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1807.02225 | Cheeger inequalities for graph limits | We introduce notions of Cheeger constants for graphons and graphings. We
prove Cheeger and Buser inequalities for these. On the way we prove co-area
formulae for graphons and graphings.
| math.GT math.CO math.PR |
1807.02226 | A Concept Specification and Abstraction-based Semantic Representation:
Addressing the Barriers to Rule-based Machine Translation | Rule-based machine translation is more data efficient than the big data-based
machine translation approaches, making it appropriate for languages with low
bilingual corpus resources -- i.e., minority languages. However, the rule-based
approach has declined in popularity relative to its big data cousins primarily
because of the extensive training and labour required to define the language
rules. To address this, we present a semantic representation that 1) treats all
bits of meaning as individual concepts that 2) modify or further specify one
another to build a network that relates entities in space and time. Also, the
representation can 3) encapsulate propositions and thereby define concepts in
terms of other concepts, supporting the abstraction of underlying linguistic
and ontological details. These features afford an exact, yet intuitive semantic
representation aimed at handling the great variety in language and reducing
labour and training time. The proposed natural language generation, parsing,
and translation strategies are also amenable to probabilistic modeling and thus
to learning the necessary rules from example data.
| cs.CL |
1807.02227 | Beating the curse of dimensionality in options pricing and optimal
stopping | The fundamental problems of pricing high-dimensional path-dependent options
and optimal stopping are central to applied probability and financial
engineering. Modern approaches, often relying on ADP, simulation, and/or
duality, have limited rigorous guarantees, which may scale poorly and/or
require previous knowledge of basis functions. A key difficulty with many
approaches is that to yield stronger guarantees, they would necessitate the
computation of deeply nested conditional expectations, with the depth scaling
with the time horizon T.
We overcome this fundamental obstacle by providing an algorithm which can
trade-off between the guaranteed quality of approximation and the level of
nesting required in a principled manner, without requiring a set of good basis
functions. We develop a novel pure-dual approach, inspired by a connection to
network flows. This leads to a representation for the optimal value as an
infinite sum for which: 1. each term is the expectation of an elegant
recursively defined infimum; 2. the first k terms only require k levels of
nesting; and 3. truncating at the first k terms yields an error of 1/k. This
enables us to devise a simple randomized algorithm whose runtime is effectively
independent of the dimension, beyond the need to simulate sample paths of the
underlying process. Indeed, our algorithm is completely data-driven in that it
only needs the ability to simulate the original process, and requires no prior
knowledge of the underlying distribution. Our method allows one to elegantly
trade-off between accuracy and runtime through a parameter epsilon controlling
the associated performance guarantee, with computational and sample complexity
both polynomial in T (and effectively independent of the dimension) for any
fixed epsilon, in contrast to past methods typically requiring a complexity
scaling exponentially in these parameters.
| math.PR cs.DS math.OC q-fin.CP q-fin.MF |
1807.02228 | Bayesian State Space Modeling of Physical Processes in Industrial
Hygiene | Exposure assessment models are deterministic models derived from
physical-chemical laws. In real workplace settings, chemical concentration
measurements can be noisy and indirectly measured. In addition, inference on
important parameters such as generation and ventilation rates are usually of
interest since they are difficult to obtain. In this paper we outline a
flexible Bayesian framework for parameter inference and exposure prediction. In
particular, we propose using Bayesian state space models by discretizing the
differential equation models and incorporating information from observed
measurements and expert prior knowledge. At each time point, a new measurement
is available that contains some noise, so using the physical model and the
available measurements, we try to obtain a more accurate state estimate, which
can be called filtering. We consider Monte Carlo sampling methods for parameter
estimation and inference under nonlinear and non-Gaussian assumptions. The
performance of the different methods is studied on computer-simulated and
controlled laboratory-generated data. We consider some commonly used exposure
models representing different physical hypotheses.
| stat.AP |
1807.02229 | Electrically driven dynamic three-dimensional solitons in nematic liquid
crystals | Electric field induced collective reorientation of nematic molecules placed
between two flat parallel electrodes is of importance for both fundamental
science and practical applications. This reorientation is either homogeneous
over the area of electrodes, as in liquid crystal displays, or periodically
modulated, as in the phenomenon called electroconvection1, similar to
Rayleigh-B\'enard thermal convection. The question is whether the electric
field can produce spatially localized propagating solitons of molecular
orientation. Here we demonstrate electrically driven three-dimensional
particle-like solitons representing self-trapped waves of oscillating molecular
orientation. The solitons propagate with a very high speed perpendicularly to
both the electric field and the initial alignment direction. The propulsion is
enabled by rapid collective reorientations of the molecules with the frequency
of the applied electric field and by lack of fore-aft symmetry. The solitons
preserve spatially-confined shapes while moving over distances hundreds of
times larger than their size and survive collisions. During collisions, the
solitons show repulsions and attractions, depending on the impact parameter.
The solitons are topologically equivalent to the uniform state and have no
static analogs, thus exhibiting a particle-wave duality. We anticipate the
observations to be a starting point for a broad range of studies since the
system allows for a precise control over a broad range of parameters that
determine the shape, propagation speed, and interactions of the solitons.
| cond-mat.soft physics.chem-ph |
1807.02230 | Coastline Kriging: A Bayesian Approach | Statistical interpolation of chemical concentrations at new locations is an
important step in assessing a worker's exposure level. When measurements are
available from coastlines, as is the case in coastal clean-up operations in oil
spills, one may need a mechanism to carry out spatial interpolation at new
locations along the coast. In this paper we present a simple model for
analyzing spatial data that is observed over a coastline. We demonstrate four
different models using two different representations of the coast using curves.
The four models were demonstrated on simulated data and one of them was also
demonstrated on a dataset from the GuLF STUDY. Our contribution here is to
offer practicing hygienists and exposure assessors with a simple and easy
method to implement Bayesian hierarchical models for analyzing and
interpolating coastal chemical concentrations.
| stat.AP |
1807.02231 | Inversion Problems for Fourier Transforms of Particle Distributions | Collective coordinates in a many-particle system are complex Fourier
components of the particle density, and often provide useful physical insights.
However, given collective coordinates, it is desirable to infer the particle
coordinates via inverse transformations. In principle, a sufficiently large set
of collective coordinates are equivalent to particle coordinates, but the
nonlinear relation between collective and particle coordinates makes the
inversion procedure highly nontrivial. Given a "target" configuration in
one-dimensional Euclidean space, we investigate the minimal set of its
collective coordinates that can be uniquely inverted into particle coordinates.
For this purpose, we treat a finite number $M$ of the real and/or the imaginary
parts of collective coordinates of the target configuration as constraints, and
then reconstruct "solution" configurations whose collective coordinates satisfy
these constraints. Both theoretical and numerical investigations reveal that
the number of numerically distinct solutions depends sensitively on the chosen
collective-coordinate constraints and target configurations. From detailed
analysis, we conclude that collective coordinates at the
$\lceil\frac{N}{2}\rceil$ smallest wavevectors is the minimal set of
constraints for unique inversion, where $\lceil{\cdot}\rceil$ represents the
ceiling function. This result provides useful groundwork to the inverse
transform of collective coordinates in higher-dimensional systems.
| cond-mat.stat-mech math-ph math.MP |
1807.02232 | Progressive Spatial Recurrent Neural Network for Intra Prediction | Intra prediction is an important component of modern video codecs, which is
able to efficiently squeeze out the spatial redundancy in video frames. With
preceding pixels as the context, traditional intra prediction schemes generate
linear predictions based on several predefined directions (i.e. modes) for
blocks to be encoded. However, these modes are relatively simple and their
predictions may fail when facing blocks with complex textures, which leads to
additional bits encoding the residue. In this paper, we design a Progressive
Spatial Recurrent Neural Network (PS-RNN) that learns to conduct intra
prediction. Specifically, our PS-RNN consists of three spatial recurrent units
and progressively generates predictions by passing information along from
preceding contents to blocks to be encoded. To make our network generate
predictions considering both distortion and bit-rate, we propose to use Sum of
Absolute Transformed Difference (SATD) as the loss function to train PS-RNN
since SATD is able to measure rate-distortion cost of encoding a residue block.
Moreover, our method supports variable-block-size for intra prediction, which
is more practical in real coding conditions. The proposed intra prediction
scheme achieves on average 2.5% bit-rate reduction on variable-block-size
settings under the same reconstruction quality compared with HEVC.
| cs.CV |
1807.02233 | U-SLADS: Unsupervised Learning Approach for Dynamic Dendrite Sampling | Novel data acquisition schemes have been an emerging need for scanning
microscopy based imaging techniques to reduce the time in data acquisition and
to minimize probing radiation in sample exposure. Varies sparse sampling
schemes have been studied and are ideally suited for such applications where
the images can be reconstructed from a sparse set of measurements. Dynamic
sparse sampling methods, particularly supervised learning based iterative
sampling algorithms, have shown promising results for sampling pixel locations
on the edges or boundaries during imaging. However, dynamic sampling for
imaging skeleton-like objects such as metal dendrites remains difficult. Here,
we address a new unsupervised learning approach using Hierarchical Gaussian
Mixture Mod- els (HGMM) to dynamically sample metal dendrites. This technique
is very useful if the users are interested in fast imaging the primary and
secondary arms of metal dendrites in solidification process in materials
science.
| eess.IV cs.LG eess.SP stat.ML |
1807.02234 | Distributed Self-Paced Learning in Alternating Direction Method of
Multipliers | Self-paced learning (SPL) mimics the cognitive process of humans, who
generally learn from easy samples to hard ones. One key issue in SPL is the
training process required for each instance weight depends on the other samples
and thus cannot easily be run in a distributed manner in a large-scale dataset.
In this paper, we reformulate the self-paced learning problem into a
distributed setting and propose a novel Distributed Self-Paced Learning method
(DSPL) to handle large-scale datasets. Specifically, both the model and
instance weights can be optimized in parallel for each batch based on a
consensus alternating direction method of multipliers. We also prove the
convergence of our algorithm under mild conditions. Extensive experiments on
both synthetic and real datasets demonstrate that our approach is superior to
those of existing methods.
| cs.LG stat.ML |
1807.02235 | Towards more Reliable Transfer Learning | Multi-source transfer learning has been proven effective when within-target
labeled data is scarce. Previous work focuses primarily on exploiting domain
similarities and assumes that source domains are richly or at least comparably
labeled. While this strong assumption is never true in practice, this paper
relaxes it and addresses challenges related to sources with diverse labeling
volume and diverse reliability. The first challenge is combining domain
similarity and source reliability by proposing a new transfer learning method
that utilizes both source-target similarities and inter-source relationships.
The second challenge involves pool-based active learning where the oracle is
only available in source domains, resulting in an integrated active transfer
learning framework that incorporates distribution matching and uncertainty
sampling. Extensive experiments on synthetic and two real-world datasets
clearly demonstrate the superiority of our proposed methods over several
baselines including state-of-the-art transfer learning methods.
| cs.LG stat.ML |
1807.02236 | Entanglement Detection via Direct-Sum Majorization Uncertainty Relations | In this paper we investigate the relationship between direct-sum majorization
formulation of uncertainty relations and entanglement, for the case of two and
many observables. Our primary results are entanglement detection methods based
on direct-sum majorization uncertainty relations. These nonlinear detectors
provide a set of necessary conditions for detecting entanglement whose number
grows with the dimension of the state being detected.
| quant-ph |
1807.02237 | Building Transmission Backbone for Highway Vehicular Networks: Framework
and Analysis | The highway vehicular ad hoc networks, where vehicles are wirelessly
inter-connected, rely on the multi-hop transmissions for end-to-end
communications. This, however, is severely challenged by the unreliable
wireless connections, signal attenuation and channel contentions in the dynamic
vehicular environment. To overcome the network dynamics, selecting appropriate
relays for end-to-end connections is important. Different from the previous
efforts (\emph{e.g.}, clustering and cooperative downloading), this paper
explores the existence of stable vehicles and propose building a stable
multi-hop transmission backbone network in the highway vehicular ad hoc
network. Our work is composed of three parts. Firstly, by analyzing the
real-world vehicle traffic traces, we observe that the large-size vehicles,
\emph{e.g.}, trucks, are typically stable with low variations of mobility and
stable channel condition of low signal attenuation; this makes their
inter-connections stable in both connection time and transmission rate.
