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Title: The time geography of segregation during working hours,
Abstract: Understanding segregation is essential to develop planning tools for building
more inclusive cities. Theoretically, segregation at the work place has been
described as lower compared to residential segregation given the importance of
skill complementarity among other productive factors shaping the economies of
cities. This paper tackles segregation during working hours from a dynamical
perspective by focusing on the movement of urbanites across the city. In
contrast to measuring residential patterns of segregation, we used mobile phone
data to infer home-work trajectory net- works and apply a community detection
algorithm to the example city of Santiago, Chile. We then describe
qualitatively and quantitatively outlined communities, in terms of their socio
eco- nomic composition. We then evaluate segregation for each of these
communities as the probability that a person from a specific community will
interact with a co-worker from the same commu- nity. Finally, we compare these
results with simulations where a new work location is set for each real user,
following the empirical probability distributions of home-work distances and
angles of direction for each community. Methodologically, this study shows that
segregation during working hours for Santiago is unexpectedly high for most of
the city with the exception of its central and business district. In fact, the
only community that is not statistically segregated corresponds to the downtown
area of Santiago, described as a zone of encounter and integration across the
city. | [
1,
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] |
Title: Image transformations on locally compact spaces,
Abstract: An image is here defined to be a set which is either open or closed and an
image transformation is structure preserving in the following sense: It
corresponds to an algebra homomorphism for each singly generated algebra. The
results extend parts of results of J.F. Aarnes on quasi-measures, -states,
-homomorphisms, and image-transformations from the setting compact Hausdorff
spaces to locally compact Hausdorff spaces. | [
0,
0,
1,
1,
0,
0
] |
Title: From Half-metal to Semiconductor: Electron-correlation Effects in Zigzag SiC Nanoribbons From First Principles,
Abstract: We performed electronic structure calculations based on the first-principles
many-body theory approach in order to study quasiparticle band gaps, and
optical absorption spectra of hydrogen-passivated zigzag SiC nanoribbons.
Self-energy corrections are included using the GW approximation, and excitonic
effects are included using the Bethe-Salpeter equation. We have systematically
studied nanoribbons that have widths between 0.6 $\text{nm}$ and 2.2
$\text{nm}$. Quasiparticle corrections widened the Kohn-Sham band gaps because
of enhanced interaction effects, caused by reduced dimensionality. Zigzag SiC
nanoribbons with widths larger than 1 nm, exhibit half-metallicity at the
mean-field level. The self-energy corrections increased band gaps
substantially, thereby transforming the half-metallic zigzag SiC nanoribbons,
to narrow gap spin-polarized semiconductors. Optical absorption spectra of
these nanoribbons get dramatically modified upon inclusion of electron-hole
interactions, and the narrowest ribbon exhibits strongly bound excitons, with
binding energy of 2.1 eV. Thus, the narrowest zigzag SiC nanoribbon has the
potential to be used in optoelectronic devices operating in the IR region of
the spectrum, while the broader ones, exhibiting spin polarization, can be
utilized in spintronic applications. | [
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1,
0,
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] |
Title: On the fundamental group of semi-Riemannian manifolds with positive curvature operator,
Abstract: This paper presents an investigation of the relation between some positivity
of the curvature and the finiteness of fundamental groups in semi-Riemannian
geometry. We consider semi-Riemannian submersions $\pi : (E, g) \rightarrow (B,
-g_{B}) $ under the condition with $(B, g_{B})$ Riemannian, the fiber closed
Riemannian, and the horizontal distribution integrable. Then we prove that, if
the lightlike geodesically complete or timelike geodesically complete
semi-Riemannian manifold $E$ has some positivity of curvature, then the
fundamental group of the fiber is finite. Moreover we construct an example of
semi-Riemannian submersions with some positivity of curvature, non-integrable
horizontal distribution, and the finiteness of the fundamental group of the
fiber. | [
0,
0,
1,
0,
0,
0
] |
Title: SRN: Side-output Residual Network for Object Symmetry Detection in the Wild,
Abstract: In this paper, we establish a baseline for object symmetry detection in
complex backgrounds by presenting a new benchmark and an end-to-end deep
learning approach, opening up a promising direction for symmetry detection in
the wild. The new benchmark, named Sym-PASCAL, spans challenges including
object diversity, multi-objects, part-invisibility, and various complex
backgrounds that are far beyond those in existing datasets. The proposed
symmetry detection approach, named Side-output Residual Network (SRN),
leverages output Residual Units (RUs) to fit the errors between the object
symmetry groundtruth and the outputs of RUs. By stacking RUs in a
deep-to-shallow manner, SRN exploits the 'flow' of errors among multiple scales
to ease the problems of fitting complex outputs with limited layers,
suppressing the complex backgrounds, and effectively matching object symmetry
of different scales. Experimental results validate both the benchmark and its
challenging aspects related to realworld images, and the state-of-the-art
performance of our symmetry detection approach. The benchmark and the code for
SRN are publicly available at this https URL. | [
1,
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] |
Title: A binary main belt comet,
Abstract: The asteroids are primitive solar system bodies which evolve both
collisionally and through disruptions due to rapid rotation [1]. These
processes can lead to the formation of binary asteroids [2-4] and to the
release of dust [5], both directly and, in some cases, through uncovering
frozen volatiles. In a sub-set of the asteroids called main-belt comets (MBCs),
the sublimation of excavated volatiles causes transient comet-like activity
[6-8]. Torques exerted by sublimation measurably influence the spin rates of
active comets [9] and might lead to the splitting of bilobate comet nuclei
[10]. The kilometer-sized main-belt asteroid 288P (300163) showed activity for
several months around its perihelion 2011 [11], suspected to be sustained by
the sublimation of water ice [12] and supported by rapid rotation [13], while
at least one component rotates slowly with a period of 16 hours [14]. 288P is
part of a young family of at least 11 asteroids that formed from a ~10km
diameter precursor during a shattering collision 7.5 million years ago [15].
Here we report that 288P is a binary main-belt comet. It is different from the
known asteroid binaries for its combination of wide separation, near-equal
component size, high eccentricity, and comet-like activity. The observations
also provide strong support for sublimation as the driver of activity in 288P
and show that sublimation torques may play a significant role in binary orbit
evolution. | [
0,
1,
0,
0,
0,
0
] |
Title: Dynamic Clustering Algorithms via Small-Variance Analysis of Markov Chain Mixture Models,
Abstract: Bayesian nonparametrics are a class of probabilistic models in which the
model size is inferred from data. A recently developed methodology in this
field is small-variance asymptotic analysis, a mathematical technique for
deriving learning algorithms that capture much of the flexibility of Bayesian
nonparametric inference algorithms, but are simpler to implement and less
computationally expensive. Past work on small-variance analysis of Bayesian
nonparametric inference algorithms has exclusively considered batch models
trained on a single, static dataset, which are incapable of capturing time
evolution in the latent structure of the data. This work presents a
small-variance analysis of the maximum a posteriori filtering problem for a
temporally varying mixture model with a Markov dependence structure, which
captures temporally evolving clusters within a dataset. Two clustering
algorithms result from the analysis: D-Means, an iterative clustering algorithm
for linearly separable, spherical clusters; and SD-Means, a spectral clustering
algorithm derived from a kernelized, relaxed version of the clustering problem.
Empirical results from experiments demonstrate the advantages of using D-Means
and SD-Means over contemporary clustering algorithms, in terms of both
computational cost and clustering accuracy. | [
0,
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] |
Title: Deep Depth From Focus,
Abstract: Depth from focus (DFF) is one of the classical ill-posed inverse problems in
computer vision. Most approaches recover the depth at each pixel based on the
focal setting which exhibits maximal sharpness. Yet, it is not obvious how to
reliably estimate the sharpness level, particularly in low-textured areas. In
this paper, we propose `Deep Depth From Focus (DDFF)' as the first end-to-end
learning approach to this problem. One of the main challenges we face is the
hunger for data of deep neural networks. In order to obtain a significant
amount of focal stacks with corresponding groundtruth depth, we propose to
leverage a light-field camera with a co-calibrated RGB-D sensor. This allows us
to digitally create focal stacks of varying sizes. Compared to existing
benchmarks our dataset is 25 times larger, enabling the use of machine learning
for this inverse problem. We compare our results with state-of-the-art DFF
methods and we also analyze the effect of several key deep architectural
components. These experiments show that our proposed method `DDFFNet' achieves
state-of-the-art performance in all scenes, reducing depth error by more than
75% compared to the classical DFF methods. | [
1,
0,
0,
0,
0,
0
] |
Title: Two-Stream 3D Convolutional Neural Network for Skeleton-Based Action Recognition,
Abstract: It remains a challenge to efficiently extract spatialtemporal information
from skeleton sequences for 3D human action recognition. Although most recent
action recognition methods are based on Recurrent Neural Networks which present
outstanding performance, one of the shortcomings of these methods is the
tendency to overemphasize the temporal information. Since 3D convolutional
neural network(3D CNN) is a powerful tool to simultaneously learn features from
both spatial and temporal dimensions through capturing the correlations between
three dimensional signals, this paper proposes a novel two-stream model using
3D CNN. To our best knowledge, this is the first application of 3D CNN in
skeleton-based action recognition. Our method consists of three stages. First,
skeleton joints are mapped into a 3D coordinate space and then encoding the
spatial and temporal information, respectively. Second, 3D CNN models are
seperately adopted to extract deep features from two streams. Third, to enhance
the ability of deep features to capture global relationships, we extend every
stream into multitemporal version. Extensive experiments on the SmartHome
dataset and the large-scale NTU RGB-D dataset demonstrate that our method
outperforms most of RNN-based methods, which verify the complementary property
between spatial and temporal information and the robustness to noise. | [
1,
0,
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0,
0,
0
] |
Title: A fast reconstruction algorithm for geometric inverse problems using topological sensitivity analysis and Dirichlet-Neumann cost functional approach,
Abstract: This paper is concerned with the detection of objects immersed in anisotropic
media from boundary measurements. We propose an accurate approach based on the
Kohn-Vogelius formulation and the topological sensitivity analysis method. The
inverse problem is formulated as a topology optimization one minimizing an
energy like functional. A topological asymptotic expansion is derived for the
anisotropic Laplace operator. The unknown object is reconstructed using a
level-set curve of the topological gradient. The efficiency and accuracy of the
proposed algorithm are illustrated by some numerical results. MOTS-CLÉS :
Problème inverse géométrique, Laplace anisotrope, formulation de
Kohn-Vogelius, analyse de sensibilité, optimisation topologique. | [
0,
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] |
Title: Tomographic X-ray data of carved cheese,
Abstract: This is the documentation of the tomographic X-ray data of a carved cheese
slice. Data are available at www.fips.fi/dataset.php, and can be freely used
for scientific purposes with appropriate references to them, and to this
document in this http URL. The data set consists of (1) the X-ray sinogram
of a single 2D slice of the cheese slice with three different resolutions and
(2) the corresponding measurement matrices modeling the linear operation of the
X-ray transform. Each of these sinograms was obtained from a measured
360-projection fan-beam sinogram by down-sampling and taking logarithms. The
original (measured) sinogram is also provided in its original form and
resolution. | [
0,
1,
0,
0,
0,
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] |
Title: Quantile Markov Decision Process,
Abstract: In this paper, we consider the problem of optimizing the quantiles of the
cumulative rewards of Markov Decision Processes (MDP), to which we refers as
Quantile Markov Decision Processes (QMDP). Traditionally, the goal of a Markov
Decision Process (MDP) is to maximize expected cumulative reward over a defined
horizon (possibly to be infinite). In many applications, however, a decision
maker may be interested in optimizing a specific quantile of the cumulative
reward instead of its expectation. Our framework of QMDP provides analytical
results characterizing the optimal QMDP solution and presents the algorithm for
solving the QMDP. We provide analytical results characterizing the optimal QMDP
solution and present the algorithms for solving the QMDP. We illustrate the
model with two experiments: a grid game and a HIV optimal treatment experiment. | [
1,
0,
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0,
0,
0
] |
Title: Generation of $1/f$ noise motivated by a model for musical melodies,
Abstract: We present a model to generate power spectrum noise with intensity
proportional to 1/f as a function of frequency f. The model arises from a
broken-symmetry variable which corresponds to absolute pitch, where
fluctuations occur in an attempt to restore that symmetry, influenced by
interactions in the creation of musical melodies. | [
0,
1,
0,
0,
0,
0
] |
Title: The geometry of the generalized algebraic Riccati equation and of the singular Hamiltonian system,
Abstract: This paper analyzes the properties of the solutions of the generalized
continuous algebraic Riccati equation from a geometric perspective. This
analysis reveals the presence of a subspace that may provide an appropriate
degree of freedom to stabilize the system in the related optimal control
problem even in cases where the Riccati equation does not admit a stabilizing
solution. | [
0,
0,
1,
0,
0,
0
] |
Title: Astrophotonics: molding the flow of light in astronomical instruments,
Abstract: Since its emergence two decades ago, astrophotonics has found broad
application in scientific instruments at many institutions worldwide. The case
for astrophotonics becomes more compelling as telescopes push for AO-assisted,
diffraction-limited performance, a mode of observing that is central to the
next-generation of extremely large telescopes (ELTs). Even AO systems are
beginning to incorporate advanced photonic principles as the community pushes
for higher performance and more complex guide-star configurations. Photonic
instruments like Gravity on the Very Large Telescope achieve milliarcsec
resolution at 2000 nm which would be very difficult to achieve with
conventional optics. While space photonics is not reviewed here, we foresee
that remote sensing platforms will become a major beneficiary of astrophotonic
components in the years ahead. The field has given back with the development of
new technologies (e.g. photonic lantern, large area multi-core fibres) already
finding widespread use in other fields; Google Scholar lists more than 400
research papers making reference to this technology. This short review covers
representative key developments since the 2009 Focus issue on Astrophotonics. | [
0,
1,
0,
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0,
0
] |
Title: Stack Overflow: A Code Laundering Platform?,
Abstract: Developers use Question and Answer (Q&A) websites to exchange knowledge and
expertise. Stack Overflow is a popular Q&A website where developers discuss
coding problems and share code examples. Although all Stack Overflow posts are
free to access, code examples on Stack Overflow are governed by the Creative
Commons Attribute-ShareAlike 3.0 Unported license that developers should obey
when reusing code from Stack Overflow or posting code to Stack Overflow. In
this paper, we conduct a case study with 399 Android apps, to investigate
whether developers respect license terms when reusing code from Stack Overflow
posts (and the other way around). We found 232 code snippets in 62 Android apps
from our dataset that were potentially reused from Stack Overflow, and 1,226
Stack Overflow posts containing code examples that are clones of code released
in 68 Android apps, suggesting that developers may have copied the code of
these apps to answer Stack Overflow questions. We investigated the licenses of
these pieces of code and observed 1,279 cases of potential license violations
(related to code posting to Stack overflow or code reuse from Stack overflow).
