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VIP: Vortex Image Processing package for high-contrast direct imaging | We present the Vortex Image Processing (VIP) library, a python package
dedicated to astronomical high-contrast imaging. Our package relies on the
extensive python stack of scientific libraries and aims to provide a flexible
framework for high-contrast data and image processing. In this paper, we
describe the capabilities of VIP related to processing image sequences acquired
using the angular differential imaging (ADI) observing technique. VIP
implements functionalities for building high-contrast data processing
pipelines, encompass- ing pre- and post-processing algorithms, potential
sources position and flux estimation, and sensitivity curves generation. Among
the reference point-spread function subtraction techniques for ADI
post-processing, VIP includes several flavors of principal component analysis
(PCA) based algorithms, such as annular PCA and incremental PCA algorithm
capable of processing big datacubes (of several gigabytes) on a computer with
limited memory. Also, we present a novel ADI algorithm based on non-negative
matrix factorization (NMF), which comes from the same family of low-rank matrix
approximations as PCA and provides fairly similar results. We showcase the ADI
capabilities of the VIP library using a deep sequence on HR8799 taken with the
LBTI/LMIRCam and its recently commissioned L-band vortex coronagraph. Using VIP
we investigated the presence of additional companions around HR8799 and did not
find any significant additional point source beyond the four known planets. VIP
is available at this http URL and is accompanied with
Jupyter notebook tutorials illustrating the main functionalities of the
library.
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Domain-Sharding for Faster HTTP/2 in Lossy Cellular Networks | HTTP/2 (h2) is a new standard for Web communications that already delivers a
large share of Web traffic. Unlike HTTP/1, h2 uses only one underlying TCP
connection. In a cellular network with high loss and sudden spikes in latency,
which the TCP stack might interpret as loss, using a single TCP connection can
negatively impact Web performance. In this paper, we perform an extensive
analysis of real world cellular network traffic and design a testbed to emulate
loss characteristics in cellular networks. We use the emulated cellular network
to measure h2 performance in comparison to HTTP/1.1, for webpages synthesized
from HTTP Archive repository data.
Our results show that, in lossy conditions, h2 achieves faster page load
times (PLTs) for webpages with small objects. For webpages with large objects,
h2 degrades the PLT. We devise a new domain-sharding technique that isolates
large and small object downloads on separate connections. Using sharding, we
show that under lossy cellular conditions, h2 over multiple connections
improves the PLT compared to h2 with one connection and HTTP/1.1 with six
connections. Finally, we recommend content providers and content delivery
networks to apply h2-aware domain-sharding on webpages currently served over h2
for improved mobile Web performance.
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Extremal copositive matrices with minimal zero supports of cardinality two | Let $A \in {\cal C}^n$ be an extremal copositive matrix with unit diagonal.
Then the minimal zeros of $A$ all have supports of cardinality two if and only
if the elements of $A$ are all from the set $\{-1,0,1\}$. Thus the extremal
copositive matrices with minimal zero supports of cardinality two are exactly
those matrices which can be obtained by diagonal scaling from the extremal
$\{-1,0,1\}$ unit diagonal matrices characterized by Hoffman and Pereira in
1973.
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A Projected Inverse Dynamics Approach for Dual-arm Cartesian Impedance Control | We propose a method for dual-arm manipulation of rigid objects, subject to
external disturbance. The problem is formulated as a Cartesian impedance
controller within a projected inverse dynamics framework. We use the
constrained component of the controller to enforce contact and the
unconstrained controller to accomplish the task with a desired 6-DOF impedance
behaviour. Furthermore, the proposed method optimises the torque required to
maintain contact, subject to unknown disturbances, and can do so without direct
measurement of external force. The techniques are evaluated on a single-arm
wiping a table and a dual-arm platform manipulating a rigid object of unknown
mass and with human interaction.
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AC-Biased Shift Registers as Fabrication Process Benchmark Circuits and Flux Trapping Diagnostic Tool | We develop an ac-biased shift register introduced in our previous work (V.K.
Semenov et al., IEEE Trans. Appl. Supercond., vol. 25, no. 3, 1301507, June
2015) into a benchmark circuit for evaluation of superconductor electronics
fabrication technology. The developed testing technique allows for extracting
margins of all individual cells in the shift register, which in turn makes it
possible to estimate statistical distribution of Josephson junctions in the
circuit. We applied this approach to successfully test registers having 8, 16,
36, and 202 thousand cells and, respectively, about 33000, 65000, 144000, and
809000 Josephson junctions. The circuits were fabricated at MIT Lincoln
Laboratory, using a fully planarized process, 0.4 {\mu}m inductor linewidth,
and 1.33x10^6 cm^-2 junction density. They are presently the largest
operational superconducting SFQ circuits ever made. The developed technique
distinguishes between hard defects (fabrication-related) and soft defects
(measurement-related) and locates them in the circuit. The soft defects are
specific to superconducting circuits and caused by magnetic flux trapping
either inside the active cells or in the dedicated flux-trapping moats near the
cells. The number and distribution of soft defects depend on the ambient
magnetic field and vary with thermal cycling even if done in the same magnetic
environment.
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Designing diagnostic platforms for analysis of disease patterns and probing disease emergence | The emerging era of personalized medicine relies on medical decisions,
practices, and products being tailored to the individual patient. Point-of-care
systems, at the heart of this model, play two important roles. First, they are
required for identifying subjects for optimal therapies based on their genetic
make-up and epigenetic profile. Second, they will be used for assessing the
progression of such therapies. Central to this vision is designing systems
that, with minimal user-intervention, can transduce complex signals from
biosystems in complement with clinical information to inform medical decision
within point-of-care settings. To reach our ultimate goal of developing
point-of-care systems and realizing personalized medicine, we are taking a
multistep systems-level approach towards understanding cellular processes and
biomolecular profiles, to quantify disease states and external interventions.
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Stopping GAN Violence: Generative Unadversarial Networks | While the costs of human violence have attracted a great deal of attention
from the research community, the effects of the network-on-network (NoN)
violence popularised by Generative Adversarial Networks have yet to be
addressed. In this work, we quantify the financial, social, spiritual,
cultural, grammatical and dermatological impact of this aggression and address
the issue by proposing a more peaceful approach which we term Generative
Unadversarial Networks (GUNs). Under this framework, we simultaneously train
two models: a generator G that does its best to capture whichever data
distribution it feels it can manage, and a motivator M that helps G to achieve
its dream. Fighting is strictly verboten and both models evolve by learning to
respect their differences. The framework is both theoretically and electrically
grounded in game theory, and can be viewed as a winner-shares-all two-player
game in which both players work as a team to achieve the best score.
Experiments show that by working in harmony, the proposed model is able to
claim both the moral and log-likelihood high ground. Our work builds on a rich
history of carefully argued position-papers, published as anonymous YouTube
comments, which prove that the optimal solution to NoN violence is more GUNs.
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Cas d'existence de solutions d'EDP | We give some examples of the existence of solutions of geometric PDEs (Yamabe
equation, Prescribed Scalar Curvature Equation, Gaussian curvature).We also
give some remarks on second order PDE and Green functions and on the maximum
principles.
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The Onset of Thermally Unstable Cooling from the Hot Atmospheres of Giant Galaxies in Clusters - Constraints on Feedback Models | We present accurate mass and thermodynamic profiles for a sample of 56 galaxy
clusters observed with the Chandra X-ray Observatory. We investigate the
effects of local gravitational acceleration in central cluster galaxies, and we
explore the role of the local free-fall time (t$_{\rm ff}$) in thermally
unstable cooling. We find that the local cooling time (t$_{\rm cool}$) is as
effective an indicator of cold gas, traced through its nebular emission, as the
ratio of t$_{\rm cool}$/t$_{\rm ff}$. Therefore, t$_{\rm cool}$ alone
apparently governs the onset of thermally unstable cooling in hot atmospheres.
The location of the minimum t$_{\rm cool}$/t$_{\rm ff}$, a thermodynamic
parameter that simulations suggest may be key in driving thermal instability,
is unresolved in most systems. As a consequence, selection effects bias the
value and reduce the observed range in measured t$_{\rm cool}$/t$_{\rm ff}$
minima. The entropy profiles of cool-core clusters are characterized by broken
power-laws down to our resolution limit, with no indication of isentropic
cores. We show, for the first time, that mass isothermality and the $K \propto
r^{2/3}$ entropy profile slope imply a floor in t$_{\rm cool}$/t$_{\rm ff}$
profiles within central galaxies. No significant departures of t$_{\rm
cool}$/t$_{\rm ff}$ below 10 are found, which is inconsistent with many recent
feedback models. The inner densities and cooling times of cluster atmospheres
are resilient to change in response to powerful AGN activity, suggesting that
the energy coupling between AGN heating and atmospheric gas is gentler than
most models predict.
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The linearized Calderon problem in transversally anisotropic geometries | In this article we study the linearized anisotropic Calderon problem. In a
compact manifold with boundary, this problem amounts to showing that products
of harmonic functions form a complete set. Assuming that the manifold is
transversally anisotropic, we show that the boundary measurements determine an
FBI type transform at certain points in the transversal manifold. This leads to
recovery of transversal singularities in the linearized problem. The method
requires a geometric condition on the transversal manifold related to pairs of
intersecting geodesics, but it does not involve the geodesic X-ray transform
which has limited earlier results on this problem.
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Almost isometries between Teichmüller spaces | We prove that the Teichmüller space of surfaces with given boundary lengths
equipped with the arc metric (resp. the Teichmüller metric) is almost
isometric to the Teichmüller space of punctured surfaces equipped with the
Thurston metric (resp. the Teichmüller metric).
| 0 | 0 | 1 | 0 | 0 | 0 |
Distributed Policy Iteration for Scalable Approximation of Cooperative Multi-Agent Policies | Decision making in multi-agent systems (MAS) is a great challenge due to
enormous state and joint action spaces as well as uncertainty, making
centralized control generally infeasible. Decentralized control offers better
scalability and robustness but requires mechanisms to coordinate on joint tasks
and to avoid conflicts. Common approaches to learn decentralized policies for
cooperative MAS suffer from non-stationarity and lacking credit assignment,
which can lead to unstable and uncoordinated behavior in complex environments.
In this paper, we propose Strong Emergent Policy approximation (STEP), a
scalable approach to learn strong decentralized policies for cooperative MAS
with a distributed variant of policy iteration. For that, we use function
approximation to learn from action recommendations of a decentralized
multi-agent planning algorithm. STEP combines decentralized multi-agent
planning with centralized learning, only requiring a generative model for
distributed black box optimization. We experimentally evaluate STEP in two
challenging and stochastic domains with large state and joint action spaces and
show that STEP is able to learn stronger policies than standard multi-agent
reinforcement learning algorithms, when combining multi-agent open-loop
planning with centralized function approximation. The learned policies can be
reintegrated into the multi-agent planning process to further improve
performance.
| 1 | 0 | 0 | 0 | 0 | 0 |
Integrable Floquet dynamics | We discuss several classes of integrable Floquet systems, i.e. systems which
do not exhibit chaotic behavior even under a time dependent perturbation. The
first class is associated with finite-dimensional Lie groups and
infinite-dimensional generalization thereof. The second class is related to the
row transfer matrices of the 2D statistical mechanics models. The third class
of models, called here "boost models", is constructed as a periodic interchange
of two Hamiltonians - one is the integrable lattice model Hamiltonian, while
the second is the boost operator. The latter for known cases coincides with the
entanglement Hamiltonian and is closely related to the corner transfer matrix
of the corresponding 2D statistical models. We present several explicit
examples. As an interesting application of the boost models we discuss a
possibility of generating periodically oscillating states with the period
different from that of the driving field. In particular, one can realize an
oscillating state by performing a static quench to a boost operator. We term
this state a "Quantum Boost Clock". All analyzed setups can be readily realized
experimentally, for example in cod atoms.
| 0 | 1 | 1 | 0 | 0 | 0 |
Topologically independent sets in precompact groups | It is a simple fact that a subgroup generated by a subset $A$ of an abelian
group is the direct sum of the cyclic groups $\langle a\rangle$, $a\in A$ if
and only if the set $A$ is independent. In [5] the concept of an $independent$
set in an abelian group was generalized to a $topologically$ $independent$
$set$ in a topological abelian group (these two notions coincide in discrete
abelian groups). It was proved that a topological subgroup generated by a
subset $A$ of an abelian topological group is the Tychonoff direct sum of the
cyclic topological groups $\langle a\rangle$, $a\in A$ if and only if the set
$A$ is topologically independent and absolutely Cauchy summable. Further, it
was shown, that the assumption of absolute Cauchy summability of $A$ can not be
removed in general in this result. In our paper we show that it can be removed
in precompact groups.
