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16,401 | Spitzer Secondary Eclipses of Qatar-1b | Previous secondary eclipse observations of the hot Jupiter Qatar-1b in the Ks
band suggest that it may have an unusually high day side temperature,
indicative of minimal heat redistribution. There have also been indications
that the orbit may be slightly eccentric, possibly forced by another planet in
the system. We investigate the day side temperature and orbital eccentricity
using secondary eclipse observations with Spitzer. We observed the secondary
eclipse with Spitzer/IRAC in subarray mode, in both 3.6 and 4.5 micron
wavelengths. We used pixel-level decorrelation to correct for Spitzer's
intra-pixel sensitivity variations and thereby obtain accurate eclipse depths
and central phases. Our 3.6 micron eclipse depth is 0.149 +/- 0.051% and the
4.5 micron depth is 0.273 +/- 0.049%. Fitting a blackbody planet to our data
and two recent Ks band eclipse depths indicates a brightness temperature of
1506 +/- 71K. Comparison to model atmospheres for the planet indicates that its
degree of longitudinal heat redistribution is intermediate between fully
uniform and day side only. The day side temperature of the planet is unlikely
to be as high (1885K) as indicated by the ground-based eclipses in the Ks band,
unless the planet's emergent spectrum deviates strongly from model atmosphere
predictions. The average central phase for our Spitzer eclipses is 0.4984 +/-
0.0017, yielding e cos(omega) = -0.0028 +/- 0.0027. Our results are consistent
with a circular orbit, and we constrain e cos(omega) much more strongly than
has been possible with previous observations.
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16,402 | Dielectric response of Anderson and pseudogapped insulators | Using a combination of analytic and numerical methods, we study the
polarizability of a (non-interacting) Anderson insulator in one, two, and three
dimensions and demonstrate that, in a wide range of parameters, it scales
proportionally to the square of the localization length, contrary to earlier
claims based on the effective-medium approximation. We further analyze the
effect of electron-electron interactions on the dielectric constant in
quasi-1D, quasi-2D and 3D materials with large localization length, including
both Coulomb repulsion and phonon-mediated attraction. The phonon-mediated
attraction (in the pseudogapped state on the insulating side of the
Superconductor-Insulator Transition) produces a correction to the dielectric
constant, which may be detected from a linear response of a dielectric constant
to an external magnetic field.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,403 | Epidemiological modeling of the 2005 French riots: a spreading wave and the role of contagion | As a large-scale instance of dramatic collective behaviour, the 2005 French
riots started in a poor suburb of Paris, then spread in all of France, lasting
about three weeks. Remarkably, although there were no displacements of rioters,
the riot activity did travel. Access to daily national police data has allowed
us to explore the dynamics of riot propagation. Here we show that an
epidemic-like model, with just a few parameters and a single sociological
variable characterizing neighbourhood deprivation, accounts quantitatively for
the full spatio-temporal dynamics of the riots. This is the first time that
such data-driven modelling involving contagion both within and between cities
(through geographic proximity or media) at the scale of a country, and on a
daily basis, is performed. Moreover, we give a precise mathematical
characterization to the expression "wave of riots", and provide a visualization
of the propagation around Paris, exhibiting the wave in a way not described
before. The remarkable agreement between model and data demonstrates that
geographic proximity played a major role in the propagation, even though
information was readily available everywhere through media. Finally, we argue
that our approach gives a general framework for the modelling of the dynamics
of spontaneous collective uprisings.
| 1 | 1 | 0 | 0 | 0 | 0 |
16,404 | Option Pricing in Illiquid Markets with Jumps | The classical linear Black--Scholes model for pricing derivative securities
is a popular model in financial industry. It relies on several restrictive
assumptions such as completeness, and frictionless of the market as well as the
assumption on the underlying asset price dynamics following a geometric
Brownian motion. The main purpose of this paper is to generalize the classical
Black--Scholes model for pricing derivative securities by taking into account
feedback effects due to an influence of a large trader on the underlying asset
price dynamics exhibiting random jumps. The assumption that an investor can
trade large amounts of assets without affecting the underlying asset price
itself is usually not satisfied, especially in illiquid markets. We generalize
the Frey--Stremme nonlinear option pricing model for the case the underlying
asset follows a Levy stochastic process with jumps. We derive and analyze a
fully nonlinear parabolic partial-integro differential equation for the price
of the option contract. We propose a semi-implicit numerical discretization
scheme and perform various numerical experiments showing influence of a large
trader and intensity of jumps on the option price.
| 0 | 0 | 0 | 0 | 0 | 1 |
16,405 | A Knowledge-Based Analysis of the Blockchain Protocol | At the heart of the Bitcoin is a blockchain protocol, a protocol for
achieving consensus on a public ledger that records bitcoin transactions. To
the extent that a blockchain protocol is used for applications such as contract
signing and making certain transactions (such as house sales) public, we need
to understand what guarantees the protocol gives us in terms of agents'
knowledge. Here, we provide a complete characterization of agent's knowledge
when running a blockchain protocol using a variant of common knowledge that
takes into account the fact that agents can enter and leave the system, it is
not known which agents are in fact following the protocol (some agents may want
to deviate if they can gain by doing so), and the fact that the guarantees
provided by blockchain protocols are probabilistic. We then consider some
scenarios involving contracts and show that this level of knowledge suffices
for some scenarios, but not others.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,406 | Convergence Analysis of Optimization Algorithms | The regret bound of an optimization algorithms is one of the basic criteria
for evaluating the performance of the given algorithm. By inspecting the
differences between the regret bounds of traditional algorithms and adaptive
one, we provide a guide for choosing an optimizer with respect to the given
data set and the loss function. For analysis, we assume that the loss function
is convex and its gradient is Lipschitz continuous.
| 1 | 0 | 1 | 1 | 0 | 0 |
16,407 | On the combinatorics of the 2-class classification problem | A set of points $X = X_B \cup X_R \subseteq \mathbb{R}^d$ is linearly
separable if the convex hulls of $X_B$ and $X_R$ are disjoint, hence there
exists a hyperplane separating $X_B$ from $X_R$. Such a hyperplane provides a
method for classifying new points, according to which side of the hyperplane
the new points lie. When such a linear separation is not possible, it may still
be possible to partition $X_B$ and $X_R$ into prespecified numbers of groups,
in such a way that every group from $X_B$ is linearly separable from every
group from $X_R$. We may also discard some points as outliers, and seek to
minimize the number of outliers necessary to find such a partition. Based on
these ideas, Bertsimas and Shioda proposed the classification and regression by
integer optimization (CRIO) method in 2007. In this work we explore the integer
programming aspects of the classification part of CRIO, in particular
theoretical properties of the associated formulation. We are able to find
facet-inducing inequalities coming from the stable set polytope, hence showing
that this classification problem has exploitable combinatorial properties.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,408 | Laboratory evidence of dynamo amplification of magnetic fields in a turbulent plasma | Magnetic fields are ubiquitous in the Universe. Extragalactic disks, halos
and clusters have consistently been shown, via diffuse radio-synchrotron
emission and Faraday rotation measurements, to exhibit magnetic field strengths
ranging from a few nG to tens of $\mu$G. The energy density of these fields is
typically comparable to the energy density of the fluid motions of the plasma
in which they are embedded, making magnetic fields essential players in the
dynamics of the luminous matter. The standard theoretical model for the origin
of these strong magnetic fields is through the amplification of tiny seed
fields via turbulent dynamo to the level consistent with current observations.
Here we demonstrate, using laser-produced colliding plasma flows, that
turbulence is indeed capable of rapidly amplifying seed fields to near
equipartition with the turbulent fluid motions. These results support the
notion that turbulent dynamo is a viable mechanism responsible for the observed
present-day magnetization of the Universe.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,409 | Radiating Electron Interaction with Multiple Colliding Electromagnetic Waves: Random Walk Trajectories, Levy Flights, Limit Circles, and Attractors (Survey of the Structurally Determinate Patterns) | The multiple colliding laser pulse concept formulated in Ref. [1] is
beneficial for achieving an extremely high amplitude of coherent
electromagnetic field. Since the topology of electric and magnetic fields
oscillating in time of multiple colliding laser pulses is far from trivial and
the radiation friction effects are significant in the high field limit, the
dynamics of charged particles interacting with the multiple colliding laser
pulses demonstrates remarkable features corresponding to random walk
trajectories, limit circles, attractors, regular patterns and Levy flights.
Under extremely high intensity conditions the nonlinear dissipation mechanism
stabilizes the particle motion resulting in the charged particle trajectory
being located within narrow regions and in the occurrence of a new class of
regular patterns made by the particle ensembles.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,410 | Towards End-to-end Text Spotting with Convolutional Recurrent Neural Networks | In this work, we jointly address the problem of text detection and
recognition in natural scene images based on convolutional recurrent neural
networks. We propose a unified network that simultaneously localizes and
recognizes text with a single forward pass, avoiding intermediate processes
like image cropping and feature re-calculation, word separation, or character
grouping. In contrast to existing approaches that consider text detection and
recognition as two distinct tasks and tackle them one by one, the proposed
framework settles these two tasks concurrently. The whole framework can be
trained end-to-end, requiring only images, the ground-truth bounding boxes and
text labels. Through end-to-end training, the learned features can be more
informative, which improves the overall performance. The convolutional features
are calculated only once and shared by both detection and recognition, which
saves processing time. Our proposed method has achieved competitive performance
on several benchmark datasets.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,411 | Evidence for OH or H2O on the surface of 433 Eros and 1036 Ganymed | Water and hydroxyl, once thought to be found only in the primitive airless
bodies that formed beyond roughly 2.5-3 AU, have recently been detected on the
Moon and Vesta, which both have surfaces dominated by evolved, non-primitive
compositions. In both these cases, the water/OH is thought to be exogenic,
either brought in via impacts with comets or hydrated asteroids or created via
solar wind interactions with silicates in the regolith or both. Such exogenic
processes should also be occurring on other airless body surfaces. To test this
hypothesis, we used the NASA Infrared Telescope Facility (IRTF) to measure
reflectance spectra (2.0 to 4.1 {\mu}m) of two large near-Earth asteroids
(NEAs) with compositions generally interpreted as anhydrous: 433 Eros and 1036
Ganymed. OH is detected on both of these bodies in the form of absorption
features near 3 {\mu}m. The spectra contain a component of thermal emission at
longer wavelengths, from which we estimate thermal of 167+/- 98 J m-2s-1/2K-1
for Eros (consistent with previous estimates) and 214+/- 80 J m-2s-1/2K-1 for
Ganymed, the first reported measurement of thermal inertia for this object.
