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Title: Efficient Online Bandit Multiclass Learning with $\tilde{O}(\sqrt{T})$ Regret,
Abstract: We present an efficient second-order algorithm with
$\tilde{O}(\frac{1}{\eta}\sqrt{T})$ regret for the bandit online multiclass
problem. The regret bound holds simultaneously with respect to a family of loss
functions parameterized by $\eta$, for a range of $\eta$ restricted by the norm
of the competitor. The family of loss functions ranges from hinge loss
($\eta=0$) to squared hinge loss ($\eta=1$). This provides a solution to the
open problem of (J. Abernethy and A. Rakhlin. An efficient bandit algorithm for
$\sqrt{T}$-regret in online multiclass prediction? In COLT, 2009). We test our
algorithm experimentally, showing that it also performs favorably against
earlier algorithms. | [
0,
0,
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1,
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] |
Title: Towards exascale real-time RFI mitigation,
Abstract: We describe the design and implementation of an extremely scalable real-time
RFI mitigation method, based on the offline AOFlagger. All algorithms scale
linearly in the number of samples. We describe how we implemented the flagger
in the LOFAR real-time pipeline, on both CPUs and GPUs. Additionally, we
introduce a novel simple history-based flagger that helps reduce the impact of
our small window on the data.
By examining an observation of a known pulsar, we demonstrate that our
flagger can achieve much higher quality than a simple thresholder, even when
running in real time, on a distributed system. The flagger works on visibility
data, but also on raw voltages, and beam formed data. The algorithms are
scale-invariant, and work on microsecond to second time scales. We are
currently implementing a prototype for the time domain pipeline of the SKA
central signal processor. | [
0,
1,
0,
0,
0,
0
] |
Title: Cayley properties of the line graphs induced by of consecutive layers of the hypercube,
Abstract: Let $n >3$ and $ 0< k < \frac{n}{2} $ be integers. In this paper, we
investigate some algebraic properties of the line graph of the graph $
{Q_n}(k,k+1) $ where $ {Q_n}(k,k+1) $ is the subgraph of the hypercube $Q_n$
which is induced by the set of vertices of weights $k$ and $k+1$. In the first
step, we determine the automorphism groups of these graphs for all values of
$k$. In the second step, we study Cayley properties of the line graph of these
graphs. In particular, we show that for $ k>2, $ if $ 2k+1 \neq n$, then the
line graph of the graph $ {Q_n}(k,k+1) $ is a vertex-transitive non Cayley
graph. Also, we show that the line graph of the graph $ {Q_n}(1,2) $ is a
Cayley graph if and only if $ n$ is a power of a prime $p$. | [
0,
0,
1,
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0,
0
] |
Title: Beyond the technical challenges for deploying Machine Learning solutions in a software company,
Abstract: Recently software development companies started to embrace Machine Learning
(ML) techniques for introducing a series of advanced functionality in their
products such as personalisation of the user experience, improved search,
content recommendation and automation. The technical challenges for tackling
these problems are heavily researched in literature. A less studied area is a
pragmatic approach to the role of humans in a complex modern industrial
environment where ML based systems are developed. Key stakeholders affect the
system from inception and up to operation and maintenance. Product managers
want to embed "smart" experiences for their users and drive the decisions on
what should be built next; software engineers are challenged to build or
utilise ML software tools that require skills that are well outside of their
comfort zone; legal and risk departments may influence design choices and data
access; operations teams are requested to maintain ML systems which are
non-stationary in their nature and change behaviour over time; and finally ML
practitioners should communicate with all these stakeholders to successfully
build a reliable system. This paper discusses some of the challenges we faced
in Atlassian as we started investing more in the ML space. | [
1,
0,
0,
1,
0,
0
] |
Title: J-MOD$^{2}$: Joint Monocular Obstacle Detection and Depth Estimation,
Abstract: In this work, we propose an end-to-end deep architecture that jointly learns
to detect obstacles and estimate their depth for MAV flight applications. Most
of the existing approaches either rely on Visual SLAM systems or on depth
estimation models to build 3D maps and detect obstacles. However, for the task
of avoiding obstacles this level of complexity is not required. Recent works
have proposed multi task architectures to both perform scene understanding and
depth estimation. We follow their track and propose a specific architecture to
jointly estimate depth and obstacles, without the need to compute a global map,
but maintaining compatibility with a global SLAM system if needed. The network
architecture is devised to exploit the joint information of the obstacle
detection task, that produces more reliable bounding boxes, with the depth
estimation one, increasing the robustness of both to scenario changes. We call
this architecture J-MOD$^{2}$. We test the effectiveness of our approach with
experiments on sequences with different appearance and focal lengths and
compare it to SotA multi task methods that jointly perform semantic
segmentation and depth estimation. In addition, we show the integration in a
full system using a set of simulated navigation experiments where a MAV
explores an unknown scenario and plans safe trajectories by using our detection
model. | [
1,
0,
0,
0,
0,
0
] |
Title: Star Formation Activity in the molecular cloud G35.20$-$0.74: onset of cloud-cloud collision,
Abstract: To probe the star-formation (SF) processes, we present results of an analysis
of the molecular cloud G35.20$-$0.74 (hereafter MCG35.2) using multi-frequency
observations. The MCG35.2 is depicted in a velocity range of 30-40 km s$^{-1}$.