In other words, we prove that if $A$ is a subset of a {\em precompact}
abelian group, then the topological subgroup generated by $A$ is the Tychonoff
direct sum of the topological cyclic subgroups $\langle a\rangle$, $a\in A$ if
and only if $A$ is topologically independent. We show that precompactness can
not be replaced by local compactness in this result.
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Efficient Simulation of Temperature Evolution of Overhead Transmission Lines Based on Analytical Solution and NWP | Transmission lines are vital components in power systems. Tripping of
transmission lines caused by over-temperature is a major threat to the security
of system operations, so it is necessary to efficiently simulate line
temperature under both normal operation conditions and foreseen fault
conditions. Existing methods based on thermal-steady-state analyses cannot
reflect transient temperature evolution, and thus cannot provide timing
information needed for taking remedial actions. Moreover, conventional
numerical method requires huge computational efforts and barricades system-wide
analysis. In this regard, this paper derives an approximate analytical solution
of transmission-line temperature evolution enabling efficient analysis on
multiple operation states. Considering the uncertainties in environmental
parameters, the region of over-temperature is constructed in the environmental
parameter space to realize the over-temperature risk assessment in both the
planning stage and real-time operations. A test on a typical conductor model
verifies the accuracy of the approximate analytical solution. Based on the
analytical solution and numerical weather prediction (NWP) data, an efficient
simulation method for temperature evolution of transmission systems under
multiple operation states is proposed. As demonstrated on an NPCC 140-bus
system, it achieves over 1000 times of efficiency enhancement, verifying its
potentials in online risk assessment and decision support.
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Multiuser Communication Based on the DFT Eigenstructure | The eigenstructure of the discrete Fourier transform (DFT) is examined and
new systematic procedures to generate eigenvectors of the unitary DFT are
proposed. DFT eigenvectors are suggested as user signatures for data
communication over the real adder channel (RAC). The proposed multiuser
communication system over the 2-user RAC is detailed.
| 1 | 0 | 0 | 1 | 0 | 0 |
Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classification | This paper introduces a new Urban Point Cloud Dataset for Automatic
Segmentation and Classification acquired by Mobile Laser Scanning (MLS). We
describe how the dataset is obtained from acquisition to post-processing and
labeling. This dataset can be used to learn classification algorithm, however,
given that a great attention has been paid to the split between the different
objects, this dataset can also be used to learn the segmentation. The dataset
consists of around 2km of MLS point cloud acquired in two cities. The number of
points and range of classes make us consider that it can be used to train
Deep-Learning methods. Besides we show some results of automatic segmentation
and classification. The dataset is available at:
this http URL
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Viden: Attacker Identification on In-Vehicle Networks | Various defense schemes --- which determine the presence of an attack on the
in-vehicle network --- have recently been proposed. However, they fail to
identify which Electronic Control Unit (ECU) actually mounted the attack.
Clearly, pinpointing the attacker ECU is essential for fast/efficient forensic,
isolation, security patch, etc. To meet this need, we propose a novel scheme,
called Viden (Voltage-based attacker identification), which can identify the
attacker ECU by measuring and utilizing voltages on the in-vehicle network. The
first phase of Viden, called ACK learning, determines whether or not the
measured voltage signals really originate from the genuine message transmitter.
Viden then exploits the voltage measurements to construct and update the
transmitter ECUs' voltage profiles as their fingerprints. It finally uses the
voltage profiles to identify the attacker ECU. Since Viden adapts its profiles
to changes inside/outside of the vehicle, it can pinpoint the attacker ECU
under various conditions. Moreover, its efficiency and design-compliance with
modern in-vehicle network implementations make Viden practical and easily
deployable. Our extensive experimental evaluations on both a CAN bus prototype
and two real vehicles have shown that Viden can accurately fingerprint ECUs
based solely on voltage measurements and thus identify the attacker ECU with a
low false identification rate of 0.2%.
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Constraining black hole spins with low-frequency quasi-periodic oscillations in soft states | Black hole X-ray transients show a variety of state transitions during their
outburst phases, characterized by changes in their spectral and timing
properties. In particular, power density spectra (PDS) show quasi periodic
oscillations (QPOs) that can be related to the accretion regime of the source.
We looked for type-C QPOs in the disc-dominated state (i.e. the high soft
state) and in the ultra-luminous state in the RXTE archival data of 12
transient black hole X-ray binaries known to show QPOs during their outbursts.
We detected 6 significant QPOs in the soft state that can be classified as
type-C QPOs. Under the assumption that the accretion disc in disc-dominated
states extends down or close to the innermost stable circular orbit (ISCO) and
that type-C QPOs would arise at the inner edge of the accretion flow, we use
the relativistic precession model (RPM) to place constraints on the black hole
spin. We were able to place lower limits on the spin value for all the 12
sources of our sample while we could place also an upper limit on the spin for
5 sources.
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On a generalization of Lie($k$): a CataLAnKe theorem | We define a generalization of the free Lie algebra based on an $n$-ary
commutator and call it the free LAnKe. We show that the action of the symmetric
group $S_{2n-1}$ on the multilinear component with $2n-1$ generators is given
by the representation $S^{2^{n-1}1}$, whose dimension is the $n$th Catalan
number. An application involving Specht modules of staircase shape is
presented. We also introduce a conjecture that extends the relation between the
Whitehouse representation and Lie($k$).
| 0 | 0 | 1 | 0 | 0 | 0 |
Inference for Multiple Change-points in Linear and Non-linear Time Series Models | In this paper we develop a generalized likelihood ratio scan method (GLRSM)
for multiple change-points inference in piecewise stationary time series, which
estimates the number and positions of change-points and provides a confidence
interval for each change-point. The computational complexity of using GLRSM for
multiple change-points detection is as low as $O(n(\log n)^3)$ for a series of
length $n$. Consistency of the estimated numbers and positions of the
change-points is established. Extensive simulation studies are provided to
demonstrate the effectiveness of the proposed methodology under different
scenarios.
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Acceleration of Convergence of Some Infinite Sequences $\boldsymbol{\{A_n\}}$ Whose Asymptotic Expansions Involve Fractional Powers of $\boldsymbol{n}$ | In this paper, we deal with the acceleration of the convergence of infinite
series $\sum^\infty_{n=1}a_n$, when the terms $a_n$ are in general complex and
have asymptotic expansions that can be expressed in the form $$
a_n\sim[\Gamma(n)]^{s/m}\exp\left[Q(n)\right]\sum^\infty_{i=0}w_i
n^{\gamma-i/m}\quad\text{as $n\to\infty$},$$ where $\Gamma(z)$ is the gamma
function, $m\geq1$ is an arbitrary integer, $Q(n)=\sum^{m}_{i=0}q_in^{i/m}$ is
a polynomial of degree at most $m$ in $n^{1/m}$, $s$ is an arbitrary integer,
and $\gamma$ is an arbitrary complex number. This can be achieved effectively
by applying the $\tilde{d}^{(m)}$ transformation of the author to the sequence
$\{A_n\}$ of the partial sums $A_n=\sum^n_{k=1}a_k$, $n=1,2,\dots\ .$
We give a detailed review of the properties of such series and of the
$\tilde{d}^{(m)}$ transformation and the recursive W-algorithm that implements
it. We illustrate with several numerical examples of varying nature the
remarkable performance of this transformation on both convergent and divergent
series. We also show that the $\tilde{d}^{(m)}$ transformation can be used
efficiently to accelerate the convergence of some infinite products of the form
$\prod^\infty_{n=1}(1+v_n)$, where $$v_n\sim
\sum^\infty_{i=0}e_in^{-t/m-i/m}\quad \text{as $n\to\infty$,\ \ $t\geq m+1$ an
integer,}$$ and illustrate this with numerical examples. We put special
emphasis on the issue of numerical stability, we show how to monitor stability,
or lack thereof, numerically, and discuss how it can be achieved/improved in
suitable ways.
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Fiber Orientation Estimation Guided by a Deep Network | Diffusion magnetic resonance imaging (dMRI) is currently the only tool for
noninvasively imaging the brain's white matter tracts. The fiber orientation
(FO) is a key feature computed from dMRI for fiber tract reconstruction.
Because the number of FOs in a voxel is usually small, dictionary-based sparse
reconstruction has been used to estimate FOs with a relatively small number of
diffusion gradients. However, accurate FO estimation in regions with complex FO
configurations in the presence of noise can still be challenging. In this work
we explore the use of a deep network for FO estimation in a dictionary-based
framework and propose an algorithm named Fiber Orientation Reconstruction
guided by a Deep Network (FORDN). FORDN consists of two steps. First, we use a
smaller dictionary encoding coarse basis FOs to represent the diffusion
signals. To estimate the mixture fractions of the dictionary atoms (and thus
coarse FOs), a deep network is designed specifically for solving the sparse
reconstruction problem. Here, the smaller dictionary is used to reduce the
computational cost of training. Second, the coarse FOs inform the final FO
estimation, where a larger dictionary encoding dense basis FOs is used and a
weighted l1-norm regularized least squares problem is solved to encourage FOs
that are consistent with the network output. FORDN was evaluated and compared
with state-of-the-art algorithms that estimate FOs using sparse reconstruction
on simulated and real dMRI data, and the results demonstrate the benefit of
using a deep network for FO estimation.
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The hypotensive effect of activated apelin receptor is correlated with \b{eta}-arrestin recruitment | The apelinergic system is an important player in the regulation of both
vascular tone and cardiovascular function, making this physiological system an
attractive target for drug development for hypertension, heart failure and
ischemic heart disease. Indeed, apelin exerts a positive inotropic effect in
humans whilst reducing peripheral vascular resistance. In this study, we
investigated the signaling pathways through which apelin exerts its hypotensive
action. We synthesized a series of apelin-13 analogs whereby the C-terminal
Phe13 residue was replaced by natural or unnatural amino acids. In HEK293 cells
expressing APJ, we evaluated the relative efficacy of these compounds to
activate G{\alpha}i1 and G{\alpha}oA G-proteins, recruit \b{eta}-arrestins 1
and 2 (\b{eta}arrs), and inhibit cAMP production. Calculating the transduction
ratio for each pathway allowed us to identify several analogs with distinct
signaling profiles. Furthermore, we found that these analogs delivered i.v. to
Sprague-Dawley rats exerted a wide range of hypotensive responses. Indeed, two
compounds lost their ability to lower blood pressure, while other analogs
significantly reduced blood pressure as apelin-13. Interestingly, analogs that
did not lower blood pressure were less effective at recruiting \b{eta}arrs.
Finally, using Spearman correlations, we established that the hypotensive
response was significantly correlated with \b{eta}arr recruitment but not with
G protein- dependent signaling. In conclusion, our results demonstrated that
the \b{eta}arr recruitment potency is involved in the hypotensive efficacy of
activated APJ.
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The Shape of Bouncing Universes | What happens to the most general closed oscillating universes in general
relativity? We sketch the development of interest in cyclic universes from the
early work of Friedmann and Tolman to modern variations introduced by the
presence of a cosmological constant. Then we show what happens in the cyclic
evolution of the most general closed anisotropic universes provided by the
Mixmaster universe. We show that in the presence of entropy increase its cycles
grow in size and age, increasingly approaching flatness. But these cycles also
grow increasingly anisotropic at their expansion maxima. If there is a positive
cosmological constant, or dark energy, present then these oscillations always
end and the last cycle evolves from an anisotropic inflexion point towards a de
Sitter future of everlasting expansion.
