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Statistical Latent Space Approach for Mixed Data Modelling and Applications | The analysis of mixed data has been raising challenges in statistics and
machine learning. One of two most prominent challenges is to develop new
statistical techniques and methodologies to effectively handle mixed data by
making the data less heterogeneous with minimum loss of information. The other
challenge is that such methods must be able to apply in large-scale tasks when
dealing with huge amount of mixed data. To tackle these challenges, we
introduce parameter sharing and balancing extensions to our recent model, the
mixed-variate restricted Boltzmann machine (MV.RBM) which can transform
heterogeneous data into homogeneous representation. We also integrate
structured sparsity and distance metric learning into RBM-based models. Our
proposed methods are applied in various applications including latent patient
profile modelling in medical data analysis and representation learning for
image retrieval. The experimental results demonstrate the models perform better
than baseline methods in medical data and outperform state-of-the-art rivals in
image dataset.
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Near-field coupling of gold plasmonic antennas for sub-100 nm magneto-thermal microscopy | The development of spintronic technology with increasingly dense, high-speed,
and complex devices will be accelerated by accessible microscopy techniques
capable of probing magnetic phenomena on picosecond time scales and at deeply
sub-micron length scales. A recently developed time-resolved magneto-thermal
microscope provides a path towards this goal if it is augmented with a
picosecond, nanoscale heat source. We theoretically study adiabatic
nanofocusing and near-field heat induction using conical gold plasmonic
antennas to generate sub-100 nm thermal gradients for time-resolved
magneto-thermal imaging. Finite element calculations of antenna-sample
interactions reveal focused electromagnetic loss profiles that are either
peaked directly under the antenna or are annular, depending on the sample's
conductivity, the antenna's apex radius, and the tip-sample separation. We find
that the thermal gradient is confined to 40 nm to 60 nm full width at half
maximum for realistic ranges of sample conductivity and apex radius. To
mitigate this variation, which is undesirable for microscopy, we investigate
the use of a platinum capping layer on top of the sample as a thermal
transduction layer to produce heat uniformly across different sample materials.
After determining the optimal capping layer thickness, we simulate the
evolution of the thermal gradient in the underlying sample layer, and find that
the temporal width is below 10 ps. These results lay a theoretical foundation
for nanoscale, time-resolved magneto-thermal imaging.
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A reproducible effect size is more useful than an irreproducible hypothesis test to analyze high throughput sequencing datasets | Motivation: P values derived from the null hypothesis significance testing
framework are strongly affected by sample size, and are known to be
irreproducible in underpowered studies, yet no suitable replacement has been
proposed. Results: Here we present implementations of non-parametric
standardized median effect size estimates, dNEF, for high-throughput sequencing
datasets. Case studies are shown for transcriptome and tag-sequencing datasets.
The dNEF measure is shown to be more repro- ducible and robust than P values
and requires sample sizes as small as 3 to reproducibly identify differentially
abundant features. Availability: Source code and binaries freely available at:
this https URL, omicplotR, and
this https URL.
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High temperature thermodynamics of the honeycomb-lattice Kitaev-Heisenberg model: A high temperature series expansion study | We develop high temperature series expansions for the thermodynamic
properties of the honeycomb-lattice Kitaev-Heisenberg model. Numerical results
for uniform susceptibility, heat capacity and entropy as a function of
temperature for different values of the Kitaev coupling $K$ and Heisenberg
exachange coupling $J$ (with $|J|\le |K|$) are presented. These expansions show
good convergence down to a temperature of a fraction of $K$ and in some cases
down to $T=K/10$. In the Kitaev exchange dominated regime, the inverse
susceptibility has a nearly linear temperature dependence over a wide
temperature range. However, we show that already at temperatures $10$-times the
Curie-Weiss temperature, the effective Curie-Weiss constant estimated from the
data can be off by a factor of 2. We find that the magnitude of the heat
capacity maximum at the short-range order peak, is substantially smaller for
small $J/K$ than for $J$ of order or larger than $K$. We suggest that this
itself represents a simple marker for the relative importance of the Kitaev
terms in these systems. Somewhat surprisingly, both heat capacity and
susceptibility data on Na$_2$IrO$_3$ are consistent with a dominant {\it
antiferromagnetic} Kitaev exchange constant of about $300-400$ $K$.
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Laplace Beltrami operator in the Baran metric and pluripotential equilibrium measure: the ball, the simplex and the sphere | The Baran metric $\delta_E$ is a Finsler metric on the interior of $E\subset
\R^n$ arising from Pluripotential Theory. We consider the few instances, namely
$E$ being the ball, the simplex, or the sphere, where $\delta_E$ is known to be
Riemaniann and we prove that the eigenfunctions of the associated Laplace
Beltrami operator (with no boundary conditions) are the orthogonal polynomials
with respect to the pluripotential equilibrium measure $\mu_E$ of $E.$ We
conjecture that this may hold in a wider generality.
The considered differential operators have been already introduced in the
framework of orthogonal polynomials and studied in connection with certain
symmetry groups. In this work instead we highlight the relationships between
orthogonal polynomials with respect to $\mu_E$ and the Riemaniann structure
naturally arising from Pluripotential Theory
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Magnetic polarons in a nonequilibrium polariton condensate | We consider a condensate of exciton-polaritons in a diluted magnetic
semiconductor microcavity. Such system may exhibit magnetic self-trapping in
the case of sufficiently strong coupling between polaritons and magnetic ions
embedded in the semiconductor. We investigate the effect of the nonequilibrium
nature of exciton-polaritons on the physics of the resulting self-trapped
magnetic polarons. We find that multiple polarons can exist at the same time,
and derive a critical condition for self-trapping which is different to the one
predicted previously in the equilibrium case. Using the Bogoliubov-de Gennes
approximation, we calculate the excitation spectrum and provide a physical
explanation in terms of the effective magnetic attraction between polaritons,
mediated by the ion subsystem.
| 0 | 1 | 0 | 0 | 0 | 0 |
Inference in Sparse Graphs with Pairwise Measurements and Side Information | We consider the statistical problem of recovering a hidden "ground truth"
binary labeling for the vertices of a graph up to low Hamming error from noisy
edge and vertex measurements. We present new algorithms and a sharp
finite-sample analysis for this problem on trees and sparse graphs with poor
expansion properties such as hypergrids and ring lattices. Our method
generalizes and improves over that of Globerson et al. (2015), who introduced
the problem for two-dimensional grid lattices.
For trees we provide a simple, efficient, algorithm that infers the ground
truth with optimal Hamming error has optimal sample complexity and implies
recovery results for all connected graphs. Here, the presence of side
information is critical to obtain a non-trivial recovery rate. We then show how
to adapt this algorithm to tree decompositions of edge-subgraphs of certain
graph families such as lattices, resulting in optimal recovery error rates that
can be obtained efficiently
The thrust of our analysis is to 1) use the tree decomposition along with
edge measurements to produce a small class of viable vertex labelings and 2)
apply an analysis influenced by statistical learning theory to show that we can
infer the ground truth from this class using vertex measurements. We show the
power of our method in several examples including hypergrids, ring lattices,
and the Newman-Watts model for small world graphs. For two-dimensional grids,
our results improve over Globerson et al. (2015) by obtaining optimal recovery
in the constant-height regime.
| 1 | 0 | 0 | 0 | 0 | 0 |
Oracle Importance Sampling for Stochastic Simulation Models | We consider the problem of estimating an expected outcome from a stochastic
simulation model using importance sampling. We propose a two-stage procedure
that involves a regression stage and a sampling stage to construct our
estimator. We introduce a parametric and a nonparametric regression estimator
in the first stage and study how the allocation between the two stages affects
the performance of final estimator. We derive the oracle property for both
approaches. We analyze the empirical performances of our approaches using two
simulated data and a case study on wind turbine reliability evaluation.
| 0 | 0 | 0 | 1 | 0 | 0 |
The Generalized Cross Validation Filter | Generalized cross validation (GCV) is one of the most important approaches
used to estimate parameters in the context of inverse problems and
regularization techniques. A notable example is the determination of the
smoothness parameter in splines. When the data are generated by a state space
model, like in the spline case, efficient algorithms are available to evaluate
the GCV score with complexity that scales linearly in the data set size.
However, these methods are not amenable to on-line applications since they rely
on forward and backward recursions. Hence, if the objective has been evaluated
at time $t-1$ and new data arrive at time t, then O(t) operations are needed to
update the GCV score. In this paper we instead show that the update cost is
$O(1)$, thus paving the way to the on-line use of GCV. This result is obtained
by deriving the novel GCV filter which extends the classical Kalman filter
equations to efficiently propagate the GCV score over time. We also illustrate
applications of the new filter in the context of state estimation and on-line
regularized linear system identification.
| 1 | 0 | 0 | 1 | 0 | 0 |
Coherence of Biochemical Oscillations is Bounded by Driving Force and Network Topology | Biochemical oscillations are prevalent in living organisms. Systems with a
small number of constituents cannot sustain coherent oscillations for an
indefinite time because of fluctuations in the period of oscillation. We show
that the number of coherent oscillations that quantifies the precision of the
oscillator is universally bounded by the thermodynamic force that drives the
system out of equilibrium and by the topology of the underlying biochemical
network of states. Our results are valid for arbitrary Markov processes, which
are commonly used to model biochemical reactions. We apply our results to a
model for a single KaiC protein and to an activator-inhibitor model that
consists of several molecules. From a mathematical perspective, based on strong
numerical evidence, we conjecture a universal constraint relating the imaginary
and real parts of the first non-trivial eigenvalue of a stochastic matrix.
| 0 | 1 | 0 | 0 | 0 | 0 |
How Do Classifiers Induce Agents To Invest Effort Strategically? | Algorithms are often used to produce decision-making rules that classify or
evaluate individuals. When these individuals have incentives to be classified a
certain way, they may behave strategically to influence their outcomes. We
develop a model for how strategic agents can invest effort in order to change
the outcomes they receive, and we give a tight characterization of when such
agents can be incentivized to invest specified forms of effort into improving
their outcomes as opposed to "gaming" the classifier. We show that whenever any
"reasonable" mechanism can do so, a simple linear mechanism suffices.
| 0 | 0 | 0 | 1 | 0 | 0 |
Guiding Reinforcement Learning Exploration Using Natural Language | In this work we present a technique to use natural language to help
reinforcement learning generalize to unseen environments. This technique uses
neural machine translation, specifically the use of encoder-decoder networks,
to learn associations between natural language behavior descriptions and
state-action information. We then use this learned model to guide agent
exploration using a modified version of policy shaping to make it more
effective at learning in unseen environments. We evaluate this technique using
the popular arcade game, Frogger, under ideal and non-ideal conditions. This
evaluation shows that our modified policy shaping algorithm improves over a
Q-learning agent as well as a baseline version of policy shaping.
| 1 | 0 | 0 | 1 | 0 | 0 |
Of the People: Voting Is More Effective with Representative Candidates | In light of the classic impossibility results of Arrow and Gibbard and
Satterthwaite regarding voting with ordinal rules, there has been recent
interest in characterizing how well common voting rules approximate the social
optimum. In order to quantify the quality of approximation, it is natural to
consider the candidates and voters as embedded within a common metric space,
and to ask how much further the chosen candidate is from the population as
compared to the socially optimal one. We use this metric preference model to
explore a fundamental and timely question: does the social welfare of a
population improve when candidates are representative of the population? If so,
then by how much, and how does the answer depend on the complexity of the
metric space?