Secondly, by exploring the stable vehicles, we propose a distributed protocol
to build a multi-hop backbone link for end-to-end transmissions, accordingly
forming a two-tier network architecture in highway vehicular ad hoc networks.
Lastly, to show the resulting data performance, we develop a queueing analysis
model to evaluate the end-to-end transmission delay and throughput.
Using extensive simulations, we show that the proposed transmission backbone
can significantly improve the reliability of multi-hop data transmissions with
higher throughput, less transmission interruptions and end-to-end delay.
| cs.NI |
1807.02238 | Expanding polynomials: A generalization of the Elekes-R\'onyai theorem
to $d$ variables | We prove the following statement. Let $f\in\mathbb{R}[x_1,\ldots,x_d]$, for
some $d\ge 3$, and assume that $f$ depends non-trivially in each of
$x_1,\ldots,x_d$. Then one of the following holds. (i) For every finite sets
$A_1,\ldots,A_d\subset \mathbb{R}$, each of size $n$, we have
$$|f(A_1\times\ldots\times A_d)|=\Omega(n^{3/2}), $$ with constant of
proportionality that depends on ${\rm deg} f$. (ii) $f$ is of one of the forms
\begin{align*} f(x_1,\ldots, x_d)&=h(p_1(x_1)+\cdots+p_d(x_d))~~\text{or}\\
f(x_1,\ldots, x_d)&=h(p_1(x_1)\cdot\ldots\cdot p_d(x_d)), \end{align*} for some
univariate real polynomials $h(x)$, $p_1(x),\ldots,p_d(x)$. This generalizes
the results from [ER00,RSS, RSdZ], which treat the cases $d=2$ and $d=3$.
| math.CO |
1807.02239 | A Flexible Joint Longitudinal-Survival Model for Analysis of End-Stage
Renal Disease Data | We propose a flexible joint longitudinal-survival framework to examine the
association between longitudinally collected biomarkers and a time-to-event
endpoint. More specifically, we use our method for analyzing the survival
outcome of end-stage renal disease patients with time-varying serum albumin
measurements. Our proposed method is robust to common parametric assumptions in
that it avoids explicit distributional assumptions on longitudinal measures and
allows for subject-specific baseline hazard in the survival component. Fully
joint estimation is performed to account for the uncertainty in the estimated
longitudinal biomarkers included in the survival model.
| stat.AP |
1807.02240 | Independently tunable dual-spectral electromagnetically induced
transparency in a terahertz metal-graphene metamaterial | We theoretically investigate the interaction between the conductive graphene
layer with the dual-spectral electromagnetically induced transparency (EIT)
metamaterial and achieve independent amplitude modulation of the transmission
peaks in terahertz (THz) regime. The dual-spectral EIT resonance results from
the strong near field coupling effects between the bright cut wire resonator
(CWR) in the middle and two dark double-split ring resonators (DSRRs) on the
two sides. By integrating monolayer graphene under the dark mode resonators,
the two transmission peaks of the EIT resonance can exhibit independent
amplitude modulation via shifting the Fermi level of the corresponding graphene
layer. The physical mechanism of the modulation can be attributed to the
variation of damping factors of the dark mode resonators arising from the
tunable conductivity of graphene. This work shows great prospects in designing
multiple-spectral THz functional devices with highly flexible tunability and
implies promising applications in multi-channel selective switching, modulation
and slow light.
| physics.optics physics.app-ph |
1807.02241 | Three-dimensional interstellar dust reddening maps of the Galactic plane | We present new three-dimensional (3D) interstellar dust reddening maps of the
Galactic plane in three colours, E(G-Ks), E(Bp-Rp) and E(H-Ks). The maps have a
spatial angular resolution of 6 arcmin and covers over 7000 deg$^2$ of the
Galactic plane for Galactic longitude 0 deg $<$ $l$ $<$ 360 deg and latitude
$|b|$ $<$ $10$ deg. The maps are constructed from robust parallax estimates
from the Gaia Data Release 2 (Gaia DR2) combined with the high-quality optical
photometry from the Gaia DR2 and the infrared photometry from the 2MASS and
WISE surveys. We estimate the colour excesses, E(G-Ks), E(Bp-Rp) and E(H-Ks),
of over 56 million stars with the machine learning algorithm Random Forest
regression, using a training data set constructed from the large-scale
spectroscopic surveys LAMOST, SEGUE and APOGEE. The results reveal the
large-scale dust distribution in the Galactic disk, showing a number of
features consistent with the earlier studies. The Galactic dust disk is clearly
warped and show complex structures possibly spatially associated with the
Sagittarius, Local and Perseus arms. We also provide the empirical extinction
coefficients for the Gaia photometry that can be used to convert the colour
excesses presented here to the line-of-sight extinction values in the Gaia
photometric bands.
| astro-ph.GA |
1807.02242 | Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting
Text with Arbitrary Shapes | Recently, models based on deep neural networks have dominated the fields of
scene text detection and recognition. In this paper, we investigate the problem
of scene text spotting, which aims at simultaneous text detection and
recognition in natural images. An end-to-end trainable neural network model for
scene text spotting is proposed. The proposed model, named as Mask TextSpotter,
is inspired by the newly published work Mask R-CNN. Different from previous
methods that also accomplish text spotting with end-to-end trainable deep
neural networks, Mask TextSpotter takes advantage of simple and smooth
end-to-end learning procedure, in which precise text detection and recognition
are acquired via semantic segmentation. Moreover, it is superior to previous
methods in handling text instances of irregular shapes, for example, curved
text. Experiments on ICDAR2013, ICDAR2015 and Total-Text demonstrate that the
proposed method achieves state-of-the-art results in both scene text detection
and end-to-end text recognition tasks.
| cs.CV |
1807.02243 | Generalization of Doob's Inequality and A Tighter Estimate on Look-back
Option Price | In this short note, we will strengthen the classic Doob's $L^p$ inequality
for sub-martingale processes. Because this inequality is of fundamental
importance to the theory of stochastic process, we believe this generalization
will find many interesting applications.
| q-fin.MF |
1807.02244 | A Bayesian Framework for Non-Collapsible Models | In this paper, we discuss the non-collapsibility concept and propose a new
approach based on Dirichlet process mixtures to estimate the conditional effect
of covariates in non-collapsible models. Using synthetic data, we evaluate the
performance of our proposed method and examine its sensitivity under different
settings. We also apply our method to real data on access failure among
hemodialysis patients.
| stat.ME |
1807.02245 | Isomorphism of the cubical and categorical cohomology groups of a
higher-rank graph | We use category-theoretic techniques to provide two proofs showing that for a
higher-rank graph $\Lambda$, its cubical (co-)homology and categorical
(co-)homology groups are isomorphic in all degrees, thus answering a question
of Kumjian, Pask and Sims in the positive. Our first proof uses the topological
realization of a higher-rank graph, which was introduced by Kaliszewski,
Kumjian, Quigg, and Sims. In our more combinatorial second proof, we construct,
explicitly and in both directions, maps on the level of (co-)chain complexes
that implement said isomorphism. Along the way, we extend the definition of
cubical (co-)homology to allow arbitrary coefficient modules.
| math.OA math.AT math.CO math.KT |
1807.02246 | The nuclear dimension of $C^*$-algebras associated to topological flows
and orientable line foliations | We show that for any locally compact Hausdorff space $Y$ with finite covering
dimension and for any continuous flow $\mathbb{R} \curvearrowright Y$, the
resulting crossed product $C^*$-algebra $C_0(Y) \rtimes \mathbb{R}$ has finite
nuclear dimension. This generalizes previous results for free flows, where this
was proved using Rokhlin dimension techniques. As an application, we obtain
bounds for the nuclear dimension of $C^*$-algebras associated to
one-dimensional orientable foliations. This result is analogous to the one we
obtained earlier for non-free actions of $\mathbb{Z}$. Some novel techniques in
our proof include the use of a conditional expectation constructed from the
inclusion of a clopen subgroupoid, as well as the introduction of what we call
fiberwise groupoid coverings that help us build a link between foliation
$C^*$-algebras and crossed products.
| math.OA math.DS |
1807.02247 | Adversarial Learning for Fine-grained Image Search | Fine-grained image search is still a challenging problem due to the
difficulty in capturing subtle differences regardless of pose variations of
objects from fine-grained categories. In practice, a dynamic inventory with new
fine-grained categories adds another dimension to this challenge. In this work,
we propose an end-to-end network, called FGGAN, that learns discriminative
representations by implicitly learning a geometric transformation from
multi-view images for fine-grained image search. We integrate a generative
adversarial network (GAN) that can automatically handle complex view and pose
variations by converting them to a canonical view without any predefined
transformations. Moreover, in an open-set scenario, our network is able to
better match images from unseen and unknown fine-grained categories. Extensive
experiments on two public datasets and a newly collected dataset have
demonstrated the outstanding robust performance of the proposed FGGAN in both
closed-set and open-set scenarios, providing as much as 10% relative
improvement compared to baselines.
| cs.CV |
1807.02248 | State-Varying Factor Models of Large Dimensions | This paper develops an inferential theory for state-varying factor models of
large dimensions. Unlike constant factor models, loadings are general functions
of some recurrent state process. We develop an estimator for the latent factors
and state-varying loadings under a large cross-section and time dimension. Our
estimator combines nonparametric methods with principal component analysis. We
derive the rate of convergence and limiting normal distribution for the
factors, loadings and common components. In addition, we develop a statistical
test for a change in the factor structure in different states. We apply the
estimator to U.S. Treasury yields and S&P500 stock returns. The systematic
factor structure in treasury yields differs in times of booms and recessions as
well as in periods of high market volatility. State-varying factors based on
the VIX capture significantly more variation and pricing information in
individual stocks than constant factor models.
| econ.EM |
1807.02249 | Towards Better Problem Finding and Creativity in Graduate School
Education | The current graduate school education system has largely been focusing on
producing better learners and problem solvers. The rise of problem based
learning approaches are testimonial to the importance of such skills at all
levels of education from early childhood to graduate school level. However,
most of the programs so far have focused primarily on producing better problem
solvers neglecting problem finding at large. Problem finding, an important
skill is a subset and first step in creative problem solving. Most studies on
problem finding skills have only focused on industries and corporations for
training employees to think out of the box for innovative product design and
development. At school or university level, students are generally given a
well-defined problem in most Problem Based Learning (PBL) scenarios and problem
discovery or how to deal with ill-structured problems is mostly ignored. In
this study, we present the Nitobe School Program and discuss our unique
curriculum to teach problem finding in graduate school education. We show how
introducing problem finding at graduate level increases student's ability to
comprehend difficult and wicked problems in a team based learning environment.