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Multi-sensor authentication to improve smartphone security | The widespread use of smartphones gives rise to new security and privacy
concerns. Smartphone thefts account for the largest percentage of thefts in
recent crime statistics. Using a victim's smartphone, the attacker can launch
impersonation attacks, which threaten the security of the victim and other
users in the network. Our threat model includes the attacker taking over the
phone after the user has logged on with his password or pin. Our goal is to
design a mechanism for smartphones to better authenticate the current user,
continuously and implicitly, and raise alerts when necessary. In this paper, we
propose a multi-sensors-based system to achieve continuous and implicit
authentication for smartphone users. The system continuously learns the owner's
behavior patterns and environment characteristics, and then authenticates the
current user without interrupting user-smartphone interactions. Our method can
adaptively update a user's model considering the temporal change of user's
patterns. Experimental results show that our method is efficient, requiring
less than 10 seconds to train the model and 20 seconds to detect the abnormal
user, while achieving high accuracy (more than 90%). Also the combination of
more sensors provide better accuracy. Furthermore, our method enables adjusting
the security level by changing the sampling rate.
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The ALF (Algorithms for Lattice Fermions) project release 1.0. Documentation for the auxiliary field quantum Monte Carlo code | The Algorithms for Lattice Fermions package provides a general code for the
finite temperature auxiliary field quantum Monte Carlo algorithm. The code is
engineered to be able to simulate any model that can be written in terms of
sums of single-body operators, of squares of single-body operators and
single-body operators coupled to an Ising field with given dynamics. We provide
predefined types that allow the user to specify the model, the Bravais lattice
as well as equal time and time displaced observables. The code supports an MPI
implementation. Examples such as the Hubbard model on the honeycomb lattice and
the Hubbard model on the square lattice coupled to a transverse Ising field are
provided and discussed in the documentation. We furthermore discuss how to use
the package to implement the Kondo lattice model and the
$SU(N)$-Hubbard-Heisenberg model. One can download the code from our Git
instance at this https URL and sign in to file issues.
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A Survey on Blockchain Technology and Its Potential Applications in Distributed Control and Cooperative Robots | As a disruptive technology, blockchain, particularly its original form of
bitcoin as a type of digital currency, has attracted great attentions. The
innovative distributed decision making and security mechanism lay the technical
foundation for its success, making us consider to penetrate the power of
blockchain technology to distributed control and cooperative robotics, in which
the distributed and secure mechanism is also highly demanded. Actually,
security and distributed communication have long been unsolved problems in the
field of distributed control and cooperative robotics. It has been reported on
the network failure and intruder attacks of distributed control and
multi-robotic systems. Blockchain technology provides promise to remedy this
situation thoroughly. This work is intended to create a global picture of
blockchain technology on its working principle and key elements in the language
of control and robotics, to provide a shortcut for beginners to step into this
research field.
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Toroidal trapped surfaces and isoperimetric inequalities | We analytically construct an infinite number of trapped toroids in
spherically symmetric Cauchy hypersurfaces of the Einstein equations. We focus
on initial data which represent "constant density stars" momentarily at rest.
There exists an infinite number of constant mean curvature tori, but we also
deal with more general configurations. The marginally trapped toroids have been
found analytically and numerically; they are unstable. The topologically
toroidal trapped surfaces appear in a finite region surrounded by the
Schwarzschild horizon.
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Federated Tensor Factorization for Computational Phenotyping | Tensor factorization models offer an effective approach to convert massive
electronic health records into meaningful clinical concepts (phenotypes) for
data analysis. These models need a large amount of diverse samples to avoid
population bias. An open challenge is how to derive phenotypes jointly across
multiple hospitals, in which direct patient-level data sharing is not possible
(e.g., due to institutional policies). In this paper, we developed a novel
solution to enable federated tensor factorization for computational phenotyping
without sharing patient-level data. We developed secure data harmonization and
federated computation procedures based on alternating direction method of
multipliers (ADMM). Using this method, the multiple hospitals iteratively
update tensors and transfer secure summarized information to a central server,
and the server aggregates the information to generate phenotypes. We
demonstrated with real medical datasets that our method resembles the
centralized training model (based on combined datasets) in terms of accuracy
and phenotypes discovery while respecting privacy.
| 1 | 0 | 0 | 1 | 0 | 0 |
Optical fluxes in coupled $\cal PT$-symmetric photonic structures | In this work we first examine transverse and longitudinal fluxes in a $\cal
PT$-symmetric photonic dimer using a coupled-mode theory. Several surprising
understandings are obtained from this perspective: The longitudinal flux shows
that the $\cal PT$ transition in a dimer can be regarded as a classical effect,
despite its analogy to $\cal PT$-symmetric quantum mechanics. The longitudinal
flux also indicates that the so-called giant amplification in the $\cal
PT$-symmetric phase is a sub-exponential behavior and does not outperform a
single gain waveguide. The transverse flux, on the other hand, reveals that the
apparent power oscillations between the gain and loss waveguides in the $\cal
PT$-symmetric phase can be deceiving in certain cases, where the transverse
power transfer is in fact unidirectional. We also show that this power transfer
cannot be arbitrarily fast even when the exceptional point is approached.
Finally, we go beyond the coupled-mode theory by using the paraxial wave
equation and also extend our discussions to a $\cal PT$ diamond and a
one-dimensional periodic lattice.
| 0 | 1 | 0 | 0 | 0 | 0 |
QCRI Machine Translation Systems for IWSLT 16 | This paper describes QCRI's machine translation systems for the IWSLT 2016
evaluation campaign. We participated in the Arabic->English and English->Arabic
tracks. We built both Phrase-based and Neural machine translation models, in an
effort to probe whether the newly emerged NMT framework surpasses the
traditional phrase-based systems in Arabic-English language pairs. We trained a
very strong phrase-based system including, a big language model, the Operation
Sequence Model, Neural Network Joint Model and Class-based models along with
different domain adaptation techniques such as MML filtering, mixture modeling
and using fine tuning over NNJM model. However, a Neural MT system, trained by
stacking data from different genres through fine-tuning, and applying ensemble
over 8 models, beat our very strong phrase-based system by a significant 2 BLEU
points margin in Arabic->English direction. We did not obtain similar gains in
the other direction but were still able to outperform the phrase-based system.
We also applied system combination on phrase-based and NMT outputs.
| 1 | 0 | 0 | 0 | 0 | 0 |
Detecting the direction of a signal on high-dimensional spheres: Non-null and Le Cam optimality results | We consider one of the most important problems in directional statistics,
namely the problem of testing the null hypothesis that the spike direction
${\pmb \theta}$ of a Fisher-von Mises-Langevin distribution on the
$p$-dimensional unit hypersphere is equal to a given direction ${\pmb
\theta}_0$. After a reduction through invariance arguments, we derive local
asymptotic normality (LAN) results in a general high-dimensional framework
where the dimension $p_n$ goes to infinity at an arbitrary rate with the sample
size $n$, and where the concentration $\kappa_n$ behaves in a completely free
way with $n$, which offers a spectrum of problems ranging from arbitrarily easy
to arbitrarily challenging ones. We identify seven asymptotic regimes,
depending on the convergence/divergence properties of $(\kappa_n)$, that yield
different contiguity rates and different limiting experiments. In each regime,
we derive Le Cam optimal tests under specified $\kappa_n$ and we compute, from
the Le Cam third lemma, asymptotic powers of the classical Watson test under
contiguous alternatives. We further establish LAN results with respect to both
spike direction and concentration, which allows us to discuss optimality also
under unspecified $\kappa_n$. To obtain a full understanding of the non-null
behavior of the Watson test, we use martingale CLTs to derive its local
asymptotic powers in the broader, semiparametric, model of rotationally
symmetric distributions. A Monte Carlo study shows that the finite-sample
behaviors of the various tests remarkably agree with our asymptotic results.
| 0 | 0 | 1 | 1 | 0 | 0 |
Hamiltonicity is Hard in Thin or Polygonal Grid Graphs, but Easy in Thin Polygonal Grid Graphs | In 2007, Arkin et al. initiated a systematic study of the complexity of the
Hamiltonian cycle problem on square, triangular, or hexagonal grid graphs,
restricted to polygonal, thin, superthin, degree-bounded, or solid grid graphs.
They solved many combinations of these problems, proving them either
polynomially solvable or NP-complete, but left three combinations open. In this
paper, we prove two of these unsolved combinations to be NP-complete:
Hamiltonicity of Square Polygonal Grid Graphs and Hamiltonicity of Hexagonal
Thin Grid Graphs. We also consider a new restriction, where the grid graph is
both thin and polygonal, and prove that Hamiltonicity then becomes polynomially
solvable for square, triangular, and hexagonal grid graphs.
| 1 | 0 | 0 | 0 | 0 | 0 |
More on cyclic amenability of the Lau product of Banach algebras defined by a Banach algebra morphism | For two Banach algebras $A$ and $B$, the $T$-Lau product $A\times_T B$, was
recently introduced and studied for some bounded homomorphism $T:B\to A$ with
$\|T\|\leq 1$. Here, we give general nessesary and sufficent conditions for
$A\times_T B$ to be (approximately) cyclic amenable. In particular, we extend
some recent results on (approximate) cyclic amenability of direct product
$A\oplus B$ and $T$-Lau product $A\times_T B$ and answer a question on cyclic
amenability of $A\times_T B$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Data-Augmented Contact Model for Rigid Body Simulation | Accurately modeling contact behaviors for real-world, near-rigid materials
remains a grand challenge for existing rigid-body physics simulators. This
paper introduces a data-augmented contact model that incorporates analytical
solutions with observed data to predict the 3D contact impulse which could
result in rigid bodies bouncing, sliding or spinning in all directions. Our
method enhances the expressiveness of the standard Coulomb contact model by
learning the contact behaviors from the observed data, while preserving the
fundamental contact constraints whenever possible. For example, a classifier is
trained to approximate the transitions between static and dynamic frictions,
while non-penetration constraint during collision is enforced analytically. Our
method computes the aggregated effect of contact for the entire rigid body,
instead of predicting the contact force for each contact point individually,
removing the exponential decline in accuracy as the number of contact points
increases.
| 1 | 0 | 0 | 0 | 0 | 0 |
Assessing the Performance of Deep Learning Algorithms for Newsvendor Problem | In retailer management, the Newsvendor problem has widely attracted attention
as one of basic inventory models. In the traditional approach to solving this
problem, it relies on the probability distribution of the demand. In theory, if
the probability distribution is known, the problem can be considered as fully
solved. However, in any real world scenario, it is almost impossible to even
approximate or estimate a better probability distribution for the demand. In
recent years, researchers start adopting machine learning approach to learn a
demand prediction model by using other feature information. In this paper, we
propose a supervised learning that optimizes the demand quantities for products
based on feature information. We demonstrate that the original Newsvendor loss
function as the training objective outperforms the recently suggested quadratic
loss function. The new algorithm has been assessed on both the synthetic data
and real-world data, demonstrating better performance.
| 1 | 0 | 0 | 1 | 0 | 0 |
STWalk: Learning Trajectory Representations in Temporal Graphs | Analyzing the temporal behavior of nodes in time-varying graphs is useful for
many applications such as targeted advertising, community evolution and outlier
detection. In this paper, we present a novel approach, STWalk, for learning
trajectory representations of nodes in temporal graphs. The proposed framework
makes use of structural properties of graphs at current and previous time-steps
to learn effective node trajectory representations. STWalk performs random
walks on a graph at a given time step (called space-walk) as well as on graphs
from past time-steps (called time-walk) to capture the spatio-temporal behavior
of nodes. We propose two variants of STWalk to learn trajectory
representations. In one algorithm, we perform space-walk and time-walk as part
of a single step. In the other variant, we perform space-walk and time-walk
separately and combine the learned representations to get the final trajectory
embedding. Extensive experiments on three real-world temporal graph datasets
validate the effectiveness of the learned representations when compared to
three baseline methods. We also show the goodness of the learned trajectory
embeddings for change point detection, as well as demonstrate that arithmetic
operations on these trajectory representations yield interesting and
interpretable results.
| 1 | 0 | 0 | 1 | 0 | 0 |
A Study on Performance and Power Efficiency of Dense Non-Volatile Caches in Multi-Core Systems | In this paper, we present a novel cache design based on Multi-Level Cell
Spin-Transfer Torque RAM (MLC STTRAM) that can dynamically adapt the set
capacity and associativity to use efficiently the full potential of MLC STTRAM.
