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Stability analysis of a system coupled to a heat equation | As a first approach to the study of systems coupling finite and infinite
dimensional natures, this article addresses the stability of a system of
ordinary differential equations coupled with a classic heat equation using a
Lyapunov functional technique. Inspired from recent developments in the area of
time delay systems, a new methodology to study the stability of such a class of
distributed parameter systems is presented here. The idea is to use a
polynomial approximation of the infinite dimensional state of the heat equation
in order to build an enriched energy functional. A well known efficient
integral inequality (Bessel inequality) will allow to obtain stability
conditions expressed in terms of linear matrix inequalities. We will eventually
test our approach on academic examples in order to illustrate the efficiency of
our theoretical results.
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Corral Framework: Trustworthy and Fully Functional Data Intensive Parallel Astronomical Pipelines | Data processing pipelines represent an important slice of the astronomical
software library that include chains of processes that transform raw data into
valuable information via data reduction and analysis. In this work we present
Corral, a Python framework for astronomical pipeline generation. Corral
features a Model-View-Controller design pattern on top of an SQL Relational
Database capable of handling: custom data models; processing stages; and
communication alerts, and also provides automatic quality and structural
metrics based on unit testing. The Model-View-Controller provides concept
separation between the user logic and the data models, delivering at the same
time multi-processing and distributed computing capabilities. Corral represents
an improvement over commonly found data processing pipelines in Astronomy since
the design pattern eases the programmer from dealing with processing flow and
parallelization issues, allowing them to focus on the specific algorithms
needed for the successive data transformations and at the same time provides a
broad measure of quality over the created pipeline. Corral and working examples
of pipelines that use it are available to the community at
this https URL.
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Radial orbit instability in systems of highly eccentric orbits: Antonov problem reviewed | Stationary stellar systems with radially elongated orbits are subject to
radial orbit instability -- an important phenomenon that structures galaxies.
Antonov (1973) presented a formal proof of the instability for spherical
systems in the limit of purely radial orbits. However, such spheres have highly
inhomogeneous density distributions with singularity $\sim 1/r^2$, resulting in
an inconsistency in the proof. The proof can be refined, if one considers an
orbital distribution close to purely radial, but not entirely radial, which
allows to avoid the central singularity. For this purpose we employ
non-singular analogs of generalised polytropes elaborated recently in our work
in order to derive and solve new integral equations adopted for calculation of
unstable eigenmodes in systems with nearly radial orbits. In addition, we
establish a link between our and Antonov's approaches and uncover the meaning
of infinite entities in the purely radial case. Maximum growth rates tend to
infinity as the system becomes more and more radially anisotropic. The
instability takes place both for even and odd spherical harmonics, with all
unstable modes developing rapidly, i.e. having eigenfrequencies comparable to
or greater than typical orbital frequencies. This invalidates orbital
approximation in the case of systems with all orbits very close to purely
radial.
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Short-range wakefields generated in the blowout regime of plasma-wakefield acceleration | In the past, calculation of wakefields generated by an electron bunch
propagating in a plasma has been carried out in linear approximation, where the
plasma perturbation can be assumed small and plasma equations of motion
linearized. This approximation breaks down in the blowout regime where a
high-density electron driver expels plasma electrons from its path and creates
a cavity void of electrons in its wake. In this paper, we develop a technique
that allows to calculate short-range longitudinal and transverse wakes
generated by a witness bunch being accelerated inside the cavity. Our results
can be used for studies of the beam loading and the hosing instability of the
witness bunch in PWFA and LWFA.
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Scaled Nuclear Norm Minimization for Low-Rank Tensor Completion | Minimizing the nuclear norm of a matrix has been shown to be very efficient
in reconstructing a low-rank sampled matrix. Furthermore, minimizing the sum of
nuclear norms of matricizations of a tensor has been shown to be very efficient
in recovering a low-Tucker-rank sampled tensor. In this paper, we propose to
recover a low-TT-rank sampled tensor by minimizing a weighted sum of nuclear
norms of unfoldings of the tensor. We provide numerical results to show that
our proposed method requires significantly less number of samples to recover to
the original tensor in comparison with simply minimizing the sum of nuclear
norms since the structure of the unfoldings in the TT tensor model is
fundamentally different from that of matricizations in the Tucker tensor model.
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Stability and Transparency Analysis of a Bilateral Teleoperation in Presence of Data Loss | This paper presents a novel approach for stability and transparency analysis
for bilateral teleoperation in the presence of data loss in communication
media. A new model for data loss is proposed based on a set of periodic
continuous pulses and its finite series representation. The passivity of the
overall system is shown using wave variable approach including the newly
defined model for data loss. Simulation results are presented to show the
effectiveness of the proposed approach.
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Quantum eigenstate tomography with qubit tunneling spectroscopy | Measurement of the energy eigenvalues (spectrum) of a multi-qubit system has
recently become possible by qubit tunneling spectroscopy (QTS). In the standard
QTS experiments, an incoherent probe qubit is strongly coupled to one of the
qubits of the system in such a way that its incoherent tunneling rate provides
information about the energy eigenvalues of the original (source) system. In
this paper, we generalize QTS by coupling the probe qubit to many source
qubits. We show that by properly choosing the couplings, one can perform
projective measurements of the source system energy eigenstates in an arbitrary
basis, thus performing quantum eigenstate tomography. As a practical example of
a limited tomography, we apply our scheme to probe the eigenstates of a kink in
a frustrated transverse Ising chain.
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Binary hermitian forms and optimal embeddings | Fix a quadratic order over the ring of integers. An embedding of the
quadratic order into a quaternionic order naturally gives an integral binary
hermitian form over the quadratic order. We show that, in certain cases, this
correspondence is a discriminant preserving bijection between the isomorphism
classes of embeddings and integral binary hermitian forms.
| 0 | 0 | 1 | 0 | 0 | 0 |
An Improved Training Procedure for Neural Autoregressive Data Completion | Neural autoregressive models are explicit density estimators that achieve
state-of-the-art likelihoods for generative modeling. The D-dimensional data
distribution is factorized into an autoregressive product of one-dimensional
conditional distributions according to the chain rule. Data completion is a
more involved task than data generation: the model must infer missing variables
for any partially observed input vector. Previous work introduced an
order-agnostic training procedure for data completion with autoregressive
models. Missing variables in any partially observed input vector can be imputed
efficiently by choosing an ordering where observed dimensions precede
unobserved ones and by computing the autoregressive product in this order. In
this paper, we provide evidence that the order-agnostic (OA) training procedure
is suboptimal for data completion. We propose an alternative procedure (OA++)
that reaches better performance in fewer computations. It can handle all data
completion queries while training fewer one-dimensional conditional
distributions than the OA procedure. In addition, these one-dimensional
conditional distributions are trained proportionally to their expected usage at
inference time, reducing overfitting. Finally, our OA++ procedure can exploit
prior knowledge about the distribution of inference completion queries, as
opposed to OA. We support these claims with quantitative experiments on
standard datasets used to evaluate autoregressive generative models.
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A Stochastic Formulation of the Resolution of Identity: Application to Second Order Møller-Plesset Perturbation Theory | A stochastic orbital approach to the resolution of identity (RI)
approximation for 4-index 2-electron electron repulsion integrals (ERIs) is
presented. The stochastic RI-ERIs are then applied to M\o ller-Plesset
perturbation theory (MP2) utilizing a \textit{multiple stochastic orbital
approach}. The introduction of multiple stochastic orbitals results in an $N^3$
scaling for both the stochastic RI-ERIs and stochastic RI-MP2. We demonstrate
that this method exhibits a small prefactor and an observed scaling of
$N^{2.4}$ for a range of water clusters, already outperforming MP2 for clusters
with as few as 21 water molecules.
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Automatic Mapping of NES Games with Mappy | Game maps are useful for human players, general-game-playing agents, and
data-driven procedural content generation. These maps are generally made by
hand-assembling manually-created screenshots of game levels. Besides being
tedious and error-prone, this approach requires additional effort for each new
game and level to be mapped. The results can still be hard for humans or
computational systems to make use of, privileging visual appearance over
semantic information. We describe a software system, Mappy, that produces a
good approximation of a linked map of rooms given a Nintendo Entertainment
System game program and a sequence of button inputs exploring its world. In
addition to visual maps, Mappy outputs grids of tiles (and how they change over
time), positions of non-tile objects, clusters of similar rooms that might in
fact be the same room, and a set of links between these rooms. We believe this
is a necessary step towards developing larger corpora of high-quality
semantically-annotated maps for PCG via machine learning and other
applications.
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Proportional Closeness Estimation of Probability of Contamination Under Group Testing | The paper is focused on the problem of estimating the probability $p$ of
individual contaminated sample, under group testing. The precision of the
estimator is given by the probability of proportional closeness, a concept
defined in the Introduction. Two-stage and sequential sampling procedures are
characterized. An adaptive procedure is examined.
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Utility of General and Specific Word Embeddings for Classifying Translational Stages of Research | Conventional text classification models make a bag-of-words assumption
reducing text into word occurrence counts per document. Recent algorithms such
as word2vec are capable of learning semantic meaning and similarity between
words in an entirely unsupervised manner using a contextual window and doing so
much faster than previous methods. Each word is projected into vector space
such that similar meaning words such as "strong" and "powerful" are projected
into the same general Euclidean space. Open questions about these embeddings
include their utility across classification tasks and the optimal properties
and source of documents to construct broadly functional embeddings. In this
work, we demonstrate the usefulness of pre-trained embeddings for
classification in our task and demonstrate that custom word embeddings, built
in the domain and for the tasks, can improve performance over word embeddings
learnt on more general data including news articles or Wikipedia.
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The AKARI IRC asteroid flux catalogue: updated diameters and albedos | The AKARI IRC All-sky survey provided more than twenty thousand thermal
infrared observations of over five thousand asteroids. Diameters and albedos
were obtained by fitting an empirically calibrated version of the standard
thermal model to these data. After the publication of the flux catalogue in
October 2016, our aim here is to present the AKARI IRC all-sky survey data and
discuss valuable scientific applications in the field of small-body physical
properties studies. As an example, we update the catalogue of asteroid
diameters and albedos based on AKARI using the near-Earth asteroid thermal
model (NEATM). We fit the NEATM to derive asteroid diameters and, whenever
possible, infrared beaming parameters. We obtained a total of 8097 diameters
and albedos for 5170 asteroids, and we fitted the beaming parameter for almost
two thousand of them. When it was not possible to fit the beaming parameter, we
used a straight line fit to our sample's beaming parameter-versus-phase angle
plot to set the default value for each fit individually instead of using a
single average value. Our diameters agree with stellar-occultation-based
diameters well within the accuracy expected for the model. They also match the
previous AKARI-based catalogue at phase angles lower than 50 degrees, but we
find a systematic deviation at higher phase angles, at which near-Earth and
Mars-crossing asteroids were observed. The AKARI IRC All-sky survey provides
observations at different observation geometries, rotational coverages and
aspect angles. For example, by comparing in more detail a few asteroids for
which dimensions were derived from occultations, we discuss how the multiple
observations per object may already provide three-dimensional information about
elongated objects even based on an idealised model like the NEATM.
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Finite sample Bernstein - von Mises theorems for functionals and spectral projectors of covariance matrix | We demonstrate that a prior influence on the posterior distribution of
covariance matrix vanishes as sample size grows. The assumptions on a prior are
explicit and mild. The results are valid for a finite sample and admit the
dimension $p$ growing with the sample size $n$. We exploit the described fact
to derive the finite sample Bernstein - von Mises theorem for functionals of
covariance matrix (e.g. eigenvalues) and to find the posterior distribution of
the Frobenius distance between spectral projector and empirical spectral
projector. This can be useful for constructing sharp confidence sets for the
true value of the functional or for the true spectral projector.
| 0 | 0 | 1 | 1 | 0 | 0 |
Low-temperature behavior of the multicomponent Widom-Rowlison model on finite square lattices | We consider the multicomponent Widom-Rowlison with Metropolis dynamics, which
describes the evolution of a particle system where $M$ different types of
particles interact subject to certain hard-core constraints. Focusing on the
scenario where the spatial structure is modeled by finite square lattices, we
study the asymptotic behavior of this interacting particle system in the
low-temperature regime, analyzing the tunneling times between its $M$
maximum-occupancy configurations, and the mixing time of the corresponding
Markov chain. In particular, we develop a novel combinatorial method that,
exploiting geometrical properties of the Widom-Rowlinson configurations on
finite square lattices, leads to the identification of the timescale at which
transitions between maximum-occupancy configurations occur and shows how this
depends on the chosen boundary conditions and the square lattice dimensions.