These observations demonstrate that processes responsible for water/OH creation
on large airless bodies also act on much smaller bodies.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,412 | Jump Locations of Jump-Diffusion Processes with State-Dependent Rates | We propose a general framework for studying jump-diffusion systems driven by
both Gaussian noise and a jump process with state-dependent intensity. Of
particular natural interest are the jump locations: the system evaluated at the
jump times. However, the state-dependence of the jump rate provides direct
coupling between the diffusion and jump components, making disentangling the
two to study individually difficult. We provide an iterative map formulation of
the sequence of distributions of jump locations. Computation of these
distributions allows for the extraction of the interjump time statistics. These
quantities reveal a relationship between the long-time distribution of jump
location and the stationary density of the full process. We provide a few
examples to demonstrate the analytical and numerical tools stemming from the
results proposed in the paper, including an application that shows a
non-monotonic dependence on the strength of diffusion.
| 0 | 1 | 1 | 0 | 0 | 0 |
16,413 | Dust evolution with active galactic nucleus feedback in elliptical galaxies | We have recently suggested that dust growth in the cold gas phase dominates
the dust abundance in elliptical galaxies while dust is efficiently destroyed
in the hot X-ray emitting plasma (hot gas). In order to understand the dust
evolution in elliptical galaxies, we construct a simple model that includes
dust growth in the cold gas and dust destruction in the hot gas. We also take
into account the effect of mass exchange between these two gas components
induced by active galactic nucleus (AGN) feedback. We survey reasonable ranges
of the relevant parameters in the model and find that AGN feedback cycles
actually produce a variety in cold gas mass and dust-to-gas ratio. By comparing
with an observational sample of nearby elliptical galaxies, we find that,
although the dust-to-gas ratio varies by an order of magnitude in our model,
the entire range of the observed dust-to-gas ratios is difficult to be
reproduced under a single parameter set. Variation of the dust growth
efficiency is the most probable solution to explain the large variety in
dust-to-gas ratio of the observational sample. Therefore, dust growth can play
a central role in creating the variation in dust-to-gas ratio through the AGN
feedback cycle and through the variation in dust growth efficiency.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,414 | Generative-Discriminative Variational Model for Visual Recognition | The paradigm shift from shallow classifiers with hand-crafted features to
end-to-end trainable deep learning models has shown significant improvements on
supervised learning tasks. Despite the promising power of deep neural networks
(DNN), how to alleviate overfitting during training has been a research topic
of interest. In this paper, we present a Generative-Discriminative Variational
Model (GDVM) for visual classification, in which we introduce a latent variable
inferred from inputs for exhibiting generative abilities towards prediction. In
other words, our GDVM casts the supervised learning task as a generative
learning process, with data discrimination to be jointly exploited for improved
classification. In our experiments, we consider the tasks of multi-class
classification, multi-label classification, and zero-shot learning. We show
that our GDVM performs favorably against the baselines or recent generative DNN
models.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,415 | PCA-Initialized Deep Neural Networks Applied To Document Image Analysis | In this paper, we present a novel approach for initializing deep neural
networks, i.e., by turning PCA into neural layers. Usually, the initialization
of the weights of a deep neural network is done in one of the three following
ways: 1) with random values, 2) layer-wise, usually as Deep Belief Network or
as auto-encoder, and 3) re-use of layers from another network (transfer
learning). Therefore, typically, many training epochs are needed before
meaningful weights are learned, or a rather similar dataset is required for
seeding a fine-tuning of transfer learning. In this paper, we describe how to
turn a PCA into an auto-encoder, by generating an encoder layer of the PCA
parameters and furthermore adding a decoding layer. We analyze the
initialization technique on real documents. First, we show that a PCA-based
initialization is quick and leads to a very stable initialization. Furthermore,
for the task of layout analysis we investigate the effectiveness of PCA-based
initialization and show that it outperforms state-of-the-art random weight
initialization methods.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,416 | On a frame theoretic measure of quality of LTI systems | It is of practical significance to define the notion of a measure of quality
of a control system, i.e., a quantitative extension of the classical notion of
controllability. In this article we demonstrate that the three standard
measures of quality involving the trace, minimum eigenvalue, and the
determinant of the controllability grammian achieve their optimum values when
the columns of the controllability matrix from a tight frame. Motivated by
this, and in view of some recent developments in frame theoretic signal
processing, we provide a measure of quality for LTI systems based on a measure
of tightness of the columns of the reachability matrix .
| 1 | 0 | 1 | 0 | 0 | 0 |
16,417 | Fuzzy Clustering Data Given on the Ordinal Scale Based on Membership and Likelihood Functions Sharing | A task of clustering data given in the ordinal scale under conditions of
overlapping clusters has been considered. It's proposed to use an approach
based on memberhsip and likelihood functions sharing. A number of performed
experiments proved effectiveness of the proposed method. The proposed method is
characterized by robustness to outliers due to a way of ordering values while
constructing membership functions.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,418 | The ALICE O2 common driver for the C-RORC and CRU read-out cards | ALICE (A Large Ion Collider Experiment) is the heavy-ion detector designed to
study the strongly interacting state of matter realized in relativistic
heavy-ion collisions at the CERN Large Hadron Collider (LHC). A major upgrade
of the experiment is planned during the 2019-2020 long shutdown. In order to
cope with a data rate 100 times higher than during LHC Run 1 and with the
continuous read-out of the Time Projection Chamber (TPC), it is necessary to
upgrade the Online and Offline Computing to a new common system called O2 . The
O2 read- out chain will use commodity x86 Linux servers equipped with custom
PCIe FPGA-based read- out cards. This paper discusses the driver architecture
for the cards that will be used in O2 : the PCIe v2 x8, Xilinx Virtex 6 based
C-RORC (Common Readout Receiver Card) and the PCIe v3 x16, Intel Arria 10 based
CRU (Common Readout Unit). Access to the PCIe cards is provided via three
layers of software. Firstly, the low-level PCIe (PCI Express) layer responsible
for the userspace interface for low-level operations such as memory mapping the
PCIe BAR (Base Address Registers) and creating scatter-gather lists, which is
provided by the PDA (Portable Driver Architecture) library developed by the
Frankfurt Institute for Advanced Studies (FIAS). Above that sits our userspace
driver which implements synchronization, controls the read-out card -- e.g.
resetting and configuring the card, providing it with bus addresses to transfer
data to and checking for data arrival -- and presents a uniform, high-level C++
interface that abstracts over the differences between the C-RORC and CRU. This
interface -- of which direct usage is principally intended for high-performance
read-out processes -- allows users to configure and use the various aspects of
the read-out cards, such as configuration, DMA transfers and commands to the
front-end. [...]
| 1 | 1 | 0 | 0 | 0 | 0 |
16,419 | Adapting control policies from simulation to reality using a pairwise loss | This paper proposes an approach to domain transfer based on a pairwise loss
function that helps transfer control policies learned in simulation onto a real
robot. We explore the idea in the context of a 'category level' manipulation
task where a control policy is learned that enables a robot to perform a mating
task involving novel objects. We explore the case where depth images are used
as the main form of sensor input. Our experimental results demonstrate that
proposed method consistently outperforms baseline methods that train only in
simulation or that combine real and simulated data in a naive way.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,420 | Evidence against a supervoid causing the CMB Cold Spot | We report the results of the 2dF-VST ATLAS Cold Spot galaxy redshift survey
(2CSz) based on imaging from VST ATLAS and spectroscopy from 2dF AAOmega over
the core of the CMB Cold Spot. We sparsely surveyed the inner 5$^{\circ}$
radius of the Cold Spot to a limit of $i_{AB} \le 19.2$, sampling $\sim7000$
galaxies at $z<0.4$. We have found voids at $z=$ 0.14, 0.26 and 0.30 but they
are interspersed with small over-densities and the scale of these voids is
insufficient to explain the Cold Spot through the $\Lambda$CDM ISW effect.
Combining with previous data out to $z\sim1$, we conclude that the CMB Cold
Spot could not have been imprinted by a void confined to the inner core of the
Cold Spot. Additionally we find that our 'control' field GAMA G23 shows a
similarity in its galaxy redshift distribution to the Cold Spot. Since the GAMA
G23 line-of-sight shows no evidence of a CMB temperature decrement we conclude
that the Cold Spot may have a primordial origin rather than being due to
line-of-sight effects.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,421 | How to Produce an Arbitrarily Small Tensor to Scalar Ratio | We construct a toy a model which demonstrates that large field single scalar
inflation can produce an arbitrarily small scalar to tensor ratio in the window
of e-foldings recoverable from CMB experiments. This is done by generalizing
the $\alpha$-attractor models to allow the potential to approach a constant as
rapidly as we desire for super-planckian field values. This implies that a
non-detection of r alone can never rule out entirely the theory of large field
inflation.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,422 | Meta Learning Shared Hierarchies | We develop a metalearning approach for learning hierarchically structured
policies, improving sample efficiency on unseen tasks through the use of shared
primitives---policies that are executed for large numbers of timesteps.
Specifically, a set of primitives are shared within a distribution of tasks,
and are switched between by task-specific policies. We provide a concrete
metric for measuring the strength of such hierarchies, leading to an
optimization problem for quickly reaching high reward on unseen tasks. We then
present an algorithm to solve this problem end-to-end through the use of any
off-the-shelf reinforcement learning method, by repeatedly sampling new tasks
and resetting task-specific policies. We successfully discover meaningful motor
primitives for the directional movement of four-legged robots, solely by
interacting with distributions of mazes. We also demonstrate the
transferability of primitives to solve long-timescale sparse-reward obstacle
courses, and we enable 3D humanoid robots to robustly walk and crawl with the
same policy.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,423 | The bright-star masks for the HSC-SSP survey | We present the procedure to build and validate the bright-star masks for the
Hyper-Suprime-Cam Strategic Subaru Proposal (HSC-SSP) survey. To identify and
mask the saturated stars in the full HSC-SSP footprint, we rely on the Gaia and
Tycho-2 star catalogues. We first assemble a pure star catalogue down to
$G_{\rm Gaia} < 18$ after removing $\sim1.5\%$ of sources that appear extended
in the Sloan Digital Sky Survey (SDSS). We perform visual inspection on the
early data from the S16A internal release of HSC-SSP, finding that our star
catalogue is $99.2\%$ pure down to $G_{\rm Gaia} < 18$. Second, we build the
mask regions in an automated way using stacked detected source measurements
around bright stars binned per $G_{\rm Gaia}$ magnitude. Finally, we validate
those masks from visual inspection and comparison with the literature of galaxy
number counts and angular two-point correlation functions. This version
(Arcturus) supersedes the previous version (Sirius) used in the S16A internal
and DR1 public releases. We publicly release the full masks and tools to flag
objects in the entire footprint of the planned HSC-SSP observations at this
address: this ftp URL.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,424 | Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step | Generative adversarial networks (GANs) are a family of generative models that
do not minimize a single training criterion. Unlike other generative models,
the data distribution is learned via a game between a generator (the generative
model) and a discriminator (a teacher providing training signal) that each
minimize their own cost. GANs are designed to reach a Nash equilibrium at which
each player cannot reduce their cost without changing the other players'
parameters. One useful approach for the theory of GANs is to show that a
divergence between the training distribution and the model distribution obtains
its minimum value at equilibrium. Several recent research directions have been
motivated by the idea that this divergence is the primary guide for the
learning process and that every step of learning should decrease the
divergence. We show that this view is overly restrictive. During GAN training,
the discriminator provides learning signal in situations where the gradients of
the divergences between distributions would not be useful. We provide empirical
counterexamples to the view of GAN training as divergence minimization.