This paper aims to raise the awareness of the software engineering community
about potential unethical code reuse activities taking place on Q&A websites
like Stack Overflow. | [
1,
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0
] |
Title: Quantification of market efficiency based on informational-entropy,
Abstract: Since the 1960s, the question whether markets are efficient or not is
controversially discussed. One reason for the difficulty to overcome the
controversy is the lack of a universal, but also precise, quantitative
definition of efficiency that is able to graduate between different states of
efficiency. The main purpose of this article is to fill this gap by developing
a measure for the efficiency of markets that fulfill all the stated
requirements. It is shown that the new definition of efficiency, based on
informational-entropy, is equivalent to the two most used definitions of
efficiency from Fama and Jensen. The new measure therefore enables steps to
settle the dispute over the state of efficiency in markets. Moreover, it is
shown that inefficiency in a market can either arise from the possibility to
use information to predict an event with higher than chance level, or can
emerge from wrong pricing/ quotes that do not reflect the right probabilities
of possible events. Finally, the calculation of efficiency is demonstrated on a
simple game (of coin tossing), to show how one could exactly quantify the
efficiency in any market-like system, if all probabilities are known. | [
0,
0,
0,
0,
0,
1
] |
Title: Note on equivalences for degenerations of Calabi-Yau manifolds,
Abstract: This note studies the equivalencies among convergences of Ricci-flat
Kähler-Einstein metrics on Calabi-Yau manifolds, cohomology classes and
potential functions. | [
0,
0,
1,
0,
0,
0
] |
Title: Improving DNN-based Music Source Separation using Phase Features,
Abstract: Music source separation with deep neural networks typically relies only on
amplitude features. In this paper we show that additional phase features can
improve the separation performance. Using the theoretical relationship between
STFT phase and amplitude, we conjecture that derivatives of the phase are a
good feature representation opposed to the raw phase. We verify this conjecture
experimentally and propose a new DNN architecture which combines amplitude and
phase. This joint approach achieves a better signal-to distortion ratio on the
DSD100 dataset for all instruments compared to a network that uses only
amplitude features. Especially, the bass instrument benefits from the phase
information. | [
1,
0,
0,
0,
0,
0
] |
Title: liquidSVM: A Fast and Versatile SVM package,
Abstract: liquidSVM is a package written in C++ that provides SVM-type solvers for
various classification and regression tasks. Because of a fully integrated
hyper-parameter selection, very carefully implemented solvers, multi-threading
and GPU support, and several built-in data decomposition strategies it provides
unprecedented speed for small training sizes as well as for data sets of tens
of millions of samples. Besides the C++ API and a command line interface,
bindings to R, MATLAB, Java, Python, and Spark are available. We present a
brief description of the package and report experimental comparisons to other
SVM packages. | [
0,
0,
0,
1,
0,
0
] |
Title: Thermalizing sterile neutrino dark matter,
Abstract: Sterile neutrinos produced through oscillations are a well motivated dark
matter candidate, but recent constraints from observations have ruled out most
of the parameter space. We analyze the impact of new interactions on the
evolution of keV sterile neutrino dark matter in the early Universe. Based on
general considerations we find a mechanism which thermalizes the sterile
neutrinos after an initial production by oscillations. The thermalization of
sterile neutrinos is accompanied by dark entropy production which increases the
yield of dark matter and leads to a lower characteristic momentum. This
resolves the growing tensions with structure formation and X-ray observations
and even revives simple non-resonant production as a viable way to produce
sterile neutrino dark matter. We investigate the parameters required for the
realization of the thermalization mechanism in a representative model and find
that a simple estimate based on energy- and entropy conservation describes the
mechanism well. | [
0,
1,
0,
0,
0,
0
] |
Title: Information Elicitation for Bayesian Auctions,
Abstract: In this paper we design information elicitation mechanisms for Bayesian
auctions. While in Bayesian mechanism design the distributions of the players'
private types are often assumed to be common knowledge, information elicitation
considers the situation where the players know the distributions better than
the decision maker. To weaken the information assumption in Bayesian auctions,
we consider an information structure where the knowledge about the
distributions is arbitrarily scattered among the players. In such an
unstructured information setting, we design mechanisms for unit-demand auctions
and additive auctions that aggregate the players' knowledge, generating revenue
that are constant approximations to the optimal Bayesian mechanisms with a
common prior. Our mechanisms are 2-step dominant-strategy truthful and the
revenue increases gracefully with the amount of knowledge the players
collectively have. | [
1,
0,
0,
0,
0,
0
] |
Title: Measurable process selection theorem and non-autonomous inclusions,
Abstract: A semi-process is an analog of the semi-flow for non-autonomous differential
equations or inclusions. We prove an abstract result on the existence of
measurable semi-processes in the situations where there is no uniqueness. Also,
we allow solutions to blow up in finite time and then obtain local
semi-processes. | [
0,
1,
1,
0,
0,
0
] |
Title: Observational evidence of galaxy assembly bias,
Abstract: We analyze the spectra of 300,000 luminous red galaxies (LRGs) with stellar
masses $M_* \gtrsim 10^{11} M_{\odot}$ from the SDSS-III Baryon Oscillation
Spectroscopic Survey (BOSS). By studying their star-formation histories, we
find two main evolutionary paths converging into the same quiescent galaxy
population at $z\sim0.55$. Fast-growing LRGs assemble $80\%$ of their stellar
mass very early on ($z\sim5$), whereas slow-growing LRGs reach the same
evolutionary state at $z\sim1.5$. Further investigation reveals that their
clustering properties on scales of $\sim$1-30 Mpc are, at a high level of
significance, also different. Fast-growing LRGs are found to be more strongly
clustered and reside in overall denser large-scale structure environments than
slow-growing systems, for a given stellar-mass threshold. Our results imply a
dependence of clustering on stellar-mass assembly history (naturally connected
to the mass-formation history of the corresponding halos) for a homogeneous
population of similar mass and color, which constitutes a strong observational
evidence of galaxy assembly bias. | [
0,
1,
0,
0,
0,
0
] |
Title: NotiMind: Utilizing Responses to Smart Phone Notifications as Affective sensors,
Abstract: Today's mobile phone users are faced with large numbers of notifications on
social media, ranging from new followers on Twitter and emails to messages
received from WhatsApp and Facebook. These digital alerts continuously disrupt
activities through instant calls for attention. This paper examines closely the
way everyday users interact with notifications and their impact on users'
emotion. Fifty users were recruited to download our application NotiMind and
use it over a five-week period. Users' phones collected thousands of social and
system notifications along with affect data collected via self-reported PANAS
tests three times a day. Results showed a noticeable correlation between
positive affective measures and keyboard activities. When large numbers of Post
and Remove notifications occur, a corresponding increase in negative affective
measures is detected. Our predictive model has achieved a good accuracy level
using three different classifiers "in the wild" (F-measure 74-78%
within-subject model, 72-76% global model). Our findings show that it is
possible to automatically predict when people are experiencing positive,
neutral or negative affective states based on interactions with notifications.
We also show how our findings open the door to a wide range of applications in
relation to emotion awareness on social and mobile communication. | [
1,
0,
0,
0,
0,
0
] |
Title: Blind Demixing and Deconvolution at Near-Optimal Rate,
Abstract: We consider simultaneous blind deconvolution of r source signals from their
noisy superposition, a problem also referred to blind demixing and
deconvolution. This signal processing problem occurs in the context of the
Internet of Things where a massive number of sensors sporadically communicate
only short messages over unknown channels. We show that robust recovery of
message and channel vectors can be achieved via convex optimization when random
linear encoding using i.i.d. complex Gaussian matrices is used at the devices
and the number of required measurements at the receiver scales with the degrees
of freedom of the overall estimation problem. Since the scaling is linear in r
our result significantly improves over recent works. | [
1,
0,
0,
0,
0,
0
] |
Title: Multivariate Locally Stationary Wavelet Process Analysis with the mvLSW R Package,
Abstract: This paper describes the R package mvLSW. The package contains a suite of
tools for the analysis of multivariate locally stationary wavelet (LSW) time
series. Key elements include: (i) the simulation of multivariate LSW time
series for a given multivariate evolutionary wavelet spectrum (EWS); (ii)
estimation of the time-dependent multivariate EWS for a given time series;
(iii) estimation of the time-dependent coherence and partial coherence between
time series channels; and, (iv) estimation of approximate confidence intervals
for multivariate EWS estimates. A demonstration of the package is presented via
both a simulated example and a case study with EuStockMarkets from the datasets
package. This paper has been accepted by the Journal of Statistical Software.