Moreover, we present how it influences creativity in graduate students
resulting in better problem solvers.
| physics.ed-ph |
1807.02250 | Face-Cap: Image Captioning using Facial Expression Analysis | Image captioning is the process of generating a natural language description
of an image. Most current image captioning models, however, do not take into
account the emotional aspect of an image, which is very relevant to activities
and interpersonal relationships represented therein. Towards developing a model
that can produce human-like captions incorporating these, we use facial
expression features extracted from images including human faces, with the aim
of improving the descriptive ability of the model. In this work, we present two
variants of our Face-Cap model, which embed facial expression features in
different ways, to generate image captions. Using all standard evaluation
metrics, our Face-Cap models outperform a state-of-the-art baseline model for
generating image captions when applied to an image caption dataset extracted
from the standard Flickr 30K dataset, consisting of around 11K images
containing faces. An analysis of the captions finds that, perhaps surprisingly,
the improvement in caption quality appears to come not from the addition of
adjectives linked to emotional aspects of the images, but from more variety in
the actions described in the captions.
| cs.CV |
1807.02251 | Minutia Texture Cylinder Codes for fingerprint matching | Minutia Cylinder Codes (MCC) are minutiae based fingerprint descriptors that
take into account minutiae information in a fingerprint image for fingerprint
matching. In this paper, we present a modification to the underlying
information of the MCC descriptor and show that using different features, the
accuracy of matching is highly affected by such changes. MCC originally being a
minutia only descriptor is transformed into a texture descriptor. The
transformation is from minutiae angular information to orientation, frequency
and energy information using Short Time Fourier Transform (STFT) analysis. The
minutia cylinder codes are converted to minutiae texture cylinder codes (MTCC).
Based on a fixed set of parameters, the proposed changes to MCC show improved
performance on FVC 2002 and 2004 data sets and surpass the traditional MCC
performance.
| cs.CV |
1807.02252 | AK-type stability theorems on cross t-intersecting families | Two families, ${\mathcal A}$ and ${\mathcal B}$, of subsets of $[n]$ are
cross $t$-intersecting if for every $A \in {\mathcal A}$ and $B \in {\mathcal
B}$, $A$ and $B$ intersect in at least $t$ elements. For a real number $p$ and
a family ${\mathcal A}$ the product measure $\mu_p ({\mathcal A})$ is defined
as the sum of $p^{|A|}(1-p)^{n-|A|}$ over all $A\in{\mathcal A}$. For every
non-negative integer $r$, and for large enough $t$, we determine, for any $p$
satisfying $\frac r{t+2r-1}\leq p\leq\frac{r+1}{t+2r+1}$, the maximum possible
value of $\mu_p ({\mathcal A})\mu_p ({\mathcal B})$ for cross $t$-intersecting
families ${\mathcal A}$ and ${\mathcal B}$. In this paper we prove a stronger
stability result which yields the above result.
| math.CO |
1807.02253 | Faster Data-access in Large-scale Systems: Network-scale Latency
Analysis under General Service-time Distributions | In cloud storage systems with a large number of servers, files are typically
not stored in single servers. Instead, they are split, replicated (to ensure
reliability in case of server malfunction) and stored in different servers. We
analyze the mean latency of such a split-and-replicate cloud storage system
under general sub-exponential service time. We present a novel scheduling
scheme that utilizes the load-balancing policy of the \textit{power of $d$
$(\geq 2)$} choices. An alternative to split-and-replicate is to use
erasure-codes, and recently, it has been observed that they can reduce latency
in data access (see \cite{longbo_delay} for details). We argue that under high
redundancy (integer redundancy factor strictly greater than or equal to 2)
regime, the mean latency of a coded system is upper bounded by that of a
split-and-replicate system (with same replication factor) and the gap between
these two is small. We validate this claim numerically under different service
distributions such as exponential, shift plus exponential and the heavy-tailed
Weibull distribution and compare the mean latency to that of an
unsplit-replicated system. We observe that the coded system outperforms the
unsplit-replication system by at least $20\%$. Furthermore, we consider the
mean latency for an erasure coded system with low redundancy (fractional
redundancy factor between 1 and 2), a scenario which is more pragmatic, given
the storage constraints (\cite{rashmi_thesis}). However under this regime, we
restrict ourselves to the special case of exponential service time distribution
and use the randomized load balancing policy namely \textit{batch-sampling}. We
obtain an upper bound on mean delay that depends on the order statistics of the
queue lengths, which, we further smooth out via a discrete to continuous
approximation.
| cs.DC cs.IT math.IT |
1807.02254 | Singing Style Transfer Using Cycle-Consistent Boundary Equilibrium
Generative Adversarial Networks | Can we make a famous rap singer like Eminem sing whatever our favorite song?
Singing style transfer attempts to make this possible, by replacing the vocal
of a song from the source singer to the target singer. This paper presents a
method that learns from unpaired data for singing style transfer using
generative adversarial networks.
| cs.SD cs.AI eess.AS |
1807.02255 | Towards a Context-Aware IDE-Based Meta Search Engine for Recommendation
about Programming Errors and Exceptions | Study shows that software developers spend about 19% of their time looking
for information in the web during software development and maintenance.
Traditional web search forces them to leave the working environment (e.g., IDE)
and look for information in the web browser. It also does not consider the
context of the problems that the developers search solutions for. The frequent
switching between web browser and the IDE is both time-consuming and
distracting, and the keyword-based traditional web search often does not help
much in problem solving. In this paper, we propose an Eclipse IDE-based web
search solution that exploits the APIs provided by three popular web search
engines-- Google, Yahoo, Bing and a popular programming Q & A site, Stack
Overflow, and captures the content-relevance, context-relevance, popularity and
search engine confidence of each candidate result against the encountered
programming problems. Experiments with 75 programming errors and exceptions
using the proposed approach show that inclusion of different types of context
information associated with a given exception can enhance the recommendation
accuracy of a given exception. Experiments both with two existing approaches
and existing web search engines confirm that our approach can perform better
than them in terms of recall, mean precision and other performance measures
with little computational cost.
| cs.SE cs.IR |
1807.02256 | SurfClipse: Context-Aware Meta Search in the IDE | Despite various debugging supports of the existing IDEs for programming
errors and exceptions, software developers often look at web for working
solutions or any up-to-date information. Traditional web search does not
consider the context of the problems that they search solutions for, and thus
it often does not help much in problem solving. In this paper, we propose a
context-aware meta search tool, SurfClipse, that analyzes an encountered
exception and its context in the IDE, and recommends not only suitable search
queries but also relevant web pages for the exception (and its context). The
tool collects results from three popular search engines and a programming Q & A
site against the exception in the IDE, refines the results for relevance
against the context of the exception, and then ranks them before
recommendation. It provides two working modes--interactive and proactive to
meet the versatile needs of the developers, and one can browse the result pages
using a customized embedded browser provided by the tool.
Tool page: www.usask.ca/~masud.rahman/surfclipse
| cs.SE |
1807.02257 | Dynamic Multimodal Instance Segmentation guided by natural language
queries | We address the problem of segmenting an object given a natural language
expression that describes it. Current techniques tackle this task by either
(\textit{i}) directly or recursively merging linguistic and visual information
in the channel dimension and then performing convolutions; or by (\textit{ii})
mapping the expression to a space in which it can be thought of as a filter,
whose response is directly related to the presence of the object at a given
spatial coordinate in the image, so that a convolution can be applied to look
for the object. We propose a novel method that integrates these two insights in
order to fully exploit the recursive nature of language. Additionally, during
the upsampling process, we take advantage of the intermediate information
generated when downsampling the image, so that detailed segmentations can be
obtained. We compare our method against the state-of-the-art approaches in four
standard datasets, in which it surpasses all previous methods in six of eight
of the splits for this task.
| cs.CV |
1807.02258 | Scalable Formal Concept Analysis algorithm for large datasets using
Spark | In the process of knowledge discovery and representation in large datasets
using formal concept analysis, complexity plays a major role in identifying all
the formal concepts and constructing the concept lattice(digraph of the
concepts). For identifying the formal concepts and constructing the digraph
from the identified concepts in very large datasets, various distributed
algorithms are available in the literature. However, the existing distributed
algorithms are not very well suitable for concept generation because it is an
iterative process. The existing algorithms are implemented using distributed
frameworks like MapReduce and Open MP, these frameworks are not appropriate for
iterative applications. Hence, in this paper we proposed efficient distributed
algorithms for both formal concept generation and concept lattice digraph
construction in large formal contexts using Apache Spark. Various performance
metrics are considered for the evaluation of the proposed work, the results of
the evaluation proves that the proposed algorithms are efficient for concept
generation and lattice graph construction in comparison with the existing
algorithms.
| cs.AI cs.DB |
1807.02259 | BKP hierarchy and Pfaffian point process | Inspired by Okounkov's work [\emph{Selecta Mathematica}, 7(1):57--81, 2001]
which relates KP hierarchy to determinant point process, we establish a
relationship between BKP hierarchy and Pfaffian point process. We prove that
the correlation function of the shifted Schur measures on strict partitions can
be expressed as a Pfaffian of skew symmetric matrix kernel, whose elememts are
certain vacuum expectations of neutral fermions. We further show that the
matrix integrals solution of BKP hierarchy can also induce a certain Pfaffian
point process.
| math-ph math.MP nlin.SI |
1807.02260 | Characterization of a metrizable space $X$ such that $F_4(X)$ is
Fr\'echet-Urysohn | Let $F(X)$ be the free topological group on a Tychonoff space $X$. For all
natural numbers $n$ we denote by $F_n(X)$ the subset of $F(X)$ consisting of
all words of reduced length $\leq n$. In \cite{Y3}, the author found equivalent
conditions on a metrizable space $X$ for $F_3(X)$ to be Fr\'echet-Urysohn, and
for $F_n(X)$ to be Fr\'echet-Urysohn for $n\geq5$. However, no equivalent
condition on $X$ for $n=4$ was found. In this paper, we give the equivalent
condition. In fact, we show that for a metrizable space $X$, if the set of all
non-isolated points of $X$ is compact, then $F_4(X)$ is Fr\'echet-Urysohn.
Consequently, for a metrizable space $X$ $F_3(X)$ is Fr\'echet-Urysohn if and
only if $F_4(X)$ is Fr\'echet-Urysohn.
| math.GN |
1807.02261 | On the Use of Context in Recommending Exception Handling Code Examples | Studies show that software developers often either misuse exception handling
features or use them inefficiently, and such a practice may lead an undergoing
software project to a fragile, insecure and non-robust application system. In
this paper, we propose a context-aware code recommendation approach that
recommends exception handling code examples from a number of popular open
source code repositories hosted at GitHub. It collects the code examples
exploiting GitHub code search API, and then analyzes, filters and ranks them
against the code under development in the IDE by leveraging not only the
structural (i.e., graph-based) and lexical features but also the heuristic
quality measures of exception handlers in the examples. Experiments with 4,400
code examples and 65 exception handling scenarios as well as comparisons with
four existing approaches show that the proposed approach is highly promising.
| cs.SE |
1807.02262 | Temporal graph-based clustering for historical record linkage | Research in the social sciences is increasingly based on large and complex
data collections, where individual data sets from different domains are linked
and integrated to allow advanced analytics. A popular type of data used in such
a context are historical censuses, as well as birth, death, and marriage
certificates. Individually, such data sets however limit the types of studies
that can be conducted. Specifically, it is impossible to track individuals,
families, or households over time. Once such data sets are linked and family
trees spanning several decades are available it is possible to, for example,
investigate how education, health, mobility, employment, and social status
influence each other and the lives of people over two or even more generations.
A major challenge is however the accurate linkage of historical data sets which
is due to data quality and commonly also the lack of ground truth data being
available. Unsupervised techniques need to be employed, which can be based on
similarity graphs generated by comparing individual records. In this paper we
present initial results from clustering birth records from Scotland where we
aim to identify all births of the same mother and group siblings into clusters.
We extend an existing clustering technique for record linkage by incorporating
temporal constraints that must hold between births by the same mother, and
propose a novel greedy temporal clustering technique. Experimental results show
improvements over non-temporary approaches, however further work is needed to
obtain links of high quality.
| cs.DB cs.AI |
1807.02263 | TextRank Based Search Term Identification for Software Change Tasks | During maintenance, software developers deal with a number of software change
requests. Each of those requests is generally written using natural language
texts, and it involves one or more domain related concepts. A developer needs
to map those concepts to exact source code locations within the project in
order to implement the requested change. This mapping generally starts with a
search within the project that requires one or more suitable search terms.