An almost horseshoe-like structure embedded within the MCG35.2 is evident in
the infrared and millimeter images and harbors the previously known sites,
ultra-compact/hyper-compact G35.20$-$0.74N H\,{\sc ii} region, Ap2-1, and
Mercer 14 at its base. The site, Ap2-1 is found to be excited by a radio
spectral type of B0.5V star where the distribution of 20 cm and H$\alpha$
emission is surrounded by the extended molecular hydrogen emission. Using the
{\it Herschel} 160-500 $\mu$m and photometric 1-24 $\mu$m data analysis,
several embedded clumps and clusters of young stellar objects (YSOs) are
investigated within the MCG35.2, revealing the SF activities. Majority of the
YSOs clusters and massive clumps (500-4250 M$_{\odot}$) are seen toward the
horseshoe-like structure. The position-velocity analysis of $^{13}$CO emission
shows a blue-shifted peak (at 33 km s$^{-1}$) and a red-shifted peak (at 37 km
s$^{-1}$) interconnected by lower intensity intermediated velocity emission,
tracing a broad bridge feature. The presence of such broad bridge feature
suggests the onset of a collision between molecular components in the MCG35.2.
A noticeable change in the H-band starlight mean polarization angles has also
been observed in the MCG35.2, probably tracing the interaction between
molecular components. Taken together, it seems that the cloud-cloud collision
process has influenced the birth of massive stars and YSOs clusters in the
MCG35.2. | [
0,
1,
0,
0,
0,
0
] |
Title: On Functional Graphs of Quadratic Polynomials,
Abstract: We study functional graphs generated by quadratic polynomials over prime
fields. We introduce efficient algorithms for methodical computations and
provide the values of various direct and cumulative statistical parameters of
interest. These include: the number of connected functional graphs, the number
of graphs having a maximal cycle, the number of cycles of fixed size, the
number of components of fixed size, as well as the shape of trees extracted
from functional graphs. We particularly focus on connected functional graphs,
that is, the graphs which contain only one component (and thus only one cycle).
Based on the results of our computations, we formulate several conjectures
highlighting the similarities and differences between these functional graphs
and random mappings. | [
0,
0,
1,
0,
0,
0
] |
Title: Spaces of orders of some one-relator groups,
Abstract: We show that certain orderable groups admit no isolated left orders. The
groups we consider are cyclic amalgamations of a free group with a general
orderable group, the HNN extensions of free groups over cyclic subgroups, and a
particular class of one-relator groups. In order to prove the results about
orders, we develop perturbation techniques for actions of these groups on the
line. | [
0,
0,
1,
0,
0,
0
] |
Title: Stellar streams as gravitational experiments I. The case of Sagittarius,
Abstract: Tidal streams of disrupting dwarf galaxies orbiting around their host galaxy
offer a unique way to constrain the shape of galactic gravitational potentials.
Such streams can be used as leaning tower gravitational experiments on galactic
scales. The most well motivated modification of gravity proposed as an
alternative to dark matter on galactic scales is Milgromian dynamics (MOND),
and we present here the first ever N-body simulations of the dynamical
evolution of the disrupting Sagittarius dwarf galaxy in this framework. Using a
realistic baryonic mass model for the Milky Way, we attempt to reproduce the
present-day spatial and kinematic structure of the Sagittarius dwarf and its
immense tidal stream that wraps around the Milky Way. With very little freedom
on the original structure of the progenitor, constrained by the total
luminosity of the Sagittarius structure and by the observed stellar mass-size
relation for isolated dwarf galaxies, we find reasonable agreement between our
simulations and observations of this system. The observed stellar velocities in
the leading arm can be reproduced if we include a massive hot gas corona around
the Milky Way that is flattened in the direction of the principal plane of its
satellites. This is the first time that tidal dissolution in MOND has been
tested rigorously at these mass and acceleration scales. | [
0,
1,
0,
0,
0,
0
] |
Title: Flows along arch filaments observed in the GRIS 'very fast spectroscopic mode',
Abstract: A new generation of solar instruments provides improved spectral, spatial,
and temporal resolution, thus facilitating a better understanding of dynamic
processes on the Sun. High-resolution observations often reveal
multiple-component spectral line profiles, e.g., in the near-infrared He I
10830 \AA\ triplet, which provides information about the chromospheric velocity
and magnetic fine structure. We observed an emerging flux region, including two
small pores and an arch filament system, on 2015 April 17 with the 'very fast
spectroscopic mode' of the GREGOR Infrared Spectrograph (GRIS) situated at the
1.5-meter GREGOR solar telescope at Observatorio del Teide, Tenerife, Spain. We
discuss this method of obtaining fast (one per minute) spectral scans of the
solar surface and its potential to follow dynamic processes on the Sun. We
demonstrate the performance of the 'very fast spectroscopic mode' by tracking
chromospheric high-velocity features in the arch filament system. | [
0,
1,
0,
0,
0,
0
] |
Title: Rethinking Information Sharing for Actionable Threat Intelligence,
Abstract: In the past decade, the information security and threat landscape has grown
significantly making it difficult for a single defender to defend against all
attacks at the same time. This called for introduc- ing information sharing, a
paradigm in which threat indicators are shared in a community of trust to
facilitate defenses. Standards for representation, exchange, and consumption of
indicators are pro- posed in the literature, although various issues are
undermined. In this paper, we rethink information sharing for actionable
intelli- gence, by highlighting various issues that deserve further explo-
ration. We argue that information sharing can benefit from well- defined use
models, threat models, well-understood risk by mea- surement and robust
scoring, well-understood and preserved pri- vacy and quality of indicators and
robust mechanism to avoid free riding behavior of selfish agent. We call for
using the differential nature of data and community structures for optimizing
sharing. | [
1,
0,
0,
0,
0,
0
] |
Title: Distributive Aronszajn trees,
Abstract: Ben-David and Shelah proved that if $\lambda$ is a singular strong-limit
cardinal and $2^\lambda=\lambda^+$, then $\square^*_\lambda$ entails the
existence of a normal $\lambda$-distributive $\lambda^+$-Aronszajn tree. Here,
it is proved that the same conclusion remains valid after replacing the
hypothesis $\square^*_\lambda$ by $\square(\lambda^+,{<}\lambda)$.
As $\square(\lambda^+,{<}\lambda)$ does not impose a bound on the order-type
of the witnessing clubs, our construction is necessarily different from that of
Ben-David and Shelah, and instead uses walks on ordinals augmented with club
guessing.
