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Semi-Supervised Approaches to Efficient Evaluation of Model Prediction Performance | In many modern machine learning applications, the outcome is expensive or
time-consuming to collect while the predictor information is easy to obtain.
Semi-supervised learning (SSL) aims at utilizing large amounts of `unlabeled'
data along with small amounts of `labeled' data to improve the efficiency of a
classical supervised approach. Though numerous SSL classification and
prediction procedures have been proposed in recent years, no methods currently
exist to evaluate the prediction performance of a working regression model. In
the context of developing phenotyping algorithms derived from electronic
medical records (EMR), we present an efficient two-step estimation procedure
for evaluating a binary classifier based on various prediction performance
measures in the semi-supervised (SS) setting. In step I, the labeled data is
used to obtain a non-parametrically calibrated estimate of the conditional risk
function. In step II, SS estimates of the prediction accuracy parameters are
constructed based on the estimated conditional risk function and the unlabeled
data. We demonstrate that under mild regularity conditions the proposed
estimators are consistent and asymptotically normal. Importantly, the
asymptotic variance of the SS estimators is always smaller than that of the
supervised counterparts under correct model specification. We also correct for
potential overfitting bias in the SS estimators in finite sample with
cross-validation and develop a perturbation resampling procedure to approximate
their distributions. Our proposals are evaluated through extensive simulation
studies and illustrated with two real EMR studies aiming to develop phenotyping
algorithms for rheumatoid arthritis and multiple sclerosis.
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Linearization of the box-ball system: an elementary approach | Kuniba, Okado, Takagi and Yamada have found that the time-evolution of the
Takahashi-Satsuma box-ball system can be linearized by considering rigged
configurations associated with states of the box-ball system. We introduce a
simple way to understand the rigged configuration of $\mathfrak{sl}_2$-type,
and give an elementary proof of the linearization property. Our approach can be
applied to a box-ball system with finite carrier, which is related to a
discrete modified KdV equation, and also to the combinatorial $R$-matrix of
$A_1^{(1)}$-type. We also discuss combinatorial statistics and related
fermionic formulas associated with the states of the box-ball systems. A
fermionic-type formula we obtain for the finite carrier case seems to be new.
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Controlling Sources of Inaccuracy in Stochastic Kriging | Scientists and engineers commonly use simulation models to study real systems
for which actual experimentation is costly, difficult, or impossible. Many
simulations are stochastic in the sense that repeated runs with the same input
configuration will result in different outputs. For expensive or time-consuming
simulations, stochastic kriging \citep{ankenman} is commonly used to generate
predictions for simulation model outputs subject to uncertainty due to both
function approximation and stochastic variation. Here, we develop and justify a
few guidelines for experimental design, which ensure accuracy of stochastic
kriging emulators. We decompose error in stochastic kriging predictions into
nominal, numeric, parameter estimation and parameter estimation numeric
components and provide means to control each in terms of properties of the
underlying experimental design. The design properties implied for each source
of error are weakly conflicting and broad principles are proposed. In brief,
space-filling properties "small fill distance" and "large separation distance"
should balance with replication at distinct input configurations, with number
of replications depending on the relative magnitudes of stochastic and process
variability. Non-stationarity implies higher input density in more active
regions, while regression functions imply a balance with traditional design
properties. A few examples are presented to illustrate the results.
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Implications of Decentralized Q-learning Resource Allocation in Wireless Networks | Reinforcement Learning is gaining attention by the wireless networking
community due to its potential to learn good-performing configurations only
from the observed results. In this work we propose a stateless variation of
Q-learning, which we apply to exploit spatial reuse in a wireless network. In
particular, we allow networks to modify both their transmission power and the
channel used solely based on the experienced throughput. We concentrate in a
completely decentralized scenario in which no information about neighbouring
nodes is available to the learners. Our results show that although the
algorithm is able to find the best-performing actions to enhance aggregate
throughput, there is high variability in the throughput experienced by the
individual networks. We identify the cause of this variability as the
adversarial setting of our setup, in which the most played actions provide
intermittent good/poor performance depending on the neighbouring decisions. We
also evaluate the effect of the intrinsic learning parameters of the algorithm
on this variability.
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Exponential Ergodicity of the Bouncy Particle Sampler | Non-reversible Markov chain Monte Carlo schemes based on piecewise
deterministic Markov processes have been recently introduced in applied
probability, automatic control, physics and statistics. Although these
algorithms demonstrate experimentally good performance and are accordingly
increasingly used in a wide range of applications, geometric ergodicity results
for such schemes have only been established so far under very restrictive
assumptions. We give here verifiable conditions on the target distribution
under which the Bouncy Particle Sampler algorithm introduced in \cite{P_dW_12}
is geometrically ergodic. This holds whenever the target satisfies a curvature
condition and has tails decaying at least as fast as an exponential and at most
as fast as a Gaussian distribution. This allows us to provide a central limit
theorem for the associated ergodic averages. When the target has tails thinner
than a Gaussian distribution, we propose an original modification of this
scheme that is geometrically ergodic. For thick-tailed target distributions,
such as $t$-distributions, we extend the idea pioneered in \cite{J_G_12} in a
random walk Metropolis context. We apply a change of variable to obtain a
transformed target satisfying the tail conditions for geometric ergodicity. By
sampling the transformed target using the Bouncy Particle Sampler and mapping
back the Markov process to the original parameterization, we obtain a
geometrically ergodic algorithm.
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Analysis and X-ray tomography | These are lecture notes for the course "MATS4300 Analysis and X-ray
tomography" given at the University of Jyväskylä in Fall 2017. The course
is a broad overview of various tools in analysis that can be used to study
X-ray tomography. The focus is on tools and ideas, not so much on technical
details and minimal assumptions. Only very basic functional analysis is assumed
as background. Exercise problems are included.
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Spatially Transformed Adversarial Examples | Recent studies show that widely used deep neural networks (DNNs) are
vulnerable to carefully crafted adversarial examples. Many advanced algorithms
have been proposed to generate adversarial examples by leveraging the
$\mathcal{L}_p$ distance for penalizing perturbations. Researchers have
explored different defense methods to defend against such adversarial attacks.
While the effectiveness of $\mathcal{L}_p$ distance as a metric of perceptual
quality remains an active research area, in this paper we will instead focus on
a different type of perturbation, namely spatial transformation, as opposed to
manipulating the pixel values directly as in prior works. Perturbations
generated through spatial transformation could result in large $\mathcal{L}_p$
distance measures, but our extensive experiments show that such spatially
transformed adversarial examples are perceptually realistic and more difficult
to defend against with existing defense systems. This potentially provides a
new direction in adversarial example generation and the design of corresponding
defenses. We visualize the spatial transformation based perturbation for
different examples and show that our technique can produce realistic
adversarial examples with smooth image deformation. Finally, we visualize the
attention of deep networks with different types of adversarial examples to
better understand how these examples are interpreted.
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Arrow Categories of Monoidal Model Categories | We prove that the arrow category of a monoidal model category, equipped with
the pushout product monoidal structure and the projective model structure, is a
monoidal model category. This answers a question posed by Mark Hovey, and has
the important consequence that it allows for the consideration of a monoidal
product in cubical homotopy theory. As illustrations we include numerous
examples of non-cofibrantly generated monoidal model categories, including
chain complexes, small categories, topological spaces, and pro-categories.
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Differential-operator representations of Weyl group and singular vectors | Given a suitable ordering of the positive root system associated with a
semisimple Lie algebra, there exists a natural correspondence between Verma
modules and related polynomial algebras. With this, the Lie algebra action on a
Verma module can be interpreted as a differential operator action on
polynomials, and thus on the corresponding truncated formal power series. We
prove that the space of truncated formal power series is a
differential-operator representation of the Weyl group $W$. We also introduce a
system of partial differential equations to investigate singular vectors in the
Verma module. It is shown that the solution space of the system in the space of
truncated formal power series is the span of $\{w(1)\ |\ w\in W\}$. Those
$w(1)$ that are polynomials correspond to singular vectors in the Verma module.
This elementary approach by partial differential equations also gives a new
proof of the well-known BGG-Verma Theorem.
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Faithful Semitoric Systems | This paper consists of two parts. The first provides a review of the basic
properties of integrable and almost-toric systems, with a particular emphasis
on the integral affine structure associated to an integrable system. The second
part introduces faithful semitoric systems, a generalization of semitoric
systems (introduced by Vu Ngoc and classified by Pelayo and Vu Ngoc) that
provides the language to develop surgeries on almost-toric systems in dimension
4. We prove that faithful semitoric systems are natural building blocks of
almost-toric systems. Moreover, we show that they enjoy many of the properties
that their (proper) semitoric counterparts do.
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HoloScope: Topology-and-Spike Aware Fraud Detection | As online fraudsters invest more resources, including purchasing large pools
of fake user accounts and dedicated IPs, fraudulent attacks become less obvious
and their detection becomes increasingly challenging. Existing approaches such
as average degree maximization suffer from the bias of including more nodes
than necessary, resulting in lower accuracy and increased need for manual
verification. Hence, we propose HoloScope, which uses information from graph
topology and temporal spikes to more accurately detect groups of fraudulent
users. In terms of graph topology, we introduce "contrast suspiciousness," a
dynamic weighting approach, which allows us to more accurately detect
fraudulent blocks, particularly low-density blocks. In terms of temporal
spikes, HoloScope takes into account the sudden bursts and drops of fraudsters'
attacking patterns. In addition, we provide theoretical bounds for how much
this increases the time cost needed for fraudsters to conduct adversarial
attacks. Additionally, from the perspective of ratings, HoloScope incorporates
the deviation of rating scores in order to catch fraudsters more accurately.
Moreover, HoloScope has a concise framework and sub-quadratic time complexity,
making the algorithm reproducible and scalable. Extensive experiments showed
that HoloScope achieved significant accuracy improvements on synthetic and real
data, compared with state-of-the-art fraud detection methods.
| 1 | 0 | 0 | 0 | 0 | 0 |
On Approximation Guarantees for Greedy Low Rank Optimization | We provide new approximation guarantees for greedy low rank matrix estimation
under standard assumptions of restricted strong convexity and smoothness. Our
novel analysis also uncovers previously unknown connections between the low
rank estimation and combinatorial optimization, so much so that our bounds are
reminiscent of corresponding approximation bounds in submodular maximization.
Additionally, we also provide statistical recovery guarantees. Finally, we
present empirical comparison of greedy estimation with established baselines on
two important real-world problems.
| 1 | 0 | 0 | 1 | 0 | 0 |
Topology Estimation in Bulk Power Grids: Guarantees on Exact Recovery | The topology of a power grid affects its dynamic operation and settlement in
the electricity market. Real-time topology identification can enable faster
control action following an emergency scenario like failure of a line. This
article discusses a graphical model framework for topology estimation in bulk
power grids (both loopy transmission and radial distribution) using
measurements of voltage collected from the grid nodes. The graphical model for
the probability distribution of nodal voltages in linear power flow models is
shown to include additional edges along with the operational edges in the true
grid. Our proposed estimation algorithms first learn the graphical model and
subsequently extract the operational edges using either thresholding or a
neighborhood counting scheme. For grid topologies containing no three-node
cycles (two buses do not share a common neighbor), we prove that an exact
extraction of the operational topology is theoretically guaranteed. This
includes a majority of distribution grids that have radial topologies. For
grids that include cycles of length three, we provide sufficient conditions
that ensure existence of algorithms for exact reconstruction. In particular,
for grids with constant impedance per unit length and uniform injection
covariances, this observation leads to conditions on geographical placement of
the buses. The performance of algorithms is demonstrated in test case
simulations.
| 1 | 0 | 1 | 1 | 0 | 0 |
Wasserstein Introspective Neural Networks | We present Wasserstein introspective neural networks (WINN) that are both a
generator and a discriminator within a single model. WINN provides a
significant improvement over the recent introspective neural networks (INN)
method by enhancing INN's generative modeling capability. WINN has three
interesting properties: (1) A mathematical connection between the formulation
of the INN algorithm and that of Wasserstein generative adversarial networks
(WGAN) is made. (2) The explicit adoption of the Wasserstein distance into INN
results in a large enhancement to INN, achieving compelling results even with a
single classifier --- e.g., providing nearly a 20 times reduction in model size
over INN for unsupervised generative modeling. (3) When applied to supervised
classification, WINN also gives rise to improved robustness against adversarial
examples in terms of the error reduction. In the experiments, we report
encouraging results on unsupervised learning problems including texture, face,
and object modeling, as well as a supervised classification task against
adversarial attacks.
| 1 | 0 | 0 | 0 | 0 | 0 |
Convexity of level lines of Martin functions and applications | Let $\Omega$ be an unbounded domain in $\mathbb{R}\times\mathbb{R}^{d}.$ A
positive harmonic function $u$ on $\Omega$ that vanishes on the boundary of
$\Omega$ is called a Martin function. In this note, we show that, when $\Omega$
is convex, the superlevel sets of a Martin function are also convex. As a
consequence we obtain that if in addition $\Omega$ is symmetric, then the
maximum of any Martin function along a slice $\Omega\cap
(\{t\}\times\mathbb{R}^d)$ is attained at $(t,0).$
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New skein invariants of links | We introduce new skein invariants of links based on a procedure where we
first apply the skein relation only to crossings of distinct components, so as
to produce collections of unlinked knots. We then evaluate the resulting knots
using a given invariant. A skein invariant can be computed on each link solely
by the use of skein relations and a set of initial conditions. The new
procedure, remarkably, leads to generalizations of the known skein invariants.
