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Title: A Stochastic Programming Approach for Electric Vehicle Charging Network Design,
Abstract: Advantages of electric vehicles (EV) include reduction of greenhouse gas and
other emissions, energy security, and fuel economy. The societal benefits of
large-scale adoption of EVs cannot be realized without adequate deployment of
publicly accessible charging stations. We propose a two-stage stochastic
programming model to determine the optimal network of charging stations for a
community considering uncertainties in arrival and dwell time of vehicles,
battery state of charge of arriving vehicles, walkable range and charging
preferences of drivers, demand during weekdays and weekends, and rate of
adoption of EVs within a community. We conducted studies using sample average
approximation (SAA) method which asymptotically converges to an optimal
solution for a two-stage stochastic problem, however it is computationally
expensive for large-scale instances. Therefore, we developed a heuristic to
produce near to optimal solutions quickly for our data instances. We conducted
computational experiments using various publicly available data sources, and
benefits of the solutions are evaluated both quantitatively and qualitatively
for a given community. | [
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Title: Autonomy in the interactive music system VIVO,
Abstract: Interactive Music Systems (IMS) have introduced a new world of music-making
modalities. But can we really say that they create music, as in true autonomous
creation? Here we discuss Video Interactive VST Orchestra (VIVO), an IMS that
considers extra-musical information by adopting a simple salience based model
of user-system interaction when simulating intentionality in automatic music
generation. Key features of the theoretical framework, a brief overview of
pilot research, and a case study providing validation of the model are
presented. This research demonstrates that a meaningful user/system interplay
is established in what we define as reflexive multidominance. | [
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] |
Title: Bayes-Optimal Entropy Pursuit for Active Choice-Based Preference Learning,
Abstract: We analyze the problem of learning a single user's preferences in an active
learning setting, sequentially and adaptively querying the user over a finite
time horizon. Learning is conducted via choice-based queries, where the user
selects her preferred option among a small subset of offered alternatives.
These queries have been shown to be a robust and efficient way to learn an
individual's preferences. We take a parametric approach and model the user's
preferences through a linear classifier, using a Bayesian prior to encode our
current knowledge of this classifier. The rate at which we learn depends on the
alternatives offered at every time epoch. Under certain noise assumptions, we
show that the Bayes-optimal policy for maximally reducing entropy of the
posterior distribution of this linear classifier is a greedy policy, and that
this policy achieves a linear lower bound when alternatives can be constructed
from the continuum. Further, we analyze a different metric called
misclassification error, proving that the performance of the optimal policy
that minimizes misclassification error is bounded below by a linear function of
differential entropy. Lastly, we numerically compare the greedy entropy
reduction policy with a knowledge gradient policy under a number of scenarios,
examining their performance under both differential entropy and
misclassification error. | [
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] |
Title: Benchmarking Decoupled Neural Interfaces with Synthetic Gradients,
Abstract: Artifical Neural Networks are a particular class of learning systems modeled
after biological neural functions with an interesting penchant for Hebbian
learning, that is "neurons that wire together, fire together". However, unlike
their natural counterparts, artificial neural networks have a close and
stringent coupling between the modules of neurons in the network. This coupling
or locking imposes upon the network a strict and inflexible structure that
prevent layers in the network from updating their weights until a full
feed-forward and backward pass has occurred. Such a constraint though may have
sufficed for a while, is now no longer feasible in the era of very-large-scale
machine learning, coupled with the increased desire for parallelization of the
learning process across multiple computing infrastructures. To solve this
problem, synthetic gradients (SG) with decoupled neural interfaces (DNI) are
introduced as a viable alternative to the backpropagation algorithm. This paper
performs a speed benchmark to compare the speed and accuracy capabilities of
SG-DNI as opposed to a standard neural interface using multilayer perceptron
MLP. SG-DNI shows good promise, in that it not only captures the learning
problem, it is also over 3-fold faster due to it asynchronous learning
capabilities. | [
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] |
Title: Core structure of two-dimensional Fermi gas vortices in the BEC-BCS crossover region,
Abstract: We report $T=0$ diffusion Monte Carlo results for the ground-state and vortex
excitation of unpolarized spin-1/2 fermions in a two-dimensional disk. We
investigate how vortex core structure properties behave over the BEC-BCS
crossover. We calculate the vortex excitation energy, density profiles, and
vortex core properties related to the current. We find a density suppression at
the vortex core on the BCS side of the crossover, and a depleted core on the
BEC limit. Size-effect dependencies in the disk geometry were carefully
studied. | [
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] |
Title: Nonlinear control for an uncertain electromagnetic actuator,
Abstract: This paper presents the design of a nonlinear control law for a typical
electromagnetic actuator system. Electromagnetic actuators are widely
implemented in industrial applications, and especially as linear positioning
system. In this work, we aim at taking into account a magnetic phenomenon that
is usually neglected: flux fringing. This issue is addressed with an uncertain
modeling approach. The proposed control law consists of two steps, a
backstepping control regulates the mechanical part and a sliding mode approach
controls the coil current and the magnetic force implicitly. An illustrative
example shows the effectiveness of the presented approach. | [
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] |
Title: Transverse spinning of light with globally unique handedness,
Abstract: Access to the transverse spin of light has unlocked new regimes in
topological photonics and optomechanics. To achieve the transverse spin of
nonzero longitudinal fields, various platforms that derive transversely
confined waves based on focusing, interference, or evanescent waves have been
suggested. Nonetheless, because of the transverse confinement inherently
accompanying sign reversal of the field derivative, the resulting transverse
spin handedness experiences spatial inversion, which leads to a mismatch
between the densities of the wavefunction and its spin component and hinders
the global observation of the transverse spin. Here, we reveal a globally pure
transverse spin in which the wavefunction density signifies the spin
distribution, by employing inverse molding of the eigenmode in the spin basis.
Starting from the target spin profile, we analytically obtain the potential
landscape and then show that the elliptic-hyperbolic transition around the
epsilon-near-zero permittivity allows for the global conservation of transverse
spin handedness across the topological interface between anisotropic
metamaterials. Extending to the non-Hermitian regime, we also develop
annihilated transverse spin modes to cover the entire Poincare sphere of the
meridional plane. Our results enable the complete transfer of optical energy to
transverse spinning motions and realize the classical analogy of 3-dimensional
quantum spin states. | [
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] |
Title: A general class of quasi-independence tests for left-truncated right-censored data,
Abstract: In survival studies, classical inferences for left-truncated data require
quasi-independence, a property that the joint density of truncation time and
failure time is factorizable into their marginal densities in the observable
region. The quasi-independence hypothesis is testable; many authors have
developed tests for left-truncated data with or without right-censoring. In
this paper, we propose a class of test statistics for testing the
quasi-independence which unifies the existing methods and generates new useful
statistics such as conditional Spearman's rank correlation coefficient.
Asymptotic normality of the proposed class of statistics is given. We show that
a new set of tests can be powerful under certain alternatives by theoretical
and empirical power comparison. | [
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] |
Title: On the higher Cheeger problem,
Abstract: We develop the notion of higher Cheeger constants for a measurable set
$\Omega \subset \mathbb{R}^N$. By the $k$-th Cheeger constant we mean the value
\[h_k(\Omega) = \inf \max \{h_1(E_1), \dots, h_1(E_k)\},\] where the infimum is
taken over all $k$-tuples of mutually disjoint subsets of $\Omega$, and
$h_1(E_i)$ is the classical Cheeger constant of $E_i$. We prove the existence
of minimizers satisfying additional "adjustment" conditions and study their
properties. A relation between $h_k(\Omega)$ and spectral minimal
$k$-partitions of $\Omega$ associated with the first eigenvalues of the
$p$-Laplacian under homogeneous Dirichlet boundary conditions is stated. The
results are applied to determine the second Cheeger constant of some planar
domains. | [
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] |
Title: Fundamental Conditions for Low-CP-Rank Tensor Completion,
Abstract: We consider the problem of low canonical polyadic (CP) rank tensor
completion. A completion is a tensor whose entries agree with the observed
entries and its rank matches the given CP rank. We analyze the manifold
structure corresponding to the tensors with the given rank and define a set of
polynomials based on the sampling pattern and CP decomposition. Then, we show
that finite completability of the sampled tensor is equivalent to having a
certain number of algebraically independent polynomials among the defined
polynomials. Our proposed approach results in characterizing the maximum number
of algebraically independent polynomials in terms of a simple geometric
structure of the sampling pattern, and therefore we obtain the deterministic
necessary and sufficient condition on the sampling pattern for finite
completability of the sampled tensor. Moreover, assuming that the entries of
the tensor are sampled independently with probability $p$ and using the
mentioned deterministic analysis, we propose a combinatorial method to derive a
lower bound on the sampling probability $p$, or equivalently, the number of
sampled entries that guarantees finite completability with high probability. We
also show that the existing result for the matrix completion problem can be
used to obtain a loose lower bound on the sampling probability $p$. In
addition, we obtain deterministic and probabilistic conditions for unique
completability. It is seen that the number of samples required for finite or
unique completability obtained by the proposed analysis on the CP manifold is
orders-of-magnitude lower than that is obtained by the existing analysis on the
Grassmannian manifold. | [
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] |
Title: Pricing for Online Resource Allocation: Intervals and Paths,
Abstract: We present pricing mechanisms for several online resource allocation problems
which obtain tight or nearly tight approximations to social welfare. In our
settings, buyers arrive online and purchase bundles of items; buyers' values
for the bundles are drawn from known distributions. This problem is closely
related to the so-called prophet-inequality of Krengel and Sucheston and its
extensions in recent literature. Motivated by applications to cloud economics,
we consider two kinds of buyer preferences. In the first, items correspond to
different units of time at which a resource is available; the items are
arranged in a total order and buyers desire intervals of items. The second
corresponds to bandwidth allocation over a tree network; the items are edges in
the network and buyers desire paths.
Because buyers' preferences have complementarities in the settings we
consider, recent constant-factor approximations via item prices do not apply,
and indeed strong negative results are known. We develop static, anonymous
bundle pricing mechanisms.
For the interval preferences setting, we show that static, anonymous bundle
pricings achieve a sublogarithmic competitive ratio, which is optimal (within
constant factors) over the class of all online allocation algorithms, truthful
or not. For the path preferences setting, we obtain a nearly-tight logarithmic
competitive ratio. Both of these results exhibit an exponential improvement
over item pricings for these settings. Our results extend to settings where the
seller has multiple copies of each item, with the competitive ratio decreasing
linearly with supply. Such a gradual tradeoff between supply and the
competitive ratio for welfare was previously known only for the single item
prophet inequality. | [
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] |
Title: Optimal designs for enzyme inhibition kinetic models,
Abstract: In this paper we present a new method for determining optimal designs for
enzyme inhibition kinetic models, which are used to model the influence of the
concentration of a substrate and an inhibition on the velocity of a reaction.