A major component of this work is the study of postprocessing functions and
their effect on square sequences. A byproduct of this study is the finding that
for $\kappa$ regular uncountable, $\square(\kappa)$ entails the existence of a
partition of $\kappa$ into $\kappa$ many fat sets. When contrasted with a
classic model of Magidor, this shows that it is equiconsistent with the
existence of a weakly compact cardinal that $\omega_2$ cannot be split into two
fat sets. | [
0,
0,
1,
0,
0,
0
] |
Title: Gated Multimodal Units for Information Fusion,
Abstract: This paper presents a novel model for multimodal learning based on gated
neural networks. The Gated Multimodal Unit (GMU) model is intended to be used
as an internal unit in a neural network architecture whose purpose is to find
an intermediate representation based on a combination of data from different
modalities. The GMU learns to decide how modalities influence the activation of
the unit using multiplicative gates. It was evaluated on a multilabel scenario
for genre classification of movies using the plot and the poster. The GMU
improved the macro f-score performance of single-modality approaches and
outperformed other fusion strategies, including mixture of experts models.
Along with this work, the MM-IMDb dataset is released which, to the best of our
knowledge, is the largest publicly available multimodal dataset for genre
prediction on movies. | [
0,
0,
0,
1,
0,
0
] |
Title: Birefringence induced by pp-wave modes in an electromagnetically active dynamic aether,
Abstract: In the framework of the Einstein-Maxwell-aether theory we study the
birefringence effect, which can occur in the pp-wave symmetric dynamic aether.
The dynamic aether is considered to be latently birefringent quasi-medium,
which displays this hidden property if and only if the aether motion is
non-uniform, i.e., when the aether flow is characterized by the non-vanishing
expansion, shear, vorticity or acceleration. In accordance with the
dynamo-optical scheme of description of the interaction between electromagnetic
waves and the dynamic aether, we shall model the susceptibility tensors by the
terms linear in the covariant derivative of the aether velocity four-vector.
When the pp-wave modes appear in the dynamic aether, we deal with a
gravitationally induced degeneracy removal with respect to hidden
susceptibility parameters. As a consequence, the phase velocities of
electromagnetic waves possessing orthogonal polarizations do not coincide, thus
displaying the birefringence effect. Two electromagnetic field configurations
are studied in detail: longitudinal and transversal with respect to the aether
pp-wave front. For both cases the solutions are found, which reveal anomalies
in the electromagnetic response on the action of the pp-wave aether mode. | [
0,
1,
0,
0,
0,
0
] |
Title: On generalizations of $p$-sets and their applications,
Abstract: The $p$-set, which is in a simple analytic form, is well distributed in unit
cubes. The well-known Weil's exponential sum theorem presents an upper bound of
the exponential sum over the $p$-set. Based on the result, one shows that the
$p$-set performs well in numerical integration, in compressed sensing as well
as in UQ. However, $p$-set is somewhat rigid since the cardinality of the
$p$-set is a prime $p$ and the set only depends on the prime number $p$. The
purpose of this paper is to present generalizations of $p$-sets, say
$\mathcal{P}_{d,p}^{{\mathbf a},\epsilon}$, which is more flexible.
Particularly, when a prime number $p$ is given, we have many different choices
of the new $p$-sets. Under the assumption that Goldbach conjecture holds, for
any even number $m$, we present a point set, say ${\mathcal L}_{p,q}$, with
cardinality $m-1$ by combining two different new $p$-sets, which overcomes a
major bottleneck of the $p$-set. We also present the upper bounds of the
exponential sums over $\mathcal{P}_{d,p}^{{\mathbf a},\epsilon}$ and ${\mathcal
L}_{p,q}$, which imply these sets have many potential applications. | [
1,
0,
1,
0,
0,
0
] |
Title: Modified Frank-Wolfe Algorithm for Enhanced Sparsity in Support Vector Machine Classifiers,
Abstract: This work proposes a new algorithm for training a re-weighted L2 Support
Vector Machine (SVM), inspired on the re-weighted Lasso algorithm of Candès
et al. and on the equivalence between Lasso and SVM shown recently by Jaggi. In
particular, the margin required for each training vector is set independently,
defining a new weighted SVM model. These weights are selected to be binary, and
they are automatically adapted during the training of the model, resulting in a
variation of the Frank-Wolfe optimization algorithm with essentially the same
computational complexity as the original algorithm. As shown experimentally,
this algorithm is computationally cheaper to apply since it requires less
iterations to converge, and it produces models with a sparser representation in
terms of support vectors and which are more stable with respect to the
selection of the regularization hyper-parameter. | [
1,
0,
0,
1,
0,
0
] |
Title: Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation,
Abstract: Learning-based approaches to robotic manipulation are limited by the
scalability of data collection and accessibility of labels. In this paper, we
present a multi-task domain adaptation framework for instance grasping in
cluttered scenes by utilizing simulated robot experiments. Our neural network
takes monocular RGB images and the instance segmentation mask of a specified
target object as inputs, and predicts the probability of successfully grasping
the specified object for each candidate motor command. The proposed transfer
learning framework trains a model for instance grasping in simulation and uses
a domain-adversarial loss to transfer the trained model to real robots using
indiscriminate grasping data, which is available both in simulation and the
real world. We evaluate our model in real-world robot experiments, comparing it
with alternative model architectures as well as an indiscriminate grasping
baseline. | [
1,
0,
0,
0,
0,
0
] |
Title: Hausdorff dimensions in $p$-adic analytic groups,
Abstract: Let $G$ be a finitely generated pro-$p$ group, equipped with the $p$-power
series. The associated metric and Hausdorff dimension function give rise to the
Hausdorff spectrum, which consists of the Hausdorff dimensions of closed
subgroups of $G$. In the case where $G$ is $p$-adic analytic, the Hausdorff
dimension function is well understood; in particular, the Hausdorff spectrum
consists of finitely many rational numbers closely linked to the analytic
dimensions of subgroups of $G$.