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Stream Graphs and Link Streams for the Modeling of Interactions over Time | Graph theory provides a language for studying the structure of relations, and
it is often used to study interactions over time too. However, it poorly
captures the both temporal and structural nature of interactions, that calls
for a dedicated formalism. In this paper, we generalize graph concepts in order
to cope with both aspects in a consistent way. We start with elementary
concepts like density, clusters, or paths, and derive from them more advanced
concepts like cliques, degrees, clustering coefficients, or connected
components. We obtain a language to directly deal with interactions over time,
similar to the language provided by graphs to deal with relations. This
formalism is self-consistent: usual relations between different concepts are
preserved. It is also consistent with graph theory: graph concepts are special
cases of the ones we introduce. This makes it easy to generalize higher-level
objects such as quotient graphs, line graphs, k-cores, and centralities. This
paper also considers discrete versus continuous time assumptions, instantaneous
links, and extensions to more complex cases.
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Supermetric Search | Metric search is concerned with the efficient evaluation of queries in metric
spaces. In general,a large space of objects is arranged in such a way that,
when a further object is presented as a query, those objects most similar to
the query can be efficiently found. Most mechanisms rely upon the triangle
inequality property of the metric governing the space. The triangle inequality
property is equivalent to a finite embedding property, which states that any
three points of the space can be isometrically embedded in two-dimensional
Euclidean space. In this paper, we examine a class of semimetric space which is
finitely four-embeddable in three-dimensional Euclidean space. In mathematics
this property has been extensively studied and is generally known as the
four-point property. All spaces with the four-point property are metric spaces,
but they also have some stronger geometric guarantees. We coin the term
supermetric space as, in terms of metric search, they are significantly more
tractable. Supermetric spaces include all those governed by Euclidean, Cosine,
Jensen-Shannon and Triangular distances, and are thus commonly used within many
domains. In previous work we have given a generic mathematical basis for the
supermetric property and shown how it can improve indexing performance for a
given exact search structure. Here we present a full investigation into its use
within a variety of different hyperplane partition indexing structures, and go
on to show some more of its flexibility by examining a search structure whose
partition and exclusion conditions are tailored, at each node, to suit the
individual reference points and data set present there. Among the results
given, we show a new best performance for exact search using a well-known
benchmark.
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Liouville-type theorems with finite Morse index for Δ_λ-Laplace operator | In this paper we study solutions, possibly unbounded and sign-changing, of
the following problem:
-\D_{\lambda} u=|x|_{\lambda}^a |u|^{p-1}u, in R^n,\;n\geq 1,\; p>1, and a
\geq 0, where \D_{\lambda} is a strongly degenerate elliptic operator, the
functions \lambda=(\lambda_1, ..., \lambda_k) : R^n \rightarrow R^k, satisfies
some certain conditions, and |.|_{\lambda} the homogeneous norm associated to
the \D_{\lambda}-Laplacian.
We prove various Liouville-type theorems for smooth solutions under the
assumption that they are stable or stable outside a compact set of R^n. First,
we establish the standard integralestimates via stability property to derive
the nonexistence results for stable solutions. Next, by mean of the Pohozaev
identity, we deduce the Liouville-type theorem for solutions stable outside a
compact set.
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Performance Impact of Base Station Antenna Heights in Dense Cellular Networks | In this paper, we present a new and significant theoretical discovery. If the
absolute height difference between base station (BS) antenna and user equipment
(UE) antenna is larger than zero, then the network performance in terms of both
the coverage probability and the area spectral efficiency (ASE) will
continuously decrease toward zero as the BS density increases for ultra-dense
(UD) small cell networks (SCNs). Such findings are completely different from
the conclusions in existing works, both quantitatively and qualitatively. In
particular, this performance behavior has a tremendous impact on the deployment
of UD SCNs in the 5th-generation (5G) era. Network operators may invest large
amounts of money in deploying more network infrastructure to only obtain an
even less network capacity. Our study results reveal that one way to address
this issue is to lower the SCN BS antenna height to the UE antenna height.
However, this requires a revolutionized approach of BS architecture and
deployment, which is explored in this paper too.
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An information-theoretic approach for selecting arms in clinical trials | The question of selecting the "best" amongst different choices is a common
problem in statistics. In drug development, our motivating setting, the
question becomes, for example: what is the dose that gives me a pre-specified
risk of toxicity or which treatment gives the best response rate. Motivated by
a recent development in the weighted information measures theory, we propose an
experimental design based on a simple and intuitive criterion which governs arm
selection in the experiment with multinomial outcomes. The criterion leads to
accurate arm selection without any parametric or monotonicity assumption. The
asymptotic properties of the design are studied for different allocation rules
and the small sample size behaviour is evaluated in simulations in the context
of Phase I and Phase II clinical trials with binary endpoints. We compare the
proposed design to currently used alternatives and discuss its practical
implementation.
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A watershed-based algorithm to segment and classify cells in fluorescence microscopy images | Imaging assays of cellular function, especially those using fluorescent
stains, are ubiquitous in the biological and medical sciences. Despite advances
in computer vision, such images are often analyzed using only manual or
rudimentary automated processes. Watershed-based segmentation is an effective
technique for identifying objects in images; it outperforms commonly used image
analysis methods, but requires familiarity with computer-vision techniques to
be applied successfully. In this report, we present and implement a
watershed-based image analysis and classification algorithm in a GUI, enabling
a broad set of users to easily understand the algorithm and adjust the
parameters to their specific needs. As an example, we implement this algorithm
to find and classify cells in a complex imaging assay for mitochondrial
function. In a second example, we demonstrate a workflow using manual
comparisons and receiver operator characteristics to optimize the algorithm
parameters for finding live and dead cells in a standard viability assay.
Overall, this watershed-based algorithm is more advanced than traditional
thresholding and can produce optimized, automated results. By incorporating
associated pre-processing steps in the GUI, the algorithm is also easily
adjusted, rendering it user-friendly.
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On the restricted Chebyshev-Boubaker polynomials | Using the language of Riordan arrays, we study a one-parameter family of
orthogonal polynomials that we call the restricted Chebyshev-Boubaker
polynomials. We characterize these polynomials in terms of the three term
recurrences that they satisfy, and we study certain central sequences defined
by their coefficient arrays. We give an integral representation for their
moments, and we show that the Hankel transforms of these moments have a simple
form. We show that the (sequence) Hankel transform of the row sums of the
corresponding moment matrix is defined by a family of polynomials closely
related to the Chebyshev polynomials of the second kind, and that these row
sums are in fact the moments of another family of orthogonal polynomials.
| 0 | 0 | 1 | 0 | 0 | 0 |
Band filling control of the Dzyaloshinskii-Moriya interaction in weakly ferromagnetic insulators | We observe and explain theoretically a dramatic evolution of the
Dzyaloshinskii-Moriya interaction in the series of isostructural weak
ferromagnets, MnCO$_3$, FeBO$_3$, CoCO$_3$ and NiCO$_3$. The sign of the
interaction is encoded in the phase of x-ray magnetic diffraction amplitude,
observed through interference with resonant quadrupole scattering. We find very
good quantitative agreement with first-principles electronic structure
calculations, reproducing both sign and magnitude through the series, and
propose a simplified `toy model' to explain the change in sign with 3 d shell
filling. The model gives a clue for qualitative understanding of the evolution
of the DMI in Mott and charge transfer insulators.
| 0 | 1 | 0 | 0 | 0 | 0 |
Reducing Estimation Risk in Mean-Variance Portfolios with Machine Learning | In portfolio analysis, the traditional approach of replacing population
moments with sample counterparts may lead to suboptimal portfolio choices. I
show that optimal portfolio weights can be estimated using a machine learning
(ML) framework, where the outcome to be predicted is a constant and the vector
of explanatory variables is the asset returns. It follows that ML specifically
targets estimation risk when estimating portfolio weights, and that
"off-the-shelf" ML algorithms can be used to estimate the optimal portfolio in
the presence of parameter uncertainty. The framework nests the traditional
approach and recently proposed shrinkage approaches as special cases. By
relying on results from the ML literature, I derive new insights for existing
approaches and propose new estimation methods. Based on simulation studies and
several datasets, I find that ML significantly reduces estimation risk compared
to both the traditional approach and the equal weight strategy.
| 0 | 0 | 0 | 0 | 0 | 1 |
CutFEM topology optimization of 3D laminar incompressible flow problems | This paper studies the characteristics and applicability of the CutFEM
approach as the core of a robust topology optimization framework for 3D laminar
incompressible flow and species transport problems at low Reynolds number (Re <
200). CutFEM is a methodology for discretizing partial differential equations
on complex geometries by immersed boundary techniques. In this study, the
geometry of the fluid domain is described by an explicit level set method,
where the parameters of a level set function are defined as functions of the
optimization variables. The fluid behavior is modeled by the incompressible
Navier-Stokes equations. Species transport is modeled by an advection-diffusion
equation. The governing equations are discretized in space by a generalized
extended finite element method. Face-oriented ghost-penalty terms are added for
stability reasons and to improve the conditioning of the system. The boundary
conditions are enforced weakly via Nit\-sc\-he's method. The emergence of
isolated volumes of fluid surrounded by solid during the optimization process
leads to a singular analysis problem. An auxiliary indicator field is modeled
to identify these volumes and to impose a constraint on the average pressure.
Numerical results for 3D, steady-state and transient problems demonstrate that
the CutFEM analyses are sufficiently accurate, and the optimized designs agree
well with results from prior studies solved in 2D or by density approaches.
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Network topology of neural systems supporting avalanche dynamics predicts stimulus propagation and recovery | Many neural systems display avalanche behavior characterized by uninterrupted
sequences of neuronal firing whose distributions of size and durations are
heavy-tailed. Theoretical models of such systems suggest that these dynamics
support optimal information transmission and storage. However, the unknown role
of network structure precludes an understanding of how variations in network
topology manifest in neural dynamics and either support or impinge upon
information processing. Here, using a generalized spiking model, we develop a
mechanistic understanding of how network topology supports information
processing through network dynamics. First, we show how network topology
determines network dynamics by analytically and numerically demonstrating that
network topology can be designed to propagate stimulus patterns for long
durations. We then identify strongly connected cycles as empirically observable
network motifs that are prevalent in such networks. Next, we show that within a
network, mathematical intuitions from network control theory are tightly linked
with dynamics initiated by node-specific stimulation and can identify stimuli
that promote long-lasting cascades. Finally, we use these network-based metrics
and control-based stimuli to demonstrate that long-lasting cascade dynamics
facilitate delayed recovery of stimulus patterns from network activity, as
measured by mutual information. Collectively, our results provide evidence that
cortical networks are structured with architectural motifs that support
long-lasting propagation and recovery of a few crucial patterns of stimulation,
especially those consisting of activity in highly controllable neurons.
Broadly, our results imply that avalanching neural networks could contribute to
cognitive faculties that require persistent activation of neuronal patterns,
such as working memory or attention.
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Comparison of ontology alignment systems across single matching task via the McNemar's test | Ontology alignment is widely-used to find the correspondences between
different ontologies in diverse fields.After discovering the alignments,several
performance scores are available to evaluate them.The scores typically require
the identified alignment and a reference containing the underlying actual
correspondences of the given ontologies.The current trend in the alignment
evaluation is to put forward a new score(e.g., precision, weighted precision,
etc.)and to compare various alignments by juxtaposing the obtained scores.
