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VQABQ: Visual Question Answering by Basic Questions | Taking an image and question as the input of our method, it can output the
text-based answer of the query question about the given image, so called Visual
Question Answering (VQA). There are two main modules in our algorithm. Given a
natural language question about an image, the first module takes the question
as input and then outputs the basic questions of the main given question. The
second module takes the main question, image and these basic questions as input
and then outputs the text-based answer of the main question. We formulate the
basic questions generation problem as a LASSO optimization problem, and also
propose a criterion about how to exploit these basic questions to help answer
main question. Our method is evaluated on the challenging VQA dataset and
yields state-of-the-art accuracy, 60.34% in open-ended task.
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Power-Constrained Secrecy Rate Maximization for Joint Relay and Jammer Selection Assisted Wireless Networks | In this paper, we examine the physical layer security for cooperative
wireless networks with multiple intermediate nodes, where the
decode-and-forward (DF) protocol is considered. We propose a new joint relay
and jammer selection (JRJS) scheme for protecting wireless communications
against eavesdropping, where an intermediate node is selected as the relay for
the sake of forwarding the source signal to the destination and meanwhile, the
remaining intermediate nodes are employed to act as friendly jammers which
broadcast the artificial noise for disturbing the eavesdropper. We further
investigate the power allocation among the source, relay and friendly jammers
for maximizing the secrecy rate of proposed JRJS scheme and derive a
closed-form sub-optimal solution. Specificially, all the intermediate nodes
which successfully decode the source signal are considered as relay candidates.
For each candidate, we derive the sub-optimal closed-form power allocation
solution and obtain the secrecy rate result of the corresponding JRJS scheme.
Then, the candidate which is capable of achieving the highest secrecy rate is
selected as the relay. Two assumptions about the channel state information
(CSI), namely the full CSI (FCSI) and partial CSI (PCSI), are considered.
Simulation results show that the proposed JRJS scheme outperforms the
conventional pure relay selection, pure jamming and GSVD based beamforming
schemes in terms of secrecy rate. Additionally, the proposed FCSI based power
allocation (FCSI-PA) and PCSI based power allocation (PCSI-PA) schemes both
achieve higher secrecy rates than the equal power allocation (EPA) scheme.
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Demagnetization of cubic Gd-Ba-Cu-O bulk superconductor by cross-fields: measurements and 3D modelling | Superconducting bulks, acting as high-field permanent magnets, are promising
for many applications. An important effect in bulk permanent magnets is
crossed-field demagnetization, which can reduce the magnetic field in
superconductors due to relatively small transverse fields. Crossed-field
demagnetization has not been studied in sample shapes such as rectangular
prisms or cubes. This contribution presents a study based on both 3D numerical
modelling and experiments. We study a cubic Gd-Ba-Cu-O bulk superconductor
sample of size 6 mm magnetized by field cooling in an external field of around
1.3 T, which is later submitted to crossed-field magnetic fields of up to 164
mT. Modelling results agree with experiments, except at transverse fields 50\%
or above of the initial trapped field. The current paths present a strong 3D
nature. For instance, at the mid-plane perpendicular to the initial magnetizing
field, the current density in this direction changes smoothly from the critical
magnitude, ${J_c}$, at the lateral sides to zero at a certain penetration
depth. This indicates a rotation of the current density with magnitude ${J_c}$,
and hence force free effects like flux cutting are expected to play a
significant role.
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Resonance-Free Light Recycling | The inability to efficiently tune the optical properties of waveguiding
structures has been one of the major hurdles for the future scalability of
integrated photonic systems. In silicon photonics, although dynamic tuning has
been achieved with various mechanisms, even the most effective thermo-optic
effect offers a refractive index change of only $1.86 \times 10^{-4} K^{-1}$.
To enhance this small change, light recycling based on resonators has been
employed in order to realize efficient modulators, phase shifters, and optical
switches. However, the resonant enhancement comes at a great cost of optical
bandwidth, fabrication tolerance and system scalability. Here we demonstrate a
scalable light recycling approach based on spatial-mode multiplexing. Our
approach offers a fabrication tolerance of ${\pm}$ 15 nm, in stark contrast to
the non-scalable subnanometer tolerance in typical silicon resonators. We
experimentally demonstrate light recycling up to 7 passes with an optical
bandwidth greater than 100 nm. We realize power-efficient thermo-optic phase
shifters that require only 1.7 mW per ${\pi}$, representing more than an 8-fold
reduction in the power consumption.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Computationally Efficient and Practically Feasible Two Microphones Blind Speech Separation Method | Traditionally, Blind Speech Separation techniques are computationally
expensive as they update the demixing matrix at every time frame index, making
them impractical to use in many Real-Time applications. In this paper, a robust
data-driven two-microphone sound source localization method is used as a
criterion to reduce the computational complexity of the Independent Vector
Analysis (IVA) Blind Speech Separation (BSS) method. IVA is used to separate
convolutedly mixed speech and noise sources. The practical feasibility of the
proposed method is proved by implementing it on a smartphone device to separate
speech and noise in Real-World scenarios for Hearing-Aid applications. The
experimental results with objective and subjective tests reveal the practical
usability of the developed method in many real-world applications.
| 1 | 0 | 0 | 0 | 0 | 0 |
Magnetic ground state and magnon-phonon interaction in multiferroic h-YMnO$_3$ | Inelastic neutron scattering has been used to study the magneto-elastic
excitations in the multiferroic manganite hexagonal YMnO$_3$. An avoided
crossing is found between magnon and phonon modes close to the Brillouin zone
boundary in the $(a,b)$-plane. Neutron polarization analysis reveals that this
mode has mixed magnon-phonon character. An external magnetic field along the
$c$-axis is observed to cause a linear field-induced splitting of one of the
spin wave branches. A theoretical description is performed, using a Heisenberg
model of localized spins, acoustic phonon modes and a magneto-elastic coupling
via the single-ion magnetostriction. The model quantitatively reproduces the
dispersion and intensities of all modes in the full Brillouin zone, describes
the observed magnon-phonon hybridized modes, and quantifies the magneto-elastic
coupling. The combined information, including the field-induced magnon
splitting, allows us to exclude several of the earlier proposed models and
point to the correct magnetic ground state symmetry, and provides an effective
dynamic model relevant for the multiferroic hexagonal manganites.
| 0 | 1 | 0 | 0 | 0 | 0 |
Neuromodulation of Neuromorphic Circuits | We present a novel methodology to enable control of a neuromorphic circuit in
close analogy with the physiological neuromodulation of a single neuron. The
methodology is general in that it only relies on a parallel interconnection of
elementary voltage-controlled current sources. In contrast to controlling a
nonlinear circuit through the parameter tuning of a state-space model, our
approach is purely input-output. The circuit elements are controlled and
interconnected to shape the current-voltage characteristics (I-V curves) of the
circuit in prescribed timescales. In turn, shaping those I-V curves determines
the excitability properties of the circuit. We show that this methodology
enables both robust and accurate control of the circuit behavior and resembles
the biophysical mechanisms of neuromodulation. As a proof of concept, we
simulate a SPICE model composed of MOSFET transconductance amplifiers operating
in the weak inversion regime.
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Frank-Wolfe Style Algorithms for Large Scale Optimization | We introduce a few variants on Frank-Wolfe style algorithms suitable for
large scale optimization. We show how to modify the standard Frank-Wolfe
algorithm using stochastic gradients, approximate subproblem solutions, and
sketched decision variables in order to scale to enormous problems while
preserving (up to constants) the optimal convergence rate
$\mathcal{O}(\frac{1}{k})$.
| 0 | 0 | 0 | 1 | 0 | 0 |
Theoretical Description of Micromaser in the Ultrastrong-Coupling Regime | We theoretically investigate an ultrastrongly-coupled micromaser based on
Rydberg atoms interacting with a superconducting LC resonator, where the common
rotating-wave approximation and slowly-varying-envelope approximation are no
longer applicable. The effect of counter-rotating terms on the masing dynamics
is studied in detail. We find that the intraresonator electric energy declines
and the microwave oscillation frequency shifts significantly in the regime of
ultrastrong coupling. Additionally, the micromaser phase fluctuation is
suppressed, resulting in a reduced spectral linewidth.
| 0 | 1 | 0 | 0 | 0 | 0 |
Compatibility of quasi-orderings and valuations; A Baer-Krull Theorem for quasi-ordered Rings | In his work of 1969, Merle E. Manis introduced valuations on commutative
rings. Recently, the class of totally quasi-ordered rings was developped by the
second author. In the present paper, we establish the notion of compatibility
between valuations and quasi-orders on rings, leading to a definition of the
rank of a quasi-ordered ring. Moreover, we prove a Baer-Krull Theorem for
quasi-ordered rings: fixing a Manis valuation v on R, we characterize all
v-compatible quasi-orders of R by lifting the quasi-orders from the residue
class ring to R itself.
| 0 | 0 | 1 | 0 | 0 | 0 |
Feature Decomposition Based Saliency Detection in Electron Cryo-Tomograms | Electron Cryo-Tomography (ECT) allows 3D visualization of subcellular
structures at the submolecular resolution in close to the native state.
However, due to the high degree of structural complexity and imaging limits,
the automatic segmentation of cellular components from ECT images is very
difficult. To complement and speed up existing segmentation methods, it is
desirable to develop a generic cell component segmentation method that is 1)
not specific to particular types of cellular components, 2) able to segment
unknown cellular components, 3) fully unsupervised and does not rely on the
availability of training data. As an important step towards this goal, in this
paper, we propose a saliency detection method that computes the likelihood that
a subregion in a tomogram stands out from the background. Our method consists
of four steps: supervoxel over-segmentation, feature extraction, feature matrix
decomposition, and computation of saliency. The method produces a distribution
map that represents the regions' saliency in tomograms. Our experiments show
that our method can successfully label most salient regions detected by a human
observer, and able to filter out regions not containing cellular components.
Therefore, our method can remove the majority of the background region, and
significantly speed up the subsequent processing of segmentation and
recognition of cellular components captured by ECT.
| 0 | 0 | 0 | 1 | 1 | 0 |
A Characterization of Integral ISS for Switched and Time-varying Systems | Most of the existing characterizations of the integral input-to-state
stability (iISS) property are not valid for time-varying or switched systems in
cases where converse Lyapunov theorems for stability are not available. This
note provides a characterization that is valid for switched and time-varying
systems, and shows that natural extensions of some of the existing
characterizations result in only sufficient but not necessary conditions. The
results provided also pinpoint suitable iISS gains and relate these to supply
functions and bounds on the function defining the system dynamics.
| 1 | 0 | 1 | 0 | 0 | 0 |
Low-temperature marginal ferromagnetism explains anomalous scale-free correlations in natural flocks | We introduce a new ferromagnetic model capable of reproducing one of the most
intriguing properties of collective behaviour in starling flocks, namely the
fact that strong collective order of the system coexists with scale-free
correlations of the modulus of the microscopic degrees of freedom, that is the
birds' speeds. The key idea of the new theory is that the single-particle
potential needed to bound the modulus of the microscopic degrees of freedom
around a finite value, is marginal, that is has zero curvature. We study the
model by using mean-field approximation and Monte Carlo simulations in three
dimensions, complemented by finite-size scaling analysis. While at the standard
critical temperature, $T_c$, the properties of the marginal model are exactly
the same as a normal ferromagnet with continuous symmetry-breaking, our results
show that a novel zero-temperature critical point emerges, so that in its
deeply ordered phase the marginal model develops divergent susceptibility and
correlation length of the modulus of the microscopic degrees of freedom, in
complete analogy with experimental data on natural flocks of starlings.
| 0 | 0 | 0 | 0 | 1 | 0 |
Historical and personal recollections of Guido Altarelli | In this paper I will present a short scientific biography of Guido Altarelli,
briefly describing some of his most important seminal works. I will analyze in
great details the paper of the $q^2$ evolution of the effective quark
distribution: I will put this paper in a historical perspective, describing our
theoretical understanding at that time and the reasons why the paper was so
successful.
| 0 | 1 | 0 | 0 | 0 | 0 |
Segment Parameter Labelling in MCMC Mean-Shift Change Detection | This work addresses the problem of segmentation in time series data with
respect to a statistical parameter of interest in Bayesian models. It is common
to assume that the parameters are distinct within each segment. As such, many
Bayesian change point detection models do not exploit the segment parameter
patterns, which can improve performance. This work proposes a Bayesian
mean-shift change point detection algorithm that makes use of repetition in
segment parameters, by introducing segment class labels that utilise a
Dirichlet process prior. The performance of the proposed approach was assessed
on both synthetic and real world data, highlighting the enhanced performance
when using parameter labelling.
| 1 | 0 | 0 | 1 | 0 | 0 |
M/G/c/c state dependent queuing model for a road traffic system of two sections in tandem | We propose in this article a M/G/c/c state dependent queuing model for road
traffic flow. The model is based on finite capacity queuing theory which
captures the stationary density-flow relationships. It is also inspired from
the deterministic Godunov scheme for the road traffic simulation. We first
present a reformulation of the existing linear case of M/G/c/c state dependent
model, in order to use flow rather than speed variables. We then extend this
model in order to consider upstream traffic demand and downstream traffic
supply. After that, we propose the model for two road sections in tandem where
both sections influence each other. In order to deal with this mutual
dependence, we solve an implicit system given by an algebraic equation.