We exploit the asymmetric nature of the MLC storage scheme to build cache lines
featuring heterogeneous performances, that is, half of the cache lines are
read-friendly, while the other is write-friendly. Furthermore, we propose to
opportunistically deactivate ways in underutilized sets to convert MLC to
Single-Level Cell (SLC) mode, which features overall better performance and
lifetime. Our ultimate goal is to build a cache architecture that combines the
capacity advantages of MLC and performance/energy advantages of SLC. Our
experiments show an improvement of 43% in total numbers of conflict misses, 27%
in memory access latency, 12% in system performance, and 26% in LLC access
energy, with a slight degradation in cache lifetime (about 7%) compared to an
SLC cache.
| 1 | 0 | 0 | 0 | 0 | 0 |
Characterization theorems for $Q$-independent random variables with values in a locally compact Abelian group | Let $X$ be a locally compact Abelian group, $Y$ be its character group.
Following A. Kagan and G. Székely we introduce a notion of $Q$-independence
for random variables with values in $X$. We prove group analogues of the
Cramér, Kac-Bernstein, Skitovich-Darmois and Heyde theorems for
$Q$-independent random variables with values in $X$. The proofs of these
theorems are reduced to solving some functional equations on the group $Y$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Controlling competing orders via non-equilibrium acoustic phonons: emergence of anisotropic electronic temperature | Ultrafast perturbations offer a unique tool to manipulate correlated systems
due to their ability to promote transient behaviors with no equilibrium
counterpart. A widely employed strategy is the excitation of coherent optical
phonons, as they can cause significant changes in the electronic structure and
interactions on short time scales. Here, we explore a promising alternative
route: the non-equilibrium excitation of acoustic phonons. We demonstrate that
it leads to the remarkable phenomenon of a momentum-dependent temperature, by
which electronic states at different regions of the Fermi surface are subject
to distinct local temperatures. Such an anisotropic electronic temperature can
have a profound effect on the delicate balance between competing ordered states
in unconventional superconductors, opening a novel avenue to control correlated
phases.
| 0 | 1 | 0 | 0 | 0 | 0 |
The Junk News Aggregator: Examining junk news posted on Facebook, starting with the 2018 US Midterm Elections | In recent years, the phenomenon of online misinformation and junk news
circulating on social media has come to constitute an important and widespread
problem affecting public life online across the globe, particularly around
important political events such as elections. At the same time, there have been
calls for more transparency around misinformation on social media platforms, as
many of the most popular social media platforms function as "walled gardens,"
where it is impossible for researchers and the public to readily examine the
scale and nature of misinformation activity as it is unfolding on the
platforms. In order to help address this, this paper, we present the Junk News
Aggregator, an interactive web tool made publicly available, which allows the
public to examine, in near real-time, all of the public content posted to
Facebook by important junk news sources in the US. It allows the public to gain
access to and examine the latest articles posted on Facebook (the most popular
social media platform in the US and one where content is not readily accessible
at scale from the open Web), as well as organise them by time, news publisher,
and keywords of interest, and sort them based on all eight engagement metrics
available on Facebook. Therefore, the Aggregator allows the public to gain
insights on the volume, content, key themes, and types and volumes of
engagement received by content posted by junk news publishers, in near real
time, hence opening up and offering transparency in these activities, at scale
across the top most popular junk news publishers and in near real time. In this
way, the Aggregator can help increase transparency around the nature, volume,
and engagement with junk news on social media, and serve as a media literacy
tool for the public.
| 1 | 0 | 0 | 0 | 0 | 0 |
Quantum Blockchain using entanglement in time | A conceptual design for a quantum blockchain is proposed. Our method involves
encoding the blockchain into a temporal GHZ (Greenberger-Horne-Zeilinger) state
of photons that do not simultaneously coexist. It is shown that the
entanglement in time, as opposed to an entanglement in space, provides the
crucial quantum advantage. All the subcomponents of this system have already
been shown to be experimentally realized. Perhaps more shockingly, our encoding
procedure can be interpreted as non-classically influencing the past; hence
this decentralized quantum blockchain can be viewed as a quantum networked time
machine.
| 0 | 0 | 0 | 0 | 0 | 1 |
Sensor Transformation Attention Networks | Recent work on encoder-decoder models for sequence-to-sequence mapping has
shown that integrating both temporal and spatial attention mechanisms into
neural networks increases the performance of the system substantially. In this
work, we report on the application of an attentional signal not on temporal and
spatial regions of the input, but instead as a method of switching among inputs
themselves. We evaluate the particular role of attentional switching in the
presence of dynamic noise in the sensors, and demonstrate how the attentional
signal responds dynamically to changing noise levels in the environment to
achieve increased performance on both audio and visual tasks in three
commonly-used datasets: TIDIGITS, Wall Street Journal, and GRID. Moreover, the
proposed sensor transformation network architecture naturally introduces a
number of advantages that merit exploration, including ease of adding new
sensors to existing architectures, attentional interpretability, and increased
robustness in a variety of noisy environments not seen during training.
Finally, we demonstrate that the sensor selection attention mechanism of a
model trained only on the small TIDIGITS dataset can be transferred directly to
a pre-existing larger network trained on the Wall Street Journal dataset,
maintaining functionality of switching between sensors to yield a dramatic
reduction of error in the presence of noise.
| 1 | 0 | 0 | 0 | 0 | 0 |
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer | The capacity of a neural network to absorb information is limited by its
number of parameters. Conditional computation, where parts of the network are
active on a per-example basis, has been proposed in theory as a way of
dramatically increasing model capacity without a proportional increase in
computation. In practice, however, there are significant algorithmic and
performance challenges. In this work, we address these challenges and finally
realize the promise of conditional computation, achieving greater than 1000x
improvements in model capacity with only minor losses in computational
efficiency on modern GPU clusters. We introduce a Sparsely-Gated
Mixture-of-Experts layer (MoE), consisting of up to thousands of feed-forward
sub-networks. A trainable gating network determines a sparse combination of
these experts to use for each example. We apply the MoE to the tasks of
language modeling and machine translation, where model capacity is critical for
absorbing the vast quantities of knowledge available in the training corpora.
We present model architectures in which a MoE with up to 137 billion parameters
is applied convolutionally between stacked LSTM layers. On large language
modeling and machine translation benchmarks, these models achieve significantly
better results than state-of-the-art at lower computational cost.
| 1 | 0 | 0 | 1 | 0 | 0 |
Finite Size Corrections and Likelihood Ratio Fluctuations in the Spiked Wigner Model | In this paper we study principal components analysis in the regime of high
dimensionality and high noise. Our model of the problem is a rank-one
deformation of a Wigner matrix where the signal-to-noise ratio (SNR) is of
constant order, and we are interested in the fundamental limits of detection of
the spike. Our main goal is to gain a fine understanding of the asymptotics for
the log-likelihood ratio process, also known as the free energy, as a function
of the SNR. Our main results are twofold. We first prove that the free energy
has a finite-size correction to its limit---the replica-symmetric
formula---which we explicitly compute. This provides a formula for the
Kullback-Leibler divergence between the planted and null models. Second, we
prove that below the reconstruction threshold, where it becomes impossible to
reconstruct the spike, the log-likelihood ratio has fluctuations of constant
order and converges in distribution to a Gaussian under both the planted and
(under restrictions) the null model. As a consequence, we provide a general
proof of contiguity between these two distributions that holds up to the
reconstruction threshold, and is valid for an arbitrary separable prior on the
spike. Formulae for the total variation distance, and the Type-I and Type-II
errors of the optimal test are also given. Our proofs are based on Gaussian
interpolation methods and a rigorous incarnation of the cavity method, as
devised by Guerra and Talagrand in their study of the Sherrington--Kirkpatrick
spin-glass model.
| 0 | 0 | 1 | 1 | 0 | 0 |
Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory | In meta-learning an agent extracts knowledge from observed tasks, aiming to
facilitate learning of novel future tasks. Under the assumption that future
tasks are 'related' to previous tasks, the accumulated knowledge should be
learned in a way which captures the common structure across learned tasks,
while allowing the learner sufficient flexibility to adapt to novel aspects of
new tasks. We present a framework for meta-learning that is based on
generalization error bounds, allowing us to extend various PAC-Bayes bounds to
meta-learning. Learning takes place through the construction of a distribution
over hypotheses based on the observed tasks, and its utilization for learning a
new task. Thus, prior knowledge is incorporated through setting an
experience-dependent prior for novel tasks. We develop a gradient-based
algorithm which minimizes an objective function derived from the bounds and
demonstrate its effectiveness numerically with deep neural networks. In
addition to establishing the improved performance available through
meta-learning, we demonstrate the intuitive way by which prior information is
manifested at different levels of the network.
| 1 | 0 | 0 | 1 | 0 | 0 |
Accurate Optical Flow via Direct Cost Volume Processing | We present an optical flow estimation approach that operates on the full
four-dimensional cost volume. This direct approach shares the structural
benefits of leading stereo matching pipelines, which are known to yield high
accuracy. To this day, such approaches have been considered impractical due to
the size of the cost volume. We show that the full four-dimensional cost volume
can be constructed in a fraction of a second due to its regularity. We then
exploit this regularity further by adapting semi-global matching to the
four-dimensional setting. This yields a pipeline that achieves significantly
higher accuracy than state-of-the-art optical flow methods while being faster
than most. Our approach outperforms all published general-purpose optical flow
methods on both Sintel and KITTI 2015 benchmarks.
| 1 | 0 | 0 | 0 | 0 | 0 |
Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls | We propose a rank-$k$ variant of the classical Frank-Wolfe algorithm to solve
convex optimization over a trace-norm ball. Our algorithm replaces the top
singular-vector computation ($1$-SVD) in Frank-Wolfe with a top-$k$
singular-vector computation ($k$-SVD), which can be done by repeatedly applying
$1$-SVD $k$ times. Alternatively, our algorithm can be viewed as a rank-$k$
restricted version of projected gradient descent. We show that our algorithm
has a linear convergence rate when the objective function is smooth and
strongly convex, and the optimal solution has rank at most $k$. This improves
the convergence rate and the total time complexity of the Frank-Wolfe method
and its variants.
| 1 | 0 | 0 | 1 | 0 | 0 |
A Note on Exponential Inequalities in Hilbert Spaces for Spatial Processes with Applications to the Functional Kernel Regression Model | In this manuscript we present exponential inequalities for spatial lattice
processes which take values in a separable Hilbert space and satisfy certain
dependence conditions. We consider two types of dependence: spatial data under
$\alpha$-mixing conditions and spatial data which satisfies a weak dependence
condition introduced by Dedecker and Prieur [2005]. We demonstrate their
usefulness in the functional kernel regression model of Ferraty and Vieu [2004]
where we study uniform consistency properties of the estimated regression
operator on increasing subsets of the underlying function space.
| 0 | 0 | 1 | 1 | 0 | 0 |
Polygons pulled from an adsorbing surface | We consider self-avoiding lattice polygons, in the hypercubic lattice, as a
model of a ring polymer adsorbed at a surface and either being desorbed by the
action of a force, or pushed towards the surface. We show that, when there is
no interaction with the surface, then the response of the polygon to the
applied force is identical (in the thermodynamic limit) for two ways in which
we apply the force. When the polygon is attracted to the surface then, when the
dimension is at least 3, we have a complete characterization of the critical
force--temperature curve in terms of the behaviour, (a) when there is no force,
and, (b) when there is no surface interaction. For the 2-dimensional case we
have upper and lower bounds on the free energy. We use both Monte Carlo and
exact enumeration and series analysis methods to investigate the form of the
phase diagram in two dimensions. We find evidence for the existence of a
\emph{mixed phase} where the free energy depends on the strength of the
interaction with the adsorbing line and on the applied force.
| 0 | 1 | 0 | 0 | 0 | 0 |
O$^2$TD: (Near)-Optimal Off-Policy TD Learning | Temporal difference learning and Residual Gradient methods are the most
widely used temporal difference based learning algorithms; however, it has been
shown that none of their objective functions is optimal w.r.t approximating the
true value function $V$. Two novel algorithms are proposed to approximate the
true value function $V$. This paper makes the following contributions: (1) A
batch algorithm that can help find the approximate optimal off-policy
prediction of the true value function $V$. (2) A linear computational cost (per
step) near-optimal algorithm that can learn from a collection of off-policy
samples. (3) A new perspective of the emphatic temporal difference learning
which bridges the gap between off-policy optimality and off-policy stability.
| 1 | 0 | 0 | 1 | 0 | 0 |
Comprehensive classification for Bose-Fermi mixtures | We present analytical studies of a boson-fermion mixture at zero temperature
with spin-polarized fermions. Using the Thomas-Fermi approximation for bosons
and the local-density approximation for fermions, we find a large variety of
different density shapes. In the case of continuous density, we obtain analytic
conditions for each configuration for attractive as well as repulsive
boson-fermion interaction. Furthermore, we analytically show that all the
scenarios we describe are minima of the grand-canonical energy functional.