However,it is substantially provocative to select one measure among others for
comparison.On top of that, claiming if one system has a better performance than
one another cannot be substantiated solely by comparing two scalars.In this
paper,we propose the statistical procedures which enable us to theoretically
favor one system over one another.The McNemar's test is the statistical means
by which the comparison of two ontology alignment systems over one matching
task is drawn.The test applies to a 2x2 contingency table which can be
constructed in two different ways based on the alignments,each of which has
their own merits/pitfalls.The ways of the contingency table construction and
various apposite statistics from the McNemar's test are elaborated in minute
detail.In the case of having more than two alignment systems for comparison,
the family-wise error rate is expected to happen. Thus, the ways of preventing
such an error are also discussed.A directed graph visualizes the outcome of the
McNemar's test in the presence of multiple alignment systems.From this graph,
it is readily understood if one system is better than one another or if their
differences are imperceptible.The proposed statistical methodologies are
applied to the systems participated in the OAEI 2016 anatomy track, and also
compares several well-known similarity metrics for the same matching problem.
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Polarizability Extraction for Waveguide-Fed Metasurfaces | We consider the design and modeling of metasurfaces that couple energy from
guided waves to propagating wavefronts. This is a first step towards a
comprehensive, multiscale modeling platform for metasurface antennas-large
arrays of metamaterial elements embedded in a waveguide structure that radiates
into free-space--in which the detailed electromagnetic responses of
metamaterial elements are replaced by polarizable dipoles. We present two
methods to extract the effective polarizability of a metamaterial element
embedded in a one- or two-dimensional waveguide. The first method invokes
surface equivalence principles, averaging over the effective surface currents
and charges within an element to obtain the effective dipole moments; the
second method is based on computing the coefficients of the scattered waves
within the waveguide, from which the effective polarizability can be inferred.
We demonstrate these methods on several variants of waveguide-fed metasurface
elements, finding excellent agreement between the two, as well as with
analytical expressions derived for irises with simpler geometries. Extending
the polarizability extraction technique to higher order multipoles, we confirm
the validity of the dipole approximation for common metamaterial elements. With
the effective polarizabilities of the metamaterial elements accurately
determined, the radiated fields generated by a metasurface antenna (inside and
outside the antenna) can be found self-consistently by including the
interactions between polarizable dipoles. The dipole description provides an
alternative language and computational framework for engineering metasurface
antennas, holograms, lenses, beam-forming arrays, and other electrically large,
waveguide-fed metasurface structures.
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Connection Scan Algorithm | We introduce the Connection Scan Algorithm (CSA) to efficiently answer
queries to timetable information systems. The input consists, in the simplest
setting, of a source position and a desired target position. The output consist
is a sequence of vehicles such as trains or buses that a traveler should take
to get from the source to the target. We study several problem variations such
as the earliest arrival and profile problems. We present algorithm variants
that only optimize the arrival time or additionally optimize the number of
transfers in the Pareto sense. An advantage of CSA is that is can easily adjust
to changes in the timetable, allowing the easy incorporation of known vehicle
delays. We additionally introduce the Minimum Expected Arrival Time (MEAT)
problem to handle possible, uncertain, future vehicle delays. We present a
solution to the MEAT problem that is based upon CSA. Finally, we extend CSA
using the multilevel overlay paradigm to answer complex queries on nation-wide
integrated timetables with trains and buses.
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A Proof of Orthogonal Double Machine Learning with $Z$-Estimators | We consider two stage estimation with a non-parametric first stage and a
generalized method of moments second stage, in a simpler setting than
(Chernozhukov et al. 2016). We give an alternative proof of the theorem given
in (Chernozhukov et al. 2016) that orthogonal second stage moments, sample
splitting and $n^{1/4}$-consistency of the first stage, imply
$\sqrt{n}$-consistency and asymptotic normality of second stage estimates. Our
proof is for a variant of their estimator, which is based on the empirical
version of the moment condition (Z-estimator), rather than a minimization of a
norm of the empirical vector of moments (M-estimator). This note is meant
primarily for expository purposes, rather than as a new technical contribution.
| 0 | 0 | 1 | 1 | 0 | 0 |
Quantile Treatment Effects in Difference in Differences Models under Dependence Restrictions and with only Two Time Periods | This paper shows that the Conditional Quantile Treatment Effect on the
Treated can be identified using a combination of (i) a conditional
Distributional Difference in Differences assumption and (ii) an assumption on
the conditional dependence between the change in untreated potential outcomes
and the initial level of untreated potential outcomes for the treated group.
The second assumption recovers the unknown dependence from the observed
dependence for the untreated group. We also consider estimation and inference
in the case where all of the covariates are discrete. We propose a uniform
inference procedure based on the exchangeable bootstrap and show its validity.
We conclude the paper by estimating the effect of state-level changes in the
minimum wage on the distribution of earnings for subgroups defined by race,
gender, and education.
| 0 | 0 | 1 | 1 | 0 | 0 |
Coppersmith's lattices and "focus groups": an attack on small-exponent RSA | We present a principled technique for reducing the matrix size in some
applications of Coppersmith's lattice method for finding roots of modular
polynomial equations. It relies on an analysis of the actual performance of
Coppersmith's attack for smaller parameter sizes, which can be thought of as
"focus group" testing. When applied to the small-exponent RSA problem, it
reduces lattice dimensions and consequently running times (sometimes by factors
of two or more). We also argue that existing metrics (such as enabling
condition bounds) are not as important as often thought for measuring the true
performance of attacks based on Coppersmith's method. Finally, experiments are
given to indicate that certain lattice reductive algorithms (such as
Nguyen-Stehlé's L2) may be particularly well-suited for Coppersmith's method.
| 1 | 0 | 1 | 0 | 0 | 0 |
Gas vs. solid phase deuterated chemistry: HDCO and D$_2$CO in massive star-forming regions | The formation of deuterated molecules is favoured at low temperatures and
high densities. Therefore, the deuteration fraction D$_{frac}$ is expected to
be enhanced in cold, dense prestellar cores and to decrease after protostellar
birth. Previous studies have shown that the deuterated forms of species such as
N2H+ (formed in the gas phase) and CH3OH (formed on grain surfaces) can be used
as evolutionary indicators and to constrain their dominant formation processes
and time-scales. Formaldehyde (H2CO) and its deuterated forms can be produced
both in the gas phase and on grain surfaces. However, the relative importance
of these two chemical pathways is unclear. Comparison of the deuteration
fraction of H2CO with respect to that of N2H+, NH3 and CH3OH can help us to
understand its formation processes and time-scales. With the new SEPIA Band 5
receiver on APEX, we have observed the J=3-2 rotational lines of HDCO and D2CO
at 193 GHz and 175 GHz toward three massive star forming regions hosting
objects at different evolutionary stages: two High-mass Starless Cores (HMSC),
two High-mass Protostellar Objects (HMPOs), and one Ultracompact HII region
(UCHII). By using previously obtained H2CO J=3-2 data, the deuteration
fractions HDCO/H2CO and D2CO/HDCO are estimated. Our observations show that
singly-deuterated H2CO is detected toward all sources and that the deuteration
fraction of H2CO increases from the HMSC to the HMPO phase and then sharply
decreases in the latest evolutionary stage (UCHII). The doubly-deuterated form
of H2CO is detected only in the earlier evolutionary stages with D2CO/H2CO
showing a pattern that is qualitatively consistent with that of HDCO/H2CO,
within current uncertainties. Our initial results show that H2CO may display a
similar D$_{frac}$ pattern as that of CH3OH in massive young stellar objects.
This finding suggests that solid state reactions dominate its formation.
| 0 | 1 | 0 | 0 | 0 | 0 |
Improved stability of optimal traffic paths | Models involving branched structures are employed to describe several
supply-demand systems such as the structure of the nerves of a leaf, the system
of roots of a tree and the nervous or cardiovascular systems. Given a flow
(traffic path) that transports a given measure $\mu^-$ onto a target measure
$\mu^+$, along a 1-dimensional network, the transportation cost per unit length
is supposed in these models to be proportional to a concave power $\alpha \in
(0,1)$ of the intensity of the flow.
In this paper we address an open problem in the book "Optimal transportation
networks" by Bernot, Caselles and Morel and we improve the stability for
optimal traffic paths in the Euclidean space $\mathbb{R}^d$, with respect to
variations of the given measures $(\mu^-,\mu^+)$, which was known up to now
only for $\alpha>1-\frac1d$. We prove it for exponents $\alpha>1-\frac1{d-1}$
(in particular, for every $\alpha \in (0,1)$ when $d=2$), for a fairly large
class of measures $\mu^+$ and $\mu^-$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Neural Code Comprehension: A Learnable Representation of Code Semantics | With the recent success of embeddings in natural language processing,
research has been conducted into applying similar methods to code analysis.
Most works attempt to process the code directly or use a syntactic tree
representation, treating it like sentences written in a natural language.
However, none of the existing methods are sufficient to comprehend program
semantics robustly, due to structural features such as function calls,
branching, and interchangeable order of statements. In this paper, we propose a
novel processing technique to learn code semantics, and apply it to a variety
of program analysis tasks. In particular, we stipulate that a robust
distributional hypothesis of code applies to both human- and machine-generated
programs. Following this hypothesis, we define an embedding space, inst2vec,
based on an Intermediate Representation (IR) of the code that is independent of
the source programming language. We provide a novel definition of contextual
flow for this IR, leveraging both the underlying data- and control-flow of the
program. We then analyze the embeddings qualitatively using analogies and
clustering, and evaluate the learned representation on three different
high-level tasks. We show that even without fine-tuning, a single RNN
architecture and fixed inst2vec embeddings outperform specialized approaches
for performance prediction (compute device mapping, optimal thread coarsening);
and algorithm classification from raw code (104 classes), where we set a new
state-of-the-art.
| 1 | 0 | 0 | 1 | 0 | 0 |
Electron conduction in solid state via time varying wavevectors | In this paper, we study electron wavepacket dynamics in electric and magnetic
fields. We rigorously derive the semiclassical equations of electron dynamics
in electric and magnetic fields. We do it both for free electron and electron
in a periodic potential. We do this by introducing time varying wavevectors
$k(t)$. In the presence of magnetic field, our wavepacket reproduces the
classical cyclotron orbits once the origin of the Schröedinger equation is
correctly chosen to be center of cyclotron orbit. In the presence of both
electric and magnetic fields, our equations for wavepacket dynamics differ from
classical Lorentz force equations. We show that in a periodic potential, on
application of electric field, the electron wave function adiabatically follows
the wavefunction of a time varying Bloch wavevector $k(t)$, with its energies
suitably shifted with time. We derive the effective mass equation and discuss
conduction in conductors and insulators.
| 0 | 1 | 0 | 0 | 0 | 0 |
On blowup of co-rotational wave maps in odd space dimensions | We consider co-rotational wave maps from the $(1+d)$-dimensional Minkowski
space into the $d$-sphere for $d\geq 3$ odd. This is an energy-supercritical
model which is known to exhibit finite-time blowup via self-similar solutions.
Based on a method developed by the second author and Schörkhuber, we prove
the asymptotic nonlinear stability of the "ground-state" self-similar solution.
| 0 | 0 | 1 | 0 | 0 | 0 |
The Young Substellar Companion ROXs 12 B: Near-Infrared Spectrum, System Architecture, and Spin-Orbit Misalignment | ROXs 12 (2MASS J16262803-2526477) is a young star hosting a directly imaged
companion near the deuterium-burning limit. We present a suite of
spectroscopic, imaging, and time-series observations to characterize the
physical and environmental properties of this system. Moderate-resolution
near-infrared spectroscopy of ROXs 12 B from Gemini-North/NIFS and Keck/OSIRIS
reveals signatures of low surface gravity including weak alkali absorption
lines and a triangular $H$-band pseudo-continuum shape. No signs of Pa$\beta$
emission are evident. As a population, however, we find that about half (46
$\pm$ 14\%) of young ($\lesssim$15 Myr) companions with masses $\lesssim$20
$M_\mathrm{Jup}$ possess actively accreting subdisks detected via Pa$\beta$
line emission, which represents a lower limit on the prevalence of
circumplanetary disks in general as some are expected to be in a quiescent
phase of accretion. The bolometric luminosity of the companion and age of the
host star (6$^{+4}_{-2}$ Myr) imply a mass of 17.5 $\pm$ 1.5 $M_\mathrm{Jup}$
for ROXs 12 B based on hot-start evolutionary models. We identify a wide (5100
AU) tertiary companion to this system, 2MASS J16262774-2527247, which is
heavily accreting and exhibits stochastic variability in its $K2$ light curve.
