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Title: Learning to Optimize Neural Nets,
Abstract: Learning to Optimize is a recently proposed framework for learning
optimization algorithms using reinforcement learning. In this paper, we explore
learning an optimization algorithm for training shallow neural nets. Such
high-dimensional stochastic optimization problems present interesting
challenges for existing reinforcement learning algorithms. We develop an
extension that is suited to learning optimization algorithms in this setting
and demonstrate that the learned optimization algorithm consistently
outperforms other known optimization algorithms even on unseen tasks and is
robust to changes in stochasticity of gradients and the neural net
architecture. More specifically, we show that an optimization algorithm trained
with the proposed method on the problem of training a neural net on MNIST
generalizes to the problems of training neural nets on the Toronto Faces
Dataset, CIFAR-10 and CIFAR-100. | [
1,
0,
1,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Notes on complexity of packing coloring,
Abstract: A packing $k$-coloring for some integer $k$ of a graph $G=(V,E)$ is a mapping
$\varphi:V\to\{1,\ldots,k\}$ such that any two vertices $u, v$ of color
$\varphi(u)=\varphi(v)$ are in distance at least $\varphi(u)+1$. This concept
is motivated by frequency assignment problems. The \emph{packing chromatic
number} of $G$ is the smallest $k$ such that there exists a packing
$k$-coloring of $G$.
Fiala and Golovach showed that determining the packing chromatic number for
chordal graphs is \NP-complete for diameter exactly 5. While the problem is
easy to solve for diameter 2, we show \NP-completeness for any diameter at
least 3. Our reduction also shows that the packing chromatic number is hard to
approximate within $n^{{1/2}-\varepsilon}$ for any $\varepsilon > 0$.
In addition, we design an \FPT algorithm for interval graphs of bounded
diameter. This leads us to exploring the problem of finding a partial coloring
that maximizes the number of colored vertices. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: Improved Absolute Frequency Measurement of the 171Yb Optical Lattice Clock at KRISS Relative to the SI Second,
Abstract: We measured the absolute frequency of the $^1S_0$ - $^3P_0$ transition of
$^{171}$Yb atoms confined in a one-dimensional optical lattice relative to the
SI second. The determined frequency was 518 295 836 590 863.38(57) Hz. The
uncertainty was reduced by a factor of 14 compared with our previously reported
value in 2013 due to the significant improvements in decreasing the systematic
uncertainties. This result is expected to contribute to the determination of a
new recommended value for the secondary representations of the second. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: A Hilbert Space of Stationary Ergodic Processes,
Abstract: Identifying meaningful signal buried in noise is a problem of interest
arising in diverse scenarios of data-driven modeling. We present here a
theoretical framework for exploiting intrinsic geometry in data that resists
noise corruption, and might be identifiable under severe obfuscation. Our
approach is based on uncovering a valid complete inner product on the space of
ergodic stationary finite valued processes, providing the latter with the
structure of a Hilbert space on the real field. This rigorous construction,
based on non-standard generalizations of the notions of sum and scalar
multiplication of finite dimensional probability vectors, allows us to
meaningfully talk about "angles" between data streams and data sources, and,
make precise the notion of orthogonal stochastic processes. In particular, the
relative angles appear to be preserved, and identifiable, under severe noise,
and will be developed in future as the underlying principle for robust
classification, clustering and unsupervised featurization algorithms. | [
0,
0,
0,
1,
0,
1
] | [
"Mathematics",
"Statistics"
] |
Title: Total variation regularized non-negative matrix factorization for smooth hyperspectral unmixing,
Abstract: Hyperspectral analysis has gained popularity over recent years as a way to
infer what materials are displayed on a picture whose pixels consist of a
mixture of spectral signatures. Computing both signatures and mixture
coefficients is known as unsupervised unmixing, a set of techniques usually
based on non-negative matrix factorization. Unmixing is a difficult non-convex
problem, and algorithms may converge to one out of many local minima, which may
be far removed from the true global minimum. Computing this true minimum is
NP-hard and seems therefore out of reach. Aiming for interesting local minima,
we investigate the addition of total variation regularization terms. Advantages
of these regularizers are two-fold. Their computation is typically rather
light, and they are deemed to preserve sharp edges in pictures. This paper
describes an algorithm for regularized hyperspectral unmixing based on the
Alternating Direction Method of Multipliers. | [
0,
0,
1,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: Efficient Antihydrogen Detection in Antimatter Physics by Deep Learning,
Abstract: Antihydrogen is at the forefront of antimatter research at the CERN
Antiproton Decelerator. Experiments aiming to test the fundamental CPT symmetry
and antigravity effects require the efficient detection of antihydrogen
annihilation events, which is performed using highly granular tracking
detectors installed around an antimatter trap. Improving the efficiency of the
antihydrogen annihilation detection plays a central role in the final
sensitivity of the experiments. We propose deep learning as a novel technique
to analyze antihydrogen annihilation data, and compare its performance with a
traditional track and vertex reconstruction method. We report that the deep
learning approach yields significant improvement, tripling event coverage while
simultaneously improving performance by over 5% in terms of Area Under Curve
(AUC). | [
1,
1,
0,
0,
0,
0
] | [
"Physics",
"Computer Science"
] |
Title: Nonparametric Cusum Charts for Angular Data with Applications in Health Science and Astrophysics,
Abstract: This paper develops non-parametric rotation invariant CUSUMs suited to the
detection of changes in the mean direction as well as changes in the
concentration parameter of angular data. The properties of the CUSUMs are
illustrated by theoretical calculations, Monte Carlo simulation and application
to sequentially observed angular data from health science and astrophysics. | [
0,
0,
0,
1,
0,
0
] | [
"Statistics",
"Physics"
] |
Title: Origin of Weak Turbulence in the Outer Regions of Protoplanetary Disks,
Abstract: The mechanism behind angular momentum transport in protoplanetary disks, and
whether this transport is turbulent in nature, is a fundamental issue in planet
formation studies. Recent ALMA observations have suggested that turbulent
velocities in the outer regions of these disks are less than ~5-10% of the
sound speed, contradicting theoretical predictions of turbulence driven by the
magnetorotational instability (MRI). These observations have generally been
interpreted to be consistent with a large-scale laminar magnetic wind driving
accretion. Here, we carry out local, shearing box simulations with varying
ionization levels and background magnetic field strengths in order to determine
which parameters produce results consistent with observations. We find that
even when the background magnetic field launches a strong largely laminar wind,
significant turbulence persists and is driven by localized regions of vertical
magnetic field (the result of zonal flows) that are unstable to the MRI. The
only conditions for which we find turbulent velocities below the observational
limits are weak background magnetic fields and ionization levels well below
that usually assumed in theoretical studies. We interpret these findings within
the context of a preliminary model in which a large scale magnetic field,
confined to the inner disk, hinders ionizing sources from reaching large radial
distances, e.g., through a sufficiently dense wind. Thus, in addition to such a
wind, this model predicts that for disks with weakly turbulent outer regions,
the outer disk will have significantly reduced ionization levels compared to
standard models and will harbor only a weak vertical magnetic field. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Real-time Monocular Visual Odometry for Turbid and Dynamic Underwater Environments,
Abstract: In the context of robotic underwater operations, the visual degradations
induced by the medium properties make difficult the exclusive use of cameras
for localization purpose. Hence, most localization methods are based on
expensive navigational sensors associated with acoustic positioning. On the
other hand, visual odometry and visual SLAM have been exhaustively studied for
aerial or terrestrial applications, but state-of-the-art algorithms fail
underwater. In this paper we tackle the problem of using a simple low-cost
camera for underwater localization and propose a new monocular visual odometry
method dedicated to the underwater environment. We evaluate different tracking
methods and show that optical flow based tracking is more suited to underwater
images than classical approaches based on descriptors. We also propose a
keyframe-based visual odometry approach highly relying on nonlinear
optimization. The proposed algorithm has been assessed on both simulated and
real underwater datasets and outperforms state-of-the-art visual SLAM methods
under many of the most challenging conditions. The main application of this
work is the localization of Remotely Operated Vehicles (ROVs) used for
underwater archaeological missions but the developed system can be used in any
other applications as long as visual information is available. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Quantitative Biology"
] |
Title: Rating Protocol Design for Extortion and Cooperation in the Crowdsourcing Contest Dilemma,
Abstract: Crowdsourcing has emerged as a paradigm for leveraging human intelligence and
activity to solve a wide range of tasks. However, strategic workers will find
enticement in their self-interest to free-ride and attack in a crowdsourcing
contest dilemma game. Hence, incentive mechanisms are of great importance to
overcome the inefficiency of the socially undesirable equilibrium. Existing
incentive mechanisms are not effective in providing incentives for cooperation
in crowdsourcing competitions due to the following features: heterogeneous
workers compete against each other in a crowdsourcing platform with imperfect
monitoring. In this paper, we take these features into consideration, and
develop a novel game-theoretic design of rating protocols, which integrates
binary rating labels with differential pricing to maximize the requester's
utility, by extorting selfish workers and enforcing cooperation among them. By
quantifying necessary and sufficient conditions for the sustainable social
norm, we formulate the problem of maximizing the revenue of the requester among
all sustainable rating protocols, provide design guidelines for optimal rating
protocols, and design a low-complexity algorithm to select optimal design
parameters which are related to differential punishments and pricing schemes.
Simulation results demonstrate how intrinsic parameters impact on design
parameters, as well as the performance gain of the proposed rating protocol. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: On the $E$-polynomial of parabolic $\mathrm{Sp}_{2n}$-character varieties,
Abstract: We find the $E$-polynomials of a family of parabolic
$\mathrm{Sp}_{2n}$-character varieties $\mathcal{M}^{\xi}_{n}$ of Riemann
surfaces by constructing a stratification, proving that each stratum has
polynomial count, applying a result of Katz regarding the counting functions,
and finally adding up the resulting $E$-polynomials of the strata. To count the
number of $\mathbb{F}_{q}$-points of the strata, we invoke a formula due to
Frobenius. Our calculation make use of a formula for the evaluation of
characters on semisimple elements coming from Deligne-Lusztig theory, applied
to the character theory of $\mathrm{Sp}{\left(2n,\mathbb{F}_{q}\right)}$, and
Möbius inversion on the poset of set-partitions. We compute the Euler
characteristic of the $\mathcal{M}^{\xi}_{n}$ with these polynomials, and show
they are connected. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Ivanov-Regularised Least-Squares Estimators over Large RKHSs and Their Interpolation Spaces,
Abstract: We study kernel least-squares estimation under a norm constraint. This form
of regularisation is known as Ivanov regularisation and it provides better
control of the norm of the estimator than the well-established Tikhonov
regularisation. This choice of regularisation allows us to dispose of the
standard assumption that the reproducing kernel Hilbert space (RKHS) has a
Mercer kernel, which is restrictive as it usually requires compactness of the
covariate set. Instead, we assume only that the RKHS is separable with a
bounded and measurable kernel. We provide rates of convergence for the expected
squared $L^2$ error of our estimator under the weak assumption that the
variance of the response variables is bounded and the unknown regression
function lies in an interpolation space between $L^2$ and the RKHS. We then
obtain faster rates of convergence when the regression function is bounded by
clipping the estimator. In fact, we attain the optimal rate of convergence.