We make skein invariants of classical links, $H[R]$, $K[Q]$ and $D[T]$, based
on the invariants of knots, $R$, $Q$ and $T$, denoting the regular isotopy
version of the Homflypt polynomial, the Kauffman polynomial and the Dubrovnik
polynomial. We provide skein theoretic proofs of the well-definedness of these
invariants. These invariants are also reformulated into summations of the
generating invariants ($R$, $Q$, $T$) on sublinks of a given link $L$, obtained
by partitioning $L$ into collections of sublinks.
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HSTREAM: A directive-based language extension for heterogeneous stream computing | Big data streaming applications require utilization of heterogeneous parallel
computing systems, which may comprise multiple multi-core CPUs and many-core
accelerating devices such as NVIDIA GPUs and Intel Xeon Phis. Programming such
systems require advanced knowledge of several hardware architectures and
device-specific programming models, including OpenMP and CUDA. In this paper,
we present HSTREAM, a compiler directive-based language extension to support
programming stream computing applications for heterogeneous parallel computing
systems. HSTREAM source-to-source compiler aims to increase the programming
productivity by enabling programmers to annotate the parallel regions for
heterogeneous execution and generate target specific code. The HSTREAM runtime
automatically distributes the workload across CPUs and accelerating devices. We
demonstrate the usefulness of HSTREAM language extension with various
applications from the STREAM benchmark. Experimental evaluation results show
that HSTREAM can keep the same programming simplicity as OpenMP, and the
generated code can deliver performance beyond what CPUs-only and GPUs-only
executions can deliver.
| 1 | 0 | 0 | 0 | 0 | 0 |
Faddeev-Jackiw approach of the noncommutative spacetime Podolsky electromagnetic theory | The interest in higher derivatives field theories has its origin mainly in
their influence concerning the renormalization properties of physical models
and to remove ultraviolet divergences. The noncommutative Podolsky theory is a
constrained system that cannot by directly quantized by the canonical way. In
this work we have used the Faddeev-Jackiw method in order to obtain the Dirac
brackets of the NC Podolsky theory.
| 0 | 1 | 0 | 0 | 0 | 0 |
Spin Transport and Accumulation in 2D Weyl Fermion System | In this work, we study the spin Hall effect and Rashba-Edelstein effect of a
2D Weyl fermion system in the clean limit using the Kubo formalism. Spin
transport is solely due to the spin-torque current in this strongly spin-orbit
coupled (SOC) system, and chiral spin-flip scattering off non-SOC scalar
impurities, with potential strength $V$ and size $a$, gives rise to a
skew-scattering mechanism for the spin Hall effect. The key result is that the
resultant spin-Hall angle has a fixed sign, with $\theta^{SH} \sim O
\left(\tfrac{V^2}{v_F^2/a^2} (k_F a)^4 \right)$ being a strongly-dependent
function of $k_F a$, with $k_F$ and $v_F$ being the Fermi wave-vector and Fermi
velocity respectively. This, therefore, allows for the possibility of tuning
the SHE by adjusting the Fermi energy or impurity size.
| 0 | 1 | 0 | 0 | 0 | 0 |
Model-Based Control Using Koopman Operators | This paper explores the application of Koopman operator theory to the control
of robotic systems. The operator is introduced as a method to generate
data-driven models that have utility for model-based control methods. We then
motivate the use of the Koopman operator towards augmenting model-based
control. Specifically, we illustrate how the operator can be used to obtain a
linearizable data-driven model for an unknown dynamical process that is useful
for model-based control synthesis. Simulated results show that with increasing
complexity in the choice of the basis functions, a closed-loop controller is
able to invert and stabilize a cart- and VTOL-pendulum systems. Furthermore,
the specification of the basis function are shown to be of importance when
generating a Koopman operator for specific robotic systems. Experimental
results with the Sphero SPRK robot explore the utility of the Koopman operator
in a reduced state representation setting where increased complexity in the
basis function improve open- and closed-loop controller performance in various
terrains, including sand.
| 1 | 0 | 0 | 0 | 0 | 0 |
Turbulence Hierarchy in a Random Fibre Laser | Turbulence is a challenging feature common to a wide range of complex
phenomena. Random fibre lasers are a special class of lasers in which the
feedback arises from multiple scattering in a one-dimensional disordered
cavity-less medium. Here, we report on statistical signatures of turbulence in
the distribution of intensity fluctuations in a continuous-wave-pumped
erbium-based random fibre laser, with random Bragg grating scatterers. The
distribution of intensity fluctuations in an extensive data set exhibits three
qualitatively distinct behaviours: a Gaussian regime below threshold, a mixture
of two distributions with exponentially decaying tails near the threshold, and
a mixture of distributions with stretched-exponential tails above threshold.
All distributions are well described by a hierarchical stochastic model that
incorporates Kolmogorov's theory of turbulence, which includes energy cascade
and the intermittence phenomenon. Our findings have implications for explaining
the remarkably challenging turbulent behaviour in photonics, using a random
fibre laser as the experimental platform.
| 0 | 1 | 0 | 0 | 0 | 0 |
Optimal Rates for Learning with Nyström Stochastic Gradient Methods | In the setting of nonparametric regression, we propose and study a
combination of stochastic gradient methods with Nyström subsampling, allowing
multiple passes over the data and mini-batches. Generalization error bounds for
the studied algorithm are provided. Particularly, optimal learning rates are
derived considering different possible choices of the step-size, the mini-batch
size, the number of iterations/passes, and the subsampling level. In comparison
with state-of-the-art algorithms such as the classic stochastic gradient
methods and kernel ridge regression with Nyström, the studied algorithm has
advantages on the computational complexity, while achieving the same optimal
learning rates. Moreover, our results indicate that using mini-batches can
reduce the total computational cost while achieving the same optimal
statistical results.
| 1 | 0 | 1 | 1 | 0 | 0 |
Run Procrustes, Run! On the convergence of accelerated Procrustes Flow | In this work, we present theoretical results on the convergence of non-convex
accelerated gradient descent in matrix factorization models. The technique is
applied to matrix sensing problems with squared loss, for the estimation of a
rank $r$ optimal solution $X^\star \in \mathbb{R}^{n \times n}$. We show that
the acceleration leads to linear convergence rate, even under non-convex
settings where the variable $X$ is represented as $U U^\top$ for $U \in
\mathbb{R}^{n \times r}$. Our result has the same dependence on the condition
number of the objective --and the optimal solution-- as that of the recent
results on non-accelerated algorithms. However, acceleration is observed in
practice, both in synthetic examples and in two real applications: neuronal
multi-unit activities recovery from single electrode recordings, and quantum
state tomography on quantum computing simulators.
| 0 | 0 | 0 | 1 | 0 | 0 |
A note on the bijectivity of antipode of a Hopf algebra and its applications | Certain sufficient homological and ring-theoretical conditions are given for
a Hopf algebra to have bijective antipode with applications to noetherian Hopf
algebras regarding their homological behaviors.
| 0 | 0 | 1 | 0 | 0 | 0 |
Perfect Edge Domination: Hard and Solvable Cases | Let $G$ be an undirected graph. An edge of $G$ dominates itself and all edges
adjacent to it. A subset $E'$ of edges of $G$ is an edge dominating set of $G$,
if every edge of the graph is dominated by some edge of $E'$. We say that $E'$
is a perfect edge dominating set of $G$, if every edge not in $E'$ is dominated
by exactly one edge of $E'$. The perfect edge dominating problem is to
determine a least cardinality perfect edge dominating set of $G$. For this
problem, we describe two NP-completeness proofs, for the classes of claw-free
graphs of degree at most 3, and for bounded degree graphs, of maximum degree at
most $d \geq 3$ and large girth. In contrast, we prove that the problem admits
an $O(n)$ time solution, for cubic claw-free graphs. In addition, we prove a
complexity dichotomy theorem for the perfect edge domination problem, based on
the results described in the paper. Finally, we describe a linear time
algorithm for finding a minimum weight perfect edge dominating set of a
$P_5$-free graph. The algorithm is robust, in the sense that, given an
arbitrary graph $G$, either it computes a minimum weight perfect edge
dominating set of $G$, or it exhibits an induced subgraph of $G$, isomorphic to
a $P_5$.
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On the presentation of Hecke-Hopf algebras for non-simply-laced type | Hecke-Hopf algebras were defined by A. Berenstein and D. Kazhdan. We give an
explicit presentation of an Hecke-Hopf algebra when the parameter $m_{ij},$
associated to any two distinct vertices $i$ and $j$ in the presentation of a
Coxeter group, equals $4,$ $5$ or $6$. As an application, we give a proof of a
conjecture of Berenstein and Kazhdan when the Coxeter group is crystallographic
and non-simply-laced. As another application, we show that another conjecture
of Berenstein and Kazhdan holds when $m_{ij},$ associated to any two distinct
vertices $i$ and $j,$ equals $4$ and that the conjecture does not hold when
some $m_{ij}$ equals $6$ by giving a counterexample to it.
| 0 | 0 | 1 | 0 | 0 | 0 |
ILP-based Alleviation of Dense Meander Segments with Prioritized Shifting and Progressive Fixing in PCB Routing | Length-matching is an important technique to bal- ance delays of bus signals
in high-performance PCB routing. Existing routers, however, may generate very
dense meander segments. Signals propagating along these meander segments
exhibit a speedup effect due to crosstalk between the segments of the same
wire, thus leading to mismatch of arrival times even under the same physical
wire length. In this paper, we present a post-processing method to enlarge the
width and the distance of meander segments and hence distribute them more
evenly on the board so that crosstalk can be reduced. In the proposed
framework, we model the sharing of available routing areas after removing dense
meander segments from the initial routing, as well as the generation of relaxed
meander segments and their groups for wire length compensation. This model is
transformed into an ILP problem and solved for a balanced distribution of wire
patterns. In addition, we adjust the locations of long wire segments according
to wire priorities to swap free spaces toward critical wires that need much
length compensation. To reduce the problem space of the ILP model, we also
introduce a progressive fixing technique so that wire patterns are grown
gradually from the edge of the routing toward the center area. Experimental
results show that the proposed method can expand meander segments significantly
even under very tight area constraints, so that the speedup effect can be
alleviated effectively in high- performance PCB designs.
| 1 | 0 | 0 | 0 | 0 | 0 |
Membrane Trafficking in the Yeast Saccharomyces cerevisiae Model | The yeast Saccharomyces cerevisiae is one of the best characterized
eukaryotic models. The secretory pathway was the first trafficking pathway
clearly understood mainly thanks to the work done in the laboratory of Randy
Schekman in the 1980s. They have isolated yeast sec mutants unable to secrete
an extracellular enzyme and these SEC genes were identified as encoding key
effectors of the secretory machinery. For this work, the 2013 Nobel Prize in
Physiology and Medicine has been awarded to Randy Schekman; the prize is shared
with James Rothman and Thomas S{ü}dhof. Here, we present the different
trafficking pathways of yeast S. cerevisiae. At the Golgi apparatus newly
synthesized proteins are sorted between those transported to the plasma
membrane (PM), or the external medium, via the exocytosis or secretory pathway
(SEC), and those targeted to the vacuole either through endosomes (vacuolar
protein sorting or VPS pathway) or directly (alkaline phosphatase or ALP
pathway). Plasma membrane proteins can be internalized by endocytosis (END) and
transported to endosomes where they are sorted between those targeted for
vacuolar degradation and those redirected to the Golgi (recycling or RCY
pathway). Studies in yeast S. cerevisiae allowed the identification of most of
the known effectors, protein complexes, and trafficking pathways in eukaryotic
cells, and most of them are conserved among eukaryotes.