By combining $v$sin$i_*$ measurements with rotation periods from $K2$, we
constrain the line-of-sight inclinations of ROXs 12 A and 2MASS
J16262774-2527247 and find that they are misaligned by
60$^{+7}_{-11}$$^{\circ}$. In addition, the orbital axis of ROXs 12 B is likely
misaligned from the spin axis of its host star ROXs 12 A, suggesting that ROXs
12 B formed akin to fragmenting binary stars or in an equatorial disk that was
torqued by the wide stellar tertiary.
| 0 | 1 | 0 | 0 | 0 | 0 |
Connecting dissipation and phase slips in a Josephson junction between fermionic superfluids | We study the emergence of dissipation in an atomic Josephson junction between
weakly-coupled superfluid Fermi gases. We find that vortex-induced phase
slippage is the dominant microscopic source of dissipation across the BEC-BCS
crossover. We explore different dynamical regimes by tuning the bias chemical
potential between the two superfluid reservoirs. For small excitations, we
observe dissipation and phase coherence to coexist, with a resistive current
followed by well-defined Josephson oscillations. We link the junction transport
properties to the phase-slippage mechanism, finding that vortex nucleation is
primarily responsible for the observed trends of conductance and critical
current. For large excitations, we observe the irreversible loss of coherence
between the two superfluids, and transport cannot be described only within an
uncorrelated phase-slip picture. Our findings open new directions for
investigating the interplay between dissipative and superfluid transport in
strongly correlated Fermi systems, and general concepts in out-of-equlibrium
quantum systems.
| 0 | 1 | 0 | 0 | 0 | 0 |
Satellite conjunction analysis and the false confidence theorem | Satellite conjunction analysis is the assessment of collision risk during a
close encounter between a satellite and another object in orbit. A
counterintuitive phenomenon has emerged in the conjunction analysis literature:
probability dilution, in which lower quality data paradoxically appear to
reduce the risk of collision. We show that probability dilution is a symptom of
a fundamental deficiency in epistemic probability distributions. In
probabilistic representations of statistical inference, there are always false
propositions that have a high probability of being assigned a high degree of
belief. We call this deficiency false confidence. In satellite conjunction
analysis, it results in a severe and persistent underestimation of collision
risk exposure.
We introduce the Martin--Liu validity criterion as a benchmark by which to
identify statistical methods that are free from false confidence. If expressed
using belief functions, such inferences will necessarily be non-additive. In
satellite conjunction analysis, we show that $K \sigma$ uncertainty ellipsoids
satisfy the validity criterion. Performing collision avoidance maneuvers based
on ellipsoid overlap will ensure that collision risk is capped at the
user-specified level. Further, this investigation into satellite conjunction
analysis provides a template for recognizing and resolving false confidence
issues as they occur in other problems of statistical inference.
| 0 | 0 | 1 | 1 | 0 | 0 |
A formula goes to court: Partisan gerrymandering and the efficiency gap | Recently, a proposal has been advanced to detect unconstitutional partisan
gerrymandering with a simple formula called the efficiency gap. The efficiency
gap is now working its way towards a possible landmark case in the Supreme
Court. This note explores some of its mathematical properties in light of the
fact that it reduces to a straight proportional comparison of votes to seats.
Though we offer several critiques, we assess that EG can still be a useful
component of a courtroom analysis. But a famous formula can take on a life of
its own and this one will need to be watched closely.
| 0 | 1 | 0 | 0 | 0 | 0 |
Quantitative aspects of linear and affine closed lambda terms | Affine $\lambda$-terms are $\lambda$-terms in which each bound variable
occurs at most once and linear $\lambda$-terms are $\lambda$-terms in which
each bound variables occurs once. and only once. In this paper we count the
number of closed affine $\lambda$-terms of size $n$, closed linear
$\lambda$-terms of size $n$, affine $\beta$-normal forms of size $n$ and linear
$\beta$-normal forms of ise $n$, for different ways of measuring the size of
$\lambda$-terms. From these formulas, we show how we can derive programs for
generating all the terms of size $n$ for each class. For this we use a specific
data structure, which are contexts taking into account all the holes at levels
of abstractions.
| 1 | 0 | 1 | 0 | 0 | 0 |
Drug response prediction by ensemble learning and drug-induced gene expression signatures | Chemotherapeutic response of cancer cells to a given compound is one of the
most fundamental information one requires to design anti-cancer drugs. Recent
advances in producing large drug screens against cancer cell lines provided an
opportunity to apply machine learning methods for this purpose. In addition to
cytotoxicity databases, considerable amount of drug-induced gene expression
data has also become publicly available. Following this, several methods that
exploit omics data were proposed to predict drug activity on cancer cells.
However, due to the complexity of cancer drug mechanisms, none of the existing
methods are perfect. One possible direction, therefore, is to combine the
strengths of both the methods and the databases for improved performance. We
demonstrate that integrating a large number of predictions by the proposed
method improves the performance for this task. The predictors in the ensemble
differ in several aspects such as the method itself, the number of tasks method
considers (multi-task vs. single-task) and the subset of data considered
(sub-sampling). We show that all these different aspects contribute to the
success of the final ensemble. In addition, we attempt to use the drug screen
data together with two novel signatures produced from the drug-induced gene
expression profiles of cancer cell lines. Finally, we evaluate the method
predictions by in vitro experiments in addition to the tests on data sets.The
predictions of the methods, the signatures and the software are available from
\url{this http URL}.
| 0 | 0 | 0 | 1 | 1 | 0 |
Eigenvalue approximation of sums of Hermitian matrices from eigenvector localization/delocalization | We propose a technique for calculating and understanding the eigenvalue
distribution of sums of random matrices from the known distribution of the
summands. The exact problem is formidably hard. One extreme approximation to
the true density amounts to classical probability, in which the matrices are
assumed to commute; the other extreme is related to free probability, in which
the eigenvectors are assumed to be in generic positions and sufficiently large.
In practice, free probability theory can give a good approximation of the
density.
We develop a technique based on eigenvector localization/delocalization that
works very well for important problems of interest where free probability is
not sufficient, but certain uniformity properties apply. The
localization/delocalization property appears in a convex combination parameter
that notably, is independent of any eigenvalue properties and yields accurate
eigenvalue density approximations.
We demonstrate this technique on a number of examples as well as discuss a
more general technique when the uniformity properties fail to apply.
| 0 | 1 | 1 | 0 | 0 | 0 |
Some Ultraspheroidal Monogenic Clifford Gegenbauer Jacobi Polynomials and Associated Wavelets | In the present paper, new classes of wavelet functions are presented in the
framework of Clifford analysis. Firstly, some classes of orthogonal polynomials
are provided based on 2-parameters weight functions. Such classes englobe the
well known ones of Jacobi and Gegenbauer polynomials when relaxing one of the
parameters. The discovered polynomial sets are next applied to introduce new
wavelet functions. Reconstruction formula as well as Fourier-Plancherel rules
have been proved.
| 0 | 0 | 1 | 0 | 0 | 0 |
Gait learning for soft microrobots controlled by light fields | Soft microrobots based on photoresponsive materials and controlled by light
fields can generate a variety of different gaits. This inherent flexibility can
be exploited to maximize their locomotion performance in a given environment
and used to adapt them to changing conditions. Albeit, because of the lack of
accurate locomotion models, and given the intrinsic variability among
microrobots, analytical control design is not possible. Common data-driven
approaches, on the other hand, require running prohibitive numbers of
experiments and lead to very sample-specific results. Here we propose a
probabilistic learning approach for light-controlled soft microrobots based on
Bayesian Optimization (BO) and Gaussian Processes (GPs). The proposed approach
results in a learning scheme that is data-efficient, enabling gait optimization
with a limited experimental budget, and robust against differences among
microrobot samples. These features are obtained by designing the learning
scheme through the comparison of different GP priors and BO settings on a
semi-synthetic data set. The developed learning scheme is validated in
microrobot experiments, resulting in a 115% improvement in a microrobot's
locomotion performance with an experimental budget of only 20 tests. These
encouraging results lead the way toward self-adaptive microrobotic systems
based on light-controlled soft microrobots and probabilistic learning control.
| 1 | 0 | 0 | 0 | 0 | 0 |
Evolution of Nagaoka phase with kinetic energy frustrating hoppings | We investigate, using the density matrix renormalization group, the evolution
of the Nagaoka state with $t'$ hoppings that frustrate the hole kinetic energy
in the $U=\infty$ Hubbard model on the anisotropic triangular lattice and the
square lattice with second-nearest neighbor hoppings. We find that the Nagaoka
ferromagnet survives up to a rather small $t'_c/t \sim 0.2.$ At this critical
value, there is a transition to an antiferromagnetic phase, that depends on the
lattice: a ${\bf Q}=(Q,0)$ spiral order, that continuously evolves with $t'$,
for the triangular lattice, and the usual ${\bf Q}=(\pi,\pi)$ Néel order for
the square lattice. Remarkably, the local magnetization takes its classical
value for all considered $t'$ ($t'/t \le 1$). Our results show that the
recently found classical kinetic antiferromagnetism, a perfect counterpart of
Nagaoka ferromagnetism, is a generic phenomenon in these kinetically frustrated
electronic systems.
| 0 | 1 | 0 | 0 | 0 | 0 |
Blocking Transferability of Adversarial Examples in Black-Box Learning Systems | Advances in Machine Learning (ML) have led to its adoption as an integral
component in many applications, including banking, medical diagnosis, and
driverless cars. To further broaden the use of ML models, cloud-based services
offered by Microsoft, Amazon, Google, and others have developed ML-as-a-service
tools as black-box systems. However, ML classifiers are vulnerable to
adversarial examples: inputs that are maliciously modified can cause the
classifier to provide adversary-desired outputs. Moreover, it is known that
adversarial examples generated on one classifier are likely to cause another
classifier to make the same mistake, even if the classifiers have different
architectures or are trained on disjoint datasets. This property, which is
known as transferability, opens up the possibility of attacking black-box
systems by generating adversarial examples on a substitute classifier and
transferring the examples to the target classifier. Therefore, the key to
protect black-box learning systems against the adversarial examples is to block
their transferability. To this end, we propose a training method that, as the
input is more perturbed, the classifier smoothly outputs lower confidence on
the original label and instead predicts that the input is "invalid". In
essence, we augment the output class set with a NULL label and train the
classifier to reject the adversarial examples by classifying them as NULL. In
experiments, we apply a wide range of attacks based on adversarial examples on
the black-box systems. We show that a classifier trained with the proposed
method effectively resists against the adversarial examples, while maintaining
the accuracy on clean data.
| 1 | 0 | 0 | 0 | 0 | 0 |
Analytic continuation of Wolynes theory into the Marcus inverted regime | The Wolynes theory of electronically nonadiabatic reaction rates [P. G.
Wolynes, J. Chem. Phys. 87, 6559 (1987)] is based on a saddle point
approximation to the time integral of a reactive flux autocorrelation function
in the nonadiabatic (golden rule) limit. The dominant saddle point is on the
imaginary time axis at $t_{\rm sp}=i\lambda_{\rm sp}\hbar$, and provided
$\lambda_{\rm sp}$ lies in the range $-\beta/2\le\lambda_{\rm sp}\le\beta/2$,
it is straightforward to evaluate the rate constant using information obtained
from an imaginary time path integral calculation. However, if $\lambda_{\rm
sp}$ lies outside this range, as it does in the Marcus inverted regime, the
path integral diverges. This has led to claims in the literature that Wolynes
theory cannot describe the correct behaviour in the inverted regime. Here we
show how the imaginary time correlation function obtained from a path integral
calculation can be analytically continued to $\lambda_{\rm sp}<-\beta/2$, and
the continuation used to evaluate the rate in the inverted regime. Comparisons
with exact golden rule results for a spin-boson model and a more demanding
(asymmetric and anharmonic) model of electronic predissociation show that the
theory it is just as accurate in the inverted regime as it is in the normal
regime.
| 0 | 1 | 0 | 0 | 0 | 0 |
JDFTx: software for joint density-functional theory | Density-functional theory (DFT) has revolutionized computational prediction
of atomic-scale properties from first principles in physics, chemistry and
materials science. Continuing development of new methods is necessary for
accurate predictions of new classes of materials and properties, and for
connecting to nano- and mesoscale properties using coarse-grained theories.
