide time protection, in analogy to the established memory protection, to
protect applications from information leakage through timing channels. Based on
a recently-proposed implementation of time protection in the seL4 microkernel,
we investigate how such an implementation could be formally proved to prevent
timing channels. We postulate that this should be possible by reasoning about a
highly abstracted representation of the shared hardware resources that cause
timing channels.
| 1 | 0 | 0 | 0 | 0 | 0 |
On some Graphs with a Unique Perfect Matching | We show that deciding whether a given graph $G$ of size $m$ has a unique
perfect matching as well as finding that matching, if it exists, can be done in
time $O(m)$ if $G$ is either a cograph, or a split graph, or an interval graph,
or claw-free. Furthermore, we provide a constructive characterization of the
claw-free graphs with a unique perfect matching.
| 1 | 0 | 0 | 0 | 0 | 0 |
Trade-Offs in Stochastic Event-Triggered Control | This paper studies the optimal output-feedback control of a linear
time-invariant system where a stochastic event-based scheduler triggers the
communication between the sensor and the controller. The primary goal of the
use of this type of scheduling strategy is to provide significant reductions in
the usage of the sensor-to-controller communication and, in turn, improve
energy expenditure in the network. In this paper, we aim to design an
admissible control policy, which is a function of the observed output, to
minimize a quadratic cost function while employing a stochastic event-triggered
scheduler that preserves the Gaussian property of the plant state and the
estimation error. For the infinite horizon case, we present analytical
expressions that quantify the trade-off between the communication cost and
control performance of such event-triggered control systems. This trade-off is
confirmed quantitatively via numerical examples.
| 1 | 0 | 0 | 0 | 0 | 0 |
Third Harmonic THz Generation from Graphene in a Parallel-Plate Waveguide | Graphene as a zero-bandgap two-dimensional semiconductor with a linear
electron band dispersion near the Dirac points has the potential to exhibit
very interesting nonlinear optical properties. In particular, third harmonic
generation of terahertz radiation should occur due to the nonlinear
relationship between the crystal momentum and the current density. In this
work, we investigate the terahertz nonlinear response of graphene inside a
parallel-plate waveguide. We optimize the plate separation and Fermi energy of
the graphene to maximize third harmonic generation, by maximizing the nonlinear
interaction while minimizing the loss and phase mismatch. The results obtained
show an increase by more than a factor of 100 in the power efficiency relative
to a normal-incidence configuration for a 2 terahertz incident field.
| 0 | 1 | 0 | 0 | 0 | 0 |
Taming the Signal-to-Noise Problem in Lattice QCD by Phase Reweighting | Path integrals describing quantum many-body systems can be calculated with
Monte Carlo sampling techniques, but average quantities are often subject to
signal-to-noise ratios that degrade exponentially with time. A
phase-reweighting technique inspired by recent observations of random walk
statistics in correlation functions is proposed that allows energy levels to be
extracted from late-time correlation functions with time-independent
signal-to-noise ratios. Phase reweighting effectively includes dynamical
refinement of source magnitudes but introduces a bias associated with the
phase. This bias can be removed by performing an extrapolation, but at the
expense of re-introducing a signal-to-noise problem. Lattice Quantum
Chromodynamics calculations of the $\rho$ and nucleon masses and of the
$\Xi\Xi$ binding energy show consistency between standard results obtained
using earlier-time correlation functions and phase-reweighted results using
late-time correlation functions inaccessible to standard statistical analysis
methods.
| 0 | 1 | 0 | 0 | 0 | 0 |
Description of radiation damage in diamond sensors using an effective defect model | The BCML system is a beam monitoring device in the CMS experiment at the LHC.
As detectors poly-crystalline diamond sensors are used. Here high particle
rates occur from the colliding beams scattering particles outside the beam
pipe. These particles cause defects, which act as traps for the ionization,
thus reducing the CCE. However, the loss in CCE was much more severe than
expected. The reason why in real experiments the CCE is so much worse than in
laboratory experiments is related to the rate of incident particles. At high
particle rates the trapping rate of the ionization is so high compared with the
detrapping rate, that space charge builds up. This space charge reduces locally
the internal electric field, which in turn increases the trapping rate and
hence reduces the CCE even further. In order to connect these macroscopic
measurements with the microscopic defects acting as traps for the ionization
charge the TCAD simulation program SILVACO was used. Two effective acceptor and
donor levels were needed to fit the data. Using this effective defect model the
highly non- linear rate dependent diamond polarization as function of the
particle rate environment and the resulting signal loss could be simulated.
| 0 | 1 | 0 | 0 | 0 | 0 |
Design and Analysis of Time-Invariant SC-LDPC Convolutional Codes With Small Constraint Length | In this paper, we deal with time-invariant spatially coupled low-density
parity-check convolutional codes (SC-LDPC-CCs). Classic design approaches
usually start from quasi-cyclic low-density parity-check (QC-LDPC) block codes
and exploit suitable unwrapping procedures to obtain SC-LDPC-CCs. We show that
the direct design of the SC-LDPC-CCs syndrome former matrix or, equivalently,
the symbolic parity-check matrix, leads to codes with smaller syndrome former
constraint lengths with respect to the best solutions available in the
literature. We provide theoretical lower bounds on the syndrome former
constraint length for the most relevant families of SC-LDPC-CCs, under
constraints on the minimum length of cycles in their Tanner graphs. We also
propose new code design techniques that approach or achieve such theoretical
limits.
| 1 | 0 | 0 | 0 | 0 | 0 |
Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network | The prediction of organic reaction outcomes is a fundamental problem in
computational chemistry. Since a reaction may involve hundreds of atoms, fully
exploring the space of possible transformations is intractable. The current
solution utilizes reaction templates to limit the space, but it suffers from
coverage and efficiency issues. In this paper, we propose a template-free
approach to efficiently explore the space of product molecules by first
pinpointing the reaction center -- the set of nodes and edges where graph edits
occur. Since only a small number of atoms contribute to reaction center, we can
directly enumerate candidate products. The generated candidates are scored by a
Weisfeiler-Lehman Difference Network that models high-order interactions
between changes occurring at nodes across the molecule. Our framework
outperforms the top-performing template-based approach with a 10\% margin,
while running orders of magnitude faster. Finally, we demonstrate that the
model accuracy rivals the performance of domain experts.
| 1 | 0 | 0 | 1 | 0 | 0 |
Legendre curves and singularities of a ruled surface according to rotation minimizing frame | In this paper, Legendre curves on unit tangent bundle are given using
rotation minimizing (RM) vector fields. Ruled surfaces corresponding to these
curves are represented. Singularities of these ruled surfaces are also analyzed
and classifed.
