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Dual Supervised Learning | Many supervised learning tasks are emerged in dual forms, e.g.,
English-to-French translation vs. French-to-English translation, speech
recognition vs. text to speech, and image classification vs. image generation.
Two dual tasks have intrinsic connections with each other due to the
probabilistic correlation between their models. This connection is, however,
not effectively utilized today, since people usually train the models of two
dual tasks separately and independently. In this work, we propose training the
models of two dual tasks simultaneously, and explicitly exploiting the
probabilistic correlation between them to regularize the training process. For
ease of reference, we call the proposed approach \emph{dual supervised
learning}. We demonstrate that dual supervised learning can improve the
practical performances of both tasks, for various applications including
machine translation, image processing, and sentiment analysis.
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Pentavalent symmetric graphs of order four times an odd square-free integer | A graph is said to be symmetric if its automorphism group is transitive on
its arcs. Guo et al. (Electronic J. Combin. 18, \#P233, 2011) and Pan et al.
(Electronic J. Combin. 20, \#P36, 2013) determined all pentavalent symmetric
graphs of order $4pq$. In this paper, we shall generalize this result by
determining all connected pentavalent symmetric graphs of order four times an
odd square-free integer. It is shown in this paper that, for each of such
graphs $\it\Gamma$, either the full automorphism group ${\sf Aut}\it\Gamma$ is
isomorphic to ${\sf PSL}(2,p)$, ${\sf PGL}(2,p)$, ${\sf
PSL}(2,p){\times}\mathbb{Z}_2$ or ${\sf PGL}(2,p){\times}\mathbb{Z}_2$, or
$\it\Gamma$ is isomorphic to one of 8 graphs.
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Applying the Polyhedral Model to Tile Time Loops in Devito | The run time of many scientific computation applications for numerical
methods is heavily dependent on just a few multi-dimensional loop nests. Since
these applications are often limited by memory bandwidth rather than
computational resources they can benefit greatly from any optimizations which
decrease the run time of their loops by improving data reuse and thus reducing
the total memory traffic. Some of the most effective of these optimizations are
not suitable for development by hand or require advanced software engineering
knowledge which is beyond the level of many researchers who are not specialists
in code optimization. Several tools exist to automate the generation of
high-performance code for numerical methods, such as Devito which produces code
for finite-difference approximations typically used in the seismic imaging
domain. We present a loop-tiling optimization which can be applied to
Devito-generated loops and improves run time by up to 27.5%, and options for
automating this optimization in the Devito framework.
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Deictic Image Maps: An Abstraction For Learning Pose Invariant Manipulation Policies | In applications of deep reinforcement learning to robotics, it is often the
case that we want to learn pose invariant policies: policies that are invariant
to changes in the position and orientation of objects in the world. For
example, consider a peg-in-hole insertion task. If the agent learns to insert a
peg into one hole, we would like that policy to generalize to holes presented
in different poses. Unfortunately, this is a challenge using conventional
methods. This paper proposes a novel state and action abstraction that is
invariant to pose shifts called \textit{deictic image maps} that can be used
with deep reinforcement learning. We provide broad conditions under which
optimal abstract policies are optimal for the underlying system. Finally, we
show that the method can help solve challenging robotic manipulation problems.
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Charged Vector Particles Tunneling From 5D Black Hole and Black Ring | In this paper, we investigate the Hawking radiation process as a
semiclassical quantum tunneling phenomenon from black ring and Myers-Perry
black holes in 5-dimensional (5D) spaces. Using Lagrangian of
Glashow-Weinberg-Salam model with background electromagnetic field (for charged
W-bosons) and the WKB approximation, we will evaluate the tunneling
rate/probability of charged vector particles through horizons by taking into
account the electromagnetic vector potential. Moreover, we investigate the
corresponding Hawking temperature values by considering Boltzmann factor for
both cases and analyze the whole spectrum generally.
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Implementing implicit OpenMP data sharing on GPUs | OpenMP is a shared memory programming model which supports the offloading of
target regions to accelerators such as NVIDIA GPUs. The implementation in
Clang/LLVM aims to deliver a generic GPU compilation toolchain that supports
both the native CUDA C/C++ and the OpenMP device offloading models. There are
situations where the semantics of OpenMP and those of CUDA diverge. One such
example is the policy for implicitly handling local variables. In CUDA, local
variables are implicitly mapped to thread local memory and thus become private
to a CUDA thread. In OpenMP, due to semantics that allow the nesting of regions
executed by different numbers of threads, variables need to be implicitly
\emph{shared} among the threads of a contention group. In this paper we
introduce a re-design of the OpenMP device data sharing infrastructure that is
responsible for the implicit sharing of local variables in the Clang/LLVM
toolchain. We introduce a new data sharing infrastructure that lowers
implicitly shared variables to the shared memory of the GPU. We measure the
amount of shared memory used by our scheme in cases that involve scalar
variables and statically allocated arrays. The evaluation is carried out by
offloading to K40 and P100 NVIDIA GPUs. For scalar variables the pressure on
shared memory is relatively low, under 26\% of shared memory utilization for
the K40, and does not negatively impact occupancy. The limiting occupancy
factor in that case is register pressure. The data sharing scheme offers the
users a simple memory model for controlling the implicit allocation of device
shared memory.
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Real-Time Impulse Noise Removal from MR Images for Radiosurgery Applications | In the recent years image processing techniques are used as a tool to improve
detection and diagnostic capabilities in the medical applications. Medical
applications have been so much affected by these techniques which some of them
are embedded in medical instruments such as MRI, CT and other medical devices.
Among these techniques, medical image enhancement algorithms play an essential
role in removal of the noise which can be produced by medical instruments and
during image transfer. It has been proved that impulse noise is a major type of
noise, which is produced during medical operations, such as MRI, CT, and
angiography, by their image capturing devices. An embeddable hardware module
which is able to denoise medical images before and during surgical operations
could be very helpful. In this paper an accurate algorithm is proposed for
real-time removal of impulse noise in medical images. All image blocks are
divided into three categories of edge, smooth, and disordered areas. A
different reconstruction method is applied to each category of blocks for the
purpose of noise removal. The proposed method is tested on MR images.
Simulation results show acceptable denoising accuracy for various levels of
noise. Also an FPAG implementation of our denoising algorithm shows acceptable
hardware resource utilization. Hence, the algorithm is suitable for embedding
in medical hardware instruments such as radiosurgery devices.
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Pitfalls of Graph Neural Network Evaluation | Semi-supervised node classification in graphs is a fundamental problem in
graph mining, and the recently proposed graph neural networks (GNNs) have
achieved unparalleled results on this task. Due to their massive success, GNNs
have attracted a lot of attention, and many novel architectures have been put
forward. In this paper we show that existing evaluation strategies for GNN
models have serious shortcomings. We show that using the same
train/validation/test splits of the same datasets, as well as making
significant changes to the training procedure (e.g. early stopping criteria)
precludes a fair comparison of different architectures. We perform a thorough
empirical evaluation of four prominent GNN models and show that considering
different splits of the data leads to dramatically different rankings of
models. Even more importantly, our findings suggest that simpler GNN
architectures are able to outperform the more sophisticated ones if the
hyperparameters and the training procedure are tuned fairly for all models.
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Randomized CP Tensor Decomposition | The CANDECOMP/PARAFAC (CP) tensor decomposition is a popular
dimensionality-reduction method for multiway data. Dimensionality reduction is
often sought since many high-dimensional tensors have low intrinsic rank
relative to the dimension of the ambient measurement space. However, the
emergence of `big data' poses significant computational challenges for
computing this fundamental tensor decomposition. Leveraging modern randomized
algorithms, we demonstrate that the coherent structure can be learned from a
smaller representation of the tensor in a fraction of the time. Moreover, the
high-dimensional signal can be faithfully approximated from the compressed
measurements. Thus, this simple but powerful algorithm enables one to compute
the approximate CP decomposition even for massive tensors. The approximation
error can thereby be controlled via oversampling and the computation of power
iterations. In addition to theoretical results, several empirical results
demonstrate the performance of the proposed algorithm.
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App Store 2.0: From Crowd Information to Actionable Feedback in Mobile Ecosystems | Given the increasing competition in mobile app ecosystems, improving the
experience of users has become a major goal for app vendors. This article
introduces a visionary app store, called APP STORE 2.0, which exploits
crowdsourced information about apps, devices and users to increase the overall
quality of the delivered mobile apps. We sketch a blueprint architecture of the
envisioned app stores and discuss the different kinds of actionable feedbacks
that app stores can generate using crowdsourced information.
| 1 | 0 | 0 | 0 | 0 | 0 |
An Information Matrix Approach for State Secrecy | This paper studies the problem of remote state estimation in the presence of
a passive eavesdropper. A sensor measures a linear plant's state and transmits
it to an authorized user over a packet-dropping channel, which is susceptible
to eavesdropping. Our goal is to design a coding scheme such that the
eavesdropper cannot infer the plant's current state, while the user
successfully decodes the sent messages. We employ a novel class of codes,
termed State-Secrecy Codes, which are fast and efficient for dynamical systems.
They apply linear time-varying transformations to the current and past states
received by the user. In this way, they force the eavesdropper's information
matrix to decrease with asymptotically the same rate as in the open-loop
prediction case, i.e. when the eavesdropper misses all messages. As a result,
the eavesdropper's minimum mean square error (mmse) for the unstable states
grows unbounded, while the respective error for the stable states converges to
the open-loop prediction one. These secrecy guarantees are achieved under
minimal conditions, which require that, at least once, the user receives the
corresponding packet while the eavesdropper fails to intercept it. Meanwhile,
the user's estimation performance remains optimal. The theoretical results are
illustrated in simulations.
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Adversarial Attacks on Node Embeddings | The goal of network representation learning is to learn low-dimensional node
embeddings that capture the graph structure and are useful for solving
downstream tasks. However, despite the proliferation of such methods there is
currently no study of their robustness to adversarial attacks. We provide the
first adversarial vulnerability analysis on the widely used family of methods
based on random walks. We derive efficient adversarial perturbations that
poison the network structure and have a negative effect on both the quality of
the embeddings and the downstream tasks. We further show that our attacks are
transferable -- they generalize to many models -- and are successful even when
the attacker has restricted actions.
| 1 | 0 | 0 | 0 | 0 | 0 |
Time-reversed magnetically controlled perturbation (TRMCP) optical focusing inside scattering media | Manipulating and focusing light deep inside biological tissue and tissue-like
complex media has been desired for long yet considered challenging. One
feasible strategy is through optical wavefront engineering, where the optical
scattering-induced phase distortions are time reversed or pre-compensated so
that photons travel along different optical paths interfere constructively at
the targeted position within a scattering medium. To define the targeted
position, an internal guidestar is needed to guide or provide a feedback for
wavefront engineering. It could be injected or embedded probes such as
fluorescence or nonlinear microspheres, ultrasonic modulation, as well as
absorption perturbation. Here we propose to use a magnetically controlled
optical absorbing microsphere as the internal guidestar. Using a digital
optical phase conjugation system, we obtained sharp optical focusing within
scattering media through time-reversing the scattered light perturbed by the
magnetic microshpere. Since the object is magnetically controlled, dynamic
optical focusing is allowed with a relatively large field-of-view by scanning
the magnetic field externally. Moreover, the magnetic microsphere can be
packaged with an organic membrane, using biological or chemical means to serve
as a carrier. Therefore the technique may find particular applications for
enhanced targeted drug delivery, and imaging and photoablation of angiogenic
vessels in tumours.
