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16,901 | Exploring cosmic origins with CORE: mitigation of systematic effects | We present an analysis of the main systematic effects that could impact the
measurement of CMB polarization with the proposed CORE space mission. We employ
timeline-to-map simulations to verify that the CORE instrumental set-up and
scanning strategy allow us to measure sky polarization to a level of accuracy
adequate to the mission science goals. We also show how the CORE observations
can be processed to mitigate the level of contamination by potentially worrying
systematics, including intensity-to-polarization leakage due to bandpass
mismatch, asymmetric main beams, pointing errors and correlated noise. We use
analysis techniques that are well validated on data from current missions such
as Planck to demonstrate how the residual contamination of the measurements by
these effects can be brought to a level low enough not to hamper the scientific
capability of the mission, nor significantly increase the overall error budget.
We also present a prototype of the CORE photometric calibration pipeline, based
on that used for Planck, and discuss its robustness to systematics, showing how
CORE can achieve its calibration requirements. While a fine-grained assessment
of the impact of systematics requires a level of knowledge of the system that
can only be achieved in a future study phase, the analysis presented here
strongly suggests that the main areas of concern for the CORE mission can be
addressed using existing knowledge, techniques and algorithms.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,902 | A non-ordinary peridynamics implementation for anisotropic materials | Peridynamics (PD) represents a new approach for modelling fracture mechanics,
where a continuum domain is modelled through particles connected via physical
bonds. This formulation allows us to model crack initiation, propagation,
branching and coalescence without special assumptions. Up to date, anisotropic
materials were modelled in the PD framework as different isotropic materials
(for instance, fibre and matrix of a composite laminate), where the stiffness
of the bond depends on its orientation. A non-ordinary state-based formulation
will enable the modelling of generally anisotropic materials, where the
material properties are directly embedded in the formulation. Other material
models include rocks, concrete and biomaterials such as bones. In this paper,
we implemented this model and validated it for anisotropic composite materials.
A composite damage criterion has been employed to model the crack propagation
behaviour. Several numerical examples have been used to validate the approach,
and compared to other benchmark solution from the finite element method (FEM)
and experimental results when available.
| 1 | 1 | 0 | 0 | 0 | 0 |
16,903 | Discrete-attractor-like Tracking in Continuous Attractor Neural Networks | Continuous attractor neural networks generate a set of smoothly connected
attractor states. In memory systems of the brain, these attractor states may
represent continuous pieces of information such as spatial locations and head
directions of animals. However, during the replay of previous experiences,
hippocampal neurons show a discontinuous sequence in which discrete transitions
of neural state are phase-locked with the slow-gamma (30-40 Hz) oscillation.
Here, we explored the underlying mechanisms of the discontinuous sequence
generation. We found that a continuous attractor neural network has several
phases depending on the interactions between external input and local
inhibitory feedback. The discrete-attractor-like behavior naturally emerges in
one of these phases without any discreteness assumption. We propose that the
dynamics of continuous attractor neural networks is the key to generate
discontinuous state changes phase-locked to the brain rhythm.
| 0 | 0 | 0 | 0 | 1 | 0 |
16,904 | Framework for an Innovative Perceptive Mobile Network Using Joint Communication and Sensing | In this paper, we develop a framework for an innovative perceptive mobile
(i.e. cellular) network that integrates sensing with communication, and
supports new applications widely in transportation, surveillance and
environmental sensing. Three types of sensing methods implemented in the
base-stations are proposed, using either uplink or downlink multiuser
communication signals. The required changes to system hardware and major
technical challenges are briefly discussed. We also demonstrate the feasibility
of estimating sensing parameters via developing a compressive sensing based
scheme and providing simulation results to validate its effectiveness.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,905 | On the smallest non-abelian quotient of $\mathrm{Aut}(F_n)$ | We show that the smallest non-abelian quotient of $\mathrm{Aut}(F_n)$ is
$\mathrm{PSL}_n(\mathbb{Z}/2\mathbb{Z}) = \mathrm{L}_n(2)$, thus confirming a
conjecture of Mecchia--Zimmermann. In the course of the proof we give an
exponential (in $n$) lower bound for the cardinality of a set on which
$\mathrm{SAut}(F_n)$, the unique index $2$ subgroup of $\mathrm{Aut}(F_n)$, can
act non-trivially. We also offer new results on the representation theory of
$\mathrm{SAut(F_n)}$ in small dimensions over small, positive characteristics,
and on rigidity of maps from $\mathrm{SAut}(F_n)$ to finite groups of Lie type
and algebraic groups in characteristic $2$.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,906 | Property Testing in High Dimensional Ising models | This paper explores the information-theoretic limitations of graph property
testing in zero-field Ising models. Instead of learning the entire graph
structure, sometimes testing a basic graph property such as connectivity, cycle
presence or maximum clique size is a more relevant and attainable objective.
Since property testing is more fundamental than graph recovery, any necessary
conditions for property testing imply corresponding conditions for graph
recovery, while custom property tests can be statistically and/or
computationally more efficient than graph recovery based algorithms.
Understanding the statistical complexity of property testing requires the
distinction of ferromagnetic (i.e., positive interactions only) and general
Ising models. Using combinatorial constructs such as graph packing and strong
monotonicity, we characterize how target properties affect the corresponding
minimax upper and lower bounds within the realm of ferromagnets. On the other
hand, by studying the detection of an antiferromagnetic (i.e., negative
interactions only) Curie-Weiss model buried in Rademacher noise, we show that
property testing is strictly more challenging over general Ising models. In
terms of methodological development, we propose two types of correlation based
tests: computationally efficient screening for ferromagnets, and score type
tests for general models, including a fast cycle presence test. Our correlation
screening tests match the information-theoretic bounds for property testing in
ferromagnets.
| 0 | 0 | 1 | 1 | 0 | 0 |
16,907 | Stratification and duality for homotopical groups | In this paper, we show that the category of module spectra over
$C^*(B\mathcal{G},\mathbb{F}_p)$ is stratified for any $p$-local compact group
$\mathcal{G}$, thereby giving a support-theoretic classification of all
localizing subcategories of this category. To this end, we generalize Quillen's
$F$-isomorphism theorem, Quillen's stratification theorem, Chouinard's theorem,
and the finite generation of cohomology rings from finite groups to homotopical
groups. Moreover, we show that $p$-compact groups admit a homotopical form of
Gorenstein duality.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,908 | Efficiently and easily integrating differential equations with JiTCODE, JiTCDDE, and JiTCSDE | We present a family of Python modules for the numerical integration of
ordinary, delay, or stochastic differential equations. The key features are
that the user enters the derivative symbolically and it is
just-in-time-compiled, allowing the user to efficiently integrate differential
equations from a higher-level interpreted language. The presented modules are
particularly suited for large systems of differential equations such as used to
describe dynamics on complex networks. Through the selected method of input,
the presented modules also allow to almost completely automatize the process of
estimating regular as well as transversal Lyapunov exponents for ordinary and
delay differential equations. We conceptually discuss the modules' design,
analyze their performance, and demonstrate their capabilities by application to
timely problems.
| 1 | 1 | 0 | 0 | 0 | 0 |
16,909 | Adaptive Diffusions for Scalable Learning over Graphs | Diffusion-based classifiers such as those relying on the Personalized
PageRank and the Heat kernel, enjoy remarkable classification accuracy at
modest computational requirements. Their performance however is affected by the
extent to which the chosen diffusion captures a typically unknown label
propagation mechanism, that can be specific to the underlying graph, and
potentially different for each class. The present work introduces a
disciplined, data-efficient approach to learning class-specific diffusion
functions adapted to the underlying network topology. The novel learning
approach leverages the notion of "landing probabilities" of class-specific
random walks, which can be computed efficiently, thereby ensuring scalability
to large graphs. This is supported by rigorous analysis of the properties of
the model as well as the proposed algorithms. Furthermore, a robust version of
the classifier facilitates learning even in noisy environments.
Classification tests on real networks demonstrate that adapting the diffusion
function to the given graph and observed labels, significantly improves the
performance over fixed diffusions; reaching -- and many times surpassing -- the
classification accuracy of computationally heavier state-of-the-art competing
methods, that rely on node embeddings and deep neural networks.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,910 | On the Discrimination Power and Effective Utilization of Active Learning Measures in Version Space Search | Active Learning (AL) methods have proven cost-saving against passive
supervised methods in many application domains. An active learner, aiming to
find some target hypothesis, formulates sequential queries to some oracle. The
set of hypotheses consistent with the already answered queries is called
version space. Several query selection measures (QSMs) for determining the best
query to ask next have been proposed. Assuming binaryoutcome queries, we
analyze various QSMs wrt. to the discrimination power of their selected queries
within the current version space. As a result, we derive superiority and
equivalence relations between these QSMs and introduce improved versions of
existing QSMs to overcome identified issues. The obtained picture gives a hint
about which QSMs should preferably be used in pool-based AL scenarios.
Moreover, we deduce properties optimal queries wrt. QSMs must satisfy. Based on
these, we demonstrate how efficient heuristic search methods for optimal
queries in query synthesis AL scenarios can be devised.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,911 | Torchbearer: A Model Fitting Library for PyTorch | We introduce torchbearer, a model fitting library for pytorch aimed at
researchers working on deep learning or differentiable programming. The
torchbearer library provides a high level metric and callback API that can be
used for a wide range of applications. We also include a series of built in
callbacks that can be used for: model persistence, learning rate decay,
logging, data visualization and more. The extensive documentation includes an
example library for deep learning and dynamic programming problems and can be
found at this http URL. The code is licensed under the MIT
License and available at this https URL.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,912 | On estimation of contamination from hydrogen cyanide in carbon monoxide line intensity mapping | Line-intensity mapping surveys probe large-scale structure through spatial
variations in molecular line emission from a population of unresolved
cosmological sources. Future such surveys of carbon monoxide line emission,
specifically the CO(1-0) line, face potential contamination from a disjoint
population of sources emitting in a hydrogen cyanide emission line, HCN(1-0).
