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On the generalized nonlinear Camassa-Holm equation | In this paper, a generalized nonlinear Camassa-Holm equation with time- and
space-dependent coefficients is considered. We show that the control of the
higher order dispersive term is possible by using an adequate weight function
to define the energy. The existence and uniqueness of solutions are obtained
via a Picard iterative method.
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802.11 Wireless Simulation and Anomaly Detection using HMM and UBM | Despite the growing popularity of 802.11 wireless networks, users often
suffer from connectivity problems and performance issues due to unstable radio
conditions and dynamic user behavior among other reasons. Anomaly detection and
distinction are in the thick of major challenges that network managers
encounter. Complication of monitoring the broaden and complex WLANs, that often
requires heavy instrumentation of the user devices, makes the anomaly detection
analysis even harder. In this paper we exploit 802.11 access point usage data
and propose an anomaly detection technique based on Hidden Markov Model (HMM)
and Universal Background Model (UBM) on data that is inexpensive to obtain. We
then generate a number of network anomalous scenarios in OMNeT++/INET network
simulator and compare the detection outcomes with those in baseline approaches
(RawData and PCA). The experimental results show the superiority of HMM and
HMM-UBM models in detection precision and sensitivity.
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Early MFCC And HPCP Fusion for Robust Cover Song Identification | While most schemes for automatic cover song identification have focused on
note-based features such as HPCP and chord profiles, a few recent papers
surprisingly showed that local self-similarities of MFCC-based features also
have classification power for this task. Since MFCC and HPCP capture
complementary information, we design an unsupervised algorithm that combines
normalized, beat-synchronous blocks of these features using cross-similarity
fusion before attempting to locally align a pair of songs. As an added bonus,
our scheme naturally incorporates structural information in each song to fill
in alignment gaps where both feature sets fail. We show a striking jump in
performance over MFCC and HPCP alone, achieving a state of the art mean
reciprocal rank of 0.87 on the Covers80 dataset. We also introduce a new
medium-sized hand designed benchmark dataset called "Covers 1000," which
consists of 395 cliques of cover songs for a total of 1000 songs, and we show
that our algorithm achieves an MRR of 0.9 on this dataset for the first
correctly identified song in a clique. We provide the precomputed HPCP and MFCC
features, as well as beat intervals, for all songs in the Covers 1000 dataset
for use in further research.
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Optimally Gathering Two Robots | We present an algorithm that ensures in finite time the gathering of two
robots in the non-rigid ASYNC model. To circumvent established impossibility
results, we assume robots are equipped with 2-colors lights and are able to
measure distances between one another. Aside from its light, a robot has no
memory of its past actions, and its protocol is deterministic. Since, in the
same model, gathering is impossible when lights have a single color, our
solution is optimal with respect to the number of used colors.
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On Grauert-Riemenschneider type criterions | Let $(X,\omega)$ be a compact Hermitian manifold of complex dimension $n$. In
this article, we first survey recent progress towards Grauert-Riemenschneider
type criterions. Secondly, we give a simplified proof of Boucksom's conjecture
given by the author under the assumption that the Hermitian metric $\omega$
satisfies $\partial\overline{\partial}\omega^l=$ for all $l$, i.e., if $T$ is a
closed positive current on $X$ such that $\int_XT_{ac}^n>0$, then the class
$\{T\}$ is big and $X$ is Kähler. Finally, as an easy observation, we point
out that Nguyen's result can be generalized as follows: if
$\partial\overline{\partial}\omega=0$, and $T$ is a closed positive current
with analytic singularities, such that $\int_XT^n_{ac}>0$, then the class
$\{T\}$ is big and $X$ is Kähler.
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P-Governance Technology: Using Big Data for Political Party Management | Information and Communication Technology (ICT) has been playing a pivotal
role since the last decade in developing countries that brings citizen services
to the doorsteps and connecting people. With this aspiration ICT has introduced
several technologies of citizen services towards all categories of people. The
purpose of this study is to examine the Governance technology perspectives for
political party, emphasizing on the basic critical steps through which it could
be operationalized. We call it P-Governance. P-Governance shows technologies to
ensure governance, management, interaction communication in a political party
by improving decision making processes using big data. P-Governance challenges
the competence perspective to apply itself more assiduously to
operationalization, including the need to choose and give definition to one or
more units of analysis (of which the routine is a promising candidate). This
paper is to focus on research challenges posed by competence to which
P-Governance can and should respond include different strategy issues faced by
particular sections. Both the qualitative as well as quantitative research
approaches were conducted. The standard of citizen services, choice &
consultation, courtesy & consultation, entrance & information, and value for
money have found the positive relation with citizen's satisfaction. This study
results how can be technology make important roles on political movements in
developing countries using big data.
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The Forgettable-Watcher Model for Video Question Answering | A number of visual question answering approaches have been proposed recently,
aiming at understanding the visual scenes by answering the natural language
questions. While the image question answering has drawn significant attention,
video question answering is largely unexplored.
Video-QA is different from Image-QA since the information and the events are
scattered among multiple frames. In order to better utilize the temporal
structure of the videos and the phrasal structures of the answers, we propose
two mechanisms: the re-watching and the re-reading mechanisms and combine them
into the forgettable-watcher model. Then we propose a TGIF-QA dataset for video
question answering with the help of automatic question generation. Finally, we
evaluate the models on our dataset. The experimental results show the
effectiveness of our proposed models.
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Analogues of the $p^n$th Hilbert symbol in characteristic $p$ (updated) | The $p$th degree Hilbert symbol $(\cdot,\cdot )_p:K^\times/K^{\times p}\times
K^\times/K^{\times p}\to{}_p{\rm Br}(K)$ from characteristic $\neq p$ has two
analogues in characteristic $p$, $$[\cdot,\cdot )_p:K/\wp (K)\times
K^\times/K^{\times p}\to{}_p{\rm Br}(K),$$ where $\wp$ is the Artin-Schreier
map $x\mapsto x^p-x$, and $$((\cdot,\cdot ))_p:K/K^p\times K/K^p\to{}_p{\rm
Br}(K).$$
The symbol $[\cdot,\cdot )_p$ generalizes to an analogue of $(\cdot,\cdot
)_{p^n}$ via the Witt vectors, $$[\cdot,\cdot )_{p^n}:W_n(K)/\wp (W_n(K))\times
K^\times/K^{\times p^n}\to{}_{p^n}{\rm Br}(K).$$
Here $W_n(K)$ is the truncation of length $n$ of the ring of $p$-typical Witt
wectors, i.e. $W_{\{1,p,\ldots,p^{n-1}\}}(K)$.
In this paper we construct similar generalizations for $((\cdot,\cdot ))_p$.
Our construction involves Witt vectors and Weyl algebras. In the process we
obtain a new kind of Weyl algebras in characteristic $p$, with many interesting
properties.
The symbols we introduce, $((\cdot,\cdot ))_{p^n}$ and, more generally,
$((\cdot,\cdot ))_{p^m,p^n}$, which here are defined in terms of central simple
algebras, coincide with the homonymous symbols we introduced in
[arXiv:1711.00980] in terms of the symbols $[\cdot,\cdot )_{p^n}$. This will be
proved in a future paper. In the present paper we only introduce the symbols
and we prove that they have the same properties with the symbols from
[arXiv:1711.00980]. These properies are enough to obtain the representation
theorem for ${}_{p^n}{\rm Br}(K)$ from [arXiv:1711.00980], Theorem 4.10.
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GAMER-2: a GPU-accelerated adaptive mesh refinement code -- accuracy, performance, and scalability | We present GAMER-2, a GPU-accelerated adaptive mesh refinement (AMR) code for
astrophysics. It provides a rich set of features, including adaptive
time-stepping, several hydrodynamic schemes, magnetohydrodynamics,
self-gravity, particles, star formation, chemistry and radiative processes with
GRACKLE, data analysis with yt, and memory pool for efficient object
allocation. GAMER-2 is fully bitwise reproducible. For the performance
optimization, it adopts hybrid OpenMP/MPI/GPU parallelization and utilizes
overlapping CPU computation, GPU computation, and CPU-GPU communication. Load
balancing is achieved using a Hilbert space-filling curve on a level-by-level
basis without the need to duplicate the entire AMR hierarchy on each MPI
process. To provide convincing demonstrations of the accuracy and performance
of GAMER-2, we directly compare with Enzo on isolated disk galaxy simulations
and with FLASH on galaxy cluster merger simulations. We show that the physical
results obtained by different codes are in very good agreement, and GAMER-2
outperforms Enzo and FLASH by nearly one and two orders of magnitude,
respectively, on the Blue Waters supercomputers using $1-256$ nodes. More
importantly, GAMER-2 exhibits similar or even better parallel scalability
compared to the other two codes. We also demonstrate good weak and strong
scaling using up to 4096 GPUs and 65,536 CPU cores, and achieve a uniform
resolution as high as $10{,}240^3$ cells. Furthermore, GAMER-2 can be adopted
as an AMR+GPUs framework and has been extensively used for the wave dark matter
($\psi$DM) simulations. GAMER-2 is open source (available at
this https URL) and new contributions are welcome.
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Partition algebras $\mathsf{P}_k(n)$ with $2k>n$ and the fundamental theorems of invariant theory for the symmetric group $\mathsf{S}_n$ | Assume $\mathsf{M}_n$ is the $n$-dimensional permutation module for the
symmetric group $\mathsf{S}_n$, and let $\mathsf{M}_n^{\otimes k}$ be its
$k$-fold tensor power. The partition algebra $\mathsf{P}_k(n)$ maps
surjectively onto the centralizer algebra
$\mathsf{End}_{\mathsf{S}_n}(\mathsf{M}_n^{\otimes k})$ for all $k, n \in
\mathbb{Z}_{\ge 1}$ and isomorphically when $n \ge 2k$. We describe the image
of the surjection $\Phi_{k,n}:\mathsf{P}_k(n) \to
\mathsf{End}_{\mathsf{S}_n}(\mathsf{M}_n^{\otimes k})$ explicitly in terms of
the orbit basis of $\mathsf{P}_k(n)$ and show that when $2k > n$ the kernel of
$\Phi_{k,n}$ is generated by a single essential idempotent $\mathsf{e}_{k,n}$,
which is an orbit basis element. We obtain a presentation for
$\mathsf{End}_{\mathsf{S}_n}(\mathsf{M}_n^{\otimes k})$ by imposing one
additional relation, $\mathsf{e}_{k,n} = 0$, to the standard presentation of
the partition algebra $\mathsf{P}_k(n)$ when $2k > n$. As a consequence, we
obtain the fundamental theorems of invariant theory for the symmetric group
$\mathsf{S}_n$. We show under the natural embedding of the partition algebra
$\mathsf{P}_n(n)$ into $\mathsf{P}_k(n)$ for $k \ge n$ that the essential
idempotent $\mathsf{e}_{n,n}$ generates the kernel of $\Phi_{k,n}$. Therefore,
the relation $\mathsf{e}_{n,n} = 0$ can replace $\mathsf{e}_{k,n} = 0$ when $k
\ge n$.
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Distributed sub-optimal resource allocation over weight-balanced graph via singular perturbation | In this paper, we consider distributed optimization design for resource
allocation problems over weight-balanced graphs. With the help of singular
perturbation analysis, we propose a simple sub-optimal continuous-time
optimization algorithm. Moreover, we prove the existence and uniqueness of the
algorithm equilibrium, and then show the convergence with an exponential rate.