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Dimensionality-strain phase diagram of strontium iridates | The competition between spin-orbit coupling, bandwidth ($W$) and
electron-electron interaction ($U$) makes iridates highly susceptible to small
external perturbations, which can trigger the onset of novel types of
electronic and magnetic states. Here we employ {\em first principles}
calculations based on density functional theory and on the constrained random
phase approximation to study how dimensionality and strain affect the strength
of $U$ and $W$ in (SrIrO$_3$)$_m$/(SrTiO$_3$) superlattices. The result is a
phase diagram explaining two different types of controllable magnetic and
electronic transitions, spin-flop and insulator-to-metal, connected with the
disruption of the $J_{eff}=1/2$ state which cannnot be understood within a
simplified local picture.
| 0 | 1 | 0 | 0 | 0 | 0 |
Herschel survey and modelling of externally-illuminated photoevaporating protoplanetary disks | Protoplanetary disks undergo substantial mass-loss by photoevaporation, a
mechanism which is crucial to their dynamical evolution. However, the processes
regulating the gas energetics have not been well constrained by observations so
far. We aim at studying the processes involved in disk photoevaporation when it
is driven by far-UV photons. We present a unique Herschel survey and new ALMA
observations of four externally-illuminated photoevaporating disks (a.k.a.
proplyds). For the analysis of these data, we developed a 1D model of the
photodissociation region (PDR) of a proplyd, based on the Meudon PDR code and
computed the far infrared line emission. We successfully reproduce most of the
observations and derive key physical parameters, i.e. densities at the disk
surface of about $10^{6}$ cm$^{-3}$ and local gas temperatures of about 1000 K.
Our modelling suggests that all studied disks are found in a transitional
regime resulting from the interplay between several heating and cooling
processes that we identify. These differ from those dominating in classical
PDRs, i.e. grain photo-electric effect and cooling by [OI] and [CII] FIR lines.
This energetic regime is associated to an equilibrium dynamical point of the
photoevaporation flow: the mass-loss rate is self-regulated to set the envelope
column density at a value that maintains the temperature at the disk surface
around 1000 K. From our best-fit models, we estimate mass-loss rates - of the
order of $10^{-7}$ $\mathrm{M}_\odot$/yr - that are in agreement with earlier
spectroscopic observation of ionised gas tracers. This holds only if we assume
an evaporation flow launched from the disk surface at sound speed
(supercritical regime). We have identified the energetic regime regulating
FUV-photoevaporation in proplyds. This regime could be implemented into models
of the dynamical evolution of protoplanetary disks.
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Observation of Spatio-temporal Instability of Femtosecond Pulses in Normal Dispersion Multimode Graded-Index Fiber | We study the spatio-temporal instability generated by a universal unstable
attractor in normal dispersion graded-index multimode fiber (GRIN MMF) for
femtosecond pulses. Our results present the generation of geometric parametric
instability (GPI) sidebands with ultrashort input pulse for the first time.
Observed GPI sidebands are 91 THz detuned from the pump wavelength, 800 nm.
Detailed analysis carried out numerically by employing coupled-mode pulse
propagation model including optical shock and Raman nonlinearity terms. A
simplified theoretical model and numerically calculated spectra are
well-aligned with experimental results. For input pulses of 200-fs duration,
formation and evolution of GPI are shown in both spatial and temporal domains.
The spatial intensity distribution of the total field and GPI sidebands are
calculated. Numerically and experimentally obtained beam shapes of first GPI
features a Gaussian-like beam profile. Our numerical results verify the unique
feature of GPI and generated sidebands preserve their inherited spatial
intensity profile from the input pulse for different propagation distances
particularly for focused and spread the total field inside the GRIN MMF.
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Spectral curves for the rogue waves | Here we find the spectral curves, corresponding to the known rational or
quasi-rational solutions of AKNS hierarchy equations, ultimately connected with
the modeling of the rogue waves events in the optical waveguides and in
hydrodynamics. We also determine spectral curves for the multi-phase
trigonometric, hyperbolic and elliptic solutions for the same hierarchy. It
seams that the nature of the related spectral curves was not sufficiently
discussed in existing literature.
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Volume functional of compact manifolds with a prescribed boundary metric | We prove that a critical metric of the volume functional on a
four-dimensional compact manifold with boundary satisfying a second-order
vanishing condition on the Weyl tensor must be isometric to a geodesic ball in
a simply connected space form $\mathbb{R}^{4}$, $\mathbb{H}^{4}$ or
$\mathbb{S}^{4}.$ Moreover, we provide an integral curvature estimate involving
the Yamabe constant for critical metrics of the volume functional, which allows
us to get a rigidity result for such critical metrics on four-dimensional
manifolds.
| 0 | 0 | 1 | 0 | 0 | 0 |
Deep Multitask Learning for Semantic Dependency Parsing | We present a deep neural architecture that parses sentences into three
semantic dependency graph formalisms. By using efficient, nearly arc-factored
inference and a bidirectional-LSTM composed with a multi-layer perceptron, our
base system is able to significantly improve the state of the art for semantic
dependency parsing, without using hand-engineered features or syntax. We then
explore two multitask learning approaches---one that shares parameters across
formalisms, and one that uses higher-order structures to predict the graphs
jointly. We find that both approaches improve performance across formalisms on
average, achieving a new state of the art. Our code is open-source and
available at this https URL.
| 1 | 0 | 0 | 0 | 0 | 0 |
Chentsov's theorem for exponential families | Chentsov's theorem characterizes the Fisher information metric on statistical
models as essentially the only Riemannian metric that is invariant under
sufficient statistics. This implies that each statistical model is naturally
equipped with a geometry, so Chentsov's theorem explains why many statistical
properties can be described in geometric terms. However, despite being one of
the foundational theorems of statistics, Chentsov's theorem has only been
proved previously in very restricted settings or under relatively strong
regularity and invariance assumptions. We therefore prove a version of this
theorem for the important case of exponential families. In particular, we
characterise the Fisher information metric as the only Riemannian metric (up to
rescaling) on an exponential family and its derived families that is invariant
under independent and identically distributed extensions and canonical
sufficient statistics. Our approach is based on the central limit theorem, so
it gives a unified proof for both discrete and continuous exponential families,
and it is less technical than previous approaches.
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Dark trions and biexcitons in WS2 and WSe2 made bright by e-e scattering | The direct band gap character and large spin-orbit splitting of the valence
band edges (at the K and K' valleys) in monolayer transition metal
dichalcogenides have put these two-dimensional materials under the spot-light
of intense experimental and theoretical studies. In particular, for Tungsten
dichalcogenides it has been found that the sign of spin splitting of conduction
band edges makes ground state excitons radiatively inactive (dark) due to spin
and momentum mismatch between the constituent electron and hole. One might
similarly assume that the ground states of charged excitons and biexcitons in
these monolayers are also dark. Here, we show that the intervalley
K$\leftrightarrows$K' electron-electron scattering mixes bright and dark states
of these complexes, and estimate the radiative lifetimes in the ground states
of these "semi-dark" trions and biexcitons to be ~ 10ps, and analyse how these
complexes appear in the temperature-dependent photoluminescence spectra of WS2
and WSe2 monolayers.
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SGDLibrary: A MATLAB library for stochastic gradient descent algorithms | We consider the problem of finding the minimizer of a function $f:
\mathbb{R}^d \rightarrow \mathbb{R}$ of the finite-sum form $\min f(w) =
1/n\sum_{i}^n f_i(w)$. This problem has been studied intensively in recent
years in the field of machine learning (ML). One promising approach for
large-scale data is to use a stochastic optimization algorithm to solve the
problem. SGDLibrary is a readable, flexible and extensible pure-MATLAB library
of a collection of stochastic optimization algorithms. The purpose of the
library is to provide researchers and implementers a comprehensive evaluation
environment for the use of these algorithms on various ML problems.
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Structured Uncertainty Prediction Networks | This paper is the first work to propose a network to predict a structured
uncertainty distribution for a synthesized image. Previous approaches have been
mostly limited to predicting diagonal covariance matrices. Our novel model
learns to predict a full Gaussian covariance matrix for each reconstruction,
which permits efficient sampling and likelihood evaluation.
We demonstrate that our model can accurately reconstruct ground truth
correlated residual distributions for synthetic datasets and generate plausible
high frequency samples for real face images. We also illustrate the use of
these predicted covariances for structure preserving image denoising.
| 0 | 0 | 0 | 1 | 0 | 0 |
Shannon's entropy and its Generalizations towards Statistics, Reliability and Information Science during 1948-2018 | Starting from the pioneering works of Shannon and Weiner in 1948, a plethora
of works have been reported on entropy in different directions. Entropy-related
review work in the direction of statistics, reliability and information
science, to the best of our knowledge, has not been reported so far. Here we
have tried to collect all possible works in this direction during the period
1948-2018 so that people interested in entropy, specially the new researchers,
get benefited.
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Understanding MIDI: A Painless Tutorial on Midi Format | A short overview demystifying the midi audio format is presented. The goal is
to explain the file structure and how the instructions are used to produce a
music signal, both in the case of monophonic signals as for polyphonic signals.
| 1 | 0 | 0 | 0 | 0 | 0 |
Optimization, fast and slow: optimally switching between local and Bayesian optimization | We develop the first Bayesian Optimization algorithm, BLOSSOM, which selects
between multiple alternative acquisition functions and traditional local
optimization at each step. This is combined with a novel stopping condition
based on expected regret. This pairing allows us to obtain the best
characteristics of both local and Bayesian optimization, making efficient use
of function evaluations while yielding superior convergence to the global
minimum on a selection of optimization problems, and also halting optimization
once a principled and intuitive stopping condition has been fulfilled.
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Optimization of exposure time division for wide field observations | The optical observations of wide fields of view encounter the problem of
selection of best exposure time. As there are usually plenty of objects
observed simultaneously, the quality of photometry of the brightest ones is
always better than of the dimmer ones. Frequently all of them are equally
interesting for the astronomers and thus it is desired to have all of them
measured with the highest possible accuracy.
In this paper we present a novel optimization algorithm dedicated for the
division of exposure time into sub-exposures, which allows to perform
photometry with more balanced noise budget. Thanks to the proposed technique,
the photometric precision of dimmer objects is increased at the expense of the
measurement fidelity of the brightest ones. We tested the method on real
observations using two telescope setups demonstrating its usefulness and good
agreement with the theoretical expectations. The main application of our
approach is a wide range of sky surveys, including the ones performed by the
space telescopes. The method can be applied for planning virtually any
photometric observations, in which the objects of interest show a wide range of
magnitudes.
| 0 | 1 | 0 | 0 | 0 | 0 |
Multi-Entity Dependence Learning with Rich Context via Conditional Variational Auto-encoder | Multi-Entity Dependence Learning (MEDL) explores conditional correlations
among multiple entities. The availability of rich contextual information
requires a nimble learning scheme that tightly integrates with deep neural
networks and has the ability to capture correlation structures among
exponentially many outcomes. We propose MEDL_CVAE, which encodes a conditional
multivariate distribution as a generating process. As a result, the variational
lower bound of the joint likelihood can be optimized via a conditional
variational auto-encoder and trained end-to-end on GPUs. Our MEDL_CVAE was
motivated by two real-world applications in computational sustainability: one
studies the spatial correlation among multiple bird species using the eBird
data and the other models multi-dimensional landscape composition and human
footprint in the Amazon rainforest with satellite images. We show that
MEDL_CVAE captures rich dependency structures, scales better than previous
methods, and further improves on the joint likelihood taking advantage of very
large datasets that are beyond the capacity of previous methods.