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Synthesizing Programs for Images using Reinforced Adversarial Learning | Advances in deep generative networks have led to impressive results in recent
years. Nevertheless, such models can often waste their capacity on the minutiae
of datasets, presumably due to weak inductive biases in their decoders. This is
where graphics engines may come in handy since they abstract away low-level
details and represent images as high-level programs. Current methods that
combine deep learning and renderers are limited by hand-crafted likelihood or
distance functions, a need for large amounts of supervision, or difficulties in
scaling their inference algorithms to richer datasets. To mitigate these
issues, we present SPIRAL, an adversarially trained agent that generates a
program which is executed by a graphics engine to interpret and sample images.
The goal of this agent is to fool a discriminator network that distinguishes
between real and rendered data, trained with a distributed reinforcement
learning setup without any supervision. A surprising finding is that using the
discriminator's output as a reward signal is the key to allow the agent to make
meaningful progress at matching the desired output rendering. To the best of
our knowledge, this is the first demonstration of an end-to-end, unsupervised
and adversarial inverse graphics agent on challenging real world (MNIST,
Omniglot, CelebA) and synthetic 3D datasets.
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A Correspondence Between Random Neural Networks and Statistical Field Theory | A number of recent papers have provided evidence that practical design
questions about neural networks may be tackled theoretically by studying the
behavior of random networks. However, until now the tools available for
analyzing random neural networks have been relatively ad-hoc. In this work, we
show that the distribution of pre-activations in random neural networks can be
exactly mapped onto lattice models in statistical physics. We argue that
several previous investigations of stochastic networks actually studied a
particular factorial approximation to the full lattice model. For random linear
networks and random rectified linear networks we show that the corresponding
lattice models in the wide network limit may be systematically approximated by
a Gaussian distribution with covariance between the layers of the network. In
each case, the approximate distribution can be diagonalized by Fourier
transformation. We show that this approximation accurately describes the
results of numerical simulations of wide random neural networks. Finally, we
demonstrate that in each case the large scale behavior of the random networks
can be approximated by an effective field theory.
| 1 | 1 | 0 | 1 | 0 | 0 |
The nature of the progenitor of the M31 North-western stream: globular clusters as milestones of its orbit | We examine the nature, possible orbits and physical properties of the
progenitor of the North-western stellar stream (NWS) in the halo of the
Andromeda galaxy (M31). The progenitor is assumed to be an accreting dwarf
galaxy with globular clusters (GCs). It is, in general, difficult to determine
the progenitor's orbit precisely because of many necessary parameters.
Recently, Veljanoski et al. 2014 reported five GCs whose positions and radial
velocities suggest an association with the stream. We use this data to
constrain the orbital motions of the progenitor using test-particle
simulations. Our simulations split the orbit solutions into two branches
according to whether the stream ends up in the foreground or in the background
of M31. Upcoming observations that will determine the distance to the NWS will
be able to reject one of the two branches. In either case, the solutions
require that the pericentric radius of any possible orbit be over 2 kpc. We
estimate the efficiency of the tidal disruption and confirm the consistency
with the assumption for the progenitor being a dwarf galaxy. The progenitor
requires the mass $\ga 2\times10^6 M_{\sun}$ and half-light radius $\ga 30$ pc.
In addition, $N$-body simulations successfully reproduce the basic observed
features of the NWS and the GCs' line-of-sight velocities.
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On codimension two flats in Fermat-type arrangements | In the present note we study certain arrangements of codimension $2$ flats in
projective spaces, we call them "Fermat arrangements". We describe algebraic
properties of their defining ideals. In particular, we show that they provide
counterexamples to an expected containment relation between ordinary and
symbolic powers of homogeneous ideals.
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Invariant Causal Prediction for Sequential Data | We investigate the problem of inferring the causal predictors of a response
$Y$ from a set of $d$ explanatory variables $(X^1,\dots,X^d)$. Classical
ordinary least squares regression includes all predictors that reduce the
variance of $Y$. Using only the causal predictors instead leads to models that
have the advantage of remaining invariant under interventions, loosely speaking
they lead to invariance across different "environments" or "heterogeneity
patterns". More precisely, the conditional distribution of $Y$ given its causal
predictors remains invariant for all observations. Recent work exploits such a
stability to infer causal relations from data with different but known
environments. We show that even without having knowledge of the environments or
heterogeneity pattern, inferring causal relations is possible for time-ordered
(or any other type of sequentially ordered) data. In particular, this allows
detecting instantaneous causal relations in multivariate linear time series
which is usually not the case for Granger causality. Besides novel methodology,
we provide statistical confidence bounds and asymptotic detection results for
inferring causal predictors, and present an application to monetary policy in
macroeconomics.
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Smoothing with Couplings of Conditional Particle Filters | In state space models, smoothing refers to the task of estimating a latent
stochastic process given noisy measurements related to the process. We propose
an unbiased estimator of smoothing expectations. The lack-of-bias property has
methodological benefits: independent estimators can be generated in parallel,
and confidence intervals can be constructed from the central limit theorem to
quantify the approximation error. To design unbiased estimators, we combine a
generic debiasing technique for Markov chains with a Markov chain Monte Carlo
algorithm for smoothing. The resulting procedure is widely applicable and we
show in numerical experiments that the removal of the bias comes at a
manageable increase in variance. We establish the validity of the proposed
estimators under mild assumptions. Numerical experiments are provided on toy
models, including a setting of highly-informative observations, and a realistic
Lotka-Volterra model with an intractable transition density.
| 0 | 0 | 0 | 1 | 0 | 0 |
Formation of High Pressure Gradients at the Free Surface of a Liquid Dielectric in a Tangential Electric Field | Nonlinear dynamics of the free surface of an ideal incompressible
non-conducting fluid with high dielectric constant subjected by strong
horizontal electric field is simulated on the base of the method of conformal
transformations. It is demonstrated that interaction of counter-propagating
waves leads to formation of regions with steep wave front at the fluid surface;
angles of the boundary inclination tend to {\pi}/2, and the curvature of
surface extremely increases. A significant concentration of the energy of the
system occurs at these points. From the physical point of view, the appearance
of these singularities corresponds to formation of regions at the fluid surface
where pressure exerted by electric field undergoes a discontinuity and
dynamical pressure increases almost an order of magnitude.
| 0 | 1 | 0 | 0 | 0 | 0 |
Subsampling for Ridge Regression via Regularized Volume Sampling | Given $n$ vectors $\mathbf{x}_i\in \mathbb{R}^d$, we want to fit a linear
regression model for noisy labels $y_i\in\mathbb{R}$. The ridge estimator is a
classical solution to this problem. However, when labels are expensive, we are
forced to select only a small subset of vectors $\mathbf{x}_i$ for which we
obtain the labels $y_i$. We propose a new procedure for selecting the subset of
vectors, such that the ridge estimator obtained from that subset offers strong
statistical guarantees in terms of the mean squared prediction error over the
entire dataset of $n$ labeled vectors. The number of labels needed is
proportional to the statistical dimension of the problem which is often much
smaller than $d$. Our method is an extension of a joint subsampling procedure
called volume sampling. A second major contribution is that we speed up volume
sampling so that it is essentially as efficient as leverage scores, which is
the main i.i.d. subsampling procedure for this task. Finally, we show
theoretically and experimentally that volume sampling has a clear advantage
over any i.i.d. sampling when labels are expensive.
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Ab initio calculations of the concentration dependent band gap reduction in dilute nitrides | While being of persistent interest for the integration of lattice-matched
laser devices with silicon circuits, the electronic structure of dilute nitride
III/V-semiconductors has presented a challenge to ab initio computational
approaches. The root of this lies in the strong distortion N atoms exert on
most host materials. Here, we resolve these issues by combining density
functional theory calculations based on the meta-GGA functional presented by
Tran and Blaha (TB09) with a supercell approach for the dilute nitride Ga(NAs).
Exploring the requirements posed to supercells, we show that the distortion
field of a single N atom must be allowed to decrease so far, that it does not
overlap with its periodic images. This also prevents spurious electronic
interactions between translational symmetric atoms, allowing to compute band
gaps in very good agreement with experimentally derived reference values. These
results open up the field of dilute nitride compound semiconductors to
predictive ab initio calculations.
| 0 | 1 | 0 | 0 | 0 | 0 |
Outliers and related problems | We define outliers as a set of observations which contradicts the proposed
mathematical (statistical) model and we discuss the frequently observed types
of the outliers. Further we explore what changes in the model have to be made
in order to avoid the occurance of the outliers. We observe that some variants
of the outliers lead to classical results in probability, such as the law of
large numbers and the concept of heavy tailed distributions.
Key words: outlier; the law of large numbers; heavy tailed distributions;
model rejection.
| 0 | 0 | 1 | 1 | 0 | 0 |
On Quadratic Convergence of DC Proximal Newton Algorithm for Nonconvex Sparse Learning in High Dimensions | We propose a DC proximal Newton algorithm for solving nonconvex regularized
sparse learning problems in high dimensions. Our proposed algorithm integrates
the proximal Newton algorithm with multi-stage convex relaxation based on the
difference of convex (DC) programming, and enjoys both strong computational and
statistical guarantees. Specifically, by leveraging a sophisticated
characterization of sparse modeling structures/assumptions (i.e., local
restricted strong convexity and Hessian smoothness), we prove that within each
stage of convex relaxation, our proposed algorithm achieves (local) quadratic
convergence, and eventually obtains a sparse approximate local optimum with
optimal statistical properties after only a few convex relaxations. Numerical
experiments are provided to support our theory.
| 1 | 0 | 1 | 1 | 0 | 0 |
Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning | In reinforcement learning, agents learn by performing actions and observing
their outcomes. Sometimes, it is desirable for a human operator to
\textit{interrupt} an agent in order to prevent dangerous situations from
happening. Yet, as part of their learning process, agents may link these
interruptions, that impact their reward, to specific states and deliberately
avoid them. The situation is particularly challenging in a multi-agent context
because agents might not only learn from their own past interruptions, but also
from those of other agents. Orseau and Armstrong defined \emph{safe
interruptibility} for one learner, but their work does not naturally extend to
multi-agent systems. This paper introduces \textit{dynamic safe
interruptibility}, an alternative definition more suited to decentralized
learning problems, and studies this notion in two learning frameworks:
\textit{joint action learners} and \textit{independent learners}. We give
realistic sufficient conditions on the learning algorithm to enable dynamic
safe interruptibility in the case of joint action learners, yet show that these
conditions are not sufficient for independent learners. We show however that if
agents can detect interruptions, it is possible to prune the observations to
ensure dynamic safe interruptibility even for independent learners.
| 1 | 0 | 0 | 1 | 0 | 0 |
Heuristic Optimization for Automated Distribution System Planning in Network Integration Studies | Network integration studies try to assess the impact of future developments,
such as the increase of Renewable Energy Sources or the introduction of Smart
Grid Technologies, on large-scale network areas. Goals can be to support
strategic alignment in the regulatory framework or to adapt the network
planning principles of Distribution System Operators. This study outlines an
approach for the automated distribution system planning that can calculate
network reconfiguration, reinforcement and extension plans in a fully automated
fashion. This allows the estimation of the expected cost in massive
probabilistic simulations of large numbers of real networks and constitutes a
core component of a framework for large-scale network integration studies.