| 0 | 0 | 1 | 0 | 0 | 0 |
Field-free nucleation of antivortices and giant vortices in non-superconducting materials | Giant vortices with higher phase-winding than $2\pi$ are usually
energetically unfavorable, but geometric symmetry constraints on a
superconductor in a magnetic field are known to stabilize such objects. Here,
we show via microscopic calculations that giant vortices can appear in
intrinsically non-superconducting materials, even without any applied magnetic
field. The enabling mechanism is the proximity effect to a host superconductor
where a current flows, and we also demonstrate that antivortices can appear in
this setup. Our results open the possibility to study electrically controllable
topological defects in unusual environments, which do not have to be exposed to
magnetic fields or intrinsically superconducting, but instead display other
types of order.
| 0 | 1 | 0 | 0 | 0 | 0 |
Quantum criticality in photorefractive optics: vortices in laser beams and antiferromagnets | We study vortex patterns in a prototype nonlinear optical system:
counterpropagating laser beams in a photorefractive crystal, with or without
the background photonic lattice. The vortices are effectively planar and
described by the winding number and the "flavor" index, stemming from the fact
that we have two parallel beams propagating in opposite directions. The problem
is amenable to the methods of statistical field theory and generalizes the
Berezinsky-Kosterlitz-Thouless transition of the XY model to the "two-flavor"
case. In addition to the familiar conductor and insulator phases, we also have
the perfect conductor (vortex proliferation in both beams/"flavors") and the
frustrated insulator (energy costs of vortex proliferation and vortex
annihilation balance each other). In the presence of disorder in the background
lattice, a novel phase appears which shows long-range correlations and absence
of long-range order, thus being analogous to spin glasses. An important benefit
of this approach is that qualitative behavior of patterns can be known without
intensive numerical work over large areas of the parameter space. More
generally, we would like to draw attention to connections between the
(classical) pattern-forming systems in photorefractive optics and the methods
of (quantum) condensed matter and field theory: on one hand, we use the
field-theoretical methods (renormalization group, replica formalism) to analyze
the patterns; on the other hand, the observed phases are analogous to those
seen in magnetic systems, and make photorefractive optics a fruitful testing
ground for condensed matter systems. As an example, we map our system to a
doped $O(3)$ antiferromagnet with $\mathbb{Z}_2$ defects, which has the same
structure of the phase diagram.
| 0 | 1 | 0 | 0 | 0 | 0 |
Burn-In Demonstrations for Multi-Modal Imitation Learning | Recent work on imitation learning has generated policies that reproduce
expert behavior from multi-modal data. However, past approaches have focused
only on recreating a small number of distinct, expert maneuvers, or have relied
on supervised learning techniques that produce unstable policies. This work
extends InfoGAIL, an algorithm for multi-modal imitation learning, to reproduce
behavior over an extended period of time. Our approach involves reformulating
the typical imitation learning setting to include "burn-in demonstrations" upon
which policies are conditioned at test time. We demonstrate that our approach
outperforms standard InfoGAIL in maximizing the mutual information between
predicted and unseen style labels in road scene simulations, and we show that
our method leads to policies that imitate expert autonomous driving systems
over long time horizons.
| 1 | 0 | 0 | 1 | 0 | 0 |
Elliptic Determinantal Processes and Elliptic Dyson Models | We introduce seven families of stochastic systems of interacting particles in
one-dimension corresponding to the seven families of irreducible reduced affine
root systems. We prove that they are determinantal in the sense that all
spatio-temporal correlation functions are given by determinants controlled by a
single function called the spatio-temporal correlation kernel. For the four
families ${A}_{N-1}$, ${B}_N$, ${C}_N$ and ${D}_N$, we identify the systems of
stochastic differential equations solved by these determinantal processes,
which will be regarded as the elliptic extensions of the Dyson model. Here we
use the notion of martingales in probability theory and the elliptic
determinant evaluations of the Macdonald denominators of irreducible reduced
affine root systems given by Rosengren and Schlosser.
| 0 | 1 | 1 | 0 | 0 | 0 |
Searching for chemical signatures of brown dwarf formation | Recent studies have shown that close-in brown dwarfs in the mass range 35-55
M$_{\rm Jup}$ are almost depleted as companions to stars, suggesting that
objects with masses above and below this gap might have different formation
mechanisms. We determine the fundamental stellar parameters, as well as
individual abundances for a large sample of stars known to have a substellar
companion in the brown dwarf regime. The sample is divided into stars hosting
"massive" and "low-mass" brown dwarfs. Following previous works a threshold of
42.5 M$_{\rm Jup}$ was considered. Our results confirm that stars with brown
dwarf companions do not follow the well-established gas-giant planet
metallicity correlation seen in main-sequence planet hosts. Stars harbouring
"massive" brown dwarfs show similar metallicity and abundance distribution as
stars without known planets or with low-mass planets. We find a tendency of
stars harbouring "less-massive" brown dwarfs of having slightly larger
metallicity, [X$_{\rm Fe}$/Fe] values, and abundances of Sc II, Mn I, and Ni I
in comparison with the stars having the massive brown dwarfs. The data suggest,
as previously reported, that massive and low-mass brown dwarfs might present
differences in period and eccentricity. We find evidence of a non-metallicity
dependent mechanism for the formation of massive brown dwarfs. Our results
agree with a scenario in which massive brown dwarfs are formed as stars. At
high-metallicities, the core-accretion mechanism might become efficient in the
formation of low-mass brown dwarfs while at lower metallicities low-mass brown
dwarfs could form by gravitational instability in turbulent protostellar discs.
| 0 | 1 | 0 | 0 | 0 | 0 |
The Klein Paradox: A New Treatment | The Dirac equation requires a treatment of the step potential that differs
fundamentally from the traditional treatment, because the Dirac plane waves,
besides momentum and spin, are characterized by a quantum number with the
physical meaning of sign of charge. Since the Hermitean operator corresponding
to this quantum number does not commute with the step potential, the time
displacement parameter used in the ansatz of the stationary state does not have
the physical meaning of energy. Therefore there are no paradoxal values of the
energy. The new solution of the Dirac equation with a step potential is
obtained. This solution, again, allows for phenomena of the Klein paradox type,
but in addition it contains a positron amplitude localized at the threshold
point of the step potential.
| 0 | 1 | 0 | 0 | 0 | 0 |
What are the most important factors that influence the changes in London Real Estate Prices? How to quantify them? | In recent years, real estate industry has captured government and public
attention around the world. The factors influencing the prices of real estate
are diversified and complex. However, due to the limitations and one-sidedness
of their respective views, they did not provide enough theoretical basis for
the fluctuation of house price and its influential factors. The purpose of this
paper is to build a housing price model to make the scientific and objective
analysis of London's real estate market trends from the year 1996 to 2016 and
proposes some countermeasures to reasonably control house prices. Specifically,
the paper analyzes eight factors which affect the house prices from two
aspects: housing supply and demand and find out the factor which is of vital
importance to the increase of housing price per square meter. The problem of a
high level of multicollinearity between them is solved by using principal
components analysis.