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A recognition algorithm for simple-triangle graphs | A simple-triangle graph is the intersection graph of triangles that are
defined by a point on a horizontal line and an interval on another horizontal
line. The time complexity of the recognition problem for simple-triangle graphs
was a longstanding open problem, which was recently settled. This paper
provides a new recognition algorithm for simple-triangle graphs to improve the
time bound from $O(n^2 \overline{m})$ to $O(nm)$, where $n$, $m$, and
$\overline{m}$ are the number of vertices, edges, and non-edges of the graph,
respectively. The algorithm uses the vertex ordering characterization that a
graph is a simple-triangle graph if and only if there is a linear ordering of
the vertices containing both an alternating orientation of the graph and a
transitive orientation of the complement of the graph. We also show, as a
byproduct, that an alternating orientation can be obtained in $O(nm)$ time for
cocomparability graphs, and it is NP-complete to decide whether a graph has an
orientation that is alternating and acyclic.
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A normalized gradient flow method with attractive-repulsive splitting for computing ground states of Bose-Einstein condensates with higher-order interaction | In this paper, we generalize the normalized gradient flow method to compute
the ground states of Bose-Einstein condensates (BEC) with higher order
interactions (HOI), which is modelled via the modified Gross-Pitaevskii
equation (MGPE). Schemes constructed in naive ways suffer from severe stability
problems due to the high restrictions on time steps. To build an efficient and
stable scheme, we split the HOI term into two parts with each part treated
separately. The part corresponding to a repulsive/positive energy is treated
semi-implicitly while the one corresponding to an attractive/negative energy is
treated fully explicitly. Based on the splitting, we construct the
BEFD-splitting and BESP-splitting schemes. A variety of numerical experiments
shows that the splitting will improve the stability of the schemes
significantly. Besides, we will show that the methods can be applied to
multidimensional problems and to the computation of the first excited state as
well.
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Truncation in Hahn Fields is Undecidable and Wild | We show that in any nontrivial Hahn field with truncation as a primitive
operation we can interpret the monadic second-order logic of the additive
monoid of natural numbers and are thus undecidable. We also specify a definable
binary relation on such a structure that has $\SOP$ and $\TP$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Direct measurement of laser aberration and ahead point from ARTEMIS satellite through strong clouds | Laser communication has advances in compared with radio frequency
communication as result of much high carrier frequency from ultraviolet to near
infrared. Very narrow laser beam is possible to form with very high power
density. But laser beam has high destruction and attenuation on clouds,
turbulence, scattering on aerosols and molecules of the atmosphere. Low Earth
orbits (LEO), Middling Earth orbits (MEO) and partly Geosynchronous Earth orbit
(GSO) satellites moving on the sky and laser light from satellites moves across
different turbulence conditions of the atmosphere, clouds, molecules of the
atmosphere H2O, O2, N2, CO, O3 and other. We performed unique experiments with
propagation of laser beams from beacon of OPALE terminal of ARTEMIS satellite
through thin clouds. We have found that small part of laser radiation is
received from ahead point there the satellite will be after time of propagation
of laser radiation from the satellite to telescope. It is in accordance with
theory of relativity for aberration of light during transition from moving to
not moving coordinate systems. It is positive effect for laser communication
through the atmosphere and clouds because will be possible to develop a system
for reduce of the atmosphere turbulence during of laser communication from
ground to the satellites. The interest is what will be during propagation of
laser radiation from the satellite through strong clouds. The detail
descriptions of laser experiment with ARTEMIS GSO satellite through strong
clouds and estimations of the laser power through strong clouds are presented
in this paper. Accordingly we must search the optimal wave lengths and power of
lasers for performs laser communication in different cloudy conditions.
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DNN-Buddies: A Deep Neural Network-Based Estimation Metric for the Jigsaw Puzzle Problem | This paper introduces the first deep neural network-based estimation metric
for the jigsaw puzzle problem. Given two puzzle piece edges, the neural network
predicts whether or not they should be adjacent in the correct assembly of the
puzzle, using nothing but the pixels of each piece. The proposed metric
exhibits an extremely high precision even though no manual feature extraction
is performed. When incorporated into an existing puzzle solver, the solution's
accuracy increases significantly, achieving thereby a new state-of-the-art
standard.
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Attack Analysis for Distributed Control Systems: An Internal Model Principle Approach | Although adverse effects of attacks have been acknowledged in many
cyber-physical systems, there is no system-theoretic comprehension of how a
compromised agent can leverage communication capabilities to maximize the
damage in distributed multi-agent systems. A rigorous analysis of
cyber-physical attacks enables us to increase the system awareness against
attacks and design more resilient control protocols. To this end, we will take
the role of the attacker to identify the worst effects of attacks on root nodes
and non-root nodes in a distributed control system. More specifically, we show
that a stealthy attack on root nodes can mislead the entire network to a wrong
understanding of the situation and even destabilize the synchronization
process. This will be called the internal model principle for the attacker and
will intensify the urgency of designing novel control protocols to mitigate
these types of attacks.
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A conservative scheme for electromagnetic simulation of magnetized plasmas with kinetic electrons | A conservative scheme has been formulated and verified for gyrokinetic
particle simulations of electromagnetic waves and instabilities in magnetized
plasmas. An electron continuity equation derived from drift kinetic equation is
used to time advance electron density perturbation by using the perturbed
mechanical flow calculated from the parallel vector potential, and the parallel
vector potential is solved by using the perturbed canonical flow from the
perturbed distribution function. In gyrokinetic particle simulations using this
new scheme, shear Alfvén wave dispersion relation in shearless slab and
continuum damping in sheared cylinder have been recovered. The new scheme
overcomes the stringent requirement in conventional perturbative simulation
method that perpendicular grid size needs to be as small as electron
collisionless skin depth even for the long wavelength Alfvén waves. The new
scheme also avoids the problem in conventional method that an unphysically
large parallel electric field arises due to the inconsistency between
electrostatic potential calculated from the perturbed density and vector
potential calculated from the perturbed canonical flow. Finally, the
gyrokinetic particle simulations of the Alfvén waves in sheared cylinder have
superior numerical properties compared with the fluid simulations, which suffer
from numerical difficulties associated with singular mode structures.
| 0 | 1 | 0 | 0 | 0 | 0 |
Corrupt Bandits for Preserving Local Privacy | We study a variant of the stochastic multi-armed bandit (MAB) problem in
which the rewards are corrupted. In this framework, motivated by privacy
preservation in online recommender systems, the goal is to maximize the sum of
the (unobserved) rewards, based on the observation of transformation of these
rewards through a stochastic corruption process with known parameters. We
provide a lower bound on the expected regret of any bandit algorithm in this
corrupted setting. We devise a frequentist algorithm, KLUCB-CF, and a Bayesian
algorithm, TS-CF and give upper bounds on their regret. We also provide the
appropriate corruption parameters to guarantee a desired level of local privacy
and analyze how this impacts the regret. Finally, we present some experimental
results that confirm our analysis.
| 1 | 0 | 0 | 1 | 0 | 0 |
Deep Reinforcement Learning based Optimal Control of Hot Water Systems | Energy consumption for hot water production is a major draw in high
efficiency buildings. Optimizing this has typically been approached from a
thermodynamics perspective, decoupled from occupant influence. Furthermore,
optimization usually presupposes existence of a detailed dynamics model for the
hot water system. These assumptions lead to suboptimal energy efficiency in the
real world. In this paper, we present a novel reinforcement learning based
methodology which optimizes hot water production. The proposed methodology is
completely generalizable, and does not require an offline step or human domain
knowledge to build a model for the hot water vessel or the heating element.
Occupant preferences too are learnt on the fly. The proposed system is applied
to a set of 32 houses in the Netherlands where it reduces energy consumption
for hot water production by roughly 20% with no loss of occupant comfort.
Extrapolating, this translates to absolute savings of roughly 200 kWh for a
single household on an annual basis. This performance can be replicated to any
domestic hot water system and optimization objective, given that the fairly
minimal requirements on sensor data are met. With millions of hot water systems
operational worldwide, the proposed framework has the potential to reduce
energy consumption in existing and new systems on a multi Gigawatt-hour scale
in the years to come.
| 0 | 0 | 0 | 1 | 0 | 0 |
A Model-Based Fuzzy Control Approach to Achieving Adaptation with Contextual Uncertainties | Self-adaptive system (SAS) is capable of adjusting its behavior in response
to meaningful changes in the operational context and itself. Due to the
inherent volatility of the open and changeable environment in which SAS is
embedded, the ability of adaptation is highly demanded by many
software-intensive systems. Two concerns, i.e., the requirements uncertainty
and the context uncertainty are most important among others. An essential issue
to be addressed is how to dynamically adapt non-functional requirements (NFRs)
and task configurations of SASs with context uncertainty. In this paper, we
propose a model-based fuzzy control approach that is underpinned by the
feedforward-feedback control mechanism. This approach identifies and represents
NFR uncertainties, task uncertainties and context uncertainties with linguistic
variables, and then designs an inference structure and rules for the fuzzy
controller based on the relations between the requirements model and the
context model. The adaptation of NFRs and task configurations is achieved
through fuzzification, inference, defuzzification and readaptation. Our
approach is demonstrated with a mobile computing application and is evaluated
through a series of simulation experiments.
| 1 | 0 | 0 | 0 | 0 | 0 |
See the Near Future: A Short-Term Predictive Methodology to Traffic Load in ITS | The Intelligent Transportation System (ITS) targets to a coordinated traffic
system by applying the advanced wireless communication technologies for road
traffic scheduling. Towards an accurate road traffic control, the short-term
traffic forecasting to predict the road traffic at the particular site in a
short period is often useful and important. In existing works, Seasonal
Autoregressive Integrated Moving Average (SARIMA) model is a popular approach.
The scheme however encounters two challenges: 1) the analysis on related data
is insufficient whereas some important features of data may be neglected; and
2) with data presenting different features, it is unlikely to have one
predictive model that can fit all situations. To tackle above issues, in this
work, we develop a hybrid model to improve accuracy of SARIMA. In specific, we
first explore the autocorrelation and distribution features existed in traffic
flow to revise structure of the time series model. Based on the Gaussian
distribution of traffic flow, a hybrid model with a Bayesian learning algorithm
is developed which can effectively expand the application scenarios of SARIMA.
We show the efficiency and accuracy of our proposal using both analysis and
experimental studies. Using the real-world trace data, we show that the
proposed predicting approach can achieve satisfactory performance in practice.
| 1 | 0 | 0 | 1 | 0 | 0 |
Single-Crystal N-polar GaN p-n Diodes by Plasma-Assisted Molecular Beam Epitaxy | N-polar GaN p-n diodes are realized on single-crystal N-polar GaN bulk wafers
by plasma-assisted molecular beam epitaxy growth. The current-voltage
characteristics show high-quality rectification and electroluminescence
characteristics with a high on/off current ratio and interband photon emission.