Finally, we derive some performance measures (throughput and expected travel
time). A comparison with results predicted by the M/G/c/c state dependent
queuing networks shows that the model we propose here captures really the
dynamics of the road traffic.
| 1 | 0 | 1 | 0 | 0 | 0 |
Laplace approximation and the natural gradient for Gaussian process regression with the heteroscedastic Student-t model | This paper considers the Laplace method to derive approximate inference for
the Gaussian process (GP) regression in the location and scale parameters of
the Student-t probabilistic model. This allows both mean and variance of the
data to vary as a function of covariates with the attractive feature that the
Student-t model has been widely used as a useful tool for robustifying data
analysis. The challenge in the approximate inference for the GP regression with
the Student-t probabilistic model, lies in the analytical intractability of the
posterior distribution and the lack of concavity of the log-likelihood
function. We present the natural gradient adaptation for the estimation process
which primarily relies on the property that the Student-t model naturally has
orthogonal parametrization with respect to the location and scale paramaters.
Due to this particular property of the model, we also introduce an alternative
Laplace approximation by using the Fisher information matrix in place of the
Hessian matrix of the negative log-likelihood function. According to
experiments this alternative approximation provides very similar posterior
approximations and predictive performance when compared to the traditional
Laplace approximation. We also compare both of these Laplace approximations
with the Monte Carlo Markov Chain (MCMC) method. Moreover, we compare our
heteroscedastic Student-t model and the GP regression with the heteroscedastic
Gaussian model. We also discuss how our approach can improve the inference
algorithm in cases where the probabilistic model assumed for the data is not
log-concave.
| 0 | 0 | 0 | 1 | 0 | 0 |
Fixed points of n-valued maps, the fixed point property and the case of surfaces -- a braid approach | We study the fixed point theory of n-valued maps of a space X using the fixed
point theory of maps between X and its configuration spaces. We give some
general results to decide whether an n-valued map can be deformed to a fixed
point free n-valued map. In the case of surfaces, we provide an algebraic
criterion in terms of the braid groups of X to study this problem. If X is
either the k-dimensional ball or an even-dimensional real or complex projective
space, we show that the fixed point property holds for n-valued maps for all n
$\ge$ 1, and we prove the same result for even-dimensional spheres for all n
$\ge$ 2. If X is the 2-torus, we classify the homotopy classes of 2-valued maps
in terms of the braid groups of X. We do not currently have a complete
characterisation of the homotopy classes of split 2-valued maps of the 2-torus
that contain a fixed point free representative, but we give an infinite family
of such homotopy classes.
| 0 | 0 | 1 | 0 | 0 | 0 |
Large Synoptic Survey Telescope Galaxies Science Roadmap | The Large Synoptic Survey Telescope (LSST) will enable revolutionary studies
of galaxies, dark matter, and black holes over cosmic time. The LSST Galaxies
Science Collaboration has identified a host of preparatory research tasks
required to leverage fully the LSST dataset for extragalactic science beyond
the study of dark energy. This Galaxies Science Roadmap provides a brief
introduction to critical extragalactic science to be conducted ahead of LSST
operations, and a detailed list of preparatory science tasks including the
motivation, activities, and deliverables associated with each. The Galaxies
Science Roadmap will serve as a guiding document for researchers interested in
conducting extragalactic science in anticipation of the forthcoming LSST era.
| 0 | 1 | 0 | 0 | 0 | 0 |
Multiresolution Coupled Vertical Equilibrium Model for Fast Flexible Simulation of CO$_2$ Storage | CO2 capture and storage is an important technology for mitigating climate
change. Design of efficient strategies for safe, long-term storage requires the
capability to efficiently simulate processes taking place on very different
temporal and spatial scales. The physical laws describing CO2 storage are the
same as for hydrocarbon recovery, but the characteristic spatial and temporal
scales are quite different. Petroleum reservoirs seldom extend more than tens
of kilometers and have operational horizons spanning decades. Injected CO2
needs to be safely contained for hundreds or thousands of years, during which
it can migrate hundreds or thousands of kilometers. Because of the vast scales
involved, conventional 3D reservoir simulation quickly becomes computationally
unfeasible. Large density difference between injected CO2 and resident brine
means that vertical segregation will take place relatively quickly, and
depth-integrated models assuming vertical equilibrium (VE) often represents a
better strategy to simulate long-term migration of CO2 in large-scale aquifer
systems. VE models have primarily been formulated for relatively simple rock
formations and have not been coupled to 3D simulation in a uniform way. In
particular, known VE simulations have not been applied to models of realistic
geology in which many flow compartments may exist in-between impermeable
layers. In this paper, we generalize the concept of VE models, formulated in
terms of well-proven reservoir simulation technology, to complex aquifer
systems with multiple layers and regions. We also introduce novel formulations
for multi-layered VE models by use of both direct spill and diffuse leakage
between individual layers. This new layered 3D model is then coupled to a
state-of-the-art, 3D black-oil type model.
| 0 | 1 | 0 | 0 | 0 | 0 |
Improved GelSight Tactile Sensor for Measuring Geometry and Slip | A GelSight sensor uses an elastomeric slab covered with a reflective membrane
to measure tactile signals. It measures the 3D geometry and contact force
information with high spacial resolution, and successfully helped many
challenging robot tasks. A previous sensor, based on a semi-specular membrane,
produces high resolution but with limited geometry accuracy. In this paper, we
describe a new design of GelSight for robot gripper, using a Lambertian
membrane and new illumination system, which gives greatly improved geometric
accuracy while retaining the compact size. We demonstrate its use in measuring
surface normals and reconstructing height maps using photometric stereo. We
also use it for the task of slip detection, using a combination of information
about relative motions on the membrane surface and the shear distortions. Using
a robotic arm and a set of 37 everyday objects with varied properties, we find
that the sensor can detect translational and rotational slip in general cases,
and can be used to improve the stability of the grasp.
| 1 | 0 | 0 | 0 | 0 | 0 |
Deep Learning for Design and Retrieval of Nano-photonic Structures | Our visual perception of our surroundings is ultimately limited by the
diffraction limit, which stipulates that optical information smaller than
roughly half the illumination wavelength is not retrievable. Over the past
decades, many breakthroughs have led to unprecedented imaging capabilities
beyond the diffraction-limit, with applications in biology and nanotechnology.
In this context, nano-photonics has revolutionized the field of optics in
recent years by enabling the manipulation of light-matter interaction with
subwavelength structures. However, despite the many advances in this field, its
impact and penetration in our daily life has been hindered by a convoluted and
iterative process, cycling through modeling, nanofabrication and
nano-characterization. The fundamental reason is the fact that not only the
prediction of the optical response is very time consuming and requires solving
Maxwell's equations with dedicated numerical packages. But, more significantly,
the inverse problem, i.e. designing a nanostructure with an on-demand optical
response, is currently a prohibitive task even with the most advanced numerical
tools due to the high non-linearity of the problem. Here, we harness the power
of Deep Learning, a new path in modern machine learning, and show its ability
to predict the geometry of nanostructures based solely on their far-field
response. This approach also addresses in a direct way the currently
inaccessible inverse problem breaking the ground for on-demand design of
optical response with applications such as sensing, imaging and also for
plasmon's mediated cancer thermotherapy.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Sparse Completely Positive Relaxation of the Modularity Maximization for Community Detection | In this paper, we consider the community detection problem under either the
stochastic block model (SBM) assumption or the degree-correlated stochastic
block model (DCSBM) assumption. The modularity maximization formulation for the
community detection problem is NP-hard in general. In this paper, we propose a
sparse and low-rank completely positive relaxation for the modularity
maximization problem, we then develop an efficient row-by-row (RBR) type block
coordinate descent (BCD) algorithm to solve the relaxation and prove an
$\mathcal{O}(1/\sqrt{N})$ convergence rate to a stationary point where $N$ is
the number of iterations. A fast rounding scheme is constructed to retrieve the
community structure from the solution. Non-asymptotic high probability bounds
on the misclassification rate are established to justify our approach. We
further develop an asynchronous parallel RBR algorithm to speed up the
convergence. Extensive numerical experiments on both synthetic and real world
networks show that the proposed approach enjoys advantages in both clustering
accuracy and numerical efficiency. Our numerical results indicate that the
newly proposed method is a quite competitive alternative for community
detection on sparse networks with over 50 million nodes.
| 0 | 0 | 1 | 0 | 0 | 0 |
Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning | In this paper, we build upon previous work on designing informative and
efficient Exploratory Landscape Analysis features for characterizing problems'
landscapes and show their effectiveness in automatically constructing algorithm
selection models in continuous black-box optimization problems. Focussing on
algorithm performance results of the COCO platform of several years, we
construct a representative set of high-performing complementary solvers and
present an algorithm selection model that - compared to the portfolio's single
best solver - on average requires less than half of the resources for solving a
given problem. Therefore, there is a huge gain in efficiency compared to
classical ensemble methods combined with an increased insight into problem
characteristics and algorithm properties by using informative features. Acting
on the assumption that the function set of the Black-Box Optimization Benchmark
is representative enough for practical applications the model allows for
selecting the best suited optimization algorithm within the considered set for
unseen problems prior to the optimization itself based on a small sample of
function evaluations. Note that such a sample can even be reused for the
initial population of an evolutionary (optimization) algorithm so that even the
feature costs become negligible.
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Car-following behavior of connected vehicles in a mixed traffic flow: modeling and stability analysis | Vehicle-to-vehicle communications can change the driving behavior of drivers
significantly by providing them rich information on downstream traffic flow
conditions. This study seeks to model the varying car-following behaviors
involving connected vehicles and human-driving vehicles in mixed traffic flow.