Studies suggest that the developers often perform poorly in coming up with good
search terms for a change task. In this paper, we propose and evaluate a novel
TextRank-based technique that automatically identifies and suggests search
terms for a software change task by analyzing its task description. Experiments
with 349 change tasks from two subject systems and comparison with one of the
latest and closely related state-of-the-art approaches show that our technique
is highly promising in terms of suggestion accuracy, mean average precision and
recall.
| cs.SE cs.IR |
1807.02264 | Variance Reduction for Reinforcement Learning in Input-Driven
Environments | We consider reinforcement learning in input-driven environments, where an
exogenous, stochastic input process affects the dynamics of the system. Input
processes arise in many applications, including queuing systems, robotics
control with disturbances, and object tracking. Since the state dynamics and
rewards depend on the input process, the state alone provides limited
information for the expected future returns. Therefore, policy gradient methods
with standard state-dependent baselines suffer high variance during training.
We derive a bias-free, input-dependent baseline to reduce this variance, and
analytically show its benefits over state-dependent baselines. We then propose
a meta-learning approach to overcome the complexity of learning a baseline that
depends on a long sequence of inputs. Our experimental results show that across
environments from queuing systems, computer networks, and MuJoCo robotic
locomotion, input-dependent baselines consistently improve training stability
and result in better eventual policies.
| cs.LG stat.ML |
1807.02265 | Parallel Convolutional Networks for Image Recognition via a
Discriminator | In this paper, we introduce a simple but quite effective recognition
framework dubbed D-PCN, aiming at enhancing feature extracting ability of CNN.
The framework consists of two parallel CNNs, a discriminator and an extra
classifier which takes integrated features from parallel networks and gives
final prediction. The discriminator is core which drives parallel networks to
focus on different regions and learn different representations. The
corresponding training strategy is introduced to ensures utilization of
discriminator. We validate D-PCN with several CNN models on benchmark datasets:
CIFAR-100, and ImageNet, D-PCN enhances all models. In particular it yields
state of the art performance on CIFAR-100 compared with related works. We also
conduct visualization experiment on fine-grained Stanford Dogs dataset to
verify our motivation. Additionally, we apply D-PCN for segmentation on PASCAL
VOC 2012 and also find promotion.
| cs.CV |
1807.02266 | Flag area measures | A flag area measure on an $n$-dimensional euclidean vector space is a
continuous translation-invariant valuation with values in the space of signed
measures on the flag manifold consisting of a unit vector $v$ and a
$(p+1)$-dimensional linear subspace containing $v$ with $0 \leq p \leq n-1$.
Using local parallel sets, Hinderer constructed examples of
$\mathrm{SO}(n)$-covariant flag area measures. There is an explicit formula for
his flag area measures evaluated on polytopes, which involves the squared
cosine of the angle between two subspaces.
We construct a more general sequence of smooth $\mathrm{SO}(n)$-covariant
flag area measures via integration over the normal cycle of appropriate
differential forms. We provide an explicit description of our measures on
polytopes, which involves an arbitrary elementary symmetric polynomial in the
squared cosines of the principal angles between two subspaces.
Moreover, we show that these flag area measures span the space of all smooth
$\mathrm{SO}(n)$-covariant flag area measures, which gives a classification
result in the spirit of Hadwiger's theorem.
| math.DG |
1807.02267 | Multi-target Joint Detection, Tracking and Classification Based on
Generalized Bayesian Risk using Radar and ESM sensors | In this paper, a novel approach is proposed for multi-target joint detection,
tracking and classification based on the labeled random finite set and
generalized Bayesian risk using Radar and ESM sensors. A new Bayesian risk is
defined for the labeled random finite set variables involving the costs of
multi-target cardinality estimation (detection), state estimation (tracking)
and classification. The inter-dependence of detection, tracking and
classification is then utilized with the minimum Bayesian risk. Furthermore,
the conditional labeled multi-Bernoulli filter is developed to calculate the
estimates and costs for different hypotheses and decisions of target classes
using attribute and dynamical measurements. Moreover, the performance is
analyzed. The effectiveness and superiority of the proposed approach are
verified using numerical simulations.
| eess.SP |
1807.02268 | EnTrans:Leveraging Kinetic Energy Harvesting Signal for Transportation
Mode Detection | Monitoring the daily transportation modes of an individual provides useful
information in many application domains, such as urban design, real-time
journey recommendation, as well as providing location-based services. In
existing systems, accelerometer and GPS are the dominantly used signal sources
for transportation context monitoring which drain out the limited battery life
of the wearable devices very quickly. To resolve the high energy consumption
issue, in this paper, we present EnTrans, which enables transportation mode
detection by using only the kinetic energy harvester as an energy-efficient
signal source. The proposed idea is based on the intuition that the vibrations
experienced by the passenger during traveling with different transportation
modes are distinctive. Thus, voltage signal generated by the energy harvesting
devices should contain sufficient features to distinguish different
transportation modes. We evaluate our system using over 28 hours of data, which
is collected by eight individuals using a practical energy harvesting
prototype. The evaluation results demonstrate that EnTrans is able to achieve
an overall accuracy over 92% in classifying five different modes while saving
more than 34% of the system power compared to conventional accelerometer-based
approaches.
| cs.HC cs.NI |
1807.02269 | Wakimoto realization of the quantum affine superalgebra
$U_q(\widehat{sl}(M|N))$ | A bosonization of the quantum affine superalgebra $U_q(\widehat{sl}(M|N))$ is
presented for an arbitrary level $k \in {\bf C}$.The Wakimoto realization is
given by using $\xi-\eta$ system. The screening operators that commute with
$U_q(\widehat{sl}(M|N))$ are presented for the level $k \neq -M+N$. New
bosonization of the affine superalgebra $\widehat{sl}(M|N)$ is obtained in the
limit $q \to 1$.
| math.QA hep-th math-ph math.MP nlin.SI |
1807.02270 | The strange case of Dr. Petit and Mr. Dulong | The Dulong-Petit limiting law for the specific heats of solids, one of the
first general results in thermodynamics, has provided Mendeleev with a powerful
tool for devising the periodic table and gave an important support to
Boltzmann's statistical mechanics. Even its failure at low temperature,
accounted for by Einstein, paved the way to the the quantum mechanical theory
of solids. These impressive consequences are even more surprising if we bear in
mind that, when this law was announced, thermal phenomena were still explained
using Lavoisier's concept of caloric and Dalton's atomic theory was in its
infancy. Recently, however, bitter criticisms charging Dulong and Petit of
`data fabrication' and fraud, have been raised. This work is an attempt to
restore a more balanced view of the work performed by these two great
scientists and to give them back the place they deserve in the framework of the
development of modern science.
| physics.hist-ph |
1807.02271 | The influence of stellar flare on dynamical state of the atmosphere of
exoplanet HD 209458b | By applying an one-dimensional aeronomic model of the upper atmosphere of the
close-in giant planet HD 209458b, we study the reaction of the planetary
atmosphere to an additional heating caused by the influence of a stellar flare.
It is shown that the absorption of additional energy of the stellar flare in
the extreme ultraviolet leads to local atmospheric heating, accompanied by
formation of two shock waves, propagating in the atmosphere. We discuss
possible observational manifestations of the shocks and feasibility of their
detection.
| astro-ph.EP |
1807.02272 | Two-dimensional ferroelectric tunnel junction: the case of monolayer
In:SnSe/SnSe/Sb:SnSe homostructure | Ferroelectric tunnel junctions, in which ferroelectric polarization and
quantum tunneling are closely coupled to induce the tunneling electroresistance
(TER) effect, have attracted considerable interest due to their potential in
non-volatile and low-power consumption memory devices. The ferroelectric size
effect, however, has hindered ferroelectric tunnel junctions from exhibiting
robust TER effect. Here, our study proposes doping engineering in a
two-dimensional in-plane ferroelectric semiconductor as an effective strategy
to design a two-dimensional ferroelectric tunnel junction composed of
homostructural $p$-type semiconductor/ferroelectric/$n$-type semiconductor.
Since the in-plane polarization persists in the monolayer ferroelectric
barrier, the vertical thickness of two-dimensional ferroelectric tunnel
junction can be as thin as monolayer. We show that the monolayer
In:SnSe/SnSe/Sb:SnSe junction provides an embodiment of this strategy.
Combining density functional theory calculations with non-equilibrium Green's
function formalism, we investigate the electron transport properties of
In:SnSe/SnSe/Sb:SnSe and reveal a giant TER effect of 1460$\%$. The dynamical
modulation of both barrier width and barrier height during the ferroelectric
switching are responsible for this giant TER effect. These findings provide an
important insight towards the understanding of the quantum behaviors of
electrons in materials at the two-dimensional limit, and enable new
possibilities for next-generation non-volatile memory devices based on flexible
two-dimensional lateral ferroelectric tunnel junctions.
| cond-mat.mtrl-sci |
1807.02273 | Commutation relations of vertex operators for $U_q(\widehat{sl}(M|N))$ | We consider commutation relations and invertibility relations of vertex
operators for the quantum affine superalgebra $U_q(\widehat{sl}(M|N))$ by using
bosonization. We show that vertex operators give a representation of the graded
Zamolodchikov-Faddeev algebra by direct computation.Invertibility relations of
type-II vertex operators for $N>M$ are very similar to those of type-I for
$M>N$.
| math.QA hep-th math-ph math.MP nlin.SI |
1807.02274 | Recommending Relevant Sections from a Webpage about Programming Errors
and Exceptions | Programming errors or exceptions are inherent in software development and
maintenance, and given today's Internet era, software developers often look at
web for finding working solutions. They make use of a search engine for
retrieving relevant pages, and then look for the appropriate solutions by
manually going through the pages one by one. However, both the manual checking
of a page's content against a given exception (and its context) and then
working an appropriate solution out are non-trivial tasks. They are even more
complex and time-consuming with the bulk of irrelevant (i.e., off-topic) and
noisy (e.g., advertisements) content in the web page. In this paper, we propose
an IDE-based and context-aware page content recommendation technique that
locates and recommends relevant sections from a given web page by exploiting
the technical details, in particular, the context of an encountered exception
in the IDE. An evaluation with 250 web pages related to 80 programming
exceptions, comparison with the only available closely related technique, and a
case study involving comparison with VSM and LSA techniques show that the
proposed technique is highly promising in terms of precision, recall and
F1-measure.
| cs.SE cs.IR |
1807.02275 | The maximum interbubble distance in relation to the radius of spherical
stable nanobubble in liquid water: A molecular dynamics study | The mechanism of superstability of nanobubbles in liquid confirmed by many
experimental studies is still in debate since the classical diffusion predicts
their lifetime on the order of a few microseconds. In this work, we study the
requirement for bulk nanobubbles to be stable by using molecular dynamics
simulations. Periodic cubic cells with different cell sizes and different
initial radii are treated to simulate the nanobubble cluster, providing the
equilibrium bubble radius and the interbubble distance. We find out that for
nanobubble with a certain radius $R$ to be stable, the interbubble distance
should be smaller than the maximum interbubble distance $L^*$ being
proportional to $R^{4/3}$.