Conversely, it is a long-standing open question whether the finiteness of the
Hausdorff spectrum implies that $G$ is $p$-adic analytic. We prove that the
answer is yes, in a strong sense, under the extra condition that $G$ is
soluble.
Furthermore, we explore the problem and related questions also for other
filtration series, such as the lower $p$-series, the Frattini series, the
modular dimension subgroup series and quite general filtration series. For
instance, we prove, for odd primes $p$, that every countably based pro-$p$
group $G$ with an open subgroup mapping onto 2 copies of the $p$-adic integers
admits a filtration series such that the corresponding Hausdorff spectrum
contains an infinite real interval. | [
0,
0,
1,
0,
0,
0
] |
Title: Real-time brain machine interaction via social robot gesture control,
Abstract: Brain-Machine Interaction (BMI) system motivates interesting and promising
results in forward/feedback control consistent with human intention. It holds
great promise for advancements in patient care and applications to
neurorehabilitation. Here, we propose a novel neurofeedback-based BCI robotic
platform using a personalized social robot in order to assist patients having
cognitive deficits through bilateral rehabilitation and mental training. For
initial testing of the platform, electroencephalography (EEG) brainwaves of a
human user were collected in real time during tasks of imaginary movements.
First, the brainwaves associated with imagined body kinematics parameters were
decoded to control a cursor on a computer screen in training protocol. Then,
the experienced subject was able to interact with a social robot via our
real-time BMI robotic platform. Corresponding to subject's imagery performance,
he/she received specific gesture movements and eye color changes as
neural-based feedback from the robot. This hands-free neurofeedback interaction
not only can be used for mind control of a social robot's movements, but also
sets the stage for application to enhancing and recovering mental abilities
such as attention via training in humans by providing real-time neurofeedback
from a social robot. | [
1,
0,
0,
0,
0,
0
] |
Title: It Takes Two to Tango: Towards Theory of AI's Mind,
Abstract: Theory of Mind is the ability to attribute mental states (beliefs, intents,
knowledge, perspectives, etc.) to others and recognize that these mental states
may differ from one's own. Theory of Mind is critical to effective
communication and to teams demonstrating higher collective performance. To
effectively leverage the progress in Artificial Intelligence (AI) to make our
lives more productive, it is important for humans and AI to work well together
in a team. Traditionally, there has been much emphasis on research to make AI
more accurate, and (to a lesser extent) on having it better understand human
intentions, tendencies, beliefs, and contexts. The latter involves making AI
more human-like and having it develop a theory of our minds. In this work, we
argue that for human-AI teams to be effective, humans must also develop a
theory of AI's mind (ToAIM) - get to know its strengths, weaknesses, beliefs,
and quirks. We instantiate these ideas within the domain of Visual Question
Answering (VQA). We find that using just a few examples (50), lay people can be
trained to better predict responses and oncoming failures of a complex VQA
model. We further evaluate the role existing explanation (or interpretability)
modalities play in helping humans build ToAIM. Explainable AI has received
considerable scientific and popular attention in recent times. Surprisingly, we
find that having access to the model's internal states - its confidence in its
top-k predictions, explicit or implicit attention maps which highlight regions
in the image (and words in the question) the model is looking at (and listening
to) while answering a question about an image - do not help people better
predict its behavior. | [
1,
0,
0,
0,
0,
0
] |
Title: Enhancing the Spectral Hardening of Cosmic TeV Photons by Mixing with Axionlike Particles in the Magnetized Cosmic Web,
Abstract: Large-scale extragalactic magnetic fields may induce conversions between
very-high-energy photons and axionlike particles (ALPs), thereby shielding the
photons from absorption on the extragalactic background light. However, in
simplified "cell" models, used so far to represent extragalactic magnetic
fields, this mechanism would be strongly suppressed by current astrophysical
bounds. Here we consider a recent model of extragalactic magnetic fields
obtained from large-scale cosmological simulations. Such simulated magnetic
fields would have large enhancement in the filaments of matter. As a result,
photon-ALP conversions would produce a significant spectral hardening for
cosmic TeV photons. This effect would be probed with the upcoming Cherenkov
Telescope Array detector. This possible detection would give a unique chance to
perform a tomography of the magnetized cosmic web with ALPs. | [
0,
1,
0,
0,
0,
0
] |
Title: Adelic point groups of elliptic curves,
Abstract: We show that for an elliptic curve E defined over a number field K, the group
E(A) of points of E over the adele ring A of K is a topological group that can
be analyzed in terms of the Galois representation associated to the torsion
points of E. An explicit description of E(A) is given, and we prove that for K
of degree n, almost all elliptic curves over K have an adelic point group
topologically isomorphic to a universal group depending on n. We also show that
there exist infinitely many elliptic curves over K having a different adelic
point group. | [
0,
0,
1,
0,
0,
0
] |
Title: Fan-type spin structure in uni-axial chiral magnets,
Abstract: We investigate the spin structure of a uni-axial chiral magnet near the
transition temperatures in low fields perpendicular to the helical axis. We
find a fan-type modulation structure where the clockwise and counterclockwise
windings appear alternatively along the propagation direction of the modulation
structure. This structure is often realized in a Yoshimori-type (non-chiral)
helimagnet but it is rarely realized in a chiral helimagnet. To discuss
underlying physics of this structure, we reconsider the phase diagram (phase
boundary and crossover lines) through the free energy and asymptotic behaviors
of isolated solitons. The fan structure appears slightly below the phase
boundary of the continuous transition of instability-type. In this region,
there are no solutions containing any types of isolated solitons to the mean
field equations. | [
0,
1,
0,
0,
0,
0
] |
Title: Direct estimation of density functionals using a polynomial basis,
Abstract: A number of fundamental quantities in statistical signal processing and
information theory can be expressed as integral functions of two probability
density functions. Such quantities are called density functionals as they map
density functions onto the real line. For example, information divergence
functions measure the dissimilarity between two probability density functions
and are useful in a number of applications. Typically, estimating these
quantities requires complete knowledge of the underlying distribution followed
by multi-dimensional integration. Existing methods make parametric assumptions
about the data distribution or use non-parametric density estimation followed
by high-dimensional integration. In this paper, we propose a new alternative.