Specifically, we demonstrate that GANs are able to learn distributions in
situations where the divergence minimization point of view predicts they would
fail. We also show that gradient penalties motivated from the divergence
minimization perspective are equally helpful when applied in other contexts in
which the divergence minimization perspective does not predict they would be
helpful. This contributes to a growing body of evidence that GAN training may
be more usefully viewed as approaching Nash equilibria via trajectories that do
not necessarily minimize a specific divergence at each step.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,425 | The Tensor Memory Hypothesis | We discuss memory models which are based on tensor decompositions using
latent representations of entities and events. We show how episodic memory and
semantic memory can be realized and discuss how new memory traces can be
generated from sensory input: Existing memories are the basis for perception
and new memories are generated via perception. We relate our mathematical
approach to the hippocampal memory indexing theory. We describe the first
detailed mathematical models for the complete processing pipeline from sensory
input and its semantic decoding, i.e., perception, to the formation of episodic
and semantic memories and their declarative semantic decodings. Our main
hypothesis is that perception includes an active semantic decoding process,
which relies on latent representations of entities and predicates, and that
episodic and semantic memories depend on the same decoding process. We
contribute to the debate between the leading memory consolidation theories,
i.e., the standard consolidation theory (SCT) and the multiple trace theory
(MTT). The latter is closely related to the complementary learning systems
(CLS) framework. In particular, we show explicitly how episodic memory can
teach the neocortex to form a semantic memory, which is a core issue in MTT and
CLS.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,426 | Data-driven Approach to Measuring the Level of Press Freedom Using Media Attention Diversity from Unfiltered News | Published by Reporters Without Borders every year, the Press Freedom Index
(PFI) reflects the fear and tension in the newsroom pushed by the government
and private sectors. While the PFI is invaluable in monitoring media
environments worldwide, the current survey-based method has inherent
limitations to updates in terms of cost and time. In this work, we introduce an
alternative way to measure the level of press freedom using media attention
diversity compiled from Unfiltered News.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,427 | Advanced Satellite-based Frequency Transfer at the 10^{-16} Level | Advanced satellite-based frequency transfers by TWCP and IPPP have been
performed between NICT and KRISS. We confirm that the disagreement between them
is less than 1x10^{-16} at an averaging time of several days. Additionally, an
intercontinental frequency ratio measurement of Sr and Yb optical lattice
clocks was directly performed by TWCP. We achieved an uncertainty at the
mid-10^{-16} level after a total measurement time of 12 hours. The frequency
ratio was consistent with the recently reported values within the uncertainty.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,428 | Are there needles in a moving haystack? Adaptive sensing for detection of dynamically evolving signals | In this paper we investigate the problem of detecting dynamically evolving
signals. We model the signal as an $n$ dimensional vector that is either zero
or has $s$ non-zero components. At each time step $t\in \mathbb{N}$ the
non-zero components change their location independently with probability $p$.
The statistical problem is to decide whether the signal is a zero vector or in
fact it has non-zero components. This decision is based on $m$ noisy
observations of individual signal components collected at times $t=1,\ldots,m$.
We consider two different sensing paradigms, namely adaptive and non-adaptive
sensing. For non-adaptive sensing the choice of components to measure has to be
decided before the data collection process started, while for adaptive sensing
one can adjust the sensing process based on observations collected earlier. We
characterize the difficulty of this detection problem in both sensing paradigms
in terms of the aforementioned parameters, with special interest to the speed
of change of the active components. In addition we provide an adaptive sensing
algorithm for this problem and contrast its performance to that of non-adaptive
detection algorithms.
| 0 | 0 | 1 | 1 | 0 | 0 |
16,429 | Image-based Localization using Hourglass Networks | In this paper, we propose an encoder-decoder convolutional neural network
(CNN) architecture for estimating camera pose (orientation and location) from a
single RGB-image. The architecture has a hourglass shape consisting of a chain
of convolution and up-convolution layers followed by a regression part. The
up-convolution layers are introduced to preserve the fine-grained information
of the input image. Following the common practice, we train our model in
end-to-end manner utilizing transfer learning from large scale classification
data. The experiments demonstrate the performance of the approach on data
exhibiting different lighting conditions, reflections, and motion blur. The
results indicate a clear improvement over the previous state-of-the-art even
when compared to methods that utilize sequence of test frames instead of a
single frame.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,430 | Suppression of Decoherence of a Spin-Boson System by Time-Periodic Control | We consider a finite-dimensional quantum system coupled to the bosonic
radiation field and subject to a time-periodic control operator. Assuming the
validity of a certain dynamic decoupling condition we approximate the system's
time evolution with respect to the non-interacting dynamics. For sufficiently
small coupling constants $g$ and control periods $T$ we show that a certain
deviation of coupled and uncoupled propagator may be estimated by
$\mathcal{O}(gt \, T)$. Our approach relies on the concept of Kato stability
and general theory on non-autonomous linear evolution equations.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,431 | Energy saving for building heating via a simple and efficient model-free control design: First steps with computer simulations | The model-based control of building heating systems for energy saving
encounters severe physical, mathematical and calibration difficulties in the
numerous attempts that has been published until now. This topic is addressed
here via a new model-free control setting, where the need of any mathematical
description disappears. Several convincing computer simulations are presented.
Comparisons with classic PI controllers and flatness-based predictive control
are provided.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,432 | Trace Properties from Separation Logic Specifications | We propose a formal approach for relating abstract separation logic library
specifications with the trace properties they enforce on interactions between a
client and a library. Separation logic with abstract predicates enforces a
resource discipline that constrains when and how calls may be made between a
client and a library. Intuitively, this can enforce a protocol on the
interaction trace. This intuition is broadly used in the separation logic
community but has not previously been formalised. We provide just such a
formalisation. Our approach is based on using wrappers which instrument library
code to induce execution traces for the properties under examination. By
considering a separation logic extended with trace resources, we prove that
when a library satisfies its separation logic specification then its wrapped
version satisfies the same specification and, moreover, maintains the trace
properties as an invariant. Consequently, any client and library implementation
that are correct with respect to the separation logic specification will
satisfy the trace properties.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,433 | Estimation of Local Degree Distributions via Local Weighted Averaging and Monte Carlo Cross-Validation | Owing to their capability of summarising interactions between elements of a
system, networks have become a common type of data in many fields. As networks
can be inhomogeneous, in that different regions of the network may exhibit
different topologies, an important topic concerns their local properties. This
paper focuses on the estimation of the local degree distribution of a vertex in
an inhomogeneous network. The contributions are twofold: we propose an
estimator based on local weighted averaging, and we set up a Monte Carlo
cross-validation procedure to pick the parameters of this estimator. Under a
specific modelling assumption we derive an oracle inequality that shows how the
model parameters affect the precision of the estimator. We illustrate our
method by several numerical experiments, on both real and synthetic data,
showing in particular that the approach considerably improves upon the natural,
empirical estimator.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,434 | The ALMA Phasing System: A Beamforming Capability for Ultra-High-Resolution Science at (Sub)Millimeter Wavelengths | The Atacama Millimeter/submillimeter Array (ALMA) Phasing Project (APP) has
developed and deployed the hardware and software necessary to coherently sum
the signals of individual ALMA antennas and record the aggregate sum in Very
Long Baseline Interferometry (VLBI) Data Exchange Format. These beamforming
capabilities allow the ALMA array to collectively function as the equivalent of
a single large aperture and participate in global VLBI arrays. The inclusion of
phased ALMA in current VLBI networks operating at (sub)millimeter wavelengths
provides an order of magnitude improvement in sensitivity, as well as
enhancements in u-v coverage and north-south angular resolution. The
availability of a phased ALMA enables a wide range of new ultra-high angular
resolution science applications, including the resolution of supermassive black
holes on event horizon scales and studies of the launch and collimation of
astrophysical jets. It also provides a high-sensitivity aperture that may be
used for investigations such as pulsar searches at high frequencies. This paper
provides an overview of the ALMA Phasing System design, implementation, and
performance characteristics.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,435 | Signatures of two-step impurity mediated vortex lattice melting in Bose-Einstein Condensates | We simulate a rotating 2D BEC to study the melting of a vortex lattice in
presence of random impurities. Impurities are introduced either through a
protocol in which vortex lattice is produced in an impurity potential or first
creating the vortex lattice in the absence of random pinning and then cranking
up the (co-rotating) impurity potential. We find that for a fixed strength,
pinning of vortices at randomly distributed impurities leads to the new states
of vortex lattice. It is unearthed that the vortex lattice follow a two-step
melting via loss of positional and orientational order. Also, the comparisons
between the states obtained in two protocols show that the vortex lattice
states are metastable states when impurities are introduced after the formation
of an ordered vortex lattice. We also show the existence of metastable states
which depend on the history of how the vortex lattice is created.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,436 | Cycle Consistent Adversarial Denoising Network for Multiphase Coronary CT Angiography | In coronary CT angiography, a series of CT images are taken at different
levels of radiation dose during the examination. Although this reduces the
total radiation dose, the image quality during the low-dose phases is
significantly degraded. To address this problem, here we propose a novel
semi-supervised learning technique that can remove the noises of the CT images
obtained in the low-dose phases by learning from the CT images in the routine
dose phases. Although a supervised learning approach is not possible due to the
differences in the underlying heart structure in two phases, the images in the
two phases are closely related so that we propose a cycle-consistent
adversarial denoising network to learn the non-degenerate mapping between the
low and high dose cardiac phases. Experimental results showed that the proposed
method effectively reduces the noise in the low-dose CT image while the
preserving detailed texture and edge information. Moreover, thanks to the
cyclic consistency and identity loss, the proposed network does not create any
artificial features that are not present in the input images. Visual grading
and quality evaluation also confirm that the proposed method provides
significant improvement in diagnostic quality.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,437 | The multidimensional truncated Moment Problem: Carathéodory Numbers | Let $\mathcal{A}$ be a finite-dimensional subspace of
$C(\mathcal{X};\mathbb{R})$, where $\mathcal{X}$ is a locally compact Hausdorff
space, and $\mathsf{A}=\{f_1,\dots,f_m\}$ a basis of $\mathcal{A}$. A sequence
$s=(s_j)_{j=1}^m$ is called a moment sequence if $s_j=\int f_j(x) \, d\mu(x)$,
$j=1,\dots,m$, for some positive Radon measure $\mu$ on $\mathcal{X}$. Each
moment sequence $s$ has a finitely atomic representing measure $\mu$. The
smallest possible number of atoms is called the Carathéodory number
$\mathcal{C}_{\mathsf{A}}(s)$. The largest number $\mathcal{C}_{\mathsf{A}}(s)$
among all moment sequences $s$ is the Carathéodory number
$\mathcal{C}_{\mathsf{A}}$. In this paper the Carathéodory numbers
$\mathcal{C}_{\mathsf{A}}(s)$ and $\mathcal{C}_{\mathsf{A}}$ are studied. In
the case of differentiable functions methods from differential geometry are
used. The main emphasis is on real polynomials. For a large class of spaces of
polynomials in one variable the number $\mathcal{C}_{\mathsf{A}}$ is
determined. In the multivariate case we obtain some lower bounds and we use
results on zeros of positive polynomials to derive upper bounds for the
Carathéodory numbers.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,438 | Inspiration, Captivation, and Misdirection: Emergent Properties in Networks of Online Navigation | The World Wide Web (WWW) has fundamentally changed the ways billions of
people are able to access information. Thus, understanding how people seek
information online is an important issue of study. Wikipedia is a hugely
important part of information provision on the web, with hundreds of millions
of users browsing and contributing to its network of knowledge. The study of
navigational behaviour on Wikipedia, due to the site's popularity and breadth
of content, can reveal more general information seeking patterns that may be
applied beyond Wikipedia and the Web. Our work addresses the relative
shortcomings of existing literature in relating how information structure
influences patterns of navigation online. We study aggregated clickstream data
for articles on the English Wikipedia in the form of a weighted, directed
navigational network. We introduce two parameters that describe how articles
act to source and spread traffic through the network, based on their in/out
strength and entropy. From these, we construct a navigational phase space where
different article types occupy different, distinct regions, indicating how the
structure of information online has differential effects on patterns of
navigation. Finally, we go on to suggest applications for this analysis in
identifying and correcting deficiencies in the Wikipedia page network that may
also be adapted to more general information networks.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,439 | Multiphase Aluminum A356 Foam Formation Process Simulation Using Lattice Boltzmann Method | Shan-Chen model is a numerical scheme to simulate multiphase fluid flows
using Lattice Boltzmann approach. The original Shan-Chen model suffers from
inability to accurately predict behavior of air bubbles interacting in a
non-aqueous fluid. In the present study, we extended the Shan-Chen model to
take the effect of the attraction-repulsion barriers among bubbles in to
account. The proposed model corrects the interaction and coalescence criterion
of the original Shan-Chen scheme in order to have a more accurate simulation of
bubbles morphology in a metal foam. The model is based on forming a thin film
(narrow channel) between merging bubbles during growth. Rupturing of the film
occurs when an oscillation in velocity and pressure arises inside the channel
followed by merging of the bubbles. Comparing numerical results obtained from
proposed model with mettallorgraphy images for aluminum A356 demonstrated a
good consistency in mean bubble size and bubbles distribution
| 1 | 1 | 0 | 0 | 0 | 0 |
16,440 | Deformations of coisotropic submanifolds in Jacobi manifolds | In this thesis, we study the deformation problem of coisotropic submanifolds
in Jacobi manifolds. In particular we attach two algebraic invariants to any
coisotropic submanifold $S$ in a Jacobi manifold, namely the
$L_\infty[1]$-algebra and the BFV-complex of $S$. Our construction generalizes
and unifies analogous constructions in symplectic, Poisson, and locally
conformal symplectic geometry. As a new special case we also attach an
$L_\infty[1]$-algebra and a BFV-complex to any coisotropic submanifold in a
contact manifold. The $L_\infty[1]$-algebra of $S$ controls the formal
coisotropic deformation problem of $S$, even under Hamiltonian equivalence. The
BFV-complex of $S$ controls the non-formal coisotropic deformation problem of
$S$, even under both Hamiltonian and Jacobi equivalence. In view of these
results, we exhibit, in the contact setting, two examples of coisotropic
submanifolds whose coisotropic deformation problem is obstructed.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,441 | Discriminate-and-Rectify Encoders: Learning from Image Transformation Sets | The complexity of a learning task is increased by transformations in the
input space that preserve class identity. Visual object recognition for example
is affected by changes in viewpoint, scale, illumination or planar
transformations. While drastically altering the visual appearance, these
changes are orthogonal to recognition and should not be reflected in the
representation or feature encoding used for learning. We introduce a framework
for weakly supervised learning of image embeddings that are robust to
transformations and selective to the class distribution, using sets of
transforming examples (orbit sets), deep parametrizations and a novel
orbit-based loss. The proposed loss combines a discriminative, contrastive part
for orbits with a reconstruction error that learns to rectify orbit
transformations. The learned embeddings are evaluated in distance metric-based
tasks, such as one-shot classification under geometric transformations, as well
as face verification and retrieval under more realistic visual variability. Our
results suggest that orbit sets, suitably computed or observed, can be used for
efficient, weakly-supervised learning of semantically relevant image
embeddings.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,442 | Covering compact metric spaces greedily | A general greedy approach to construct coverings of compact metric spaces by
metric balls is given and analyzed. The analysis is a continuous version of
Chvatal's analysis of the greedy algorithm for the weighted set cover problem.