Finally, we verify the sub-optimality of the algorithm, which can approach the
optimal solution as an adjustable parameter tends to zero.
| 0 | 0 | 1 | 0 | 0 | 0 |
Decentralized P2P Energy Trading under Network Constraints in a Low-Voltage Network | The increasing uptake of distributed energy resources (DERs) in distribution
systems and the rapid advance of technology have established new scenarios in
the operation of low-voltage networks. In particular, recent trends in
cryptocurrencies and blockchain have led to a proliferation of peer-to-peer
(P2P) energy trading schemes, which allow the exchange of energy between the
neighbors without any intervention of a conventional intermediary in the
transactions. Nevertheless, far too little attention has been paid to the
technical constraints of the network under this scenario. A major challenge to
implementing P2P energy trading is that of ensuring that network constraints
are not violated during the energy exchange. This paper proposes a methodology
based on sensitivity analysis to assess the impact of P2P transactions on the
network and to guarantee an exchange of energy that does not violate network
constraints. The proposed method is tested on a typical UK low-voltage network.
The results show that our method ensures that energy is exchanged between users
under the P2P scheme without violating the network constraints, and that users
can still capture the economic benefits of the P2P architecture.
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Fictitious GAN: Training GANs with Historical Models | Generative adversarial networks (GANs) are powerful tools for learning
generative models. In practice, the training may suffer from lack of
convergence. GANs are commonly viewed as a two-player zero-sum game between two
neural networks. Here, we leverage this game theoretic view to study the
convergence behavior of the training process. Inspired by the fictitious play
learning process, a novel training method, referred to as Fictitious GAN, is
introduced. Fictitious GAN trains the deep neural networks using a mixture of
historical models. Specifically, the discriminator (resp. generator) is updated
according to the best-response to the mixture outputs from a sequence of
previously trained generators (resp. discriminators). It is shown that
Fictitious GAN can effectively resolve some convergence issues that cannot be
resolved by the standard training approach. It is proved that asymptotically
the average of the generator outputs has the same distribution as the data
samples.
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Suppression of Hall number due to charge density wave order in high-$T_c$ cuprates | Understanding the pseudogap phase in hole-doped high temperature cuprate
superconductors remains a central challenge in condensed matter physics. From a
host of recent experiments there is now compelling evidence of translational
symmetry breaking charge density wave (CDW) order in a wide range of doping
inside this phase. Two distinct types of incommensurate charge order --
bidirectional at zero or low magnetic fields and unidirectional at high
magnetic fields close to the upper critical field $H_{c2}$ -- have been
reported so far in approximately the same doping range between $p\simeq 0.08$
and $p\simeq 0.16$. In concurrent developments, recent high field Hall
experiments have also revealed two indirect but striking signatures of Fermi
surface reconstruction in the pseudogap phase, namely, a sign change of the
Hall coefficient to negative values at low temperatures at intermediate range
of hole doping and a rapid suppression of the positive Hall number without
change in sign near optimal doping $p \sim 0.19$. We show that the assumption
of a unidirectional incommensurate CDW (with or without a coexisting weak
bidirectional order) at high magnetic fields near optimal doping and a
coexistence of both types of orders of approximately equal magnitude at high
magnetic fields at intermediate range of doping may help explain the striking
behavior of low temperature Hall effect in the entire pseudogap phase.
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The Wavefunction of the Collapsing Bose-Einstein Condensate | Bose-Einstein condensates with tunable interatomic interactions have been
studied intensely in recent experiments. The investigation of the collapse of a
condensate following a sudden change in the nature of the interaction from
repulsive to attractive has led to the observation of a remnant condensate that
did not undergo further collapse. We suggest that this high-density remnant is
in fact the absolute minimum of the energy, if the attractive atomic
interactions are nonlocal, and is therefore inherently stable. We show that a
variational trial function consisting of a superposition of two distinct
gaussians is an accurate representation of the wavefunction of the ground state
of the conventional local Gross-Pitaevskii field equation for an attractive
condensate and gives correctly the points of emergence of instability. We then
use such a superposition of two gaussians as a variational trial function in
order to calculate the minima of the energy when it includes a nonlocal
interaction term. We use experimental data in order to study the long range of
the nonlocal interaction, showing that they agree very well with a
dimensionally derived expression for this range.
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Not-So-Random Features | We propose a principled method for kernel learning, which relies on a
Fourier-analytic characterization of translation-invariant or
rotation-invariant kernels. Our method produces a sequence of feature maps,
iteratively refining the SVM margin. We provide rigorous guarantees for
optimality and generalization, interpreting our algorithm as online
equilibrium-finding dynamics in a certain two-player min-max game. Evaluations
on synthetic and real-world datasets demonstrate scalability and consistent
improvements over related random features-based methods.
| 1 | 0 | 0 | 1 | 0 | 0 |
Dual-Primal Graph Convolutional Networks | In recent years, there has been a surge of interest in developing deep
learning methods for non-Euclidean structured data such as graphs. In this
paper, we propose Dual-Primal Graph CNN, a graph convolutional architecture
that alternates convolution-like operations on the graph and its dual. Our
approach allows to learn both vertex- and edge features and generalizes the
previous graph attention (GAT) model. We provide extensive experimental
validation showing state-of-the-art results on a variety of tasks tested on
established graph benchmarks, including CORA and Citeseer citation networks as
well as MovieLens, Flixter, Douban and Yahoo Music graph-guided recommender
systems.
| 0 | 0 | 0 | 1 | 0 | 0 |
CLEAR: Coverage-based Limiting-cell Experiment Analysis for RNA-seq | Direct cDNA preamplification protocols developed for single-cell RNA-seq
(scRNA-seq) have enabled transcriptome profiling of rare cells without having
to pool multiple samples or to perform RNA extraction. We term this approach
limiting-cell RNA-seq (lcRNA-seq). Unlike scRNA-seq, which focuses on
'cell-atlasing', lcRNA-seq focuses on identifying differentially expressed
genes (DEGs) between experimental groups. This requires accounting for systems
noise which can obscure biological differences. We present CLEAR, a workflow
that identifies robust transcripts in lcRNA-seq data for between-group
comparisons. To develop CLEAR, we compared DEGs from RNA extracted from
FACS-derived CD5+ and CD5- cells from a single chronic lymphocytic leukemia
patient diluted to input RNA levels of 10-, 100- and 1,000pg. Data quality at
ultralow input levels are known to be noisy. When using CLEAR transcripts vs.
using all available transcripts, downstream analyses reveal more shared DEGs,
improved Principal Component Analysis separation of cell type, and increased
similarity between results across different input RNA amounts. CLEAR was
applied to two publicly available ultralow input RNA-seq data and an in-house
murine neural cell lcRNA-seq dataset. CLEAR provides a novel way to visualize
the public datasets while validates cell phenotype markers for astrocytes,
neural stem and progenitor cells.
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Training Deep AutoEncoders for Collaborative Filtering | This paper proposes a novel model for the rating prediction task in
recommender systems which significantly outperforms previous state-of-the art
models on a time-split Netflix data set. Our model is based on deep autoencoder
with 6 layers and is trained end-to-end without any layer-wise pre-training. We
empirically demonstrate that: a) deep autoencoder models generalize much better
than the shallow ones, b) non-linear activation functions with negative parts
are crucial for training deep models, and c) heavy use of regularization
techniques such as dropout is necessary to prevent over-fiting. We also propose
a new training algorithm based on iterative output re-feeding to overcome
natural sparseness of collaborate filtering. The new algorithm significantly
speeds up training and improves model performance. Our code is available at
this https URL
| 1 | 0 | 0 | 1 | 0 | 0 |
Unit circle rectification of the MVDR beamformer | The sample matrix inversion (SMI) beamformer implements Capon's minimum
variance distortionless (MVDR) beamforming using the sample covariance matrix
(SCM). In a snapshot limited environment, the SCM is poorly conditioned
resulting in a suboptimal performance from the SMI beamformer. Imposing
structural constraints on the SCM estimate to satisfy known theoretical
properties of the ensemble MVDR beamformer mitigates the impact of limited
snapshots on the SMI beamformer performance. Toeplitz rectification and
bounding the norm of weight vector are common approaches for such constrains.
This paper proposes the unit circle rectification technique which constraints
the SMI beamformer to satisfy a property of the ensemble MVDR beamformer: for
narrowband planewave beamforming on a uniform linear array, the zeros of the
MVDR weight array polynomial must fall on the unit circle. Numerical
simulations show that the resulting unit circle MVDR (UC MVDR) beamformer
frequently improves the suppression of both discrete interferers and white
background noise compared to the classic SMI beamformer. Moreover, the UC MVDR
beamformer is shown to suppress discrete interferers better than the MVDR
beamformer diagonally loaded to maximize the SINR.
| 1 | 0 | 0 | 0 | 0 | 0 |
Reducing Storage of Global Wind Ensembles with Stochastic Generators | Wind has the potential to make a significant contribution to future energy
resources. Locating the sources of this renewable energy on a global scale is
however extremely challenging, given the difficulty to store very large data
sets generated by modern computer models. We propose a statistical model that
aims at reproducing the data-generating mechanism of an ensemble of runs via a
Stochastic Generator (SG) of global annual wind data. We introduce an
evolutionary spectrum approach with spatially varying parameters based on
large-scale geographical descriptors such as altitude to better account for
different regimes across the Earth's orography. We consider a multi-step
conditional likelihood approach to estimate the parameters that explicitly
accounts for nonstationary features while also balancing memory storage and
distributed computation. We apply the proposed model to more than 18 million
points of yearly global wind speed. The proposed SG requires orders of
magnitude less storage for generating surrogate ensemble members from wind than
does creating additional wind fields from the climate model, even if an
effective lossy data compression algorithm is applied to the simulation output.
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Jackknife Empirical Likelihood-based inference for S-Gini indices | Widely used income inequality measure, Gini index is extended to form a
family of income inequality measures known as Single-Series Gini (S-Gini)
indices. In this study, we develop empirical likelihood (EL) and jackknife
empirical likelihood (JEL) based inference for S-Gini indices. We prove that
the limiting distribution of both EL and JEL ratio statistics are Chi-square
distribution with one degree of freedom. Using the asymptotic distribution we
construct EL and JEL based confidence intervals for realtive S-Gini indices. We
also give bootstrap-t and bootstrap calibrated empirical likelihood confidence
intervals for S-Gini indices. A numerical study is carried out to compare the
performances of the proposed confidence interval with the bootstrap methods. A
test for S-Gini indices based on jackknife empirical likelihood ratio is also
proposed. Finally we illustrate the proposed method using an income data.
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Continual One-Shot Learning of Hidden Spike-Patterns with Neural Network Simulation Expansion and STDP Convergence Predictions | This paper presents a constructive algorithm that achieves successful
one-shot learning of hidden spike-patterns in a competitive detection task. It
has previously been shown (Masquelier et al., 2008) that spike-timing-dependent
plasticity (STDP) and lateral inhibition can result in neurons competitively
tuned to repeating spike-patterns concealed in high rates of overall
presynaptic activity. One-shot construction of neurons with synapse weights
calculated as estimates of converged STDP outcomes results in immediate
selective detection of hidden spike-patterns. The capability of continual
learning is demonstrated through the successful one-shot detection of new sets
of spike-patterns introduced after long intervals in the simulation time.
Simulation expansion (Lightheart et al., 2013) has been proposed as an approach
to the development of constructive algorithms that are compatible with
simulations of biological neural networks. A simulation of a biological neural
network may have orders of magnitude fewer neurons and connections than the
related biological neural systems; therefore, simulated neural networks can be
assumed to be a subset of a larger neural system. The constructive algorithm is
developed using simulation expansion concepts to perform an operation
equivalent to the exchange of neurons between the simulation and the larger
hypothetical neural system. The dynamic selection of neurons to simulate within
a larger neural system (hypothetical or stored in memory) may be a starting
point for a wide range of developments and applications in machine learning and
the simulation of biology.