We introduce the concept of "data-driven basis functions" - functions of
distributions whose value we can estimate given only samples from the
underlying distributions without requiring distribution fitting or direct
integration. We derive a new data-driven complete basis that is similar to the
deterministic Bernstein polynomial basis and develop two methods for performing
basis expansions of functionals of two distributions. We also show that the new
basis set allows us to approximate functions of distributions as closely as
desired. Finally, we evaluate the methodology by developing data driven
estimators for the Kullback-Leibler divergences and the Hellinger distance and
by constructing empirical estimates of tight bounds on the Bayes error rate. | [
1,
0,
0,
1,
0,
0
] |
Title: From bare interactions, low--energy constants and unitary gas to nuclear density functionals without free parameters: application to neutron matter,
Abstract: We further progress along the line of Ref. [Phys. Rev. {\bf A 94}, 043614
(2016)] where a functional for Fermi systems with anomalously large $s$-wave
scattering length $a_s$ was proposed that has no free parameters. The
functional is designed to correctly reproduce the unitary limit in Fermi gases
together with the leading-order contributions in the s- and p-wave channels at
low density. The functional is shown to be predictive up to densities
$\sim0.01$ fm$^{-3}$ that is much higher densities compared to the Lee-Yang
functional, valid for $\rho < 10^{-6}$ fm$^{-3}$. The form of the functional
retained in this work is further motivated. It is shown that the new functional
corresponds to an expansion of the energy in $(a_s k_F)$ and $(r_e k_F)$ to all
orders, where $r_e$ is the effective range and $k_F$ is the Fermi momentum. One
conclusion from the present work is that, except in the extremely low--density
regime, nuclear systems can be treated perturbatively in $-(a_s k_F)^{-1}$ with
respect to the unitary limit. Starting from the functional, we introduce
density--dependent scales and show that scales associated to the bare
interaction are strongly renormalized by medium effects. As a consequence, some
of the scales at play around saturation are dominated by the unitary gas
properties and not directly to low-energy constants. For instance, we show that
the scale in the s-wave channel around saturation is proportional to the
so-called Bertsch parameter $\xi_0$ and becomes independent of $a_s$. We also
point out that these scales are of the same order of magnitude than those
empirically obtained in the Skyrme energy density functional. We finally
propose a slight modification of the functional such that it becomes accurate
up to the saturation density $\rho\simeq 0.16$ fm$^{-3}$. | [
0,
1,
0,
0,
0,
0
] |
Title: Diffusion Maps meet Nyström,
Abstract: Diffusion maps are an emerging data-driven technique for non-linear
dimensionality reduction, which are especially useful for the analysis of
coherent structures and nonlinear embeddings of dynamical systems. However, the
computational complexity of the diffusion maps algorithm scales with the number
of observations. Thus, long time-series data presents a significant challenge
for fast and efficient embedding. We propose integrating the Nyström method
with diffusion maps in order to ease the computational demand. We achieve a
speedup of roughly two to four times when approximating the dominant diffusion
map components. | [
0,
0,
0,
1,
0,
0
] |
Title: Deadly dark matter cusps vs faint and extended star clusters: Eridanus II and Andromeda XXV,
Abstract: The recent detection of two faint and extended star clusters in the central
regions of two Local Group dwarf galaxies, Eridanus II and Andromeda XXV,
raises the question of whether clusters with such low densities can survive the
tidal field of cold dark matter haloes with central density cusps. Using both
analytic arguments and a suite of collisionless N-body simulations, I show that
these clusters are extremely fragile and quickly disrupted in the presence of
central cusps $\rho\sim r^{-\alpha}$ with $\alpha\gtrsim 0.2$. Furthermore, the
scenario in which the clusters where originally more massive and sank to the
center of the halo requires extreme fine tuning and does not naturally
reproduce the observed systems. In turn, these clusters are long lived in cored
haloes, whose central regions are safe shelters for $\alpha\lesssim 0.2$. The
only viable scenario for hosts that have preserved their primoridal cusp to the
present time is that the clusters formed at rest at the bottom of the
potential, which is easily tested by measurement of the clusters proper
velocity within the host. This offers means to readily probe the central
density profile of two dwarf galaxies as faint as $L_V\sim5\times 10^5 L_\odot$
and $L_V\sim6\times10^4 L_\odot$, in which stellar feedback is unlikely to be
effective. | [
0,
1,
0,
0,
0,
0
] |
Title: Mutual Information, Relative Entropy and Estimation Error in Semi-martingale Channels,
Abstract: Fundamental relations between information and estimation have been
established in the literature for the continuous-time Gaussian and Poisson
channels, in a long line of work starting from the classical representation
theorems by Duncan and Kabanov respectively. In this work, we demonstrate that
such relations hold for a much larger family of continuous-time channels. We
introduce the family of semi-martingale channels where the channel output is a
semi-martingale stochastic process, and the channel input modulates the
characteristics of the semi-martingale. For these channels, which includes as a
special case the continuous time Gaussian and Poisson models, we establish new
representations relating the mutual information between the channel input and
output to an optimal causal filtering loss, thereby unifying and considerably
extending results from the Gaussian and Poisson settings. Extensions to the
setting of mismatched estimation are also presented where the relative entropy
between the laws governing the output of the channel under two different input
distributions is equal to the cumulative difference between the estimation loss
incurred by using the mismatched and optimal causal filters respectively. The
main tool underlying these results is the Doob--Meyer decomposition of a class
of likelihood ratio sub-martingales. The results in this work can be viewed as
the continuous-time analogues of recent generalizations for relations between
information and estimation for discrete-time Lévy channels. | [
1,
0,
0,
0,
0,
0
] |
Title: Criterion of positivity for semilinear problems with applications in biology,
Abstract: The goal of this article is to provide an useful criterion of positivity and
well-posedness for a wide range of infinite dimensional semilinear abstract
Cauchy problems. This criterion is based on some weak assumptions on the
non-linear part of the semilinear problem and on the existence of a strongly
continuous semigroup generated by the differential operator. To illustrate a
large variety of applications, we exhibit the feasibility of this criterion
through three examples in mathematical biology: epidemiology, predator-prey
interactions and oncology. | [
0,
0,
1,
0,
0,
0
] |
Title: Axiomatic quantum mechanics: Necessity and benefits for the physics studies,
Abstract: The ongoing progress in quantum theory emphasizes the crucial role of the