The approach is demonstrated in an exemplary manner to construct efficient
coverings of the n-dimensional sphere and n-dimensional Euclidean space to give
short and transparent proofs of several best known bounds obtained from
deterministic constructions in the literature on sphere coverings.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,443 | Gauge covariances and nonlinear optical responses | The formalism of the reduced density matrix is pursued in both length and
velocity gauges of the perturbation to the crystal Hamiltonian. The covariant
derivative is introduced as a convenient representation of the position
operator. This allow us to write compact expressions for the reduced density
matrix in any order of the perturbation which simplifies the calculations of
nonlinear optical responses; as an example, we compute the first and third
order contributions of the monolayer graphene. Expressions obtained in both
gauges share the same formal structure, allowing a comparison of the effects of
truncation to a finite set of bands. This truncation breaks the equivalence
between the two approaches: its proper implementation can be done directly in
the expressions derived in the length gauge, but require a revision of the
equations of motion of the reduced density matrix in the velocity gauge.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,444 | Quasi-Static Internal Magnetic Field Detected in the Pseudogap Phase of Bi$_{2+x}$Sr$_{2-x}$CaCu$_2$O$_{8+δ}$ by $μ$SR | We report muon spin relaxation ($\mu$SR) measurements of optimally-doped and
overdoped Bi$_{2+x}$Sr$_{2-x}$CaCu$_2$O$_{8+\delta}$ (Bi2212) single crystals
that reveal the presence of a weak temperature-dependent quasi-static internal
magnetic field of electronic origin in the superconducting (SC) and pseudogap
(PG) phases. In both samples the internal magnetic field persists up to 160~K,
but muon diffusion prevents following the evolution of the field to higher
temperatures. We consider the evidence from our measurments in support of PG
order parameter candidates, namely, electronic loop currents and
magnetoelectric quadrupoles.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,445 | Auto-Encoding Total Correlation Explanation | Advances in unsupervised learning enable reconstruction and generation of
samples from complex distributions, but this success is marred by the
inscrutability of the representations learned. We propose an
information-theoretic approach to characterizing disentanglement and dependence
in representation learning using multivariate mutual information, also called
total correlation. The principle of total Cor-relation Ex-planation (CorEx) has
motivated successful unsupervised learning applications across a variety of
domains, but under some restrictive assumptions. Here we relax those
restrictions by introducing a flexible variational lower bound to CorEx.
Surprisingly, we find that this lower bound is equivalent to the one in
variational autoencoders (VAE) under certain conditions. This
information-theoretic view of VAE deepens our understanding of hierarchical VAE
and motivates a new algorithm, AnchorVAE, that makes latent codes more
interpretable through information maximization and enables generation of richer
and more realistic samples.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,446 | A Characterisation of Open Bisimilarity using an Intuitionistic Modal Logic | Open bisimilarity is the original notion of bisimilarity to be introduced for
the pi-calculus that is a congruence. In open bisimilarity, free names in
processes are treated as variables that may be instantiated lazily; in contrast
to early and late bisimilarity where free names are constants. We build on the
established line of work, due to Milner, Parrow, and Walker, on classical modal
logics characterising early and late bisimilarity for the $\pi$-calculus. The
important insight is, to characterise open bisimilarity, we move to the setting
of intuitionistic modal logics. The intuitionistic modal logic introduced,
called OM, is such that modalities are closed under (respectful) substitutions,
inducing a property known as intuitionistic hereditary. Intuitionistic
hereditary reflects the lazy instantiation of names in open bisimilarity. The
soundness proof for open bisimilarity with respect to the modal logic is
mechanised in Abella. The constructive content of the completeness proof
provides an algorithm for generating distinguishing formulae, where such
formulae are useful as a certificate explaining why two processes are not open
bisimilar. We draw attention to the fact that open bisimilarity is not the only
notion of bisimilarity that is a congruence: for name-passing calculi there is
a classical/intuitionistic spectrum of bisimilarities.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,447 | Using data science as a community advocacy tool to promote equity in urban renewal programs: An analysis of Atlanta's Anti-Displacement Tax Fund | Cities across the United States are undergoing great transformation and urban
growth. Data and data analysis has become an essential element of urban
planning as cities use data to plan land use and development. One great
challenge is to use the tools of data science to promote equity along with
growth. The city of Atlanta is an example site of large-scale urban renewal
that aims to engage in development without displacement. On the Westside of
downtown Atlanta, the construction of the new Mercedes-Benz Stadium and the
conversion of an underutilized rail-line into a multi-use trail may result in
increased property values. In response to community residents' concerns and a
commitment to development without displacement, the city and philanthropic
partners announced an Anti-Displacement Tax Fund to subsidize future property
tax increases of owner occupants for the next twenty years. To achieve greater
transparency, accountability, and impact, residents expressed a desire for a
tool that would help them determine eligibility and quantify this commitment.
In support of this goal, we use machine learning techniques to analyze
historical tax assessment and predict future tax assessments. We then apply
eligibility estimates to our predictions to estimate the total cost for the
first seven years of the program. These forecasts are also incorporated into an
interactive tool for community residents to determine their eligibility for the
fund and the expected increase in their home value over the next seven years.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,448 | General Bayesian inference schemes in infinite mixture models | Bayesian statistical models allow us to formalise our knowledge about the
world and reason about our uncertainty, but there is a need for better
procedures to accurately encode its complexity. One way to do so is through
compositional models, which are formed by combining blocks consisting of
simpler models. One can increase the complexity of the compositional model by
either stacking more blocks or by using a not-so-simple model as a building
block. This thesis is an example of the latter. One first aim is to expand the
choice of Bayesian nonparametric (BNP) blocks for constructing tractable
compositional models. So far, most of the models that have a Bayesian
nonparametric component use a Dirichlet Process or a Pitman-Yor process because
of the availability of tractable and compact representations. This thesis shows
how to overcome certain intractabilities in order to obtain analogous compact
representations for the class of Poisson-Kingman priors which includes the
Dirichlet and Pitman-Yor processes.
A major impediment to the widespread use of Bayesian nonparametric building
blocks is that inference is often costly, intractable or difficult to carry
out. This is an active research area since dealing with the model's infinite
dimensional component forbids the direct use of standard simulation-based
methods. The main contribution of this thesis is a variety of inference schemes
that tackle this problem: Markov chain Monte Carlo and Sequential Monte Carlo
methods, which are exact inference schemes since they target the true
posterior. The contributions of this thesis, in a larger context, provide
general purpose exact inference schemes in the flavour or probabilistic
programming: the user is able to choose from a variety of models, focusing only
on the modelling part. Indeed, if the wide enough class of Poisson-Kingman
priors is used as one of our blocks, this objective is achieved.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,449 | Glow: Generative Flow with Invertible 1x1 Convolutions | Flow-based generative models (Dinh et al., 2014) are conceptually attractive
due to tractability of the exact log-likelihood, tractability of exact
latent-variable inference, and parallelizability of both training and
synthesis. In this paper we propose Glow, a simple type of generative flow
using an invertible 1x1 convolution. Using our method we demonstrate a
significant improvement in log-likelihood on standard benchmarks. Perhaps most
strikingly, we demonstrate that a generative model optimized towards the plain
log-likelihood objective is capable of efficient realistic-looking synthesis
and manipulation of large images. The code for our model is available at
this https URL
| 0 | 0 | 0 | 1 | 0 | 0 |
16,450 | Pohozaev identity for the fractional $p-$Laplacian on $\mathbb{R}^N$ | By virtue of a suitable approximation argument, we prove a Pohozaev identity
for nonlinear nonlocal problems on $\mathbb{R}^N$ involving the fractional
$p-$Laplacian operator. Furthermore we provide an application of the identity
to show that some relevant levels of the energy functional associated with the
problem coincide.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,451 | A Second Wave of Expanders over Finite Fields | This is an expository survey on recent sum-product results in finite fields.
We present a number of sum-product or "expander" results that say that if
$|A| > p^{2/3}$ then some set determined by sums and product of elements of $A$
is nearly as large as possible, and if $|A|<p^{2/3}$ then the set in question
is significantly larger that $A$. These results are based on a point-plane
incidence bound of Rudnev, and are quantitatively stronger than a wave of
earlier results following Bourgain, Katz, and Tao's breakthrough sum-product
result.