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Interpretable High-Dimensional Inference Via Score Projection with an Application in Neuroimaging | In the fields of neuroimaging and genetics, a key goal is testing the
association of a single outcome with a very high-dimensional imaging or genetic
variable. Often, summary measures of the high-dimensional variable are created
to sequentially test and localize the association with the outcome. In some
cases, the results for summary measures are significant, but subsequent tests
used to localize differences are underpowered and do not identify regions
associated with the outcome. Here, we propose a generalization of Rao's score
test based on projecting the score statistic onto a linear subspace of a
high-dimensional parameter space. In addition, we provide methods to localize
signal in the high-dimensional space by projecting the scores to the subspace
where the score test was performed. This allows for inference in the
high-dimensional space to be performed on the same degrees of freedom as the
score test, effectively reducing the number of comparisons. Simulation results
demonstrate the test has competitive power relative to others commonly used. We
illustrate the method by analyzing a subset of the Alzheimer's Disease
Neuroimaging Initiative dataset. Results suggest cortical thinning of the
frontal and temporal lobes may be a useful biological marker of Alzheimer's
risk.
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A maximum principle for free boundary minimal varieties of arbitrary codimension | We establish a boundary maximum principle for free boundary minimal
submanifolds in a Riemannian manifold with boundary, in any dimension and
codimension. Our result holds more generally in the context of varifolds.
| 0 | 0 | 1 | 0 | 0 | 0 |
Drawing Big Graphs using Spectral Sparsification | Spectral sparsification is a general technique developed by Spielman et al.
to reduce the number of edges in a graph while retaining its structural
properties. We investigate the use of spectral sparsification to produce good
visual representations of big graphs. We evaluate spectral sparsification
approaches on real-world and synthetic graphs. We show that spectral
sparsifiers are more effective than random edge sampling. Our results lead to
guidelines for using spectral sparsification in big graph visualization.
| 1 | 0 | 0 | 0 | 0 | 0 |
Optimal paths on the road network as directed polymers | We analyze the statistics of the shortest and fastest paths on the road
network between randomly sampled end points. To a good approximation, these
optimal paths are found to be directed in that their lengths (at large scales)
are linearly proportional to the absolute distance between them. This motivates
comparisons to universal features of directed polymers in random media. There
are similarities in scalings of fluctuations in length/time and transverse
wanderings, but also important distinctions in the scaling exponents, likely
due to long-range correlations in geographic and man-made features. At short
scales the optimal paths are not directed due to circuitous excursions governed
by a fat-tailed (power-law) probability distribution.
| 0 | 1 | 0 | 0 | 0 | 0 |
Optimal proportional reinsurance and investment for stochastic factor models | In this work we investigate the optimal proportional reinsurance-investment
strategy of an insurance company which wishes to maximize the expected
exponential utility of its terminal wealth in a finite time horizon. Our goal
is to extend the classical Cramer-Lundberg model introducing a stochastic
factor which affects the intensity of the claims arrival process, described by
a Cox process, as well as the insurance and reinsurance premia. Using the
classical stochastic control approach based on the Hamilton-Jacobi-Bellman
equation we characterize the optimal strategy and provide a verification result
for the value function via classical solutions of two backward partial
differential equations. Existence and uniqueness of these solutions are
discussed. Results under various premium calculation principles are illustrated
and a new premium calculation rule is proposed in order to get more realistic
strategies and to better fit our stochastic factor model. Finally, numerical
simulations are performed to obtain sensitivity analyses.
| 0 | 0 | 0 | 0 | 0 | 1 |
Formal Black-Box Analysis of Routing Protocol Implementations | The Internet infrastructure relies entirely on open standards for its routing
protocols. However, the majority of routers on the Internet are closed-source.
Hence, there is no straightforward way to analyze them. Specifically, one
cannot easily identify deviations of a router's routing functionality from the
routing protocol's standard. Such deviations (either deliberate or inadvertent)
are particularly important to identify since they may degrade the security or
resiliency of the network.
A model-based testing procedure is a technique that allows to systematically
generate tests based on a model of the system to be tested; thereby finding
deviations in the system compared to the model. However, applying such an
approach to a complex multi-party routing protocol requires a prohibitively
high number of tests to cover the desired functionality. We propose efficient
and practical optimizations to the model-based testing procedure that are
tailored to the analysis of routing protocols. These optimizations allow to
devise a formal black-box method to unearth deviations in closed-source routing
protocols' implementations. The method relies only on the ability to test the
targeted protocol implementation and observe its output. Identification of the
deviations is fully automatic.
We evaluate our method against one of the complex and widely used routing
protocols on the Internet -- OSPF. We search for deviations in the OSPF
implementation of Cisco. Our evaluation identified numerous significant
deviations that can be abused to compromise the security of a network. The
deviations were confirmed by Cisco. We further employed our method to analyze
the OSPF implementation of the Quagga Routing Suite. The analysis revealed one
significant deviation. Subsequent to the disclosure of the deviations some of
them were also identified by IBM, Lenovo and Huawei in their own products.
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Finding polynomial loop invariants for probabilistic programs | Quantitative loop invariants are an essential element in the verification of
probabilistic programs. Recently, multivariate Lagrange interpolation has been
applied to synthesizing polynomial invariants. In this paper, we propose an
alternative approach. First, we fix a polynomial template as a candidate of a
loop invariant. Using Stengle's Positivstellensatz and a transformation to a
sum-of-squares problem, we find sufficient conditions on the coefficients.
Then, we solve a semidefinite programming feasibility problem to synthesize the
loop invariants. If the semidefinite program is unfeasible, we backtrack after
increasing the degree of the template. Our approach is semi-complete in the
sense that it will always lead us to a feasible solution if one exists and
numerical errors are small. Experimental results show the efficiency of our
approach.
| 1 | 0 | 0 | 0 | 0 | 0 |
Active Decision Boundary Annotation with Deep Generative Models | This paper is on active learning where the goal is to reduce the data
annotation burden by interacting with a (human) oracle during training.
Standard active learning methods ask the oracle to annotate data samples.
Instead, we take a profoundly different approach: we ask for annotations of the
decision boundary. We achieve this using a deep generative model to create
novel instances along a 1d line. A point on the decision boundary is revealed
where the instances change class. Experimentally we show on three data sets
that our method can be plugged-in to other active learning schemes, that human
oracles can effectively annotate points on the decision boundary, that our
method is robust to annotation noise, and that decision boundary annotations
improve over annotating data samples.
| 1 | 0 | 0 | 0 | 0 | 0 |
Multifrequency Excitation and Detection Scheme in Apertureless Scattering Near Field Scanning Optical Microscopy | We theoretically and experimentally demonstrate a multifrequency excitation
and detection scheme in apertureless near field optical microscopy, that
exceeds current state of the art sensitivity and background suppression. By
exciting the AFM tip at its two first flexural modes, and demodulating the
detected signal at the harmonics of their sum, we extract a near field signal
with a twofold improved sensitivity and deep sub-wavelength resolution,
reaching $\lambda/230$. Furthermore, the method offers rich control over
experimental degrees of freedom, expanding the parameter space for achieving
complete optical background suppression. This approach breaks the ground for
non-interferometric complete phase and amplitude retrieval of the near field
signal, and is suitable for any multimodal excitation and higher harmonic
demodulation.
| 0 | 1 | 0 | 0 | 0 | 0 |
Extended Kitaev chain with longer-range hopping and pairing | We consider the Kitaev chain model with finite and infinite range in the
hopping and pairing parameters, looking in particular at the appearance of
Majorana zero energy modes and massive edge modes. We study the system both in
the presence and in the absence of time reversal symmetry, by means of
topological invariants and exact diagonalization, disclosing very rich phase
diagrams. In particular, for extended hopping and pairing terms, we can get as
many Majorana modes at each end of the chain as the neighbors involved in the
couplings. Finally we generalize the transfer matrix approach useful to
calculate the zero-energy Majorana modes at the edges for a generic number of
coupled neighbors.
| 0 | 1 | 0 | 0 | 0 | 0 |
Global entropy solutions to the compressible Euler equations in the isentropic nozzle flow for large data: Application of the modified Godunov scheme and the generalized invariant regions | We study the motion of isentropic gas in nozzles. This is a major subject in
fluid dynamics. In fact, the nozzle is utilized to increase the thrust of
rocket engines. Moreover, the nozzle flow is closely related to astrophysics.
These phenomena are governed by the compressible Euler equation, which is one
of crucial equations in inhomogeneous conservation laws.
In this paper, we consider its unsteady flow and devote to proving the global
existence and stability of solutions to the Cauchy problem for the general
nozzle. The theorem has been proved in (Tsuge in Arch. Ration. Mech. Anal.
209:365-400 (2013)). However, this result is limited to small data. Our aim in
the present paper is to remove this restriction, that is, we consider large
data. Although the subject is important in Mathematics, Physics and
engineering, it remained open for a long time. The problem seems to lie in a
bounded estimate of approximate solutions, because we have only method to
investigate the behavior with respect to the time variable. To solve this, we
first introduce a generalized invariant region. Compared with the existing
ones, its upper and lower bounds are extended constants to functions of the
space variable. However, we cannot apply the new invariant region to the
traditional difference method. Therefore, we invent the modified Godunov
scheme. The approximate solutions consist of some functions corresponding to
the upper and lower bounds of the invariant regions. These methods enable us to
investigate the behavior of approximate solutions with respect to the space
variable. The ideas are also applicable to other nonlinear problems involving
similar difficulties.
| 0 | 0 | 1 | 0 | 0 | 0 |
Effects of atrial fibrillation on the arterial fluid dynamics: a modelling perspective | Atrial fibrillation (AF) is the most common form of arrhythmia with
accelerated and irregular heart rate (HR), leading to both heart failure and
stroke and being responsible for an increase in cardiovascular morbidity and
mortality. In spite of its importance, the direct effects of AF on the arterial
hemodynamic patterns are not completely known to date. Based on a multiscale
modelling approach, the proposed work investigates the effects of AF on the
local arterial fluid dynamics. AF and normal sinus rhythm (NSR) conditions are
simulated extracting 2000 $\mathrm{RR}$ heartbeats and comparing the most
relevant cardiac and vascular parameters at the same HR (75 bpm). Present
outcomes evidence that the arterial system is not able to completely absorb the
AF-induced variability, which can be even amplified towards the peripheral
circulation. AF is also able to locally alter the wave dynamics, by modifying
the interplay between forward and backward signals. The sole heart rhythm
variation (i.e., from NSR to AF) promotes an alteration of the regular dynamics
at the arterial level which, in terms of pressure and peripheral perfusion,
suggests a modification of the physiological phenomena ruled by periodicity
(e.g., regular organ perfusion)and a possible vascular dysfunction due to the
prolonged exposure to irregular and extreme values. The present study
represents a first modeling approach to characterize the variability of
arterial hemodynamics in presence of AF, which surely deserves further clinical
investigation.
| 0 | 0 | 0 | 0 | 1 | 0 |
Controllability and optimal control of the transport equation with a localized vector field | We study controllability of a Partial Differential Equation of transport
type, that arises in crowd models. We are interested in controlling such system
with a control being a Lipschitz vector field on a fixed control set $\omega$.
