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Title: Discovery of Shifting Patterns in Sequence Classification,
Abstract: In this paper, we investigate the multi-variate sequence classification
problem from a multi-instance learning perspective. Real-world sequential data
commonly show discriminative patterns only at specific time periods. For
instance, we can identify a cropland during its growing season, but it looks
similar to a barren land after harvest or before planting. Besides, even within
the same class, the discriminative patterns can appear in different periods of
sequential data. Due to such property, these discriminative patterns are also
referred to as shifting patterns. The shifting patterns in sequential data
severely degrade the performance of traditional classification methods without
sufficient training data.
We propose a novel sequence classification method by automatically mining
shifting patterns from multi-variate sequence. The method employs a
multi-instance learning approach to detect shifting patterns while also
modeling temporal relationships within each multi-instance bag by an LSTM model
to further improve the classification performance. We extensively evaluate our
method on two real-world applications - cropland mapping and affective state
recognition. The experiments demonstrate the superiority of our proposed method
in sequence classification performance and in detecting discriminative shifting
patterns. | [
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] |
Title: Virtual Network Migration on the GENI Wide-Area SDN-Enabled Infrastructure,
Abstract: A virtual network (VN) contains a collection of virtual nodes and links
assigned to underlying physical resources in a network substrate. VN migration
is the process of remapping a VN's logical topology to a new set of physical
resources to provide failure recovery, energy savings, or defense against
attack. Providing VN migration that is transparent to running applications is a
significant challenge. Efficient migration mechanisms are highly dependent on
the technology deployed in the physical substrate. Prior work has considered
migration in data centers and in the PlanetLab infrastructure. However, there
has been little effort targeting an SDN-enabled wide-area networking
environment - an important building block of future networking infrastructure.
In this work, we are interested in the design, implementation and evaluation of
VN migration in GENI as a working example of such a future network. We identify
and propose techniques to address key challenges: the dynamic allocation of
resources during migration, managing hosts connected to the VN, and flow table
migration sequences to minimize packet loss. We find that GENI's virtualization
architecture makes transparent and efficient migration challenging. We suggest
alternatives that might be adopted in GENI and are worthy of adoption by
virtual network providers to facilitate migration. | [
1,
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] |
Title: Communication-efficient Algorithm for Distributed Sparse Learning via Two-way Truncation,
Abstract: We propose a communicationally and computationally efficient algorithm for
high-dimensional distributed sparse learning. At each iteration, local machines
compute the gradient on local data and the master machine solves one shifted
$l_1$ regularized minimization problem. The communication cost is reduced from
constant times of the dimension number for the state-of-the-art algorithm to
constant times of the sparsity number via Two-way Truncation procedure.
Theoretically, we prove that the estimation error of the proposed algorithm
decreases exponentially and matches that of the centralized method under mild
assumptions. Extensive experiments on both simulated data and real data verify
that the proposed algorithm is efficient and has performance comparable with
the centralized method on solving high-dimensional sparse learning problems. | [
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] |
Title: On Lasso refitting strategies,
Abstract: A well-know drawback of l_1-penalized estimators is the systematic shrinkage
of the large coefficients towards zero. A simple remedy is to treat Lasso as a
model-selection procedure and to perform a second refitting step on the
selected support. In this work we formalize the notion of refitting and provide
oracle bounds for arbitrary refitting procedures of the Lasso solution. One of
the most widely used refitting techniques which is based on Least-Squares may
bring a problem of interpretability, since the signs of the refitted estimator
might be flipped with respect to the original estimator. This problem arises
from the fact that the Least-Squares refitting considers only the support of
the Lasso solution, avoiding any information about signs or amplitudes. To this
end we define a sign consistent refitting as an arbitrary refitting procedure,
preserving the signs of the first step Lasso solution and provide Oracle
inequalities for such estimators. Finally, we consider special refitting
strategies: Bregman Lasso and Boosted Lasso. Bregman Lasso has a fruitful
property to converge to the Sign-Least-Squares refitting (Least-Squares with
sign constraints), which provides with greater interpretability. We
additionally study the Bregman Lasso refitting in the case of orthogonal
design, providing with simple intuition behind the proposed method. Boosted
Lasso, in contrast, considers information about magnitudes of the first Lasso
step and allows to develop better oracle rates for prediction. Finally, we
conduct an extensive numerical study to show advantages of one approach over
others in different synthetic and semi-real scenarios. | [
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] |
Title: A connection between MAX $κ$-CUT and the inhomogeneous Potts spin glass in the large degree limit,
Abstract: We study the asymptotic behavior of the Max $\kappa$-cut on a family of
sparse, inhomogeneous random graphs. In the large degree limit, the leading
term is a variational problem, involving the ground state of a constrained
inhomogeneous Potts spin glass. We derive a Parisi type formula for the free
energy of this model, with possible constraints on the proportions, and derive
the limiting ground state energy by a suitable zero temperature limit. | [
0,
0,
1,
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0,
0
] |
Title: Novel Structured Low-rank algorithm to recover spatially smooth exponential image time series,
Abstract: We propose a structured low rank matrix completion algorithm to recover a
time series of images consisting of linear combination of exponential
parameters at every pixel, from under-sampled Fourier measurements. The spatial
smoothness of these parameters is exploited along with the exponential
structure of the time series at every pixel, to derive an annihilation relation
in the $k-t$ domain. This annihilation relation translates into a structured
low rank matrix formed from the $k-t$ samples. We demonstrate the algorithm in
the parameter mapping setting and show significant improvement over state of
the art methods. | [
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0
] |
Title: Stochastic Assume-Guarantee Contracts for Cyber-Physical System Design Under Probabilistic Requirements,
Abstract: We develop an assume-guarantee contract framework for the design of
cyber-physical systems, modeled as closed-loop control systems, under
probabilistic requirements. We use a variant of signal temporal logic, namely,
Stochastic Signal Temporal Logic (StSTL) to specify system behaviors as well as
contract assumptions and guarantees, thus enabling automatic reasoning about
requirements of stochastic systems. Given a stochastic linear system
representation and a set of requirements captured by bounded StSTL contracts,
we propose algorithms that can check contract compatibility, consistency, and
refinement, and generate a controller to guarantee that a contract is
satisfied, following a stochastic model predictive control approach. Our
algorithms leverage encodings of the verification and control synthesis tasks
into mixed integer optimization problems, and conservative approximations of
probabilistic constraints that produce both sound and tractable problem
formulations. We illustrate the effectiveness of our approach on a few
examples, including the design of embedded controllers for aircraft power
distribution networks. | [
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] |
Title: Manipulating magnetism by ultrafast control of the exchange interaction,
Abstract: In recent years, the optical control of exchange interactions has emerged as
an exciting new direction in the study of the ultrafast optical control of
magnetic order. Here we review recent theoretical works on antiferromagnetic
systems, devoted to i) simulating the ultrafast control of exchange
interactions, ii) modeling the strongly nonequilibrium response of the magnetic
order and iii) the relation with relevant experimental works developed in
parallel. In addition to the excitation of spin precession, we discuss examples
of rapid cooling and the control of ultrafast coherent longitudinal spin
dynamics in response to femtosecond optically induced perturbations of exchange
interactions. These elucidate the potential for exploiting the control of
exchange interactions to find new scenarios for both faster and more
energy-efficient manipulation of magnetism. | [
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] |
Title: FBG-Based Control of a Continuum Manipulator Interacting With Obstacles,
Abstract: Tracking and controlling the shape of continuum dexterous manipulators (CDM)
in constraint environments is a challenging task. The imposed constraints and
interaction with unknown obstacles may conform the CDM's shape and therefore
demands for shape sensing methods which do not rely on direct line of sight. To
address these issues, we integrate a novel Fiber Bragg Grating (FBG) shape
sensing unit into a CDM, reconstruct the shape in real-time, and develop an
optimization-based control algorithm using FBG tip position feedback. The CDM
is designed for less-invasive treatment of osteolysis (bone degradation). To
evaluate the performance of the feedback control algorithm when the CDM
interacts with obstacles, we perform a set of experiments similar to the real
scenario of the CDM interaction with soft and hard lesions during the treatment
of osteolysis. In addition, we propose methods for identification of the CDM
collisions with soft or hard obstacles using the jacobian information. Results
demonstrate successful control of the CDM tip based on the FBG feedback and
indicate repeatability and robustness of the proposed method when interacting
with unknown obstacles. | [
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] |
Title: Generalized variational inequalities for maximal monotone operators,
Abstract: In this paper we present some new results on the existence of solutions of
generalized variational inequalities in real reflexive Banach spaces with
Fréchet differentiable norms. Moreover, we also give some theorems about the
structure of solution sets. The results obtained in this paper improve and
extend the ones announced by Fang and Peterson [1] to infinite dimensional
spaces. | [
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1,
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0,
0
] |
Title: Classification of Questions and Learning Outcome Statements (LOS) Into Blooms Taxonomy (BT) By Similarity Measurements Towards Extracting Of Learning Outcome from Learning Material,
Abstract: Blooms Taxonomy (BT) have been used to classify the objectives of learning
outcome by dividing the learning into three different domains; the cognitive
domain, the effective domain and the psychomotor domain. In this paper, we are
introducing a new approach to classify the questions and learning outcome
statements (LOS) into Blooms taxonomy (BT) and to verify BT verb lists, which
are being cited and used by academicians to write questions and (LOS). An
experiment was designed to investigate the semantic relationship between the
action verbs used in both questions and LOS to obtain more accurate
classification of the levels of BT. A sample of 775 different action verbs
collected from different universities allows us to measure an accurate and
clear-cut cognitive level for the action verb. It is worth mentioning that
natural language processing techniques were used to develop our rules as to
induce the questions into chunks in order to extract the action verbs. Our
proposed solution was able to classify the action verb into a precise level of
the cognitive domain. We, on our side, have tested and evaluated our proposed
solution using confusion matrix. The results of evaluation tests yielded 97%
for the macro average of precision and 90% for F1. Thus, the outcome of the
research suggests that it is crucial to analyse and verify the action verbs
cited and used by academicians to write LOS and classify their questions based
on blooms taxonomy in order to obtain a definite and more accurate
classification. | [
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] |
Title: Symmetric Rank Covariances: a Generalised Framework for Nonparametric Measures of Dependence,
Abstract: The need to test whether two random vectors are independent has spawned a
large number of competing measures of dependence. We are interested in
nonparametric measures that are invariant under strictly increasing
transformations, such as Kendall's tau, Hoeffding's D, and the more recently
discovered Bergsma--Dassios sign covariance. Each of these measures exhibits
symmetries that are not readily apparent from their definitions. Making these
symmetries explicit, we define a new class of multivariate nonparametric
measures of dependence that we refer to as Symmetric Rank Covariances. This new
class generalises all of the above measures and leads naturally to multivariate
extensions of the Bergsma--Dassios sign covariance. Symmetric Rank Covariances
may be estimated unbiasedly using U-statistics for which we prove results on
computational efficiency and large-sample behavior. The algorithms we develop
for their computation include, to the best of our knowledge, the first
efficient algorithms for the well-known Hoeffding's D statistic in the
multivariate setting. | [
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] |
Title: Diamond-colored distributive lattices, move-minimizing games, and fundamental Weyl symmetric functions: The type $\mathsf{A}$ case,
Abstract: We present some elementary but foundational results concerning
diamond-colored modular and distributive lattices and connect these structures
to certain one-player combinatorial "move-minimizing games," in particular, a