A revised car-following model is developed using an intelligent driver model
(IDM) to capture drivers' perceptions of their preceding traffic conditions
through vehicle-to-vehicle communications. Stability analysis of the mixed
traffic flow is conducted for a specific case. Numerical results show that the
stable region is apparently enlarged compared with the IDM.
| 1 | 0 | 0 | 0 | 0 | 0 |
A note on a two-weight estimate for the dyadic square function | We show that the two-weight estimate for the dyadic square function proved by
Lacey--Li in [2] is sharp.
| 0 | 0 | 1 | 0 | 0 | 0 |
The Oblique Orbit of WASP-107b from K2 Photometry | Observations of nine transits of WASP-107 during the {\it K2} mission reveal
three separate occasions when the planet crossed in front of a starspot. The
data confirm the stellar rotation period to be 17 days --- approximately three
times the planet's orbital period --- and suggest that large spots persist for
at least one full rotation. If the star had a low obliquity, at least two
additional spot crossings should have been observed. They were not observed,
giving evidence for a high obliquity. We use a simple geometric model to show
that the obliquity is likely in the range 40-140$^\circ$, i.e., both spin-orbit
alignment and anti-alignment can be ruled out. WASP-107 thereby joins the small
collection of relatively low-mass stars hosting a giant planet with a high
obliquity. Most such stars have been observed to have low obliquities; all the
exceptions, including WASP-107, involve planets with relatively wide orbits
("warm Jupiters", with $a_{\rm min}/R_\star \gtrsim 8$). This demonstrates a
connection between stellar obliquity and planet properties, in contradiction to
some theories for obliquity excitation.
| 0 | 1 | 0 | 0 | 0 | 0 |
A characterization of signed discrete infinitely divisible distributions | In this article, we give some reviews concerning negative probabilities model
and quasi-infinitely divisible at the beginning. We next extend Feller's
characterization of discrete infinitely divisible distributions to signed
discrete infinitely divisible distributions, which are discrete pseudo compound
Poisson (DPCP) distributions with connections to the Lévy-Wiener theorem.
This is a special case of an open problem which is proposed by Sato(2014),
Chaumont and Yor(2012). An analogous result involving characteristic functions
is shown for signed integer-valued infinitely divisible distributions. We show
that many distributions are DPCP by the non-zero p.g.f. property, such as the
mixed Poisson distribution and fractional Poisson process. DPCP has some
bizarre properties, and one is that the parameter $\lambda $ in the DPCP class
cannot be arbitrarily small.
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Frequency Domain Singular Value Decomposition for Efficient Spatial Audio Coding | Advances in virtual reality have generated substantial interest in accurately
reproducing and storing spatial audio in the higher order ambisonics (HOA)
representation, given its rendering flexibility. Recent standardization for HOA
compression adopted a framework wherein HOA data are decomposed into principal
components that are then encoded by standard audio coding, i.e., frequency
domain quantization and entropy coding to exploit psychoacoustic redundancy. A
noted shortcoming of this approach is the occasional mismatch in principal
components across blocks, and the resulting suboptimal transitions in the data
fed to the audio coder. Instead, we propose a framework where singular value
decomposition (SVD) is performed after transformation to the frequency domain
via the modified discrete cosine transform (MDCT). This framework not only
ensures smooth transition across blocks, but also enables frequency dependent
SVD for better energy compaction. Moreover, we introduce a novel noise
substitution technique to compensate for suppressed ambient energy in discarded
higher order ambisonics channels, which significantly enhances the perceptual
quality of the reconstructed HOA signal. Objective and subjective evaluation
results provide evidence for the effectiveness of the proposed framework in
terms of both higher compression gains and better perceptual quality, compared
to existing methods.
| 1 | 0 | 0 | 0 | 0 | 0 |
Functional Dynamical Structures in Complex Systems: an Information-Theoretic Approach | Understanding the dynamical behavior of complex systems is of exceptional
relevance in everyday life, from biology to economy. In order to describe the
dynamical organization of complex systems, existing methods require the
knowledge of the network topology. By contrast, in this thesis we develop a new
method based on Information Theory which does not require any topological
knowledge. We introduce the Dynamical Cluster Index to detect those groups of
system elements which have strong mutual interactions, named as Relevant
Subsets. Among them, we identify those which exchange most information with the
rest of the system, thus being the most influential for its dynamics. In order
to detect such Functional Dynamical Structures, we introduce another
information theoretic measure, called D-index. The experimental results make us
confident that our method can be effectively used to study both artificial and
natural complex systems.
| 0 | 1 | 0 | 0 | 0 | 0 |
Angle-resolved photoemission spectroscopy with quantum gas microscopes | Quantum gas microscopes are a promising tool to study interacting quantum
many-body systems and bridge the gap between theoretical models and real
materials. So far they were limited to measurements of instantaneous
correlation functions of the form $\langle \hat{O}(t) \rangle$, even though
extensions to frequency-resolved response functions $\langle \hat{O}(t)
\hat{O}(0) \rangle$ would provide important information about the elementary
excitations in a many-body system. For example, single particle spectral
functions, which are usually measured using photoemission experiments in
electron systems, contain direct information about fractionalization and the
quasiparticle excitation spectrum. Here, we propose a measurement scheme to
experimentally access the momentum and energy resolved spectral function in a
quantum gas microscope with currently available techniques. As an example for
possible applications, we numerically calculate the spectrum of a single hole
excitation in one-dimensional $t-J$ models with isotropic and anisotropic
antiferromagnetic couplings. A sharp asymmetry in the distribution of spectral
weight appears when a hole is created in an isotropic Heisenberg spin chain.
This effect slowly vanishes for anisotropic spin interactions and disappears
completely in the case of pure Ising interactions. The asymmetry strongly
depends on the total magnetization of the spin chain, which can be tuned in
experiments with quantum gas microscopes. An intuitive picture for the observed
behavior is provided by a slave-fermion mean field theory. The key properties
of the spectra are visible at currently accessible temperatures.
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A note on MLE of covariance matrix | For a multivariate normal set up, it is well known that the maximum
likelihood estimator of covariance matrix is neither admissible nor minimax
under the Stein loss function. For the past six decades, a bunch of researches
have followed along this line for Stein's phenomenon in the literature. In this
note, the results are two folds: Firstly, with respect to Stein type loss
function we use the full Iwasawa decomposition to enhance the unpleasant
phenomenon that the minimum risks of maximum likelihood estimators for the
different coordinate systems (Cholesky decomposition and full Iwasawa
decomposition) are different. Secondly, we introduce a new class of loss
functions to show that the minimum risks of maximum likelihood estimators for
the different coordinate systems, the Cholesky decomposition and the full
Iwasawa decomposition, are of the same, and hence the Stein's paradox
disappears.
| 0 | 0 | 1 | 1 | 0 | 0 |
Resource Management in Cloud Computing: Classification and Taxonomy | Cloud Computing is a new era of remote computing / Internet based computing
where one can access their personal resources easily from any computer through
Internet. Cloud delivers computing as a utility as it is available to the cloud
consumers on demand. It is a simple pay-per-use consumer-provider service
model. It contains large number of shared resources. So Resource Management is
always a major issue in cloud computing like any other computing paradigm. Due
to the availability of finite resources it is very challenging for cloud
providers to provide all the requested resources. From the cloud providers
perspective cloud resources must be allocated in a fair and efficient manner.
Research Survey is not available from the perspective of resource management as
a process in cloud computing. So this research paper provides a detailed
sequential view / steps on resource management in cloud computing. Firstly this
research paper classifies various resources in cloud computing. It also gives
taxonomy on resource management in cloud computing through which one can do
further research. Lastly comparisons on various resource management algorithms
has been presented.
| 1 | 0 | 0 | 0 | 0 | 0 |
What can the programming language Rust do for astrophysics? | The astrophysics community uses different tools for computational tasks such
as complex systems simulations, radiative transfer calculations or big data.
Programming languages like Fortran, C or C++ are commonly present in these
tools and, generally, the language choice was made based on the need for
performance. However, this comes at a cost: safety. For instance, a common
source of error is the access to invalid memory regions, which produces random
execution behaviors and affects the scientific interpretation of the results.
In 2015, Mozilla Research released the first stable version of a new
programming language named Rust. Many features make this new language
attractive for the scientific community, it is open source and it guarantees
memory safety while offering zero-cost abstraction.
We explore the advantages and drawbacks of Rust for astrophysics by
re-implementing the fundamental parts of Mercury-T, a Fortran code that
simulates the dynamical and tidal evolution of multi-planet systems.
| 1 | 1 | 0 | 0 | 0 | 0 |
Eliashberg theory with the external pair potential | Based on BCS model with the external pair potential formulated in a work
\emph{K.V. Grigorishin} arXiv:1605.07080, analogous model with electron-phonon
coupling and Coulomb coupling is proposed. The generalized Eliashberg equations
in the regime of renormalization of the order parameter are obtained. High
temperature asymptotics and influence of Coulomb pseudopotential on them are
investigated: as in the BCS model the order parameter asymptotically tends to
zero as temperature rises, but the accounting of the Coulomb pseudopotential
leads to existence of critical temperature. The effective Ginzburg-Landau
theory is formulated for such model, where the temperature dependencies near
$T_{c}$ of the basic characteristics of a superconductor (coherence length,
magnetic penetration depth, GL parameter, the thermodynamical critical field,
the first and the second critical fields) recovers to the temperature
dependencies as in the ordinary GL theory after the BCS model with the external
pair potential.
| 0 | 1 | 0 | 0 | 0 | 0 |
An exponential limit shape of random $q$-proportion Bulgarian solitaire | We introduce \emph{$p_n$-random $q_n$-proportion Bulgarian solitaire}
($0<p_n,q_n\le 1$), played on $n$ cards distributed in piles. In each pile, a
number of cards equal to the proportion $q_n$ of the pile size rounded upward
to the nearest integer are candidates to be picked. Each candidate card is
picked with probability $p_n$, independently of other candidate cards. This
generalizes Popov's random Bulgarian solitaire, in which there is a single
candidate card in each pile. Popov showed that a triangular limit shape is
obtained for a fixed $p$ as $n$ tends to infinity. Here we let both $p_n$ and
$q_n$ vary with $n$. We show that under the conditions $q_n^2 p_n n/{\log
n}\rightarrow \infty$ and $p_n q_n \rightarrow 0$ as $n\to\infty$, the
$p_n$-random $q_n$-proportion Bulgarian solitaire has an exponential limit
shape.
| 0 | 0 | 1 | 0 | 0 | 0 |
Constraining Effective Temperature, Mass and Radius of Hot White Dwarfs | By introducing a simplified transport model of outer layers of white dwarfs
we derive an analytical semi-empirical relation which constrains effective
temperature-mass-radius for white dwarfs. This relation is used to classify
recent data of white dwarfs according to their time evolution in non-accretion
process of cooling. This formula permit us to study the population map of white
dwarfs in the central temperature and mass plane, and discuss the relation with
the ignition temperature for C-O material. Our effective
temperature-mass-radius relation provide a quick method to estimate the mass of
newly observed white dwarfs from their spectral measurements of effective
temperature and superficial gravity.
| 0 | 1 | 0 | 0 | 0 | 0 |
Hierarchical Multinomial-Dirichlet model for the estimation of conditional probability tables | We present a novel approach for estimating conditional probability tables,
based on a joint, rather than independent, estimate of the conditional
distributions belonging to the same table. We derive exact analytical
expressions for the estimators and we analyse their properties both
analytically and via simulation. We then apply this method to the estimation of
parameters in a Bayesian network. Given the structure of the network, the
proposed approach better estimates the joint distribution and significantly
improves the classification performance with respect to traditional approaches.
| 0 | 0 | 0 | 1 | 0 | 0 |
Proceedings of the Fifth Workshop on Proof eXchange for Theorem Proving | This volume of EPTCS contains the proceedings of the Fifth Workshop on Proof
Exchange for Theorem Proving (PxTP 2017), held on September 23-24, 2017 as part
of the Tableaux, FroCoS and ITP conferences in Brasilia, Brazil. The PxTP
workshop series brings together researchers working on various aspects of
communication, integration, and cooperation between reasoning systems and
formalisms, with a special focus on proofs. The progress in computer-aided
reasoning, both automated and interactive, during the past decades, made it
possible to build deduction tools that are increasingly more applicable to a
wider range of problems and are able to tackle larger problems progressively
faster. In recent years, cooperation between such tools in larger systems has
demonstrated the potential to reduce the amount of manual intervention.