The approach uses a nonlinear transformation of the vector of predictors such
that the model in the new coordinates is given by an incomplete response
surface model. Although there exist no explicit solutions of the optimal design
problem for incomplete response surface models so far, the corresponding design
problem in the new coordinates is substantially more transparent, such that
explicit or numerical solutions can be determined more easily. The designs for
the original problem can finally be found by an inverse transformation of the
optimal designs determined for the response surface model. We illustrate the
method determining explicit solutions for the $D$-optimal design and for the
optimal design problem for estimating the individual coefficients in a
non-competitive enzyme inhibition kinetic model. | [
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] |
Title: Highly Granular Calorimeters: Technologies and Results,
Abstract: The CALICE collaboration is developing highly granular calorimeters for
experiments at a future lepton collider primarily to establish technologies for
particle flow event reconstruction. These technologies also find applications
elsewhere, such as detector upgrades for the LHC. Meanwhile, the large data
sets collected in an extensive series of beam tests have enabled detailed
studies of the properties of hadronic showers in calorimeter systems, resulting
in improved simulation models and development of sophisticated reconstruction
techniques. In this proceeding, highlights are included from studies of the
structure of hadronic showers and results on reconstruction techniques for
imaging calorimetry. In addition, current R&D activities within CALICE are
summarized, focusing on technological prototypes that address challenges from
full detector system integration and production techniques amenable to mass
production for electromagnetic and hadronic calorimeters based on silicon,
scintillator, and gas techniques. | [
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] |
Title: On a variable step size modification of Hines' method in computational neuroscience,
Abstract: For simulating large networks of neurons Hines proposed a method which uses
extensively the structure of the arising systems of ordinary differential
equations in order to obtain an efficient implementation. The original method
requires constant step sizes and produces the solution on a staggered grid. In
the present paper a one-step modification of this method is introduced and
analyzed with respect to their stability properties. The new method allows for
step size control. Local error estimators are constructed. The method has been
implemented in matlab and tested using simple Hodgkin-Huxley type models.
Comparisons with standard state-of-the-art solvers are provided. | [
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] |
Title: Neural Question Answering at BioASQ 5B,
Abstract: This paper describes our submission to the 2017 BioASQ challenge. We
participated in Task B, Phase B which is concerned with biomedical question
answering (QA). We focus on factoid and list question, using an extractive QA
model, that is, we restrict our system to output substrings of the provided
text snippets. At the core of our system, we use FastQA, a state-of-the-art
neural QA system. We extended it with biomedical word embeddings and changed
its answer layer to be able to answer list questions in addition to factoid
questions. We pre-trained the model on a large-scale open-domain QA dataset,
SQuAD, and then fine-tuned the parameters on the BioASQ training set. With our
approach, we achieve state-of-the-art results on factoid questions and
competitive results on list questions. | [
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] |
Title: Data Distillation for Controlling Specificity in Dialogue Generation,
Abstract: People speak at different levels of specificity in different situations.
Depending on their knowledge, interlocutors, mood, etc.} A conversational agent
should have this ability and know when to be specific and when to be general.
We propose an approach that gives a neural network--based conversational agent
this ability. Our approach involves alternating between \emph{data
distillation} and model training : removing training examples that are closest
to the responses most commonly produced by the model trained from the last
round and then retrain the model on the remaining dataset. Dialogue generation
models trained with different degrees of data distillation manifest different
levels of specificity.
We then train a reinforcement learning system for selecting among this pool
of generation models, to choose the best level of specificity for a given
input. Compared to the original generative model trained without distillation,
the proposed system is capable of generating more interesting and
higher-quality responses, in addition to appropriately adjusting specificity
depending on the context.
Our research constitutes a specific case of a broader approach involving
training multiple subsystems from a single dataset distinguished by differences
in a specific property one wishes to model. We show that from such a set of
subsystems, one can use reinforcement learning to build a system that tailors
its output to different input contexts at test time. | [
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] |
Title: Non-locality of the meet levels of the Trotter-Weil Hierarchy,
Abstract: We prove that the meet level $m$ of the Trotter-Weil, $\mathsf{V}_m$ is not
local for all $m \geq 1$, as conjectured in a paper by Kufleitner and Lauser.
In order to show this, we explicitly provide a language whose syntactic
semigroup is in $L \mathsf{V}_m$ and not in $\mathsf{V}_m*\mathsf{D}$. | [
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] |
Title: Backlund transformations and divisor doubling,
Abstract: In classical mechanics well-known cryptographic algorithms and protocols can
be very useful for construction canonical transformations preserving form of
Hamiltonians. We consider application of a standard generic divisor doubling
for construction of new auto Bäcklund transformations for the Lagrange top
and Hénon-Heiles system separable in parabolic coordinates. | [
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] |
Title: KATE: K-Competitive Autoencoder for Text,
Abstract: Autoencoders have been successful in learning meaningful representations from
image datasets. However, their performance on text datasets has not been widely
studied. Traditional autoencoders tend to learn possibly trivial
representations of text documents due to their confounding properties such as
high-dimensionality, sparsity and power-law word distributions. In this paper,
we propose a novel k-competitive autoencoder, called KATE, for text documents.
Due to the competition between the neurons in the hidden layer, each neuron
becomes specialized in recognizing specific data patterns, and overall the
model can learn meaningful representations of textual data. A comprehensive set
of experiments show that KATE can learn better representations than traditional
autoencoders including denoising, contractive, variational, and k-sparse
autoencoders. Our model also outperforms deep generative models, probabilistic
topic models, and even word representation models (e.g., Word2Vec) in terms of
several downstream tasks such as document classification, regression, and
retrieval. | [
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] |
Title: Learning to Address Health Inequality in the United States with a Bayesian Decision Network,
Abstract: Life-expectancy is a complex outcome driven by genetic, socio-demographic,
environmental and geographic factors. Increasing socio-economic and health
disparities in the United States are propagating the longevity-gap, making it a
cause for concern. Earlier studies have probed individual factors but an
integrated picture to reveal quantifiable actions has been missing. There is a
growing concern about a further widening of healthcare inequality caused by
Artificial Intelligence (AI) due to differential access to AI-driven services.
Hence, it is imperative to explore and exploit the potential of AI for
illuminating biases and enabling transparent policy decisions for positive
social and health impact. In this work, we reveal actionable interventions for
decreasing the longevity-gap in the United States by analyzing a County-level
data resource containing healthcare, socio-economic, behavioral, education and
demographic features. We learn an ensemble-averaged structure, draw inferences
using the joint probability distribution and extend it to a Bayesian Decision
Network for identifying policy actions. We draw quantitative estimates for the
impact of diversity, preventive-care quality and stable-families within the
unified framework of our decision network. Finally, we make this analysis and
dashboard available as an interactive web-application for enabling users and
policy-makers to validate our reported findings and to explore the impact of
ones beyond reported in this work. | [
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] |
Title: Towards Noncommutative Topological Quantum Field Theory: New invariants for 3-manifolds,
Abstract: We define some new invariants for 3-manifolds using the space of taut codim-1
foliations along with various techniques from noncommutative geometry. These
invariants originate from our attempt to generalise Topological Quantum Field
Theories in the Noncommutative geometry / topology realm. | [
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] |
Title: Chaotic Dynamic S Boxes Based Substitution Approach for Digital Images,
Abstract: In this paper, we propose an image encryption algorithm based on the chaos,
substitution boxes, nonlinear transformation in Galois field and Latin square.
Initially, the dynamic S boxes are generated using Fisher Yates shuffle method
and piece wise linear chaotic map. The algorithm utilizes advantages of keyed
Latin square and transformation to substitute highly correlated digital images
and yield encrypted image with valued performance. The chaotic behavior is
achieved using Logistic map which is used to select one of thousand S boxes and
also decides the row and column of selected S box. The selected S box value is
transformed using nonlinear transformation. Along with the keyed Latin square
generated using a 256 bit external key, used to substitute secretly plain image
pixels in cipher block chaining mode. To further strengthen the security of
algorithm, round operation are applied to obtain final ciphered image. The
experimental results are performed to evaluate algorithm and the anticipated
algorithm is compared with a recent encryption scheme. The analyses demonstrate
algorithms effectiveness in providing high security to digital media. | [
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] |
Title: Few-shot Learning by Exploiting Visual Concepts within CNNs,
Abstract: Convolutional neural networks (CNNs) are one of the driving forces for the
advancement of computer vision. Despite their promising performances on many
tasks, CNNs still face major obstacles on the road to achieving ideal machine
intelligence. One is that CNNs are complex and hard to interpret. Another is
that standard CNNs require large amounts of annotated data, which is sometimes
hard to obtain, and it is desirable to learn to recognize objects from few
examples. In this work, we address these limitations of CNNs by developing
novel, flexible, and interpretable models for few-shot learning. Our models are
based on the idea of encoding objects in terms of visual concepts (VCs), which
are interpretable visual cues represented by the feature vectors within CNNs.
We first adapt the learning of VCs to the few-shot setting, and then uncover
two key properties of feature encoding using VCs, which we call category
sensitivity and spatial pattern. Motivated by these properties, we present two
intuitive models for the problem of few-shot learning. Experiments show that
our models achieve competitive performances, while being more flexible and
interpretable than alternative state-of-the-art few-shot learning methods. We
conclude that using VCs helps expose the natural capability of CNNs for
few-shot learning. | [
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] |
Title: Noise Statistics Oblivious GARD For Robust Regression With Sparse Outliers,
Abstract: Linear regression models contaminated by Gaussian noise (inlier) and possibly
unbounded sparse outliers are common in many signal processing applications.
Sparse recovery inspired robust regression (SRIRR) techniques are shown to
deliver high quality estimation performance in such regression models.
Unfortunately, most SRIRR techniques assume \textit{a priori} knowledge of
noise statistics like inlier noise variance or outlier statistics like number
of outliers. Both inlier and outlier noise statistics are rarely known
\textit{a priori} and this limits the efficient operation of many SRIRR
algorithms. This article proposes a novel noise statistics oblivious algorithm
called residual ratio thresholding GARD (RRT-GARD) for robust regression in the
presence of sparse outliers. RRT-GARD is developed by modifying the recently
proposed noise statistics dependent greedy algorithm for robust de-noising
(GARD). Both finite sample and asymptotic analytical results indicate that
RRT-GARD performs nearly similar to GARD with \textit{a priori} knowledge of
noise statistics. Numerical simulations in real and synthetic data sets also
point to the highly competitive performance of RRT-GARD. | [
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] |
Title: Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation,
Abstract: This paper addresses the problem of depth estimation from a single still
image. Inspired by recent works on multi- scale convolutional neural networks
(CNN), we propose a deep model which fuses complementary information derived
from multiple CNN side outputs. Different from previous methods, the
integration is obtained by means of continuous Conditional Random Fields
(CRFs). In particular, we propose two different variations, one based on a
cascade of multiple CRFs, the other on a unified graphical model. By designing
a novel CNN implementation of mean-field updates for continuous CRFs, we show
that both proposed models can be regarded as sequential deep networks and that
training can be performed end-to-end. Through extensive experimental evaluation
we demonstrate the effective- ness of the proposed approach and establish new
state of the art results on publicly available datasets. | [
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] |
Title: Towards Proxemic Mobile Collocated Interactions,
Abstract: Research on mobile collocated interactions has been exploring situations
where collocated users engage in collaborative activities using their personal
mobile devices (e.g., smartphones and tablets), thus going from
personal/individual toward shared/multiuser experiences and interactions. The
proliferation of ever-smaller computers that can be worn on our wrists (e.g.,
Apple Watch) and other parts of the body (e.g., Google Glass), have expanded
the possibilities and increased the complexity of interaction in what we term
mobile collocated situations. Research on F-formations (or facing formations)
has been conducted in traditional settings (e.g., home, office, parties) where
the context and the presence of physical elements (e.g., furniture) can
strongly influence the way people socially interact with each other. While we
may be aware of how people arrange themselves spatially and interact with each
other at a dinner table, in a classroom, or at a waiting room in a hospital,
there are other less-structured, dynamic, and larger-scale spaces that present
different types of challenges and opportunities for technology to enrich how
people experience these (semi-) public spaces. In this article, the authors
explore proxemic mobile collocated interactions by looking at F-formations in
the wild. They discuss recent efforts to observe how people socially interact
in dynamic, unstructured, non-traditional settings. The authors also report the
results of exploratory F-formation observations conducted in the wild (i.e.,
tourist attraction). | [
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] |
Title: On the length of perverse sheaves and D-modules,
Abstract: We prove that the length function for perverse sheaves and algebraic regular
holonomic D-modules on a smooth complex algebraic variety Y is an absolute
Q-constructible function. One consequence is: for "any" fixed natural (derived)
functor F between constructible complexes or perverse sheaves on two smooth
varieties X and Y, the loci of rank one local systems L on X whose image F(L)
has prescribed length are Zariski constructible subsets defined over Q,
obtained from finitely many torsion-translated complex affine algebraic subtori
of the moduli of rank one local systems via a finite sequence of taking union,
intersection, and complement. | [
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] |
Title: Magnetic ground state of SrRuO$_3$ thin film and applicability of standard first-principles approximations to metallic magnetism,
Abstract: A systematic first-principles study has been performed to understand the
magnetism of thin film SrRuO$_3$ which lots of research efforts have been
devoted to but no clear consensus has been reached about its ground state
properties. The relative t$_{2g}$ level difference, lattice distortion as well
as the layer thickness play together in determining the spin order. In
particular, it is important to understand the difference between two standard
approximations, namely LDA and GGA, in describing this metallic magnetism.
