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Title: The Music Streaming Sessions Dataset,
Abstract: At the core of many important machine learning problems faced by online
streaming services is a need to model how users interact with the content.
These problems can often be reduced to a combination of 1) sequentially
recommending items to the user, and 2) exploiting the user's interactions with
the items as feedback for the machine learning model. Unfortunately, there are
no public datasets currently available that enable researchers to explore this
topic. In order to spur that research, we release the Music Streaming Sessions
Dataset (MSSD), which consists of approximately 150 million listening sessions
and associated user actions. Furthermore, we provide audio features and
metadata for the approximately 3.7 million unique tracks referred to in the
logs. This is the largest collection of such track metadata currently available
to the public. This dataset enables research on important problems including
how to model user listening and interaction behaviour in streaming, as well as
Music Information Retrieval (MIR), and session-based sequential
recommendations. | [
1,
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] |
Title: Learning Hybrid Process Models From Events: Process Discovery Without Faking Confidence,
Abstract: Process discovery techniques return process models that are either formal
(precisely describing the possible behaviors) or informal (merely a "picture"
not allowing for any form of formal reasoning). Formal models are able to
classify traces (i.e., sequences of events) as fitting or non-fitting. Most
process mining approaches described in the literature produce such models. This
is in stark contrast with the over 25 available commercial process mining tools
that only discover informal process models that remain deliberately vague on
the precise set of possible traces. There are two main reasons why vendors
resort to such models: scalability and simplicity. In this paper, we propose to
combine the best of both worlds: discovering hybrid process models that have
formal and informal elements. As a proof of concept we present a discovery
technique based on hybrid Petri nets. These models allow for formal reasoning,
but also reveal information that cannot be captured in mainstream formal
models. A novel discovery algorithm returning hybrid Petri nets has been
implemented in ProM and has been applied to several real-life event logs. The
results clearly demonstrate the advantages of remaining "vague" when there is
not enough "evidence" in the data or standard modeling constructs do not "fit".
Moreover, the approach is scalable enough to be incorporated in
industrial-strength process mining tools. | [
1,
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] |
Title: Bounds for the completely positive rank of a symmetric matrix over a tropical semiring,
Abstract: In this paper, we find an upper bound for the CP-rank of a matrix over a
tropical semiring, according to the vertex clique cover of the graph prescribed
by the pattern of the matrix. We study the graphs that beget the patterns of
matrices with the lowest possible CP-ranks and prove that any such graph must
have its diameter equal to 2. | [
0,
0,
1,
0,
0,
0
] |
Title: On Inconsistency Indices and Inconsistency Axioms in Pairwise Comparisons,
Abstract: Pairwise comparisons are an important tool of modern (multiple criteria)
decision making. Since human judgments are often inconsistent, many studies
focused on the ways how to express and measure this inconsistency, and several
inconsistency indices were proposed as an alternative to Saaty inconsistency
index and inconsistency ratio for reciprocal pairwise comparisons matrices.
This paper aims to: firstly, introduce a new measure of inconsistency of
pairwise comparisons and to prove its basic properties; secondly, to postulate
an additional axiom, an upper boundary axiom, to an existing set of axioms; and
the last, but not least, the paper provides proofs of satisfaction of this
additional axiom by selected inconsistency indices as well as it provides their
numerical comparison. | [
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] |
Title: Rapid micro fluorescence in situ hybridization in tissue sections,
Abstract: This paper describes a micro fluorescence in situ hybridization
({\mu}FISH)-based rapid detection of cytogenetic biomarkers on formalin-fixed
paraffin embedded (FFPE) tissue sections. We demonstrated this method in the
context of detecting human epidermal growth factor 2 (HER2) in breast tissue
sections. This method uses a non-contact microfluidic scanning probe (MFP),
which localizes FISH probes at the micrometer length-scale to selected cells of
the tissue section. The scanning ability of the MFP allows for a versatile
implementation of FISH on tissue sections. We demonstrated the use of
oligonucleotide FISH probes in ethylene carbonate-based buffer enabling rapid
hybridization within < 1 min for chromosome enumeration and 10-15 min for
assessment of the HER2 status in FFPE sections. We further demonstrated
recycling of FISH probes for multiple sequential tests using a defined volume
of probes by forming hierarchical hydrodynamic flow confinements. This
microscale method is compatible with the standard FISH protocols and with the
Instant Quality (IQ) FISH assay, reduces the FISH probe consumption ~100-fold
and the hybridization time 4-fold, resulting in an assay turnaround time of < 3
h. We believe rapid {\mu}FISH has the potential of being used in pathology
workflows as a standalone method or in combination with other molecular methods
for diagnostic and prognostic analysis of FFPE sections. | [
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] |
Title: Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding,
Abstract: Neural time-series data contain a wide variety of prototypical signal
waveforms (atoms) that are of significant importance in clinical and cognitive
research. One of the goals for analyzing such data is hence to extract such
'shift-invariant' atoms. Even though some success has been reported with
existing algorithms, they are limited in applicability due to their heuristic
nature. Moreover, they are often vulnerable to artifacts and impulsive noise,
which are typically present in raw neural recordings. In this study, we address
these issues and propose a novel probabilistic convolutional sparse coding
(CSC) model for learning shift-invariant atoms from raw neural signals
containing potentially severe artifacts. In the core of our model, which we
call $\alpha$CSC, lies a family of heavy-tailed distributions called
$\alpha$-stable distributions. We develop a novel, computationally efficient
Monte Carlo expectation-maximization algorithm for inference. The maximization
step boils down to a weighted CSC problem, for which we develop a
computationally efficient optimization algorithm. Our results show that the
proposed algorithm achieves state-of-the-art convergence speeds. Besides,
$\alpha$CSC is significantly more robust to artifacts when compared to three
competing algorithms: it can extract spike bursts, oscillations, and even
reveal more subtle phenomena such as cross-frequency coupling when applied to
noisy neural time series. | [
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] |
Title: Machine Learning for Drug Overdose Surveillance,
Abstract: We describe two recently proposed machine learning approaches for discovering
emerging trends in fatal accidental drug overdoses. The Gaussian Process Subset
Scan enables early detection of emerging patterns in spatio-temporal data,
accounting for both the non-iid nature of the data and the fact that detecting
subtle patterns requires integration of information across multiple spatial
areas and multiple time steps. We apply this approach to 17 years of
county-aggregated data for monthly opioid overdose deaths in the New York City
metropolitan area, showing clear advantages in the utility of discovered
patterns as compared to typical anomaly detection approaches.
To detect and characterize emerging overdose patterns that differentially
affect a subpopulation of the data, including geographic, demographic, and
behavioral patterns (e.g., which combinations of drugs are involved), we apply
the Multidimensional Tensor Scan to 8 years of case-level overdose data from
Allegheny County, PA. We discover previously unidentified overdose patterns
which reveal unusual demographic clusters, show impacts of drug legislation,
and demonstrate potential for early detection and targeted intervention. These
approaches to early detection of overdose patterns can inform prevention and
response efforts, as well as understanding the effects of policy changes. | [
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] |
Title: Linear simulation of ion temperature gradient driven instabilities in W7-X and LHD stellarators using GTC,
Abstract: The global gyrokinetic toroidal code (GTC) has been recently upgraded to do
simulations in non-axisymmetric equilibrium configuration, such as
stellarators. Linear simulation of ion temperature gradient (ITG) driven
instabilities has been done in Wendelstein7-X (W7-X) and Large Helical Device
(LHD) stellarators using GTC. Several results are discussed to study
characteristics of ITG in stellarators, including toroidal grids convergence,
nmodes number convergence, poloidal and parallel spectrums, and electrostatic
potential mode structure on flux surface. | [
0,
1,
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] |
Title: Stick-breaking processes, clumping, and Markov chain occupation laws,
Abstract: We consider the connections among `clumped' residual allocation models
(RAMs), a general class of stick-breaking processes including Dirichlet
processes, and the occupation laws of certain discrete space time-inhomogeneous
Markov chains related to simulated annealing and other applications. An
intermediate structure is introduced in a given RAM, where proportions between
successive indices in a list are added or clumped together to form another RAM.
In particular, when the initial RAM is a Griffiths-Engen-McCloskey (GEM)
sequence and the indices are given by the random times that an auxiliary Markov
chain jumps away from its current state, the joint law of the intermediate RAM
and the locations visited in the sojourns is given in terms of a `disordered'
GEM sequence, and an induced Markov chain. Through this joint law, we identify
a large class of `stick breaking' processes as the limits of empirical
occupation measures for associated time-inhomogeneous Markov chains. | [
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1,
1,
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0
] |
Title: Scaling laws of Rydberg excitons,
Abstract: Rydberg atoms have attracted considerable interest due to their huge
interaction among each other and with external fields. They demonstrate
characteristic scaling laws in dependence on the principal quantum number $n$
for features such as the magnetic field for level crossing. While bearing
striking similarities to Rydberg atoms, fundamentally new insights may be
obtained for Rydberg excitons, as the crystal environment gives easy optical
access to many states within an exciton multiplet. Here we study experimentally
and theoretically the scaling of several characteristic parameters of Rydberg
excitons with $n$. From absorption spectra in magnetic field we find for the
first crossing of levels with adjacent principal quantum numbers a $B_r \propto
n^{-4}$ dependence of the resonance field strength, $B_r$, due to the dominant
paramagnetic term unlike in the atomic case where the diamagnetic contribution
is decisive. By contrast, in electric field we find scaling laws just like for
Rydberg atoms. The resonance electric field strength scales as $E_r \propto
n^{-5}$. We observe anticrossings of the states belonging to multiplets with
different principal quantum numbers. The energy splittings at the avoided
crossings scale as $n^{-4}$ which we relate to the crystal specific deviation
of the exciton Hamiltonian from the hydrogen model. We observe the exciton
polarizability in the electric field to scale as $n^7$. In magnetic field the
crossover field strength from a hydrogen-like exciton to a magnetoexciton
dominated by electron and hole Landau level quantization scales as $n^{-3}$.
The ionization voltages demonstrate a $n^{-4}$ scaling as for atoms. The width
of the absorption lines remains constant before dissociation for high enough
$n$, while for small $n \lesssim 12$ an exponential increase with the field is
found. These results are in excellent agreement with theoretical calculations. | [
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] |
Title: Core Discovery in Hidden Graphs,
Abstract: Massive network exploration is an important research direction with many
applications. In such a setting, the network is, usually, modeled as a graph
$G$, whereas any structural information of interest is extracted by inspecting
the way nodes are connected together. In the case where the adjacency matrix or
the adjacency list of $G$ is available, one can directly apply graph mining
algorithms to extract useful knowledge. However, there are cases where this is
not possible because the graph is \textit{hidden} or \textit{implicit}, meaning
that the edges are not recorded explicitly in the form of an adjacency
representation. In such a case, the only alternative is to pose a sequence of
\textit{edge probing queries} asking for the existence or not of a particular
graph edge. However, checking all possible node pairs is costly (quadratic on
the number of nodes). Thus, our objective is to pose as few edge probing
queries as possible, since each such query is expected to be costly. In this
work, we center our focus on the \textit{core decomposition} of a hidden graph.