Cooperation between reasoning systems relies on availability of theoretical
formalisms and practical tools to exchange problems, proofs, and models. The
PxTP workshop series strives to encourage such cooperation by inviting
contributions on all aspects of cooperation between reasoning tools, whether
automatic or interactive.
| 1 | 0 | 0 | 0 | 0 | 0 |
Photoinduced charge-order melting dynamics in a one-dimensional interacting Holstein model | Transient quantum dynamics in an interacting fermion-phonon system are
investigated. In particular, a charge order (CO) melting after a short
optical-pulse irradiation and roles of the quantum phonons on the transient
dynamics are focused on. A spinless-fermion model in a one-dimensional chain
coupled with local phonons is analyzed numerically. The infinite time-evolving
block decimation algorithm is adopted as a reliable numerical method for
one-dimensional quantum many-body systems. Numerical results for the
photoinduced CO melting dynamics without phonons are well interpreted by the
soliton picture for the CO domains. This interpretation is confirmed by the
numerical simulation for an artificial local excitation and the classical
soliton model. In the case of the large phonon frequency corresponding to the
antiadiabatic condition, the CO melting is induced by propagations of the
polaronic solitons with the renormalized soliton velocity. On the other hand,
in the case of the small phonon frequency corresponding to the adiabatic
condition, the first stage of the CO melting dynamics occurs due to the energy
transfer from the fermionic to phononic systems, and the second stage is
brought about by the soliton motions around the bottom of the soliton band.
Present analyses provide a standard reference for the photoinduced CO melting
dynamics in low-dimensional many-body quantum systems.
| 0 | 1 | 0 | 0 | 0 | 0 |
Compactness of the automorphism group of a topological parallelism on real projective 3-space: The disconnected case | We prove that the automorphism group of a topological parallelism on real
projective 3-space is compact. In a preceding article it was proved that at
least the connected component of the identity is compact. The present proof
does not depend on that earlier result.
| 0 | 0 | 1 | 0 | 0 | 0 |
A model provides insight into electric field-induced rupture mechanism of water-in-toluene emulsion films | This paper presents the first MD simulations of a model, which we have
designed for understanding the development of electro-induced instability of a
thin toluene emulsion film in contact with saline aqueous phase. This study
demonstrates the charge accumulation role in toluene film rupture when a DC
electric field is applied. The critical value of the external field at which
film ruptures, thin film charge distribution, capacitance, number densities and
film structure have been obtained in simulating the system within NVT and NPT
ensembles. A mechanism of thin film rupture driven by the electric discharge is
suggested.We show that NPT ensemble with a constant surface tension is a better
choice for further modeling of the systems that resemble more close the real
films.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Rich-Variant Architecture for a User-Aware multi-tenant SaaS approach | Software as a Service cloud computing model favorites the Multi-Tenancy as a
key factor to exploit economies of scale. However Multi-Tenancy present several
disadvantages. Therein, our approach comes to assign instances to multi-tenants
with an optimal solution while ensuring more economies of scale and avoiding
tenants hesitation to share resources. The present paper present the
architecture of our user-aware multi-tenancy SaaS approach based on the use of
rich-variant components. The proposed approach seek to model services
functional customization as well as automation of computing the optimal
distribution of instances by tenants. The proposed model takes into
consideration tenants functional requirements and tenants deployment
requirements to deduce an optimal distribution using essentially a specific
variability engine and a graph-based execution framework.
| 1 | 0 | 0 | 0 | 0 | 0 |
Comparison of Parallelisation Approaches, Languages, and Compilers for Unstructured Mesh Algorithms on GPUs | Efficiently exploiting GPUs is increasingly essential in scientific
computing, as many current and upcoming supercomputers are built using them. To
facilitate this, there are a number of programming approaches, such as CUDA,
OpenACC and OpenMP 4, supporting different programming languages (mainly C/C++
and Fortran). There are also several compiler suites (clang, nvcc, PGI, XL)
each supporting different combinations of languages. In this study, we take a
detailed look at some of the currently available options, and carry out a
comprehensive analysis and comparison using computational loops and
applications from the domain of unstructured mesh computations. Beyond runtimes
and performance metrics (GB/s), we explore factors that influence performance
such as register counts, occupancy, usage of different memory types,
instruction counts, and algorithmic differences. Results of this work show how
clang's CUDA compiler frequently outperform NVIDIA's nvcc, performance issues
with directive-based approaches on complex kernels, and OpenMP 4 support
maturing in clang and XL; currently around 10% slower than CUDA.
| 1 | 0 | 0 | 0 | 0 | 0 |
Notes on Growing a Tree in a Graph | We study the height of a spanning tree $T$ of a graph $G$ obtained by
starting with a single vertex of $G$ and repeatedly selecting, uniformly at
random, an edge of $G$ with exactly one endpoint in $T$ and adding this edge to
$T$.
| 1 | 0 | 0 | 0 | 0 | 0 |
Vortex pinning by the point potential in topological superconductors: a scheme for braiding Majorana bound states | We propose theoretically an effective scheme for braiding Majorana bound
states by manipulating the point potential. The vortex pinning effect is
carefully elucidated. This effect may be used to control the vortices and
Majorana bound states in topological superconductors. The exchange of two
vortices induced by moving the potentials is simulated numerically. The
zero-energy state in the vortex core is robust with respect to the strength of
the potential. The Majorana bound states in a pinned vortex are identified
numerically.
| 0 | 1 | 0 | 0 | 0 | 0 |
T* : A Heuristic Search Based Algorithm for Motion Planning with Temporal Goals | Motion planning is the core problem to solve for developing any application
involving an autonomous mobile robot. The fundamental motion planning problem
involves generating a trajectory for a robot for point-to-point navigation
while avoiding obstacles. Heuristic-based search algorithms like A* have been
shown to be extremely efficient in solving such planning problems. Recently,
there has been an increased interest in specifying complex motion plans using
temporal logic. In the state-of-the-art algorithm, the temporal logic motion
planning problem is reduced to a graph search problem and Dijkstra's shortest
path algorithm is used to compute the optimal trajectory satisfying the
specification.
The A* algorithm when used with a proper heuristic for the distance from the
destination can generate an optimal path in a graph efficiently. The primary
challenge for using A* algorithm in temporal logic path planning is that there
is no notion of a single destination state for the robot. In this thesis, we
present a novel motion planning algorithm T* that uses the A* search procedure
in temporal logic path planning \emph{opportunistically} to generate an optimal
trajectory satisfying a temporal logic query. Our experimental results
demonstrate that T* achieves an order of magnitude improvement over the
state-of-the-art algorithm to solve many temporal logic motion planning
problems.
| 1 | 0 | 0 | 0 | 0 | 0 |
Discontinuous Homomorphisms of $C(X)$ with $2^{\aleph_0}>\aleph_2$ | Assume that $M$ is a c.t.m. of $ZFC+CH$ containing a simplified
$(\omega_1,2)$-morass, $P\in M$ is the poset adding $\aleph_3$ generic reals
and $G$ is $P$-generic over $M$. In $M$ we construct a function between sets of
terms in the forcing language, that interpreted in $M[G]$ is an $\mathbb
R$-linear order-preserving monomorphism from the finite elements of an
ultrapower of the reals, over a non-principal ultrafilter on $\omega$, into the
Esterle algebra of formal power series. Therefore it is consistent that
$2^{\aleph_0}=\aleph_3$ and, for any infinite compact Hausdorff space $X$,
there exists a discontinuous homomorphism of $C(X)$, the algebra of continuous
real-valued functions on $X$. For $n\in \mathbb N$, If $M$ contains a
simplified $(\omega_1,n)$-morass, then in the Cohen extension of $M$ adding
$\aleph_n$ generic reals there exists a discontinuous homomorphism of $C(X)$,
for any infinite compact Hausdorff space $X$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Investigation of beam self-polarization in the future $e^{+}e^{-}$ circular collider | The use of resonant depolarization has been suggested for precise beam energy
measurements (better than 100 keV) in the $e^{+}e^{-}$ Future Circular Collider
(FCC-$e^{+}e^{-}$) for Z and WW physics at 45 and 80 GeV beam energy
respectively. Longitudinal beam polarization would benefit the Z peak physics
program; however it is not essential and therefore it will be not investigated
here. In this paper the possibility of self-polarized leptons is considered.
Preliminary results of simulations in presence of quadrupole misalignments and
beam position monitors (BPMs) errors for a simplified FCC-$e^{+}e^{-}$ ring are
presented.
| 0 | 1 | 0 | 0 | 0 | 0 |
Learning to Grasp from a Single Demonstration | Learning-based approaches for robotic grasping using visual sensors typically
require collecting a large size dataset, either manually labeled or by many
trial and errors of a robotic manipulator in the real or simulated world. We
propose a simpler learning-from-demonstration approach that is able to detect
the object to grasp from merely a single demonstration using a convolutional
neural network we call GraspNet. In order to increase robustness and decrease
the training time even further, we leverage data from previous demonstrations
to quickly fine-tune a GrapNet for each new demonstration. We present some
preliminary results on a grasping experiment with the Franka Panda cobot for
which we can train a GraspNet with only hundreds of train iterations.
| 1 | 0 | 0 | 0 | 0 | 0 |
Tensor Minkowski Functionals for random fields on the sphere | We generalize the translation invariant tensor-valued Minkowski Functionals
which are defined on two-dimensional flat space to the unit sphere. We apply
them to level sets of random fields. The contours enclosing boundaries of level
sets of random fields give a spatial distribution of random smooth closed
curves. We obtain analytic expressions for the ensemble expectation values for
the matrix elements of the tensor-valued Minkowski Functionals for isotropic
Gaussian and Rayleigh fields. We elucidate the way in which the elements of the
tensor Minkowski Functionals encode information about the nature and
statistical isotropy (or departure from isotropy) of the field. We then
implement our method to compute the tensor-valued Minkowski Functionals
numerically and demonstrate how they encode statistical anisotropy and
departure from Gaussianity by applying the method to maps of the Galactic
foreground emissions from the PLANCK data.
| 0 | 1 | 0 | 0 | 0 | 0 |
Measuring galaxy cluster masses with CMB lensing using a Maximum Likelihood estimator: Statistical and systematic error budgets for future experiments | We develop a Maximum Likelihood estimator (MLE) to measure the masses of
galaxy clusters through the impact of gravitational lensing on the temperature
and polarization anisotropies of the cosmic microwave background (CMB). We show
that, at low noise levels in temperature, this optimal estimator outperforms
the standard quadratic estimator by a factor of two. For polarization, we show
that the Stokes Q/U maps can be used instead of the traditional E- and B-mode
maps without losing information. We test and quantify the bias in the recovered
lensing mass for a comprehensive list of potential systematic errors. Using
realistic simulations, we examine the cluster mass uncertainties from
CMB-cluster lensing as a function of an experiment's beam size and noise level.