very basic principles of quantum theory. However, this is not properly followed
in teaching quantum mechanics on the graduate and undergraduate levels of
physics studies. The existing textbooks typically avoid the axiomatic
presentation of the theory. We emphasize usefulness of the systematic,
axiomatic approach to the basics of quantum theory as well as its importance in
the light of the modern scientific-research context. | [
0,
1,
0,
0,
0,
0
] |
Title: Kinetic modelling of competition and depletion of shared miRNAs by competing endogenous RNAs,
Abstract: Non-conding RNAs play a key role in the post-transcriptional regulation of
mRNA translation and turnover in eukaryotes. miRNAs, in particular, interact
with their target RNAs through protein-mediated, sequence-specific binding,
giving rise to extended and highly heterogeneous miRNA-RNA interaction
networks. Within such networks, competition to bind miRNAs can generate an
effective positive coupling between their targets. Competing endogenous RNAs
(ceRNAs) can in turn regulate each other through miRNA-mediated crosstalk.
Albeit potentially weak, ceRNA interactions can occur both dynamically,
affecting e.g. the regulatory clock, and at stationarity, in which case ceRNA
networks as a whole can be implicated in the composition of the cell's
proteome. Many features of ceRNA interactions, including the conditions under
which they become significant, can be unraveled by mathematical and in silico
models. We review the understanding of the ceRNA effect obtained within such
frameworks, focusing on the methods employed to quantify it, its role in the
processing of gene expression noise, and how network topology can determine its
reach. | [
0,
0,
0,
0,
1,
0
] |
Title: Fast Meta-Learning for Adaptive Hierarchical Classifier Design,
Abstract: We propose a new splitting criterion for a meta-learning approach to
multiclass classifier design that adaptively merges the classes into a
tree-structured hierarchy of increasingly difficult binary classification
problems. The classification tree is constructed from empirical estimates of
the Henze-Penrose bounds on the pairwise Bayes misclassification rates that
rank the binary subproblems in terms of difficulty of classification. The
proposed empirical estimates of the Bayes error rate are computed from the
minimal spanning tree (MST) of the samples from each pair of classes. Moreover,
a meta-learning technique is presented for quantifying the one-vs-rest Bayes
error rate for each individual class from a single MST on the entire dataset.
Extensive simulations on benchmark datasets show that the proposed hierarchical
method can often be learned much faster than competing methods, while achieving
competitive accuracy. | [
1,
0,
0,
1,
0,
0
] |
Title: Identification and Off-Policy Learning of Multiple Objectives Using Adaptive Clustering,
Abstract: In this work, we present a methodology that enables an agent to make
efficient use of its exploratory actions by autonomously identifying possible
objectives in its environment and learning them in parallel. The identification
of objectives is achieved using an online and unsupervised adaptive clustering
algorithm. The identified objectives are learned (at least partially) in
parallel using Q-learning. Using a simulated agent and environment, it is shown
that the converged or partially converged value function weights resulting from
off-policy learning can be used to accumulate knowledge about multiple
objectives without any additional exploration. We claim that the proposed
approach could be useful in scenarios where the objectives are initially
unknown or in real world scenarios where exploration is typically a time and
energy intensive process. The implications and possible extensions of this work
are also briefly discussed. | [
1,
0,
0,
0,
0,
0
] |
Title: Spoken English Intelligibility Remediation with PocketSphinx Alignment and Feature Extraction Improves Substantially over the State of the Art,
Abstract: We use automatic speech recognition to assess spoken English learner
pronunciation based on the authentic intelligibility of the learners' spoken
responses determined from support vector machine (SVM) classifier or deep
learning neural network model predictions of transcription correctness. Using
numeric features produced by PocketSphinx alignment mode and many recognition
passes searching for the substitution and deletion of each expected phoneme and
insertion of unexpected phonemes in sequence, the SVM models achieve 82 percent
agreement with the accuracy of Amazon Mechanical Turk crowdworker
transcriptions, up from 75 percent reported by multiple independent
researchers. Using such features with SVM classifier probability prediction
models can help computer-aided pronunciation teaching (CAPT) systems provide
intelligibility remediation. | [
1,
0,
0,
1,
0,
0
] |
Title: Second-Order Analysis and Numerical Approximation for Bang-Bang Bilinear Control Problems,
Abstract: We consider bilinear optimal control problems, whose objective functionals do
not depend on the controls. Hence, bang-bang solutions will appear. We
investigate sufficient second-order conditions for bang-bang controls, which
guarantee local quadratic growth of the objective functional in $L^1$. In
addition, we prove that for controls that are not bang-bang, no such growth can
be expected. Finally, we study the finite-element discretization, and prove
error estimates of bang-bang controls in $L^1$-norms. | [
0,
0,
1,
0,
0,
0
] |
Title: Robust Orchestration of Concurrent Application Workflows in Mobile Device Clouds,
Abstract: A hybrid mobile/fixed device cloud that harnesses sensing, computing,
communication, and storage capabilities of mobile and fixed devices in the
field as well as those of computing and storage servers in remote datacenters
is envisioned. Mobile device clouds can be harnessed to enable innovative
pervasive applications that rely on real-time, in-situ processing of sensor
data collected in the field. To support concurrent mobile applications on the
device cloud, a robust and secure distributed computing framework, called
Maestro, is proposed. The key components of Maestro are (i) a task scheduling
mechanism that employs controlled task replication in addition to task
reallocation for robustness and (ii) Dedup for task deduplication among
concurrent pervasive workflows. An architecture-based solution that relies on
task categorization and authorized access to the categories of tasks is
proposed for different levels of protection. Experimental evaluation through
prototype testbed of Android- and Linux-based mobile devices as well as
simulations is performed to demonstrate Maestro's capabilities. | [
1,
0,
0,
0,
0,
0
] |
Title: Anisotropy and multiband superconductivity in Sr2RuO4,
Abstract: Despite numerous studies the exact nature of the order parameter in
superconducting Sr2RuO4 remains unresolved. We have extended previous
small-angle neutron scattering studies of the vortex lattice in this material
to a wider field range, higher temperatures, and with the field applied close
to both the <100> and <110> basal plane directions. Measurements at high field
were made possible by the use of both spin polarization and analysis to improve
the signal-to-noise ratio. Rotating the field towards the basal plane causes a
distortion of the square vortex lattice observed for H // <001>, and also a
symmetry change to a distorted triangular symmetry for fields close to <100>.