The measured electroluminescence spectrum is dominated by strong near-band edge
emission, while deep level luminescence is greatly suppressed. A very low
dislocation density leads to a high reverse breakdown electric field. The low
leakage current N-polar diodes open up several potential applications in
polarization-engineered photonic and electronic devices.
| 0 | 1 | 0 | 0 | 0 | 0 |
Particle Identification with the TOP and ARICH detectors at Belle II | Particle identification at the Belle II experiment will be provided by two
ring imaging Cherenkov devices, the time of propagation counters in the central
region and the proximity focusing RICH with aerogel radiator in the forward
end-cap region. The key features of these two detectors, the performance
studies, and the construction progress is presented.
| 0 | 1 | 0 | 0 | 0 | 0 |
Face Detection and Face Recognition In the Wild Using Off-the-Shelf Freely Available Components | This paper presents an easy and efficient face detection and face recognition
approach using free software components from the internet. Face detection and
face recognition problems have wide applications in home and office security.
Therefore this work will helpful for those searching for a free face
off-the-shelf face detection system. Using this system, faces can be detected
in uncontrolled environments. In the detection phase, every individual face is
detected and in the recognition phase the detected faces are compared with the
faces in a given data set and recognized.
| 1 | 0 | 0 | 0 | 0 | 0 |
End-to-end Learning of Deterministic Decision Trees | Conventional decision trees have a number of favorable properties, including
interpretability, a small computational footprint and the ability to learn from
little training data. However, they lack a key quality that has helped fuel the
deep learning revolution: that of being end-to-end trainable, and to learn from
scratch those features that best allow to solve a given supervised learning
problem. Recent work (Kontschieder 2015) has addressed this deficit, but at the
cost of losing a main attractive trait of decision trees: the fact that each
sample is routed along a small subset of tree nodes only. We here propose a
model and Expectation-Maximization training scheme for decision trees that are
fully probabilistic at train time, but after a deterministic annealing process
become deterministic at test time. We also analyze the learned oblique split
parameters on image datasets and show that Neural Networks can be trained at
each split node. In summary, we present the first end-to-end learning scheme
for deterministic decision trees and present results on par with or superior to
published standard oblique decision tree algorithms.
| 1 | 0 | 0 | 1 | 0 | 0 |
Conceptualization of Object Compositions Using Persistent Homology | A topological shape analysis is proposed and utilized to learn concepts that
reflect shape commonalities. Our approach is two-fold: i) a spatial topology
analysis of point cloud segment constellations within objects. Therein
constellations are decomposed and described in an hierarchical manner - from
single segments to segment groups until a single group reflects an entire
object. ii) a topology analysis of the description space in which segment
decompositions are exposed in. Inspired by Persistent Homology, hidden groups
of shape commonalities are revealed from object segment decompositions.
Experiments show that extracted persistent groups of commonalities can
represent semantically meaningful shape concepts. We also show the
generalization capability of the proposed approach considering samples of
external datasets.
| 1 | 0 | 0 | 0 | 0 | 0 |
Long-term Blood Pressure Prediction with Deep Recurrent Neural Networks | Existing methods for arterial blood pressure (BP) estimation directly map the
input physiological signals to output BP values without explicitly modeling the
underlying temporal dependencies in BP dynamics. As a result, these models
suffer from accuracy decay over a long time and thus require frequent
calibration. In this work, we address this issue by formulating BP estimation
as a sequence prediction problem in which both the input and target are
temporal sequences. We propose a novel deep recurrent neural network (RNN)
consisting of multilayered Long Short-Term Memory (LSTM) networks, which are
incorporated with (1) a bidirectional structure to access larger-scale context
information of input sequence, and (2) residual connections to allow gradients
in deep RNN to propagate more effectively. The proposed deep RNN model was
tested on a static BP dataset, and it achieved root mean square error (RMSE) of
3.90 and 2.66 mmHg for systolic BP (SBP) and diastolic BP (DBP) prediction
respectively, surpassing the accuracy of traditional BP prediction models. On a
multi-day BP dataset, the deep RNN achieved RMSE of 3.84, 5.25, 5.80 and 5.81
mmHg for the 1st day, 2nd day, 4th day and 6th month after the 1st day SBP
prediction, and 1.80, 4.78, 5.0, 5.21 mmHg for corresponding DBP prediction,
respectively, which outperforms all previous models with notable improvement.
The experimental results suggest that modeling the temporal dependencies in BP
dynamics significantly improves the long-term BP prediction accuracy.
| 1 | 0 | 0 | 1 | 0 | 0 |
Risks for life on habitable planets from superflares of their host stars | We explore some of the ramifications arising from superflares on the
evolutionary history of Earth, other planets in the Solar system, and
exoplanets. We propose that the most powerful superflares can serve as
plausible drivers of extinction events, and that their periodicity could
correspond to certain patterns in the terrestrial fossil diversity record. On
the other hand, weaker superflares may play a positive role in enabling the
origin of life through the formation of key organic compounds. Superflares
could also prove to be quite detrimental to the evolution of complex life on
present-day Mars and exoplanets in the habitable zone of M- and K-dwarfs. We
conclude that the risk posed by superflares has not been sufficiently
appreciated, and that humanity might potentially witness a superflare event in
the next $\sim 10^3$ years leading to devastating economic and technological
losses. In light of the many uncertainties and assumptions associated with our
analysis, we recommend that these results should be viewed with due caution.
| 0 | 1 | 0 | 0 | 0 | 0 |
Thermodynamics of Spin-1/2 Kagomé Heisenberg Antiferromagnet: Algebraic Paramagnetic Liquid and Finite-Temperature Phase Diagram | Quantum fluctuations from frustration can trigger quantum spin liquids (QSLs)
at zero temperature. However, it is unclear how thermal fluctuations affect a
QSL. We employ state-of-the-art tensor network-based methods to explore the
ground state and thermodynamic properties of the spin-1/2 kagome Heisenberg
antiferromagnet (KHA). Its ground state is shown to be consistent with a
gapless QSL by observing the absence of zero-magnetization plateau as well as
the algebraic behaviors of susceptibility and specific heat at low
temperatures, respectively. We show that there exists an \textit{algebraic
paramagnetic liquid} (APL) that possesses both the paramagnetic properties and
the algebraic behaviors inherited from the QSL. The APL is induced under the
interplay between quantum fluctuations from geometrical frustration and thermal
fluctuations. By studying the temperature-dependent behaviors of specific heat
and magnetic susceptibility, a finite-temperature phase diagram in a magnetic
field is suggested, where various phases are identified. This present study
gains useful insight into the thermodynamic properties of the spin-1/2 KHA with
or without a magnetic field and is helpful for relevant experimental studies.
| 0 | 1 | 0 | 0 | 0 | 0 |
Fast Rigid 3D Registration Solution: A Simple Method Free of SVD and Eigen-Decomposition | A novel solution is obtained to solve the rigid 3D registration problem,
motivated by previous eigen-decomposition approaches. Different from existing
solvers, the proposed algorithm does not require sophisticated matrix
operations e.g. singular value decomposition or eigenvalue decomposition.
Instead, the optimal eigenvector of the point cross-covariance matrix can be
computed within several iterations. It is also proven that the optimal rotation
matrix can be directly computed for cases without need of quaternion. The
simple framework provides very easy approach of integer-implementation on
embedded platforms. Simulations on noise-corrupted point clouds have verified
the robustness and computation speed of the proposed method. The final results
indicate that the proposed algorithm is accurate, robust and owns over $60\%
\sim 80\%$ less computation time than representatives. It has also been applied
to real-world applications for faster relative robotic navigation.
| 1 | 0 | 0 | 0 | 0 | 0 |
Massive MIMO 5G Cellular Networks: mm-wave vs. μ-wave Frequencies | Enhanced mobile broadband (eMBB) is one of the key use-cases for the
development of the new standard 5G New Radio for the next generation of mobile
wireless networks. Large-scale antenna arrays, a.k.a. Massive MIMO, the usage
of carrier frequencies in the range 10-100 GHz, the so-called millimeter wave
(mm-wave) band, and the network densification with the introduction of
small-sized cells are the three technologies that will permit implementing eMBB
services and realizing the Gbit/s mobile wireless experience. This paper is
focused on the massive MIMO technology; initially conceived for conventional
cellular frequencies in the sub-6 GHz range (\mu-wave), the massive MIMO
concept has been then progressively extended to the case in which mm-wave
frequencies are used. However, due to different propagation mechanisms in urban
scenarios, the resulting MIMO channel models at \mu-wave and mm-wave are
radically different. Six key basic differences are pinpointed in this paper,
along with the implications that they have on the architecture and algorithms
of the communication transceivers and on the attainable performance in terms of
reliability and multiplexing capabilities.
| 1 | 0 | 0 | 0 | 0 | 0 |
Independent Set Size Approximation in Graph Streams | We study the problem of estimating the size of independent sets in a graph
$G$ defined by a stream of edges. Our approach relies on the Caro-Wei bound,
which expresses the desired quantity in terms of a sum over nodes of the
reciprocal of their degrees, denoted by $\beta(G)$. Our results show that
$\beta(G)$ can be approximated accurately, based on a provided lower bound on
$\beta$. Stronger results are possible when the edges are promised to arrive
grouped by an incident node. In this setting, we obtain a value that is at most
a logarithmic factor below the true value of $\beta$ and no more than the true
independent set size. To justify the form of this bound, we also show an
$\Omega(n/\beta)$ lower bound on any algorithm that approximates $\beta$ up to
a constant factor.
| 1 | 0 | 0 | 0 | 0 | 0 |
Two-Person Zero-Sum Games with Unbounded Payoff Functions and Uncertain Expected Payoffs | This paper provides sufficient conditions for the existence of values and
solutions for two-person zero-sum one-step games with possibly noncompact
action sets for both players and possibly unbounded payoff functions, which may
be neither convex nor concave. For such games payoffs may not be defined for
some pairs of strategies. In addition to the existence of values and solutions,
this paper investigates continuity properties of the value functions and
solution multifunctions for families of games with possibly noncompact action
sets and unbounded payoff functions, when action sets and payoffs depend on a
parameter.
| 0 | 0 | 1 | 0 | 0 | 0 |
Scale relativistic formulation of non-differentiable mechanics II: The Schroedinger picture | This article is the second in a series of two presenting the Scale
Relativistic approach to non-differentiability in mechanics and its relation to
quantum mechanics. Here, we show Schroedinger's equation to be a reformulation
of Newton's fundamental relation of dynamics as generalized to
non-differentiable geometries in the first paper \cite{paper1}. It motivates an
alternative interpretation of the other axioms of standard quantum mechanics in
a coherent picture. This exercise validates the Scale Relativistic approach
and, at the same time, it allows to identify macroscopic chaotic systems
considered at time scales exceeding their horizon of predictability as
candidates in which to search for quantum-like structuring or behavior.
| 0 | 1 | 0 | 0 | 0 | 0 |
Compact arrangement for femtosecond laser induced generation of broadband hard x-ray pulses | We present a simple apparatus for femtosecond laser induced generation of
X-rays. The apparatus consists of a vacuum chamber containing an off-axis
parabolic focusing mirror, a reel system, a debris protection setup, a quartz
window for the incoming laser beam, and an X-ray window. Before entering the
vacuum chamber, the femtosecond laser is expanded with an all reflective
telescope design to minimize laser intensity losses and pulse broadening while
allowing for focusing as well as peak intensity optimization. The laser pulse
duration was characterized by second-harmonic generation frequency resolved
optical gating. A high spatial resolution knife-edge technique was implemented
to characterize the beam size at the focus of the X-ray generation apparatus.