Exemplary case study results are presented that were performed in cooperation
with different major distribution system operators. The case studies cover the
estimation of expected network reinforcement costs, technical and economical
assessment of smart grid technologies and structural network optimisation.
| 1 | 0 | 0 | 0 | 0 | 0 |
The Sizes and Depletions of the Dust and Gas Cavities in the Transitional Disk J160421.7-213028 | We report ALMA Cycle 2 observations of 230 GHz (1.3 mm) dust continuum
emission, and $^{12}$CO, $^{13}$CO, and C$^{18}$O J = 2-1 line emission, from
the Upper Scorpius transitional disk [PZ99] J160421.7-213028, with an angular
resolution of ~0".25 (35 AU). Armed with these data and existing H-band
scattered light observations, we measure the size and depth of the disk's
central cavity, and the sharpness of its outer edge, in three components:
sub-$\mu$m-sized "small" dust traced by scattered light, millimeter-sized "big"
dust traced by the millimeter continuum, and gas traced by line emission. Both
dust populations feature a cavity of radius $\sim$70 AU that is depleted by
factors of at least 1000 relative to the dust density just outside. The
millimeter continuum data are well explained by a cavity with a sharp edge.
Scattered light observations can be fitted with a cavity in small dust that has
either a sharp edge at 60 AU, or an edge that transitions smoothly over an
annular width of 10 AU near 60 AU. In gas, the data are consistent with a
cavity that is smaller, about 15 AU in radius, and whose surface density at 15
AU is $10^{3\pm1}$ times smaller than the surface density at 70 AU; the gas
density grades smoothly between these two radii. The CO isotopologue
observations rule out a sharp drop in gas surface density at 30 AU or a
double-drop model as found by previous modeling. Future observations are needed
to assess the nature of these gas and dust cavities, e.g., whether they are
opened by multiple as-yet-unseen planets or photoevaporation.
| 0 | 1 | 0 | 0 | 0 | 0 |
Demystifying AlphaGo Zero as AlphaGo GAN | The astonishing success of AlphaGo Zero\cite{Silver_AlphaGo} invokes a
worldwide discussion of the future of our human society with a mixed mood of
hope, anxiousness, excitement and fear. We try to dymystify AlphaGo Zero by a
qualitative analysis to indicate that AlphaGo Zero can be understood as a
specially structured GAN system which is expected to possess an inherent good
convergence property. Thus we deduct the success of AlphaGo Zero may not be a
sign of a new generation of AI.
| 1 | 0 | 0 | 1 | 0 | 0 |
Effects of pressure and magnetic field on the re-entrant superconductor Eu(Fe$_{0.93}$Rh$_{0.07}$)$_2$As$_2$ | Electron-doped Eu(Fe$_{0.93}$Rh$_{0.07}$)$_2$As$_2$ has been systematically
studied by high pressure investigations of the magnetic and electrical
transport properties, in order to unravel the complex interplay of
superconductivity and magnetism. The compound reveals an exceedingly broad
re-entrant transition to the superconducting state between $T_{\rm{c,on}} =
19.8$ K and $T_{\rm{c,0}} = 5.2$ K due to a canted A-type antiferromagnetic
ordering of the Eu$^{2+}$ moments at $T_{\rm{N}} = 16.6$ K and a re-entrant
spin glass transition at $T_{\rm{SG}} = 14.1$ K. At ambient pressure evidences
for the coexistence of superconductivity and ferromagnetism could be observed,
as well as a magnetic-field-induced enhancement of the zero-resistance
temperature $T_{\rm{c,0}}$ up to $7.2$ K with small magnetic fields applied
parallel to the \textit{ab}-plane of the crystal. We attribute the
field-induced-enhancement of superconductivity to the suppression of the
ferromagnetic component of the Eu$^{2+}$ moments along the \textit{c}-axis,
which leads to a reduction of the orbital pair breaking effect. Application of
hydrostatic pressure suppresses the superconducting state around $14$ kbar
along with a linear temperature dependence of the resistivity, implying that a
non-Fermi liquid region is located at the boundary of the superconducting
phase. At intermediate pressure, an additional feature in the resistivity
curves is identified, which can be suppressed by external magnetic fields and
competes with the superconducting phase. We suggest that the effect of negative
pressure by the chemical Rh substitution in
Eu(Fe$_{0.93}$Rh$_{0.07}$)$_2$As$_2$ is partially reversed, leading to a
re-activation of the spin density wave.
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Commissioning and Operation | Chapter 16 in High-Luminosity Large Hadron Collider (HL-LHC) : Preliminary
Design Report. The Large Hadron Collider (LHC) is one of the largest scientific
instruments ever built. Since opening up a new energy frontier for exploration
in 2010, it has gathered a global user community of about 7,000 scientists
working in fundamental particle physics and the physics of hadronic matter at
extreme temperature and density. To sustain and extend its discovery potential,
the LHC will need a major upgrade in the 2020s. This will increase its
luminosity (rate of collisions) by a factor of five beyond the original design
value and the integrated luminosity (total collisions created) by a factor ten.
The LHC is already a highly complex and exquisitely optimised machine so this
upgrade must be carefully conceived and will require about ten years to
implement. The new configuration, known as High Luminosity LHC (HL-LHC), will
rely on a number of key innovations that push accelerator technology beyond its
present limits. Among these are cutting-edge 11-12 tesla superconducting
magnets, compact superconducting cavities for beam rotation with ultra-precise
phase control, new technology and physical processes for beam collimation and
300 metre-long high-power superconducting links with negligible energy
dissipation. The present document describes the technologies and components
that will be used to realise the project and is intended to serve as the basis
for the detailed engineering design of HL-LHC.
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Only in the standard representation the Dirac theory is a quantum theory of a single fermion | It is shown that the relativistic quantum mechanics of a single fermion can
be developed only on the basis of the standard representation of the Dirac
bispinor. As in the nonrelativistic quantum mechanics, the arbitrariness in
defining the bispinor, as a four-component wave function, is restricted by its
multiplication by an arbitrary phase factor. We reveal the role of the large
and small components of the bispinor, establish their link in the
nonrelativistic limit with the Pauli spinor, as well as explain the role of
states with negative energies. The Klein tunneling is treated here as a
physical phenomenon analogous to the propagation of the electromagnetic wave in
a medium with negative dielectric permittivity and permeability. For the case
of localized stationary states we define the effective one-particle operators
which act in the space of the large component but contain the contributions of
both components. The effective operator of energy is presented in a compact
analytical form.
| 0 | 1 | 0 | 0 | 0 | 0 |
Stable absorbing boundary conditions for molecular dynamics in general domains | A new type of absorbing boundary conditions for molecular dynamics
simulations are presented. The exact boundary conditions for crystalline solids
with harmonic approximation are expressed as a dynamic Dirichlet- to-Neumann
(DtN) map. It connects the displacement of the atoms at the boundary to the
traction on these atoms. The DtN map is valid for a domain with general
geometry. To avoid evaluating the time convo- lution of the dynamic DtN map, we
approximate the associated kernel function by rational functions in the Laplace
domain. The parameters in the approximations are determined by interpolations.
The explicit forms of the zeroth, first, and second order approximations will
be presented. The stability of the molecular dynamics model, supplemented with
these absorbing boundary conditions is established. Two numerical simulations
are performed to demonstrate the effectiveness of the methods.
| 0 | 1 | 0 | 0 | 0 | 0 |
Algebraic operads up to homotopy | This paper deals with the homotopy theory of differential graded operads. We
endow the Koszul dual category of curved conilpotent cooperads, where the
notion of quasi-isomorphism barely makes sense, with a model category structure
Quillen equivalent to that of operads. This allows us to describe the homotopy
properties of differential graded operads in a simpler and richer way, using
obstruction methods.
| 0 | 0 | 1 | 0 | 0 | 0 |
Nonlinear Kalman Filtering with Divergence Minimization | We consider the nonlinear Kalman filtering problem using Kullback-Leibler
(KL) and $\alpha$-divergence measures as optimization criteria. Unlike linear
Kalman filters, nonlinear Kalman filters do not have closed form Gaussian
posteriors because of a lack of conjugacy due to the nonlinearity in the
likelihood. In this paper we propose novel algorithms to optimize the forward
and reverse forms of the KL divergence, as well as the alpha-divergence which
contains these two as limiting cases. Unlike previous approaches, our
algorithms do not make approximations to the divergences being optimized, but
use Monte Carlo integration techniques to derive unbiased algorithms for direct
optimization. We assess performance on radar and sensor tracking, and options
pricing problems, showing general improvement over the UKF and EKF, as well as
competitive performance with particle filtering.
| 0 | 0 | 1 | 1 | 0 | 0 |
On Chern number inequality in dimension 3 | We prove that if $X---> X^+$ is a threefold terminal flip, then
$c_1(X).c_2(X)\leq c_1(X^+).c_2(X^+)$ where $c_1(X)$ and $c_2(X)$ denote the
Chern classes. This gives the affirmative answer to a Question by Xie
\cite{Xie2}. We obtain the similar but weaker result in the case of divisorial
contraction to curves.
| 0 | 0 | 1 | 0 | 0 | 0 |
Enhancing SDO/HMI images using deep learning | The Helioseismic and Magnetic Imager (HMI) provides continuum images and
magnetograms with a cadence better than one per minute. It has been
continuously observing the Sun 24 hours a day for the past 7 years. The obvious
trade-off between full disk observations and spatial resolution makes HMI not
enough to analyze the smallest-scale events in the solar atmosphere. Our aim is
to develop a new method to enhance HMI data, simultaneously deconvolving and
super-resolving images and magnetograms. The resulting images will mimic
observations with a diffraction-limited telescope twice the diameter of HMI.
Our method, which we call Enhance, is based on two deep fully convolutional
neural networks that input patches of HMI observations and output deconvolved
and super-resolved data. The neural networks are trained on synthetic data
obtained from simulations of the emergence of solar active regions. We have
obtained deconvolved and supper-resolved HMI images. To solve this ill-defined
problem with infinite solutions we have used a neural network approach to add
prior information from the simulations. We test Enhance against Hinode data
that has been degraded to a 28 cm diameter telescope showing very good
consistency. The code is open source.
| 1 | 1 | 0 | 0 | 0 | 0 |
Suppression of the superconductivity in ultrathin amorphous Mo$_{78}$Ge$_{22}$ thin films observed by STM | In contact with a superconductor, a normal metal modifies its properties due
to Andreev reflection. In the current work, the local density of states (LDOS)
of superconductor - normal metal Mo$_{78}$Ge$_{22}$ - Au bilayers are studied
by means of STM applied from the Au side. Three bilayers have been prepared on
silicate glass substrate consisting of 100, 10 and 5 nm MoGe thin films covered
always by 5 nm Au layer. The tunneling spectra were measured at temperatures
from 0.5 K to 7 K. The two-dimensional cross-correlation between topography and
normalized zero-bias conductance (ZBC) indicates a proximity effect between 100
and 10 nm MoGe thin films and Au layer where a superconducting gap slightly
smaller than that of bulk MoGe is observed. The effect of the thinnest 5 nm
MoGe layer on Au leads to much smaller gap moreover the LDOS reveals almost
completely suppressed coherence peaks. This is attributed to a strong
pair-breaking effect of spin-flip processes at the interface between MoGe films
and the substrate.
| 0 | 1 | 0 | 0 | 0 | 0 |
Functional data analysis in the Banach space of continuous functions | Functional data analysis is typically conducted within the $L^2$-Hilbert
space framework. There is by now a fully developed statistical toolbox allowing
for the principled application of the functional data machinery to real-world
problems, often based on dimension reduction techniques such as functional
principal component analysis. At the same time, there have recently been a
number of publications that sidestep dimension reduction steps and focus on a
fully functional $L^2$-methodology. This paper goes one step further and
develops data analysis methodology for functional time series in the space of
all continuous functions. The work is motivated by the fact that objects with
rather different shapes may still have a small $L^2$-distance and are therefore
identified as similar when using an $L^2$-metric. However, in applications it
is often desirable to use metrics reflecting the visualization of the curves in
the statistical analysis. The methodological contributions are focused on
developing two-sample and change-point tests as well as confidence bands, as
these procedures appear do be conducive to the proposed setting. Particular
interest is put on relevant differences; that is, on not trying to test for
exact equality, but rather for pre-specified deviations under the null
hypothesis.