so-called "domino game." The objective of this game is to find, if possible,
the least number of "domino moves" to get from one partition to another, where
a domino move is, with one exception, the addition or removal of a
domino-shaped pair of tiles. We solve this domino game by demonstrating the
somewhat surprising fact that the associated "game graphs" coincide with a
well-known family of diamond-colored distributive lattices which shall be
referred to as the "type $\mathsf{A}$ fundamental lattices." These lattices
arise as supporting graphs for the fundamental representations of the special
linear Lie algebras and as splitting posets for type $\mathsf{A}$ fundamental
symmetric functions, connections which are further explored in sequel papers
for types $\mathsf{A}$, $\mathsf{C}$, and $\mathsf{B}$. In this paper, this
connection affords a solution to the proposed domino game as well as new
descriptions of the type $\mathsf{A}$ fundamental lattices. | [
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0,
1,
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] |
Title: Hidden Truncation Hyperbolic Distributions, Finite Mixtures Thereof, and Their Application for Clustering,
Abstract: A hidden truncation hyperbolic (HTH) distribution is introduced and finite
mixtures thereof are applied for clustering. A stochastic representation of the
HTH distribution is given and a density is derived. A hierarchical
representation is described, which aids in parameter estimation. Finite
mixtures of HTH distributions are presented and their identifiability is
proved. The convexity of the HTH distribution is discussed, which is important
in clustering applications, and some theoretical results in this direction are
presented. The relationship between the HTH distribution and other skewed
distributions in the literature is discussed. Illustrations are provided ---
both of the HTH distribution and application of finite mixtures thereof for
clustering. | [
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] |
Title: No evidence for a significant AGN contribution to cosmic hydrogen reionization,
Abstract: We reinvestigate a claimed sample of 22 X-ray detected active galactic nuclei
(AGN) at redshifts z > 4, which has reignited the debate as to whether young
galaxies or AGN reionized the Universe. These sources lie within the
GOODS-S/CANDELS field, and we examine both the robustness of the claimed X-ray
detections (within the Chandra 4Ms imaging) and perform an independent analysis
of the photometric redshifts of the optical/infrared counterparts. We confirm
the reality of only 15 of the 22 reported X-ray detections, and moreover find
that only 12 of the 22 optical/infrared counterpart galaxies actually lie
robustly at z > 4. Combining these results we find convincing evidence for only
7 X-ray AGN at z > 4 in the GOODS-S field, of which only one lies at z > 5. We
recalculate the evolving far-UV (1500 Angstrom) luminosity density produced by
AGN at high redshift, and find that it declines rapidly from z = 4 to z = 6, in
agreement with several other recent studies of the evolving AGN luminosity
function. The associated rapid decline in inferred hydrogen-ionizing emissivity
contributed by AGN falls an order-of-magnitude short of the level required to
maintain hydrogen ionization at z ~ 6. We conclude that all available evidence
continues to favour a scenario in which young galaxies reionized the Universe,
with AGN making, at most, a very minor contribution to cosmic hydrogen
reionization. | [
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] |
Title: On the kinetic equation in Zakharov's wave turbulence theory for capillary waves,
Abstract: The wave turbulence equation is an effective kinetic equation that describes
the dynamics of wave spectrum in weakly nonlinear and dispersive media. Such a
kinetic model has been derived by physicists in the sixties, though the
well-posedness theory remains open, due to the complexity of resonant
interaction kernels. In this paper, we provide a global unique radial strong
solution, the first such a result, to the wave turbulence equation for
capillary waves. | [
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] |
Title: Regularizing Model Complexity and Label Structure for Multi-Label Text Classification,
Abstract: Multi-label text classification is a popular machine learning task where each
document is assigned with multiple relevant labels. This task is challenging
due to high dimensional features and correlated labels. Multi-label text
classifiers need to be carefully regularized to prevent the severe over-fitting
in the high dimensional space, and also need to take into account label
dependencies in order to make accurate predictions under uncertainty. We
demonstrate significant and practical improvement by carefully regularizing the
model complexity during training phase, and also regularizing the label search
space during prediction phase. Specifically, we regularize the classifier
training using Elastic-net (L1+L2) penalty for reducing model complexity/size,
and employ early stopping to prevent overfitting. At prediction time, we apply
support inference to restrict the search space to label sets encountered in the
training set, and F-optimizer GFM to make optimal predictions for the F1
metric. We show that although support inference only provides density
estimations on existing label combinations, when combined with GFM predictor,
the algorithm can output unseen label combinations. Taken collectively, our
experiments show state of the art results on many benchmark datasets. Beyond
performance and practical contributions, we make some interesting observations.
Contrary to the prior belief, which deems support inference as purely an
approximate inference procedure, we show that support inference acts as a
strong regularizer on the label prediction structure. It allows the classifier
to take into account label dependencies during prediction even if the
classifiers had not modeled any label dependencies during training. | [
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] |
Title: Network Design with Probabilistic Capacities,
Abstract: We consider a network design problem with random arc capacities and give a
formulation with a probabilistic capacity constraint on each cut of the
network. To handle the exponentially-many probabilistic constraints a
separation procedure that solves a nonlinear minimum cut problem is introduced.
For the case with independent arc capacities, we exploit the supermodularity of
the set function defining the constraints and generate cutting planes based on
the supermodular covering knapsack polytope. For the general correlated case,
we give a reformulation of the constraints that allows to uncover and utilize
the submodularity of a related function. The computational results indicate
that exploiting the underlying submodularity and supermodularity arising with
the probabilistic constraints provides significant advantages over the
classical approaches. | [
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] |
Title: Mapping stellar content to dark matter halos - III.Environmental dependence and conformity of galaxy colours,
Abstract: Recent studies suggest that the quenching properties of galaxies are
correlated over several mega-parsecs. The large-scale "galactic conformity"
phenomenon around central galaxies has been regarded as a potential signature
of "galaxy assembly bias" or "pre-heating", both of which interpret conformity
as a result of direct environmental effects acting on galaxy formation.
Building on the iHOD halo quenching framework developed in Zu & Mandelbaum
(2015, 2016), we discover that our fiducial halo mass quenching model, without
any galaxy assembly bias, can successfully explain the overall environmental
dependence and the conformity of galaxy colours in SDSS, as measured by the
mark correlation functions of galaxy colours and the red galaxy fractions
around isolated primaries, respectively. Our fiducial iHOD halo quenching mock
also correctly predicts the differences in the spatial clustering and
galaxy-galaxy lensing signals between the more vs. less red galaxy subsamples,
split by the red-sequence ridge-line at fixed stellar mass. Meanwhile, models
that tie galaxy colours fully or partially to halo assembly bias have
difficulties in matching all these observables simultaneously. Therefore, we
demonstrate that the observed environmental dependence of galaxy colours can be
naturally explained by the combination of 1) halo quenching and 2) the
variation of halo mass function with environment --- an indirect environmental
effect mediated by two separate physical processes. | [
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] |
Title: ConsiDroid: A Concolic-based Tool for Detecting SQL Injection Vulnerability in Android Apps,
Abstract: In this paper, we present a concolic execution technique for detecting SQL
injection vulnerabilities in Android apps, with a new tool we called
ConsiDroid. We extend the source code of apps with mocking technique, such that
the execution of original source code is not affected. The extended source
codes can be treated as Java applications and may be executed by SPF with
concolic execution. We automatically produce a DummyMain class out of static
analysis such that the essential functions are called sequentially and, the
events leading to vulnerable functions are triggered. We extend SPF with taint
analysis in ConsiDroid. For making taint analysis possible, we introduce a new
technique of symbolic mock classes in order to ease the propagation of tainted
values in the code. An SQL injection vulnerability is detected through
receiving a tainted value by a vulnerable function. Besides, ConsiDroid takes
advantage of static analysis to adjust SPF in order to inspect only suspicious
paths. To illustrate the applicability of ConsiDroid, we have inspected
randomly selected 140 apps from F-Droid repository. From these apps, we found
three apps vulnerable to SQL injection. To verify their vulnerability, we
analyzed the apps manually based on ConsiDroid's reports by using Robolectric. | [
1,
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] |
Title: Alternative derivation of exact law for compressible and isothermal magnetohydrodynamics turbulence,
Abstract: The exact law for fully developed homogeneous compressible
magnetohydrodynamics (CMHD) turbulence is derived. For an isothermal plasma,
without the assumption of isotropy, the exact law is expressed as a function of
the plasma velocity field, the compressible Alfvén velocity and the scalar
density, instead of the Elsässer variables used in previous works. The
theoretical results show four different types of terms that are involved in the
nonlinear cascade of the total energy in the inertial range. Each category is
examined in detail, in particular those that can be written either as source or
flux terms. Finally, the role of the background magnetic field $B_0$ is
highlighted and comparison with the incompressible MHD (IMHD) model is
discussed. This point is particularly important when testing the exact law on
numerical simulations and in situ observations in space plasmas. | [
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1,
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0,
0
] |
Title: Is Flat Fielding Safe for Precision CCD Astronomy?,
Abstract: The ambitious goals of precision cosmology with wide-field optical surveys
such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope
(LSST) demand, as their foundation, precision CCD astronomy. This in turn
requires an understanding of previously uncharacterized sources of systematic
error in CCD sensors, many of which manifest themselves as static effective
variations in pixel area. Such variation renders a critical assumption behind
the traditional procedure of flat fielding--that a sensor's pixels comprise a
uniform grid--invalid. In this work, we present a method to infer a curl-free
model of a sensor's underlying pixel grid from flat field images, incorporating
the superposition of all electrostatic sensor effects--both known and
unknown--present in flat field data. We use these pixel grid models to estimate
the overall impact of sensor systematics on photometry, astrometry, and PSF
shape measurements in a representative sensor from the Dark Energy Camera
(DECam) and a prototype LSST sensor. Applying the method to DECam data recovers
known significant sensor effects for which corrections are currently being
developed within DES. For an LSST prototype CCD with pixel-response
non-uniformity (PRNU) of 0.4%, we find the impact of "improper" flat-fielding
on these observables is negligible in nominal .7" seeing conditions. These
errors scale linearly with the PRNU, so for future LSST production sensors,
which may have larger PRNU, our method provides a way to assess whether
pixel-level calibration beyond flat fielding will be required. | [
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] |
Title: Towards Practical Differential Privacy for SQL Queries,
Abstract: Differential privacy promises to enable general data analytics while
protecting individual privacy, but existing differential privacy mechanisms do
not support the wide variety of features and databases used in real-world
SQL-based analytics systems.
This paper presents the first practical approach for differential privacy of
SQL queries. Using 8.1 million real-world queries, we conduct an empirical
study to determine the requirements for practical differential privacy, and
discuss limitations of previous approaches in light of these requirements. To
meet these requirements we propose elastic sensitivity, a novel method for
approximating the local sensitivity of queries with general equijoins. We prove
that elastic sensitivity is an upper bound on local sensitivity and can
therefore be used to enforce differential privacy using any local
sensitivity-based mechanism.
We build FLEX, a practical end-to-end system to enforce differential privacy
for SQL queries using elastic sensitivity. We demonstrate that FLEX is
compatible with any existing database, can enforce differential privacy for
real-world SQL queries, and incurs negligible (0.03%) performance overhead. | [
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] |
Title: A Survey on QoE-oriented Wireless Resources Scheduling,
Abstract: Future wireless systems are expected to provide a wide range of services to
more and more users. Advanced scheduling strategies thus arise not only to
perform efficient radio resource management, but also to provide fairness among
the users. On the other hand, the users' perceived quality, i.e., Quality of
Experience (QoE), is becoming one of the main drivers within the schedulers
design. In this context, this paper starts by providing a comprehension of what
is QoE and an overview of the evolution of wireless scheduling techniques.