We prove that, for each initial and final configuration, one can steer one to
another with such class of controls only if the uncontrolled dynamics allows to
cross the control set $\omega$. We also prove a minimal time result for such
systems. We show that the minimal time to steer one initial configuration to
another is related to the condition of having enough mass in $\omega$ to feed
the desired final configuration.
| 0 | 0 | 1 | 0 | 0 | 0 |
Gravitational instabilities in a protosolar-like disc II: continuum emission and mass estimates | Gravitational instabilities (GIs) are most likely a fundamental process
during the early stages of protoplanetary disc formation. Recently, there have
been detections of spiral features in young, embedded objects that appear
consistent with GI-driven structure. It is crucial to perform hydrodynamic and
radiative transfer simulations of gravitationally unstable discs in order to
assess the validity of GIs in such objects, and constrain optimal targets for
future observations. We utilise the radiative transfer code LIME to produce
continuum emission maps of a $0.17\,\mathrm{M}_{\odot}$ self-gravitating
protosolar-like disc. We note the limitations of using LIME as is and explore
methods to improve upon the default gridding. We use CASA to produce synthetic
observations of 270 continuum emission maps generated across different
frequencies, inclinations and dust opacities. We find that the spiral structure
of our protosolar-like disc model is distinguishable across the majority of our
parameter space after 1 hour of observation, and is especially prominent at
230$\,$GHz due to the favourable combination of angular resolution and
sensitivity. Disc mass derived from the observations is sensitive to the
assumed dust opacities and temperatures, and therefore can be underestimated by
a factor of at least 30 at 850$\,$GHz and 2.5 at 90$\,$GHz. As a result, this
effect could retrospectively validate GIs in discs previously thought not
massive enough to be gravitationally unstable, which could have a significant
impact on the understanding of the formation and evolution of protoplanetary
discs.
| 0 | 1 | 0 | 0 | 0 | 0 |
Information Processing by Networks of Quantum Decision Makers | We suggest a model of a multi-agent society of decision makers taking
decisions being based on two criteria, one is the utility of the prospects and
the other is the attractiveness of the considered prospects. The model is the
generalization of quantum decision theory, developed earlier for single
decision makers realizing one-step decisions, in two principal aspects. First,
several decision makers are considered simultaneously, who interact with each
other through information exchange. Second, a multistep procedure is treated,
when the agents exchange information many times. Several decision makers
exchanging information and forming their judgement, using quantum rules, form a
kind of a quantum information network, where collective decisions develop in
time as a result of information exchange. In addition to characterizing
collective decisions that arise in human societies, such networks can describe
dynamical processes occurring in artificial quantum intelligence composed of
several parts or in a cluster of quantum computers. The practical usage of the
theory is illustrated on the dynamic disjunction effect for which three
quantitative predictions are made: (i) the probabilistic behavior of decision
makers at the initial stage of the process is described; (ii) the decrease of
the difference between the initial prospect probabilities and the related
utility factors is proved; (iii) the existence of a common consensus after
multiple exchange of information is predicted. The predicted numerical values
are in very good agreement with empirical data.
| 1 | 0 | 0 | 0 | 0 | 0 |
The Value of Sharing Intermittent Spectrum | Recent initiatives by regulatory agencies to increase spectrum resources
available for broadband access include rules for sharing spectrum with
high-priority incumbents. We study a model in which wireless Service Providers
(SPs) charge for access to their own exclusive-use (licensed) band along with
access to an additional shared band. The total, or delivered price in each band
is the announced price plus a congestion cost, which depends on the load, or
total users normalized by the bandwidth. The shared band is intermittently
available with some probability, due to incumbent activity, and when
unavailable, any traffic carried on that band must be shifted to licensed
bands. The SPs then compete for quantity of users. We show that the value of
the shared band depends on the relative sizes of the SPs: large SPs with more
bandwidth are better able to absorb the variability caused by intermittency
than smaller SPs. However, as the amount of shared spectrum increases, the
large SPs may not make use of it. In that scenario shared spectrum creates more
value than splitting it among the SPs for exclusive use. We also show that
fixing the average amount of available shared bandwidth, increasing the
reliability of the band is preferable to increasing the bandwidth.
| 1 | 0 | 0 | 0 | 0 | 0 |
Origin of layer dependence in band structures of two-dimensional materials | We study the origin of layer dependence in band structures of two-dimensional
materials. We find that the layer dependence, at the density functional theory
(DFT) level, is a result of quantum confinement and the non-linearity of the
exchange-correlation functional. We use this to develop an efficient scheme for
performing DFT and GW calculations of multilayer systems. We show that the DFT
and quasiparticle band structures of a multilayer system can be derived from a
single calculation on a monolayer of the material. We test this scheme on
multilayers of MoS$_2$, graphene and phosphorene. This new scheme yields
results in excellent agreement with the standard methods at a fraction of the
computation cost. This helps overcome the challenge of performing fully
converged GW calculations on multilayers of 2D materials, particularly in the
case of transition metal dichalcogenides which involve very stringent
convergence parameters.
| 0 | 1 | 0 | 0 | 0 | 0 |
From Strings to Sets | A complete proof is given of relative interpretability of Adjunctive Set
Theory with Extensionality in an elementary concatenation theory.
| 0 | 0 | 1 | 0 | 0 | 0 |
Sharp measure contraction property for generalized H-type Carnot groups | We prove that H-type Carnot groups of rank $k$ and dimension $n$ satisfy the
$\mathrm{MCP}(K,N)$ if and only if $K\leq 0$ and $N \geq k+3(n-k)$. The latter
integer coincides with the geodesic dimension of the Carnot group. The same
result holds true for the larger class of generalized H-type Carnot groups
introduced in this paper, and for which we compute explicitly the optimal
synthesis. This constitutes the largest class of Carnot groups for which the
curvature exponent coincides with the geodesic dimension. We stress that
generalized H-type Carnot groups have step 2, include all corank 1 groups and,
in general, admit abnormal minimizing curves.
As a corollary, we prove the absolute continuity of the Wasserstein geodesics
for the quadratic cost on all generalized H-type Carnot groups.
| 0 | 0 | 1 | 0 | 0 | 0 |
Twisted Quantum Double Model of Topological Orders with Boundaries | We generalize the twisted quantum double model of topological orders in two
dimensions to the case with boundaries by systematically constructing the
boundary Hamiltonians. Given the bulk Hamiltonian defined by a gauge group $G$
and a three-cocycle in the third cohomology group of $G$ over $U(1)$, a
boundary Hamiltonian can be defined by a subgroup $K$ of $G$ and a two-cochain
in the second cochain group of $K$ over $U(1)$. The consistency between the
bulk and boundary Hamiltonians is dictated by what we call the Frobenius
condition that constrains the two-cochain given the three-cocyle. We offer a
closed-form formula computing the ground state degeneracy of the model on a
cylinder in terms of the input data only, which can be naturally generalized to
surfaces with more boundaries. We also explicitly write down the ground-state
wavefunction of the model on a disk also in terms of the input data only.
| 0 | 1 | 1 | 0 | 0 | 0 |
Evolutionary game of coalition building under external pressure | We study the fragmentation-coagulation (or merging and splitting)
evolutionary control model as introduced recently by one of the authors, where
$N$ small players can form coalitions to resist to the pressure exerted by the
principal. It is a Markov chain in continuous time and the players have a
common reward to optimize. We study the behavior as $N$ grows and show that the
problem converges to a (one player) deterministic optimization problem in
continuous time, in the infinite dimensional state space.
| 0 | 0 | 1 | 0 | 0 | 0 |
Nearly resolution V plans on blocks of small size | In Bagchi (2010) main effect plans "orthogonal through the block factor"
(POTB) have been constructed. The main advantages of a POTB are that (a) it may
exist in a set up where an "usual" orthogonal main effect plan (OMEP) cannot
exist and (b) the data analysis is nearly as simple as an OMEP. In the present
paper we extend this idea and define the concept of orthogonality between a
pair of factorial effects ( main effects or interactions) "through the block
factor" in the context of a symmetrical experiment. We consider plans generated
from an initial plan by adding runs. For such a plan we have derived necessary
and sufficient conditions for a pair of effects to be orthogonal through the
block factor in terms of the generators. We have also derived a sufficient
condition on the generators so as to turn a pair of effects aliased in the
initial plan separated in the final plan. The theory developed is illustrated
with plans for experiments with three-level factors in situations where
interactions between three or more factors are absent. We have constructed
plans with blocks of size four and fewer runs than a resolution $V$ plan
estimating all main effects and all but at most one two-factor interactions.
| 0 | 0 | 1 | 1 | 0 | 0 |
Graphene-based electron transport layers in perovskite solar cells: a step-up for an efficient carrier collection | The electron transport layer (ETL) plays a fundamental role in perovskite
solar cells. Recently, graphene-based ETLs have been proved to be good
candidate for scalable fabrication processes and to achieve higher carrier
injection with respect to most commonly used ETLs. In this work we
experimentally study the effects of different graphene-based ETLs in sensitized
MAPI solar cells. By means of time-integrated and picosecond time-resolved
photoluminescence techniques, the carrier recombination dynamics in MAPI films
embedded in different ETLs is investigated. Using graphene doped mesoporous
TiO2 (G+mTiO2) with the addition of a lithium-neutralized graphene oxide
(GO-Li) interlayer as ETL, we find that the carrier collection efficiency is
increased by about a factor two with respect to standard mTiO2. Taking
advantage of the absorption coefficient dispersion, we probe the MAPI layer
morphology, along the thickness, finding that the MAPI embedded in the ETL
composed by G+mTiO2 plus GO-Li brings to a very good crystalline quality of the
MAPI layer with a trap density about one order of magnitude lower than that
found with the other ETLs. In addition, this ETL freezes MAPI at the tetragonal
phase, regardless of the temperature. Graphene-based ETLs can open the way to
significant improvement of perovskite solar cells.
| 0 | 1 | 0 | 0 | 0 | 0 |
Learning and Transferring IDs Representation in E-commerce | Many machine intelligence techniques are developed in E-commerce and one of
the most essential components is the representation of IDs, including user ID,
item ID, product ID, store ID, brand ID, category ID etc. The classical
encoding based methods (like one-hot encoding) are inefficient in that it
suffers sparsity problems due to its high dimension, and it cannot reflect the
relationships among IDs, either homogeneous or heterogeneous ones. In this
paper, we propose an embedding based framework to learn and transfer the
representation of IDs. As the implicit feedbacks of users, a tremendous amount
of item ID sequences can be easily collected from the interactive sessions. By
jointly using these informative sequences and the structural connections among
IDs, all types of IDs can be embedded into one low-dimensional semantic space.