This paper explores the potential range of the strength of HCN emission and its
effect on the CO auto power spectrum, using simulations with an empirical model
of the CO/HCN--halo connection. We find that effects on the observed CO power
spectrum depend on modeling assumptions but are very small for our fiducial
model based on our understanding of the galaxy--halo connection, with the bias
in overall CO detection significance due to HCN expected to be less than 1%.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,913 | Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints | Classifiers can be trained with data-dependent constraints to satisfy
fairness goals, reduce churn, achieve a targeted false positive rate, or other
policy goals. We study the generalization performance for such constrained
optimization problems, in terms of how well the constraints are satisfied at
evaluation time, given that they are satisfied at training time. To improve
generalization performance, we frame the problem as a two-player game where one
player optimizes the model parameters on a training dataset, and the other
player enforces the constraints on an independent validation dataset. We build
on recent work in two-player constrained optimization to show that if one uses
this two-dataset approach, then constraint generalization can be significantly
improved. As we illustrate experimentally, this approach works not only in
theory, but also in practice.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,914 | Automated Website Fingerprinting through Deep Learning | Several studies have shown that the network traffic that is generated by a
visit to a website over Tor reveals information specific to the website through
the timing and sizes of network packets. By capturing traffic traces between
users and their Tor entry guard, a network eavesdropper can leverage this
meta-data to reveal which website Tor users are visiting. The success of such
attacks heavily depends on the particular set of traffic features that are used
to construct the fingerprint. Typically, these features are manually engineered
and, as such, any change introduced to the Tor network can render these
carefully constructed features ineffective. In this paper, we show that an
adversary can automate the feature engineering process, and thus automatically
deanonymize Tor traffic by applying our novel method based on deep learning. We
collect a dataset comprised of more than three million network traces, which is
the largest dataset of web traffic ever used for website fingerprinting, and
find that the performance achieved by our deep learning approaches is
comparable to known methods which include various research efforts spanning
over multiple years. The obtained success rate exceeds 96% for a closed world
of 100 websites and 94% for our biggest closed world of 900 classes. In our
open world evaluation, the most performant deep learning model is 2% more
accurate than the state-of-the-art attack. Furthermore, we show that the
implicit features automatically learned by our approach are far more resilient
to dynamic changes of web content over time. We conclude that the ability to
automatically construct the most relevant traffic features and perform accurate
traffic recognition makes our deep learning based approach an efficient,
flexible and robust technique for website fingerprinting.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,915 | DataCite as a novel bibliometric source: Coverage, strengths and limitations | This paper explores the characteristics of DataCite to determine its
possibilities and potential as a new bibliometric data source to analyze the
scholarly production of open data. Open science and the increasing data sharing
requirements from governments, funding bodies, institutions and scientific
journals has led to a pressing demand for the development of data metrics. As a
very first step towards reliable data metrics, we need to better comprehend the
limitations and caveats of the information provided by sources of open data. In
this paper, we critically examine records downloaded from the DataCite's OAI
API and elaborate a series of recommendations regarding the use of this source
for bibliometric analyses of open data. We highlight issues related to metadata
incompleteness, lack of standardization, and ambiguous definitions of several
fields. Despite these limitations, we emphasize DataCite's value and potential
to become one of the main sources for data metrics development.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,916 | Parameter Estimation of Complex Fractional Ornstein-Uhlenbeck Processes with Fractional Noise | We obtain strong consistency and asymptotic normality of a least squares
estimator of the drift coefficient for complex-valued Ornstein-Uhlenbeck
processes disturbed by fractional noise, extending the result of Y. Hu and D.
Nualart, [Statist. Probab. Lett., 80 (2010), 1030-1038] to a special
2-dimensions. The strategy is to exploit the Garsia-Rodemich-Rumsey inequality
and complex fourth moment theorems. The main ingredients of this paper are the
sample path regularity of a multiple Wiener-Ito integral and two equivalent
conditions of complex fourth moment theorems in terms of the contractions of
integral kernels and complex Malliavin derivatives.
| 0 | 0 | 1 | 1 | 0 | 0 |
16,917 | E-learning Information Technology Based on an Ontology Driven Learning Engine | In the article, proposed is a new e-learning information technology based on
an ontology driven learning engine, which is matched with modern pedagogical
technologies. With the help of proposed engine and developed question database
we have conducted an experiment, where students were tested. The developed
ontology driven system of e-learning facilitates the creation of favorable
conditions for the development of personal qualities and creation of a holistic
understanding of the subject area among students throughout the educational
process.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,918 | Global regularity for 1D Eulerian dynamics with singular interaction forces | The Euler-Poisson-Alignment (EPA) system appears in mathematical biology and
is used to model, in a hydrodynamic limit, a set agents interacting through
mutual attraction/repulsion as well as alignment forces. We consider
one-dimensional EPA system with a class of singular alignment terms as well as
natural attraction/repulsion terms. The singularity of the alignment kernel
produces an interesting effect regularizing the solutions of the equation and
leading to global regularity for wide range of initial data. This was recently
observed in the paper by Do, Kiselev, Ryzhik and Tan. Our goal in this paper is
to generalize the result and to incorporate the attractive/repulsive potential.
We prove that global regularity persists for these more general models.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,919 | A $q$-generalization of the para-Racah polynomials | New bispectral orthogonal polynomials are obtained from an unconventional
truncation of the Askey-Wilson polynomials. In the limit $q \to 1$, they reduce
to the para-Racah polynomials which are orthogonal with respect to a quadratic
bi-lattice. The three term recurrence relation and q-difference equation are
obtained through limits of those of the Askey-Wilson polynomials. An explicit
expression in terms of hypergeometric series and the orthogonality relation are
provided. A $q$-generalization of the para-Krawtchouk polynomials is obtained
as a special case. Connections with the $q$-Racah and dual-Hahn polynomials are
also presented.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,920 | Data Poisoning Attack against Unsupervised Node Embedding Methods | Unsupervised node embedding methods (e.g., DeepWalk, LINE, and node2vec) have
attracted growing interests given their simplicity and effectiveness. However,
although these methods have been proved effective in a variety of applications,
none of the existing work has analyzed the robustness of them. This could be
very risky if these methods are attacked by an adversarial party. In this
paper, we take the task of link prediction as an example, which is one of the
most fundamental problems for graph analysis, and introduce a data positioning
attack to node embedding methods. We give a complete characterization of
attacker's utilities and present efficient solutions to adversarial attacks for
two popular node embedding methods: DeepWalk and LINE. We evaluate our proposed
attack model on multiple real-world graphs. Experimental results show that our
proposed model can significantly affect the results of link prediction by
slightly changing the graph structures (e.g., adding or removing a few edges).
We also show that our proposed model is very general and can be transferable
across different embedding methods. Finally, we conduct a case study on a
coauthor network to better understand our attack method.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,921 | Entanglement properties of the two-dimensional SU(3) AKLT state | Two-dimensional (spin-$2$) Affleck-Kennedy-Lieb-Tasaki (AKLT) type valence
bond solids on the square lattice are known to be symmetry protected
topological (SPT) gapped spin liquids [Shintaro Takayoshi, Pierre Pujol, and
Akihiro Tanaka Phys. Rev. B ${\bf 94}$, 235159 (2016)]. Using the projected
entangled pair state (PEPS) framework, we extend the construction of the AKLT
state to the case of $SU(3)$, relevant for cold atom systems. The entanglement
spectrum is shown to be described by an alternating $SU(3)$ chain of "quarks"
and "antiquarks", subject to exponentially decaying (with distance) Heisenberg
interactions, in close similarity with its $SU(2)$ analog. We discuss the SPT
feature of the state.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,922 | Heart Rate Variability during Periods of Low Blood Pressure as a Predictor of Short-Term Outcome in Preterms | Efficient management of low blood pressure (BP) in preterm neonates remains
challenging with a considerable variability in clinical practice. The ability
to assess preterm wellbeing during episodes of low BP will help to decide when
and whether hypotension treatment should be initiated. This work aims to
investigate the relationship between heart rate variability (HRV), BP and the
short-term neurological outcome in preterm infants less than 32 weeks
gestational age (GA). The predictive power of common HRV features with respect
to the outcome is assessed and shown to improve when HRV is observed during
episodes of low mean arterial pressure (MAP) - with a single best feature
leading to an AUC of 0.87. Combining multiple features with a boosted decision
tree classifier achieves an AUC of 0.97. The work presents a promising step
towards the use of multimodal data in building an objective decision support
tool for clinical prediction of short-term outcome in preterms who suffer
episodes of low BP.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,923 | Understanding News Outlets' Audience-Targeting Patterns | The power of the press to shape the informational landscape of a population
is unparalleled, even now in the era of democratic access to all information
outlets. However, it is known that news outlets (particularly more traditional
ones) tend to discriminate who they want to reach, and who to leave aside. In
this work, we attempt to shed some light on the audience targeting patterns of
newspapers, using the Chilean media ecosystem. First, we use the gravity model
to analyze geography as a factor in explaining audience reachability. This
shows that some newspapers are indeed driven by geographical factors (mostly
local news outlets) but some others are not (national-distribution outlets).
For those which are not, we use a regression model to study the influence of
socioeconomic and political characteristics in news outlets adoption. We
conclude that indeed larger, national-distribution news outlets target
populations based on these factors, rather than on geography or immediacy.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,924 | Deriving a Representative Vector for Ontology Classes with Instance Word Vector Embeddings | Selecting a representative vector for a set of vectors is a very common
requirement in many algorithmic tasks. Traditionally, the mean or median vector
is selected. Ontology classes are sets of homogeneous instance objects that can
be converted to a vector space by word vector embeddings. This study proposes a
methodology to derive a representative vector for ontology classes whose
instances were converted to the vector space. We start by deriving five
candidate vectors which are then used to train a machine learning model that
would calculate a representative vector for the class. We show that our
methodology out-performs the traditional mean and median vector
representations.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,925 | The ESA Gaia Archive: Data Release 1 | ESA Gaia mission is producing the more accurate source catalogue in astronomy
up to now. That represents a challenge on the archiving area to make accessible
this information to the astronomers in an efficient way. Also, new astronomical
missions have reinforced the change on the development of archives. Archives,
as simple applications to access the data are being evolving into complex data
center structures where computing power services are available for users and
data mining tools are integrated into the server side. In the case of astronomy
science that involves the use of big catalogues, as in Gaia (or Euclid to
come), the common ways to work on the data need to be changed to a new paradigm
"move code close to the data", what implies that data mining functionalities
are becoming a must to allow the science exploitation. To enable these
capabilities, a TAP+ interface, crossmatch capabilities, full catalogue
histograms, serialisation of intermediate results in cloud resources like
VOSpace, etc have been implemented for the Gaia DR1, to enable the exploitation
of these science resources by the community without the bottlenecks on the
connection bandwidth. We present the architecture, infrastructure and tools
already available in the Gaia Archive Data Release 1
(this http URL) and we describe capabilities and
infrastructure.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,926 | Efficient algorithm for large spectral partitions | We present an amelioration of current known algorithms for optimal spectral
partitioning problems. The idea is to use the advantage of a representation
using density functions while decreasing the computational time. This is done
by restricting the computation to neighbourhoods of regions where the
associated densities are above a certain threshold. The algorithm extends and
improves known methods in the plane and on surfaces in dimension 3. It also
makes possible to make some of the first computations of volumic 3D spectral
partitions on sufficiently large discretizations.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,927 | A Martian Origin for the Mars Trojan Asteroids | Seven of the nine known Mars Trojan asteroids belong to an orbital cluster
named after its largest member 5261 Eureka. Eureka is likely the progenitor of
the whole cluster, which formed at least 1 Gyr ago. It was suggested that the
thermal YORP effect spun-up Eureka resulting with fragments being ejected by
the rotational-fission mechanism. Eureka's spectrum exhibits a broad and deep
absorption band around 1 {\mu}m, indicating an olivine-rich composition. Here
we show evidence that the Trojan Eureka cluster progenitor could have
originated as impact debris excavated from the Martian mantle. We present new
near-infrared observations of two Trojans (311999 2007 NS2 and 385250 2001
DH47) and find that both exhibit an olivine-rich reflectance spectrum similar
to Eureka's. These measurements confirm that the progenitor of the cluster has
an achondritic composition. Olivine-rich reflectance spectra are rare amongst
asteroids but are seen around the largest basins on Mars. They are also
consistent with some Martian meteorites (e.g. Chassigny), and with the material
comprising much of the Martian mantle. Using numerical simulations, we show
that the Mars Trojans are more likely to be impact ejecta from Mars than
captured olivine-rich asteroids transported from the main belt. This result
links directly specific asteroids to debris from the forming planets.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,928 | Far-infrared metallicity diagnostics: Application to local ultraluminous infrared galaxies | The abundance of metals in galaxies is a key parameter which permits to
distinguish between different galaxy formation and evolution models. Most of
the metallicity determinations are based on optical line ratios. However, the
optical spectral range is subject to dust extinction and, for high-z objects (z
> 3), some of the lines used in optical metallicity diagnostics are shifted to
wavelengths not accessible to ground based observatories. For this reason, we
explore metallicity diagnostics using far-infrared (IR) line ratios which can
provide a suitable alternative in such situations. To investigate these far-IR
line ratios, we modeled the emission of a starburst with the photoionization
code CLOUDY. The most sensitive far-IR ratios to measure metallicities are the
[OIII]52$\mu$m and 88$\mu$m to [NIII]57$\mu$m ratios. We show that this ratio
produces robust metallicities in the presence of an AGN and is insensitive to
changes in the age of the ionizing stellar. Another metallicity sensitive ratio
is the [OIII]88$\mu$m/[NII]122$\mu$m ratio, although it depends on the
ionization parameter. We propose various mid- and far-IR line ratios to break
this dependency. Finally, we apply these far-IR diagnostics to a sample of 19
local ultraluminous IR galaxies (ULIRGs) observed with Herschel and Spitzer. We
find that the gas-phase metallicity in these local ULIRGs is in the range 0.7 <
Z_gas/Z_sun < 1.5, which corresponds to 8.5 < 12 + log (O/H) < 8.9. The
inferred metallicities agree well with previous estimates for local ULIRGs and
this confirms that they lie below the local mass-metallicity relation.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,929 | Quantum communication by means of collapse of the wave function | We show that quantum communication by means of collapse of the wave function
is possible. In this study, quantum communication does not mean quantum
teleportation or quantum cryptography, but transmission of information itself.
