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Analysis of nonsmooth stochastic approximation: the differential inclusion approach | In this paper we address the convergence of stochastic approximation when the
functions to be minimized are not convex and nonsmooth. We show that the
"mean-limit" approach to the convergence which leads, for smooth problems, to
the ODE approach can be adapted to the non-smooth case. The limiting dynamical
system may be shown to be, under appropriate assumption, a differential
inclusion. Our results expand earlier works in this direction by Benaim et al.
(2005) and provide a general framework for proving convergence for
unconstrained and constrained stochastic approximation problems, with either
explicit or implicit updates. In particular, our results allow us to establish
the convergence of stochastic subgradient and proximal stochastic gradient
descent algorithms arising in a large class of deep learning and
high-dimensional statistical inference with sparsity inducing penalties.
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Clicks and Cliques. Exploring the Soul of the Community | In the paper we analyze 26 communities across the United States with the
objective to understand what attaches people to their community and how this
attachment differs among communities. How different are attached people from
unattached? What attaches people to their community? How different are the
communities? What are key drivers behind emotional attachment? To address these
questions, graphical, supervised and unsupervised learning tools were used and
information from the Census Bureau and the Knight Foundation were combined.
Using the same pre-processed variables as Knight (2010) most likely will drive
the results towards the same conclusions than the Knight foundation, so this
paper does not use those variables.
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Quickest Change Detection under Transient Dynamics: Theory and Asymptotic Analysis | The problem of quickest change detection (QCD) under transient dynamics is
studied, where the change from the initial distribution to the final persistent
distribution does not happen instantaneously, but after a series of transient
phases. The observations within the different phases are generated by different
distributions. The objective is to detect the change as quickly as possible,
while controlling the average run length (ARL) to false alarm, when the
durations of the transient phases are completely unknown. Two algorithms are
considered, the dynamic Cumulative Sum (CuSum) algorithm, proposed in earlier
work, and a newly constructed weighted dynamic CuSum algorithm. Both algorithms
admit recursions that facilitate their practical implementation, and they are
adaptive to the unknown transient durations. Specifically, their asymptotic
optimality is established with respect to both Lorden's and Pollak's criteria
as the ARL to false alarm and the durations of the transient phases go to
infinity at any relative rate. Numerical results are provided to demonstrate
the adaptivity of the proposed algorithms, and to validate the theoretical
results.
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The Block Point Process Model for Continuous-Time Event-Based Dynamic Networks | Many application settings involve the analysis of timestamped relations or
events between a set of entities, e.g. messages between users of an on-line
social network. Static and discrete-time network models are typically used as
analysis tools in these settings; however, they discard a significant amount of
information by aggregating events over time to form network snapshots. In this
paper, we introduce a block point process model (BPPM) for dynamic networks
evolving in continuous time in the form of events at irregular time intervals.
The BPPM is inspired by the well-known stochastic block model (SBM) for static
networks and is a simpler version of the recently-proposed Hawkes infinite
relational model (IRM). We show that networks generated by the BPPM follow an
SBM in the limit of a growing number of nodes and leverage this property to
develop an efficient inference procedure for the BPPM. We fit the BPPM to
several real network data sets, including a Facebook network with over 3, 500
nodes and 130, 000 events, several orders of magnitude larger than the Hawkes
IRM and other existing point process network models.
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Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks | This work demonstrates the potential of deep reinforcement learning
techniques for transmit power control in emerging and future wireless networks.
Various techniques have been proposed in the literature to find near-optimal
power allocations, often by solving a challenging optimization problem. Most of
these algorithms are not scalable to large networks in real-world scenarios
because of their computational complexity and instantaneous cross-cell channel
state information (CSI) requirement. In this paper, a model-free distributively
executed dynamic power allocation scheme is developed based on deep
reinforcement learning. Each transmitter collects CSI and quality of service
(QoS) information from several neighbors and adapts its own transmit power
accordingly. The objective is to maximize a weighted sum-rate utility function,
which can be particularized to achieve maximum sum-rate or proportionally fair
scheduling (with weights that are changing over time). Both random variations
and delays in the CSI are inherently addressed using deep Q-learning. For a
typical network architecture, the proposed algorithm is shown to achieve
near-optimal power allocation in real time based on delayed CSI measurements
available to the agents. This work indicates that deep reinforcement learning
based radio resource management can be very fast and deliver highly competitive
performance, especially in practical scenarios where the system model is
inaccurate and CSI delay is non-negligible.
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Investor Reaction to Financial Disclosures Across Topics: An Application of Latent Dirichlet Allocation | This paper provides a holistic study of how stock prices vary in their
response to financial disclosures across different topics. Thereby, we
specifically shed light into the extensive amount of filings for which no a
priori categorization of their content exists. For this purpose, we utilize an
approach from data mining - namely, latent Dirichlet allocation - as a means of
topic modeling. This technique facilitates our task of automatically
categorizing, ex ante, the content of more than 70,000 regulatory 8-K filings
from U.S. companies. We then evaluate the subsequent stock market reaction. Our
empirical evidence suggests a considerable discrepancy among various types of
news stories in terms of their relevance and impact on financial markets. For
instance, we find a statistically significant abnormal return in response to
earnings results and credit rating, but also for disclosures regarding business
strategy, the health sector, as well as mergers and acquisitions. Our results
yield findings that benefit managers, investors and policy-makers by indicating
how regulatory filings should be structured and the topics most likely to
precede changes in stock valuations.
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Truncated Variational EM for Semi-Supervised Neural Simpletrons | Inference and learning for probabilistic generative networks is often very
challenging and typically prevents scalability to as large networks as used for
deep discriminative approaches. To obtain efficiently trainable, large-scale
and well performing generative networks for semi-supervised learning, we here
combine two recent developments: a neural network reformulation of hierarchical
Poisson mixtures (Neural Simpletrons), and a novel truncated variational EM
approach (TV-EM). TV-EM provides theoretical guarantees for learning in
generative networks, and its application to Neural Simpletrons results in
particularly compact, yet approximately optimal, modifications of learning
equations. If applied to standard benchmarks, we empirically find, that
learning converges in fewer EM iterations, that the complexity per EM iteration
is reduced, and that final likelihood values are higher on average. For the
task of classification on data sets with few labels, learning improvements
result in consistently lower error rates if compared to applications without
truncation. Experiments on the MNIST data set herein allow for comparison to
standard and state-of-the-art models in the semi-supervised setting. Further
experiments on the NIST SD19 data set show the scalability of the approach when
a manifold of additional unlabeled data is available.
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The nature and origin of heavy tails in retweet activity | Modern social media platforms facilitate the rapid spread of information
online. Modelling phenomena such as social contagion and information diffusion
are contingent upon a detailed understanding of the information-sharing
processes. In Twitter, an important aspect of this occurs with retweets, where
users rebroadcast the tweets of other users. To improve our understanding of
how these distributions arise, we analyse the distribution of retweet times. We
show that a power law with exponential cutoff provides a better fit than the
power laws previously suggested. We explain this fit through the burstiness of
human behaviour and the priorities individuals place on different tasks.
| 1 | 1 | 0 | 1 | 0 | 0 |
Opinion Polarization by Learning from Social Feedback | We explore a new mechanism to explain polarization phenomena in opinion
dynamics in which agents evaluate alternative views on the basis of the social
feedback obtained on expressing them. High support of the favored opinion in
the social environment, is treated as a positive feedback which reinforces the
value associated to this opinion. In connected networks of sufficiently high
modularity, different groups of agents can form strong convictions of competing
opinions. Linking the social feedback process to standard equilibrium concepts
we analytically characterize sufficient conditions for the stability of
bi-polarization. While previous models have emphasized the polarization effects
of deliberative argument-based communication, our model highlights an affective
experience-based route to polarization, without assumptions about negative
influence or bounded confidence.
| 1 | 1 | 0 | 0 | 0 | 0 |
Estimating Quality in Multi-Objective Bandits Optimization | Many real-world applications are characterized by a number of conflicting
performance measures. As optimizing in a multi-objective setting leads to a set
of non-dominated solutions, a preference function is required for selecting the
solution with the appropriate trade-off between the objectives. The question
is: how good do estimations of these objectives have to be in order for the
solution maximizing the preference function to remain unchanged? In this paper,
we introduce the concept of preference radius to characterize the robustness of
the preference function and provide guidelines for controlling the quality of
estimations in the multi-objective setting. More specifically, we provide a
general formulation of multi-objective optimization under the bandits setting.
We show how the preference radius relates to the optimal gap and we use this
concept to provide a theoretical analysis of the Thompson sampling algorithm
from multivariate normal priors. We finally present experiments to support the
theoretical results and highlight the fact that one cannot simply scalarize
multi-objective problems into single-objective problems.
| 1 | 0 | 0 | 1 | 0 | 0 |
Adaptive IGAFEM with optimal convergence rates: Hierarchical B-splines | We consider an adaptive algorithm for finite element methods for the
isogeometric analysis (IGAFEM) of elliptic (possibly non-symmetric)
second-order partial differential equations in arbitrary space dimension
$d\ge2$. We employ hierarchical B-splines of arbitrary degree and different
order of smoothness. We propose a refinement strategy to generate a sequence of
locally refined meshes and corresponding discrete solutions. Adaptivity is
driven by some weighted residual a posteriori error estimator. We prove linear
convergence of the error estimator (resp. the sum of energy error plus data
oscillations) with optimal algebraic rates. Numerical experiments underpin the
theoretical findings.
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Spin dynamics of quadrupole nuclei in InGaAs quantum dots | Photoluminescence polarization is experimentally studied for samples with
(In,Ga)As/GaAs selfassembled quantum dots in transverse magnetic field (Hanle
effect) under slow modulation of the excitation light polarization from
fractions of Hz to tens of kHz. The polarization reflects the evolution of
strongly coupled electron-nuclear spin system in the quantum dots. Strong
modification of the Hanle curves under variation of the modulation period is
attributed to the peculiarities of the spin dynamics of quadrupole nuclei,
which states are split due to deformation of the crystal lattice in the quantum
dots. Analysis of the Hanle curves is fulfilled in the framework of a
phenomenological model considering a separate dynamics of a nuclear field BNd
determined by the +/- 12 nuclear spin states and of a nuclear field BNq
determined by the split-off states +/- 3/2, +/- 5/2, etc. It is found that the
characteristic relaxation time for the nuclear field BNd is of order of 0.5 s,
while the relaxation of the field BNq is faster by three orders of magnitude.
| 0 | 1 | 0 | 0 | 0 | 0 |
Calibration for Weak Variance-Alpha-Gamma Processes | The weak variance-alpha-gamma process is a multivariate Lévy process
constructed by weakly subordinating Brownian motion, possibly with correlated
components with an alpha-gamma subordinator. It generalises the
variance-alpha-gamma process of Semeraro constructed by traditional
subordination. We compare three calibration methods for the weak
variance-alpha-gamma process, method of moments, maximum likelihood estimation
(MLE) and digital moment estimation (DME). We derive a condition for Fourier
invertibility needed to apply MLE and show in our simulations that MLE produces
a better fit when this condition holds, while DME produces a better fit when it
is violated. We also find that the weak variance-alpha-gamma process exhibits a
wider range of dependence and produces a significantly better fit than the
variance-alpha-gamma process on an S&P500-FTSE100 data set, and that DME
produces the best fit in this situation.