Presented code extracts demonstrating the mvLSW package is performed under
version 1.2.1. | [
0,
0,
0,
1,
0,
0
] |
Title: Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario,
Abstract: Item cold-start is a classical issue in recommender systems that affects
anime and manga recommendations as well. This problem can be framed as follows:
how to predict whether a user will like a manga that received few ratings from
the community? Content-based techniques can alleviate this issue but require
extra information, that is usually expensive to gather. In this paper, we use a
deep learning technique, Illustration2Vec, to easily extract tag information
from the manga and anime posters (e.g., sword, or ponytail). We propose BALSE
(Blended Alternate Least Squares with Explanation), a new model for
collaborative filtering, that benefits from this extra information to recommend
mangas. We show, using real data from an online manga recommender system called
Mangaki, that our model improves substantially the quality of recommendations,
especially for less-known manga, and is able to provide an interpretation of
the taste of the users. | [
1,
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0,
1,
0,
0
] |
Title: Generalized least squares can overcome the critical threshold in respondent-driven sampling,
Abstract: In order to sample marginalized and/or hard-to-reach populations,
respondent-driven sampling (RDS) and similar techniques reach their
participants via peer referral. Under a Markov model for RDS, previous research
has shown that if the typical participant refers too many contacts, then the
variance of common estimators does not decay like $O(n^{-1})$, where $n$ is the
sample size. This implies that confidence intervals will be far wider than
under a typical sampling design. Here we show that generalized least squares
(GLS) can effectively reduce the variance of RDS estimates. In particular, a
theoretical analysis indicates that the variance of the GLS estimator is
$O(n^{-1})$. We then derive two classes of feasible GLS estimators. The first
class is based upon a Degree Corrected Stochastic Blockmodel for the underlying
social network. The second class is based upon a rank-two model. It might be of
independent interest that in both model classes, the theoretical results show
that it is possible to estimate the spectral properties of the population
network from the sampled observations. Simulations on empirical social networks
show that the feasible GLS (fGLS) estimators can have drastically smaller error
and rarely increase the error. A diagnostic plot helps to identify where fGLS
will aid estimation. The fGLS estimators continue to outperform standard
estimators even when they are built from a misspecified model and when there is
preferential recruitment. | [
0,
0,
1,
1,
0,
0
] |
Title: Micro-sized cold atmospheric plasma source for brain and breast cancer treatment,
Abstract: Micro-sized cold atmospheric plasma (uCAP) has been developed to expand the
applications of CAP in cancer therapy. In this paper, uCAP devices with
different nozzle lengths were applied to investigate effects on both brain
(glioblastoma U87) and breast (MDA-MB-231) cancer cells. Various diagnostic
techniques were employed to evaluate the parameters of uCAP devices with
different lengths such as potential distribution, electron density, and optical
emission spectroscopy. The generation of short- and long-lived species (such as
hydroxyl radical (.OH), superoxide (O2-), hydrogen peroxide (H2O2), nitrite
(NO2-), et al) were studied. These data revealed that uCAP treatment with a 20
mm length tube has a stronger effect than that of the 60 mm tube due to the
synergetic effects of reactive species and free radicals. Reactive species
generated by uCAP enhanced tumor cell death in a dose-dependent fashion and was
not specific with regards to tumor cell type. | [
0,
0,
0,
0,
1,
0
] |
Title: Some Sharpening and Generalizations of a result of T. J. Rivlin,
Abstract: Let $p(z)=a_0+a_1z+a_2z^2+a_3z^3+\cdots+a_nz^n$ be a polynomial of degree
$n$. Rivlin \cite{Rivlin} proved that if $p(z)\neq 0$ in the unit disk, then
for $0<r\leq 1$, $\displaystyle{\max_{|z| = r}|p(z)|} \geq
\Big(\dfrac{r+1}{2}\Big)^n \displaystyle{\max_{|z|=1} |p(z)|}.$ ~In this paper,
we prove a sharpening and generalization of this result, and show by means of
examples that for some polynomials our result can significantly improve the
bound obtained by the Rivlin's Theorem. | [
0,
0,
1,
0,
0,
0
] |
Title: Is charge order induced near an antiferromagnetic quantum critical point?,
Abstract: We investigate the interplay between charge order and superconductivity near
an antiferromagnetic quantum critical point using sign-problem-free Quantum
Monte Carlo simulations. We establish that, when the electronic dispersion is
particle-hole symmetric, the system has an emergent SU(2) symmetry that implies
a degeneracy between $d$-wave superconductivity and charge order with $d$-wave
form factor. Deviations from particle-hole symmetry, however, rapidly lift this
degeneracy, despite the fact that the SU(2) symmetry is preserved at low
energies. As a result, we find a strong suppression of charge order caused by
the competing, leading superconducting instability. Across the
antiferromagnetic phase transition, we also observe a shift in the charge order
wave-vector from diagonal to axial. We discuss the implications of our results
to the universal phase diagram of antiferromagnetic quantum-critical metals and
to the elucidation of the charge order experimentally observed in the cuprates. | [
0,
1,
0,
0,
0,
0
] |
Title: Metal nanospheres under intense continuous wave illumination - a unique case of non-perturbative nonlinear nanophotonics,
Abstract: We show that the standard perturbative (i.e., cubic) description of the
thermal nonlinear response of small metal nanospheres to intense continuous
wave illumination is insufficient already beyond temperature rises of a few
tens of degrees. In some cases, a cubic-quintic nonlinear response is
sufficient to describe accurately the intensity dependence of the temperature,
permittivity and field, while in other cases, a full non-perturbative
description is required. We further analyze the relative importance of the
various contributions to the thermal nonlinearity, identify a qualitative
difference between Au and Ag, and show that the thermo-optical nonlinearity of
the host typically plays a minor role, but its thermal conductivity is
important. | [
0,
1,
0,
0,
0,
0
] |
Title: Behind Every Great Tree is a Great (Phylogenetic) Network,
Abstract: In Francis and Steel (2015), it was shown that there exists non-trivial
networks on $4$ leaves upon which the distance metric affords a metric on a
tree which is not the base tree of the network. In this paper we extend this
result in two directions. We show that for any tree $T$ there exists a family
of non-trivial HGT networks $N$ for which the distance metric $d_N$ affords a
metric on $T$. We additionally provide a class of networks on any number of
leaves upon which the distance metric affords a metric on a tree which is not
the base tree of the network.
The family of networks are all "floating" networks, a subclass of a novel
family of networks introduced in this paper, and referred to as "versatile"
networks. Versatile networks are then characterised.
Additionally, we find a lower bound for the number of `useful' HGT arcs in
such networks, in a sense explained in the paper. This lower bound is equal to
the number of HGT arcs required for each floating network in the main results,
and thus our networks are minimal in this sense. | [
0,
0,
1,
0,
0,
0
] |
Title: Cosmic-ray induced destruction of CO in star-forming galaxies,
Abstract: We explore the effects of the expected higher cosmic ray (CR) ionization
rates $\zeta_{\rm CR}$ on the abundances of carbon monoxide (CO), atomic carbon
(C), and ionized carbon (C$^+$) in the H$_2$ clouds of star-forming galaxies.
The study of Bisbas et al. (2015) is expanded by: a) using realistic
inhomogeneous Giant Molecular Cloud (GMC) structures, b) a detailed chemical
analysis behind the CR-induced destruction of CO, and c) exploring the thermal
state of CR-irradiated molecular gas. CRs permeating the interstellar medium
with $\zeta_{\rm CR}$$\gtrsim 10\times$(Galactic) are found to significantly
reduce the [CO]/[H$_2$] abundance ratios throughout the mass of a GMC. CO
rotational line imaging will then show much clumpier structures than the actual
ones. For $\zeta_{\rm CR}$$\gtrsim 100\times$(Galactic) this bias becomes
severe, limiting the utility of CO lines for recovering structural and
dynamical characteristics of H$_2$-rich galaxies throughout the Universe,
including many of the so-called Main Sequence (MS) galaxies where the bulk of
cosmic star formation occurs. Both C$^+$ and C abundances increase with rising
$\zeta_{\rm CR}$, with C remaining the most abundant of the two throughout
H$_2$ clouds, when $\zeta_{\rm CR}\sim (1-100)\times$(Galactic). C$^+$ starts
to dominate for $\zeta_{\rm CR}$$\gtrsim 10^3\times$(Galactic). The thermal
state of the gas in the inner and denser regions of GMCs is invariant with
$T_{\rm gas}\sim 10\,{\rm K}$ for $\zeta_{\rm CR}\sim (1-10)\times$(Galactic).
For $\zeta_{\rm CR}$$\sim 10^3\times$(Galactic) this is no longer the case and
$T_{\rm gas}\sim 30-50\,{\rm K}$ are reached. Finally we identify OH as the key
species whose $T_{\rm gas}-$sensitive abundance could mitigate the destruction
of CO at high temperatures. | [
0,
1,
0,
0,
0,
0
] |
Title: News Session-Based Recommendations using Deep Neural Networks,
Abstract: News recommender systems are aimed to personalize users experiences and help
them to discover relevant articles from a large and dynamic search space.
Therefore, news domain is a challenging scenario for recommendations, due to
its sparse user profiling, fast growing number of items, accelerated item's
value decay, and users preferences dynamic shift. Some promising results have
been recently achieved by the usage of Deep Learning techniques on Recommender
Systems, specially for item's feature extraction and for session-based
recommendations with Recurrent Neural Networks. In this paper, it is proposed
an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News
Recommender Systems. This architecture is composed of two modules, the first
responsible to learn news articles representations, based on their text and
metadata, and the second module aimed to provide session-based recommendations
using Recurrent Neural Networks. The recommendation task addressed in this work
is next-item prediction for users sessions: "what is the next most likely
article a user might read in a session?" Users sessions context is leveraged by
the architecture to provide additional information in such extreme cold-start
scenario of news recommendation. Users' behavior and item features are both
merged in an hybrid recommendation approach. A temporal offline evaluation
method is also proposed as a complementary contribution, for a more realistic
evaluation of such task, considering dynamic factors that affect global
readership interests like popularity, recency, and seasonality. Experiments
with an extensive number of session-based recommendation methods were performed
and the proposed instantiation of CHAMELEON meta-architecture obtained a
significant relative improvement in top-n accuracy and ranking metrics (10% on
Hit Rate and 13% on MRR) over the best benchmark methods. | [
0,
0,
0,
1,
0,
0
] |
Title: Loop Tiling in Large-Scale Stencil Codes at Run-time with OPS,
Abstract: The key common bottleneck in most stencil codes is data movement, and prior
research has shown that improving data locality through optimisations that
schedule across loops do particularly well. However, in many large PDE
applications it is not possible to apply such optimisations through compilers
because there are many options, execution paths and data per grid point, many
dependent on run-time parameters, and the code is distributed across different
compilation units. In this paper, we adapt the data locality improving
optimisation called iteration space slicing for use in large OPS applications
both in shared-memory and distributed-memory systems, relying on run-time
analysis and delayed execution. We evaluate our approach on a number of
applications, observing speedups of 2$\times$ on the Cloverleaf 2D/3D proxy
application, which contain 83/141 loops respectively, $3.5\times$ on the linear
solver TeaLeaf, and $1.7\times$ on the compressible Navier-Stokes solver
OpenSBLI. We demonstrate strong and weak scalability up to 4608 cores of
CINECA's Marconi supercomputer. We also evaluate our algorithms on Intel's
Knights Landing, demonstrating maintained throughput as the problem size grows
beyond 16GB, and we do scaling studies up to 8704 cores. The approach is
generally applicable to any stencil DSL that provides per loop data access
information. | [
1,
0,
0,
0,
0,
0
] |
Title: Semiclassical Prediction of Large Spectral Fluctuations in Interacting Kicked Spin Chains,
Abstract: While plenty of results have been obtained for single-particle quantum
systems with chaotic dynamics through a semiclassical theory, much less is
known about quantum chaos in the many-body setting. We contribute to recent
efforts to make a semiclassical analysis of many-body systems feasible. This is
nontrivial due to both the enormous density of states and the exponential
proliferation of periodic orbits with the number of particles. As a model
system we study kicked interacting spin chains employing semiclassical methods
supplemented by a newly developed duality approach. We show that for this model
the line between integrability and chaos becomes blurred. Due to the
interaction structure the system features (non-isolated) manifolds of periodic
orbits possessing highly correlated, collective dynamics. As with the invariant
tori in integrable systems, their presence lead to significantly enhanced
spectral fluctuations, which by order of magnitude lie in-between integrable
and chaotic cases. | [
0,
1,
0,
0,
0,
0
] |
Title: If it ain't broke, don't fix it: Sparse metric repair,
Abstract: Many modern data-intensive computational problems either require, or benefit
from distance or similarity data that adhere to a metric. The algorithms run
faster or have better performance guarantees. Unfortunately, in real
applications, the data are messy and values are noisy. The distances between
the data points are far from satisfying a metric. Indeed, there are a number of
different algorithms for finding the closest set of distances to the given ones
that also satisfy a metric (sometimes with the extra condition of being
Euclidean). These algorithms can have unintended consequences, they can change
a large number of the original data points, and alter many other features of
the data.