We restrict attention to the most fundamental and common social choice
setting: a population of voters, two independently drawn candidates, and a
majority rule election. When candidates are not representative of the
population, it is known that the candidate selected by the majority rule can be
thrice as far from the population as the socially optimal one. We examine how
this ratio improves when candidates are drawn independently from the population
of voters. Our results are two-fold: When the metric is a line, the ratio
improves from $3$ to $4-2\sqrt{2}$, roughly $1.1716$; this bound is tight. When
the metric is arbitrary, we show a lower bound of $1.5$ and a constant upper
bound strictly better than $2$ on the approximation ratio of the majority rule.
The positive result depends in part on the assumption that candidates are
independent and identically distributed. However, we show that independence
alone is not enough to achieve the upper bound: even when candidates are drawn
independently, if the population of candidates can be different from the
voters, then an upper bound of $2$ on the approximation is tight.
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Cell Coverage Extension with Orthogonal Random Precoding for Massive MIMO Systems | In this paper, we investigate a coverage extension scheme based on orthogonal
random precoding (ORP) for the downlink of massive multiple-input
multiple-output (MIMO) systems. In this scheme, a precoding matrix consisting
of orthogonal vectors is employed at the transmitter to enhance the maximum
signal-to-interference-plus-noise ratio (SINR) of the user. To analyze and
optimize the ORP scheme in terms of cell coverage, we derive the analytical
expressions of the downlink coverage probability for two receiver structures,
namely, the single-antenna (SA) receiver and multiple-antenna receiver with
antenna selection (AS). The simulation results show that the analytical
expressions accurately capture the coverage behaviors of the systems employing
the ORP scheme. It is also shown that the optimal coverage performance is
achieved when a single precoding vector is used under the condition that the
threshold of the signal-to-noise ratio of the coverage is greater than one. The
performance of the ORP scheme is further analyzed when different random
precoder groups are utilized over multiple time slots to exploit precoding
diversity. The numerical results show that the proposed ORP scheme over
multiple time slots provides a substantial coverage gain over the space-time
coding scheme despite its low feedback overhead.
| 1 | 0 | 1 | 0 | 0 | 0 |
Hidden Community Detection in Social Networks | We introduce a new paradigm that is important for community detection in the
realm of network analysis. Networks contain a set of strong, dominant
communities, which interfere with the detection of weak, natural community
structure. When most of the members of the weak communities also belong to
stronger communities, they are extremely hard to be uncovered. We call the weak
communities the hidden community structure.
We present a novel approach called HICODE (HIdden COmmunity DEtection) that
identifies the hidden community structure as well as the dominant community
structure. By weakening the strength of the dominant structure, one can uncover
the hidden structure beneath. Likewise, by reducing the strength of the hidden
structure, one can more accurately identify the dominant structure. In this
way, HICODE tackles both tasks simultaneously.
Extensive experiments on real-world networks demonstrate that HICODE
outperforms several state-of-the-art community detection methods in uncovering
both the dominant and the hidden structure. In the Facebook university social
networks, we find multiple non-redundant sets of communities that are strongly
associated with residential hall, year of registration or career position of
the faculties or students, while the state-of-the-art algorithms mainly locate
the dominant ground truth category. In the Due to the difficulty of labeling
all ground truth communities in real-world datasets, HICODE provides a
promising approach to pinpoint the existing latent communities and uncover
communities for which there is no ground truth. Finding this unknown structure
is an extremely important community detection problem.
| 1 | 1 | 0 | 1 | 0 | 0 |
Two-photon exchange correction to the hyperfine splitting in muonic hydrogen | We reevaluate the Zemach, recoil and polarizability corrections to the
hyperfine splitting in muonic hydrogen expressing them through the low-energy
proton structure constants and obtain the precise values of the Zemach radius
and two-photon exchange (TPE) contribution. The uncertainty of TPE correction
to S energy levels in muonic hydrogen of 105 ppm exceeds the ppm accuracy level
of the forthcoming 1S hyperfine splitting measurements at PSI, J-PARC and
RIKEN-RAL.
| 0 | 1 | 0 | 0 | 0 | 0 |
Ising Models with Latent Conditional Gaussian Variables | Ising models describe the joint probability distribution of a vector of
binary feature variables. Typically, not all the variables interact with each
other and one is interested in learning the presumably sparse network structure
of the interacting variables. However, in the presence of latent variables, the
conventional method of learning a sparse model might fail. This is because the
latent variables induce indirect interactions of the observed variables. In the
case of only a few latent conditional Gaussian variables these spurious
interactions contribute an additional low-rank component to the interaction
parameters of the observed Ising model. Therefore, we propose to learn a sparse
+ low-rank decomposition of the parameters of an Ising model using a convex
regularized likelihood problem. We show that the same problem can be obtained
as the dual of a maximum-entropy problem with a new type of relaxation, where
the sample means collectively need to match the expected values only up to a
given tolerance. The solution to the convex optimization problem has
consistency properties in the high-dimensional setting, where the number of
observed binary variables and the number of latent conditional Gaussian
variables are allowed to grow with the number of training samples.
| 1 | 0 | 0 | 1 | 0 | 0 |
Quasiparticles and charge transfer at the two surfaces of the honeycomb iridate Na$_2$IrO$_3$ | Direct experimental investigations of the low-energy electronic structure of
the Na$_2$IrO$_3$ iridate insulator are sparse and draw two conflicting
pictures. One relies on flat bands and a clear gap, the other involves
dispersive states approaching the Fermi level, pointing to surface metallicity.
Here, by a combination of angle-resolved photoemission, photoemission electron
microscopy, and x-ray absorption, we show that the correct picture is more
complex and involves an anomalous band, arising from charge transfer from Na
atoms to Ir-derived states. Bulk quasiparticles do exist, but in one of the two
possible surface terminations the charge transfer is smaller and they remain
elusive.
| 0 | 1 | 0 | 0 | 0 | 0 |
Breaking Bivariate Records | We establish a fundamental property of bivariate Pareto records for
independent observations uniformly distributed in the unit square. We prove
that the asymptotic conditional distribution of the number of records broken by
an observation given that the observation sets a record is Geometric with
parameter 1/2.
| 0 | 0 | 1 | 1 | 0 | 0 |
A Bag-of-Paths Node Criticality Measure | This work compares several node (and network) criticality measures
quantifying to which extend each node is critical with respect to the
communication flow between nodes of the network, and introduces a new measure
based on the Bag-of-Paths (BoP) framework. Network disconnection simulation
experiments show that the new BoP measure outperforms all the other measures on
a sample of Erdos-Renyi and Albert-Barabasi graphs. Furthermore, a faster
(still O(n^3)), approximate, BoP criticality relying on the Sherman-Morrison
rank-one update of a matrix is introduced for tackling larger networks. This
approximate measure shows similar performances as the original, exact, one.
| 1 | 1 | 0 | 0 | 0 | 0 |
Generation and analysis of lamplighter programs | We consider a programming language based on the lamplighter group that uses
only composition and iteration as control structures. We derive generating
functions and counting formulas for this language and special subsets of it,
establishing lower and upper bounds on the growth rate of semantically distinct
programs. Finally, we show how to sample random programs and analyze the
distribution of runtimes induced by such sampling.
| 1 | 0 | 1 | 0 | 0 | 0 |
A Projection-Based Reformulation and Decomposition Algorithm for Global Optimization of a Class of Mixed Integer Bilevel Linear Programs | We propose an extended variant of the reformulation and decomposition
algorithm for solving a special class of mixed-integer bilevel linear programs
(MIBLPs) where continuous and integer variables are involved in both upper- and
lower-level problems. In particular, we consider MIBLPs with upper-level
constraints that involve lower-level variables. We assume that the inducible
region is nonempty and all variables are bounded. By using the reformulation
and decomposition scheme, an MIBLP is first converted into its equivalent
single-level formulation, then computed by a column-and-constraint generation
based decomposition algorithm. The solution procedure is enhanced by a
projection strategy that does not require the relatively complete response
property. To ensure its performance, we prove that our new method converges to
the global optimal solution in a finite number of iterations. A large-scale
computational study on random instances and instances of hierarchical supply
chain planning are presented to demonstrate the effectiveness of the algorithm.
| 0 | 0 | 1 | 0 | 0 | 0 |
Preduals for spaces of operators involving Hilbert spaces and trace-class operators | Continuing the study of preduals of spaces $\mathcal{L}(H,Y)$ of bounded,
linear maps, we consider the situation that $H$ is a Hilbert space. We
establish a natural correspondence between isometric preduals of
$\mathcal{L}(H,Y)$ and isometric preduals of $Y$.
The main ingredient is a Tomiyama-type result which shows that every
contractive projection that complements $\mathcal{L}(H,Y)$ in its bidual is
automatically a right $\mathcal{L}(H)$-module map.
As an application, we show that isometric preduals of
$\mathcal{L}(\mathcal{S}_1)$, the algebra of operators on the space of
trace-class operators, correspond to isometric preduals of $\mathcal{S}_1$
itself (and there is an abundance of them). On the other hand, the compact
operators are the unique predual of $\mathcal{S}_1$ making its multiplication
separately weak* continuous.
| 0 | 0 | 1 | 0 | 0 | 0 |
Computing maximum cliques in $B_2$-EPG graphs | EPG graphs, introduced by Golumbic et al. in 2009, are edge-intersection
graphs of paths on an orthogonal grid. The class $B_k$-EPG is the subclass of
EPG graphs where the path on the grid associated to each vertex has at most $k$
bends. Epstein et al. showed in 2013 that computing a maximum clique in
$B_1$-EPG graphs is polynomial. As remarked in [Heldt et al., 2014], when the
number of bends is at least $4$, the class contains $2$-interval graphs for
which computing a maximum clique is an NP-hard problem. The complexity status
of the Maximum Clique problem remains open for $B_2$ and $B_3$-EPG graphs. In
this paper, we show that we can compute a maximum clique in polynomial time in
$B_2$-EPG graphs given a representation of the graph.