We have characterized x-ray spectra obtained with three different samples:
titanium, iron:chromium alloy, and copper. In all three cases, the femtosecond
laser generated X-rays give spectral lines consistent with literature reports.
We present a rms amplitude analysis of the generated X-ray pulses, and provide
an upper bound for the duration of the X-ray pulses.
| 0 | 1 | 0 | 0 | 0 | 0 |
Hierarchical Summarization of Metric Changes | We study changes in metrics that are defined on a cartesian product of trees.
Such metrics occur naturally in many practical applications, where a global
metric (such as revenue) can be broken down along several hierarchical
dimensions (such as location, gender, etc).
Given a change in such a metric, our goal is to identify a small set of
non-overlapping data segments that account for the change. An organization
interested in improving the metric can then focus their attention on these data
segments.
Our key contribution is an algorithm that mimics the operation of a
hierarchical organization of analysts. The algorithm has been successfully
applied, for example within Google Adwords to help advertisers triage the
performance of their advertising campaigns.
We show that the algorithm is optimal for two dimensions, and has an
approximation ratio $\log^{d-2}(n+1)$ for $d \geq 3$ dimensions, where $n$ is
the number of input data segments. For the Adwords application, we can show
that our algorithm is in fact a $2$-approximation.
Mathematically, we identify a certain data pattern called a \emph{conflict}
that both guides the design of the algorithm, and plays a central role in the
hardness results. We use these conflicts to both derive a lower bound of
$1.144^{d-2}$ (again $d\geq3$) for our algorithm, and to show that the problem
is NP-hard, justifying the focus on approximation.
| 1 | 0 | 0 | 0 | 0 | 0 |
Manifold learning with bi-stochastic kernels | In this paper we answer the following question: what is the infinitesimal
generator of the diffusion process defined by a kernel that is normalized such
that it is bi-stochastic with respect to a specified measure? More precisely,
under the assumption that data is sampled from a Riemannian manifold we
determine how the resulting infinitesimal generator depends on the potentially
nonuniform distribution of the sample points, and the specified measure for the
bi-stochastic normalization. In a special case, we demonstrate a connection to
the heat kernel. We consider both the case where only a single data set is
given, and the case where a data set and a reference set are given. The
spectral theory of the constructed operators is studied, and Nyström
extension formulas for the gradients of the eigenfunctions are computed.
Applications to discrete point sets and manifold learning are discussed.
| 0 | 0 | 1 | 1 | 0 | 0 |
The importance of the weak: Interaction modifiers in artificial spin ices | The modification of geometry and interactions in two-dimensional magnetic
nanosystems has enabled a range of studies addressing the magnetic order,
collective low-energy dynamics, and emergent magnetic properties, in e.g.
artificial spin ice structures. The common denominator of all these
investigations is the use of Ising-like mesospins as building blocks, in the
form of elongated magnetic islands. Here we introduce a new approach: single
interaction modifiers, using slave-mesospins in the form of discs, within which
the mesospin is free to rotate in the disc plane. We show that by placing these
on the vertices of square artificial spin ice arrays and varying their
diameter, it is possible to tailor the strength and the ratio of the
interaction energies. We demonstrate the existence of degenerate ice-rule
obeying states in square artificial spin ice structures, enabling the
exploration of thermal dynamics in a spin liquid manifold. Furthermore, we even
observe the emergence of flux lattices on larger length-scales, when the energy
landscape of the vertices is reversed. The work highlights the potential of a
design strategy for two-dimensional magnetic nano-architectures, through which
mixed dimensionality of mesospins can be used to promote thermally emergent
mesoscale magnetic states.
| 0 | 1 | 0 | 0 | 0 | 0 |
How To Extract Fashion Trends From Social Media? A Robust Object Detector With Support For Unsupervised Learning | With the proliferation of social media, fashion inspired from celebrities,
reputed designers as well as fashion influencers has shortened the cycle of
fashion design and manufacturing. However, with the explosion of fashion
related content and large number of user generated fashion photos, it is an
arduous task for fashion designers to wade through social media photos and
create a digest of trending fashion. This necessitates deep parsing of fashion
photos on social media to localize and classify multiple fashion items from a
given fashion photo. While object detection competitions such as MSCOCO have
thousands of samples for each of the object categories, it is quite difficult
to get large labeled datasets for fast fashion items. Moreover,
state-of-the-art object detectors do not have any functionality to ingest large
amount of unlabeled data available on social media in order to fine tune object
detectors with labeled datasets. In this work, we show application of a generic
object detector, that can be pretrained in an unsupervised manner, on 24
categories from recently released Open Images V4 dataset. We first train the
base architecture of the object detector using unsupervisd learning on 60K
unlabeled photos from 24 categories gathered from social media, and then
subsequently fine tune it on 8.2K labeled photos from Open Images V4 dataset.
On 300 X 300 image inputs, we achieve 72.7% mAP on a test dataset of 2.4K
photos while performing 11% to 17% better as compared to the state-of-the-art
object detectors. We show that this improvement is due to our choice of
architecture that lets us do unsupervised learning and that performs
significantly better in identifying small objects.
| 0 | 0 | 0 | 1 | 0 | 0 |
Multi-Task Learning Using Neighborhood Kernels | This paper introduces a new and effective algorithm for learning kernels in a
Multi-Task Learning (MTL) setting. Although, we consider a MTL scenario here,
our approach can be easily applied to standard single task learning, as well.
As shown by our empirical results, our algorithm consistently outperforms the
traditional kernel learning algorithms such as uniform combination solution,
convex combinations of base kernels as well as some kernel alignment-based
models, which have been proven to give promising results in the past. We
present a Rademacher complexity bound based on which a new Multi-Task Multiple
Kernel Learning (MT-MKL) model is derived. In particular, we propose a Support
Vector Machine-regularized model in which, for each task, an optimal kernel is
learned based on a neighborhood-defining kernel that is not restricted to be
positive semi-definite. Comparative experimental results are showcased that
underline the merits of our neighborhood-defining framework in both
classification and regression problems.
| 1 | 0 | 0 | 1 | 0 | 0 |
Optimized Bucket Wheel Design for Asteroid Excavation | Current spacecraft need to launch with all of their required fuel for travel.
This limits the system performance, payload capacity, and mission flexibility.
One compelling alternative is to perform In-Situ Resource Utilization (ISRU) by
extracting fuel from small bodies in local space such as asteroids or small
satellites. Compared to the Moon or Mars, the microgravity on an asteroid
demands a fraction of the energy for digging and accessing hydrated regolith
just below the surface. Previous asteroid excavation efforts have focused on
discrete capture events (an extension of sampling technology) or whole-asteroid
capture and processing. This paper proposes an optimized bucket wheel design
for surface excavation of an asteroid or small-body. Asteroid regolith is
excavated and water extracted for use as rocket propellant. Our initial study
focuses on system design, bucket wheel mechanisms, and capture dynamics applied
to ponded materials known to exist on asteroids like Itokawa and Eros and small
satellites like Phobos and Deimos. For initial evaluation of
material-spacecraft dynamics and mechanics, we assume lunar-like regolith for
bulk density, particle size and cohesion. We shall present our estimates for
the energy balance of excavation and processing versus fuel gained.
Conventional electrolysis of water is used to produce hydrogen and oxygen. It
is compared with steam for propulsion and both show significant delta-v. We
show that a return trip from Deimos to Earth is possible for a 12 kg craft
using ISRU processed fuel.
| 1 | 1 | 0 | 0 | 0 | 0 |
Fraction of the X-ray selected AGNs with optical emission lines in galaxy groups | Compared with numerous X-ray dominant active galactic nuclei (AGNs) without
emission-line signatures in their optical spectra, the X-ray selected AGNs with
optical emission lines are probably still in the high-accretion phase of black
hole growth. This paper presents an investigation on the fraction of these
X-ray detected AGNs with optical emission-line spectra in 198 galaxy groups at
$z<1$ in a rest frame 0.1-2.4 keV luminosity range 41.3 <log(L_X/erg s-1) <
44.1 within the COSMOS field, as well as its variations with redshift and group
richness. For various selection criteria of member galaxies, the numbers of
galaxies and the AGNs with optical emission lines in each galaxy group are
obtained. It is found that, in total 198 X-ray groups, there are 27 AGNs
detected in 26 groups. AGN fraction is on everage less than $4.6 (\pm 1.2)\%$
for individual groups hosting at least one AGN. The corrected overall AGN
fraction for whole group sample is less than $0.98 (\pm 0.11) \%$. The
normalized locations of group AGNs show that 15 AGNs are found to be located in
group centers, including all 6 low-luminosity group AGNs. A week rising
tendency with $z$ are found: overall AGN fraction is 0.30-0.43% for the groups
at $z<0.5$, and 0.55-0.64% at 0.5 < z < 1.0. For the X-ray groups at $z>0.5$,
most member AGNs are X-ray bright, optically dull, which results in a lower AGN
fractions at higher redshifts. The AGN fraction in isolated fields also
exhibits a rising trend with redshift, and the slope is consistent with that in
groups. The environment of galaxy groups seems to make no difference in
detection probability of the AGNs with emission lines. Additionally, a larger
AGN fractions are found in poorer groups, which implies that the AGNs in poorer
groups might still be in the high-accretion phase, whereas the AGN population
in rich clusters is mostly in the low-accretion, X-ray dominant phase.
| 0 | 1 | 0 | 0 | 0 | 0 |
A local search 2.917-approximation algorithm for duo-preservation string mapping | We study the {\em maximum duo-preservation string mapping} ({\sc Max-Duo})
problem, which is the complement of the well studied {\em minimum common string
partition} ({\sc MCSP}) problem. Both problems have applications in many fields
including text compression and bioinformatics. Motivated by an earlier local
search algorithm, we present an improved approximation and show that its
performance ratio is no greater than ${35}/{12} < 2.917$. This beats the
current best $3.25$-approximation for {\sc Max-Duo}. The performance analysis
of our algorithm is done through a complex yet interesting amortization. Two
lower bounds on the locality gap of our algorithm are also provided.
| 1 | 0 | 0 | 0 | 0 | 0 |
Towards Automatic Learning of Heuristics for Mechanical Transformations of Procedural Code | The current trends in next-generation exascale systems go towards integrating
a wide range of specialized (co-)processors into traditional supercomputers.