| 0 | 0 | 0 | 1 | 0 | 1 |
A Multi-Wavelength Analysis of Dust and Gas in the SR 24S Transition Disk | We present new Atacama Large Millimeter/sub-millimeter Array (ALMA) 1.3 mm
continuum observations of the SR 24S transition disk with an angular resolution
$\lesssim0.18"$ (12 au radius). We perform a multi-wavelength investigation by
combining new data with previous ALMA data at 0.45 mm. The visibilities and
images of the continuum emission at the two wavelengths are well characterized
by a ring-like emission. Visibility modeling finds that the ring-like emission
is narrower at longer wavelengths, in good agreement with models of dust
trapping in pressure bumps, although there are complex residuals that suggest
potentially asymmetric structures. The 0.45 mm emission has a shallower profile
inside the central cavity than the 1.3 mm emission. In addition, we find that
the $^{13}$CO and C$^{18}$O (J=2-1) emission peaks at the center of the
continuum cavity. We do not detect either continuum or gas emission from the
northern companion to this system (SR 24N), which is itself a binary system.
The upper limit for the dust disk mass of SR 24N is $\lesssim
0.12\,M_{\bigoplus}$, which gives a disk mass ratio in dust between the two
components of $M_{\mathrm{dust, SR\,24S}}/M_{\mathrm{dust,
SR\,24N}}\gtrsim840$. The current ALMA observations may imply that either
planets have already formed in the SR 24N disk or that dust growth to mm-sizes
is inhibited there and that only warm gas, as seen by ro-vibrational CO
emission inside the truncation radii of the binary, is present.
| 0 | 1 | 0 | 0 | 0 | 0 |
Step bunching with both directions of the current: Vicinal W(110) surfaces versus atomistic scale model | We report for the first time the observation of bunching of monoatomic steps
on vicinal W(110) surfaces induced by step up or step down currents across the
steps. Measurements reveal that the size scaling exponent {\gamma}, connecting
the maximal slope of a bunch with its height, differs depending on the current
direction. We provide a numerical perspective by using an atomistic scale model
with a conserved surface flux to mimic experimental conditions, and also for
the first time show that there is an interval of parameters in which the
vicinal surface is unstable against step bunching for both directions of the
adatom drift.
| 0 | 1 | 0 | 0 | 0 | 0 |
Causal Bandits with Propagating Inference | Bandit is a framework for designing sequential experiments. In each
experiment, a learner selects an arm $A \in \mathcal{A}$ and obtains an
observation corresponding to $A$. Theoretically, the tight regret lower-bound
for the general bandit is polynomial with respect to the number of arms
$|\mathcal{A}|$. This makes bandit incapable of handling an exponentially large
number of arms, hence the bandit problem with side-information is often
considered to overcome this lower bound. Recently, a bandit framework over a
causal graph was introduced, where the structure of the causal graph is
available as side-information. A causal graph is a fundamental model that is
frequently used with a variety of real problems. In this setting, the arms are
identified with interventions on a given causal graph, and the effect of an
intervention propagates throughout all over the causal graph. The task is to
find the best intervention that maximizes the expected value on a target node.
Existing algorithms for causal bandit overcame the
$\Omega(\sqrt{|\mathcal{A}|/T})$ simple-regret lower-bound; however, their
algorithms work only when the interventions $\mathcal{A}$ are localized around
a single node (i.e., an intervention propagates only to its neighbors).
We propose a novel causal bandit algorithm for an arbitrary set of
interventions, which can propagate throughout the causal graph. We also show
that it achieves $O(\sqrt{ \gamma^*\log(|\mathcal{A}|T) / T})$ regret bound,
where $\gamma^*$ is determined by using a causal graph structure. In
particular, if the in-degree of the causal graph is bounded, then $\gamma^* =
O(N^2)$, where $N$ is the number $N$ of nodes.
| 0 | 0 | 0 | 1 | 0 | 0 |
The 2D Tree Sliding Window Discrete Fourier Transform | We present a new algorithm for the 2D Sliding Window Discrete Fourier
Transform (SWDFT). Our algorithm avoids repeating calculations in overlapping
windows by storing them in a tree data-structure based on the ideas of the
Cooley- Tukey Fast Fourier Transform (FFT). For an $N_0 \times N_1$ array and
$n_0 \times n_1$ windows, our algorithm takes $O(N_0 N_1 n_0 n_1)$ operations.
We provide a C implementation of our algorithm for the Radix-2 case, compare
ours with existing algorithms, and show how our algorithm easily extends to
higher dimensions.
| 1 | 0 | 0 | 1 | 0 | 0 |
Towards security defect prediction with AI | In this study, we investigate the limits of the current state of the art AI
system for detecting buffer overflows and compare it with current static
analysis tools. To do so, we developed a code generator, s-bAbI, capable of
producing an arbitrarily large number of code samples of controlled complexity.
We found that the static analysis engines we examined have good precision, but
poor recall on this dataset, except for a sound static analyzer that has good
precision and recall. We found that the state of the art AI system, a memory
network modeled after Choi et al. [1], can achieve similar performance to the
static analysis engines, but requires an exhaustive amount of training data in
order to do so. Our work points towards future approaches that may solve these
problems; namely, using representations of code that can capture appropriate
scope information and using deep learning methods that are able to perform
arithmetic operations.
| 0 | 0 | 0 | 1 | 0 | 0 |
Pseudo-Recursal: Solving the Catastrophic Forgetting Problem in Deep Neural Networks | In general, neural networks are not currently capable of learning tasks in a
sequential fashion. When a novel, unrelated task is learnt by a neural network,
it substantially forgets how to solve previously learnt tasks. One of the
original solutions to this problem is pseudo-rehearsal, which involves learning
the new task while rehearsing generated items representative of the previous
task/s. This is very effective for simple tasks. However, pseudo-rehearsal has
not yet been successfully applied to very complex tasks because in these tasks
it is difficult to generate representative items. We accomplish
pseudo-rehearsal by using a Generative Adversarial Network to generate items so
that our deep network can learn to sequentially classify the CIFAR-10, SVHN and
MNIST datasets. After training on all tasks, our network loses only 1.67%
absolute accuracy on CIFAR-10 and gains 0.24% absolute accuracy on SVHN. Our
model's performance is a substantial improvement compared to the current state
of the art solution.