Subsequently, the learned representations are utilized and transferred in four
scenarios: (i) measuring the similarity between items, (ii) transferring from
seen items to unseen items, (iii) transferring across different domains, (iv)
transferring across different tasks. We deploy and evaluate the proposed
approach in Hema App and the results validate its effectiveness.
| 1 | 0 | 0 | 1 | 0 | 0 |
Differentiable Supervector Extraction for Encoding Speaker and Phrase Information in Text Dependent Speaker Verification | In this paper, we propose a new differentiable neural network alignment
mechanism for text-dependent speaker verification which uses alignment models
to produce a supervector representation of an utterance. Unlike previous works
with similar approaches, we do not extract the embedding of an utterance from
the mean reduction of the temporal dimension. Our system replaces the mean by a
phrase alignment model to keep the temporal structure of each phrase which is
relevant in this application since the phonetic information is part of the
identity in the verification task. Moreover, we can apply a convolutional
neural network as front-end, and thanks to the alignment process being
differentiable, we can train the whole network to produce a supervector for
each utterance which will be discriminative with respect to the speaker and the
phrase simultaneously. As we show, this choice has the advantage that the
supervector encodes the phrase and speaker information providing good
performance in text-dependent speaker verification tasks. In this work, the
process of verification is performed using a basic similarity metric, due to
simplicity, compared to other more elaborate models that are commonly used. The
new model using alignment to produce supervectors was tested on the
RSR2015-Part I database for text-dependent speaker verification, providing
competitive results compared to similar size networks using the mean to extract
embeddings.
| 1 | 0 | 0 | 0 | 0 | 0 |
In situ accretion of gaseous envelopes on to planetary cores embedded in evolving protoplanetary discs | The core accretion hypothesis posits that planets with significant gaseous
envelopes accreted them from their protoplanetary discs after the formation of
rocky/icy cores. Observations indicate that such exoplanets exist at a broad
range of orbital radii, but it is not known whether they accreted their
envelopes in situ, or originated elsewhere and migrated to their current
locations. We consider the evolution of solid cores embedded in evolving
viscous discs that undergo gaseous envelope accretion in situ with orbital
radii in the range $0.1-10\rm au$. Additionally, we determine the long-term
evolution of the planets that had no runaway gas accretion phase after disc
dispersal. We find: (i) Planets with $5 \rm M_{\oplus}$ cores never undergo
runaway accretion. The most massive envelope contained $2.8 \rm M_{\oplus}$
with the planet orbiting at $10 \rm au$. (ii) Accretion is more efficient onto
$10 \rm M_{\oplus}$ and $15 \rm M_{\oplus}$ cores. For orbital radii $a_{\rm p}
\ge 0.5 \rm au$, $15 \rm M_{\oplus}$ cores always experienced runaway gas
accretion. For $a_{\rm p} \ge 5 \rm au$, all but one of the $10 \rm M_{\oplus}$
cores experienced runaway gas accretion. No planets experienced runaway growth
at $a_{\rm p} = 0.1 \rm au$. (iii) We find that, after disc dispersal, planets
with significant gaseous envelopes cool and contract on Gyr time-scales, the
contraction time being sensitive to the opacity assumed. Our results indicate
that Hot Jupiters with core masses $\lesssim 15 \rm M_{\oplus}$ at $\lesssim
0.1 \rm au$ likely accreted their gaseous envelopes at larger distances and
migrated inwards. Consistently with the known exoplanet population,
Super-Earths and mini-Neptunes at small radii during the disc lifetime, accrete
only modest gaseous envelopes.
| 0 | 1 | 0 | 0 | 0 | 0 |
New Two Step Laplace Adam-Bashforth Method for Integer an Non integer Order Partial Differential Equations | This paper presents a novel method that allows to generalise the use of the
Adam-Bashforth to Partial Differential Equations with local and non local
operator. The Method derives a two step Adam-Bashforth numerical scheme in
Laplace space and the solution is taken back into the real space via inverse
Laplace transform. The method yields a powerful numerical algorithm for
fractional order derivative where the usually very difficult to manage
summation in the numerical scheme disappears. Error Analysis of the method is
also presented. Applications of the method and numerical simulations are
presented on a wave-equation like, and on a fractional order diffusion
equation.
| 0 | 0 | 1 | 0 | 0 | 0 |
The Impact of Alternation | Alternating automata have been widely used to model and verify systems that
handle data from finite domains, such as communication protocols or hardware.
The main advantage of the alternating model of computation is that
complementation is possible in linear time, thus allowing to concisely encode
trace inclusion problems that occur often in verification. In this paper we
consider alternating automata over infinite alphabets, whose transition rules
are formulae in a combined theory of booleans and some infinite data domain,
that relate past and current values of the data variables. The data theory is
not fixed, but rather it is a parameter of the class. We show that union,
intersection and complementation are possible in linear time in this model and,
though the emptiness problem is undecidable, we provide two efficient
semi-algorithms, inspired by two state-of-the-art abstraction refinement model
checking methods: lazy predicate abstraction \cite{HJMS02} and the \impact~
semi-algorithm \cite{mcmillan06}. We have implemented both methods and report
the results of an experimental comparison.
| 1 | 0 | 0 | 0 | 0 | 0 |
Partial-wave Coulomb t-matrices for like-charged particles at ground-state energy | We study a special case at which the analytical solution of the
Lippmann-Schwinger integral equation for the partial wave two-body Coulomb
transition matrix for likely charged particles at negative energy is possible.
With the use of the Fock's method of the stereographic projection of the
momentum space onto the four-dimensional unit sphere, the analytical
expressions for s-, p- and d-wave partial Coulomb transition matrices for
repulsively interacting particles at bound-state energy have been derived.
| 0 | 1 | 0 | 0 | 0 | 0 |
Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness | Causal effect estimation from observational data is an important and much
studied research topic. The instrumental variable (IV) and local causal
discovery (LCD) patterns are canonical examples of settings where a closed-form
expression exists for the causal effect of one variable on another, given the
presence of a third variable. Both rely on faithfulness to infer that the
latter only influences the target effect via the cause variable. In reality, it
is likely that this assumption only holds approximately and that there will be
at least some form of weak interaction. This brings about the paradoxical
situation that, in the large-sample limit, no predictions are made, as
detecting the weak edge invalidates the setting. We introduce an alternative
approach by replacing strict faithfulness with a prior that reflects the
existence of many 'weak' (irrelevant) and 'strong' interactions. We obtain a
posterior distribution over the target causal effect estimator which shows
that, in many cases, we can still make good estimates. We demonstrate the
approach in an application on a simple linear-Gaussian setting, using the
MultiNest sampling algorithm, and compare it with established techniques to
show our method is robust even when strict faithfulness is violated.
| 1 | 0 | 0 | 1 | 0 | 0 |
The Cross-section of a Spherical Double Cone | We show that the poset of $SL(n)$-orbit closures in the product of two
partial flag varieties is a lattice if the action of $SL(n)$ is spherical.
| 0 | 0 | 1 | 0 | 0 | 0 |
Understanding looping kinetics of a long polymer molecule in solution. Exact solution for delocalized sink model | The fundamental understanding of loop formation of long polymer chains in
solution has been an important thread of research for several theoretical and
experimental studies. Loop formations are important phenomenological parameters
in many important biological processes. Here we give a general method for
finding an exact analytical solution for the occurrence of looping of a long
polymer chains in solution modeled by using a Smoluchowski-like equation with a
delocalized sink. The average rate constant for the delocalized sink is
explicitly expressed in terms of the corresponding rate constants for localized
sinks with different initial conditions. Simple analytical expressions are
provided for average rate constant.
| 0 | 1 | 0 | 0 | 0 | 0 |
SESA: Supervised Explicit Semantic Analysis | In recent years supervised representation learning has provided state of the
art or close to the state of the art results in semantic analysis tasks
including ranking and information retrieval. The core idea is to learn how to
embed items into a latent space such that they optimize a supervised objective
in that latent space. The dimensions of the latent space have no clear
semantics, and this reduces the interpretability of the system. For example, in
personalization models, it is hard to explain why a particular item is ranked
high for a given user profile. We propose a novel model of representation
learning called Supervised Explicit Semantic Analysis (SESA) that is trained in
a supervised fashion to embed items to a set of dimensions with explicit
semantics. The model learns to compare two objects by representing them in this
explicit space, where each dimension corresponds to a concept from a knowledge
base. This work extends Explicit Semantic Analysis (ESA) with a supervised
model for ranking problems. We apply this model to the task of Job-Profile
relevance in LinkedIn in which a set of skills defines our explicit dimensions
of the space. Every profile and job are encoded to this set of skills their
similarity is calculated in this space. We use RNNs to embed text input into
this space. In addition to interpretability, our model makes use of the
web-scale collaborative skills data that is provided by users for each LinkedIn
profile. Our model provides state of the art result while it remains
interpretable.
| 1 | 0 | 0 | 0 | 0 | 0 |
High-Level Concepts for Affective Understanding of Images | This paper aims to bridge the affective gap between image content and the
emotional response of the viewer it elicits by using High-Level Concepts
(HLCs). In contrast to previous work that relied solely on low-level features
or used convolutional neural network (CNN) as a black-box, we use HLCs
generated by pretrained CNNs in an explicit way to investigate the
relations/associations between these HLCs and a (small) set of Ekman's
emotional classes. As a proof-of-concept, we first propose a linear admixture
model for modeling these relations, and the resulting computational framework
allows us to determine the associations between each emotion class and certain
HLCs (objects and places). This linear model is further extended to a nonlinear
model using support vector regression (SVR) that aims to predict the viewer's
emotional response using both low-level image features and HLCs extracted from
images. These class-specific regressors are then assembled into a regressor
ensemble that provide a flexible and effective predictor for predicting
viewer's emotional responses from images. Experimental results have
demonstrated that our results are comparable to existing methods, with a clear
view of the association between HLCs and emotional classes that is ostensibly
missing in most existing work.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Network of Networks Approach to Interconnected Power Grids | We present two different approaches to model power grids as interconnected
networks of networks. Both models are derived from a model for spatially
embedded mono-layer networks and are generalised to handle an arbitrary number
of network layers. The two approaches are distinguished by their use case. The
static glue stick construction model yields a multi-layer network from a
predefined layer interconnection scheme, i.e. different layers are attached
with transformer edges. It is especially suited to construct multi-layer power
grids with a specified number of nodes in and transformers between layers. We
contrast it with a genuine growth model which we label interconnected layer
growth model.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Nonparametric Method for Producing Isolines of Bivariate Exceedance Probabilities | We present a method for drawing isolines indicating regions of equal joint
exceedance probability for bivariate data. The method relies on bivariate
regular variation, a dependence framework widely used for extremes. This
framework enables drawing isolines corresponding to very low exceedance
probabilities and these lines may lie beyond the range of the data. The method
we utilize for characterizing dependence in the tail is largely nonparametric.
Furthermore, we extend this method to the case of asymptotic independence and
propose a procedure which smooths the transition from asymptotic independence
in the interior to the first-order behavior on the axes. We propose a
diagnostic plot for assessing isoline estimate and choice of smoothing, and a
bootstrap procedure to visually assess uncertainty.
| 0 | 0 | 0 | 1 | 0 | 0 |
Finite element error analysis for measure-valued optimal control problems governed by a 1D wave equation with variable coefficients | This work is concerned with the optimal control problems governed by a 1D
wave equation with variable coefficients and the control spaces $\mathcal M_T$
of either measure-valued functions $L_{w^*}^2(I,\mathcal M(\Omega))$ or vector
measures $\mathcal M(\Omega,L^2(I))$. The cost functional involves the standard
quadratic tracking terms and the regularization term $\alpha\|u\|_{\mathcal
M_T}$ with $\alpha>0$. We construct and study three-level in time bilinear
finite element discretizations for this class of problems. The main focus lies
on the derivation of error estimates for the optimal state variable and the
error measured in the cost functional. The analysis is mainly based on some
previous results of the authors. The numerical results are included.
| 0 | 0 | 1 | 0 | 0 | 0 |
Marked points on translation surfaces | We show that all GL(2,R) equivariant point markings over orbit closures of
translation surfaces arise from branched covering constructions and periodic
points, completely classify such point markings over strata of quadratic
differentials, and give applications to the finite blocking problem.
| 0 | 0 | 1 | 0 | 0 | 0 |
Multitask Learning for Fundamental Frequency Estimation in Music | Fundamental frequency (f0) estimation from polyphonic music includes the
tasks of multiple-f0, melody, vocal, and bass line estimation. Historically
these problems have been approached separately, and only recently, using
learning-based approaches. We present a multitask deep learning architecture
that jointly estimates outputs for various tasks including multiple-f0, melody,
vocal and bass line estimation, and is trained using a large,
semi-automatically annotated dataset. We show that the multitask model
outperforms its single-task counterparts, and explore the effect of various
design decisions in our approach, and show that it performs better or at least
competitively when compared against strong baseline methods.