In particular, we provide an efficient algorithm to detect the maximal subgraph
of $S_k$ of $G$ where the induced degree of every node $u \in S_k$ is at least
$k$. Performance evaluation results demonstrate that significant performance
improvements are achieved in comparison to baseline approaches. | [
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] |
Title: Some exact Bradlow vortex solutions,
Abstract: We consider the Bradlow equation for vortices which was recently found by
Manton and find a two-parameter class of analytic solutions in closed form on
nontrivial geometries with non-constant curvature. The general solution to our
class of metrics is given by a hypergeometric function and the area of the
vortex domain by the Gaussian hypergeometric function. | [
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1,
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0,
0
] |
Title: Limitations on Variance-Reduction and Acceleration Schemes for Finite Sum Optimization,
Abstract: We study the conditions under which one is able to efficiently apply
variance-reduction and acceleration schemes on finite sum optimization
problems. First, we show that, perhaps surprisingly, the finite sum structure
by itself, is not sufficient for obtaining a complexity bound of
$\tilde{\cO}((n+L/\mu)\ln(1/\epsilon))$ for $L$-smooth and $\mu$-strongly
convex individual functions - one must also know which individual function is
being referred to by the oracle at each iteration. Next, we show that for a
broad class of first-order and coordinate-descent finite sum algorithms
(including, e.g., SDCA, SVRG, SAG), it is not possible to get an `accelerated'
complexity bound of $\tilde{\cO}((n+\sqrt{n L/\mu})\ln(1/\epsilon))$, unless
the strong convexity parameter is given explicitly. Lastly, we show that when
this class of algorithms is used for minimizing $L$-smooth and convex finite
sums, the optimal complexity bound is $\tilde{\cO}(n+L/\epsilon)$, assuming
that (on average) the same update rule is used in every iteration, and
$\tilde{\cO}(n+\sqrt{nL/\epsilon})$, otherwise. | [
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0
] |
Title: Some Sphere Theorems in Linear Potential Theory,
Abstract: In this paper we analyze the capacitary potential due to a charged body in
order to deduce sharp analytic and geometric inequalities, whose equality cases
are saturated by domains with spherical symmetry. In particular, for a regular
bounded domain $\Omega \subset \mathbb{R}^n$, $n\geq 3$, we prove that if the
mean curvature $H$ of the boundary obeys the condition $$ - \bigg[
\frac{1}{\text{Cap}(\Omega)} \bigg]^{\frac{1}{n-2}} \leq \frac{H}{n-1} \leq
\bigg[ \frac{1}{\text{Cap}(\Omega)} \bigg]^{\frac{1}{n-2}} , $$ then $\Omega$
is a round ball. | [
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] |
Title: Non-Gaussian Component Analysis using Entropy Methods,
Abstract: Non-Gaussian component analysis (NGCA) is a problem in multidimensional data
analysis which, since its formulation in 2006, has attracted considerable
attention in statistics and machine learning. In this problem, we have a random
variable $X$ in $n$-dimensional Euclidean space. There is an unknown subspace
$\Gamma$ of the $n$-dimensional Euclidean space such that the orthogonal
projection of $X$ onto $\Gamma$ is standard multidimensional Gaussian and the
orthogonal projection of $X$ onto $\Gamma^{\perp}$, the orthogonal complement
of $\Gamma$, is non-Gaussian, in the sense that all its one-dimensional
marginals are different from the Gaussian in a certain metric defined in terms
of moments. The NGCA problem is to approximate the non-Gaussian subspace
$\Gamma^{\perp}$ given samples of $X$.
Vectors in $\Gamma^{\perp}$ correspond to `interesting' directions, whereas
vectors in $\Gamma$ correspond to the directions where data is very noisy. The
most interesting applications of the NGCA model is for the case when the
magnitude of the noise is comparable to that of the true signal, a setting in
which traditional noise reduction techniques such as PCA don't apply directly.
NGCA is also related to dimension reduction and to other data analysis problems
such as ICA. NGCA-like problems have been studied in statistics for a long time
using techniques such as projection pursuit.
We give an algorithm that takes polynomial time in the dimension $n$ and has
an inverse polynomial dependence on the error parameter measuring the angle
distance between the non-Gaussian subspace and the subspace output by the
algorithm. Our algorithm is based on relative entropy as the contrast function
and fits under the projection pursuit framework. The techniques we develop for
analyzing our algorithm maybe of use for other related problems. | [
0,
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] |
Title: Computational Thinking in Education: Where does it Fit? A systematic literary review,
Abstract: Computational Thinking (CT) has been described as an essential skill which
everyone should learn and can therefore include in their skill set. Seymour
Papert is credited as concretising Computational Thinking in 1980 but since
Wing popularised the term in 2006 and brought it to the international
community's attention, more and more research has been conducted on CT in
education. The aim of this systematic literary review is to give educators and
education researchers an overview of what work has been carried out in the
domain, as well as potential gaps and opportunities that still exist.
Overall it was found in this review that, although there is a lot of work
currently being done around the world in many different educational contexts,
the work relating to CT is still in its infancy. Along with the need to create
an agreed-upon definition of CT lots of countries are still in the process of,
or have not yet started, introducing CT into curriculums in all levels of
education. It was also found that Computer Science/Computing, which could be
the most obvious place to teach CT, has yet to become a mainstream subject in
some countries, although this is improving. Of encouragement to educators is
the wealth of tools and resources being developed to help teach CT as well as
more and more work relating to curriculum development. For those teachers
looking to incorporate CT into their schools or classes then there are
bountiful options which include programming, hands-on exercises and more. The
need for more detailed lesson plans and curriculum structure however, is
something that could be of benefit to teachers. | [
1,
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] |
Title: A New Take on Protecting Cyclists in Smart Cities,
Abstract: Pollution in urban centres is becoming a major societal problem. While
pollution is a concern for all urban dwellers, cyclists are one of the most
exposed groups due to their proximity to vehicle tailpipes. Consequently, new
solutions are required to help protect citizens, especially cyclists, from the
harmful effects of exhaust-gas emissions. In this context, hybrid vehicles
(HVs) offer new actuation possibilities that can be exploited in this
direction. More specifically, such vehicles when working together as a group,
have the ability to dynamically lower the emissions in a given area, thus
benefiting citizens, whilst still giving the vehicle owner the flexibility of
using an Internal Combustion Engine (ICE). This paper aims to develop an
algorithm, that can be deployed in such vehicles, whereby geofences (virtual
geographic boundaries) are used to specify areas of low pollution around
cyclists. The emissions level inside the geofence is controlled via a coin
tossing algorithm to switch the HV motor into, and out of, electric mode, in a
manner that is in some sense optimal. The optimality criterion is based on how
polluting vehicles inside the geofence are, and the expected density of
cyclists near each vehicle. The algorithm is triggered once a vehicle detects a
cyclist. Implementations are presented, both in simulation, and in a real
vehicle, and the system is tested using a Hardware-In-the-Loop (HIL) platform
(video provided). | [
0,
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1,
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0
] |
Title: The effect upon neutrinos of core-collapse supernova accretion phase turbulence,
Abstract: During the accretion phase of a core-collapse supernovae, large amplitude
turbulence is generated by the combination of the standing accretion shock
instability and convection driven by neutrino heating. The turbulence directly
affects the dynamics of the explosion, but there is also the possibility of an
additional, indirect, feedback mechanism due to the effect turbulence can have
upon neutrino flavor evolution and thus the neutrino heating. In this paper we
consider the effect of turbulence during the accretion phase upon neutrino
evolution, both numerically and analytically. Adopting representative supernova
profiles taken from the accretion phase of a supernova simulation, we find the
numerical calculations exhibit no effect from turbulence. We explain this
absence using two analytic descriptions: the Stimulated Transition model and
the Distorted Phase Effect model. In the Stimulated Transition model turbulence
effects depend upon six different lengthscales, and three criteria must be
satisfied between them if one is to observe a change in the flavor evolution
due to Stimulated Transition. We further demonstrate that the Distorted Phase
Effect depends upon the presence of multiple semi-adiabatic MSW resonances or
discontinuities that also can be expressed as a relationship between three of
the same lengthscales. When we examine the supernova profiles used in the
numerical calculations we find the three Stimulated Transition criteria cannot
be satisfied, independent of the form of the turbulence power spectrum, and
that the same supernova profiles lack the multiple semi-adiabatic MSW
resonances or discontinuities necessary to produce a Distorted Phase Effect.
Thus we conclude that even though large amplitude turbulence is present in
supernova during the accretion phase, it has no effect upon neutrino flavor
evolution. | [
0,
1,
0,
0,
0,
0
] |
Title: Positive-Unlabeled Learning with Non-Negative Risk Estimator,
Abstract: From only positive (P) and unlabeled (U) data, a binary classifier could be
trained with PU learning, in which the state of the art is unbiased PU
learning. However, if its model is very flexible, empirical risks on training
data will go negative, and we will suffer from serious overfitting. In this
paper, we propose a non-negative risk estimator for PU learning: when getting
minimized, it is more robust against overfitting, and thus we are able to use
very flexible models (such as deep neural networks) given limited P data.
Moreover, we analyze the bias, consistency, and mean-squared-error reduction of
the proposed risk estimator, and bound the estimation error of the resulting
empirical risk minimizer. Experiments demonstrate that our risk estimator fixes
the overfitting problem of its unbiased counterparts. | [
1,
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] |
Title: Privacy Preserving Face Retrieval in the Cloud for Mobile Users,
Abstract: Recently, cloud storage and processing have been widely adopted. Mobile users
in one family or one team may automatically backup their photos to the same
shared cloud storage space. The powerful face detector trained and provided by
a 3rd party may be used to retrieve the photo collection which contains a
specific group of persons from the cloud storage server. However, the privacy
of the mobile users may be leaked to the cloud server providers. In the
meanwhile, the copyright of the face detector should be protected. Thus, in
this paper, we propose a protocol of privacy preserving face retrieval in the
cloud for mobile users, which protects the user photos and the face detector
simultaneously. The cloud server only provides the resources of storage and
computing and can not learn anything of the user photos and the face detector.
We test our protocol inside several families and classes. The experimental
results reveal that our protocol can successfully retrieve the proper photos
from the cloud server and protect the user photos and the face detector. | [
1,
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] |
Title: Averages of Unlabeled Networks: Geometric Characterization and Asymptotic Behavior,
Abstract: It is becoming increasingly common to see large collections of network data
objects -- that is, data sets in which a network is viewed as a fundamental
unit of observation. As a result, there is a pressing need to develop
network-based analogues of even many of the most basic tools already standard
for scalar and vector data. In this paper, our focus is on averages of
unlabeled, undirected networks with edge weights. Specifically, we (i)
characterize a certain notion of the space of all such networks, (ii) describe
key topological and geometric properties of this space relevant to doing
probability and statistics thereupon, and (iii) use these properties to
establish the asymptotic behavior of a generalized notion of an empirical mean
under sampling from a distribution supported on this space. Our results rely on
a combination of tools from geometry, probability theory, and statistical shape
analysis. In particular, the lack of vertex labeling necessitates working with
a quotient space modding out permutations of labels. This results in a
nontrivial geometry for the space of unlabeled networks, which in turn is found
to have important implications on the types of probabilistic and statistical
results that may be obtained and the techniques needed to obtain them. | [
0,
0,
1,
1,
0,
0
] |
Title: The Abelian distribution,
Abstract: We define the Abelian distribution and study its basic properties. Abelian
distributions arise in the context of neural modeling and describe the size of
neural avalanches in fully-connected integrate-and-fire models of
self-organized criticality in neural systems. | [
0,
1,
0,
0,
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] |
Title: A Random Sample Partition Data Model for Big Data Analysis,
Abstract: Big data sets must be carefully partitioned into statistically similar data
subsets that can be used as representative samples for big data analysis tasks.