Moreover, we show that a simple counting argument provides a
${2(k+1)}$-approximation for the coloring problem on $B_k$-EPG graphs without
knowing the representation of the graph. It generalizes a result of [Epstein et
al, 2013] on $B_1$-EPG graphs (where the representation was needed).
| 1 | 0 | 0 | 0 | 0 | 0 |
Interactions between Health Searchers and Search Engines | The Web is an important resource for understanding and diagnosing medical
conditions. Based on exposure to online content, people may develop undue
health concerns, believing that common and benign symptoms are explained by
serious illnesses. In this paper, we investigate potential strategies to mine
queries and searcher histories for clues that could help search engines choose
the most appropriate information to present in response to exploratory medical
queries. To do this, we performed a longitudinal study of health search
behavior using the logs of a popular search engine. We found that query
variations which might appear innocuous (e.g. "bad headache" vs "severe
headache") may hold valuable information about the searcher which could be used
by search engines to improve performance. Furthermore, we investigated how
medically concerned users respond differently to search engine result pages
(SERPs) and find that their disposition for clicking on concerning pages is
pronounced, potentially leading to a self-reinforcement of concern. Finally, we
studied to which degree variations in the SERP impact future search and
real-world health-seeking behavior and obtained some surprising results (e.g.,
viewing concerning pages may lead to a short-term reduction of real-world
health seeking).
| 1 | 0 | 0 | 0 | 0 | 0 |
Effect algebras as presheaves on finite Boolean algebras | For an effect algebra $A$, we examine the category of all morphisms from
finite Boolean algebras into $A$. This category can be described as a category
of elements of a presheaf $R(A)$ on the category of finite Boolean algebras. We
prove that some properties (being an orthoalgebra, the Riesz decomposition
property, being a Boolean algebra) of an effect algebra $A$ can be
characterized by properties of the category of elements of the presheaf $R(A)$.
We prove that the tensor product of of effect algebras arises as a left Kan
extension of the free product of finite Boolean algebras along the inclusion
functor. As a consequence, the tensor product of effect algebras can be
expressed by means of the Day convolution of presheaves on finite Boolean
algebras.
| 0 | 0 | 1 | 0 | 0 | 0 |
Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices | In a previous work we have detailed the requirements to obtain a maximal
performance benefit by implementing fully connected deep neural networks (DNN)
in form of arrays of resistive devices for deep learning. This concept of
Resistive Processing Unit (RPU) devices we extend here towards convolutional
neural networks (CNNs). We show how to map the convolutional layers to RPU
arrays such that the parallelism of the hardware can be fully utilized in all
three cycles of the backpropagation algorithm. We find that the noise and bound
limitations imposed due to analog nature of the computations performed on the
arrays effect the training accuracy of the CNNs. Noise and bound management
techniques are presented that mitigate these problems without introducing any
additional complexity in the analog circuits and can be addressed by the
digital circuits. In addition, we discuss digitally programmable update
management and device variability reduction techniques that can be used
selectively for some of the layers in a CNN. We show that combination of all
those techniques enables a successful application of the RPU concept for
training CNNs. The techniques discussed here are more general and can be
applied beyond CNN architectures and therefore enables applicability of RPU
approach for large class of neural network architectures.
| 1 | 0 | 0 | 1 | 0 | 0 |
Absolute versus convective helical magnetorotational instabilities in Taylor-Couette flows | We study magnetic Taylor-Couette flow in a system having nondimensional radii
$r_i=1$ and $r_o=2$, and periodic in the axial direction with wavelengths
$h\ge100$. The rotation ratio of the inner and outer cylinders is adjusted to
be slightly in the Rayleigh-stable regime, where magnetic fields are required
to destabilize the flow, in this case triggering the axisymmetric helical
magnetorotational instability (HMRI). Two choices of imposed magnetic field are
considered, both having the same azimuthal component $B_\phi=r^{-1}$, but
differing axial components. The first choice has $B_z=0.1$, and yields the
familiar HMRI, consisting of unidirectionally traveling waves. The second
choice has $B_z\approx0.1\sin(2\pi z/h)$, and yields HMRI waves that travel in
opposite directions depending on the sign of $B_z$. The first configuration
corresponds to a convective instability, the second to an absolute instability.
The two variants behave very similarly regarding both linear onset as well as
nonlinear equilibration.
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Symmetries and multipeakon solutions for the modified two-component Camassa-Holm system | Compared with the two-component Camassa-Holm system, the modified
two-component Camassa-Holm system introduces a regularized density which makes
possible the existence of solutions of lower regularity, and in particular of
multipeakon solutions. In this paper, we derive a new pointwise invariant for
the modified two-component Camassa-Holm system. The derivation of the invariant
uses directly the symmetry of the system, following the classical argument of
Noether's theorem. The existence of the multipeakon solutions can be directly
inferred from this pointwise invariant. This derivation shows the strong
connection between symmetries and the existence of special solutions. The
observation also holds for the scalar Camassa-Holm equation and, for
comparison, we have also included the corresponding derivation. Finally, we
compute explicitly the solutions obtained for the peakon-antipeakon case. We
observe the existence of a periodic solution which has not been reported in the
literature previously. This case shows the attractive effect that the
introduction of an elastic potential can have on the solutions.
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Selection of quasi-stationary states in the Navier-Stokes equation on the torus | The two dimensional incompressible Navier-Stokes equation on $D_\delta := [0,
2\pi\delta] \times [0, 2\pi]$ with $\delta \approx 1$, periodic boundary
conditions, and viscosity $0 < \nu \ll 1$ is considered. Bars and dipoles, two
explicitly given quasi-stationary states of the system, evolve on the time
scale $\mathcal{O}(e^{-\nu t})$ and have been shown to play a key role in its
long-time evolution. Of particular interest is the role that $\delta$ plays in
selecting which of these two states is observed. Recent numerical studies
suggest that, after a transient period of rapid decay of the high Fourier
modes, the bar state will be selected if $\delta \neq 1$, while the dipole will
be selected if $\delta = 1$. Our results support this claim and seek to
mathematically formalize it. We consider the system in Fourier space, project
it onto a center manifold consisting of the lowest eight Fourier modes, and use
this as a model to study the selection of bars and dipoles. It is shown for
this ODE model that the value of $\delta$ controls the behavior of the
asymptotic ratio of the low modes, thus determining the likelihood of observing
a bar state or dipole after an initial transient period. Moreover, in our
model, for all $\delta \approx 1$, there is an initial time period in which the
high modes decay at the rapid rate $\mathcal{O}(e^{-t/\nu})$, while the low
modes evolve at the slower $\mathcal{O}(e^{-\nu t})$ rate. The results for the
ODE model are proven using energy estimates and invariant manifolds and further
supported by formal asymptotic expansions and numerics.
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Geometric Enclosing Networks | Training model to generate data has increasingly attracted research attention
and become important in modern world applications. We propose in this paper a
new geometry-based optimization approach to address this problem. Orthogonal to
current state-of-the-art density-based approaches, most notably VAE and GAN, we
present a fresh new idea that borrows the principle of minimal enclosing ball
to train a generator G\left(\bz\right) in such a way that both training and
generated data, after being mapped to the feature space, are enclosed in the
same sphere. We develop theory to guarantee that the mapping is bijective so
that its inverse from feature space to data space results in expressive
nonlinear contours to describe the data manifold, hence ensuring data generated
are also lying on the data manifold learned from training data. Our model
enjoys a nice geometric interpretation, hence termed Geometric Enclosing
Networks (GEN), and possesses some key advantages over its rivals, namely
simple and easy-to-control optimization formulation, avoidance of mode
collapsing and efficiently learn data manifold representation in a completely
unsupervised manner. We conducted extensive experiments on synthesis and
real-world datasets to illustrate the behaviors, strength and weakness of our
proposed GEN, in particular its ability to handle multi-modal data and quality
of generated data.
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A pliable lasso for the Cox model | We introduce a pliable lasso method for estimation of interaction effects in
the Cox proportional hazards model framework. The pliable lasso is a linear
model that includes interactions between covariates X and a set of modifying
variables Z and assumes sparsity of the main effects and interaction effects.
The hierarchical penalty excludes interaction effects when the corresponding
main effects are zero: this avoids overfitting and an explosion of model
complexity. We extend this method to the Cox model for survival data,
incorporating modifiers that are either fixed or varying in time into the
partial likelihood. For example, this allows modeling of survival times that
differ based on interactions of genes with age, gender, or other demographic
information. The optimization is done by blockwise coordinate descent on a
second order approximation of the objective.
| 0 | 0 | 0 | 1 | 0 | 0 |
Localized magnetic moments with tunable spin exchange in a gas of ultracold fermions | We report on the experimental realization of a state-dependent lattice for a
two-orbital fermionic quantum gas with strong interorbital spin exchange. In
our state-dependent lattice, the ground and metastable excited electronic
states of $^{173}$Yb take the roles of itinerant and localized magnetic
moments, respectively. Repulsive on-site interactions in conjunction with the
tunnel mobility lead to spin exchange between mobile and localized particles,
modeling the coupling term in the well-known Kondo Hamiltonian. In addition, we
find that this exchange process can be tuned resonantly by varying the on-site
confinement. We attribute this to a resonant coupling to center-of-mass excited
bound states of one interorbital scattering channel.
| 0 | 1 | 0 | 0 | 0 | 0 |
Khintchine's Theorem with random fractions | We prove versions of Khintchine's Theorem (1924) for approximations by
rational numbers whose numerators lie in randomly chosen sets of integers, and
we explore the extent to which the monotonicity assumption can be removed.
Roughly speaking, we show that if the number of available fractions for each
denominator grows too fast, then the monotonicity assumption cannot be removed.
There are questions in this random setting which may be seen as cognates of the
Duffin-Schaeffer Conjecture (1941), and are likely to be more accessible. We
point out that the direct random analogue of the Duffin-Schaeffer Conjecture,
like the Duffin-Schaeffer Conjecture itself, implies Catlin's Conjecture
(1976). It is not obvious whether the Duffin-Schaeffer Conjecture and its
random version imply one another, and it is not known whether Catlin's
Conjecture implies either of them. The question of whether Catlin implies
Duffin-Schaeffer has been unsettled for decades.
| 0 | 0 | 1 | 0 | 0 | 0 |
A Method of Generating Random Weights and Biases in Feedforward Neural Networks with Random Hidden Nodes | Neural networks with random hidden nodes have gained increasing interest from
researchers and practical applications. This is due to their unique features
such as very fast training and universal approximation property. In these
networks the weights and biases of hidden nodes determining the nonlinear
feature mapping are set randomly and are not learned. Appropriate selection of
the intervals from which weights and biases are selected is extremely
important. This topic has not yet been sufficiently explored in the literature.
In this work a method of generating random weights and biases is proposed. This
method generates the parameters of the hidden nodes in such a way that
nonlinear fragments of the activation functions are located in the input space
regions with data and can be used to construct the surface approximating a
nonlinear target function. The weights and biases are dependent on the input
data range and activation function type. The proposed methods allows us to
control the generalization degree of the model. These all lead to improvement
in approximation performance of the network. Several experiments show very
promising results.