| 0 | 0 | 0 | 1 | 0 | 0 |
Multivariate Hadamard self-similarity: testing fractal connectivity | While scale invariance is commonly observed in each component of real world
multivariate signals, it is also often the case that the inter-component
correlation structure is not fractally connected, i.e., its scaling behavior is
not determined by that of the individual components. To model this situation in
a versatile manner, we introduce a class of multivariate Gaussian stochastic
processes called Hadamard fractional Brownian motion (HfBm). Its theoretical
study sheds light on the issues raised by the joint requirement of entry-wise
scaling and departures from fractal connectivity. An asymptotically normal
wavelet-based estimator for its scaling parameter, called the Hurst matrix, is
proposed, as well as asymptotically valid confidence intervals. The latter are
accompanied by original finite sample procedures for computing confidence
intervals and testing fractal connectivity from one single and finite size
observation. Monte Carlo simulation studies are used to assess the estimation
performance as a function of the (finite) sample size, and to quantify the
impact of omitting wavelet cross-correlation terms. The simulation studies are
shown to validate the use of approximate confidence intervals, together with
the significance level and power of the fractal connectivity test. The test
performance and properties are further studied as functions of the HfBm
parameters.
| 0 | 0 | 1 | 1 | 0 | 0 |
Efficient barycentric point sampling on meshes | We present an easy-to-implement and efficient analytical inversion algorithm
for the unbiased random sampling of a set of points on a triangle mesh whose
surface density is specified by barycentric interpolation of non-negative
per-vertex weights. The correctness of the inversion algorithm is verified via
statistical tests, and we show that it is faster on average than rejection
sampling.
| 1 | 0 | 0 | 0 | 0 | 0 |
Femtosecond Optical Superregular Breathers | Superregular (SR) breathers are nonlinear wave structures formed by a unique
nonlinear superposition of pairs of quasi-Akhmediev breathers. They describe a
complete scenario of modulation instability that develops from localized small
perturbations as well as an unusual quasiannihilation of breather collision.
Here, we demonstrate that femtosecond optical SR breathers in optical fibers
exhibit intriguing half-transition and full-suppression states, which are
absent in the picosecond regime governed by the standard nonlinear
Schrödinger equation. In particular, the full-suppression mode, which is
strictly associated with the regime of vanishing growth rate of modulation
instability, reveals a crucial \textit{non-amplifying} nonlinear dynamics of
localized small perturbations. We numerically confirm the robustness of such
different SR modes excited from ideal and nonideal initial states in both
integrable and nonintegrable cases.
| 0 | 1 | 0 | 0 | 0 | 0 |
Diversity, Topology, and the Risk of Node Re-identification in Labeled Social Graphs | Real network datasets provide significant benefits for understanding
phenomena such as information diffusion or network evolution. Yet the privacy
risks raised from sharing real graph datasets, even when stripped of user
identity information, are significant. When nodes have associated attributes,
the privacy risks increase. In this paper we quantitatively study the impact of
binary node attributes on node privacy by employing machine-learning-based
re-identification attacks and exploring the interplay between graph topology
and attribute placement. Our experiments show that the population's diversity
on the binary attribute consistently degrades anonymity.
| 1 | 0 | 0 | 0 | 0 | 0 |
Braid group action and root vectors for the $q$-Onsager algebra | We define two algebra automorphisms $T_0$ and $T_1$ of the $q$-Onsager
algebra $B_c$, which provide an analog of G. Lusztig's braid group action for
quantum groups. These automorphisms are used to define root vectors which give
rise to a PBW basis for $B_c$. We show that the root vectors satisfy
$q$-analogs of Onsager's original commutation relations. The paper is much
inspired by I. Damiani's construction and investigation of root vectors for the
quantized enveloping algebra of $\widehat{\mathfrak{sl}}_2$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Underapproximation of Reach-Avoid Sets for Discrete-Time Stochastic Systems via Lagrangian Methods | We examine Lagrangian techniques for computing underapproximations of
finite-time horizon, stochastic reach-avoid level-sets for discrete-time,
nonlinear systems. We use the concept of reachability of a target tube in the
control literature to define robust reach-avoid sets which are parameterized by
the target set, safe set, and the set in which the disturbance is drawn from.
We unify two existing Lagrangian approaches to compute these sets and establish
that there exists an optimal control policy of the robust reach-avoid sets
which is a Markov policy. Based on these results, we characterize the subset of
the disturbance space whose corresponding robust reach-avoid set for the given
target and safe set is a guaranteed underapproximation of the stochastic
reach-avoid level-set of interest. The proposed approach dramatically improves
the computational efficiency for obtaining an underapproximation of stochastic
reach-avoid level-sets when compared to the traditional approaches based on
gridding. Our method, while conservative, does not rely on a grid, implying
scalability as permitted by the known computational geometry constraints. We
demonstrate the method on two examples: a simple two-dimensional integrator,
and a space vehicle rendezvous-docking problem.
| 1 | 0 | 1 | 0 | 0 | 0 |
A Domain-Specific Language and Editor for Parallel Particle Methods | Domain-specific languages (DSLs) are of increasing importance in scientific
high-performance computing to reduce development costs, raise the level of
abstraction and, thus, ease scientific programming. However, designing and
implementing DSLs is not an easy task, as it requires knowledge of the
application domain and experience in language engineering and compilers.
Consequently, many DSLs follow a weak approach using macros or text generators,
which lack many of the features that make a DSL a comfortable for programmers.
Some of these features---e.g., syntax highlighting, type inference, error
reporting, and code completion---are easily provided by language workbenches,
which combine language engineering techniques and tools in a common ecosystem.
In this paper, we present the Parallel Particle-Mesh Environment (PPME), a DSL
and development environment for numerical simulations based on particle methods
and hybrid particle-mesh methods. PPME uses the meta programming system (MPS),
a projectional language workbench. PPME is the successor of the Parallel
Particle-Mesh Language (PPML), a Fortran-based DSL that used conventional
implementation strategies. We analyze and compare both languages and
demonstrate how the programmer's experience can be improved using static
analyses and projectional editing. Furthermore, we present an explicit domain
model for particle abstractions and the first formal type system for particle
methods.
| 1 | 0 | 0 | 0 | 0 | 0 |
Manifold regularization based on Nystr{ö}m type subsampling | In this paper, we study the Nystr{ö}m type subsampling for large scale
kernel methods to reduce the computational complexities of big data. We discuss
the multi-penalty regularization scheme based on Nystr{ö}m type subsampling
which is motivated from well-studied manifold regularization schemes. We
develop a theoretical analysis of multi-penalty least-square regularization
scheme under the general source condition in vector-valued function setting,
therefore the results can also be applied to multi-task learning problems. We
achieve the optimal minimax convergence rates of multi-penalty regularization
using the concept of effective dimension for the appropriate subsampling size.