Due to the efficiency of heterogeneous systems in terms of Watts and FLOPS per
surface unit, opening the access of heterogeneous platforms to a wider range of
users is an important problem to be tackled. However, heterogeneous platforms
limit the portability of the applications and increase development complexity
due to the programming skills required. Program transformation can help make
programming heterogeneous systems easier by defining a step-wise transformation
process that translates a given initial code into a semantically equivalent
final code, but adapted to a specific platform. Program transformation systems
require the definition of efficient transformation strategies to tackle the
combinatorial problem that emerges due to the large set of transformations
applicable at each step of the process. In this paper we propose a machine
learning-based approach to learn heuristics to define program transformation
strategies. Our approach proposes a novel combination of reinforcement learning
and classification methods to efficiently tackle the problems inherent to this
type of systems. Preliminary results demonstrate the suitability of this
approach.
| 1 | 0 | 0 | 0 | 0 | 0 |
Using Graphs of Classifiers to Impose Declarative Constraints on Semi-supervised Learning | We propose a general approach to modeling semi-supervised learning (SSL)
algorithms. Specifically, we present a declarative language for modeling both
traditional supervised classification tasks and many SSL heuristics, including
both well-known heuristics such as co-training and novel domain-specific
heuristics. In addition to representing individual SSL heuristics, we show that
multiple heuristics can be automatically combined using Bayesian optimization
methods. We experiment with two classes of tasks, link-based text
classification and relation extraction. We show modest improvements on
well-studied link-based classification benchmarks, and state-of-the-art results
on relation-extraction tasks for two realistic domains.
| 1 | 0 | 0 | 1 | 0 | 0 |
Fabrication of a centimeter-long cavity on a nanofiber for cavity QED | We report the fabrication of a 1.2 cm long cavity directly on a nanofiber
using femtosecond laser ablation. The cavity modes with finesse value in the
range 200-400 can still maintain the transmission between 40-60%, which can
enable "strong-coupling" regime of cavity QED for a single atom trapped 200 nm
away from the fiber surface. For such cavity modes, we estimate the one-pass
intra-cavity transmission to be 99.53%. Other cavity modes, which can enable
high cooperativity in the range 3-10, show transmission over 60-85% and are
suitable for fiber-based single photon sources and quantum nonlinear optics in
the "Purcell" regime.
| 0 | 1 | 0 | 0 | 0 | 0 |
Optimized Deformed Laplacian for Spectrum-based Community Detection in Sparse Heterogeneous Graphs | Spectral clustering is one of the most popular, yet still incompletely
understood, methods for community detection on graphs. In this article we study
spectral clustering based on the deformed Laplacian matrix $D-rA$, for sparse
heterogeneous graphs (following a two-class degree-corrected stochastic block
model). For a specific value $r = \zeta$, we show that, unlike competing
methods such as the Bethe Hessian or non-backtracking operator approaches,
clustering is insensitive to the graph heterogeneity. Based on heuristic
arguments, we study the behavior of the informative eigenvector of $D-\zeta A$
and, as a result, we accurately predict the clustering accuracy. Via extensive
simulations and application to real networks, the resulting clustering
algorithm is validated and observed to systematically outperform
state-of-the-art competing methods.
| 1 | 0 | 0 | 1 | 0 | 0 |
High dimensional deformed rectangular matrices with applications in matrix denoising | We consider the recovery of a low rank $M \times N$ matrix $S$ from its noisy
observation $\tilde{S}$ in two different regimes. Under the assumption that $M$
is comparable to $N$, we propose two consistent estimators for $S$. Our
analysis relies on the local behavior of the large dimensional rectangular
matrices with finite rank perturbation. We also derive the convergent limits
and rates for the singular values and vectors of such matrices.
| 0 | 0 | 1 | 1 | 0 | 0 |
Validation of the 3-under-2 principle of cell wall growth in Gram-positive bacteria by simulation of a simple coarse-grained model | The aim of this work is to propose a first coarse-grained model of Bacillus
subtilis cell wall, handling explicitly the existence of multiple layers of
peptidoglycans. In this first work, we aim at the validation of the recently
proposed "three under two" principle.
| 0 | 1 | 0 | 0 | 0 | 0 |
Self-bound quantum droplets in atomic mixtures | Self-bound quantum droplets are a newly discovered phase in the context of
ultracold atoms. In this work we report their experimental realization
following the original proposal by Petrov [Phys. Rev. Lett. 115, 155302
(2015)], using an attractive bosonic mixture. In this system spherical droplets
form due to the balance of competing attractive and repulsive forces, provided
by the mean-field energy close to the collapse threshold and the first-order
correction due to quantum fluctuations. Thanks to an optical levitating
potential with negligible residual confinement we observe self-bound droplets
in free space and we characterize the conditions for their formation as well as
their equilibrium properties. This work sets the stage for future studies on
quantum droplets, from the measurement of their peculiar excitation spectrum,
to the exploration of their superfluid nature.
| 0 | 1 | 0 | 0 | 0 | 0 |
Control of Gene Regulatory Networks with Noisy Measurements and Uncertain Inputs | This paper is concerned with the problem of stochastic control of gene
regulatory networks (GRNs) observed indirectly through noisy measurements and
with uncertainty in the intervention inputs. The partial observability of the
gene states and uncertainty in the intervention process are accounted for by
modeling GRNs using the partially-observed Boolean dynamical system (POBDS)
signal model with noisy gene expression measurements. Obtaining the optimal
infinite-horizon control strategy for this problem is not attainable in
general, and we apply reinforcement learning and Gaussian process techniques to
find a near-optimal solution. The POBDS is first transformed to a
directly-observed Markov Decision Process in a continuous belief space, and the
Gaussian process is used for modeling the cost function over the belief and
intervention spaces. Reinforcement learning then is used to learn the cost
function from the available gene expression data. In addition, we employ
sparsification, which enables the control of large partially-observed GRNs. The
performance of the resulting algorithm is studied through a comprehensive set
of numerical experiments using synthetic gene expression data generated from a
melanoma gene regulatory network.
| 0 | 0 | 0 | 1 | 0 | 0 |
On Memory System Design for Stochastic Computing | Growing uncertainty in design parameters (and therefore, in design
functionality) renders stochastic computing particularly promising, which
represents and processes data as quantized probabilities. However, due to the
difference in data representation, integrating conventional memory (designed
and optimized for non-stochastic computing) in stochastic computing systems
inevitably incurs a significant data conversion overhead. Barely any stochastic
computing proposal to-date covers the memory impact. In this paper, as the
first study of its kind to the best of our knowledge, we rethink the memory
system design for stochastic computing. The result is a seamless stochastic
system, StochMem, which features analog memory to trade the energy and area
overhead of data conversion for computation accuracy. In this manner StochMem
can reduce the energy (area) overhead by up-to 52.8% (93.7%) at the cost of at
most 0.7% loss in computation accuracy.
| 1 | 0 | 0 | 0 | 0 | 0 |
Strongly convex stochastic online optimization on a unit simplex with application to the mixing least square regression | In this paper we propose a new approach to obtain mixing least square
regression estimate by means of stochastic online mirror descent in
non-euclidian set-up.
| 0 | 0 | 1 | 0 | 0 | 0 |
Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos | Despite rapid advances in face recognition, there remains a clear gap between
the performance of still image-based face recognition and video-based face
recognition, due to the vast difference in visual quality between the domains
and the difficulty of curating diverse large-scale video datasets. This paper
addresses both of those challenges, through an image to video feature-level
domain adaptation approach, to learn discriminative video frame
representations. The framework utilizes large-scale unlabeled video data to
reduce the gap between different domains while transferring discriminative
knowledge from large-scale labeled still images. Given a face recognition
network that is pretrained in the image domain, the adaptation is achieved by
(i) distilling knowledge from the network to a video adaptation network through
feature matching, (ii) performing feature restoration through synthetic data
augmentation and (iii) learning a domain-invariant feature through a domain
adversarial discriminator. We further improve performance through a
discriminator-guided feature fusion that boosts high-quality frames while
eliminating those degraded by video domain-specific factors. Experiments on the
YouTube Faces and IJB-A datasets demonstrate that each module contributes to
our feature-level domain adaptation framework and substantially improves video
face recognition performance to achieve state-of-the-art accuracy. We
demonstrate qualitatively that the network learns to suppress diverse artifacts
in videos such as pose, illumination or occlusion without being explicitly
trained for them.
| 1 | 0 | 0 | 0 | 0 | 0 |
Unifying DAGs and UGs | We introduce a new class of graphical models that generalizes
Lauritzen-Wermuth-Frydenberg chain graphs by relaxing the semi-directed
acyclity constraint so that only directed cycles are forbidden. Moreover, up to
two edges are allowed between any pair of nodes. Specifically, we present
local, pairwise and global Markov properties for the new graphical models and
prove their equivalence. We also present an equivalent factorization property.
Finally, we present a causal interpretation of the new models.
| 1 | 0 | 0 | 1 | 0 | 0 |
Program Completionin the Input Language of GRINGO | We argue that turning a logic program into a set of completed definitions can
be sometimes thought of as the "reverse engineering" process of generating a
set of conditions that could serve as a specification for it. Accordingly, it
may be useful to define completion for a large class of ASP programs and to
automate the process of generating and simplifying completion formulas.
Examining the output produced by this kind of software may help programmers to
see more clearly what their program does, and to what degree its behavior
conforms with their expectations. As a step toward this goal, we propose here a
definition of program completion for a large class of programs in the input
language of the ASP grounder GRINGO, and study its properties. This note is
under consideration for publication in Theory and Practice of Logic
Programming.
| 1 | 0 | 0 | 0 | 0 | 0 |
Numerical modelling of surface water wave interaction with a moving wall | In the present manuscript, we consider the practical problem of wave
interaction with a vertical wall. However, the novelty here consists in the
fact that the wall can move horizontally due to a system of springs. The water
wave evolution is described with the free surface potential flow model. Then, a
semi-analytical numerical method is presented. It is based on a mapping
technique and a finite difference scheme in the transformed domain. The idea is
to pose the equations on a fixed domain. This method is thoroughly tested and
validated in our study. By choosing specific values of spring parameters, this
system can be used to damp (or in other words to extract the energy of)
incident water waves.
| 1 | 1 | 0 | 0 | 0 | 0 |
Dynamic Curriculum Learning for Imbalanced Data Classification | Human attribute analysis is a challenging task in the field of computer
vision, since the data is largely imbalance-distributed. Common techniques such
as re-sampling and cost-sensitive learning require prior-knowledge to train the
system. To address this problem, we propose a unified framework called Dynamic
Curriculum Learning (DCL) to online adaptively adjust the sampling strategy and
loss learning in single batch, which resulting in better generalization and
discrimination. Inspired by the curriculum learning, DCL consists of two level
curriculum schedulers: (1) sampling scheduler not only manages the data
distribution from imbalanced to balanced but also from easy to hard; (2) loss
scheduler controls the learning importance between classification and metric
learning loss. Learning from these two schedulers, we demonstrate our DCL
framework with the new state-of-the-art performance on the widely used face
attribute dataset CelebA and pedestrian attribute dataset RAP.
| 1 | 0 | 0 | 0 | 0 | 0 |
Causal Discovery in the Presence of Measurement Error: Identifiability Conditions | Measurement error in the observed values of the variables can greatly change
the output of various causal discovery methods. This problem has received much
attention in multiple fields, but it is not clear to what extent the causal
model for the measurement-error-free variables can be identified in the
presence of measurement error with unknown variance. In this paper, we study
precise sufficient identifiability conditions for the measurement-error-free
causal model and show what information of the causal model can be recovered
from observed data. In particular, we present two different sets of
identifiability conditions, based on the second-order statistics and
higher-order statistics of the data, respectively. The former was inspired by
the relationship between the generating model of the
measurement-error-contaminated data and the factor analysis model, and the
latter makes use of the identifiability result of the over-complete independent
component analysis problem.