In addition, we present two geometric results: an incidence bound due to
Stevens and de Zeeuw, and bound on collinear triples, and an example of an
expander that breaks the threshold of $p^{2/3}$ required by the other results.
We have simplified proofs wherever possible, and hope that this survey may
serve as a compact guide to recent advances in arithmetic combinatorics over
finite fields. We do not claim originality for any of the results.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,452 | Homology of torus knots | Using the method of Elias-Hogancamp and combinatorics of toric braids we give
an explicit formula for the triply graded Khovanov-Rozansky homology of an
arbitrary torus knot, thereby proving some of the conjectures of
Aganagic-Shakirov, Cherednik, Gorsky-Negut and Oblomkov-Rasmussen-Shende.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,453 | 2-associahedra | For any $r\geq 1$ and $\mathbf{n} \in \mathbb{Z}_{\geq0}^r \setminus
\{\mathbf0\}$ we construct a poset $W_{\mathbf{n}}$ called a 2-associahedron.
The 2-associahedra arose in symplectic geometry, where they are expected to
control maps between Fukaya categories of different symplectic manifolds. We
prove that the completion $\widehat{W_{\mathbf{n}}}$ is an abstract polytope of
dimension $|\mathbf{n}|+r-3$. There are forgetful maps $W_{\mathbf{n}} \to
K_r$, where $K_r$ is the $(r-2)$-dimensional associahedron, and the
2-associahedra specialize to the associahedra (in two ways) and to the
multiplihedra. In an appendix, we work out the 2- and 3-dimensional
associahedra in detail.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,454 | Anisotropic thermophoresis | Colloidal migration in temperature gradient is referred to as thermophoresis.
In contrast to particles with spherical shape, we show that elongated colloids
may have a thermophoretic response that varies with the colloid orientation.
Remarkably, this can translate into a non-vanishing thermophoretic force in the
direction perpendicular to the temperature gradient. Oppositely to the friction
force, the thermophoretic force of a rod oriented with the temperature gradient
can be larger or smaller than when oriented perpendicular to it. The precise
anisotropic thermophoretic behavior clearly depends on the colloidal rod aspect
ratio, and also on its surface details, which provides an interesting
tunability to the devices constructed based on this principle. By means of
mesoscale hydrodynamic simulations, we characterize this effect for different
types of rod-like colloids.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,455 | Ultra-Wideband Aided Fast Localization and Mapping System | This paper proposes an ultra-wideband (UWB) aided localization and mapping
system that leverages on inertial sensor and depth camera. Inspired by the fact
that visual odometry (VO) system, regardless of its accuracy in the short term,
still faces challenges with accumulated errors in the long run or under
unfavourable environments, the UWB ranging measurements are fused to remove the
visual drift and improve the robustness. A general framework is developed which
consists of three parallel threads, two of which carry out the visual-inertial
odometry (VIO) and UWB localization respectively. The other mapping thread
integrates visual tracking constraints into a pose graph with the proposed
smooth and virtual range constraints, such that an optimization is performed to
provide robust trajectory estimation. Experiments show that the proposed system
is able to create dense drift-free maps in real-time even running on an
ultra-low power processor in featureless environments.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,456 | Modular Sensor Fusion for Semantic Segmentation | Sensor fusion is a fundamental process in robotic systems as it extends the
perceptual range and increases robustness in real-world operations. Current
multi-sensor deep learning based semantic segmentation approaches do not
provide robustness to under-performing classes in one modality, or require a
specific architecture with access to the full aligned multi-sensor training
data. In this work, we analyze statistical fusion approaches for semantic
segmentation that overcome these drawbacks while keeping a competitive
performance. The studied approaches are modular by construction, allowing to
have different training sets per modality and only a much smaller subset is
needed to calibrate the statistical models. We evaluate a range of statistical
fusion approaches and report their performance against state-of-the-art
baselines on both real-world and simulated data. In our experiments, the
approach improves performance in IoU over the best single modality segmentation
results by up to 5%. We make all implementations and configurations publicly
available.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,457 | Thickness-dependent electronic and magnetic properties of $γ'$-Fe$_{\mathrm 4}$N atomic layers on Cu(001) | Growth, electronic and magnetic properties of $\gamma'$-Fe$_{4}$N atomic
layers on Cu(001) are studied by scanning tunneling microscopy/spectroscopy and
x-ray absorption spectroscopy/magnetic circular dichroism. A continuous film of
ordered trilayer $\gamma'$-Fe$_{4}$N is obtained by Fe deposition under N$_{2}$
atmosphere onto monolayer Fe$_{2}$N/Cu(001), while the repetition of a
bombardment with 0.5 keV N$^{+}$ ions during growth cycles results in imperfect
bilayer $\gamma'$-Fe$_{4}$N. The increase in the sample thickness causes the
change of the surface electronic structure, as well as the enhancement in the
spin magnetic moment of Fe atoms reaching $\sim$ 1.4 $\mu_{\mathrm B}$/atom in
the trilayer sample. The observed thickness-dependent properties of the system
are well interpreted by layer-resolved density of states calculated using first
principles, which demonstrates the strongly layer-dependent electronic states
within each surface, subsurface, and interfacial plane of the
$\gamma'$-Fe$_{4}$N atomic layers on Cu(001).
| 0 | 1 | 0 | 0 | 0 | 0 |
16,458 | Error Characterization, Mitigation, and Recovery in Flash Memory Based Solid-State Drives | NAND flash memory is ubiquitous in everyday life today because its capacity
has continuously increased and cost has continuously decreased over decades.
This positive growth is a result of two key trends: (1) effective process
technology scaling, and (2) multi-level (e.g., MLC, TLC) cell data coding.
Unfortunately, the reliability of raw data stored in flash memory has also
continued to become more difficult to ensure, because these two trends lead to
(1) fewer electrons in the flash memory cell (floating gate) to represent the
data and (2) larger cell-to-cell interference and disturbance effects. Without
mitigation, worsening reliability can reduce the lifetime of NAND flash memory.
As a result, flash memory controllers in solid-state drives (SSDs) have become
much more sophisticated: they incorporate many effective techniques to ensure
the correct interpretation of noisy data stored in flash memory cells.
In this article, we review recent advances in SSD error characterization,
mitigation, and data recovery techniques for reliability and lifetime
improvement. We provide rigorous experimental data from state-of-the-art MLC
and TLC NAND flash devices on various types of flash memory errors, to motivate
the need for such techniques. Based on the understanding developed by the
experimental characterization, we describe several mitigation and recovery
techniques, including (1) cell-to-cell interference mitigation, (2) optimal
multi-level cell sensing, (3) error correction using state-of-the-art
algorithms and methods, and (4) data recovery when error correction fails. We
quantify the reliability improvement provided by each of these techniques.
Looking forward, we briefly discuss how flash memory and these techniques could
evolve into the future.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,459 | Unified Backpropagation for Multi-Objective Deep Learning | A common practice in most of deep convolutional neural architectures is to
employ fully-connected layers followed by Softmax activation to minimize
cross-entropy loss for the sake of classification. Recent studies show that
substitution or addition of the Softmax objective to the cost functions of
support vector machines or linear discriminant analysis is highly beneficial to
improve the classification performance in hybrid neural networks. We propose a
novel paradigm to link the optimization of several hybrid objectives through
unified backpropagation. This highly alleviates the burden of extensive
boosting for independent objective functions or complex formulation of
multiobjective gradients. Hybrid loss functions are linked by basic probability
assignment from evidence theory. We conduct our experiments for a variety of
scenarios and standard datasets to evaluate the advantage of our proposed
unification approach to deliver consistent improvements into the classification
performance of deep convolutional neural networks.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,460 | Information-Theoretic Representation Learning for Positive-Unlabeled Classification | Recent advances in weakly supervised classification allow us to train a
classifier only from positive and unlabeled (PU) data. However, existing PU
classification methods typically require an accurate estimate of the
class-prior probability, which is a critical bottleneck particularly for
high-dimensional data. This problem has been commonly addressed by applying
principal component analysis in advance, but such unsupervised dimension
reduction can collapse underlying class structure. In this paper, we propose a
novel representation learning method from PU data based on the
information-maximization principle. Our method does not require class-prior
estimation and thus can be used as a preprocessing method for PU
classification. Through experiments, we demonstrate that our method combined
with deep neural networks highly improves the accuracy of PU class-prior
estimation, leading to state-of-the-art PU classification performance.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,461 | Aspects of Chaitin's Omega | The halting probability of a Turing machine,also known as Chaitin's Omega, is
an algorithmically random number with many interesting properties. Since
Chaitin's seminal work, many popular expositions have appeared, mainly focusing
on the metamathematical or philosophical significance of Omega (or debating
against it). At the same time, a rich mathematical theory exploring the
properties of Chaitin's Omega has been brewing in various technical papers,
which quietly reveals the significance of this number to many aspects of
contemporary algorithmic information theory. The purpose of this survey is to
expose these developments and tell a story about Omega, which outlines its
multifaceted mathematical properties and roles in algorithmic randomness.
| 1 | 0 | 1 | 0 | 0 | 0 |
16,462 | This Looks Like That: Deep Learning for Interpretable Image Recognition | When we are faced with challenging image classification tasks, we often
explain our reasoning by dissecting the image, and pointing out prototypical
aspects of one class or another. The mounting evidence for each of the classes
helps us make our final decision. In this work, we introduce a deep network
architecture that reasons in a similar way: the network dissects the image by
finding prototypical parts, and combines evidence from the prototypes to make a
final classification. The model thus reasons in a way that is qualitatively
similar to the way ornithologists, physicians, geologists, architects, and
others would explain to people on how to solve challenging image classification
tasks. The network uses only image-level labels for training, meaning that
there are no labels for parts of images. We demonstrate our method on the
CUB-200-2011 dataset and the CBIS-DDSM dataset. Our experiments show that our
interpretable network can achieve comparable accuracy with its analogous
standard non-interpretable counterpart as well as other interpretable deep
models.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,463 | Deep Learning in the Automotive Industry: Applications and Tools | Deep Learning refers to a set of machine learning techniques that utilize
neural networks with many hidden layers for tasks, such as image
classification, speech recognition, language understanding. Deep learning has
been proven to be very effective in these domains and is pervasively used by
many Internet services. In this paper, we describe different automotive uses
cases for deep learning in particular in the domain of computer vision. We
surveys the current state-of-the-art in libraries, tools and infrastructures
(e.\,g.\ GPUs and clouds) for implementing, training and deploying deep neural
networks. We particularly focus on convolutional neural networks and computer
vision use cases, such as the visual inspection process in manufacturing plants
and the analysis of social media data. To train neural networks, curated and
labeled datasets are essential. In particular, both the availability and scope
of such datasets is typically very limited. A main contribution of this paper
is the creation of an automotive dataset, that allows us to learn and
automatically recognize different vehicle properties. We describe an end-to-end
deep learning application utilizing a mobile app for data collection and
process support, and an Amazon-based cloud backend for storage and training.
For training we evaluate the use of cloud and on-premises infrastructures
(including multiple GPUs) in conjunction with different neural network
architectures and frameworks. We assess both the training times as well as the
accuracy of the classifier. Finally, we demonstrate the effectiveness of the
trained classifier in a real world setting during manufacturing process.