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Real eigenvalues of a non-self-adjoint perturbation of the self-adjoint Zakharov-Shabat operator | We study the eigenvalues of the self-adjoint Zakharov-Shabat operator
corresponding to the defocusing nonlinear Schrodinger equation in the inverse
scattering method. Real eigenvalues exist when the square of the potential has
a simple well. We derive two types of quantization condition for the
eigenvalues by using the exact WKB method, and show that the eigenvalues stay
real for a sufficiently small non-self-adjoint perturbation when the potential
has some PT-like symmetry.
| 0 | 0 | 1 | 0 | 0 | 0 |
Planning with Multiple Biases | Recent work has considered theoretical models for the behavior of agents with
specific behavioral biases: rather than making decisions that optimize a given
payoff function, the agent behaves inefficiently because its decisions suffer
from an underlying bias. These approaches have generally considered an agent
who experiences a single behavioral bias, studying the effect of this bias on
the outcome.
In general, however, decision-making can and will be affected by multiple
biases operating at the same time. How do multiple biases interact to produce
the overall outcome? Here we consider decisions in the presence of a pair of
biases exhibiting an intuitively natural interaction: present bias -- the
tendency to value costs incurred in the present too highly -- and sunk-cost
bias -- the tendency to incorporate costs experienced in the past into one's
plans for the future.
We propose a theoretical model for planning with this pair of biases, and we
show how certain natural behavioral phenomena can arise in our model only when
agents exhibit both biases. As part of our model we differentiate between
agents that are aware of their biases (sophisticated) and agents that are
unaware of them (naive). Interestingly, we show that the interaction between
the two biases is quite complex: in some cases, they mitigate each other's
effects while in other cases they might amplify each other. We obtain a number
of further results as well, including the fact that the planning problem in our
model for an agent experiencing and aware of both biases is computationally
hard in general, though tractable under more relaxed assumptions.
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Zhu reduction for Jacobi $n$-point functions and applications | We establish precise Zhu reduction formulas for Jacobi $n$-point functions
which show the absence of any possible poles arising in these formulas. We then
exploit this to produce results concerning the structure of strongly regular
vertex operator algebras, and also to motivate new differential operators
acting on Jacobi forms. Finally, we apply the reduction formulas to the Fermion
model in order to create polynomials of quasi-Jacobi forms which are Jacobi
forms.
| 0 | 0 | 1 | 0 | 0 | 0 |
Fourier-like multipliers and applications for integral operators | Timelimited functions and bandlimited functions play a fundamental role in
signal and image processing. But by the uncertainty principles, a signal cannot
be simultaneously time and bandlimited. A natural assumption is thus that a
signal is almost time and almost bandlimited. The aim of this paper is to prove
that the set of almost time and almost bandlimited signals is not excluded from
the uncertainty principles. The transforms under consideration are integral
operators with bounded kernels for which there is a Parseval Theorem. Then we
define the wavelet multipliers for this class of operators, and study their
boundedness and Schatten class properties. We show that the wavelet multiplier
is unitary equivalent to a scalar multiple of the phase space restriction
operator. Moreover we prove that a signal which is almost time and almost
bandlimited can be approximated by its projection on the span of the first
eigenfunctions of the phase space restriction operator, corresponding to the
largest eigenvalues which are close to one.
| 0 | 0 | 1 | 0 | 0 | 0 |
Inferring Properties of the ISM from Supernova Remnant Size Distributions | We model the size distribution of supernova remnants to infer the surrounding
ISM density. Using simple, yet standard SNR evolution models, we find that the
distribution of ambient densities is remarkably narrow; either the standard
assumptions about SNR evolution are wrong, or observable SNRs are biased to a
narrow range of ambient densities. We show that the size distributions are
consistent with log-normal, which severely limits the number of model
parameters in any SNR population synthesis model. Simple Monte Carlo
simulations demonstrate that the size distribution is indistinguishable from
log-normal when the SNR sample size is less than 600. This implies that these
SNR distributions provide only information on the mean and variance, yielding
additional information only when the sample size grows larger than $\sim{600}$
SNRs. To infer the parameters of the ambient density, we use Bayesian
statistical inference under the assumption that SNR evolution is dominated by
the Sedov phase. In particular, we use the SNR sizes and explosion energies to
estimate the mean and variance of the ambient medium surrounding SNR
progenitors. We find that the mean ISM particle density around our sample of
SNRs is $\mu_{\log{n}} = -1.33$, in $\log_{10}$ of particles per cubic
centimeter, with variance $\sigma^2_{\log{n}} = 0.49$. If interpreted at face
value, this implies that most SNRs result from supernovae propagating in the
warm, ionized medium. However, it is also likely that either SNR evolution is
not dominated by the simple Sedov evolution or SNR samples are biased to the
warm, ionized medium (WIM).
| 0 | 1 | 0 | 0 | 0 | 0 |
Mining Target Attribute Subspace and Set of Target Communities in Large Attributed Networks | Community detection provides invaluable help for various applications, such
as marketing and product recommendation. Traditional community detection
methods designed for plain networks may not be able to detect communities with
homogeneous attributes inside on attributed networks with attribute
information. Most of recent attribute community detection methods may fail to
capture the requirements of a specific application and not be able to mine the
set of required communities for a specific application. In this paper, we aim
to detect the set of target communities in the target subspace which has some
focus attributes with large importance weights satisfying the requirements of a
specific application. In order to improve the university of the problem, we
address the problem in an extreme case where only two sample nodes in any
potential target community are provided. A Target Subspace and Communities
Mining (TSCM) method is proposed. In TSCM, a sample information extension
method is designed to extend the two sample nodes to a set of exemplar nodes
from which the target subspace is inferred. Then the set of target communities
are located and mined based on the target subspace. Experiments on synthetic
datasets demonstrate the effectiveness and efficiency of our method and
applications on real-world datasets show its application values.
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Achieving rental harmony with a secretive roommate | Given the subjective preferences of n roommates in an n-bedroom apartment,
one can use Sperner's lemma to find a division of the rent such that each
roommate is content with a distinct room. At the given price distribution, no
roommate has a strictly stronger preference for a different room. We give a new
elementary proof that the subjective preferences of only n-1 of the roommates
actually suffice to achieve this envy-free rent division. Our proof, in
particular, yields an algorithm to find such a fair division of rent. The
techniques also give generalizations of Sperner's lemma including a new proof
of a conjecture of the third author.
| 0 | 0 | 1 | 0 | 0 | 0 |
A Channel-Based Perspective on Conjugate Priors | A desired closure property in Bayesian probability is that an updated
posterior distribution be in the same class of distributions --- say Gaussians
--- as the prior distribution. When the updating takes place via a statistical
model, one calls the class of prior distributions the `conjugate priors' of the
model. This paper gives (1) an abstract formulation of this notion of conjugate
prior, using channels, in a graphical language, (2) a simple abstract proof
that such conjugate priors yield Bayesian inversions, and (3) a logical
description of conjugate priors that highlights the required closure of the
priors under updating. The theory is illustrated with several standard
examples, also covering multiple updating.
| 1 | 0 | 0 | 0 | 0 | 0 |
Cascaded Coded Distributed Computing on Heterogeneous Networks | Coded distributed computing (CDC) introduced by Li et al. in 2015 offers an
efficient approach to trade computing power to reduce the communication load in
general distributed computing frameworks such as MapReduce. For the more
general cascaded CDC, Map computations are repeated at $r$ nodes to
significantly reduce the communication load among nodes tasked with computing
$Q$ Reduce functions $s$ times. While an achievable cascaded CDC scheme was
proposed, it only operates on homogeneous networks, where the storage,
computation load and communication load of each computing node is the same. In
this paper, we address this limitation by proposing a novel combinatorial
design which operates on heterogeneous networks where nodes have varying
storage and computing capabilities. We provide an analytical characterization
of the computation-communication trade-off and show that it is optimal within a
constant factor and could outperform the state-of-the-art homogeneous schemes.
| 1 | 0 | 0 | 0 | 0 | 0 |
A FEL Based on a Superlattice | The motion and photon emission of electrons in a superlattice may be
described as in an undulator. Therefore, there is a close analogy between
ballistic electrons in a superlattice and electrons in a free electron laser
(FEL). Touching upon this analogy the intensity of photon emission in the IR
region and the gain are calculated. It is shown that the amplification can be
significant, reaching tens of percent.
| 0 | 1 | 0 | 0 | 0 | 0 |
Thermal and non-thermal emission from the cocoon of a gamma-ray burst jet | We present hydrodynamic simulations of the hot cocoon produced when a
relativistic jet passes through the gamma-ray burst (GRB) progenitor star and
its environment, and we compute the lightcurve and spectrum of the radiation
emitted by the cocoon. The radiation from the cocoon has a nearly thermal
spectrum with a peak in the X-ray band, and it lasts for a few minutes in the
observer frame; the cocoon radiation starts at roughly the same time as when
$\gamma$-rays from a burst trigger detectors aboard GRB satellites. The
isotropic cocoon luminosity ($\sim 10^{47}$ erg s$^{-1}$) is of the same order
of magnitude as the X-ray luminosity of a typical long-GRB afterglow during the
plateau phase. This radiation should be identifiable in the Swift data because
of its nearly thermal spectrum which is distinct from the somewhat brighter
power-law component. The detection of this thermal component would provide
information regarding the size and density stratification of the GRB progenitor
star. Photons from the cocoon are also inverse-Compton (IC) scattered by
electrons in the relativistic jet. We present the IC lightcurve and spectrum,
by post-processing the results of the numerical simulations. The IC spectrum
lies in 10 keV--MeV band for typical GRB parameters. The detection of this IC
component would provide an independent measurement of GRB jet Lorentz factor
and it would also help to determine the jet magnetisation parameter.
| 0 | 1 | 0 | 0 | 0 | 0 |
Least Squares Polynomial Chaos Expansion: A Review of Sampling Strategies | As non-institutive polynomial chaos expansion (PCE) techniques have gained
growing popularity among researchers, we here provide a comprehensive review of
major sampling strategies for the least squares based PCE. Traditional sampling
methods, such as Monte Carlo, Latin hypercube, quasi-Monte Carlo, optimal
design of experiments (ODE), Gaussian quadratures, as well as more recent
techniques, such as coherence-optimal and randomized quadratures are discussed.
We also propose a hybrid sampling method, dubbed alphabetic-coherence-optimal,
that employs the so-called alphabetic optimality criteria used in the context
of ODE in conjunction with coherence-optimal samples. A comparison between the
empirical performance of the selected sampling methods applied to three
numerical examples, including high-order PCE's, high-dimensional problems, and
low oversampling ratios, is presented to provide a road map for practitioners
seeking the most suitable sampling technique for a problem at hand. We observed
that the alphabetic-coherence-optimal technique outperforms other sampling
methods, specially when high-order ODE are employed and/or the oversampling
ratio is low.