| 1 | 0 | 0 | 1 | 0 | 0 |
Elliptic fibrations on covers of the elliptic modular surface of level 5 | We consider the K3 surfaces that arise as double covers of the elliptic
modular surface of level 5, $R_{5,5}$. Such surfaces have a natural elliptic
fibration induced by the fibration on $R_{5,5}$. Moreover, they admit several
other elliptic fibrations. We describe such fibrations in terms of linear
systems of curves on $R_{5,5}$. This has a major advantage over other methods
of classification of elliptic fibrations, namely, a simple algorithm that has
as input equations of linear systems of curves in the projective plane yields a
Weierstrass equation for each elliptic fibration. We deal in detail with the
cases for which the double cover is branched over the two reducible fibers of
type $I_5$ and for which it is branched over two smooth fibers, giving a
complete list of elliptic fibrations for these two scenarios.
| 0 | 0 | 1 | 0 | 0 | 0 |
A wide field-of-view crossed Dragone optical system using the anamorphic aspherical surfaces | A side-fed crossed Dragone telescope provides a wide field-of-view. This type
of a telescope is commonly employed in the measurement of cosmic microwave
background (CMB) polarization, which requires an image-space telecentric
telescope with a large focal plane over broadband coverage. We report the
design of the wide field-of-view crossed Dragone optical system using the
anamorphic aspherical surfaces with correction terms up to the 10th order. We
achieved the Strehl ratio larger than 0.95 over 32 by 18 square degrees at 150
GHz. This design is an image-space telecentric and fully diffraction-limited
system below 400 GHz. We discuss the optical performance in the uniformity of
the axially symmetric point spread function and telecentricity over the
field-of-view. We also address the analysis to evaluate the polarization
properties, including the instrumental polarization, extinction rate, and
polarization angle rotation. This work is a part of programs to design a
compact multi-color wide field-of-view telescope for LiteBIRD, which is a next
generation CMB polarization satellite.
| 0 | 1 | 0 | 0 | 0 | 0 |
Searching for Biophysically Realistic Parameters for Dynamic Neuron Models by Genetic Algorithms from Calcium Imaging Recording | Individual Neurons in the nervous systems exploit various dynamics. To
capture these dynamics for single neurons, we tune the parameters of an
electrophysiological model of nerve cells, to fit experimental data obtained by
calcium imaging. A search for the biophysical parameters of this model is
performed by means of a genetic algorithm, where the model neuron is exposed to
a predefined input current representing overall inputs from other parts of the
nervous system. The algorithm is then constrained for keeping the ion-channel
currents within reasonable ranges, while producing the best fit to a calcium
imaging time series of the AVA interneuron, from the brain of the soil-worm, C.
elegans. Our settings enable us to project a set of biophysical parameters to
the the neuron kinetics observed in neuronal imaging.
| 1 | 0 | 0 | 0 | 0 | 0 |
Matrix elements of irreducible representations of $\mathrm{SU}(n+1)\times\mathrm{SU}(n+1)$ and multivariable matrix-valued orthogonal polynomials | In Part 1 we study the spherical functions on compact symmetric pairs of
arbitrary rank under a suitable multiplicity freeness assumption and additional
conditions on the branching rules. The spherical functions are taking values in
the spaces of linear operators of a finite dimensional representation of the
subgroup, so the spherical functions are matrix-valued. Under these assumptions
these functions can be described in terms of matrix-valued orthogonal
polynomials in several variables, where the number of variables is the rank of
the compact symmetric pair. Moreover, these polynomials are uniquely determined
as simultaneous eigenfunctions of a commutative algebra of differential
operators.
In Part 2 we verify that the group case $\mathrm{SU}(n+1)$ meets all the
conditions that we impose in Part 1. For any $k\in\mathbb{N}_{0}$ we obtain
families of orthogonal polynomials in $n$ variables with values in the $N\times
N$-matrices, where $N=\binom{n+k}{k}$. The case $k=0$ leads to the classical
Heckman-Opdam polynomials of type $A_{n}$ with geometric parameter. For $k=1$
we obtain the most complete results. In this case we give an explicit
expression of the matrix weight, which we show to be irreducible whenever
$n\ge2$. We also give explicit expressions of the spherical functions that
determine the matrix weight for $k=1$. These expressions are used to calculate
the spherical functions that determine the matrix weight for general $k$ up to
invertible upper-triangular matrices. This generalizes and gives a new proof of
a formula originally obtained by Koornwinder for the case $n=1$. The commuting
family of differential operators that have the matrix-valued polynomials as
simultaneous eigenfunctions contains an element of order one. We give explicit
formulas for differential operators of order one and two for $(n,k)$ equal to
$(2,1)$ and $(3,1)$.
| 0 | 0 | 1 | 0 | 0 | 0 |
On the variance of internode distance under the multispecies coalescent | We consider the problem of estimating species trees from unrooted gene tree
topologies in the presence of incomplete lineage sorting, a common phenomenon
that creates gene tree heterogeneity in multilocus datasets. One popular class
of reconstruction methods in this setting is based on internode distances, i.e.
the average graph distance between pairs of species across gene trees. While
statistical consistency in the limit of large numbers of loci has been
established in some cases, little is known about the sample complexity of such
methods. Here we make progress on this question by deriving a lower bound on
the worst-case variance of internode distance which depends linearly on the
corresponding graph distance in the species tree. We also discuss some
algorithmic implications.
| 0 | 0 | 0 | 0 | 1 | 0 |
DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN | Recently, the introduction of the generative adversarial network (GAN) and
its variants has enabled the generation of realistic synthetic samples, which
has been used for enlarging training sets. Previous work primarily focused on
data augmentation for semi-supervised and supervised tasks. In this paper, we
instead focus on unsupervised anomaly detection and propose a novel generative
data augmentation framework optimized for this task. In particular, we propose
to oversample infrequent normal samples - normal samples that occur with small
probability, e.g., rare normal events. We show that these samples are
responsible for false positives in anomaly detection. However, oversampling of
infrequent normal samples is challenging for real-world high-dimensional data
with multimodal distributions. To address this challenge, we propose to use a
GAN variant known as the adversarial autoencoder (AAE) to transform the
high-dimensional multimodal data distributions into low-dimensional unimodal
latent distributions with well-defined tail probability. Then, we
systematically oversample at the `edge' of the latent distributions to increase
the density of infrequent normal samples. We show that our oversampling
pipeline is a unified one: it is generally applicable to datasets with
different complex data distributions. To the best of our knowledge, our method
is the first data augmentation technique focused on improving performance in
unsupervised anomaly detection. We validate our method by demonstrating
consistent improvements across several real-world datasets.
| 0 | 0 | 0 | 1 | 0 | 0 |
Online Human Gesture Recognition using Recurrent Neural Networks and Wearable Sensors | Gestures are a natural communication modality for humans. The ability to
interpret gestures is fundamental for robots aiming to naturally interact with
humans. Wearable sensors are promising to monitor human activity, in particular
the usage of triaxial accelerometers for gesture recognition have been
explored. Despite this, the state of the art presents lack of systems for
reliable online gesture recognition using accelerometer data. The article
proposes SLOTH, an architecture for online gesture recognition, based on a
wearable triaxial accelerometer, a Recurrent Neural Network (RNN) probabilistic
classifier and a procedure for continuous gesture detection, relying on
modelling gesture probabilities, that guarantees (i) good recognition results
in terms of precision and recall, (ii) immediate system reactivity.
| 1 | 0 | 0 | 0 | 0 | 0 |
Conditional Variance Penalties and Domain Shift Robustness | When training a deep network for image classification, one can broadly
distinguish between two types of latent features of images that will drive the
classification. Following the notation of Gong et al. (2016), we can divide
latent features into (i) "core" features $X^\text{core}$ whose distribution
$X^\text{core}\vert Y$ does not change substantially across domains and (ii)
"style" features $X^{\text{style}}$ whose distribution $X^{\text{style}}\vert
Y$ can change substantially across domains. These latter orthogonal features
would generally include features such as rotation, image quality or brightness
but also more complex ones like hair color or posture for images of persons.
Guarding against future adversarial domain shifts implies that the influence of
the second type of style features in the prediction has to be limited. We
assume that the domain itself is not observed and hence a latent variable. We
do assume, however, that we can sometimes observe a typically discrete
identifier or $\mathrm{ID}$ variable. We know in some applications, for
example, that two images show the same person, and $\mathrm{ID}$ then refers to
the identity of the person. The method requires only a small fraction of images
to have an $\mathrm{ID}$ variable. We group data samples if they share the same
class and identifier $(Y,\mathrm{ID})=(y,\mathrm{id})$ and penalize the
conditional variance of the prediction if we condition on $(Y,\mathrm{ID})$.
Using this approach is shown to protect against shifts in the distribution of
the style variables for both regression and classification models.
Specifically, the conditional variance penalty CoRe is shown to be equivalent
to minimizing the risk under noise interventions in a regression setting and is
shown to lead to adversarial risk consistency in a partially linear
classification setting.
| 1 | 0 | 0 | 1 | 0 | 0 |
The First Optical Spectra of Wolf Rayet Stars in M101 Revealed with Gemini/GMOS | Deep narrow-band HST imaging of the iconic spiral galaxy M101 has revealed
over a thousand new Wolf Rayet (WR) candidates. We report spectrographic
confirmation of 10 HeII emission line sources hosting 15 WR stars. We find WR
stars present at both sub- and super-solar metalicities with WC stars favouring
more metal-rich regions compared to WN stars. We investigate the association of
WR stars with HII regions using archival HST imaging and conclude that the
majority of WR stars are in or associated with HII regions. Of the 10 emission
lines sources, only one appears to be unassociated with a star-forming region.
Our spectroscopic survey provides confidence that our narrow-band photometric
candidates are in fact bonafide WR stars, which will allow us to characterise
the progenitors of any core-collapse supernovae that erupt in the future in
M101.
| 0 | 1 | 0 | 0 | 0 | 0 |
Machine Learning Topological Invariants with Neural Networks | In this Letter we supervisedly train neural networks to distinguish different
topological phases in the context of topological band insulators. After
training with Hamiltonians of one-dimensional insulators with chiral symmetry,
the neural network can predict their topological winding numbers with nearly
100% accuracy, even for Hamiltonians with larger winding numbers that are not
included in the training data. These results show a remarkable success that the
neural network can capture the global and nonlinear topological features of
quantum phases from local inputs. By opening up the neural network, we confirm
that the network does learn the discrete version of the winding number formula.