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16,464 | Design, Engineering and Optimization of a Grid-Tie Multicell Inverter for Energy Storage Applications | Multilevel converters have found many applications within renewable energy
systems thanks to their unique capability of generating multiple voltage
levels. However, these converters need multiple DC sources and the voltage
balancing over capacitors for these systems is cumbersome. In this work, a new
grid-tie multicell inverter with high level of safety has been designed,
engineered and optimized for integrating energy storage devices to the electric
grid. The multilevel converter proposed in this work is capable of maintaining
the flying capacitors voltage in the desired value. The solar cells are the
primary energy sources for proposed inverter where the maximum power density is
obtained. Finally, the performance of the inverter and its control method
simulated using PSCAD/EMTDC software package and good agreement achieved with
experimental data.
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16,465 | An infinitely differentiable function with compact support: Definition and properties | This is the English translation of my old paper 'Definición y estudio de
una función indefinidamente diferenciable de soporte compacto', Rev. Real
Acad. Ciencias 76 (1982) 21-38. In it a function (essentially Fabius function)
is defined and given its main properties, including: unicity, interpretation as
a probability, partition of unity with its translates, formulas for its $n$-th
derivates, rationality of its values at dyadic points, formulas for the
effective computation of these values, and some arithmetical properties of
these values. Since I need it now for a reference, I have translated it.
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16,466 | Analysis of bacterial population growth using extended logistic growth model with distributed delay | In the present work, we develop a delayed Logistic growth model to study the
effects of decontamination on the bacterial population in the ambient
environment. Using the linear stability analysis, we study different case
scenarios, where bacterial population may establish at the positive equilibrium
or go extinct due to increased decontamination. The results are verified using
numerical simulation of the model.
| 0 | 0 | 0 | 0 | 1 | 0 |
16,467 | Reverse Quantum Annealing Approach to Portfolio Optimization Problems | We investigate a hybrid quantum-classical solution method to the
mean-variance portfolio optimization problems. Starting from real financial
data statistics and following the principles of the Modern Portfolio Theory, we
generate parametrized samples of portfolio optimization problems that can be
related to quadratic binary optimization forms programmable in the analog
D-Wave Quantum Annealer 2000Q. The instances are also solvable by an
industry-established Genetic Algorithm approach, which we use as a classical
benchmark. We investigate several options to run the quantum computation
optimally, ultimately discovering that the best results in terms of expected
time-to-solution as a function of number of variables for the hardest instances
set are obtained by seeding the quantum annealer with a solution candidate
found by a greedy local search and then performing a reverse annealing
protocol. The optimized reverse annealing protocol is found to be more than 100
times faster than the corresponding forward quantum annealing on average.
| 0 | 0 | 0 | 0 | 0 | 1 |
16,468 | GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations | Modern deep transfer learning approaches have mainly focused on learning
generic feature vectors from one task that are transferable to other tasks,
such as word embeddings in language and pretrained convolutional features in
vision. However, these approaches usually transfer unary features and largely
ignore more structured graphical representations. This work explores the
possibility of learning generic latent relational graphs that capture
dependencies between pairs of data units (e.g., words or pixels) from
large-scale unlabeled data and transferring the graphs to downstream tasks. Our
proposed transfer learning framework improves performance on various tasks
including question answering, natural language inference, sentiment analysis,
and image classification. We also show that the learned graphs are generic
enough to be transferred to different embeddings on which the graphs have not
been trained (including GloVe embeddings, ELMo embeddings, and task-specific
RNN hidden unit), or embedding-free units such as image pixels.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,469 | Learning to cluster in order to transfer across domains and tasks | This paper introduces a novel method to perform transfer learning across
domains and tasks, formulating it as a problem of learning to cluster. The key
insight is that, in addition to features, we can transfer similarity
information and this is sufficient to learn a similarity function and
clustering network to perform both domain adaptation and cross-task transfer
learning. We begin by reducing categorical information to pairwise constraints,
which only considers whether two instances belong to the same class or not.
This similarity is category-agnostic and can be learned from data in the source
domain using a similarity network. We then present two novel approaches for
performing transfer learning using this similarity function. First, for
unsupervised domain adaptation, we design a new loss function to regularize
classification with a constrained clustering loss, hence learning a clustering
network with the transferred similarity metric generating the training inputs.
Second, for cross-task learning (i.e., unsupervised clustering with unseen
categories), we propose a framework to reconstruct and estimate the number of
semantic clusters, again using the clustering network. Since the similarity
network is noisy, the key is to use a robust clustering algorithm, and we show
that our formulation is more robust than the alternative constrained and
unconstrained clustering approaches. Using this method, we first show state of
the art results for the challenging cross-task problem, applied on Omniglot and
ImageNet. Our results show that we can reconstruct semantic clusters with high
accuracy. We then evaluate the performance of cross-domain transfer using
images from the Office-31 and SVHN-MNIST tasks and present top accuracy on both
datasets. Our approach doesn't explicitly deal with domain discrepancy. If we
combine with a domain adaptation loss, it shows further improvement.
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16,470 | Modeling Perceptual Aliasing in SLAM via Discrete-Continuous Graphical Models | Perceptual aliasing is one of the main causes of failure for Simultaneous
Localization and Mapping (SLAM) systems operating in the wild. Perceptual
aliasing is the phenomenon where different places generate a similar visual
(or, in general, perceptual) footprint. This causes spurious measurements to be
fed to the SLAM estimator, which typically results in incorrect localization
and mapping results. The problem is exacerbated by the fact that those outliers
are highly correlated, in the sense that perceptual aliasing creates a large
number of mutually-consistent outliers. Another issue stems from the fact that
most state-of-the-art techniques rely on a given trajectory guess (e.g., from
odometry) to discern between inliers and outliers and this makes the resulting
pipeline brittle, since the accumulation of error may result in incorrect
choices and recovery from failures is far from trivial. This work provides a
unified framework to model perceptual aliasing in SLAM and provides practical
algorithms that can cope with outliers without relying on any initial guess. We
present two main contributions. The first is a Discrete-Continuous Graphical
Model (DC-GM) for SLAM: the continuous portion of the DC-GM captures the
standard SLAM problem, while the discrete portion describes the selection of
the outliers and models their correlation. The second contribution is a
semidefinite relaxation to perform inference in the DC-GM that returns
estimates with provable sub-optimality guarantees. Experimental results on
standard benchmarking datasets show that the proposed technique compares
favorably with state-of-the-art methods while not relying on an initial guess
for optimization.
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16,471 | Sparse distributed representation, hierarchy, critical periods, metaplasticity: the keys to lifelong fixed-time learning and best-match retrieval | Among the more important hallmarks of human intelligence, which any
artificial general intelligence (AGI) should have, are the following. 1. It
must be capable of on-line learning, including with single/few trials. 2.
Memories/knowledge must be permanent over lifelong durations, safe from
catastrophic forgetting. Some confabulation, i.e., semantically plausible
retrieval errors, may gradually accumulate over time. 3. The time to both: a)
learn a new item, and b) retrieve the best-matching / most relevant item(s),
i.e., do similarity-based retrieval, must remain constant throughout the
lifetime. 4. The system should never become full: it must remain able to store
new information, i.e., make new permanent memories, throughout very long
lifetimes. No artificial computational system has been shown to have all these
properties. Here, we describe a neuromorphic associative memory model, Sparsey,
which does, in principle, possess them all. We cite prior results supporting
possession of hallmarks 1 and 3 and sketch an argument, hinging on strongly
recursive, hierarchical, part-whole compositional structure of natural data,
that Sparsey also possesses hallmarks 2 and 4.
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16,472 | Efficient implementations of the modified Gram-Schmidt orthogonalization with a non-standard inner product | The modified Gram-Schmidt (MGS) orthogonalization is one of the most
well-used algorithms for computing the thin QR factorization. MGS can be
straightforwardly extended to a non-standard inner product with respect to a
symmetric positive definite matrix $A$. For the thin QR factorization of an $m
\times n$ matrix with the non-standard inner product, a naive implementation of
MGS requires $2n$ matrix-vector multiplications (MV) with respect to $A$. In
this paper, we propose $n$-MV implementations: a high accuracy (HA) type and a
high performance (HP) type, of MGS. We also provide error bounds of the HA-type
implementation. Numerical experiments and analysis indicate that the proposed
implementations have competitive advantages over the naive implementation in
terms of both computational cost and accuracy.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,473 | On the isoperimetric constant, covariance inequalities and $L_p$-Poincaré inequalities in dimension one | Firstly, we derive in dimension one a new covariance inequality of
$L_{1}-L_{\infty}$ type that characterizes the isoperimetric constant as the
best constant achieving the inequality. Secondly, we generalize our result to
$L_{p}-L_{q}$ bounds for the covariance. Consequently, we recover Cheeger's
inequality without using the co-area formula. We also prove a generalized
weighted Hardy type inequality that is needed to derive our covariance
inequalities and that is of independent interest. Finally, we explore some
consequences of our covariance inequalities for $L_{p}$-Poincaré
inequalities and moment bounds. In particular, we obtain optimal constants in
general $L_{p}$-Poincaré inequalities for measures with finite
isoperimetric constant, thus generalizing in dimension one Cheeger's
inequality, which is a $L_{p}$-Poincaré inequality for $p=2$, to any real
$p\geq 1$.
| 0 | 0 | 1 | 1 | 0 | 0 |
16,474 | Faster arbitrary-precision dot product and matrix multiplication | We present algorithms for real and complex dot product and matrix
multiplication in arbitrary-precision floating-point and ball arithmetic. A
low-overhead dot product is implemented on the level of GMP limb arrays; it is
about twice as fast as previous code in MPFR and Arb at precision up to several
hundred bits. Up to 128 bits, it is 3-4 times as fast, costing 20-30 cycles per
term for floating-point evaluation and 40-50 cycles per term for balls. We
handle large matrix multiplications even more efficiently via blocks of scaled
integer matrices. The new methods are implemented in Arb and significantly
speed up polynomial operations and linear algebra.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,475 | Improving Distributed Representations of Tweets - Present and Future | Unsupervised representation learning for tweets is an important research
field which helps in solving several business applications such as sentiment
analysis, hashtag prediction, paraphrase detection and microblog ranking. A
good tweet representation learning model must handle the idiosyncratic nature
of tweets which poses several challenges such as short length, informal words,
unusual grammar and misspellings. However, there is a lack of prior work which
surveys the representation learning models with a focus on tweets. In this
work, we organize the models based on its objective function which aids the
understanding of the literature. We also provide interesting future directions,
which we believe are fruitful in advancing this field by building high-quality
tweet representation learning models.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,476 | Tverberg-type theorems for matroids: A counterexample and a proof | Bárány, Kalai, and Meshulam recently obtained a topological Tverberg-type
theorem for matroids, which guarantees multiple coincidences for continuous
maps from a matroid complex to d-dimensional Euclidean space, if the matroid
has sufficiently many disjoint bases. They make a conjecture on the
connectivity of k-fold deleted joins of a matroid with many disjoint bases,
which would yield a much tighter result - but we provide a counterexample
already for the case of k=2, where a tight Tverberg-type theorem would be a
topological Radon theorem for matroids. Nevertheless, we prove the topological
Radon theorem for the counterexample family of matroids by an index
calculation, despite the failure of the connectivity-based approach.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,477 | Near-Perfect Conversion of a Propagating Plane Wave into a Surface Wave Using Metasurfaces | In this paper, theoretical and numerical studies of perfect/nearly-perfect
conversion of a plane wave into a surface wave are presented. The problem of
determining the electromagnetic properties of an inhomogeneous lossless
boundary which would fully transform an incident plane wave into a surface wave
propagating along the boundary is considered. An approximate field solution
which produces a slowly growing surface wave and satisfies the energy
conservation law is discussed and numerically demonstrated. The results of the
study are of great importance for the future development of such devices as
perfect leaky-wave antennas and can potentially lead to many novel
applications.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,478 | A National Research Agenda for Intelligent Infrastructure | Our infrastructure touches the day-to-day life of each of our fellow
citizens, and its capabilities, integrity and sustainability are crucial to the
overall competitiveness and prosperity of our country. Unfortunately, the
current state of U.S. infrastructure is not good: the American Society of Civil
Engineers' latest report on America's infrastructure ranked it at a D+ -- in
need of $3.9 trillion in new investments. This dire situation constrains the
growth of our economy, threatens our quality of life, and puts our global
leadership at risk. The ASCE report called out three actions that need to be
taken to address our infrastructure problem: 1) investment and planning in the
system; 2) bold leadership by elected officials at the local and federal state;
and 3) planning sustainability and resiliency in our infrastructure.