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Generalized Coordinated Transaction Scheduling: A Market Approach to Seamless Interfaces | A generalization of the coordinated transaction scheduling (CTS)---the
state-of-the-art interchange scheduling---is proposed. Referred to as
generalized coordinated transaction scheduling (GCTS), the proposed approach
addresses major seams issues of CTS: the ad hoc use of proxy buses, the
presence of loop flow as a result of proxy bus approximation, and difficulties
in dealing with multiple interfaces. By allowing market participants to submit
bids across market boundaries, GCTS also generalizes the joint economic
dispatch that achieves seamless interchange without market participants. It is
shown that GCTS asymptotically achieves seamless interface under certain
conditions. GCTS is also shown to be revenue adequate in that each regional
market has a non-negative net revenue that is equal to its congestion rent.
Numerical examples are presented to illustrate the quantitative improvement of
the proposed approach.
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Boundaries as an Enhancement Technique for Physical Layer Security | In this paper, we study the receiver performance with physical layer security
in a Poisson field of interferers. We compare the performance in two deployment
scenarios: (i) the receiver is located at the corner of a quadrant, (ii) the
receiver is located in the infinite plane. When the channel state information
(CSI) of the eavesdropper is not available at the transmitter, we calculate the
probability of secure connectivity using the Wyner coding scheme, and we show
that hiding the receiver at the corner is beneficial at high rates of the
transmitted codewords and detrimental at low transmission rates. When the CSI
is available, we show that the average secrecy capacity is higher when the
receiver is located at the corner, even if the intensity of interferers in this
case is four times higher than the intensity of interferers in the bulk.
Therefore boundaries can also be used as a secrecy enhancement technique for
high data rate applications.
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Magnetically induced Ferroelectricity in Bi$_2$CuO$_4$ | The tetragonal copper oxide Bi$_2$CuO$_4$ has an unusual crystal structure
with a three-dimensional network of well separated CuO$_4$ plaquettes. This
material was recently predicted to host electronic excitations with an
unconventional spectrum and the spin structure of its magnetically ordered
state appearing at T$_N$ $\sim$43 K remains controversial. Here we present the
results of detailed studies of specific heat, magnetic and dielectric
properties of Bi$_2$CuO$_4$ single crystals grown by the floating zone
technique, combined with the polarized neutron scattering and high-resolution
X-ray measurements. Our polarized neutron scattering data show Cu spins are
parallel to the $ab$ plane. Below the onset of the long range antiferromagnetic
ordering we observe an electric polarization induced by an applied magnetic
field, which indicates inversion symmetry breaking by the ordered state of Cu
spins. For the magnetic field applied perpendicular to the tetragonal axis, the
spin-induced ferroelectricity is explained in terms of the linear
magnetoelectric effect that occurs in a metastable magnetic state. A relatively
small electric polarization induced by the field parallel to the tetragonal
axis may indicate a more complex magnetic ordering in Bi$_2$CuO$_4$.
| 0 | 1 | 0 | 0 | 0 | 0 |
Probabilistic Matrix Factorization for Automated Machine Learning | In order to achieve state-of-the-art performance, modern machine learning
techniques require careful data pre-processing and hyperparameter tuning.
Moreover, given the ever increasing number of machine learning models being
developed, model selection is becoming increasingly important. Automating the
selection and tuning of machine learning pipelines consisting of data
pre-processing methods and machine learning models, has long been one of the
goals of the machine learning community. In this paper, we tackle this
meta-learning task by combining ideas from collaborative filtering and Bayesian
optimization. Using probabilistic matrix factorization techniques and
acquisition functions from Bayesian optimization, we exploit experiments
performed in hundreds of different datasets to guide the exploration of the
space of possible pipelines. In our experiments, we show that our approach
quickly identifies high-performing pipelines across a wide range of datasets,
significantly outperforming the current state-of-the-art.
| 0 | 0 | 0 | 1 | 0 | 0 |
Stochastic Multi-objective Optimization on a Budget: Application to multi-pass wire drawing with quantified uncertainties | Design optimization of engineering systems with multiple competing objectives
is a painstakingly tedious process especially when the objective functions are
expensive-to-evaluate computer codes with parametric uncertainties. The
effectiveness of the state-of-the-art techniques is greatly diminished because
they require a large number of objective evaluations, which makes them
impractical for problems of the above kind. Bayesian global optimization (BGO),
has managed to deal with these challenges in solving single-objective
optimization problems and has recently been extended to multi-objective
optimization (MOO). BGO models the objectives via probabilistic surrogates and
uses the epistemic uncertainty to define an information acquisition function
(IAF) that quantifies the merit of evaluating the objective at new designs.
This iterative data acquisition process continues until a stopping criterion is
met. The most commonly used IAF for MOO is the expected improvement over the
dominated hypervolume (EIHV) which in its original form is unable to deal with
parametric uncertainties or measurement noise. In this work, we provide a
systematic reformulation of EIHV to deal with stochastic MOO problems. The
primary contribution of this paper lies in being able to filter out the noise
and reformulate the EIHV without having to observe or estimate the stochastic
parameters. An addendum of the probabilistic nature of our methodology is that
it enables us to characterize our confidence about the predicted Pareto front.
We verify and validate the proposed methodology by applying it to synthetic
test problems with known solutions. We demonstrate our approach on an
industrial problem of die pass design for a steel wire drawing process.
| 0 | 0 | 1 | 0 | 0 | 0 |
Generation High resolution 3D model from natural language by Generative Adversarial Network | We present a method of generating high resolution 3D shapes from natural
language descriptions. To achieve this goal, we propose two steps that
generating low resolution shapes which roughly reflect texts and generating
high resolution shapes which reflect the detail of texts. In a previous paper,
the authors have shown a method of generating low resolution shapes. We improve
it to generate 3D shapes more faithful to natural language and test the
effectiveness of the method. To generate high resolution 3D shapes, we use the
framework of Conditional Wasserstein GAN. We propose two roles of Critic
separately, which calculate the Wasserstein distance between two probability
distribution, so that we achieve generating high quality shapes or acceleration
of learning speed of model. To evaluate our approach, we performed quantitive
evaluation with several numerical metrics for Critic models. Our method is
first to realize the generation of high quality model by propagating text
embedding information to high resolution task when generating 3D model.
| 1 | 0 | 0 | 1 | 0 | 0 |
A survey on policy search algorithms for learning robot controllers in a handful of trials | Most policy search algorithms require thousands of training episodes to find
an effective policy, which is often infeasible with a physical robot. This
survey article focuses on the extreme other end of the spectrum: how can a
robot adapt with only a handful of trials (a dozen) and a few minutes? By
analogy with the word "big-data", we refer to this challenge as "micro-data
reinforcement learning". We show that a first strategy is to leverage prior
knowledge on the policy structure (e.g., dynamic movement primitives), on the
policy parameters (e.g., demonstrations), or on the dynamics (e.g.,
simulators). A second strategy is to create data-driven surrogate models of the
expected reward (e.g., Bayesian optimization) or the dynamical model (e.g.,
model-based policy search), so that the policy optimizer queries the model
instead of the real system. Overall, all successful micro-data algorithms
combine these two strategies by varying the kind of model and prior knowledge.
The current scientific challenges essentially revolve around scaling up to
complex robots (e.g., humanoids), designing generic priors, and optimizing the
computing time.
| 1 | 0 | 0 | 1 | 0 | 0 |
Non-linear Cyclic Codes that Attain the Gilbert-Varshamov Bound | We prove that there exist non-linear binary cyclic codes that attain the
Gilbert-Varshamov bound.
| 1 | 0 | 1 | 0 | 0 | 0 |
Consistency of Lipschitz learning with infinite unlabeled data and finite labeled data | We study the consistency of Lipschitz learning on graphs in the limit of
infinite unlabeled data and finite labeled data. Previous work has conjectured
that Lipschitz learning is well-posed in this limit, but is insensitive to the
distribution of the unlabeled data, which is undesirable for semi-supervised
learning. We first prove that this conjecture is true in the special case of a
random geometric graph model with kernel-based weights. Then we go on to show
that on a random geometric graph with self-tuning weights, Lipschitz learning
is in fact highly sensitive to the distribution of the unlabeled data, and we
show how the degree of sensitivity can be adjusted by tuning the weights. In
both cases, our results follow from showing that the sequence of learned
functions converges to the viscosity solution of an $\infty$-Laplace type
equation, and studying the structure of the limiting equation.
| 1 | 0 | 0 | 0 | 0 | 0 |
On the Synthesis of Guaranteed-Quality Plans for Robot Fleets in Logistics Scenarios via Optimization Modulo Theories | In manufacturing, the increasing involvement of autonomous robots in
production processes poses new challenges on the production management. In this
paper we report on the usage of Optimization Modulo Theories (OMT) to solve
certain multi-robot scheduling problems in this area. Whereas currently
existing methods are heuristic, our approach guarantees optimality for the
computed solution. We do not only present our final method but also its
chronological development, and draw some general observations for the
development of OMT-based approaches.
| 1 | 0 | 0 | 0 | 0 | 0 |
Geometrical morphology | We explore inflectional morphology as an example of the relationship of the
discrete and the continuous in linguistics. The grammar requests a form of a
lexeme by specifying a set of feature values, which corresponds to a corner M
of a hypercube in feature value space. The morphology responds to that request
by providing a morpheme, or a set of morphemes, whose vector sum is
geometrically closest to the corner M. In short, the chosen morpheme $\mu$ is
the morpheme (or set of morphemes) that maximizes the inner product of $\mu$
and M.
| 1 | 0 | 0 | 0 | 0 | 0 |
A framework for cost-constrained genome rearrangement under Double Cut and Join | The study of genome rearrangement has many flavours, but they all are somehow
tied to edit distances on variations of a multi-graph called the breakpoint
graph. We study a weighted 2-break distance on Eulerian 2-edge-colored
multi-graphs, which generalizes weighted versions of several Double Cut and
Join problems, including those on genomes with unequal gene content. We affirm
the connection between cycle decompositions and edit scenarios first discovered
with the Sorting By Reversals problem. Using this we show that the problem of
finding a parsimonious scenario of minimum cost on an Eulerian 2-edge-colored
multi-graph - with a general cost function for 2-breaks - can be solved by
decomposing the problem into independent instances on simple alternating
cycles. For breakpoint graphs, and a more constrained cost function, based on
coloring the vertices, we give a polynomial-time algorithm for finding a
parsimonious 2-break scenario of minimum cost, while showing that finding a
non-parsimonious 2-break scenario of minimum cost is NP-Hard.
| 0 | 0 | 0 | 0 | 1 | 0 |
Dual combination combination multi switching synchronization of eight chaotic systems | In this paper, a novel scheme for synchronizing four drive and four response
systems is proposed by the authors. The idea of multi switching and dual
combination synchronization is extended to dual combination-combination multi
switching synchronization involving eight chaotic systems and is a first of its
kind. Due to the multiple combination of chaotic systems and multi switching
the resultant dynamic behaviour is so complex that, in communication theory,
transmission and security of the resultant signal is more effective. Using
Lyapunov stability theory, sufficient conditions are achieved and suitable
controllers are designed to realise the desired synchronization. Corresponding
theoretical analysis is presented and numerical simulations performed to
demonstrate the effectiveness of the proposed scheme.
| 1 | 0 | 1 | 0 | 0 | 0 |
An Optimization Based Control Framework for Balancing and Walking: Implementation on the iCub Robot | A whole-body torque control framework adapted for balancing and walking tasks
is presented in this paper. In the proposed approach, centroidal momentum terms
are excluded in favor of a hierarchy of high-priority position and orientation
tasks and a low-priority postural task. More specifically, the controller
stabilizes the position of the center of mass, the orientation of the pelvis
frame, as well as the position and orientation of the feet frames. The
low-priority postural task provides reference positions for each joint of the
robot. Joint torques and contact forces to stabilize tasks are obtained through
quadratic programming optimization. Besides the exclusion of centroidal
momentum terms, part of the novelty of the approach lies in the definition of
control laws in SE(3) which do not require the use of Euler parameterization.