In this paper, we propose the random sample partition (RSP) data model to
represent a big data set as a set of non-overlapping data subsets, called RSP
data blocks, where each RSP data block has a probability distribution similar
to the whole big data set. Under this data model, efficient block level
sampling is used to randomly select RSP data blocks, replacing expensive record
level sampling to select sample data from a big distributed data set on a
computing cluster. We show how RSP data blocks can be employed to estimate
statistics of a big data set and build models which are equivalent to those
built from the whole big data set. In this approach, analysis of a big data set
becomes analysis of few RSP data blocks which have been generated in advance on
the computing cluster. Therefore, the new method for data analysis based on RSP
data blocks is scalable to big data. | [
1,
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1,
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0
] |
Title: Deep Learning for micro-Electrocorticographic (μECoG) Data,
Abstract: Machine learning can extract information from neural recordings, e.g.,
surface EEG, ECoG and {\mu}ECoG, and therefore plays an important role in many
research and clinical applications. Deep learning with artificial neural
networks has recently seen increasing attention as a new approach in brain
signal decoding. Here, we apply a deep learning approach using convolutional
neural networks to {\mu}ECoG data obtained with a wireless, chronically
implanted system in an ovine animal model. Regularized linear discriminant
analysis (rLDA), a filter bank component spatial pattern (FBCSP) algorithm and
convolutional neural networks (ConvNets) were applied to auditory evoked
responses captured by {\mu}ECoG. We show that compared with rLDA and FBCSP,
significantly higher decoding accuracy can be obtained by ConvNets trained in
an end-to-end manner, i.e., without any predefined signal features. Deep
learning thus proves a promising technique for {\mu}ECoG-based brain-machine
interfacing applications. | [
0,
0,
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1,
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] |
Title: Clipped Matrix Completion: A Remedy for Ceiling Effects,
Abstract: We consider the problem of recovering a low-rank matrix from its clipped
observations. Clipping is conceivable in many scientific areas that obstructs
statistical analyses. On the other hand, matrix completion (MC) methods can
recover a low-rank matrix from various information deficits by using the
principle of low-rank completion. However, the current theoretical guarantees
for low-rank MC do not apply to clipped matrices, as the deficit depends on the
underlying values. Therefore, the feasibility of clipped matrix completion
(CMC) is not trivial. In this paper, we first provide a theoretical guarantee
for the exact recovery of CMC by using a trace-norm minimization algorithm.
Furthermore, we propose practical CMC algorithms by extending ordinary MC
methods. Our extension is to use the squared hinge loss in place of the squared
loss for reducing the penalty of over-estimation on clipped entries. We also
propose a novel regularization term tailored for CMC. It is a combination of
two trace-norm terms, and we theoretically bound the recovery error under the
regularization. We demonstrate the effectiveness of the proposed methods
through experiments using both synthetic and benchmark data for recommendation
systems. | [
0,
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1,
0,
0
] |
Title: Distributed Convolutional Dictionary Learning (DiCoDiLe): Pattern Discovery in Large Images and Signals,
Abstract: Convolutional dictionary learning (CDL) estimates shift invariant basis
adapted to multidimensional data. CDL has proven useful for image denoising or
inpainting, as well as for pattern discovery on multivariate signals. As
estimated patterns can be positioned anywhere in signals or images,
optimization techniques face the difficulty of working in extremely high
dimensions with millions of pixels or time samples, contrarily to standard
patch-based dictionary learning. To address this optimization problem, this
work proposes a distributed and asynchronous algorithm, employing locally
greedy coordinate descent and an asynchronous locking mechanism that does not
require a central server. This algorithm can be used to distribute the
computation on a number of workers which scales linearly with the encoded
signal's size. Experiments confirm the scaling properties which allows us to
learn patterns on large scales images from the Hubble Space Telescope. | [
1,
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] |
Title: Performance Analysis of Robust Stable PID Controllers Using Dominant Pole Placement for SOPTD Process Models,
Abstract: This paper derives new formulations for designing dominant pole placement
based proportional-integral-derivative (PID) controllers to handle second order
processes with time delays (SOPTD). Previously, similar attempts have been made
for pole placement in delay-free systems. The presence of the time delay term
manifests itself as a higher order system with variable number of interlaced
poles and zeros upon Pade approximation, which makes it difficult to achieve
precise pole placement control. We here report the analytical expressions to
constrain the closed loop dominant and non-dominant poles at the desired
locations in the complex s-plane, using a third order Pade approximation for
the delay term. However, invariance of the closed loop performance with
different time delay approximation has also been verified using increasing
order of Pade, representing a closed to reality higher order delay dynamics.
The choice of the nature of non-dominant poles e.g. all being complex, real or
a combination of them modifies the characteristic equation and influences the
achievable stability regions. The effect of different types of non-dominant
poles and the corresponding stability regions are obtained for nine test-bench
processes indicating different levels of open-loop damping and lag to delay
ratio. Next, we investigate which expression yields a wider stability region in
the design parameter space by using Monte Carlo simulations while uniformly
sampling a chosen design parameter space. Various time and frequency domain
control performance parameters are investigated next, as well as their
deviations with uncertain process parameters, using thousands of Monte Carlo
simulations, around the robust stable solution for each of the nine test-bench
processes. | [
0,
0,
0,
1,
0,
0
] |
Title: On Conjugates and Adjoint Descent,
Abstract: In this note we present an $\infty$-categorical framework for descent along
adjunctions and a general formula for counting conjugates up to equivalence
which unifies several known formulae from different fields. | [
0,
0,
1,
0,
0,
0
] |
Title: Learning to Parse and Translate Improves Neural Machine Translation,
Abstract: There has been relatively little attention to incorporating linguistic prior
to neural machine translation. Much of the previous work was further
constrained to considering linguistic prior on the source side. In this paper,
we propose a hybrid model, called NMT+RNNG, that learns to parse and translate
by combining the recurrent neural network grammar into the attention-based
neural machine translation. Our approach encourages the neural machine
translation model to incorporate linguistic prior during training, and lets it
translate on its own afterward. Extensive experiments with four language pairs
show the effectiveness of the proposed NMT+RNNG. | [
1,
0,
0,
0,
0,
0
] |
Title: An Improved Video Analysis using Context based Extension of LSH,
Abstract: Locality Sensitive Hashing (LSH) based algorithms have already shown their
promise in finding approximate nearest neighbors in high dimen- sional data
space. However, there are certain scenarios, as in sequential data, where the
proximity of a pair of points cannot be captured without considering their
surroundings or context. In videos, as for example, a particular frame is
meaningful only when it is seen in the context of its preceding and following
frames. LSH has no mechanism to handle the con- texts of the data points. In
this article, a novel scheme of Context based Locality Sensitive Hashing
(conLSH) has been introduced, in which points are hashed together not only
based on their closeness, but also because of similar context. The contribution
made in this article is three fold. First, conLSH is integrated with a recently
proposed fast optimal sequence alignment algorithm (FOGSAA) using a layered
approach. The resultant method is applied to video retrieval for extracting
similar sequences. The pro- posed algorithm yields more than 80% accuracy on an
average in different datasets. It has been found to save 36.3% of the total
time, consumed by the exhaustive search. conLSH reduces the search space to
approximately 42% of the entire dataset, when compared with an exhaustive
search by the aforementioned FOGSAA, Bag of Words method and the standard LSH
implementations. Secondly, the effectiveness of conLSH is demon- strated in
action recognition of the video clips, which yields an average gain of 12.83%
in terms of classification accuracy over the state of the art methods using
STIP descriptors. The last but of great significance is that this article
provides a way of automatically annotating long and composite real life videos.
The source code of conLSH is made available at
this http URL | [
1,
0,
0,
0,
0,
0
] |
Title: Exact energy stability of Bénard-Marangoni convection at infinite Prandtl number,
Abstract: Using the energy method we investigate the stability of pure conduction in
Pearson's model for Bénard-Marangoni convection in a layer of fluid at
infinite Prandtl number. Upon extending the space of admissible perturbations
to the conductive state, we find an exact solution to the energy stability
variational problem for a range of thermal boundary conditions describing
perfectly conducting, imperfectly conducting, and insulating boundaries. Our
analysis extends and improves previous results, and shows that with the energy
method global stability can be proven up to the linear instability threshold
only when the top and bottom boundaries of the fluid layer are insulating.
Contrary to the well-known Rayleigh-Bénard convection setup, therefore,
energy stability theory does not exclude the possibility of subcritical
instabilities against finite-amplitude perturbations. | [
0,
1,
0,
0,
0,
0
] |
Title: A sharpening of a problem on Bernstein polynomials and convex functions,
Abstract: We present an elementary proof of a conjecture proposed by I. Rasa in 2017
which is an inequality involving Bernstein basis polynomials and convex
functions. It was affirmed in positive by A. Komisarski and T. Rajba very
recently by the use of stochastic convex orderings. | [
0,
0,
1,
0,
0,
0
] |
Title: Multiple core hole formation by free-electron laser radiation in molecular nitrogen,
Abstract: We investigate the formation of multiple-core-hole states of molecular
nitrogen interacting with a free-electron laser pulse. We obtain bound and
continuum molecular orbitals in the single-center expansion scheme and use
these orbitals to calculate photo-ionization and Auger decay rates. Using these
rates, we compute the atomic ion yields generated in this interaction. We track
the population of all states throughout this interaction and compute the
proportion of the population which accesses different core-hole states. We also
investigate the pulse parameters that favor the formation of these core-hole
states for 525 eV and 1100 eV photons. | [
0,
1,
0,
0,
0,
0
] |
Title: Uniformly recurrent subgroups and the ideal structure of reduced crossed products,
Abstract: We study the ideal structure of reduced crossed product of topological
dynamical systems of a countable discrete group. More concretely, for a compact
Hausdorff space $X$ with an action of a countable discrete group $\Gamma$, we
consider the absence of a non-zero ideals in the reduced crossed product $C(X)
\rtimes_r \Gamma$ which has a zero intersection with $C(X)$. We characterize
this condition by a property for amenable subgroups of the stabilizer subgroups
of $X$ in terms of the Chabauty space of $\Gamma$. This generalizes Kennedy's
algebraic characterization of the simplicity for a reduced group
$\mathrm{C}^{*}$-algebra of a countable discrete group. | [
0,
0,
1,
0,
0,
0
] |
Title: Modularity Matters: Learning Invariant Relational Reasoning Tasks,
Abstract: We focus on two supervised visual reasoning tasks whose labels encode a
semantic relational rule between two or more objects in an image: the MNIST
Parity task and the colorized Pentomino task. The objects in the images undergo
random translation, scaling, rotation and coloring transformations. Thus these
tasks involve invariant relational reasoning. We report uneven performance of
various deep CNN models on these two tasks. For the MNIST Parity task, we
report that the VGG19 model soundly outperforms a family of ResNet models.
Moreover, the family of ResNet models exhibits a general sensitivity to random
initialization for the MNIST Parity task. For the colorized Pentomino task, now
both the VGG19 and ResNet models exhibit sluggish optimization and very poor
test generalization, hovering around 30% test error. The CNN we tested all
learn hierarchies of fully distributed features and thus encode the distributed
representation prior. We are motivated by a hypothesis from cognitive
neuroscience which posits that the human visual cortex is modularized, and this
allows the visual cortex to learn higher order invariances. To this end, we
consider a modularized variant of the ResNet model, referred to as a Residual
Mixture Network (ResMixNet) which employs a mixture-of-experts architecture to
interleave distributed representations with more specialized, modular
representations. We show that very shallow ResMixNets are capable of learning
each of the two tasks well, attaining less than 2% and 1% test error on the
MNIST Parity and the colorized Pentomino tasks respectively. Most importantly,
the ResMixNet models are extremely parameter efficient: generalizing better
than various non-modular CNNs that have over 10x the number of parameters.