Furthermore, we provide a high-probability bound under the stronger assumption
that the response variables have subgaussian errors and that the regression
function lies in an interpolation space between $L^\infty$ and the RKHS.
Finally, we derive adaptive results for the settings in which the regression
function is bounded. | [
0,
0,
1,
1,
0,
0
] | [
"Statistics",
"Mathematics",
"Computer Science"
] |
Title: Using Ice and Dust Lines to Constrain the Surface Densities of Protoplanetary Disks,
Abstract: We present a novel method for determining the surface density of
protoplanetary disks through consideration of disk 'dust lines' which indicate
the observed disk radial scale at different observational wavelengths. This
method relies on the assumption that the processes of particle growth and drift
control the radial scale of the disk at late stages of disk evolution such that
the lifetime of the disk is equal to both the drift timescale and growth
timescale of the maximum particle size at a given dust line. We provide an
initial proof of concept of our model through an application to the disk TW Hya
and are able to estimate the disk dust-to-gas ratio, CO abundance, and
accretion rate in addition to the total disk surface density. We find that our
derived surface density profile and dust-to-gas ratio are consistent with the
lower limits found through measurements of HD gas. The CO ice line also depends
on surface density through grain adsorption rates and drift and we find that
our theoretical CO ice line estimates have clear observational analogues. We
further apply our model to a large parameter space of theoretical disks and
find three observational diagnostics that may be used to test its validity.
First we predict that the dust lines of disks other than TW Hya will be
consistent with the normalized CO surface density profile shape for those
disks. Second, surface density profiles that we derive from disk ice lines
should match those derived from disk dust lines. Finally, we predict that disk
dust and ice lines will scale oppositely, as a function of surface density,
across a large sample of disks. | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Astrophysics"
] |
Title: Quaternionic Projective Bundle Theorem and Gysin Triangle in MW-Motivic Cohomology,
Abstract: In this paper, we show that the motive $HP^n$ of the quaternionic
Grassmannian (as defined by I. Panin and C. Walter) splits in the category of
effective MW-motives (as defined by B. Calmès, F. Déglise and J. Fasel).
Moreover, we extend this result to an arbitrary symplectic bundle, obtaining
the so-called quaternionic projective bundle theorem. This enables us to define
Pontryagin classes of symplectic bundles in the Chow-Witt ring.
As an application, we prove that there is a Gysin triangle in MW-motivic
cohomology in case the normal bundle is symplectic. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Summary of a Literature Review in Scalability of QoS-aware Service Composition,
Abstract: This paper shows that authors have no consistent way to characterize the
scalability of their solutions, and so consider only a limited number of
scaling characteristics. This review aimed at establishing the evidence that
the route for designing and evaluating the scalability of dynamic QoS-aware
service composition mechanisms has been lacking systematic guidance, and has
been informed by a very limited set of criteria. For such, we analyzed 47
papers, from 2004 to 2018. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: On the functional window of the avian compass,
Abstract: The functional window is an experimentally observed property of the avian
compass that refers to its selectivity around the geomagnetic field strength.
We show that the radical-pair model, using biologically feasible hyperfine
parameters, can qualitatively explain the salient features of the avian compass
as observed from behavioral experiments: its functional window, as well as
disruption of the compass action by an RF field of specific frequencies.
Further, we show that adjustment of the hyperfine parameters can tune the
functional window, suggesting a possible mechanism for its observed
adaptability to field variation. While these lend strong support to the
radical-pair model, we find it impossible to explain quantitatively the
observed width of the functional window within this model, or even with simple
augmentations thereto. This suggests that a deeper generalization of this model
may be called for; we conjecture that environmental coupling may be playing a
subtle role here that has not been captured accurately. Lastly, we examine a
possible biological purpose to the functional window; assuming evolutionary
benefit from radical-pair magnetoreception, we conjecture that the functional
window is simply a corollary thereof and brings no additional advantage. | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Quantitative Biology"
] |
Title: Automated text summarisation and evidence-based medicine: A survey of two domains,
Abstract: The practice of evidence-based medicine (EBM) urges medical practitioners to
utilise the latest research evidence when making clinical decisions. Because of
the massive and growing volume of published research on various medical topics,
practitioners often find themselves overloaded with information. As such,
natural language processing research has recently commenced exploring
techniques for performing medical domain-specific automated text summarisation
(ATS) techniques-- targeted towards the task of condensing large medical texts.
However, the development of effective summarisation techniques for this task
requires cross-domain knowledge. We present a survey of EBM, the
domain-specific needs for EBM, automated summarisation techniques, and how they
have been applied hitherto. We envision that this survey will serve as a first
resource for the development of future operational text summarisation
techniques for EBM. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Quantitative Biology"
] |
Title: Generalized Moran sets Generated by Step-wise Adjustable Iterated Function Systems,
Abstract: In this article we provide a systematic way of creating generalized Moran
sets using an analogous iterated function system (IFS) procedure. We use a
step-wise adjustable IFS to introduce some variance (such as
non-self-similarity) in the fractal limit sets. The process retains the
computational simplicity of a standard IFS procedure. In our construction of
the generalized Moran sets, we also weaken the fourth Moran Structure Condition
that requires the same pattern of diameter ratios be used across a generation.
Moreover, we provide upper and lower bounds for the Hausdorff dimension of the
fractals created from this generalized process. Specific examples (Cantor-like
sets, Sierpinski-like Triangles, etc) with the calculations of their
corresponding dimensions are studied. | [
0,
1,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Isospectrality For Orbifold Lens Spaces,
Abstract: We answer Mark Kac's famous question, "can one hear the shape of a drum?" in
the positive for orbifolds that are 3-dimensional and 4-dimensional lens
spaces; we thus complete the answer to this question for orbifold lens spaces
in all dimensions. We also show that the coefficients of the asymptotic
expansion of the trace of the heat kernel are not sufficient to determine the
above results. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics",
"Physics"
] |
Title: DCCO: Towards Deformable Continuous Convolution Operators,
Abstract: Discriminative Correlation Filter (DCF) based methods have shown competitive
performance on tracking benchmarks in recent years. Generally, DCF based
trackers learn a rigid appearance model of the target. However, this reliance
on a single rigid appearance model is insufficient in situations where the
target undergoes non-rigid transformations. In this paper, we propose a unified
formulation for learning a deformable convolution filter. In our framework, the
deformable filter is represented as a linear combination of sub-filters. Both
the sub-filter coefficients and their relative locations are inferred jointly
in our formulation. Experiments are performed on three challenging tracking
benchmarks: OTB- 2015, TempleColor and VOT2016. Our approach improves the
baseline method, leading to performance comparable to state-of-the-art. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: New X-ray bound on density of primordial black holes,
Abstract: We set a new upper limit on the abundance of primordial black holes (PBH)
based on existing X-ray data. PBH interactions with interstellar medium should
result in significant fluxes of X-ray photons, which would contribute to the
observed number density of compact X-ray objects in galaxies. The data
constrain PBH number density in the mass range from a few $M_\odot$ to $2\times
10^7 M_\odot$. PBH density needed to account for the origin of black holes
detected by LIGO is marginally allowed. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations,
Abstract: We introduce physics informed neural networks -- neural networks that are
trained to solve supervised learning tasks while respecting any given law of
physics described by general nonlinear partial differential equations. In this
second part of our two-part treatise, we focus on the problem of data-driven
discovery of partial differential equations. Depending on whether the available
data is scattered in space-time or arranged in fixed temporal snapshots, we
introduce two main classes of algorithms, namely continuous time and discrete
time models. The effectiveness of our approach is demonstrated using a wide
range of benchmark problems in mathematical physics, including conservation
laws, incompressible fluid flow, and the propagation of nonlinear shallow-water
waves. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Physics",
"Mathematics"
] |
Title: The HoTT reals coincide with the Escardó-Simpson reals,
Abstract: Escardó and Simpson defined a notion of interval object by a universal
property in any category with binary products. The Homotopy Type Theory book
defines a higher-inductive notion of reals, and suggests that the interval may
satisfy this universal property. We show that this is indeed the case in the
category of sets of any universe. We also show that the type of HoTT reals is
the least Cauchy complete subset of the Dedekind reals containing the
rationals. | [
1,
0,
1,
0,
0,
0
] | [
"Mathematics",
"Computer Science"
] |
Title: Noise2Noise: Learning Image Restoration without Clean Data,
Abstract: We apply basic statistical reasoning to signal reconstruction by machine
learning -- learning to map corrupted observations to clean signals -- with a
simple and powerful conclusion: it is possible to learn to restore images by
only looking at corrupted examples, at performance at and sometimes exceeding
training using clean data, without explicit image priors or likelihood models
of the corruption. In practice, we show that a single model learns photographic
noise removal, denoising synthetic Monte Carlo images, and reconstruction of
undersampled MRI scans -- all corrupted by different processes -- based on
noisy data only. | [
0,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Jeffrey's prior sampling of deep sigmoidal networks,
Abstract: Neural networks have been shown to have a remarkable ability to uncover low
dimensional structure in data: the space of possible reconstructed images form
a reduced model manifold in image space. We explore this idea directly by
analyzing the manifold learned by Deep Belief Networks and Stacked Denoising
Autoencoders using Monte Carlo sampling. The model manifold forms an only
slightly elongated hyperball with actual reconstructed data appearing
predominantly on the boundaries of the manifold. In connection with the results
we present, we discuss problems of sampling high-dimensional manifolds as well
as recent work [M. Transtrum, G. Hart, and P. Qiu, Submitted (2014)] discussing
the relation between high dimensional geometry and model reduction. | [
1,
1,
0,
0,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Faster Learning by Reduction of Data Access Time,
Abstract: Nowadays, the major challenge in machine learning is the Big Data challenge.
The big data problems due to large number of data points or large number of
features in each data point, or both, the training of models have become very
slow. The training time has two major components: Time to access the data and
time to process (learn from) the data. So far, the research has focused only on
the second part, i.e., learning from the data. In this paper, we have proposed
one possible solution to handle the big data problems in machine learning. The
idea is to reduce the training time through reducing data access time by
proposing systematic sampling and cyclic/sequential sampling to select
mini-batches from the dataset. To prove the effectiveness of proposed sampling
techniques, we have used Empirical Risk Minimization, which is commonly used
machine learning problem, for strongly convex and smooth case. The problem has
been solved using SAG, SAGA, SVRG, SAAG-II and MBSGD (Mini-batched SGD), each
using two step determination techniques, namely, constant step size and
backtracking line search method. Theoretical results prove the same convergence
for systematic sampling, cyclic sampling and the widely used random sampling
technique, in expectation. Experimental results with bench marked datasets
prove the efficacy of the proposed sampling techniques and show up to six times
faster training. | [
0,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: ScaleSimulator: A Fast and Cycle-Accurate Parallel Simulator for Architectural Exploration,
Abstract: Design of next generation computer systems should be supported by simulation
infrastructure that must achieve a few contradictory goals such as fast
execution time, high accuracy, and enough flexibility to allow comparison
between large numbers of possible design points. Most existing architecture
level simulators are designed to be flexible and to execute the code in
parallel for greater efficiency, but at the cost of scarified accuracy. This
paper presents the ScaleSimulator simulation environment, which is based on a
new design methodology whose goal is to achieve near cycle accuracy while still
being flexible enough to simulate many different future system architectures
and efficient enough to run meaningful workloads. We achieve these goals by
making the parallelism a first-class citizen in our methodology. Thus, this
paper focuses mainly on the ScaleSimulator design points that enable better
parallel execution while maintaining the scalability and cycle accuracy of a
simulated architecture. The paper indicates that the new proposed
ScaleSimulator tool can (1) efficiently parallelize the execution of a
cycle-accurate architecture simulator, (2) efficiently simulate complex
architectures (e.g., out-of-order CPU pipeline, cache coherency protocol, and
network) and massive parallel systems, and (3) use meaningful workloads, such
as full simulation of OLTP benchmarks, to examine future architectural choices. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: Kahler-Einstein metrics and algebraic geometry,
Abstract: This is a survey article, based on the author's lectures in the 2015 Current
developments in Mathematics meeting; published in "Current developments in
Mathematics". Version 2, references corrected and added. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks,
Abstract: Heterogeneous information networks (HINs) are ubiquitous in real-world
applications. Due to the heterogeneity in HINs, the typed edges may not fully
align with each other. In order to capture the semantic subtlety, we propose
the concept of aspects with each aspect being a unit representing one
underlying semantic facet. Meanwhile, network embedding has emerged as a
powerful method for learning network representation, where the learned
embedding can be used as features in various downstream applications.