Landau free energy analysis and the magnetization-energy-ratio plot clearly
show the different tendency of favoring the magnetic moment formation, and it
is magnified when applied to the thin film limit where the experimental
information is severely limited. As a result, LDA gives a qualitatively
different prediction from GGA in the experimentally relevant region of strain
whereas both approximations give reasonable results for the bulk phase. We
discuss the origin of this difference and the applicability of standard methods
to the correlated oxide and the metallic magnetic systems. | [
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] |
Title: RelNN: A Deep Neural Model for Relational Learning,
Abstract: Statistical relational AI (StarAI) aims at reasoning and learning in noisy
domains described in terms of objects and relationships by combining
probability with first-order logic. With huge advances in deep learning in the
current years, combining deep networks with first-order logic has been the
focus of several recent studies. Many of the existing attempts, however, only
focus on relations and ignore object properties. The attempts that do consider
object properties are limited in terms of modelling power or scalability. In
this paper, we develop relational neural networks (RelNNs) by adding hidden
layers to relational logistic regression (the relational counterpart of
logistic regression). We learn latent properties for objects both directly and
through general rules. Back-propagation is used for training these models. A
modular, layer-wise architecture facilitates utilizing the techniques developed
within deep learning community to our architecture. Initial experiments on
eight tasks over three real-world datasets show that RelNNs are promising
models for relational learning. | [
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] |
Title: A Continuum Poisson-Boltzmann Model for Membrane Channel Proteins,
Abstract: Membrane proteins constitute a large portion of the human proteome and
perform a variety of important functions as membrane receptors, transport
proteins, enzymes, signaling proteins, and more. The computational studies of
membrane proteins are usually much more complicated than those of globular
proteins. Here we propose a new continuum model for Poisson-Boltzmann
calculations of membrane channel proteins. Major improvements over the existing
continuum slab model are as follows: 1) The location and thickness of the slab
model are fine-tuned based on explicit-solvent MD simulations. 2) The highly
different accessibility in the membrane and water regions are addressed with a
two-step, two-probe grid labeling procedure, and 3) The water pores/channels
are automatically identified. The new continuum membrane model is optimized (by
adjusting the membrane probe, as well as the slab thickness and center) to best
reproduce the distributions of buried water molecules in the membrane region as
sampled in explicit water simulations. Our optimization also shows that the
widely adopted water probe of 1.4 {\AA} for globular proteins is a very
reasonable default value for membrane protein simulations. It gives an overall
minimum number of inconsistencies between the continuum and explicit
representations of water distributions in membrane channel proteins, at least
in the water accessible pore/channel regions that we focus on. Finally, we
validate the new membrane model by carrying out binding affinity calculations
for a potassium channel, and we observe a good agreement with experiment
results. | [
0,
1,
0,
0,
0,
0
] |
Title: On the structure of Hausdorff moment sequences of complex matrices,
Abstract: The paper treats several aspects of the truncated matricial
$[\alpha,\beta]$-Hausdorff type moment problems. It is shown that each
$[\alpha,\beta]$-Hausdorff moment sequence has a particular intrinsic
structure. More precisely, each element of this sequence varies within a closed
bounded matricial interval. The case that the corresponding moment coincides
with one of the endpoints of the interval plays a particular important role.
This leads to distinguished molecular solutions of the truncated matricial
$[\alpha,\beta]$-Hausdorff moment problem, which satisfy some extremality
properties. The proofs are mainly of algebraic character. The use of the
parallel sum of matrices is an essential tool in the proofs. | [
0,
0,
1,
0,
0,
0
] |
Title: Minimal Approximately Balancing Weights: Asymptotic Properties and Practical Considerations,
Abstract: In observational studies and sample surveys, and regression settings,
weighting methods are widely used to adjust for or balance observed covariates.
Recently, a few weighting methods have been proposed that focus on directly
balancing the covariates while minimizing the dispersion of the weights. In
this paper, we call this class of weights minimal approximately balancing
weights (MABW); we study their asymptotic properties and address two
practicalities. We show that, under standard technical conditions, MABW are
consistent estimates of the true inverse probability weights; the resulting
weighting estimator is consistent, asymptotically normal, and
semiparametrically efficient. For applications, we present a finite sample
oracle inequality showing that the loss incurred by balancing too many
functions of the covariates is limited in MABW. We also provide an algorithm
for choosing the degree of approximate balancing in MABW. Finally, we conclude
with numerical results that suggest approximate balancing is preferable to
exact balancing, especially when there is limited overlap in covariate
distributions: the root mean squared error of the weighting estimator can be
reduced by nearly a half. | [
0,
0,
1,
1,
0,
0
] |
Title: Phonetic-attention scoring for deep speaker features in speaker verification,
Abstract: Recent studies have shown that frame-level deep speaker features can be
derived from a deep neural network with the training target set to discriminate
speakers by a short speech segment. By pooling the frame-level features,
utterance-level representations, called d-vectors, can be derived and used in
the automatic speaker verification (ASV) task. This simple average pooling,
however, is inherently sensitive to the phonetic content of the utterance. An
interesting idea borrowed from machine translation is the attention-based
mechanism, where the contribution of an input word to the translation at a
particular time is weighted by an attention score. This score reflects the
relevance of the input word and the present translation. We can use the same
idea to align utterances with different phonetic contents. This paper proposes
a phonetic-attention scoring approach for d-vector systems. By this approach,
an attention score is computed for each frame pair. This score reflects the
similarity of the two frames in phonetic content, and is used to weigh the
contribution of this frame pair in the utterance-based scoring. This new
scoring approach emphasizes the frame pairs with similar phonetic contents,
which essentially provides a soft alignment for utterances with any phonetic
contents. Experimental results show that compared with the naive average
pooling, this phonetic-attention scoring approach can deliver consistent
performance improvement in ASV tasks of both text-dependent and
text-independent. | [
1,
0,
0,
0,
0,
0
] |
Title: The Broad Consequences of Narrow Banking,
Abstract: We investigate the macroeconomic consequences of narrow banking in the
context of stock-flow consistent models. We begin with an extension of the
Goodwin-Keen model incorporating time deposits, government bills, cash, and
central bank reserves to the base model with loans and demand deposits and use
it to describe a fractional reserve banking system. We then characterize narrow
banking by a full reserve requirement on demand deposits and describe the
resulting separation between the payment system and lending functions of the
resulting banking sector. By way of numerical examples, we explore the
properties of fractional and full reserve versions of the model and compare
their asymptotic properties. We find that narrow banking does not lead to any
loss in economic growth when the models converge to a finite equilibrium, while
allowing for more direct monitoring and prevention of financial breakdowns in
the case of explosive asymptotic behaviour. | [
0,
0,
0,
0,
0,
1
] |
Title: Neurofeedback: principles, appraisal and outstanding issues,
Abstract: Neurofeedback is a form of brain training in which subjects are fed back
information about some measure of their brain activity which they are
instructed to modify in a way thought to be functionally advantageous. Over the
last twenty years, NF has been used to treat various neurological and
psychiatric conditions, and to improve cognitive function in various contexts.
However, despite its growing popularity, each of the main steps in NF comes
with its own set of often covert assumptions. Here we critically examine some
conceptual and methodological issues associated with the way general objectives
and neural targets of NF are defined, and review the neural mechanisms through
which NF may act, and the way its efficacy is gauged. The NF process is
characterised in terms of functional dynamics, and possible ways in which it
may be controlled are discussed. Finally, it is proposed that improving NF will
require better understanding of various fundamental aspects of brain dynamics
and a more precise definition of functional brain activity and brain-behaviour
relationships. | [
0,
0,
0,
0,
1,
0
] |
Title: Automating Image Analysis by Annotating Landmarks with Deep Neural Networks,
Abstract: Image and video analysis is often a crucial step in the study of animal
behavior and kinematics. Often these analyses require that the position of one
or more animal landmarks are annotated (marked) in numerous images. The process
of annotating landmarks can require a significant amount of time and tedious
labor, which motivates the need for algorithms that can automatically annotate
landmarks. In the community of scientists that use image and video analysis to
study the 3D flight of animals, there has been a trend of developing more
automated approaches for annotating landmarks, yet they fall short of being
generally applicable. Inspired by the success of Deep Neural Networks (DNNs) on
many problems in the field of computer vision, we investigate how suitable DNNs
are for accurate and automatic annotation of landmarks in video datasets
representative of those collected by scientists studying animals.