The goal of sparse metric repair is to make as few changes as possible to the
original data set or underlying distances so as to ensure the resulting
distances satisfy the properties of a metric. In other words, we seek to
minimize the sparsity (or the $\ell_0$ "norm") of the changes we make to the
distances subject to the new distances satisfying a metric. We give three
different combinatorial algorithms to repair a metric sparsely. In one setting
the algorithm is guaranteed to return the sparsest solution and in the other
settings, the algorithms repair the metric. Without prior information, the
algorithms run in time proportional to the cube of the number of input data
points and, with prior information we can reduce the running time considerably. | [
1,
0,
0,
1,
0,
0
] |
Title: Optimal one-shot quantum algorithm for EQUALITY and AND,
Abstract: We study the computation complexity of Boolean functions in the quantum black
box model. In this model our task is to compute a function
$f:\{0,1\}\to\{0,1\}$ on an input $x\in\{0,1\}^n$ that can be accessed by
querying the black box. Quantum algorithms are inherently probabilistic; we are
interested in the lowest possible probability that the algorithm outputs
incorrect answer (the error probability) for a fixed number of queries. We show
that the lowest possible error probability for $AND_n$ and $EQUALITY_{n+1}$ is
$1/2-n/(n^2+1)$. | [
1,
0,
0,
0,
0,
0
] |
Title: Polarisation of submillimetre lines from interstellar medium,
Abstract: Magnetic fields play important roles in many astrophysical processes.
However, there is no universal diagnostic for the magnetic fields in the
interstellar medium (ISM) and each magnetic tracer has its limitation. Any new
detection method is thus valuable. Theoretical studies have shown that
submillimetre fine-structure lines are polarised due to atomic alignment by
Ultraviolet (UV) photon-excitation, which opens up a new avenue to probe
interstellar magnetic fields. We will, for the first time, perform synthetic
observations on the simulated three-dimensional ISM to demonstrate the
measurability of the polarisation of submillimetre atomic lines. The maximum
polarisation for different absorption and emission lines expected from various
sources, including Star-Forming Regions (SFRs) are provided. Our results
demonstrate that the polarisation of submillimetre atomic lines is a powerful
magnetic tracer and add great value to the observational studies of the
submilimetre astronomy. | [
0,
1,
0,
0,
0,
0
] |
Title: Photometric Redshifts with the LSST: Evaluating Survey Observing Strategies,
Abstract: In this paper we present and characterize a nearest-neighbors color-matching
photometric redshift estimator that features a direct relationship between the
precision and accuracy of the input magnitudes and the output photometric
redshifts. This aspect makes our estimator an ideal tool for evaluating the
impact of changes to LSST survey parameters that affect the measurement errors
of the photometry, which is the main motivation of our work (i.e., it is not
intended to provide the "best" photometric redshifts for LSST data). We show
how the photometric redshifts will improve with time over the 10-year LSST
survey and confirm that the nominal distribution of visits per filter provides
the most accurate photo-$z$ results. The LSST survey strategy naturally
produces observations over a range of airmass, which offers the opportunity of
using an SED- and $z$-dependent atmospheric affect on the observed photometry
as a color-independent redshift indicator. We show that measuring this airmass
effect and including it as a prior has the potential to improve the photometric
redshifts and can ameliorate extreme outliers, but that it will only be
adequately measured for the brightest galaxies, which limits its overall impact
on LSST photometric redshifts. We furthermore demonstrate how this airmass
effect can induce a bias in the photo-$z$ results, and caution against survey
strategies that prioritize high-airmass observations for the purpose of
improving this prior. Ultimately, we intend for this work to serve as a guide
for the expectations and preparations of the LSST science community with
regards to the minimum quality of photo-$z$ as the survey progresses. | [
0,
1,
0,
0,
0,
0
] |
Title: Modelling diverse sources of Clostridium difficile in the community: importance of animals, infants and asymptomatic carriers,
Abstract: Clostridium difficile infections (CDIs) affect patients in hospitals and in
the community, but the relative importance of transmission in each setting is
unknown. We developed a mathematical model of C. difficile transmission in a
hospital and surrounding community that included infants, adults, and
transmission from animal reservoirs. We assessed the role of these transmission
routes in maintaining disease and evaluated the recommended classification
system for hospital and community-acquired CDIs. The reproduction number in the
hospital was <1 (range: 0.16-0.46) for all scenarios. Outside the hospital, the
reproduction number was >1 for nearly all scenarios without transmission from
animal reservoirs (range: 1.0-1.34). However, the reproduction number for the
human population was <1 if a minority (>3.5-26.0%) of human exposures
originated from animal reservoirs. Symptomatic adults accounted for <10%
transmission in the community. Under conservative assumptions, infants
accounted for 17% of community transmission. An estimated 33-40% of
community-acquired cases were reported but 28-39% of these reported cases were
misclassified as hospital-acquired by recommended definitions. Transmission
could be plausibly sustained by asymptomatically colonized adults and infants
in the community or exposure to animal reservoirs, but not hospital
transmission alone. Underreporting of community-onset cases and systematic
misclassification underplays the role of community transmission. | [
0,
0,
0,
0,
1,
0
] |
Title: Quivers with potentials for cluster varieties associated to braid semigroups,
Abstract: Let $C$ be a simply laced generalized Cartan matrix. Given an element $b$ of
the generalized braid semigroup related to $C$, we construct a collection of
mutation-equivalent quivers with potentials. A quiver with potential in such a
collection corresponds to an expression of $b$ in terms of the standard
generators. For two expressions that differ by a braid relation, the
corresponding quivers with potentials are related by a mutation.
The main application of this result is a construction of a family of $CY_3$
$A_\infty$-categories associated to elements of the braid semigroup related to
$C$. In particular, we construct a canonical up to equivalence $CY_3$
$A_\infty$-category associated to quotient of any Double Bruhat cell
$G^{u,v}/{\rm Ad} H$ in a simply laced reductive Lie group $G$.
We describe the full set of parameters these categories depend on by defining
a 2-dimensional CW-complex and proving that the set of parameters is identified
with second cohomology group of this complex. | [
0,
0,
1,
0,
0,
0
] |
Title: Calibrating the Planck Cluster Mass Scale with Cluster Velocity Dispersions,
Abstract: We measure the Planck cluster mass bias using dynamical mass measurements
based on velocity dispersions of a subsample of 17 Planck-detected clusters.
The velocity dispersions were calculated using redshifts determined from
spectra obtained at Gemini observatory with the GMOS multi-object spectrograph.
We correct our estimates for effects due to finite aperture, Eddington bias and
correlated scatter between velocity dispersion and the Planck mass proxy. The
result for the mass bias parameter, $(1-b)$, depends on the value of the galaxy
velocity bias $b_v$ adopted from simulations: $(1-b)=(0.51\pm0.09) b_v^3$.
Using a velocity bias of $b_v=1.08$ from Munari et al., we obtain
$(1-b)=0.64\pm 0.11$, i.e, an error of 17% on the mass bias measurement with 17
clusters. This mass bias value is consistent with most previous weak lensing
determinations. It lies within $1\sigma$ of the value needed to reconcile the
Planck cluster counts with the Planck primary CMB constraints. We emphasize
that uncertainty in the velocity bias severely hampers precision measurements
of the mass bias using velocity dispersions. On the other hand, when we fix the
Planck mass bias using the constraints from Penna-Lima et al., based on weak
lensing measurements, we obtain a positive velocity bias $b_v \gtrsim 0.9$ at
$3\sigma$. | [
0,
1,
0,
0,
0,
0
] |
Title: Boundedness in a fully parabolic chemotaxis system with nonlinear diffusion and sensitivity, and logistic source,
Abstract: In this paper we study the zero-flux chemotaxis-system \begin{equation*}
\begin{cases} u_{ t}=\nabla \cdot ((u+1)^{m-1} \nabla u-(u+1)^\alpha
\chi(v)\nabla v) + ku-\mu u^2 & x\in \Omega, t>0, \\ v_{t} = \Delta v-vu & x\in
\Omega, t>0,\\ \end{cases} \end{equation*} $\Omega$ being a bounded and smooth
domain of $\mathbb{R}^n$, $n\geq 1$, and where $m,k \in \mathbb{R}$, $\mu>0$
and $\alpha < \frac{m+1}{2}$. For any $v\geq 0$ the chemotactic sensitivity
function is assumed to behave as the prototype $\chi(v) =
\frac{\chi_0}{(1+av)^2}$, with $a\geq 0$ and $\chi_0>0$. We prove that for
nonnegative and sufficiently regular initial data $u(x,0)$ and $v(x,0),$ the
corresponding initial-boundary value problem admits a global bounded classical
solution provided $\mu$ is large enough. | [
0,
0,
1,
0,
0,
0
] |
Title: From Infinite to Finite Programs: Explicit Error Bounds with Applications to Approximate Dynamic Programming,
Abstract: We consider linear programming (LP) problems in infinite dimensional spaces
that are in general computationally intractable. Under suitable assumptions, we
develop an approximation bridge from the infinite-dimensional LP to tractable
finite convex programs in which the performance of the approximation is
quantified explicitly. To this end, we adopt the recent developments in two
areas of randomized optimization and first order methods, leading to a priori
as well as a posterior performance guarantees. We illustrate the generality and
implications of our theoretical results in the special case of the long-run
average cost and discounted cost optimal control problems for Markov decision
processes on Borel spaces. The applicability of the theoretical results is
demonstrated through a constrained linear quadratic optimal control problem and
a fisheries management problem. | [
1,
0,
1,
0,
0,
0
] |
Title: An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting,
Abstract: The key component in forecasting demand and consumption of resources in a
supply network is an accurate prediction of real-valued time series. Indeed,
both service interruptions and resource waste can be reduced with the
implementation of an effective forecasting system. Significant research has
thus been devoted to the design and development of methodologies for short term
load forecasting over the past decades. A class of mathematical models, called
Recurrent Neural Networks, are nowadays gaining renewed interest among
researchers and they are replacing many practical implementation of the
forecasting systems, previously based on static methods. Despite the undeniable
expressive power of these architectures, their recurrent nature complicates
their understanding and poses challenges in the training procedures. Recently,
new important families of recurrent architectures have emerged and their
applicability in the context of load forecasting has not been investigated
completely yet. In this paper we perform a comparative study on the problem of
Short-Term Load Forecast, by using different classes of state-of-the-art
Recurrent Neural Networks. We test the reviewed models first on controlled
synthetic tasks and then on different real datasets, covering important
practical cases of study. We provide a general overview of the most important
architectures and we define guidelines for configuring the recurrent networks
to predict real-valued time series. | [
1,
0,
0,
0,
0,
0
] |
Title: Existence of regular solutions for a certain type of non-Newtonian Navier-Stokes equations,
Abstract: We are concerned with existence of regular solutions for non-Newtonian fluids
in dimension three. For a certain type of non-Newtonian fluids we prove local
existence of unique regular solutions, provided that the initial data are
sufficiently smooth. Moreover, if the $H^3$-norm of initial data is
sufficiently small, then the regular solution exists globally in time. | [
0,
0,
1,
0,
0,
0
] |
Title: Multiferroic Quantum Criticality,
Abstract: The zero-temperature limit of a continuous phase transition is marked by a
quantum critical point, which can generate exotic physics that extends to
elevated temperatures. Magnetic quantum criticality is now well known, and has
been explored in systems ranging from heavy fermion metals to quantum Ising
materials. Ferroelectric quantum critical behaviour has also been recently
established, motivating a flurry of research investigating its consequences.
Here, we introduce the concept of multiferroic quantum criticality, in which
both magnetic and ferroelectric quantum criticality occur in the same system.