JDFTx is a fully-featured open-source electronic DFT software designed
specifically to facilitate rapid development of new theories, models and
algorithms. Using an algebraic formulation as an abstraction layer, compact
C++11 code automatically performs well on diverse hardware including GPUs. This
code hosts the development of joint density-functional theory (JDFT) that
combines electronic DFT with classical DFT and continuum models of liquids for
first-principles calculations of solvated and electrochemical systems. In
addition, the modular nature of the code makes it easy to extend and interface
with, facilitating the development of multi-scale toolkits that connect to ab
initio calculations, e.g. photo-excited carrier dynamics combining electron and
phonon calculations with electromagnetic simulations.
| 0 | 1 | 0 | 0 | 0 | 0 |
Experimental realization of purely excitonic lasing in ZnO microcrystals at room temperature: transition from exciton-exciton to exciton-electron scattering | Since the seminal observation of room-temperature laser emission from ZnO
thin films and nanowires, numerous attempts have been carried out for detailed
understanding of the lasing mechanism in ZnO. In spite of the extensive efforts
performed over the last decades, the origin of optical gain at room temperature
is still a matter of considerable discussion,. We show that ZnO microcrystals
with a size of a few micrometers exhibit purely excitonic lasing at room
temperature without showing any symptoms of electron-hole plasma emission. We
then present the distinct experimental evidence that the room-temperature
excitonic lasing is achieved not by exciton-exciton scattering, as has been
generally believed, but by exciton-electron scattering. As the temperature is
lowered below ~150 K, the lasing mechanism is shifted from the exciton-electron
scattering to the exciton-exciton scattering. We also argue that the ease of
carrier diffusion plays a significant role in showing room-temperature
excitonic lasing.
| 0 | 1 | 0 | 0 | 0 | 0 |
Selective probing of hidden spin-polarized states in inversion-symmetric bulk MoS2 | Spin- and angle-resolved photoemission spectroscopy is used to reveal that a
large spin polarization is observable in the bulk centrosymmetric transition
metal dichalcogenide MoS2. It is found that the measured spin polarization can
be reversed by changing the handedness of incident circularly-polarized light.
Calculations based on a three-step model of photoemission show that the valley
and layer-locked spin-polarized electronic states can be selectively addressed
by circularly-polarized light, therefore providing a novel route to probe these
hidden spin-polarized states in inversion-symmetric systems as predicted by
Zhang et al. [Nature Physics 10, 387 (2014)].
| 0 | 1 | 0 | 0 | 0 | 0 |
Classification without labels: Learning from mixed samples in high energy physics | Modern machine learning techniques can be used to construct powerful models
for difficult collider physics problems. In many applications, however, these
models are trained on imperfect simulations due to a lack of truth-level
information in the data, which risks the model learning artifacts of the
simulation. In this paper, we introduce the paradigm of classification without
labels (CWoLa) in which a classifier is trained to distinguish statistical
mixtures of classes, which are common in collider physics. Crucially, neither
individual labels nor class proportions are required, yet we prove that the
optimal classifier in the CWoLa paradigm is also the optimal classifier in the
traditional fully-supervised case where all label information is available.
After demonstrating the power of this method in an analytical toy example, we
consider a realistic benchmark for collider physics: distinguishing quark-
versus gluon-initiated jets using mixed quark/gluon training samples. More
generally, CWoLa can be applied to any classification problem where labels or
class proportions are unknown or simulations are unreliable, but statistical
mixtures of the classes are available.
| 0 | 0 | 0 | 1 | 0 | 0 |
Semantic Annotation for Microblog Topics Using Wikipedia Temporal Information | Trending topics in microblogs such as Twitter are valuable resources to
understand social aspects of real-world events. To enable deep analyses of such
trends, semantic annotation is an effective approach; yet the problem of
annotating microblog trending topics is largely unexplored by the research
community. In this work, we tackle the problem of mapping trending Twitter
topics to entities from Wikipedia. We propose a novel model that complements
traditional text-based approaches by rewarding entities that exhibit a high
temporal correlation with topics during their burst time period. By exploiting
temporal information from the Wikipedia edit history and page view logs, we
have improved the annotation performance by 17-28\%, as compared to the
competitive baselines.
| 1 | 0 | 0 | 0 | 0 | 0 |
Twin-beam real-time position estimation of micro-objects in 3D | Various optical methods for measuring positions of micro-objects in 3D have
been reported in the literature. Nevertheless, majority of them are not
suitable for real-time operation, which is needed, for example, for feedback
position control. In this paper, we present a method for real-time estimation
of the position of micro-objects in 3D; the method is based on twin-beam
illumination and it requires only a very simple hardware setup whose essential
part is a standard image sensor without any lens. Performance of the proposed
method is tested during a micro-manipulation task in which the estimated
position served as a feedback for the controller. The experiments show that the
estimate is accurate to within ~3 um in the lateral position and ~7 um in the
axial distance with the refresh rate of 10 Hz. Although the experiments are
done using spherical objects, the presented method could be modified to handle
non-spherical objects as well.
| 1 | 1 | 0 | 0 | 0 | 0 |
Asymmetric metallicity patterns in the stellar velocity space with RAVE | We explore the correlations between velocity and metallicity and the possible
distinct chemical signatures of the velocity over-densities of the local
Galactic neighbourhood. We use the large spectroscopic survey RAVE and the
Geneva Copenhagen Survey. We compare the metallicity distribution of regions in
the velocity plane ($v_R,v_\phi$) with that of their symmetric counterparts
($-v_R,v_\phi$). We expect similar metallicity distributions if there are no
tracers of a sub-population (e.g., a dispersed cluster, accreted stars), if the
disk of the Galaxy is axisymmetric, and if the orbital effects of the spiral
arms and the bar are weak. We find that the metallicity-velocity space of the
solar neighbourhood is highly patterned. A large fraction of the velocity plane
shows differences in the metallicity distribution when comparing symmetric
$v_R$ regions. The typical differences in the median metallicity are of $0.05$
dex with a statistical significance of at least $95\%$, and with values up to
$0.6$ dex. For low azimuthal velocity $v_\phi$, stars moving outwards in the
Galaxy have on average higher metallicity than those moving inwards. These
include stars in the Hercules and Hyades moving groups and other velocity
branch-like structures. For higher $v_\phi$, the stars moving inwards have
higher metallicity than those moving outwards. The most likely interpretation
of the metallicity asymmetry is that it is due to the orbital effects of the
bar and the radial metallicity gradient of the disk. We present a simulation
that supports this idea. We have also discovered a positive gradient in
$v_\phi$ with respect to metallicity at high metallicities, apart from the two
known positive and negative gradients for the thick and thin disks,
respectively.
| 0 | 1 | 0 | 0 | 0 | 0 |
End-to-End Attention based Text-Dependent Speaker Verification | A new type of End-to-End system for text-dependent speaker verification is
presented in this paper. Previously, using the phonetically
discriminative/speaker discriminative DNNs as feature extractors for speaker
verification has shown promising results. The extracted frame-level (DNN
bottleneck, posterior or d-vector) features are equally weighted and aggregated
to compute an utterance-level speaker representation (d-vector or i-vector). In
this work we use speaker discriminative CNNs to extract the noise-robust
frame-level features. These features are smartly combined to form an
utterance-level speaker vector through an attention mechanism. The proposed
attention model takes the speaker discriminative information and the phonetic
information to learn the weights. The whole system, including the CNN and
attention model, is joint optimized using an end-to-end criterion. The training
algorithm imitates exactly the evaluation process --- directly mapping a test
utterance and a few target speaker utterances into a single verification score.
The algorithm can automatically select the most similar impostor for each
target speaker to train the network. We demonstrated the effectiveness of the
proposed end-to-end system on Windows $10$ "Hey Cortana" speaker verification
task.
| 1 | 0 | 0 | 1 | 0 | 0 |
Automated Discovery of Process Models from Event Logs: Review and Benchmark | Process mining allows analysts to exploit logs of historical executions of
business processes to extract insights regarding the actual performance of
these processes. One of the most widely studied process mining operations is
automated process discovery. An automated process discovery method takes as
input an event log, and produces as output a business process model that
captures the control-flow relations between tasks that are observed in or
implied by the event log. Various automated process discovery methods have been
proposed in the past two decades, striking different tradeoffs between
scalability, accuracy and complexity of the resulting models. However, these
methods have been evaluated in an ad-hoc manner, employing different datasets,
experimental setups, evaluation measures and baselines, often leading to
incomparable conclusions and sometimes unreproducible results due to the use of
closed datasets. This article provides a systematic review and comparative
evaluation of automated process discovery methods, using an open-source
benchmark and covering twelve publicly-available real-life event logs, twelve
proprietary real-life event logs, and nine quality metrics. The results
highlight gaps and unexplored tradeoffs in the field, including the lack of
scalability of some methods and a strong divergence in their performance with
respect to the different quality metrics used.
| 1 | 0 | 0 | 0 | 0 | 0 |
Criteria for strict monotonicity of the mixed volume of convex polytopes | Let $P_1,\dots, P_n$ and $Q_1,\dots, Q_n$ be convex polytopes in
$\mathbb{R}^n$ such that $P_i\subset Q_i$. It is well-known that the mixed
volume has the monotonicity property: $V(P_1,\dots,P_n)\leq V(Q_1,\dots,Q_n)$.
We give two criteria for when this inequality is strict in terms of essential
collections of faces as well as mixed polyhedral subdivisions. This geometric
result allows us to characterize sparse polynomial systems with Newton
polytopes $P_1,\dots,P_n$ whose number of isolated solutions equals the
normalized volume of the convex hull of $P_1\cup\dots\cup P_n$. In addition, we
obtain an analog of Cramer's rule for sparse polynomial systems.
| 0 | 0 | 1 | 0 | 0 | 0 |
Colorings with Fractional Defect | Consider a coloring of a graph such that each vertex is assigned a fraction
of each color, with the total amount of colors at each vertex summing to $1$.
We define the fractional defect of a vertex $v$ to be the sum of the overlaps
with each neighbor of $v$, and the fractional defect of the graph to be the
maximum of the defects over all vertices. Note that this coincides with the
usual definition of defect if every vertex is monochromatic. We provide results
on the minimum fractional defect of $2$-colorings of some graphs.
| 0 | 0 | 1 | 0 | 0 | 0 |
The new concepts of measurement error's regularities and effect characteristics | In several literatures, the authors give a new thinking of measurement theory
system based on error non-classification philosophy, which completely
overthrows the existing measurement concept system of precision, trueness and
accuracy. In this paper, by focusing on the issues of error's regularities and
effect characteristics, the authors will do a thematic interpretation, and
prove that the error's regularities actually come from different cognitive
perspectives, are also unable to be used for classifying errors, and that the
error's effect characteristics actually depend on artificial condition rules of
repeated measurement, and are still unable to be used for classifying errors.