Because of consistency with special relativity, the possibility of the quantum
communication leads to another conclusion that the collapse of the wave
function must propagate at the speed of light or slower.
We show this requirement is consistent with nonlocality in quantum mechanics.
We also demonstrate that the Einstein-Podolsky-Rosen experiment does not
disprove our conclusion.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,930 | DeepTerramechanics: Terrain Classification and Slip Estimation for Ground Robots via Deep Learning | Terramechanics plays a critical role in the areas of ground vehicles and
ground mobile robots since understanding and estimating the variables
influencing the vehicle-terrain interaction may mean the success or the failure
of an entire mission. This research applies state-of-the-art algorithms in deep
learning to two key problems: estimating wheel slip and classifying the terrain
being traversed by a ground robot. Three data sets collected by ground robotic
platforms (MIT single-wheel testbed, MSL Curiosity rover, and tracked robot
Fitorobot) are employed in order to compare the performance of traditional
machine learning methods (i.e. Support Vector Machine (SVM) and Multi-layer
Perceptron (MLP)) against Deep Neural Networks (DNNs) and Convolutional Neural
Networks (CNNs). This work also shows the impact that certain tuning parameters
and the network architecture (MLP, DNN and CNN) play on the performance of
those methods. This paper also contributes a deep discussion with the lessons
learned in the implementation of DNNs and CNNs and how these methods can be
extended to solve other problems.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,931 | Characterizations of multinormality and corresponding tests of fit, including for Garch models | We provide novel characterizations of multivariate normality that incorporate
both the characteristic function and the moment generating function, and we
employ these results to construct a class of affine invariant, consistent and
easy-to-use goodness-of-fit tests for normality. The test statistics are
suitably weighted $L^2$-statistics, and we provide their asymptotic behavior
both for i.i.d. observations as well as in the context of testing that the
innovation distribution of a multivariate GARCH model is Gaussian. We also
study the finite-sample behavior of the new tests and compare the new criteria
with alternative existing tests.
| 0 | 0 | 1 | 1 | 0 | 0 |
16,932 | Grouped Gaussian Processes for Solar Power Prediction | We consider multi-task regression models where the observations are assumed
to be a linear combination of several latent node functions and weight
functions, which are both drawn from Gaussian process priors. Driven by the
problem of developing scalable methods for forecasting distributed solar and
other renewable power generation, we propose coupled priors over groups of
(node or weight) processes to exploit spatial dependence between functions. We
estimate forecast models for solar power at multiple distributed sites and
ground wind speed at multiple proximate weather stations. Our results show that
our approach maintains or improves point-prediction accuracy relative to
competing solar benchmarks and improves over wind forecast benchmark models on
all measures. Our approach consistently dominates the equivalent model without
coupled priors, achieving faster gains in forecast accuracy. At the same time
our approach provides better quantification of predictive uncertainties.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,933 | Modeling epidemics on d-cliqued graphs | Since social interactions have been shown to lead to symmetric clusters, we
propose here that symmetries play a key role in epidemic modeling. Mathematical
models on d-ary tree graphs were recently shown to be particularly effective
for modeling epidemics in simple networks [Seibold & Callender, 2016]. To
account for symmetric relations, we generalize this to a new type of networks
modeled on d-cliqued tree graphs, which are obtained by adding edges to regular
d-trees to form d-cliques. This setting gives a more realistic model for
epidemic outbreaks originating, for example, within a family or classroom and
which could reach a population by transmission via children in schools.
Specifically, we quantify how an infection starting in a clique (e.g. family)
can reach other cliques through the body of the graph (e.g. public places).
Moreover, we propose and study the notion of a safe zone, a subset that has a
negligible probability of infection.
| 1 | 0 | 0 | 0 | 1 | 0 |
16,934 | On the K-theory stable bases of the Springer resolution | Cohomological and K-theoretic stable bases originated from the study of
quantum cohomology and quantum K-theory. Restriction formula for cohomological
stable bases played an important role in computing the quantum connection of
cotangent bundle of partial flag varieties. In this paper we study the
K-theoretic stable bases of cotangent bundles of flag varieties. We describe
these bases in terms of the action of the affine Hecke algebra and the twisted
group algebra of Kostant-Kumar. Using this algebraic description and the method
of root polynomials, we give a restriction formula of the stable bases. We
apply it to obtain the restriction formula for partial flag varieties. We also
build a relation between the stable basis and the Casselman basis in the
principal series representations of the Langlands dual group. As an
application, we give a closed formula for the transition matrix between
Casselman basis and the characteristic functions.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,935 | Recency Bias in the Era of Big Data: The Need to Strengthen the Status of History of Mathematics in Nigerian Schools | The amount of information available to the mathematics teacher is so enormous
that the selection of desirable content is gradually becoming a huge task in
itself. With respect to the inclusion of elements of history of mathematics in
mathematics instruction, the era of Big Data introduces a high likelihood of
Recency Bias, a hitherto unconnected challenge for stakeholders in mathematics
education. This tendency to choose recent information at the expense of
relevant older, composite, historical facts stands to defeat the aims and
objectives of the epistemological and cultural approach to mathematics
instructional delivery. This study is a didactic discourse with focus on this
threat to the history and pedagogy of mathematics, particularly as it affects
mathematics education in Nigeria. The implications for mathematics curriculum
developers, teacher-training programmes, teacher lesson preparation, and
publication of mathematics instructional materials were also deeply considered.
| 1 | 0 | 1 | 0 | 0 | 0 |
16,936 | Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings | This paper presents a convergence analysis of kernel-based quadrature rules
in misspecified settings, focusing on deterministic quadrature in Sobolev
spaces. In particular, we deal with misspecified settings where a test
integrand is less smooth than a Sobolev RKHS based on which a quadrature rule
is constructed. We provide convergence guarantees based on two different
assumptions on a quadrature rule: one on quadrature weights, and the other on
design points. More precisely, we show that convergence rates can be derived
(i) if the sum of absolute weights remains constant (or does not increase
quickly), or (ii) if the minimum distance between design points does not
decrease very quickly. As a consequence of the latter result, we derive a rate
of convergence for Bayesian quadrature in misspecified settings. We reveal a
condition on design points to make Bayesian quadrature robust to
misspecification, and show that, under this condition, it may adaptively
achieve the optimal rate of convergence in the Sobolev space of a lesser order
(i.e., of the unknown smoothness of a test integrand), under a slightly
stronger regularity condition on the integrand.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,937 | The toric Frobenius morphism and a conjecture of Orlov | We combine the Bondal-Uehara method for producing exceptional collections on
toric varieties with a result of the first author and Favero to expand the set
of varieties satisfying Orlov's Conjecture on derived dimension.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,938 | Friendship Maintenance and Prediction in Multiple Social Networks | Due to the proliferation of online social networks (OSNs), users find
themselves participating in multiple OSNs. These users leave their activity
traces as they maintain friendships and interact with other users in these
OSNs. In this work, we analyze how users maintain friendship in multiple OSNs
by studying users who have accounts in both Twitter and Instagram.
Specifically, we study the similarity of a user's friendship and the evenness
of friendship distribution in multiple OSNs. Our study shows that most users in
Twitter and Instagram prefer to maintain different friendships in the two OSNs,
keeping only a small clique of common friends in across the OSNs. Based upon
our empirical study, we conduct link prediction experiments to predict missing
friendship links in multiple OSNs using the neighborhood features, neighborhood
friendship maintenance features and cross-link features. Our link prediction
experiments shows that un- supervised methods can yield good accuracy in
predicting links in one OSN using another OSN data and the link prediction
accuracy can be further improved using supervised method with friendship
maintenance and others measures as features.
| 1 | 1 | 0 | 0 | 0 | 0 |
16,939 | Learning to Generate Music with BachProp | As deep learning advances, algorithms of music composition increase in
performance. However, most of the successful models are designed for specific
musical structures. Here, we present BachProp, an algorithmic composer that can
generate music scores in many styles given sufficient training data. To adapt
BachProp to a broad range of musical styles, we propose a novel representation
of music and train a deep network to predict the note transition probabilities
of a given music corpus. In this paper, new music scores generated by BachProp
are compared with the original corpora as well as with different network
architectures and other related models. We show that BachProp captures
important features of the original datasets better than other models and invite
the reader to a qualitative comparison on a large collection of generated
songs.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,940 | How to Quantize $n$ Outputs of a Binary Symmetric Channel to $n-1$ Bits? | Suppose that $Y^n$ is obtained by observing a uniform Bernoulli random vector
$X^n$ through a binary symmetric channel with crossover probability $\alpha$.