Afterwards, a survey on the most recent QoE-based scheduling strategies for
wireless systems is presented, highlighting the application/service of the
different approaches reported in the literature, as well as the parameters that
were taken into account for QoE optimization. Therefore, this paper aims at
helping readers interested in learning the basic concepts of QoE-oriented
wireless resources scheduling, as well as getting in touch with the present
time research frontier. | [
1,
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0,
0,
0
] |
Title: Wandering domains for diffeomorphisms of the k-torus: a remark on a theorem by Norton and Sullivan,
Abstract: We show that there is no C^{k+1} diffeomorphism of the k-torus which is
semiconjugate to a minimal translation and has a wandering domain all of whose
iterates are Euclidean balls. | [
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0,
1,
0,
0,
0
] |
Title: Disentangling group and link persistence in Dynamic Stochastic Block models,
Abstract: We study the inference of a model of dynamic networks in which both
communities and links keep memory of previous network states. By considering
maximum likelihood inference from single snapshot observations of the network,
we show that link persistence makes the inference of communities harder,
decreasing the detectability threshold, while community persistence tends to
make it easier. We analytically show that communities inferred from single
network snapshot can share a maximum overlap with the underlying communities of
a specific previous instant in time. This leads to time-lagged inference: the
identification of past communities rather than present ones. Finally we compute
the time lag and propose a corrected algorithm, the Lagged Snapshot Dynamic
(LSD) algorithm, for community detection in dynamic networks. We analytically
and numerically characterize the detectability transitions of such algorithm as
a function of the memory parameters of the model and we make a comparison with
a full dynamic inference. | [
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] |
Title: An intracardiac electrogram model to bridge virtual hearts and implantable cardiac devices,
Abstract: Virtual heart models have been proposed to enhance the safety of implantable
cardiac devices through closed loop validation. To communicate with a virtual
heart, devices have been driven by cardiac signals at specific sites. As a
result, only the action potentials of these sites are sensed. However, the real
device implanted in the heart will sense a complex combination of near and
far-field extracellular potential signals. Therefore many device functions,
such as blanking periods and refractory periods, are designed to handle these
unexpected signals. To represent these signals, we develop an intracardiac
electrogram (IEGM) model as an interface between the virtual heart and the
device. The model can capture not only the local excitation but also far-field
signals and pacing afterpotentials. Moreover, the sensing controller can
specify unipolar or bipolar electrogram (EGM) sensing configurations and
introduce various oversensing and undersensing modes. The simulation results
show that the model is able to reproduce clinically observed sensing problems,
which significantly extends the capabilities of the virtual heart model in the
context of device validation. | [
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] |
Title: Dynamic scaling analysis of the long-range RKKY Ising spin glass Dy$_{x}$Y$_{1-x}$Ru$_{2}$Si$_{2}$,
Abstract: Dynamic scaling analyses of linear and nonlinear ac susceptibilities in a
model magnet of the long-rang RKKY Ising spin glass (SG)
Dy$_{x}$Y$_{1-x}$Ru$_{2}$Si$_{2}$ were examined. The obtained set of the
critical exponents, $\gamma$ $\sim$ 1, $\beta$ $\sim$ 1, $\delta$ $\sim$ 2, and
$z\nu$ $\sim$ 3.4, indicates the SG phase transition belongs to a different
universality class from either the canonical (Heisenberg) or the short-range
Ising SGs. The analyses also reveal a finite-temperature SG transition with the
same critical exponents under a magnetic field and the phase transition line
$T_{\mbox{g}}(H)$ described by $T_{\mbox{g}}(H)$ $=$
$T_{\mbox{g}}(0)(1-AH^{2/\phi})$ with $\phi$ $\sim$ 2. The crossover exponent
$\phi$ obeys the scaling relation $\phi$ $=$ $\gamma + \beta$ within the margin
of errors. These results strongly suggest the spontaneous
replica-symmetry-breaking (RSB) with a {\it non- or marginal-mean-field
universality class} in the long-range RKKY Ising SG. | [
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] |
Title: A Bayesian hierarchical model for related densities using Polya trees,
Abstract: Bayesian hierarchical models are used to share information between related
samples and obtain more accurate estimates of sample-level parameters, common
structure, and variation between samples. When the parameter of interest is the
distribution or density of a continuous variable, a hierarchical model for
distributions is required. A number of such models have been described in the
literature using extensions of the Dirichlet process and related processes,
typically as a distribution on the parameters of a mixing kernel. We propose a
new hierarchical model based on the Polya tree, which allows direct modeling of
densities and enjoys some computational advantages over the Dirichlet process.
The Polya tree also allows more flexible modeling of the variation between
samples, providing more informed shrinkage and permitting posterior inference
on the dispersion function, which quantifies the variation among sample
densities. We also show how the model can be extended to cluster samples in
situations where the observed samples are believed to have been drawn from
several latent populations. | [
0,
0,
0,
1,
0,
0
] |
Title: Groups of fast homeomorphisms of the interval and the ping-pong argument,
Abstract: We adapt the Ping-Pong Lemma, which historically was used to study free
products of groups, to the setting of the homeomorphism group of the unit
interval. As a consequence, we isolate a large class of generating sets for
subgroups of $\mathrm{Homeo}_+(I)$ for which certain finite dynamical data can
be used to determine the marked isomorphism type of the groups which they
generate. As a corollary, we will obtain a criteria for embedding subgroups of
$\mathrm{Homeo}_+(I)$ into Richard Thompson's group $F$. In particular, every
member of our class of generating sets generates a group which embeds into $F$
and in particular is not a free product. An analogous abstract theory is also
developed for groups of permutations of an infinite set. | [
0,
0,
1,
0,
0,
0
] |
Title: The CodRep Machine Learning on Source Code Competition,
Abstract: CodRep is a machine learning competition on source code data. It is carefully
designed so that anybody can enter the competition, whether professional
researchers, students or independent scholars, without specific knowledge in
machine learning or program analysis. In particular, it aims at being a common
playground on which the machine learning and the software engineering research
communities can interact. The competition has started on April 14th 2018 and
has ended on October 14th 2018. The CodRep data is hosted at
this https URL. | [
1,
0,
0,
0,
0,
0
] |
Title: Learning Hard Alignments with Variational Inference,
Abstract: There has recently been significant interest in hard attention models for
tasks such as object recognition, visual captioning and speech recognition.
Hard attention can offer benefits over soft attention such as decreased
computational cost, but training hard attention models can be difficult because
of the discrete latent variables they introduce. Previous work used REINFORCE
and Q-learning to approach these issues, but those methods can provide
high-variance gradient estimates and be slow to train. In this paper, we tackle
the problem of learning hard attention for a sequential task using variational
inference methods, specifically the recently introduced VIMCO and NVIL.
Furthermore, we propose a novel baseline that adapts VIMCO to this setting. We
demonstrate our method on a phoneme recognition task in clean and noisy
environments and show that our method outperforms REINFORCE, with the
difference being greater for a more complicated task. | [
1,
0,
0,
1,
0,
0
] |
Title: A New Sparse and Robust Adaptive Lasso Estimator for the Independent Contamination Model,
Abstract: Many problems in signal processing require finding sparse solutions to
under-determined, or ill-conditioned, linear systems of equations. When dealing
with real-world data, the presence of outliers and impulsive noise must also be
accounted for. In past decades, the vast majority of robust linear regression
estimators has focused on robustness against rowwise contamination. Even so
called `high breakdown' estimators rely on the assumption that a majority of
rows of the regression matrix is not affected by outliers. Only very recently,
the first cellwise robust regression estimation methods have been developed. In
this paper, we define robust oracle properties, which an estimator must have in
order to perform robust model selection for under-determined, or
ill-conditioned linear regression models that are contaminated by cellwise
outliers in the regression matrix. We propose and analyze a robustly weighted
and adaptive Lasso type regularization term which takes into account cellwise
outliers for model selection. The proposed regularization term is integrated
into the objective function of the MM-estimator, which yields the proposed
MM-Robust Weighted Adaptive Lasso (MM-RWAL), for which we prove that at least
the weak robust oracle properties hold. A performance comparison to existing
robust Lasso estimators is provided using Monte Carlo experiments. Further, the
MM-RWAL is applied to determine the temporal releases of the European Tracer
Experiment (ETEX) at the source location. This ill-conditioned linear inverse
problem contains cellwise and rowwise outliers and is sparse both in the
regression matrix and the parameter vector. The proposed RWAL penalty is not
limited to the MM-estimator but can easily be integrated into the objective
function of other robust estimators. | [
0,
0,
1,
1,
0,
0
] |
Title: Comparison of electricity market designs for stable decentralized power grids,
Abstract: In this study, we develop a theoretical model of strategic equilibrium
bidding and price-setting behaviour by heterogeneous and boundedly rational
electricity producers and a grid operator in a single electricity market under
uncertain information about production capabilities and electricity demand.
We compare eight different market design variants and several levels of
centralized electricity production that influence the spatial distribution of
producers in the grid, their unit production and curtailment costs, and the
mean and standard deviation of their production capabilities.
Our market design variants differ in three aspects. Producers are either paid
their individual bid price ("pay as bid") or the (higher) market price set by
the grid operator ("uniform pricing"). They are either paid for their bid
quantity ("pay requested") or for their actual supply ("pay supplied") which
may differ due to production uncertainty. Finally, excess production is either
required to be curtailed or may be supplied to the grid.
Overall, we find the combination of uniform pricing, paying for requested
amounts, and required curtailment to perform best or second best in many
respects, and to provide the best compromise between the goals of low economic
costs, low consumer costs, positive profits, low balancing, low workload, and
honest bidding behaviour. | [
0,
1,
0,
0,
0,
0
] |
Title: Maximal Jacobian-based Saliency Map Attack,
Abstract: The Jacobian-based Saliency Map Attack is a family of adversarial attack
methods for fooling classification models, such as deep neural networks for
image classification tasks. By saturating a few pixels in a given image to
their maximum or minimum values, JSMA can cause the model to misclassify the
resulting adversarial image as a specified erroneous target class. We propose
two variants of JSMA, one which removes the requirement to specify a target
class, and another that additionally does not need to specify whether to only
increase or decrease pixel intensities. Our experiments highlight the
competitive speeds and qualities of these variants when applied to datasets of
hand-written digits and natural scenes. | [
0,
0,
0,
1,
0,
0
] |
Title: Spin Susceptibility of the Topological Superconductor UPt3 from Polarized Neutron Diffraction,
Abstract: Experiment and theory indicate that UPt3 is a topological superconductor in
an odd-parity state, based in part from temperature independence of the NMR
Knight shift. However, quasiparticle spin-flip scattering near a surface, where
the Knight shift is measured, might be responsible. We use polarized neutron
scattering to measure the bulk susceptibility with H||c, finding consistency
with the Knight shift but inconsistent with theory for this field orientation.
We infer that neither spin susceptibility nor Knight shift are a reliable
indication of odd-parity. | [
0,
1,
0,
0,
0,
0
] |
Title: Factorization systems on (stable) derivators,
Abstract: We define triangulated factorization systems on triangulated categories, and
prove that a suitable subclass thereof (the normal triangulated torsion
theories) corresponds bijectively to $t$-structures on the same category. This
result is then placed in the framework of derivators regarding a triangulated
category as the base of a stable derivator. More generally, we define derivator
factorization systems in the 2-category $\mathrm{PDer}$, describing them as
algebras for a suitable strict 2-monad (this result is of independent
interest), and prove that a similar characterization still holds true: for a
stable derivator $\mathbb D$, a suitable class of derivator factorization
systems (the normal derivator torsion theories) correspond bijectively with
$t$-structures on the base $\mathbb{D}(\mathbb{1})$ of the derivator. These two
result can be regarded as the triangulated- and derivator- analogues,
respectively, of the theorem that says that `$t$-structures are normal torsion
theories' in the setting of stable $\infty$-categories, showing how the result
remains true whatever the chosen model for stable homotopy theory is. | [
0,
0,
1,
0,
0,
0
] |
Title: Dynamic Provable Data Possession Protocols with Public Verifiability and Data Privacy,
Abstract: Cloud storage services have become accessible and used by everyone.