We also make a couple of remarks regarding the role of the symmetry and the
opposite effect of regularization techniques when applying machine learning to
physical systems.
| 1 | 1 | 0 | 0 | 0 | 0 |
Topics and Label Propagation: Best of Both Worlds for Weakly Supervised Text Classification | We propose a Label Propagation based algorithm for weakly supervised text
classification. We construct a graph where each document is represented by a
node and edge weights represent similarities among the documents. Additionally,
we discover underlying topics using Latent Dirichlet Allocation (LDA) and
enrich the document graph by including the topics in the form of additional
nodes. The edge weights between a topic and a text document represent level of
"affinity" between them. Our approach does not require document level
labelling, instead it expects manual labels only for topic nodes. This
significantly minimizes the level of supervision needed as only a few topics
are observed to be enough for achieving sufficiently high accuracy. The Label
Propagation Algorithm is employed on this enriched graph to propagate labels
among the nodes. Our approach combines the advantages of Label Propagation
(through document-document similarities) and Topic Modelling (for minimal but
smart supervision). We demonstrate the effectiveness of our approach on various
datasets and compare with state-of-the-art weakly supervised text
classification approaches.
| 1 | 0 | 0 | 0 | 0 | 0 |
Parallel Markov Chain Monte Carlo for the Indian Buffet Process | Indian Buffet Process based models are an elegant way for discovering
underlying features within a data set, but inference in such models can be
slow. Inferring underlying features using Markov chain Monte Carlo either
relies on an uncollapsed representation, which leads to poor mixing, or on a
collapsed representation, which leads to a quadratic increase in computational
complexity. Existing attempts at distributing inference have introduced
additional approximation within the inference procedure. In this paper we
present a novel algorithm to perform asymptotically exact parallel Markov chain
Monte Carlo inference for Indian Buffet Process models. We take advantage of
the fact that the features are conditionally independent under the
beta-Bernoulli process. Because of this conditional independence, we can
partition the features into two parts: one part containing only the finitely
many instantiated features and the other part containing the infinite tail of
uninstantiated features. For the finite partition, parallel inference is simple
given the instantiation of features. But for the infinite tail, performing
uncollapsed MCMC leads to poor mixing and hence we collapse out the features.
The resulting hybrid sampler, while being parallel, produces samples
asymptotically from the true posterior.
| 0 | 0 | 0 | 1 | 0 | 0 |
The Robot Routing Problem for Collecting Aggregate Stochastic Rewards | We propose a new model for formalizing reward collection problems on graphs
with dynamically generated rewards which may appear and disappear based on a
stochastic model. The *robot routing problem* is modeled as a graph whose nodes
are stochastic processes generating potential rewards over discrete time. The
rewards are generated according to the stochastic process, but at each step, an
existing reward disappears with a given probability. The edges in the graph
encode the (unit-distance) paths between the rewards' locations. On visiting a
node, the robot collects the accumulated reward at the node at that time, but
traveling between the nodes takes time. The optimization question asks to
compute an optimal (or epsilon-optimal) path that maximizes the expected
collected rewards.
We consider the finite and infinite-horizon robot routing problems. For
finite-horizon, the goal is to maximize the total expected reward, while for
infinite horizon we consider limit-average objectives. We study the
computational and strategy complexity of these problems, establish NP-lower
bounds and show that optimal strategies require memory in general. We also
provide an algorithm for computing epsilon-optimal infinite paths for arbitrary
epsilon > 0.
| 1 | 0 | 1 | 0 | 0 | 0 |
Estimating occupation time functionals | We study the estimation of integral type functionals $\int_{0}^{t}f(X_{r})dr$
for a function $f$ and a $d$-dimensional càdlàg process $X$ with respect to
discrete observations by a Riemann-sum estimator. Based on novel semimartingale
approximations in the Fourier domain, central limit theorems are proved for
$L^{2}$-Sobolev functions $f$ with fractional smoothness and continuous Itô
semimartingales $X$. General $L^{2}(\mathbb{P})$-upper bounds on the error for
càdlàg processes are given under weak assumptions. These bounds combine and
generalize all previously obtained results in the literature and apply also to
non-Markovian processes. Several detailed examples are discussed. As
application the approximation of local times for fractional Brownian motion is
studied. The optimality of the $L^{2}(\mathbb{P})$-upper bounds is shown by
proving the corresponding lower bounds in case of Brownian motion.
| 0 | 0 | 1 | 1 | 0 | 0 |
Bootstrapping a Lexicon for Emotional Arousal in Software Engineering | Emotional arousal increases activation and performance but may also lead to
burnout in software development. We present the first version of a Software
Engineering Arousal lexicon (SEA) that is specifically designed to address the
problem of emotional arousal in the software developer ecosystem. SEA is built
using a bootstrapping approach that combines word embedding model trained on
issue-tracking data and manual scoring of items in the lexicon. We show that
our lexicon is able to differentiate between issue priorities, which are a
source of emotional activation and then act as a proxy for arousal. The best
performance is obtained by combining SEA (428 words) with a previously created
general purpose lexicon by Warriner et al. (13,915 words) and it achieves
Cohen's d effect sizes up to 0.5.
| 1 | 0 | 0 | 0 | 0 | 0 |
Early Solar System irradiation quantified by linked vanadium and beryllium isotope variations in meteorites | X-ray emission in young stellar objects (YSOs) is orders of magnitude more
intense than in main sequence stars1,2, suggestive of cosmic ray irradiation of
surrounding accretion disks. Protoplanetary disk irradiation has been detected
around YSOs by HERSCHEL3. In our solar system, short-lived 10Be (half-life =
1.39 My4), which cannot be produced by stellar nucleosynthesis, was discovered
in the oldest solar system solids, the calcium-aluminium-rich inclusions
(CAIs)5. The high 10Be abundance, as well as detection of other irradiation
tracers6,7, suggest 10Be likely originates from cosmic ray irradiation caused
by solar flares8. Nevertheless, the nature of these flares (gradual or
impulsive), the target (gas or dust), and the duration and location of
irradiation remain unknown. Here we use the vanadium isotopic composition,
together with initial 10Be abundance to quantify irradiation conditions in the
early Solar System9. For the initial 10Be abundances recorded in CAIs, 50V
excesses of a few per mil relative to chondrites have been predicted10,11. We
report 50V excesses in CAIs up to 4.4 per mil that co-vary with 10Be abundance.
Their co-variation dictates that excess 50V and 10Be were synthesised through
irradiation of refractory dust. Modelling of the production rate of 50V and
10Be demonstrates that the dust was exposed to solar cosmic rays produced by
gradual flares for less than 300 years at about 0.1 au from the protoSun.
| 0 | 1 | 0 | 0 | 0 | 0 |
Two classes of nonlocal Evolution Equations related by a shared Traveling Wave Problem | We consider reaction-diffusion equations and Korteweg-de Vries-Burgers (KdVB)
equations, i.e. scalar conservation laws with diffusive-dispersive
regularization. We review the existence of traveling wave solutions for these
two classes of evolution equations. For classical equations the traveling wave
problem (TWP) for a local KdVB equation can be identified with the TWP for a
reaction-diffusion equation. In this article we study this relationship for
these two classes of evolution equations with nonlocal diffusion/dispersion.
This connection is especially useful, if the TW equation is not studied
directly, but the existence of a TWS is proven using one of the evolution
equations instead. Finally, we present three models from fluid dynamics and
discuss the TWP via its link to associated reaction-diffusion equations.
| 0 | 0 | 1 | 0 | 0 | 0 |
Analyzing IO Amplification in Linux File Systems | We present the first systematic analysis of read, write, and space
amplification in Linux file systems. While many researchers are tackling write
amplification in key-value stores, IO amplification in file systems has been
largely unexplored. We analyze data and metadata operations on five widely-used
Linux file systems: ext2, ext4, XFS, btrfs, and F2FS. We find that data
operations result in significant write amplification (2-32X) and that metadata
operations have a large IO cost. For example, a single rename requires 648 KB
write IO in btrfs. We also find that small random reads result in read
amplification of 2-13X. Based on these observations, we present the CReWS
conjecture about the relationship between IO amplification, consistency, and
storage space utilization. We hope this paper spurs people to design future
file systems with less IO amplification, especially for non-volatile memory
technologies.
| 1 | 0 | 0 | 0 | 0 | 0 |
Feature analysis of multidisciplinary scientific collaboration patterns based on PNAS | The features of collaboration patterns are often considered to be different
from discipline to discipline. Meanwhile, collaborating among disciplines is an
obvious feature emerged in modern scientific research, which incubates several
interdisciplines. The features of collaborations in and among the disciplines
of biological, physical and social sciences are analyzed based on 52,803 papers
published in a multidisciplinary journal PNAS during 1999 to 2013. From those
data, we found similar transitivity and assortativity of collaboration patterns
as well as the identical distribution type of collaborators per author and that
of papers per author, namely a mixture of generalized Poisson and power-law
distributions. In addition, we found that interdisciplinary research is
undertaken by a considerable fraction of authors, not just those with many
collaborators or those with many papers. This case study provides a window for
understanding aspects of multidisciplinary and interdisciplinary collaboration
patterns.
| 1 | 1 | 0 | 0 | 0 | 0 |
Inflationary magneto-(non)genesis, increasing kinetic couplings, and the strong coupling problem | We study the generation of magnetic fields during inflation making use of a
coupling of the inflaton and moduli fields to electromagnetism via the photon
kinetic term, and assuming that the coupling is an increasing function of time.
We demonstrate that the strong coupling problem of inflationary magnetogenesis
can be avoided by incorporating the destabilization of moduli fields after
inflation. The magnetic field always dominates over the electric one, and thus
the severe constraints on the latter from backreaction, which are the demanding
obstacles in the case of a decreasing coupling function, do not apply to the
current scenario. However, we show that this loophole to the strong coupling
problem comes at a price: the normalization of the amplitude of magnetic fields
is determined by this coupling term and is therefore suppressed by a large
factor after the moduli destabilization completes. From this we conclude that
there is no self-consistent and generic realization of primordial
magnetogenesis producing scale-invariant fields in the case of an increasing
kinetic coupling.
| 0 | 1 | 0 | 0 | 0 | 0 |
HornDroid: Practical and Sound Static Analysis of Android Applications by SMT Solving | We present HornDroid, a new tool for the static analysis of information flow
properties in Android applications. The core idea underlying HornDroid is to
use Horn clauses for soundly abstracting the semantics of Android applications
and to express security properties as a set of proof obligations that are
automatically discharged by an off-the-shelf SMT solver. This approach makes it
possible to fine-tune the analysis in order to achieve a high degree of
precision while still using off-the-shelf verification tools, thereby
leveraging the recent advances in this field. As a matter of fact, HornDroid
outperforms state-of-the-art Android static analysis tools on benchmarks
proposed by the community. Moreover, HornDroid is the first static analysis
tool for Android to come with a formal proof of soundness, which covers the
core of the analysis technique: besides yielding correctness assurances, this
proof allowed us to identify some critical corner-cases that affect the
soundness guarantees provided by some of the previous static analysis tools for
Android.
| 1 | 0 | 0 | 0 | 0 | 0 |
Assessing the reliability polynomial based on percolation theory | In this paper, we study the robustness of network topologies. We use the
concept of percolation as measuring tool to assess the reliability polynomial
of those systems which can be modeled as a general inhomogeneous random graph
as well as scale-free random graph.
| 0 | 0 | 1 | 1 | 0 | 0 |
Interval Exchange Transformations and Low-Discrepancy | In [Mas82] and [Vee78] it was proved independently that almost every interval
exchange transformation is uniquely ergodic. The Birkhoff ergodic theorem
implies that these maps mainly have uniformly distributed orbits. This raises
the question under which conditions the orbits yield low-discrepancy sequences.
The case of $n=2$ intervals corresponds to circle rotation, where conditions
for low-discrepancy are well-known. In this paper, we give corresponding
conditions in the case $n=3$. Furthermore, we construct infinitely many
interval exchange transformations with low-discrepancy orbits for $n \geq 4$.
We also show that these examples do not coincide with $LS$-sequences if $S \geq
2$.
| 0 | 0 | 1 | 0 | 0 | 0 |
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