Validation of the framework was achieved in a scenario where the robot kept
balance while walking in place. Experiments have been conducted with the iCub
robot, in simulation and in real-world experiments.
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Self-similar groups of type FP_{n} | We construct new classes of self-similar groups : S-aritmetic groups, affine
groups and metabelian groups. Most of the soluble ones are finitely presented
and of type FP_{n} for appropriate n.
| 0 | 0 | 1 | 0 | 0 | 0 |
Operator Fitting for Parameter Estimation of Stochastic Differential Equations | Estimation of parameters is a crucial part of model development. When models
are deterministic, one can minimise the fitting error; for stochastic systems
one must be more careful. Broadly parameterisation methods for stochastic
dynamical systems fit into maximum likelihood estimation- and method of
moment-inspired techniques. We propose a method where one matches a finite
dimensional approximation of the Koopman operator with the implied Koopman
operator as generated by an extended dynamic mode decomposition approximation.
One advantage of this approach is that the objective evaluation cost can be
independent the number of samples for some dynamical systems. We test our
approach on two simple systems in the form of stochastic differential
equations, compare to benchmark techniques, and consider limited
eigen-expansions of the operators being approximated. Other small variations on
the technique are also considered, and we discuss the advantages to our
formulation.
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Binomial transform of products | Given two infinite sequences with known binomial transforms, we compute the
binomial transform of the product sequence. Various identities are obtained and
numerous examples are given involving sequences of special numbers: Harmonic
numbers, Bernoulli numbers, Fibonacci numbers, and also Laguerre polynomials.
| 0 | 0 | 1 | 0 | 0 | 0 |
Strength Factors: An Uncertainty System for a Quantified Modal Logic | We present a new system S for handling uncertainty in a quantified modal
logic (first-order modal logic). The system is based on both probability theory
and proof theory. The system is derived from Chisholm's epistemology. We
concretize Chisholm's system by grounding his undefined and primitive (i.e.
foundational) concept of reasonablenes in probability and proof theory. S can
be useful in systems that have to interact with humans and provide
justifications for their uncertainty. As a demonstration of the system, we
apply the system to provide a solution to the lottery paradox. Another
advantage of the system is that it can be used to provide uncertainty values
for counterfactual statements. Counterfactuals are statements that an agent
knows for sure are false. Among other cases, counterfactuals are useful when
systems have to explain their actions to users. Uncertainties for
counterfactuals fall out naturally from our system.
Efficient reasoning in just simple first-order logic is a hard problem.
Resolution-based first-order reasoning systems have made significant progress
over the last several decades in building systems that have solved non-trivial
tasks (even unsolved conjectures in mathematics). We present a sketch of a
novel algorithm for reasoning that extends first-order resolution.
Finally, while there have been many systems of uncertainty for propositional
logics, first-order logics and propositional modal logics, there has been very
little work in building systems of uncertainty for first-order modal logics.
The work described below is in progress; and once finished will address this
lack.
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Binary companions of nearby supernova remnants found with Gaia | We search for runaway former companions of the progenitors of nearby Galactic
core-collapse supernova remnants (SNRs) in the Tycho-Gaia astrometric solution
(TGAS). We look for candidates for a sample of ten SNRs with distances less
than $2\;\mathrm{kpc}$, taking astrometry and $G$ magnitude from TGAS and $B,V$
magnitudes from the AAVSO Photometric All-Sky Survey (APASS). A simple method
of tracking back stars and finding the closest point to the SNR centre is shown
to have several failings when ranking candidates. In particular, it neglects
our expectation that massive stars preferentially have massive companions. We
evolve a grid of binary stars to exploit these covariances in the distribution
of runaway star properties in colour - magnitude - ejection velocity space. We
construct an analytic model which predicts the properties of a runaway star, in
which the model parameters are the properties of the progenitor binary and the
properties of the SNR. Using nested sampling we calculate the Bayesian evidence
for each candidate to be the runaway and simultaneously constrain the
properties of that runaway and of the SNR itself. We identify four likely
runaway companions of the Cygnus Loop, HB 21, S147 and the Monoceros Loop. HD
37424 has previously been suggested as the companion of S147, however the other
three stars are new candidates. The favoured companion of HB 21 is the Be star
BD+50 3188 whose emission-line features could be explained by pre-supernova
mass transfer from the primary. There is a small probability that the
$2\;\mathrm{M}_{\odot}$ candidate runaway TYC 2688-1556-1 associated with the
Cygnus Loop is a hypervelocity star. If the Monoceros Loop is related to the
on-going star formation in the Mon OB2 association, the progenitor of the
Monoceros Loop is required to be more massive than $40\;\mathrm{M}_{\odot}$
which is in tension with the posterior for HD 261393.
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Some Distributions on Finite Rooted Binary Trees | We introduce some natural families of distributions on rooted binary ranked
plane trees with a view toward unifying ideas from various fields, including
macroevolution, epidemiology, computational group theory, search algorithms and
other fields. In the process we introduce the notions of split-exchangeability
and plane-invariance of a general Markov splitting model in order to readily
obtain probabilities over various equivalence classes of trees that arise in
statistics, phylogenetics, epidemiology and group theory.
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Programmable DNA-mediated decision maker | DNA-mediated computing is a novel technology that seeks to capitalize on the
enormous informational capacity of DNA and has tremendous computational ability
to compete with the current silicon-mediated computing, due to massive
parallelism and unique characteristics inherent in DNA interaction. In this
paper, the methodology of DNA-mediated computing is utilized to enrich decision
theory, by demonstrating how a novel programmable DNA-mediated normative
decision-making apparatus is able to capture rational choice under uncertainty.
| 1 | 0 | 0 | 0 | 0 | 0 |
The Effects of Ram Pressure on the Cold Clouds in the Centers of Galaxy Clusters | We discuss the effect of ram pressure on the cold clouds in the centers of
cool-core galaxy clusters, and in particular, how it reduces cloud velocity and
sometimes causes an offset between the cold gas and young stars. The velocities
of the molecular gas in both observations and our simulations fall in the range
of $100-400$ km/s, much lower than expected if they fall from a few tens of kpc
ballistically. If the intra-cluster medium (ICM) is at rest, the ram pressure
of the ICM only slightly reduces the velocity of the clouds. When we assume
that the clouds are actually "fluffier" because they are co-moving with a
warm-hot layer, the velocity becomes smaller. If we also consider the AGN wind
in the cluster center by adding a wind profile measured from the simulation,
the clouds are further slowed down at small radii, and the resulting velocities
are in general agreement with the observations and simulations. Because ram
pressure only affects gas but not stars, it can cause a separation between a
filament and young stars that formed in the filament as they move through the
ICM together. This separation has been observed in Perseus and also exists in
our simulations. We show that the star-filament offset combined with
line-of-sight velocity measurements can help determine the true motion of the
cold gas, and thus distinguish between inflows and outflows.
| 0 | 1 | 0 | 0 | 0 | 0 |
Time-delayed SIS epidemic model with population awareness | This paper analyses the dynamics of infectious disease with a concurrent
spread of disease awareness. The model includes local awareness due to contacts
with aware individuals, as well as global awareness due to reported cases of
infection and awareness campaigns. We investigate the effects of time delay in
response of unaware individuals to available information on the epidemic
dynamics by establishing conditions for the Hopf bifurcation of the endemic
steady state of the model. Analytical results are supported by numerical
bifurcation analysis and simulations.
| 0 | 1 | 0 | 0 | 0 | 0 |
Determining Song Similarity via Machine Learning Techniques and Tagging Information | The task of determining item similarity is a crucial one in a recommender
system. This constitutes the base upon which the recommender system will work
to determine which items are more likely to be enjoyed by a user, resulting in
more user engagement. In this paper we tackle the problem of determining song
similarity based solely on song metadata (such as the performer, and song
title) and on tags contributed by users. We evaluate our approach under a
series of different machine learning algorithms. We conclude that tf-idf
achieves better results than Word2Vec to model the dataset to feature vectors.
We also conclude that k-NN models have better performance than SVMs and Linear
Regression for this problem.
| 1 | 0 | 0 | 1 | 0 | 0 |
Uplink Performance Analysis in D2D-Enabled mmWave Cellular Networks | In this paper, we provide an analytical framework to analyze the uplink
performance of device-to-device (D2D)-enabled millimeter wave (mmWave) cellular
networks. Signal-to- interference-plus-noise ratio (SINR) outage probabilities
are derived for both cellular and D2D links using tools from stochastic
geometry. The distinguishing features of mmWave communications such as
directional beamforming and having different path loss laws for line-of-sight
(LOS) and non-line-of-sight (NLOS) links are incorporated into the outage
analysis by employing a flexible mode selection scheme and Nakagami fading.