Finally, we provide a full classification of all possible ground states in the
interpenetrative regime. Our results also apply to binary mixtures of bosons.
| 0 | 1 | 0 | 0 | 0 | 0 |
Navigation Objects Extraction for Better Content Structure Understanding | Existing works for extracting navigation objects from webpages focus on
navigation menus, so as to reveal the information architecture of the site.
However, web 2.0 sites such as social networks, e-commerce portals etc. are
making the understanding of the content structure in a web site increasingly
difficult. Dynamic and personalized elements such as top stories, recommended
list in a webpage are vital to the understanding of the dynamic nature of web
2.0 sites. To better understand the content structure in web 2.0 sites, in this
paper we propose a new extraction method for navigation objects in a webpage.
Our method will extract not only the static navigation menus, but also the
dynamic and personalized page-specific navigation lists. Since the navigation
objects in a webpage naturally come in blocks, we first cluster hyperlinks into
different blocks by exploiting spatial locations of hyperlinks, the
hierarchical structure of the DOM-tree and the hyperlink density. Then we
identify navigation objects from those blocks using the SVM classifier with
novel features such as anchor text lengths etc. Experiments on real-world data
sets with webpages from various domains and styles verified the effectiveness
of our method.
| 1 | 0 | 0 | 0 | 0 | 0 |
Source Selection for Cluster Weak Lensing Measurements in the Hyper Suprime-Cam Survey | We present optimized source galaxy selection schemes for measuring cluster
weak lensing (WL) mass profiles unaffected by cluster member dilution from the
Subaru Hyper Suprime-Cam Strategic Survey Program (HSC-SSP). The ongoing
HSC-SSP survey will uncover thousands of galaxy clusters to $z\lesssim1.5$. In
deriving cluster masses via WL, a critical source of systematics is
contamination and dilution of the lensing signal by cluster {members, and by
foreground galaxies whose photometric redshifts are biased}. Using the
first-year CAMIRA catalog of $\sim$900 clusters with richness larger than 20
found in $\sim$140 deg$^2$ of HSC-SSP data, we devise and compare several
source selection methods, including selection in color-color space (CC-cut),
and selection of robust photometric redshifts by applying constraints on their
cumulative probability distribution function (PDF; P-cut). We examine the
dependence of the contamination on the chosen limits adopted for each method.
Using the proper limits, these methods give mass profiles with minimal dilution
in agreement with one another. We find that not adopting either the CC-cut or
P-cut methods results in an underestimation of the total cluster mass
($13\pm4\%$) and the concentration of the profile ($24\pm11\%$). The level of
cluster contamination can reach as high as $\sim10\%$ at $R\approx 0.24$
Mpc/$h$ for low-z clusters without cuts, while employing either the P-cut or
CC-cut results in cluster contamination consistent with zero to within the 0.5%
uncertainties. Our robust methods yield a $\sim60\sigma$ detection of the
stacked CAMIRA surface mass density profile, with a mean mass of
$M_\mathrm{200c} = (1.67\pm0.05({\rm {stat}}))\times 10^{14}\,M_\odot/h$.
| 0 | 1 | 0 | 0 | 0 | 0 |
Dense blowup for parabolic SPDEs | The main result of this paper is that there are examples of stochastic
partial differential equations [hereforth, SPDEs] of the type $$ \partial_t
u=\frac12\Delta u +\sigma(u)\eta \qquad\text{on
$(0\,,\infty)\times\mathbb{R}^3$}$$ such that the solution exists and is unique
as a random field in the sense of Dalang and Walsh, yet the solution has
unbounded oscillations in every open neighborhood of every space-time point. We
are not aware of the existence of such a construction in spatial dimensions
below $3$. En route, it will be proved that there exist a large family of
parabolic SPDEs whose moment Lyapunov exponents grow at least sub exponentially
in its order parameter in the sense that there exist $A_1,\beta\in(0\,,1)$ such
that \[
\underline{\gamma}(k) :=
\liminf_{t\to\infty}t^{-1}\inf_{x\in\mathbb{R}^3}
\log\mathbb{E}\left(|u(t\,,x)|^k\right) \ge A_1\exp(A_1 k^\beta)
\qquad\text{for all $k\ge 2$}.
\] This sort of "super intermittency" is combined with a local linearization
of the solution, and with techniques from Gaussian analysis in order to
establish the unbounded oscillations of the sample functions of the solution to
our SPDE.
| 0 | 0 | 1 | 0 | 0 | 0 |
Batched High-dimensional Bayesian Optimization via Structural Kernel Learning | Optimization of high-dimensional black-box functions is an extremely
challenging problem. While Bayesian optimization has emerged as a popular
approach for optimizing black-box functions, its applicability has been limited
to low-dimensional problems due to its computational and statistical challenges
arising from high-dimensional settings. In this paper, we propose to tackle
these challenges by (1) assuming a latent additive structure in the function
and inferring it properly for more efficient and effective BO, and (2)
performing multiple evaluations in parallel to reduce the number of iterations
required by the method. Our novel approach learns the latent structure with
Gibbs sampling and constructs batched queries using determinantal point
processes. Experimental validations on both synthetic and real-world functions
demonstrate that the proposed method outperforms the existing state-of-the-art
approaches.
| 1 | 0 | 1 | 1 | 0 | 0 |
Classifying Time-Varying Complex Networks on the Tensor Manifold | At the core of understanding dynamical systems is the ability to maintain and
control the systems behavior that includes notions of robustness,
heterogeneity, and/or regime-shift detection. Recently, to explore such
functional properties, a convenient representation has been to model such
dynamical systems as a weighted graph consisting of a finite, but very large
number of interacting agents. This said, there exists very limited relevant
statistical theory that is able cope with real-life data, i.e., how does
perform simple analysis and/or statistics over a family of networks as opposed
to a specific network or network-to-network variation. Here, we are interested
in the analysis of network families whereby each network represents a point on
an underlying statistical manifold. From this, we explore the Riemannian
structure of the statistical (tensor) manifold in order to define notions of
geodesics or shortest distance amongst such points as well as a statistical
framework for time-varying complex networks for which we can utilize in higher
order classification tasks.
| 1 | 0 | 0 | 0 | 0 | 0 |
An Adversarial Regularisation for Semi-Supervised Training of Structured Output Neural Networks | We propose a method for semi-supervised training of structured-output neural
networks. Inspired by the framework of Generative Adversarial Networks (GAN),
we train a discriminator network to capture the notion of a quality of network
output. To this end, we leverage the qualitative difference between outputs
obtained on the labelled training data and unannotated data. We then use the
discriminator as a source of error signal for unlabelled data. This effectively
boosts the performance of a network on a held out test set. Initial experiments
in image segmentation demonstrate that the proposed framework enables achieving
the same network performance as in a fully supervised scenario, while using two
times less annotations.
| 1 | 0 | 0 | 0 | 0 | 0 |
Elliptic operators on refined Sobolev scales on vector bundles | We introduce a refined Sobolev scale on a vector bundle over a closed
infinitely smooth manifold. This scale consists of inner product Hörmander
spaces parametrized with a real number and a function varying slowly at
infinity in the sense of Karamata. We prove that these spaces are obtained by
the interpolation with a function parameter between inner product Sobolev
spaces. An arbitrary classical elliptic pseudodifferential operator acting
between vector bundles of the same rank is investigated on this scale. We prove
that this operator is bounded and Fredholm on pairs of appropriate Hörmander
spaces. We also prove that the solutions to the corresponding elliptic equation
satisfy a certain a priori estimate on these spaces. The local regularity of
these solutions is investigated on the refined Sobolev scale. We find new
sufficient conditions for the solutions to have continuous derivatives of a
given order.
| 0 | 0 | 1 | 0 | 0 | 0 |
CoDraw: Collaborative Drawing as a Testbed for Grounded Goal-driven Communication | In this work, we propose a goal-driven collaborative task that contains
language, vision, and action in a virtual environment as its core components.
Specifically, we develop a Collaborative image-Drawing game between two agents,
called CoDraw. Our game is grounded in a virtual world that contains movable
clip art objects. The game involves two players: a Teller and a Drawer. The
Teller sees an abstract scene containing multiple clip art pieces in a
semantically meaningful configuration, while the Drawer tries to reconstruct
the scene on an empty canvas using available clip art pieces. The two players
communicate via two-way communication using natural language. We collect the
CoDraw dataset of ~10K dialogs consisting of ~138K messages exchanged between
human agents. We define protocols and metrics to evaluate the effectiveness of
learned agents on this testbed, highlighting the need for a novel crosstalk
condition which pairs agents trained independently on disjoint subsets of the
training data for evaluation. We present models for our task, including simple
but effective nearest-neighbor techniques and neural network approaches trained
using a combination of imitation learning and goal-driven training. All models
are benchmarked using both fully automated evaluation and by playing the game
with live human agents.
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Learning Fast and Slow: PROPEDEUTICA for Real-time Malware Detection | In this paper, we introduce and evaluate PROPEDEUTICA, a novel methodology
and framework for efficient and effective real-time malware detection,
leveraging the best of conventional machine learning (ML) and deep learning
(DL) algorithms. In PROPEDEUTICA, all software processes in the system start
execution subjected to a conventional ML detector for fast classification. If a
piece of software receives a borderline classification, it is subjected to
further analysis via more performance expensive and more accurate DL methods,
via our newly proposed DL algorithm DEEPMALWARE. Further, we introduce delays
to the execution of software subjected to deep learning analysis as a way to
"buy time" for DL analysis and to rate-limit the impact of possible malware in
the system. We evaluated PROPEDEUTICA with a set of 9,115 malware samples and
877 commonly used benign software samples from various categories for the
Windows OS. Our results show that the false positive rate for conventional ML
methods can reach 20%, and for modern DL methods it is usually below 6%.