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The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification | Artificial neural networks have been successfully applied to a variety of
machine learning tasks, including image recognition, semantic segmentation, and
machine translation. However, few studies fully investigated ensembles of
artificial neural networks. In this work, we investigated multiple widely used
ensemble methods, including unweighted averaging, majority voting, the Bayes
Optimal Classifier, and the (discrete) Super Learner, for image recognition
tasks, with deep neural networks as candidate algorithms. We designed several
experiments, with the candidate algorithms being the same network structure
with different model checkpoints within a single training process, networks
with same structure but trained multiple times stochastically, and networks
with different structure. In addition, we further studied the over-confidence
phenomenon of the neural networks, as well as its impact on the ensemble
methods. Across all of our experiments, the Super Learner achieved best
performance among all the ensemble methods in this study.
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Representation of big data by dimension reduction | Suppose the data consist of a set $S$ of points $x_j, 1 \leq j \leq J$,
distributed in a bounded domain $D \subset R^N$, where $N$ and $J$ are large
numbers. In this paper an algorithm is proposed for checking whether there
exists a manifold $\mathbb{M}$ of low dimension near which many of the points
of $S$ lie and finding such $\mathbb{M}$ if it exists. There are many dimension
reduction algorithms, both linear and non-linear. Our algorithm is simple to
implement and has some advantages compared with the known algorithms. If there
is a manifold of low dimension near which most of the data points lie, the
proposed algorithm will find it. Some numerical results are presented
illustrating the algorithm and analyzing its performance compared to the
classical PCA (principal component analysis) and Isomap.
| 1 | 0 | 0 | 1 | 0 | 0 |
Out-degree reducing partitions of digraphs | Let $k$ be a fixed integer. We determine the complexity of finding a
$p$-partition $(V_1, \dots, V_p)$ of the vertex set of a given digraph such
that the maximum out-degree of each of the digraphs induced by $V_i$, ($1\leq
i\leq p$) is at least $k$ smaller than the maximum out-degree of $D$. We show
that this problem is polynomial-time solvable when $p\geq 2k$ and ${\cal
NP}$-complete otherwise. The result for $k=1$ and $p=2$ answers a question
posed in \cite{bangTCS636}. We also determine, for all fixed non-negative
integers $k_1,k_2,p$, the complexity of deciding whether a given digraph of
maximum out-degree $p$ has a $2$-partition $(V_1,V_2)$ such that the digraph
induced by $V_i$ has maximum out-degree at most $k_i$ for $i\in [2]$. It
follows from this characterization that the problem of deciding whether a
digraph has a 2-partition $(V_1,V_2)$ such that each vertex $v\in V_i$ has at
least as many neighbours in the set $V_{3-i}$ as in $V_i$, for $i=1,2$ is
${\cal NP}$-complete. This solves a problem from \cite{kreutzerEJC24} on
majority colourings.
| 1 | 0 | 0 | 0 | 0 | 0 |
Introduction to Plasma Physics | These notes are intended to provide a brief primer in plasma physics,
introducing common definitions, basic properties, and typical processes found
in plasmas. These concepts are inherent in contemporary plasma-based
accelerator schemes, and thus provide a foundation for the more advanced
expositions that follow in this volume. No prior knowledge of plasma physics is
required, but the reader is assumed to be familiar with basic electrodynamics
and fluid mechanics.
| 0 | 1 | 0 | 0 | 0 | 0 |
Presymplectic convexity and (ir)rational polytopes | In this paper, we extend the Atiyah--Guillemin--Sternberg convexity theorem
and Delzant's classification of symplectic toric manifolds to presymplectic
manifolds. We also define and study the Morita equivalence of presymplectic
toric manifolds and of their corresponding framed momentum polytopes, which may
be rational or non-rational. Toric orbifolds, quasifolds and non-commutative
toric varieties may be viewed as the quotient of our presymplectic toric
manifolds by the kernel isotropy foliation of the presymplectic form.
| 0 | 0 | 1 | 0 | 0 | 0 |
Unsupervised Learning of Mixture Regression Models for Longitudinal Data | This paper is concerned with learning of mixture regression models for
individuals that are measured repeatedly. The adjective "unsupervised" implies
that the number of mixing components is unknown and has to be determined,
ideally by data driven tools. For this purpose, a novel penalized method is
proposed to simultaneously select the number of mixing components and to
estimate the mixing proportions and unknown parameters in the models. The
proposed method is capable of handling both continuous and discrete responses
by only requiring the first two moment conditions of the model distribution. It
is shown to be consistent in both selecting the number of components and
estimating the mixing proportions and unknown regression parameters. Further, a
modified EM algorithm is developed to seamlessly integrate model selection and
estimation. Simulation studies are conducted to evaluate the finite sample
performance of the proposed procedure. And it is further illustrated via an
analysis of a primary biliary cirrhosis data set.
| 0 | 0 | 0 | 1 | 0 | 0 |
Anomalous electron states | By the certain macroscopic perturbations in condensed matter anomalous
electron wells can be formed due to a local reduction of electromagnetic zero
point energy. These wells are narrow, of the width $\sim 10^{-11}cm$, and with
the depth $\sim 1MeV$. Such anomalous states, from the formal standpoint of
quantum mechanics, correspond to a singular solution of a wave equation
produced by the non-physical $\delta(\vec R)$ source. The resolution, on the
level of the Standard Model, of the tiny region around the formal singularity
shows that the state is physical. The creation of those states in an atomic
system is of the formal probability $\exp(-1000)$. The probability becomes not
small under a perturbation which rapidly varies in space, on the scale
$10^{-11}cm$. In condensed matter such perturbation may relate to acoustic
shock waves. In this process the short scale is the length of the standing de
Broglie wave of a reflected lattice atom. Under electron transitions in the
anomalous well (anomalous atom) $keV$ X-rays are expected to be emitted. A
macroscopic amount of anomalous atoms, of the size $10^{-11}cm$ each, can be
formed in a solid resulting in ${\it collapsed}$ ${\it matter}$ with $10^9$
times enhanced density.
| 0 | 1 | 0 | 0 | 0 | 0 |
Theoretical calculation of the fine-structure constant and the permittivity of the vacuum | Light traveling through the vacuum interacts with virtual particles similarly
to the way that light traveling through a dielectric interacts with ordinary
matter. And just as the permittivity of a dielectric can be calculated, the
permittivity $\epsilon_0$ of the vacuum can be calculated, yielding an equation
for the fine-structure constant $\alpha$. The most important contributions to
the value of $\alpha$ arise from interactions in the vacuum of photons with
virtual, bound states of charged lepton-antilepton pairs. Considering only
these contributions, the fully screened $\alpha \cong 1/(8^2\sqrt{3\pi/2})
\cong 1/139$.
| 0 | 1 | 0 | 0 | 0 | 0 |
LEADER: fast estimates of asteroid shape elongation and spin latitude distributions from scarce photometry | Many asteroid databases with lightcurve brightness measurements (e.g. WISE,
Pan-STARRS1) contain enormous amounts of data for asteroid shape and spin
modelling. While lightcurve inversion is not plausible for individual targets
with scarce data, it is possible for large populations with thousands of
asteroids, where the distributions of the shape and spin characteristics of the
populations are obtainable.
We aim to introduce a software implementation of a method that computes the
joint shape elongation p and spin latitude beta distributions for a population,
with the brightness observations given in an asteroid database. Other main
goals are to include a method for performing validity checks of the algorithm,
and a tool for a statistical comparison of populations.
The LEADER software package read the brightness measurement data for a
user-defined subpopulation from a given database. The observations were used to
compute estimates of the brightness variations of the population members. A
cumulative distribution function (CDF) was constructed of these estimates. A
superposition of known analytical basis functions yielded this CDF as a
function of the (shape, spin) distribution. The joint distribution can be
reconstructed by solving a linear constrained inverse problem. To test the
validity of the method, the algorithm can be run with synthetic asteroid
models, where the shape and spin characteristics are known, and by using the
geometries taken from the examined database.
LEADER is a fast and robust software package for solving shape and spin
distributions for large populations. There are major differences in the quality
and coverage of measurements depending on the database used, so synthetic
simulations are always necessary before a database can be reliably used. We
show examples of differences in the results when switching to another database.
| 0 | 1 | 0 | 0 | 0 | 0 |
Calibrated Projection in MATLAB: Users' Manual | We present the calibrated-projection MATLAB package implementing the method
to construct confidence intervals proposed by Kaido, Molinari and Stoye (2017).
This manual provides details on how to use the package for inference on
projections of partially identified parameters. It also explains how to use the
MATLAB functions we developed to compute confidence intervals on solutions of
nonlinear optimization problems with estimated constraints.
| 0 | 0 | 0 | 1 | 0 | 0 |
Atomic Clock Measurements of Quantum Scattering Phase Shifts Spanning Feshbach Resonances at Ultralow Fields | We use an atomic fountain clock to measure quantum scattering phase shifts
precisely through a series of narrow, low-field Feshbach resonances at average
collision energies below $1\,\mu$K. Our low spread in collision energy yields
phase variations of order $\pm \pi/2$ for target atoms in several $F,m_F$
states. We compare them to a theoretical model and establish the accuracy of
the measurements and the theoretical uncertainties from the fitted potential.
We find overall excellent agreement, with small statistically significant
differences that remain unexplained.
| 0 | 1 | 0 | 0 | 0 | 0 |
Temporal processing and context dependency in C. elegans mechanosensation | A quantitative understanding of how sensory signals are transformed into
motor outputs places useful constraints on brain function and helps reveal the
brain's underlying computations. We investigate how the nematode C. elegans
responds to time-varying mechanosensory signals using a high-throughput
optogenetic assay and automated behavior quantification. In the prevailing
picture of the touch circuit, the animal's behavior is determined by which
neurons are stimulated and by the stimulus amplitude. In contrast, we find that
the behavioral response is tuned to temporal properties of mechanosensory
signals, like its integral and derivative, that extend over many seconds.
Mechanosensory signals, even in the same neurons, can be tailored to elicit
different behavioral responses. Moreover, we find that the animal's response
also depends on its behavioral context. Most dramatically, the animal ignores
all tested mechanosensory stimuli during turns. Finally, we present a
linear-nonlinear model that predicts the animal's behavioral response to
stimulus.
| 0 | 0 | 0 | 0 | 1 | 0 |
On the putative essential discreteness of q-generalized entropies | It has been argued in [EPL {\bf 90} (2010) 50004], entitled {\it Essential
discreteness in generalized thermostatistics with non-logarithmic entropy},
that "continuous Hamiltonian systems with long-range interactions and the
so-called q-Gaussian momentum distributions are seen to be outside the scope of
non-extensive statistical mechanics". The arguments are clever and appealing.