Thus, from the perspectives of error's regularities and effect characteristics,
the existing error classification philosophy is still incorrect; and an
uncertainty concept system, which must be interpreted by the error
non-classification philosophy, naturally becomes the only way out of
measurement theory.
| 0 | 0 | 1 | 1 | 0 | 0 |
The cavity approach for Steiner trees packing problems | The Belief Propagation approximation, or cavity method, has been recently
applied to several combinatorial optimization problems in its zero-temperature
implementation, the max-sum algorithm. In particular, recent developments to
solve the edge-disjoint paths problem and the prize-collecting Steiner tree
problem on graphs have shown remarkable results for several classes of graphs
and for benchmark instances. Here we propose a generalization of these
techniques for two variants of the Steiner trees packing problem where multiple
"interacting" trees have to be sought within a given graph. Depending on the
interaction among trees we distinguish the vertex-disjoint Steiner trees
problem, where trees cannot share nodes, from the edge-disjoint Steiner trees
problem, where edges cannot be shared by trees but nodes can be members of
multiple trees. Several practical problems of huge interest in network design
can be mapped into these two variants, for instance, the physical design of
Very Large Scale Integration (VLSI) chips. The formalism described here relies
on two components edge-variables that allows us to formulate a massage-passing
algorithm for the V-DStP and two algorithms for the E-DStP differing in the
scaling of the computational time with respect to some relevant parameters. We
will show that one of the two formalisms used for the edge-disjoint variant
allow us to map the max-sum update equations into a weighted maximum matching
problem over proper bipartite graphs. We developed a heuristic procedure based
on the max-sum equations that shows excellent performance in synthetic networks
(in particular outperforming standard multi-step greedy procedures by large
margins) and on large benchmark instances of VLSI for which the optimal
solution is known, on which the algorithm found the optimum in two cases and
the gap to optimality was never larger than 4 %.
| 1 | 0 | 0 | 0 | 0 | 0 |
Observational Learning by Reinforcement Learning | Observational learning is a type of learning that occurs as a function of
observing, retaining and possibly replicating or imitating the behaviour of
another agent. It is a core mechanism appearing in various instances of social
learning and has been found to be employed in several intelligent species,
including humans. In this paper, we investigate to what extent the explicit
modelling of other agents is necessary to achieve observational learning
through machine learning. Especially, we argue that observational learning can
emerge from pure Reinforcement Learning (RL), potentially coupled with memory.
Through simple scenarios, we demonstrate that an RL agent can leverage the
information provided by the observations of an other agent performing a task in
a shared environment. The other agent is only observed through the effect of
its actions on the environment and never explicitly modeled. Two key aspects
are borrowed from observational learning: i) the observer behaviour needs to
change as a result of viewing a 'teacher' (another agent) and ii) the observer
needs to be motivated somehow to engage in making use of the other agent's
behaviour. The later is naturally modeled by RL, by correlating the learning
agent's reward with the teacher agent's behaviour.
| 1 | 0 | 0 | 1 | 0 | 0 |
P4-compatible High-level Synthesis of Low Latency 100 Gb/s Streaming Packet Parsers in FPGAs | Packet parsing is a key step in SDN-aware devices. Packet parsers in SDN
networks need to be both reconfigurable and fast, to support the evolving
network protocols and the increasing multi-gigabit data rates. The combination
of packet processing languages with FPGAs seems to be the perfect match for
these requirements. In this work, we develop an open-source FPGA-based
configurable architecture for arbitrary packet parsing to be used in SDN
networks. We generate low latency and high-speed streaming packet parsers
directly from a packet processing program. Our architecture is pipelined and
entirely modeled using templated C++ classes. The pipeline layout is derived
from a parser graph that corresponds a P4 code after a series of graph
transformation rounds. The RTL code is generated from the C++ description using
Xilinx Vivado HLS and synthesized with Xilinx Vivado. Our architecture achieves
100 Gb/s data rate in a Xilinx Virtex-7 FPGA while reducing the latency by 45%
and the LUT usage by 40% compared to the state-of-the-art.
| 1 | 0 | 0 | 0 | 0 | 0 |
Uniruledness of Strata of Holomorphic Differentials in Small Genus | We address the question concerning the birational geometry of the strata of
holomorphic and quadratic differentials. We show strata of holomorphic and
quadratic differentials to be uniruled in small genus by constructing rational
curves via pencils on K3 and del Pezzo surfaces respectively. Restricting to
genus $3\leq g\leq6$, we construct projective bundles over a rational varieties
that dominate the holomorphic strata with length at most $g-1$, hence showing
in addition that these strata are unirational.
| 0 | 0 | 1 | 0 | 0 | 0 |
Conservation laws, vertex corrections, and screening in Raman spectroscopy | We present a microscopic theory for the Raman response of a clean multiband
superconductor accounting for the effects of vertex corrections and long-range
Coulomb interaction. The measured Raman intensity, $R(\Omega)$, is proportional
to the imaginary part of the fully renormalized particle-hole correlator with
Raman form-factors $\gamma(\vec k)$. In a BCS superconductor, a bare Raman
bubble is non-zero for any $\gamma(\vec k)$ and diverges at $\Omega = 2\Delta
+0$, where $\Delta$ is the largest gap along the Fermi surface. However, for
$\gamma(\vec k) =$ const, the full $R(\Omega)$ is expected to vanish due to
particle number conservation. It was long thought that this vanishing is due to
the singular screening by long-range Coulomb interaction. We argue that this
vanishing actually holds due to vertex corrections from the same short-range
interaction that gives rise to superconductivity. We further argue that
long-range Coulomb interaction does not affect the Raman signal for $any$
$\gamma(\vec k)$. We argue that vertex corrections eliminate the divergence at
$2\Delta$ and replace it with a maximum at a somewhat larger frequency. We also
argue that vertex corrections give rise to sharp peaks in $R(\Omega)$ at
$\Omega < 2\Delta$, when $\Omega$ coincides with the frequency of one of
collective modes in a superconductor, e.g, Leggett mode, Bardasis-Schrieffer
mode, or an excitonic mode.
| 0 | 1 | 0 | 0 | 0 | 0 |
The distribution of symmetry of a naturally reductive nilpotent Lie group | We show that the distribution of symmetry of a naturally reductive nilpotent
Lie group coincides with the invariant distribution induced by the set of fixed
vectors of the isotropy. This extends a known result on compact naturally
reductive spaces. We also address the study of the quotient by the foliation of
symmetry.
| 0 | 0 | 1 | 0 | 0 | 0 |
Odd-integer quantum Hall states and giant spin susceptibility in p-type few-layer WSe2 | We fabricate high-mobility p-type few-layer WSe2 field-effect transistors and
surprisingly observe a series of quantum Hall (QH) states following an
unconventional sequence predominated by odd-integer states under a moderate
strength magnetic field. By tilting the magnetic field, we discover Landau
level (LL) crossing effects at ultra-low coincident angles, revealing that the
Zeeman energy is about three times as large as the cyclotron energy near the
valence band top at {\Gamma} valley. This result implies the significant roles
played by the exchange interactions in p-type few-layer WSe2, in which
itinerant or QH ferromagnetism likely occurs. Evidently, the {\Gamma} valley of
few-layer WSe2 offers a unique platform with unusually heavy hole-carriers and
a substantially enhanced g-factor for exploring strongly correlated phenomena.
| 0 | 1 | 0 | 0 | 0 | 0 |
Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning | In the field of reinforcement learning there has been recent progress towards
safety and high-confidence bounds on policy performance. However, to our
knowledge, no practical methods exist for determining high-confidence policy
performance bounds in the inverse reinforcement learning setting---where the
true reward function is unknown and only samples of expert behavior are given.
We propose a sampling method based on Bayesian inverse reinforcement learning
that uses demonstrations to determine practical high-confidence upper bounds on
the $\alpha$-worst-case difference in expected return between any evaluation
policy and the optimal policy under the expert's unknown reward function. We
evaluate our proposed bound on both a standard grid navigation task and a
simulated driving task and achieve tighter and more accurate bounds than a
feature count-based baseline. We also give examples of how our proposed bound
can be utilized to perform risk-aware policy selection and risk-aware policy
improvement. Because our proposed bound requires several orders of magnitude
fewer demonstrations than existing high-confidence bounds, it is the first
practical method that allows agents that learn from demonstration to express
confidence in the quality of their learned policy.
| 1 | 0 | 0 | 1 | 0 | 0 |
Face Identification and Clustering | In this thesis, we study two problems based on clustering algorithms. In the
first problem, we study the role of visual attributes using an agglomerative
clustering algorithm to whittle down the search area where the number of
classes is high to improve the performance of clustering. We observe that as we
add more attributes, the clustering performance increases overall. In the
second problem, we study the role of clustering in aggregating templates in a
1:N open set protocol using multi-shot video as a probe. We observe that by
increasing the number of clusters, the performance increases with respect to
the baseline and reaches a peak, after which increasing the number of clusters
causes the performance to degrade. Experiments are conducted using recently
introduced unconstrained IARPA Janus IJB-A, CS2, and CS3 face recognition
datasets.
| 1 | 0 | 0 | 0 | 0 | 0 |
Properties of Hydrogen Bonds in the Protic Ionic Liquid Ethylammonium Nitrate. DFT versus DFTB Molecular Dynamics | Comparative molecular dynamics simulations of a hexamer cluster of the protic
ionic liquid ethylammonium nitrate are performed using density functional
theory (DFT) and density functional-based tight binding (DFTB) methods. The
focus is on assessing the performance of the DFTB approach to describe the
dynamics and infrared spectroscopic signatures of hydrogen bonding between the
ions. Average geometries and geometric correlations are found to be rather
similar. The same holds true for the far-infrared spectral region. Differences
are more pronounced for the NH- and CH-stretching band, where DFTB predicts a
broader intensity distribution. DFTB completely fails to describe the
fingerprint range shaped by nitrate anion vibrations. Finally, charge
fluctuations within the H-bonds are characterized yielding moderate
dependencies on geometry. On the basis of these results, DFTB is recommend for
the simulation of H-bond properties of this type of ionic liquids.
| 0 | 1 | 0 | 0 | 0 | 0 |
On Abruptly-Changing and Slowly-Varying Multiarmed Bandit Problems | We study the non-stationary stochastic multiarmed bandit (MAB) problem and
propose two generic algorithms, namely, the limited memory deterministic
sequencing of exploration and exploitation (LM-DSEE) and the Sliding-Window
Upper Confidence Bound# (SW-UCB#). We rigorously analyze these algorithms in
abruptly-changing and slowly-varying environments and characterize their
performance. We show that the expected cumulative regret for these algorithms
under either of the environments is upper bounded by sublinear functions of
time, i.e., the time average of the regret asymptotically converges to zero. We
complement our analytic results with numerical illustrations.
| 0 | 0 | 0 | 1 | 0 | 0 |
Validation of small Kepler transiting planet candidates in or near the habitable zone | A main goal of NASA's Kepler Mission is to establish the frequency of
potentially habitable Earth-size planets (eta Earth). Relatively few such
candidates identified by the mission can be confirmed to be rocky via dynamical
measurement of their mass. Here we report an effort to validate 18 of them
statistically using the BLENDER technique, by showing that the likelihood they
are true planets is far greater than that of a false positive. Our analysis
incorporates follow-up observations including high-resolution optical and
near-infrared spectroscopy, high-resolution imaging, and information from the
analysis of the flux centroids of the Kepler observations themselves. While
many of these candidates have been previously validated by others, the
confidence levels reported typically ignore the possibility that the planet may
transit a different star than the target along the same line of sight. If that
were the case, a planet that appears small enough to be rocky may actually be
considerably larger and therefore less interesting from the point of view of
habitability. We take this into consideration here, and are able to validate 15
of our candidates at a 99.73% (3 sigma) significance level or higher, and the
other three at slightly lower confidence. We characterize the GKM host stars
using available ground-based observations and provide updated parameters for
the planets, with sizes between 0.8 and 2.9 Earth radii. Seven of them
(KOI-0438.02, 0463.01, 2418.01, 2626.01, 3282.01, 4036.01, and 5856.01) have a
better than 50% chance of being smaller than 2 Earth radii and being in the
habitable zone of their host stars.