We develop the phenomenology of multiferroic quantum critical behaviour,
describe the associated experimental signatures, and propose material systems
and schemes to realize it. | [
0,
1,
0,
0,
0,
0
] |
Title: Marginally compact fractal trees with semiflexibility,
Abstract: We study marginally compact macromolecular trees that are created by means of
two different fractal generators. In doing so, we assume Gaussian statistics
for the vectors connecting nodes of the trees. Moreover, we introduce bond-bond
correlations that make the trees locally semiflexible. The symmetry of the
structures allows an iterative construction of full sets of eigenmodes
(notwithstanding the additional interactions that are present due to
semiflexibility constraints), enabling us to get physical insights about the
trees' behavior and to consider larger structures. Due to the local stiffness
the self-contact density gets drastically reduced. | [
0,
1,
0,
0,
0,
0
] |
Title: Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC,
Abstract: There has been considerable recent activity applying deep convolutional
neural nets (CNNs) to data from particle physics experiments. Current
approaches on ATLAS/CMS have largely focussed on a subset of the calorimeter,
and for identifying objects or particular particle types. We explore approaches
that use the entire calorimeter, combined with track information, for directly
conducting physics analyses: i.e. classifying events as known-physics
background or new-physics signals.
We use an existing RPV-Supersymmetry analysis as a case study and explore
CNNs on multi-channel, high-resolution sparse images: applied on GPU and
multi-node CPU architectures (including Knights Landing (KNL) Xeon Phi nodes)
on the Cori supercomputer at NERSC. | [
1,
0,
0,
0,
0,
0
] |
Title: Central limit theorem for linear spectral statistics of general separable sample covariance matrices with applications,
Abstract: In this paper, we consider the separable covariance model, which plays an
important role in wireless communications and spatio-temporal statistics and
describes a process where the time correlation does not depend on the spatial
location and the spatial correlation does not depend on time. We established a
central limit theorem for linear spectral statistics of general separable
sample covariance matrices in the form of $\mathbf S_n=\frac1n\mathbf
T_{1n}\mathbf X_n\mathbf T_{2n}\mathbf X_n^*\mathbf T_{1n}^*$ where $\mathbf
X_n=(x_{jk})$ is of $m_1\times m_2$ dimension, the entries $\{x_{jk},
j=1,...,m_1, k=1,...,m_2\}$ are independent and identically distributed complex
variables with zero means and unit variances, $\mathbf T_{1n}$ is a $p\times
m_1 $ complex matrix and $\mathbf T_{2n}$ is an $m_2\times m_2$ Hermitian
matrix. We then apply this general central limit theorem to the problem of
testing white noise in time series. | [
0,
0,
1,
1,
0,
0
] |
Title: Malnormality and join-free subgroups in right-angled Coxeter groups,
Abstract: In this paper, we prove that all finitely generated malnormal subgroups of
one-ended right-angled Coxeter groups are strongly quasiconvex and they are in
particular quasiconvex when the ambient groups are hyperbolic. The key idea is
to prove all infinite proper malnormal subgroups of one-ended right-angled
Coxeter groups are join-free and then prove the strong quasiconvexity and the
virtual freeness of these subgroups. We also study the subgroup divergence of
join-free subgroups in right-angled Coxeter groups and compare them with the
analogous subgroups in right-angled Artin groups. We characterize almost
malnormal parabolic subgroups in terms of their defining graphs and also
recognize them as strongly quasiconvex subgroups by the recent work of Genevois
and Russell-Spriano-Tran. Finally, we discuss some results on hyperbolically
embedded subgroups in right-angled Coxeter groups. | [
0,
0,
1,
0,
0,
0
] |
Title: Mapping the Americanization of English in Space and Time,
Abstract: As global political preeminence gradually shifted from the United Kingdom to
the United States, so did the capacity to culturally influence the rest of the
world. In this work, we analyze how the world-wide varieties of written English
are evolving. We study both the spatial and temporal variations of vocabulary
and spelling of English using a large corpus of geolocated tweets and the
Google Books datasets corresponding to books published in the US and the UK.
The advantage of our approach is that we can address both standard written
language (Google Books) and the more colloquial forms of microblogging messages
(Twitter). We find that American English is the dominant form of English
outside the UK and that its influence is felt even within the UK borders.
Finally, we analyze how this trend has evolved over time and the impact that
some cultural events have had in shaping it. | [
1,
0,
0,
1,
0,
0
] |
Title: Robot Localisation and 3D Position Estimation Using a Free-Moving Camera and Cascaded Convolutional Neural Networks,
Abstract: Many works in collaborative robotics and human-robot interaction focuses on
identifying and predicting human behaviour while considering the information
about the robot itself as given. This can be the case when sensors and the
robot are calibrated in relation to each other and often the reconfiguration of
the system is not possible, or extra manual work is required. We present a deep
learning based approach to remove the constraint of having the need for the
robot and the vision sensor to be fixed and calibrated in relation to each
other. The system learns the visual cues of the robot body and is able to
localise it, as well as estimate the position of robot joints in 3D space by
just using a 2D color image. The method uses a cascaded convolutional neural
network, and we present the structure of the network, describe our own
collected dataset, explain the network training and achieved results. A fully
trained system shows promising results in providing an accurate mask of where
the robot is located and a good estimate of its joints positions in 3D. The
accuracy is not good enough for visual servoing applications yet, however, it
can be sufficient for general safety and some collaborative tasks not requiring
very high precision. The main benefit of our method is the possibility of the
vision sensor to move freely. This allows it to be mounted on moving objects,
for example, a body of the person or a mobile robot working in the same
environment as the robots are operating in. | [
1,
0,
0,
0,
0,
0
] |
Title: Data-driven regularization of Wasserstein barycenters with an application to multivariate density registration,
Abstract: We present a framework to simultaneously align and smooth data in the form of
multiple point clouds sampled from unknown densities with support in a
$d$-dimensional Euclidean space. This work is motivated by applications in
bioinformatics where researchers aim to automatically homogenize large datasets
to compare and analyze characteristics within a same cell population.
Inconveniently, the information acquired is most certainly noisy due to
mis-alignment caused by technical variations of the environment. To overcome
this problem, we propose to register multiple point clouds by using the notion
of regularized barycenters (or Fréchet mean) of a set of probability
measures with respect to the Wasserstein metric. A first approach consists in
penalizing a Wasserstein barycenter with a convex functional as recently
proposed in Bigot and al. (2018). A second strategy is to transform the
Wasserstein metric itself into an entropy regularized transportation cost
between probability measures as introduced in Cuturi (2013). The main
contribution of this work is to propose data-driven choices for the
regularization parameters involved in each approach using the
Goldenshluger-Lepski's principle. Simulated data sampled from Gaussian mixtures
are used to illustrate each method, and an application to the analysis of flow
cytometry data is finally proposed. | [
0,
0,
0,
1,
0,
0
] |
Title: BCS quantum critical phenomena,
Abstract: Theoretically, we recently showed that the scaling relation between the
transition temperature T_c and the superfluid density at zero temperature n_s
(0) might exhibit a parabolic pattern [Scientific Reports 6 (2016) 23863]. It
is significantly different from the linear scaling described by Homes' law,
which is well known as a mean-field result. More recently, Bozovic et al. have
observed such a parabolic scaling in the overdoped copper oxides with a
sufficiently low transition temperature T_c [Nature 536 (2016) 309-311]. They
further point out that this experimental finding is incompatible with the
standard Bardeen-Cooper-Schrieffer (BCS) description. Here we report that if
T_c is sufficiently low, applying the renormalization group approach into the
BCS action at zero temperature will naturally lead to the parabolic scaling.
Our result indicates that when T_c sufficiently approaches zero, quantum
fluctuations will be overwhelmingly amplified so that the mean-field
approximation may break down at zero temperature. | [
0,
1,
0,
0,
0,
0
] |
Title: Action preserving (weak) topologies on the category of presheaves,
Abstract: Let $\mathcal{C}$ be a finitely complete small category. In this paper, first
we construct two weak (Lawvere-Tierney) topologies on the category of
presheaves. One of them is established by means of a subfunctor of the Yoneda
functor and the other one, is constructed by an admissible class on
$\mathcal{C}$ and the internal existential quantifier in the presheaf topos
$\widehat{\mathcal{C}}$. Moreover, by using an admissible class on
$\mathcal{C},$ we are able to define an action on the subobject classifier
$\Omega$ of $\widehat{\mathcal{C}}$. Then we find some necessary conditions for
that the two weak topologies and also the double negation topology $\neg\neg$
on $\widehat{\mathcal{C}}$ to be action preserving maps. Finally, among other
things, we constitute an action preserving weak topology on
$\widehat{\mathcal{C}}$. | [
0,
0,
1,
0,
0,
0
] |
Title: Evidence synthesis for stochastic epidemic models,
Abstract: In recent years the role of epidemic models in informing public health
policies has progressively grown. Models have become increasingly realistic and
more complex, requiring the use of multiple data sources to estimate all
quantities of interest. This review summarises the different types of
stochastic epidemic models that use evidence synthesis and highlights current
challenges. | [
0,
0,
0,
1,
0,
0
] |
Title: Enhanced activity of the Southern Taurids in 2005 and 2015,
Abstract: The paper presents an analysis of Polish Fireball Network (PFN) observations
of enhanced activity of the Southern Taurid meteor shower in 2005 and 2015. In
2005, between October 20 and November 10, seven stations of PFN determined 107
accurate orbits with 37 of them belonging to the Southern Taurid shower. In the
same period of 2015, 25 stations of PFN recorded 719 accurate orbits with 215
orbits of the Southern Taurids. Both maxima were rich in fireballs which
accounted to 17% of all observed Taurids. The whole sample of Taurid fireballs
is quite uniform in the sense of starting and terminal heights of the
trajectory. On the other hand a clear decreasing trend in geocentric velocity
with increasing solar longitude was observed.
Orbital parameters of observed Southern Taurids were compared to orbital
elements of Near Earth Objects (NEO) from the NEODYS-2 database. Using the
Drummond criterion $D'$ with threshold as low as 0.06, we found over 100
fireballs strikingly similar to the orbit of asteroid 2015 TX24. Several dozens
of Southern Taurids have orbits similar to three other asteroids, namely: 2005
TF50, 2005 UR and 2010 TU149. All mentioned NEOs have orbital periods very
close to the 7:2 resonance with Jupiter's orbit. It confirms a theory of a
"resonant meteoroid swarm" within the Taurid complex that predicts that in
specific years, the Earth is hit by a greater number of meteoroids capable of
producing fireballs. | [
0,
1,
0,
0,
0,
0
] |
Title: Comparing anticyclotomic Selmer groups of positive coranks for congruent modular forms,
Abstract: We study the variation of Iwasawa invariants of the anticyclotomic Selmer
groups of congruent modular forms under the Heegner hypothesis. In particular,
we show that even if the Selmer groups we study may have positive coranks, the
mu-invariant vanishes for one modular form if and only if it vanishes for the
other, and that their lambda-invariants are related by an explicit formula.
This generalizes results of Greenberg-Vatsal for the cyclotomic extension, as
well as results of Pollack-Weston and Castella-Kim-Longo for the anticyclotomic
extension when the Selmer groups in question are cotorsion. | [
0,
0,
1,
0,
0,
0
] |
Title: Predicting the Results of LTL Model Checking using Multiple Machine Learning Algorithms,
Abstract: In this paper, we study how to predict the results of LTL model checking
using some machine learning algorithms. Some Kripke structures and LTL formulas
and their model checking results are made up data set. The approaches based on
the Random Forest (RF), K-Nearest Neighbors (KNN), Decision tree (DT), and
Logistic Regression (LR) are used to training and prediction. The experiment
results show that the average computation efficiencies of the RF, LR, DT, and
KNN-based approaches are 2066181, 2525333, 1894000 and 294 times than that of
the existing approach, respectively. | [
1,
0,
0,
0,
0,
0
] |
Title: Elastic collision and molecule formation of spatiotemporal light bullets in a cubic-quintic nonlinear medium,
Abstract: We consider the statics and dynamics of a stable, mobile three-dimensional
(3D) spatiotemporal light bullet in a cubic-quintic nonlinear medium with a
focusing cubic nonlinearity above a critical value and any defocusing quintic
nonlinearity. The 3D light bullet can propagate with a constant velocity in any
direction. Stability of the light bullet under a small perturbation is
established numerically.We consider frontal collision between two light bullets
with different relative velocities. At large velocities the collision is
elastic with the bullets emerge after collision with practically no distortion.