While our immediate infrastructure needs are critical, it would be
shortsighted to simply replicate more of what we have today. By doing so, we
miss the opportunity to create Intelligent Infrastructure that will provide the
foundation for increased safety and resilience, improved efficiencies and civic
services, and broader economic opportunities and job growth. Indeed, our
challenge is to proactively engage the declining, incumbent national
infrastructure system and not merely repair it, but to enhance it; to create an
internationally competitive cyber-physical system that provides an immediate
opportunity for better services for citizens and that acts as a platform for a
21st century, high-tech economy and beyond.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,479 | Surface Edge Explorer (SEE): Planning Next Best Views Directly from 3D Observations | Surveying 3D scenes is a common task in robotics. Systems can do so
autonomously by iteratively obtaining measurements. This process of planning
observations to improve the model of a scene is called Next Best View (NBV)
planning.
NBV planning approaches often use either volumetric (e.g., voxel grids) or
surface (e.g., triangulated meshes) representations. Volumetric approaches
generalise well between scenes as they do not depend on surface geometry but do
not scale to high-resolution models of large scenes. Surface representations
can obtain high-resolution models at any scale but often require tuning of
unintuitive parameters or multiple survey stages.
This paper presents a scene-model-free NBV planning approach with a density
representation. The Surface Edge Explorer (SEE) uses the density of current
measurements to detect and explore observed surface boundaries. This approach
is shown experimentally to provide better surface coverage in lower computation
time than the evaluated state-of-the-art volumetric approaches while moving
equivalent distances.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,480 | N/O abundance ratios in gamma-ray burst and supernova host galaxies at z<4. Comparison with AGN, starburst and HII regions | The distribution of N/O abundance ratios calculated by the detailed modelling
of different galaxy spectra at z<4 is investigated. Supernova (SN) and long
gamma-ray-burst (LGRB) host galaxies cover different redshift domains. N/O in
SN hosts increases due to secondary N production towards low z (0.01)
accompanying the growing trend of active galaxies (AGN, LINER). N/O in LGRB
hosts decreases rapidly between z>1 and z ~0.1 following the N/H trend and
reach the characteristic N/O ratios calculated for the HII regions in local and
nearby galaxies. The few short period GRB (SGRB) hosts included in the galaxy
sample show N/H <0.04 solar and O/H solar. They seem to continue the low bound
N/H trend of SN hosts at z<0.3. The distribution of N/O as function of
metallicity for SN and LGRB hosts is compared with star chemical evolution
models. The results show that several LGRB hosts can be explained by star
multi-bursting models when 12+log(O/H) <8.5, while some objects follow the
trend of continuous star formation models. N/O in SN hosts at log(O/H)+12 <8.5
are not well explained by stellar chemical evolution models calculated for
starburst galaxies. At 12+log(O/H) >8.5 many different objects are nested close
to O/H solar with N/O ranging between the maximum corresponding to starburst
galaxies and AGN and the minimum corresponding to HII regions and SGRB.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,481 | Baselines and a datasheet for the Cerema AWP dataset | This paper presents the recently published Cerema AWP (Adverse Weather
Pedestrian) dataset for various machine learning tasks and its exports in
machine learning friendly format. We explain why this dataset can be
interesting (mainly because it is a greatly controlled and fully annotated
image dataset) and present baseline results for various tasks. Moreover, we
decided to follow the very recent suggestions of datasheets for dataset, trying
to standardize all the available information of the dataset, with a
transparency objective.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,482 | Characterizing Directed and Undirected Networks via Multidimensional Walks with Jumps | Estimating distributions of node characteristics (labels) such as number of
connections or citizenship of users in a social network via edge and node
sampling is a vital part of the study of complex networks. Due to its low cost,
sampling via a random walk (RW) has been proposed as an attractive solution to
this task. Most RW methods assume either that the network is undirected or that
walkers can traverse edges regardless of their direction. Some RW methods have
been designed for directed networks where edges coming into a node are not
directly observable. In this work, we propose Directed Unbiased Frontier
Sampling (DUFS), a sampling method based on a large number of coordinated
walkers, each starting from a node chosen uniformly at random. It is applicable
to directed networks with invisible incoming edges because it constructs, in
real-time, an undirected graph consistent with the walkers trajectories, and
due to the use of random jumps which prevent walkers from being trapped. DUFS
generalizes previous RW methods and is suited for undirected networks and to
directed networks regardless of in-edges visibility. We also propose an
improved estimator of node label distributions that combines information from
the initial walker locations with subsequent RW observations. We evaluate DUFS,
compare it to other RW methods, investigate the impact of its parameters on
estimation accuracy and provide practical guidelines for choosing them. In
estimating out-degree distributions, DUFS yields significantly better estimates
of the head of the distribution than other methods, while matching or exceeding
estimation accuracy of the tail. Last, we show that DUFS outperforms uniform
node sampling when estimating distributions of node labels of the top 10%
largest degree nodes, even when sampling a node uniformly has the same cost as
RW steps.
| 1 | 1 | 0 | 0 | 0 | 0 |
16,483 | Tensorial Recurrent Neural Networks for Longitudinal Data Analysis | Traditional Recurrent Neural Networks assume vectorized data as inputs.
However many data from modern science and technology come in certain structures
such as tensorial time series data. To apply the recurrent neural networks for
this type of data, a vectorisation process is necessary, while such a
vectorisation leads to the loss of the precise information of the spatial or
longitudinal dimensions. In addition, such a vectorized data is not an optimum
solution for learning the representation of the longitudinal data. In this
paper, we propose a new variant of tensorial neural networks which directly
take tensorial time series data as inputs. We call this new variant as
Tensorial Recurrent Neural Network (TRNN). The proposed TRNN is based on tensor
Tucker decomposition.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,484 | End-to-End Learning for the Deep Multivariate Probit Model | The multivariate probit model (MVP) is a popular classic model for studying
binary responses of multiple entities. Nevertheless, the computational
challenge of learning the MVP model, given that its likelihood involves
integrating over a multidimensional constrained space of latent variables,
significantly limits its application in practice. We propose a flexible deep
generalization of the classic MVP, the Deep Multivariate Probit Model (DMVP),
which is an end-to-end learning scheme that uses an efficient parallel sampling
process of the multivariate probit model to exploit GPU-boosted deep neural
networks. We present both theoretical and empirical analysis of the convergence
behavior of DMVP's sampling process with respect to the resolution of the
correlation structure. We provide convergence guarantees for DMVP and our
empirical analysis demonstrates the advantages of DMVP's sampling compared with
standard MCMC-based methods. We also show that when applied to multi-entity
modelling problems, which are natural DMVP applications, DMVP trains faster
than classical MVP, by at least an order of magnitude, captures rich
correlations among entities, and further improves the joint likelihood of
entities compared with several competitive models.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,485 | Purity and separation for oriented matroids | Leclerc and Zelevinsky, motivated by the study of quasi-commuting quantum
flag minors, introduced the notions of strongly separated and weakly separated
collections. These notions are closely related to the theory of cluster
algebras, to the combinatorics of the double Bruhat cells, and to the totally
positive Grassmannian.
A key feature, called the purity phenomenon, is that every maximal by
inclusion strongly (resp., weakly) separated collection of subsets in $[n]$ has
the same cardinality.
In this paper, we extend these notions and define $\mathcal{M}$-separated
collections, for any oriented matroid $\mathcal{M}$.
We show that maximal by size $\mathcal{M}$-separated collections are in
bijection with fine zonotopal tilings (if $\mathcal{M}$ is a realizable
oriented matroid), or with one-element liftings of $\mathcal{M}$ in general
position (for an arbitrary oriented matroid).
We introduce the class of pure oriented matroids for which the purity
phenomenon holds: an oriented matroid $\mathcal{M}$ is pure if
$\mathcal{M}$-separated collections form a pure simplicial complex, i.e., any
maximal by inclusion $\mathcal{M}$-separated collection is also maximal by
size.
We pay closer attention to several special classes of oriented matroids:
oriented matroids of rank $3$, graphical oriented matroids, and uniform
oriented matroids. We classify pure oriented matroids in these cases. An
oriented matroid of rank $3$ is pure if and only if it is a positroid (up to
reorienting and relabeling its ground set). A graphical oriented matroid is
pure if and only if its underlying graph is an outerplanar graph, that is, a
subgraph of a triangulation of an $n$-gon.
We give a simple conjectural characterization of pure oriented matroids by
forbidden minors and prove it for the above classes of matroids (rank $3$,
graphical, uniform).
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16,486 | Random Manifolds have no Totally Geodesic Submanifolds | For $n\geq 4$ we show that generic closed Riemannian $n$-manifolds have no
nontrivial totally geodesic submanifolds, answering a question of Spivak. An
immediate consequence is a severe restriction on the isometry group of a
generic Riemannian metric. Both results are widely believed to be true, but we
are not aware of any proofs in the literature.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,487 | Kinematically Redundant Octahedral Motion Platform for Virtual Reality Simulations | We propose a novel design of a parallel manipulator of Stewart Gough type for
virtual reality application of single individuals; i.e. an omni-directional
treadmill is mounted on the motion platform in order to improve VR immersion by
giving feedback to the human body. For this purpose we modify the well-known
octahedral manipulator in a way that it has one degree of kinematical
redundancy; namely an equiform reconfigurability of the base. The instantaneous
kinematics and singularities of this mechanism are studied, where especially
"unavoidable singularities" are characterized. These are poses of the motion
platform, which can only be realized by singular configurations of the
mechanism despite its kinematic redundancy.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,488 | Loss Max-Pooling for Semantic Image Segmentation | We introduce a novel loss max-pooling concept for handling imbalanced
training data distributions, applicable as alternative loss layer in the
context of deep neural networks for semantic image segmentation. Most
real-world semantic segmentation datasets exhibit long tail distributions with
few object categories comprising the majority of data and consequently biasing
the classifiers towards them. Our method adaptively re-weights the
contributions of each pixel based on their observed losses, targeting
under-performing classification results as often encountered for
under-represented object classes. Our approach goes beyond conventional
cost-sensitive learning attempts through adaptive considerations that allow us
to indirectly address both, inter- and intra-class imbalances. We provide a
theoretical justification of our approach, complementary to experimental
analyses on benchmark datasets. In our experiments on the Cityscapes and Pascal
VOC 2012 segmentation datasets we find consistently improved results,
demonstrating the efficacy of our approach.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,489 | Nonlinear Traveling Internal Waves in Depth-Varying Currents | In this work, we study the nonlinear traveling waves in density stratified
fluids with depth varying shear currents. Beginning the formulation of the
water-wave problem due to [1], we extend the work of [4] and [18] to examine
the interface between two fluids of differing densities and varying linear
shear. We derive as systems of equations depending only on variables at the
interface, and numerically solve for periodic traveling wave solutions using
numerical continuation. Here we consider only branches which bifurcate from
solutions where there is no slip in the tangential velocity at the interface
for the trivial flow. The spectral stability of these solutions is then
determined using a numerical Fourier-Floquet technique. We find that the
strength of the linear shear in each fluid impacts the stability of the
corresponding traveling wave solutions. Specifically, opposing shears may
amplify or suppress instabilities.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,490 | Instabilities of Internal Gravity Wave Beams | Internal gravity waves play a primary role in geophysical fluids: they
contribute significantly to mixing in the ocean and they redistribute energy
and momentum in the middle atmosphere. Until recently, most studies were
focused on plane wave solutions. However, these solutions are not a
satisfactory description of most geophysical manifestations of internal gravity
waves, and it is now recognized that internal wave beams with a confined
profile are ubiquitous in the geophysical context.