We predict the cluster mass uncertainties will be 3 - 6% for SPT-3G, AdvACT,
and Simons Array experiments with 10,000 clusters and less than 1% for the
CMB-S4 experiment with a sample containing 100,000 clusters. The mass
constraints from CMB polarization are very sensitive to the experimental beam
size and map noise level: for a factor of three reduction in either the beam
size or noise level, the lensing signal-to-noise improves by roughly a factor
of two.
| 0 | 1 | 0 | 0 | 0 | 0 |
The Incremental Proximal Method: A Probabilistic Perspective | In this work, we highlight a connection between the incremental proximal
method and stochastic filters. We begin by showing that the proximal operators
coincide, and hence can be realized with, Bayes updates. We give the explicit
form of the updates for the linear regression problem and show that there is a
one-to-one correspondence between the proximal operator of the least-squares
regression and the Bayes update when the prior and the likelihood are Gaussian.
We then carry out this observation to a general sequential setting: We consider
the incremental proximal method, which is an algorithm for large-scale
optimization, and show that, for a linear-quadratic cost function, it can
naturally be realized by the Kalman filter. We then discuss the implications of
this idea for nonlinear optimization problems where proximal operators are in
general not realizable. In such settings, we argue that the extended Kalman
filter can provide a systematic way for the derivation of practical procedures.
| 0 | 0 | 0 | 1 | 0 | 0 |
List Decoding of Insertions and Deletions | List decoding of insertions and deletions in the Levenshtein metric is
considered. The Levenshtein distance between two sequences is the minimum
number of insertions and deletions needed to turn one of the sequences into the
other. In this paper, a Johnson-like upper bound on the maximum list size when
list decoding in the Levenshtein metric is derived. This bound depends only on
the length and minimum Levenshtein distance of the code, the length of the
received word, and the alphabet size. It shows that polynomial-time list
decoding beyond half the Levenshtein distance is possible for many parameters.
Further, we also prove a lower bound on list decoding of deletions with with
the well-known binary Varshamov-Tenengolts (VT) codes which shows that the
maximum list size grows exponentially with the number of deletions. Finally, an
efficient list decoding algorithm for two insertions/deletions with VT codes is
given. This decoder can be modified to a polynomial-time list decoder of any
constant number of insertions/deletions.
| 1 | 0 | 1 | 0 | 0 | 0 |
Almost sure scattering for the energy-critical NLS with radial data below $H^1(\mathbb{R}^4)$ | We prove almost sure global existence and scattering for the energy-critical
nonlinear Schrödinger equation with randomized spherically symmetric initial
data in $H^s(\mathbb{R}^4)$ with $\frac56<s<1$. We were inspired to consider
this problem by the recent work of Dodson--Lührmann--Mendelson, which treated
the analogous problem for the energy-critical wave equation.
| 0 | 0 | 1 | 0 | 0 | 0 |
Almost Boltzmann Exploration | Boltzmann exploration is widely used in reinforcement learning to provide a
trade-off between exploration and exploitation. Recently, in (Cesa-Bianchi et
al., 2017) it has been shown that pure Boltzmann exploration does not perform
well from a regret perspective, even in the simplest setting of stochastic
multi-armed bandit (MAB) problems. In this paper, we show that a simple
modification to Boltzmann exploration, motivated by a variation of the standard
doubling trick, achieves $O(K\log^{1+\alpha} T)$ regret for a stochastic MAB
problem with $K$ arms, where $\alpha>0$ is a parameter of the algorithm. This
improves on the result in (Cesa-Bianchi et al., 2017), where an algorithm
inspired by the Gumbel-softmax trick achieves $O(K\log^2 T)$ regret. We also
show that our algorithm achieves $O(\beta(G) \log^{1+\alpha} T)$ regret in
stochastic MAB problems with graph-structured feedback, without knowledge of
the graph structure, where $\beta(G)$ is the independence number of the
feedback graph. Additionally, we present extensive experimental results on real
datasets and applications for multi-armed bandits with both traditional bandit
feedback and graph-structured feedback. In all cases, our algorithm performs as
well or better than the state-of-the-art.
| 1 | 0 | 0 | 1 | 0 | 0 |
A Time Localization System in Smart Home Using Hierarchical Structure and Dynamic Frequency | Both GPS and WiFi based localization have been exploited in recent years, yet
most researches focus on localizing at home without environment context.
Besides, the near home or workplace area is complex and has little attention in
smart home or IOT. Therefore, after exploring the realistic route in and out of
building, we conducted a time localization system (TLS) based on off-the-shelf
smart phones with WiFi identification. TLS can identify the received signal
strength indication (RSSI) of home and construct radio map of users' time route
without site survey. As designed to service the smart devices in home, TLS
applies the time interval as the distance of positions and as the variables of
WiFi environment to mark time points. Experimental results with real users show
that TLS as a service system for timeline localization achieves a median
accuracy of 70 seconds and is more robust compared with nearest neighbor
localization approach.
| 1 | 0 | 0 | 0 | 0 | 0 |
Vision-based Real Estate Price Estimation | Since the advent of online real estate database companies like Zillow, Trulia
and Redfin, the problem of automatic estimation of market values for houses has
received considerable attention. Several real estate websites provide such
estimates using a proprietary formula. Although these estimates are often close
to the actual sale prices, in some cases they are highly inaccurate. One of the
key factors that affects the value of a house is its interior and exterior
appearance, which is not considered in calculating automatic value estimates.
In this paper, we evaluate the impact of visual characteristics of a house on
its market value. Using deep convolutional neural networks on a large dataset
of photos of home interiors and exteriors, we develop a method for estimating
the luxury level of real estate photos. We also develop a novel framework for
automated value assessment using the above photos in addition to home
characteristics including size, offered price and number of bedrooms. Finally,
by applying our proposed method for price estimation to a new dataset of real
estate photos and metadata, we show that it outperforms Zillow's estimates.
| 1 | 0 | 0 | 0 | 0 | 0 |
Deep Rewiring: Training very sparse deep networks | Neuromorphic hardware tends to pose limits on the connectivity of deep
networks that one can run on them. But also generic hardware and software
implementations of deep learning run more efficiently for sparse networks.
Several methods exist for pruning connections of a neural network after it was
trained without connectivity constraints. We present an algorithm, DEEP R, that
enables us to train directly a sparsely connected neural network. DEEP R
automatically rewires the network during supervised training so that
connections are there where they are most needed for the task, while its total
number is all the time strictly bounded. We demonstrate that DEEP R can be used
to train very sparse feedforward and recurrent neural networks on standard
benchmark tasks with just a minor loss in performance. DEEP R is based on a
rigorous theoretical foundation that views rewiring as stochastic sampling of
network configurations from a posterior.
| 1 | 0 | 0 | 1 | 0 | 0 |
Analyzing and Disentangling Interleaved Interrupt-driven IoT Programs | In the Internet of Things (IoT) community, Wireless Sensor Network (WSN) is a
key technique to enable ubiquitous sensing of environments and provide reliable
services to applications. WSN programs, typically interrupt-driven, implement
the functionalities via the collaboration of Interrupt Procedure Instances
(IPIs, namely executions of interrupt processing logic). However, due to the
complicated concurrency model of WSN programs, the IPIs are interleaved
intricately and the program behaviours are hard to predicate from the source
codes. Thus, to improve the software quality of WSN programs, it is significant
to disentangle the interleaved executions and develop various IPI-based program
analysis techniques, including offline and online ones. As the common
foundation of those techniques, a generic efficient and real-time algorithm to
identify IPIs is urgently desired. However, the existing
instance-identification approach cannot satisfy the desires. In this paper, we
first formally define the concept of IPI. Next, we propose a generic
IPI-identification algorithm, and prove its correctness, real-time and
efficiency. We also conduct comparison experiments to illustrate that our
algorithm is more efficient than the existing one in terms of both time and
space. As the theoretical analyses and empirical studies exhibit, our algorithm
provides the groundwork for IPI-based analyses of WSN programs in IoT
environment.
| 1 | 0 | 0 | 0 | 0 | 0 |
Absence of replica symmetry breaking in the transverse and longitudinal random field Ising model | It is proved that replica symmetry is not broken in the transverse and
longitudinal random field Ising model. In this model, the variance of spin
overlap of any component vanishes in any dimension almost everywhere in the
coupling constant space in the infinite volume limit. The weak
Fortuin-Kasteleyn-Ginibre property in this model and the Ghirlanda-Guerra
identities in artificial models in a path integral representation based on the
Lie-Trotter-Suzuki formula enable us to extend Chatterjee's proof for the
random field Ising model to the quantum model.
| 0 | 1 | 0 | 0 | 0 | 0 |
Specialization of Generic Array Accesses After Inlining | We have implemented an optimization that specializes type-generic array
accesses after inlining of polymorphic functions in the native-code OCaml
compiler. Polymorphic array operations (read and write) in OCaml require
runtime type dispatch because of ad hoc memory representations of integer and
float arrays. It cannot be removed even after being monomorphized by inlining
because the intermediate language is mostly untyped. We therefore extended it
with explicit type application like System F (while keeping implicit type
abstraction by means of unique identifiers for type variables). Our
optimization has achieved up to 21% speed-up of numerical programs.
| 1 | 0 | 0 | 0 | 0 | 0 |
Exact tensor completion with sum-of-squares | We obtain the first polynomial-time algorithm for exact tensor completion
that improves over the bound implied by reduction to matrix completion. The
algorithm recovers an unknown 3-tensor with $r$ incoherent, orthogonal
components in $\mathbb R^n$ from $r\cdot \tilde O(n^{1.5})$ randomly observed
entries of the tensor. This bound improves over the previous best one of
$r\cdot \tilde O(n^{2})$ by reduction to exact matrix completion. Our bound
also matches the best known results for the easier problem of approximate
tensor completion (Barak & Moitra, 2015).
Our algorithm and analysis extends seminal results for exact matrix
completion (Candes & Recht, 2009) to the tensor setting via the sum-of-squares
method. The main technical challenge is to show that a small number of randomly
chosen monomials are enough to construct a degree-3 polynomial with precisely
planted orthogonal global optima over the sphere and that this fact can be
certified within the sum-of-squares proof system.
| 1 | 0 | 0 | 1 | 0 | 0 |
Outlier Cluster Formation in Spectral Clustering | Outlier detection and cluster number estimation is an important issue for
clustering real data. This paper focuses on spectral clustering, a time-tested
clustering method, and reveals its important properties related to outliers.