At small velocities two bullets coalesce to form a bullet molecule. At a small
range of intermediate velocities the localized bullets could form a single
entity which expands indefinitely leading to a destruction of the bullets after
collision. The present study is based on an analytic Lagrange variational
approximation and a full numerical solution of the 3D nonlinear Schrödinger
equation. | [
0,
1,
0,
0,
0,
0
] |
Title: From Query-By-Keyword to Query-By-Example: LinkedIn Talent Search Approach,
Abstract: One key challenge in talent search is to translate complex criteria of a
hiring position into a search query, while it is relatively easy for a searcher
to list examples of suitable candidates for a given position. To improve search
efficiency, we propose the next generation of talent search at LinkedIn, also
referred to as Search By Ideal Candidates. In this system, a searcher provides
one or several ideal candidates as the input to hire for a given position. The
system then generates a query based on the ideal candidates and uses it to
retrieve and rank results. Shifting from the traditional Query-By-Keyword to
this new Query-By-Example system poses a number of challenges: How to generate
a query that best describes the candidates? When moving to a completely
different paradigm, how does one leverage previous product logs to learn
ranking models and/or evaluate the new system with no existing usage logs?
Finally, given the different nature between the two search paradigms, the
ranking features typically used for Query-By-Keyword systems might not be
optimal for Query-By-Example. This paper describes our approach to solving
these challenges. We present experimental results confirming the effectiveness
of the proposed solution, particularly on query building and search ranking
tasks. As of writing this paper, the new system has been available to all
LinkedIn members. | [
1,
0,
0,
0,
0,
0
] |
Title: On Bayesian Exponentially Embedded Family for Model Order Selection,
Abstract: In this paper, we derive a Bayesian model order selection rule by using the
exponentially embedded family method, termed Bayesian EEF. Unlike many other
Bayesian model selection methods, the Bayesian EEF can use vague proper priors
and improper noninformative priors to be objective in the elicitation of
parameter priors. Moreover, the penalty term of the rule is shown to be the sum
of half of the parameter dimension and the estimated mutual information between
parameter and observed data. This helps to reveal the EEF mechanism in
selecting model orders and may provide new insights into the open problems of
choosing an optimal penalty term for model order selection and choosing a good
prior from information theoretic viewpoints. The important example of linear
model order selection is given to illustrate the algorithms and arguments.
Lastly, the Bayesian EEF that uses Jeffreys prior coincides with the EEF rule
derived by frequentist strategies. This shows another interesting relationship
between the frequentist and Bayesian philosophies for model selection. | [
0,
0,
0,
1,
0,
0
] |
Title: Revisiting Activation Regularization for Language RNNs,
Abstract: Recurrent neural networks (RNNs) serve as a fundamental building block for
many sequence tasks across natural language processing. Recent research has
focused on recurrent dropout techniques or custom RNN cells in order to improve
performance. Both of these can require substantial modifications to the machine
learning model or to the underlying RNN configurations. We revisit traditional
regularization techniques, specifically L2 regularization on RNN activations
and slowness regularization over successive hidden states, to improve the
performance of RNNs on the task of language modeling. Both of these techniques
require minimal modification to existing RNN architectures and result in
performance improvements comparable or superior to more complicated
regularization techniques or custom cell architectures. These regularization
techniques can be used without any modification on optimized LSTM
implementations such as the NVIDIA cuDNN LSTM. | [
1,
0,
0,
0,
0,
0
] |
Title: On the scaling of entropy viscosity in high order methods,
Abstract: In this work, we outline the entropy viscosity method and discuss how the
choice of scaling influences the size of viscosity for a simple shock problem.
We present examples to illustrate the performance of the entropy viscosity
method under two distinct scalings. | [
0,
0,
1,
0,
0,
0
] |
Title: An Architecture Combining Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for Image Classification,
Abstract: Convolutional neural networks (CNNs) are similar to "ordinary" neural
networks in the sense that they are made up of hidden layers consisting of
neurons with "learnable" parameters. These neurons receive inputs, performs a
dot product, and then follows it with a non-linearity. The whole network
expresses the mapping between raw image pixels and their class scores.
Conventionally, the Softmax function is the classifier used at the last layer
of this network. However, there have been studies (Alalshekmubarak and Smith,
2013; Agarap, 2017; Tang, 2013) conducted to challenge this norm. The cited
studies introduce the usage of linear support vector machine (SVM) in an
artificial neural network architecture. This project is yet another take on the
subject, and is inspired by (Tang, 2013). Empirical data has shown that the
CNN-SVM model was able to achieve a test accuracy of ~99.04% using the MNIST
dataset (LeCun, Cortes, and Burges, 2010). On the other hand, the CNN-Softmax
was able to achieve a test accuracy of ~99.23% using the same dataset. Both
models were also tested on the recently-published Fashion-MNIST dataset (Xiao,
Rasul, and Vollgraf, 2017), which is suppose to be a more difficult image
classification dataset than MNIST (Zalandoresearch, 2017). This proved to be
the case as CNN-SVM reached a test accuracy of ~90.72%, while the CNN-Softmax
reached a test accuracy of ~91.86%. The said results may be improved if data
preprocessing techniques were employed on the datasets, and if the base CNN
model was a relatively more sophisticated than the one used in this study. | [
1,
0,
0,
1,
0,
0
] |
Title: Derivation of the cutoff length from the quantum quadratic enhancement of a mass in vacuum energy constant Lambda,
Abstract: Ultraviolet self-interaction energies in field theory sometimes contain
meaningful physical quantities. The self-energies in such as classical
electrodynamics are usually subtracted from the rest mass. For the consistent
treatment of energies as sources of curvature in the Einstein field equations,
this study includes these subtracted self-energies into vacuum energy expressed
by the constant Lambda (used in such as Lambda-CDM). In this study, the
self-energies in electrodynamics and macroscopic classical Einstein field
equations are examined, using the formalisms with the ultraviolet cutoff
scheme. One of the cutoff formalisms is the field theory in terms of the
step-function-type basis functions, developed by the present authors. The other
is a continuum theory of a fundamental particle with the same cutoff length.
Based on the effectiveness of the continuum theory with the cutoff length shown
in the examination, the dominant self-energy is the quadratic term of the Higgs
field at a quantum level (classical self-energies are reduced to logarithmic
forms by quantum corrections). The cutoff length is then determined to
reproduce today's tiny value of Lambda for vacuum energy. Additionally, a field
with nonperiodic vanishing boundary conditions is treated, showing that the
field has no zero-point energy. | [
0,
1,
0,
0,
0,
0
] |
Title: Study on a Poisson's Equation Solver Based On Deep Learning Technique,
Abstract: In this work, we investigated the feasibility of applying deep learning
techniques to solve Poisson's equation. A deep convolutional neural network is
set up to predict the distribution of electric potential in 2D or 3D cases.
With proper training data generated from a finite difference solver, the strong
approximation capability of the deep convolutional neural network allows it to
make correct prediction given information of the source and distribution of
permittivity. With applications of L2 regularization, numerical experiments
show that the predication error of 2D cases can reach below 1.5\% and the
predication of 3D cases can reach below 3\%, with a significant reduction in
CPU time compared with the traditional solver based on finite difference
methods. | [
1,
1,
0,
0,
0,
0
] |
Title: On multifractals: a non-linear study of actigraphy data,
Abstract: This work aimed, to determine the characteristics of activity series from
fractal geometry concepts application, in addition to evaluate the possibility
of identifying individuals with fibromyalgia. Activity level data were
collected from 27 healthy subjects and 27 fibromyalgia patients, with the use
of clock-like devices equipped with accelerometers, for about four weeks, all
day long. The activity series were evaluated through fractal and multifractal
methods. Hurst exponent analysis exhibited values according to other studies
($H>0.5$) for both groups ($H=0.98\pm0.04$ for healthy subjects and
$H=0.97\pm0.03$ for fibromyalgia patients), however, it is not possible to
distinguish between the two groups by such analysis. Activity time series also
exhibited a multifractal pattern. A paired analysis of the spectra indices for
the sleep and awake states revealed differences between healthy subjects and
fibromyalgia patients. The individuals feature differences between awake and
sleep states, having statistically significant differences for $\alpha_{q-} -
\alpha_{0}$ in healthy subjects ($p = 0.014$) and $D_{0}$ for patients with
fibromyalgia ($p = 0.013$). The approach has proven to be an option on the
characterisation of such kind of signals and was able to differ between both
healthy and fibromyalgia groups. This outcome suggests changes in the
physiologic mechanisms of movement control. | [
0,
1,
0,
1,
0,
0
] |
Title: Universality for eigenvalue algorithms on sample covariance matrices,
Abstract: We prove a universal limit theorem for the halting time, or iteration count,
of the power/inverse power methods and the QR eigenvalue algorithm.
Specifically, we analyze the required number of iterations to compute extreme
eigenvalues of random, positive-definite sample covariance matrices to within a
prescribed tolerance. The universality theorem provides a complexity estimate
for the algorithms which, in this random setting, holds with high probability.
The method of proof relies on recent results on the statistics of the
eigenvalues and eigenvectors of random sample covariance matrices (i.e.,
delocalization, rigidity and edge universality). | [
0,
0,
1,
0,
0,
0
] |
Title: Similarity-based Multi-label Learning,
Abstract: Multi-label classification is an important learning problem with many
applications. In this work, we propose a principled similarity-based approach
for multi-label learning called SML. We also introduce a similarity-based
approach for predicting the label set size. The experimental results
demonstrate the effectiveness of SML for multi-label classification where it is
shown to compare favorably with a wide variety of existing algorithms across a
range of evaluation criterion. | [
1,
0,
0,
1,
0,
0
] |
Title: Burst Synchronization in A Scale-Free Neuronal Network with Inhibitory Spike-Timing-Dependent Plasticity,
Abstract: We are concerned about burst synchronization (BS), related to neural
information processes in health and disease, in the Barabási-Albert
scale-free network (SFN) composed of inhibitory bursting Hindmarsh-Rose
neurons. This inhibitory neuronal population has adaptive dynamic synaptic
strengths governed by the inhibitory spike-timing-dependent plasticity (iSTDP).
In previous works without considering iSTDP, BS was found to appear in a range
of noise intensities for fixed synaptic inhibition strengths. In contrast, in
our present work, we take into consideration iSTDP and investigate its effect
on BS by varying the noise intensity. Our new main result is to find occurrence
of a Matthew effect in inhibitory synaptic plasticity: good BS gets better via
LTD, while bad BS get worse via LTP. This kind of Matthew effect in inhibitory
synaptic plasticity is in contrast to that in excitatory synaptic plasticity
where good (bad) synchronization gets better (worse) via LTP (LTD). We note
that, due to inhibition, the roles of LTD and LTP in inhibitory synaptic
plasticity are reversed in comparison with those in excitatory synaptic
plasticity. Moreover, emergences of LTD and LTP of synaptic inhibition
strengths are intensively investigated via a microscopic method based on the
distributions of time delays between the pre- and the post-synaptic burst onset
times. Finally, in the presence of iSTDP we investigate the effects of network
architecture on BS by varying the symmetric attachment degree $l^*$ and the
asymmetry parameter $\Delta l$ in the SFN. | [
0,
0,
0,
0,
1,
0
] |
Title: The Arrow of Time in the collapse of collisionless self-gravitating systems: non-validity of the Vlasov-Poisson equation during violent relaxation,
Abstract: The collapse of a collisionless self-gravitating system, with the fast
achievement of a quasi-stationary state, is driven by violent relaxation, with
a typical particle interacting with the time-changing collective potential. It
is traditionally assumed that this evolution is governed by the Vlasov-Poisson
equation, in which case entropy must be conserved. We run N-body simulations of
isolated self-gravitating systems, using three simulation codes: NBODY-6
(direct summation without softening), NBODY-2 (direct summation with softening)
and GADGET-2 (tree code with softening), for different numbers of particles and
initial conditions. At each snapshot, we estimate the Shannon entropy of the
distribution function with three different techniques: Kernel, Nearest Neighbor
and EnBiD. For all simulation codes and estimators, the entropy evolution
converges to the same limit as N increases. During violent relaxation, the
entropy has a fast increase followed by damping oscillations, indicating that
violent relaxation must be described by a kinetic equation other than the
Vlasov-Poisson, even for N as large as that of astronomical structures. This
indicates that violent relaxation cannot be described by a time-reversible
equation, shedding some light on the so-called "fundamental paradox of stellar
dynamics". The long-term evolution is well described by the orbit-averaged
Fokker-Planck model, with Coulomb logarithm values in the expected range 10-12.