Our work shows, through extensive experimentation on videos of hawkmoths,
that DNNs are suitable for automatic and accurate landmark localization. In
particular, we show that one of our proposed DNNs is more accurate than the
current best algorithm for automatic localization of landmarks on hawkmoth
videos. Moreover, we demonstrate how these annotations can be used to
quantitatively analyze the 3D flight of a hawkmoth. To facilitate the use of
DNNs by scientists from many different fields, we provide a self contained
explanation of what DNNs are, how they work, and how to apply them to other
datasets using the freely available library Caffe and supplemental code that we
provide. | [
1,
0,
0,
0,
0,
0
] |
Title: Cognitive Subscore Trajectory Prediction in Alzheimer's Disease,
Abstract: Accurate diagnosis of Alzheimer's Disease (AD) entails clinical evaluation of
multiple cognition metrics and biomarkers. Metrics such as the Alzheimer's
Disease Assessment Scale - Cognitive test (ADAS-cog) comprise multiple
subscores that quantify different aspects of a patient's cognitive state such
as learning, memory, and language production/comprehension. Although
computer-aided diagnostic techniques for classification of a patient's current
disease state exist, they provide little insight into the relationship between
changes in brain structure and different aspects of a patient's cognitive state
that occur over time in AD. We have developed a Convolutional Neural Network
architecture that can concurrently predict the trajectories of the 13 subscores
comprised by a subject's ADAS-cog examination results from a current minimally
preprocessed structural MRI scan up to 36 months from image acquisition time
without resorting to manual feature extraction. Mean performance metrics are
within range of those of existing techniques that require manual feature
selection and are limited to predicting aggregate scores. | [
1,
0,
0,
1,
0,
0
] |
Title: PICOSEC: Charged particle Timing to 24 picosecond Precision with MicroPattern Gas Detectors,
Abstract: The prospect of pileup induced backgrounds at the High Luminosity LHC
(HL-LHC) has stimulated intense interest in technology for charged particle
timing at high rates. In contrast to the role of timing for particle
identification, which has driven incremental improvements in timing, the LHC
timing challenge dictates a specific level of timing performance- roughly 20-30
picoseconds. Since the elapsed time for an LHC bunch crossing (with standard
design book parameters) has an rms spread of 170 picoseconds, the $\sim50-100$
picosecond resolution now commonly achieved in TOF systems would be
insufficient to resolve multiple "in-time" pileup. Here we present a MicroMegas
based structure which achieves the required time precision (ie 24 picoseconds
for 150 GeV $\mu$'s) and could potentially offer an inexpensive solution
covering large areas with $\sim 1$ cm$^2$ pixel size. We present here a
proof-of-principle which motivates further work in our group toward realizing a
practical design capable of long-term survival in a high rate experiment. | [
0,
1,
0,
0,
0,
0
] |
Title: Measuring scientific buzz,
Abstract: Keywords are important for information retrieval. They are used to classify
and sort papers. However, these terms can also be used to study trends within
and across fields. We want to explore the lifecycle of new keywords. How often
do new terms come into existence and how long till they fade out? In this
paper, we present our preliminary analysis where we measure the burstiness of
keywords within the field of AI. We examine 150k keywords in approximately 100k
journal and conference papers. We find that nearly 80\% of the keywords die off
before year one for both journals and conferences but that terms last longer in
journals versus conferences. We also observe time periods of thematic bursts in
AI -- one where the terms are more neuroscience inspired and one more oriented
to computational optimization. This work shows promise of using author keywords
to better understand dynamics of buzz within science. | [
1,
0,
0,
0,
0,
0
] |
Title: On Nonlinear Dimensionality Reduction, Linear Smoothing and Autoencoding,
Abstract: We develop theory for nonlinear dimensionality reduction (NLDR). A number of
NLDR methods have been developed, but there is limited understanding of how
these methods work and the relationships between them. There is limited basis
for using existing NLDR theory for deriving new algorithms. We provide a novel
framework for analysis of NLDR via a connection to the statistical theory of
linear smoothers. This allows us to both understand existing methods and derive
new ones. We use this connection to smoothing to show that asymptotically,
existing NLDR methods correspond to discrete approximations of the solutions of
sets of differential equations given a boundary condition. In particular, we
can characterize many existing methods in terms of just three limiting
differential operators and boundary conditions. Our theory also provides a way
to assert that one method is preferable to another; indeed, we show Local
Tangent Space Alignment is superior within a class of methods that assume a
global coordinate chart defines an isometric embedding of the manifold. | [
0,
0,
0,
1,
0,
0
] |
Title: Asymmetric Deep Supervised Hashing,
Abstract: Hashing has been widely used for large-scale approximate nearest neighbor
search because of its storage and search efficiency. Recent work has found that
deep supervised hashing can significantly outperform non-deep supervised
hashing in many applications. However, most existing deep supervised hashing
methods adopt a symmetric strategy to learn one deep hash function for both
query points and database (retrieval) points. The training of these symmetric
deep supervised hashing methods is typically time-consuming, which makes them
hard to effectively utilize the supervised information for cases with
large-scale database. In this paper, we propose a novel deep supervised hashing
method, called asymmetric deep supervised hashing (ADSH), for large-scale
nearest neighbor search. ADSH treats the query points and database points in an
asymmetric way. More specifically, ADSH learns a deep hash function only for
query points, while the hash codes for database points are directly learned.
The training of ADSH is much more efficient than that of traditional symmetric
deep supervised hashing methods. Experiments show that ADSH can achieve
state-of-the-art performance in real applications. | [
1,
0,
0,
1,
0,
0
] |
Title: Pseudo-Separation for Assessment of Structural Vulnerability of a Network,
Abstract: Based upon the idea that network functionality is impaired if two nodes in a
network are sufficiently separated in terms of a given metric, we introduce two
combinatorial \emph{pseudocut} problems generalizing the classical min-cut and
multi-cut problems. We expect the pseudocut problems will find broad relevance
to the study of network reliability. We comprehensively analyze the
computational complexity of the pseudocut problems and provide three
approximation algorithms for these problems.
Motivated by applications in communication networks with strict
Quality-of-Service (QoS) requirements, we demonstrate the utility of the
pseudocut problems by proposing a targeted vulnerability assessment for the
structure of communication networks using QoS metrics; we perform experimental
evaluations of our proposed approximation algorithms in this context. | [
1,
0,
0,
0,
0,
0
] |
Title: Out-of-Sample Testing for GANs,
Abstract: We propose a new method to evaluate GANs, namely EvalGAN. EvalGAN relies on a
test set to directly measure the reconstruction quality in the original sample
space (no auxiliary networks are necessary), and it also computes the
(log)likelihood for the reconstructed samples in the test set. Further, EvalGAN
is agnostic to the GAN algorithm and the dataset. We decided to test it on
three state-of-the-art GANs over the well-known CIFAR-10 and CelebA datasets. | [
1,
0,
0,
1,
0,
0
] |
Title: Quantum periodicity in the critical current of superconducting rings with asymmetric link-up of current leads,
Abstract: We use superconducting rings with asymmetric link-up of current leads for
experimental investigation of winding number change at magnetic field
corresponding to the half of the flux quantum inside the ring. According to the
conventional theory, the critical current of such rings should change by jump
due to this change. Experimental data obtained at measurements of aluminum
rings agree with theoretical prediction in magnetic flux region close to
integer numbers of the flux quantum and disagree in the region close to the
half of the one, where a smooth change is observed instead of the jump. First
measurements of tantalum ring give a hope for the jump. Investigation of this
problem may have both fundamental and practical importance. | [
0,
1,
0,
0,
0,
0
] |
Title: A Story of Parametric Trace Slicing, Garbage and Static Analysis,
Abstract: This paper presents a proposal (story) of how statically detecting
unreachable objects (in Java) could be used to improve a particular runtime
verification approach (for Java), namely parametric trace slicing. Monitoring
algorithms for parametric trace slicing depend on garbage collection to (i)
cleanup data-structures storing monitored objects, ensuring they do not become
unmanageably large, and (ii) anticipate the violation of (non-safety)
properties that cannot be satisfied as a monitored object can no longer appear
later in the trace. The proposal is that both usages can be improved by making
the unreachability of monitored objects explicit in the parametric property and
statically introducing additional instrumentation points generating related
events. The ideas presented in this paper are still exploratory and the
intention is to integrate the described techniques into the MarQ monitoring
tool for quantified event automata. | [
1,
0,
0,
0,
0,
0
] |
Title: Extended TQFT arising from enriched multi-fusion categories,
Abstract: We define a symmetric monoidal (4,3)-category with duals whose objects are
certain enriched multi-fusion categories. For every modular tensor category
$\mathcal{C}$, there is a self enriched multi-fusion category $\mathfrak{C}$
giving rise to an object of this symmetric monoidal (4,3)-category. We
conjecture that the extended 3D TQFT given by the fully dualizable object
$\mathfrak{C}$ extends the 1-2-3-dimensional Reshetikhin-Turaev TQFT associated
to the modular tensor category $\mathcal{C}$ down to dimension zero. | [
0,
0,
1,
0,
0,
0
] |
Title: Dynamic Laplace: Efficient Centrality Measure for Weighted or Unweighted Evolving Networks,
Abstract: With its origin in sociology, Social Network Analysis (SNA), quickly emerged
and spread to other areas of research, including anthropology, biology,
information science, organizational studies, political science, and computer
science. Being it's objective the investigation of social structures through
the use of networks and graph theory, Social Network Analysis is, nowadays, an
important research area in several domains. Social Network Analysis cope with
different problems namely network metrics, models, visualization and
information spreading, each one with several approaches, methods and
algorithms. One of the critical areas of Social Network Analysis involves the
calculation of different centrality measures (i.e.: the most important vertices
within a graph). Today, the challenge is how to do this fast and efficiently,
as many increasingly larger datasets are available. Recently, the need to apply
such centrality algorithms to non static networks (i.e.: networks that evolve
over time) is also a new challenge. Incremental and dynamic versions of
centrality measures are starting to emerge (betweenness, closeness, etc). Our
contribution is the proposal of two incremental versions of the Laplacian
Centrality measure, that can be applied not only to large graphs but also to,
weighted or unweighted, dynamically changing networks. The experimental
evaluation was performed with several tests in different types of evolving
networks, incremental or fully dynamic. Results have shown that our incremental
versions of the algorithm can calculate node centralities in large networks,
faster and efficiently than the corresponding batch version in both incremental
and full dynamic network setups. | [
1,
0,
0,
0,
0,
0
] |
Title: Fighting Accounting Fraud Through Forensic Data Analytics,
Abstract: Accounting fraud is a global concern representing a significant threat to the
financial system stability due to the resulting diminishing of the market
confidence and trust of regulatory authorities. Several tricks can be used to
commit accounting fraud, hence the need for non-static regulatory interventions
that take into account different fraudulent patterns. Accordingly, this study
aims to improve the detection of accounting fraud via the implementation of
several machine learning methods to better differentiate between fraud and
non-fraud companies, and to further assist the task of examination within the
riskier firms by evaluating relevant financial indicators. Out-of-sample
results suggest there is a great potential in detecting falsified financial
statements through statistical modelling and analysis of publicly available
accounting information. The proposed methodology can be of assistance to public
auditors and regulatory agencies as it facilitates auditing processes, and
supports more targeted and effective examinations of accounting reports. | [
0,
0,
0,
1,
0,
0
] |
Title: Electric properties of carbon nano-onion/polyaniline composites: a combined electric modulus and ac conductivity study,
Abstract: The complex electric modulus and the ac conductivity of carbon
nanoonion/polyaniline composites were studied from 1 mHz to 1 MHz at isothermal
conditions ranging from 15 K to room temperature. The temperature dependence of
the electric modulus and the dc conductivity analyses indicate a couple of
hopping mechanisms. The distinction between thermally activated processes and
the determination of cross-over temperature were achieved by exploring the
temperature dependence of the fractional exponent of the dispersive ac
conductivity and the bifurcation of the scaled ac conductivity isotherms. The
results are analyzed by combining the granular metal model(inter-grain charge
tunneling of extended electron states located within mesoscopic highly
conducting polyaniline grains) and a 3D Mott variable range hopping model
(phonon assisted tunneling within the carbon nano-onions and clusters). | [
0,
1,
0,
0,
0,
0
] |
Title: Uniform Rates of Convergence of Some Representations of Extremes : a first approach,
Abstract: Uniform convergence rates are provided for asymptotic representations of
sample extremes. These bounds which are universal in the sense that they do not
depend on the extreme value index are meant to be extended to arbitrary samples
extremes in coming papers. | [
0,
0,
0,
1,
0,
0
] |
Title: Eigensolutions and spectral analysis of a model for vertical gene transfer of plasmids,
Abstract: Plasmids are autonomously replicating genetic elements in bacteria. At cell
division plasmids are distributed among the two daughter cells. This gene
transfer from one generation to the next is called vertical gene transfer. We
study the dynamics of a bacterial population carrying plasmids and are in
particular interested in the long-time distribution of plasmids. Starting with
a model for a bacterial population structured by the discrete number of
plasmids, we proceed to the continuum limit in order to derive a continuous
model. The model incorporates plasmid reproduction, division and death of
bacteria, and distribution of plasmids at cell division. It is a hyperbolic
integro-differential equation and a so-called growth-fragmentation-death model.