The highlights of this paper are the following two mathematical observations:
first, spectral clustering's intrinsic property of an outlier cluster
formation, and second, the singularity of an outlier cluster with a valid
cluster number. Based on these observations, we designed a function that
evaluates clustering and outlier detection results. In experiments, we prepared
two scenarios, face clustering in photo album and person re-identification in a
camera network. We confirmed that the proposed method detects outliers and
estimates the number of clusters properly in both problems. Our method
outperforms state-of-the-art methods in both the 128-dimensional sparse space
for face clustering and the 4,096-dimensional non-sparse space for person
re-identification.
| 1 | 0 | 0 | 0 | 0 | 0 |
Stored Electromagnetic Field Energies in General Materials | The most general expressions of the stored energies for time-harmonic
electromagnetic fields are derived from the time-domain Poynting theorem, and
are valuable in characterizing the energy storage and transport properties of
complex media. A new energy conservation law for the time-harmonic
electromagnetic fields, which involves the derived general expressions of the
stored energies, is introduced. In contrast to the well-established Poynting
theorem for time-harmonic fields, the real part of the new energy conservation
law gives an equation for the sum of stored electric and magnetic field
energies; the imaginary part involves an equation related to the difference
between the dissipated electric and magnetic field energies. In a lossless
isotropic and homogeneous medium, the new energy conservation law has a clear
physical implication: the stored electromagnetic field energy of a radiating
system enclosed by a surface is equal to the total field energy inside the
surface subtracted by the field energy flowing out of the surface.
| 0 | 1 | 0 | 0 | 0 | 0 |
From Relational Data to Graphs: Inferring Significant Links using Generalized Hypergeometric Ensembles | The inference of network topologies from relational data is an important
problem in data analysis. Exemplary applications include the reconstruction of
social ties from data on human interactions, the inference of gene
co-expression networks from DNA microarray data, or the learning of semantic
relationships based on co-occurrences of words in documents. Solving these
problems requires techniques to infer significant links in noisy relational
data. In this short paper, we propose a new statistical modeling framework to
address this challenge. It builds on generalized hypergeometric ensembles, a
class of generative stochastic models that give rise to analytically tractable
probability spaces of directed, multi-edge graphs. We show how this framework
can be used to assess the significance of links in noisy relational data. We
illustrate our method in two data sets capturing spatio-temporal proximity
relations between actors in a social system. The results show that our
analytical framework provides a new approach to infer significant links from
relational data, with interesting perspectives for the mining of data on social
systems.
| 1 | 1 | 0 | 1 | 0 | 0 |
A Relaxed Kačanov Iteration for the $p$-Poisson Problem | In this paper, we introduce an iterative linearization scheme that allows to
approximate the weak solution of the $p$-Poisson problem
\begin{align*}
-\operatorname{div}(|\nabla u|^{p-2}\nabla u) &= f\quad\text{in }\Omega,
u&= 0\quad\text{on}\partial\Omega
\end{align*} for $1 < p \leq 2$. The algorithm can be interpreted as a
relaxed Kačanov iteration. We prove that the algorithm converges at least
with an algebraic rate.
| 0 | 0 | 1 | 0 | 0 | 0 |
Explosive Percolation on Directed Networks Due to Monotonic Flow of Activity | An important class of real-world networks have directed edges, and in
addition, some rank ordering on the nodes, for instance the "popularity" of
users in online social networks. Yet, nearly all research related to explosive
percolation has been restricted to undirected networks. Furthermore,
information on such rank ordered networks typically flows from higher ranked to
lower ranked individuals, such as follower relations, replies and retweets on
Twitter.
Here we introduce a simple percolation process on an ordered, directed
network where edges are added monotonically with respect to the rank ordering.
We show with a numerical approach that the emergence of a dominant strongly
connected component appears to be discontinuous. Large scale connectivity
occurs at very high density compared with most percolation processes, and this
holds not just for the strongly connected component structure but for the
weakly connected component structure as well. We present analysis with
branching processes which explains this unusual behavior and gives basic
intuition for the underlying mechanisms. We also show that before the emergence
of a dominant strongly connected component, multiple giant strongly connected
components may exist simultaneously. By adding a competitive percolation rule
with a small bias to link uses of similar rank, we show this leads to formation
of two distinct components, one of high ranked users, and one of low ranked
users, with little flow between the two components.
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Experimental observation of self excited co--rotating multiple vortices in a dusty plasma with inhomogeneous plasma background | We report an experimental observation of multiple co--rotating vortices in a
extended dust column in the background of non--uniform diffused plasma.
Inductively coupled RF discharge is initiated in the background of argon gas in
the source region which later found to diffuse in the main experimental
chamber. A secondary DC glow discharge plasma is produced to introduce the dust
particles into the plasma. These micron sized poly-disperse dust particles get
charged in the plasma environment and transported by the ambipolar electric
field of the diffused plasma and found to confine in the potential well, where
the resultant electric field of the diffused plasma (ambipolar E--field) and
glass wall charging (sheath E--field) hold the micron sized particles against
the gravity. Multiple co--rotating (anti--clockwise) dust vortices are observed
in the dust cloud for a particular discharge condition. The transition from
multiple to single dust vortex is observed when input RF power is lowered.
Occurrence of these vortices are explained on the basis of the charge gradient
of dust particles which is orthogonal to the ion drag force. The charge
gradient is a consequence of the plasma inhomogeneity along the dust cloud
length. The detailed nature and the reason for multiple vortices are still
under investigation through further experiments, however, preliminary
qualitative understanding is discussed based on characteristic scale length of
dust vortex. There is a characteristic size of the vortex in the dusty plasma
so that multiple vortices is possible to form in the extended dusty plasma with
inhomogeneous plasma background. The experimental results on the vortex motion
of particles are compared with a theoretical model and found some agreement.
| 0 | 1 | 0 | 0 | 0 | 0 |
Analysis of Computational Science Papers from ICCS 2001-2016 using Topic Modeling and Graph Theory | This paper presents results of topic modeling and network models of topics
using the International Conference on Computational Science corpus, which
contains domain-specific (computational science) papers over sixteen years (a
total of 5695 papers). We discuss topical structures of International
Conference on Computational Science, how these topics evolve over time in
response to the topicality of various problems, technologies and methods, and
how all these topics relate to one another. This analysis illustrates
multidisciplinary research and collaborations among scientific communities, by
constructing static and dynamic networks from the topic modeling results and
the keywords of authors. The results of this study give insights about the past
and future trends of core discussion topics in computational science. We used
the Non-negative Matrix Factorization topic modeling algorithm to discover
topics and labeled and grouped results hierarchically.
| 1 | 0 | 0 | 0 | 0 | 0 |
A maximal Boolean sublattice that is not the range of a Banaschewski function | We construct a countable bounded sublattice of the lattice of all subspaces
of a vector space with two non-isomorphic maximal Boolean sublattice. We
represent one of them as the range of a Banschewski function and we prove that
this is not the case of the other. Hereby we solve a problem of F. Wehrung.
| 0 | 0 | 1 | 0 | 0 | 0 |
Bayesian inference for stationary data on finite state spaces | In this work the issue of Bayesian inference for stationary data is
addressed. Therefor a parametrization of a statistically suitable subspace of
the the shift-ergodic probability measures on a Cartesian product of some
finite state space is given using an inverse limit construction. Moreover, an
explicit model for the prior is given by taking into account an additional step
in the usual stepwise sampling scheme of data. An update to the posterior is
defined by exploiting this augmented sample scheme. Thereby, its model-step is
updated using a measurement of the empirical distances between the model
classes.
| 0 | 0 | 1 | 1 | 0 | 0 |
Why is solar cycle 24 an inefficient producer of high-energy particle events? | The aim of the study is to investigate the reason for the low productivity of
high-energy SEPs in the present solar cycle. We employ scaling laws derived
from diffusive shock acceleration theory and simulation studies including
proton-generated upstream Alfvén waves to find out how the changes observed
in the long-term average properties of the erupting and ambient coronal and/or
solar wind plasma would affect the ability of shocks to accelerate particles to
the highest energies. Provided that self-generated turbulence dominates
particle transport around coronal shocks, it is found that the most crucial
factors controlling the diffusive shock acceleration process are the number
density of seed particles and the plasma density of the ambient medium.
Assuming that suprathermal populations provide a fraction of the particles
injected to shock acceleration in the corona, we show that the lack of most
energetic particle events as well as the lack of low charge-to-mass ratio ion
species in the present cycle can be understood as a result of the reduction of
average coronal plasma and suprathermal densities in the present cycle over the
previous one.
| 0 | 1 | 0 | 0 | 0 | 0 |
E2M2: Energy Efficient Mobility Management in Dense Small Cells with Mobile Edge Computing | Merging mobile edge computing with the dense deployment of small cell base
stations promises enormous benefits such as a real proximity, ultra-low latency
access to cloud functionalities. However, the envisioned integration creates
many new challenges and one of the most significant is mobility management,
which is becoming a key bottleneck to the overall system performance. Simply
applying existing solutions leads to poor performance due to the highly
overlapped coverage areas of multiple base stations in the proximity of the
user and the co-provisioning of radio access and computing services. In this
paper, we develop a novel user-centric mobility management scheme, leveraging
Lyapunov optimization and multi-armed bandits theories, in order to maximize
the edge computation performance for the user while keeping the user's
communication energy consumption below a constraint. The proposed scheme
effectively handles the uncertainties present at multiple levels in the system
and provides both short-term and long-term performance guarantee. Simulation
results show that our proposed scheme can significantly improve the computation
performance (compared to state of the art) while satisfying the communication
energy constraint.
| 1 | 0 | 0 | 0 | 0 | 0 |
Big Data in HEP: A comprehensive use case study | Experimental Particle Physics has been at the forefront of analyzing the
worlds largest datasets for decades. The HEP community was the first to develop
suitable software and computing tools for this task. In recent times, new
toolkits and systems collectively called Big Data technologies have emerged to
support the analysis of Petabyte and Exabyte datasets in industry. While the
principles of data analysis in HEP have not changed (filtering and transforming
experiment-specific data formats), these new technologies use different
approaches and promise a fresh look at analysis of very large datasets and
could potentially reduce the time-to-physics with increased interactivity. In
this talk, we present an active LHC Run 2 analysis, searching for dark matter
with the CMS detector, as a testbed for Big Data technologies. We directly
compare the traditional NTuple-based analysis with an equivalent analysis using
Apache Spark on the Hadoop ecosystem and beyond. In both cases, we start the
analysis with the official experiment data formats and produce publication
physics plots. We will discuss advantages and disadvantages of each approach
and give an outlook on further studies needed.
| 1 | 0 | 0 | 0 | 0 | 0 |
Criticality & Deep Learning II: Momentum Renormalisation Group | Guided by critical systems found in nature we develop a novel mechanism
consisting of inhomogeneous polynomial regularisation via which we can induce
scale invariance in deep learning systems. Technically, we map our deep
learning (DL) setup to a genuine field theory, on which we act with the
Renormalisation Group (RG) in momentum space and produce the flow equations of
the couplings; those are translated to constraints and consequently interpreted
as "critical regularisation" conditions in the optimiser; the resulting
equations hence prove to be sufficient conditions for - and serve as an elegant
and simple mechanism to induce scale invariance in any deep learning setup.
| 1 | 0 | 0 | 0 | 0 | 0 |
The Price of Diversity in Assignment Problems | We introduce and analyze an extension to the matching problem on a weighted
bipartite graph: Assignment with Type Constraints. The two parts of the graph
are partitioned into subsets called types and blocks; we seek a matching with
the largest sum of weights under the constraint that there is a pre-specified
cap on the number of vertices matched in every type-block pair. Our primary
motivation stems from the public housing program of Singapore, accounting for
over 70% of its residential real estate. To promote ethnic diversity within its
housing projects, Singapore imposes ethnicity quotas: each new housing
development comprises blocks of flats and each ethnicity-based group in the
population must not own more than a certain percentage of flats in a block.
Other domains using similar hard capacity constraints include matching
prospective students to schools or medical residents to hospitals. Limiting
agents' choices for ensuring diversity in this manner naturally entails some
welfare loss. One of our goals is to study the trade-off between diversity and
social welfare in such settings. We first show that, while the classic
assignment program is polynomial-time computable, adding diversity constraints
makes it computationally intractable; however, we identify a
$\tfrac{1}{2}$-approximation algorithm, as well as reasonable assumptions on
the weights that permit poly-time algorithms. Next, we provide two upper bounds
on the price of diversity -- a measure of the loss in welfare incurred by
imposing diversity constraints -- as functions of natural problem parameters.