| 0 | 1 | 0 | 0 | 0 | 0 |
CP-decomposition with Tensor Power Method for Convolutional Neural Networks Compression | Convolutional Neural Networks (CNNs) has shown a great success in many areas
including complex image classification tasks. However, they need a lot of
memory and computational cost, which hinders them from running in relatively
low-end smart devices such as smart phones. We propose a CNN compression method
based on CP-decomposition and Tensor Power Method. We also propose an iterative
fine tuning, with which we fine-tune the whole network after decomposing each
layer, but before decomposing the next layer. Significant reduction in memory
and computation cost is achieved compared to state-of-the-art previous work
with no more accuracy loss.
| 1 | 0 | 0 | 0 | 0 | 0 |
Stoic Ethics for Artificial Agents | We present a position paper advocating the notion that Stoic philosophy and
ethics can inform the development of ethical A.I. systems. This is in sharp
contrast to most work on building ethical A.I., which has focused on
Utilitarian or Deontological ethical theories. We relate ethical A.I. to
several core Stoic notions, including the dichotomy of control, the four
cardinal virtues, the ideal Sage, Stoic practices, and Stoic perspectives on
emotion or affect. More generally, we put forward an ethical view of A.I. that
focuses more on internal states of the artificial agent rather than on external
actions of the agent. We provide examples relating to near-term A.I. systems as
well as hypothetical superintelligent agents.
| 1 | 0 | 0 | 0 | 0 | 0 |
From quarks to nucleons in dark matter direct detection | We provide expressions for the nonperturbative matching of the effective
field theory describing dark matter interactions with quarks and gluons to the
effective theory of nonrelativistic dark matter interacting with
nonrelativistic nucleons. We give the leading and subleading order expressions
in chiral counting. In general, a single partonic operator already matches onto
several nonrelativistic operators at leading order in chiral counting. Thus,
keeping only one operator at the time in the nonrelativistic effective theory
does not properly describe the scattering in direct detection. Moreover, the
matching of the axial--axial partonic level operator, as well as the matching
of the operators coupling DM to the QCD anomaly term, naively include momentum
suppressed terms. However, these are still of leading chiral order due to pion
poles and can be numerically important. We illustrate the impact of these
effects with several examples.
| 0 | 1 | 0 | 0 | 0 | 0 |
Sharpened Strichartz estimates and bilinear restriction for the mass-critical quantum harmonic oscillator | We develop refined Strichartz estimates at $L^2$ regularity for a class of
time-dependent Schrödinger operators. Such refinements begin to
characterize the near-optimizers of the Strichartz estimate, and play a pivotal
part in the global theory of mass-critical NLS. On one hand, the harmonic
analysis is quite subtle in the $L^2$-critical setting due to an enormous group
of symmetries, while on the other hand, the spacetime Fourier analysis employed
by the existing approaches to the constant-coefficient equation are not adapted
to nontranslation-invariant situations, especially with potentials as large as
those considered in this article.
Using phase space techniques, we reduce to proving certain analogues of
(adjoint) bilinear Fourier restriction estimates. Then we extend Tao's bilinear
restriction estimate for paraboloids to more general Schrödinger operators.
As a particular application, the resulting inverse Strichartz theorem and
profile decompositions constitute a key harmonic analysis input for studying
large data solutions to the $L^2$-critical NLS with a harmonic oscillator
potential in dimensions $\ge 2$. This article builds on recent work of Killip,
Visan, and the author in one space dimension.
| 0 | 0 | 1 | 0 | 0 | 0 |
A fast and stable test to check if a weakly diagonally dominant matrix is a nonsingular M-matrix | We present a test for determining if a substochastic matrix is convergent. By
establishing a duality between weakly chained diagonally dominant (w.c.d.d.)
L-matrices and convergent substochastic matrices, we show that this test can be
trivially extended to determine whether a weakly diagonally dominant (w.d.d.)
matrix is a nonsingular M-matrix. The test's runtime is linear in the order of
the input matrix if it is sparse and quadratic if it is dense. This is a
partial strengthening of the cubic test in [J. M. Peña., A stable test to
check if a matrix is a nonsingular M-matrix, Math. Comp., 247, 1385-1392,
2004]. As a by-product of our analysis, we prove that a nonsingular w.d.d.
M-matrix is a w.c.d.d. L-matrix, a fact whose converse has been known since at
least 1964. We point out that this strengthens some recent results on
M-matrices in the literature.
| 1 | 0 | 1 | 0 | 0 | 0 |
Task-Oriented Query Reformulation with Reinforcement Learning | Search engines play an important role in our everyday lives by assisting us
in finding the information we need. When we input a complex query, however,
results are often far from satisfactory. In this work, we introduce a query
reformulation system based on a neural network that rewrites a query to
maximize the number of relevant documents returned. We train this neural
network with reinforcement learning. The actions correspond to selecting terms
to build a reformulated query, and the reward is the document recall. We
evaluate our approach on three datasets against strong baselines and show a
relative improvement of 5-20% in terms of recall. Furthermore, we present a
simple method to estimate a conservative upper-bound performance of a model in
a particular environment and verify that there is still large room for
improvements.
| 1 | 0 | 0 | 0 | 0 | 0 |
Inverse scattering transform for the nonlocal reverse space-time Sine-Gordon, Sinh-Gordon and nonlinear Schrödinger equations with nonzero boundary conditions | The reverse space-time (RST) Sine-Gordon, Sinh-Gordon and nonlinear
Schrödinger equations were recently introduced and shown to be integrable
infinite-dimensional dynamical systems. The inverse scattering transform (IST)
for rapidly decaying data was also constructed. In this paper, IST for these
equations with nonzero boundary conditions (NZBCs) at infinity is presented.
The NZBC problem is more complicated due to the associated branching structure
of the associated linear eigenfunctions. With constant amplitude at infinity,
four cases are analyzed; they correspond to two different signs of nonlinearity
and two different values of the phase at infinity. Special soliton solutions
are discussed and explicit 1-soliton and 2-soliton solutions are found. In
terms of IST, the difference between the RST Sine-Gordon/Sinh-Gordon equations
and the RST NLS equation is the time dependence of the scattering data.
Spatially dependent boundary conditions are also briefly considered.
| 0 | 1 | 0 | 0 | 0 | 0 |
Relieving the frustration through Mn$^{3+}$ substitution in Holmium Gallium Garnet | We present a study on the impact of Mn$^{3+}$ substitution in the
geometrically frustrated Ising garnet Ho$_3$Ga$_5$O$_{12}$ using bulk magnetic
measurements and low temperature powder neutron diffraction. We find that the
transition temperature, $T_N$ = 5.8 K, for Ho$_3$MnGa$_4$O$_{12}$ is raised by
almost 20 when compared to Ho$_3$Ga$_5$O$_{12}$. Powder neutron diffraction on
Ho$_3$Mn$_x$Ga$_{5-x}$O$_{12}$ ($x$ = 0.5, 1) below $T_N$ shows the formation
of a long range ordered ordered state with $\mathbf{k}$ = (0,0,0). Ho$^{3+}$
spins are aligned antiferromagnetically along the six crystallographic axes
with no resultant moment while the Mn$^{3+}$ spins are oriented along the body
diagonals, such that there is a net moment along [111]. The magnetic structure
can be visualised as ten-membered rings of corner-sharing triangles of
Ho$^{3+}$ spins with the Mn$^{3+}$ spins ferromagnetically coupled to each
individual Ho$^{3+}$ spin in the triangle. Substitution of Mn$^{3+}$ completely
relieves the magnetic frustration with $f = \theta_{CW}/T_N \approx 1.1$ for
Ho$_3$MnGa$_4$O$_{12}$.
| 0 | 1 | 0 | 0 | 0 | 0 |
Inference on Auctions with Weak Assumptions on Information | Given a sample of bids from independent auctions, this paper examines the
question of inference on auction fundamentals (e.g. valuation distributions,
welfare measures) under weak assumptions on information structure. The question
is important as it allows us to learn about the valuation distribution in a
robust way, i.e., without assuming that a particular information structure
holds across observations. We leverage the recent contributions of
\cite{Bergemann2013} in the robust mechanism design literature that exploit the
link between Bayesian Correlated Equilibria and Bayesian Nash Equilibria in
incomplete information games to construct an econometrics framework for
learning about auction fundamentals using observed data on bids. We showcase
our construction of identified sets in private value and common value auctions.
Our approach for constructing these sets inherits the computational simplicity
of solving for correlated equilibria: checking whether a particular valuation
distribution belongs to the identified set is as simple as determining whether
a {\it linear} program is feasible. A similar linear program can be used to
construct the identified set on various welfare measures and counterfactual
objects. For inference and to summarize statistical uncertainty, we propose
novel finite sample methods using tail inequalities that are used to construct
confidence regions on sets. We also highlight methods based on Bayesian
bootstrap and subsampling. A set of Monte Carlo experiments show adequate
finite sample properties of our inference procedures. We illustrate our methods
using data from OCS auctions.
| 1 | 0 | 1 | 0 | 0 | 0 |
Introduction to OXPath | Contemporary web pages with increasingly sophisticated interfaces rival
traditional desktop applications for interface complexity and are often called
web applications or RIA (Rich Internet Applications). They often require the
execution of JavaScript in a web browser and can call AJAX requests to
dynamically generate the content, reacting to user interaction. From the
automatic data acquisition point of view, thus, it is essential to be able to
correctly render web pages and mimic user actions to obtain relevant data from
the web page content. Briefly, to obtain data through existing Web interfaces
and transform it into structured form, contemporary wrappers should be able to:
1) interact with sophisticated interfaces of web applications; 2) precisely
acquire relevant data; 3) scale with the number of crawled web pages or states
of web application; 4) have an embeddable programming API for integration with
existing web technologies. OXPath is a state-of-the-art technology, which is
compliant with these requirements and demonstrated its efficiency in
comprehensive experiments. OXPath integrates Firefox for correct rendering of
web pages and extends XPath 1.0 for the DOM node selection, interaction, and
extraction. It provides means for converting extracted data into different
formats, such as XML, JSON, CSV, and saving data into relational databases.
This tutorial explains main features of the OXPath language and the setup of
a suitable working environment. The guidelines for using OXPath are provided in
the form of prototypical examples.
| 1 | 0 | 0 | 0 | 0 | 0 |
Image Registration for the Alignment of Digitized Historical Documents | In this work, we conducted a survey on different registration algorithms and
investigated their suitability for hyperspectral historical image registration
applications. After the evaluation of different algorithms, we choose an
intensity based registration algorithm with a curved transformation model. For
the transformation model, we select cubic B-splines since they should be
capable to cope with all non-rigid deformations in our hyperspectral images.
From a number of similarity measures, we found that residual complexity and
localized mutual information are well suited for the task at hand. In our
evaluation, both measures show an acceptable performance in handling all
difficulties, e.g., capture range, non-stationary and spatially varying
intensity distortions or multi-modality that occur in our application.
| 1 | 0 | 0 | 0 | 0 | 0 |
Jointly Attentive Spatial-Temporal Pooling Networks for Video-based Person Re-Identification | Person Re-Identification (person re-id) is a crucial task as its applications
in visual surveillance and human-computer interaction. In this work, we present
a novel joint Spatial and Temporal Attention Pooling Network (ASTPN) for
video-based person re-identification, which enables the feature extractor to be
aware of the current input video sequences, in a way that interdependency from
the matching items can directly influence the computation of each other's
representation. Specifically, the spatial pooling layer is able to select
regions from each frame, while the attention temporal pooling performed can
select informative frames over the sequence, both pooling guided by the
information from distance matching. Experiments are conduced on the iLIDS-VID,
PRID-2011 and MARS datasets and the results demonstrate that this approach
outperforms existing state-of-art methods. We also analyze how the joint
pooling in both dimensions can boost the person re-id performance more
effectively than using either of them separately.
| 1 | 0 | 0 | 1 | 0 | 0 |
Inertia, positive definiteness and $\ell_p$ norm of GCD and LCM matrices and their unitary analogs | Let $S=\{x_1,x_2,\dots,x_n\}$ be a set of distinct positive integers, and let
$f$ be an arithmetical function. The GCD matrix $(S)_f$ on $S$ associated with
$f$ is defined as the $n\times n$ matrix having $f$ evaluated at the greatest
common divisor of $x_i$ and $x_j$ as its $ij$ entry. The LCM matrix $[S]_f$ is
defined similarly. We consider inertia, positive definiteness and $\ell_p$ norm
of GCD and LCM matrices and their unitary analogs. Proofs are based on matrix
factorizations and convolutions of arithmetical functions.
| 0 | 0 | 1 | 0 | 0 | 0 |
Low-Latency Millimeter-Wave Communications: Traffic Dispersion or Network Densification? | This paper investigates two strategies to reduce the communication delay in
future wireless networks: traffic dispersion and network densification. A
hybrid scheme that combines these two strategies is also considered. The
probabilistic delay and effective capacity are used to evaluate performance.