Also, the effect of beamforming alignment errors on the outage probability is
investigated to get insight on the performance in practical scenarios.
| 1 | 0 | 0 | 0 | 0 | 0 |
A critical topology for $L^p$-Carleman classes with $0<p<1$ | In this paper, we explain a sharp phase transition phenomenon which occurs
for $L^p$-Carleman classes with exponents $0<p<1$. In principle, these classes
are defined as usual, only the traditional $L^\infty$-bounds are replaced by
corresponding $L^p$-bounds. To mirror the classical definition, we add the
feature of dilatation invariance as well, and consider a larger soft-topology
space, the $L^p$-Carleman class. A particular degenerate instance is when we
obtain the $L^p$-Sobolev spaces, analyzed previously by Peetre, following an
initial insight by Douady. Peetre found that these $L^p$-Sobolev spaces are
highly degenerate for $0<p<1$. Essentially, the contact is lost between the
function and its derivatives. Here, we analyze this degeneracy for the more
general $L^p$-Carleman classes defined by a weight sequence. Under some
reasonable growth and regularity properties, and a condition on the collection
of test functions, we find that there is a sharp boundary, defined in terms of
the weight sequence: on the one side, we get Douady-Peetre's phenomenon of
"disconnexion" between the function and its derivatives, while on the other, we
obtain a collection of highly smooth functions. We also look at the more
standard second phase transition, between non-quasianalyticity and
quasianalyticity, in the $L^p$ setting, with $0<p<1$.
| 0 | 0 | 1 | 0 | 0 | 0 |
The GAN Landscape: Losses, Architectures, Regularization, and Normalization | Generative adversarial networks (GANs) are a class of deep generative models
which aim to learn a target distribution in an unsupervised fashion. While they
were successfully applied to many problems, training a GAN is a notoriously
challenging task and requires a significant amount of hyperparameter tuning,
neural architecture engineering, and a non-trivial amount of "tricks". The
success in many practical applications coupled with the lack of a measure to
quantify the failure modes of GANs resulted in a plethora of proposed losses,
regularization and normalization schemes, and neural architectures. In this
work we take a sober view of the current state of GANs from a practical
perspective. We reproduce the current state of the art and go beyond fairly
exploring the GAN landscape. We discuss common pitfalls and reproducibility
issues, open-source our code on Github, and provide pre-trained models on
TensorFlow Hub.
| 0 | 0 | 0 | 1 | 0 | 0 |
An energy-based equilibrium contact angle boundary condition on jagged surfaces for phase-field methods | We consider an energy-based boundary condition to impose an equilibrium
wetting angle for the Cahn-Hilliard-Navier-Stokes phase-field model on
voxel-set-type computational domains. These domains typically stem from the
micro-CT imaging of porous rock and approximate a (on {\mu}m scale) smooth
domain with a certain resolution. Planar surfaces that are perpendicular to the
main axes are naturally approximated by a layer of voxels. However, planar
surfaces in any other directions and curved surfaces yield a jagged/rough
surface approximation by voxels. For the standard Cahn-Hilliard formulation,
where the contact angle between the diffuse interface and the domain boundary
(fluid-solid interface/wall) is 90 degrees, jagged surfaces have no impact on
the contact angle. However, a prescribed contact angle smaller or larger than
90 degrees on jagged voxel surfaces is amplified in either direction. As a
remedy, we propose the introduction of surface energy correction factors for
each fluid-solid voxel face that counterbalance the difference of the voxel-set
surface area with the underlying smooth one. The discretization of the model
equations is performed with the discontinuous Galerkin method, however, the
presented semi-analytical approach of correcting the surface energy is equally
applicable to other direct numerical methods such as finite elements, finite
volumes, or finite differences, since the correction factors appear in the
strong formulation of the model.
| 0 | 1 | 0 | 0 | 0 | 0 |
Period polynomials, derivatives of $L$-functions, and zeros of polynomials | Period polynomials have long been fruitful tools for the study of values of
$L$-functions in the context of major outstanding conjectures. In this paper,
we survey some facets of this study from the perspective of Eichler cohomology.
We discuss ways to incorporate non-cuspidal modular forms and values of
derivatives of $L$-functions into the same framework. We further review
investigations of the location of zeros of the period polynomial as well as of
its analogue for $L$-derivatives.
| 0 | 0 | 1 | 0 | 0 | 0 |
Forming disc galaxies in major mergers II. The central mass concentration problem and a comparison of GADGET3 with GIZMO | Context: In a series of papers, we study the major merger of two disk
galaxies in order to establish whether or not such a merger can produce a disc
galaxy. Aims: Our aim here is to describe in detail the technical aspects of
our numerical experiments. Methods: We discuss the initial conditions of our
major merger, which consist of two protogalaxies on a collision orbit. We show
that such merger simulations can produce a non-realistic central mass
concentration, and we propose simple, parametric, AGN-like feedback as a
solution to this problem. Our AGN-like feedback algorithm is very simple: at
each time-step we take all particles whose local volume density is above a
given threshold value and increase their temperature to a preset value. We also
compare the GADGET3 and GIZMO codes, by applying both of them to the same
initial conditions. Results: We show that the evolution of isolated
protogalaxies resembles the evolution of disk galaxies, thus arguing that our
protogalaxies are well suited for our merger simulations. We demonstrate that
the problem with the unphysical central mass concentration in our merger
simulations is further aggravated when we increase the resolution. We show that
our AGN-like feedback removes this non-physical central mass concentration, and
thus allows the formation of realistic bars. Note that our AGN-like feedback
mainly affects the central region of a model, without significantly modifying
the rest of the galaxy. We demonstrate that, in the context of our kind of
simulation, GADGET3 gives results which are very similar to those obtained with
the PSPH (density independent SPH) flavor of GIZMO. Moreover, in the examples
we tried, the differences between the results of the two flavors of GIZMO,
namely PSPH, and MFM (mesh-less algorithm) are similar to and, in some
comparisons, larger than the differences between the results of GADGET3 and
PSPH.
| 0 | 1 | 0 | 0 | 0 | 0 |
Measuring Systematic Risk with Neural Network Factor Model | In this paper, we measure systematic risk with a new nonparametric factor
model, the neural network factor model. The suitable factors for systematic
risk can be naturally found by inserting daily returns on a wide range of
assets into the bottleneck network. The network-based model does not stick to a
probabilistic structure unlike parametric factor models, and it does not need
feature engineering because it selects notable features by itself. In addition,
we compare performance between our model and the existing models using 20-year
data of S&P 100 components. Although the new model can not outperform the best
ones among the parametric factor models due to limitations of the variational
inference, the estimation method used for this study, it is still noteworthy in
that it achieves the performance as best the comparable models could without
any prior knowledge.
| 0 | 0 | 0 | 0 | 0 | 1 |
Obstacle Avoidance Using Stereo Camera | In this paper we present a novel method for obstacle avoidance using the
stereo camera. The conventional obstacle avoidance methods and their
limitations are discussed. A new algorithm is developed for the real-time
obstacle avoidance which responds faster to unexpected obstacles. In this
approach the depth map is divided into optimized number of regions and the
minimum depth at each section is assigned as the depth of that region. A fuzzy
controller is designed to create the drive commands for the robot/quadcopter.
The system was tested on multiple paths with different obstacles and the
results demonstrated the high accuracy of the developed system.
| 1 | 0 | 0 | 0 | 0 | 0 |
Detecting Outliers in Data with Correlated Measures | Advances in sensor technology have enabled the collection of large-scale
datasets. Such datasets can be extremely noisy and often contain a significant
amount of outliers that result from sensor malfunction or human operation
faults. In order to utilize such data for real-world applications, it is
critical to detect outliers so that models built from these datasets will not
be skewed by outliers.
In this paper, we propose a new outlier detection method that utilizes the
correlations in the data (e.g., taxi trip distance vs. trip time). Different
from existing outlier detection methods, we build a robust regression model
that explicitly models the outliers and detects outliers simultaneously with
the model fitting.
We validate our approach on real-world datasets against methods specifically
designed for each dataset as well as the state of the art outlier detectors.
Our outlier detection method achieves better performances, demonstrating the
robustness and generality of our method. Last, we report interesting case
studies on some outliers that result from atypical events.
| 0 | 0 | 0 | 1 | 0 | 0 |
Active bialkali photocathodes on free-standing graphene substrates | The hexagonal structure of graphene gives rise to the property of gas
impermeability, motivating its investigation for a new application: protection
of semiconductor photocathodes in electron accelerators. These materials are
extremely susceptible to degradation in efficiency through multiple mechanisms
related to contamination from the local imperfect vacuum environment of the
host photoinjector. Few-layer graphene has been predicted to permit a modified
photoemission response of protected photocathode surfaces, and recent
experiments of single-layer graphene on copper have begun to confirm these
predictions for single crystal metallic photocathodes. Unlike metallic
photoemitters, the integration of an ultra-thin graphene barrier film with
conventional semiconductor photocathode growth processes is not
straightforward. A first step toward addressing this challenge is the growth
and characterization of technologically relevant, high quantum efficiency
bialkali photocathodes grown on ultra-thin free-standing graphene substrates.
Photocathode growth on free-standing graphene provides the opportunity to
integrate these two materials and study their interaction. Specifically,
spectral response features and photoemission stability of cathodes grown on
graphene substrates are compared to those deposited on established substrates.
In addition we observed an increase of work function for the graphene
encapsulated bialkali photocathode surfaces, which is predicted by our
calculations. The results provide a unique demonstration of bialkali
photocathodes on free-standing substrates, and indicate promise towards our
goal of fabricating high-performance graphene encapsulated photocathodes with
enhanced lifetime for accelerator applications.
| 0 | 1 | 0 | 0 | 0 | 0 |
Reconstruction of a compact Riemannian manifold from the scattering data of internal sources | Given a smooth non-trapping compact manifold with strictly con- vex boundary,
we consider an inverse problem of reconstructing the manifold from the
scattering data initiated from internal sources. This data consist of the exit
directions of geodesics that are emaneted from interior points of the manifold.
We show that under certain generic assumption of the metric, one can
reconstruct an isometric copy of the manifold from such scattering data
measured on the boundary.
| 0 | 0 | 1 | 0 | 0 | 0 |
Lensing and the Warm Hot Intergalactic Medium | The correlation of weak lensing and Cosmic Microwave Anisotropy (CMB) data
traces the pressure distribution of the hot, ionized gas and the underlying
matter density field. The measured correlation is dominated by baryons residing
in halos. Detecting the contribution from unbound gas by measuring the residual
cross-correlation after masking all known halos requires a theoretical
understanding of this correlation and its dependence with model parameters. Our
model assumes that the gas in filaments is well described by a log-normal
probability distribution function, with temperatures $10^{5-7}$K and
overdensities $\xi\le 100$. The lensing-comptonization cross-correlation is
dominated by gas with overdensities in the range $\xi\approx[3-33]$; the signal
is generated at redshifts $z\le 1$. If only 10\% of the measured
cross-correlation is due to unbound gas, then the most recent measurements set
an upper limit of $\bar{T}_e\lesssim 10^6$K on the mean temperature of Inter
Galactic Medium. The amplitude is proportional to the baryon fraction stored in
filaments. The lensing-comptonization power spectrum peaks at a different scale
than the gas in halos making it possible to distinguish both contributions. To
trace the distribution of the low density and low temperature plasma on
cosmological scales, the effect of halos will have to be subtracted from the
data, requiring observations with larger signal-to-noise ratio than currently
available.
| 0 | 1 | 0 | 0 | 0 | 0 |
Guaranteed Simultaneous Asymmetric Tensor Decomposition via Orthogonalized Alternating Least Squares | We consider the asymmetric orthogonal tensor decomposition problem, and
present an orthogonalized alternating least square algorithm that converges to
rank-$r$ of the true tensor factors simultaneously in
$O(\log(\log(\frac{1}{\epsilon})))$ steps under our proposed Trace Based
Initialization procedure. Trace Based Initialization requires $O(1/{\log
(\frac{\lambda_{r}}{\lambda_{r+1}})})$ number of matrix subspace iterations to
guarantee a "good" initialization for the simultaneous orthogonalized ALS
method, where $\lambda_r$ is the $r$-th largest singular value of the tensor.