However, the classification time for DL can be 100X longer than conventional ML
methods. PROPEDEUTICA improved the detection F1-score from 77.54% (conventional
ML method) to 90.25%, and reduced the detection time by 54.86%. Further, the
percentage of software subjected to DL analysis was approximately 40% on
average. Further, the application of delays in software subjected to ML reduced
the detection time by approximately 10%. Finally, we found and discussed a
discrepancy between the detection accuracy offline (analysis after all traces
are collected) and on-the-fly (analysis in tandem with trace collection). Our
insights show that conventional ML and modern DL-based malware detectors in
isolation cannot meet the needs of efficient and effective malware detection:
high accuracy, low false positive rate, and short classification time.
| 1 | 0 | 0 | 1 | 0 | 0 |
Metachronal motion of artificial magnetic cilia | Organisms use hair-like cilia that beat in a metachronal fashion to actively
transport fluid and suspended particles. Metachronal motion emerges due to a
phase difference between beating cycles of neighboring cilia and appears as
traveling waves propagating along ciliary carpet. In this work, we demonstrate
biomimetic artificial cilia capable of metachronal motion. The cilia are
micromachined magnetic thin filaments attached at one end to a substrate and
actuated by a uniform rotating magnetic field. We show that the difference in
magnetic cilium length controls the phase of the beating motion. We use this
property to induce metachronal waves within a ciliary array and explore the
effect of operation parameters on the wave motion. The metachronal motion in
our artificial system is shown to depend on the magnetic and elastic properties
of the filaments, unlike natural cilia, where metachronal motion arises due to
fluid coupling. Our approach enables an easy integration of metachronal
magnetic cilia in lab-on-a-chip devices for enhanced fluid and particle
manipulations.
| 0 | 0 | 0 | 0 | 1 | 0 |
Power-of-$d$-Choices with Memory: Fluid Limit and Optimality | In multi-server distributed queueing systems, the access of stochastically
arriving jobs to resources is often regulated by a dispatcher, also known as
load balancer. A fundamental problem consists in designing a load balancing
algorithm that minimizes the delays experienced by jobs. During the last two
decades, the power-of-$d$-choice algorithm, based on the idea of dispatching
each job to the least loaded server out of $d$ servers randomly sampled at the
arrival of the job itself, has emerged as a breakthrough in the foundations of
this area due to its versatility and appealing asymptotic properties. In this
paper, we consider the power-of-$d$-choice algorithm with the addition of a
local memory that keeps track of the latest observations collected over time on
the sampled servers. Then, each job is sent to a server with the lowest
observation. We show that this algorithm is asymptotically optimal in the sense
that the load balancer can always assign each job to an idle server in the
large-server limit. This holds true if and only if the system load $\lambda$ is
less than $1-\frac{1}{d}$. If this condition is not satisfied, we show that
queue lengths are bounded by $j^\star+1$, where $j^\star\in\mathbb{N}$ is given
by the solution of a polynomial equation. This is in contrast with the classic
version of the power-of-$d$-choice algorithm, where queue lengths are
unbounded. Our upper bound on the size of the most loaded server, $j^*+1$, is
tight and increases slowly when $\lambda$ approaches its critical value from
below. For instance, when $\lambda= 0.995$ and $d=2$ (respectively, $d=3$), we
find that no server will contain more than just $5$ ($3$) jobs in equilibrium.
Our results quantify and highlight the importance of using memory as a means to
enhance performance in randomized load balancing.
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Structural Compression of Convolutional Neural Networks Based on Greedy Filter Pruning | Convolutional neural networks (CNNs) have state-of-the-art performance on
many problems in machine vision. However, networks with superior performance
often have millions of weights so that it is difficult or impossible to use
CNNs on computationally limited devices or to humanly interpret them. A myriad
of CNN compression approaches have been proposed and they involve pruning and
compressing the weights and filters. In this article, we introduce a greedy
structural compression scheme that prunes filters in a trained CNN. We define a
filter importance index equal to the classification accuracy reduction (CAR) of
the network after pruning that filter (similarly defined as RAR for
regression). We then iteratively prune filters based on the CAR index. This
algorithm achieves substantially higher classification accuracy in AlexNet
compared to other structural compression schemes that prune filters. Pruning
half of the filters in the first or second layer of AlexNet, our CAR algorithm
achieves 26% and 20% higher classification accuracies respectively, compared to
the best benchmark filter pruning scheme. Our CAR algorithm, combined with
further weight pruning and compressing, reduces the size of first or second
convolutional layer in AlexNet by a factor of 42, while achieving close to
original classification accuracy through retraining (or fine-tuning) network.
Finally, we demonstrate the interpretability of CAR-compressed CNNs by showing
that our algorithm prunes filters with visually redundant functionalities. In
fact, out of top 20 CAR-pruned filters in AlexNet, 17 of them in the first
layer and 14 of them in the second layer are color-selective filters as opposed
to shape-selective filters. To our knowledge, this is the first reported result
on the connection between compression and interpretability of CNNs.
| 1 | 0 | 0 | 0 | 0 | 0 |
Testing the Young Neutron Star Scenario with Persistent Radio Emission Associated with FRB 121102 | Recently a repeating fast radio burst (FRB) 121102 has been confirmed to be
an extragalactic event and a persistent radio counterpart has been identified.
While other possibilities are not ruled out, the emission properties are
broadly consistent with Murase et al. (2016) that theoretically proposed
quasi-steady radio emission as a counterpart of both FRBs and pulsar-driven
supernovae. Here we constrain the model parameters of such a young neutron star
scenario for FRB 121102. If the associated supernova has a conventional ejecta
mass of $M_{\rm ej}\gtrsim{\rm a \ few}\ M_\odot$, a neutron star with an age
of $t_{\rm age} \sim 10-100 \ \rm yrs$, an initial spin period of $P_{i}
\lesssim$ a few ms, and a dipole magnetic field of $B_{\rm dip} \lesssim {\rm a
\ few} \times 10^{13} \ \rm G$ can be compatible with the observations.
However, in this case, the magnetically-powered scenario may be favored as an
FRB energy source because of the efficiency problem in the rotation-powered
scenario. On the other hand, if the associated supernova is an ultra-stripped
one or the neutron star is born by the accretion-induced collapse with $M_{\rm
ej} \sim 0.1 \ M_\odot$, a younger neutron star with $t_{\rm age} \sim 1-10$
yrs can be the persistent radio source and might produce FRBs with the
spin-down power. These possibilities can be distinguished by the decline rate
of the quasi-steady radio counterpart.
| 0 | 1 | 0 | 0 | 0 | 0 |
Critical Vertices and Edges in $H$-free Graphs | A vertex or edge in a graph is critical if its deletion reduces the chromatic
number of the graph by 1. We consider the problems of deciding whether a graph
has a critical vertex or edge, respectively. We give a complexity dichotomy for
both problems restricted to $H$-free graphs, that is, graphs with no induced
subgraph isomorphic to $H$. Moreover, we show that an edge is critical if and
only if its contraction reduces the chromatic number by 1. Hence, we also
obtain a complexity dichotomy for the problem of deciding if a graph has an
edge whose contraction reduces the chromatic number by 1.
| 1 | 0 | 0 | 0 | 0 | 0 |
Transverse Weitzenböck formulas and de Rham cohomology of totally geodesic foliations | We prove transverse Weitzenböck identities for the horizontal Laplacians of
a totally geodesic foliation. As a consequence, we obtain nullity theorems for
the de Rham cohomology assuming only the positivity of curvature quantities
transverse to the leaves. Those curvature quantities appear in the adiabatic
limit of the canonical variation of the metric.
| 0 | 0 | 1 | 0 | 0 | 0 |
An adaptive Newton algorithm for optimal control problems with application to optimal electrode design | In this work we present an adaptive Newton-type method to solve nonlinear
constrained optimization problems in which the constraint is a system of
partial differential equations discretized by the finite element method. The
adaptive strategy is based on a goal-oriented a posteriori error estimation for
the discretization and for the iteration error. The iteration error stems from
an inexact solution of the nonlinear system of first order optimality
conditions by the Newton-type method. This strategy allows to balance the two
errors and to derive effective stopping criteria for the Newton-iterations. The
algorithm proceeds with the search of the optimal point on coarse grids which
are refined only if the discretization error becomes dominant. Using computable
error indicators the mesh is refined locally leading to a highly efficient
solution process. The performance of the algorithm is shown with several
examples and in particular with an application in the neurosciences: the
optimal electrode design for the study of neuronal networks.
| 0 | 0 | 1 | 0 | 0 | 0 |
Characterization of the Two-Dimensional Five-Fold Lattice Tiles | In 1885, Fedorov discovered that a convex domain can form a lattice tiling of
the Euclidean plane if and only if it is a parallelogram or a centrally
symmetric hexagon. It is known that there is no other convex domain which can
form a two-, three- or four-fold lattice tiling in the Euclidean plane, but
there is a centrally symmetric convex decagon which can form a five-fold
lattice tiling. This paper characterizes all the convex domains which can form
a five-fold lattice tiling of the Euclidean plane.
| 0 | 0 | 1 | 0 | 0 | 0 |
Projectors separating spectra for $L^2$ on pseudounitary groups $U(p,q)$ | The spectrum of $L^2$ on a pseudo-unitary group $U(p,q)$ (we assume $p\ge q$
naturally splits into $q+1$ types. We write explicitly orthogonal projectors in
$L^2$ to subspaces with uniform spectra (this is an old question formulated by
Gelfand and Gindikin). We also write two finer separations of $L^2$. In the
first case pieces are enumerated by $r=0$, 1,..., $q$ and representations of
discrete series of $U(p-r,q-r)$, where $r=0$, \dots, $q$. In the second case
pieces are enumerated by all discrete parameters of the tempered spectrum of
$U(p,q)$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Predicting Individual Physiologically Acceptable States for Discharge from a Pediatric Intensive Care Unit | Objective: Predict patient-specific vitals deemed medically acceptable for
discharge from a pediatric intensive care unit (ICU). Design: The means of each
patient's hr, sbp and dbp measurements between their medical and physical
discharge from the ICU were computed as a proxy for their physiologically
acceptable state space (PASS) for successful ICU discharge. These individual
PASS values were compared via root mean squared error (rMSE) to population
age-normal vitals, a polynomial regression through the PASS values of a
Pediatric ICU (PICU) population and predictions from two recurrent neural
network models designed to predict personalized PASS within the first twelve
hours following ICU admission. Setting: PICU at Children's Hospital Los Angeles
(CHLA). Patients: 6,899 PICU episodes (5,464 patients) collected between 2009
and 2016. Interventions: None. Measurements: Each episode data contained 375
variables representing vitals, labs, interventions, and drugs. They also
included a time indicator for PICU medical discharge and physical discharge.
Main Results: The rMSEs between individual PASS values and population
age-normals (hr: 25.9 bpm, sbp: 13.4 mmHg, dbp: 13.0 mmHg) were larger than the
rMSEs corresponding to the polynomial regression (hr: 19.1 bpm, sbp: 12.3 mmHg,
dbp: 10.8 mmHg). The rMSEs from the best performing RNN model were the lowest
(hr: 16.4 bpm; sbp: 9.9 mmHg, dbp: 9.0 mmHg). Conclusion: PICU patients are a
unique subset of the general population, and general age-normal vitals may not
be suitable as target values indicating physiologic stability at discharge.
Age-normal vitals that were specifically derived from the medical-to-physical
discharge window of ICU patients may be more appropriate targets for
'acceptable' physiologic state for critical care patients. Going beyond simple
age bins, an RNN model can provide more personalized target values.