The "most informative Boolean function" conjecture postulates that the maximal
mutual information between $Y^n$ and any Boolean function $\mathrm{b}(X^n)$ is
attained by a dictator function. In this paper, we consider the "complementary"
case in which the Boolean function is replaced by
$f:\left\{0,1\right\}^n\to\left\{0,1\right\}^{n-1}$, namely, an $n-1$ bit
quantizer, and show that $I(f(X^n);Y^n)\leq (n-1)\cdot\left(1-h(\alpha)\right)$
for any such $f$. Thus, in this case, the optimal function is of the form
$f(x^n)=(x_1,\ldots,x_{n-1})$.
| 1 | 0 | 1 | 0 | 0 | 0 |
16,941 | Semi-Supervised Deep Learning for Monocular Depth Map Prediction | Supervised deep learning often suffers from the lack of sufficient training
data. Specifically in the context of monocular depth map prediction, it is
barely possible to determine dense ground truth depth images in realistic
dynamic outdoor environments. When using LiDAR sensors, for instance, noise is
present in the distance measurements, the calibration between sensors cannot be
perfect, and the measurements are typically much sparser than the camera
images. In this paper, we propose a novel approach to depth map prediction from
monocular images that learns in a semi-supervised way. While we use sparse
ground-truth depth for supervised learning, we also enforce our deep network to
produce photoconsistent dense depth maps in a stereo setup using a direct image
alignment loss. In experiments we demonstrate superior performance in depth map
prediction from single images compared to the state-of-the-art methods.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,942 | Approximation Schemes for Clustering with Outliers | Clustering problems are well-studied in a variety of fields such as data
science, operations research, and computer science. Such problems include
variants of centre location problems, $k$-median, and $k$-means to name a few.
In some cases, not all data points need to be clustered; some may be discarded
for various reasons.
We study clustering problems with outliers. More specifically, we look at
Uncapacitated Facility Location (UFL), $k$-Median, and $k$-Means. In UFL with
outliers, we have to open some centres, discard up to $z$ points of $\cal X$
and assign every other point to the nearest open centre, minimizing the total
assignment cost plus centre opening costs. In $k$-Median and $k$-Means, we have
to open up to $k$ centres but there are no opening costs. In $k$-Means, the
cost of assigning $j$ to $i$ is $\delta^2(j,i)$. We present several results.
Our main focus is on cases where $\delta$ is a doubling metric or is the
shortest path metrics of graphs from a minor-closed family of graphs. For
uniform-cost UFL with outliers on such metrics we show that a multiswap simple
local search heuristic yields a PTAS. With a bit more work, we extend this to
bicriteria approximations for the $k$-Median and $k$-Means problems in the same
metrics where, for any constant $\epsilon > 0$, we can find a solution using
$(1+\epsilon)k$ centres whose cost is at most a $(1+\epsilon)$-factor of the
optimum and uses at most $z$ outliers. We also show that natural local search
heuristics that do not violate the number of clusters and outliers for
$k$-Median (or $k$-Means) will have unbounded gap even in Euclidean metrics.
Furthermore, we show how our analysis can be extended to general metrics for
$k$-Means with outliers to obtain a $(25+\epsilon,1+\epsilon)$ bicriteria.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,943 | Order preserving pattern matching on trees and DAGs | The order preserving pattern matching (OPPM) problem is, given a pattern
string $p$ and a text string $t$, find all substrings of $t$ which have the
same relative orders as $p$. In this paper, we consider two variants of the
OPPM problem where a set of text strings is given as a tree or a DAG. We show
that the OPPM problem for a single pattern $p$ of length $m$ and a text tree
$T$ of size $N$ can be solved in $O(m+N)$ time if the characters of $p$ are
drawn from an integer alphabet of polynomial size. The time complexity becomes
$O(m \log m + N)$ if the pattern $p$ is over a general ordered alphabet. We
then show that the OPPM problem for a single pattern and a text DAG is
NP-complete.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,944 | Categorical Probabilistic Theories | We present a simple categorical framework for the treatment of probabilistic
theories, with the aim of reconciling the fields of Categorical Quantum
Mechanics (CQM) and Operational Probabilistic Theories (OPTs). In recent years,
both CQM and OPTs have found successful application to a number of areas in
quantum foundations and information theory: they present many similarities,
both in spirit and in formalism, but they remain separated by a number of
subtle yet important differences. We attempt to bridge this gap, by adopting a
minimal number of operationally motivated axioms which provide clean
categorical foundations, in the style of CQM, for the treatment of the problems
that OPTs are concerned with.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,945 | Maximal polynomial modulations of singular integrals | Let $K$ be a standard Hölder continuous Calderón--Zygmund kernel on
$\mathbb{R}^{\mathbf{d}}$ whose truncations define $L^2$ bounded operators. We
show that the maximal operator obtained by modulating $K$ by polynomial phases
of a fixed degree is bounded on $L^p(\mathbb{R}^{\mathbf{d}})$ for $1 < p <
\infty$. This extends Sjölin's multidimensional Carleson theorem and Lie's
polynomial Carleson theorem.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,946 | Effects of Hubbard term correction on the structural parameters and electronic properties of wurtzite Zn | The effects of including the Hubbard on-site Coulombic correction to the
structural parameters and valence energy states of wurtzite ZnO was explored.
Due to the changes in the structural parameters caused by correction of
hybridization between Zn d states and O p states, suitable parameters of
Hubbard terms have to be determined for an accurate prediction of ZnO
properties. Using the LDA+${U}$ method by applying Hubbard corrections $U_p$ to
Zn 3d states and $U_p$ to O 2p states, the lattice constants were
underestimated for all tested Hubbard parameters. The combination of both $U_d$
and $U_p$ correction terms managed to widen the band gap of wurtzite ZnO to the
experimental value. Pairs of $U_p$ and $U_p$ parameters with the correct
positioning of d-band and accurate bandwidths were selected, in addition to
predicting an accurate band gap value. Inspection of vibrational properties,
however, revealed mismatches between the estimated gamma phonon frequencies and
experimental values. The selection of Hubbard terms based on electronic band
properties alone cannot ensure an accurate vibrational description in LDA+${U}$
calculation.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,947 | A multi-scale Gaussian beam parametrix for the wave equation: the Dirichlet boundary value problem | We present a construction of a multi-scale Gaussian beam parametrix for the
Dirichlet boundary value problem associated with the wave equation, and study
its convergence rate to the true solution in the highly oscillatory regime. The
construction elaborates on the wave-atom parametrix of Bao, Qian, Ying, and
Zhang and extends to a multi-scale setting the technique of Gaussian beam
propagation from a boundary of Katchalov, Kurylev and Lassas.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,948 | Uncertainty and sensitivity analysis of functional risk curves based on Gaussian processes | A functional risk curve gives the probability of an undesirable event as a
function of the value of a critical parameter of a considered physical system.
In several applicative situations, this curve is built using phenomenological
numerical models which simulate complex physical phenomena. To avoid cpu-time
expensive numerical models, we propose to use Gaussian process regression to
build functional risk curves. An algorithm is given to provide confidence
bounds due to this approximation. Two methods of global sensitivity analysis of
the models' random input parameters on the functional risk curve are also
studied. In particular, the PLI sensitivity indices allow to understand the
effect of misjudgment on the input parameters' probability density functions.
| 0 | 0 | 1 | 1 | 0 | 0 |
16,949 | Global optimization for low-dimensional switching linear regression and bounded-error estimation | The paper provides global optimization algorithms for two particularly
difficult nonconvex problems raised by hybrid system identification: switching
linear regression and bounded-error estimation. While most works focus on local
optimization heuristics without global optimality guarantees or with guarantees
valid only under restrictive conditions, the proposed approach always yields a
solution with a certificate of global optimality. This approach relies on a
branch-and-bound strategy for which we devise lower bounds that can be
efficiently computed. In order to obtain scalable algorithms with respect to
the number of data, we directly optimize the model parameters in a continuous
optimization setting without involving integer variables. Numerical experiments
show that the proposed algorithms offer a higher accuracy than convex
relaxations with a reasonable computational burden for hybrid system
identification. In addition, we discuss how bounded-error estimation is related
to robust estimation in the presence of outliers and exact recovery under
sparse noise, for which we also obtain promising numerical results.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,950 | An Incremental Self-Organizing Architecture for Sensorimotor Learning and Prediction | During visuomotor tasks, robots must compensate for temporal delays inherent
in their sensorimotor processing systems. Delay compensation becomes crucial in
a dynamic environment where the visual input is constantly changing, e.g.,
during the interacting with a human demonstrator. For this purpose, the robot
must be equipped with a prediction mechanism for using the acquired perceptual
experience to estimate possible future motor commands. In this paper, we
present a novel neural network architecture that learns prototypical visuomotor
representations and provides reliable predictions on the basis of the visual
input. These predictions are used to compensate for the delayed motor behavior
in an online manner. We investigate the performance of our method with a set of
experiments comprising a humanoid robot that has to learn and generate visually
perceived arm motion trajectories. We evaluate the accuracy in terms of mean
prediction error and analyze the response of the network to novel movement
demonstrations. Additionally, we report experiments with incomplete data
sequences, showing the robustness of the proposed architecture in the case of a
noisy and faulty visual sensor.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,951 | A CutFEM method for two-phase flow problems | In this article, we present a cut finite element method for two-phase
Navier-Stokes flows. The main feature of the method is the formulation of a
unified continuous interior penalty stabilisation approach for, on the one
hand, stabilising advection and the pressure-velocity coupling and, on the
other hand, stabilising the cut region. The accuracy of the algorithm is
enhanced by the development of extended fictitious domains to guarantee a well
defined velocity from previous time steps in the current geometry. Finally, the
robustness of the moving-interface algorithm is further improved by the
introduction of a curvature smoothing technique that reduces spurious
velocities. The algorithm is shown to perform remarkably well for low capillary
number flows, and is a first step towards flexible and robust CutFEM algorithms
for the simulation of microfluidic devices.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,952 | Learning under selective labels in the presence of expert consistency | We explore the problem of learning under selective labels in the context of
algorithm-assisted decision making. Selective labels is a pervasive selection
bias problem that arises when historical decision making blinds us to the true
outcome for certain instances. Examples of this are common in many
applications, ranging from predicting recidivism using pre-trial release data
to diagnosing patients. In this paper we discuss why selective labels often
cannot be effectively tackled by standard methods for adjusting for sample
selection bias, even if there are no unobservables. We propose a data
augmentation approach that can be used to either leverage expert consistency to
mitigate the partial blindness that results from selective labels, or to
empirically validate whether learning under such framework may lead to
unreliable models prone to systemic discrimination.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,953 | Opacity limit for supermassive protostars | We present a model for the evolution of supermassive protostars from their
formation at $M_\star \simeq 0.1\,\text{M}_\odot$ until their growth to
$M_\star \simeq 10^5\,\text{M}_\odot$. To calculate the initial properties of
the object in the optically thick regime we follow two approaches: based on
idealized thermodynamic considerations, and on a more detailed one-zone model.