For probabilistic delay, the violation probability of delay, i.e., the
probability that the delay exceeds a given tolerance level, is characterized in
terms of upper bounds, which are derived by applying stochastic network
calculus theory. In addition, to characterize the maximum affordable arrival
traffic for mmWave systems, the effective capacity, i.e., the service
capability with a given quality-of-service (QoS) requirement, is studied. The
derived bounds on the probabilistic delay and effective capacity are validated
through simulations. These numerical results show that, for a given average
system gain, traffic dispersion, network densification, and the hybrid scheme
exhibit different potentials to reduce the end-to-end communication delay. For
instance, traffic dispersion outperforms network densification, given high
average system gain and arrival rate, while it could be the worst option,
otherwise. Furthermore, it is revealed that, increasing the number of
independent paths and/or relay density is always beneficial, while the
performance gain is related to the arrival rate and average system gain,
jointly. Therefore, a proper transmission scheme should be selected to optimize
the delay performance, according to the given conditions on arrival traffic and
system service capability.
| 1 | 0 | 0 | 0 | 0 | 0 |
The structure of multiplicative tilings of the real line | Suppose $\Omega, A \subseteq \RR\setminus\Set{0}$ are two sets, both of mixed
sign, that $\Omega$ is Lebesgue measurable and $A$ is a discrete set. We study
the problem of when $A \cdot \Omega$ is a (multiplicative) tiling of the real
line, that is when almost every real number can be uniquely written as a
product $a\cdot \omega$, with $a \in A$, $\omega \in \Omega$. We study both the
structure of the set of multiples $A$ and the structure of the tile $\Omega$.
We prove strong results in both cases. These results are somewhat analogous to
the known results about the structure of translational tiling of the real line.
There is, however, an extra layer of complexity due to the presence of sign in
the sets $A$ and $\Omega$, which makes multiplicative tiling roughly equivalent
to translational tiling on the larger group $\ZZ_2 \times \RR$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Supporting Crowd-Powered Science in Economics: FRACTI, a Conceptual Framework for Large-Scale Collaboration and Transparent Investigation in Financial Markets | Modern investigation in economics and in other sciences requires the ability
to store, share, and replicate results and methods of experiments that are
often multidisciplinary and yield a massive amount of data. Given the
increasing complexity and growing interaction across diverse bodies of
knowledge it is becoming imperative to define a platform to properly support
collaborative research and track origin, accuracy and use of data. This paper
starts by defining a set of methods leveraging scientific principles and
advocating the importance of those methods in multidisciplinary, computer
intensive fields like computational finance. The next part of this paper
defines a class of systems called scientific support systems, vis-a-vis usages
in other research fields such as bioinformatics, physics and engineering. We
outline a basic set of fundamental concepts, and list our goals and motivation
for leveraging such systems to enable large-scale investigation, "crowd powered
science", in economics. The core of this paper provides an outline of FRACTI in
five steps. First we present definitions related to scientific support systems
intrinsic to finance and describe common characteristics of financial use
cases. The second step concentrates on what can be exchanged through the
definition of shareable entities called contributions. The third step is the
description of a classification system for building blocks of the conceptual
framework, called facets. The fourth step introduces the meta-model that will
enable provenance tracking and representation of data fragments and simulation.
Finally we describe intended cases of use to highlight main strengths of
FRACTI: application of the scientific method for investigation in computational
finance, large-scale collaboration and simulation.
| 0 | 0 | 0 | 0 | 0 | 1 |
The effect of temperature on generic stable periodic structures in the parameter space of dissipative relativistic standard map | In this work, we have characterized changes in the dynamics of a
two-dimensional relativistic standard map in the presence of dissipation and
specially when it is submitted to thermal effects modeled by a Gaussian noise
reservoir. By the addition of thermal noise in the dissipative relativistic
standard map (DRSM) it is possible to suppress typical stable periodic
structures (SPSs) embedded in the chaotic domains of parameter space for large
enough temperature strengths. Smaller SPSs are first affected by thermal
effects, starting from their borders, as a function of temperature. To estimate
the necessary temperature strength capable to destroy those SPSs we use the
largest Lyapunov exponent to obtain the critical temperature ($T_C$) diagrams.
For critical temperatures the chaotic behavior takes place with the suppression
of periodic motion, although, the temperature strengths considered in this work
are not so large to convert the deterministic features of the underlying system
into a stochastic ones.
| 0 | 1 | 0 | 0 | 0 | 0 |
The $u^n$-invariant and the Symbol Length of $H_2^n(F)$ | Given a field $F$ of $\operatorname{char}(F)=2$, we define $u^n(F)$ to be the
maximal dimension of an anisotropic form in $I_q^n F$. For $n=1$ it recaptures
the definition of $u(F)$. We study the relations between this value and the
symbol length of $H_2^n(F)$, denoted by $sl_2^n(F)$. We show for any $n \geq 2$
that if $2^n \leq u^n(F) \leq u^2(F) < \infty$ then $sl_2^n(F) \leq
\prod_{i=2}^n (\frac{u^i(F)}{2}+1-2^{i-1})$. As a result, if $u(F)$ is finite
then $sl_2^n(F)$ is finite for any $n$, a fact which was previously proven when
$\operatorname{char}(F) \neq 2$ by Saltman and Krashen. We also show that if
$sl_2^n(F)=1$ then $u^n(F)$ is either $2^n$ or $2^{n+1}$.
| 0 | 0 | 1 | 0 | 0 | 0 |
An Affective Robot Companion for Assisting the Elderly in a Cognitive Game Scenario | Being able to recognize emotions in human users is considered a highly
desirable trait in Human-Robot Interaction (HRI) scenarios. However, most
contemporary approaches rarely attempt to apply recognized emotional features
in an active manner to modulate robot decision-making and dialogue for the
benefit of the user. In this position paper, we propose a method of
incorporating recognized emotions into a Reinforcement Learning (RL) based
dialogue management module that adapts its dialogue responses in order to
attempt to make cognitive training tasks, like the 2048 Puzzle Game, more
enjoyable for the users.
| 1 | 0 | 0 | 0 | 0 | 0 |
Rao-Blackwellization to give Improved Estimates in Multi-List Studies | Sufficient statistics are derived for the population size and parameters of
commonly used closed population mark-recapture models. Rao-Blackwellization
details for improving estimators that are not functions of the statistics are
presented. As Rao-Blackwellization entails enumerating all sample reorderings
consistent with the sufficient statistic, Markov chain Monte Carlo resampling
procedures are provided to approximate the computationally intensive
estimators. Simulation studies demonstrate that significant improvements can be
made with the strategy. Supplementary materials for this article are available
online.
| 0 | 0 | 0 | 1 | 0 | 0 |
Bernstein Polynomial Model for Nonparametric Multivariate Density | In this paper, we study the Bernstein polynomial model for estimating the
multivariate distribution functions and densities with bounded support. As a
mixture model of multivariate beta distributions, the maximum (approximate)
likelihood estimate can be obtained using EM algorithm. A change-point method
of choosing optimal degrees of the proposed Bernstein polynomial model is
presented. Under some conditions the optimal rate of convergence in the mean
$\chi^2$-divergence of new density estimator is shown to be nearly parametric.
The method is illustrated by an application to a real data set. Finite sample
performance of the proposed method is also investigated by simulation study and
is shown to be much better than the kernel density estimate but close to the
parametric ones.
| 0 | 0 | 0 | 1 | 0 | 0 |
Small-space encoding LCE data structure with constant-time queries | The \emph{longest common extension} (\emph{LCE}) problem is to preprocess a
given string $w$ of length $n$ so that the length of the longest common prefix
between suffixes of $w$ that start at any two given positions is answered
quickly. In this paper, we present a data structure of $O(z \tau^2 +
\frac{n}{\tau})$ words of space which answers LCE queries in $O(1)$ time and
can be built in $O(n \log \sigma)$ time, where $1 \leq \tau \leq \sqrt{n}$ is a
parameter, $z$ is the size of the Lempel-Ziv 77 factorization of $w$ and
$\sigma$ is the alphabet size. This is an \emph{encoding} data structure, i.e.,
it does not access the input string $w$ when answering queries and thus $w$ can
be deleted after preprocessing. On top of this main result, we obtain further
results using (variants of) our LCE data structure, which include the
following:
- For highly repetitive strings where the $z\tau^2$ term is dominated by
$\frac{n}{\tau}$, we obtain a \emph{constant-time and sub-linear space} LCE
query data structure.
- Even when the input string is not well compressible via Lempel-Ziv 77
factorization, we still can obtain a \emph{constant-time and sub-linear space}
LCE data structure for suitable $\tau$ and for $\sigma \leq 2^{o(\log n)}$.
- The time-space trade-off lower bounds for the LCE problem by Bille et al.
[J. Discrete Algorithms, 25:42-50, 2014] and by Kosolobov [CoRR,
abs/1611.02891, 2016] can be "surpassed" in some cases with our LCE data
structure.
| 1 | 0 | 0 | 0 | 0 | 0 |
Machine learning application in the life time of materials | Materials design and development typically takes several decades from the
initial discovery to commercialization with the traditional trial and error
development approach. With the accumulation of data from both experimental and
computational results, data based machine learning becomes an emerging field in
materials discovery, design and property prediction. This manuscript reviews
the history of materials science as a disciplinary the most common machine
learning method used in materials science, and specifically how they are used
in materials discovery, design, synthesis and even failure detection and
analysis after materials are deployed in real application. Finally, the
limitations of machine learning for application in materials science and
challenges in this emerging field is discussed.
| 1 | 1 | 0 | 0 | 0 | 0 |
A Unified Framework for Stochastic Matrix Factorization via Variance Reduction | We propose a unified framework to speed up the existing stochastic matrix
factorization (SMF) algorithms via variance reduction. Our framework is general
and it subsumes several well-known SMF formulations in the literature. We
perform a non-asymptotic convergence analysis of our framework and derive
computational and sample complexities for our algorithm to converge to an
$\epsilon$-stationary point in expectation. In addition, extensive experiments
for a wide class of SMF formulations demonstrate that our framework
consistently yields faster convergence and a more accurate output dictionary
vis-à-vis state-of-the-art frameworks.
| 1 | 0 | 1 | 1 | 0 | 0 |
Short Term Power Demand Prediction Using Stochastic Gradient Boosting | Power prediction demand is vital in power system and delivery engineering
fields. By efficiently predicting the power demand, we can forecast the total
energy to be consumed in a certain city or district. Thus, exact resources
required to produce the demand power can be allocated. In this paper, a
Stochastic Gradient Boosting (aka Treeboost) model is used to predict the short
term power demand for the Emirate of Sharjah in the United Arab Emirates (UAE).
Results show that the proposed model gives promising results in comparison to
the model used by Sharjah Electricity and Water Authority (SEWA).
| 0 | 0 | 0 | 1 | 0 | 0 |
Learning Qualitatively Diverse and Interpretable Rules for Classification | There has been growing interest in developing accurate models that can also
be explained to humans. Unfortunately, if there exist multiple distinct but
accurate models for some dataset, current machine learning methods are unlikely
to find them: standard techniques will likely recover a complex model that
combines them. In this work, we introduce a way to identify a maximal set of
distinct but accurate models for a dataset. We demonstrate empirically that, in
situations where the data supports multiple accurate classifiers, we tend to
recover simpler, more interpretable classifiers rather than more complex ones.
| 0 | 0 | 0 | 1 | 0 | 0 |
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