| physics.chem-ph cond-mat.soft |
1807.02276 | A simple projective setup to study optical cloaking in the classroom | Optical cloaking consists in hiding from sight an object by properly
deviating the light that comes from it. An optical cloaking device (OCD) is an
artifact that hides the object and, at the same time, its presence is not (or
should not be) noticeable for the observer, who will have the impression of
being looking through it. At the level of paraxial geometrical optics, suitable
for undergraduate courses, simple OCDs can be built by combining a series of
lenses. With this motivation, here we present an analysis of a simple
projective OCD arrangement. First, a simple theoretical account in terms of the
transfer matrix method is provided, and then the outcomes from a series of
teaching experiments carried out with this device, easy to conduct in the
classroom, are discussed. In particular, the performance of such an OCD is
investigated by determining the effect of the hidden object, role here played
by the opaque zone of an iris-type diaphragm, on the projected image of an
illuminated transparent slide (test object). That is, cloaking is analyzed in
terms of the optimal position and opening diameter of a diaphragm that still
warrants an almost unaffected projected image. Because the lenses are not
high-quality ones, the OCD is not aberration-free, which is advantageously
considered to determine acceptable cloaking conditions (i.e., the tolerance of
the device).
| physics.ed-ph physics.optics |
1807.02277 | First-principles study on the chemical decomposition of inorganic
perovskites \ce{CsPbI3} and \ce{RbPbI3} at finite temperature and pressure | Inorganic halide perovskite \ce{Cs(Rb)PbI3} has attracted significant
research interest in the application of light-absorbing material of perovskite
solar cells (PSCs). Although there have been extensive studies on structural
and electronic properties of inorganic halide perovskites, the investigation on
their thermodynamic stability is lack. Thus, we investigate the effect of
substituting Rb for Cs in \ce{CsPbI3} on the chemical decomposition and
thermodynamic stability using first-principles thermodynamics. By calculating
the formation energies of solid solutions \ce{Cs$_{1-x}$Rb$_x$PbI3} from their
ingredients \ce{Cs$_{1-x}$Rb$_x$I} and \ce{PbI2}, we find that the best match
between efficiency and stability can be achieved at the Rb content $x\approx$
0.7. The calculated Helmholtz free energy of solid solutions indicates that
\ce{Cs$_{1-x}$Rb$_x$PbI3} has a good thermodynamic stability at room
temperature due to a good miscibility of \ce{CsPbI3} and \ce{RbPbI3}. Through
lattice-dynamics calculations, we further highlight that \ce{RbPbI3} never
stabilize in cubic phase at any temperature and pressure due to the chemical
decomposition into its ingredients \ce{RbI} and \ce{PbI2}, while \ce{CsPbI3}
can be stabilized in the cubic phase at the temperature range of 0$-$600 K and
the pressure range of 0$-$4 GPa. Our work reasonably explains the experimental
observations, and paves the way for understanding material stability of the
inorganic halide perovskites and designing efficient inorganic halide PSCs.
| cond-mat.mtrl-sci |
1807.02278 | Recommending Insightful Comments for Source Code using Crowdsourced
Knowledge | Recently, automatic code comment generation is proposed to facilitate program
comprehension. Existing code comment generation techniques focus on describing
the functionality of the source code. However, there are other aspects such as
insights about quality or issues of the code, which are overlooked by earlier
approaches. In this paper, we describe a mining approach that recommends
insightful comments about the quality, deficiencies or scopes for further
improvement of the source code. First, we conduct an exploratory study that
motivates crowdsourced knowledge from Stack Overflow discussions as a potential
resource for source code comment recommendation. Second, based on the findings
from the exploratory study, we propose a heuristic-based technique for mining
insightful comments from Stack Overflow Q & A site for source code comment
recommendation. Experiments with 292 Stack Overflow code segments and 5,039
discussion comments show that our approach has a promising recall of 85.42%. We
also conducted a complementary user study which confirms the accuracy and
usefulness of the recommended comments.
| cs.SE |
1807.02279 | Fabrication of Hollow AlAu2 Nanoparticles by Solid State Dewetting and
Oxidation of Al on Sapphire Substrate | The Al-Au binary diffusion couple is a classic example of the system
exhibiting Kirkendall voiding during interdiffusion. We demonstrate that this
effect, which is a major reason for failures of the wire bonds in
microelectronics, can be utilized for producing hollow AlAu2 nanoparticles
attached to sapphire substrate. To this end, we produced the core-shell Al-Au
nanoparticles by performing a solid state dewetting treatment of Al thin film
deposited on sapphire substrate, followed by the deposition of thin Au layer on
the top of dewetted sample. Annealing of the core-shell nanoparticles in air
resulted in outdiffusion of Al from the particles, formation of pores, and
growth of the AlAu2 intermetallic phase in the particles. We demonstrated that
the driving force for hollowing is the oxidation reaction of the Al atoms at
the Au-sapphire interface, leading to the homoepitaxial growth of newly formed
alumina at the interface. We developed a kinetic model of hollowing controlled
by diffusion of oxygen through the Au thin film, and estimated the solubility
of oxygen in solid Au. Our work demonstrates that the core-shell nanoparticles
attached to the substrate can be hollowed by the Kirkendall effect in the thin
film spatially separated from the particles.
| cond-mat.mtrl-sci cond-mat.mes-hall |
1807.02280 | System stability and truncation schemes to the Dyson-Schwinger Equations | With decades of years development, although important progresses have been
made by the pioneers of this field, providing a sophisticated truncation scheme
is still a great challenge up to now if the Dyson-Schwinger Equations(DSEs) of
both quark and gluon propagators (or including even more DSEs) remain after
truncation. In this work we view the coupled reminiscent DSEs of the gluon and
quark propagators after truncation as a system with feedback. Then studying the
stability of this equation array gives useful results. Our calculation shows
that the sum of the gluon and ghost loops plays the most important role in
keeping this system stable and having reasonable solutions. The quark-gluon
vertex plays a relative smaller but also important role. Our method also could
give constraints and inspirations on fabricating a more sophisticated model of
the quark-gluon vertex.
| nucl-th |
1807.02281 | Angular analyses of $b \to s \mu^+ \mu^-$ transitions at CMS | The flavour changing neutral current decays can be interesting probes for
searching for new physics. Angular distributions of $b \to s \ell^+ \ell^-$
transition processes of both $\mathrm{B}^0 \to \mathrm{K}^{*0} \mu^ +\mu^-$ and
$\mathrm{B}^+ \to \mathrm{K}^+ \mu^+\mu^-$ are studied using a sample of
proton-proton collisions at $\sqrt{s} = 8~\mathrm{TeV}$ collected with the CMS
detector at the LHC, corresponding to an integrated luminosity of
$20.5~\mathrm{fb}^{-1}$. Angular analyses are performed to determine $P_1$ and
$P_5'$ angular parameters for $\mathrm{B}^0 \to \mathrm{K}^{*0} \mu^ +\mu^-$
and $A_{FB}$ and $F_{H}$ parameters for $\mathrm{B}^+ \to \mathrm{K}^+
\mu^+\mu^-$, all as functions of the dimuon invariant mass squared. The $P_5'$
parameter is of particular interest due to recent measurements that indicate a
potential discrepancy with the standard model. All the measurements are
consistent with the standard model predictions. Efforts with more channels and
more coming data will be continued to further test the standard model in higher
precision in future.
| hep-ex |
1807.02282 | CoMID: Context-based Multi-Invariant Detection for Monitoring
Cyber-Physical Software | Cyber-physical software continually interacts with its physical environment
for adaptation in order to deliver smart services. However, the interactions
can be subject to various errors when the software's assumption on its
environment no longer holds, thus leading to unexpected misbehavior or even
failure. To address this problem, one promising way is to conduct runtime
monitoring of invariants, so as to prevent cyber-physical software from
entering such errors (a.k.a. abnormal states). To effectively detect abnormal
states, we in this article present an approach, named Context-based
Multi-Invariant Detection (CoMID), which consists of two techniques:
context-based trace grouping and multi-invariant detection. The former infers
contexts to distinguish different effective scopes for CoMID's derived
invariants, and the latter conducts ensemble evaluation of multiple invariants
to detect abnormal states. We experimentally evaluate CoMID on real-world
cyber-physical software. The results show that CoMID achieves a 5.7-28.2%
higher true-positive rate and a 6.8-37.6% lower false-positive rate in
detecting abnormal states, as compared with state-of-the-art approaches (i.e.,
Daikon and ZoomIn). When deployed in field tests, CoMID's runtime monitoring
improves the success rate of cyber-physical software in its task executions by
15.3-31.7%.
| cs.SE |
1807.02283 | Theoretical method for generating regular spatiotemporal pulsed-beam
with controlled transverse-spatiotemporal dispersion | Herein we theoretically report a method that generates a
transverse-spatiotemporal dispersion (T-STD), which is distinct from previous
spatial, temporal, and longitudinal-spatiotemporal optics dispersions. By
modulating T-STD, two not yet reported spatiotemporally structured beams
(STSBs), i.e., the honeycomb beam and the picket-fence beam, can be generated
in the space-time domain. The generated STSBs have novel and tunable periodic
distributions. T-STD, STSB and their inner physical relationship are analyzed
and introduced. We believe that this method might open a path towards new
optical beams for potential applications, such as ultrafast optical fabrication
and detection.
| physics.optics |
1807.02284 | Continuous-Scale Kinetic Fluid Simulation | Kinetic approaches, i.e., methods based on the lattice Boltzmann equations,
have long been recognized as an appealing alternative for solving
incompressible Navier-Stokes equations in computational fluid dynamics.
However, such approaches have not been widely adopted in graphics mainly due to
the underlying inaccuracy, instability and inflexibility. In this paper, we try
to tackle these problems in order to make kinetic approaches practical for
graphical applications. To achieve more accurate and stable simulations, we
propose to employ the non-orthogonal central-moment-relaxation model, where we
develop a novel adaptive relaxation method to retain both stability and
accuracy in turbulent flows. To achieve flexibility, we propose a novel
continuous-scale formulation that enables samples at arbitrary resolutions to
easily communicate with each other in a more continuous sense and with loose
geometrical constraints, which allows efficient and adaptive sample
construction to better match the physical scale. Such a capability directly
leads to an automatic sample construction which generates static and dynamic
scales at initialization and during simulation, respectively. This effectively
makes our method suitable for simulating turbulent flows with arbitrary
geometrical boundaries. Our simulation results with applications to smoke
animations show the benefits of our method, with comparisons for justification
and verification.
| cs.GR |
1807.02285 | Willis metamaterial on a structured beam | Bianisotropy is common in electromagnetics whenever a cross-coupling between
electric and magnetic responses exists. However, the analogous concept for
elastic waves in solids, termed as Willis coupling, is more challenging to
observe. It requires coupling between stress and velocity or momentum and
strain fields, which is difficult to induce in non-negligible levels, even when
using metamaterial structures. Here, we report the experimental realization of
a Willis metamaterial for flexural waves. Based on a cantilever bending
resonance, we demonstrate asymmetric reflection amplitudes and phases due to
Willis coupling. We also show that, by introducing loss in the metamaterial,
the asymmetric amplitudes can be controlled and can be used to approach an
exceptional point of the non-Hermitian system, at which unidirectional zero
reflection occurs. The present work extends conventional propagation theory in
plates and beams to include Willis coupling, and provides new avenues to tailor
flexural waves using artificial structures.
| physics.app-ph |
1807.02286 | Dissociative electron attachment to sulfur dioxide : A theoretical
approach | In this article, density functional theory (DFT) and natural bond orbital
(NBO) calculations are performed to understand experimental observations of
dissociative electron attachment (DEA) to SO$_2$. The molecular structure,
fundamental vibrational frequencies with their corresponding intensities and
molecular electrostatic potential (MEP) map of SO$_2$ and SO$_2^-$ are
interpreted from respective ground state optimized electronic structures
calculated using DFT. The quantified MEPs and the second order perturbation
energies for different oxygen lone pair (n) to $\sigma^*$ and $\pi^*$
interactions of S-O bond orbitals have been calculated by carrying out NBO
analysis. The change in the electronic structure of the molecule after the
attachment of a low-energy ($\leq$ 15 eV) electron, thus forming a transient
negative ion, can be interpreted from the $n\rightarrow\sigma^*$ and
$n\rightarrow\pi^*$ interactions. The results of the calculations are used to
interpret the dissociative electron attachment process. The dissociation of the
anion SO$_2^-$ into negative and neutral fragments has been explained by
interpreting the infrared spectrum and different vibration modes. It could be
observed that the dissociation of SO_{2}^{-} into S^{-} occurs as a result of
simultaneous symmetric stretching and bending modes of the molecular anion.