These experimental results support the hypothesis that modularity is a robust
prior for learning invariant relational reasoning. | [
0,
0,
0,
1,
1,
0
] |
Title: Equivalence between Differential Inclusions Involving Prox-regular sets and maximal monotone operators,
Abstract: In this paper, we study the existence and the stability in the sense of
Lyapunov of solutions for\ differential inclusions governed by the normal cone
to a prox-regular set and subject to a Lipschitzian perturbation. We prove that
such, apparently, more general nonsmooth dynamics can be indeed remodelled into
the classical theory of differential inclusions involving maximal monotone
operators. This result is new in the literature and permits us to make use of
the rich and abundant achievements in this class of monotone operators to
derive the desired existence result and stability analysis, as well as the
continuity and differentiability properties of the solutions. This going back
and forth between these two models of differential inclusions is made possible
thanks to a viability result for maximal monotone operators. As an application,
we study a Luenberger-like observer, which is shown to converge exponentially
to the actual state when the initial value of the state's estimation remains in
a neighborhood of the initial value of the original system. | [
0,
0,
1,
0,
0,
0
] |
Title: Effect of iron oxide loading on magnetoferritin structure in solution as revealed by SAXS and SANS,
Abstract: Synthetic biological macromolecule of magnetoferritin containing an iron
oxide core inside a protein shell (apoferritin) is prepared with different
content of iron. Its structure in aqueous solution is analyzed by small-angle
synchrotron X-ray (SAXS) and neutron (SANS) scattering. The loading factor (LF)
defined as the average number of iron atoms per protein is varied up to LF=800.
With an increase of the LF, the scattering curves exhibit a relative increase
in the total scattered intensity, a partial smearing and a shift of the match
point in the SANS contrast variation data. The analysis shows an increase in
the polydispersity of the proteins and a corresponding effective increase in
the relative content of magnetic material against the protein moiety of the
shell with the LF growth. At LFs above ~150, the apoferritin shell undergoes
structural changes, which is strongly indicative of the fact that the shell
stability is affected by iron oxide presence. | [
0,
1,
0,
0,
0,
0
] |
Title: Volumetric Super-Resolution of Multispectral Data,
Abstract: Most multispectral remote sensors (e.g. QuickBird, IKONOS, and Landsat 7
ETM+) provide low-spatial high-spectral resolution multispectral (MS) or
high-spatial low-spectral resolution panchromatic (PAN) images, separately. In
order to reconstruct a high-spatial/high-spectral resolution multispectral
image volume, either the information in MS and PAN images are fused (i.e.
pansharpening) or super-resolution reconstruction (SRR) is used with only MS
images captured on different dates. Existing methods do not utilize temporal
information of MS and high spatial resolution of PAN images together to improve
the resolution. In this paper, we propose a multiframe SRR algorithm using
pansharpened MS images, taking advantage of both temporal and spatial
information available in multispectral imagery, in order to exceed spatial
resolution of given PAN images. We first apply pansharpening to a set of
multispectral images and their corresponding PAN images captured on different
dates. Then, we use the pansharpened multispectral images as input to the
proposed wavelet-based multiframe SRR method to yield full volumetric SRR. The
proposed SRR method is obtained by deriving the subband relations between
multitemporal MS volumes. We demonstrate the results on Landsat 7 ETM+ images
comparing our method to conventional techniques. | [
1,
0,
0,
0,
0,
0
] |
Title: Comparison of methods for early-readmission prediction in a high-dimensional heterogeneous covariates and time-to-event outcome framework,
Abstract: Background: Choosing the most performing method in terms of outcome
prediction or variables selection is a recurring problem in prognosis studies,
leading to many publications on methods comparison. But some aspects have
received little attention. First, most comparison studies treat prediction
performance and variable selection aspects separately. Second, methods are
either compared within a binary outcome setting (based on an arbitrarily chosen
delay) or within a survival setting, but not both. In this paper, we propose a
comparison methodology to weight up those different settings both in terms of
prediction and variables selection, while incorporating advanced machine
learning strategies. Methods: Using a high-dimensional case study on a
sickle-cell disease (SCD) cohort, we compare 8 statistical methods. In the
binary outcome setting, we consider logistic regression (LR), support vector
machine (SVM), random forest (RF), gradient boosting (GB) and neural network
(NN); while on the survival analysis setting, we consider the Cox Proportional
Hazards (PH), the CURE and the C-mix models. We then compare performances of
all methods both in terms of risk prediction and variable selection, with a
focus on the use of Elastic-Net regularization technique. Results: Among all
assessed statistical methods assessed, the C-mix model yields the better
performances in both the two considered settings, as well as interesting
interpretation aspects. There is some consistency in selected covariates across
methods within a setting, but not much across the two settings. Conclusions: It
appears that learning withing the survival setting first, and then going back
to a binary prediction using the survival estimates significantly enhance
binary predictions. | [
0,
0,
0,
1,
0,
0
] |
Title: How the notion of ACCESS guides the organization of a European research infrastructure: the example of DARIAH,
Abstract: This contribution will show how Access play a strong role in the creation and
structuring of DARIAH, a European Digital Research Infrastructure in Arts and
Humanities.To achieve this goal, this contribution will develop the concept of
Access from five examples: Interdisciplinarity point of view, Manage
contradiction between national and international perspectives, Involve
different communities (not only researchers stakeholders), Manage tools and
services, Develop and use new collaboration tools. We would like to demonstrate
that speaking about Access always implies a selection, a choice, even in the
perspective of "Open Access". | [
1,
0,
0,
0,
0,
0
] |
Title: Soliton-potential interactions for nonlinear Schrödinger equation in $\mathbb{R}^3$,
Abstract: In this work we mainly consider the dynamics and scattering of a narrow
soliton of NLS equation with a potential in $\mathbb{R}^3$, where the
asymptotic state of the system can be far from the initial state in parameter
space. Specifically, if we let a narrow soliton state with initial velocity
$\upsilon_{0}$ to interact with an extra potential $V(x)$, then the velocity
$\upsilon_{+}$ of outgoing solitary wave in infinite time will in general be
very different from $\upsilon_{0}$. In contrast to our present work, previous
works proved that the soliton is asymptotically stable under the assumption
that $\upsilon_{+}$ stays close to $\upsilon_{0}$ in a certain manner. | [
0,
0,
1,
0,
0,
0
] |
Title: Incommensurately modulated twin structure of nyerereite Na1.64K0.36Ca(CO3)2,
Abstract: Incommensurately modulated twin structure of nyerereite Na1.64K0.36Ca(CO3)2
has been first determined in the (3+1)D symmetry group Cmcm({\alpha}00)00s with
modulation vector q = 0.383a*. Unit-cell values are a = 5.062(1), b = 8.790(1),
c = 12.744(1) {\AA}. Three orthorhombic components are related by threefold
rotation about [001]. Discontinuous crenel functions are used to describe
occupation modulation of Ca and some CO3 groups. Strong displacive modulation
of the oxygen atoms in vertexes of such CO3 groups is described using
x-harmonics in crenel intervals. The Na, K atoms occupy mixed sites whose
occupation modulation is described by two ways using either complementary
harmonic functions or crenels. The nyerereite structure has been compared both
with commensurately modulated structure of K-free Na2Ca(CO3)2 and with widely
known incommensurately modulated structure of {\gamma}-Na2CO3. | [
0,
1,
0,
0,
0,
0
] |
Title: Topological phase of the interlayer exchange coupling with application to magnetic switching,
Abstract: We show, theoretically, that the phase of the interlayer exchange coupling
(IEC) undergoes a topological change of approximately $2\pi$ as the chemical
potential of the ferromagnetic (FM) lead moves across a hybridization gap (HG).
The effect is largely independent of the detailed parameters of the system, in
particular the width of the gap. The implication is that for a narrow gap, a
small perturbation in the chemical potential of the lead can give a sign
reversal of the exchange coupling. This offers the possibility of controlling
magnetization switching in spintronic devices such as MRAM, with little power
consumption. Furthermore we believe that this effect has already been
indirectly observed, in existing measurements of the IEC as a function of
temperature and of doping of the leads. | [
0,
1,
0,
0,
0,
0
] |
Title: Distributed Triangle Counting in the Graphulo Matrix Math Library,
Abstract: Triangle counting is a key algorithm for large graph analysis. The Graphulo
library provides a framework for implementing graph algorithms on the Apache
Accumulo distributed database. In this work we adapt two algorithms for
counting triangles, one that uses the adjacency matrix and another that also
uses the incidence matrix, to the Graphulo library for server-side processing
inside Accumulo. Cloud-based experiments show a similar performance profile for
these different approaches on the family of power law Graph500 graphs, for
which data skew increasingly bottlenecks. These results motivate the design of
skew-aware hybrid algorithms that we propose for future work. | [
1,
0,
0,
0,
0,
0
] |
Title: Spin-Frustrated Pyrochlore Chains in the Volcanic Mineral Kamchatkite (KCu3OCl(SO4)2),
Abstract: Search of new frustrated magnetic systems is of a significant importance for
physics studying the condensed matter. The platform for geometric frustration
of magnetic systems can be provided by copper oxocentric tetrahedra (OCu4)
forming the base of crystalline structures of copper minerals from Tolbachik
volcanos in Kamchatka. The present work was devoted to a new frustrated
antiferromagnetic - kamchatkite (KCu3OCl(SO4)2). The calculation of the sign
and strength of magnetic couplings in KCu3OCl(SO4)2 has been performed on the
basis of structural data by the phenomenological crystal chemistry method with
taking into account corrections on the Jahn-Teller orbital degeneracy of Cu2.