By means of NBODY-2, we also study the dependence of the 2-body relaxation
time-scale on the softening length. The approach presented in the current work
can potentially provide a general method for testing any kinetic equation
intended to describe the macroscopic evolution of N-body systems. | [
0,
1,
0,
0,
0,
0
] |
Title: Neighborhood-Based Label Propagation in Large Protein Graphs,
Abstract: Understanding protein function is one of the keys to understanding life at
the molecular level. It is also important in several scenarios including human
disease and drug discovery. In this age of rapid and affordable biological
sequencing, the number of sequences accumulating in databases is rising with an
increasing rate. This presents many challenges for biologists and computer
scientists alike. In order to make sense of this huge quantity of data, these
sequences should be annotated with functional properties. UniProtKB consists of
two components: i) the UniProtKB/Swiss-Prot database containing protein
sequences with reliable information manually reviewed by expert bio-curators
and ii) the UniProtKB/TrEMBL database that is used for storing and processing
the unknown sequences. Hence, for all proteins we have available the sequence
along with few more information such as the taxon and some structural domains.
Pairwise similarity can be defined and computed on proteins based on such
attributes. Other important attributes, while present for proteins in
Swiss-Prot, are often missing for proteins in TrEMBL, such as their function
and cellular localization. The enormous number of protein sequences now in
TrEMBL calls for rapid procedures to annotate them automatically. In this work,
we present DistNBLP, a novel Distributed Neighborhood-Based Label Propagation
approach for large-scale annotation of proteins. To do this, the functional
annotations of reviewed proteins are used to predict those of non-reviewed
proteins using label propagation on a graph representation of the protein
database. DistNBLP is built on top of the "akka" toolkit for building resilient
distributed message-driven applications. | [
1,
0,
0,
0,
0,
0
] |
Title: Compressive Sensing with Cross-Validation and Stop-Sampling for Sparse Polynomial Chaos Expansions,
Abstract: Compressive sensing is a powerful technique for recovering sparse solutions
of underdetermined linear systems, which is often encountered in uncertainty
quantification analysis of expensive and high-dimensional physical models. We
perform numerical investigations employing several compressive sensing solvers
that target the unconstrained LASSO formulation, with a focus on linear systems
that arise in the construction of polynomial chaos expansions. With core
solvers of l1_ls, SpaRSA, CGIST, FPC_AS, and ADMM, we develop techniques to
mitigate overfitting through an automated selection of regularization constant
based on cross-validation, and a heuristic strategy to guide the stop-sampling
decision. Practical recommendations on parameter settings for these techniques
are provided and discussed. The overall method is applied to a series of
numerical examples of increasing complexity, including large eddy simulations
of supersonic turbulent jet-in-crossflow involving a 24-dimensional input.
Through empirical phase-transition diagrams and convergence plots, we
illustrate sparse recovery performance under structures induced by polynomial
chaos, accuracy and computational tradeoffs between polynomial bases of
different degrees, and practicability of conducting compressive sensing for a
realistic, high-dimensional physical application. Across test cases studied in
this paper, we find ADMM to have demonstrated empirical advantages through
consistent lower errors and faster computational times. | [
0,
1,
0,
1,
0,
0
] |
Title: Closure Properties in the Class of Multiple Context Free Groups,
Abstract: We show that the class of groups with $k$-multiple context-free word problem
is closed under graphs of groups with finite edge groups. | [
1,
0,
1,
0,
0,
0
] |
Title: Random gauge models of the superconductor-insulator transition in two-dimensional disordered superconductors,
Abstract: We study numerically the superconductor-insulator transition in
two-dimensional inhomogeneous superconductors with gauge disorder, described by
four different quantum rotor models: a gauge glass, a flux glass, a binary
phase glass and a Gaussian phase glass. The first two models, describe the
combined effect of geometrical disorder in the array of local superconducting
islands and a uniform external magnetic field while the last two describe the
effects of random negative Josephson-junction couplings or $\pi$ junctions.
Monte Carlo simulations in the path-integral representation of the models are
used to determine the critical exponents and the universal conductivity at the
quantum phase transition. The gauge and flux glass models display the same
critical behavior, within the estimated numerical uncertainties. Similar
agreement is found for the binary and Gaussian phase-glass models. Despite the
different symmetries and disorder correlations, we find that the universal
conductivity of these models is approximately the same. In particular, the
ratio of this value to that of the pure model agrees with recent experiments on
nanohole thin film superconductors in a magnetic field, in the large disorder
limit. | [
0,
1,
0,
0,
0,
0
] |
Title: A polynomial time knot polynomial,
Abstract: We present the strongest known knot invariant that can be computed
effectively (in polynomial time). | [
0,
0,
1,
0,
0,
0
] |
Title: Geometric Biplane Graphs I: Maximal Graphs,
Abstract: We study biplane graphs drawn on a finite planar point set $S$ in general
position. This is the family of geometric graphs whose vertex set is $S$ and
can be decomposed into two plane graphs. We show that two maximal biplane
graphs---in the sense that no edge can be added while staying biplane---may
differ in the number of edges, and we provide an efficient algorithm for adding
edges to a biplane graph to make it maximal. We also study extremal properties
of maximal biplane graphs such as the maximum number of edges and the largest
maximum connectivity over $n$-element point sets. | [
1,
0,
0,
0,
0,
0
] |
Title: Neural Architecture for Question Answering Using a Knowledge Graph and Web Corpus,
Abstract: In Web search, entity-seeking queries often trigger a special Question
Answering (QA) system. It may use a parser to interpret the question to a
structured query, execute that on a knowledge graph (KG), and return direct
entity responses. QA systems based on precise parsing tend to be brittle: minor
syntax variations may dramatically change the response. Moreover, KG coverage
is patchy. At the other extreme, a large corpus may provide broader coverage,
but in an unstructured, unreliable form. We present AQQUCN, a QA system that
gracefully combines KG and corpus evidence. AQQUCN accepts a broad spectrum of
query syntax, between well-formed questions to short `telegraphic' keyword
sequences. In the face of inherent query ambiguities, AQQUCN aggregates signals
from KGs and large corpora to directly rank KG entities, rather than commit to
one semantic interpretation of the query. AQQUCN models the ideal
interpretation as an unobservable or latent variable. Interpretations and
candidate entity responses are scored as pairs, by combining signals from
multiple convolutional networks that operate collectively on the query, KG and
corpus. On four public query workloads, amounting to over 8,000 queries with
diverse query syntax, we see 5--16% absolute improvement in mean average
precision (MAP), compared to the entity ranking performance of recent systems.
Our system is also competitive at entity set retrieval, almost doubling F1
scores for challenging short queries. | [
1,
0,
0,
0,
0,
0
] |
Title: A spatially explicit capture recapture model for partially identified individuals when trap detection rate is less than one,
Abstract: Spatially explicit capture recapture (SECR) models have gained enormous
popularity to solve abundance estimation problems in ecology. In this study, we
develop a novel Bayesian SECR model that disentangles the process of animal
movement through a detector from the process of recording data by a detector in
the face of imperfect detection. We integrate this complexity into an advanced
version of a recent SECR model involving partially identified individuals
(Royle, 2015). We assess the performance of our model over a range of realistic
simulation scenarios and demonstrate that estimates of population size $N$
improve when we utilize the proposed model relative to the model that does not
explicitly estimate trap detection probability (Royle, 2015). We confront and
investigate the proposed model with a spatial capture-recapture data set from a
camera trapping survey on tigers (\textit{Panthera tigris}) in Nagarahole,
southern India. Trap detection probability is estimated at 0.489 and therefore
justifies the necessity to utilize our model in field situations. We discuss
possible extensions, future work and relevance of our model to other
statistical applications beyond ecology. | [
0,
0,
0,
1,
0,
0
] |
Title: Non-Gaussian Stochastic Volatility Model with Jumps via Gibbs Sampler,
Abstract: In this work, we propose a model for estimating volatility from financial
time series, extending the non-Gaussian family of space-state models with exact
marginal likelihood proposed by Gamerman, Santos and Franco (2013). On the
literature there are models focused on estimating financial assets risk,
however, most of them rely on MCMC methods based on Metropolis algorithms,
since full conditional posterior distributions are not known. We present an
alternative model capable of estimating the volatility, in an automatic way,
since all full conditional posterior distributions are known, and it is
possible to obtain an exact sample of parameters via Gibbs Sampler. The
incorporation of jumps in returns allows the model to capture speculative
movements of the data, so that their influence does not propagate to
volatility. We evaluate the performance of the algorithm using synthetic and
real data time series.