We discuss an aggregation approach based on linear function strategy to combine
various Nystr{ö}m approximants. Finally, we demonstrate the performance of
multi-penalty regularization based on Nystr{ö}m type subsampling on
Caltech-101 data set for multi-class image classification and NSL-KDD benchmark
data set for intrusion detection problem.
| 1 | 0 | 0 | 1 | 0 | 0 |
Path integral molecular dynamics with surface hopping for thermal equilibrium sampling of nonadiabatic systems | In this work, a novel ring polymer representation for multi-level quantum
system is proposed for thermal average calculations. The proposed presentation
keeps the discreteness of the electronic states: besides position and momentum,
each bead in the ring polymer is also characterized by a surface index
indicating the electronic energy surface. A path integral molecular dynamics
with surface hopping (PIMD-SH) dynamics is also developed to sample the
equilibrium distribution of ring polymer configurational space. The PIMD-SH
sampling method is validated theoretically and by numerical examples.
| 0 | 1 | 1 | 0 | 0 | 0 |
The interplay of the collisionless nonlinear thin-shell instability with the ion acoustic instability | The nonlinear thin-shell instability (NTSI) may explain some of the turbulent
hydrodynamic structures that are observed close to the collision boundary of
energetic astrophysical outflows. It develops in nonplanar shells that are
bounded on either side by a hydrodynamic shock, provided that the amplitude of
the seed oscillations is sufficiently large. The hydrodynamic NTSI has a
microscopic counterpart in collisionless plasma. A sinusoidal displacement of a
thin shell, which is formed by the collision of two clouds of unmagnetized
electrons and protons, grows and saturates on timescales of the order of the
inverse proton plasma frequency. Here we increase the wavelength of the seed
perturbation by a factor 4 compared to that in a previous study. Like in the
case of the hydrodynamic NTSI, the increase in the wavelength reduces the
growth rate of the microscopic NTSI. The prolonged growth time of the
microscopic NTSI allows the waves, which are driven by the competing ion
acoustic instability, to grow to a large amplitude before the NTSI saturates
and they disrupt the latter. The ion acoustic instability thus imposes a limit
on the largest wavelength that can be destabilized by the NTSI in collisionless
plasma. The limit can be overcome by binary collisions. We bring forward
evidence for an overstability of the collisionless NTSI.
| 0 | 1 | 0 | 0 | 0 | 0 |
Multiple Scaled Contaminated Normal Distribution and Its Application in Clustering | The multivariate contaminated normal (MCN) distribution represents a simple
heavy-tailed generalization of the multivariate normal (MN) distribution to
model elliptical contoured scatters in the presence of mild outliers, referred
to as "bad" points. The MCN can also automatically detect bad points. The price
of these advantages is two additional parameters, both with specific and useful
interpretations: proportion of good observations and degree of contamination.
However, points may be bad in some dimensions but good in others. The use of an
overall proportion of good observations and of an overall degree of
contamination is limiting. To overcome this limitation, we propose a multiple
scaled contaminated normal (MSCN) distribution with a proportion of good
observations and a degree of contamination for each dimension. Once the model
is fitted, each observation has a posterior probability of being good with
respect to each dimension. Thanks to this probability, we have a method for
simultaneous directional robust estimation of the parameters of the MN
distribution based on down-weighting and for the automatic directional
detection of bad points by means of maximum a posteriori probabilities. The
term "directional" is added to specify that the method works separately for
each dimension. Mixtures of MSCN distributions are also proposed as an
application of the proposed model for robust clustering. An extension of the EM
algorithm is used for parameter estimation based on the maximum likelihood
approach. Real and simulated data are used to show the usefulness of our
mixture with respect to well-established mixtures of symmetric distributions
with heavy tails.
| 0 | 0 | 0 | 1 | 0 | 0 |
The Galaxy-Halo Connection Over The Last 13.3 Gyrs | We present new determinations of the stellar-to-halo mass relation (SHMR) at
$z=0-10$ that match the evolution of the galaxy stellar mass function, the
SFR$-M_*$ relation,and the cosmic star formation rate. We utilize a compilation
of 40 observational studies from the literature and correct them for potential
biases. Using our robust determinations of halo mass assembly and the SHMR, we
infer star formation histories, merger rates, and structural properties for
average galaxies, combining star-forming and quenched galaxies. Our main
findings: (1) The halo mass $M_{50}$ above which 50\% of galaxies are quenched
coincides with sSFR/sMAR$\sim1$, where sMAR is the specific halo mass accretion
rate. (2) $M_{50}$ increases with redshift, presumably due to cold streams
being more efficient at high redshift while virial shocks and AGN feedback
become more relevant at lower redshifts. (3) The ratio sSFR/sMAR has a peak
value, which occurs around $M_{\rm vir}\sim2\times10^{11}M_{\odot}$. (4) The
stellar mass density within 1 kpc, $\Sigma_1$, is a good indicator of the
galactic global sSFR. (5) Galaxies are statistically quenched after they reach
a maximum in $\Sigma_1$, consistent with theoretical expectations of the gas
compaction model; this maximum depends on redshift. (6) In-situ star formation
is responsible for most galactic stellar mass growth, especially for lower-mass
galaxies. (7) Galaxies grow inside out. The marked change in the slope of the
size--mass relation when galaxies became quenched, from $d\log R_{\rm
eff}/d\log M_*\sim0.35$ to $\sim2.5$, could be the result of dry minor mergers.
| 0 | 1 | 0 | 0 | 0 | 0 |
State of the art of Trust and Reputation Systems in E-Commerce Context | This article proposes in depth comparative study of the most popular, used
and analyzed Trust and Reputation System (TRS) according to the trust and
reputation literature and in terms of specific trustworthiness criteria. This
survey is realized relying on a selection of trustworthiness criteria that
analyze and evaluate the maturity and effectiveness of TRS. These criteria
describe the utility, the usability, the performance and the effectiveness of
the TRS. We also provide a summary table of the compared TRS within a detailed
and granular selection of trust and reputation aspects.
| 1 | 0 | 0 | 0 | 0 | 0 |
Placing your Coins on a Shelf | We consider the problem of packing a family of disks "on a shelf", that is,
such that each disk touches the $x$-axis from above and such that no two disks
overlap. We prove that the problem of minimizing the distance between the
leftmost point and the rightmost point of any disk is NP-hard. On the positive
side, we show how to approximate this problem within a factor of 4/3 in $O(n
\log n)$ time, and provide an $O(n \log n)$-time exact algorithm for a special
case, in particular when the ratio between the largest and smallest radius is
at most four.
| 1 | 0 | 1 | 0 | 0 | 0 |
Randomized Rumor Spreading in Ad Hoc Networks with Buffers | The randomized rumor spreading problem generates a big interest in the area
of distributed algorithms due to its simplicity, robustness and wide range of
applications. The two most popular communication paradigms used for spreading
the rumor are Push and Pull algorithms. The former protocol allows nodes to
send the rumor to a randomly selected neighbor at each step, while the latter
is based on sending a request and downloading the rumor from a randomly
selected neighbor, provided the neighbor has it. Previous analysis of these
protocols assumed that every node could process all such push/pull operations
within a single step, which could be unrealistic in practical situations.