It has been established that kamchatkite (KCu3OCl(SO4)2) contains AFM
spin-frustrated chains of the pyrochlore type composed of cone-sharing Cu4
tetrahedra. Strong AFM intrachain and interchain couplings compete with each
other. Frustration of magnetic couplings in tetrahedral chains is combined with
the presence of electric polarization. | [
0,
1,
0,
0,
0,
0
] |
Title: Detail-revealing Deep Video Super-resolution,
Abstract: Previous CNN-based video super-resolution approaches need to align multiple
frames to the reference. In this paper, we show that proper frame alignment and
motion compensation is crucial for achieving high quality results. We
accordingly propose a `sub-pixel motion compensation' (SPMC) layer in a CNN
framework. Analysis and experiments show the suitability of this layer in video
SR. The final end-to-end, scalable CNN framework effectively incorporates the
SPMC layer and fuses multiple frames to reveal image details. Our
implementation can generate visually and quantitatively high-quality results,
superior to current state-of-the-arts, without the need of parameter tuning. | [
1,
0,
0,
0,
0,
0
] |
Title: Generalized Results on Monoids as Memory,
Abstract: We show that some results from the theory of group automata and monoid
automata still hold for more general classes of monoids and models. Extending
previous work for finite automata over commutative groups, we demonstrate a
context-free language that can not be recognized by any rational monoid
automaton over a finitely generated permutable monoid. We show that the class
of languages recognized by rational monoid automata over finitely generated
completely simple or completely 0-simple permutable monoids is a semi-linear
full trio. Furthermore, we investigate valence pushdown automata, and prove
that they are only as powerful as (finite) valence automata. We observe that
certain results proven for monoid automata can be easily lifted to the case of
context-free valence grammars. | [
1,
0,
0,
0,
0,
0
] |
Title: Estimating the Spectral Density of Large Implicit Matrices,
Abstract: Many important problems are characterized by the eigenvalues of a large
matrix. For example, the difficulty of many optimization problems, such as
those arising from the fitting of large models in statistics and machine
learning, can be investigated via the spectrum of the Hessian of the empirical
loss function. Network data can be understood via the eigenstructure of a graph
Laplacian matrix using spectral graph theory. Quantum simulations and other
many-body problems are often characterized via the eigenvalues of the solution
space, as are various dynamic systems. However, naive eigenvalue estimation is
computationally expensive even when the matrix can be represented; in many of
these situations the matrix is so large as to only be available implicitly via
products with vectors. Even worse, one may only have noisy estimates of such
matrix vector products. In this work, we combine several different techniques
for randomized estimation and show that it is possible to construct unbiased
estimators to answer a broad class of questions about the spectra of such
implicit matrices, even in the presence of noise. We validate these methods on
large-scale problems in which graph theory and random matrix theory provide
ground truth. | [
0,
0,
0,
1,
0,
0
] |
Title: Robust 3D Distributed Formation Control with Application to Quadrotors,
Abstract: We present a distributed control strategy for a team of quadrotors to
autonomously achieve a desired 3D formation. Our approach is based on local
relative position measurements and does not require global position information
or inter-vehicle communication. We assume that quadrotors have a common sense
of direction, which is chosen as the direction of gravitational force measured
by their onboard IMU sensors. However, this assumption is not crucial, and our
approach is robust to inaccuracies and effects of acceleration on gravitational
measurements. In particular, converge to the desired formation is unaffected if
each quadrotor has a velocity vector that projects positively onto the desired
velocity vector provided by the formation control strategy. We demonstrate the
validity of proposed approach in an experimental setup and show that a team of
quadrotors achieve a desired 3D formation. | [
1,
0,
0,
0,
0,
0
] |
Title: Discrete Extremes,
Abstract: Our contribution is to widen the scope of extreme value analysis applied to
discrete-valued data. Extreme values of a random variable $X$ are commonly
modeled using the generalized Pareto distribution, a method that often gives
good results in practice. When $X$ is discrete, we propose two other methods
using a discrete generalized Pareto and a generalized Zipf distribution
respectively. Both are theoretically motivated and we show that they perform
well in estimating rare events in several simulated and real data cases such as
word frequency, tornado outbreaks and multiple births. | [
0,
0,
1,
1,
0,
0
] |
Title: Rapid Adaptation with Conditionally Shifted Neurons,
Abstract: We describe a mechanism by which artificial neural networks can learn rapid
adaptation - the ability to adapt on the fly, with little data, to new tasks -
that we call conditionally shifted neurons. We apply this mechanism in the
framework of metalearning, where the aim is to replicate some of the
flexibility of human learning in machines. Conditionally shifted neurons modify
their activation values with task-specific shifts retrieved from a memory
module, which is populated rapidly based on limited task experience. On
metalearning benchmarks from the vision and language domains, models augmented
with conditionally shifted neurons achieve state-of-the-art results. | [
1,
0,
0,
1,
0,
0
] |
Title: Condition number and matrices,
Abstract: It is well known the concept of the condition number $\kappa(A) =
\|A\|\|A^{-1}\|$, where $A$ is a $n \times n$ real or complex matrix and the
norm used is the spectral norm. Although it is very common to think in
$\kappa(A)$ as "the" condition number of $A$, the truth is that condition
numbers are associated to problems, not just instance of problems. Our goal is
to clarify this difference. We will introduce the general concept of condition
number and apply it to the particular case of real or complex matrices. After
this, we will introduce the classic condition number $\kappa(A)$ of a matrix
and show some known results. | [
0,
0,
1,
0,
0,
0
] |
Title: Dirac fermions in borophene,
Abstract: Honeycomb structures of group IV elements can host massless Dirac fermions
with non-trivial Berry phases. Their potential for electronic applications has
attracted great interest and spurred a broad search for new Dirac materials
especially in monolayer structures. We present a detailed investigation of the
\beta 12 boron sheet, which is a borophene structure that can form
spontaneously on a Ag(111) surface. Our tight-binding analysis revealed that
the lattice of the \beta 12-sheet could be decomposed into two triangular
sublattices in a way similar to that for a honeycomb lattice, thereby hosting
Dirac cones. Furthermore, each Dirac cone could be split by introducing
periodic perturbations representing overlayer-substrate interactions. These
unusual electronic structures were confirmed by angle-resolved photoemission
spectroscopy and validated by first-principles calculations. Our results
suggest monolayer boron as a new platform for realizing novel high-speed
low-dissipation devices. | [
0,
1,
0,
0,
0,
0
] |
Title: Benchmarking gate-based quantum computers,
Abstract: With the advent of public access to small gate-based quantum processors, it
becomes necessary to develop a benchmarking methodology such that independent
researchers can validate the operation of these processors. We explore the
usefulness of a number of simple quantum circuits as benchmarks for gate-based
quantum computing devices and show that circuits performing identity operations
are very simple, scalable and sensitive to gate errors and are therefore very
well suited for this task. We illustrate the procedure by presenting benchmark
results for the IBM Quantum Experience, a cloud-based platform for gate-based
quantum computing. | [
0,
1,
0,
0,
0,
0
] |
Title: Transfer Operator Based Approach for Optimal Stabilization of Stochastic System,
Abstract: In this paper we develop linear transfer Perron Frobenius operator-based
approach for optimal stabilization of stochastic nonlinear system. One of the
main highlight of the proposed transfer operator based approach is that both
the theory and computational framework developed for the optimal stabilization
of deterministic dynamical system in [1] carries over to the stochastic case
with little change. The optimal stabilization problem is formulated as an
infinite dimensional linear program. Set oriented numerical methods are
proposed for the finite dimensional approximation of the transfer operator and
the controller. Simulation results are presented to verify the developed
framework. | [
1,
0,
1,
0,
0,
0
] |
Title: Regulating Highly Automated Robot Ecologies: Insights from Three User Studies,
Abstract: Highly automated robot ecologies (HARE), or societies of independent
autonomous robots or agents, are rapidly becoming an important part of much of
the world's critical infrastructure. As with human societies, regulation,
wherein a governing body designs rules and processes for the society, plays an
important role in ensuring that HARE meet societal objectives. However, to
date, a careful study of interactions between a regulator and HARE is lacking.
In this paper, we report on three user studies which give insights into how to
design systems that allow people, acting as the regulatory authority, to
effectively interact with HARE. As in the study of political systems in which
governments regulate human societies, our studies analyze how interactions
between HARE and regulators are impacted by regulatory power and individual
(robot or agent) autonomy. Our results show that regulator power, decision
support, and adaptive autonomy can each diminish the social welfare of HARE,
and hint at how these seemingly desirable mechanisms can be designed so that
they become part of successful HARE. | [
1,
0,
0,
0,
0,
0
] |
Title: Flipping growth orientation of nanographitic structures by plasma enhanced chemical vapor deposition,
Abstract: Nanographitic structures (NGSs) with multitude of morphological features are
grown on SiO2/Si substrates by electron cyclotron resonance - plasma enhanced
chemical vapor deposition (ECR-PECVD). CH4 is used as source gas with Ar and H2
as dilutants. Field emission scanning electron microscopy, high resolution
transmission electron microscopy (HRTEM) and Raman spectroscopy are used to
study the structural and morphological features of the grown films. Herein, we
demonstrate, how the morphology can be tuned from planar to vertical structure
using single control parameter namely, dilution of CH4 with Ar and/or H2. Our
results show that the competitive growth and etching processes dictate the
morphology of the NGSs. While Ar-rich composition favors vertically oriented
graphene nanosheets, H2-rich composition aids growth of planar films. Raman
analysis reveals dilution of CH4 with either Ar or H2 or in combination helps
to improve the structural quality of the films. Line shape analysis of Raman 2D
band shows nearly symmetric Lorentzian profile which confirms the turbostratic
nature of the grown NGSs. Further, this aspect is elucidated by HRTEM studies
by observing elliptical diffraction pattern. Based on these experiments, a
comprehensive understanding is obtained on the growth and structural properties
of NGSs grown over a wide range of feedstock compositions. | [
0,
1,
0,
0,
0,
0
] |
Title: Numerical prediction of the piezoelectric transducer response in the acoustic nearfield using a one-dimensional electromechanical finite difference approach,
Abstract: We present a simple electromechanical finite difference model to study the
response of a piezoelectric polyvinylidenflourid (PVDF) transducer to
optoacoustic (OA) pressure waves in the acoustic nearfield prior to thermal
relaxation of the OA source volume. The assumption of nearfield conditions,
i.e. the absence of acoustic diffraction, allows to treat the problem using a
one-dimensional numerical approach. Therein, the computational domain is
modeled as an inhomogeneous elastic medium, characterized by its local wave
velocities and densities, allowing to explore the effect of stepwise impedance
changes on the stress wave propagation. The transducer is modeled as a thin
piezoelectric sensing layer and the electromechanical coupling is accomplished
by means of the respective linear constituting equations. Considering a
low-pass characteristic of the full experimental setup, we obtain the resulting
transducer signal. Complementing transducer signals measured in a controlled
laboratory experiment with numerical simulations that result from a model of
the experimental setup, we find that, bearing in mind the apparent limitations
of the one-dimensional approach, the simulated transducer signals can be used
very well to predict and interpret the experimental findings. | [
0,
1,
0,
0,
0,
0
] |
Title: Improved Point Source Detection in Crowded Fields using Probabilistic Cataloging,
Abstract: Cataloging is challenging in crowded fields because sources are extremely
covariant with their neighbors and blending makes even the number of sources
ambiguous. We present the first optical probabilistic catalog, cataloging a
crowded (~0.1 sources per pixel brighter than 22nd magnitude in F606W) Sloan
Digital Sky Survey r band image from M2. Probabilistic cataloging returns an
ensemble of catalogs inferred from the image and thus can capture source-source
covariance and deblending ambiguities. By comparing to a traditional catalog of
the same image and a Hubble Space Telescope catalog of the same region, we show
that our catalog ensemble better recovers sources from the image. It goes more
than a magnitude deeper than the traditional catalog while having a lower false
discovery rate brighter than 20th magnitude. We also present an algorithm for
reducing this catalog ensemble to a condensed catalog that is similar to a
traditional catalog, except it explicitly marginalizes over source-source
covariances and nuisance parameters. We show that this condensed catalog has a
similar completeness and false discovery rate to the catalog ensemble. Future
telescopes will be more sensitive, and thus more of their images will be
crowded. Probabilistic cataloging performs better than existing software in
crowded fields and so should be considered when creating photometric pipelines
in the Large Synoptic Space Telescope era. | [
0,
1,
0,
0,
0,
0
] |
Title: Local Convergence of Proximal Splitting Methods for Rank Constrained Problems,
Abstract: We analyze the local convergence of proximal splitting algorithms to solve
optimization problems that are convex besides a rank constraint. For this, we
show conditions under which the proximal operator of a function involving the
rank constraint is locally identical to the proximal operator of its convex
envelope, hence implying local convergence. The conditions imply that the
non-convex algorithms locally converge to a solution whenever a convex
relaxation involving the convex envelope can be expected to solve the
non-convex problem. | [
1,
0,
0,
1,
0,
0
] |
Title: On the boundary between qualitative and quantitative measures of causal effects,
Abstract: Causal relationships among variables are commonly represented via directed
acyclic graphs. There are many methods in the literature to quantify the
strength of arrows in a causal acyclic graph. These methods, however, have
undesirable properties when the causal system represented by a directed acyclic
graph is degenerate. In this paper, we characterize a degenerate causal system
using multiplicity of Markov boundaries, and show that in this case, it is
impossible to quantify causal effects in a reasonable fashion. We then propose
algorithms to identify such degenerate scenarios from observed data.