As we are interested in the long-time distribution of plasmids we study the
associated eigenproblem and show existence of eigensolutions. The stability of
this solution is studied by analyzing the spectrum of the integro-differential
operator given by the eigenproblem. By relating the spectrum with the spectrum
of an integral operator we find a simple real dominating eigenvalue with a
non-negative corresponding eigenfunction. Moreover, we describe an iterative
method for the numerical construction of the eigenfunction. | [
0,
0,
0,
0,
1,
0
] |
Title: Non-Euclidean geometry, nontrivial topology and quantum vacuum effects,
Abstract: Space out of a topological defect of the Abrikosov-Nielsen-Olesen vortex type
is locally flat but non-Euclidean. If a spinor field is quantized in such a
space, then a variety of quantum effects is induced in the vacuum. Basing on
the continuum model for long-wavelength electronic excitations, originating in
the tight-binding approximation for the nearest neighbor interaction of atoms
in the crystal lattice, we consider quantum ground state effects in monolayer
structures warped into nanocones by a disclination; the nonzero size of the
disclination is taken into account, and a boundary condition at the edge of the
disclination is chosen to ensure self-adjointness of the Dirac-Weyl Hamiltonian
operator. In the case of carbon nanocones, we find circumstances when the
quantum ground state effects are independent of the boundary parameter and the
disclination size. | [
0,
1,
0,
0,
0,
0
] |
Title: Theory of ground states for classical Heisenberg spin systems I,
Abstract: We formulate part I of a rigorous theory of ground states for classical,
finite, Heisenberg spin systems. The main result is that all ground states can
be constructed from the eigenvectors of a real, symmetric matrix with entries
comprising the coupling constants of the spin system as well as certain
Lagrange parameters. The eigenvectors correspond to the unique maximum of the
minimal eigenvalue considered as a function of the Lagrange parameters.
However, there are rare cases where all ground states obtained in this way have
unphysical dimensions $M>3$ and the theory would have to be extended. Further
results concern the degree of additional degeneracy, additional to the trivial
degeneracy of ground states due to rotations or reflections. The theory is
illustrated by a couple of elementary examples. | [
0,
1,
1,
0,
0,
0
] |
Title: Spatial analysis of airborne laser scanning point clouds for predicting forest variables,
Abstract: With recent developments in remote sensing technologies, plot-level forest
resources can be predicted utilizing airborne laser scanning (ALS). The
prediction is often assisted by mostly vertical summaries of the ALS point
clouds. We present a spatial analysis of the point cloud by studying the
horizontal distribution of the pulse returns through canopy height models
thresholded at different height levels. The resulting patterns of patches of
vegetation and gabs on each layer are summarized to spatial ALS features. We
propose new features based on the Euler number, which is the number of patches
minus the number of gaps, and the empty-space function, which is a spatial
summary function of the gab space. The empty-space function is also used to
describe differences in the gab structure between two different layers. We
illustrate usefulness of the proposed spatial features for predicting different
forest variables that summarize the spatial structure of forests or their
breast height diameter distribution. We employ the proposed spatial features,
in addition to commonly used features from literature, in the well-known k-nn
estimation method to predict the forest variables. We present the methodology
on the example of a study site in Central Finland. | [
0,
0,
0,
1,
1,
0
] |
Title: Analytic and arithmetic properties of the $(Γ,χ)$-automorphic reproducing kernel function,
Abstract: We consider the reproducing kernel function of the theta Bargmann-Fock
Hilbert space associated to given full-rank lattice and pseudo-character, and
we deal with some of its analytical and arithmetical properties. Specially, the
distribution and discreteness of its zeros are examined and analytic sets
inside a product of fundamental cells is characterized and shown to be finite
and of cardinal less or equal to the dimension of the theta Bargmann-Fock
Hilbert space. Moreover, we obtain some remarkable lattice sums by evaluating
the so-called complex Hermite-Taylor coefficients. Some of them generalize some
of the arithmetic identities established by Perelomov in the framework of
coherent states for the specific case of von Neumann lattice. Such complex
Hermite-Taylor coefficients are nontrivial examples of the so-called lattice's
functions according the Serre terminology. The perfect use of the basic
properties of the complex Hermite polynomials is crucial in this framework. | [
0,
0,
1,
0,
0,
0
] |
Title: Simulated Annealing for JPEG Quantization,
Abstract: JPEG is one of the most widely used image formats, but in some ways remains
surprisingly unoptimized, perhaps because some natural optimizations would go
outside the standard that defines JPEG. We show how to improve JPEG compression
in a standard-compliant, backward-compatible manner, by finding improved
default quantization tables. We describe a simulated annealing technique that
has allowed us to find several quantization tables that perform better than the
industry standard, in terms of both compressed size and image fidelity.
Specifically, we derive tables that reduce the FSIM error by over 10% while
improving compression by over 20% at quality level 95 in our tests; we also
provide similar results for other quality levels. While we acknowledge our
approach can in some images lead to visible artifacts under large
magnification, we believe use of these quantization tables, or additional
tables that could be found using our methodology, would significantly reduce
JPEG file sizes with improved overall image quality. | [
1,
0,
0,
0,
0,
0
] |
Title: All-but-the-Top: Simple and Effective Postprocessing for Word Representations,
Abstract: Real-valued word representations have transformed NLP applications; popular
examples are word2vec and GloVe, recognized for their ability to capture
linguistic regularities. In this paper, we demonstrate a {\em very simple}, and
yet counter-intuitive, postprocessing technique -- eliminate the common mean
vector and a few top dominating directions from the word vectors -- that
renders off-the-shelf representations {\em even stronger}. The postprocessing
is empirically validated on a variety of lexical-level intrinsic tasks (word
similarity, concept categorization, word analogy) and sentence-level tasks
(semantic textural similarity and { text classification}) on multiple datasets
and with a variety of representation methods and hyperparameter choices in
multiple languages; in each case, the processed representations are
consistently better than the original ones. | [
1,
0,
0,
1,
0,
0
] |
Title: Detecting Near Duplicates in Software Documentation,
Abstract: Contemporary software documentation is as complicated as the software itself.
During its lifecycle, the documentation accumulates a lot of near duplicate
fragments, i.e. chunks of text that were copied from a single source and were
later modified in different ways. Such near duplicates decrease documentation
quality and thus hamper its further utilization. At the same time, they are
hard to detect manually due to their fuzzy nature. In this paper we give a
formal definition of near duplicates and present an algorithm for their
detection in software documents. This algorithm is based on the exact software
clone detection approach: the software clone detection tool Clone Miner was
adapted to detect exact duplicates in documents. Then, our algorithm uses these
exact duplicates to construct near ones. We evaluate the proposed algorithm
using the documentation of 19 open source and commercial projects. Our
evaluation is very comprehensive - it covers various documentation types:
design and requirement specifications, programming guides and API
documentation, user manuals. Overall, the evaluation shows that all kinds of
software documentation contain a significant number of both exact and near
duplicates. Next, we report on the performed manual analysis of the detected
near duplicates for the Linux Kernel Documentation. We present both quantative
and qualitative results of this analysis, demonstrate algorithm strengths and
weaknesses, and discuss the benefits of duplicate management in software
documents. | [
1,
0,
0,
0,
0,
0
] |
Title: Comparing Rule-Based and Deep Learning Models for Patient Phenotyping,
Abstract: Objective: We investigate whether deep learning techniques for natural
language processing (NLP) can be used efficiently for patient phenotyping.
Patient phenotyping is a classification task for determining whether a patient
has a medical condition, and is a crucial part of secondary analysis of
healthcare data. We assess the performance of deep learning algorithms and
compare them with classical NLP approaches.
Materials and Methods: We compare convolutional neural networks (CNNs),
n-gram models, and approaches based on cTAKES that extract pre-defined medical
concepts from clinical notes and use them to predict patient phenotypes. The
performance is tested on 10 different phenotyping tasks using 1,610 discharge
summaries extracted from the MIMIC-III database.
Results: CNNs outperform other phenotyping algorithms in all 10 tasks. The
average F1-score of our model is 76 (PPV of 83, and sensitivity of 71) with our
model having an F1-score up to 37 points higher than alternative approaches. We
additionally assess the interpretability of our model by presenting a method
that extracts the most salient phrases for a particular prediction.
Conclusion: We show that NLP methods based on deep learning improve the
performance of patient phenotyping. Our CNN-based algorithm automatically
learns the phrases associated with each patient phenotype. As such, it reduces
the annotation complexity for clinical domain experts, who are normally
required to develop task-specific annotation rules and identify relevant
phrases. Our method performs well in terms of both performance and
interpretability, which indicates that deep learning is an effective approach
to patient phenotyping based on clinicians' notes. | [
1,
0,
0,
1,
0,
0
] |
Title: Jackknife multiplier bootstrap: finite sample approximations to the $U$-process supremum with applications,
Abstract: This paper is concerned with finite sample approximations to the supremum of
a non-degenerate $U$-process of a general order indexed by a function class. We
are primarily interested in situations where the function class as well as the
underlying distribution change with the sample size, and the $U$-process itself
is not weakly convergent as a process. Such situations arise in a variety of
modern statistical problems. We first consider Gaussian approximations, namely,
approximate the $U$-process supremum by the supremum of a Gaussian process, and
derive coupling and Kolmogorov distance bounds. Such Gaussian approximations
are, however, not often directly applicable in statistical problems since the
covariance function of the approximating Gaussian process is unknown. This
motivates us to study bootstrap-type approximations to the $U$-process
supremum. We propose a novel jackknife multiplier bootstrap (JMB) tailored to
the $U$-process, and derive coupling and Kolmogorov distance bounds for the
proposed JMB method. All these results are non-asymptotic, and established
under fairly general conditions on function classes and underlying
distributions. Key technical tools in the proofs are new local maximal
inequalities for $U$-processes, which may be useful in other problems. We also
discuss applications of the general approximation results to testing for
qualitative features of nonparametric functions based on generalized local
$U$-processes. | [
0,
0,
1,
1,
0,
0
] |
Title: On the universality of anomalous scaling exponents of structure functions in turbulent flows,
Abstract: All previous experiments in open turbulent flows (e.g. downstream of grids,
jet and atmospheric boundary layer) have produced quantitatively consistent
values for the scaling exponents of velocity structure functions. The only
measurement in closed turbulent flow (von Kármán swirling flow) using
Taylor-hypothesis, however, produced scaling exponents that are significantly
smaller, suggesting that the universality of these exponents are broken with
respect to change of large scale geometry of the flow. Here, we report
measurements of longitudinal structure functions of velocity in a von
Kármán setup without the use of Taylor-hypothesis. The measurements are
made using Stereo Particle Image Velocimetry at 4 different ranges of spatial
scales, in order to observe a combined inertial subrange spanning roughly one
and a half order of magnitude. We found scaling exponents (up to 9th order)
that are consistent with values from open turbulent flows, suggesting that they
might be in fact universal. | [
0,
1,
0,
0,
0,
0
] |
Title: Tree-based networks: characterisations, metrics, and support trees,
Abstract: Phylogenetic networks generalise phylogenetic trees and allow for the
accurate representation of the evolutionary history of a set of present-day
species whose past includes reticulate events such as hybridisation and lateral
gene transfer. One way to obtain such a network is by starting with a (rooted)
phylogenetic tree $T$, called a base tree, and adding arcs between arcs of $T$.