We show here that, however, some mathematical subtleties render them
unconvincing
| 0 | 1 | 0 | 0 | 0 | 0 |
Spin Distribution of Primordial Black Holes | We estimate the spin distribution of primordial black holes based on the
recent study of the critical phenomena in the gravitational collapse of a
rotating radiation fluid. We find that primordial black holes are mostly slowly
rotating.
| 0 | 1 | 0 | 0 | 0 | 0 |
Automated flow for compressing convolution neural networks for efficient edge-computation with FPGA | Deep convolutional neural networks (CNN) based solutions are the current
state- of-the-art for computer vision tasks. Due to the large size of these
models, they are typically run on clusters of CPUs or GPUs. However, power
requirements and cost budgets can be a major hindrance in adoption of CNN for
IoT applications. Recent research highlights that CNN contain significant
redundancy in their structure and can be quantized to lower bit-width
parameters and activations, while maintaining acceptable accuracy. Low
bit-width and especially single bit-width (binary) CNN are particularly
suitable for mobile applications based on FPGA implementation, due to the
bitwise logic operations involved in binarized CNN. Moreover, the transition to
lower bit-widths opens new avenues for performance optimizations and model
improvement. In this paper, we present an automatic flow from trained
TensorFlow models to FPGA system on chip implementation of binarized CNN. This
flow involves quantization of model parameters and activations, generation of
network and model in embedded-C, followed by automatic generation of the FPGA
accelerator for binary convolutions. The automated flow is demonstrated through
implementation of binarized "YOLOV2" on the low cost, low power Cyclone- V FPGA
device. Experiments on object detection using binarized YOLOV2 demonstrate
significant performance benefit in terms of model size and inference speed on
FPGA as compared to CPU and mobile CPU platforms. Furthermore, the entire
automated flow from trained models to FPGA synthesis can be completed within
one hour.
| 1 | 0 | 0 | 0 | 0 | 0 |
Pulse rate estimation using imaging photoplethysmography: generic framework and comparison of methods on a publicly available dataset | Objective: to establish an algorithmic framework and a benchmark dataset for
comparing methods of pulse rate estimation using imaging photoplethysmography
(iPPG). Approach: first we reveal essential steps of pulse rate estimation from
facial video and review methods applied at each of the steps. Then we
investigate performance of these methods for DEAP dataset
www.eecs.qmul.ac.uk/mmv/datasets/deap/ containing facial videos and reference
contact photoplethysmograms. Main results: best assessment precision is
achieved when pulse rate is estimated using continuous wavelet transform from
iPPG extracted by the POS method (overall mean absolute error below 2 heart
beats per minute). Significance: we provide a generic framework for theoretical
comparison of methods for pulse rate estimation from iPPG and report results
for the most popular methods on a publicly available dataset that can be used
as a benchmark.
| 0 | 1 | 0 | 0 | 0 | 0 |
Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution | Convolutional neural networks have recently demonstrated high-quality
reconstruction for single-image super-resolution. In this paper, we propose the
Laplacian Pyramid Super-Resolution Network (LapSRN) to progressively
reconstruct the sub-band residuals of high-resolution images. At each pyramid
level, our model takes coarse-resolution feature maps as input, predicts the
high-frequency residuals, and uses transposed convolutions for upsampling to
the finer level. Our method does not require the bicubic interpolation as the
pre-processing step and thus dramatically reduces the computational complexity.
We train the proposed LapSRN with deep supervision using a robust Charbonnier
loss function and achieve high-quality reconstruction. Furthermore, our network
generates multi-scale predictions in one feed-forward pass through the
progressive reconstruction, thereby facilitates resource-aware applications.
Extensive quantitative and qualitative evaluations on benchmark datasets show
that the proposed algorithm performs favorably against the state-of-the-art
methods in terms of speed and accuracy.
| 1 | 0 | 0 | 0 | 0 | 0 |
Foundation for a series of efficient simulation algorithms | Compute the coarsest simulation preorder included in an initial preorder is
used to reduce the resources needed to analyze a given transition system. This
technique is applied on many models like Kripke structures, labeled graphs,
labeled transition systems or even word and tree automata. Let (Q,
$\rightarrow$) be a given transition system and Rinit be an initial preorder
over Q. Until now, algorithms to compute Rsim , the coarsest simulation
included in Rinit , are either memory efficient or time efficient but not both.
In this paper we propose the foundation for a series of efficient simulation
algorithms with the introduction of the notion of maximal transitions and the
notion of stability of a preorder with respect to a coarser one. As an
illustration we solve an open problem by providing the first algorithm with the
best published time complexity, O(|Psim |.|$\rightarrow$|), and a bit space
complexity in O(|Psim |^2. log(|Psim |) + |Q|. log(|Q|)), with Psim the
partition induced by Rsim.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Review of Macroscopic Motion in Thermodynamic Equilibrium | A principle on the macroscopic motion of systems in thermodynamic
equilibrium, rarely discussed in texts, is reviewed: Very small but still
macroscopic parts of a fully isolated system in thermal equilibrium move as if
points of a rigid body, macroscopic energy being dissipated to increase
internal energy, and increase entropy along. It appears particularly important
in Space physics, when dissipation involves long-range fields at
Electromagnetism and Gravitation, rather than short-range contact forces. It is
shown how new physics, Special Relativity as regards Electromagnetism, first
Newtonian theory then General Relativity as regards Gravitation, determine
different dissipative processes involved in the approach to that equilibrium.
| 0 | 1 | 0 | 0 | 0 | 0 |
Emergent electronic structure of CaFe2As2 | CaFe2As2 exhibits collapsed tetragonal (cT) structure and varied exotic
behavior under pressure at low temperatures that led to debate on linking the
structural changes to its exceptional electronic properties like
superconductivity, magnetism, etc. Here, we investigate the electronic
structure of CaFe2As2 forming in different structures employing density
functional theory. The results indicate better stability of the cT phase with
enhancement in hybridization induced effects and shift of the energy bands
towards lower energies. The Fermi surface centered around $\Gamma$ point
gradually vanishes with the increase in pressure. Consequently, the nesting
between the hole and electron Fermi surfaces associated to the spin density
wave state disappears indicating a pathway to achieve the proximity to quantum
fluctuations. The magnetic moment at the Fe sites diminishes in the cT phase
consistent with the magnetic susceptibility results. Notably, the hybridization
of Ca 4s states (Ca-layer may be treated as a charge reservoir layer akin to
those in cuprate superconductors) is significantly enhanced in the cT phase
revealing its relevance in its interesting electronic properties.
| 0 | 1 | 0 | 0 | 0 | 0 |
Lord Kelvin's method of images approach to the Rotenberg model and its asymptotics | We study a mathematical model of cell populations dynamics proposed by M.
Rotenberg and investigated by M. Boulanouar. Here, a cell is characterized by
her maturity and speed of maturation. The growth of cell populations is
described by a partial differential equation with a boundary condition. In the
first part of the paper we exploit semigroup theory approach and apply Lord
Kelvin's method of images in order to give a new proof that the model is well
posed. Next, we use a semi-explicit formula for the semigroup related to the
model obtained by the method of images in order to give growth estimates for
the semigroup. The main part of the paper is devoted to the asymptotic
behaviour of the semigroup. We formulate conditions for the asymptotic
stability of the semigroup in the case in which the average number of viable
daughters per mitosis equals one. To this end we use methods developed by K.
Pichór and R. Rudnicki.
| 0 | 0 | 1 | 0 | 0 | 0 |
Study of the Magnetizing Relationship of the Kickers for CSNS | The extraction system of CSNS mainly consists of two kinds of magnets: eight
kickers and one lambertson magnet. In this paper, firstly, the magnetic test
results of the eight kickers were introduced and then the filed uniformity and
magnetizing relationship of the kickers were given. Secondly, during the beam
commissioning in the future, in order to obtain more accurate magnetizing
relationship, a new method to measure the magnetizing coefficients of the
kickers by the real extraction beam was given and the data analysis would also
be processed.
| 0 | 1 | 0 | 0 | 0 | 0 |
Smart "Predict, then Optimize" | Many real-world analytics problems involve two significant challenges:
prediction and optimization. Due to the typically complex nature of each
challenge, the standard paradigm is to predict, then optimize. By and large,
machine learning tools are intended to minimize prediction error and do not
account for how the predictions will be used in a downstream optimization
problem. In contrast, we propose a new and very general framework, called Smart
"Predict, then Optimize" (SPO), which directly leverages the optimization
problem structure, i.e., its objective and constraints, for designing
successful analytics tools. A key component of our framework is the SPO loss
function, which measures the quality of a prediction by comparing the objective
values of the solutions generated using the predicted and observed parameters,
respectively. Training a model with respect to the SPO loss is computationally
challenging, and therefore we also develop a surrogate loss function, called
the SPO+ loss, which upper bounds the SPO loss, has desirable convexity
properties, and is statistically consistent under mild conditions. We also
propose a stochastic gradient descent algorithm which allows for situations in
which the number of training samples is large, model regularization is desired,
and/or the optimization problem of interest is nonlinear or integer. Finally,
we perform computational experiments to empirically verify the success of our
SPO framework in comparison to the standard predict-then-optimize approach.
| 1 | 0 | 0 | 1 | 0 | 0 |
U-SLADS: Unsupervised Learning Approach for Dynamic Dendrite Sampling | Novel data acquisition schemes have been an emerging need for scanning
microscopy based imaging techniques to reduce the time in data acquisition and
to minimize probing radiation in sample exposure. Varies sparse sampling
schemes have been studied and are ideally suited for such applications where
the images can be reconstructed from a sparse set of measurements. Dynamic
sparse sampling methods, particularly supervised learning based iterative
sampling algorithms, have shown promising results for sampling pixel locations
on the edges or boundaries during imaging. However, dynamic sampling for
imaging skeleton-like objects such as metal dendrites remains difficult. Here,
we address a new unsupervised learning approach using Hierarchical Gaussian
Mixture Mod- els (HGMM) to dynamically sample metal dendrites. This technique
is very useful if the users are interested in fast imaging the primary and
secondary arms of metal dendrites in solidification process in materials
science.
| 0 | 0 | 0 | 1 | 0 | 0 |
On a registration-based approach to sensor network localization | We consider a registration-based approach for localizing sensor networks from
range measurements. This is based on the assumption that one can find
overlapping cliques spanning the network. That is, for each sensor, one can
identify geometric neighbors for which all inter-sensor ranges are known. Such
cliques can be efficiently localized using multidimensional scaling. However,
since each clique is localized in some local coordinate system, we are required
to register them in a global coordinate system. In other words, our approach is
based on transforming the localization problem into a problem of registration.