Therefore, we are motivated to propose a novel embedding learning
framework---AspEm---to preserve the semantic information in HINs based on
multiple aspects. Instead of preserving information of the network in one
semantic space, AspEm encapsulates information regarding each aspect
individually. In order to select aspects for embedding purpose, we further
devise a solution for AspEm based on dataset-wide statistics. To corroborate
the efficacy of AspEm, we conducted experiments on two real-words datasets with
two types of applications---classification and link prediction. Experiment
results demonstrate that AspEm can outperform baseline network embedding
learning methods by considering multiple aspects, where the aspects can be
selected from the given HIN in an unsupervised manner. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: The energy-momentum tensor of electromagnetic fields in matter,
Abstract: We present a complete resolution of the Abraham-Minkowski controversy . This
is done by considering several new aspects which invalidate previous
discussions. We show that: 1)For polarized matter the center of mass theorem is
no longer valid in its usual form. A contribution related to microscopic spin
should be considered. 2)The electromagnetic dipolar energy density contributes
to the inertia of matter and should be incorporated covariantly to the the
energy-momentum tensor of matter. Then there is an electromagnetic component in
matter's momentum density whose variation explains the results of the only
experiment which supports Abraham's force. 3)Averaging the microscopic
Lorentz's force results in the unambiguos expression for the force density
exerted by the field. This force density is consistent with all the
experimental evidence. 4)Momentum conservation determines the electromagnetic
energy-momentum tensor. This tensor is different from Abraham's and Minkowski's
tensors, but one recovers Minkowski's expression for the momentum density. The
energy density is different from Poynting's expression but Poynting's vector
remains the same. Our tensor is non-symmetric which allows the field to exert a
distributed torque on matter. We use our results to discuss momentum and
angular momentum exchange in various situations of physical interest. We find
complete consistency of our equations in the description of the systems
considered. We also show that several alternative expressions of the field
energy-momentum tensor and force-density cannot be successfully used in all our
examples. In particular we verify in two of these examples that the center of
mass and spin introduced by us moves with constant velocity, but that the
standard center of mass does not. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Tuning Free Orthogonal Matching Pursuit,
Abstract: Orthogonal matching pursuit (OMP) is a widely used compressive sensing (CS)
algorithm for recovering sparse signals in noisy linear regression models. The
performance of OMP depends on its stopping criteria (SC). SC for OMP discussed
in literature typically assumes knowledge of either the sparsity of the signal
to be estimated $k_0$ or noise variance $\sigma^2$, both of which are
unavailable in many practical applications. In this article we develop a
modified version of OMP called tuning free OMP or TF-OMP which does not require
a SC. TF-OMP is proved to accomplish successful sparse recovery under the usual
assumptions on restricted isometry constants (RIC) and mutual coherence of
design matrix. TF-OMP is numerically shown to deliver a highly competitive
performance in comparison with OMP having \textit{a priori} knowledge of $k_0$
or $\sigma^2$. Greedy algorithm for robust de-noising (GARD) is an OMP like
algorithm proposed for efficient estimation in classical overdetermined linear
regression models corrupted by sparse outliers. However, GARD requires the
knowledge of inlier noise variance which is difficult to estimate. We also
produce a tuning free algorithm (TF-GARD) for efficient estimation in the
presence of sparse outliers by extending the operating principle of TF-OMP to
GARD. TF-GARD is numerically shown to achieve a performance comparable to that
of the existing implementation of GARD. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Mathematics",
"Statistics"
] |
Title: New constructions of MDS codes with complementary duals,
Abstract: Linear complementary-dual (LCD for short) codes are linear codes that
intersect with their duals trivially. LCD codes have been used in certain
communication systems. It is recently found that LCD codes can be applied in
cryptography. This application of LCD codes renewed the interest in the
construction of LCD codes having a large minimum distance. MDS codes are
optimal in the sense that the minimum distance cannot be improved for given
length and code size. Constructing LCD MDS codes is thus of significance in
theory and practice. Recently, Jin (\cite{Jin}, IEEE Trans. Inf. Theory, 2016)
constructed several classes of LCD MDS codes through generalized Reed-Solomon
codes. In this paper, a different approach is proposed to obtain new LCD MDS
codes from generalized Reed-Solomon codes. Consequently, new code constructions
are provided and certain previously known results in \cite{Jin} are extended. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: Porosity and regularity in metric measure spaces,
Abstract: This is a report of a joint work with E. Järvenpää, M. Järvenpää,
T. Rajala, S. Rogovin, and V. Suomala. In [3], we characterized uniformly
porous sets in $s$-regular metric spaces in terms of regular sets by verifying
that a set $A$ is uniformly porous if and only if there is $t < s$ and a
$t$-regular set $F \supset A$. Here we outline the main idea of the proof and
also present an alternative proof for the crucial lemma needed in the proof of
the result. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Strong-coupling superconductivity induced by calcium intercalation in bilayer transition-metal dichalcogenides,
Abstract: We theoretically investigate the possibility of achieving a superconducting
state in transition-metal dichalcogenide bilayers through intercalation, a
process previously and widely used to achieve metallization and superconducting
states in novel superconductors. For the Ca-intercalated bilayers MoS$_2$ and
WS$_2$, we find that the superconducting state is characterized by an
electron-phonon coupling constant larger than $1.0$ and a superconducting
critical temperature of $13.3$ and $9.3$ K, respectively. These results are
superior to other predicted or experimentally observed two-dimensional
conventional superconductors and suggest that the investigated materials may be
good candidates for nanoscale superconductors. More interestingly, we proved
that the obtained thermodynamic properties go beyond the predictions of the
mean-field Bardeen--Cooper--Schrieffer approximation and that the calculations
conducted within the framework of the strong-coupling Eliashberg theory should
be treated as those that yield quantitative results. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Global existence for the nonlinear fractional Schrödinger equation with fractional dissipation,
Abstract: We consider the initial value problem for the fractional nonlinear
Schrödinger equation with a fractional dissipation. Global existence and
scattering are proved depending on the order of the fractional dissipation. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics",
"Physics"
] |
Title: Statistical properties of an enstrophy conserving discretisation for the stochastic quasi-geostrophic equation,
Abstract: A framework of variational principles for stochastic fluid dynamics was
presented by Holm (2015), and these stochastic equations were also derived by
Cotter et al. (2017). We present a conforming finite element discretisation for
the stochastic quasi-geostrophic equation that was derived from this framework.
The discretisation preserves the first two moments of potential vorticity, i.e.
the mean potential vorticity and the enstrophy. Following the work of Dubinkina
and Frank (2007), who investigated the statistical mechanics of discretisations
of the deterministic quasi-geostrophic equation, we investigate the statistical
mechanics of our discretisation of the stochastic quasi-geostrophic equation.
We compare the statistical properties of our discretisation with the Gibbs
distribution under assumption of these conserved quantities, finding that there
is agreement between the statistics under a wide range of set-ups. | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Mathematics",
"Statistics"
] |
Title: Improved electronic structure and magnetic exchange interactions in transition metal oxides,
Abstract: We discuss the application of the Agapito Curtarolo and Buongiorno Nardelli
(ACBN0) pseudo-hybrid Hubbard density functional to several transition metal
oxides. ACBN0 is a fast, accurate and parameter-free alternative to traditional
DFT+$U$ and hybrid exact exchange methods. In ACBN0, the Hubbard energy of
DFT+$U$ is calculated via the direct evaluation of the local Coulomb and
exchange integrals in which the screening of the bare Coulomb potential is
accounted for by a renormalization of the density matrix. We demonstrate the
success of the ACBN0 approach for the electronic properties of a series
technologically relevant mono-oxides (MnO, CoO, NiO, FeO, both at equilibrium
and under pressure). We also present results on two mixed valence compounds,
Co$_3$O$_4$ and Mn$_3$O$_4$. Our results, obtained at the computational cost of
a standard LDA/PBE calculation, are in excellent agreement with hybrid
functionals, the GW approximation and experimental measurements. | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Computer Science"
] |
Title: Neon2: Finding Local Minima via First-Order Oracles,
Abstract: We propose a reduction for non-convex optimization that can (1) turn an
stationary-point finding algorithm into an local-minimum finding one, and (2)
replace the Hessian-vector product computations with only gradient
computations. It works both in the stochastic and the deterministic settings,
without hurting the algorithm's performance.
As applications, our reduction turns Natasha2 into a first-order method
without hurting its performance. It also converts SGD, GD, SCSG, and SVRG into
algorithms finding approximate local minima, outperforming some best known
results. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: Simple Countermeasures to Mitigate the Effect of Pollution Attack in Network Coding Based Peer-to-Peer Live Streaming,
Abstract: Network coding based peer-to-peer streaming represents an effective solution
to aggregate user capacities and to increase system throughput in live
multimedia streaming. Nonetheless, such systems are vulnerable to pollution
attacks where a handful of malicious peers can disrupt the communication by
transmitting just a few bogus packets which are then recombined and relayed by
unaware honest nodes, further spreading the pollution over the network. Whereas
previous research focused on malicious nodes identification schemes and
pollution-resilient coding, in this paper we show pollution countermeasures
which make a standard network coding scheme resilient to pollution attacks.