We are the first to give a theoretical guarantee on orthogonal asymmetric
tensor decomposition using Trace Based Initialization procedure and the
orthogonalized alternating least squares. Our Trace Based Initialization also
improves convergence for symmetric orthogonal tensor decomposition.
| 0 | 0 | 0 | 1 | 0 | 0 |
The average sizes of two-torsion subgroups in quotients of class groups of cubic fields | We prove a generalization of a result of Bhargava regarding the average size
$\mathrm{Cl}(K)[2]$ as $K$ varies among cubic fields. For a fixed set of
rational primes $S$, we obtain a formula for the average size of
$\mathrm{Cl}(K)/\langle S \rangle[2]$ as $K$ varies among cubic fields with a
fixed signature, where $\langle S \rangle$ is the subgroup of $\mathrm{Cl}(K)$
generated by the classes of primes of $K$ above primes in $S$.
As a consequence, we are able to calculate the average sizes of
$K_{2n}(\mathcal{O}_K)[2]$ for $n > 0$ and for the relaxed Selmer group
$\mathrm{Sel}_2^S(K)$ as $K$ varies in these same families.
| 0 | 0 | 1 | 0 | 0 | 0 |
A Strongly Consistent Finite Difference Scheme for Steady Stokes Flow and its Modified Equations | We construct and analyze a strongly consistent second-order finite difference
scheme for the steady two-dimensional Stokes flow. The pressure Poisson
equation is explicitly incorporated into the scheme. Our approach suggested by
the first two authors is based on a combination of the finite volume method,
difference elimination, and numerical integration. We make use of the
techniques of the differential and difference Janet/Groebner bases. In order to
prove strong consistency of the generated scheme we correlate the differential
ideal generated by the polynomials in the Stokes equations with the difference
ideal generated by the polynomials in the constructed difference scheme.
Additionally, we compute the modified differential system of the obtained
scheme and analyze the scheme's accuracy and strong consistency by considering
this system. An evaluation of our scheme against the established
marker-and-cell method is carried out.
| 1 | 0 | 0 | 0 | 0 | 0 |
Classification of $δ(2,n-2)$-ideal Lagrangian submanifolds in $n$-dimensional complex space forms | It was proven in [B.-Y. Chen, F. Dillen, J. Van der Veken and L. Vrancken,
Curvature inequalities for Lagrangian submanifolds: the final solution, Differ.
Geom. Appl. 31 (2013), 808-819] that every Lagrangian submanifold $M$ of a
complex space form $\tilde M^{n}(4c)$ of constant holomorphic sectional
curvature $4c$ satisfies the following optimal inequality: \begin{align*}
\delta(2,n-2) \leq \frac{n^2(n-2)}{4(n-1)} H^2 + 2(n-2) c, \end{align*} where
$H^2$ is the squared mean curvature and $\delta(2,n-2)$ is a $\delta$-invariant
on $M$. In this paper we classify Lagrangian submanifolds of complex space
forms $\tilde M^{n}(4c)$, $n \geq 5$, which satisfy the equality case of this
inequality at every point.
| 0 | 0 | 1 | 0 | 0 | 0 |
Robust stability analysis of DC microgrids with constant power loads | This paper studies stability analysis of DC microgrids with uncertain
constant power loads (CPLs). It is well known that CPLs have negative impedance
effects, which may cause instability in a DC microgrid. Existing works often
study the stability around a given equilibrium based on some nominal values of
CPLs. However, in real applications, the equilibrium of a DC microgrid depends
on the loading condition that often changes over time. Different from many
previous results, this paper develops a framework that can analyze the DC
microgrid stability for a given range of CPLs. The problem is formulated as a
robust stability problem of a polytopic uncertain linear system. By exploiting
the structure of the problem, we derive a set of sufficient conditions that can
guarantee robust stability. The conditions can be efficiently checked by
solving a convex optimization problem whose complexity does not grow with the
number of buses in the microgrid. The effectiveness and non-conservativeness of
the proposed framework are demonstrated using simulation examples.
| 0 | 0 | 1 | 0 | 0 | 0 |
Dimensional Analysis in Economics: A Study of the Neoclassical Economic Growth Model | The fundamental purpose of the present research article is to introduce the
basic principles of Dimensional Analysis in the context of the neoclassical
economic theory, in order to apply such principles to the fundamental relations
that underlay most models of economic growth. In particular, basic instruments
from Dimensional Analysis are used to evaluate the analytical consistency of
the Neoclassical economic growth model. The analysis shows that an adjustment
to the model is required in such a way that the principle of dimensional
homogeneity is satisfied.
| 0 | 0 | 0 | 0 | 0 | 1 |
A high precision semi-analytic mass function | In this paper, extending past works of Del Popolo, we show how a high
precision mass function (MF) can be obtained using the excursion set approach
and an improved barrier taking implicitly into account a non-zero cosmological
constant, the angular momentum acquired by tidal interaction of
proto-structures and dynamical friction. In the case of the $\Lambda$CDM
paradigm, we find that our MF is in agreement at the 3\% level to Klypin's
Bolshoi simulation, in the mass range $M_{\rm vir} = 5 \times 10^9 h^{-1}
M_{\odot} -- 5 \times 10^{14} h^{-1} M_{\odot}$ and redshift range $0 \lesssim
z \lesssim 10$. For $z=0$ we also compared our MF to several fitting formulae,
and found in particular agreement with Bhattacharya's within 3\% in the mass
range $10^{12}-10^{16} h^{-1} M_{\odot}$. Moreover, we discuss our MF validity
for different cosmologies.
| 0 | 1 | 0 | 0 | 0 | 0 |
Fast Compressed Self-Indexes with Deterministic Linear-Time Construction | We introduce a compressed suffix array representation that, on a text $T$ of
length $n$ over an alphabet of size $\sigma$, can be built in $O(n)$
deterministic time, within $O(n\log\sigma)$ bits of working space, and counts
the number of occurrences of any pattern $P$ in $T$ in time $O(|P| + \log\log_w
\sigma)$ on a RAM machine of $w=\Omega(\log n)$-bit words. This new index
outperforms all the other compressed indexes that can be built in linear
deterministic time, and some others. The only faster indexes can be built in
linear time only in expectation, or require $\Theta(n\log n)$ bits. We also
show that, by using $O(n\log\sigma)$ bits, we can build in linear time an index
that counts in time $O(|P|/\log_\sigma n + \log n(\log\log n)^2)$, which is
RAM-optimal for $w=\Theta(\log n)$ and sufficiently long patterns.
| 1 | 0 | 0 | 0 | 0 | 0 |
Solvability of the operator Riccati equation in the Feshbach case | We consider a bounded block operator matrix of the form $$
L=\left(\begin{array}{cc} A & B \\ C & D \end{array} \right), $$ where the
main-diagonal entries $A$ and $D$ are self-adjoint operators on Hilbert spaces
$H_{_A}$ and $H_{_D}$, respectively; the coupling $B$ maps $H_{_D}$ to $H_{_A}$
and $C$ is an operator from $H_{_A}$ to $H_{_D}$. It is assumed that the
spectrum $\sigma_{_D}$ of $D$ is absolutely continuous and uniform, being
presented by a single band $[\alpha,\beta]\subset\mathbb{R}$, $\alpha<\beta$,
and the spectrum $\sigma_{_A}$ of $A$ is embedded into $\sigma_{_D}$, that is,
$\sigma_{_A}\subset(\alpha,\beta)$. We formulate conditions under which there
are bounded solutions to the operator Riccati equations associated with the
complexly deformed block operator matrix $L$; in such a case the deformed
operator matrix $L$ admits a block diagonalization. The same conditions also
ensure the Markus-Matsaev-type factorization of the Schur complement
$M_{_A}(z)=A-z-B(D-z)^{-1}C$ analytically continued onto the unphysical
sheet(s) of the complex $z$ plane adjacent to the band $[\alpha,\beta]$. We
prove that the operator roots of the continued Schur complement $M_{_A}$ are
explicitly expressed through the respective solutions to the deformed Riccati
equations.
| 0 | 0 | 1 | 0 | 0 | 0 |
Comparison results for first order linear operators with reflection and periodic boundary value conditions | This work is devoted to the study of the first order operator
$x'(t)+m\,x(-t)$ coupled with periodic boundary value conditions. We describe
the eigenvalues of the operator and obtain the expression of its related
Green's function in the non resonant case. We also obtain the range of the
values of the real parameter $m$ for which the integral kernel, which provides
the unique solution, has constant sign. In this way, we automatically establish
maximum and anti-maximum principles for the equation. Some applications to the
existence of nonlinear periodic boundary value problems are showed.
| 0 | 0 | 1 | 0 | 0 | 0 |
A remark on oscillatory integrals associated with fewnomials | We prove that the $L^2$ bound of an oscillatory integral associated with a
polynomial depends only on the number of monomials that this polynomial
consists of.
| 0 | 0 | 1 | 0 | 0 | 0 |
Quantitative stochastic homogenization and regularity theory of parabolic equations | We develop a quantitative theory of stochastic homogenization for linear,
uniformly parabolic equations with coefficients depending on space and time.
Inspired by recent works in the elliptic setting, our analysis is focused on
certain subadditive quantities derived from a variational interpretation of
parabolic equations. These subadditive quantities are intimately connected to
spatial averages of the fluxes and gradients of solutions. We implement a
renormalization-type scheme to obtain an algebraic rate for their convergence,
which is essentially a quantification of the weak convergence of the gradients
and fluxes of solutions to their homogenized limits. As a consequence, we
obtain estimates of the homogenization error for the Cauchy-Dirichlet problem
which are optimal in stochastic integrability. We also develop a higher
regularity theory for solutions of the heterogeneous equation, including a
uniform $C^{0,1}$-type estimate and a Liouville theorem of every finite order.
| 0 | 0 | 1 | 0 | 0 | 0 |
On the periodicity problem of residual r-Fubini sequences | For any positive integer $r$, the $r$-Fubini number with parameter $n$,
denoted by $F_{n,r}$, is equal to the number of ways that the elements of a set
with $n+r$ elements can be weak ordered such that the $r$ least elements are in
distinct orders. In this article we focus on the sequence of residues of the
$r$-Fubini numbers modulo a positive integer $s$ and show that this sequence is
periodic and then, exhibit how to calculate its period length. As an extra
result, an explicit formula for the $r$-Stirling numbers is obtained which is
frequently used in calculations.
| 0 | 0 | 1 | 0 | 0 | 0 |
Boundedness of the Bergman projection on generalized Fock-Sobolev spaces on ${\mathbb C}^n$ | In this paper we solve a problem posed by H. Bommier-Hato, M. Engliš and
E.H. Youssfi in [3] on the boundedness of the Bergman-type projections in
generalized Fock spaces. It will be a consequence of two facts: a full
description of the embeddings between generalized Fock-Sobolev spaces and a
complete characterization of the boundedness of the above Bergman type
projections between weighted $L^p$-spaces related to generalized Fock-Sobolev
spaces.
| 0 | 0 | 1 | 0 | 0 | 0 |
Support Vector Machines and generalisation in HEP | We review the concept of Support Vector Machines (SVMs) and discuss examples
of their use in a number of scenarios. Several SVM implementations have been
used in HEP and we exemplify this algorithm using the Toolkit for Multivariate
Analysis (TMVA) implementation. We discuss examples relevant to HEP including
background suppression for $H\to\tau^+\tau^-$ at the LHC with several different
kernel functions. Performance benchmarking leads to the issue of generalisation
of hyper-parameter selection. The avoidance of fine tuning (over training or
over fitting) in MVA hyper-parameter optimisation, i.e. the ability to ensure
generalised performance of an MVA that is independent of the training,
validation and test samples, is of utmost importance. We discuss this issue and
compare and contrast performance of hold-out and k-fold cross-validation. We
have extended the SVM functionality and introduced tools to facilitate cross
validation in TMVA and present results based on these improvements.