The class of phylogenetic networks that can be obtained in this way is called
tree-based networks and includes the prominent classes of tree-child and
reticulation-visible networks. Initially defined for binary phylogenetic
networks, tree-based networks naturally extend to arbitrary phylogenetic
networks. In this paper, we generalise recent tree-based characterisations and
associated proximity measures for binary phylogenetic networks to arbitrary
phylogenetic networks. These characterisations are in terms of matchings in
bipartite graphs, path partitions, and antichains. Some of the generalisations
are straightforward to establish using the original approach, while others
require a very different approach. Furthermore, for an arbitrary tree-based
network $N$, we characterise the support trees of $N$, that is, the tree-based
embeddings of $N$. We use this characterisation to give an explicit formula for
the number of support trees of $N$ when $N$ is binary. This formula is written
in terms of the components of a bipartite graph. | [
1,
0,
0,
0,
0,
0
] |
Title: Urban Dreams of Migrants: A Case Study of Migrant Integration in Shanghai,
Abstract: Unprecedented human mobility has driven the rapid urbanization around the
world. In China, the fraction of population dwelling in cities increased from
17.9% to 52.6% between 1978 and 2012. Such large-scale migration poses
challenges for policymakers and important questions for researchers. To
investigate the process of migrant integration, we employ a one-month complete
dataset of telecommunication metadata in Shanghai with 54 million users and 698
million call logs. We find systematic differences between locals and migrants
in their mobile communication networks and geographical locations. For
instance, migrants have more diverse contacts and move around the city with a
larger radius than locals after they settle down. By distinguishing new
migrants (who recently moved to Shanghai) from settled migrants (who have been
in Shanghai for a while), we demonstrate the integration process of new
migrants in their first three weeks. Moreover, we formulate classification
problems to predict whether a person is a migrant. Our classifier is able to
achieve an F1-score of 0.82 when distinguishing settled migrants from locals,
but it remains challenging to identify new migrants because of class imbalance.
This classification setup holds promise for identifying new migrants who will
successfully integrate into locals (new migrants that misclassified as locals). | [
1,
1,
0,
0,
0,
0
] |
Title: Dynamic Rank Maximal Matchings,
Abstract: We consider the problem of matching applicants to posts where applicants have
preferences over posts. Thus the input to our problem is a bipartite graph G =
(A U P,E), where A denotes a set of applicants, P is a set of posts, and there
are ranks on edges which denote the preferences of applicants over posts. A
matching M in G is called rank-maximal if it matches the maximum number of
applicants to their rank 1 posts, subject to this the maximum number of
applicants to their rank 2 posts, and so on.
We consider this problem in a dynamic setting, where vertices and edges can
be added and deleted at any point. Let n and m be the number of vertices and
edges in an instance G, and r be the maximum rank used by any rank-maximal
matching in G. We give a simple O(r(m+n))-time algorithm to update an existing
rank-maximal matching under each of these changes. When r = o(n), this is
faster than recomputing a rank-maximal matching completely using a known
algorithm like that of Irving et al., which takes time O(min((r + n,
r*sqrt(n))m). | [
1,
0,
0,
0,
0,
0
] |
Title: Distance-to-Mean Continuous Conditional Random Fields to Enhance Prediction Problem in Traffic Flow Data,
Abstract: The increase of vehicle in highways may cause traffic congestion as well as
in the normal roadways. Predicting the traffic flow in highways especially, is
demanded to solve this congestion problem. Predictions on time-series
multivariate data, such as in the traffic flow dataset, have been largely
accomplished through various approaches. The approach with conventional
prediction algorithms, such as with Support Vector Machine (SVM), is only
capable of accommodating predictions that are independent in each time unit.
Hence, the sequential relationships in this time series data is hardly
explored. Continuous Conditional Random Field (CCRF) is one of Probabilistic
Graphical Model (PGM) algorithms which can accommodate this problem. The
neighboring aspects of sequential data such as in the time series data can be
expressed by CCRF so that its predictions are more reliable. In this article, a
novel approach called DM-CCRF is adopted by modifying the CCRF prediction
algorithm to strengthen the probability of the predictions made by the baseline
regressor. The result shows that DM-CCRF is superior in performance compared to
CCRF. This is validated by the error decrease of the baseline up to 9%
significance. This is twice the standard CCRF performance which can only
decrease baseline error by 4.582% at most. | [
1,
0,
0,
0,
0,
0
] |
Title: Partial and Total Dielectronic Recombination Rate Coefficients for W$^{55+}$ to W$^{38+}$,
Abstract: Dielectronic recombination (DR) is the dominant mode of recombination in
magnetically confined fusion plasmas for intermediate to low-charged ions of W.
Complete, final-state resolved partial isonuclear W DR rate coefficient data is
required for detailed collisional-radiative modelling for such plasmas in
preparation for the upcoming fusion experiment ITER. To realize this
requirement, we continue {\it The Tungsten Project} by presenting our
calculations for tungsten ions W$^{55+}$ to W$^{38+}$. As per our prior
calculations for W$^{73+}$ to W$^{56+}$, we use the collision package {\sc
autostructure} to calculate partial and total DR rate coefficients for all
relevant core-excitations in intermediate coupling (IC) and configuration
average (CA) using $\kappa$-averaged relativistic wavefunctions. Radiative
recombination (RR) rate coefficients are also calculated for the purpose of
evaluating ionization fractions. Comparison of our DR rate coefficients for
W$^{46+}$ with other authors yields agreement to within 7-19\% at peak
abundance verifying the reliability of our method. Comparison of partial DR
rate coefficients calculated in IC and CA yield differences of a factor
$\sim{2}$ at peak abundance temperature, highlighting the importance of
relativistic configuration mixing. Large differences are observed between
ionization fractions calculated using our recombination rate coefficient data
and that of Pütterich~\etal [Plasma Phys. and Control. Fusion 50 085016,
(2008)]. These differences are attributed to deficiencies in the average-atom
method used by the former to calculate their data. | [
0,
1,
0,
0,
0,
0
] |
Title: Congenial Causal Inference with Binary Structural Nested Mean Models,
Abstract: Structural nested mean models (SNMMs) are among the fundamental tools for
inferring causal effects of time-dependent exposures from longitudinal studies.
With binary outcomes, however, current methods for estimating multiplicative
and additive SNMM parameters suffer from variation dependence between the
causal SNMM parameters and the non-causal nuisance parameters. Estimating
methods for logistic SNMMs do not suffer from this dependence. Unfortunately,
in contrast with the multiplicative and additive models, unbiased estimation of
the causal parameters of a logistic SNMM rely on additional modeling
assumptions even when the treatment probabilities are known. These difficulties
have hindered the uptake of SNMMs in epidemiological practice, where binary
outcomes are common. We solve the variation dependence problem for the binary
multiplicative SNMM by a reparametrization of the non-causal nuisance
parameters. Our novel nuisance parameters are variation independent of the
causal parameters, and hence allows the fitting of a multiplicative SNMM by
unconstrained maximum likelihood. It also allows one to construct true (i.e.
congenial) doubly robust estimators of the causal parameters. Along the way, we
prove that an additive SNMM with binary outcomes does not admit a variation
independent parametrization, thus explaining why we restrict ourselves to the
multiplicative SNMM. | [
0,
0,
0,
1,
0,
0
] |
Title: Improving Search through A3C Reinforcement Learning based Conversational Agent,
Abstract: We develop a reinforcement learning based search assistant which can assist
users through a set of actions and sequence of interactions to enable them
realize their intent. Our approach caters to subjective search where the user
is seeking digital assets such as images which is fundamentally different from
the tasks which have objective and limited search modalities. Labeled
conversational data is generally not available in such search tasks and
training the agent through human interactions can be time consuming. We propose
a stochastic virtual user which impersonates a real user and can be used to
sample user behavior efficiently to train the agent which accelerates the
bootstrapping of the agent. We develop A3C algorithm based context preserving
architecture which enables the agent to provide contextual assistance to the
user. We compare the A3C agent with Q-learning and evaluate its performance on
average rewards and state values it obtains with the virtual user in validation
episodes. Our experiments show that the agent learns to achieve higher rewards
and better states. | [
1,
0,
0,
0,
0,
0
] |
Title: Distributions-oriented wind forecast verification by a hidden Markov model for multivariate circular-linear data,
Abstract: Winds from the North-West quadrant and lack of precipitation are known to
lead to an increase of PM10 concentrations over a residential neighborhood in
the city of Taranto (Italy). In 2012 the local government prescribed a
reduction of industrial emissions by 10% every time such meteorological
conditions are forecasted 72 hours in advance. Wind forecasting is addressed
using the Weather Research and Forecasting (WRF) atmospheric simulation system
by the Regional Environmental Protection Agency. In the context of
distributions-oriented forecast verification, we propose a comprehensive
model-based inferential approach to investigate the ability of the WRF system
to forecast the local wind speed and direction allowing different performances
for unknown weather regimes. Ground-observed and WRF-forecasted wind speed and
direction at a relevant location are jointly modeled as a 4-dimensional time
series with an unknown finite number of states characterized by homogeneous
distributional behavior. The proposed model relies on a mixture of joint
projected and skew normal distributions with time-dependent states, where the
temporal evolution of the state membership follows a first order Markov
process. Parameter estimates, including the number of states, are obtained by a
Bayesian MCMC-based method. Results provide useful insights on the performance
of WRF forecasts in relation to different combinations of wind speed and
direction. | [
0,
0,
0,
1,
0,
0
] |
Title: Automorphisms and deformations of conformally Kähler, Einstein-Maxwell metrics,
Abstract: We obtain a structure theorem for the group of holomorphic automorphisms of a
conformally Kähler, Einstein-Maxwell metric, extending the classical results
of Matsushima, Licherowicz and Calabi in the Kähler-Einstein, cscK, and
extremal Kähler cases. Combined with previous results of LeBrun,
Apostolov-Maschler and Futaki-Ono, this completes the classification of the
conformally Kähler, Einstein--Maxwell metrics on $\mathbb{CP}^1 \times
\mathbb{CP}^1$. We also use our result in order to introduce a (relative)
Mabuchi energy in the more general context of $(K, q, a)$-extremal Kähler
metrics in a given Kähler class, and show that the existence of $(K, q,
a)$-extremal Kähler metrics is stable under small deformation of the Kähler
class, the Killing vector field $K$ and the normalization constant $a$. | [
0,
0,
1,
0,
0,
0
] |
Title: Human-Level Intelligence or Animal-Like Abilities?,
Abstract: The vision systems of the eagle and the snake outperform everything that we
can make in the laboratory, but snakes and eagles cannot build an eyeglass or a
telescope or a microscope. (Judea Pearl) | [
1,
0,
0,
1,
0,
0
] |
Title: Meta-Learning for Contextual Bandit Exploration,
Abstract: We describe MELEE, a meta-learning algorithm for learning a good exploration
policy in the interactive contextual bandit setting. Here, an algorithm must
take actions based on contexts, and learn based only on a reward signal from
the action taken, thereby generating an exploration/exploitation trade-off.
MELEE addresses this trade-off by learning a good exploration strategy for
offline tasks based on synthetic data, on which it can simulate the contextual
bandit setting. Based on these simulations, MELEE uses an imitation learning
strategy to learn a good exploration policy that can then be applied to true
contextual bandit tasks at test time. We compare MELEE to seven strong baseline
contextual bandit algorithms on a set of three hundred real-world datasets, on
which it outperforms alternatives in most settings, especially when differences
in rewards are large. Finally, we demonstrate the importance of having a rich
feature representation for learning how to explore. | [
1,
0,
0,
1,
0,
0
] |
Title: Ride Sharing and Dynamic Networks Analysis,
Abstract: The potential of an efficient ride-sharing scheme to significantly reduce
traffic congestion, lower emission level, as well as facilitating the
introduction of smart cities has been widely demonstrated. This positive thrust
however is faced with several delaying factors, one of which is the volatility
and unpredictability of the potential benefit (or utilization) of ride-sharing
at different times, and in different places. In this work the following
research questions are posed: (a) Is ride-sharing utilization stable over time
or does it undergo significant changes? (b) If ride-sharing utilization is
dynamic, can it be correlated with some traceable features of the traffic? and
(c) If ride-sharing utilization is dynamic, can it be predicted ahead of time?