Nevertheless, stored data are dependable on the behavior of the cloud servers,
and losses and damages often occur. One solution is to regularly audit the
cloud servers in order to check the integrity of the stored data. The Dynamic
Provable Data Possession scheme with Public Verifiability and Data Privacy
presented in ACISP'15 is a straightforward design of such solution. However,
this scheme is threatened by several attacks. In this paper, we carefully
recall the definition of this scheme as well as explain how its security is
dramatically menaced. Moreover, we proposed two new constructions for Dynamic
Provable Data Possession scheme with Public Verifiability and Data Privacy
based on the scheme presented in ACISP'15, one using Index Hash Tables and one
based on Merkle Hash Trees. We show that the two schemes are secure and
privacy-preserving in the random oracle model. | [
1,
0,
0,
0,
0,
0
] |
Title: Robustness of persistent currents in two-dimensional Dirac systems with disorders,
Abstract: We consider two-dimensional (2D) Dirac quantum ring systems formed by the
infinite mass constraint. When an Aharonov-Bohm magnetic flux is present, e.g.,
through the center of the ring domain, persistent currents, i.e., permanent
currents without dissipation, can arise. In real materials, impurities and
defects are inevitable, raising the issue of robustness of the persistent
currents. Using localized random potential to simulate the disorders, we
investigate how the ensemble averaged current magnitude varies with the
disorder density. For comparison, we study the nonrelativistic quantum
counterpart by analyzing the solutions of the Schrödinger equation under
the same geometrical and disorder settings. We find that, for the Dirac ring
system, as the disorder density is systematically increased, the average
current decreases slowly initially and then plateaus at a finite nonzero value,
indicating remarkable robustness of the persistent currents. The physical
mechanism responsible for the robustness is the emergence of a class of
boundary states - whispering gallery modes. In contrast, in the Schrödinger
ring system, such boundary states cannot form and the currents diminish rapidly
to zero with increase in the disorder density. We develop a physical theory
based on a quasi one-dimensional approximation to understand the striking
contrast in the behaviors of the persistent currents in the Dirac and
Schrödinger rings. Our 2D Dirac ring systems with disorders can be
experimentally realized, e.g., on the surface of a topological insulator with
natural or deliberately added impurities from the fabrication process. | [
0,
1,
0,
0,
0,
0
] |
Title: Fitting 3D Shapes from Partial and Noisy Point Clouds with Evolutionary Computing,
Abstract: Point clouds obtained from photogrammetry are noisy and incomplete models of
reality. We propose an evolutionary optimization methodology that is able to
approximate the underlying object geometry on such point clouds. This approach
assumes a priori knowledge on the 3D structure modeled and enables the
identification of a collection of primitive shapes approximating the scene.
Built-in mechanisms that enforce high shape diversity and adaptive population
size make this method suitable to modeling both simple and complex scenes. We
focus here on the case of cylinder approximations and we describe, test, and
compare a set of mutation operators designed for optimal exploration of their
search space. We assess the robustness and limitations of this algorithm
through a series of synthetic examples, and we finally demonstrate its general
applicability on two real-life cases in vegetation and industrial settings. | [
1,
0,
0,
0,
1,
0
] |
Title: Decomposition Algorithm for Distributionally Robust Optimization using Wasserstein Metric,
Abstract: We study distributionally robust optimization (DRO) problems where the
ambiguity set is defined using the Wasserstein metric. We show that this class
of DRO problems can be reformulated as semi-infinite programs. We give an
exchange method to solve the reformulated problem for the general nonlinear
model, and a central cutting-surface method for the convex case, assuming that
we have a separation oracle. We used a distributionally robust generalization
of the logistic regression model to test our algorithm. Numerical experiments
on the distributionally robust logistic regression models show that the number
of oracle calls are typically 20 ? 50 to achieve 5-digit precision. The
solution found by the model is generally better in its ability to predict with
a smaller standard error. | [
0,
0,
1,
0,
0,
0
] |
Title: PD-ML-Lite: Private Distributed Machine Learning from Lighweight Cryptography,
Abstract: Privacy is a major issue in learning from distributed data. Recently the
cryptographic literature has provided several tools for this task. However,
these tools either reduce the quality/accuracy of the learning
algorithm---e.g., by adding noise---or they incur a high performance penalty
and/or involve trusting external authorities.
We propose a methodology for {\sl private distributed machine learning from
light-weight cryptography} (in short, PD-ML-Lite). We apply our methodology to
two major ML algorithms, namely non-negative matrix factorization (NMF) and
singular value decomposition (SVD). Our resulting protocols are communication
optimal, achieve the same accuracy as their non-private counterparts, and
satisfy a notion of privacy---which we define---that is both intuitive and
measurable. Our approach is to use lightweight cryptographic protocols (secure
sum and normalized secure sum) to build learning algorithms rather than wrap
complex learning algorithms in a heavy-cost MPC framework.
We showcase our algorithms' utility and privacy on several applications: for
NMF we consider topic modeling and recommender systems, and for SVD, principal
component regression, and low rank approximation. | [
1,
0,
0,
1,
0,
0
] |
Title: ISS Property with Respect to Boundary Disturbances for a Class of Riesz-Spectral Boundary Control Systems,
Abstract: This paper deals with the establishment of Input-to-State Stability (ISS)
properties for infinite dimensional systems with respect to both boundary and
distributed disturbances. First, an ISS estimate is established with respect to
finite dimensional boundary disturbances for a class of Riesz-spectral boundary
control systems satisfying certain eigenvalue constraints. Second, a concept of
weak solutions is introduced in order to relax the disturbances regularity
assumptions required to ensure the existence of strong solutions. The proposed
concept of weak solutions, that applies to a large class of boundary control
systems which is not limited to the Riesz-spectral ones, provides a natural
extension of the concept of both strong and mild solutions. Assuming that an
ISS estimate holds true for strong solutions, we show the existence, the
uniqueness, and the ISS property of the weak solutions. | [
1,
0,
0,
0,
0,
0
] |
Title: TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension,
Abstract: We present TriviaQA, a challenging reading comprehension dataset containing
over 650K question-answer-evidence triples. TriviaQA includes 95K
question-answer pairs authored by trivia enthusiasts and independently gathered
evidence documents, six per question on average, that provide high quality
distant supervision for answering the questions. We show that, in comparison to
other recently introduced large-scale datasets, TriviaQA (1) has relatively
complex, compositional questions, (2) has considerable syntactic and lexical
variability between questions and corresponding answer-evidence sentences, and
(3) requires more cross sentence reasoning to find answers. We also present two
baseline algorithms: a feature-based classifier and a state-of-the-art neural
network, that performs well on SQuAD reading comprehension. Neither approach
comes close to human performance (23% and 40% vs. 80%), suggesting that
TriviaQA is a challenging testbed that is worth significant future study. Data
and code available at -- this http URL | [
1,
0,
0,
0,
0,
0
] |
Title: Impact of Traditional Sparse Optimizations on a Migratory Thread Architecture,
Abstract: Achieving high performance for sparse applications is challenging due to
irregular access patterns and weak locality. These properties preclude many
static optimizations and degrade cache performance on traditional systems. To
address these challenges, novel systems such as the Emu architecture have been
proposed. The Emu design uses light-weight migratory threads, narrow memory,
and near-memory processing capabilities to address weak locality and reduce the
total load on the memory system. Because the Emu architecture is fundamentally
different than cache based hierarchical memory systems, it is crucial to
understand the cost-benefit tradeoffs of standard sparse algorithm
optimizations on Emu hardware. In this work, we explore sparse matrix-vector
multiplication (SpMV) on the Emu architecture. We investigate the effects of
different sparse optimizations such as dense vector data layouts, work
distributions, and matrix reorderings. Our study finds that initially
distributing work evenly across the system is inadequate to maintain load
balancing over time due to the migratory nature of Emu threads. In severe
cases, matrix sparsity patterns produce hot-spots as many migratory threads
converge on a single resource. We demonstrate that known matrix reordering
techniques can improve SpMV performance on the Emu architecture by as much as
70% by encouraging more consistent load balancing. This can be compared with a
performance gain of no more than 16% on a cache-memory based system. | [
1,
0,
0,
0,
0,
0
] |
Title: Fractional Driven Damped Oscillator,
Abstract: The resonances associated with a fractional damped oscillator which is driven
by an oscillatory external force are studied. It is shown that such resonances
can be manipulated by tuning up either the coefficient of the fractional
damping or the order of the corresponding fractional derivatives. | [
0,
1,
0,
0,
0,
0
] |
Title: Bootstrap confidence sets for spectral projectors of sample covariance,
Abstract: Let $X_{1},\ldots,X_{n}$ be i.i.d. sample in $\mathbb{R}^{p}$ with zero mean
and the covariance matrix $\mathbf{\Sigma}$. The problem of recovering the
projector onto an eigenspace of $\mathbf{\Sigma}$ from these observations
naturally arises in many applications. Recent technique from [Koltchinskii,
Lounici, 2015] helps to study the asymptotic distribution of the distance in
the Frobenius norm $\| \mathbf{P}_r - \widehat{\mathbf{P}}_r \|_{2}$ between
the true projector $\mathbf{P}_r$ on the subspace of the $r$-th eigenvalue and
its empirical counterpart $\widehat{\mathbf{P}}_r$ in terms of the effective
rank of $\mathbf{\Sigma}$. This paper offers a bootstrap procedure for building
sharp confidence sets for the true projector $\mathbf{P}_r$ from the given
data. This procedure does not rely on the asymptotic distribution of $\|
\mathbf{P}_r - \widehat{\mathbf{P}}_r \|_{2}$ and its moments. It could be
applied for small or moderate sample size $n$ and large dimension $p$. The main
result states the validity of the proposed procedure for finite samples with an
explicit error bound for the error of bootstrap approximation. This bound
involves some new sharp results on Gaussian comparison and Gaussian
anti-concentration in high-dimensional spaces. Numeric results confirm a good
performance of the method in realistic examples. | [
0,
0,
1,
1,
0,
0
] |
Title: Locally recoverable codes from algebraic curves and surfaces,
Abstract: A locally recoverable code is a code over a finite alphabet such that the
value of any single coordinate of a codeword can be recovered from the values
of a small subset of other coordinates. Building on work of Barg, Tamo, and
Vlăduţ, we present several constructions of locally recoverable codes
from algebraic curves and surfaces. | [
1,
0,
1,
0,
0,
0
] |
Title: Specification properties on uniform spaces,
Abstract: In the following text we introduce specification property (stroboscopical
property) for dynamical systems on uniform space. We focus on two classes of
dynamical systems: generalized shifts and dynamical systems with Alexandroff
compactification of a discrete space as phase space. We prove that for a
discrete finite topological space $X$ with at least two elements, a nonempty
set $\Gamma$ and a self--map $\varphi:\Gamma\to\Gamma$ the generalized shift
dynamical system $(X^\Gamma,\sigma_\varphi)$: \begin{itemize} \item has
(almost) weak specification property if and only if $\varphi:\Gamma\to\Gamma$
does not have any periodic point,
\item has (uniform) stroboscopical property if and only if
$\varphi:\Gamma\to\Gamma$
is one-to-one. \end{itemize} | [
0,
0,
1,
0,
0,
0
] |
Title: Learning by Playing - Solving Sparse Reward Tasks from Scratch,
Abstract: We propose Scheduled Auxiliary Control (SAC-X), a new learning paradigm in
the context of Reinforcement Learning (RL). SAC-X enables learning of complex
behaviors - from scratch - in the presence of multiple sparse reward signals.
To this end, the agent is equipped with a set of general auxiliary tasks, that
it attempts to learn simultaneously via off-policy RL. The key idea behind our
method is that active (learned) scheduling and execution of auxiliary policies
allows the agent to efficiently explore its environment - enabling it to excel
at sparse reward RL. Our experiments in several challenging robotic
manipulation settings demonstrate the power of our approach. | [
1,
0,
0,
1,
0,
0
] |
Title: Dynamics of Charged Bulk Viscous Collapsing Cylindrical Source With Heat Flux,
Abstract: In this paper, we have explored the effects of dissipation on the dynamics of
charged bulk viscous collapsing cylindrical source which allows the out follow
of heat flux in the form of radiations. Misner-Sharp formulism has been
implemented to drive the dynamical equation in term of proper time and radial
derivatives. We have investigated the effects of charge and bulk viscosity on
the dynamics of collapsing cylinder. To determine the effects of radial heat
flux, we have formulated the heat transport equations in the context of
M$\ddot{u}$ller-Israel-Stewart theory by assuming that thermodynamics
viscous/heat coupling coefficients can be neglected within some approximations.