Thanks to a simple yet effective analytical model of a reference node
collecting packets by malicious and honest neighbors, we demonstrate that i)
packets received earlier are less likely to be polluted and ii) short
generations increase the likelihood to recover a clean generation. Therefore,
we propose a recombination scheme where nodes draw packets to be recombined
according to their age in the input queue, paired with a decoding scheme able
to detect the reception of polluted packets early in the decoding process and
short generations. The effectiveness of our approach is experimentally
evaluated in a real system we developed and deployed on hundreds to thousands
peers. Experimental evidence shows that, thanks to our simple countermeasures,
the effect of a pollution attack is almost canceled and the video quality
experienced by the peers is comparable to pre-attack levels. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: Small-scale structure and the Lyman-$α$ forest baryon acoustic oscillation feature,
Abstract: The baryon-acoustic oscillation (BAO) feature in the Lyman-$\alpha$ forest is
one of the key probes of the cosmic expansion rate at redshifts z~2.5, well
before dark energy is believed to have become dynamically significant. A key
advantage of the BAO as a standard ruler is that it is a sharp feature and
hence is more robust against broadband systematic effects than other
cosmological probes. However, if the Lyman-$\alpha$ forest transmission is
sensitive to the initial streaming velocity of the baryons relative to the dark
matter, then the BAO peak position can be shifted. Here we investigate this
sensitivity using a suite of hydrodynamic simulations of small regions of the
intergalactic medium with a range of box sizes and physics assumptions; each
simulation starts from initial conditions at the kinematic decoupling era
(z~1059), undergoes a discrete change from neutral gas to ionized gas thermal
evolution at reionization (z~8), and is finally processed into a Lyman-$\alpha$
forest transmitted flux cube. Streaming velocities suppress small-scale
structure, leading to less violent relaxation after reionization. The changes
in the gas distribution and temperature-density relation at low redshift are
more subtle, due to the convergent temperature evolution in the ionized phase.
The change in the BAO scale is estimated to be of the order of 0.12% at z=2.5;
some of the major uncertainties and avenues for future improvement are
discussed. The predicted streaming velocity shift would be a subdominant but
not negligible effect (of order $0.26\sigma$) for the upcoming DESI
Lyman-$\alpha$ forest survey, and exceeds the cosmic variance floor. It is
hoped that this study will motivate additional theoretical work on the
magnitude of the BAO shift, both in the Lyman-$\alpha$ forest and in other
tracers of large-scale structure. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: InfoCatVAE: Representation Learning with Categorical Variational Autoencoders,
Abstract: This paper describes InfoCatVAE, an extension of the variational autoencoder
that enables unsupervised disentangled representation learning. InfoCatVAE uses
multimodal distributions for the prior and the inference network and then
maximizes the evidence lower bound objective (ELBO). We connect the new ELBO
derived for our model with a natural soft clustering objective which explains
the robustness of our approach. We then adapt the InfoGANs method to our
setting in order to maximize the mutual information between the categorical
code and the generated inputs and obtain an improved model. | [
0,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Ulrich bundles on smooth projective varieties of minimal degree,
Abstract: We classify the Ulrich vector bundles of arbitrary rank on smooth projective
varieties of minimal degree. In the process, we prove the stability of the
sheaves of relative differentials on rational scrolls. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: $ε$-Regularity and Structure of 4-dimensional Shrinking Ricci Solitons,
Abstract: A closed four dimensional manifold cannot possess a non-flat Ricci soliton
metric with arbitrarily small $L^2$-norm of the curvature. In this paper, we
localize this fact in the case of shrinking Ricci solitons by proving an
$\varepsilon$-regularity theorem, thus confirming a conjecture of Cheeger-Tian.
As applications, we will also derive structural results concerning the
degeneration of the metrics on a family of complete non-compact four
dimensional shrinking Ricci solitons without a uniform entropy lower bound. In
the appendix, we provide a detailed account of the equivariant good chopping
theorem when collapsing with locally bounded curvature happens. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Deep Memory Networks for Attitude Identification,
Abstract: We consider the task of identifying attitudes towards a given set of entities
from text. Conventionally, this task is decomposed into two separate subtasks:
target detection that identifies whether each entity is mentioned in the text,
either explicitly or implicitly, and polarity classification that classifies
the exact sentiment towards an identified entity (the target) into positive,
negative, or neutral.
Instead, we show that attitude identification can be solved with an
end-to-end machine learning architecture, in which the two subtasks are
interleaved by a deep memory network. In this way, signals produced in target
detection provide clues for polarity classification, and reversely, the
predicted polarity provides feedback to the identification of targets.
Moreover, the treatments for the set of targets also influence each other --
the learned representations may share the same semantics for some targets but
vary for others. The proposed deep memory network, the AttNet, outperforms
methods that do not consider the interactions between the subtasks or those
among the targets, including conventional machine learning methods and the
state-of-the-art deep learning models. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: Discrete flow posteriors for variational inference in discrete dynamical systems,
Abstract: Each training step for a variational autoencoder (VAE) requires us to sample
from the approximate posterior, so we usually choose simple (e.g. factorised)
approximate posteriors in which sampling is an efficient computation that fully
exploits GPU parallelism. However, such simple approximate posteriors are often
insufficient, as they eliminate statistical dependencies in the posterior.
While it is possible to use normalizing flow approximate posteriors for
continuous latents, some problems have discrete latents and strong statistical
dependencies. The most natural approach to model these dependencies is an
autoregressive distribution, but sampling from such distributions is inherently
sequential and thus slow. We develop a fast, parallel sampling procedure for
autoregressive distributions based on fixed-point iterations which enables
efficient and accurate variational inference in discrete state-space latent
variable dynamical systems. To optimize the variational bound, we considered
two ways to evaluate probabilities: inserting the relaxed samples directly into
the pmf for the discrete distribution, or converting to continuous logistic
latent variables and interpreting the K-step fixed-point iterations as a
normalizing flow. We found that converting to continuous latent variables gave
considerable additional scope for mismatch between the true and approximate
posteriors, which resulted in biased inferences, we thus used the former
approach. Using our fast sampling procedure, we were able to realize the
benefits of correlated posteriors, including accurate uncertainty estimates for
one cell, and accurate connectivity estimates for multiple cells, in an order
of magnitude less time. | [
0,
0,
0,
1,
1,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Thermoelectric power factor enhancement by spin-polarized currents - a nanowire case study,
Abstract: Thermoelectric (TE) measurements have been performed on the workhorses of
today's data storage devices, exhibiting either the giant or the anisotropic
magnetoresistance effect (GMR and AMR). The temperature-dependent (50-300 K)
and magnetic field-dependent (up to 1 T) TE power factor (PF) has been
determined for several Co-Ni alloy nanowires with varying Co:Ni ratios as well
as for Co-Ni/Cu multilayered nanowires with various Cu layer thicknesses, which
were all synthesized via a template-assisted electrodeposition process. A
systematic investigation of the resistivity, as well as the Seebeck
coefficient, is performed for Co-Ni alloy nanowires and Co-Ni/Cu multilayered
nanowires. At room temperature, measured values of TE PFs up to 3.6 mWK-2m-1
for AMR samples and 2.0 mWK-2m-1 for GMR nanowires are obtained. Furthermore,
the TE PF is found to increase by up to 13.1 % for AMR Co-Ni alloy nanowires
and by up to 52 % for GMR Co-Ni/Cu samples in an external applied magnetic
field. The magnetic nanowires exhibit TE PFs that are of the same order of
magnitude as TE PFs of Bi-Sb-Se-Te based thermoelectric materials and,
additionally, give the opportunity to adjust the TE power output to changing
loads and hotspots through external magnetic fields. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Risk-Sensitive Cooperative Games for Human-Machine Systems,
Abstract: Autonomous systems can substantially enhance a human's efficiency and
effectiveness in complex environments. Machines, however, are often unable to
observe the preferences of the humans that they serve. Despite the fact that
the human's and machine's objectives are aligned, asymmetric information, along
with heterogeneous sensitivities to risk by the human and machine, make their
joint optimization process a game with strategic interactions. We propose a
framework based on risk-sensitive dynamic games; the human seeks to optimize
her risk-sensitive criterion according to her true preferences, while the
machine seeks to adaptively learn the human's preferences and at the same time
provide a good service to the human. We develop a class of performance measures
for the proposed framework based on the concept of regret. We then evaluate
their dependence on the risk-sensitivity and the degree of uncertainty. We
present applications of our framework to self-driving taxis, and robo-financial
advising. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Quantitative Finance"
] |
Title: On Gauge Invariance and Covariant Derivatives in Metric Spaces,
Abstract: In this manuscript, we will discuss the construction of covariant derivative
operator in quantum gravity. We will find it is appropriate to use affine
connections more general than metric compatible connections in quantum gravity.
We will demonstrate this using the canonical quantization procedure. This is
valid irrespective of the presence and nature of sources. The standard Palatini
formalism, where metric and affine connections are the independent variables,
is not sufficient to construct a source-free theory of gravity with affine
connections more general than the metric compatible Levi-Civita connections.
This is also valid for minimally coupled interacting theories where sources
only couple with metric by using the metric compatible Levi-Civita connections
exclusively. We will discuss a potential formalism and possible extensions of
the action to introduce nonmetricity in these cases. This is also required to
construct a general interacting quantum theory with dynamical general affine
connections. We will have to use a modified Ricci tensor to state Einstein's
equation in the Palatini formalism. General affine connections can be described
by a third rank tensor with one contravariant and two covariant indices.
Antisymmetric part of this tensor in the lower indices gives torsion with a
half factor. In the Palatini formalism or its generalizations considered here,
symmetric part of this tensor in the lower indices is finite when torsion is
finite. This part can give a massless scalar field in a potential formalism. We
will have to extend the local conservation laws when we use general affine
connections. General affine connections can become significant to solve
cosmological problems. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: A Compressed Sensing Approach for Distribution Matching,
Abstract: In this work, we formulate the fixed-length distribution matching as a
Bayesian inference problem. Our proposed solution is inspired from the
compressed sensing paradigm and the sparse superposition (SS) codes. First, we
introduce sparsity in the binary source via position modulation (PM). We then
present a simple and exact matcher based on Gaussian signal quantization. At
the receiver, the dematcher exploits the sparsity in the source and performs
low-complexity dematching based on generalized approximate message-passing
(GAMP). We show that GAMP dematcher and spatial coupling lead to asymptotically
optimal performance, in the sense that the rate tends to the entropy of the
target distribution with vanishing reconstruction error in a proper limit.
Furthermore, we assess the performance of the dematcher on practical
Hadamard-based operators. A remarkable feature of our proposed solution is the
possibility to: i) perform matching at the symbol level (nonbinary); ii)
perform joint channel coding and matching. | [
0,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Mathematics",
"Statistics"
] |
Title: Deviation from the dipole-ice model in the new spinel spin-ice candidate, MgEr$_2$Se$_4$,
Abstract: In spin ice research, small variations in structure or interactions drive a
multitude of different behaviors, yet the collection of known materials relies
heavily on the `227' pyrochlore structure. Here, we present thermodynamic,
structural and inelastic neutron scattering data on a new spin-ice material,
MgEr$_2$Se$_4$, which contributes to the relatively underexplored family of
rare-earth spinel chalcogenides. X-ray and neutron diffraction confirm a normal
spinel structure, and places Er$^{3+}$ moments on an ideal pyrochlore
sublattice. Measurement of crystal electric field excitations with neutron
inelastic scattering confirms that the moments have perfect Ising character,
and further identifies the ground state Kramers doublet as having
dipole-octupolar form with a significant multipolar character. Heat capacity
and magnetic neutron diffuse scattering have ice-like features, but are
inconsistent with Monte Carlo simulations of the nearest-neighbor and
next-nearest-neighbor dipolar spin-ice (DSI) models. A significant remnant
entropy is observed as T$\rightarrow$0 K, but again falls short of the full
Pauling expectation for DSI, unless significant disorder is added. We show that
these observations are fully in-line with what is recently reported for
CdEr$_2$Se$_4$, and point to the importance of quantum fluctuations in these
materials. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Generating Nontrivial Melodies for Music as a Service,
Abstract: We present a hybrid neural network and rule-based system that generates pop
music. Music produced by pure rule-based systems often sounds mechanical. Music
produced by machine learning sounds better, but still lacks hierarchical
temporal structure. We restore temporal hierarchy by augmenting machine
learning with a temporal production grammar, which generates the music's
overall structure and chord progressions. A compatible melody is then generated
by a conditional variational recurrent autoencoder. The autoencoder is trained
with eight-measure segments from a corpus of 10,000 MIDI files, each of which
has had its melody track and chord progressions identified heuristically. The
autoencoder maps melody into a multi-dimensional feature space, conditioned by
the underlying chord progression. A melody is then generated by feeding a
random sample from that space to the autoencoder's decoder, along with the
chord progression generated by the grammar. The autoencoder can make musically
plausible variations on an existing melody, suitable for recurring motifs. It
can also reharmonize a melody to a new chord progression, keeping the rhythm
and contour. The generated music compares favorably with that generated by
other academic and commercial software designed for the music-as-a-service
industry. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Quantitative Biology"
] |
Title: Data-Driven Estimation of Travel Latency Cost Functions via Inverse Optimization in Multi-Class Transportation Networks,
Abstract: We develop a method to estimate from data travel latency cost functions in
multi-class transportation networks, which accommodate different types of
vehicles with very different characteristics (e.g., cars and trucks).