The procedures are justified through large-sample theory. To ensure
practicability, non-standard bootstrap procedures are developed and
investigated addressing particular features that arise in the problem of
testing relevant hypotheses. The finite sample properties are explored through
a simulation study and an application to annual temperature profiles.
| 0 | 0 | 1 | 1 | 0 | 0 |
Bayesian Recurrent Neural Networks | In this work we explore a straightforward variational Bayes scheme for
Recurrent Neural Networks. Firstly, we show that a simple adaptation of
truncated backpropagation through time can yield good quality uncertainty
estimates and superior regularisation at only a small extra computational cost
during training, also reducing the amount of parameters by 80\%. Secondly, we
demonstrate how a novel kind of posterior approximation yields further
improvements to the performance of Bayesian RNNs. We incorporate local gradient
information into the approximate posterior to sharpen it around the current
batch statistics. We show how this technique is not exclusive to recurrent
neural networks and can be applied more widely to train Bayesian neural
networks. We also empirically demonstrate how Bayesian RNNs are superior to
traditional RNNs on a language modelling benchmark and an image captioning
task, as well as showing how each of these methods improve our model over a
variety of other schemes for training them. We also introduce a new benchmark
for studying uncertainty for language models so future methods can be easily
compared.
| 1 | 0 | 0 | 1 | 0 | 0 |
Cross-Correlation Redshift Calibration Without Spectroscopic Calibration Samples in DES Science Verification Data | Galaxy cross-correlations with high-fidelity redshift samples hold the
potential to precisely calibrate systematic photometric redshift uncertainties
arising from the unavailability of complete and representative training and
validation samples of galaxies. However, application of this technique in the
Dark Energy Survey (DES) is hampered by the relatively low number density,
small area, and modest redshift overlap between photometric and spectroscopic
samples. We propose instead using photometric catalogs with reliable
photometric redshifts for photo-z calibration via cross-correlations. We verify
the viability of our proposal using redMaPPer clusters from the Sloan Digital
Sky Survey (SDSS) to successfully recover the redshift distribution of SDSS
spectroscopic galaxies. We demonstrate how to combine photo-z with
cross-correlation data to calibrate photometric redshift biases while
marginalizing over possible clustering bias evolution in either the calibration
or unknown photometric samples. We apply our method to DES Science Verification
(DES SV) data in order to constrain the photometric redshift distribution of a
galaxy sample selected for weak lensing studies, constraining the mean of the
tomographic redshift distributions to a statistical uncertainty of $\Delta z
\sim \pm 0.01$. We forecast that our proposal can in principle control
photometric redshift uncertainties in DES weak lensing experiments at a level
near the intrinsic statistical noise of the experiment over the range of
redshifts where redMaPPer clusters are available. Our results provide strong
motivation to launch a program to fully characterize the systematic errors from
bias evolution and photo-z shapes in our calibration procedure.
| 0 | 1 | 0 | 0 | 0 | 0 |
Completely bounded bimodule maps and spectral synthesis | We initiate the study of the completely bounded multipliers of the Haagerup
tensor product $A(G)\otimes_{\rm h} A(G)$ of two copies of the Fourier algebra
$A(G)$ of a locally compact group $G$. If $E$ is a closed subset of $G$ we let
$E^{\sharp} = \{(s,t) : st\in E\}$ and show that if $E^{\sharp}$ is a set of
spectral synthesis for $A(G)\otimes_{\rm h} A(G)$ then $E$ is a set of local
spectral synthesis for $A(G)$. Conversely, we prove that if $E$ is a set of
spectral synthesis for $A(G)$ and $G$ is a Moore group then $E^{\sharp}$ is a
set of spectral synthesis for $A(G)\otimes_{\rm h} A(G)$. Using the natural
identification of the space of all completely bounded weak* continuous
$VN(G)'$-bimodule maps with the dual of $A(G)\otimes_{\rm h} A(G)$, we show
that, in the case $G$ is weakly amenable, such a map leaves the multiplication
algebra of $L^{\infty}(G)$ invariant if and only if its support is contained in
the antidiagonal of $G$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation | Black-box risk scoring models permeate our lives, yet are typically
proprietary or opaque. We propose Distill-and-Compare, a model distillation and
comparison approach to audit such models. To gain insight into black-box
models, we treat them as teachers, training transparent student models to mimic
the risk scores assigned by black-box models. We compare the student model
trained with distillation to a second un-distilled transparent model trained on
ground-truth outcomes, and use differences between the two models to gain
insight into the black-box model. Our approach can be applied in a realistic
setting, without probing the black-box model API. We demonstrate the approach
on four public data sets: COMPAS, Stop-and-Frisk, Chicago Police, and Lending
Club. We also propose a statistical test to determine if a data set is missing
key features used to train the black-box model. Our test finds that the
ProPublica data is likely missing key feature(s) used in COMPAS.
| 1 | 0 | 0 | 1 | 0 | 0 |
An influence-based fast preceding questionnaire model for elderly assessments | To improve the efficiency of elderly assessments, an influence-based fast
preceding questionnaire model (FPQM) is proposed. Compared with traditional
assessments, the FPQM optimizes questionnaires by reordering their attributes.
The values of low-ranking attributes can be predicted by the values of the
high-ranking attributes. Therefore, the number of attributes can be reduced
without redesigning the questionnaires. A new function for calculating the
influence of the attributes is proposed based on probability theory. Reordering
and reducing algorithms are given based on the attributes' influences. The
model is verified through a practical application. The practice in an
elderly-care company shows that the FPQM can reduce the number of attributes by
90.56% with a prediction accuracy of 98.39%. Compared with other methods, such
as the Expert Knowledge, Rough Set and C4.5 methods, the FPQM achieves the best
performance. In addition, the FPQM can also be applied to other questionnaires.
| 1 | 0 | 0 | 0 | 0 | 0 |
A GPU Accelerated Discontinuous Galerkin Incompressible Flow Solver | We present a GPU-accelerated version of a high-order discontinuous Galerkin
discretization of the unsteady incompressible Navier-Stokes equations. The
equations are discretized in time using a semi-implicit scheme with explicit
treatment of the nonlinear term and implicit treatment of the split Stokes
operators. The pressure system is solved with a conjugate gradient method
together with a fully GPU-accelerated multigrid preconditioner which is
designed to minimize memory requirements and to increase overall performance. A
semi-Lagrangian subcycling advection algorithm is used to shift the
computational load per timestep away from the pressure Poisson solve by
allowing larger timestep sizes in exchange for an increased number of advection
steps. Numerical results confirm we achieve the design order accuracy in time
and space. We optimize the performance of the most time-consuming kernels by
tuning the fine-grain parallelism, memory utilization, and maximizing
bandwidth. To assess overall performance we present an empirically calibrated
roofline performance model for a target GPU to explain the achieved efficiency.
We demonstrate that, in the most cases, the kernels used in the solver are
close to their empirically predicted roofline performance.
| 1 | 0 | 0 | 0 | 0 | 0 |
The Auger Engineering Radio Array and multi-hybrid cosmic ray detection (TAUP 2015) | The Auger Engineering Radio Array (AERA) aims at the detection of air showers
induced by high-energy cosmic rays. As an extension of the Pierre Auger
Observatory, it measures complementary information to the particle detectors,
fluorescence telescopes and to the muon scintillators of the Auger Muons and
Infill for the Ground Array (AMIGA). AERA is sensitive to all fundamental
parameters of an extensive air shower such as the arrival direction, energy and
depth of shower maximum. Since the radio emission is induced purely by the
electromagnetic component of the shower, in combination with the AMIGA muon
counters, AERA is perfect for separate measurements of the electrons and muons
in the shower, if combined with a muon counting detector like AMIGA. In
addition to the depth of the shower maximum, the ratio of the electron and muon
number serves as a measure of the primary particle mass.
| 0 | 1 | 0 | 0 | 0 | 0 |
Historic Emergence of Diversity in Painting: Heterogeneity in Chromatic Distance in Images and Characterization of Massive Painting Data Set | Painting is an art form that has long functioned as a major channel for the
creative expression and communication of humans, its evolution taking place
under an interplay with the science, technology, and social environments of the
times. Therefore, understanding the process based on comprehensive data could
shed light on how humans acted and manifested creatively under changing
conditions. Yet, there exist few systematic frameworks that characterize the
process for painting, which would require robust statistical methods for
defining painting characteristics and identifying human's creative
developments, and data of high quality and sufficient quantity. Here we propose
that the color contrast of a painting image signifying the heterogeneity in
inter-pixel chromatic distance can be a useful representation of its style,
integrating both the color and geometry. From the color contrasts of paintings
from a large-scale, comprehensive archive of 179,853 high-quality images
spanning several centuries we characterize the temporal evolutionary patterns
of paintings, and present a deep study of an extraordinary expansion in
creative diversity and individuality that came to define the modern era.
| 1 | 1 | 0 | 0 | 0 | 0 |
Cage Size and Jump Precursors in Glass-Forming Liquids: Experiment and Simulations | Glassy dynamics is intermittent, as particles suddenly jump out of the cage
formed by their neighbours, and heterogeneous, as these jumps are not uniformly
distributed across the system. Relating these features of the dynamics to the
diverse local environments explored by the particles is essential to
rationalize the relaxation process. Here we investigate this issue
characterizing the local environment of a particle with the amplitude of its
short time vibrational motion, as determined by segmenting in cages and jumps
the particle trajectories. Both simulations of supercooled liquids and
experiments on colloidal suspensions show that particles in large cages are
likely to jump after a small time-lag, and that, on average, the cage enlarges
shortly before the particle jumps. At large time-lags, the cage has essentially
a constant value, which is smaller for longer-lasting cages. Finally, we
clarify how this coupling between cage size and duration controls the average
behaviour and opens the way to a better understanding of the relaxation process
in glass--forming liquids.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Comprehensive Survey on Bengali Phoneme Recognition | Hidden Markov model based various phoneme recognition methods for Bengali
language is reviewed. Automatic phoneme recognition for Bengali language using
multilayer neural network is reviewed. Usefulness of multilayer neural network
over single layer neural network is discussed. Bangla phonetic feature table
construction and enhancement for Bengali speech recognition is also discussed.