In our discussion, we have introduced the viscosity by the standard
(non-casual) thermodynamics approach. The dynamical equations have been coupled
with the heat transport equation equation, the consequences of resulting
coupled heat equation have been analyzed in detail. | [
0,
1,
0,
0,
0,
0
] |
Title: Towards Comfortable Cycling: A Practical Approach to Monitor the Conditions in Cycling Paths,
Abstract: This is a no brainer. Using bicycles to commute is the most sustainable form
of transport, is the least expensive to use and are pollution-free. Towns and
cities have to be made bicycle-friendly to encourage their wide usage.
Therefore, cycling paths should be more convenient, comfortable, and safe to
ride. This paper investigates a smartphone application, which passively
monitors the road conditions during cyclists ride. To overcome the problems of
monitoring roads, we present novel algorithms that sense the rough cycling
paths and locate road bumps. Each event is detected in real time to improve the
user friendliness of the application. Cyclists may keep their smartphones at
any random orientation and placement. Moreover, different smartphones sense the
same incident dissimilarly and hence report discrepant sensor values. We
further address the aforementioned difficulties that limit such crowd-sourcing
application. We evaluate our sensing application on cycling paths in Singapore,
and show that it can successfully detect such bad road conditions. | [
1,
0,
0,
0,
0,
0
] |
Title: Empathy in Bimatrix Games,
Abstract: Although the definition of what empathetic preferences exactly are is still
evolving, there is a general consensus in the psychology, science and
engineering communities that the evolution toward players' behaviors in
interactive decision-making problems will be accompanied by the exploitation of
their empathy, sympathy, compassion, antipathy, spitefulness, selfishness,
altruism, and self-abnegating states in the payoffs. In this article, we study
one-shot bimatrix games from a psychological game theory viewpoint. A new
empathetic payoff model is calculated to fit empirical observations and both
pure and mixed equilibria are investigated. For a realized empathy structure,
the bimatrix game is categorized among four generic class of games. Number of
interesting results are derived. A notable level of involvement can be observed
in the empathetic one-shot game compared the non-empathetic one and this holds
even for games with dominated strategies. Partial altruism can help in breaking
symmetry, in reducing payoff-inequality and in selecting social welfare and
more efficient outcomes. By contrast, partial spite and self-abnegating may
worsen payoff equity. Empathetic evolutionary game dynamics are introduced to
capture the resulting empathetic evolutionarily stable strategies under wide
range of revision protocols including Brown-von Neumann-Nash, Smith, imitation,
replicator, and hybrid dynamics. Finally, mutual support and Berge solution are
investigated and their connection with empathetic preferences are established.
We show that pure altruism is logically inconsistent, only by balancing it with
some partial selfishness does it create a consistent psychology. | [
1,
0,
0,
0,
0,
0
] |
Title: Free transport for convex potentials,
Abstract: We construct non-commutative analogs of transport maps among free Gibbs state
satisfying a certain convexity condition. Unlike previous constructions, our
approach is non-perturbative in nature and thus can be used to construct
transport maps between free Gibbs states associated to potentials which are far
from quadratic, i.e., states which are far from the semicircle law. An
essential technical ingredient in our approach is the extension of free
stochastic analysis to non-commutative spaces of functions based on the
Haagerup tensor product. | [
0,
0,
1,
0,
0,
0
] |
Title: Chemical exfoliation of MoS2 leads to semiconducting 1T' phase and not the metallic 1T phase,
Abstract: A trigonal phase existing only as small patches on chemically exfoliated few
layer, thermodynamically stable 1H phase of MoS2 is believed to influence
critically properties of MoS2 based devices. This phase has been most often
attributed to the metallic 1T phase. We investigate the electronic structure of
chemically exfoliated MoS2 few layered systems using spatially resolved (lesser
than 120 nm resolution) photoemission spectroscopy and Raman spectroscopy in
conjunction with state-of-the-art electronic structure calculations. On the
basis of these results, we establish that the ground state of this phase is a
small gap (~90 meV) semiconductor in contrast to most claims in the literature;
we also identify the specific trigonal (1T') structure it has among many
suggested ones. | [
0,
1,
0,
0,
0,
0
] |
Title: A note on the approximate admissibility of regularized estimators in the Gaussian sequence model,
Abstract: We study the problem of estimating an unknown vector $\theta$ from an
observation $X$ drawn according to the normal distribution with mean $\theta$
and identity covariance matrix under the knowledge that $\theta$ belongs to a
known closed convex set $\Theta$. In this general setting, Chatterjee (2014)
proved that the natural constrained least squares estimator is "approximately
admissible" for every $\Theta$. We extend this result by proving that the same
property holds for all convex penalized estimators as well. Moreover, we
simplify and shorten the original proof considerably. We also provide explicit
upper and lower bounds for the universal constant underlying the notion of
approximate admissibility. | [
0,
0,
1,
1,
0,
0
] |
Title: Extracting Epistatic Interactions in Type 2 Diabetes Genome-Wide Data Using Stacked Autoencoder,
Abstract: 2 Diabetes is a leading worldwide public health concern, and its increasing
prevalence has significant health and economic importance in all nations. The
condition is a multifactorial disorder with a complex aetiology. The genetic
determinants remain largely elusive, with only a handful of identified
candidate genes. Genome wide association studies (GWAS) promised to
significantly enhance our understanding of genetic based determinants of common
complex diseases. To date, 83 single nucleotide polymorphisms (SNPs) for type 2
diabetes have been identified using GWAS. Standard statistical tests for single
and multi-locus analysis such as logistic regression, have demonstrated little
effect in understanding the genetic architecture of complex human diseases.
Logistic regression is modelled to capture linear interactions but neglects the
non-linear epistatic interactions present within genetic data. There is an
urgent need to detect epistatic interactions in complex diseases as this may
explain the remaining missing heritability in such diseases. In this paper, we
present a novel framework based on deep learning algorithms that deal with
non-linear epistatic interactions that exist in genome wide association data.
Logistic association analysis under an additive genetic model, adjusted for
genomic control inflation factor, is conducted to remove statistically
improbable SNPs to minimize computational overheads. | [
0,
0,
0,
1,
0,
0
] |
Title: A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication,
Abstract: In recent years, randomized methods for numerical linear algebra have
received growing interest as a general approach to large-scale problems.
Typically, the essential ingredient of these methods is some form of randomized
dimension reduction, which accelerates computations, but also creates random
approximation error. In this way, the dimension reduction step encodes a
tradeoff between cost and accuracy. However, the exact numerical relationship
between cost and accuracy is typically unknown, and consequently, it may be
difficult for the user to precisely know (1) how accurate a given solution is,
or (2) how much computation is needed to achieve a given level of accuracy. In
the current paper, we study randomized matrix multiplication (sketching) as a
prototype setting for addressing these general problems. As a solution, we
develop a bootstrap method for {directly estimating} the accuracy as a function
of the reduced dimension (as opposed to deriving worst-case bounds on the
accuracy in terms of the reduced dimension). From a computational standpoint,
the proposed method does not substantially increase the cost of standard
sketching methods, and this is made possible by an "extrapolation" technique.
In addition, we provide both theoretical and empirical results to demonstrate
the effectiveness of the proposed method. | [
1,
0,
0,
1,
0,
0
] |
Title: Nanoscale superconducting memory based on the kinetic inductance of asymmetric nanowire loops,
Abstract: The demand for low-dissipation nanoscale memory devices is as strong as ever.
As Moore's Law is staggering, and the demand for a low-power-consuming
supercomputer is high, the goal of making information processing circuits out
of superconductors is one of the central goals of modern technology and
physics. So far, digital superconducting circuits could not demonstrate their
immense potential. One important reason for this is that a dense
superconducting memory technology is not yet available. Miniaturization of
traditional superconducting quantum interference devices is difficult below a
few micrometers because their operation relies on the geometric inductance of
the superconducting loop. Magnetic memories do allow nanometer-scale
miniaturization, but they are not purely superconducting (Baek et al 2014 Nat.
Commun. 5 3888). Our approach is to make nanometer scale memory cells based on
the kinetic inductance (and not geometric inductance) of superconducting
nanowire loops, which have already shown many fascinating properties (Aprili
2006 Nat. Nanotechnol. 1 15; Hopkins et al 2005 Science 308 1762). This allows
much smaller devices and naturally eliminates magnetic-field cross-talk. We
demonstrate that the vorticity, i.e., the winding number of the order
parameter, of a closed superconducting loop can be used for realizing a
nanoscale nonvolatile memory device. We demonstrate how to alter the vorticity
in a controlled fashion by applying calibrated current pulses. A reliable
read-out of the memory is also demonstrated. We present arguments that such
memory can be developed to operate without energy dissipation. | [
0,
1,
0,
0,
0,
0
] |
Title: The Autonomic Architecture of the Licas System,
Abstract: Licas (lightweight internet-based communication for autonomic services) is a
distributed framework for building service-based systems. The framework
provides a p2p server and more intelligent processing of information through
its AI algorithms. Distributed communication includes XML-RPC, REST, HTTP and
Web Services. It can now provide a robust platform for building different types
of system, where Microservices or SOA would be possible. However, the system
may be equally suited for the IoT, as it provides classes to connect with
external sources and has an optional Autonomic Manager with a MAPE control loop
integrated into the communication process. The system is also mobile-compatible
with Android. This paper focuses in particular on the autonomic setup and how
that might be used. A novel linking mechanism has been described previously [5]
that can be used to dynamically link sources and this is also considered, as
part of the autonomous framework. | [
1,
0,
0,
0,
0,
0
] |
Title: Some Characterizations on the Normalized Lommel, Struve and Bessel Functions of the First Kind,
Abstract: In this paper, we introduce new technique for determining some necessary and
sufficient conditions of the normalized Bessel functions $j_{\nu}$, normalized
Struve functions $h_{\nu}$ and normalized Lommel functions $s_{\mu,\nu}$ of the
first kind, to be in the subclasses of starlike and convex functions of order
$\alpha$ and type $\beta$. | [
0,
0,
1,
0,
0,
0
] |
Title: On the origin of the hydraulic jump in a thin liquid film,
Abstract: For more than a century, it has been believed that all hydraulic jumps are
created due to gravity. However, we found that thin-film hydraulic jumps are
not induced by gravity. This study explores the initiation of thin-film
hydraulic jumps. For circular jumps produced by the normal impingement of a jet
onto a solid surface, we found that the jump is formed when surface tension and
viscous forces balance the momentum in the film and gravity plays no
significant role. Experiments show no dependence on the orientation of the
surface and a scaling relation balancing viscous forces and surface tension
collapses the experimental data. Experiments on thin film planar jumps in a
channel also show that the predominant balance is with surface tension,
although for the thickness of the films we studied gravity also played a role
in the jump formation. A theoretical analysis shows that the downstream
transport of surface tension energy is the previously neglected, critical
ingredient in these flows and that capillary waves play the role of gravity
waves in a traditional jump in demarcating the transition from the
supercritical to subcritical flow associated with these jumps. | [
0,
1,
0,
0,
0,
0
] |
Title: Mammography Assessment using Multi-Scale Deep Classifiers,
Abstract: Applying deep learning methods to mammography assessment has remained a
challenging topic. Dense noise with sparse expressions, mega-pixel raw data
resolution, lack of diverse examples have all been factors affecting
performance. The lack of pixel-level ground truths have especially limited
segmentation methods in pushing beyond approximately bounding regions. We
propose a classification approach grounded in high performance tissue
assessment as an alternative to all-in-one localization and assessment models
that is also capable of pinpointing the causal pixels. First, the objective of
the mammography assessment task is formalized in the context of local tissue
classifiers. Then, the accuracy of a convolutional neural net is evaluated on
classifying patches of tissue with suspicious findings at varying scales, where
highest obtained AUC is above $0.9$. The local evaluations of one such expert
tissue classifier is used to augment the results of a heatmap regression model
and additionally recover the exact causal regions at high resolution as a
saliency image suitable for clinical settings. | [
0,
0,
0,
1,
0,
0
] |
Title: Studies to Understand and Optimize the Performance of Scintillation Counters for the Mu2e Cosmic Ray Veto System,
Abstract: In order to optimize the performance of the CRV, reflection studies and aging
studies were conducted. | [
0,
1,
0,
0,
0,
0
] |
Title: Linear Parameter Varying Representation of a class of MIMO Nonlinear Systems,
Abstract: Linear parameter-varying (LPV) models form a powerful model class to analyze
and control a (nonlinear) system of interest. Identifying an LPV model of a
nonlinear system can be challenging due to the difficulty of selecting the
scheduling variable(s) a priori, especially if a first principles based
understanding of the system is unavailable. Converting a nonlinear model to an
LPV form is also non-trivial and requires systematic methods to automate the
process.