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Efficient Dense Labeling of Human Activity Sequences from Wearables using Fully Convolutional Networks | Recognizing human activities in a sequence is a challenging area of research
in ubiquitous computing. Most approaches use a fixed size sliding window over
consecutive samples to extract features---either handcrafted or learned
features---and predict a single label for all samples in the window. Two key
problems emanate from this approach: i) the samples in one window may not
always share the same label. Consequently, using one label for all samples
within a window inevitably lead to loss of information; ii) the testing phase
is constrained by the window size selected during training while the best
window size is difficult to tune in practice. We propose an efficient algorithm
that can predict the label of each sample, which we call dense labeling, in a
sequence of human activities of arbitrary length using a fully convolutional
network. In particular, our approach overcomes the problems posed by the
sliding window step. Additionally, our algorithm learns both the features and
classifier automatically. We release a new daily activity dataset based on a
wearable sensor with hospitalized patients. We conduct extensive experiments
and demonstrate that our proposed approach is able to outperform the
state-of-the-arts in terms of classification and label misalignment measures on
three challenging datasets: Opportunity, Hand Gesture, and our new dataset.
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Higher Tetrahedral Algebras | We introduce and study the higher tetrahedral algebras, an exotic family of
finite-dimensional tame symmetric algebras over an algebraically closed field.
The Gabriel quiver of such an algebra is the triangulation quiver associated to
the coherent orientation of the tetrahedron. Surprisingly, these algebras
occurred in the classification of all algebras of generalised quaternion type,
but are not weighted surface algebras. We prove that a higher tetrahedral
algebra is periodic if and only if it is non-singular.
| 0 | 0 | 1 | 0 | 0 | 0 |
Adaptive multi-penalty regularization based on a generalized Lasso path | For many algorithms, parameter tuning remains a challenging and critical
task, which becomes tedious and infeasible in a multi-parameter setting.
Multi-penalty regularization, successfully used for solving undetermined sparse
regression of problems of unmixing type where signal and noise are additively
mixed, is one of such examples. In this paper, we propose a novel algorithmic
framework for an adaptive parameter choice in multi-penalty regularization with
a focus on the correct support recovery. Building upon the theory of
regularization paths and algorithms for single-penalty functionals, we extend
these ideas to a multi-penalty framework by providing an efficient procedure
for the construction of regions containing structurally similar solutions,
i.e., solutions with the same sparsity and sign pattern, over the whole range
of parameters. Combining this with a model selection criterion, we can choose
regularization parameters in a data-adaptive manner. Another advantage of our
algorithm is that it provides an overview on the solution stability over the
whole range of parameters. This can be further exploited to obtain additional
insights into the problem of interest. We provide a numerical analysis of our
method and compare it to the state-of-the-art single-penalty algorithms for
compressed sensing problems in order to demonstrate the robustness and power of
the proposed algorithm.
| 1 | 0 | 0 | 1 | 0 | 0 |
Shape Generation using Spatially Partitioned Point Clouds | We propose a method to generate 3D shapes using point clouds. Given a
point-cloud representation of a 3D shape, our method builds a kd-tree to
spatially partition the points. This orders them consistently across all
shapes, resulting in reasonably good correspondences across all shapes. We then
use PCA analysis to derive a linear shape basis across the spatially
partitioned points, and optimize the point ordering by iteratively minimizing
the PCA reconstruction error. Even with the spatial sorting, the point clouds
are inherently noisy and the resulting distribution over the shape coefficients
can be highly multi-modal. We propose to use the expressive power of neural
networks to learn a distribution over the shape coefficients in a
generative-adversarial framework. Compared to 3D shape generative models
trained on voxel-representations, our point-based method is considerably more
light-weight and scalable, with little loss of quality. It also outperforms
simpler linear factor models such as Probabilistic PCA, both qualitatively and
quantitatively, on a number of categories from the ShapeNet dataset.
Furthermore, our method can easily incorporate other point attributes such as
normal and color information, an additional advantage over voxel-based
representations.
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Open Vocabulary Scene Parsing | Recognizing arbitrary objects in the wild has been a challenging problem due
to the limitations of existing classification models and datasets. In this
paper, we propose a new task that aims at parsing scenes with a large and open
vocabulary, and several evaluation metrics are explored for this problem. Our
proposed approach to this problem is a joint image pixel and word concept
embeddings framework, where word concepts are connected by semantic relations.
We validate the open vocabulary prediction ability of our framework on ADE20K
dataset which covers a wide variety of scenes and objects. We further explore
the trained joint embedding space to show its interpretability.
| 1 | 0 | 0 | 0 | 0 | 0 |
Parameter Estimation in Mean Reversion Processes with Periodic Functional Tendency | This paper describes the procedure to estimate the parameters in mean
reversion processes with functional tendency defined by a periodic continuous
deterministic function, expressed as a series of truncated Fourier. Two phases
of estimation are defined, in the first phase through Gaussian techniques using
the Euler-Maruyama discretization, we obtain the maximum likelihood function,
that will allow us to find estimators of the external parameters and an
estimation of the expected value of the process. In the second phase, a
reestimate of the periodic functional tendency with it's parameters of phase
and amplitude is carried out, this will allow, improve the initial estimation.
Some experimental result using simulated data sets are graphically illustrated.
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User Interface (UI) Design Issues for the Multilingual Users: A Case Study | A multitude of web and desktop applications are now widely available in
diverse human languages. This paper explores the design issues that are
specifically relevant for multilingual users. It reports on the continued
studies of Information System (IS) issues and users' behaviour across
cross-cultural and transnational boundaries. Taking the BBC website as a model
that is internationally recognised, usability tests were conducted to compare
different versions of the website. The dependant variables derived from the
questionnaire were analysed (via descriptive statistics) to elucidate the
multilingual UI design issues. Using Principal Component Analysis (PCA), five
de-correlated variables were identified which were then used for hypotheses
tests. A modified version of Herzberg's Hygiene-motivational Theory about the
Workplace was applied to assess the components used in the website. Overall, it
was concluded that the English versions of the website gave superior usability
results and this implies the need for deeper study of the problems in usability
of the translated versions.
| 1 | 0 | 0 | 0 | 0 | 0 |
NetSpam: a Network-based Spam Detection Framework for Reviews in Online Social Media | Nowadays, a big part of people rely on available content in social media in
their decisions (e.g. reviews and feedback on a topic or product). The
possibility that anybody can leave a review provide a golden opportunity for
spammers to write spam reviews about products and services for different
interests. Identifying these spammers and the spam content is a hot topic of
research and although a considerable number of studies have been done recently
toward this end, but so far the methodologies put forth still barely detect
spam reviews, and none of them show the importance of each extracted feature
type. In this study, we propose a novel framework, named NetSpam, which
utilizes spam features for modeling review datasets as heterogeneous
information networks to map spam detection procedure into a classification
problem in such networks. Using the importance of spam features help us to
obtain better results in terms of different metrics experimented on real-world
review datasets from Yelp and Amazon websites. The results show that NetSpam
outperforms the existing methods and among four categories of features;
including review-behavioral, user-behavioral, reviewlinguistic,
user-linguistic, the first type of features performs better than the other
categories.
| 1 | 1 | 0 | 0 | 0 | 0 |
Proving Non-Deterministic Computations in Agda | We investigate proving properties of Curry programs using Agda. First, we
address the functional correctness of Curry functions that, apart from some
syntactic and semantic differences, are in the intersection of the two
languages. Second, we use Agda to model non-deterministic functions with two
distinct and competitive approaches incorporating the non-determinism. The
first approach eliminates non-determinism by considering the set of all
non-deterministic values produced by an application. The second approach
encodes every non-deterministic choice that the application could perform. We
consider our initial experiment a success. Although proving properties of
programs is a notoriously difficult task, the functional logic paradigm does
not seem to add any significant layer of difficulty or complexity to the task.
| 1 | 0 | 0 | 0 | 0 | 0 |
NeuroRule: A Connectionist Approach to Data Mining | Classification, which involves finding rules that partition a given data set
into disjoint groups, is one class of data mining problems. Approaches proposed
so far for mining classification rules for large databases are mainly decision
tree based symbolic learning methods. The connectionist approach based on
neural networks has been thought not well suited for data mining. One of the
major reasons cited is that knowledge generated by neural networks is not
explicitly represented in the form of rules suitable for verification or
interpretation by humans. This paper examines this issue. With our newly
developed algorithms, rules which are similar to, or more concise than those
generated by the symbolic methods can be extracted from the neural networks.
The data mining process using neural networks with the emphasis on rule
extraction is described. Experimental results and comparison with previously
published works are presented.
| 1 | 0 | 0 | 0 | 0 | 0 |
Contextual Explanation Networks | Modern learning algorithms excel at producing accurate but complex models of
the data. However, deploying such models in the real-world requires extra care:
we must ensure their reliability, robustness, and absence of undesired biases.
This motivates the development of models that are equally accurate but can be
also easily inspected and assessed beyond their predictive performance. To this
end, we introduce contextual explanation networks (CENs)---a class of
architectures that learn to predict by generating and utilizing intermediate,
simplified probabilistic models. Specifically, CENs generate parameters for
intermediate graphical models which are further used for prediction and play
the role of explanations. Contrary to the existing post-hoc model-explanation
tools, CENs learn to predict and to explain jointly. Our approach offers two
major advantages: (i) for each prediction, valid, instance-specific
explanations are generated with no computational overhead and (ii) prediction
via explanation acts as a regularizer and boosts performance in low-resource
settings. We analyze the proposed framework theoretically and experimentally.
Our results on image and text classification and survival analysis tasks
demonstrate that CENs are not only competitive with the state-of-the-art
methods but also offer additional insights behind each prediction, that are
valuable for decision support. We also show that while post-hoc methods may
produce misleading explanations in certain cases, CENs are always consistent
and allow to detect such cases systematically.
| 1 | 0 | 0 | 1 | 0 | 0 |
Inference for Differential Equation Models using Relaxation via Dynamical Systems | Statistical regression models whose mean functions are represented by
ordinary differential equations (ODEs) can be used to describe phenomenons
dynamical in nature, which are abundant in areas such as biology, climatology
and genetics. The estimation of parameters of ODE based models is essential for
understanding its dynamics, but the lack of an analytical solution of the ODE
makes the parameter estimation challenging. The aim of this paper is to propose
a general and fast framework of statistical inference for ODE based models by
relaxation of the underlying ODE system. Relaxation is achieved by a properly
chosen numerical procedure, such as the Runge-Kutta, and by introducing
additive Gaussian noises with small variances. Consequently, filtering methods
can be applied to obtain the posterior distribution of the parameters in the
Bayesian framework. The main advantage of the proposed method is computation
speed. In a simulation study, the proposed method was at least 14 times faster
than the other methods. Theoretical results which guarantee the convergence of
the posterior of the approximated dynamical system to the posterior of true
model are presented. Explicit expressions are given that relate the order and
the mesh size of the Runge-Kutta procedure to the rate of convergence of the
approximated posterior as a function of sample size.
| 0 | 0 | 0 | 1 | 0 | 0 |
Joint Trajectory and Communication Design for UAV-Enabled Multiple Access | Unmanned aerial vehicles (UAVs) have attracted significant interest recently
in wireless communication due to their high maneuverability, flexible
deployment, and low cost. This paper studies a UAV-enabled wireless network
where the UAV is employed as an aerial mobile base station (BS) to serve a
group of users on the ground. To achieve fair performance among users, we
maximize the minimum throughput over all ground users by jointly optimizing the
multiuser communication scheduling and UAV trajectory over a finite horizon.