Comparison among these methods is discussed.
| 1 | 0 | 0 | 0 | 0 | 0 |
Factorization of arithmetic automorphic periods | In this paper, we prove that the arithmetic automorphic periods for $GL_{n}$
over a CM field factorize through the infinite places. This generalizes a
conjecture of Shimura in 1983, and is predicted by the Langlands correspondence
between automorphic representations and motives.
| 0 | 0 | 1 | 0 | 0 | 0 |
Multielectronic processes in particle and antiparticle collisions with rare gases | In this chapter we analyze the multiple ionization by impact of |Z|=1
projectiles: electrons, positrons, protons and antiprotons. Differences and
similarities among the cross sections by these four projectiles allows us to
have an insight on the physics involved. Mass and charge effects, energy
thresholds, and relative importance of collisional and post-collisional
processes are discussed. For this purpose, we performed a detailed
theoretical-experimental comparison for single up to quintuple ionization of
Ne, Ar, Kr and Xe by particles and antiparticles. We include an extensive
compilation of the available data for the sixteen collisional systems, and the
theoretical cross sections by means of the continuum distorted wave eikonal
initial state approximation. We underline here that post-collisional ionization
is decisive to describe multiple ionization by light projectiles, covering
almost the whole energy range, from threshold to high energies. The
normalization of positron and antiproton measurements to electron impact ones,
the lack of data in certain cases, and the future prospects are presented and
discussed.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Floating Cylinder on An Unbounded Bath | In this paper, we reconsider a circular cylinder horizontally floating on an
unbounded reservoir in a gravitational field directed downwards, which was
studied by Bhatnargar and Finn in 2006. We follow their approach but with some
modifications. We establish the relation between the total energy relative to
the undisturbed state and the total force. There is a monotone relation between
the height of the centre and the wetting angle. We study the number of
equilibria, the floating configurations and their stability for all parameter
values. We find that the system admits at most two equilibrium points for
arbitrary contact angle, the one with smaller wetting angle is stable and the
one with larger wetting angle is unstable. The initial model has a limitation
that the fluid interfaces may intersect. We show that the stable equilibrium
point never lies in the intersection region, while the unstable equilibrium
point may lie in the intersection region.
| 0 | 1 | 1 | 0 | 0 | 0 |
Switching between Limit Cycles in a Model of Running Using Exponentially Stabilizing Discrete Control Lyapunov Function | This paper considers the problem of switching between two periodic motions,
also known as limit cycles, to create agile running motions. For each limit
cycle, we use a control Lyapunov function to estimate the region of attraction
at the apex of the flight phase. We switch controllers at the apex, only if the
current state of the robot is within the region of attraction of the subsequent
limit cycle. If the intersection between two limit cycles is the null set, then
we construct additional limit cycles till we are able to achieve sufficient
overlap of the region of attraction between sequential limit cycles.
Additionally, we impose an exponential convergence condition on the control
Lyapunov function that allows us to rapidly transition between limit cycles.
Using the approach we demonstrate switching between 5 limit cycles in about 5
steps with the speed changing from 2 m/s to 5 m/s.
| 1 | 0 | 0 | 0 | 0 | 0 |
Locally Private Bayesian Inference for Count Models | As more aspects of social interaction are digitally recorded, there is a
growing need to develop privacy-preserving data analysis methods. Social
scientists will be more likely to adopt these methods if doing so entails
minimal change to their current methodology. Toward that end, we present a
general and modular method for privatizing Bayesian inference for Poisson
factorization, a broad class of models that contains some of the most widely
used models in the social sciences. Our method satisfies local differential
privacy, which ensures that no single centralized server need ever store the
non-privatized data. To formulate our local-privacy guarantees, we introduce
and focus on limited-precision local privacy---the local privacy analog of
limited-precision differential privacy (Flood et al., 2013). We present two
case studies, one involving social networks and one involving text corpora,
that test our method's ability to form the posterior distribution over latent
variables under different levels of noise, and demonstrate our method's utility
over a naïve approach, wherein inference proceeds as usual, treating the
privatized data as if it were not privatized.
| 1 | 0 | 0 | 1 | 0 | 0 |
Carrier Diffusion in Thin-Film CH3NH3PbI3 Perovskite Measured using Four-Wave Mixing | We report the application of femtosecond four-wave mixing (FWM) to the study
of carrier transport in solution-processed CH3NH3PbI3. The diffusion
coefficient was extracted through direct detection of the lateral diffusion of
carriers utilizing the transient grating technique, coupled with simultaneous
measurement of decay kinetics exploiting the versatility of the boxcar
excitation beam geometry. The observation of exponential decay of the transient
grating versus interpulse delay indicates diffusive transport with negligible
trapping within the first nanosecond following excitation. The in-plane
transport geometry in our experiments enabled the diffusion length to be
compared directly with the grain size, indicating that carriers move across
multiple grain boundaries prior to recombination. Our experiments illustrate
the broad utility of FWM spectroscopy for rapid characterization of macroscopic
film transport properties.
| 0 | 1 | 0 | 0 | 0 | 0 |
On effective Birkhoff's ergodic theorem for computable actions of amenable groups | We introduce computable actions of computable groups and prove the following
versions of effective Birkhoff's ergodic theorem. Let $\Gamma$ be a computable
amenable group, then there always exists a canonically computable tempered
two-sided F{\o}lner sequence $(F_n)_{n \geq
1}$ in $\Gamma$. For a computable, measure-preserving, ergodic action of
$\Gamma$ on a Cantor space $\{0,1\}^{\mathbb N}$ endowed with a computable
probability measure $\mu$, it is shown that for every bounded lower
semicomputable function $f$ on $\{0,1\}^{\mathbb N}$ and for every Martin-Löf
random $\omega \in \{0,1\}^{\mathbb N}$ the equality \[ \lim\limits_{n \to
\infty} \frac{1}{|F_n|} \sum\limits_{g \in F_n} f(g \cdot \omega) = \int\limits
f d \mu \] holds, where the averages are taken with respect to a canonically
computable tempered two-sided F{\o}lner sequence $(F_n)_{n \geq
1}$. We also prove the same identity for all lower semicomputable $f$'s in
the special case when $\Gamma$ is a computable group of polynomial growth and
$F_n:=\mathrm{B}(n)$ is the F{\o}lner sequence of balls around the neutral
element of $\Gamma$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Gender Differences in Participation and Reward on Stack Overflow | Programming is a valuable skill in the labor market, making the
underrepresentation of women in computing an increasingly important issue.
Online question and answer platforms serve a dual purpose in this field: they
form a body of knowledge useful as a reference and learning tool, and they
provide opportunities for individuals to demonstrate credible, verifiable
expertise. Issues, such as male-oriented site design or overrepresentation of
men among the site's elite may therefore compound the issue of women's
underrepresentation in IT. In this paper we audit the differences in behavior
and outcomes between men and women on Stack Overflow, the most popular of these
Q&A sites. We observe significant differences in how men and women participate
in the platform and how successful they are. For example, the average woman has
roughly half of the reputation points, the primary measure of success on the
site, of the average man. Using an Oaxaca-Blinder decomposition, an econometric
technique commonly applied to analyze differences in wages between groups, we
find that most of the gap in success between men and women can be explained by
differences in their activity on the site and differences in how these
activities are rewarded. Specifically, 1) men give more answers than women and
2) are rewarded more for their answers on average, even when controlling for
possible confounders such as tenure or buy-in to the site. Women ask more
questions and gain more reward per question. We conclude with a hypothetical
redesign of the site's scoring system based on these behavioral differences,
cutting the reputation gap in half.
| 1 | 0 | 0 | 0 | 0 | 0 |
Invariant surface area functionals and singular Yamabe problem in 3-dimensional CR geometry | We express two CR invariant surface area elements in terms of quantities in
pseudohermitian geometry. We deduce the Euler-Lagrange equations of the
associated energy functionals. Many solutions are given and discussed. In
relation to the singular CR Yamabe problem, we show that one of the energy
functionals appears as the coefficient (up to a constant multiple) of the log
term in the associated volume renormalization.
| 0 | 0 | 1 | 0 | 0 | 0 |
Dynamic dipole polarizabilities of heteronuclear alkali dimers: optical response, trapping and control of ultracold molecules | In this article we address the general approach for calculating dynamical
dipole polarizabilities of small quantum systems, based on a sum-over-states
formula involving in principle the entire energy spectrum of the system. We
complement this method by a few-parameter model involving a limited number of
effective transitions, allowing for a compact and accurate representation of
both the isotropic and anisotropic components of the polarizability. We apply
the method to the series of ten heteronuclear molecules composed of two of
($^7$Li,$^{23}$Na,$^{39}$K,$^{87}$Rb,$^{133}$Cs) alkali-metal atoms. We rely on
both up-to-date spectroscopically-determined potential energy curves for the
lowest electronic states, and on our systematic studies of these systems
performed during the last decade for higher excited states and for permanent
and transition dipole moments. Such a compilation is timely for the
continuously growing researches on ultracold polar molecules. Indeed the
knowledge of the dynamic dipole polarizabilities is crucial to model the
optical response of molecules when trapped in optical lattices, and to
determine optimal lattice frequencies ensuring optimal transfer to the absolute
ground state of initially weakly-bound molecules. When they exist, we determine
the so-called "magic frequencies" where the ac-Stark shift and thus the viewed
trap depth, is the same for both weakly-bound and ground-state molecules.
| 0 | 1 | 0 | 0 | 0 | 0 |
Minor-free graphs have light spanners | We show that every $H$-minor-free graph has a light $(1+\epsilon)$-spanner,
resolving an open problem of Grigni and Sissokho and proving a conjecture of
Grigni and Hung. Our lightness bound is
\[O\left(\frac{\sigma_H}{\epsilon^3}\log \frac{1}{\epsilon}\right)\] where
$\sigma_H = |V(H)|\sqrt{\log |V(H)|}$ is the sparsity coefficient of
$H$-minor-free graphs. That is, it has a practical dependency on the size of
the minor $H$. Our result also implies that the polynomial time approximation
scheme (PTAS) for the Travelling Salesperson Problem (TSP) in $H$-minor-free
graphs by Demaine, Hajiaghayi and Kawarabayashi is an efficient PTAS whose
running time is $2^{O_H\left(\frac{1}{\epsilon^4}\log
\frac{1}{\epsilon}\right)}n^{O(1)}$ where $O_H$ ignores dependencies on the
size of $H$. Our techniques significantly deviate from existing lines of
research on spanners for $H$-minor-free graphs, but build upon the work of
Chechik and Wulff-Nilsen for spanners of general graphs.
| 1 | 0 | 0 | 0 | 0 | 0 |
Adversarial Generation of Natural Language | Generative Adversarial Networks (GANs) have gathered a lot of attention from
the computer vision community, yielding impressive results for image
generation. Advances in the adversarial generation of natural language from
noise however are not commensurate with the progress made in generating images,
and still lag far behind likelihood based methods. In this paper, we take a
step towards generating natural language with a GAN objective alone. We
introduce a simple baseline that addresses the discrete output space problem
without relying on gradient estimators and show that it is able to achieve
state-of-the-art results on a Chinese poem generation dataset. We present
quantitative results on generating sentences from context-free and
probabilistic context-free grammars, and qualitative language modeling results.
A conditional version is also described that can generate sequences conditioned
on sentence characteristics.
| 1 | 0 | 0 | 1 | 0 | 0 |
Likely Transiting Exocomets Detected by Kepler | We present the first good evidence for exocomet transits of a host star in
continuum light in data from the Kepler mission. The Kepler star in question,
KIC 3542116, is of spectral type F2V and is quite bright at K_p = 10. The
transits have a distinct asymmetric shape with a steeper ingress and slower
egress that can be ascribed to objects with a trailing dust tail passing over
the stellar disk. There are three deeper transits with depths of ~0.1% that
last for about a day, and three that are several times more shallow and of
shorter duration. The transits were found via an exhaustive visual search of
the entire Kepler photometric data set, which we describe in some detail. We
review the methods we use to validate the Kepler data showing the comet
transits, and rule out instrumental artefacts as sources of the signals. We fit
the transits with a simple dust-tail model, and find that a transverse comet
speed of ~35-50 km/s and a minimum amount of dust present in the tail of ~10^16
g are required to explain the larger transits. For a dust replenishment time of
~10 days, and a comet lifetime of only ~300 days, this implies a total cometary
mass of > 3 x 10^17 g, or about the mass of Halley's comet. We also discuss the
number of comets and orbital geometry that would be necessary to explain the
six transits detected over the four years of Kepler prime-field observations.