While the formation of O$^-$ and SO$^-$ occurs as a result of anti-symmetric
stretching of the molecular anion. The calculated symmetries of the TNI state
contributing to the first resonant peak at around 5.2 eV and second resonant
peak at around 7.5 eV was observed from time-dependent density functional
theory calculations to be an A$_1$ and a combination of A$_1$+B$_2$ states for
the two resonant peaks, respectively. These findings strongly support our
recent experimental observations for DEA to SO$_2$ [Jana and Nandi, Phys. Rev.
A, 97, 042706 (2018)].
| physics.atm-clus |
1807.02287 | Outperforming Good-Turing: Preliminary Report | Estimating a large alphabet probability distribution from a limited number of
samples is a fundamental problem in machine learning and statistics. A variety
of estimation schemes have been proposed over the years, mostly inspired by the
early work of Laplace and the seminal contribution of Good and Turing. One of
the basic assumptions shared by most commonly-used estimators is the unique
correspondence between the symbol's sample frequency and its estimated
probability. In this work we tackle this paradigmatic assumption; we claim that
symbols with "similar" frequencies shall be assigned the same estimated
probability value. This way we regulate the number of parameters and improve
generalization. In this preliminary report we show that by applying an ensemble
of such regulated estimators, we introduce a dramatic enhancement in the
estimation accuracy (typically up to 50%), compared to currently known methods.
An implementation of our suggested method is publicly available at the first
author's web-page.
| stat.ML cs.LG |
1807.02288 | Shared features of endothelial dysfunction between sepsis and its
preceding risk factors (aging and chronic disease) | Acute vascular endothelial dysfunction is a central event in the pathogenesis
of sepsis,increasing vascular permeability, promoting activation of the
coagulation cascade, tissue edema and compromising perfusion of vital organs.
Aging and chronic diseases(hypertension,dyslipidaemia,diabetes mellitus,chronic
kidney disease,cardiovascular disease,cerebrovascular disease, chronic
pulmonary disease,liver disease or cancer)are recognized risk factors for
sepsis. In this article we review the features of endothelial dysfunction
shared by sepsis,aging and the chronic conditions preceding this disease.
Clinical studies and review articles on endothelial dysfunction associated to
sepsis,aging and chronic diseases published in PubMed were considered. The main
features of endothelial dysfunction shared by sepsis,aging and chronic diseases
were 1.increased oxidative stress and systemic inflammation, 2.glycocalyx
degradation and shedding, 3.disassembly of intercellular junctions,endothelial
cell death,blood tissue barrier disruption, 4.enhanced leukocyte adhesion and
extravasation, 5.induction of a pro-coagulant and anti-fibrinolytic state. In
addition,chronic diseases impair the mechanisms of endothelial reparation. In
conclusion,sepsis,aging and chronic diseases induce similar features of
endothelial dysfunction. The potential contribution of the pre-existent degree
of endothelial dysfunction to sepsis pathogenesis deserves to be further
investigated
| q-bio.TO |
1807.02289 | Interleaved lattice-based maximin distance designs | We propose a new method to construct maximin distance designs with arbitrary
number of dimensions and points. The proposed designs hold interleaved-layer
structures and are by far the best maximin distance designs in four or more
dimensions. Applicable to distance measures with equal or unequal weights, our
method is useful for emulating computer experiments when a relatively accurate
priori guess on the variable importance is available.
| stat.ME |
1807.02290 | Differentially Private Online Submodular Optimization | In this paper we develop the first algorithms for online submodular
minimization that preserve differential privacy under full information feedback
and bandit feedback. A sequence of $T$ submodular functions over a collection
of $n$ elements arrive online, and at each timestep the algorithm must choose a
subset of $[n]$ before seeing the function. The algorithm incurs a cost equal
to the function evaluated on the chosen set, and seeks to choose a sequence of
sets that achieves low expected regret.
Our first result is in the full information setting, where the algorithm can
observe the entire function after making its decision at each timestep. We give
an algorithm in this setting that is $\epsilon$-differentially private and
achieves expected regret
$\tilde{O}\left(\frac{n^{3/2}\sqrt{T}}{\epsilon}\right)$. This algorithm works
by relaxing submodular function to a convex function using the Lovasz
extension, and then simulating an algorithm for differentially private online
convex optimization.
Our second result is in the bandit setting, where the algorithm can only see
the cost incurred by its chosen set, and does not have access to the entire
function. This setting is significantly more challenging because the algorithm
does not receive enough information to compute the Lovasz extension or its
subgradients. Instead, we construct an unbiased estimate using a single-point
estimation, and then simulate private online convex optimization using this
estimate. Our algorithm using bandit feedback is $\epsilon$-differentially
private and achieves expected regret
$\tilde{O}\left(\frac{n^{3/2}T^{3/4}}{\epsilon}\right)$.
| cs.DS cs.LG stat.ML |
1807.02291 | Sliced Recurrent Neural Networks | Recurrent neural networks have achieved great success in many NLP tasks.
However, they have difficulty in parallelization because of the recurrent
structure, so it takes much time to train RNNs. In this paper, we introduce
sliced recurrent neural networks (SRNNs), which could be parallelized by
slicing the sequences into many subsequences. SRNNs have the ability to obtain
high-level information through multiple layers with few extra parameters. We
prove that the standard RNN is a special case of the SRNN when we use linear
activation functions. Without changing the recurrent units, SRNNs are 136 times
as fast as standard RNNs and could be even faster when we train longer
sequences. Experiments on six largescale sentiment analysis datasets show that
SRNNs achieve better performance than standard RNNs.
| cs.CL |
1807.02292 | Evolution of urban scaling: evidence from Brazil | During the last years, the new science of municipalities has been established
as a fertile quantitative approach to systematically understand the urban
phenomena. One of its main pillars is the proposition that urban systems
display universal scaling behavior regarding socioeconomic, infrastructural and
individual basic services variables. This paper discusses the extension of the
universality proposition by testing it against a broad range of urban metrics
in a developing country urban system. We present an exploration of the scaling
exponents for over 6$ variables for the Brazilian urban system. As Brazilian
municipalities can deviate significantly from urban settlements, urban-like
municipalities were selected based on a systematic density cut-off procedure
and the scaling exponents were estimated for this new subset of municipalities.
To validate our findings we compared the results for overlaying variables with
other studies based on alternative methods. It was found that the analyzed
socioeconomic variables follow a superlinear scaling relationship with the
population size, and most of the infrastructure and individual basic services
variables follow expected sublinear and linear scaling, respectively. However,
some infrastructural and individual basic services variables deviated from
their expected regimes, challenging the universality hypothesis of urban
scaling. We propose that these deviations are a product of top-down
decisions/policies. Our analysis spreads over a time-range of 10 years, what is
not enough to draw conclusive observations, nevertheless we found hints that
the scaling exponent of these variables are evolving towards the expected
scaling regime, indicating that the deviations might be temporally constrained
and that the urban systems might eventually reach the expected scaling regime.
| physics.soc-ph |
1807.02293 | Interpolation theory for Sobolev functions with partially vanishing
trace on irregular open sets | A full interpolation theory for Sobolev functions with smoothness between 0
and 1 and vanishing trace on a part of the boundary of an open set is
established. Geometric assumptions are of mostly measure theoretic nature and
reach beyond Lipschitz regular domains. Previous results were limited to
regular geometric configurations or Hilbertian Sobolev spaces. Sets with porous
boundary and their characteristic multipliers on smoothness spaces play a major
role in the arguments.
| math.CA math.AP |
1807.02294 | Combining SLAM with muti-spectral photometric stereo for real-time dense
3D reconstruction | Obtaining dense 3D reconstrution with low computational cost is one of the
important goals in the field of SLAM. In this paper we propose a dense 3D
reconstruction framework from monocular multispectral video sequences using
jointly semi-dense SLAM and Multispectral Photometric Stereo approaches.
Starting from multispectral video, SALM (a) reconstructs a semi-dense 3D shape
that will be densified;(b) recovers relative sparse depth map that is then fed
as prioris into optimization-based multispectral photometric stereo for a more
accurate dense surface normal recovery;(c)obtains camera pose that is
subsequently used for conversion of view in the process of fusion where we
combine the relative sparse point cloud with the dense surface normal using the
automated cross-scale fusion method proposed in this paper to get a dense point
cloud with subtle texture information. Experiments show that our method can
effectively obtain denser 3D reconstructions.
| cs.CV |
1807.02295 | Modeling the Mechanosensitivity of Fast-Crawling Cells on Cyclically
Stretched Substrates | The mechanosensitivity of cells, which determines how they are able to
respond to mechanical signals received from their environment, is crucial for
the functioning of all biological systems. In experiments, cells placed on
cyclically stretched substrates have been shown to reorient in a direction that
depends not only on the type of cell, but also on the mechanical properties of
the substrate, and the amplitude and rate of stretching. However, the
underlying biochemical and mechanical mechanisms responsible for this
realignment are still not completely understood. In this study, we introduce a
computational model for fast crawling on cyclically stretched substrates that
accounts for the sub-cellular processes responsible for the cell shape and
motility, as well as the coupling to the substrate through the focal adhesion
sites. In particular, we focus on the role of the focal adhesion dynamics, and
show that the reorientation under cyclic stretching is strongly dependent on
the frequency, as has been observed experimentally. Furthermore, we show that
an asymmetry during the loading and unloading phases of the stretching, whether
coming from the response of the cell itself, or from the stretching protocol,
can be used to selectively align the cells in either the parallel or
perpendicular directions.
| physics.bio-ph cond-mat.soft q-bio.CB |
1807.02296 | Low Noise Readout Circuits for Particle and Radiation Sensors | The present thesis follows a three years' work in design, realization and
operation of electronic circuits for the readout of particle and radiation
sensors, carried out in close collaboration with the Istituto Nazionale di
Fisica Nucleare (INFN), sezione di Milano Bicocca. The work was mainly focused
to applications in particle physics experiments which are currently in the
construction phase, or to existing experiments which planned major hardware
upgrades in the next years, involving the design of new front-end circuits. The
circuits developed are in principle applicable also outside the field of pure
science research, for applications in nuclear instrumentation, medical imaging,
security and industrial scanners, and others.
| physics.ins-det |
1807.02297 | Combinatorial Bandits for Incentivizing Agents with Dynamic Preferences | The design of personalized incentives or recommendations to improve user
engagement is gaining prominence as digital platform providers continually
emerge. We propose a multi-armed bandit framework for matching incentives to
users, whose preferences are unknown a priori and evolving dynamically in time,
in a resource constrained environment. We design an algorithm that combines
ideas from three distinct domains: (i) a greedy matching paradigm, (ii) the
upper confidence bound algorithm (UCB) for bandits, and (iii) mixing times from
the theory of Markov chains. For this algorithm, we provide theoretical bounds
on the regret and demonstrate its performance via both synthetic and realistic
(matching supply and demand in a bike-sharing platform) examples.
| cs.LG cs.AI cs.SY stat.ML |
1807.02298 | Fluid mixtures in nanotubes | The aim of the paper is the study of fluid mixtures in nanotubes by the
methods of continuum mechanics. The model starts from a statistical
distribution in mean-field molecular theory and uses a density expansion of
Taylor series. We get a continuous expression of the volume free energy with
density's spatial-derivatives limited at the second order. The nanotubes can be
filled with liquid or vapor according to the chemical characteristics of the
walls and of liquid or vapor mixture-bulks. An example of two-fluid mixture
constituted of water and ethanol inside carbon nanotubes at 20{\textdegree} C
is considered. When diameters are small enough, nanotubes are filled with
liquid-mixture whatever are the liquid or vapor mixture-bulks. The carbon wall
influences the ratio of the fluid components in favor of ethanol. The
fluid-mixture flows across nanotubes can be much more important than classical
ones and if the external bulk is vapor, the flow can be several hundred
thousand times larger than Poiseuille flow.