Performance of our algorithms is investigated through synthetic data analysis. | [
0,
0,
1,
1,
0,
0
] |
Title: How Could Polyhedral Theory Harness Deep Learning?,
Abstract: The holy grail of deep learning is to come up with an automatic method to
design optimal architectures for different applications. In other words, how
can we effectively dimension and organize neurons along the network layers
based on the computational resources, input size, and amount of training data?
We outline promising research directions based on polyhedral theory and
mixed-integer representability that may offer an analytical approach to this
question, in contrast to the empirical techniques often employed. | [
0,
0,
0,
1,
0,
0
] |
Title: Optimal top dag compression,
Abstract: It is shown that for a given ordered node-labelled tree of size $n$ and with
$s$ many different node labels, one can construct in linear time a top dag of
height $O(\log n)$ and size $O(n / \log_\sigma n) \cap O(d \cdot \log n)$,
where $\sigma = \max\{ 2, s\}$ and $d$ is the size of the minimal dag. The size
bound $O(n / \log_\sigma n)$ is optimal and improves on previous bounds. | [
1,
0,
0,
0,
0,
0
] |
Title: Load Thresholds for Cuckoo Hashing with Overlapping Blocks,
Abstract: Dietzfelbinger and Weidling [DW07] proposed a natural variation of cuckoo
hashing where each of $cn$ objects is assigned $k = 2$ intervals of size $\ell$
in a linear (or cyclic) hash table of size $n$ and both start points are chosen
independently and uniformly at random. Each object must be placed into a table
cell within its intervals, but each cell can only hold one object. Experiments
suggested that this scheme outperforms the variant with blocks in which
intervals are aligned at multiples of $\ell$. In particular, the load threshold
is higher, i.e. the load $c$ that can be achieved with high probability. For
instance, Lehman and Panigrahy [LP09] empirically observed the threshold for
$\ell = 2$ to be around $96.5\%$ as compared to roughly $89.7\%$ using blocks.
They managed to pin down the asymptotics of the thresholds for large $\ell$,
but the precise values resisted rigorous analysis.
We establish a method to determine these load thresholds for all $\ell \geq
2$, and, in fact, for general $k \geq 2$. For instance, for $k = \ell = 2$ we
get $\approx 96.4995\%$. The key tool we employ is an insightful and general
theorem due to Leconte, Lelarge, and Massoulié [LLM13], which adapts methods
from statistical physics to the world of hypergraph orientability. In effect,
the orientability thresholds for our graph families are determined by belief
propagation equations for certain graph limits. As a side note we provide
experimental evidence suggesting that placements can be constructed in linear
time with loads close to the threshold using an adapted version of an algorithm
by Khosla [Kho13]. | [
1,
0,
0,
0,
0,
0
] |
Title: Evaluating the hot hand phenomenon using predictive memory selection for multistep Markov Chains: LeBron James' error correcting free throws,
Abstract: Consider the problem of modeling memory for discrete-state random walks using
higher-order Markov chains. This Letter introduces a general Bayesian framework
under the principle of minimizing prediction error to select, from data, the
number of prior states of recent history upon which a trajectory is
statistically dependent. In this framework, I provide closed-form expressions
for several alternative model selection criteria that approximate model
prediction error for future data. Using simulations, I evaluate the statistical
power of these criteria. These methods, when applied to data from the
2016--2017 NBA season, demonstrate evidence of statistical dependencies in
LeBron James' free throw shooting. In particular, a model depending on the
previous shot (single-step Markovian) is approximately as predictive as a model
with independent outcomes. A hybrid jagged model of two parameters, where James
shoots a higher percentage after a missed free throw than otherwise, is more
predictive than either model. | [
0,
1,
0,
1,
0,
0
] |
Title: Closed almost-Kähler 4-manifolds of constant non-negative Hermitian holomorphic sectional curvature are Kähler,
Abstract: We show that a closed almost Kähler 4-manifold of globally constant
holomorphic sectional curvature $k\geq 0$ with respect to the canonical
Hermitian connection is automatically Kähler. The same result holds for $k<0$
if we require in addition that the Ricci curvature is J-invariant. The proofs
are based on the observation that such manifolds are self-dual, so that
Chern-Weil theory implies useful integral formulas, which are then combined
with results from Seiberg--Witten theory. | [
0,
0,
1,
0,
0,
0
] |
Title: Outcrop fracture characterization on suppositional planes cutting through digital outcrop models (DOMs),
Abstract: Conventional fracture data collection methods are usually implemented on
planar surfaces or assuming they are planar; these methods may introduce
sampling errors on uneven outcrop surfaces. Consequently, data collected on
limited types of outcrop surfaces (mainly bedding surfaces) may not be a
sufficient representation of fracture network characteristic in outcrops.
Recent development of techniques that obtain DOMs from outcrops and extract the
full extent of individual fractures offers the opportunity to address the
problem of performing the conventional sampling methods on uneven outcrop
surfaces. In this study, we propose a new method that performs outcrop fracture
characterization on suppositional planes cutting through DOMs. The
suppositional plane is the best fit plane of the outcrop surface, and the
fracture trace map is extracted on the suppositional plane so that the fracture
network can be further characterized. The amount of sampling errors introduced
by the conventional methods and avoided by the new method on 16 uneven outcrop
surfaces with different roughnesses are estimated. The results show that the
conventional sampling methods don't apply to outcrops other than bedding
surfaces or outcrops whose roughness > 0.04 m, and that the proposed method can
greatly extend the types of outcrop surfaces for outcrop fracture
characterization with the suppositional plane cutting through DOMs. | [
1,
1,
0,
0,
0,
0
] |
Title: Consistent Inter-Model Specification for Time-Homogeneous SPX Stochastic Volatility and VIX Market Models,
Abstract: This paper shows how to recover stochastic volatility models (SVMs) from
market models for the VIX futures term structure. Market models have more
flexibility for fitting of curves than do SVMs, and therefore they are
better-suited for pricing VIX futures and derivatives. But the VIX itself is a
derivative of the S&P500 (SPX) and it is common practice to price SPX
derivatives using an SVM. Hence, a consistent model for both SPX and VIX
derivatives would be one where the SVM is obtained by inverting the market
model. This paper's main result is a method for the recovery of a stochastic
volatility function as the output of an inverse problem, with the inputs given
by a VIX futures market model. Analysis will show that some conditions need to
be met in order for there to not be any inter-model arbitrage or mis-priced
derivatives. Given these conditions the inverse problem can be solved. Several
models are analyzed and explored numerically to gain a better understanding of
the theory and its limitations. | [
0,
0,
0,
0,
0,
1
] |
Title: One-step and Two-step Classification for Abusive Language Detection on Twitter,
Abstract: Automatic abusive language detection is a difficult but important task for
online social media. Our research explores a two-step approach of performing
classification on abusive language and then classifying into specific types and
compares it with one-step approach of doing one multi-class classification for
detecting sexist and racist languages. With a public English Twitter corpus of
20 thousand tweets in the type of sexism and racism, our approach shows a
promising performance of 0.827 F-measure by using HybridCNN in one-step and
0.824 F-measure by using logistic regression in two-steps. | [
1,
0,
0,
0,
0,
0
] |
Title: Massive data compression for parameter-dependent covariance matrices,
Abstract: We show how the massive data compression algorithm MOPED can be used to
reduce, by orders of magnitude, the number of simulated datasets that are
required to estimate the covariance matrix required for the analysis of
gaussian-distributed data. This is relevant when the covariance matrix cannot
be calculated directly. The compression is especially valuable when the
covariance matrix varies with the model parameters. In this case, it may be
prohibitively expensive to run enough simulations to estimate the full
covariance matrix throughout the parameter space. This compression may be
particularly valuable for the next-generation of weak lensing surveys, such as
proposed for Euclid and LSST, for which the number of summary data (such as
band power or shear correlation estimates) is very large, $\sim 10^4$, due to
the large number of tomographic redshift bins that the data will be divided
into. In the pessimistic case where the covariance matrix is estimated
separately for all points in an MCMC analysis, this may require an unfeasible
$10^9$ simulations. We show here that MOPED can reduce this number by a factor
of 1000, or a factor of $\sim 10^6$ if some regularity in the covariance matrix
is assumed, reducing the number of simulations required to a manageable $10^3$,
making an otherwise intractable analysis feasible. | [
0,
1,
0,
1,
0,
0
] |
Title: A Novel Data-Driven Framework for Risk Characterization and Prediction from Electronic Medical Records: A Case Study of Renal Failure,
Abstract: Electronic medical records (EMR) contain longitudinal information about
patients that can be used to analyze outcomes. Typically, studies on EMR data
have worked with established variables that have already been acknowledged to
be associated with certain outcomes. However, EMR data may also contain
hitherto unrecognized factors for risk association and prediction of outcomes
for a disease. In this paper, we present a scalable data-driven framework to
analyze EMR data corpus in a disease agnostic way that systematically uncovers
important factors influencing outcomes in patients, as supported by data and
without expert guidance. We validate the importance of such factors by using
the framework to predict for the relevant outcomes. Specifically, we analyze
EMR data covering approximately 47 million unique patients to characterize
renal failure (RF) among type 2 diabetic (T2DM) patients. We propose a
specialized L1 regularized Cox Proportional Hazards (CoxPH) survival model to
identify the important factors from those available from patient encounter
history. To validate the identified factors, we use a specialized generalized
linear model (GLM) to predict the probability of renal failure for individual
patients within a specified time window. Our experiments indicate that the
factors identified via our data-driven method overlap with the patient
characteristics recognized by experts. Our approach allows for scalable,
repeatable and efficient utilization of data available in EMRs, confirms prior
medical knowledge and can generate new hypothesis without expert supervision. | [
1,
0,
0,
1,
0,
0
] |
Title: Knotted solutions for linear and nonlinear theories: electromagnetism and fluid dynamics,
Abstract: We examine knotted solutions, the most simple of which is the "Hopfion", from
the point of view of relations between electromagnetism and ideal fluid
dynamics. A map between fluid dynamics and electromagnetism works for initial
conditions or for linear perturbations, allowing us to find new knotted fluid
solutions. Knotted solutions are also found to to be solutions of nonlinear
generalizations of electromagnetism, and of quantum-corrected actions for
electromagnetism coupled to other modes. For null configurations,
electromagnetism can be described as a null pressureless fluid, for which we
can find solutions from the knotted solutions of electromagnetism. We also map
them to solutions of Euler's equations, obtained from a type of nonrelativistic
reduction of the relativistic fluid equations. | [
0,
1,
0,
0,
0,
0
] |
Title: Improving Legal Information Retrieval by Distributional Composition with Term Order Probabilities,
Abstract: Legal professionals worldwide are currently trying to get up-to-pace with the
explosive growth in legal document availability through digital means. This
drives a need for high efficiency Legal Information Retrieval (IR) and Question
Answering (QA) methods. The IR task in particular has a set of unique
challenges that invite the use of semantic motivated NLP techniques. In this
work, a two-stage method for Legal Information Retrieval is proposed, combining
lexical statistics and distributional sentence representations in the context
of Competition on Legal Information Extraction/Entailment (COLIEE). The
combination is done with the use of disambiguation rules, applied over the
rankings obtained through n-gram statistics. After the ranking is done, its
results are evaluated for ambiguity, and disambiguation is done if a result is
decided to be unreliable for a given query. Competition and experimental
results indicate small gains in overall retrieval performance using the
proposed approach. Additionally, an analysis of error and improvement cases is
presented for a better understanding of the contributions. | [
1,
0,
0,
0,
0,
0
] |
Title: On the structure of join tensors with applications to tensor eigenvalue problems,
Abstract: We investigate the structure of join tensors, which may be regarded as the
multivariable extension of lattice-theoretic join matrices. Explicit formulae
for a polyadic decomposition (i.e., a linear combination of rank-1 tensors) and
a tensor-train decomposition of join tensors are derived on general join
semilattices. We discuss conditions under which the obtained decompositions are
optimal in rank, and examine numerically the storage complexity of the obtained
decompositions for a class of LCM tensors as a special case of join tensors. In
addition, we investigate numerically the sharpness of a theoretical upper bound
on the tensor eigenvalues of LCM tensors. | [
0,
0,
1,
0,
0,
0
] |
Title: Learning Light Transport the Reinforced Way,
Abstract: We show that the equations of reinforcement learning and light transport
simulation are related integral equations. Based on this correspondence, a
scheme to learn importance while sampling path space is derived. The new
approach is demonstrated in a consistent light transport simulation algorithm
that uses reinforcement learning to progressively learn where light comes from.