Both methods derive a similar value of $n_{\rm F} \simeq 2 \times 10^{17}
\,\text{cm}^{-3}$ for the density of the object when opacity becomes important,
i.e. the opacity limit. The subsequent evolution of the growing protostar is
determined by the accretion of gas onto the object and can be described by a
mass-radius relation of the form $R_\star \propto M_\star^{1/3}$ during the
early stages, and of the form $R_\star \propto M_\star^{1/2}$ when internal
luminosity becomes important. For the case of a supermassive protostar, this
implies that the radius of the star grows from $R_\star \simeq 0.65 \,{\rm AU}$
to $R_\star \simeq 250 \,{\rm AU}$ during its evolution. Finally, we use this
model to construct a sub-grid recipe for accreting sink particles in numerical
simulations. A prime ingredient thereof is a physically motivated prescription
for the accretion radius and the effective temperature of the growing protostar
embedded inside it. From the latter, we can conclude that photo-ionization
feedback can be neglected until very late in the assembly process of the
supermassive object.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,954 | Learning to Imagine Manipulation Goals for Robot Task Planning | Prospection is an important part of how humans come up with new task plans,
but has not been explored in depth in robotics. Predicting multiple task-level
is a challenging problem that involves capturing both task semantics and
continuous variability over the state of the world. Ideally, we would combine
the ability of machine learning to leverage big data for learning the semantics
of a task, while using techniques from task planning to reliably generalize to
new environment. In this work, we propose a method for learning a model
encoding just such a representation for task planning. We learn a neural net
that encodes the $k$ most likely outcomes from high level actions from a given
world. Our approach creates comprehensible task plans that allow us to predict
changes to the environment many time steps into the future. We demonstrate this
approach via application to a stacking task in a cluttered environment, where
the robot must select between different colored blocks while avoiding
obstacles, in order to perform a task. We also show results on a simple
navigation task. Our algorithm generates realistic image and pose predictions
at multiple points in a given task.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,955 | On the Combinatorial Power of the Weisfeiler-Lehman Algorithm | The classical Weisfeiler-Lehman method WL[2] uses edge colors to produce a
powerful graph invariant. It is at least as powerful in its ability to
distinguish non-isomorphic graphs as the most prominent algebraic graph
invariants. It determines not only the spectrum of a graph, and the angles
between standard basis vectors and the eigenspaces, but even the angles between
projections of standard basis vectors into the eigenspaces. Here, we
investigate the combinatorial power of WL[2]. For sufficiently large k, WL[k]
determines all combinatorial properties of a graph. Many traditionally used
combinatorial invariants are determined by WL[k] for small k. We focus on two
fundamental invariants, the num- ber of cycles Cp of length p, and the number
of cliques Kp of size p. We show that WL[2] determines the number of cycles of
lengths up to 6, but not those of length 8. Also, WL[2] does not determine the
number of 4-cliques.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,956 | Learning to Generate Reviews and Discovering Sentiment | We explore the properties of byte-level recurrent language models. When given
sufficient amounts of capacity, training data, and compute time, the
representations learned by these models include disentangled features
corresponding to high-level concepts. Specifically, we find a single unit which
performs sentiment analysis. These representations, learned in an unsupervised
manner, achieve state of the art on the binary subset of the Stanford Sentiment
Treebank. They are also very data efficient. When using only a handful of
labeled examples, our approach matches the performance of strong baselines
trained on full datasets. We also demonstrate the sentiment unit has a direct
influence on the generative process of the model. Simply fixing its value to be
positive or negative generates samples with the corresponding positive or
negative sentiment.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,957 | Designing magnetism in Fe-based Heusler alloys: a machine learning approach | Combining material informatics and high-throughput electronic structure
calculations offers the possibility of a rapid characterization of complex
magnetic materials. Here we demonstrate that datasets of electronic properties
calculated at the ab initio level can be effectively used to identify and
understand physical trends in magnetic materials, thus opening new avenues for
accelerated materials discovery. Following a data-centric approach, we utilize
a database of Heusler alloys calculated at the density functional theory level
to identify the ideal ions neighbouring Fe in the $X_2$Fe$Z$ Heusler prototype.
The hybridization of Fe with the nearest neighbour $X$ ion is found to cause
redistribution of the on-site Fe charge and a net increase of its magnetic
moment proportional to the valence of $X$. Thus, late transition metals are
ideal Fe neighbours for producing high-moment Fe-based Heusler magnets. At the
same time a thermodynamic stability analysis is found to restrict $Z$ to main
group elements. Machine learning regressors, trained to predict magnetic moment
and volume of Heusler alloys, are used to determine the magnetization for all
materials belonging to the proposed prototype. We find that Co$_2$Fe$Z$ alloys,
and in particular Co$_2$FeSi, maximize the magnetization, which reaches values
up to 1.2T. This is in good agreement with both ab initio and experimental
data. Furthermore, we identify the Cu$_2$Fe$Z$ family to be a cost-effective
materials class, offering a magnetization of approximately 0.65T.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,958 | On a diffuse interface model for tumour growth with non-local interactions and degenerate mobilities | We study a non-local variant of a diffuse interface model proposed by
Hawkins--Darrud et al. (2012) for tumour growth in the presence of a chemical
species acting as nutrient. The system consists of a Cahn--Hilliard equation
coupled to a reaction-diffusion equation. For non-degenerate mobilities and
smooth potentials, we derive well-posedness results, which are the non-local
analogue of those obtained in Frigeri et al. (European J. Appl. Math. 2015).
Furthermore, we establish existence of weak solutions for the case of
degenerate mobilities and singular potentials, which serves to confine the
order parameter to its physically relevant interval. Due to the non-local
nature of the equations, under additional assumptions continuous dependence on
initial data can also be shown.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,959 | Gradient Descent Can Take Exponential Time to Escape Saddle Points | Although gradient descent (GD) almost always escapes saddle points
asymptotically [Lee et al., 2016], this paper shows that even with fairly
natural random initialization schemes and non-pathological functions, GD can be
significantly slowed down by saddle points, taking exponential time to escape.
On the other hand, gradient descent with perturbations [Ge et al., 2015, Jin et
al., 2017] is not slowed down by saddle points - it can find an approximate
local minimizer in polynomial time. This result implies that GD is inherently
slower than perturbed GD, and justifies the importance of adding perturbations
for efficient non-convex optimization. While our focus is theoretical, we also
present experiments that illustrate our theoretical findings.
| 1 | 0 | 1 | 1 | 0 | 0 |
16,960 | Spectral parameter power series for arbitrary order linear differential equations | Let $L$ be the $n$-th order linear differential operator $Ly = \phi_0y^{(n)}
+ \phi_1y^{(n-1)} + \cdots + \phi_ny$ with variable coefficients. A
representation is given for $n$ linearly independent solutions of $Ly=\lambda r
y$ as power series in $\lambda$, generalizing the SPPS (spectral parameter
power series) solution which has been previously developed for $n=2$. The
coefficient functions in these series are obtained by recursively iterating a
simple integration process, begining with a solution system for $\lambda=0$. It
is shown how to obtain such an initializing system working upwards from
equations of lower order. The values of the successive derivatives of the power
series solutions at the basepoint of integration are given, which provides a
technique for numerical solution of $n$-th order initial value problems and
spectral problems.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,961 | Antropologia de la Informatica Social: Teoria de la Convergencia Tecno-Social | The traditional humanism of the twentieth century, inspired by the culture of
the book, systematically distanced itself from the new society of digital
information; the Internet and tools of information processing revolutionized
the world, society during this period developed certain adaptive
characteristics based on coexistence (Human - Machine), this transformation
sets based on the impact of three technology segments: devices, applications
and infrastructure of social communication, which are involved in various
physical, behavioural and cognitive changes of the human being; and the
emergence of new models of influence and social control through the new
ubiquitous communication; however in this new process of conviviality new
models like the "collaborative thinking" and "InfoSharing" develop; managing
social information under three Human ontological dimensions (h) - Information
(i) - Machine (m), which is the basis of a new physical-cyber ecosystem, where
they coexist and develop new social units called "virtual communities ". This
new communication infrastructure and social management of information given
discovered areas of vulnerability "social perspective of risk", impacting all
social units through massive impact vector (i); The virtual environment "H + i
+ M"; and its components, as well as the life cycle management of social
information allows us to understand the path of integration "Techno - Social"
and setting a new contribution to cybernetics, within the convergence of
technology with society and the new challenges of coexistence, aimed at a new
holistic and not pragmatic vision, as the human component (h) in the virtual
environment is the precursor of the future and needs to be studied not as an
application, but as the hub of a new society.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,962 | A Deterministic Approach to Avoid Saddle Points | Loss functions with a large number of saddle points are one of the main
obstacles to training many modern machine learning models. Gradient descent
(GD) is a fundamental algorithm for machine learning and converges to a saddle
point for certain initial data. We call the region formed by these initial
values the "attraction region." For quadratic functions, GD converges to a
saddle point if the initial data is in a subspace of up to n-1 dimensions. In
this paper, we prove that a small modification of the recently proposed
Laplacian smoothing gradient descent (LSGD) [Osher, et al., arXiv:1806.06317]
contributes to avoiding saddle points without sacrificing the convergence rate
of GD. In particular, we show that the dimension of the LSGD's attraction
region is at most floor((n-1)/2) for a class of quadratic functions which is
significantly smaller than GD's (n-1)-dimensional attraction region.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,963 | Automatic Generation of Typographic Font from a Small Font Subset | This paper addresses the automatic generation of a typographic font from a
subset of characters. Specifically, we use a subset of a typographic font to
extrapolate additional characters. Consequently, we obtain a complete font
containing a number of characters sufficient for daily use. The automated
generation of Japanese fonts is in high demand because a Japanese font requires
over 1,000 characters. Unfortunately, professional typographers create most
fonts, resulting in significant financial and time investments for font
generation. The proposed method can be a great aid for font creation because
designers do not need to create the majority of the characters for a new font.
The proposed method uses strokes from given samples for font generation. The
strokes, from which we construct characters, are extracted by exploiting a
character skeleton dataset. This study makes three main contributions: a novel
method of extracting strokes from characters, which is applicable to both
standard fonts and their variations; a fully automated approach for
constructing characters; and a selection method for sample characters. We
demonstrate our proposed method by generating 2,965 characters in 47 fonts.
Objective and subjective evaluations verify that the generated characters are
similar to handmade characters.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,964 | The Second Postulate of Euclid and the Hyperbolic Geometry | The article deals with the connection between the second postulate of Euclid
and non-Euclidean geometry. It is shown that the violation of the second
postulate of Euclid inevitably leads to hyperbolic geometry. This eliminates
misunderstandings about the sums of some divergent series. The connection
between hyperbolic geometry and relativistic computations is noted.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,965 | Transkernel: An Executor for Commodity Kernels on Peripheral Cores | Modern mobile and embedded platforms see a large number of ephemeral tasks
driven by background activities. In order to execute such a task, the OS kernel
wakes up the platform beforehand and puts it back to sleep afterwards. In doing
so, the kernel operates various IO devices and orchestrates their power state
transitions. Such kernel execution phases are lengthy, having high energy cost,
and yet difficult to optimize. We advocate for relieving the CPU from these
kernel phases by executing them on a low-power, microcontroller-like core,
dubbed peripheral core, hence leaving the CPU off. Yet, for a peripheral core
to execute phases in a complex commodity kernel (e.g. Linux), existing
approaches either incur high engineering effort or high runtime overhead. We
take a radical approach with a new executor model called transkernel. Running
on a peripheral core, a transkernel executes the binary of the commodity kernel
through cross-ISA, dynamic binary translation (DBT). The transkernel translates
stateful kernel code while emulating a small set of stateless kernel services;
it sets a narrow, stable binary interface for emulated services; it specializes
for kernel's beaten paths; it exploits ISA similarities for low DBT cost. With
a concrete implementation on a heterogeneous ARM SoC, we demonstrate the
feasibility and benefit of transkernel. Our result contributes a new OS
structure that combines cross-ISA DBT and emulation for harnessing a
heterogeneous SoC. Our result demonstrates that while cross-ISA DBT is
typically used under the assumption of efficiency loss, it can be used for
efficiency gain, even atop off-the-shelf hardware.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,966 | No iterated identities satisfied by all finite groups | We show that there is no iterated identity satisfied by all finite groups.