Therefore we propose a new framework for analysis rumor spreading accommodating
buffers, in which a node can process only one push/pull message or push request
at a time. We develop upper and lower bounds for randomized rumor spreading
time in the new framework, and compare the results with analogous in the old
framework without buffers.
| 1 | 0 | 0 | 0 | 0 | 0 |
A simple and efficient feedback control strategy for wastewater denitrification | Due to severe mathematical modeling and calibration difficulties open-loop
feedforward control is mainly employed today for wastewater denitrification,
which is a key ecological issue. In order to improve the resulting poor
performances a new model-free control setting and its corresponding
"intelligent" controller are introduced. The pitfall of regulating two output
variables via a single input variable is overcome by introducing also an
open-loop knowledge-based control deduced from the plant behavior. Several
convincing computer simulations are presented and discussed.
| 1 | 0 | 1 | 0 | 0 | 0 |
The interaction of Airy waves and solitons in the three-wave system | We employ the generic three-wave system, with the $\chi ^{(2)}$ interaction
between two components of the fundamental-frequency (FF) wave and
second-harmonic (SH) one, to consider collisions of truncated Airy waves (TAWs)
and three-wave solitons in a setting which is not available in other nonlinear
systems. The advantage is that the single-wave TAWs, carried by either one of
the FF component, are not distorted by the nonlinearity and are stable,
three-wave solitons being stable too in the same system. The collision between
mutually symmetric TAWs, carried by the different FF components, transforms
them into a set of solitons, the number of which decreases with the increase of
the total power. The TAW absorbs an incident small-power soliton, and a
high-power soliton absorbs the TAW. Between these limits, the collision with an
incident soliton converts the TAW into two solitons, with a remnant of the TAW
attached to one of them, or leads to formation of a complex TAW-soliton bound
state. At large velocities, the collisions become quasi-elastic.
| 0 | 1 | 0 | 0 | 0 | 0 |
A penalty criterion for score forecasting in soccer | This note proposes a penalty criterion for assessing correct score
forecasting in a soccer match. The penalty is based on hierarchical priorities
for such a forecast i.e., i) Win, Draw and Loss exact prediction and ii)
normalized Euclidian distance between actual and forecast scores. The procedure
is illustrated on typical scores, and different alternatives on the penalty
components are discussed.
| 0 | 0 | 0 | 1 | 0 | 0 |
A robust RUV-testing procedure via gamma-divergence | Identification of differentially expressed genes (DE-genes) is commonly
conducted in modern biomedical researches. However, unwanted variation
inevitably arises during the data collection process, which could make the
detection results heavily biased. It is suggested to remove the unwanted
variation while keeping the biological variation to ensure a reliable analysis
result. Removing Unwanted Variation (RUV) is recently proposed for this purpose
by the virtue of negative control genes. On the other hand, outliers are
frequently appear in modern high-throughput genetic data that can heavily
affect the performances of RUV and its downstream analysis. In this work, we
propose a robust RUV-testing procedure via gamma-divergence. The advantages of
our method are twofold: (1) it does not involve any modeling for the outlier
distribution, which is applicable to various situations, (2) it is easy to
implement in the sense that its robustness is controlled by a single tuning
parameter gamma of gamma-divergence, and a data-driven criterion is developed
to select $\gamma$. In the Gender Study, our method can successfully remove
unwanted variation, and is able to identify more DE-genes than conventional
methods.
| 0 | 0 | 0 | 1 | 0 | 0 |
On the Distribution, Model Selection Properties and Uniqueness of the Lasso Estimator in Low and High Dimensions | We derive expressions for the finite-sample distribution of the Lasso
estimator in the context of a linear regression model with normally distributed
errors in low as well as in high dimensions by exploiting the structure of the
optimization problem defining the estimator. In low dimensions we assume full
rank of the regressor matrix and present expressions for the cumulative
distribution function as well as the densities of the absolutely continuous
parts of the estimator. Additionally, we establish an explicit formula for the
correspondence between the Lasso and the least-squares estimator. We derive
analogous results for the distribution in less explicit form in high dimensions
where we make no assumptions on the regressor matrix at all. In this setting,
we also investigate the model selection properties of the Lasso and show that
possibly only a subset of models might be selected by the estimator, completely
independently of the observed response vector. Finally, we present a condition
for uniqueness of the estimator that is necessary as well as sufficient.
| 0 | 0 | 1 | 1 | 0 | 0 |
Transductive Boltzmann Machines | We present transductive Boltzmann machines (TBMs), which firstly achieve
transductive learning of the Gibbs distribution. While exact learning of the
Gibbs distribution is impossible by the family of existing Boltzmann machines
due to combinatorial explosion of the sample space, TBMs overcome the problem
by adaptively constructing the minimum required sample space from data to avoid
unnecessary generalization. We theoretically provide bias-variance
decomposition of the KL divergence in TBMs to analyze its learnability, and
empirically demonstrate that TBMs are superior to the fully visible Boltzmann
machines and popularly used restricted Boltzmann machines in terms of
efficiency and effectiveness.
| 0 | 0 | 0 | 1 | 0 | 0 |
Human-Robot Trust Integrated Task Allocation and Symbolic Motion planning for Heterogeneous Multi-robot Systems | This paper presents a human-robot trust integrated task allocation and motion
planning framework for multi-robot systems (MRS) in performing a set of tasks
concurrently. A set of task specifications in parallel are conjuncted with MRS
to synthesize a task allocation automaton. Each transition of the task
allocation automaton is associated with the total trust value of human in
corresponding robots. Here, the human-robot trust model is constructed with a
dynamic Bayesian network (DBN) by considering individual robot performance,
safety coefficient, human cognitive workload and overall evaluation of task
allocation. Hence, a task allocation path with maximum encoded human-robot
trust can be searched based on the current trust value of each robot in the
task allocation automaton. Symbolic motion planning (SMP) is implemented for
each robot after they obtain the sequence of actions. The task allocation path
can be intermittently updated with this DBN based trust model. The overall
strategy is demonstrated by a simulation with 5 robots and 3 parallel subtask
automata.
| 1 | 0 | 0 | 0 | 0 | 0 |
A five-decision testing procedure to infer on unidimensional parameter | A statistical test can be seen as a procedure to produce a decision based on
observed data, where some decisions consist of rejecting a hypothesis (yielding
a significant result) and some do not, and where one controls the probability
to make a wrong rejection at some pre-specified significance level. Whereas
traditional hypothesis testing involves only two possible decisions (to reject
or not a null hypothesis), Kaiser's directional two-sided test as well as the
more recently introduced Jones and Tukey's testing procedure involve three
possible decisions to infer on unidimensional parameter. The latter procedure
assumes that a point null hypothesis is impossible (e.g. that two treatments
cannot have exactly the same effect), allowing a gain of statistical power.