Inspired by these challenges, a systematic LPV embedding approach starting
from multiple-input multiple-output (MIMO) linear fractional representations
with a nonlinear feedback block (NLFR) is proposed. This NLFR model class is
embedded into the LPV model class by an automated factorization of the
(possibly MIMO) static nonlinear block present in the model. As a result of the
factorization, an LPV-LFR or an LPV state-space model with affine dependency on
the scheduling is obtained. This approach facilitates the selection of the
scheduling variable and the connected mapping of system variables. Such a
conversion method enables to use nonlinear identification tools to estimate LPV
models.
The potential of the proposed approach is illustrated on a 2-DOF nonlinear
mass-spring-damper example. | [
1,
0,
0,
0,
0,
0
] |
Title: First-Principles Many-Body Investigation of Correlated Oxide Heterostructures: Few-Layer-Doped SmTiO$_3$,
Abstract: Correlated oxide heterostructures pose a challenging problem in condensed
matter research due to their structural complexity interweaved with demanding
electron states beyond the effective single-particle picture. By exploring the
correlated electronic structure of SmTiO$_3$ doped with few layers of SrO, we
provide an insight into the complexity of such systems. Furthermore, it is
shown how the advanced combination of band theory on the level of Kohn-Sham
density functional theory with explicit many-body theory on the level of
dynamical mean-field theory provides an adequate tool to cope with the problem.
Coexistence of band-insulating, metallic and Mott-critical electronic regions
is revealed in individual heterostructures with multi-orbital manifolds.
Intriguing orbital polarizations, that qualitatively vary between the metallic
and the Mott layers are also encountered. | [
0,
1,
0,
0,
0,
0
] |
Title: Faster Boosting with Smaller Memory,
Abstract: The two state-of-the-art implementations of boosted trees: XGBoost and
LightGBM, can process large training sets extremely fast. However, this
performance requires that memory size is sufficient to hold a 2-3 multiple of
the training set size. This paper presents an alternative approach to
implementing boosted trees. which achieves a significant speedup over XGBoost
and LightGBM, especially when memory size is small. This is achieved using a
combination of two techniques: early stopping and stratified sampling, which
are explained and analyzed in the paper. We describe our implementation and
present experimental results to support our claims. | [
1,
0,
0,
1,
0,
0
] |
Title: Coexistence of pressure-induced structural phases in bulk black phosphorus: a combined x-ray diffraction and Raman study up to 18 GPa,
Abstract: We report a study of the structural phase transitions induced by pressure in
bulk black phosphorus by using both synchrotron x-ray diffraction for pressures
up to 12.2 GPa and Raman spectroscopy up to 18.2 GPa. Very recently black
phosphorus attracted large attention because of the unique properties of
fewlayers samples (phosphorene), but some basic questions are still open in the
case of the bulk system. As concerning the presence of a Raman spectrum above
10 GPa, which should not be observed in an elemental simple cubic system, we
propose a new explanation by attributing a key role to the non-hydrostatic
conditions occurring in Raman experiments. Finally, a combined analysis of
Raman and XRD data allowed us to obtain quantitative information on presence
and extent of coexistences between different structural phases from ~5 up to
~15 GPa. This information can have an important role in theoretical studies on
pressure-induced structural and electronic phase transitions in black
phosphorus. | [
0,
1,
0,
0,
0,
0
] |
Title: Analytical methods for vacuum simulations in high energy accelerators for future machines based on the LHC performance,
Abstract: The Future Circular Collider (FCC), currently in the design phase, will
address many outstanding questions in particle physics. The technology to
succeed in this 100 km circumference collider goes beyond present limits.
Ultra-high vacuum conditions in the beam pipe is one essential requirement to
provide a smooth operation. Different physics phenomena as photon-, ion- and
electron- induced desorption and thermal outgassing of the chamber walls
challenge this requirement. This paper presents an analytical model and a
computer code PyVASCO that supports the design of a stable vacuum system by
providing an overview of all the gas dynamics happening inside the beam pipes.
A mass balance equation system describes the density distribution of the four
dominating gas species $\text{H}_2, \text{CH}_4$, $\text{CO}$ and
$\text{CO}_2$. An appropriate solving algorithm is discussed in detail and a
validation of the model including a comparison of the output to the readings of
LHC gauges is presented. This enables the evaluation of different designs for
the FCC. | [
0,
1,
0,
0,
0,
0
] |
Title: On the semigroup rank of a group,
Abstract: For an arbitrary group $G$, it is shown that either the semigroup rank $G{\rm
rk}S$ equals the group rank $G{\rm rk}G$, or $G{\rm rk}S = G{\rm rk}G+1$. This
is the starting point for the rest of the article, where the semigroup rank for
diverse kinds of groups is analysed. The semigroup rank of relatively free
groups, for any variety of groups, is computed. For a finitely generated
abelian group~$G$, it is proven that $G{\rm rk}S = G{\rm rk}G+1$ if and only if
$G$ is torsion-free. In general, this is not true. Partial results are obtained
in the nilpotent case. It is also proven that if $M$ is a connected closed
surface, then $(\pi_1(M)){\rm rk}S = (\pi_1(M)){\rm rk}G+1$ if and only if $M$
is orientable. | [
0,
0,
1,
0,
0,
0
] |
Title: On the primary spacing and microsegregation of cellular dendrites in laser deposited Ni-Nb alloys,
Abstract: In this study, an alloy phase-field model is used to simulate solidification
microstructures at different locations within a solidified molten pool. The
temperature gradient $G$ and the solidification velocity $V$ are obtained from
a macroscopic heat transfer finite element simulation and provided as input to
the phase-field model. The effects of laser beam speed and the location within
the melt pool on the primary arm spacing and on the extent of Nb partitioning
at the cell tips are investigated. Simulated steady-state primary spacings are
compared with power law and geometrical models. Cell tip compositions are
compared to a dendrite growth model. The extent of non-equilibrium interface
partitioning of the phase-field model is investigated. Although the phase-field
model has an anti-trapping solute flux term meant to maintain local interface
equilibrium, we have found that during simulations it was insufficient at
maintaining equilibrium. This is due to the fact that the additive
manufacturing solidification conditions fall well outside the allowed limits of
this flux term. | [
0,
1,
0,
0,
0,
0
] |
Title: Parallel mining of time-faded heavy hitters,
Abstract: We present PFDCMSS, a novel message-passing based parallel algorithm for
mining time-faded heavy hitters. The algorithm is a parallel version of the
recently published FDCMSS sequential algorithm. We formally prove its
correctness by showing that the underlying data structure, a sketch augmented
with a Space Saving stream summary holding exactly two counters, is mergeable.
Whilst mergeability of traditional sketches derives immediately from theory, we
show that merging our augmented sketch is non trivial. Nonetheless, the
resulting parallel algorithm is fast and simple to implement. To the best of
our knowledge, PFDCMSS is the first parallel algorithm solving the problem of
mining time-faded heavy hitters on message-passing parallel architectures.
Extensive experimental results confirm that PFDCMSS retains the extreme
accuracy and error bound provided by FDCMSS whilst providing excellent parallel
scalability. | [
1,
0,
0,
0,
0,
0
] |
Title: Instantaneous Arbitrage and the CAPM,
Abstract: This paper studies the concept of instantaneous arbitrage in continuous time
and its relation to the instantaneous CAPM. Absence of instantaneous arbitrage
is equivalent to the existence of a trading strategy which satisfies the CAPM
beta pricing relation in place of the market. Thus the difference between the
arbitrage argument and the CAPM argument in Black and Scholes (1973) is this:
the arbitrage argument assumes that there exists some portfolio satisfying the
capm equation, whereas the CAPM argument assumes, in addition, that this
portfolio is the market portfolio. | [
0,
0,
0,
0,
0,
1
] |
Title: Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces,
Abstract: Modern implicit generative models such as generative adversarial networks
(GANs) are generally known to suffer from instability and lack of
interpretability as it is difficult to diagnose what aspects of the target
distribution are missed by the generative model. In this work, we propose a
theoretically grounded solution to these issues by augmenting the GAN's loss
function with a kernel-based regularization term that magnifies local
discrepancy between the distributions of generated and real samples. The
proposed method relies on so-called witness points in the data space which are
jointly trained with the generator and provide an interpretable indication of
where the two distributions locally differ during the training procedure. In
addition, the proposed algorithm is scaled to higher dimensions by learning the
witness locations in a latent space of an autoencoder. We theoretically
investigate the dynamics of the training procedure, prove that a desirable
equilibrium point exists, and the dynamical system is locally stable around
this equilibrium. Finally, we demonstrate different aspects of the proposed
algorithm by numerical simulations of analytical solutions and empirical
results for low and high-dimensional datasets. | [
1,
0,
0,
1,
0,
0
] |
Title: Heterogeneous Transfer Learning: An Unsupervised Approach,
Abstract: Transfer learning leverages the knowledge in one domain, the source domain,
to improve learning efficiency in another domain, the target domain. Existing
transfer learning research is relatively well-progressed, but only in
situations where the feature spaces of the domains are homogeneous and the
target domain contains at least a few labeled instances. However, transfer
learning has not been well-studied in heterogeneous settings with an unlabeled
target domain. To contribute to the research in this emerging field, this paper
presents: (1) an unsupervised knowledge transfer theorem that prevents negative
transfer; and (2) a principal angle-based metric to measure the distance
between two pairs of domains. The metric shows the extent to which homogeneous
representations have preserved the information in original source and target
domains. The unsupervised knowledge transfer theorem sets out the transfer
conditions necessary to prevent negative transfer. Linear monotonic maps meet
the transfer conditions of the theorem and, hence, are used to construct
homogeneous representations of the heterogeneous domains, which in principle
prevents negative transfer. The metric and the theorem have been implemented in
an innovative transfer model, called a Grassmann-LMM-geodesic flow kernel
(GLG), that is specifically designed for knowledge transfer across
heterogeneous domains. The GLG model learns homogeneous representations of
heterogeneous domains by minimizing the proposed metric. Knowledge is
transferred through these learned representations via a geodesic flow kernel.
Notably, the theorem presented in this paper provides the sufficient transfer
conditions needed to guarantee that knowledge is transferred from a source
domain to an unlabeled target domain with correctness. | [
0,
0,
0,
1,
0,
0
] |
Title: Diffusion transformations, Black-Scholes equation and optimal stopping,
Abstract: We develop a new class of path transformations for one-dimensional diffusions
that are tailored to alter their long-run behaviour from transient to recurrent
or vice versa. This immediately leads to a formula for the distribution of the
first exit times of diffusions, which is recently characterised by Karatzas and
Ruf \cite{KR} as the minimal solution of an appropriate Cauchy problem under
more stringent conditions. A particular limit of these transformations also
turn out to be instrumental in characterising the stochastic solutions of
Cauchy problems defined by the generators of strict local martingales, which
are well-known for not having unique solutions even when one restricts
solutions to have linear growth. Using an appropriate diffusion transformation
we show that the aforementioned stochastic solution can be written in terms of
the unique classical solution of an {\em alternative} Cauchy problem with
suitable boundary conditions. This in particular resolves the long-standing
issue of non-uniqueness with the Black-Scholes equations in derivative pricing
in the presence of {\em bubbles}. Finally, we use these path transformations to
propose a unified framework for solving explicitly the optimal stopping problem
for one-dimensional diffusions with discounting, which in particular is
relevant for the pricing and the computation of optimal exercise boundaries of
perpetual American options. | [
0,
0,
1,
0,
0,
0
] |
Title: Generation of concept-representative symbols,
Abstract: The visual representation of concepts or ideas through the use of simple
shapes has always been explored in the history of Humanity, and it is believed
to be the origin of writing. We focus on computational generation of visual
symbols to represent concepts. We aim to develop a system that uses background
knowledge about the world to find connections among concepts, with the goal of
generating symbols for a given concept. We are also interested in exploring the
system as an approach to visual dissociation and visual conceptual blending.