We will discuss the reason for the ubiquity of wave beams in stratified
fluids, related to the fact that they are solutions of the nonlinear governing
equations. We will focus more specifically on situations with a constant
buoyancy frequency. Moreover, in light of recent experimental and analytical
studies of internal gravity beams, it is timely to discuss the two main
mechanisms of instability for those beams. i) The Triadic Resonant Instability
generating two secondary wave beams. ii) The streaming instability
corresponding to the spontaneous generation of a mean flow.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,491 | Multiphoton-Excited Fluorescence of Silicon-Vacancy Color Centers in Diamond | Silicon-vacancy color centers in nanodiamonds are promising as fluorescent
labels for biological applications, with a narrow, non-bleaching emission line
at 738\,nm. Two-photon excitation of this fluorescence offers the possibility
of low-background detection at significant tissue depth with high
three-dimensional spatial resolution. We have measured the two-photon
fluorescence cross section of a negatively-charged silicon vacancy (SiV$^-$) in
ion-implanted bulk diamond to be $0.74(19) \times 10^{-50}{\rm cm^4\;s/photon}$
at an excitation wavelength of 1040\,nm. In comparison to the diamond nitrogen
vacancy (NV) center, the expected detection threshold of a two-photon excited
SiV center is more than an order of magnitude lower, largely due to its much
narrower linewidth. We also present measurements of two- and three-photon
excitation spectra, finding an increase in the two-photon cross section with
decreasing wavelength, and discuss the physical interpretation of the spectra
in the context of existing models of the SiV energy-level structure.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,492 | Exploring Latent Semantic Factors to Find Useful Product Reviews | Online reviews provided by consumers are a valuable asset for e-Commerce
platforms, influencing potential consumers in making purchasing decisions.
However, these reviews are of varying quality, with the useful ones buried deep
within a heap of non-informative reviews. In this work, we attempt to
automatically identify review quality in terms of its helpfulness to the end
consumers. In contrast to previous works in this domain exploiting a variety of
syntactic and community-level features, we delve deep into the semantics of
reviews as to what makes them useful, providing interpretable explanation for
the same. We identify a set of consistency and semantic factors, all from the
text, ratings, and timestamps of user-generated reviews, making our approach
generalizable across all communities and domains. We explore review semantics
in terms of several latent factors like the expertise of its author, his
judgment about the fine-grained facets of the underlying product, and his
writing style. These are cast into a Hidden Markov Model -- Latent Dirichlet
Allocation (HMM-LDA) based model to jointly infer: (i) reviewer expertise, (ii)
item facets, and (iii) review helpfulness. Large-scale experiments on five
real-world datasets from Amazon show significant improvement over
state-of-the-art baselines in predicting and ranking useful reviews.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,493 | Thermodynamically-consistent semi-classical $\ell$-changing rates | We compare the results of the semi-classical (SC) and quantum-mechanical (QM)
formalisms for angular-momentum changing transitions in Rydberg atom collisions
given by Vrinceanu & Flannery, J. Phys. B 34, L1 (2001), and Vrinceanu, Onofrio
& Sadeghpour, ApJ 747, 56 (2012), with those of the SC formalism using a
modified Monte Carlo realization. We find that this revised SC formalism agrees
well with the QM results. This provides further evidence that the rates derived
from the QM treatment are appropriate to be used when modelling recombination
through Rydberg cascades, an important process in understanding the state of
material in the early universe. The rates for $\Delta\ell=\pm1$ derived from
the QM formalism diverge when integrated to sufficiently large impact
parameter, $b$. Further to the empirical limits to the $b$ integration
suggested by Pengelly & Seaton, MNRAS 127, 165 (1964), we suggest that the
fundamental issue causing this divergence in the theory is that it does not
fully cater for the finite time taken for such distant collisions to complete.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,494 | A Fourier transform for the quantum Toda lattice | We introduce an algebraic Fourier transform for the quantum Toda lattice.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,495 | Semi-supervised learning | Semi-supervised learning deals with the problem of how, if possible, to take
advantage of a huge amount of not classified data, to perform classification,
in situations when, typically, the labelled data are few. Even though this is
not always possible (it depends on how useful is to know the distribution of
the unlabelled data in the inference of the labels), several algorithm have
been proposed recently. A new algorithm is proposed, that under almost
neccesary conditions, attains asymptotically the performance of the best
theoretical rule, when the size of unlabeled data tends to infinity. The set of
necessary assumptions, although reasonables, show that semi-parametric
classification only works for very well conditioned problems.
| 0 | 0 | 1 | 1 | 0 | 0 |
16,496 | The Cosmic-Ray Neutron Rover - Mobile Surveys of Field Soil Moisture and the Influence of Roads | Measurements of root-zone soil moisture across spatial scales of tens to
thousands of meters have been a challenge for many decades. The mobile
application of Cosmic-Ray Neutron Sensing (CRNS) is a promising approach to
measure field soil moisture non-invasively by surveying large regions with a
ground-based vehicle. Recently, concerns have been raised about a potentially
biasing influence of local structures and roads. We employed neutron transport
simulations and dedicated experiments to quantify the influence of different
road types on the CRNS measurement. We found that the presence of roads
introduces a bias in the CRNS estimation of field soil moisture compared to
non-road scenarios. However, this effect becomes insignificant at distances
beyond a few meters from the road. Measurements from the road could
overestimate the field value by up to 40 % depending on road material, width,
and the surrounding field water content. The bias could be successfully removed
with an analytical correction function that accounts for these parameters.
Additionally, an empirical approach is proposed that can be used on-the-fly
without prior knowledge of field soil moisture. Tests at different study sites
demonstrated good agreement between road-effect corrected measurements and
field soil moisture observations. However, if knowledge about the road
characteristics is missing, any measurements on the road could substantially
reduce the accuracy of this method. Our results constitute a practical
advancement of the mobile CRNS methodology, which is important for providing
unbiased estimates of field-scale soil moisture to support applications in
hydrology, remote sensing, and agriculture.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,497 | Invariance in Constrained Switching | We study discrete time linear constrained switching systems with additive
disturbances, in which the switching may be on the system matrices, the
disturbance sets, the state constraint sets or a combination of the above. In
our general setting, a switching sequence is admissible if it is accepted by an
automaton. For this family of systems, stability does not necessarily imply the
existence of an invariant set. Nevertheless, it does imply the existence of an
invariant multi-set, which is a relaxation of invariance and the object of our
work. First, we establish basic results concerning the characterization,
approximation and computation of the minimal and the maximal admissible
invariant multi-set. Second, by exploiting the topological properties of the
directed graph which defines the switching constraints, we propose invariant
multi-set constructions with several benefits. We illustrate our results in
benchmark problems in control.
| 1 | 0 | 1 | 0 | 0 | 0 |
16,498 | On the choice of the low-dimensional domain for global optimization via random embeddings | The challenge of taking many variables into account in optimization problems
may be overcome under the hypothesis of low effective dimensionality. Then, the
search of solutions can be reduced to the random embedding of a low dimensional
space into the original one, resulting in a more manageable optimization
problem. Specifically, in the case of time consuming black-box functions and
when the budget of evaluations is severely limited, global optimization with
random embeddings appears as a sound alternative to random search. Yet, in the
case of box constraints on the native variables, defining suitable bounds on a
low dimensional domain appears to be complex. Indeed, a small search domain
does not guarantee to find a solution even under restrictive hypotheses about
the function, while a larger one may slow down convergence dramatically. Here
we tackle the issue of low-dimensional domain selection based on a detailed
study of the properties of the random embedding, giving insight on the
aforementioned difficulties. In particular, we describe a minimal
low-dimensional set in correspondence with the embedded search space. We
additionally show that an alternative equivalent embedding procedure yields
simultaneously a simpler definition of the low-dimensional minimal set and
better properties in practice. Finally, the performance and robustness gains of
the proposed enhancements for Bayesian optimization are illustrated on
numerical examples.
| 0 | 0 | 1 | 1 | 0 | 0 |
16,499 | Distributed resource allocation through utility design - Part II: applications to submodular, supermodular and set covering problems | A fundamental component of the game theoretic approach to distributed control
is the design of local utility functions. In Part I of this work we showed how
to systematically design local utilities so as to maximize the induced worst
case performance. The purpose of the present manuscript is to specialize the
general results obtained in Part I to a class of monotone submodular,
supermodular and set covering problems. In the case of set covering problems,
we show how any distributed algorithm capable of computing a Nash equilibrium
inherits a performance certificate matching the well known 1-1/e approximation
of Nemhauser. Relative to the class of submodular maximization problems
considered here, we show how the performance offered by the game theoretic
approach improves on existing approximation algorithms. We briefly discuss the
algorithmic complexity of computing (pure) Nash equilibria and show how our
approach generalizes and subsumes previously fragmented results in the area of
optimal utility design. Two applications and corresponding numerics are
presented: the vehicle target assignment problem and a coverage problem arising
in distributed caching for wireless networks.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,500 | Quasiparticle band structure engineering in van der Waals heterostructures via dielectric screening | The idea of combining different two-dimensional (2D) crystals in van der
Waals heterostructures (vdWHs) has led to a new paradigm for band structure
engineering with atomic precision. Due to the weak interlayer couplings, the
band structures of the individual 2D crystals are largely preserved upon
formation of the heterostructure. However, regardless of the details of the
interlayer hybridisation, the size of the 2D crystal band gaps are always
reduced due to the enhanced dielectric screening provided by the surrounding
layers. The effect can be on the order of electron volts, but its precise
magnitude is non-trivial to predict because of the non-local nature of the
screening in quasi-2D materials, and it is not captured by effective
single-particle methods such as density functional theory. Here we present an
efficient and general method for calculating the band gap renormalization of a
2D material embedded in an arbitrary vdWH. The method evaluates the change in
the GW self-energy of the 2D material from the change in the screened Coulomb
interaction. The latter is obtained using the quantum-electrostatic
heterostructure (QEH) model. We benchmark the G$\Delta$W method against full
first-principles GW calculations and use it to unravel the importance of
screening-induced band structure renormalisation in various vdWHs. A main
result is the observation that the size of the band gap reduction of a given 2D
material when inserted into a heterostructure scales inversely with the
polarisability of the 2D material. Our work demonstrates that dielectric
engineering \emph{via} van der Waals heterostructuring represents a promising
strategy for tailoring the band structure of 2D materials.
| 0 | 1 | 0 | 0 | 0 | 0 |
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