Finally, we also report the discovery of a single comet-shaped transit in KIC
11084727 with very similar transit and host-star properties.
| 0 | 1 | 0 | 0 | 0 | 0 |
Origins of bond and spin order in rare-earth nickelate bulk and heterostructures | We analyze the charge- and spin response functions of rare-earth nickelates
RNiO3 and their heterostructures using random-phase approximation in a two-band
Hubbard model. The inter-orbital charge fluctuation is found to be the driving
mechanism for the rock-salt type bond order in bulk RNiO3, and good agreement
of the ordering temperature with experimental values is achieved for all RNiO3
using realistic crystal structures and interaction parameters. We further show
that magnetic ordering in bulk is not driven by the spin fluctuation and should
be instead explained as ordering of localized moments. This picture changes for
low-dimensional heterostructures, where the charge fluctuation is suppressed
and overtaken by the enhanced spin instability, which results in a
spin-density-wave ground state observed in recent experiments. Predictions for
spectroscopy allow for further experimental testing of our claims.
| 0 | 1 | 0 | 0 | 0 | 0 |
Canonical Truth | We introduce and study a notion of canonical set theoretical truth, which
means truth in a `canonical model', i.e. a transitive class model that is
uniquely characterized by some $\in$-formula. We show that this notion of truth
is `informative', i.e. there are statements that hold in all canonical models
but do not follow from ZFC, such as Reitz' ground model axiom or the
nonexistence of measurable cardinals. We also show that ZF+$V=L[\mathbb{R}]$+AD
has no canonical models. On the other hand, we show that there are canonical
models for `every real has sharp'. Moreover, we consider `theory-canonical'
statements that only fix a transitive class model of ZFC up to elementary
equivalence and show that it is consistent relative to large cardinals that
there are theory-canonical models with measurable cardinals and that
theory-canonicity is still informative in the sense explained above.
| 0 | 0 | 1 | 0 | 0 | 0 |
AACT: Application-Aware Cooperative Time Allocation for Internet of Things | As the number of Internet of Things (IoT) devices keeps increasing, data is
required to be communicated and processed by these devices at unprecedented
rates. Cooperation among wireless devices by exploiting Device-to-Device (D2D)
connections is promising, where aggregated resources in a cooperative setup can
be utilized by all devices, which would increase the total utility of the
setup. In this paper, we focus on the resource allocation problem for
cooperating IoT devices with multiple heterogeneous applications. In
particular, we develop Application-Aware Cooperative Time allocation (AACT)
framework, which optimizes the time that each application utilizes the
aggregated system resources by taking into account heterogeneous device
constraints and application requirements. AACT is grounded on the concept of
Rolling Horizon Control (RHC) where decisions are made by iteratively solving a
convex optimization problem over a moving control window of estimated system
parameters. The simulation results demonstrate significant performance gains.
| 1 | 0 | 0 | 0 | 0 | 0 |
COPA: Constrained PARAFAC2 for Sparse & Large Datasets | PARAFAC2 has demonstrated success in modeling irregular tensors, where the
tensor dimensions vary across one of the modes. An example scenario is modeling
treatments across a set of patients with the varying number of medical
encounters over time. Despite recent improvements on unconstrained PARAFAC2,
its model factors are usually dense and sensitive to noise which limits their
interpretability. As a result, the following open challenges remain: a) various
modeling constraints, such as temporal smoothness, sparsity and non-negativity,
are needed to be imposed for interpretable temporal modeling and b) a scalable
approach is required to support those constraints efficiently for large
datasets. To tackle these challenges, we propose a {\it CO}nstrained {\it
PA}RAFAC2 (COPA) method, which carefully incorporates optimization constraints
such as temporal smoothness, sparsity, and non-negativity in the resulting
factors. To efficiently support all those constraints, COPA adopts a hybrid
optimization framework using alternating optimization and alternating direction
method of multiplier (AO-ADMM). As evaluated on large electronic health record
(EHR) datasets with hundreds of thousands of patients, COPA achieves
significant speedups (up to 36 times faster) over prior PARAFAC2 approaches
that only attempt to handle a subset of the constraints that COPA enables.
Overall, our method outperforms all the baselines attempting to handle a subset
of the constraints in terms of speed, while achieving the same level of
accuracy. Through a case study on temporal phenotyping of medically complex
children, we demonstrate how the constraints imposed by COPA reveal concise
phenotypes and meaningful temporal profiles of patients. The clinical
interpretation of both the phenotypes and the temporal profiles was confirmed
by a medical expert.
| 0 | 0 | 0 | 1 | 0 | 0 |
Effect of Composition Gradient on Magnetothermal Instability Modified by Shear and Rotation | We model the intracluster medium as a weakly collisional plasma that is a
binary mixture of the hydrogen and the helium ions, along with free electrons.
When, owing to the helium sedimentation, the gradient of the mean molecular
weight (or equivalently, composition or helium ions' concentration) of the
plasma is not negligible, it can have appreciable influence on the stability
criteria of the thermal convective instabilities, e.g., the heat-flux-buoyancy
instability and the magnetothermal instability (MTI). These instabilities are
consequences of the anisotropic heat conduction occurring preferentially along
the magnetic field lines. In this paper, without ignoring the magnetic tension,
we first present the mathematical criterion for the onset of composition
gradient modified MTI. Subsequently, we relax the commonly adopted equilibrium
state in which the plasma is at rest, and assume that the plasma is in a
sheared state which may be due to differential rotation. We discuss how the
concentration gradient affects the coupling between the Kelvin--Helmholtz
instability and the MTI in rendering the plasma unstable or stable. We derive
exact stability criterion by working with the sharp boundary case in which the
physical variables---temperature, mean molecular weight, density, and magnetic
field---change discontinuously from one constant value to another on crossing
the boundary. Finally, we perform the linear stability analysis for the case of
the differentially rotating plasma that is thermally and compositionally
stratified as well. By assuming axisymmetric perturbations, we find the
corresponding dispersion relation and the explicit mathematical expression
determining the onset of the modified MTI.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Verified Algorithm Enumerating Event Structures | An event structure is a mathematical abstraction modeling concepts as
causality, conflict and concurrency between events. While many other
mathematical structures, including groups, topological spaces, rings, abound
with algorithms and formulas to generate, enumerate and count particular sets
of their members, no algorithm or formulas are known to generate or count all
the possible event structures over a finite set of events. We present an
algorithm to generate such a family, along with a functional implementation
verified using Isabelle/HOL. As byproducts, we obtain a verified enumeration of
all possible preorders and partial orders. While the integer sequences counting
preorders and partial orders are already listed on OEIS (On-line Encyclopedia
of Integer Sequences), the one counting event structures is not. We therefore
used our algorithm to submit a formally verified addition, which has been
successfully reviewed and is now part of the OEIS.
| 1 | 0 | 0 | 0 | 0 | 0 |
Missing Data as Part of the Social Behavior in Real-World Financial Complex Systems | Many real-world networks are known to exhibit facts that counter our
knowledge prescribed by the theories on network creation and communication
patterns. A common prerequisite in network analysis is that information on
nodes and links will be complete because network topologies are extremely
sensitive to missing information of this kind. Therefore, many real-world
networks that fail to meet this criterion under random sampling may be
discarded.
In this paper we offer a framework for interpreting the missing observations
in network data under the hypothesis that these observations are not missing at
random. We demonstrate the methodology with a case study of a financial trade
network, where the awareness of agents to the data collection procedure by a
self-interested observer may result in strategic revealing or withholding of
information. The non-random missingness has been overlooked despite the
possibility of this being an important feature of the processes by which the
network is generated. The analysis demonstrates that strategic information
withholding may be a valid general phenomenon in complex systems. The evidence
is sufficient to support the existence of an influential observer and to offer
a compelling dynamic mechanism for the creation of the network.
| 0 | 0 | 0 | 1 | 0 | 0 |
Quantum Monte Carlo simulation of a two-dimensional Majorana lattice model | We study interacting Majorana fermions in two dimensions as a low-energy
effective model of a vortex lattice in two-dimensional time-reversal-invariant
topological superconductors. For that purpose, we implement ab-initio quantum
Monte Carlo simulation to the Majorana fermion system in which the
path-integral measure is given by a semi-positive Pfaffian. We discuss
spontaneous breaking of time-reversal symmetry at finite temperature.
| 0 | 1 | 0 | 0 | 0 | 0 |
Geometric Rescaling Algorithms for Submodular Function Minimization | We present a new class of polynomial-time algorithms for submodular function
minimization (SFM), as well as a unified framework to obtain strongly
polynomial SFM algorithms. Our new algorithms are based on simple iterative
methods for the minimum-norm problem, such as the conditional gradient and the
Fujishige-Wolfe algorithms. We exhibit two techniques to turn simple iterative
methods into polynomial-time algorithms.
Firstly, we use the geometric rescaling technique, which has recently gained
attention in linear programming. We adapt this technique to SFM and obtain a
weakly polynomial bound $O((n^4\cdot EO + n^5)\log (n L))$.
Secondly, we exhibit a general combinatorial black-box approach to turn any
strongly polynomial $\varepsilon L$-approximate SFM oracle into a strongly
polynomial exact SFM algorithm. This framework can be applied to a wide range
of combinatorial and continuous algorithms, including pseudo-polynomial ones.
In particular, we can obtain strongly polynomial algorithms by a repeated
application of the conditional gradient or of the Fujishige-Wolfe algorithm.
Combined with the geometric rescaling technique, the black-box approach
provides a $O((n^5\cdot EO + n^6)\log^2 n)$ algorithm. Finally, we show that
one of the techniques we develop in the paper can also be combined with the
cutting-plane method of Lee, Sidford, and Wong, yielding a simplified variant
of their $O(n^3 \log^2 n \cdot EO + n^4\log^{O(1)} n)$ algorithm.
| 1 | 0 | 1 | 0 | 0 | 0 |
Statistical PT-symmetric lasing in an optical fiber network | PT-symmetry in optics is a condition whereby the real and imaginary parts of
the refractive index across a photonic structure are deliberately balanced.
This balance can lead to a host of novel optical phenomena, such as
unidirectional invisibility, loss-induced lasing, single-mode lasing from
multimode resonators, and non-reciprocal effects in conjunction with
nonlinearities. Because PT-symmetry has been thought of as fragile,
experimental realizations to date have been usually restricted to on-chip
micro-devices. Here, we demonstrate that certain features of PT-symmetry are
sufficiently robust to survive the statistical fluctuations associated with a
macroscopic optical cavity. We construct optical-fiber-based coupled-cavities
in excess of a kilometer in length (the free spectral range is less than 0.8
fm) with balanced gain and loss in two sub-cavities and examine the lasing
dynamics. In such a macroscopic system, fluctuations can lead to a
cavity-detuning exceeding the free spectral range. Nevertheless, by varying the
gain-loss contrast, we observe that both the lasing threshold and the growth of
the laser power follow the predicted behavior of a stable PT-symmetric
structure. Furthermore, a statistical symmetry-breaking point is observed upon
varying the cavity loss. These findings indicate that PT-symmetry is a more
robust optical phenomenon than previously expected, and points to potential
applications in optical fiber networks and fiber lasers.
| 0 | 1 | 0 | 0 | 0 | 0 |
Transforming Single Domain Magnetic CoFe2O4 Nanoparticles from Hydrophobic to Hydrophilic By Novel Mechanochemical Ligand Exchange | Single phase, uniform size (~9 nm) Cobalt Ferrite (CFO) nanoparticles have
been synthesized by hydrothermal synthesis using oleic acid as a surfactant.