The vortex lattice distortion allows us to determine the intrinsic
superconducting anisotropy between the c-axis and the Ru-O basal plane,
yielding a value of ~60 at low temperature and low to intermediate fields. This
greatly exceeds the upper critical field anisotropy of ~20 at low temperature,
reminiscent of Pauli limiting. Indirect evidence for Pauli paramagnetic effects
on the unpaired quasiparticles in the vortex cores are observed, but a direct
detection lies below the measurement sensitivity. The superconducting
anisotropy is found to be independent of temperature but increases for fields >
1 T, indicating multiband superconductvity in Sr2RuO4. Finally, the temperature
dependence of the scattered intensity provides further support for gap nodes or
deep minima in the superconducting gap. | [
0,
1,
0,
0,
0,
0
] |
Title: Comparative Investigation of the High Pressure Autoignition of the Butanol Isomers,
Abstract: Investigation of the autoignition delay of the butanol isomers has been
performed at elevated pressures of 15 bar and 30 bar and low to intermediate
temperatures of 680-860 K. The reactivity of the stoichiometric isomers of
butanol, in terms of inverse ignition delay, was ranked as n-butanol >
sec-butanol ~ iso-butanol > tert-butanol at a compressed pressure of 15 bar but
changed to n-butanol > tert-butanol > sec-butanol > iso-butanol at 30 bar. For
the temperature and pressure conditions in this study, no NTC or two-stage
ignition behavior were observed. However, for both of the compressed pressures
studied in this work, tert-butanol exhibited unique pre-ignition heat release
characteristics. As such, tert-butanol was further studied at two additional
equivalence ratios ($\phi$ = 0.5 and 2.0) to help determine the cause of the
heat release. | [
0,
1,
0,
0,
0,
0
] |
Title: Selecting optimal minimum spanning trees that share a topological correspondence with phylogenetic trees,
Abstract: Choi et. al (2011) introduced a minimum spanning tree (MST)-based method
called CLGrouping, for constructing tree-structured probabilistic graphical
models, a statistical framework that is commonly used for inferring
phylogenetic trees. While CLGrouping works correctly if there is a unique MST,
we observe an indeterminacy in the method in the case that there are multiple
MSTs. In this work we remove this indeterminacy by introducing so-called
vertex-ranked MSTs. We note that the effectiveness of CLGrouping is inversely
related to the number of leaves in the MST. This motivates the problem of
finding a vertex-ranked MST with the minimum number of leaves (MLVRMST). We
provide a polynomial time algorithm for the MLVRMST problem, and prove its
correctness for graphs whose edges are weighted with tree-additive distances. | [
1,
0,
1,
0,
0,
0
] |
Title: A Game of Life on Penrose tilings,
Abstract: We define rules for cellular automata played on quasiperiodic tilings of the
plane arising from the multigrid method in such a way that these cellular
automata are isomorphic to Conway's Game of Life. Although these tilings are
nonperiodic, determining the next state of each tile is a local computation,
requiring only knowledge of the local structure of the tiling and the states of
finitely many nearby tiles. As an example, we show a version of a "glider"
moving through a region of a Penrose tiling. This constitutes a potential
theoretical framework for a method of executing computations in
non-periodically structured substrates such as quasicrystals. | [
0,
1,
1,
0,
0,
0
] |
Title: Dimension-free Wasserstein contraction of nonlinear filters,
Abstract: For a class of partially observed diffusions, sufficient conditions are given
for the map from initial condition of the signal to filtering distribution to
be contractive with respect to Wasserstein distances, with rate which has no
dependence on the dimension of the state-space and is stable under tensor
products of the model. The main assumptions are that the signal has affine
drift and constant diffusion coefficient, and that the likelihood functions are
log-concave. Contraction estimates are obtained from an $h$-process
representation of the transition probabilities of the signal reweighted so as
to condition on the observations. | [
0,
0,
1,
1,
0,
0
] |
Title: Vortex Nucleation Limited Mobility of Free Electron Bubbles in the Gross-Pitaevskii Model of a Superfluid,
Abstract: We study the motion of an electron bubble in the zero temperature limit where
neither phonons nor rotons provide a significant contribution to the drag
exerted on an ion moving within the superfluid. By using the Gross-Clark model,
in which a Gross-Pitaevskii equation for the superfluid wavefunction is coupled
to a Schrödinger equation for the electron wavefunction, we study how
vortex nucleation affects the measured drift velocity of the ion. We use
parameters that give realistic values of the ratio of the radius of the bubble
with respect to the healing length in superfluid $^4$He at a pressure of one
bar. By performing fully 3D spatio-temporal simulations of the superfluid
coupled to an electron, that is modelled within an adiabatic approximation and
moving under the influence of an applied electric field, we are able to recover
the key dynamics of the ion-vortex interactions that arise and the subsequent
ion-vortex complexes that can form. Using the numerically computed drift
velocity of the ion as a function of the applied electric field, we determine
the vortex-nucleation limited mobility of the ion to recover values in
reasonable agreement with measured data. | [
0,
1,
0,
0,
0,
0
] |
Title: Learning to Drive in a Day,
Abstract: We demonstrate the first application of deep reinforcement learning to
autonomous driving. From randomly initialised parameters, our model is able to
learn a policy for lane following in a handful of training episodes using a
single monocular image as input. We provide a general and easy to obtain
reward: the distance travelled by the vehicle without the safety driver taking
control. We use a continuous, model-free deep reinforcement learning algorithm,
with all exploration and optimisation performed on-vehicle. This demonstrates a
new framework for autonomous driving which moves away from reliance on defined
logical rules, mapping, and direct supervision. We discuss the challenges and
opportunities to scale this approach to a broader range of autonomous driving
tasks. | [
1,
0,
0,
1,
0,
0
] |
Title: Strong-coupling of WSe2 in ultra-compact plasmonic nanocavities at room temperature,
Abstract: Strong-coupling of monolayer metal dichalcogenide semiconductors with light
offers encouraging prospects for realistic exciton devices at room temperature.