| 0 | 1 | 0 | 0 | 0 | 0 |
A sequent calculus for the Tamari order | We introduce a sequent calculus with a simple restriction of Lambek's product
rules that precisely captures the classical Tamari order, i.e., the partial
order on fully-bracketed words (equivalently, binary trees) induced by a
semi-associative law (equivalently, tree rotation). We establish a focusing
property for this sequent calculus (a strengthening of cut-elimination), which
yields the following coherence theorem: every valid entailment in the Tamari
order has exactly one focused derivation. One combinatorial application of this
coherence theorem is a new proof of the Tutte-Chapoton formula for the number
of intervals in the Tamari lattice $Y_n$. We also apply the sequent calculus
and the coherence theorem to build a surprising bijection between intervals of
the Tamari order and a certain fragment of lambda calculus, consisting of the
$\beta$-normal planar lambda terms with no closed proper subterms.
| 1 | 0 | 1 | 0 | 0 | 0 |
A Measurement of CMB Cluster Lensing with SPT and DES Year 1 Data | Clusters of galaxies gravitationally lens the cosmic microwave background
(CMB) radiation, resulting in a distinct imprint in the CMB on arcminute
scales. Measurement of this effect offers a promising way to constrain the
masses of galaxy clusters, particularly those at high redshift. We use CMB maps
from the South Pole Telescope Sunyaev-Zel'dovich (SZ) survey to measure the CMB
lensing signal around galaxy clusters identified in optical imaging from first
year observations of the Dark Energy Survey. The cluster catalog used in this
analysis contains 3697 members with mean redshift of $\bar{z} = 0.45$. We
detect lensing of the CMB by the galaxy clusters at $8.1\sigma$ significance.
Using the measured lensing signal, we constrain the amplitude of the relation
between cluster mass and optical richness to roughly $17\%$ precision, finding
good agreement with recent constraints obtained with galaxy lensing. The error
budget is dominated by statistical noise but includes significant contributions
from systematic biases due to the thermal SZ effect and cluster miscentering.
| 0 | 1 | 0 | 0 | 0 | 0 |
Seebeck Effect in Nanoscale Ferromagnets | We present a theory of the Seebeck effect in nanoscale ferromagnets with
dimensions smaller than the spin diffusion length. The spin accumulation
generated by a temperature gradient strongly affects the thermopower. We also
identify a correction arising from the transverse temperature gradient induced
by the anomalous Ettingshausen effect. The effect of an induced spin-heat accu-
mulation gradient is considered as well. The importance of these effects for
nanoscale ferromagnets is illustrated by ab initio calculations for dilute
ferromagnetic alloys.
| 0 | 1 | 0 | 0 | 0 | 0 |
Fast Asymmetric Fronts Propagation for Image Segmentation | In this paper, we introduce a generalized asymmetric fronts propagation model
based on the geodesic distance maps and the Eikonal partial differential
equations. One of the key ingredients for the computation of the geodesic
distance map is the geodesic metric, which can govern the action of the
geodesic distance level set propagation. We consider a Finsler metric with the
Randers form, through which the asymmetry and anisotropy enhancements can be
taken into account to prevent the fronts leaking problem during the fronts
propagation. These enhancements can be derived from the image edge-dependent
vector field such as the gradient vector flow. The numerical implementations
are carried out by the Finsler variant of the fast marching method, leading to
very efficient interactive segmentation schemes. We apply the proposed Finsler
fronts propagation model to image segmentation applications. Specifically, the
foreground and background segmentation is implemented by the Voronoi index map.
In addition, for the application of tubularity segmentation, we exploit the
level set lines of the geodesic distance map associated to the proposed Finsler
metric providing that a thresholding value is given.
| 1 | 0 | 0 | 0 | 0 | 0 |
Efficient injection from large telescopes into single-mode fibres: Enabling the era of ultra-precision astronomy | Photonic technologies offer numerous advantages for astronomical instruments
such as spectrographs and interferometers owing to their small footprints and
diverse range of functionalities. Operating at the diffraction-limit, it is
notoriously difficult to efficiently couple such devices directly with large
telescopes. We demonstrate that with careful control of both the non-ideal
pupil geometry of a telescope and residual wavefront errors, efficient coupling
with single-mode devices can indeed be realised. A fibre injection was built
within the Subaru Coronagraphic Extreme Adaptive Optics (SCExAO) instrument.
Light was coupled into a single-mode fibre operating in the near-IR (J-H bands)
which was downstream of the extreme adaptive optics system and the pupil
apodising optics. A coupling efficiency of 86% of the theoretical maximum limit
was achieved at 1550 nm for a diffraction-limited beam in the laboratory, and
was linearly correlated with Strehl ratio. The coupling efficiency was constant
to within <30% in the range 1250-1600 nm. Preliminary on-sky data with a Strehl
ratio of 60% in the H-band produced a coupling efficiency into a single-mode
fibre of ~50%, consistent with expectations. The coupling was >40% for 84% of
the time and >50% for 41% of the time. The laboratory results allow us to
forecast that extreme adaptive optics levels of correction (Strehl ratio >90%
in H-band) would allow coupling of >67% (of the order of coupling to multimode
fibres currently). For Strehl ratios <20%, few-port photonic lanterns become a
superior choice but the signal-to-noise must be considered. These results
illustrate a clear path to efficient on-sky coupling into a single-mode fibre,
which could be used to realise modal-noise-free radial velocity machines,
very-long-baseline optical/near-IR interferometers and/or simply exploit
photonic technologies in future instrument design.
| 0 | 1 | 0 | 0 | 0 | 0 |
An upwind method for genuine weakly hyperbolic systems | In this article, we attempted to develop an upwind scheme based on Flux
Difference Splitting using Jordan canonical forms to simulate genuine weakly
hyperbolic systems. Theory of Jordan Canonical Forms is being used to complete
defective set of linear independent eigenvectors. Proposed FDS-J scheme is
capable of recognizing various shocks accurately.
| 0 | 0 | 1 | 0 | 0 | 0 |
Semi-tied Units for Efficient Gating in LSTM and Highway Networks | Gating is a key technique used for integrating information from multiple
sources by long short-term memory (LSTM) models and has recently also been
applied to other models such as the highway network. Although gating is
powerful, it is rather expensive in terms of both computation and storage as
each gating unit uses a separate full weight matrix. This issue can be severe
since several gates can be used together in e.g. an LSTM cell. This paper
proposes a semi-tied unit (STU) approach to solve this efficiency issue, which
uses one shared weight matrix to replace those in all the units in the same
layer. The approach is termed "semi-tied" since extra parameters are used to
separately scale each of the shared output values. These extra scaling factors
are associated with the network activation functions and result in the use of
parameterised sigmoid, hyperbolic tangent, and rectified linear unit functions.
Speech recognition experiments using British English multi-genre broadcast data
showed that using STUs can reduce the calculation and storage cost by a factor
of three for highway networks and four for LSTMs, while giving similar word
error rates to the original models.
| 0 | 0 | 0 | 1 | 0 | 0 |
A step towards Twist Conjecture | Under the assumption that a defining graph of a Coxeter group admits only
twists in $\mathbb{Z}_2$ and is of type FC, we prove Mühlherr's Twist
Conjecture.
| 0 | 0 | 1 | 0 | 0 | 0 |
Achieveing reliable UDP transmission at 10 Gb/s using BSD socket for data acquisition systems | User Datagram Protocol (UDP) is a commonly used protocol for data
transmission in small embedded systems. UDP as such is unreliable and packet
losses can occur. The achievable data rates can suffer if optimal packet sizes
are not used. The alternative, Transmission Control Protocol (TCP) guarantees
the ordered delivery of data and automatically adjusts transmission to match
the capability of the transmission link. Nevertheless UDP is often favored over
TCP due to its simplicity, small memory and instruction footprints. Both UDP
and TCP are implemented in all larger operating systems and commercial embedded
frameworks. In addition UDP also supported on a variety of small hardware
platforms such as Digital Signal Processors (DSP) Field Programmable Gate
Arrays (FPGA). This is not so common for TCP. This paper describes how high
speed UDP based data transmission with very low packet error ratios was
achieved. The near-reliable communications link is used in a data acquisition
(DAQ) system for the next generation of extremely intense neutron source,
European Spallation Source. This paper presents measurements of UDP performance
and reliability as achieved by employing several optimizations. The
measurements were performed on Xeon E5 based CentOS (Linux) servers. The
measured data rates are very close to the 10 Gb/s line rate, and zero packet
loss was achieved. The performance was obtained utilizing a single processor
core as transmitter and a single core as receiver. The results show that
support for transmitting large data packets is a key parameter for good
performance.
Optimizations for throughput are: MTU, packet sizes, tuning Linux kernel
parameters, thread affinity, core locality and efficient timers.
| 1 | 1 | 0 | 0 | 0 | 0 |
Epidemic Threshold in Continuous-Time Evolving Networks | Current understanding of the critical outbreak condition on temporal networks
relies on approximations (time scale separation, discretization) that may bias
the results. We propose a theoretical framework to compute the epidemic
threshold in continuous time through the infection propagator approach. We
introduce the {\em weak commutation} condition allowing the interpretation of
annealed networks, activity-driven networks, and time scale separation into one
formalism. Our work provides a coherent connection between discrete and
continuous time representations applicable to realistic scenarios.
| 0 | 1 | 0 | 0 | 0 | 0 |
Demo Abstract: CDMA-based IoT Services with Shared Band Operation of LTE in 5G | With the vision of deployment of massive Internet-of-Things (IoTs) in 5G
network, existing 4G network and protocols are inefficient to handle sporadic
IoT traffic with requirements of low-latency, low control overhead and low
power. To suffice these requirements, we propose a design of a PHY/MAC layer
using Software Defined Radios (SDRs) that is backward compatible with existing
OFDM based LTE protocols and supports CDMA based transmissions for low power
IoT devices as well. This demo shows our implemented system based on that
design and the viability of the proposal under different network scenarios.
| 1 | 0 | 0 | 0 | 0 | 0 |
Can Two-Way Direct Communication Protocols Be Considered Secure? | We consider attacks on two-way quantum key distribution protocols in which an
undetectable eavesdropper copies all messages in the message mode. We show that
under the attacks there is no disturbance in the message mode and that the
mutual information between the sender and the receiver is always constant and
equal to one. It follows that recent proofs of security for two-way protocols
cannot be considered complete since they do not cover the considered attacks.
| 1 | 0 | 0 | 0 | 0 | 0 |
Using Deep Neural Network Approximate Bayesian Network | We present a new method to approximate posterior probabilities of Bayesian
Network using Deep Neural Network. Experiment results on several public
Bayesian Network datasets shows that Deep Neural Network is capable of learning
joint probability distri- bution of Bayesian Network by learning from a few
observation and posterior probability distribution pairs with high accuracy.