In this context, the main contributions are as follows. First, we describe an
efficient method for partitioning the network into overlapping cliques. Second,
we study the problem of registering the localized cliques, and formulate a
necessary rigidity condition for uniquely recovering the global sensor
coordinates. In particular, we present a method for efficiently testing
rigidity, and a proposal for augmenting the partitioned network to enforce
rigidity. A recently proposed semidefinite relaxation of global registration is
used for registering the cliques. We present simulation results on random and
structured sensor networks to demonstrate that the proposed method compares
favourably with state-of-the-art methods in terms of run-time, accuracy, and
scalability.
| 1 | 0 | 1 | 0 | 0 | 0 |
Density estimation on small datasets | How might a smooth probability distribution be estimated, with accurately
quantified uncertainty, from a limited amount of sampled data? Here we describe
a field-theoretic approach that addresses this problem remarkably well in one
dimension, providing an exact nonparametric Bayesian posterior without relying
on tunable parameters or large-data approximations. Strong non-Gaussian
constraints, which require a non-perturbative treatment, are found to play a
major role in reducing distribution uncertainty. A software implementation of
this method is provided.
| 1 | 0 | 0 | 0 | 1 | 0 |
Generalized Euler classes, differential forms and commutative DGAs | In the context of commutative differential graded algebras over $\mathbb Q$,
we show that an iteration of "odd spherical fibration" creates a "total space"
commutative differential graded algebra with only odd degree cohomology. Then
we show for such a commutative differential graded algebra that, for any of its
"fibrations" with "fiber" of finite cohomological dimension, the induced map on
cohomology is injective.
| 0 | 0 | 1 | 0 | 0 | 0 |
Episodic memory for continual model learning | Both the human brain and artificial learning agents operating in real-world
or comparably complex environments are faced with the challenge of online model
selection. In principle this challenge can be overcome: hierarchical Bayesian
inference provides a principled method for model selection and it converges on
the same posterior for both off-line (i.e. batch) and online learning. However,
maintaining a parameter posterior for each model in parallel has in general an
even higher memory cost than storing the entire data set and is consequently
clearly unfeasible. Alternatively, maintaining only a limited set of models in
memory could limit memory requirements. However, sufficient statistics for one
model will usually be insufficient for fitting a different kind of model,
meaning that the agent loses information with each model change. We propose
that episodic memory can circumvent the challenge of limited memory-capacity
online model selection by retaining a selected subset of data points. We design
a method to compute the quantities necessary for model selection even when the
data is discarded and only statistics of one (or few) learnt models are
available. We demonstrate on a simple model that a limited-sized episodic
memory buffer, when the content is optimised to retain data with statistics not
matching the current representation, can resolve the fundamental challenge of
online model selection.
| 1 | 0 | 0 | 1 | 0 | 0 |
Security Trust Zone in 5G Networks | Fifth Generation (5G) telecommunication system is going to deliver a flexible
radio access network (RAN). Security functions such as authorization,
authentication and accounting (AAA) are expected to be distributed from central
clouds to edge clouds. We propose a novel architectural security solution that
applies to 5G networks. It is called Trust Zone (TZ) that is designed as an
enhancement of the 5G AAA in the edge cloud. TZ also provides an autonomous and
decentralized security policy for different tenants under variable network
conditions. TZ also initiates an ability of disaster cognition and extends the
security functionalities to a set of flexible and highly available emergency
services in the edge cloud.
| 1 | 0 | 0 | 0 | 0 | 0 |
Upper-Bounding the Regularization Constant for Convex Sparse Signal Reconstruction | Consider reconstructing a signal $x$ by minimizing a weighted sum of a convex
differentiable negative log-likelihood (NLL) (data-fidelity) term and a convex
regularization term that imposes a convex-set constraint on $x$ and enforces
its sparsity using $\ell_1$-norm analysis regularization. We compute upper
bounds on the regularization tuning constant beyond which the regularization
term overwhelmingly dominates the NLL term so that the set of minimum points of
the objective function does not change. Necessary and sufficient conditions for
irrelevance of sparse signal regularization and a condition for the existence
of finite upper bounds are established. We formulate an optimization problem
for finding these bounds when the regularization term can be globally minimized
by a feasible $x$ and also develop an alternating direction method of
multipliers (ADMM) type method for their computation. Simulation examples show
that the derived and empirical bounds match.
| 0 | 0 | 1 | 1 | 0 | 0 |
On the Privacy of the Opal Data Release: A Response | This document is a response to a report from the University of Melbourne on
the privacy of the Opal dataset release. The Opal dataset was released by
Data61 (CSIRO) in conjunction with the Transport for New South Wales (TfNSW).
The data consists of two separate weeks of "tap-on/tap-off" data of individuals
who used any of the four different modes of public transport from TfNSW: buses,
light rail, train and ferries. These taps are recorded through the smart
ticketing system, known as Opal, available in the state of New South Wales,
Australia.
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Long time behavior of Gross-Pitaevskii equation at positive temperature | The stochastic Gross-Pitaevskii equation is used as a model to describe
Bose-Einstein condensation at positive temperature. The equation is a complex
Ginzburg Landau equation with a trapping potential and an additive space-time
white noise. Two important questions for this system are the global existence
of solutions in the support of the Gibbs measure, and the convergence of those
solutions to the equilibrium for large time. In this paper, we give a proof of
these two results in one space dimension. In order to prove the convergence to
equilibrium, we use the associated purely dissipative equation as an auxiliary
equation, for which the convergence may be obtained using standard techniques.
Global existence is obtained for all initial data, and not almost surely with
respect to the invariant measure.
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Isomorphism and Morita equivalence classes for crossed products of irrational rotation algebras by cyclic subgroups of $SL_2(\mathbb{Z})$ | Let $\theta, \theta'$ be irrational numbers and $A, B$ be matrices in
$SL_2(\mathbb{Z})$ of infinite order. We compute the $K$-theory of the crossed
product $\mathcal{A}_{\theta}\rtimes_A \mathbb{Z}$ and show that
$\mathcal{A}_{\theta} \rtimes_A\mathbb{Z}$ and $\mathcal{A}_{\theta'} \rtimes_B
\mathbb{Z}$ are $*$-isomorphic if and only if $\theta = \pm\theta'
\pmod{\mathbb{Z}}$ and $I-A^{-1}$ is matrix equivalent to $I-B^{-1}$. Combining
this result and an explicit construction of equivariant bimodules, we show that
$\mathcal{A}_{\theta} \rtimes_A\mathbb{Z}$ and $\mathcal{A}_{\theta'} \rtimes_B
\mathbb{Z}$ are Morita equivalent if and only if $\theta$ and $\theta'$ are in
the same $GL_2(\mathbb{Z})$ orbit and $I-A^{-1}$ is matrix equivalent to
$I-B^{-1}$. Finally, we determine the Morita equivalence class of
$\mathcal{A}_{\theta} \rtimes F$ for any finite subgroup $F$ of
$SL_2(\mathbb{Z})$.
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Model Predictive Control for Distributed Microgrid Battery Energy Storage Systems | This paper proposes a new convex model predictive control strategy for
dynamic optimal power flow between battery energy storage systems distributed
in an AC microgrid. The proposed control strategy uses a new problem
formulation, based on a linear d-q reference frame voltage-current model and
linearised power flow approximations. This allows the optimal power flows to be
solved as a convex optimisation problem, for which fast and robust solvers
exist. The proposed method does not assume real and reactive power flows are
decoupled, allowing line losses, voltage constraints and converter current
constraints to be addressed. In addition, non-linear variations in the charge
and discharge efficiencies of lithium ion batteries are analysed and included
in the control strategy. Real-time digital simulations were carried out for an
islanded microgrid based on the IEEE 13 bus prototypical feeder, with
distributed battery energy storage systems and intermittent photovoltaic
generation. It is shown that the proposed control strategy approaches the
performance of a strategy based on non-convex optimisation, while reducing the
required computation time by a factor of 1000, making it suitable for a
real-time model predictive control implementation.
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On noncommutative geometry of the Standard Model: fermion multiplet as internal forms | We unveil the geometric nature of the multiplet of fundamental fermions in
the Standard Model of fundamental particles as a noncommutative analogue of de
Rham forms on the internal finite quantum space.
| 0 | 0 | 1 | 0 | 0 | 0 |
A Review of Dynamic Network Models with Latent Variables | We present a selective review of statistical modeling of dynamic networks. We
focus on models with latent variables, specifically, the latent space models
and the latent class models (or stochastic blockmodels), which investigate both
the observed features and the unobserved structure of networks. We begin with
an overview of the static models, and then we introduce the dynamic extensions.
For each dynamic model, we also discuss its applications that have been studied
in the literature, with the data source listed in Appendix. Based on the
review, we summarize a list of open problems and challenges in dynamic network
modeling with latent variables.
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LevelHeaded: Making Worst-Case Optimal Joins Work in the Common Case | Pipelines combining SQL-style business intelligence (BI) queries and linear
algebra (LA) are becoming increasingly common in industry. As a result, there
is a growing need to unify these workloads in a single framework.
Unfortunately, existing solutions either sacrifice the inherent benefits of
exclusively using a relational database (e.g. logical and physical
independence) or incur orders of magnitude performance gaps compared to
specialized engines (or both). In this work we study applying a new type of
query processing architecture to standard BI and LA benchmarks. To do this we
present a new in-memory query processing engine called LevelHeaded. LevelHeaded
uses worst-case optimal joins as its core execution mechanism for both BI and
LA queries. With LevelHeaded, we show how crucial optimizations for BI and LA
queries can be captured in a worst-case optimal query architecture. Using these
optimizations, LevelHeaded outperforms other relational database engines
(LogicBlox, MonetDB, and HyPer) by orders of magnitude on standard LA
benchmarks, while performing on average within 31% of the best-of-breed BI
(HyPer) and LA (Intel MKL) solutions on their own benchmarks. Our results show
that such a single query processing architecture is capable of delivering
competitive performance on both BI and LA queries.
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Few-shot learning of neural networks from scratch by pseudo example optimization | In this paper, we propose a simple but effective method for training neural
networks with a limited amount of training data. Our approach inherits the idea
of knowledge distillation that transfers knowledge from a deep or wide
reference model to a shallow or narrow target model. The proposed method
employs this idea to mimic predictions of reference estimators that are more
robust against overfitting than the network we want to train. Different from
almost all the previous work for knowledge distillation that requires a large
amount of labeled training data, the proposed method requires only a small
amount of training data. Instead, we introduce pseudo training examples that
are optimized as a part of model parameters. Experimental results for several
benchmark datasets demonstrate that the proposed method outperformed all the
other baselines, such as naive training of the target model and standard
knowledge distillation.
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Identities and congruences involving the Fubini polynomials | In this paper, we investigate the umbral representation of the Fubini
polynomials $F_{x}^{n}:=F_{n}(x)$ to derive some properties involving these
polynomials. For any prime number $p$ and any polynomial $f$ with integer
coefficients, we show $(f(F_{x}))^{p}\equiv f(F_{x})$ and we give other curious
congruences.
| 0 | 0 | 1 | 0 | 0 | 0 |
Introduction to Delay Models and Their Wave Solutions | In this paper, a brief review of delay population models and their
applications in ecology is provided. The inclusion of diffusion and nonlocality
terms in delay models has given more capabilities to these models enabling them
to capture several ecological phenomena such as the Allee effect, waves of
invasive species and spatio-temporal competitions of interacting species.
Moreover, recent advances in the studies of traveling and stationary wave
solutions of delay models are outlined. In particular, the existence of
stationary and traveling wave solutions of delay models, stability of wave
solutions, formation of wavefronts in the special domain, and possible outcomes
of delay models are discussed.
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On Dummett's Pragmatist Justification Procedure | I show that propositional intuitionistic logic is complete with respect to an
adaptation of Dummett's pragmatist justification procedure. In particular,
given a pragmatist justification of an argument, I show how to obtain a natural
deduction derivation of the conclusion of the argument from, at most, the same
assumptions.