Leveraging our earlier work on inverse variational inequalities, we develop a
data-driven approach to estimate the travel latency cost functions. Extensive
numerical experiments using benchmark networks, ranging from moderate-sized to
large-sized, demonstrate the effectiveness and efficiency of our approach. | [
1,
0,
1,
0,
0,
0
] | [
"Computer Science",
"Mathematics",
"Statistics"
] |
Title: Guiding Chemical Synthesis: Computational Prediction of the Regioselectivity of CH Functionalization,
Abstract: We will develop a computational method (RegioSQM) for predicting the
regioselectivity of CH functionalization reactions that can be used by
synthetic chemists who are not experts in computational chemistry through a
simple web interface (regiosqm.org). CH functionalization, i.e. replacing the
hydrogen atom in a CH bond with another atom or molecule, is arguably single
most promising technique for increasing the efficiency of chemical synthesis,
but there are no generally applicable predictive tools that predict which CH
bond is most reactive. RegioSQM uses semiempirical quantum chemistry methods to
predict relative stabilities of reaction intermediates which correlates with
reaction rate and our goal is to determine which quantum method and
intermediate give the best result for each reaction type. Finally, we will
experimentally demonstrate how RegioSQM can be used to make the chemical
synthesis part of drug discovery more efficient. | [
0,
1,
0,
0,
0,
0
] | [
"Computer Science",
"Physics"
] |
Title: Potential-Function Proofs for First-Order Methods,
Abstract: This note discusses proofs for convergence of first-order methods based on
simple potential-function arguments. We cover methods like gradient descent
(for both smooth and non-smooth settings), mirror descent, and some accelerated
variants. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: Multidimensional $p$-adic continued fraction algorithms,
Abstract: We give a new class of multidimensional $p$-adic continued fraction
algorithms. We propose an algorithm in the class for which we can expect that
multidimensional $p$-adic version of Lagrange's Theorem holds. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Expected Policy Gradients,
Abstract: We propose expected policy gradients (EPG), which unify stochastic policy
gradients (SPG) and deterministic policy gradients (DPG) for reinforcement
learning. Inspired by expected sarsa, EPG integrates across the action when
estimating the gradient, instead of relying only on the action in the sampled
trajectory. We establish a new general policy gradient theorem, of which the
stochastic and deterministic policy gradient theorems are special cases. We
also prove that EPG reduces the variance of the gradient estimates without
requiring deterministic policies and, for the Gaussian case, with no
computational overhead. Finally, we show that it is optimal in a certain sense
to explore with a Gaussian policy such that the covariance is proportional to
the exponential of the scaled Hessian of the critic with respect to the
actions. We present empirical results confirming that this new form of
exploration substantially outperforms DPG with the Ornstein-Uhlenbeck heuristic
in four challenging MuJoCo domains. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: A new Hysteretic Nonlinear Energy Sink (HNES),
Abstract: The behavior of a new Hysteretic Nonlinear Energy Sink (HNES) coupled to a
linear primary oscillator is investigated in shock mitigation. Apart from a
small mass and a nonlinear elastic spring of the Duffing oscillator, the HNES
is also comprised of a purely hysteretic and a linear elastic spring of
potentially negative stiffness, connected in parallel. The Bouc-Wen model is
used to describe the force produced by both the purely hysteretic and linear
elastic springs. Coupling the primary oscillator with the HNES three nonlinear
equations of motion are derived, in terms of the two displacements and the
dimensionless hysteretic variable, which are integrated numerically using the
analog equation method. The performance of the HNES is examined by quantifying
the percentage of the initially induced energy in the primary system that is
passively transferred and dissipated by the HNES. Remarkable results are
achieved for a wide range of initial input energies. The great performance of
the HNES is mostly evidenced when the linear spring stiffness takes on negative
values. | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Mathematics"
] |
Title: Measuring High-Energy Spectra with HAWC,
Abstract: The High-Altitude Water-Cherenkov (HAWC) experiment is a TeV $\gamma$-ray
observatory located \unit[4100]{m} above sea level on the Sierra Negra mountain
in Puebla, Mexico. The detector consists of 300 water-filled tanks, each
instrumented with 4 photomultiplier tubes that utilize the water-Cherenkov
technique to detect atmospheric air showers produced by cosmic $\gamma$ rays.
Construction of HAWC was completed in March of 2015. The experiment's wide
instantaneous field of view (\unit[2]{sr}) and high duty cycle (> 95\%) make it
a powerful survey instrument sensitive to pulsars, supernova remnants, and
other $\gamma$-ray sources. The mechanisms of particle acceleration at these
sources can be studied by analyzing their high-energy spectra. To this end, we
have developed an event-by-event energy-reconstruction algorithm using an
artificial neural network to estimate energies of primary $\gamma$ rays at
HAWC. We will present the details of this technique and its performance as well
as the current progress toward using it to measure energy spectra of
$\gamma$-ray sources. | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Computer Science"
] |
Title: On the Three Properties of Stationary Populations and knotting with Non-Stationary Populations,
Abstract: We propose three properties that are related to the stationary population
identity (SPI) of population biology by connecting it with stationary
populations and non-stationary populations which are approaching stationarity.
These properties provide deeper insights into cohort formation in real-world
populations and the length of the duration for which stationary and
non-stationary conditions hold. The new concepts are based on the time gap
between the occurrence of stationary and non-stationary populations within the
SPI framework that we refer to as Oscillatory SPI and the Amplitude of SPI. | [
0,
0,
0,
0,
1,
0
] | [
"Quantitative Biology"
] |
Title: Generating and designing DNA with deep generative models,
Abstract: We propose generative neural network methods to generate DNA sequences and
tune them to have desired properties. We present three approaches: creating
synthetic DNA sequences using a generative adversarial network; a DNA-based
variant of the activation maximization ("deep dream") design method; and a
joint procedure which combines these two approaches together. We show that
these tools capture important structures of the data and, when applied to
designing probes for protein binding microarrays, allow us to generate new
sequences whose properties are estimated to be superior to those found in the
training data. We believe that these results open the door for applying deep
generative models to advance genomics research. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Quantitative Biology"
] |
Title: Asymptotic Theory for the Maximum of an Increasing Sequence of Parametric Functions,
Abstract: \cite{HillMotegi2017} present a new general asymptotic theory for the maximum
of a random array $\{\mathcal{X}_{n}(i)$ $:$ $1$ $\leq $ $i$ $\leq $
$\mathcal{L}\}_{n\geq 1}$, where each $\mathcal{X}_{n}(i)$ is assumed to
converge in probability as $n$ $\rightarrow $ $\infty $. The array dimension
$\mathcal{L}$ is allowed to increase with the sample size $n$. Existing extreme
value theory arguments focus on observed data $\mathcal{X}_{n}(i)$, and require
a well defined limit law for $\max_{1\leq i\leq
\mathcal{L}}|\mathcal{X}_{n}(i)|$ by restricting dependence across $i$. The
high dimensional central limit theory literature presumes approximability by a
Gaussian law, and also restricts attention to observed data.
\cite{HillMotegi2017} do not require $\max_{1\leq i\leq
\mathcal{L}_{n}}|\mathcal{X}_{n}(i)|$ to have a well defined limit nor be
approximable by a Gaussian random variable, and we do not make any assumptions
about dependence across $i$. We apply the theory to filtered data when the
variable of interest $\mathcal{X}_{n}(i,\theta _{0})$ is not observed, but its
sample counterpart $\mathcal{X}_{n}(i,\hat{\theta}_{n})$ is observed where
$\hat{\theta}_{n}$ estimates $\theta _{0}$. The main results are illustrated by
looking at unit root tests for a high dimensional random variable, and a
residuals white noise test. | [
0,
0,
1,
1,
0,
0
] | [
"Mathematics",
"Statistics"
] |
Title: Resilient Active Information Gathering with Mobile Robots,
Abstract: Applications of safety, security, and rescue in robotics, such as multi-robot
target tracking, involve the execution of information acquisition tasks by
teams of mobile robots. However, in failure-prone or adversarial environments,
robots get attacked, their communication channels get jammed, and their sensors
may fail, resulting in the withdrawal of robots from the collective task, and
consequently the inability of the remaining active robots to coordinate with
each other. As a result, traditional design paradigms become insufficient and,
in contrast, resilient designs against system-wide failures and attacks become
important. In general, resilient design problems are hard, and even though they
often involve objective functions that are monotone or submodular, scalable
approximation algorithms for their solution have been hitherto unknown. In this
paper, we provide the first algorithm, enabling the following capabilities:
minimal communication, i.e., the algorithm is executed by the robots based only
on minimal communication between them; system-wide resiliency, i.e., the
algorithm is valid for any number of denial-of-service attacks and failures;
and provable approximation performance, i.e., the algorithm ensures for all
monotone (and not necessarily submodular) objective functions a solution that
is finitely close to the optimal. We quantify our algorithm's approximation
performance using a notion of curvature for monotone set functions. We support
our theoretical analyses with simulated and real-world experiments, by
considering an active information gathering scenario, namely, multi-robot
target tracking. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Robotics"
] |
Title: Optical properties of a four-layer waveguiding nanocomposite structure in near-IR regime,
Abstract: The theoretical study of the optical properties of TE- and TM- modes in a
four-layer structure composed of the magneto-optical yttrium iron garnet
guiding layer on a dielectric substrate covered by planar nanocomposite guiding
multilayer is presented. The dispersion equation is obtained taking into
account the bigyrotropic properties of yttrium-iron garnet, and an original
algorithm for the guided modes identification is proposed. The dispersion
spectra are analyzed and the energy flux distributions across the structure are
calculated. The fourfold difference between the partial power fluxes within the
waveguide layers is achieved in the wavelength range of 200 nm. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Source Forager: A Search Engine for Similar Source Code,
Abstract: Developers spend a significant amount of time searching for code: e.g., to
understand how to complete, correct, or adapt their own code for a new context.