For $w$ being a non-trivial word of length $l$, we show that there exists a
finite group $G$ of cardinality at most $\exp(l^C)$ which does not satisfy the
iterated identity $w$. The proof uses the approach of Borisov and Sapir, who
used dynamics of polynomial mappings for the proof of non residual finiteness
of some groups.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,967 | Optimization Methods for Supervised Machine Learning: From Linear Models to Deep Learning | The goal of this tutorial is to introduce key models, algorithms, and open
questions related to the use of optimization methods for solving problems
arising in machine learning. It is written with an INFORMS audience in mind,
specifically those readers who are familiar with the basics of optimization
algorithms, but less familiar with machine learning. We begin by deriving a
formulation of a supervised learning problem and show how it leads to various
optimization problems, depending on the context and underlying assumptions. We
then discuss some of the distinctive features of these optimization problems,
focusing on the examples of logistic regression and the training of deep neural
networks. The latter half of the tutorial focuses on optimization algorithms,
first for convex logistic regression, for which we discuss the use of
first-order methods, the stochastic gradient method, variance reducing
stochastic methods, and second-order methods. Finally, we discuss how these
approaches can be employed to the training of deep neural networks, emphasizing
the difficulties that arise from the complex, nonconvex structure of these
models.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,968 | A Unified Parallel Algorithm for Regularized Group PLS Scalable to Big Data | Partial Least Squares (PLS) methods have been heavily exploited to analyse
the association between two blocs of data. These powerful approaches can be
applied to data sets where the number of variables is greater than the number
of observations and in presence of high collinearity between variables.
Different sparse versions of PLS have been developed to integrate multiple data
sets while simultaneously selecting the contributing variables. Sparse
modelling is a key factor in obtaining better estimators and identifying
associations between multiple data sets. The cornerstone of the sparsity
version of PLS methods is the link between the SVD of a matrix (constructed
from deflated versions of the original matrices of data) and least squares
minimisation in linear regression. We present here an accurate description of
the most popular PLS methods, alongside their mathematical proofs. A unified
algorithm is proposed to perform all four types of PLS including their
regularised versions. Various approaches to decrease the computation time are
offered, and we show how the whole procedure can be scalable to big data sets.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,969 | Asymptotic behaviour of the fifth Painlevé transcendents in the space of initial values | We study the asymptotic behaviour of the solutions of the fifth Painlevé
equation as the independent variable approaches zero and infinity in the space
of initial values. We show that the limit set of each solution is compact and
connected and, moreover, that any solution with the essential singularity at
zero has an infinite number of poles and zeroes, and any solution with the
essential singularity at infinity has infinite number of poles and takes value
$1$ infinitely many times.
| 0 | 1 | 1 | 0 | 0 | 0 |
16,970 | Hidden Treasures - Recycling Large-Scale Internet Measurements to Study the Internet's Control Plane | Internet-wide scans are a common active measurement approach to study the
Internet, e.g., studying security properties or protocol adoption. They involve
probing large address ranges (IPv4 or parts of IPv6) for specific ports or
protocols. Besides their primary use for probing (e.g., studying protocol
adoption), we show that - at the same time - they provide valuable insights
into the Internet control plane informed by ICMP responses to these probes - a
currently unexplored secondary use. We collect one week of ICMP responses
(637.50M messages) to several Internet-wide ZMap scans covering multiple TCP
and UDP ports as well as DNS-based scans covering > 50% of the domain name
space. This perspective enables us to study the Internet's control plane as a
by-product of Internet measurements. We receive ICMP messages from ~171M
different IPs in roughly 53K different autonomous systems. Additionally, we
uncover multiple control plane problems, e.g., we detect a plethora of outdated
and misconfigured routers and uncover the presence of large-scale persistent
routing loops in IPv4.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,971 | Image Registration and Predictive Modeling: Learning the Metric on the Space of Diffeomorphisms | We present a method for metric optimization in the Large Deformation
Diffeomorphic Metric Mapping (LDDMM) framework, by treating the induced
Riemannian metric on the space of diffeomorphisms as a kernel in a machine
learning context. For simplicity, we choose the kernel Fischer Linear
Discriminant Analysis (KLDA) as the framework. Optimizing the kernel parameters
in an Expectation-Maximization framework, we define model fidelity via the
hinge loss of the decision function. The resulting algorithm optimizes the
parameters of the LDDMM norm-inducing differential operator as a solution to a
group-wise registration and classification problem. In practice, this may lead
to a biology-aware registration, focusing its attention on the predictive task
at hand such as identifying the effects of disease. We first tested our
algorithm on a synthetic dataset, showing that our parameter selection improves
registration quality and classification accuracy. We then tested the algorithm
on 3D subcortical shapes from the Schizophrenia cohort Schizconnect. Our
Schizpohrenia-Control predictive model showed significant improvement in ROC
AUC compared to baseline parameters.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,972 | On a direct algorithm for constructing recursion operators and Lax pairs for integrable models | We suggested an algorithm for searching the recursion operators for nonlinear
integrable equations. It was observed that the recursion operator $R$ can be
represented as a ratio of the form $R=L_1^{-1}L_2$ where the linear
differential operators $L_1$ and $L_2$ are chosen in such a way that the
ordinary differential equation $(L_2-\lambda L_1)U=0$ is consistent with the
linearization of the given nonlinear integrable equation for any value of the
parameter $\lambda\in \textbf{C}$. For constructing the operator $L_1$ we use
the concept of the invariant manifold which is a generalization of the
symmetry. Then for searching $L_2$ we take an auxiliary linear equation
connected with the linearized equation by the Darboux transformation.
Connection of the invariant manifold with the Lax pairs and the
Dubrovin-Weierstrass equations is discussed.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,973 | Network Classification and Categorization | To the best of our knowledge, this paper presents the first large-scale study
that tests whether network categories (e.g., social networks vs. web graphs)
are distinguishable from one another (using both categories of real-world
networks and synthetic graphs). A classification accuracy of $94.2\%$ was
achieved using a random forest classifier with both real and synthetic
networks. This work makes two important findings. First, real-world networks
from various domains have distinct structural properties that allow us to
predict with high accuracy the category of an arbitrary network. Second,
classifying synthetic networks is trivial as our models can easily distinguish
between synthetic graphs and the real-world networks they are supposed to
model.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,974 | A Polynomial-Time Algorithm for Solving the Minimal Observability Problem in Conjunctive Boolean Networks | Many complex systems in biology, physics, and engineering include a large
number of state-variables, and measuring the full state of the system is often
impossible. Typically, a set of sensors is used to measure part of the
state-variables. A system is called observable if these measurements allow to
reconstruct the entire state of the system. When the system is not observable,
an important and practical problem is how to add a \emph{minimal} number of
sensors so that the system becomes observable. This minimal observability
problem is practically useful and theoretically interesting, as it pinpoints
the most informative nodes in the system. We consider the minimal observability
problem for an important special class of Boolean networks, called conjunctive
Boolean networks (CBNs). Using a graph-theoretic approach, we provide a
necessary and sufficient condition for observability of a CBN with $n$
state-variables, and an efficient~$O(n^2)$-time algorithm for solving the
minimal observability problem. We demonstrate the usefulness of these results
by studying the properties of a class of random CBNs.
| 1 | 0 | 1 | 0 | 0 | 0 |
16,975 | The Description and Scaling Behavior for the Inner Region of the Boundary Layer for 2-D Wall-bounded Flows | A second derivative-based moment method is proposed for describing the
thickness and shape of the region where viscous forces are dominant in
turbulent boundary layer flows. Rather than the fixed location sublayer model
presently employed, the new method defines thickness and shape parameters that
are experimentally accessible without differentiation. It is shown
theoretically that one of the new length parameters used as a scaling parameter
is also a similarity parameter for the velocity profile. In fact, we show that
this new length scale parameter removes one of the theoretical inconsistencies
present in the traditional Prandtl Plus scalings. Furthermore, the new length
parameter and the Prandtl Plus scaling parameters perform identically when
operating on experimental datasets. This means that many of the past successes
ascribed to the Prandtl Plus scaling also apply to the new parameter set but
without one of the theoretical inconsistencies. Examples are offered to show
how the new description method is useful in exploring the actual physics of the
boundary layer.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,976 | Completely Sidon sets in $C^*$-algebras (New title) | A sequence in a $C^*$-algebra $A$ is called completely Sidon if its span in
$A$ is completely isomorphic to the operator space version of the space
$\ell_1$ (i.e. $\ell_1$ equipped with its maximal operator space structure).
The latter can also be described as the span of the free unitary generators in
the (full) $C^*$-algebra of the free group $\F_\infty$ with countably
infinitely many generators. Our main result is a generalization to this context
of Drury's classical theorem stating that Sidon sets are stable under finite
unions. In the particular case when $A=C^*(G)$ the (maximal) $C^*$-algebra of a
discrete group $G$, we recover the non-commutative (operator space) version of
Drury's theorem that we recently proved. We also give several non-commutative
generalizations of our recent work on uniformly bounded orthonormal systems to
the case of von Neumann algebras equipped with normal faithful tracial states.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,977 | Conflict-Free Coloring of Planar Graphs | A conflict-free k-coloring of a graph assigns one of k different colors to
some of the vertices such that, for every vertex v, there is a color that is
assigned to exactly one vertex among v and v's neighbors. Such colorings have
applications in wireless networking, robotics, and geometry, and are
well-studied in graph theory. Here we study the natural problem of the
conflict-free chromatic number chi_CF(G) (the smallest k for which
conflict-free k-colorings exist). We provide results both for closed
neighborhoods N[v], for which a vertex v is a member of its neighborhood, and
for open neighborhoods N(v), for which vertex v is not a member of its
neighborhood.
For closed neighborhoods, we prove the conflict-free variant of the famous
Hadwiger Conjecture: If an arbitrary graph G does not contain K_{k+1} as a
minor, then chi_CF(G) <= k. For planar graphs, we obtain a tight worst-case
bound: three colors are sometimes necessary and always sufficient. We also give
a complete characterization of the computational complexity of conflict-free
coloring. Deciding whether chi_CF(G)<= 1 is NP-complete for planar graphs G,
but polynomial for outerplanar graphs. Furthermore, deciding whether
chi_CF(G)<= 2 is NP-complete for planar graphs G, but always true for
outerplanar graphs. For the bicriteria problem of minimizing the number of
colored vertices subject to a given bound k on the number of colors, we give a
full algorithmic characterization in terms of complexity and approximation for
outerplanar and planar graphs.