We conclude the paper with simulations based on publicly available data from
two diversity-constrained allocation problems -- Singapore Public Housing and
Chicago School Choice -- which shed light on how the constrained maximization
as well as lottery-based variants perform in practice.
| 1 | 0 | 0 | 0 | 0 | 0 |
X-ray spectral analyses of AGNs from the 7Ms Chandra Deep Field-South survey: the distribution, variability, and evolution of AGN's obscuration | We present a detailed spectral analysis of the brightest Active Galactic
Nuclei (AGN) identified in the 7Ms Chandra Deep Field South (CDF-S) survey over
a time span of 16 years. Using a model of an intrinsically absorbed power-law
plus reflection, with possible soft excess and narrow Fe K$\alpha$ line, we
perform a systematic X-ray spectral analysis, both on the total 7Ms exposure
and in four different periods with lengths of 2-21 months. With this approach,
we not only present the power-law slopes, column densities $N_H$, observed
fluxes, and absorption-corrected 2-10~keV luminosities $L_X$ for our sample of
AGNs, but also identify significant spectral variabilities among them on time
scales of years. We find that the $N_H$ variabilities can be ascribed to two
different types of mechanisms, either flux-driven or flux-independent. We also
find that the correlation between the narrow Fe line EW and $N_H$ can be well
explained by the continuum suppression with increasing $N_H$. Accounting for
the sample incompleteness and bias, we measure the intrinsic distribution of
$N_H$ for the CDF-S AGN population and present re-selected subsamples which are
complete with respect to $N_H$. The $N_H$-complete subsamples enable us to
decouple the dependences of $N_H$ on $L_X$ and on redshift. Combining our data
with that from C-COSMOS, we confirm the anti-correlation between the average
$N_H$ and $L_X$ of AGN, and find a significant increase of the AGN obscured
fraction with redshift at any luminosity. The obscured fraction can be
described as $f_{obscured}\thickapprox 0.42\ (1+z)^{0.60}$.
| 0 | 1 | 0 | 0 | 0 | 0 |
Muon spin relaxation and inelastic neutron scattering investigations of all-in/all-out antiferromagnet Nd2Hf2O7 | Nd2Hf2O7, belonging to the family of geometrically frustrated cubic rare
earth pyrochlore oxides, was recently identified to order antiferromagnetically
below T_N = 0.55 K with an all-in/all-out arrangement of Nd3+ moments, however
with a much reduced ordered state moment. Herein we investigate the spin
dynamics and crystal field states of Nd2Hf2O7 using muon spin relaxation (muSR)
and inelastic neutron scattering (INS) measurements. Our muSR study confirms
the long range magnetic ordering and shows evidence for coexisting persistent
dynamic spin fluctuations deep inside the ordered state down to 42 mK. The INS
data show the crytal electric field (CEF) excitations due to the transitions
both within the ground state multiplet and to the first excited state
multiplet. The INS data are analyzed by a model based on CEF and crystal field
states are determined. Strong Ising-type anisotropy is inferred from the ground
state wavefunction. The CEF parameters indicate the CEF-split Kramers doublet
ground state of Nd3+ to be consistent with the dipolar-octupolar character.
| 0 | 1 | 0 | 0 | 0 | 0 |
Modeling and Soft-fault Diagnosis of Underwater Thrusters with Recurrent Neural Networks | Noncritical soft-faults and model deviations are a challenge for Fault
Detection and Diagnosis (FDD) of resident Autonomous Underwater Vehicles
(AUVs). Such systems may have a faster performance degradation due to the
permanent exposure to the marine environment, and constant monitoring of
component conditions is required to ensure their reliability. This works
presents an evaluation of Recurrent Neural Networks (RNNs) for a data-driven
fault detection and diagnosis scheme for underwater thrusters with empirical
data. The nominal behavior of the thruster was modeled using the measured
control input, voltage, rotational speed and current signals. We evaluated the
performance of fault classification using all the measured signals compared to
using the computed residuals from the nominal model as features.
| 1 | 0 | 0 | 1 | 0 | 0 |
Epidemic Spreading on Activity-Driven Networks with Attractiveness | We study SIS epidemic spreading processes unfolding on a recent
generalisation of the activity-driven modelling framework. In this model of
time-varying networks each node is described by two variables: activity and
attractiveness. The first, describes the propensity to form connections. The
second, defines the propensity to attract them. We derive analytically the
epidemic threshold considering the timescale driving the evolution of contacts
and the contagion as comparable. The solutions are general and hold for any
joint distribution of activity and attractiveness. The theoretical picture is
confirmed via large-scale numerical simulations performed considering
heterogeneous distributions and different correlations between the two
variables. We find that heterogeneous distributions of attractiveness alter the
contagion process. In particular, in case of uncorrelated and positive
correlations between the two variables, heterogeneous attractiveness
facilitates the spreading. On the contrary, negative correlations between
activity and attractiveness hamper the spreading. The results presented
contribute to the understanding of the dynamical properties of time-varying
networks and their effects on contagion phenomena unfolding on their fabric.
| 1 | 1 | 0 | 0 | 0 | 0 |
A generalized family of anisotropic compact object in general relativity | We present model for anisotropic compact star under the general theory of
relativity of Einstein. In the study a 4-dimensional spacetime has been
considered which is embedded into the 5-dimensional flat metric so that the
spherically symmetric metric has class 1 when the condition
$e^{\lambda}=\left(\,1+C\,e^{\nu} \,{\nu'}^2\,\right)$ is satisfied ($\lambda$
and $\nu$ being the metric potentials along with a constant $C$). A set of
solutions for the field equations are found depending on the index $n$ involved
in the physical parameters. The interior solutions have been matched smoothly
at the boundary of the spherical distribution to the exterior Schwarzschild
solution which necessarily provides values of the unknown constants. We have
chosen the values of $n$ as $n=2$ and $n$=10 to 20000 for which interesting and
physically viable results can be found out. The numerical values of the
parameters and arbitrary constants for different compact stars are assumed in
the graphical plots and tables as follows: (i) LMC X-4 : $a=0.0075$,
$b=0.000821$ for $n=2$ and $a=0.0075$, $nb=0.00164$ for $n\ge 10$, (ii) SMC
X-1: $a=0.00681$, $b=0.00078$ for $n=2$, and $a=0.00681$, $nb=0.00159$ for $n
\ge 10$. The investigations on the physical features of the model include
several astrophysical issues, like (i) regularity behavior of stars at the
centre, (ii) well behaved condition for velocity of sound, (iii) energy
conditions, (iv) stabilty of the system via the following three techniques -
adiabatic index, Herrera cracking concept and TOV equation, (v) total mass,
effective mass and compactification factor and (vi) surface redshift. Specific
numerical values of the compact star candidates LMC X-4 and SMC X-1 are
calculated for central and surface densities as well as central pressure to
compare the model value with actual observational data.
| 0 | 1 | 0 | 0 | 0 | 0 |
Automated capture and delivery of assistive task guidance with an eyewear computer: The GlaciAR system | In this paper we describe and evaluate a mixed reality system that aims to
augment users in task guidance applications by combining automated and
unsupervised information collection with minimally invasive video guides. The
result is a self-contained system that we call GlaciAR (Glass-enabled
Contextual Interactions for Augmented Reality), that operates by extracting
contextual interactions from observing users performing actions. GlaciAR is
able to i) automatically determine moments of relevance based on a head motion
attention model, ii) automatically produce video guidance information, iii)
trigger these video guides based on an object detection method, iv) learn
without supervision from observing multiple users and v) operate fully on-board
a current eyewear computer (Google Glass). We describe the components of
GlaciAR together with evaluations on how users are able to use the system to
achieve three tasks. We see this work as a first step toward the development of
systems that aim to scale up the notoriously difficult authoring problem in
guidance systems and where people's natural abilities are enhanced via
minimally invasive visual guidance.
| 1 | 0 | 0 | 0 | 0 | 0 |
V2X Meets NOMA: Non-Orthogonal Multiple Access for 5G Enabled Vehicular Networks | Benefited from the widely deployed infrastructure, the LTE network has
recently been considered as a promising candidate to support the
vehicle-to-everything (V2X) services. However, with a massive number of devices
accessing the V2X network in the future, the conventional OFDM-based LTE
network faces the congestion issues due to its low efficiency of orthogonal
access, resulting in significant access delay and posing a great challenge
especially to safety-critical applications. The non-orthogonal multiple access
(NOMA) technique has been well recognized as an effective solution for the
future 5G cellular networks to provide broadband communications and massive
connectivity. In this article, we investigate the applicability of NOMA in
supporting cellular V2X services to achieve low latency and high reliability.
Starting with a basic V2X unicast system, a novel NOMA-based scheme is proposed
to tackle the technical hurdles in designing high spectral efficient scheduling
and resource allocation schemes in the ultra dense topology. We then extend it
to a more general V2X broadcasting system. Other NOMA-based extended V2X
applications and some open issues are also discussed.
| 1 | 0 | 0 | 0 | 0 | 0 |
Theoretical Foundation of Co-Training and Disagreement-Based Algorithms | Disagreement-based approaches generate multiple classifiers and exploit the
disagreement among them with unlabeled data to improve learning performance.
Co-training is a representative paradigm of them, which trains two classifiers
separately on two sufficient and redundant views; while for the applications
where there is only one view, several successful variants of co-training with
two different classifiers on single-view data instead of two views have been
proposed. For these disagreement-based approaches, there are several important
issues which still are unsolved, in this article we present theoretical
analyses to address these issues, which provides a theoretical foundation of
co-training and disagreement-based approaches.
| 1 | 0 | 0 | 1 | 0 | 0 |
Schrödinger's Man | What if someone built a "box" that applies quantum superposition not just to
quantum bits in the microscopic but also to macroscopic everyday "objects",
such as Schrödinger's cat or a human being? If that were possible, and if the
different "copies" of a man could exploit quantum interference to synchronize
and collapse into their preferred state, then one (or they?) could in a sense
choose their future, win the lottery, break codes and other security devices,
and become king of the world, or actually of the many-worlds. We set up the
plot-line of a new episode of Black Mirror to reflect on what might await us if
one were able to build such a technology.
| 1 | 0 | 0 | 0 | 0 | 0 |
High-Resolution Multispectral Dataset for Semantic Segmentation | Unmanned aircraft have decreased the cost required to collect remote sensing
imagery, which has enabled researchers to collect high-spatial resolution data
from multiple sensor modalities more frequently and easily. The increase in
data will push the need for semantic segmentation frameworks that are able to
classify non-RGB imagery, but this type of algorithmic development requires an
increase in publicly available benchmark datasets with class labels. In this
paper, we introduce a high-resolution multispectral dataset with image labels.
This new benchmark dataset has been pre-split into training/testing folds in
order to standardize evaluation and continue to push state-of-the-art
classification frameworks for non-RGB imagery.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Methodology for the Selection of Requirement Elicitation Techniques | In this paper, we present an approach to select a subset of requirement
elicitation technique for an optimum result in the requirement elicitation
process. Our approach consists of three steps. First, we identify various
attribute in three important dimensions namely project, people and the process
of software development that can influence the outcome of an elicitation
process. Second, we construct three p matrix (3PM) separately for each
dimension, that shows a relation between the elicitation techniques and three
dimensions of a software. Third, we provide a mapping criteria and use them in
the selection of a subset of elicitation techniques. We demonstrate the
applicability of the proposed approach using case studies to evaluate and
provide the contextual knowledge of selecting requirement elicitation
technique.
| 1 | 0 | 0 | 0 | 0 | 0 |
Nonclassical Light Generation from III-V and Group-IV Solid-State Cavity Quantum Systems | In this chapter, we present the state-of-the-art in the generation of
nonclassical states of light using semiconductor cavity quantum electrodynamics
(QED) platforms. Our focus is on the photon blockade effects that enable the
generation of indistinguishable photon streams with high purity and efficiency.