The formulated problem is shown to be a mixed integer non-convex optimization
problem that is difficult to solve in general. We thus propose an efficient
iterative algorithm by applying the block coordinate descent and successive
convex optimization techniques, which is guaranteed to converge to at least a
locally optimal solution. To achieve fast convergence and stable throughput, we
further propose a low-complexity initialization scheme for the UAV trajectory
design based on the simple circular trajectory. Extensive simulation results
are provided which show significant throughput gains of the proposed design as
compared to other benchmark schemes.
| 1 | 0 | 1 | 0 | 0 | 0 |
Imaging the Schwarzschild-radius-scale Structure of M87 with the Event Horizon Telescope using Sparse Modeling | We propose a new imaging technique for radio and optical/infrared
interferometry. The proposed technique reconstructs the image from the
visibility amplitude and closure phase, which are standard data products of
short-millimeter very long baseline interferometers such as the Event Horizon
Telescope (EHT) and optical/infrared interferometers, by utilizing two
regularization functions: the $\ell_1$-norm and total variation (TV) of the
brightness distribution. In the proposed method, optimal regularization
parameters, which represent the sparseness and effective spatial resolution of
the image, are derived from data themselves using cross validation (CV). As an
application of this technique, we present simulated observations of M87 with
the EHT based on four physically motivated models. We confirm that $\ell_1$+TV
regularization can achieve an optimal resolution of $\sim 20-30$% of the
diffraction limit $\lambda/D_{\rm max}$, which is the nominal spatial
resolution of a radio interferometer. With the proposed technique, the EHT can
robustly and reasonably achieve super-resolution sufficient to clearly resolve
the black hole shadow. These results make it promising for the EHT to provide
an unprecedented view of the event-horizon-scale structure in the vicinity of
the super-massive black hole in M87 and also the Galactic center Sgr A*.
| 0 | 1 | 0 | 0 | 0 | 0 |
The process of purely event-driven programs | Using process algebra, this paper describes the formalisation of the
process/semantics behind the purely event-driven programming language.
| 1 | 0 | 0 | 0 | 0 | 0 |
Partial dust obscuration in active galactic nuclei as a cause of broad-line profile and lag variability, and apparent accretion disc inhomogeneities | The profiles of the broad emission lines of active galactic nuclei (AGNs) and
the time delays in their response to changes in the ionizing continuum ("lags")
give information about the structure and kinematics of the inner regions of
AGNs. Line profiles are also our main way of estimating the masses of the
supermassive black holes (SMBHs). However, the profiles often show
ill-understood, asymmetric structure and velocity-dependent lags vary with
time. Here we show that partial obscuration of the broad-line region (BLR) by
outflowing, compact, dusty clumps produces asymmetries and velocity-dependent
lags similar to those observed. Our model explains previously inexplicable
changes in the ratios of the hydrogen lines with time and velocity, the lack of
correlation of changes in line profiles with variability of the central engine,
the velocity dependence of lags, and the change of lags with time. We propose
that changes on timescales longer than the light-crossing time do not come from
dynamical changes in the BLR, but are a natural result of the effect of
outflowing dusty clumps driven by radiation pressure acting on the dust. The
motion of these clumps offers an explanation of long-term changes in
polarization. The effects of the dust complicate the study of the structure and
kinematics of the BLR and the search for sub-parsec SMBH binaries. Partial
obscuration of the accretion disc can also provide the local fluctuations in
luminosity that can explain sizes deduced from microlensing.
| 0 | 1 | 0 | 0 | 0 | 0 |
From Plants to Landmarks: Time-invariant Plant Localization that uses Deep Pose Regression in Agricultural Fields | Agricultural robots are expected to increase yields in a sustainable way and
automate precision tasks, such as weeding and plant monitoring. At the same
time, they move in a continuously changing, semi-structured field environment,
in which features can hardly be found and reproduced at a later time.
Challenges for Lidar and visual detection systems stem from the fact that
plants can be very small, overlapping and have a steadily changing appearance.
Therefore, a popular way to localize vehicles with high accuracy is based on
ex- pensive global navigation satellite systems and not on natural landmarks.
The contribution of this work is a novel image- based plant localization
technique that uses the time-invariant stem emerging point as a reference. Our
approach is based on a fully convolutional neural network that learns landmark
localization from RGB and NIR image input in an end-to-end manner. The network
performs pose regression to generate a plant location likelihood map. Our
approach allows us to cope with visual variances of plants both for different
species and different growth stages. We achieve high localization accuracies as
shown in detailed evaluations of a sugar beet cultivation phase. In experiments
with our BoniRob we demonstrate that detections can be robustly reproduced with
centimeter accuracy.
| 1 | 0 | 0 | 0 | 0 | 0 |
There's more to the multimedia effect than meets the eye: is seeing pictures believing? | Textbooks in applied mathematics often use graphs to explain the meaning of
formulae, even though their benefit is still not fully explored. To test
processes underlying this assumed multimedia effect we collected performance
scores, eye movements, and think-aloud protocols from students solving problems
in vector calculus with and without graphs. Results showed no overall
multimedia effect, but instead an effect to confirm statements that were
accompanied by graphs, irrespective of whether these statements were true or
false. Eye movement and verbal data shed light on this surprising finding.
Students looked proportionally less at the text and the problem statement when
a graph was present. Moreover, they experienced more mental effort with the
graph, as indicated by more silent pauses in thinking aloud. Hence, students
actively processed the graphs. This, however, was not sufficient. Further
analysis revealed that the more students looked at the statement, the better
they performed. Thus, in the multimedia condition the graph drew students'
attention and cognitive capacities away from focusing on the statement. A good
alternative strategy in the multimedia condition was to frequently look between
graph and problem statement, and thus to integrate their information. In
conclusion, graphs influence where students look and what they process, and may
even mislead them into believing accompanying information. Thus, teachers and
textbook designers should be very critical on when to use graphs and carefully
consider how the graphs are integrated with other parts of the problem.
| 0 | 1 | 1 | 0 | 0 | 0 |
Methods of Enumerating Two Vertex Maps of Arbitrary Genus | This paper provides an alternate proof to parts of the Goulden-Slofstra
formula for enumerating two vertex maps by genus, which is an extension of the
famous Harer-Zagier formula that computes the Euler characteristic of the
moduli space of curves. This paper also shows a further simplification to the
Goulden-Slofstra formula. Portions of this alternate proof will be used in a
subsequent paper, where it forms a basis for a more general result that applies
for a certain class of maps with an arbitrary number of vertices.
| 0 | 0 | 1 | 0 | 0 | 0 |
Higher Theory and the Three Problems of Physics | According to the Butterfield--Isham proposal, to understand quantum gravity
we must revise the way we view the universe of mathematics. However, this paper
demonstrates that the current elaborations of this programme neglect quantum
interactions. The paper then introduces the Faddeev--Mickelsson anomaly which
obstructs the renormalization of Yang--Mills theory, suggesting that to
theorise on many-particle systems requires a many-topos view of mathematics
itself: higher theory. As our main contribution, the topos theoretic framework
is used to conceptualise the fact that there are principally three different
quantisation problems, the differences of which have been ignored not just by
topos physicists but by most philosophers of science. We further argue that if
higher theory proves out to be necessary for understanding quantum gravity, its
implications to philosophy will be foundational: higher theory challenges the
propositional concept of truth and thus the very meaning of theorising in
science.
| 0 | 1 | 1 | 0 | 0 | 0 |
Light emission by accelerated electric, toroidal and anapole dipolar sources | Emission of electromagnetic radiation by accelerated particles with electric,
toroidal and anapole dipole moments is analyzed. It is shown that ellipticity
of the emitted light can be used to differentiate between electric and toroidal
dipole sources, and that anapoles, elementary neutral non-radiating
configurations, which consist of electric and toroidal dipoles, can emit light
under uniform acceleration. The existence of non-radiating configurations in
electrodynamics implies that it is impossible to fully determine the internal
makeup of the emitter given only the distribution of the emitted light. Here we
demonstrate that there is a loop-hole in this `inverse source problem'. Our
results imply that there may be a whole range of new phenomena to be discovered
by studying the electromagnetic response of matter under acceleration.
| 0 | 1 | 0 | 0 | 0 | 0 |
Bayesian Approaches to Distribution Regression | Distribution regression has recently attracted much interest as a generic
solution to the problem of supervised learning where labels are available at
the group level, rather than at the individual level. Current approaches,
however, do not propagate the uncertainty in observations due to sampling
variability in the groups. This effectively assumes that small and large groups
are estimated equally well, and should have equal weight in the final
regression. We account for this uncertainty with a Bayesian distribution
regression formalism, improving the robustness and performance of the model
when group sizes vary. We frame our models in a neural network style, allowing
for simple MAP inference using backpropagation to learn the parameters, as well
as MCMC-based inference which can fully propagate uncertainty. We demonstrate
our approach on illustrative toy datasets, as well as on a challenging problem
of predicting age from images.
| 1 | 0 | 0 | 1 | 0 | 0 |
Atomic-Scale Structure Relaxation, Chemistry and Charge Distribution of Dislocation Cores in SrTiO3 | By using the state-of-the-art microscopy and spectroscopy in
aberration-corrected scanning transmission electron microscopes, we determine
the atomic arrangements, occupancy, elemental distribution, and the electronic
structures of dislocation cores in the 10°tilted SrTiO3 bicrystal. We
identify that there are two different types of oxygen deficient dislocation
cores, i.e., the SrO plane terminated Sr0.82Ti0.85O3-x (Ti3.67+, 0.48<x<0.91)
and TiO2 plane terminated Sr0.63Ti0.90O3-y (Ti3.60+, 0.57<y<1). They have the
same Burgers vector of a[100] but different atomic arrangements and chemical
properties. Besides the oxygen vacancies, Sr vacancies and rocksalt-like
titanium oxide reconstruction are also identified in the dislocation core with
TiO2 plane termination. Our atomic-scale study reveals the true atomic
structures and chemistry of individual dislocation cores, providing useful
insights into understanding the properties of dislocations and grain
boundaries.
| 0 | 1 | 0 | 0 | 0 | 0 |
Transição de fase no sistema de Hénon-Heiles (Phase transition in the Henon-Heiles system) | The Henon-Heiles system was originally proposed to describe the dynamical
behavior of galaxies, but this system has been widely applied in dynamical
systems by exhibit great details in phase space. This work presents the
formalism to describe Henon-Heiles system and a qualitative approach of
dynamics behavior. The growth of chaotic region in phase space is observed by
Poincare Surface of Section when the total energy increases. Island of
regularity remain around stable points and relevants phenomena appear, such as
sticky.
| 0 | 1 | 0 | 0 | 0 | 0 |
Self-similar solutions of fragmentation equations revisited | We study the large time behaviour of the mass (size) of particles described
by the fragmentation equation with homogeneous breakup kernel. We give
necessary and sufficient conditions for the convergence of solutions to the
unique self-similar solution.
| 0 | 0 | 1 | 0 | 0 | 0 |
Refining Source Representations with Relation Networks for Neural Machine Translation | Although neural machine translation (NMT) with the encoder-decoder framework
has achieved great success in recent times, it still suffers from some
drawbacks: RNNs tend to forget old information which is often useful and the
encoder only operates through words without considering word relationship. To
solve these problems, we introduce a relation networks (RN) into NMT to refine
the encoding representations of the source. In our method, the RN first
augments the representation of each source word with its neighbors and reasons
all the possible pairwise relations between them. Then the source
representations and all the relations are fed to the attention module and the
decoder together, keeping the main encoder-decoder architecture unchanged.
Experiments on two Chinese-to-English data sets in different scales both show
that our method can outperform the competitive baselines significantly.
| 1 | 0 | 0 | 0 | 0 | 0 |
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