Keywords: Financial time series, Stochastic volatility, Gibbs Sampler,
Dynamic linear models. | [
0,
0,
0,
0,
0,
1
] |
Title: Smart Guiding Glasses for Visually Impaired People in Indoor Environment,
Abstract: To overcome the travelling difficulty for the visually impaired group, this
paper presents a novel ETA (Electronic Travel Aids)-smart guiding device in the
shape of a pair of eyeglasses for giving these people guidance efficiently and
safely. Different from existing works, a novel multi sensor fusion based
obstacle avoiding algorithm is proposed, which utilizes both the depth sensor
and ultrasonic sensor to solve the problems of detecting small obstacles, and
transparent obstacles, e.g. the French door. For totally blind people, three
kinds of auditory cues were developed to inform the direction where they can go
ahead. Whereas for weak sighted people, visual enhancement which leverages the
AR (Augment Reality) technique and integrates the traversable direction is
adopted. The prototype consisting of a pair of display glasses and several low
cost sensors is developed, and its efficiency and accuracy were tested by a
number of users. The experimental results show that the smart guiding glasses
can effectively improve the user's travelling experience in complicated indoor
environment. Thus it serves as a consumer device for helping the visually
impaired people to travel safely. | [
1,
0,
0,
0,
0,
0
] |
Title: Recurrent Poisson Factorization for Temporal Recommendation,
Abstract: Poisson factorization is a probabilistic model of users and items for
recommendation systems, where the so-called implicit consumer data is modeled
by a factorized Poisson distribution. There are many variants of Poisson
factorization methods who show state-of-the-art performance on real-world
recommendation tasks. However, most of them do not explicitly take into account
the temporal behavior and the recurrent activities of users which is essential
to recommend the right item to the right user at the right time. In this paper,
we introduce Recurrent Poisson Factorization (RPF) framework that generalizes
the classical PF methods by utilizing a Poisson process for modeling the
implicit feedback. RPF treats time as a natural constituent of the model and
brings to the table a rich family of time-sensitive factorization models. To
elaborate, we instantiate several variants of RPF who are capable of handling
dynamic user preferences and item specification (DRPF), modeling the
social-aspect of product adoption (SRPF), and capturing the consumption
heterogeneity among users and items (HRPF). We also develop a variational
algorithm for approximate posterior inference that scales up to massive data
sets. Furthermore, we demonstrate RPF's superior performance over many
state-of-the-art methods on synthetic dataset, and large scale real-world
datasets on music streaming logs, and user-item interactions in M-Commerce
platforms. | [
1,
0,
0,
1,
0,
0
] |
Title: Categorification of sign-skew-symmetric cluster algebras and some conjectures on g-vectors,
Abstract: Using the unfolding method given in \cite{HL}, we prove the conjectures on
sign-coherence and a recurrence formula respectively of ${\bf g}$-vectors for
acyclic sign-skew-symmetric cluster algebras. As a following consequence, the
conjecture is affirmed in the same case which states that the ${\bf g}$-vectors
of any cluster form a basis of $\mathbb Z^n$. Also, the additive
categorification of an acyclic sign-skew-symmetric cluster algebra $\mathcal
A(\Sigma)$ is given, which is realized as $(\mathcal C^{\widetilde Q},\Gamma)$
for a Frobenius $2$-Calabi-Yau category $\mathcal C^{\widetilde Q}$ constructed
from an unfolding $(Q,\Gamma)$ of the acyclic exchange matrix $B$ of $\mathcal
A(\Sigma)$. | [
0,
0,
1,
0,
0,
0
] |
Title: Is ram-pressure stripping an efficient mechanism to remove gas in galaxies?,
Abstract: We study how the gas in a sample of galaxies (M* > 10e9 Msun) in clusters,
obtained in a cosmological simulation, is affected by the interaction with the
intra-cluster medium (ICM). The dynamical state of each elemental parcel of gas
is studied using the total energy. At z ~ 2, the galaxies in the simulation are
evenly distributed within clusters, moving later on towards more central
locations. In this process, gas from the ICM is accreted and mixed with the gas
in the galactic halo. Simultaneously, the interaction with the environment
removes part of the gas. A characteristic stellar mass around M* ~ 10e10 Msun
appears as a threshold marking two differentiated behaviours. Below this mass,
galaxies are located at the external part of clusters and have eccentric
orbits. The effect of the interaction with the environment is marginal. Above,
galaxies are mainly located at the inner part of clusters with mostly radial
orbits with low velocities. In these massive systems, part of the gas, strongly
correlated with the stellar mass of the galaxy, is removed. The amount of
removed gas is sub-dominant compared with the quantity of retained gas which is
continuously influenced by the hot gas coming from the ICM. The analysis of
individual galaxies reveals the existence of a complex pattern of flows,
turbulence and a constant fuelling of gas to the hot corona from the ICM that
could make the global effect of the interaction of galaxies with their
environment to be substantially less dramatic than previously expected. | [
0,
1,
0,
0,
0,
0
] |
Title: Hidden Fluid Mechanics: A Navier-Stokes Informed Deep Learning Framework for Assimilating Flow Visualization Data,
Abstract: We present hidden fluid mechanics (HFM), a physics informed deep learning
framework capable of encoding an important class of physical laws governing
fluid motions, namely the Navier-Stokes equations. In particular, we seek to
leverage the underlying conservation laws (i.e., for mass, momentum, and
energy) to infer hidden quantities of interest such as velocity and pressure
fields merely from spatio-temporal visualizations of a passive scaler (e.g.,
dye or smoke), transported in arbitrarily complex domains (e.g., in human
arteries or brain aneurysms). Our approach towards solving the aforementioned
data assimilation problem is unique as we design an algorithm that is agnostic
to the geometry or the initial and boundary conditions. This makes HFM highly
flexible in choosing the spatio-temporal domain of interest for data
acquisition as well as subsequent training and predictions. Consequently, the
predictions made by HFM are among those cases where a pure machine learning
strategy or a mere scientific computing approach simply cannot reproduce. The
proposed algorithm achieves accurate predictions of the pressure and velocity
fields in both two and three dimensional flows for several benchmark problems
motivated by real-world applications. Our results demonstrate that this
relatively simple methodology can be used in physical and biomedical problems
to extract valuable quantitative information (e.g., lift and drag forces or
wall shear stresses in arteries) for which direct measurements may not be
possible. | [
0,
0,
0,
1,
0,
0
] |
Title: Hierarchical Detail Enhancing Mesh-Based Shape Generation with 3D Generative Adversarial Network,
Abstract: Automatic mesh-based shape generation is of great interest across a wide
range of disciplines, from industrial design to gaming, computer graphics and
various other forms of digital art. While most traditional methods focus on
primitive based model generation, advances in deep learning made it possible to
learn 3-dimensional geometric shape representations in an end-to-end manner.
However, most current deep learning based frameworks focus on the
representation and generation of voxel and point-cloud based shapes, making it
not directly applicable to design and graphics communities. This study
addresses the needs for automatic generation of mesh-based geometries, and
propose a novel framework that utilizes signed distance function representation
that generates detail preserving three-dimensional surface mesh by a deep
learning based approach. | [
1,
0,
0,
0,
0,
0
] |
Title: Combined analysis of galaxy cluster number count, thermal Sunyaev-Zel'dovich power spectrum, and bispectrum,
Abstract: The Sunyaev-Zel'dovich (SZ) effect is a powerful probe of the evolution of
structures in the universe, and is thus highly sensitive to cosmological
parameters $\sigma_8$ and $\Omega_m$, though its power is hampered by the
current uncertainties on the cluster mass calibration. In this analysis we
revisit constraints on these cosmological parameters as well as the hydrostatic
mass bias, by performing (i) a robust estimation of the tSZ power-spectrum,
(ii) a complete modeling and analysis of the tSZ bispectrum, and (iii) a
combined analysis of galaxy clusters number count, tSZ power spectrum, and tSZ
bispectrum. From this analysis, we derive as final constraints $\sigma_8 = 0.79
\pm 0.02$, $\Omega_{\rm m} = 0.29 \pm 0.02$, and $(1-b) = 0.71 \pm 0.07$. These
results favour a high value for the hydrostatic mass bias compared to numerical
simulations and weak-lensing based estimations. They are furthermore consistent
with both previous tSZ analyses, CMB derived cosmological parameters, and
ancillary estimations of the hydrostatic mass bias. | [
0,
1,
0,
0,
0,
0
] |
Title: On the least upper bound for the settling time of a class of fixed-time stable systems,
Abstract: This paper deals with the convergence time analysis of a class of fixed-time
stable systems with the aim to provide a new non-conservative upper bound for
its settling time. Our contribution is threefold. First, we revisit a
well-known class of fixed-time stable systems showing the conservatism of the
classical upper estimate of the settling time. Second, we provide the smallest
constant that uniformly upper bounds the settling time of any trajectory of the
system under consideration. Then, introducing a slight modification of the
previous class of fixed-time systems, we propose a new predefined-time
convergent algorithm where the least upper bound of the settling time is set a
priori as a parameter of the system. This calculation is a valuable
contribution toward online differentiators, observers, and controllers in
applications with real-time constraints. | [
1,
0,
0,
0,
0,
0
] |
Title: Nonequilibrium Work and its Hamiltonian Connection for a Microstate in Nonequilibrium Statistical Thermodynamics: A Case of Mistaken Identity,
Abstract: Nonequilibrium work-Hamiltonian connection for a microstate plays a central
role in diverse branches of statistical thermodynamics (fluctuation theorems,
quantum thermodynamics, stochastic thermodynamics, etc.). We show that the
change in the Hamiltonian for a microstate should be identified with the work
done by it, and not the work done on it. This contradicts the current practice
in the field. The difference represents a contribution whose average gives the
work that is dissipated due to irreversibility. As the latter has been
overlooked, the current identification does not properly account for
irreversibilty. As an example, we show that the corrected version of
Jarzynski's relation can be applied to free expansion, where the original
relation fails. Thus, the correction has far-reaching consequences and requires
reassessment of current applications. | [
0,
1,
0,
0,
0,
0
] |
Title: Thick Subcategories of the stable category of modules over the exterior algebra I,
Abstract: We study thick subcategories defined by modules of complexity one in
$\underline{\md}R$, where $R$ is the exterior algebra in $n+1$ indeterminates. | [
0,
0,
1,
0,
0,
0
] |
Title: Nonparametric mean curvature type flows of graphs with contact angle conditions,
Abstract: In this paper we study nonparametric mean curvature type flows in
$M\times\mathbb{R}$ which are represented as graphs $(x,u(x,t))$ over a domain
in a Riemannian manifold $M$ with prescribed contact angle. The speed of $u$ is
the mean curvature speed minus an admissible function $\psi(x,u,Du)$. Long time
existence and uniformly convergence are established if $\psi(x,u, Du)\equiv 0$
with vertical contact angle and $\psi(x,u,Du)=h(x,u)\omega$ with $h_u(x,u)\geq
h_0>0$ and $\omega=\sqrt{1+|Du|^2}$. Their applications include mean curvature
type equations with prescribed contact angle boundary condition and the
asymptotic behavior of nonparametric mean curvature flows of graphs over a
convex domain in $M^2$ which is a surface with nonnegative Ricci curvature. | [
0,
0,
1,
0,
0,
0
] |
Title: Spontaneous symmetry breaking as a triangular relation between pairs of Goldstone bosons and the degenerate vacuum: Interactions of D-branes,
Abstract: We formulate the Nambu-Goldstone theorem as a triangular relation between
pairs of Goldstone bosons with the degenerate vacuum. The vacuum degeneracy is
then a natural consequence of this relation. Inside the scenario of String
Theory, we then find that there is a correspondence between the way how the
$D$-branes interact and the properties of the Goldstone bosons. | [
0,
1,
0,
0,
0,
0
] |
Title: Differentially Private Dropout,
Abstract: Large data collections required for the training of neural networks often
contain sensitive information such as the medical histories of patients, and
the privacy of the training data must be preserved. In this paper, we introduce
a dropout technique that provides an elegant Bayesian interpretation to
dropout, and show that the intrinsic noise added, with the primary goal of
regularization, can be exploited to obtain a degree of differential privacy.
The iterative nature of training neural networks presents a challenge for
privacy-preserving estimation since multiple iterations increase the amount of
noise added. We overcome this by using a relaxed notion of differential
privacy, called concentrated differential privacy, which provides tighter
estimates on the overall privacy loss. We demonstrate the accuracy of our
privacy-preserving dropout algorithm on benchmark datasets. | [
1,
0,
0,
1,
0,
0
] |
Title: Quantitative Connection Between Ensemble Thermodynamics and Single-Molecule Kinetics: A Case Study Using Cryogenic Electron Microscopy and Single-Molecule Fluorescence Resonance Energy Transfer Investigations of the Ribosome,
Abstract: At equilibrium, thermodynamic and kinetic information can be extracted from
biomolecular energy landscapes by many techniques. However, while static,
ensemble techniques yield thermodynamic data, often only dynamic,
single-molecule techniques can yield the kinetic data that describes
transition-state energy barriers. Here we present a generalized framework based
upon dwell-time distributions that can be used to connect such static, ensemble
techniques with dynamic, single-molecule techniques, and thus characterize
energy landscapes to greater resolutions. We demonstrate the utility of this
framework by applying it to cryogenic electron microscopy (cryo-EM) and
single-molecule fluorescence resonance energy transfer (smFRET) studies of the
bacterial ribosomal pre-translocation complex. Among other benefits,
application of this framework to these data explains why two transient,
intermediate conformations of the pre-translocation complex, which are observed
in a cryo-EM study, may not be observed in several smFRET studies. | [
0,
1,
0,
0,
0,
0
] |
Title: Non-parametric estimation of Jensen-Shannon Divergence in Generative Adversarial Network training,
Abstract: Generative Adversarial Networks (GANs) have become a widely popular framework
for generative modelling of high-dimensional datasets. However their training
is well-known to be difficult. This work presents a rigorous statistical
analysis of GANs providing straight-forward explanations for common training
pathologies such as vanishing gradients. Furthermore, it proposes a new
training objective, Kernel GANs, and demonstrates its practical effectiveness
on large-scale real-world data sets. A key element in the analysis is the
distinction between training with respect to the (unknown) data distribution,
and its empirical counterpart. To overcome issues in GAN training, we pursue
the idea of smoothing the Jensen-Shannon Divergence (JSD) by incorporating
noise in the input distributions of the discriminator. As we show, this
effectively leads to an empirical version of the JSD in which the true and the
generator densities are replaced by kernel density estimates, which leads to
Kernel GANs. | [
0,
0,
0,
1,
0,
0
] |
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