Compared with traditional approximate method likelihood weighting sampling
algorithm, our method is much faster and gains higher accuracy in medium sized
Bayesian Network. Another advantage of our method is that our method can be
parallelled much easier in GPU without extra effort. We also ex- plored the
connection between the accuracy of our model and the number of training
examples. The result shows that our model saturate as the number of training
examples grow and we don't need many training examples to get reasonably good
result. Another contribution of our work is that we have shown discriminative
model like Deep Neural Network can approximate generative model like Bayesian
Network.
| 0 | 0 | 0 | 1 | 0 | 0 |
Simple Classification using Binary Data | Binary, or one-bit, representations of data arise naturally in many
applications, and are appealing in both hardware implementations and algorithm
design. In this work, we study the problem of data classification from binary
data and propose a framework with low computation and resource costs. We
illustrate the utility of the proposed approach through stylized and realistic
numerical experiments, and provide a theoretical analysis for a simple case. We
hope that our framework and analysis will serve as a foundation for studying
similar types of approaches.
| 1 | 0 | 0 | 1 | 0 | 0 |
Unstable normalized standing waves for the space periodic NLS | For the stationary nonlinear Schrödinger equation $-\Delta u+ V(x)u- f(u) =
\lambda u$ with periodic potential $V$ we study the existence and stability
properties of multibump solutions with prescribed $L^2$-norm. To this end we
introduce a new nondegeneracy condition and develop new superposition
techniques which allow to match the $L^2$-constraint. In this way we obtain the
existence of infinitely many geometrically distinct solutions to the stationary
problem. We then calculate the Morse index of these solutions with respect to
the restriction of the underlying energy functional to the associated
$L^2$-sphere, and we show their orbital instability with respect to the
Schrödinger flow. Our results apply in both, the mass-subcritical and the
mass-supercritical regime.
| 0 | 0 | 1 | 0 | 0 | 0 |
Inverse regression for ridge recovery: A data-driven approach for parameter reduction in computer experiments | Parameter reduction can enable otherwise infeasible design and uncertainty
studies with modern computational science models that contain several input
parameters. In statistical regression, techniques for sufficient dimension
reduction (SDR) use data to reduce the predictor dimension of a regression
problem. A computational scientist hoping to use SDR for parameter reduction
encounters a problem: a computer prediction is best represented by a
deterministic function of the inputs, so data comprised of computer simulation
queries fail to satisfy the SDR assumptions. To address this problem, we
interpret SDR methods sliced inverse regression (SIR) and sliced average
variance estimation (SAVE) as estimating the directions of a ridge function,
which is a composition of a low-dimensional linear transformation with a
nonlinear function. Within this interpretation, SIR and SAVE estimate matrices
of integrals whose column spaces are contained in the ridge directions' span;
we analyze and numerically verify convergence of these column spaces as the
number of computer model queries increases. Moreover, we show example functions
that are not ridge functions but whose inverse conditional moment matrices are
low-rank. Consequently, the computational scientist should beware when using
SIR and SAVE for parameter reduction, since SIR and SAVE may mistakenly suggest
that truly important directions are unimportant.
| 0 | 0 | 1 | 0 | 0 | 0 |
Thermodynamics of Higher Order Entropy Corrected Schwarzschild-Beltrami-de Sitter Black Hole | In this paper, we consider higher order correction of the entropy and study
the thermodynamical properties of recently proposed Schwarzschild-Beltrami-de
Sitter black hole, which is indeed an exact solution of Einstein equation with
a positive cosmological constant. By using the corrected entropy and Hawking
temperature we extract some thermodynamical quantities like Gibbs and Helmholtz
free energies and heat capacity. We also investigate the first and second laws
of thermodynamics. We find that presence of higher order corrections, which
come from thermal fluctuations, may remove some instabilities of the black
hole. Also unstable to stable phase transition is possible in presence of the
first and second order corrections.
| 0 | 1 | 0 | 0 | 0 | 0 |
Robust, high brightness, degenerate entangled photon source at room temperature | We report on a compact, simple and robust high brightness entangled photon
source at room temperature. Based on a 30 mm long periodically poled potassium
titanyl phosphate (PPKTP), the source produces non-collinear, type0 phase
matched, degenerate photons at 810 nm with pair production rate as high 39.13
MHz per mW at room temperature. To the best of our knowledge, this is the
highest photon pair rate generated using bulk crystals pump with
continuous-wave laser. Combined with the inherently stable polarization Sagnac
interferometer, the source produces entangled state violating the Bells
inequality by nearly 10 standard deviations and a Bell state fidelity of 0.96.
The compact footprint, simple and robust experimental design and room
temperature operation, make our source ideal for various quantum communication
experiments including long distance free space and satellite communications.
| 0 | 1 | 0 | 0 | 0 | 0 |
Emergence of superconductivity in the cuprates via a universal percolation process | A pivotal step toward understanding unconventional superconductors would be
to decipher how superconductivity emerges from the unusual normal state upon
cooling. In the cuprates, traces of superconducting pairing appear above the
macroscopic transition temperature $T_c$, yet extensive investigation has led
to disparate conclusions. The main difficulty has been the separation of
superconducting contributions from complex normal state behaviour. Here we
avoid this problem by measuring the nonlinear conductivity, an observable that
is zero in the normal state. We uncover for several representative cuprates
that the nonlinear conductivity vanishes exponentially above $T_c$, both with
temperature and magnetic field, and exhibits temperature-scaling characterized
by a nearly universal scale $T_0$. Attempts to model the response with the
frequently evoked Ginzburg-Landau theory are unsuccessful. Instead, our
findings are captured by a simple percolation model that can also explain other
properties of the cuprates. We thus resolve a long-standing conundrum by
showing that the emergence of superconductivity in the cuprates is dominated by
their inherent inhomogeneity.
| 0 | 1 | 0 | 0 | 0 | 0 |
Exploiting Spatial Degrees of Freedom for High Data Rate Ultrasound Communication with Implantable Devices | We propose and demonstrate an ultrasonic communication link using spatial
degrees of freedom to increase data rates for deeply implantable medical
devices. Low attenuation and millimeter wavelengths make ultrasound an ideal
communication medium for miniaturized low-power implants. While small spectral
bandwidth has drastically limited achievable data rates in conventional
ultrasonic implants, large spatial bandwidth can be exploited by using multiple
transducers in a multiple-input/multiple-output system to provide spatial
multiplexing gain without additional power, larger bandwidth, or complicated
packaging. We experimentally verify the communication link in mineral oil with
a transmitter and receiver 5 cm apart, each housing two custom-designed
mm-sized piezoelectric transducers operating at the same frequency. Two streams
of data modulated with quadrature phase-shift keying at 125 kbps are
simultaneously transmitted and received on both channels, effectively doubling
the data rate to 250 kbps with a measured bit error rate below 1e-4. We also
evaluate the performance and robustness of the channel separation network by
testing the communication link after introducing position offsets. These
results demonstrate the potential of spatial multiplexing to enable more
complex implant applications requiring higher data rates.
| 1 | 1 | 0 | 0 | 0 | 0 |
Global sensitivity analysis in the context of imprecise probabilities (p-boxes) using sparse polynomial chaos expansions | Global sensitivity analysis aims at determining which uncertain input
parameters of a computational model primarily drives the variance of the output
quantities of interest. Sobol' indices are now routinely applied in this
context when the input parameters are modelled by classical probability theory
using random variables. In many practical applications however, input
parameters are affected by both aleatory and epistemic (so-called polymorphic)
uncertainty, for which imprecise probability representations have become
popular in the last decade. In this paper, we consider that the uncertain input
parameters are modelled by parametric probability boxes (p-boxes). We propose
interval-valued (so-called imprecise) Sobol' indices as an extension of their
classical definition. An original algorithm based on the concepts of augmented
space, isoprobabilistic transforms and sparse polynomial chaos expansions is
devised to allow for the computation of these imprecise Sobol' indices at
extremely low cost. In particular, phantoms points are introduced to build an
experimental design in the augmented space (necessary for the calibration of
the sparse PCE) which leads to a smart reuse of runs of the original
computational model. The approach is illustrated on three analytical and
engineering examples which allows one to validate the proposed algorithms
against brute-force double-loop Monte Carlo simulation.
| 0 | 0 | 0 | 1 | 0 | 0 |
Unexpected Enhancement of Three-Dimensional Low-Energy Spin Correlations in Quasi-Two-Dimensional Fe$_{1+y}$Te$_{1-x}$Se$_{x}$ System at High Temperature | We report inelastic neutron scattering measurements of low energy ($\hbar
\omega < 10$ meV) magnetic excitations in the "11" system
Fe$_{1+y}$Te$_{1-x}$Se$_{x}$. The spin correlations are two-dimensional (2D) in
the superconducting samples at low temperature, but appear much more
three-dimensional when the temperature rises well above $T_c \sim 15$ K, with a
clear increase of the (dynamic) spin correlation length perpendicular to the Fe
planes. The spontaneous change of dynamic spin correlations from 2D to 3D on
warming is unexpected and cannot be naturally explained when only the spin
degree of freedom is considered. Our results suggest that the low temperature
physics in the "11" system, in particular the evolution of low energy spin
excitations towards %better satisfying the nesting condition for mediating
superconducting pairing, is driven by changes in orbital correlations.
| 0 | 1 | 0 | 0 | 0 | 0 |
Agile Software Development Methods: Review and Analysis | Agile - denoting "the quality of being agile, readiness for motion,
nimbleness, activity, dexterity in motion" - software development methods are
attempting to offer an answer to the eager business community asking for
lighter weight along with faster and nimbler software development processes.
This is especially the case with the rapidly growing and volatile Internet
software industry as well as for the emerging mobile application environment.
The new agile methods have evoked substantial amount of literature and debates.