Starting with the leading platform of InGaAs quantum dots in optical
nanocavities, we review the physics of a single quantum emitter strongly
coupled to a cavity. Furthermore, we propose a complete model for photon
blockade and tunneling in III-V quantum dot cavity QED systems. Turning toward
quantum emitters with small inhomogeneous broadening, we propose a direction
for novel experiments for nonclassical light generation based on group-IV
color-center systems. We present a model of a multi-emitter cavity QED
platform, which features richer dressed-states ladder structures, and show how
it can offer opportunities for studying new regimes of high-quality photon
blockade.
| 0 | 1 | 0 | 0 | 0 | 0 |
INtERAcT: Interaction Network Inference from Vector Representations of Words | In recent years, the number of biomedical publications has steadfastly grown,
resulting in a rich source of untapped new knowledge. Most biomedical facts are
however not readily available, but buried in the form of unstructured text, and
hence their exploitation requires the time-consuming manual curation of
published articles. Here we present INtERAcT, a novel approach to extract
protein-protein interactions from a corpus of biomedical articles related to a
broad range of scientific domains in a completely unsupervised way. INtERAcT
exploits vector representation of words, computed on a corpus of domain
specific knowledge, and implements a new metric that estimates an interaction
score between two molecules in the space where the corresponding words are
embedded. We demonstrate the power of INtERAcT by reconstructing the molecular
pathways associated to 10 different cancer types using a corpus of
disease-specific articles for each cancer type. We evaluate INtERAcT using
STRING database as a benchmark, and show that our metric outperforms currently
adopted approaches for similarity computation at the task of identifying known
molecular interactions in all studied cancer types. Furthermore, our approach
does not require text annotation, manual curation or the definition of semantic
rules based on expert knowledge, and hence it can be easily and efficiently
applied to different scientific domains. Our findings suggest that INtERAcT may
increase our capability to summarize the understanding of a specific disease
using the published literature in an automated and completely unsupervised
fashion.
| 0 | 0 | 0 | 0 | 1 | 0 |
Distributed Deep Transfer Learning by Basic Probability Assignment | Transfer learning is a popular practice in deep neural networks, but
fine-tuning of large number of parameters is a hard task due to the complex
wiring of neurons between splitting layers and imbalance distributions of data
in pretrained and transferred domains. The reconstruction of the original
wiring for the target domain is a heavy burden due to the size of
interconnections across neurons. We propose a distributed scheme that tunes the
convolutional filters individually while backpropagates them jointly by means
of basic probability assignment. Some of the most recent advances in evidence
theory show that in a vast variety of the imbalanced regimes, optimizing of
some proper objective functions derived from contingency matrices prevents
biases towards high-prior class distributions. Therefore, the original filters
get gradually transferred based on individual contributions to overall
performance of the target domain. This largely reduces the expected complexity
of transfer learning whilst highly improves precision. Our experiments on
standard benchmarks and scenarios confirm the consistent improvement of our
distributed deep transfer learning strategy.
| 1 | 0 | 0 | 1 | 0 | 0 |
Parameter Learning and Change Detection Using a Particle Filter With Accelerated Adaptation | This paper presents the construction of a particle filter, which incorporates
elements inspired by genetic algorithms, in order to achieve accelerated
adaptation of the estimated posterior distribution to changes in model
parameters. Specifically, the filter is designed for the situation where the
subsequent data in online sequential filtering does not match the model
posterior filtered based on data up to a current point in time. The examples
considered encompass parameter regime shifts and stochastic volatility. The
filter adapts to regime shifts extremely rapidly and delivers a clear heuristic
for distinguishing between regime shifts and stochastic volatility, even though
the model dynamics assumed by the filter exhibit neither of those features.
| 0 | 0 | 0 | 1 | 0 | 1 |
Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation | Enabling robots to autonomously navigate complex environments is essential
for real-world deployment. Prior methods approach this problem by having the
robot maintain an internal map of the world, and then use a localization and
planning method to navigate through the internal map. However, these approaches
often include a variety of assumptions, are computationally intensive, and do
not learn from failures. In contrast, learning-based methods improve as the
robot acts in the environment, but are difficult to deploy in the real-world
due to their high sample complexity. To address the need to learn complex
policies with few samples, we propose a generalized computation graph that
subsumes value-based model-free methods and model-based methods, with specific
instantiations interpolating between model-free and model-based. We then
instantiate this graph to form a navigation model that learns from raw images
and is sample efficient. Our simulated car experiments explore the design
decisions of our navigation model, and show our approach outperforms
single-step and $N$-step double Q-learning. We also evaluate our approach on a
real-world RC car and show it can learn to navigate through a complex indoor
environment with a few hours of fully autonomous, self-supervised training.
Videos of the experiments and code can be found at github.com/gkahn13/gcg
| 1 | 0 | 0 | 0 | 0 | 0 |
Magnetization reversal by superconducting current in $φ_0$ Josephson junctions | We study magnetization reversal in a $\varphi_0$ Josephson junction with
direct coupling between magnetic moment and Josephson current. Our simulations
of magnetic moment dynamics show that by applying an electric current pulse, we
can realize the full magnetization reversal. We propose different protocols of
full magnetization reversal based on the variation of the Josephson junction
and pulse parameters, particularly, electric current pulse amplitude, damping
of magnetization and spin-orbit interaction. We discuss experiments which can
probe the magnetization reversal in $\varphi_0$-junctions.
| 0 | 1 | 0 | 0 | 0 | 0 |
Higher Order Accurate Space-Time Schemes for Computational Astrophysics -- Part I -- Finite Volume Methods | As computational astrophysics comes under pressure to become a precision
science, there is an increasing need to move to high accuracy schemes for
computational astrophysics. Hence the need for a specialized review on higher
order schemes for computational astrophysics.
The focus here is on weighted essentially non-oscillatory (WENO) schemes,
discontinuous Galerkin (DG) schemes and PNPM schemes. WENO schemes are higher
order extensions of traditional second order finite volume schemes which are
already familiar to most computational astrophysicists. DG schemes, on the
other hand, evolve all the moments of the solution, with the result that they
are more accurate than WENO schemes. PNPM schemes occupy a compromise position
between WENO and PNPM schemes. They evolve an Nth order spatial polynomial,
while reconstructing higher order terms up to Mth order. As a result, the
timestep can be larger.
Time-dependent astrophysical codes need to be accurate in space and time.
This is realized with the help of SSP-RK (strong stability preserving
Runge-Kutta) schemes and ADER (Arbitrary DERivative in space and time) schemes.
The most popular approaches to SSP-RK and ADER schemes are also described.
The style of this review is to assume that readers have a basic understanding
of hyperbolic systems and one-dimensional Riemann solvers. Such an
understanding can be acquired from a sequence of prepackaged lectures available
from this http URL. We then build on this
understanding to give the reader a practical introduction to the schemes
described here. The emphasis is on computer-implementable ideas, not
necessarily on the underlying theory, because it was felt that this would be
most interesting to most computational astrophysicists.
| 0 | 1 | 0 | 0 | 0 | 0 |
Building Robust Deep Neural Networks for Road Sign Detection | Deep Neural Networks are built to generalize outside of training set in mind
by using techniques such as regularization, early stopping and dropout. But
considerations to make them more resilient to adversarial examples are rarely
taken. As deep neural networks become more prevalent in mission-critical and
real-time systems, miscreants start to attack them by intentionally making deep
neural networks to misclassify an object of one type to be seen as another
type. This can be catastrophic in some scenarios where the classification of a
deep neural network can lead to a fatal decision by a machine. In this work, we
used GTSRB dataset to craft adversarial samples by Fast Gradient Sign Method
and Jacobian Saliency Method, used those crafted adversarial samples to attack
another Deep Convolutional Neural Network and built the attacked network to be
more resilient against adversarial attacks by making it more robust by
Defensive Distillation and Adversarial Training
| 1 | 0 | 0 | 1 | 0 | 0 |
Statman's Hierarchy Theorem | In the Simply Typed $\lambda$-calculus Statman investigates the reducibility
relation $\leq_{\beta\eta}$ between types: for $A,B \in \mathbb{T}^0$, types
freely generated using $\rightarrow$ and a single ground type $0$, define $A
\leq_{\beta\eta} B$ if there exists a $\lambda$-definable injection from the
closed terms of type $A$ into those of type $B$. Unexpectedly, the induced
partial order is the (linear) well-ordering (of order type) $\omega + 4$.
In the proof a finer relation $\leq_{h}$ is used, where the above injection
is required to be a Böhm transformation, and an (a posteriori) coarser
relation $\leq_{h^+}$, requiring a finite family of Böhm transformations that
is jointly injective.
We present this result in a self-contained, syntactic, constructive and
simplified manner. En route similar results for $\leq_h$ (order type $\omega +
5$) and $\leq_{h^+}$ (order type $8$) are obtained. Five of the equivalence
classes of $\leq_{h^+}$ correspond to canonical term models of Statman, one to
the trivial term model collapsing all elements of the same type, and one does
not even form a model by the lack of closed terms of many types.
| 1 | 0 | 0 | 0 | 0 | 0 |
Anisotropic Dzyaloshinskii-Moriya Interaction in ultra-thin epitaxial Au/Co/W(110) | We have used Brillouin Light Scattering spectroscopy to independently
determine the in-plane Magneto-Crystalline Anisotropy and the
Dzyaloshinskii-Moriya Interaction (DMI) in out-of-plane magnetized
Au/Co/W(110). We found that the DMI strength is 2-3 times larger along the
bcc$[\bar{1}10]$ than along the bcc$[001]$ direction. We use analytical
considerations to illustrate the relationship between the crystal symmetry of
the stack and the anisotropy of microscopic DMI. Such an anisotropic DMI is the
first step to realize isolated elliptical skyrmions or anti-skyrmions in thin
film systems with $C_{2v}$ symmetry.
| 0 | 1 | 0 | 0 | 0 | 0 |
Modeling and Simulation of Robotic Finger Powered by Nylon Artificial Muscles- Equations with Simulink model | This paper shows a detailed modeling of three-link robotic finger that is
actuated by nylon artificial muscles and a simulink model that can be used for
numerical study of a robotic finger. The robotic hand prototype was recently
demonstrated in recent publication Wu, L., Jung de Andrade, M., Saharan,
L.,Rome, R., Baughman, R., and Tadesse, Y., 2017, Compact and Low-cost Humanoid
Hand Powered by Nylon Artificial Muscles, Bioinspiration & Biomimetics, 12 (2).
The robotic hand is a 3D printed, lightweight and compact hand actuated by
silver-coated nylon muscles, often called Twisted and coiled Polymer (TCP)
muscles. TCP muscles are thermal actuators that contract when they are heated
and they are getting attention for application in robotics. The purpose of this
paper is to demonstrate the modeling equations that were derived based on Euler
Lagrangian approach that is suitable for implementation in simulink model.
| 1 | 0 | 0 | 0 | 0 | 0 |
Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo | A key task in Bayesian statistics is sampling from distributions that are
only specified up to a partition function (i.e., constant of proportionality).
However, without any assumptions, sampling (even approximately) can be #P-hard,
and few works have provided "beyond worst-case" guarantees for such settings.
For log-concave distributions, classical results going back to Bakry and
Émery (1985) show that natural continuous-time Markov chains called Langevin
diffusions mix in polynomial time. The most salient feature of log-concavity
violated in practice is uni-modality: commonly, the distributions we wish to
sample from are multi-modal. In the presence of multiple deep and
well-separated modes, Langevin diffusion suffers from torpid mixing.
We address this problem by combining Langevin diffusion with simulated
tempering. The result is a Markov chain that mixes more rapidly by
transitioning between different temperatures of the distribution. We analyze
this Markov chain for the canonical multi-modal distribution: a mixture of
gaussians (of equal variance). The algorithm based on our Markov chain provably
samples from distributions that are close to mixtures of gaussians, given
access to the gradient of the log-pdf. For the analysis, we use a spectral
decomposition theorem for graphs (Gharan and Trevisan, 2014) and a Markov chain
decomposition technique (Madras and Randall, 2002).
| 1 | 0 | 0 | 1 | 0 | 0 |
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