We analyze a dataset of over 14 Million taxi trips taken in New York City. We
propose a dynamic travel network approach for modeling and forecasting the
potential ride-sharing utilization over time, showing it to be highly volatile.
In order to model the utilization's dynamics we propose a network-centric
approach, projecting the aggregated traffic taken from continuous time periods
into a feature space comprised of topological features of the network implied
by this traffic. This feature space is then used to model the dynamics of
ride-sharing utilization over time. The results of our analysis demonstrate the
significant volatility of ride-sharing utilization over time, indicating that
any policy, design or plan that would disregard this aspect and chose a static
paradigm would undoubtably be either highly inefficient or provide insufficient
resources. We show that using our suggested approach it is possible to model
the potential utilization of ride sharing based on the topological properties
of the rides network. We also show that using this method the potential
utilization can be forecasting a few hours ahead of time. | [
1,
1,
0,
0,
0,
0
] |
Title: Gas Adsorption and Dynamics in Pillared Graphene Frameworks,
Abstract: Pillared Graphene Frameworks are a novel class of microporous materials made
by graphene sheets separated by organic spacers. One of their main features is
that the pillar type and density can be chosen to tune the material properties.
In this work, we present a computer simulation study of adsorption and dynamics
of H$_{4}$, CH$_{2}$, CO$_{2}$, N$_{2}$ and O$_{2}$ and binary mixtures
thereof, in Pillared Graphene Frameworks with nitrogen-containing organic
spacers. In general, we find that pillar density plays the most important role
in determining gas adsorption. In the low-pressure regime (< 10 bar) the amount
of gas adsorbed is an increasing function of pillar density. At higher
pressures the opposite trend is observed. Diffusion coefficients were computed
for representative structures taking into account the framework flexibility
that is essential in assessing the dynamical properties of the adsorbed gases.
Good performance for the gas separation in CH$_{4}$/H$_{2}$, CO$_{2}$/H$_{2}$
and CO$_{2}$/N$_{2}$ mixtures was found with values comparable to those of
metal-organic frameworks and zeolites. | [
0,
1,
0,
0,
0,
0
] |
Title: Conditional Mean and Quantile Dependence Testing in High Dimension,
Abstract: Motivated by applications in biological science, we propose a novel test to
assess the conditional mean dependence of a response variable on a large number
of covariates. Our procedure is built on the martingale difference divergence
recently proposed in Shao and Zhang (2014), and it is able to detect a certain
type of departure from the null hypothesis of conditional mean independence
without making any specific model assumptions. Theoretically, we establish the
asymptotic normality of the proposed test statistic under suitable assumption
on the eigenvalues of a Hermitian operator, which is constructed based on the
characteristic function of the covariates. These conditions can be simplified
under banded dependence structure on the covariates or Gaussian design. To
account for heterogeneity within the data, we further develop a testing
procedure for conditional quantile independence at a given quantile level and
provide an asymptotic justification. Empirically, our test of conditional mean
independence delivers comparable results to the competitor, which was
constructed under the linear model framework, when the underlying model is
linear. It significantly outperforms the competitor when the conditional mean
admits a nonlinear form. | [
0,
0,
1,
1,
0,
0
] |
Title: Cohomology of the flag variety under PBW degenerations,
Abstract: PBW degenerations are a particularly nice family of flat degenerations of
type A flag varieties. We show that the cohomology of any PBW degeneration of
the flag variety surjects onto the cohomology of the original flag variety, and
that this holds in an equivariant setting too. We also prove that the same is
true in the symplectic setting when considering Feigin's linear degeneration of
the symplectic flag variety. | [
0,
0,
1,
0,
0,
0
] |
Title: Exact MAP inference in general higher-order graphical models using linear programming,
Abstract: This paper is concerned with the problem of exact MAP inference in general
higher-order graphical models by means of a traditional linear programming
relaxation approach. In fact, the proof that we have developed in this paper is
a rather simple algebraic proof being made straightforward, above all, by the
introduction of two novel algebraic tools. Indeed, on the one hand, we
introduce the notion of delta-distribution which merely stands for the
difference of two arbitrary probability distributions, and which mainly serves
to alleviate the sign constraint inherent to a traditional probability
distribution. On the other hand, we develop an approximation framework of
general discrete functions by means of an orthogonal projection expressing in
terms of linear combinations of function margins with respect to a given
collection of point subsets, though, we rather exploit the latter approach for
the purpose of modeling locally consistent sets of discrete functions from a
global perspective. After that, as a first step, we develop from scratch the
expectation optimization framework which is nothing else than a reformulation,
on stochastic grounds, of the convex-hull approach, as a second step, we
develop the traditional LP relaxation of such an expectation optimization
approach, and we show that it enables to solve the MAP inference problem in
graphical models under rather general assumptions. Last but not least, we
describe an algorithm which allows to compute an exact MAP solution from a
perhaps fractional optimal (probability) solution of the proposed LP
relaxation. | [
1,
0,
0,
0,
0,
0
] |
Title: Dissolution of topological Fermi arcs in a dirty Weyl semimetal,
Abstract: Weyl semimetals (WSMs) have recently attracted a great deal of attention as
they provide condensed matter realization of chiral anomaly, feature
topologically protected Fermi arc surface states and sustain sharp chiral Weyl
quasiparticles up to a critical disorder at which a continuous quantum phase
transition (QPT) drives the system into a metallic phase. We here numerically
demonstrate that with increasing strength of disorder the Fermi arc gradually
looses its sharpness, and close to the WSM-metal QPT it completely dissolves
into the metallic bath of the bulk. Predicted topological nature of the
WSM-metal QPT and the resulting bulk-boundary correspondence across this
transition can directly be observed in
angle-resolved-photo-emmision-spectroscopy (ARPES) and Fourier transformed
scanning-tunneling-microscopy (STM) measurements by following the continuous
deformation of the Fermi arcs with increasing disorder in recently discovered
Weyl materials. | [
0,
1,
0,
0,
0,
0
] |
Title: Use of First and Third Person Views for Deep Intersection Classification,
Abstract: We explore the problem of intersection classification using monocular
on-board passive vision, with the goal of classifying traffic scenes with
respect to road topology. We divide the existing approaches into two broad
categories according to the type of input data: (a) first person vision (FPV)
approaches, which use an egocentric view sequence as the intersection is
passed; and (b) third person vision (TPV) approaches, which use a single view
immediately before entering the intersection. The FPV and TPV approaches each
have advantages and disadvantages. Therefore, we aim to combine them into a
unified deep learning framework. Experimental results show that the proposed
FPV-TPV scheme outperforms previous methods and only requires minimal FPV/TPV
measurements. | [
1,
0,
0,
0,
0,
0
] |
Title: Excitable behaviors,
Abstract: This chapter revisits the concept of excitability, a basic system property of
neurons. The focus is on excitable systems regarded as behaviors rather than
dynamical systems. By this we mean open systems modulated by specific
interconnection properties rather than closed systems classified by their
parameter ranges. Modeling, analysis, and synthesis questions can be formulated
in the classical language of circuit theory. The input-output characterization
of excitability is in terms of the local sensitivity of the current-voltage
relationship. It suggests the formulation of novel questions for non-linear
system theory, inspired by questions from experimental neurophysiology. | [
1,
0,
0,
0,
0,
0
] |
Title: Laplacian solitons: questions and homogeneous examples,
Abstract: We give the first examples of closed Laplacian solitons which are shrinking,
and in particular produce closed Laplacian flow solutions with a finite-time
singularity. Extremally Ricci pinched G2-structures (introduced by Bryant)
which are steady Laplacian solitons have also been found. All the examples are
left-invariant G2-structures on solvable Lie groups. | [
0,
0,
1,
0,
0,
0
] |
Title: The equivariant index of twisted dirac operators and semi-classical limits,
Abstract: Consider a spin manifold M, equipped with a line bundle L and an action of a
compact Lie group G. We can attach to this data a family Theta(k) of
distributions on the dual of the Lie algebra of G. The aim of this paper is to
study the asymptotic behaviour of Theta(k) when k is large, and M possibly non
compact, and to explore a functorial consequence of this formula for reduced
spaces. | [
0,
0,
1,
0,
0,
0
] |
Title: Mass transfer in asymptotic-giant-branch binary systems,
Abstract: Binary stars can interact via mass transfer when one member (the primary)
ascends onto a giant branch. The amount of gas ejected by the binary and the
amount of gas accreted by the secondary over the lifetime of the primary
influence the subsequent binary phenomenology. Some of the gas ejected by the
binary will remain gravitationally bound and its distribution will be closely
related to the formation of planetary nebulae. We investigate the nature of
mass transfer in binary systems containing an AGB star by adding radiative
transfer to the AstroBEAR AMR Hydro/MHD code. | [
0,
1,
0,
0,
0,
0
] |
Title: On polar relative normalizations of ruled surfaces,
Abstract: This paper deals with skew ruled surfaces in the Euclidean space
$\mathbb{E}^{3}$ which are equipped with polar normalizations, that is,
relative normalizations such that the relative normal at each point of the
ruled surface lies on the corresponding polar plane. We determine the
invariants of a such normalized ruled surface and we study some properties of
the Tchebychev vector field and the support vector field of a polar
normalization. Furthermore, we study a special polar normalization, the
relative image of which degenerates into a curve. | [
0,
0,
1,
0,
0,
0
] |
Title: The Rank Effect,
Abstract: We decompose returns for portfolios of bottom-ranked, lower-priced assets
relative to the market into rank crossovers and changes in the relative price
of those bottom-ranked assets. This decomposition is general and consistent
with virtually any asset pricing model. Crossovers measure changes in rank and
are smoothly increasing over time, while return fluctuations are driven by
volatile relative price changes. Our results imply that in a closed,
dividend-free market in which the relative price of bottom-ranked assets is
approximately constant, a portfolio of those bottom-ranked assets will
outperform the market portfolio over time. We show that bottom-ranked relative
commodity futures prices have increased only slightly, and confirm the
existence of substantial excess returns predicted by our theory. If these
excess returns did not exist, then top-ranked relative prices would have had to
be much higher in 2018 than those actually observed -- this would imply a
radically different commodity price distribution. | [
0,
0,
0,
0,
0,
1
] |
Title: The Generalized Label Correcting Method for Optimal Kinodynamic Motion Planning,
Abstract: Nearly all autonomous robotic systems use some form of motion planning to
compute reference motions through their environment. An increasing use of
autonomous robots in a broad range of applications creates a need for
efficient, general purpose motion planning algorithms that are applicable in
any of these new application domains.
This thesis presents a resolution complete optimal kinodynamic motion
planning algorithm based on a direct forward search of the set of admissible
input signals to a dynamical model. The advantage of this generalized label
correcting method is that it does not require a local planning subroutine as in
the case of related methods.
Preliminary material focuses on new topological properties of the canonical
problem formulation that are used to show continuity of the performance
objective. These observations are used to derive a generalization of Bellman's
principle of optimality in the context of kinodynamic motion planning. A
generalized label correcting algorithm is then proposed which leverages these
results to prune candidate input signals from the search when their cost is
greater than related signals.
The second part of this thesis addresses admissible heuristics for
kinodynamic motion planning. An admissibility condition is derived that can be
used to verify the admissibility of candidate heuristics for a particular
problem. This condition also characterizes a convex set of admissible
heuristics.