| physics.flu-dyn cond-mat.soft |
1807.02299 | On the Equilibrium of Query Reformulation and Document Retrieval | In this paper, we study jointly query reformulation and document relevance
estimation, the two essential aspects of information retrieval (IR). Their
interactions are modelled as a two-player strategic game: one player, a query
formulator, taking actions to produce the optimal query, is expected to
maximize its own utility with respect to the relevance estimation of documents
produced by the other player, a retrieval modeler; simultaneously, the
retrieval modeler, taking actions to produce the document relevance scores,
needs to optimize its likelihood from the training data with respect to the
refined query produced by the query formulator. Their equilibrium or equilibria
will be reached when both are the best responses to each other. We derive our
equilibrium theory of IR using normal-form representations: when a standard
relevance feedback algorithm is coupled with a retrieval model, they would
share the same objective function and thus form a partnership game; by
contrast, pseudo relevance feedback pursues a rather different objective than
that of retrieval models, therefore the interaction between them would lead to
a general-sum game (though implicitly collaborative). Our game-theoretical
analyses not only yield useful insights into the two major aspects of IR, but
also offer new practical algorithms for achieving the equilibrium state of
retrieval which have been shown to bring consistent performance improvements in
both text retrieval and item recommendation.
| cs.IR cs.GT |
1807.02300 | Risk Forms: Representation, Disintegration, and Application to Partially
Observable Two-Stage Systems | We introduce the concept of a risk form, which is a real functional of two
arguments: a measurable function on a Polish space and a measure on that space.
We generalize the duality theory and the Kusuoka representation to this
setting. For a risk form acting on a product of Polish spaces, we define
marginal and conditional forms and we prove a disintegration formula, which
represents a risk form as a composition of its marginal and conditional forms.
We apply the proposed approach to two-stage stochastic programming problems
with partial information and decision-dependent observation distribution.
| math.OC |
1807.02301 | Sequential Copying Networks | Copying mechanism shows effectiveness in sequence-to-sequence based neural
network models for text generation tasks, such as abstractive sentence
summarization and question generation. However, existing works on modeling
copying or pointing mechanism only considers single word copying from the
source sentences. In this paper, we propose a novel copying framework, named
Sequential Copying Networks (SeqCopyNet), which not only learns to copy single
words, but also copies sequences from the input sentence. It leverages the
pointer networks to explicitly select a sub-span from the source side to target
side, and integrates this sequential copying mechanism to the generation
process in the encoder-decoder paradigm. Experiments on abstractive sentence
summarization and question generation tasks show that the proposed SeqCopyNet
can copy meaningful spans and outperforms the baseline models.
| cs.CL |
1807.02302 | Asymptotics in Fourier space of self-similar solutions to the modified
Korteweg-de Vries equation | We give the asymptotics of the Fourier transform of self-similar solutions to
the modified Korteweg-de Vries equation, through a fixed point argument in
weighted W^{1,\infty} around a carefully chosen, two term ansatz. Such
knowledge is crucial in the study of stability properties of the self-similar
solutions for the modified Korteweg-de Vries flow. In the defocusing case, the
self-similar profiles are solutions to the Painlev\'e II equation. Although
they were extensively studied in physical space, no result to our knowledge
describe their behavior in Fourier space. We are able to relate the constants
involved in the description in Fourier space with those involved in the
description in physical space.
| math.AP |
1807.02303 | A survey on policy search algorithms for learning robot controllers in a
handful of trials | Most policy search algorithms require thousands of training episodes to find
an effective policy, which is often infeasible with a physical robot. This
survey article focuses on the extreme other end of the spectrum: how can a
robot adapt with only a handful of trials (a dozen) and a few minutes? By
analogy with the word "big-data", we refer to this challenge as "micro-data
reinforcement learning". We show that a first strategy is to leverage prior
knowledge on the policy structure (e.g., dynamic movement primitives), on the
policy parameters (e.g., demonstrations), or on the dynamics (e.g.,
simulators). A second strategy is to create data-driven surrogate models of the
expected reward (e.g., Bayesian optimization) or the dynamical model (e.g.,
model-based policy search), so that the policy optimizer queries the model
instead of the real system. Overall, all successful micro-data algorithms
combine these two strategies by varying the kind of model and prior knowledge.
The current scientific challenges essentially revolve around scaling up to
complex robots (e.g., humanoids), designing generic priors, and optimizing the
computing time.
| cs.RO cs.AI cs.LG stat.ML |
1807.02304 | Broad-Band Negative Refraction via Simultaneous Multi-Electron
Transitions | We analyze different factors which influence the negative refraction in
solids and multi-atom molecules. We find that this negative refraction is
significantly influenced by simultaneous multi-electron transitions with the
same transition frequency and dipole redistribution over different eigenstates.
We show that these simultaneous multi-electron transitions and enhanced
transition dipole broaden the bandwidth of the negative refraction by at least
one order of magnitude. This work provides additional connection between
metamaterials and Mobius strips.
| physics.optics cond-mat.mes-hall physics.atom-ph quant-ph |
1807.02305 | Neural Document Summarization by Jointly Learning to Score and Select
Sentences | Sentence scoring and sentence selection are two main steps in extractive
document summarization systems. However, previous works treat them as two
separated subtasks. In this paper, we present a novel end-to-end neural network
framework for extractive document summarization by jointly learning to score
and select sentences. It first reads the document sentences with a hierarchical
encoder to obtain the representation of sentences. Then it builds the output
summary by extracting sentences one by one. Different from previous methods,
our approach integrates the selection strategy into the scoring model, which
directly predicts the relative importance given previously selected sentences.
Experiments on the CNN/Daily Mail dataset show that the proposed framework
significantly outperforms the state-of-the-art extractive summarization models.
| cs.CL |
1807.02306 | A moment approach for entropy solutions to nonlinear hyperbolic PDEs | We propose to solve polynomial hyperbolic partial differential equations
(PDEs) with convex optimization. This approach is based on a very weak notion
of solution of the nonlinear equation, namely the measure-valued (mv) solution,
satisfying a linear equation in the space of Borel measures. The aim of this
paper is, first, to provide the conditions that ensure the equivalence between
the two formulations and, second, to introduce a method which approximates the
infinite-dimensional linear problem by a hierarchy of convex,
finite-dimensional, semidefinite programming problems. This result is then
illustrated on the celebrated Burgers equation. We also compare our results
with an existing numerical scheme, namely the Godunov scheme.
| math.AP math.OC |
1807.02307 | z-TORCH: An Automated NFV Orchestration and Monitoring Solution | Autonomous management and orchestration (MANO) of virtualized resources and
services, especially in large-scale Network Function Virtualization (NFV)
environments, is a big challenge owing to the stringent delay and performance
requirements expected of a variety of network services. The
Quality-of-Decisions (QoD) of a Management and Orchestration (MANO) system
depends on the quality and timeliness of the information received from the
underlying monitoring system. The data generated by monitoring systems is a
significant contributor to the network and processing load of MANO systems,
impacting thus their performance. This raises a unique challenge: how to
jointly optimize the QoD of MANO systems while at the same minimizing their
monitoring loads at runtime? This is the main focus of this paper.
In this context, we propose a novel automated NFV orchestration solution,
namely z-TORCH (zero Touch Orchestration) that jointly optimizes the
orchestration and monitoring processes by exploiting machine-learning-based
techniques. The objective is to enhance the QoD of MANO systems achieving a
near-optimal placement of Virtualized Network Functions (VNFs) at minimum
monitoring costs.
| cs.NI |
1807.02308 | Vertex partition of hypergraphs and maximum degenerate subhypergraphs | In 2007 Matamala proved that if $G$ is a simple graph with maximum degree
$\Delta\geq 3$ not containing $K_{\Delta +1}$ as a subgraph and $s, t$ are
positive integers such that $s+t \geq \Delta$, then the vertex set of $G$
admits a partition $(S,T)$ such that $G[S]$ is a maximum order
$(s-1)$-degenerate subgraph of $G$ and $G[T]$ is a $(t-1)$-degenerate subgraph
of $G$. This result extended earlier results obtained by Borodin, by Bollob\'as
and Manvel, by Catlin, by Gerencs\'{e}r and by Catlin and Lai. In this paper we
prove a hypergraph version of this result and extend it to variable degeneracy
and to partitions into more than two parts, thereby extending a result by
Borodin, Kostochka, and Toft.
| math.CO |
1807.02309 | Formation of globular clusters with multiple stellar populations from
massive gas clumps in high-z gas-rich dwarf galaxies | One of the currently favored scenarios for the formation of globular clusters
(GCs) with multiple stellar populations is that an initial massive stellar
system forms (`first generation', FG), subsequently giving rise to gaseous
ejecta which is converted into a second generation (SG) of stars to form a GC.
We investigate, for the first time, the sequential formation processes of both
FG and SG stars from star-forming massive gas clumps in gas-rich dwarf disk
galaxies. We adopt a novel approach to resolve the two-stage formation of GCs
in hydrodynamical simulations of dwarf galaxies.In the new simulations, new gas
particles that are much less massive than their parent star particle are
generated around each new star particle when the new star enters into the
asymptotic giant branch (AGB) phase. Furthermore, much finer maximum time step
width (<10^5 yr) and smaller softening length (<2 pc) are adopted for such AGB
gas particles to properly resolve the ejection of gas from AGB stars and AGB
feedback effects. Therefore, secondary star formation from AGB ejecta can be
properly investigated in galaxy-scale simulations. An FG stellar system can
first form from a massive gas clump developing due to gravitational instability
within its host gas-rich dwarf galaxy. Initially the FG stellar system is not a
single massive cluster, but instead is composed of several irregular stellar
clumps (or filaments) with a total mass larger than 10^6 Msun. While the FG
system is dynamically relaxing, gaseous ejecta from AGB stars can be
gravitationally trapped by the FG system and subsequently converted into new
stars to form a compact SG stellar system within the FG system. Interestingly,
about 40% of AGB ejecta is from stars that do not belong to the FG system
(`external gas accretion'). The mass-density relation for SG stellar systems
can be approximated as rho_SG ~ M_SG^1.5.
| astro-ph.GA astro-ph.SR |
1807.02310 | Relativistic and Newton-Cartan Particle in de Broglie-Bohm Theory | This paper is devoted to the analysis of massive particle in general and
Newton-Cartan Background in de Broglie-Bohm Theory. We find classical and
quantum version of Hamilton-Jacobi equations and find their relations to wave
equations. We also discuss fundamental difference between classical and quantum
description of these two systems.
| quant-ph |
1807.02311 | Energy and Latency Control for Edge Computing in Dense V2X Networks | This study focuses on edge computing in dense millimeter wave
vehicle-to-everything (V2X) networks. A control problem is formulated to
minimize the energy consumption under delay constraint resulting from vehicle
mobility. A tractable algorithm is proposed to solve this problem by optimizing
the offloaded computing tasks and transmit power of vehicles and road side
units. The proposed dynamic solution can well coordinate the interference
without requiring global channel state information, and makes a tradeoff
between energy consumption and task computing latency.
| cs.NI |
1807.02312 | Exponential Convergence for Functional SDEs with H\"older Continuous
Drift | Applying Zvonkin's transform, the exponential convergence in Wasserstein
distance for a class of functional SDEs with H\"older continuous drift is
obtained. This combining with log-Harnack inequality implies the same
convergence in the sense of entropy, which also yields the convergence in total
variation norm by Pinsker's inequality.
| math.PR |
1807.02313 | Ramsey goodness of cycles | Given a pair of graphs $G$ and $H$, the Ramsey number $R(G,H)$ is the
smallest $N$ such that every red-blue coloring of the edges of the complete
graph $K_N$ contains a red copy of $G$ or a blue copy of $H$. If a graph $G$ is
connected, it is well known and easy to show that $R(G,H) \geq
(|G|-1)(\chi(H)-1)+\sigma(H)$, where $\chi(H)$ is the chromatic number of $H$
and $\sigma(H)$ is the size of the smallest color class in a $\chi(H)$-coloring
of $H$. A graph $G$ is called $H$-good if $R(G,H)=
(|G|-1)(\chi(H)-1)+\sigma(H)$. The notion of Ramsey goodness was introduced by
Burr and Erd\H{o}s in 1983 and has been extensively studied since then.