As using this information for importance sampling includes information about
visibility, too, the number of light transport paths with zero contribution is
dramatically reduced, resulting in much less noisy images within a fixed time
budget. | [
1,
0,
0,
0,
0,
0
] |
Title: Posterior Asymptotic Normality for an Individual Coordinate in High-dimensional Linear Regression,
Abstract: We consider the sparse high-dimensional linear regression model
$Y=Xb+\epsilon$ where $b$ is a sparse vector. For the Bayesian approach to this
problem, many authors have considered the behavior of the posterior
distribution when, in truth, $Y=X\beta+\epsilon$ for some given $\beta$. There
have been numerous results about the rate at which the posterior distribution
concentrates around $\beta$, but few results about the shape of that posterior
distribution. We propose a prior distribution for $b$ such that the marginal
posterior distribution of an individual coordinate $b_i$ is asymptotically
normal centered around an asymptotically efficient estimator, under the truth.
Such a result gives Bayesian credible intervals that match with the confidence
intervals obtained from an asymptotically efficient estimator for $b_i$. We
also discuss ways of obtaining such asymptotically efficient estimators on
individual coordinates. We compare the two-step procedure proposed by Zhang and
Zhang (2014) and a one-step modified penalization method. | [
0,
0,
1,
1,
0,
0
] |
Title: Subdeterminant Maximization via Nonconvex Relaxations and Anti-concentration,
Abstract: Several fundamental problems that arise in optimization and computer science
can be cast as follows: Given vectors $v_1,\ldots,v_m \in \mathbb{R}^d$ and a
constraint family ${\cal B}\subseteq 2^{[m]}$, find a set $S \in \cal{B}$ that
maximizes the squared volume of the simplex spanned by the vectors in $S$. A
motivating example is the data-summarization problem in machine learning where
one is given a collection of vectors that represent data such as documents or
images. The volume of a set of vectors is used as a measure of their diversity,
and partition or matroid constraints over $[m]$ are imposed in order to ensure
resource or fairness constraints. Recently, Nikolov and Singh presented a
convex program and showed how it can be used to estimate the value of the most
diverse set when ${\cal B}$ corresponds to a partition matroid. This result was
recently extended to regular matroids in works of Straszak and Vishnoi, and
Anari and Oveis Gharan. The question of whether these estimation algorithms can
be converted into the more useful approximation algorithms -- that also output
a set -- remained open.
The main contribution of this paper is to give the first approximation
algorithms for both partition and regular matroids. We present novel
formulations for the subdeterminant maximization problem for these matroids;
this reduces them to the problem of finding a point that maximizes the absolute
value of a nonconvex function over a Cartesian product of probability
simplices. The technical core of our results is a new anti-concentration
inequality for dependent random variables that allows us to relate the optimal
value of these nonconvex functions to their value at a random point. Unlike
prior work on the constrained subdeterminant maximization problem, our proofs
do not rely on real-stability or convexity and could be of independent interest
both in algorithms and complexity. | [
1,
0,
1,
1,
0,
0
] |
Title: On The Complexity of Enumeration,
Abstract: We investigate the relationship between several enumeration complexity
classes and focus in particular on problems having enumeration algorithms with
incremental and polynomial delay (IncP and DelayP respectively). We show that,
for some algorithms, we can turn an average delay into a worst case delay
without increasing the space complexity, suggesting that IncP_1 = DelayP even
with polynomially bounded space. We use the Exponential Time Hypothesis to
exhibit a strict hierarchy inside IncP which gives the first separation of
DelayP and IncP. Finally we relate the uniform generation of solutions to
probabilistic enumeration algorithms with polynomial delay and polynomial
space. | [
1,
0,
0,
0,
0,
0
] |
Title: Combinatorial formulas for Kazhdan-Lusztig polynomials with respect to W-graph ideals,
Abstract: In \cite{y1} Yin generalized the definition of $W$-graph ideal $E_J$ in
weighted Coxeter groups and introduced the weighted Kazhdan-Lusztig polynomials
$ \left \{ P_{x,y} \mid x,y\in E_J\right \}$, where $J$ is a subset of simple
generators $S$. In this paper, we study the combinatorial formulas for those
polynomials, which extend the results of Deodhar \cite{v3} and Tagawa
\cite{h1}. | [
0,
0,
1,
0,
0,
0
] |
Title: Characterizing time-irreversibility in disordered fermionic systems by the effect of local perturbations,
Abstract: We study the effects of local perturbations on the dynamics of disordered
fermionic systems in order to characterize time-irreversibility. We focus on
three different systems, the non-interacting Anderson and Aubry-André-Harper
(AAH-) models, and the interacting spinless disordered t-V chain. First, we
consider the effect on the full many-body wave-functions by measuring the
Loschmidt echo (LE). We show that in the extended/ergodic phase the LE decays
exponentially fast with time, while in the localized phase the decay is
algebraic. We demonstrate that the exponent of the decay of the LE in the
localized phase diverges proportionally to the single-particle localization
length as we approach the metal-insulator transition in the AAH model. Second,
we probe different phases of disordered systems by studying the time
expectation value of local observables evolved with two Hamiltonians that
differ by a spatially local perturbation. Remarkably, we find that many-body
localized systems could lose memory of the initial state in the long-time
limit, in contrast to the non-interacting localized phase where some memory is
always preserved. | [
0,
1,
0,
0,
0,
0
] |
Title: A Lower Bound for the Number of Central Configurations on H^2,
Abstract: We study the indices of the geodesic central configurations on $\H^2$. We
then show that central configurations are bounded away from the singularity
set. With Morse's inequality, we get a lower bound for the number of central
configurations on $\H^2$. | [
0,
0,
1,
0,
0,
0
] |
Title: Motional Ground State Cooling Outside the Lamb-Dicke Regime,
Abstract: We report Raman sideband cooling of a single sodium atom to its
three-dimensional motional ground state in an optical tweezer. Despite a large
Lamb-Dicke parameter, high initial temperature, and large differential light
shifts between the excited state and the ground state, we achieve a ground
state population of $93.5(7)$% after $53$ ms of cooling. Our technique includes
addressing high-order sidebands, where several motional quanta are removed by a
single laser pulse, and fast modulation of the optical tweezer intensity. We
demonstrate that Raman sideband cooling to the 3D motional ground state is
possible, even without tight confinement and low initial temperature. | [
0,
1,
0,
0,
0,
0
] |
Title: Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction,
Abstract: Spatial-temporal prediction is a fundamental problem for constructing smart
city, which is useful for tasks such as traffic control, taxi dispatching, and
environmental policy making. Due to data collection mechanism, it is common to
see data collection with unbalanced spatial distributions. For example, some
cities may release taxi data for multiple years while others only release a few
days of data; some regions may have constant water quality data monitored by
sensors whereas some regions only have a small collection of water samples. In
this paper, we tackle the problem of spatial-temporal prediction for the cities
with only a short period of data collection. We aim to utilize the long-period
data from other cities via transfer learning. Different from previous studies
that transfer knowledge from one single source city to a target city, we are
the first to leverage information from multiple cities to increase the
stability of transfer. Specifically, our proposed model is designed as a
spatial-temporal network with a meta-learning paradigm. The meta-learning
paradigm learns a well-generalized initialization of the spatial-temporal
network, which can be effectively adapted to target cities. In addition, a
pattern-based spatial-temporal memory is designed to distill long-term temporal
information (i.e., periodicity). We conduct extensive experiments on two tasks:
traffic (taxi and bike) prediction and water quality prediction. The
experiments demonstrate the effectiveness of our proposed model over several
competitive baseline models. | [
1,
0,
0,
1,
0,
0
] |
Title: Numerical Simulations of Regolith Sampling Processes,
Abstract: We present recent improvements in the simulation of regolith sampling
processes in microgravity using the numerical particle method smooth particle
hydrodynamics (SPH). We use an elastic-plastic soil constitutive model for
large deformation and failure flows for dynamical behaviour of regolith. In the
context of projected small body (asteroid or small moons) sample return
missions, we investigate the efficiency and feasibility of a particular
material sampling method: Brushes sweep material from the asteroid's surface
into a collecting tray. We analyze the influence of different material
parameters of regolith such as cohesion and angle of internal friction on the
sampling rate. Furthermore, we study the sampling process in two environments
by varying the surface gravity (Earth's and Phobos') and we apply different
rotation rates for the brushes. We find good agreement of our sampling
simulations on Earth with experiments and provide estimations for the influence
of the material properties on the collecting rate. | [
0,
1,
0,
0,
0,
0
] |
Title: Lexical analysis of automated accounts on Twitter,
Abstract: In recent years, social bots have been using increasingly more sophisticated,
challenging detection strategies. While many approaches and features have been
proposed, social bots evade detection and interact much like humans making it
difficult to distinguish real human accounts from bot accounts. For detection
systems, various features under the broader categories of account profile,
tweet content, network and temporal pattern have been utilised. The use of
tweet content features is limited to analysis of basic terms such as URLs,
hashtags, name entities and sentiment. Given a set of tweet contents with no
obvious pattern can we distinguish contents produced by social bots from that
of humans? We aim to answer this question by analysing the lexical richness of
tweets produced by the respective accounts using large collections of different
datasets. Our results show a clear margin between the two classes in lexical
diversity, lexical sophistication and distribution of emoticons. We found that
the proposed lexical features significantly improve the performance of
classifying both account types. These features are useful for training a
standard machine learning classifier for effective detection of social bot
accounts. A new dataset is made freely available for further exploration. | [
1,
0,
0,
0,
0,
0
] |
Title: Deep Reinforcement Learning for De-Novo Drug Design,
Abstract: We propose a novel computational strategy for de novo design of molecules
with desired properties termed ReLeaSE (Reinforcement Learning for Structural
Evolution). Based on deep and reinforcement learning approaches, ReLeaSE
integrates two deep neural networks - generative and predictive - that are
trained separately but employed jointly to generate novel targeted chemical
libraries. ReLeaSE employs simple representation of molecules by their SMILES
strings only. Generative models are trained with stack-augmented memory network
to produce chemically feasible SMILES strings, and predictive models are
derived to forecast the desired properties of the de novo generated compounds.
In the first phase of the method, generative and predictive models are trained
separately with a supervised learning algorithm. In the second phase, both
models are trained jointly with the reinforcement learning approach to bias the
generation of new chemical structures towards those with the desired physical
and/or biological properties. In the proof-of-concept study, we have employed
the ReLeaSE method to design chemical libraries with a bias toward structural
complexity or biased toward compounds with either maximal, minimal, or specific
range of physical properties such as melting point or hydrophobicity, as well
as to develop novel putative inhibitors of JAK2. The approach proposed herein
can find a general use for generating targeted chemical libraries of novel
compounds optimized for either a single desired property or multiple
properties. | [
1,
0,
0,
1,
0,
0
] |
Title: Batch Data Processing and Gaussian Two-Armed Bandit,
Abstract: We consider the two-armed bandit problem as applied to data processing if
there are two alternative processing methods available with different a priori
unknown efficiencies. One should determine the most effective method and
provide its predominant application. Gaussian two-armed bandit describes the
batch, and possibly parallel, processing when the same methods are applied to
sufficiently large packets of data and accumulated incomes are used for the
control. If the number of packets is large enough then such control does not
deteriorate the control performance, i.e. does not increase the minimax risk.