A linear program is formulated to obtain a heuristic which is as close to the
optimal cost-to-go as possible while remaining admissible. This optimization is
justified by showing its solution coincides with the solution to the
Hamilton-Jacobi-Bellman equation. Lastly, a sum-of-squares relaxation of this
infinite-dimensional linear program is proposed for obtaining provably
admissible approximate solutions. | [
1,
0,
0,
0,
0,
0
] |
Title: Formally continuous functions on Baire space,
Abstract: A function from Baire space to the natural numbers is called formally
continuous if it is induced by a morphism between the corresponding formal
spaces. We compare formal continuity to two other notions of continuity on
Baire space working in Bishop constructive mathematics: one is a function
induced by a Brouwer-operation (i.e. inductively defined neighbourhood
function); the other is a function uniformly continuous near every compact
image. We show that formal continuity is equivalent to the former while it is
strictly stronger than the latter. | [
1,
0,
1,
0,
0,
0
] |
Title: Universal Joint Image Clustering and Registration using Partition Information,
Abstract: We consider the problem of universal joint clustering and registration of
images and define algorithms using multivariate information functionals. We
first study registering two images using maximum mutual information and prove
its asymptotic optimality. We then show the shortcomings of pairwise
registration in multi-image registration, and design an asymptotically optimal
algorithm based on multiinformation. Further, we define a novel multivariate
information functional to perform joint clustering and registration of images,
and prove consistency of the algorithm. Finally, we consider registration and
clustering of numerous limited-resolution images, defining algorithms that are
order-optimal in scaling of number of pixels in each image with the number of
images. | [
1,
0,
1,
1,
0,
0
] |
Title: Quantitative statistical stability and speed of convergence to equilibrium for partially hyperbolic skew products,
Abstract: We consider a general relation between fixed point stability of suitably
perturbed transfer operators and convergence to equilibrium (a notion which is
strictly related to decay of correlations). We apply this relation to
deterministic perturbations of a class of (piecewise) partially hyperbolic skew
products whose behavior on the preserved fibration is dominated by the
expansion of the base map. In particular we apply the results to power law
mixing toral extensions. It turns out that in this case, the dependence of the
physical measure on small deterministic perturbations, in a suitable
anisotropic metric is at least Holder continuous, with an exponent which is
explicitly estimated depending on the arithmetical properties of the system. We
show explicit examples of toral extensions having actually Holder stability and
non differentiable dependence of the physical measure on perturbations. | [
0,
0,
1,
0,
0,
0
] |
Title: Minimax Rényi Redundancy,
Abstract: The redundancy for universal lossless compression of discrete memoryless
sources in Campbell's setting is characterized as a minimax Rényi divergence,
which is shown to be equal to the maximal $\alpha$-mutual information via a
generalized redundancy-capacity theorem. Special attention is placed on the
analysis of the asymptotics of minimax Rényi divergence, which is determined
up to a term vanishing in blocklength. | [
1,
0,
1,
0,
0,
0
] |
Title: Dynamical control of atoms with polarized bichromatic weak field,
Abstract: We propose ultranarrow dynamical control of population oscillation (PO)
between ground states through the polarization content of an input bichromatic
field. Appropriate engineering of classical interference between optical fields
results in PO arising exclusively from optical pumping. Contrary to the
expected broad spectral response associated with optical pumping, we obtain
subnatural linewidth in complete absence of quantum interference. The
ellipticity of the light polarizations can be used for temporal shaping of the
PO leading to generation of multiple sidebands even at low light level. | [
0,
1,
0,
0,
0,
0
] |
Title: Twisting and Mixing,
Abstract: We present a framework that connects three interesting classes of groups: the
twisted groups (also known as Suzuki-Ree groups), the mixed groups and the
exotic pseudo-reductive groups.
For a given characteristic p, we construct categories of twisted and mixed
schemes. Ordinary schemes are a full subcategory of the mixed schemes. Mixed
schemes arise from a twisted scheme by base change, although not every mixed
scheme arises this way. The group objects in these categories are called
twisted and mixed group schemes.
Our main theorems state: (1) The twisted Chevalley groups ${}^2\mathsf B_2$,
${}^2\mathsf G_2$ and ${}^2\mathsf F_4$ arise as rational points of twisted
group schemes. (2) The mixed groups in the sense of Tits arise as rational
points of mixed group schemes over mixed fields. (3) The exotic
pseudo-reductive groups of Conrad, Gabber and Prasad are Weil restrictions of
mixed group schemes. | [
0,
0,
1,
0,
0,
0
] |
Title: KeyVec: Key-semantics Preserving Document Representations,
Abstract: Previous studies have demonstrated the empirical success of word embeddings
in various applications. In this paper, we investigate the problem of learning
distributed representations for text documents which many machine learning
algorithms take as input for a number of NLP tasks.
We propose a neural network model, KeyVec, which learns document
representations with the goal of preserving key semantics of the input text. It
enables the learned low-dimensional vectors to retain the topics and important
information from the documents that will flow to downstream tasks. Our
empirical evaluations show the superior quality of KeyVec representations in
two different document understanding tasks. | [
1,
0,
0,
0,
0,
0
] |
Title: Probabilistic Assessment of PV-Battery System Impacts on LV Distribution Networks,
Abstract: The increasing uptake of residential batteries has led to suggestions that
the prevalence of batteries on LV networks will serendipitously mitigate the
technical problems induced by PV installations. However, in general, the
effects of PV-battery systems on LV networks have not been well studied. Given
this background, in this paper, we test the assertion that the uncoordinated
operation of batteries improves network performance. In order to carry out this
assessment, we develop a methodology for incorporating home energy management
(HEM) operational decisions within a Monte Carlo (MC) power flow analysis
comprising three parts. First, due to the unavailability of large number of
load and PV traces required for MC analysis, we used a maximum a-posteriori
Dirichlet process to generate statistically representative synthetic profiles.
Second, a policy function approximation (PFA) that emulates the outputs of the
HEM solver is implemented to provide battery scheduling policies for a pool of
customers, making simulation of optimization-based HEM feasible within MC
studies. Third, the resulting net loads are used in a MC power flow time series
study. The efficacy of our method is shown on three typical LV feeders. Our
assessment finds that uncoordinated PV-battery systems have little beneficial
impact on LV networks. | [
1,
0,
0,
0,
0,
0
] |
Title: Extremely broadband ultralight thermally emissive metasurfaces,
Abstract: We report the design, fabrication and characterization of ultralight highly
emissive metaphotonic structures with record-low mass/area that emit thermal
radiation efficiently over a broad spectral (2 to 35 microns) and angular (0-60
degrees) range. The structures comprise one to three pairs of alternating
nanometer-scale metallic and dielectric layers, and have measured effective 300
K hemispherical emissivities of 0.7 to 0.9. To our knowledge, these structures,
which are all subwavelength in thickness are the lightest reported metasurfaces
with comparable infrared emissivity. The superior optical properties, together
with their mechanical flexibility, low outgassing, and low areal mass, suggest
that these metasurfaces are candidates for thermal management in applications
demanding of ultralight flexible structures, including aerospace applications,
ultralight photovoltaics, lightweight flexible electronics, and textiles for
thermal insulation. | [
0,
1,
0,
0,
0,
0
] |
Title: Optimal Scheduling of Multi-Energy Systems with Flexible Electrical and Thermal Loads,
Abstract: This paper proposes a detailed optimal scheduling model of an exemplar
multi-energy system comprising combined cycle power plants (CCPPs), battery
energy storage systems, renewable energy sources, boilers, thermal energy
storage systems,electric loads and thermal loads. The proposed model considers
the detailed start-up and shutdown power trajectories of the gas turbines,
steam turbines and boilers. Furthermore, a practical,multi-energy load
management scheme is proposed within the framework of the optimal scheduling
problem. The proposed load management scheme utilizes the flexibility offered
by system components such as flexible electrical pump loads, electrical
interruptible loads and a flexible thermal load to reduce the overall energy
cost of the system. The efficacy of the proposed model in reducing the energy
cost of the system is demonstrated in the context of a day-ahead scheduling
problem using four illustrative scenarios. | [
1,
0,
0,
0,
0,
0
] |
Title: AFT*: Integrating Active Learning and Transfer Learning to Reduce Annotation Efforts,
Abstract: The splendid success of convolutional neural networks (CNNs) in computer
vision is largely attributed to the availability of large annotated datasets,
such as ImageNet and Places. However, in biomedical imaging, it is very
challenging to create such large annotated datasets, as annotating biomedical
images is not only tedious, laborious, and time consuming, but also demanding
of costly, specialty-oriented skills, which are not easily accessible. To
dramatically reduce annotation cost, this paper presents a novel method to
naturally integrate active learning and transfer learning (fine-tuning) into a
single framework, called AFT*, which starts directly with a pre-trained CNN to
seek "worthy" samples for annotation and gradually enhance the (fine-tuned) CNN
via continuous fine-tuning. We have evaluated our method in three distinct
biomedical imaging applications, demonstrating that it can cut the annotation
cost by at least half, in comparison with the state-of-the-art method. This
performance is attributed to the several advantages derived from the advanced
active, continuous learning capability of our method. Although AFT* was
initially conceived in the context of computer-aided diagnosis in biomedical
imaging, it is generic and applicable to many tasks in computer vision and
image analysis; we illustrate the key ideas behind AFT* with the Places
database for scene interpretation in natural images. | [
0,
0,
0,
1,
0,
0
] |
Title: Do triangle-free planar graphs have exponentially many 3-colorings?,
Abstract: Thomassen conjectured that triangle-free planar graphs have an exponential
number of $3$-colorings. We show this conjecture to be equivalent to the
following statement: there exists a positive real $\alpha$ such that whenever
$G$ is a planar graph and $A$ is a subset of its edges whose deletion makes $G$
triangle-free, there exists a subset $A'$ of $A$ of size at least $\alpha|A|$
such that $G-(A\setminus A')$ is $3$-colorable. This equivalence allows us to
study restricted situations, where we can prove the statement to be true. | [
0,
0,
1,
0,
0,
0
] |
Title: Self-sustained activity in balanced networks with low firing-rate,
Abstract: The brain can display self-sustained activity (SSA), which is the persistent
firing of neurons in the absence of external stimuli. This spontaneous activity
shows low neuronal firing rates and is observed in diverse in vitro and in vivo
situations. In this work, we study the influence of excitatory/inhibitory
balance, connection density, and network size on the self-sustained activity of
a neuronal network model. We build a random network of adaptive exponential
integrate-and-fire (AdEx) neuron models connected through inhibitory and
excitatory chemical synapses. The AdEx model mimics several behaviours of
biological neurons, such as spike initiation, adaptation, and bursting
patterns. In an excitation/inhibition balanced state, if the mean connection
degree (K) is fixed, the firing rate does not depend on the network size (N),
whereas for fixed N, the firing rate decreases when K increases. However, for
large K, SSA states can appear only for large N. We show the existence of SSA
states with similar behaviours to those observed in experimental recordings,
such as very low and irregular neuronal firing rates, and spike-train power
spectra with slow fluctuations, only for balanced networks of large size. | [
0,
0,
0,
0,
1,
0
] |
Title: Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data,
Abstract: Several important applications, such as streaming PCA and semidefinite
programming, involve a large-scale positive-semidefinite (psd) matrix that is
presented as a sequence of linear updates. Because of storage limitations, it
may only be possible to retain a sketch of the psd matrix. This paper develops
a new algorithm for fixed-rank psd approximation from a sketch. The approach
combines the Nystrom approximation with a novel mechanism for rank truncation.
Theoretical analysis establishes that the proposed method can achieve any
prescribed relative error in the Schatten 1-norm and that it exploits the
spectral decay of the input matrix. Computer experiments show that the proposed
method dominates alternative techniques for fixed-rank psd matrix approximation
across a wide range of examples. | [
1,
0,
0,
1,
0,
0
] |
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