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Evidence for a radiatively driven disc-wind in PDS 456? | We present a newly discovered correlation between the wind outflow velocity
and the X-ray luminosity in the luminous ($L_{\rm bol}\sim10^{47}\,\rm
erg\,s^{-1}$) nearby ($z=0.184$) quasar PDS\,456. All the contemporary
XMM-Newton, NuSTAR and Suzaku observations from 2001--2014 were revisited and
we find that the centroid energy of the blueshifted Fe\,K absorption profile
increases with luminosity. This translates into a correlation between the wind
outflow velocity and the hard X-ray luminosity (between 7--30\,keV) where we
find that $v_{\rm w}/c \propto L_{7-30}^{\gamma}$ where $\gamma=0.22\pm0.04$.
We also show that this is consistent with a wind that is predominately
radiatively driven, possibly resulting from the high Eddington ratio of
PDS\,456.
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From a normal insulator to a topological insulator in plumbene | Plumbene, similar to silicene, has a buckled honeycomb structure with a large
band gap ($\sim 400$ meV). All previous studies have shown that it is a normal
insulator. Here, we perform first-principles calculations and employ a
sixteen-band tight-binding model with nearest-neighbor and
next-nearest-neighbor hopping terms to investigate electronic structures and
topological properties of the plumbene monolayer. We find that it can become a
topological insulator with a large bulk gap ($\sim 200$ meV) through electron
doping, and the nontrivial state is very robust with respect to external
strain. Plumbene can be an ideal candidate for realizing the quantum spin Hall
effect at room temperature. By investigating effects of external electric and
magnetic fields on electronic structures and transport properties of plumbene,
we present two rich phase diagrams with and without electron doping, and
propose a theoretical design for a four-state spin-valley filter.
| 0 | 1 | 0 | 0 | 0 | 0 |
High-sensitivity Kinetic Inductance Detectors for CALDER | Providing a background discrimination tool is crucial for enhancing the
sensitivity of next-generation experiments searching for neutrinoless double-
beta decay. The development of high-sensitivity (< 20 eV RMS) cryogenic light
detectors allows simultaneous read-out of the light and heat signals and
enables background suppression through particle identification. The Cryogenic
wide- Area Light Detector with Excellent Resolution (CALDER) R&D already proved
the potential of this technique using the phonon-mediated Kinetic Inductance
Detectors (KIDs) approach. The first array prototype with 4 Aluminum KIDs on a
2 $\times$ 2 cm2 Silicon substrate showed a baseline resolution of 154 $\pm$ 7
eV RMS. Improving the design and the readout of the resonator, the next CALDER
prototype featured an energy resolution of 82 $\pm$ 4 eV, by sampling the same
substrate with a single Aluminum KID.
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Bounding the composition length of primitive permutation groups and completely reducible linear groups | We obtain upper bounds on the composition length of a finite permutation
group in terms of the degree and the number of orbits, and analogous bounds for
primitive, quasiprimitive and semiprimitive groups. Similarly, we obtain upper
bounds on the composition length of a finite completely reducible linear group
in terms of some of its parameters. In almost all cases we show that the bounds
are sharp, and describe the extremal examples.
| 0 | 0 | 1 | 0 | 0 | 0 |
A Bernstein Inequality For Spatial Lattice Processes | In this article we present a Bernstein inequality for sums of random
variables which are defined on a spatial lattice structure. The inequality can
be used to derive concentration inequalities. It can be useful to obtain
consistency properties for nonparametric estimators of conditional expectation
functions.
| 0 | 0 | 1 | 1 | 0 | 0 |
An Exploration of Approaches to Integrating Neural Reranking Models in Multi-Stage Ranking Architectures | We explore different approaches to integrating a simple convolutional neural
network (CNN) with the Lucene search engine in a multi-stage ranking
architecture. Our models are trained using the PyTorch deep learning toolkit,
which is implemented in C/C++ with a Python frontend. One obvious integration
strategy is to expose the neural network directly as a service. For this, we
use Apache Thrift, a software framework for building scalable cross-language
services. In exploring alternative architectures, we observe that once trained,
the feedforward evaluation of neural networks is quite straightforward.
Therefore, we can extract the parameters of a trained CNN from PyTorch and
import the model into Java, taking advantage of the Java Deeplearning4J library
for feedforward evaluation. This has the advantage that the entire end-to-end
system can be implemented in Java. As a third approach, we can extract the
neural network from PyTorch and "compile" it into a C++ program that exposes a
Thrift service. We evaluate these alternatives in terms of performance (latency
and throughput) as well as ease of integration. Experiments show that
feedforward evaluation of the convolutional neural network is significantly
slower in Java, while the performance of the compiled C++ network does not
consistently beat the PyTorch implementation.
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Dispersive Regimes of the Dicke Model | We study two dispersive regimes in the dynamics of $N$ two-level atoms
interacting with a bosonic mode for long interaction times. Firstly, we analyze
the dispersive multiqubit quantum Rabi model for the regime in which the qubit
frequencies are equal and smaller than the mode frequency, and for values of
the coupling strength similar or larger than the mode frequency, namely, the
deep strong coupling regime. Secondly, we address an interaction that is
dependent on the photon number, where the coupling strength is comparable to
the geometric mean of the qubit and mode frequencies. We show that the
associated dynamics is analytically tractable and provide useful frameworks
with which to analyze the system behavior. In the deep strong coupling regime,
we unveil the structure of unexpected resonances for specific values of the
coupling, present for $N\ge2$, and in the photon-number-dependent regime we
demonstrate that all the nontrivial dynamical behavior occurs in the atomic
degrees of freedom for a given Fock state. We verify these assertions with
numerical simulations of the qubit population and photon-statistic dynamics.
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ZebraLancer: Crowdsource Knowledge atop Open Blockchain, Privately and Anonymously | We design and implement the first private and anonymous decentralized
crowdsourcing system ZebraLancer. It realizes the fair exchange (i.e. security
against malicious workers and dishonest requesters) without using any
third-party arbiter. More importantly, it overcomes two fundamental challenges
of decentralization, i.e. data leakage and identity breach.
First, our outsource-then-prove methodology resolves the critical tension
between blockchain transparency and data confidentiality without sacrificing
the fairness of exchange. ZebraLancer ensures: a requester will not pay more
than what data deserve, according to a policy announced when her task is
published through the blockchain; each worker indeed gets a payment based on
the policy, if submits data to the blockchain; the above properties are
realized not only without a central arbiter, but also without leaking the data
to blockchain network.
Furthermore, the blockchain transparency might allow one to infer private
information of workers/requesters through their participation history.
ZebraLancer solves the problem by allowing anonymous participations without
surrendering user accountability. Specifically, workers cannot misuse anonymity
to submit multiple times to reap rewards, and an anonymous requester cannot
maliciously submit colluded answers to herself to repudiate payments. The idea
behind is a subtle linkability: if one authenticates twice in a task, everybody
can tell, or else staying anonymous. To realize such delicate linkability, we
put forth a novel cryptographic notion, the common-prefix-linkable anonymous
authentication.
Finally, we implement our protocol for a common image annotation task and
deploy it in a test net of Ethereum. The experiment results show the
applicability of our protocol and highlight subtleties of tailoring the
protocol to be compatible with the existing real-world open blockchain.
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Fast, Better Training Trick -- Random Gradient | In this paper, we will show an unprecedented method to accelerate training
and improve performance, which called random gradient (RG). This method can be
easier to the training of any model without extra calculation cost, we use
Image classification, Semantic segmentation, and GANs to confirm this method
can improve speed which is training model in computer vision. The central idea
is using the loss multiplied by a random number to random reduce the
back-propagation gradient. We can use this method to produce a better result in
Pascal VOC, Cifar, Cityscapes datasets.
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Expressions of Sentiments During Code Reviews: Male vs. Female | Background: As most of the software development organizations are
male-dominated, female developers encountering various negative workplace
experiences reported feeling like they "do not belong". Exposures to
discriminatory expletives or negative critiques from their male colleagues may
further exacerbate those feelings. Aims: The primary goal of this study is to
identify the differences in expressions of sentiments between male and female
developers during various software engineering tasks. Method: On this goal, we
mined the code review repositories of six popular open source projects. We used
a semi-automated approach leveraging the name as well as multiple social
networks to identify the gender of a developer. Using SentiSE, a customized and
state-of-the-art sentiment analysis tool for the software engineering domain,
we classify each communication as negative, positive, or neutral. We also
compute the frequencies of sentiment words, emoticons, and expletives used by
each developer. Results: Our results suggest that the likelihood of using
sentiment words, emoticons, and expletives during code reviews varies based on
the gender of a developer, as females are significantly less likely to express
sentiments than males. Although female developers were more neutral to their
male colleagues than to another female, male developers from three out of the
six projects were not only writing more frequent negative comments but also
withholding positive encouragements from their female counterparts. Conclusion:
Our results provide empirical evidence of another factor behind the negative
work place experiences encountered by the female developers that may be
contributing to the diminishing number of females in the SE industry.
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Monotonicity patterns and functional inequalities for classical and generalized Wright functions | In this paper our aim is to present the completely monotonicity and convexity
properties for the Wright function. As consequences of these results, we
present some functional inequalities. Moreover, we derive the monotonicity and
log-convexity results for the generalized Wright functions. As applications, we
present several new inequalities (like Turán type inequalities) and we prove
some geometric properties for four--parametric Mittag--Leffler functions.
| 0 | 0 | 1 | 0 | 0 | 0 |
Multiple VLAD encoding of CNNs for image classification | Despite the effectiveness of convolutional neural networks (CNNs) especially
in image classification tasks, the effect of convolution features on learned
representations is still limited. It mostly focuses on the salient object of
the images, but ignores the variation information on clutter and local. In this
paper, we propose a special framework, which is the multiple VLAD encoding
method with the CNNs features for image classification. Furthermore, in order
to improve the performance of the VLAD coding method, we explore the
multiplicity of VLAD encoding with the extension of three kinds of encoding
algorithms, which are the VLAD-SA method, the VLAD-LSA and the VLAD-LLC method.
Finally, we equip the spatial pyramid patch (SPM) on VLAD encoding to add the
spatial information of CNNs feature. In particular, the power of SPM leads our
framework to yield better performance compared to the existing method.
| 1 | 0 | 0 | 0 | 0 | 0 |
Index of Dirac operators and classification of topological insulators | Real and complex Clifford bundles and Dirac operators defined on them are
considered. By using the index theorems of Dirac operators, table of
topological invariants is constructed from the Clifford chessboard. Through the
relations between K-theory groups, Grothendieck groups and symmetric spaces,
the periodic table of topological insulators and superconductors is obtained.