There are however situations where a point hypothesis is indeed plausible, for
example when considering hypotheses derived from Einstein's theories. In this
article, we introduce a five-decision rule testing procedure, which combines
the advantages of the testing procedures of Kaiser (no assumption on a point
hypothesis being impossible) and of Jones and Tukey (higher power), allowing
for a non-negligible (typically 20%) reduction of the sample size needed to
reach a given statistical power to get a significant result, compared to the
traditional approach.
| 0 | 0 | 1 | 1 | 0 | 0 |
PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts | We present PubMed 200k RCT, a new dataset based on PubMed for sequential
sentence classification. The dataset consists of approximately 200,000
abstracts of randomized controlled trials, totaling 2.3 million sentences. Each
sentence of each abstract is labeled with their role in the abstract using one
of the following classes: background, objective, method, result, or conclusion.
The purpose of releasing this dataset is twofold. First, the majority of
datasets for sequential short-text classification (i.e., classification of
short texts that appear in sequences) are small: we hope that releasing a new
large dataset will help develop more accurate algorithms for this task. Second,
from an application perspective, researchers need better tools to efficiently
skim through the literature. Automatically classifying each sentence in an
abstract would help researchers read abstracts more efficiently, especially in
fields where abstracts may be long, such as the medical field.
| 1 | 0 | 0 | 1 | 0 | 0 |
Convergence radius of perturbative Lindblad driven non-equilibrium steady states | We address the problem of analyzing the radius of convergence of perturbative
expansion of non-equilibrium steady states of Lindblad driven spin chains. A
simple formal approach is developed for systematically computing the
perturbative expansion of small driven systems. We consider the paradigmatic
model of an open $XXZ$ spin 1/2 chain with boundary supported ultralocal
Lindblad dissipators and treat two different perturbative cases: (i) expansion
in system-bath coupling parameter and (ii) expansion in driving (bias)
parameter. In the first case (i) we find that the radius of convergence quickly
shrinks with increasing the system size, while in the second case (ii) we find
that the convergence radius is always larger than $1$, and in particular it
approaches $1$ from above as we change the anisotropy from easy plane ($XY$) to
easy axis (Ising) regime.
| 0 | 1 | 0 | 0 | 0 | 0 |
A 2D metamaterial with auxetic out-of-plane behavior and non-auxetic in-plane behavior | Customarily, in-plane auxeticity and synclastic bending behavior (i.e.
out-of-plane auxeticity) are not independent, being the latter a manifestation
of the former. Basically, this is a feature of three-dimensional bodies. At
variance, two-dimensional bodies have more freedom to deform than
three-dimensional ones. Here, we exploit this peculiarity and propose a
two-dimensional honeycomb structure with out-of-plane auxetic behavior opposite
to the in-plane one. With a suitable choice of the lattice constitutive
parameters, in its continuum description such a structure can achieve the whole
range of values for the bending Poisson coefficient, while retaining a
membranal Poisson coefficient equal to 1. In particular, this structure can
reach the extreme values, $-1$ and $+1$, of the bending Poisson coefficient.
Analytical calculations are supported by numerical simulations, showing the
accuracy of the continuum formulas in predicting the response of the discrete
structure.
| 0 | 1 | 0 | 0 | 0 | 0 |
Isolated Loops in Quantum Feedback Networks | A scheme making use of an isolated feedback loop was recently proposed in
\cite{GP_} for creating an arbitrary bilinear Hamiltonian interaction between
two multi-mode Linear Quantum Stochastic Systems (LQSSs). In this work we
examine the presence of an isolated feedback loop in a general SLH network, and
derive the modified Hamiltonian of the network due to the presence of the loop.
In the case of a bipartite network with an isolated loop running through both
parts, this results in modified Hamiltonians for each subnetwork, as well as a
Hamiltonian interaction between them. As in the LQSS case, by engineering
appropriate ports in each subnetwork, we may create desired interactions
between them. Examples are provided that illustrate the general theory.
| 0 | 0 | 1 | 0 | 0 | 0 |
Distance-based Depths for Directional Data | Directional data are constrained to lie on the unit sphere of~$\mathbb{R}^q$
for some~$q\geq 2$. To address the lack of a natural ordering for such data,
depth functions have been defined on spheres. However, the depths available
either lack flexibility or are so computationally expensive that they can only
be used for very small dimensions~$q$. In this work, we improve on this by
introducing a class of distance-based depths for directional data. Irrespective
of the distance adopted, these depths can easily be computed in high dimensions
too. We derive the main structural properties of the proposed depths and study
how they depend on the distance used. We discuss the asymptotic and robustness
properties of the corresponding deepest points. We show the practical relevance
of the proposed depths in two applications, related to (i) spherical location
estimation and (ii) supervised classification. For both problems, we show
through simulation studies that distance-based depths have strong advantages
over their competitors.
| 0 | 0 | 1 | 1 | 0 | 0 |
Two-dimensional Schrödinger symmetry and three-dimensional breathers and Kelvin-ripple complexes as quasi-massive-Nambu-Goldstone modes | Bose-Einstein condensates (BECs) confined in a two-dimensional (2D) harmonic
trap are known to possess a hidden 2D Schrödinger symmetry, that is, the
Schrödinger symmetry modified by a trapping potential. Spontaneous breaking
of this symmetry gives rise to a breathing motion of the BEC, whose oscillation
frequency is robustly determined by the strength of the harmonic trap. In this
paper, we demonstrate that the concept of the 2D Schrödinger symmetry can be
applied to predict the nature of three dimensional (3D) collective modes
propagating along a condensate confined in an elongated trap. We find three
kinds of collective modes whose existence is robustly ensured by the
Schrödinger symmetry, which are physically interpreted as one breather mode
and two Kelvin-ripple complex modes, i.e., composite modes in which the vortex
core and the condensate surface oscillate interactively. We provide analytical
expressions for the dispersion relations (energy-momentum relation) of these
modes using the Bogoliubov theory [D. A. Takahashi and M. Nitta, Ann. Phys.