In this paper we show that if $n\geq 10^{60}|H|$ and $\sigma(H)\geq
\chi(H)^{22}$ then the $n$-vertex cycle $C_n$ is $H$-good. For graphs $H$ with
high $\chi(H)$ and $\sigma(H)$, this proves in a strong form a conjecture of
Allen, Brightwell, and Skokan.
| math.CO |
1807.02314 | JUMPER: Learning When to Make Classification Decisions in Reading | In early years, text classification is typically accomplished by
feature-based machine learning models; recently, deep neural networks, as a
powerful learning machine, make it possible to work with raw input as the text
stands. However, exiting end-to-end neural networks lack explicit
interpretation of the prediction. In this paper, we propose a novel framework,
JUMPER, inspired by the cognitive process of text reading, that models text
classification as a sequential decision process. Basically, JUMPER is a neural
system that scans a piece of text sequentially and makes classification
decisions at the time it wishes. Both the classification result and when to
make the classification are part of the decision process, which is controlled
by a policy network and trained with reinforcement learning. Experimental
results show that a properly trained JUMPER has the following properties: (1)
It can make decisions whenever the evidence is enough, therefore reducing total
text reading by 30-40% and often finding the key rationale of prediction. (2)
It achieves classification accuracy better than or comparable to
state-of-the-art models in several benchmark and industrial datasets.
| cs.IR cs.AI cs.CL cs.LG |
1807.02315 | On the use of circulant matrices for the stability analysis of recent
weakly compressible SPH methods | In this study, a linear stability analysis is performed for different Weakly
Compressible Smooth Particle Hydrodynamics (WCSPH) methods on a 1D periodic
domain describing an incompressible base flow. The perturbation equation can be
vectorized and written as an ordinary differential equation where the
coefficients are circulant matrices. The diagonalization of the system is
equivalent to apply a spatial discrete Fourier transform. This leads to
stability conditions expressed by the discrete Fourier transform of the first
and second derivatives of the kernel. Although spurious modes are highlighted,
no tensile nor pairing instabilities are found in the present study, suggesting
that the perturbations of the stresses are always damped if the base flow is
incompressible. The perturbations equation is solved in the Laplace domain,
allowing to derive an analytical solution of the transient state. Also, it is
demonstrated analytically that a positive background pressure combined with the
uncorrected gradient operator leads to a reordering of the particle lattice. It
is also shown that above a critical value, the background pressure leads to
instabilities. Finally, the dispersion curves for inviscid and viscous flows
are plotted for different WCSPH methods and compared to the continuum solution.
It is observed that a background pressure equal to $\rho c^2$ gives the best
fidelity to predict the propagation of a sound wave. When viscosity effects are
taken into account, the damping of pressure fluctuations show the best
agreement with the continuum for $p_{back} \sim \rho c^2/2$.
| physics.comp-ph |
1807.02316 | The maximal flow from a compact convex subset to infinity in first
passage percolation on Z^d | We consider the standard first passage percolation model on Z^d with a
distribution G on R+ that admits an exponential moment. We study the maximal
flow between a compact convex subset A of R^d and infinity. The study of
maximal flow is associated with the study of sets of edges of minimal capacity
that cut A from infinity. We prove that the rescaled maximal flow between nA
and infinity $\phi$(nA)/n^ (d--1) almost surely converges towards a
deterministic constant depending on A. This constant corresponds to the
capacity of the boundary $\partial$A of A and is the integral of a
deterministic function over $\partial$A. This result was shown in dimension 2
and conjectured for higher dimensions by Garet in [6].
| math.PR |
1807.02317 | Coordinate-free study of Finsler spaces of $H_{p}$-scalar curvature | The aim of the present paper is to provide an \emph{intrinsic} investigation
of special Finsler spaces of $H_{p}$-scalar curvature and of $H_{p}\,$-constant
curvature. Characterizations of such spaces are shown. Sufficient condition for
Finsler space of $H_{p}$-scalar curvature to be of perpendicular scalar
curvature is investigated. Necessary and sufficient condition under which a
Finsler space of scalar curvature turns into a Finsler space of $H_{p}$-scalar
curvature is given. Further, certain conditions under which a Finsler manifolds
of $H_{p}$-scalar curvature and of scalar curvature reduce to a Finsler
manifold of $H_{p}$-constant curvature are obtained. Finally, various examples
are studied and constructed.
| math.DG |
1807.02318 | A study on finding a buried obstacle in a layered medium having the
influence of the total reflection phenomena via the time domain enclosure
method | An inverse obstacle problem for the wave governed by the wave equation in a
two layered medium is considered under the framework of the time domain
enclosure method. The wave is generated by an initial data supported on a
closed ball in the upper half-space, and observed on the same ball over a
finite time interval. The unknown obstacle is penetrable and embedded in the
lower half-space. It is assumed that the propagation speed of the wave in the
upper half-space is greater than that of the wave in the lower half-space,
which is excluded in the previous study: Ikehata and Kawashita (2018) to
appear, Inverse Problems and Imaging. In the present case, when the reflected
waves from the obstacle enter the upper layer, the total reflection phenomena
occur, which give singularities to the integral representation of the
fundamental solution for the reduced transmission problem in the background
medium. This fact makes the problem more complicated. However, it is shown that
these waves do not have any influence on the leading profile of the indicator
function of the time domain enclosure method.
| math.AP |
1807.02319 | Approximately Reachable Directions for Piecewise Linear Switched Systems | This paper deals with some reachability issues for piecewise linear switched
systems with time-dependent coefficients and multiplicative noise. Namely, it
aims at characterizing data that are almost reachable at some fixed time T > 0
(belong to the closure of the reachable set in a suitable L 2-sense). From a
mathematical point of view, this provides the missing link between approximate
controllability towards 0 and approximate controllability towards given
targets. The methods rely on linear-quadratic control and Riccati equations.
The main novelty is that we consider an LQ problem with controlled backward
stochastic dynamics and, since the coefficients are not deterministic (unlike
some of the cited references), neither is the backward stochastic Riccati
equation. Existence and uniqueness of the solution of such equations rely on
structure arguments (inspired by [7]). Besides solvability, Riccati
representation of the resulting control problem is provided as is the synthesis
of optimal (non-Markovian) control. Several examples are discussed.
| math.OC math.PR |
1807.02320 | Weak periodic solutions and numerical case studies of the
Fornberg-Whitham equation | Spatially periodic solutions of the Fornberg-Whitham equation are studied to
illustrate the mechanism of wave breaking and the formation of shocks for a
large class of initial data. We show that these solutions can be considered to
be weak solutions satisfying the entropy condition. By numerical experiments,
we show that the breaking waves become shock-wave type in the time evolution.
| math.AP |
1807.02321 | Limits of topological protection under local periodic driving | The bulk-edge correspondence guarantees that the interface between two
topologically distinct insulators supports at least one topological edge state
that is robust against static perturbations. Here, we address the question of
how dynamic perturbations of the interface affect the robustness of edge
states. We illuminate the limits of topological protection for Floquet systems
in the special case of a static bulk. We use two independent dynamic quantum
simulators based on coupled plasmonic and dielectric photonic waveguides to
implement the topological Su-Schriefer-Heeger model with convenient control of
the full space- and time-dependence of the Hamiltonian. Local time periodic
driving of the interface does not change the topological character of the
system but nonetheless leads to dramatic changes of the edge state, which
becomes rapidly depopulated in a certain frequency window. A theoretical
Floquet analysis shows that the coupling of Floquet replicas to the bulk bands
is responsible for this effect. Additionally, we determine the depopulation
rate of the edge state and compare it to numerical simulations.
| physics.optics quant-ph |
1807.02322 | Memory Augmented Policy Optimization for Program Synthesis and Semantic
Parsing | We present Memory Augmented Policy Optimization (MAPO), a simple and novel
way to leverage a memory buffer of promising trajectories to reduce the
variance of policy gradient estimate. MAPO is applicable to deterministic
environments with discrete actions, such as structured prediction and
combinatorial optimization tasks. We express the expected return objective as a
weighted sum of two terms: an expectation over the high-reward trajectories
inside the memory buffer, and a separate expectation over trajectories outside
the buffer. To make an efficient algorithm of MAPO, we propose: (1) memory
weight clipping to accelerate and stabilize training; (2) systematic
exploration to discover high-reward trajectories; (3) distributed sampling from
inside and outside of the memory buffer to scale up training. MAPO improves the
sample efficiency and robustness of policy gradient, especially on tasks with
sparse rewards. We evaluate MAPO on weakly supervised program synthesis from
natural language (semantic parsing). On the WikiTableQuestions benchmark, we
improve the state-of-the-art by 2.6%, achieving an accuracy of 46.3%. On the
WikiSQL benchmark, MAPO achieves an accuracy of 74.9% with only weak
supervision, outperforming several strong baselines with full supervision. Our
source code is available at
https://github.com/crazydonkey200/neural-symbolic-machines
| cs.LG cs.AI cs.CL stat.ML |
1807.02323 | Optimal Sensor Data Fusion Architecture for Object Detection in Adverse
Weather Conditions | A good and robust sensor data fusion in diverse weather conditions is a quite
challenging task. There are several fusion architectures in the literature,
e.g. the sensor data can be fused right at the beginning (Early Fusion), or
they can be first processed separately and then concatenated later (Late
Fusion). In this work, different fusion architectures are compared and
evaluated by means of object detection tasks, in which the goal is to recognize
and localize predefined objects in a stream of data. Usually, state-of-the-art
object detectors based on neural networks are highly optimized for good weather
conditions, since the well-known benchmarks only consist of sensor data
recorded in optimal weather conditions. Therefore, the performance of these
approaches decreases enormously or even fails in adverse weather conditions. In
this work, different sensor fusion architectures are compared for good and
adverse weather conditions for finding the optimal fusion architecture for
diverse weather situations. A new training strategy is also introduced such
that the performance of the object detector is greatly enhanced in adverse
weather scenarios or if a sensor fails. Furthermore, the paper responds to the
question if the detection accuracy can be increased further by providing the
neural network with a-priori knowledge such as the spatial calibration of the
sensors.
| cs.CV |
1807.02324 | Sum-Product Networks for Sequence Labeling | We consider higher-order linear-chain conditional random fields (HO-LC-CRFs)
for sequence modelling, and use sum-product networks (SPNs) for representing
higher-order input- and output-dependent factors. SPNs are a recently
introduced class of deep models for which exact and efficient inference can be
performed. By combining HO-LC-CRFs with SPNs, expressive models over both the
output labels and the hidden variables are instantiated while still enabling
efficient exact inference. Furthermore, the use of higher-order factors allows
us to capture relations of multiple input segments and multiple output labels
as often present in real-world data. These relations can not be modelled by the
commonly used first-order models and higher-order models with local factors
including only a single output label. We demonstrate the effectiveness of our
proposed models for sequence labeling. In extensive experiments, we outperform
other state-of-the-art methods in optical character recognition and achieve
competitive results in phone classification.
| cs.LG stat.ML |
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