For open neighborhoods, we show that every planar bipartite graph has a
conflict-free coloring with at most four colors; on the other hand, we prove
that for k in {1,2,3}, it is NP-complete to decide whether a planar bipartite
graph has a conflict-free k-coloring. Moreover, we establish that any general}
planar graph has a conflict-free coloring with at most eight colors.
| 1 | 0 | 1 | 0 | 0 | 0 |
16,978 | Explicit solutions to utility maximization problems in a regime-switching market model via Laplace transforms | We study the problem of utility maximization from terminal wealth in which an
agent optimally builds her portfolio by investing in a bond and a risky asset.
The asset price dynamics follow a diffusion process with regime-switching
coefficients modeled by a continuous-time finite-state Markov chain. We
consider an investor with a Constant Relative Risk Aversion (CRRA) utility
function. We deduce the associated Hamilton-Jacobi-Bellman equation to
construct the solution and the optimal trading strategy and verify optimality
by showing that the value function is the unique constrained viscosity solution
of the HJB equation. By means of a Laplace transform method, we show how to
explicitly compute the value function and illustrate the method with the two-
and three-states cases. This method is interesting in its own right and can be
adapted in other applications involving hybrid systems and using other types of
transforms with basic properties similar to the Laplace transform.
| 0 | 0 | 0 | 0 | 0 | 1 |
16,979 | Spectroscopic study of the elusive globular cluster ESO452-SC11 and its surroundings | Globular clusters (GCs) are amongst the oldest objects in the Galaxy and play
a pivotal role in deciphering its early history. We present the first
spectroscopic study of the GC ESO452-SC11 using the AAOmega spectrograph at
medium resolution. Given the sparsity of this object and high degree of
foreground contamination due to its location toward the bulge, few details are
known for this cluster: there is no consensus of its age, metallicity, or its
association with the disk or bulge. We identify 5 members based on radial
velocity, metallicity, and position within the GC. Using spectral synthesis,
accurate abundances of Fe and several $\alpha$-, Fe-peak, neutron-capture
elements (Si,Ca,Ti,Cr,Co,Ni,Sr,Eu) were measured. Two of the 5 cluster
candidates are likely non-members, as they have deviant Fe abundances and
[$\alpha$/Fe] ratios. The mean radial velocity is 19$\pm$2 km s$^{-1}$ with a
low dispersion of 2.8$\pm$3.4 km s$^{-1}$, in line with its low mass. The mean
Fe-abundance from spectral fitting is $-0.88\pm0.03$, with a spread driven by
observational errors. The $\alpha$-elements of the GC candidates are marginally
lower than expected for the bulge at similar metallicities. As spectra of
hundreds of stars were collected in a 2 degree field around ESO452-SC11,
detailed abundances in the surrounding field were measured. Most non-members
have higher [$\alpha$/Fe] ratios, typical of the nearby bulge population. Stars
with measured Fe-peak abundances show a large scatter around Solar values,
though with large uncertainties. Our study provides the first systematic
measurement of Sr in a Galactic bulge GC. The Eu and Sr abundances of the GC
candidates are consistent with a disk or bulge association. Our calculations
place ESO452 on an elliptical orbit in the central 3 kpc of the bulge. We find
no evidence of extratidal stars in our data. (Abridged)
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16,980 | The Fundamental Infinity-Groupoid of a Parametrized Family | Given an infinity-category C, one can naturally construct an
infinity-category Fam(C) of families of objects in C indexed by
infinity-groupoids. An ordinary categorical version of this construction was
used by Borceux and Janelidze in the study of generalized covering maps in
categorical Galois theory. In this paper, we develop the homotopy theory of
such "parametrized families" as generalization of the classical homotopy theory
of spaces. In particular, we study homotopy-theoretical constructions that
arise from the fundamental infinity-groupoids of families in an
infinity-category. In the same spirit, we show that Fam(C) admits a
Grothendieck topology which generalizes the canonical/epimorphism topology on
the infinity-topos of infinity-groupoids in the sense of Carchedi.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,981 | Leveraging Pre-Trained 3D Object Detection Models For Fast Ground Truth Generation | Training 3D object detectors for autonomous driving has been limited to small
datasets due to the effort required to generate annotations. Reducing both task
complexity and the amount of task switching done by annotators is key to
reducing the effort and time required to generate 3D bounding box annotations.
This paper introduces a novel ground truth generation method that combines
human supervision with pretrained neural networks to generate per-instance 3D
point cloud segmentation, 3D bounding boxes, and class annotations. The
annotators provide object anchor clicks which behave as a seed to generate
instance segmentation results in 3D. The points belonging to each instance are
then used to regress object centroids, bounding box dimensions, and object
orientation. Our proposed annotation scheme requires 30x lower human annotation
time. We use the KITTI 3D object detection dataset to evaluate the efficiency
and the quality of our annotation scheme. We also test the the proposed scheme
on previously unseen data from the Autonomoose self-driving vehicle to
demonstrate generalization capabilities of the network.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,982 | Epidemic spreading in multiplex networks influenced by opinion exchanges on vaccination | We study the changes of opinions about vaccination together with the
evolution of a disease. In our model we consider a multiplex network consisting
of two layers. One of the layers corresponds to a social network where people
share their opinions and influence others opinions. The social model that rules
the dynamic is the M-model, which takes into account two different processes
that occurs in a society: persuasion and compromise. This two processes are
related through a parameter $r$, $r<1$ describes a moderate and committed
society, for $r>1$ the society tends to have extremist opinions, while $r=1$
represents a neutral society. This social network may be of real or virtual
contacts. On the other hand, the second layer corresponds to a network of
physical contacts where the disease spreading is described by the SIR-Model. In
this model the individuals may be in one of the following four states:
Susceptible ($S$), Infected($I$), Recovered ($R$) or Vaccinated ($V$). A
Susceptible individual can: i) get vaccinated, if his opinion in the other
layer is totally in favor of the vaccine, ii) get infected, with probability
$\beta$ if he is in contact with an infected neighbor. Those $I$ individuals
recover after a certain period $t_r=6$. Vaccinated individuals have an
extremist positive opinion that does not change. We consider that the vaccine
has a certain effectiveness $\omega$ and as a consequence vaccinated nodes can
be infected with probability $\beta (1 - \omega)$ if they are in contact with
an infected neighbor. In this case, if the infection process is successful, the
new infected individual changes his opinion from extremist positive to totally
against the vaccine. We find that depending on the trend in the opinion of the
society, which depends on $r$, different behaviors in the spread of the
epidemic occurs. An epidemic threshold was found.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,983 | CASP Solutions for Planning in Hybrid Domains | CASP is an extension of ASP that allows for numerical constraints to be added
in the rules. PDDL+ is an extension of the PDDL standard language of automated
planning for modeling mixed discrete-continuous dynamics.
In this paper, we present CASP solutions for dealing with PDDL+ problems,
i.e., encoding from PDDL+ to CASP, and extensions to the algorithm of the EZCSP
CASP solver in order to solve CASP programs arising from PDDL+ domains. An
experimental analysis, performed on well-known linear and non-linear variants
of PDDL+ domains, involving various configurations of the EZCSP solver, other
CASP solvers, and PDDL+ planners, shows the viability of our solution.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,984 | Primordial black holes from inflaton and spectator field perturbations in a matter-dominated era | We study production of primordial black holes (PBHs) during an early
matter-dominated phase. As a source of perturbations, we consider either the
inflaton field with a running spectral index or a spectator field that has a
blue spectrum and thus provides a significant contribution to the PBH
production at small scales. First, we identify the region of the parameter
space where a significant fraction of the observed dark matter can be produced,
taking into account all current PBH constraints. Then, we present constraints
on the amplitude and spectral index of the spectator field as a function of the
reheating temperature. We also derive constraints on the running of the
inflaton spectral index, ${\rm d}n/{\rm d}{\rm ln}k \lesssim -0.002$, which are
comparable to those from the Planck satellite for a scenario where the
spectator field is absent.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,985 | State Space Decomposition and Subgoal Creation for Transfer in Deep Reinforcement Learning | Typical reinforcement learning (RL) agents learn to complete tasks specified
by reward functions tailored to their domain. As such, the policies they learn
do not generalize even to similar domains. To address this issue, we develop a
framework through which a deep RL agent learns to generalize policies from
smaller, simpler domains to more complex ones using a recurrent attention
mechanism. The task is presented to the agent as an image and an instruction
specifying the goal. This meta-controller guides the agent towards its goal by
designing a sequence of smaller subtasks on the part of the state space within
the attention, effectively decomposing it. As a baseline, we consider a setup
without attention as well. Our experiments show that the meta-controller learns
to create subgoals within the attention.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,986 | Robust Imitation of Diverse Behaviors | Deep generative models have recently shown great promise in imitation
learning for motor control. Given enough data, even supervised approaches can
do one-shot imitation learning; however, they are vulnerable to cascading
failures when the agent trajectory diverges from the demonstrations. Compared
to purely supervised methods, Generative Adversarial Imitation Learning (GAIL)
can learn more robust controllers from fewer demonstrations, but is inherently
mode-seeking and more difficult to train. In this paper, we show how to combine
the favourable aspects of these two approaches. The base of our model is a new
type of variational autoencoder on demonstration trajectories that learns
semantic policy embeddings. We show that these embeddings can be learned on a 9
DoF Jaco robot arm in reaching tasks, and then smoothly interpolated with a
resulting smooth interpolation of reaching behavior. Leveraging these policy
representations, we develop a new version of GAIL that (1) is much more robust
than the purely-supervised controller, especially with few demonstrations, and
(2) avoids mode collapse, capturing many diverse behaviors when GAIL on its own
does not. We demonstrate our approach on learning diverse gaits from
demonstration on a 2D biped and a 62 DoF 3D humanoid in the MuJoCo physics
environment.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,987 | Efficient Measurement of the Vibrational Rogue Waves by Compressive Sampling Based Wavelet Analysis | In this paper we discuss the possible usage of the compressive sampling based
wavelet analysis for the efficient measurement and for the early detection of
one dimensional (1D) vibrational rogue waves. We study the construction of the
triangular (V-shaped) wavelet spectra using compressive samples of rogue waves
that can be modeled as Peregrine and Akhmediev-Peregrine solitons. We show that
triangular wavelet spectra can be sensed by compressive measurements at the
early stages of the development of vibrational rogue waves. Our results may
lead to development of the efficient vibrational rogue wave measurement and
early sensing systems with reduced memory requirements which use the
compressive sampling algorithms. In typical solid mechanics applications,
compressed measurements can be acquired by randomly positioning single sensor
and multisensors.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,988 | SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes | This paper presents SceneCut, a novel approach to jointly discover previously
unseen objects and non-object surfaces using a single RGB-D image. SceneCut's
joint reasoning over scene semantics and geometry allows a robot to detect and
segment object instances in complex scenes where modern deep learning-based
methods either fail to separate object instances, or fail to detect objects
that were not seen during training. SceneCut automatically decomposes a scene
into meaningful regions which either represent objects or scene surfaces. The
decomposition is qualified by an unified energy function over objectness and
geometric fitting. We show how this energy function can be optimized
efficiently by utilizing hierarchical segmentation trees. Moreover, we leverage
a pre-trained convolutional oriented boundary network to predict accurate
boundaries from images, which are used to construct high-quality region
hierarchies. We evaluate SceneCut on several different indoor environments, and
the results show that SceneCut significantly outperforms all the existing
methods.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,989 | An Invariant Model of the Significance of Different Body Parts in Recognizing Different Actions | In this paper, we show that different body parts do not play equally
important roles in recognizing a human action in video data. We investigate to
what extent a body part plays a role in recognition of different actions and
hence propose a generic method of assigning weights to different body points.