The as synthesized oleic acid coated CFO (OA-CFO) nanoparticles were well
dispersible in nonpolar solvents but not dispersible in water. The OA-CFO
nanoparticles have been successfully transformed to highly water dispersible
citric acid coated CFO (CA-CFO) nanoparticles using a novel single step ligand
exchange process by mechanochemical milling, in which small chain citric acid
molecules replace the original large chain oleic acid molecules available on
CFO nanoparticles. The contact angle measurement shows that OA-CFO
nanoparticles are hydrophobic whereas CA-CFO nanoparticles are superhydrophilic
in nature. The potentiality of as synthesized OA-CFO and mechanochemically
transformed CA-CFO nanoparticles for the demulsification of highly stabilized
water-in-oil and oil-in-water emulsions has been demonstrated.
| 0 | 1 | 0 | 0 | 0 | 0 |
Probing the dusty stellar populations of the Local Volume Galaxies with JWST/MIRI | The Mid-Infrared Instrument (MIRI) for the {\em James Webb Space Telescope}
(JWST) will revolutionize our understanding of infrared stellar populations in
the Local Volume. Using the rich {\em Spitzer}-IRS spectroscopic data-set and
spectral classifications from the Surveying the Agents of Galaxy Evolution
(SAGE)-Spectroscopic survey of over a thousand objects in the Magellanic
Clouds, the Grid of Red supergiant and Asymptotic giant branch star ModelS
({\sc grams}), and the grid of YSO models by Robitaille et al. (2006), we
calculate the expected flux-densities and colors in the MIRI broadband filters
for prominent infrared stellar populations. We use these fluxes to explore the
{\em JWST}/MIRI colours and magnitudes for composite stellar population studies
of Local Volume galaxies. MIRI colour classification schemes are presented;
these diagrams provide a powerful means of identifying young stellar objects,
evolved stars and extragalactic background galaxies in Local Volume galaxies
with a high degree of confidence. Finally, we examine which filter combinations
are best for selecting populations of sources based on their JWST colours.
| 0 | 1 | 0 | 0 | 0 | 0 |
PythonRobotics: a Python code collection of robotics algorithms | This paper describes an Open Source Software (OSS) project: PythonRobotics.
This is a collection of robotics algorithms implemented in the Python
programming language. The focus of the project is on autonomous navigation, and
the goal is for beginners in robotics to understand the basic ideas behind each
algorithm. In this project, the algorithms which are practical and widely used
in both academia and industry are selected. Each sample code is written in
Python3 and only depends on some standard modules for readability and ease of
use. It includes intuitive animations to understand the behavior of the
simulation.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Liouville theorem for the Euler equations in the plane | This paper is concerned with qualitative properties of bounded steady flows
of an ideal incompressible fluid with no stagnation point in the
two-dimensional plane R^2. We show that any such flow is a shear flow, that is,
it is parallel to some constant vector. The proof of this Liouville-type result
is firstly based on the study of the geometric properties of the level curves
of the stream function and secondly on the derivation of some estimates on the
at most logarithmic growth of the argument of the flow. These estimates lead to
the conclusion that the streamlines of the flow are all parallel lines.
| 0 | 0 | 1 | 0 | 0 | 0 |
First On-Site True Gamma-Ray Imaging-Spectroscopy of Contamination near Fukushima Plant | We have developed an Electron Tracking Compton Camera (ETCC), which provides
a well-defined Point Spread Function (PSF) by reconstructing a direction of
each gamma as a point and realizes simultaneous measurement of brightness and
spectrum of MeV gamma-rays for the first time. Here, we present the results of
our on-site pilot gamma-imaging-spectroscopy with ETCC at three contaminated
locations in the vicinity of the Fukushima Daiichi Nuclear Power Plants in
Japan in 2014. The obtained distribution of brightness (or emissivity) with
remote-sensing observations is unambiguously converted into the dose
distribution. We confirm that the dose distribution is consistent with the one
taken by conventional mapping measurements with a dosimeter physically placed
at each grid point. Furthermore, its imaging spectroscopy, boosted by
Compton-edge-free spectra, reveals complex radioactive features in a
quantitative manner around each individual target point in the
background-dominated environment. Notably, we successfully identify a "micro
hot spot" of residual caesium contamination even in an already decontaminated
area. These results show that the ETCC performs exactly as the geometrical
optics predicts, demonstrates its versatility in the field radiation
measurement, and reveals potentials for application in many fields, including
the nuclear industry, medical field, and astronomy.
| 0 | 1 | 0 | 0 | 0 | 0 |
Weak Versus Strong Disorder Superfluid-Bose Glass Transition in One Dimension | Using large-scale simulations based on matrix product state and quantum Monte
Carlo techniques, we study the superfluid to Bose glass-transition for
one-dimensional attractive hard-core bosons at zero temperature, across the
full regime from weak to strong disorder. As a function of interaction and
disorder strength, we identify a Berezinskii-Kosterlitz-Thouless critical line
with two different regimes. At small attraction where critical disorder is weak
compared to the bandwidth, the critical Luttinger parameter $K_c$ takes its
universal Giamarchi-Schulz value $K_{c}=3/2$. Conversely, a non-universal
$K_c>3/2$ emerges for stronger attraction where weak-link physics is relevant.
In this strong disorder regime, the transition is characterized by self-similar
power-law distributed weak links with a continuously varying characteristic
exponent $\alpha$.
| 0 | 1 | 0 | 0 | 0 | 0 |
Quantum Structures in Human Decision-making: Towards Quantum Expected Utility | {\it Ellsberg thought experiments} and empirical confirmation of Ellsberg
preferences pose serious challenges to {\it subjective expected utility theory}
(SEUT). We have recently elaborated a quantum-theoretic framework for human
decisions under uncertainty which satisfactorily copes with the Ellsberg
paradox and other puzzles of SEUT. We apply here the quantum-theoretic
framework to the {\it Ellsberg two-urn example}, showing that the paradox can
be explained by assuming a state change of the conceptual entity that is the
object of the decision ({\it decision-making}, or {\it DM}, {\it entity}) and
representing subjective probabilities by quantum probabilities. We also model
the empirical data we collected in a DM test on human participants within the
theoretic framework above. The obtained results are relevant, as they provide a
line to model real life, e.g., financial and medical, decisions that show the
same empirical patterns as the two-urn experiment.
| 0 | 0 | 0 | 0 | 1 | 1 |
Fine-grained Event Learning of Human-Object Interaction with LSTM-CRF | Event learning is one of the most important problems in AI. However,
notwithstanding significant research efforts, it is still a very complex task,
especially when the events involve the interaction of humans or agents with
other objects, as it requires modeling human kinematics and object movements.
This study proposes a methodology for learning complex human-object interaction
(HOI) events, involving the recording, annotation and classification of event
interactions. For annotation, we allow multiple interpretations of a motion
capture by slicing over its temporal span, for classification, we use
Long-Short Term Memory (LSTM) sequential models with Conditional Randon Field
(CRF) for constraints of outputs. Using a setup involving captures of
human-object interaction as three dimensional inputs, we argue that this
approach could be used for event types involving complex spatio-temporal
dynamics.
| 1 | 0 | 0 | 0 | 0 | 0 |
Book Review Interferometry and Synthesis in Radio Astronomy - 3rd Ed | Review of the third edition of "Interferometry and Synthesis in Radio
Astronomy" by Thompson, Moran and Swenson
| 0 | 1 | 0 | 0 | 0 | 0 |
A Kernel Theory of Modern Data Augmentation | Data augmentation, a technique in which a training set is expanded with
class-preserving transformations, is ubiquitous in modern machine learning
pipelines. In this paper, we seek to establish a theoretical framework for
understanding modern data augmentation techniques. We start by showing that for
kernel classifiers, data augmentation can be approximated by first-order
feature averaging and second-order variance regularization components. We
connect this general approximation framework to prior work in invariant
kernels, tangent propagation, and robust optimization. Next, we explicitly
tackle the compositional aspect of modern data augmentation techniques,
proposing a novel model of data augmentation as a Markov process. Under this
model, we show that performing $k$-nearest neighbors with data augmentation is
asymptotically equivalent to a kernel classifier. Finally, we illustrate ways
in which our theoretical framework can be leveraged to accelerate machine
learning workflows in practice, including reducing the amount of computation
needed to train on augmented data, and predicting the utility of a
transformation prior to training.
| 0 | 0 | 0 | 1 | 0 | 0 |
Carrier driven coupling in ferromagnetic oxide heterostructures | Transition metal oxides are well known for their complex magnetic and
electrical properties. When brought together in heterostructure geometries,
they show particular promise for spintronics and colossal magnetoresistance
applications. In this letter, we propose a new mechanism for the coupling
between layers of itinerant ferromagnetic materials in heterostructures. The
coupling is mediated by charge carriers that strive to maximally delocalize
through the heterostructure to gain kinetic energy. In doing so, they force a
ferromagnetic or antiferromagnetic coupling between the constituent layers. To
illustrate this, we focus on heterostructures composed of SrRuO$_3$ and
La$_{1-x}$A$_{x}$MnO$_3$ (A=Ca/Sr). Our mechanism is consistent with
antiferromagnetic alignment that is known to occur in multilayers of
SrRuO$_3$-La$_{1-x}$A$_{x}$MnO$_3$. To support our assertion, we present a
minimal Kondo-lattice model which reproduces the known magnetization properties
of such multilayers. In addition, we discuss a quantum well model for
heterostructures and argue that the spin-dependent density of states determines
the nature of the coupling. As a smoking gun signature, we propose that
bilayers with the same constituents will oscillate between ferromagnetic and
antiferromagnetic coupling upon tuning the relative thicknesses of the layers.
| 0 | 1 | 0 | 0 | 0 | 0 |
Data Dropout in Arbitrary Basis for Deep Network Regularization | An important problem in training deep networks with high capacity is to
ensure that the trained network works well when presented with new inputs
outside the training dataset. Dropout is an effective regularization technique
to boost the network generalization in which a random subset of the elements of
the given data and the extracted features are set to zero during the training
process. In this paper, a new randomized regularization technique in which we
withhold a random part of the data without necessarily turning off the
neurons/data-elements is proposed. In the proposed method, of which the
conventional dropout is shown to be a special case, random data dropout is
performed in an arbitrary basis, hence the designation Generalized Dropout. We
also present a framework whereby the proposed technique can be applied
efficiently to convolutional neural networks. The presented numerical
experiments demonstrate that the proposed technique yields notable performance
gain. Generalized Dropout provides new insight into the idea of dropout, shows
that we can achieve different performance gains by using different bases
matrices, and opens up a new research question as of how to choose optimal
bases matrices that achieve maximal performance gain.
| 1 | 0 | 0 | 1 | 0 | 0 |
Efficient algorithms to discover alterations with complementary functional association in cancer | Recent large cancer studies have measured somatic alterations in an
unprecedented number of tumours. These large datasets allow the identification
of cancer-related sets of genetic alterations by identifying relevant
combinatorial patterns. Among such patterns, mutual exclusivity has been
employed by several recent methods that have shown its effectivenes in
characterizing gene sets associated to cancer. Mutual exclusivity arises
because of the complementarity, at the functional level, of alterations in
genes which are part of a group (e.g., a pathway) performing a given function.
The availability of quantitative target profiles, from genetic perturbations or
from clinical phenotypes, provides additional information that can be leveraged
to improve the identification of cancer related gene sets by discovering groups
with complementary functional associations with such targets.
In this work we study the problem of finding groups of mutually exclusive
alterations associated with a quantitative (functional) target. We propose a
combinatorial formulation for the problem, and prove that the associated
computation problem is computationally hard. We design two algorithms to solve
the problem and implement them in our tool UNCOVER. We provide analytic
evidence of the effectiveness of UNCOVER in finding high-quality solutions and
show experimentally that UNCOVER finds sets of alterations significantly
associated with functional targets in a variety of scenarios. In addition, our
algorithms are much faster than the state-of-the-art, allowing the analysis of
large datasets of thousands of target profiles from cancer cell lines. We show
that on one such dataset from project Achilles our methods identify several
significant gene sets with complementary functional associations with targets.
| 0 | 0 | 0 | 0 | 1 | 0 |
Laplace operators on holomorphic Lie algebroids | The paper introduces Laplace-type operators for functions defined on the
tangent space of a Finsler Lie algebroid, using a volume form on the
prolongation of the algebroid. It also presents the construction of a
horizontal Laplace operator for forms defined on the prolongation of the
algebroid. All of the Laplace operators considered in the paper are also
locally expressed using the Chern-Finsler connection of the algebroid.
| 0 | 0 | 1 | 0 | 0 | 0 |
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