This has a great potential in the area of Graphic Design as a tool to both
stimulate creativity and aid in brainstorming in projects such as logo,
pictogram or signage design. | [
1,
0,
0,
0,
0,
0
] |
Title: Continuous User Authentication via Unlabeled Phone Movement Patterns,
Abstract: In this paper, we propose a novel continuous authentication system for
smartphone users. The proposed system entirely relies on unlabeled phone
movement patterns collected through smartphone accelerometer. The data was
collected in a completely unconstrained environment over five to twelve days.
The contexts of phone usage were identified using k-means clustering. Multiple
profiles, one for each context, were created for every user. Five machine
learning algorithms were employed for classification of genuine and impostors.
The performance of the system was evaluated over a diverse population of 57
users. The mean equal error rates achieved by Logistic Regression, Neural
Network, kNN, SVM, and Random Forest were 13.7%, 13.5%, 12.1%, 10.7%, and 5.6%
respectively. A series of statistical tests were conducted to compare the
performance of the classifiers. The suitability of the proposed system for
different types of users was also investigated using the failure to enroll
policy. | [
1,
0,
0,
0,
0,
0
] |
Title: How strong are correlations in strongly recurrent neuronal networks?,
Abstract: Cross-correlations in the activity in neural networks are commonly used to
characterize their dynamical states and their anatomical and functional
organizations. Yet, how these latter network features affect the spatiotemporal
structure of the correlations in recurrent networks is not fully understood.
Here, we develop a general theory for the emergence of correlated neuronal
activity from the dynamics in strongly recurrent networks consisting of several
populations of binary neurons. We apply this theory to the case in which the
connectivity depends on the anatomical or functional distance between the
neurons. We establish the architectural conditions under which the system
settles into a dynamical state where correlations are strong, highly robust and
spatially modulated. We show that such strong correlations arise if the network
exhibits an effective feedforward structure. We establish how this feedforward
structure determines the way correlations scale with the network size and the
degree of the connectivity. In networks lacking an effective feedforward
structure correlations are extremely small and only weakly depend on the number
of connections per neuron. Our work shows how strong correlations can be
consistent with highly irregular activity in recurrent networks, two key
features of neuronal dynamics in the central nervous system. | [
0,
0,
0,
0,
1,
0
] |
Title: Director Field Analysis (DFA): Exploring Local White Matter Geometric Structure in diffusion MRI,
Abstract: In Diffusion Tensor Imaging (DTI) or High Angular Resolution Diffusion
Imaging (HARDI), a tensor field or a spherical function field (e.g., an
orientation distribution function field), can be estimated from measured
diffusion weighted images. In this paper, inspired by the microscopic
theoretical treatment of phases in liquid crystals, we introduce a novel
mathematical framework, called Director Field Analysis (DFA), to study local
geometric structural information of white matter based on the reconstructed
tensor field or spherical function field: 1) We propose a set of mathematical
tools to process general director data, which consists of dyadic tensors that
have orientations but no direction. 2) We propose Orientational Order (OO) and
Orientational Dispersion (OD) indices to describe the degree of alignment and
dispersion of a spherical function in a single voxel or in a region,
respectively; 3) We also show how to construct a local orthogonal coordinate
frame in each voxel exhibiting anisotropic diffusion; 4) Finally, we define
three indices to describe three types of orientational distortion (splay, bend,
and twist) in a local spatial neighborhood, and a total distortion index to
describe distortions of all three types. To our knowledge, this is the first
work to quantitatively describe orientational distortion (splay, bend, and
twist) in general spherical function fields from DTI or HARDI data. The
proposed DFA and its related mathematical tools can be used to process not only
diffusion MRI data but also general director field data, and the proposed
scalar indices are useful for detecting local geometric changes of white matter
for voxel-based or tract-based analysis in both DTI and HARDI acquisitions. The
related codes and a tutorial for DFA will be released in DMRITool. | [
1,
1,
0,
0,
0,
0
] |
Title: Advantages of versatile neural-network decoding for topological codes,
Abstract: Finding optimal correction of errors in generic stabilizer codes is a
computationally hard problem, even for simple noise models. While this task can
be simplified for codes with some structure, such as topological stabilizer
codes, developing good and efficient decoders still remains a challenge. In our
work, we systematically study a very versatile class of decoders based on
feedforward neural networks. To demonstrate adaptability, we apply neural
decoders to the triangular color and toric codes under various noise models
with realistic features, such as spatially-correlated errors. We report that
neural decoders provide significant improvement over leading efficient decoders
in terms of the error-correction threshold. Using neural networks simplifies
the process of designing well-performing decoders, and does not require prior
knowledge of the underlying noise model. | [
0,
0,
0,
1,
0,
0
] |
Title: A retrieval-based dialogue system utilizing utterance and context embeddings,
Abstract: Finding semantically rich and computer-understandable representations for
textual dialogues, utterances and words is crucial for dialogue systems (or
conversational agents), as their performance mostly depends on understanding
the context of conversations. Recent research aims at finding distributed
vector representations (embeddings) for words, such that semantically similar
words are relatively close within the vector-space. Encoding the "meaning" of
text into vectors is a current trend, and text can range from words, phrases
and documents to actual human-to-human conversations. In recent research
approaches, responses have been generated utilizing a decoder architecture,
given the vector representation of the current conversation. In this paper, the
utilization of embeddings for answer retrieval is explored by using
Locality-Sensitive Hashing Forest (LSH Forest), an Approximate Nearest Neighbor
(ANN) model, to find similar conversations in a corpus and rank possible
candidates. Experimental results on the well-known Ubuntu Corpus (in English)
and a customer service chat dataset (in Dutch) show that, in combination with a
candidate selection method, retrieval-based approaches outperform generative
ones and reveal promising future research directions towards the usability of
such a system. | [
1,
0,
0,
0,
0,
0
] |
Title: Testing homogeneity of proportions from sparse binomial data with a large number of groups,
Abstract: In this paper, we consider testing the homogeneity for proportions in
independent binomial distributions especially when data are sparse for large
number of groups. We provide broad aspects of our proposed tests such as
theoretical studies, simulations and real data application. We present the
asymptotic null distributions and asymptotic powers for our proposed tests and
compare their performance with existing tests. Our simulation studies show that
none of tests dominate the others, however our proposed test and a few tests
are expected to control given sizes and obtain significant powers. We also
present a real example regarding safety concerns associated with Avandiar
(rosiglitazone) in Nissen and Wolsky (2007). | [
0,
0,
1,
1,
0,
0
] |
Title: Henkin measures for the Drury-Arveson space,
Abstract: We exhibit Borel probability measures on the unit sphere in $\mathbb C^d$ for
$d \ge 2$ which are Henkin for the multiplier algebra of the Drury-Arveson
space, but not Henkin in the classical sense. This provides a negative answer
to a conjecture of Clouâtre and Davidson. | [
0,
0,
1,
0,
0,
0
] |
Title: Discriminative Bimodal Networks for Visual Localization and Detection with Natural Language Queries,
Abstract: Associating image regions with text queries has been recently explored as a
new way to bridge visual and linguistic representations. A few pioneering
approaches have been proposed based on recurrent neural language models trained
generatively (e.g., generating captions), but achieving somewhat limited
localization accuracy. To better address natural-language-based visual entity
localization, we propose a discriminative approach. We formulate a
discriminative bimodal neural network (DBNet), which can be trained by a
classifier with extensive use of negative samples. Our training objective
encourages better localization on single images, incorporates text phrases in a
broad range, and properly pairs image regions with text phrases into positive
and negative examples. Experiments on the Visual Genome dataset demonstrate the
proposed DBNet significantly outperforms previous state-of-the-art methods both
for localization on single images and for detection on multiple images. We we
also establish an evaluation protocol for natural-language visual detection. | [
1,
0,
0,
1,
0,
0
] |
Title: Particle-without-Particle: a practical pseudospectral collocation method for linear partial differential equations with distributional sources,
Abstract: Partial differential equations with distributional sources---in particular,
involving (derivatives of) delta distributions---have become increasingly
ubiquitous in numerous areas of physics and applied mathematics. It is often of
considerable interest to obtain numerical solutions for such equations, but any
singular ("particle"-like) source modeling invariably introduces nontrivial
computational obstacles. A common method to circumvent these is through some
form of delta function approximation procedure on the computational grid;
however, this often carries significant limitations on the efficiency of the
numerical convergence rates, or sometimes even the resolvability of the problem
at all.
In this paper, we present an alternative technique for tackling such
equations which avoids the singular behavior entirely: the
"Particle-without-Particle" method. Previously introduced in the context of the
self-force problem in gravitational physics, the idea is to discretize the
computational domain into two (or more) disjoint pseudospectral
(Chebyshev-Lobatto) grids such that the "particle" is always at the interface
between them; thus, one only needs to solve homogeneous equations in each
domain, with the source effectively replaced by jump (boundary) conditions
thereon. We prove here that this method yields solutions to any linear PDE the
source of which is any linear combination of delta distributions and
derivatives thereof supported on a one-dimensional subspace of the problem
domain. We then implement it to numerically solve a variety of relevant PDEs:
hyperbolic (with applications to neuroscience and acoustics), parabolic (with
applications to finance), and elliptic. We generically obtain improved
convergence rates relative to typical past implementations relying on delta
function approximations. | [
0,
0,
0,
0,
1,
1
] |
Title: Genetic Algorithm for Epidemic Mitigation by Removing Relationships,
Abstract: Min-SEIS-Cluster is an optimization problem which aims at minimizing the
infection spreading in networks. In this problem, nodes can be susceptible to
an infection, exposed to an infection, or infectious. One of the main features
of this problem is the fact that nodes have different dynamics when interacting
with other nodes from the same community. Thus, the problem is characterized by
distinct probabilities of infecting nodes from both the same and from different
communities. This paper presents a new genetic algorithm that solves the
Min-SEIS-Cluster problem. This genetic algorithm surpassed the current
heuristic of this problem significantly, reducing the number of infected nodes
during the simulation of the epidemics. The results therefore suggest that our
new genetic algorithm is the state-of-the-art heuristic to solve this problem. | [
1,
0,
1,
0,
0,
0
] |
Title: Angpow: a software for the fast computation of accurate tomographic power spectra,
Abstract: The statistical distribution of galaxies is a powerful probe to constrain
cosmological models and gravity. In particular the matter power spectrum $P(k)$
brings information about the cosmological distance evolution and the galaxy
clustering together. However the building of $P(k)$ from galaxy catalogues
needs a cosmological model to convert angles on the sky and redshifts into
distances, which leads to difficulties when comparing data with predicted
$P(k)$ from other cosmological models, and for photometric surveys like LSST.
The angular power spectrum $C_\ell(z_1,z_2)$ between two bins located at
redshift $z_1$ and $z_2$ contains the same information than the matter power
spectrum, is free from any cosmological assumption, but the prediction of
$C_\ell(z_1,z_2)$ from $P(k)$ is a costly computation when performed exactly.