Unfortunately, the state of the art in code search has not evolved much beyond
text search over tokenized source. Code has much richer structure and semantics
than normal text, and this property can be exploited to specialize the
code-search process for better querying, searching, and ranking of code-search
results.
We present a new code-search engine named Source Forager. Given a query in
the form of a C/C++ function, Source Forager searches a pre-populated code
database for similar C/C++ functions. Source Forager preprocesses the database
to extract a variety of simple code features that capture different aspects of
code. A search returns the $k$ functions in the database that are most similar
to the query, based on the various extracted code features.
We tested the usefulness of Source Forager using a variety of code-search
queries from two domains. Our experiments show that the ranked results returned
by Source Forager are accurate, and that query-relevant functions can be
reliably retrieved even when searching through a large code database that
contains very few query-relevant functions.
We believe that Source Forager is a first step towards much-needed tools that
provide a better code-search experience. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: A New Test of Multivariate Nonlinear Causality,
Abstract: The multivariate nonlinear Granger causality developed by Bai et al. (2010)
plays an important role in detecting the dynamic interrelationships between two
groups of variables. Following the idea of Hiemstra-Jones (HJ) test proposed by
Hiemstra and Jones (1994), they attempt to establish a central limit theorem
(CLT) of their test statistic by applying the asymptotical property of
multivariate $U$-statistic. However, Bai et al. (2016) revisit the HJ test and
find that the test statistic given by HJ is NOT a function of $U$-statistics
which implies that the CLT neither proposed by Hiemstra and Jones (1994) nor
the one extended by Bai et al. (2010) is valid for statistical inference. In
this paper, we re-estimate the probabilities and reestablish the CLT of the new
test statistic. Numerical simulation shows that our new estimates are
consistent and our new test performs decent size and power. | [
0,
0,
0,
1,
0,
0
] | [
"Statistics",
"Mathematics"
] |
Title: Nonlinear dynamics of polar regions in paraelectric phase of (Ba1-x,Srx)TiO3 ceramics,
Abstract: The dynamic dielectric nonlinearity of barium strontium titanate
(Ba1-x,Srx)TiO3 ceramics is investigated in their paraelectric phase. With the
goal to contribute to the identification of the mechanisms that govern the
dielectric nonlinearity in this family, we analyze the amplitude and the phase
angles of the first and the third harmonics of polarization. Our study shows
that an interpretation of the field-dependent polarization in paraelectric
(Ba1-x,Srx)TiO3 ceramics in terms of the Rayleigh-type dynamics is inadequate
for our samples and that their nonlinear response rather resembles that
observed in canonical relaxor Pb(Mg1/3Nb2/3)O3. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Finite Sample Analysis of Two-Timescale Stochastic Approximation with Applications to Reinforcement Learning,
Abstract: Two-timescale Stochastic Approximation (SA) algorithms are widely used in
Reinforcement Learning (RL). Their iterates have two parts that are updated
using distinct stepsizes. In this work, we develop a novel recipe for their
finite sample analysis. Using this, we provide a concentration bound, which is
the first such result for a two-timescale SA. The type of bound we obtain is
known as `lock-in probability'. We also introduce a new projection scheme, in
which the time between successive projections increases exponentially. This
scheme allows one to elegantly transform a lock-in probability into a
convergence rate result for projected two-timescale SA. From this latter
result, we then extract key insights on stepsize selection. As an application,
we finally obtain convergence rates for the projected two-timescale RL
algorithms GTD(0), GTD2, and TDC. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Mathematics",
"Statistics"
] |
Title: Existence and uniqueness of solutions to Y-systems and TBA equations,
Abstract: We consider Y-system functional equations of the form $$
Y_n(x+i)Y_n(x-i)=\prod_{m=1}^N (1+Y_m(x))^{G_{nm}}$$ and the corresponding
nonlinear integral equations of the Thermodynamic Bethe Ansatz. We prove an
existence and uniqueness result for solutions of these equations, subject to
appropriate conditions on the analytical properties of the $Y_n$, in particular
the absence of zeros in a strip around the real axis. The matrix $G_{nm}$ must
have non-negative real entries, and be irreducible and diagonalisable over
$\mathbb{R}$ with spectral radius less than 2. This includes the adjacency
matrices of finite Dynkin diagrams, but covers much more as we do not require
$G_{nm}$ to be integers. Our results specialise to the constant Y-system,
proving existence and uniqueness of a strictly positive solution in that case. | [
0,
1,
0,
0,
0,
0
] | [
"Mathematics",
"Physics"
] |
Title: Normalization of Neural Networks using Analytic Variance Propagation,
Abstract: We address the problem of estimating statistics of hidden units in a neural
network using a method of analytic moment propagation. These statistics are
useful for approximate whitening of the inputs in front of saturating
non-linearities such as a sigmoid function. This is important for
initialization of training and for reducing the accumulated scale and bias
dependencies (compensating covariate shift), which presumably eases the
learning. In batch normalization, which is currently a very widely applied
technique, sample estimates of statistics of hidden units over a batch are
used. The proposed estimation uses an analytic propagation of mean and variance
of the training set through the network. The result depends on the network
structure and its current weights but not on the specific batch input. The
estimates are suitable for initialization and normalization, efficient to
compute and independent of the batch size. The experimental verification well
supports these claims. However, the method does not share the generalization
properties of BN, to which our experiments give some additional insight. | [
0,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Delta sets for symmetric numerical semigroups with embedding dimension three,
Abstract: This work extends the results known for the Delta sets of non-symmetric
numerical semigroups with embedding dimension three to the symmetric case.
Thus, we have a fast algorithm to compute the Delta set of any embedding
dimension three numerical semigroup. Also, as a consequence of these resutls,
the sets that can be realized as Delta sets of numerical semigroups of
embedding dimension three are fully characterized. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Unsure When to Stop? Ask Your Semantic Neighbors,
Abstract: In iterative supervised learning algorithms it is common to reach a point in
the search where no further induction seems to be possible with the available
data. If the search is continued beyond this point, the risk of overfitting
increases significantly. Following the recent developments in inductive
semantic stochastic methods, this paper studies the feasibility of using
information gathered from the semantic neighborhood to decide when to stop the
search. Two semantic stopping criteria are proposed and experimentally assessed
in Geometric Semantic Genetic Programming (GSGP) and in the Semantic Learning
Machine (SLM) algorithm (the equivalent algorithm for neural networks). The
experiments are performed on real-world high-dimensional regression datasets.
The results show that the proposed semantic stopping criteria are able to
detect stopping points that result in a competitive generalization for both
GSGP and SLM. This approach also yields computationally efficient algorithms as
it allows the evolution of neural networks in less than 3 seconds on average,
and of GP trees in at most 10 seconds. The usage of the proposed semantic
stopping criteria in conjunction with the computation of optimal
mutation/learning steps also results in small trees and neural networks. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Warming trend in cold season of the Yangtze River Delta and its correlation with Siberian high,
Abstract: Based on the meteorological data from 1960 to 2010, we investigated the
temperature variation in the Yangtze River Delta (YRD) by using Mann-Kendall
nonparametric test and explored the correlation between the temperature in the
cold season and the Siberian high intensity (SHI) by using correlation analysis
method. The main results are that (a) the temperature in YRD increased
remarkably during the study period, (b) the warming trend in the cold season
made the higher contribution to annual warming, and (c) there was a significant
negative correlation between the temperature in the cold season and the SHI. | [
0,
0,
0,
1,
0,
0
] | [
"Physics",
"Statistics"
] |
Title: Modeling and Quantifying the Forces Driving Online Video Popularity Evolution,
Abstract: Video popularity is an essential reference for optimizing resource allocation
and video recommendation in online video services. However, there is still no
convincing model that can accurately depict a video's popularity evolution. In
this paper, we propose a dynamic popularity model by modeling the video
information diffusion process driven by various forms of recommendation.
Through fitting the model with real traces collected from a practical system,
we can quantify the strengths of the recommendation forces. Such quantification
can lead to characterizing video popularity patterns, user behaviors and
recommendation strategies, which is illustrated by a case study of TV episodes. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Measurement of the Lorentz-FitzGerald Body Contraction,
Abstract: A complete foundational discussion of acceleration in context of Special
Relativity is presented. Acceleration allows the measurement of a
Lorentz-FitzGerald body contraction created. It is argued that in the back
scattering of a probing laser beam from a relativistic flying electron cloud
mirror generated by an ultra-intense laser pulse, a first measurement of a
Lorentz-FitzGerald body contraction is feasible. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Information Directed Sampling for Stochastic Bandits with Graph Feedback,
Abstract: We consider stochastic multi-armed bandit problems with graph feedback, where
the decision maker is allowed to observe the neighboring actions of the chosen
action. We allow the graph structure to vary with time and consider both
deterministic and Erdős-Rényi random graph models. For such a graph
feedback model, we first present a novel analysis of Thompson sampling that
leads to tighter performance bound than existing work. Next, we propose new
Information Directed Sampling based policies that are graph-aware in their
decision making. Under the deterministic graph case, we establish a Bayesian
regret bound for the proposed policies that scales with the clique cover number
of the graph instead of the number of actions. Under the random graph case, we
provide a Bayesian regret bound for the proposed policies that scales with the
ratio of the number of actions over the expected number of observations per
iteration. To the best of our knowledge, this is the first analytical result
for stochastic bandits with random graph feedback. Finally, using numerical
evaluations, we demonstrate that our proposed IDS policies outperform existing
approaches, including adaptions of upper confidence bound, $\epsilon$-greedy
and Exp3 algorithms. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Hausdorff operators on holomorphic Hardy spaces and applications,
Abstract: The aim of this paper is to characterize the nonnegative functions $\varphi$
defined on $(0,\infty)$ for which the Hausdorff operator
$$\mathscr H_\varphi f(z)= \int_0^\infty
f\left(\frac{z}{t}\right)\frac{\varphi(t)}{t}dt$$ is bounded on the Hardy
spaces of the upper half-plane $\mathcal H_a^p(\mathbb C_+)$, $p\in[1,\infty]$.
The corresponding operator norms and their applications are also given. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Does Your Phone Know Your Touch?,
Abstract: This paper explores supervised techniques for continuous anomaly detection
from biometric touch screen data. A capacitive sensor array used to mimic a
touch screen as used to collect touch and swipe gestures from participants. The
gestures are recorded over fixed segments of time, with position and force
measured for each gesture. Support Vector Machine, Logistic Regression, and
Gaussian mixture models were tested to learn individual touch patterns. Test
results showed true negative and true positive scores of over 95% accuracy for
all gesture types, with logistic regression models far outperforming the other
methods. A more expansive and varied data collection over longer periods of
time is needed to determine pragmatic usage of these results. | [
0,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Nucleus: A Pilot Project,
Abstract: Early in 2016, an environmental scan was conducted by the Research Library
Data Working Group for three purposes:
1.) Perform a survey of the data management landscape at Los Alamos National
Laboratory in order to identify local gaps in data management services.
2.) Conduct an environmental scan of external institutions to benchmark
budgets, infrastructure, and personnel dedicated to data management.
3.) Draft a research data infrastructure model that aligns with the current
workflow and classification restrictions at Los Alamos National Laboratory.