This gives the result that the periodic table of real and complex topological
phases is originated from the Clifford chessboard and index theorems.
| 0 | 1 | 0 | 0 | 0 | 0 |
Centroid vetting of transiting planet candidates from the Next Generation Transit Survey | The Next Generation Transit Survey (NGTS), operating in Paranal since 2016,
is a wide-field survey to detect Neptunes and super-Earths transiting bright
stars, which are suitable for precise radial velocity follow-up and
characterisation. Thereby, its sub-mmag photometric precision and ability to
identify false positives are crucial. Particularly, variable background objects
blended in the photometric aperture frequently mimic Neptune-sized transits and
are costly in follow-up time. These objects can best be identified with the
centroiding technique: if the photometric flux is lost off-centre during an
eclipse, the flux centroid shifts towards the centre of the target star.
Although this method has successfully been employed by the Kepler mission, it
has previously not been implemented from the ground. We present a
fully-automated centroid vetting algorithm developed for NGTS, enabled by our
high-precision auto-guiding. Our method allows detecting centroid shifts with
an average precision of 0.75 milli-pixel, and down to 0.25 milli-pixel for
specific targets, for a pixel size of 4.97 arcsec. The algorithm is now part of
the NGTS candidate vetting pipeline and automatically employed for all detected
signals. Further, we develop a joint Bayesian fitting model for all photometric
and centroid data, allowing to disentangle which object (target or background)
is causing the signal, and what its astrophysical parameters are. We
demonstrate our method on two NGTS objects of interest. These achievements make
NGTS the first ground-based wide-field transit survey ever to successfully
apply the centroiding technique for automated candidate vetting, enabling the
production of a robust candidate list before follow-up.
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Galaxy And Mass Assembly: the evolution of the cosmic spectral energy distribution from z = 1 to z = 0 | We present the evolution of the Cosmic Spectral Energy Distribution (CSED)
from $z = 1 - 0$. Our CSEDs originate from stacking individual spectral energy
distribution fits based on panchromatic photometry from the Galaxy and Mass
Assembly (GAMA) and COSMOS datasets in ten redshift intervals with completeness
corrections applied. Below $z = 0.45$, we have credible SED fits from 100 nm to
1 mm. Due to the relatively low sensitivity of the far-infrared data, our
far-infrared CSEDs contain a mix of predicted and measured fluxes above $z =
0.45$. Our results include appropriate errors to highlight the impact of these
corrections. We show that the bolometric energy output of the Universe has
declined by a factor of roughly four -- from $5.1 \pm 1.0$ at $z \sim 1$ to
$1.3 \pm 0.3 \times 10^{35}~h_{70}$~W~Mpc$^{-3}$ at the current epoch. We show
that this decrease is robust to cosmic variance, SED modelling and other
various types of error. Our CSEDs are also consistent with an increase in the
mean age of stellar populations. We also show that dust attenuation has
decreased over the same period, with the photon escape fraction at 150~nm
increasing from $16 \pm 3$ at $z \sim 1$ to $24 \pm 5$ per cent at the current
epoch, equivalent to a decrease in $A_\mathrm{FUV}$ of 0.4~mag. Our CSEDs
account for $68 \pm 12$ and $61 \pm 13$ per cent of the cosmic optical and
infrared backgrounds respectively as defined from integrated galaxy counts and
are consistent with previous estimates of the cosmic infrared background with
redshift.
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Large sums of Hecke eigenvalues of holomorphic cusp forms | Let $f$ be a Hecke cusp form of weight $k$ for the full modular group, and
let $\{\lambda_f(n)\}_{n\geq 1}$ be the sequence of its normalized Fourier
coefficients. Motivated by the problem of the first sign change of
$\lambda_f(n)$, we investigate the range of $x$ (in terms of $k$) for which
there are cancellations in the sum $S_f(x)=\sum_{n\leq x} \lambda_f(n)$. We
first show that $S_f(x)=o(x\log x)$ implies that $\lambda_f(n)<0$ for some
$n\leq x$. We also prove that $S_f(x)=o(x\log x)$ in the range $\log x/\log\log
k\to \infty$ assuming the Riemann hypothesis for $L(s, f)$, and furthermore
that this range is best possible unconditionally. More precisely, we establish
the existence of many Hecke cusp forms $f$ of large weight $k$, for which
$S_f(x)\gg_A x\log x$, when $x=(\log k)^A.$ Our results are $GL_2$ analogues of
work of Granville and Soundararajan for character sums, and could also be
generalized to other families of automorphic forms.
| 0 | 0 | 1 | 0 | 0 | 0 |
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples | Recent studies have highlighted the vulnerability of deep neural networks
(DNNs) to adversarial examples - a visually indistinguishable adversarial image
can easily be crafted to cause a well-trained model to misclassify. Existing
methods for crafting adversarial examples are based on $L_2$ and $L_\infty$
distortion metrics. However, despite the fact that $L_1$ distortion accounts
for the total variation and encourages sparsity in the perturbation, little has
been developed for crafting $L_1$-based adversarial examples. In this paper, we
formulate the process of attacking DNNs via adversarial examples as an
elastic-net regularized optimization problem. Our elastic-net attacks to DNNs
(EAD) feature $L_1$-oriented adversarial examples and include the
state-of-the-art $L_2$ attack as a special case. Experimental results on MNIST,
CIFAR10 and ImageNet show that EAD can yield a distinct set of adversarial
examples with small $L_1$ distortion and attains similar attack performance to
the state-of-the-art methods in different attack scenarios. More importantly,
EAD leads to improved attack transferability and complements adversarial
training for DNNs, suggesting novel insights on leveraging $L_1$ distortion in
adversarial machine learning and security implications of DNNs.
| 1 | 0 | 0 | 1 | 0 | 0 |
Playtime Measurement with Survival Analysis | Maximizing product use is a central goal of many businesses, which makes
retention and monetization two central analytics metrics in games. Player
retention may refer to various duration variables quantifying product use:
total playtime or session playtime are popular research targets, and active
playtime is well-suited for subscription games. Such research often has the
goal of increasing player retention or conversely decreasing player churn.
Survival analysis is a framework of powerful tools well suited for retention
type data. This paper contributes new methods to game analytics on how playtime
can be analyzed using survival analysis without covariates. Survival and hazard
estimates provide both a visual and an analytic interpretation of the playtime
phenomena as a funnel type nonparametric estimate. Metrics based on the
survival curve can be used to aggregate this playtime information into a single
statistic. Comparison of survival curves between cohorts provides a scientific
AB-test. All these methods work on censored data and enable computation of
confidence intervals. This is especially important in time and sample limited
data which occurs during game development. Throughout this paper, we illustrate
the application of these methods to real world game development problems on the
Hipster Sheep mobile game.
| 1 | 0 | 0 | 1 | 0 | 0 |
Asymptotic formula of the number of Newton polygons | In this paper, we enumerate Newton polygons asymptotically. The number of
Newton polygons is computable by a simple recurrence equation, but unexpectedly
the asymptotic formula of its logarithm contains growing oscillatory terms. As
the terms come from non-trivial zeros of the Riemann zeta function, an
estimation of the amplitude of the oscillating part is equivalent to the
Riemann hypothesis.
| 0 | 0 | 1 | 0 | 0 | 0 |
Invariant-based inverse engineering of crane control parameters | By applying invariant-based inverse engineering in the small-oscillations
regime, we design the time dependence of the control parameters of an overhead
crane (trolley displacement and rope length), to transport a load between two
positions at different heights with minimal final energy excitation for a
microcanonical ensemble of initial conditions. The analogies between ion
transport in multisegmented traps or neutral atom transport in moving optical
lattices and load manipulation by cranes opens a route for a useful transfer of
techniques among very different fields.
| 0 | 1 | 0 | 0 | 0 | 0 |
Leaf Space Isometries of Singular Riemannian Foliations and Their Spectral Properties | In this paper, the authors consider leaf spaces of singular Riemannian
foliations $\mathcal{F}$ on compact manifolds $M$ and the associated
$\mathcal{F}$-basic spectrum on $M$, $spec_B(M, \mathcal{F}),$ counted with
multiplicities. Recently, a notion of smooth isometry $\varphi:
M_1/\mathcal{F}_1\rightarrow M_2/\mathcal{F}_2$ between the leaf spaces of such
singular Riemannian foliations $(M_1,\mathcal{F}_1)$ and $(M_2,\mathcal{F}_2)$
has appeared in the literature. In this paper, the authors provide an example
to show that the existence a smooth isometry of leaf spaces as above is not
sufficient to guarantee the equality of $spec_B(M_1,\mathcal{F}_1)$ and
$spec_B(M_2,\mathcal{F}_2).$ The authors then prove that if some additional
conditions involving the geometry of the leaves are satisfied, then the
equality of $spec_B(M_1,\mathcal{F}_1)$ and $spec_B(M_2,\mathcal{F}_2)$ is
guaranteed. Consequences and applications to orbifold spectral theory,
isometric group actions, and their reductions are also explored.
| 0 | 0 | 1 | 0 | 0 | 0 |
Backward Monte-Carlo applied to muon transport | We discuss a backward Monte-Carlo technique for muon transport problem, with
emphasis on its application in muography. Backward Monte-Carlo allows exclusive
sampling of a final state by reversing the simulation flow. In practice it can
be made analogous to an adjoint Monte-Carlo, though it is more versatile for
muon transport. A backward Monte-Carlo was implemented as a dedicated muon
transport library: PUMAS. It is shown for case studies relevant for muography
imaging that the implementations of forward and backward Monte-Carlo schemes
agree to better than 1%.
| 0 | 1 | 0 | 0 | 0 | 0 |
Functional importance of noise in neuronal information processing | Noise is an inherent part of neuronal dynamics, and thus of the brain. It can
be observed in neuronal activity at different spatiotemporal scales, including
in neuronal membrane potentials, local field potentials,
electroencephalography, and magnetoencephalography. A central research topic in
contemporary neuroscience is to elucidate the functional role of noise in
neuronal information processing. Experimental studies have shown that a
suitable level of noise may enhance the detection of weak neuronal signals by
means of stochastic resonance. In response, theoretical research, based on the
theory of stochastic processes, nonlinear dynamics, and statistical physics,
has made great strides in elucidating the mechanism and the many benefits of
stochastic resonance in neuronal systems. In this perspective, we review recent
research dedicated to neuronal stochastic resonance in biophysical mathematical
models. We also explore the regulation of neuronal stochastic resonance, and we
outline important open questions and directions for future research. A deeper
understanding of neuronal stochastic resonance may afford us new insights into
the highly impressive information processing in the brain.
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Stochastic Variance Reduction Methods for Policy Evaluation | Policy evaluation is a crucial step in many reinforcement-learning
procedures, which estimates a value function that predicts states' long-term
value under a given policy. In this paper, we focus on policy evaluation with
linear function approximation over a fixed dataset. We first transform the
empirical policy evaluation problem into a (quadratic) convex-concave saddle
point problem, and then present a primal-dual batch gradient method, as well as
two stochastic variance reduction methods for solving the problem. These
algorithms scale linearly in both sample size and feature dimension. Moreover,
they achieve linear convergence even when the saddle-point problem has only
strong concavity in the dual variables but no strong convexity in the primal
variables. Numerical experiments on benchmark problems demonstrate the
effectiveness of our methods.
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