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Unsupervised learning of object frames by dense equivariant image labelling | One of the key challenges of visual perception is to extract abstract models
of 3D objects and object categories from visual measurements, which are
affected by complex nuisance factors such as viewpoint, occlusion, motion, and
deformations. Starting from the recent idea of viewpoint factorization, we
propose a new approach that, given a large number of images of an object and no
other supervision, can extract a dense object-centric coordinate frame. This
coordinate frame is invariant to deformations of the images and comes with a
dense equivariant labelling neural network that can map image pixels to their
corresponding object coordinates. We demonstrate the applicability of this
method to simple articulated objects and deformable objects such as human
faces, learning embeddings from random synthetic transformations or optical
flow correspondences, all without any manual supervision.
| 1 | 0 | 0 | 1 | 0 | 0 |
Channel surfaces in Lie sphere geometry | We discuss channel surfaces in the context of Lie sphere geometry and
characterise them as certain $\Omega_{0}$-surfaces. Since $\Omega_{0}$-surfaces
possess a rich transformation theory, we study the behaviour of channel
surfaces under these transformations. Furthermore, by using certain Dupin
cyclide congruences, we characterise Ribaucour pairs of channel surfaces.
| 0 | 0 | 1 | 0 | 0 | 0 |
A Parallelizable Acceleration Framework for Packing Linear Programs | This paper presents an acceleration framework for packing linear programming
problems where the amount of data available is limited, i.e., where the number
of constraints m is small compared to the variable dimension n. The framework
can be used as a black box to speed up linear programming solvers dramatically,
by two orders of magnitude in our experiments. We present worst-case guarantees
on the quality of the solution and the speedup provided by the algorithm,
showing that the framework provides an approximately optimal solution while
running the original solver on a much smaller problem. The framework can be
used to accelerate exact solvers, approximate solvers, and parallel/distributed
solvers. Further, it can be used for both linear programs and integer linear
programs.
| 1 | 0 | 0 | 1 | 0 | 0 |
$L^p$ estimates for the Bergman projection on some Reinhardt domains | We obtain $L^p$ regularity for the Bergman projection on some Reinhardt
domains. We start with a bounded initial domain $\Omega$ with some symmetry
properties and generate successor domains in higher {dimensions}. We prove: If
the Bergman kernel on $\Omega$ satisfies appropriate estimates, then the
Bergman projection on the successor is $L^p$ bounded. For example, the Bergman
projection on successors of strictly pseudoconvex initial domains is bounded on
$L^p$ for $1<p<\infty$. The successor domains need not have smooth boundary nor
be strictly pseudoconvex.
| 0 | 0 | 1 | 0 | 0 | 0 |
Generic Axiomatization of Families of Noncrossing Graphs in Dependency Parsing | We present a simple encoding for unlabeled noncrossing graphs and show how
its latent counterpart helps us to represent several families of directed and
undirected graphs used in syntactic and semantic parsing of natural language as
context-free languages. The families are separated purely on the basis of
forbidden patterns in latent encoding, eliminating the need to differentiate
the families of non-crossing graphs in inference algorithms: one algorithm
works for all when the search space can be controlled in parser input.
| 1 | 0 | 0 | 0 | 0 | 0 |
Computation of Optimal Transport on Discrete Metric Measure Spaces | In this paper we investigate the numerical approximation of an analogue of
the Wasserstein distance for optimal transport on graphs that is defined via a
discrete modification of the Benamou--Brenier formula. This approach involves
the logarithmic mean of measure densities on adjacent nodes of the graph. For
this model a variational time discretization of the probability densities on
graph nodes and the momenta on graph edges is proposed. A robust descent
algorithm for the action functional is derived, which in particular uses a
proximal splitting with an edgewise nonlinear projection on the convex subgraph
of the logarithmic mean. Thereby, suitable chosen slack variables avoid a
global coupling of probability densities on all graph nodes in the projection
step. For the time discrete action functional $\Gamma$--convergence to the time
continuous action is established. Numerical results for a selection of test
cases show qualitative and quantitative properties of the optimal transport on
graphs. Finally, we use our algorithm to implement a JKO scheme for the
gradient flow of the entropy in the discrete transportation distance, which is
known to coincide with the underlying Markov semigroup, and test our results
against a classical backward Euler discretization of this discrete heat flow.
| 0 | 0 | 1 | 0 | 0 | 0 |
Torsions of integral homology and cohomology of real Grassmannians | According to a result of Ehresmann, the torsions of integral homology of real
Grassmannian are all of order $2$. In this note, We compute the
$\mathbb{Z}_2$-dimensions of torsions in the integral homology and cohomology
of real Grassmannian.
| 0 | 0 | 1 | 0 | 0 | 0 |
PACO: Signal Restoration via PAtch COnsensus | Many signal processing algorithms operate by breaking the target signal into
possibly overlapping segments (typically called windows or patches), processing
them separately, and then stitching them back into place to produce a unified
output. In most cases where pach overlapping occurs, the final value of those
samples that are estimated by more than one patch is resolved by averaging
those estimates; this includes many recent image processing algorithms. In
other cases, typically frequency-based restoration methods, the average is
implicitly weighted by some window function such as Hanning, Blackman, etc.
which is applied prior to the Fourier/DCT transform in order to avoid Gibbs
oscillations in the processed patches. Such averaging may incidentally help in
covering up artifacts in the restoration process, but more often will simply
degrade the overall result, posing an upper limit to the size of the patches
that can be used. In order to avoid such drawbacks, we propose a new
methodology where the different estimates of any given sample are forced to be
identical. We show that, together, these consensus constraints constitute a
non-empty convex feasible set, provide a general formulation of the resulting
constrained optimization problem which can be applied to a wide variety of
signal restoration tasks, and propose an efficient algorithm for finding the
corresponding solutions. Finally, we describe in detail the application of the
proposed methodology to three different signal processing problems, in some
cases surpassing the state of the art by a significant margin.
| 0 | 0 | 0 | 1 | 0 | 0 |
Hyperplane arrangements associated to symplectic quotient singularities | We study the hyperplane arrangements associated, via the minimal model
programme, to symplectic quotient singularities. We show that this hyperplane
arrangement equals the arrangement of CM-hyperplanes coming from the
representation theory of restricted rational Cherednik algebras. We explain
some of the interesting consequences of this identification for the
representation theory of restricted rational Cherednik algebras. We also show
that the Calogero-Moser space is smooth if and only if the Calogero-Moser
families are trivial. We describe the arrangements of CM-hyperplanes associated
to several exceptional complex reflection groups, some of which are free.
| 0 | 0 | 1 | 0 | 0 | 0 |
CTCModel: a Keras Model for Connectionist Temporal Classification | We report an extension of a Keras Model, called CTCModel, to perform the
Connectionist Temporal Classification (CTC) in a transparent way. Combined with
Recurrent Neural Networks, the Connectionist Temporal Classification is the
reference method for dealing with unsegmented input sequences, i.e. with data
that are a couple of observation and label sequences where each label is
related to a subset of observation frames. CTCModel makes use of the CTC
implementation in the Tensorflow backend for training and predicting sequences
of labels using Keras. It consists of three branches made of Keras models: one
for training, computing the CTC loss function; one for predicting, providing
sequences of labels; and one for evaluating that returns standard metrics for
analyzing sequences of predictions.
| 1 | 0 | 0 | 1 | 0 | 0 |
Software Distribution Transparency and Auditability | A large user base relies on software updates provided through package
managers. This provides a unique lever for improving the security of the
software update process. We propose a transparency system for software updates
and implement it for a widely deployed Linux package manager, namely APT. Our
system is capable of detecting targeted backdoors without producing overhead
for maintainers. In addition, in our system, the availability of source code is
ensured, the binding between source and binary code is verified using
reproducible builds, and the maintainer responsible for distributing a specific
package can be identified. We describe a novel "hidden version" attack against
current software transparency systems and propose as well as integrate a
suitable defense. To address equivocation attacks by the transparency log
server, we introduce tree root cross logging, where the log's Merkle tree root
is submitted into a separately operated log server. This significantly relaxes
the inter-operator cooperation requirements compared to other systems. Our
implementation is evaluated by replaying over 3000 updates of the Debian
operating system over the course of two years, demonstrating its viability and
identifying numerous irregularities.
| 1 | 0 | 0 | 0 | 0 | 0 |
Imputation Approaches for Animal Movement Modeling | The analysis of telemetry data is common in animal ecological studies. While
the collection of telemetry data for individual animals has improved
dramatically, the methods to properly account for inherent uncertainties (e.g.,
measurement error, dependence, barriers to movement) have lagged behind. Still,
many new statistical approaches have been developed to infer unknown quantities
affecting animal movement or predict movement based on telemetry data.
Hierarchical statistical models are useful to account for some of the
aforementioned uncertainties, as well as provide population-level inference,
but they often come with an increased computational burden. For certain types
of statistical models, it is straightforward to provide inference if the latent
true animal trajectory is known, but challenging otherwise. In these cases,
approaches related to multiple imputation have been employed to account for the
uncertainty associated with our knowledge of the latent trajectory. Despite the
increasing use of imputation approaches for modeling animal movement, the
general sensitivity and accuracy of these methods have not been explored in
detail. We provide an introduction to animal movement modeling and describe how
imputation approaches may be helpful for certain types of models. We also
assess the performance of imputation approaches in a simulation study. Our
simulation study suggests that inference for model parameters directly related
to the location of an individual may be more accurate than inference for
parameters associated with higher-order processes such as velocity or
acceleration. Finally, we apply these methods to analyze a telemetry data set
involving northern fur seals (Callorhinus ursinus) in the Bering Sea.
| 0 | 0 | 0 | 1 | 0 | 0 |
Motion planning in high-dimensional spaces | Motion planning is a key tool that allows robots to navigate through an
environment without collisions. The problem of robot motion planning has been
studied in great detail over the last several decades, with researchers
initially focusing on systems such as planar mobile robots and low
degree-of-freedom (DOF) robotic arms. The increased use of high DOF robots that
must perform tasks in real time in complex dynamic environments spurs the need
for fast motion planning algorithms. In this overview, we discuss several types
of strategies for motion planning in high dimensional spaces and dissect some
of them, namely grid search based, sampling based and trajectory optimization
based approaches. We compare them and outline their advantages and
disadvantages, and finally, provide an insight into future research
opportunities.
| 1 | 0 | 0 | 0 | 0 | 0 |
Emergent low-energy bound states in the two-orbital Hubbard model | A repulsive Coulomb interaction between electrons in different orbitals in
correlated materials can give rise to bound quasiparticle states. We study the
non-hybridized two-orbital Hubbard model with intra (inter)-orbital interaction
$U$ ($U_{12}$) and different band widths using an improved dynamical mean field
theory numerical technique which leads to reliable spectra on the real energy
axis directly at zero temperature. We find that a finite density of states at
the Fermi energy in one band is correlated with the emergence of well defined
quasiparticle states at excited energies $\Delta=U-U_{12}$ in the other band.
These excitations are inter-band holon-doublon bound states. At the symmetric
point $U=U_{12}$, the quasiparticle peaks are located at the Fermi energy,
leading to a simultaneous and continuous Mott transition settling a
long-standing controversy.
| 0 | 1 | 0 | 0 | 0 | 0 |
Improved thermal lattice Boltzmann model for simulation of liquid-vapor phase change | In this paper, an improved thermal lattice Boltzmann (LB) model is proposed
for simulating liquid-vapor phase change, which is aimed at improving an
existing thermal LB model for liquid-vapor phase change [S. Gong and P. Cheng,
Int. J. Heat Mass Transfer 55, 4923 (2012)]. First, we emphasize that the
replacement of \[{\left( {\rho {c_V}} \right)^{ - 1}}\nabla \cdot \left(
{\lambda \nabla T} \right)\] with \[\nabla \cdot \left( {\chi \nabla T}
\right)\] is an inappropriate treatment for diffuse interface modeling of
liquid-vapor phase change. Furthermore, the error terms ${\partial_{t0}}\left(
{Tv} \right) + \nabla \cdot \left( {Tvv} \right)$, which exist in the
macroscopic temperature equation recovered from the standard thermal LB
equation, are eliminated in the present model through a way that is consistent
with the philosophy of the LB method. In addition, the discrete effect of the
source term is also eliminated in the present model. Numerical simulations are
performed for droplet evaporation and bubble nucleation to validate the
capability of the improved model for simulating liquid-vapor phase change.