354, 101 (2015)]. Furthermore, we point out that these modes can be interpreted
as "quasi-massive-Nambu-Goldstone (NG) modes", that is, they have the
properties of both quasi-NG and massive NG modes: quasi-NG modes appear when a
symmetry of a part of a Lagrangian, which is not a symmetry of full a
Lagrangian, is spontaneously broken, while massive NG modes appear when a
modified symmetry is spontaneously broken.
| 0 | 1 | 0 | 0 | 0 | 0 |
Groupoid of morphisms of groupoids | In this paper we construct two groupoids from morphisms of groupoids, with
one from a categorical viewpoint and the other from a geometric viewpoint. We
show that for each pair of groupoids, the two kinds of groupoids of morphisms
are equivalent. Then we study the automorphism groupoid of a groupoid.
| 0 | 0 | 1 | 0 | 0 | 0 |
Spin alignment of stars in old open clusters | Stellar clusters form by gravitational collapse of turbulent molecular
clouds, with up to several thousand stars per cluster. They are thought to be
the birthplace of most stars and therefore play an important role in our
understanding of star formation, a fundamental problem in astrophysics. The
initial conditions of the molecular cloud establish its dynamical history until
the stellar cluster is born. However, the evolution of the cloud's angular
momentum during cluster formation is not well understood. Current observations
have suggested that turbulence scrambles the angular momentum of the
cluster-forming cloud, preventing spin alignment amongst stars within a
cluster. Here we use asteroseismology to measure the inclination angles of spin
axes in 48 stars from the two old open clusters NGC~6791 and NGC~6819. The
stars within each cluster show strong alignment. Three-dimensional
hydrodynamical simulations of proto-cluster formation show that at least 50 %
of the initial proto-cluster kinetic energy has to be rotational in order to
obtain strong stellar-spin alignment within a cluster. Our result indicates
that the global angular momentum of the cluster-forming clouds was efficiently
transferred to each star and that its imprint has survived after several
gigayears since the clusters formed.
| 0 | 1 | 0 | 0 | 0 | 0 |
Global well-posedness of critical surface quasigeostrophic equation on the sphere | In this paper we prove global well-posedness of the critical surface
quasigeostrophic equation on the two dimensional sphere building on some
earlier work of the authors. The proof relies on an improving of the previously
known pointwise inequality for fractional laplacians as in the work of
Constantin and Vicol for the euclidean setting.
| 0 | 0 | 1 | 0 | 0 | 0 |
On the Limitation of Local Intrinsic Dimensionality for Characterizing the Subspaces of Adversarial Examples | Understanding and characterizing the subspaces of adversarial examples aid in
studying the robustness of deep neural networks (DNNs) to adversarial
perturbations. Very recently, Ma et al. (ICLR 2018) proposed to use local
intrinsic dimensionality (LID) in layer-wise hidden representations of DNNs to
study adversarial subspaces. It was demonstrated that LID can be used to
characterize the adversarial subspaces associated with different attack
methods, e.g., the Carlini and Wagner's (C&W) attack and the fast gradient sign
attack.
In this paper, we use MNIST and CIFAR-10 to conduct two new sets of
experiments that are absent in existing LID analysis and report the limitation
of LID in characterizing the corresponding adversarial subspaces, which are (i)
oblivious attacks and LID analysis using adversarial examples with different
confidence levels; and (ii) black-box transfer attacks. For (i), we find that
the performance of LID is very sensitive to the confidence parameter deployed
by an attack, and the LID learned from ensembles of adversarial examples with
varying confidence levels surprisingly gives poor performance. For (ii), we
find that when adversarial examples are crafted from another DNN model, LID is
ineffective in characterizing their adversarial subspaces. These two findings
together suggest the limited capability of LID in characterizing the subspaces
of adversarial examples.
| 0 | 0 | 0 | 1 | 0 | 0 |
A properly embedded holomorphic disc in the ball with finite area and dense boundary curve | In this paper we construct a properly embedded holomorphic disc in the unit
ball $\mathbb{B}^2$ of $\mathbb{C}^2$ having a surprising combination of
properties: on the one hand, it has finite area and hence is the zero set of a
bounded holomorphic function on $\mathbb{B}^2$; on the other hand, its boundary
curve is everywhere dense in the sphere $b\mathbb{B}^2$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Kähler differential algebras for 0-dimensional schemes | Given a 0-dimensional scheme in a projective space $\mathbb{P}^n$ over a
field $K$, we study the Kähler differential algebra $\Omega_{R/K}$ of its
homogeneous coordinate ring $R$. Using explicit presentations of the modules
$\Omega^m_{R/K}$ of Kähler differential $m$-forms, we determine many values
of their Hilbert functions explicitly and bound their Hilbert polynomials and
regularity indices. Detailed results are obtained for subschemes of
$\mathbb{P}^1$, fat point schemes, and subschemes of $\mathbb{P}^2$ supported
on a conic.
| 0 | 0 | 1 | 0 | 0 | 0 |
Benchmarks and reliable DFT results for spin-crossover complexes | DFT is used throughout nanoscience, especially when modeling spin-dependent
properties that are important in spintronics. But standard quantum chemical
methods (both CCSD(T) and self-consistent semilocal density functional
calculations) fail badly for the spin adiabatic energy difference in Fe(II)
spin-crossover complexes. We show that all-electron fixed-node diffusion Monte
Carlo can be converged at significant computational cost, and that the B3LYP
single-determinant has sufficiently accurate nodes, providing benchmarks for
these systems. We also find that density-corrected DFT, using Hartree-Fock
densities (HF-DFT), greatly improves accuracy and reduces dependence on
approximations for these calculations. The small gap in the self-consistent DFT
calculations for the high-spin state is consistent with this. For the spin
adiabatic energy differences in these complexes, HF-DFT is both accurate and
reliable, and we make a strong prediction for the Fe-Porphyrin complex. The
"parameter-dilemma" of needing different amounts of mixing for different
properties is eliminated by HF-DFT.
| 0 | 1 | 0 | 0 | 0 | 0 |
The Road to Success: Assessing the Fate of Linguistic Innovations in Online Communities | We investigate the birth and diffusion of lexical innovations in a large
dataset of online social communities. We build on sociolinguistic theories and
focus on the relation between the spread of a novel term and the social role of
the individuals who use it, uncovering characteristics of innovators and
adopters. Finally, we perform a prediction task that allows us to anticipate
whether an innovation will successfully spread within a community.
| 1 | 0 | 0 | 0 | 0 | 0 |
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