For example, in case of 50 packets the minimax risk is about 2% larger than
that one corresponding to one-by-one optimal processing. However, this is
completely true only for methods with close efficiencies because otherwise
there may be significant expected losses at the initial stage of control when
both actions are applied turn-by-turn. To avoid significant losses at the
initial stage of control one should take initial packets of data having smaller
sizes. | [
0,
0,
1,
1,
0,
0
] |
Title: Estimating Under Five Mortality in Space and Time in a Developing World Context,
Abstract: Accurate estimates of the under-5 mortality rate (U5MR) in a developing world
context are a key barometer of the health of a nation. This paper describes new
models to analyze survey data on mortality in this context. We are interested
in both spatial and temporal description, that is, wishing to estimate U5MR
across regions and years, and to investigate the association between the U5MR
and spatially-varying covariate surfaces. We illustrate the methodology by
producing yearly estimates for subnational areas in Kenya over the period 1980
- 2014 using data from demographic health surveys (DHS). We use a binomial
likelihood with fixed effects for the urban/rural stratification to account for
the complex survey design. We carry out smoothing using Bayesian hierarchical
models with continuous spatial and temporally discrete components. A key
component of the model is an offset to adjust for bias due to the effects of
HIV epidemics. Substantively, there has been a sharp decline in U5MR in the
period 1980 - 2014, but large variability in estimated subnational rates
remains. A priority for future research is understanding this variability.
Temperature, precipitation and a measure of malaria infection prevalence were
candidates for inclusion in the covariate model. | [
0,
0,
0,
1,
0,
0
] |
Title: Cyclotomic Construction of Strong External Difference Families in Finite Fields,
Abstract: Strong external difference family (SEDF) and its generalizations GSEDF,
BGSEDF in a finite abelian group $G$ are combinatorial designs raised by
Paterson and Stinson [7] in 2016 and have applications in communication theory
to construct optimal strong algebraic manipulation detection codes. In this
paper we firstly present some general constructions of these combinatorial
designs by using difference sets and partial difference sets in $G$. Then, as
applications of the general constructions, we construct series of SEDF, GSEDF
and BGSEDF in finite fields by using cyclotomic classes. | [
1,
0,
1,
0,
0,
0
] |
Title: Acoustic double negativity induced by position correlations within a disordered set of monopolar resonators,
Abstract: Using a Multiple Scattering Theory algorithm, we investigate numerically the
transmission of ultrasonic waves through a disordered locally resonant
metamaterial containing only monopolar resonators. By comparing the cases of a
perfectly random medium with its pair correlated counterpart, we show that the
introduction of short range correlation can substantially impact the effective
parameters of the sample. We report, notably, the opening of an acoustic
transparency window in the region of the hybridization band gap. Interestingly,
the transparency window is found to be associated with negative values of both
effective compressibility and density. Despite this feature being unexpected
for a disordered medium of monopolar resonators, we show that it can be fully
described analytically and that it gives rise to negative refraction of waves. | [
0,
1,
0,
0,
0,
0
] |
Title: Interoceptive robustness through environment-mediated morphological development,
Abstract: Typically, AI researchers and roboticists try to realize intelligent behavior
in machines by tuning parameters of a predefined structure (body plan and/or
neural network architecture) using evolutionary or learning algorithms. Another
but not unrelated longstanding property of these systems is their brittleness
to slight aberrations, as highlighted by the growing deep learning literature
on adversarial examples. Here we show robustness can be achieved by evolving
the geometry of soft robots, their control systems, and how their material
properties develop in response to one particular interoceptive stimulus
(engineering stress) during their lifetimes. By doing so we realized robots
that were equally fit but more robust to extreme material defects (such as
might occur during fabrication or by damage thereafter) than robots that did
not develop during their lifetimes, or developed in response to a different
interoceptive stimulus (pressure). This suggests that the interplay between
changes in the containing systems of agents (body plan and/or neural
architecture) at different temporal scales (evolutionary and developmental)
along different modalities (geometry, material properties, synaptic weights)
and in response to different signals (interoceptive and external perception)
all dictate those agents' abilities to evolve or learn capable and robust
strategies. | [
1,
0,
0,
0,
0,
0
] |
Title: Strichartz estimates for non-degenerate Schrödinger equations,
Abstract: We consider Schrödinger equation with a non-degenerate metric on the
Euclidean space. We study local in time Strichartz estimates for the
Schrödinger equation without loss of derivatives including the endpoint case.
In contrast to the Riemannian metric case, we need the additional assumptions
for the well-posedness of our Schrödinger equation and for proving Strichartz
estimates without loss. | [
0,
0,
1,
0,
0,
0
] |
Title: The diffusion equation with nonlocal data,
Abstract: We study the diffusion (or heat) equation on a finite 1-dimensional spatial
domain, but we replace one of the boundary conditions with a "nonlocal
condition", through which we specify a weighted average of the solution over
the spatial interval. We provide conditions on the regularity of both the data
and weight for the problem to admit a unique solution, and also provide a
solution representation in terms of contour integrals. The solution and
well-posedness results rely upon an extension of the Fokas (or unified)
transform method to initial-nonlocal value problems for linear equations; the
necessary extensions are described in detail. Despite arising naturally from
the Fokas transform method, the uniqueness argument appears to be novel even
for initial-boundary value problems. | [
0,
0,
1,
0,
0,
0
] |
Title: Exploring Neural Transducers for End-to-End Speech Recognition,
Abstract: In this work, we perform an empirical comparison among the CTC,
RNN-Transducer, and attention-based Seq2Seq models for end-to-end speech
recognition. We show that, without any language model, Seq2Seq and
RNN-Transducer models both outperform the best reported CTC models with a
language model, on the popular Hub5'00 benchmark. On our internal diverse
dataset, these trends continue - RNNTransducer models rescored with a language
model after beam search outperform our best CTC models. These results simplify
the speech recognition pipeline so that decoding can now be expressed purely as
neural network operations. We also study how the choice of encoder architecture
affects the performance of the three models - when all encoder layers are
forward only, and when encoders downsample the input representation
aggressively. | [
1,
0,
0,
0,
0,
0
] |
Title: Phase Transitions of Spectral Initialization for High-Dimensional Nonconvex Estimation,
Abstract: We study a spectral initialization method that serves a key role in recent
work on estimating signals in nonconvex settings. Previous analysis of this
method focuses on the phase retrieval problem and provides only performance
bounds. In this paper, we consider arbitrary generalized linear sensing models
and present a precise asymptotic characterization of the performance of the
method in the high-dimensional limit. Our analysis also reveals a phase
transition phenomenon that depends on the ratio between the number of samples
and the signal dimension. When the ratio is below a minimum threshold, the
estimates given by the spectral method are no better than random guesses drawn
from a uniform distribution on the hypersphere, thus carrying no information;
above a maximum threshold, the estimates become increasingly aligned with the
target signal. The computational complexity of the method, as measured by the
spectral gap, is also markedly different in the two phases. Worked examples and
numerical results are provided to illustrate and verify the analytical
predictions. In particular, simulations show that our asymptotic formulas
provide accurate predictions for the actual performance of the spectral method
even at moderate signal dimensions. | [
1,
0,
0,
1,
0,
0
] |
Title: Analysis of Footnote Chasing and Citation Searching in an Academic Search Engine,
Abstract: In interactive information retrieval, researchers consider the user behavior
towards systems and search tasks in order to adapt search results by analyzing
their past interactions. In this paper, we analyze the user behavior towards
Marcia Bates' search stratagems such as 'footnote chasing' and 'citation
search' in an academic search engine. We performed a preliminary analysis of
their frequency and stage of use in the social sciences search engine sowiport.
In addition, we explored the impact of these stratagems on the whole search
process performance. We can conclude that the appearance of these two search
features in real retrieval sessions lead to an improvement of the precision in
terms of positive interactions with 16% when using footnote chasing and 17% for
the citation search stratagem. | [
1,
0,
0,
0,
0,
0
] |
Title: Temporal Convolution Networks for Real-Time Abdominal Fetal Aorta Analysis with Ultrasound,
Abstract: The automatic analysis of ultrasound sequences can substantially improve the
efficiency of clinical diagnosis. In this work we present our attempt to
automate the challenging task of measuring the vascular diameter of the fetal
abdominal aorta from ultrasound images. We propose a neural network
architecture consisting of three blocks: a convolutional layer for the
extraction of imaging features, a Convolution Gated Recurrent Unit (C-GRU) for
enforcing the temporal coherence across video frames and exploiting the
temporal redundancy of a signal, and a regularized loss function, called
\textit{CyclicLoss}, to impose our prior knowledge about the periodicity of the
observed signal. We present experimental evidence suggesting that the proposed
architecture can reach an accuracy substantially superior to previously
proposed methods, providing an average reduction of the mean squared error from
$0.31 mm^2$ (state-of-art) to $0.09 mm^2$, and a relative error reduction from
$8.1\%$ to $5.3\%$. The mean execution speed of the proposed approach of 289
frames per second makes it suitable for real time clinical use. | [
0,
0,
0,
1,
0,
0
] |
Title: The Role of Data Analysis in the Development of Intelligent Energy Networks,
Abstract: Data analysis plays an important role in the development of intelligent
energy networks (IENs). This article reviews and discusses the application of
data analysis methods for energy big data. The installation of smart energy
meters has provided a huge volume of data at different time resolutions,
suggesting data analysis is required for clustering, demand forecasting, energy
generation optimization, energy pricing, monitoring and diagnostics. The
currently adopted data analysis technologies for IENs include pattern
recognition, machine learning, data mining, statistics methods, etc. However,
existing methods for data analysis cannot fully meet the requirements for
processing the big data produced by the IENs and, therefore, more comprehensive
data analysis methods are needed to handle the increasing amount of data and to
mine more valuable information. | [
1,
0,
0,
0,
0,
0
] |
Title: Quantification of the memory effect of steady-state currents from interaction-induced transport in quantum systems,
Abstract: Dynamics of a system in general depends on its initial state and how the
system is driven, but in many-body systems the memory is usually averaged out
during evolution. Here, interacting quantum systems without external
relaxations are shown to retain long-time memory effects in steady states. To
identify memory effects, we first show quasi-steady state currents form in
finite, isolated Bose and Fermi Hubbard models driven by interaction imbalance
and they become steady-state currents in the thermodynamic limit. By comparing
the steady state currents from different initial states or ramping rates of the
imbalance, long-time memory effects can be quantified. While the memory effects
of initial states are more ubiquitous, the memory effects of switching
protocols are mostly visible in interaction-induced transport in lattices. Our
simulations suggest the systems enter a regime governed by a generalized Fick's
law and memory effects lead to initial-state dependent diffusion coefficients.
We also identify conditions for enhancing memory effects and discuss possible
experimental implications. | [
0,
1,
0,
0,
0,
0
] |
Title: Quandle rings,
Abstract: In this paper, a theory of quandle rings is proposed for quandles analogous
to the classical theory of group rings for groups, and interconnections between
quandles and associated quandle rings are explored. | [
0,
0,
1,
0,
0,
0
] |
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