This report is a summary of those activities and the draft for a pilot data
management project. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Non Volatile MoS$_{2}$ Field Effect Transistors Directly Gated By Single Crystalline Epitaxial Ferroelectric,
Abstract: We demonstrate non-volatile, n-type, back-gated, MoS$_{2}$ transistors,
placed directly on an epitaxial grown, single crystalline,
PbZr$_{0.2}$Ti$_{0.8}$O$_{3}$ (PZT) ferroelectric. The transistors show decent
ON current (19 ${\mu}A/{\mu}$m), high on-off ratio (10$^{7}$), and a
subthreshold swing of (SS ~ 92 mV/dec) with a 100 nm thick PZT layer as the
back gate oxide. Importantly, the ferroelectric polarization can directly
control the channel charge, showing a clear anti-clockwise hysteresis. We have
selfconsistently confirmed the switching of the ferroelectric and corresponding
change in channel current from a direct time-dependent measurement. Our results
demonstrate that it is possible to obtain transistor operation directly on
polar surfaces and therefore it should be possible to integrate 2D electronics
with single crystalline functional oxides. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Integral Equations and Machine Learning,
Abstract: As both light transport simulation and reinforcement learning are ruled by
the same Fredholm integral equation of the second kind, reinforcement learning
techniques may be used for photorealistic image synthesis: Efficiency may be
dramatically improved by guiding light transport paths by an approximate
solution of the integral equation that is learned during rendering. In the
light of the recent advances in reinforcement learning for playing games, we
investigate the representation of an approximate solution of an integral
equation by artificial neural networks and derive a loss function for that
purpose. The resulting Monte Carlo and quasi-Monte Carlo methods train neural
networks with standard information instead of linear information and naturally
are able to generate an arbitrary number of training samples. The methods are
demonstrated for applications in light transport simulation. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: Experiments on bright field and dark field high energy electron imaging with thick target material,
Abstract: Using a high energy electron beam for the imaging of high density matter with
both high spatial-temporal and areal density resolution under extreme states of
temperature and pressure is one of the critical challenges in high energy
density physics . When a charged particle beam passes through an opaque target,
the beam will be scattered with a distribution that depends on the thickness of
the material. By collecting the scattered beam either near or off axis,
so-called bright field or dark field images can be obtained. Here we report on
an electron radiography experiment using 45 MeV electrons from an S-band
photo-injector, where scattered electrons, after interacting with a sample, are
collected and imaged by a quadrupole imaging system. We achieved a few
micrometers (about 4 micrometers) spatial resolution and about 10 micrometers
thickness resolution for a silicon target of 300-600 micron thickness. With
addition of dark field images that are captured by selecting electrons with
large scattering angle, we show that more useful information in determining
external details such as outlines, boundaries and defects can be obtained. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: SOLAR: Deep Structured Latent Representations for Model-Based Reinforcement Learning,
Abstract: Model-based reinforcement learning (RL) methods can be broadly categorized as
global model methods, which depend on learning models that provide sensible
predictions in a wide range of states, or local model methods, which
iteratively refit simple models that are used for policy improvement. While
predicting future states that will result from the current actions is
difficult, local model methods only attempt to understand system dynamics in
the neighborhood of the current policy, making it possible to produce local
improvements without ever learning to predict accurately far into the future.
The main idea in this paper is that we can learn representations that make it
easy to retrospectively infer simple dynamics given the data from the current
policy, thus enabling local models to be used for policy learning in complex
systems. To that end, we focus on learning representations with probabilistic
graphical model (PGM) structure, which allows us to devise an efficient local
model method that infers dynamics from real-world rollouts with the PGM as a
global prior. We compare our method to other model-based and model-free RL
methods on a suite of robotics tasks, including manipulation tasks on a real
Sawyer robotic arm directly from camera images. Videos of our results are
available at this https URL | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Robust and Fast Decoding of High-Capacity Color QR Codes for Mobile Applications,
Abstract: The use of color in QR codes brings extra data capacity, but also inflicts
tremendous challenges on the decoding process due to chromatic distortion,
cross-channel color interference and illumination variation. Particularly, we
further discover a new type of chromatic distortion in high-density color QR
codes, cross-module color interference, caused by the high density which also
makes the geometric distortion correction more challenging. To address these
problems, we propose two approaches, namely, LSVM-CMI and QDA-CMI, which
jointly model these different types of chromatic distortion. Extended from SVM
and QDA, respectively, both LSVM-CMI and QDA-CMI optimize over a particular
objective function to learn a color classifier. Furthermore, a robust geometric
transformation method and several pipeline refinements are proposed to boost
the decoding performance for mobile applications. We put forth and implement a
framework for high-capacity color QR codes equipped with our methods, called
HiQ. To evaluate the performance of HiQ, we collect a challenging large-scale
color QR code dataset, CUHK-CQRC, which consists of 5390 high-density color QR
code samples. The comparison with the baseline method [2] on CUHK-CQRC shows
that HiQ at least outperforms [2] by 188% in decoding success rate and 60% in
bit error rate. Our implementation of HiQ in iOS and Android also demonstrates
the effectiveness of our framework in real-world applications. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: The quest for H$_3^+$ at Neptune: deep burn observations with NASA IRTF iSHELL,
Abstract: Emission from the molecular ion H$_3^+$ is a powerful diagnostic of the upper
atmosphere of Jupiter, Saturn, and Uranus, but it remains undetected at
Neptune. In search of this emission, we present near-infrared spectral
observations of Neptune between 3.93 and 4.00 $\mu$m taken with the newly
commissioned iSHELL instrument on the NASA Infrared Telescope Facility in
Hawaii, obtained 17-20 August 2017. We spent 15.4 h integrating across the disk
of the planet, yet were unable to unambiguously identify any H$_3^+$ line
emissions. Assuming a temperature of 550 K, we derive an upper limit on the
column integrated density of $1.0^{+1.2}_{-0.8}\times10^{13}$ m$^{-2}$, which
is an improvement of 30\% on the best previous observational constraint. This
result means that models are over-estimating the density by at least a factor
of 5, highlighting the need for renewed modelling efforts. A potential solution
is strong vertical mixing of polyatomic neutral species from Neptune's upper
stratosphere to the thermosphere, reacting with H$_3^+$, thus greatly reducing
the column integrated H$_3^+$ densities. This upper limit also provide
constraints on future attempts at detecting H$_3^+$ using the James Webb Space
Telescope. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Redistributing Funds across Charitable Crowdfunding Campaigns,
Abstract: On Kickstarter only 36% of crowdfunding campaigns successfully raise
sufficient funds for their projects. In this paper, we explore the possibility
of redistribution of crowdfunding donations to increase the chances of success.
We define several intuitive redistribution policies and, using data from a real
crowdfunding platform, LaunchGood, we assess the potential improvement in
campaign fundraising success rates. We find that an aggressive redistribution
scheme can boost campaign success rates from 37% to 79%, but such
choice-agnostic redistribution schemes come at the cost of disregarding donor
preferences. Taking inspiration from offline giving societies and donor clubs,
we build a case for choice preserving redistribution schemes that strike a
balance between increasing the number of successful campaigns and respecting
giving preference. We find that choice-preserving redistribution can easily
achieve campaign success rates of 48%. Finally, we discuss the implications of
these different redistribution schemes for the various stakeholders in the
crowdfunding ecosystem. | [
1,
0,
0,
0,
0,
0
] | [
"Quantitative Finance",
"Statistics"
] |
Title: Far-HO: A Bilevel Programming Package for Hyperparameter Optimization and Meta-Learning,
Abstract: In (Franceschi et al., 2018) we proposed a unified mathematical framework,
grounded on bilevel programming, that encompasses gradient-based hyperparameter
optimization and meta-learning. We formulated an approximate version of the
problem where the inner objective is solved iteratively, and gave sufficient
conditions ensuring convergence to the exact problem. In this work we show how
to optimize learning rates, automatically weight the loss of single examples
and learn hyper-representations with Far-HO, a software package based on the
popular deep learning framework TensorFlow that allows to seamlessly tackle
both HO and ML problems. | [
0,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: Femtosecond mega-electron-volt electron microdiffraction,
Abstract: Instruments to visualize transient structural changes of inhomogeneous
materials on the nanometer scale with atomic spatial and temporal resolution
are demanded to advance materials science, bioscience, and fusion sciences. One
such technique is femtosecond electron microdiffraction, in which a short pulse
of electrons with femtosecond-scale duration is focused into a micron-scale
spot and used to obtain diffraction images to resolve ultrafast structural
dynamics over localized crystalline domain. In this letter, we report the
experimental demonstration of time-resolved mega-electron-volt electron
microdiffraction which achieves a 5 {\mu}m root-mean-square (rms) beam size on
the sample and a 100 fs rms temporal resolution. Using pulses of 10k electrons
at 4.2 MeV energy with a normalized emittance 3 nm-rad, we obtained high
quality diffraction from a single 10 {\mu}m paraffin (C_44 H_90) crystal. The
phonon softening mode in optical-pumped polycrystalline Bi was also
time-resolved, demonstrating the temporal resolution limits of our instrument
design. This new characterization capability will open many research
opportunities in material and biological sciences. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Deep Recurrent Neural Network for Protein Function Prediction from Sequence,
Abstract: As high-throughput biological sequencing becomes faster and cheaper, the need
to extract useful information from sequencing becomes ever more paramount,
often limited by low-throughput experimental characterizations. For proteins,
accurate prediction of their functions directly from their primary amino-acid
sequences has been a long standing challenge. Here, machine learning using
artificial recurrent neural networks (RNN) was applied towards classification
of protein function directly from primary sequence without sequence alignment,
heuristic scoring or feature engineering. The RNN models containing
long-short-term-memory (LSTM) units trained on public, annotated datasets from
UniProt achieved high performance for in-class prediction of four important
protein functions tested, particularly compared to other machine learning
algorithms using sequence-derived protein features. RNN models were used also
for out-of-class predictions of phylogenetically distinct protein families with
similar functions, including proteins of the CRISPR-associated nuclease,
ferritin-like iron storage and cytochrome P450 families. Applying the trained
RNN models on the partially unannotated UniRef100 database predicted not only
candidates validated by existing annotations but also currently unannotated
sequences. Some RNN predictions for the ferritin-like iron sequestering
function were experimentally validated, even though their sequences differ
significantly from known, characterized proteins and from each other and cannot
be easily predicted using popular bioinformatics methods. As sequencing and
experimental characterization data increases rapidly, the machine-learning
approach based on RNN could be useful for discovery and prediction of
homologues for a wide range of protein functions. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Quantitative Biology"
] |
Title: CubemapSLAM: A Piecewise-Pinhole Monocular Fisheye SLAM System,
Abstract: We present a real-time feature-based SLAM (Simultaneous Localization and
Mapping) system for fisheye cameras featured by a large field-of-view (FoV).