The approach is inspired by the strong evidence in the applied perception
community that humans perform recognition in a foveated manner, that is they
recognize events or objects by only focusing on visually significant aspects.
An important contribution of our method is that the computation of the weights
assigned to body parts is invariant to viewing directions and camera parameters
in the input data. We have performed extensive experiments to validate the
proposed approach and demonstrate its significance. In particular, results show
that considerable improvement in performance is gained by taking into account
the relative importance of different body parts as defined by our approach.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,990 | Forecasting Crime with Deep Learning | The objective of this work is to take advantage of deep neural networks in
order to make next day crime count predictions in a fine-grain city partition.
We make predictions using Chicago and Portland crime data, which is augmented
with additional datasets covering weather, census data, and public
transportation. The crime counts are broken into 10 bins and our model predicts
the most likely bin for a each spatial region at a daily level. We train this
data using increasingly complex neural network structures, including variations
that are suited to the spatial and temporal aspects of the crime prediction
problem. With our best model we are able to predict the correct bin for overall
crime count with 75.6% and 65.3% accuracy for Chicago and Portland,
respectively. The results show the efficacy of neural networks for the
prediction problem and the value of using external datasets in addition to
standard crime data.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,991 | A family of compact semitoric systems with two focus-focus singularities | About 6 years ago, semitoric systems were classified by Pelayo & Vu Ngoc by
means of five invariants. Standard examples are the coupled spin oscillator on
$\mathbb{S}^2 \times \mathbb{R}^2$ and coupled angular momenta on $\mathbb{S}^2
\times \mathbb{S}^2$, both having exactly one focus-focus singularity. But so
far there were no explicit examples of systems with more than one focus-focus
singularity which are semitoric in the sense of that classification. This paper
introduces a 6-parameter family of integrable systems on $\mathbb{S}^2 \times
\mathbb{S}^2$ and proves that, for certain ranges of the parameters, it is a
compact semitoric system with precisely two focus-focus singularities. Since
the twisting index (one of the semitoric invariants) is related to the
relationship between different focus-focus points, this paper provides systems
for the future study of the twisting index.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,992 | Mixed Threefolds Isogenous to a Product | In this paper we study \emph{threefolds isogenous to a product of mixed type}
i.e. quotients of a product of three compact Riemann surfaces $C_i$ of genus at
least two by the action of a finite group $G$, which is free, but not diagonal.
In particular, we are interested in the systematic construction and
classification of these varieties. Our main result is the full classification
of threefolds isogenous to a product of mixed type with $\chi(\mathcal O_X)=-1$
assuming that any automorphism in $G$, which restricts to the trivial element
in $Aut(C_i)$ for some $C_i$, is the identity on the product. Since the
holomorphic Euler-Poincaré-characteristic of a smooth threefold of general
type with ample canonical class is always negative, these examples lie on the
boundary, in the sense of threefold geography. To achieve our result we use
techniques from computational group theory. Indeed, we develop a MAGMA
algorithm to classify these threefolds for any given value of $\chi(\mathcal
O_X)$.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,993 | Discriminatory Transfer | We observe standard transfer learning can improve prediction accuracies of
target tasks at the cost of lowering their prediction fairness -- a phenomenon
we named discriminatory transfer. We examine prediction fairness of a standard
hypothesis transfer algorithm and a standard multi-task learning algorithm, and
show they both suffer discriminatory transfer on the real-world Communities and
Crime data set. The presented case study introduces an interaction between
fairness and transfer learning, as an extension of existing fairness studies
that focus on single task learning.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,994 | Ultrafast relaxation of hot phonons in Graphene-hBN Heterostructures | Fast carrier cooling is important for high power graphene based devices.
Strongly Coupled Optical Phonons (SCOPs) play a major role in the relaxation of
photoexcited carriers in graphene. Heterostructures of graphene and hexagonal
boron nitride (hBN) have shown exceptional mobility and high saturation
current, which makes them ideal for applications, but the effect of the hBN
substrate on carrier cooling mechanisms is not understood. We track the cooling
of hot photo-excited carriers in graphene-hBN heterostructures using ultrafast
pump-probe spectroscopy. We find that the carriers cool down four times faster
in the case of graphene on hBN than on a silicon oxide substrate thus
overcoming the hot phonon (HP) bottleneck that plagues cooling in graphene
devices.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,995 | Non-linear Associative-Commutative Many-to-One Pattern Matching with Sequence Variables | Pattern matching is a powerful tool which is part of many functional
programming languages as well as computer algebra systems such as Mathematica.
Among the existing systems, Mathematica offers the most expressive pattern
matching. Unfortunately, no open source alternative has comparable pattern
matching capabilities. Notably, these features include support for associative
and/or commutative function symbols and sequence variables. While those
features have individually been subject of previous research, their
comprehensive combination has not yet been investigated. Furthermore, in many
applications, a fixed set of patterns is matched repeatedly against different
subjects. This many-to-one matching can be sped up by exploiting similarities
between patterns. Discrimination nets are the state-of-the-art solution for
many-to-one matching. In this thesis, a generalized discrimination net which
supports the full feature set is presented. All algorithms have been
implemented as an open-source library for Python. In experiments on real world
examples, significant speedups of many-to-one over one-to-one matching have
been observed.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,996 | Pair Background Envelopes in the SiD Detector | The beams at the ILC produce electron positron pairs due to beam-beam
interactions. This note presents for the first time a study of these processes
in a detailed simulation, which shows that these pair background particles
appear at angles that extend to the inner layers of the detector. The full data
set of pairs produced in one bunch crossing was used to calculate the helix
tracks, which the particles form in the solenoid field of the SiD detector. The
results suggest to further study the reduction of the beam pipe radius and
therefore to either add another SiD vertex detector layer, or reduce the radius
of the existing vertex detector layers, without increasing the detector
occupancy significantly. This has to go along with additional studies whether
the improvement in physics reconstruction methods, like c-tagging, is worth the
increased background level at smaller radii.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,997 | Expansion of percolation critical points for Hamming graphs | The Hamming graph $H(d,n)$ is the Cartesian product of $d$ complete graphs on
$n$ vertices. Let $m=d(n-1)$ be the degree and $V = n^d$ be the number of
vertices of $H(d,n)$. Let $p_c^{(d)}$ be the critical point for bond
percolation on $H(d,n)$. We show that, for $d \in \mathbb N$ fixed and $n \to
\infty$,
\begin{equation*}
p_c^{(d)}= \dfrac{1}{m} + \dfrac{2d^2-1}{2(d-1)^2}\dfrac{1}{m^2}
+ O(m^{-3}) + O(m^{-1}V^{-1/3}),
\end{equation*} which extends the asymptotics found in
\cite{BorChaHofSlaSpe05b} by one order. The term $O(m^{-1}V^{-1/3})$ is the
width of the critical window. For $d=4,5,6$ we have $m^{-3} =
O(m^{-1}V^{-1/3})$, and so the above formula represents the full asymptotic
expansion of $p_c^{(d)}$. In \cite{FedHofHolHul16a} \st{we show that} this
formula is a crucial ingredient in the study of critical bond percolation on
$H(d,n)$ for $d=2,3,4$. The proof uses a lace expansion for the upper bound and
a novel comparison with a branching random walk for the lower bound. The proof
of the lower bound also yields a refined asymptotics for the susceptibility of
a subcritical Erdős-Rényi random graph.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,998 | Efficiently Manifesting Asynchronous Programming Errors in Android Apps | Android, the #1 mobile app framework, enforces the single-GUI-thread model,
in which a single UI thread manages GUI rendering and event dispatching. Due to
this model, it is vital to avoid blocking the UI thread for responsiveness. One
common practice is to offload long-running tasks into async threads. To achieve
this, Android provides various async programming constructs, and leaves
developers themselves to obey the rules implied by the model. However, as our
study reveals, more than 25% apps violate these rules and introduce
hard-to-detect, fail-stop errors, which we term as aysnc programming errors
(APEs). To this end, this paper introduces APEChecker, a technique to
automatically and efficiently manifest APEs. The key idea is to characterize
APEs as specific fault patterns, and synergistically combine static analysis
and dynamic UI exploration to detect and verify such errors. Among the 40
real-world Android apps, APEChecker unveils and processes 61 APEs, of which 51
are confirmed (83.6% hit rate). Specifically, APEChecker detects 3X more APEs
than the state-of-art testing tools (Monkey, Sapienz and Stoat), and reduces
testing time from half an hour to a few minutes. On a specific type of APEs,
APEChecker confirms 5X more errors than the data race detection tool,
EventRacer, with very few false alarms.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,999 | AI Challenges in Human-Robot Cognitive Teaming | Among the many anticipated roles for robots in the future is that of being a
human teammate. Aside from all the technological hurdles that have to be
overcome with respect to hardware and control to make robots fit to work with
humans, the added complication here is that humans have many conscious and
subconscious expectations of their teammates - indeed, we argue that teaming is
mostly a cognitive rather than physical coordination activity. This introduces
new challenges for the AI and robotics community and requires fundamental
changes to the traditional approach to the design of autonomy. With this in
mind, we propose an update to the classical view of the intelligent agent
architecture, highlighting the requirements for mental modeling of the human in
the deliberative process of the autonomous agent. In this article, we outline
briefly the recent efforts of ours, and others in the community, towards
developing cognitive teammates along these guidelines.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,000 | Generalizing the MVW involution, and the contragredient | For certain quasi-split reductive groups $G$ over a general field $F$, we
construct an automorphism $\iota_G$ of $G$ over $F$, well-defined as an element
of ${\rm Aut}(G)(F)/jG(F)$ where $j:G(F) \rightarrow {\rm Aut}(G)(F)$ is the
inner-conjugation action of $G(F)$ on $G$. The automorphism $\iota_G$
generalizes (although only for quasi-split groups) an involution due to
Moeglin-Vigneras-Waldspurger in [MVW] for classical groups which takes any
irreducible admissible representation $\pi$ of $G(F)$ for $G$ a classical group
and $F$ a local field, to its contragredient $\pi^\vee$. The paper also
formulates a conjecture on the contragredient of an irreducible admissible
representation of $G(F)$ for $G$ a reductive algebraic group over a local field
$F$ in terms of the (enhanced) Langlands parameter of the representation.
| 0 | 0 | 1 | 0 | 0 | 0 |
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