Numerical comparisons show that the aforementioned replacement leads to
significant numerical errors and the error terms in the recovered macroscopic
temperature equation also result in considerable errors.
| 0 | 1 | 0 | 0 | 0 | 0 |
A fresh look at effect aliasing and interactions: some new wine in old bottles | Interactions and effect aliasing are among the fundamental concepts in
experimental design. In this paper, some new insights and approaches are
provided on these subjects. In the literature, the "de-aliasing" of aliased
effects is deemed to be impossible. We argue that this "impossibility" can
indeed be resolved by employing a new approach which consists of
reparametrization of effects and exploitation of effect non-orthogonality. This
approach is successfully applied to three classes of designs: regular and
nonregular two-level fractional factorial designs, and three-level fractional
factorial designs. For reparametrization, the notion of conditional main
effects (cme's) is employed for two-level regular designs, while the
linear-quadratic system is used for three-level designs. For nonregular
two-level designs, reparametrization is not needed because the partial aliasing
of their effects already induces non-orthogonality. The approach can be
extended to general observational data by using a new bi-level variable
selection technique based on the cme's. A historical recollection is given on
how these ideas were discovered.
| 0 | 0 | 0 | 1 | 0 | 0 |
Effects of sampling skewness of the importance-weighted risk estimator on model selection | Importance-weighting is a popular and well-researched technique for dealing
with sample selection bias and covariate shift. It has desirable
characteristics such as unbiasedness, consistency and low computational
complexity. However, weighting can have a detrimental effect on an estimator as
well. In this work, we empirically show that the sampling distribution of an
importance-weighted estimator can be skewed. For sample selection bias
settings, and for small sample sizes, the importance-weighted risk estimator
produces overestimates for datasets in the body of the sampling distribution,
i.e. the majority of cases, and large underestimates for data sets in the tail
of the sampling distribution. These over- and underestimates of the risk lead
to suboptimal regularization parameters when used for importance-weighted
validation.
| 0 | 0 | 0 | 1 | 0 | 0 |
Minimizing the Cost of Team Exploration | A group of mobile agents is given a task to explore an edge-weighted graph
$G$, i.e., every vertex of $G$ has to be visited by at least one agent. There
is no centralized unit to coordinate their actions, but they can freely
communicate with each other. The goal is to construct a deterministic strategy
which allows agents to complete their task optimally. In this paper we are
interested in a cost-optimal strategy, where the cost is understood as the
total distance traversed by agents coupled with the cost of invoking them. Two
graph classes are analyzed, rings and trees, in the off-line and on-line
setting, i.e., when a structure of a graph is known and not known to agents in
advance. We present algorithms that compute the optimal solutions for a given
ring and tree of order $n$, in $O(n)$ time units. For rings in the on-line
setting, we give the $2$-competitive algorithm and prove the lower bound of
$3/2$ for the competitive ratio for any on-line strategy. For every strategy
for trees in the on-line setting, we prove the competitive ratio to be no less
than $2$, which can be achieved by the $DFS$ algorithm.
| 1 | 0 | 0 | 0 | 0 | 0 |
Exponential Source/Channel Duality | We propose a source/channel duality in the exponential regime, where
success/failure in source coding parallels error/correctness in channel coding,
and a distortion constraint becomes a log-likelihood ratio (LLR) threshold. We
establish this duality by first deriving exact exponents for lossy coding of a
memoryless source P, at distortion D, for a general i.i.d. codebook
distribution Q, for both encoding success (R < R(P,Q,D)) and failure (R >
R(P,Q,D)). We then turn to maximum likelihood (ML) decoding over a memoryless
channel P with an i.i.d. input Q, and show that if we substitute P=QP, Q=Q, and
D=0 under the LLR distortion measure, then the exact exponents for
decoding-error (R < I(Q, P)) and strict correct-decoding (R > I(Q, P)) follow
as special cases of the exponents for source encoding success/failure,
respectively. Moreover, by letting the threshold D take general values, the
exact random-coding exponents for erasure (D > 0) and list decoding (D < 0)
under the simplified Forney decoder are obtained. Finally, we derive the exact
random-coding exponent for Forney's optimum tradeoff erasure/list decoder, and
show that at the erasure regime it coincides with Forney's lower bound and with
the simplified decoder exponent.
| 1 | 0 | 0 | 0 | 0 | 0 |
Delta Theorem in the Age of High Dimensions | We provide a new version of delta theorem, that takes into account of high
dimensional parameter estimation. We show that depending on the structure of
the function, the limits of functions of estimators have faster or slower rate
of convergence than the limits of estimators. We illustrate this via two
examples. First, we use it for testing in high dimensions, and second in
estimating large portfolio risk. Our theorem works in the case of larger number
of parameters, $p$, than the sample size, $n$: $p>n$.
| 0 | 0 | 1 | 1 | 0 | 0 |
Deep Learning Interior Tomography for Region-of-Interest Reconstruction | Interior tomography for the region-of-interest (ROI) imaging has advantages
of using a small detector and reducing X-ray radiation dose. However, standard
analytic reconstruction suffers from severe cupping artifacts due to existence
of null space in the truncated Radon transform. Existing penalized
reconstruction methods may address this problem but they require extensive
computations due to the iterative reconstruction. Inspired by the recent deep
learning approaches to low-dose and sparse view CT, here we propose a deep
learning architecture that removes null space signals from the FBP
reconstruction. Experimental results have shown that the proposed method
provides near-perfect reconstruction with about 7-10 dB improvement in PSNR
over existing methods in spite of significantly reduced run-time complexity.
| 1 | 0 | 0 | 1 | 0 | 0 |
Structured Parallel Programming for Monte Carlo Tree Search | In this paper, we present a new algorithm for parallel Monte Carlo tree
search (MCTS). It is based on the pipeline pattern and allows flexible
management of the control flow of the operations in parallel MCTS. The pipeline
pattern provides for the first structured parallel programming approach to
MCTS. Moreover, we propose a new lock-free tree data structure for parallel
MCTS which removes synchronization overhead. The Pipeline Pattern for Parallel
MCTS algorithm (called 3PMCTS), scales very well to higher numbers of cores
when compared to the existing methods.
| 1 | 0 | 0 | 0 | 0 | 0 |
Single-Atom Scale Structural Selectivity in Te Nanowires Encapsulated inside Ultra-Narrow, Single-Walled Carbon Nanotubes | Extreme nanowires (ENs) represent the ultimate class of crystals: They are
the smallest possible periodic materials. With atom-wide motifs repeated in one
dimension (1D), they offer a privileged perspective into the Physics and
Chemistry of low-dimensional systems. Single-walled carbon nanotubes (SWCNTs)
provide ideal environments for the creation of such materials. Here we present
a comprehensive study of Te ENs encapsulated inside ultra- narrow SWCNTs with
diameters between 0.7 nm and 1.1 nm. We combine state-of-the-art imaging
techniques and 1D-adapted ab initio structure prediction to treat both
confinement and periodicity effects. The studied Te ENs adopt a variety of
structures, exhibiting a true 1D realisation of a Peierls structural distortion
and transition from metallic to insulating behaviour as a function of
encapsulating diameter. We analyse the mechanical stability of the encapsulated
ENs and show that nanoconfinement is not only a useful means to produce ENs,
but may actually be necessary, in some cases, to prevent them from
disintegrating. The ability to control functional properties of these ENs with
confinement has numerous applications in future device technologies, and we
anticipate that our study will set the basic paradigm to be adopted in the
characterisation and understanding of such systems.
| 0 | 1 | 0 | 0 | 0 | 0 |
Navigating through the R packages for movement | The advent of miniaturized biologging devices has provided ecologists with
unparalleled opportunities to record animal movement across scales, and led to
the collection of ever-increasing quantities of tracking data. In parallel,
sophisticated tools to process, visualize and analyze tracking data have been
developed in abundance. Within the R software alone, we listed 57 focused on
these tasks, called here tracking packages. Here, we reviewed these tracking
packages, as an introduction to this set of packages for researchers, and to
provide feedback and recommendations to package developers, from a user
perspective. We described each package based on a workflow centered around
tracking data (i.e. (x,y,t)), broken down in three stages: pre-processing,
post-processing, and analysis (data visualization, track description, path
reconstruction, behavioral pattern identification, space use characterization,
trajectory simulation and others).
Supporting documentation is key to the accessibility of a package for users.
Based on a user survey, we reviewed the quality of packages' documentation, and
identified $12$ packages with good or excellent documentation. Links between
packages were assessed through a network graph analysis. Although a large group
of packages shows some degree of connectivity (either depending on functions or
suggesting the use of another tracking package), a third of tracking packages
work on isolation, reflecting a fragmentation in the R Movement-Ecology
programming community.
Finally, we provide recommendations for users to choose packages, and for
developers to maximize usefulness of their contribution and strengthen the
links between the programming community.
| 0 | 0 | 0 | 0 | 1 | 0 |
Image classification and retrieval with random depthwise signed convolutional neural networks | We study image classification and retrieval performance in a feature space
given by random depthwise convolutional neural networks. Intuitively our
network can be interpreted as applying random hyperplanes to the space of all
patches of input images followed by average pooling to obtain final features.
We show that the ratio of image pixel distribution similarity across classes to
within classes and the average margin of the linear support vector machine on
test data are both higher in our network's final layer compared to the input
space. We then apply the linear support vector machine for image classification
and $k$-nearest neighbor for image similarity detection on our network's final
layer. We show that for classification our network attains higher accuracies
than previous random networks and is not far behind in accuracy to trained
state of the art networks, especially in the top-k setting. For example the
top-2 accuracy of our network is near 90\% on both CIFAR10 and a 10-class mini
ImageNet, and 85\% on STL10. In the problem of image similarity we find that
$k$-nearest neighbor gives a comparable precision on the Corel Princeton Image
Similarity Benchmark than if we were to use the last hidden layer of trained
networks. We highlight sensitivity of our network to background color as a
potential pitfall. Overall our work pushes the boundary of what can be achieved
with random weights.
| 0 | 0 | 0 | 1 | 0 | 0 |
Spatial dynamics of flower organ formation | Understanding the emergence of biological structures and their changes is a
complex problem. On a biochemical level, it is based on gene regulatory
networks (GRN) consisting on interactions between the genes responsible for
cell differentiation and coupled in a greater scale with external factors. In
this work we provide a systematic methodological framework to construct
Waddington's epigenetic landscape of the GRN involved in cellular determination
during the early stages of development of angiosperms. As a specific example we
consider the flower of the plant \textit{Arabidopsis thaliana}. Our model,
which is based on experimental data, recovers accurately the spatial
configuration of the flower during cell fate determination, not only for the
wild type, but for its homeotic mutants as well. The method developed in this
project is general enough to be used in the study of the relationship between
genotype-phenotype in other living organisms.
| 0 | 0 | 0 | 0 | 1 | 0 |
Percent Change Estimation in Large Scale Online Experiments | Online experiments are a fundamental component of the development of
web-facing products. Given the large user-base, even small product improvements
can have a large impact on an absolute scale. As a result, accurately
estimating the relative impact of these changes is extremely important. I
propose an approach based on an objective Bayesian model to improve the
sensitivity of percent change estimation in A/B experiments. Leveraging
pre-period information, this approach produces more robust and accurate point
estimates and up to 50% tighter credible intervals than traditional methods.
The R package abpackage provides an implementation of the approach.
| 0 | 0 | 0 | 1 | 0 | 0 |
Characterization of Near-Earth Asteroids using KMTNet-SAAO | We present here VRI spectrophotometry of 39 near-Earth asteroids (NEAs)
observed with the Sutherland, South Africa, node of the Korea Microlensing
Telescope Network (KMTNet). Of the 39 NEAs, 19 were targeted, but because of
KMTNet's large 2 deg by 2 deg field of view, 20 serendipitous NEAs were also
captured in the observing fields. Targeted observations were performed within
44 days (median: 16 days, min: 4 days) of each NEA's discovery date. Our
broadband spectrophotometry is reliable enough to distinguish among four
asteroid taxonomies and we were able to confidently categorize 31 of the 39
observed targets as either a S-, C-, X- or D-type asteroid by means of a
Machine Learning (ML) algorithm approach. Our data suggest that the ratio
between "stony" S-type NEAs and "not-stony" (C+X+D)-type NEAs, with H
magnitudes between 15 and 25, is roughly 1:1. Additionally, we report ~1-hour
light curve data for each NEA and of the 39 targets we were able to resolve the
complete rotation period and amplitude for six targets and report lower limits
for the remaining targets.
| 0 | 1 | 0 | 0 | 0 | 0 |
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