Large FoV cameras are beneficial for large-scale outdoor SLAM applications,
because they increase visual overlap between consecutive frames and capture
more pixels belonging to the static parts of the environment. However, current
feature-based SLAM systems such as PTAM and ORB-SLAM limit their camera model
to pinhole only. To compensate for the vacancy, we propose a novel SLAM system
with the cubemap model that utilizes the full FoV without introducing
distortion from the fisheye lens, which greatly benefits the feature matching
pipeline. In the initialization and point triangulation stages, we adopt a
unified vector-based representation to efficiently handle matches across
multiple faces, and based on this representation we propose and analyze a novel
inlier checking metric. In the optimization stage, we design and test a novel
multi-pinhole reprojection error metric that outperforms other metrics by a
large margin. We evaluate our system comprehensively on a public dataset as
well as a self-collected dataset that contains real-world challenging
sequences. The results suggest that our system is more robust and accurate than
other feature-based fisheye SLAM approaches. The CubemapSLAM system has been
released into the public domain. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: $J$-holomorphic disks with pre-Lagrangian boundary conditions,
Abstract: The purpose of this paper is to carry out a classical construction of a
non-constant holomorphic disk with boundary on (the suspension of) a Lagrangian
submanifold in $\mathbb{R}^{2 n}$ in the case the Lagrangian is the lift of a
coisotropic (a.k.a. pre-Lagrangian) submanifold in (a subset $U$ of)
$\mathbb{R}^{2 n - 1}$. We show that the positive lower and finite upper bounds
for the area of such a disk (which are due to M. Gromov and J.-C. Sikorav and
F. Laudenbach-Sikorav for general Lagrangians) depend on the coisotropic
submanifold only but not on its lift to the symplectization. The main
application is to a $C^0$-characterization of contact embeddings in terms of
coisotropic embeddings in another paper by the present author. Moreover, we
prove a version of Gromov's non-existence of exact Lagrangian embeddings into
standard $\mathbb{R}^{2 n}$ for coisotropic embeddings into $S^1 \times
\mathbb{R}^{2 n}$. This allows us to distinguish different contact structures
on the latter by means of the (modified) contact shape invariant. As in the
general Lagrangian case, all of the existence results are based on Gromov's
theory of $J$-holomorphic curves and his compactness theorem (or persistence
principle). Analytical difficulties arise mainly at the ends of the cone
$\mathbb{R}_+ \times U$. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics",
"Physics"
] |
Title: Neutron activation and prompt gamma intensity in Ar/CO$_{2}$-filled neutron detectors at the European Spallation Source,
Abstract: Monte Carlo simulations using MCNP6.1 were performed to study the effect of
neutron activation in Ar/CO$_{2}$ neutron detector counting gas. A general MCNP
model was built and validated with simple analytical calculations. Simulations
and calculations agree that only the $^{40}$Ar activation can have a
considerable effect. It was shown that neither the prompt gamma intensity from
the $^{40}$Ar neutron capture nor the produced $^{41}$Ar activity have an
impact in terms of gamma dose rate around the detector and background level. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Solving 1ODEs with functions,
Abstract: Here we present a new approach to deal with first order ordinary differential
equations (1ODEs), presenting functions. This method is an alternative to the
one we have presented in [1]. In [2], we have establish the theoretical
background to deal, in the extended Prelle-Singer approach context, with
systems of 1ODEs. In this present paper, we will apply these results in order
to produce a method that is more efficient in a great number of cases.
Directly, the solving of 1ODEs is applicable to any problem presenting
parameters to which the rate of change is related to the parameter itself.
Apart from that, the solving of 1ODEs can be a part of larger mathematical
processes vital to dealing with many problems. | [
0,
1,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Strong Consistency of Spectral Clustering for Stochastic Block Models,
Abstract: In this paper we prove the strong consistency of several methods based on the
spectral clustering techniques that are widely used to study the community
detection problem in stochastic block models (SBMs). We show that under some
weak conditions on the minimal degree, the number of communities, and the
eigenvalues of the probability block matrix, the K-means algorithm applied to
the eigenvectors of the graph Laplacian associated with its first few largest
eigenvalues can classify all individuals into the true community uniformly
correctly almost surely. Extensions to both regularized spectral clustering and
degree-corrected SBMs are also considered. We illustrate the performance of
different methods on simulated networks. | [
0,
0,
0,
1,
0,
0
] | [
"Statistics",
"Mathematics",
"Computer Science"
] |
Title: Machine learning out-of-equilibrium phases of matter,
Abstract: Neural network based machine learning is emerging as a powerful tool for
obtaining phase diagrams when traditional regression schemes using local
equilibrium order parameters are not available, as in many-body localized or
topological phases. Nevertheless, instances of machine learning offering new
insights have been rare up to now. Here we show that a single feed-forward
neural network can decode the defining structures of two distinct MBL phases
and a thermalizing phase, using entanglement spectra obtained from individual
eigenstates. For this, we introduce a simplicial geometry based method for
extracting multi-partite phase boundaries. We find that this method outperforms
conventional metrics (like the entanglement entropy) for identifying MBL phase
transitions, revealing a sharper phase boundary and shedding new insight into
the topology of the phase diagram. Furthermore, the phase diagram we acquire
from a single disorder configuration confirms that the machine-learning based
approach we establish here can enable speedy exploration of large phase spaces
that can assist with the discovery of new MBL phases. To our knowledge this
work represents the first example of a machine learning approach revealing new
information beyond conventional knowledge. | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Computer Science"
] |
Title: Maximal solutions for the Infinity-eigenvalue problem,
Abstract: In this article we prove that the first eigenvalue of the $\infty-$Laplacian
$$ \left\{ \begin{array}{rclcl}
\min\{ -\Delta_\infty v,\, |\nabla v|-\lambda_{1, \infty}(\Omega) v \} & = &
0 & \text{in} & \Omega v & = & 0 & \text{on} & \partial \Omega, \end{array}
\right. $$ has a unique (up to scalar multiplication) maximal solution. This
maximal solution can be obtained as the limit as $\ell \nearrow 1$ of concave
problems of the form $$ \left\{ \begin{array}{rclcl}
\min\{ -\Delta_\infty v_{\ell},\, |\nabla v_{\ell}|-\lambda_{1,
\infty}(\Omega) v_{\ell}^{\ell} \} & = & 0 & \text{in} & \Omega v_{\ell} & = &
0 & \text{on} & \partial \Omega. \end{array} \right. $$ In this way we obtain
that the maximal eigenfunction is the unique one that is the limit of the
concave problems as happens for the usual eigenvalue problem for the
$p-$Laplacian for a fixed $1<p<\infty$. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: BL-MNE: Emerging Heterogeneous Social Network Embedding through Broad Learning with Aligned Autoencoder,
Abstract: Network embedding aims at projecting the network data into a low-dimensional
feature space, where the nodes are represented as a unique feature vector and
network structure can be effectively preserved. In recent years, more and more
online application service sites can be represented as massive and complex
networks, which are extremely challenging for traditional machine learning
algorithms to deal with. Effective embedding of the complex network data into
low-dimension feature representation can both save data storage space and
enable traditional machine learning algorithms applicable to handle the network
data. Network embedding performance will degrade greatly if the networks are of
a sparse structure, like the emerging networks with few connections. In this
paper, we propose to learn the embedding representation for a target emerging
network based on the broad learning setting, where the emerging network is
aligned with other external mature networks at the same time. To solve the
problem, a new embedding framework, namely "Deep alIgned autoencoder based
eMbEdding" (DIME), is introduced in this paper. DIME handles the diverse link
and attribute in a unified analytic based on broad learning, and introduces the
multiple aligned attributed heterogeneous social network concept to model the
network structure. A set of meta paths are introduced in the paper, which
define various kinds of connections among users via the heterogeneous link and
attribute information. The closeness among users in the networks are defined as
the meta proximity scores, which will be fed into DIME to learn the embedding
vectors of users in the emerging network. Extensive experiments have been done
on real-world aligned social networks, which have demonstrated the
effectiveness of DIME in learning the emerging network embedding vectors. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: Multi-channel discourse as an indicator for Bitcoin price and volume movements,
Abstract: This research aims to identify how Bitcoin-related news publications and
online discourse are expressed in Bitcoin exchange movements of price and
volume. Being inherently digital, all Bitcoin-related fundamental data (from
exchanges, as well as transactional data directly from the blockchain) is
available online, something that is not true for traditional businesses or
currencies traded on exchanges. This makes Bitcoin an interesting subject for
such research, as it enables the mapping of sentiment to fundamental events
that might otherwise be inaccessible. Furthermore, Bitcoin discussion largely
takes place on online forums and chat channels. In stock trading, the value of
sentiment data in trading decisions has been demonstrated numerous times [1]
[2] [3], and this research aims to determine whether there is value in such
data for Bitcoin trading models. To achieve this, data over the year 2015 has
been collected from Bitcointalk.org, (the biggest Bitcoin forum in post
volume), established news sources such as Bloomberg and the Wall Street
Journal, the complete /r/btc and /r/Bitcoin subreddits, and the bitcoin-otc and
bitcoin-dev IRC channels. By analyzing this data on sentiment and volume, we
find weak to moderate correlations between forum, news, and Reddit sentiment
and movements in price and volume from 1 to 5 days after the sentiment was
expressed. A Granger causality test confirms the predictive causality of the
sentiment on the daily percentage price and volume movements, and at the same
time underscores the predictive causality of market movements on sentiment
expressions in online communities | [
0,
0,
0,
0,
0,
1
] | [
"Quantitative Finance",
"Statistics"
] |
Title: Fano Resonances in a Photonic Crystal Covered with a Perforated Gold Film and its Application to Biosensing,
Abstract: Optical properties of the photonic crystal covered with a perforated metal
film were investigated and the existence of the Fano-type resonances was shown.
The Fano resonances originate from the interaction between the optical Tamm
state and the waveguide modes of the photonic crystal. It manifests itself as a
narrow dip in a broad peak in the transmission spectrum related to the optical
Tamm state. The design of a sensor based on this Fano resonance that is
sensitive to the change of the environment refractive index is suggested. | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Quantitative Biology"
] |
Title: Non-Stationary Bandits with Habituation and Recovery Dynamics,
Abstract: Many settings involve sequential decision-making where a set of actions can
be chosen at each time step, each action provides a stochastic reward, and the
distribution for the reward of each action is initially unknown. However,
frequent selection of a specific action may reduce its expected reward, while
abstaining from choosing an action may cause its expected reward to increase.
Such non-stationary phenomena are observed in many real world settings such as
personalized healthcare-adherence improving interventions and targeted online
advertising. Though finding an optimal policy for general models with
non-stationarity is PSPACE-complete, we propose and analyze a new class of
models called ROGUE (Reducing or Gaining Unknown Efficacy) bandits, which we
show in this paper can capture these phenomena and are amenable to the design
of effective policies. We first present a consistent maximum likelihood
estimator for the parameters of these models. Next, we construct finite sample
concentration bounds that lead to an upper confidence bound policy called the
ROGUE Upper Confidence Bound (ROGUE-UCB) algorithm. We prove that under proper
conditions the ROGUE-UCB algorithm achieves logarithmic in time regret, unlike
existing algorithms which result in linear regret. We conclude with a numerical
experiment using real data from a personalized healthcare-adherence improving
intervention to increase physical activity. In this intervention, the goal is
to optimize the selection of messages (e.g., confidence increasing vs.
knowledge increasing) to send to each individual each day to increase adherence
and physical activity. Our results show that ROGUE-UCB performs better in terms
of regret and average reward as compared to state of the art algorithms, and
the use of ROGUE-UCB increases daily step counts by roughly 1,000 steps a day
(about a half-mile more of walking) as compared to other algorithms. | [
1,
0,
1,
0,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: An Approach to Controller Design Based on the Generalized Cloud Model,
Abstract: In this paper, an approach to controller design based on the cloud models,
without using the analog plant model is presented. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
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