However, academic research on the subject is still scarce, as most of existing
publications are written by practitioners or consultants. The aim of this
publication is to begin filling this gap by systematically reviewing the
existing literature on agile software development methodologies. This
publication has three purposes. First, it proposes a definition and a
classification of agile software development approaches. Second, it analyses
ten software development methods that can be characterized as being "agile"
against the defined criterion. Third, it compares these methods and highlights
their similarities and differences. Based on this analysis, future research
needs are identified and discussed.
| 1 | 0 | 0 | 0 | 0 | 0 |
Deep Learning: A Bayesian Perspective | Deep learning is a form of machine learning for nonlinear high dimensional
pattern matching and prediction. By taking a Bayesian probabilistic
perspective, we provide a number of insights into more efficient algorithms for
optimisation and hyper-parameter tuning. Traditional high-dimensional data
reduction techniques, such as principal component analysis (PCA), partial least
squares (PLS), reduced rank regression (RRR), projection pursuit regression
(PPR) are all shown to be shallow learners. Their deep learning counterparts
exploit multiple deep layers of data reduction which provide predictive
performance gains. Stochastic gradient descent (SGD) training optimisation and
Dropout (DO) regularization provide estimation and variable selection. Bayesian
regularization is central to finding weights and connections in networks to
optimize the predictive bias-variance trade-off. To illustrate our methodology,
we provide an analysis of international bookings on Airbnb. Finally, we
conclude with directions for future research.
| 1 | 0 | 0 | 1 | 0 | 0 |
Tunnel-injected sub-260 nm ultraviolet light emitting diodes | We report on tunnel-injected deep ultraviolet light emitting diodes (UV LEDs)
configured with a polarization engineered Al0.75Ga0.25N/ In0.2Ga0.8N tunnel
junction structure. Tunnel-injected UV LED structure enables n-type contacts
for both bottom and top contact layers. However, achieving Ohmic contact to
wide bandgap n-AlGaN layers is challenging and typically requires high
temperature contact metal annealing. In this work, we adopted a compositionally
graded top contact layer for non-alloyed metal contact, and obtained a low
contact resistance of Rc=4.8x10-5 Ohm cm2 on n-Al0.75Ga0.25N. We also observed
a significant reduction in the forward operation voltage from 30.9 V to 19.2 V
at 1 kA/cm2 by increasing the Mg doping concentration from 6.2x1018 cm-3 to
1.5x1019 cm-3. Non-equilibrium hole injection into wide bandgap Al0.75Ga0.25N
with Eg>5.2 eV was confirmed by light emission at 257 nm. This work
demonstrates the feasibility of tunneling hole injection into deep UV LEDs, and
provides a novel structural design towards high power deep-UV emitters.
| 0 | 1 | 0 | 0 | 0 | 0 |
Three-dimensional image reconstruction in J-PET using Filtered Back Projection method | We present a method and preliminary results of the image reconstruction in
the Jagiellonian PET tomograph. Using GATE (Geant4 Application for Tomographic
Emission), interactions of the 511 keV photons with a cylindrical detector were
generated. Pairs of such photons, flying back-to-back, originate from e+e-
annihilations inside a 1-mm spherical source. Spatial and temporal coordinates
of hits were smeared using experimental resolutions of the detector. We
incorporated the algorithm of the 3D Filtered Back Projection, implemented in
the STIR and TomoPy software packages, which differ in approximation methods.
Consistent results for the Point Spread Functions of ~5/7,mm and ~9/20, mm were
obtained, using STIR, for transverse and longitudinal directions, respectively,
with no time of flight information included.
| 0 | 1 | 0 | 0 | 0 | 0 |
Two-component domain decomposition scheme with overlapping subdomains for parabolic equations | An iteration-free method of domain decomposition is considered for
approximate solving a boundary value problem for a second-order parabolic
equation. A standard approach to constructing domain decomposition schemes is
based on a partition of unity for the domain under the consideration. Here a
new general approach is proposed for constructing domain decomposition schemes
with overlapping subdomains based on indicator functions of subdomains. The
basic peculiarity of this method is connected with a representation of the
problem operator as the sum of two operators, which are constructed for two
separate subdomains with the subtraction of the operator that is associated
with the intersection of the subdomains. There is developed a two-component
factorized scheme, which can be treated as a generalization of the standard
Alternating Direction Implicit (ADI) schemes to the case of a special
three-component splitting. There are obtained conditions for the unconditional
stability of regionally additive schemes constructed using indicator functions
of subdomains. Numerical results are presented for a model two-dimensional
problem.
| 1 | 0 | 0 | 0 | 0 | 0 |
On the representation of finite convex geometries with convex sets | Very recently Richter and Rogers proved that any convex geometry can be
represented by a family of convex polygons in the plane. We shall generalize
their construction and obtain a wide variety of convex shapes for representing
convex geometries. We present an Erdos-Szekeres type obstruction, which answers
a question of Czedli negatively, that is general convex geometries cannot be
represented with ellipses in the plane. Moreover, we shall prove that one
cannot even bound the number of common supporting lines of the pairs of the
representing convex sets. In higher dimensions we prove that all convex
geometries can be represented with ellipsoids.
| 0 | 0 | 1 | 0 | 0 | 0 |
Resolving ultrafast exciton migration in organic solids at the nanoscale | The effectiveness of molecular-based light harvesting relies on transport of
optical excitations, excitons, to charg-transfer sites. Measuring exciton
migration has, however, been challenging because of the mismatch between
nanoscale migration lengths and the diffraction limit. In organic
semiconductors, common bulk methods employ a series of films terminated at
quenching substrates, altering the spatioenergetic landscape for migration.
Here we instead define quenching boundaries all-optically with sub-diffraction
resolution, thus characterizing spatiotemporal exciton migration on its native
nanometer and picosecond scales without disturbing morphology. By transforming
stimulated emission depletion microscopy into a time-resolved ultrafast
approach, we measure a 16-nm migration length in CN-PPV conjugated polymer
films. Combining these experiments with Monte Carlo exciton hopping simulations
shows that migration in CN-PPV films is essentially diffusive because intrinsic
chromophore energetic disorder is comparable to inhomogeneous broadening among
chromophores. This framework also illustrates general trends across materials.
Our new approach's sub-diffraction resolution will enable previously
unattainable correlations of local material structure to the nature of exciton
migration, applicable not only to photovoltaic or display-destined organic
semiconductors but also to explaining the quintessential exciton migration
exhibited in photosynthesis.
| 0 | 1 | 0 | 0 | 0 | 0 |
Transfer Learning across Low-Resource, Related Languages for Neural Machine Translation | We present a simple method to improve neural translation of a low-resource
language pair using parallel data from a related, also low-resource, language
pair. The method is based on the transfer method of Zoph et al., but whereas
their method ignores any source vocabulary overlap, ours exploits it. First, we
split words using Byte Pair Encoding (BPE) to increase vocabulary overlap.
Then, we train a model on the first language pair and transfer its parameters,
including its source word embeddings, to another model and continue training on
the second language pair. Our experiments show that transfer learning helps
word-based translation only slightly, but when used on top of a much stronger
BPE baseline, it yields larger improvements of up to 4.3 BLEU.
| 1 | 0 | 0 | 0 | 0 | 0 |
Dynamical correlations in the electronic structure of BiFeO$_{3}$, as revealed by dynamical mean field theory | Using local density approximation plus dynamical mean-field theory
(LDA+DMFT), we have computed the valence band photoelectron spectra of highly
popular multiferroic BiFeO$_{3}$. Within DMFT, the local impurity problem is
tackled by exact diagonalization (ED) solver. For comparison, we also present
result from LDA+U approach, which is commonly used to compute physical
properties of this compound. Our LDA+DMFT derived spectra match adequately with
the experimental hard X-ray photoelectron spectroscopy (HAXPES) and resonant
photoelectron spectroscopy (RPES) for Fe 3$d$ states, whereas the other
theoretical method that we employed failed to capture the features of the
measured spectra. Thus, our investigation shows the importance of accurately
incorporating the dynamical aspects of electron-electron interaction among the
Fe 3$d$ orbitals in calculations to produce the experimental excitation
spectra, which establishes BiFeO$_{3}$ as a strongly correlated electron
system. The LDA+DMFT derived density of states (DOSs) exhibit significant
amount of Fe 3$d$ states at the energy of Bi lone-pairs, implying that the
latter is not as alone as previously thought in the spectral scenario. Our
study also demonstrates that the combination of orbital cross-sections for the
constituent elements and broadening schemes for the calculated spectral
function are pivotal to explain the detailed structures of the experimental
spectra.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Van-Der-Waals picture for metabolic networks from MaxEnt modeling: inherent bistability and elusive coexistence | In this work maximum entropy distributions in the space of steady states of
metabolic networks are defined upon constraining the first and second moment of
the growth rate. Inherent bistability of fast and slow phenotypes, akin to a
Van-Der Waals picture, emerges upon considering control on the average growth
(optimization/repression) and its fluctuations (heterogeneity). This is applied
to the carbon catabolic core of E.coli where it agrees with some stylized facts
on the persisters phenotype and it provides a quantitative map with metabolic
fluxes, opening for the possibility to detect coexistence from flux data.
Preliminary analysis on data for E.Coli cultures in standard conditions shows,
on the other hand, degeneracy for the inferred parameters that extend in the
coexistence region.
| 0 | 1 | 0 | 0 | 0 | 0 |
Robust Parameter Estimation of Regression Model with AR(p) Error Terms | In this paper, we consider a linear regression model with AR(p) error terms
with the assumption that the error terms have a t distribution as a heavy
tailed alternative to the normal distribution. We obtain the estimators for the
model parameters by using the conditional maximum likelihood (CML) method. We
conduct an iteratively reweighting algorithm (IRA) to find the estimates for
the parameters of interest. We provide a simulation study and three real data
examples to illustrate the performance of the proposed robust estimators based
on t distribution.
| 0 | 0 | 0 | 1 | 0 | 0 |
Measuring filament orientation: a new quantitative, local approach | The relative orientation between filamentary structures in molecular clouds
and the ambient magnetic field provides insight into filament formation and
stability. To calculate the relative orientation, a measurement of filament
orientation is first required. We propose a new method to calculate the
orientation of the one pixel wide filament skeleton that is output by filament
identification algorithms such as \textsc{filfinder}. We derive the local
filament orientation from the direction of the intensity gradient in the
skeleton image using the Sobel filter and a few simple post-processing steps.
We call this the `Sobel-gradient method'. The resulting filament orientation
map can be compared quantitatively on a local scale with the magnetic field
orientation map to then find the relative orientation of the filament with
respect to the magnetic field at each point along the filament. It can also be
used in constructing radial profiles for filament width fitting. The proposed
method facilitates automation in analysis of filament skeletons, which is
imperative in this era of `big data'.
| 0 | 1 | 0 | 0 | 0 | 0 |
Controlled dynamic screening of excitonic complexes in 2D semiconductors | We report a combined theoretical/experimental study of dynamic screening of
excitons in media with frequency-dependent dielectric functions. We develop an
analytical model showing that interparticle interactions in an exciton are
screened in the range of frequencies from zero to the characteristic binding
energy depending on the symmetries and transition energies of that exciton. The
problem of the dynamic screening is then reduced to simply solving the
Schrodinger equation with an effectively frequency-independent potential.
Quantitative predictions of the model are experimentally verified using a test
system: neutral, charged and defect-bound excitons in two-dimensional monolayer
WS2, screened by metallic, liquid, and semiconducting environments. The
screening-induced shifts of the excitonic peaks in photoluminescence spectra
are in good agreement with our model.
| 0 | 1 | 0 | 0 | 0 | 0 |
Motions about a fixed point by hypergeometric functions: new non-complex analytical solutions and integration of the herpolhode | We study four problems in the dynamics of a body moving about a fixed point,
providing a non-complex, analytical solution for all of them. For the first
two, we will work on the motion first integrals. For the symmetrical heavy
body, that is the Lagrange-Poisson case, we compute the second and third Euler
angles in explicit and real forms by means of multiple hypergeometric functions
(Lauricella, functions). Releasing the weight load but adding the complication
of the asymmetry, by means of elliptic integrals of third kind, we provide the
precession angle completing some previous treatments of the Euler-Poinsot case.
Integrating then the relevant differential equation, we reach the finite polar
equation of a special trajectory named the {\it herpolhode}. In the last
problem we keep the symmetry of the first problem, but without the weight, and
take into account a viscous dissipation. The approach of first integrals is no
longer practicable in this situation and the Euler equations are faced directly
leading to dumped goniometric functions obtained as particular occurrences of
Bessel functions of order $-1/2$.
| 0 | 0 | 1 | 0 | 0 | 0 |
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