The Angpow software aims at computing quickly and accurately the auto
($z_1=z_2$) and cross ($z_1 \neq z_2$) angular power spectra between redshift
bins. We describe the developed algorithm, based on developments on the
Chebyshev polynomial basis and on the Clenshaw-Curtis quadrature method. We
validate the results with other codes, and benchmark the performance. Angpow is
flexible and can handle any user defined power spectra, transfer functions, and
redshift selection windows. The code is fast enough to be embedded inside
programs exploring large cosmological parameter spaces through the
$C_\ell(z_1,z_2)$ comparison with data. We emphasize that the Limber's
approximation, often used to fasten the computation, gives wrong $C_\ell$
values for cross-correlations. | [
0,
1,
0,
0,
0,
0
] |
Title: Robust and Flexible Estimation of Stochastic Mediation Effects: A Proposed Method and Example in a Randomized Trial Setting,
Abstract: Causal mediation analysis can improve understanding of the mechanisms
underlying epidemiologic associations. However, the utility of natural direct
and indirect effect estimation has been limited by the assumption of no
confounder of the mediator-outcome relationship that is affected by prior
exposure---an assumption frequently violated in practice. We build on recent
work that identified alternative estimands that do not require this assumption
and propose a flexible and double robust semiparametric targeted minimum
loss-based estimator for data-dependent stochastic direct and indirect effects.
The proposed method treats the intermediate confounder affected by prior
exposure as a time-varying confounder and intervenes stochastically on the
mediator using a distribution which conditions on baseline covariates and
marginalizes over the intermediate confounder. In addition, we assume the
stochastic intervention is given, conditional on observed data, which results
in a simpler estimator and weaker identification assumptions. We demonstrate
the estimator's finite sample and robustness properties in a simple simulation
study. We apply the method to an example from the Moving to Opportunity
experiment. In this application, randomization to receive a housing voucher is
the treatment/instrument that influenced moving to a low-poverty neighborhood,
which is the intermediate confounder. We estimate the data-dependent stochastic
direct effect of randomization to the voucher group on adolescent marijuana use
not mediated by change in school district and the stochastic indirect effect
mediated by change in school district. We find no evidence of mediation. Our
estimator is easy to implement in standard statistical software, and we provide
annotated R code to further lower implementation barriers. | [
0,
0,
0,
1,
0,
0
] |
Title: On the essential spectrum of elliptic differential operators,
Abstract: Let $\mathcal{A}$ be a $C^*$-algebra of bounded uniformly continuous
functions on $X=\mathbb{R}^d$ such that $\mathcal{A}$ is stable under
translations and contains the continuous functions that have a limit at
infinity. Denote $\mathcal{A}^\dagger$ the boundary of $X$ in the character
space of $\mathcal{A}$. Then the crossed product
$\mathscr{A}=\mathcal{A}\rtimes X$ of $\mathcal{A}$ by the natural action of
$X$ on $\mathcal{A}$ is a well defined $C^*$-algebra and to each operator
$A\in\mathscr{A}$ one may naturally associate a family of bounded operators
$A_\varkappa$ on $L^2(X)$ indexed by the characters
$\varkappa\in\mathcal{A}^\dagger$. We show that the essential spectrum of $A$
is the union of the spectra of the operators $A_\varkappa$. The applications
cover very general classes of singular elliptic operators. | [
0,
0,
1,
0,
0,
0
] |
Title: Novel processes and metrics for a scientific evaluation rooted in the principles of science - Version 1,
Abstract: Scientific evaluation is a determinant of how scientists, institutions and
funders behave, and as such is a key element in the making of science. In this
article, we propose an alternative to the current norm of evaluating research
with journal rank. Following a well-defined notion of scientific value, we
introduce qualitative processes that can also be quantified and give rise to
meaningful and easy-to-use article-level metrics. In our approach, the goal of
a scientist is transformed from convincing an editorial board through a
vertical process to convincing peers through an horizontal one. We argue that
such an evaluation system naturally provides the incentives and logic needed to
constantly promote quality, reproducibility, openness and collaboration in
science. The system is legally and technically feasible and can gradually lead
to the self-organized reappropriation of the scientific process by the
scholarly community and its institutions. We propose an implementation of our
evaluation system with the platform "the Self-Journals of Science"
(www.sjscience.org). | [
1,
0,
0,
0,
0,
0
] |
Title: Topological quantization of energy transport in micro- and nano-mechanical lattices,
Abstract: Topological effects typically discussed in the context of quantum physics are
emerging as one of the central paradigms of physics. Here, we demonstrate the
role of topology in energy transport through dimerized micro- and
nano-mechanical lattices in the classical regime, i.e., essentially "masses and
springs". We show that the thermal conductance factorizes into topological and
non-topological components. The former takes on three discrete values and
arises due to the appearance of edge modes that prevent good contact between
the heat reservoirs and the bulk, giving a length-independent reduction of the
conductance. In essence, energy input at the boundary mostly stays there, an
effect robust against disorder and nonlinearity. These results bridge two
seemingly disconnected disciplines of physics, namely topology and thermal
transport, and suggest ways to engineer thermal contacts, opening a direction
to explore the ramifications of topological properties on nanoscale technology. | [
0,
1,
0,
0,
0,
0
] |
Title: Auxiliary Variables in TLA+,
Abstract: Auxiliary variables are often needed for verifying that an implementation is
correct with respect to a higher-level specification. They augment the formal
description of the implementation without changing its semantics--that is, the
set of behaviors that it describes. This paper explains rules for adding
history, prophecy, and stuttering variables to TLA+ specifications, ensuring
that the augmented specification is equivalent to the original one. The rules
are explained with toy examples, and they are used to verify the correctness of
a simplified version of a snapshot algorithm due to Afek et al. | [
1,
0,
0,
0,
0,
0
] |
Title: A Homological model for the coloured Jones polynomials,
Abstract: In this paper we will present a homological model for Coloured Jones
Polynomials. For each color $N \in \N$, we will describe the invariant
$J_N(L,q)$ as a graded intersection pairing of certain homological classes in a
covering of the configuration space on the punctured disk. This construction is
based on the Lawrence representation and a result due to Kohno that relates
quantum representations and homological representations of the braid groups. | [
0,
0,
1,
0,
0,
0
] |
Title: Property Safety Stock Policy for Correlated Commodities Based on Probability Inequality,
Abstract: Deriving the optimal safety stock quantity with which to meet customer
satisfaction is one of the most important topics in stock management. However,
it is difficult to control the stock management of correlated marketable
merchandise when using an inventory control method that was developed under the
assumption that the demands are not correlated. For this, we propose a
deterministic approach that uses a probability inequality to derive a
reasonable safety stock for the case in which we know the correlation between
various commodities. Moreover, over a given lead time, the relation between the
appropriate safety stock and the allowable stockout rate is analytically
derived, and the potential of our proposed procedure is validated by numerical
experiments. | [
0,
0,
1,
1,
0,
0
] |
Title: Carleman estimates for the time-fractional advection-diffusion equations and applications,
Abstract: In this article, we prove Carleman estimates for the generalized
time-fractional advection-diffusion equations by considering the fractional
derivative as perturbation for the first order time-derivative. As a direct
application of the Carleman estimates, we show a conditional stability of a
lateral Cauchy problem for the time-fractional advection-diffusion equation,
and we also investigate the stability of an inverse source problem. | [
0,
0,
1,
0,
0,
0
] |
Title: Generating and Aligning from Data Geometries with Generative Adversarial Networks,
Abstract: Unsupervised domain mapping has attracted substantial attention in recent
years due to the success of models based on the cycle-consistency assumption.
These models map between two domains by fooling a probabilistic discriminator,
thereby matching the probability distributions of the real and generated data.
Instead of this probabilistic approach, we cast the problem in terms of
aligning the geometry of the manifolds of the two domains. We introduce the
Manifold Geometry Matching Generative Adversarial Network (MGM GAN), which adds
two novel mechanisms to facilitate GANs sampling from the geometry of the
manifold rather than the density and then aligning two manifold geometries: (1)
an importance sampling technique that reweights points based on their density
on the manifold, making the discriminator only able to discern geometry and (2)
a penalty adapted from traditional manifold alignment literature that
explicitly enforces the geometry to be preserved. The MGM GAN leverages the
manifolds arising from a pre-trained autoencoder to bridge the gap between
formal manifold alignment literature and existing GAN work, and demonstrate the
advantages of modeling the manifold geometry over its density. | [
1,
0,
0,
1,
0,
0
] |
Title: Wolf-Rayet spin at low metallicity and its implication for Black Hole formation channels,
Abstract: The spin of Wolf-Rayet (WR) stars at low metallicity (Z) is most relevant for
our understanding of gravitational wave sources such as GW 150914, as well as
the incidence of long-duration gamma-ray bursts (GRBs). Two scenarios have been
suggested for both phenomena: one of them involves rapid rotation and
quasi-chemical homogeneous evolution (CHE), the other invokes classical
evolution through mass loss in single and binary systems. WR spin rates might
enable us to test these two scenarios. In order to obtain empirical constraints
on black hole progenitor spin, we infer wind asymmetries in all 12 known WR
stars in the Small Magellanic Cloud (SMC) at Z = 1/5 Zsun, as well as within a
significantly enlarged sample of single and binary WR stars in the Large
Magellanic Cloud (LMC at Z = 1/2 Zsun), tripling the sample of Vink (2007).
This brings the total LMC sample to 39, making it appropriate for comparison to
the Galactic sample. We measure WR wind asymmetries with VLT-FORS linear
spectropolarimetry. We report the detection of new line effects in the LMC WN
star BAT99-43 and the WC star BAT99-70, as well as the famous WR/LBV HD 5980 in
the SMC, which might be evolving chemically homogeneously. With the previous
reported line effects in the late-type WNL (Ofpe/WN9) objects BAT99-22 and
BAT99-33, this brings the total LMC WR sample to 4, i.e. a frequency of ~10%.
Perhaps surprisingly, the incidence of line effects amongst low-Z WR stars is
not found to be any higher than amongst the Galactic WR sample, challenging the
rotationally-induced CHE model. As WR mass loss is likely Z-dependent, our
Magellanic Cloud line-effect WR stars may maintain their surface rotation and
fulfill the basic conditions for producing long GRBs, both via the classical
post-red supergiant (RSG) or luminous blue variable (LBV) channel, as well as
resulting from CHE due to physics specific to very massive stars (VMS). | [
0,
1,
0,
0,
0,
0
] |
Title: Learning to Identify Ambiguous and Misleading News Headlines,
Abstract: Accuracy is one of the basic principles of journalism. However, it is
increasingly hard to manage due to the diversity of news media. Some editors of
online news tend to use catchy headlines which trick readers into clicking.
These headlines are either ambiguous or misleading, degrading the reading
experience of the audience. Thus, identifying inaccurate news headlines is a
task worth studying. Previous work names these headlines "clickbaits" and
mainly focus on the features extracted from the headlines, which limits the
performance since the consistency between headlines and news bodies is
underappreciated. In this paper, we clearly redefine the problem and identify
ambiguous and misleading headlines separately. We utilize class sequential
rules to exploit structure information when detecting ambiguous headlines. For
the identification of misleading headlines, we extract features based on the
congruence between headlines and bodies. To make use of the large unlabeled
data set, we apply a co-training method and gain an increase in performance.
The experiment results show the effectiveness of our methods. Then we use our
classifiers to detect inaccurate headlines crawled from different sources and
conduct a data analysis. | [
1,
0,
0,
0,
0,
0
] |
Title: Evidence of Complex Contagion of Information in Social Media: An Experiment Using Twitter Bots,
Abstract: It has recently become possible to study the dynamics of information
diffusion in techno-social systems at scale, due to the emergence of online
platforms, such as Twitter, with millions of users. One question that
systematically recurs is whether information spreads according to simple or
complex dynamics: does each exposure to a piece of information have an
independent probability of a user adopting it (simple contagion), or does this
probability depend instead on the number of sources of exposure, increasing
above some threshold (complex contagion)? Most studies to date are
observational and, therefore, unable to disentangle the effects of confounding
factors such as social reinforcement, homophily, limited attention, or network
community structure. Here we describe a novel controlled experiment that we
performed on Twitter using `social bots' deployed to carry out coordinated
attempts at spreading information. We propose two Bayesian statistical models
describing simple and complex contagion dynamics, and test the competing
hypotheses. We provide experimental evidence that the complex contagion model
describes the observed information diffusion behavior more accurately than
simple contagion. Future applications of our results include more effective
defenses against malicious propaganda campaigns on social media, improved
marketing and advertisement strategies, and design of effective network
intervention techniques. | [
1,
1,
0,
0,
0,
0
] |
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