However, the nature of this coupling depends extremely sensitively on the
optical confinement and the orientation of electronic dipoles and fields. Here,
we show how plasmon strong coupling can be achieved in compact robust
easily-assembled gold nano-gap resonators at room temperature. We prove that
strong coupling is impossible with monolayers due to the large exciton
coherence size, but resolve clear anti-crossings for 8 layer devices with Rabi
splittings exceeding 135 meV. We show that such structures improve on prospects
for nonlinear exciton functionalities by at least 10^4, while retaining quantum
efficiencies above 50%. | [
0,
1,
0,
0,
0,
0
] |
Title: Stigmergy-based modeling to discover urban activity patterns from positioning data,
Abstract: Positioning data offer a remarkable source of information to analyze crowds
urban dynamics. However, discovering urban activity patterns from the emergent
behavior of crowds involves complex system modeling. An alternative approach is
to adopt computational techniques belonging to the emergent paradigm, which
enables self-organization of data and allows adaptive analysis. Specifically,
our approach is based on stigmergy. By using stigmergy each sample position is
associated with a digital pheromone deposit, which progressively evaporates and
aggregates with other deposits according to their spatiotemporal proximity.
Based on this principle, we exploit positioning data to identify high density
areas (hotspots) and characterize their activity over time. This
characterization allows the comparison of dynamics occurring in different days,
providing a similarity measure exploitable by clustering techniques. Thus, we
cluster days according to their activity behavior, discovering unexpected urban
activity patterns. As a case study, we analyze taxi traces in New York City
during 2015. | [
1,
1,
0,
0,
0,
0
] |
Title: Bounded gaps between primes in short intervals,
Abstract: Baker, Harman, and Pintz showed that a weak form of the Prime Number Theorem
holds in intervals of the form $[x-x^{0.525},x]$ for large $x$. In this paper,
we extend a result of Maynard and Tao concerning small gaps between primes to
intervals of this length. More precisely, we prove that for any $\delta\in
[0.525,1]$ there exist positive integers $k,d$ such that for sufficiently large
$x$, the interval $[x-x^\delta,x]$ contains $\gg_{k} \frac{x^\delta}{(\log
x)^k}$ pairs of consecutive primes differing by at most $d$. This confirms a
speculation of Maynard that results on small gaps between primes can be refined
to the setting of short intervals of this length. | [
0,
0,
1,
0,
0,
0
] |
Title: Handover analysis of the Improved Phantom Cells,
Abstract: Improved Phantom cell is a new scenario which has been introduced recently to
enhance the capacity of Heterogeneous Networks (HetNets). The main trait of
this scenario is that, besides maximizing the total network capacity in both
indoor and outdoor environments, it claims to reduce the handover number
compared to the conventional scenarios. In this paper, by a comprehensive
review of the Improved Phantom cells structure, an appropriate algorithm will
be introduced for the handover procedure of this scenario. To reduce the number
of handover in the proposed algorithm, various parameters such as the received
Signal to Interference plus Noise Ratio (SINR) at the user equipment (UE),
users access conditions to the phantom cells, and users staying time in the
target cell based on its velocity, has been considered. Theoretical analyses
and simulation results show that applying the suggested algorithm the improved
phantom cell structure has a much better performance than conventional HetNets
in terms of the number of handover. | [
1,
0,
0,
0,
0,
0
] |
Title: Software correlator for Radioastron mission,
Abstract: In this paper we discuss the characteristics and operation of Astro Space
Center (ASC) software FX correlator that is an important component of
space-ground interferometer for Radioastron project. This project performs
joint observations of compact radio sources using 10 meter space radio
telescope (SRT) together with ground radio telescopes at 92, 18, 6 and 1.3 cm
wavelengths. In this paper we describe the main features of space-ground VLBI
data processing of Radioastron project using ASC correlator. Quality of
implemented fringe search procedure provides positive results without
significant losses in correlated amplitude. ASC Correlator has a computational
power close to real time operation. The correlator has a number of processing
modes: "Continuum", "Spectral Line", "Pulsars", "Giant Pulses","Coherent".
Special attention is paid to peculiarities of Radioastron space-ground VLBI
data processing. The algorithms of time delay and delay rate calculation are
also discussed, which is a matter of principle for data correlation of
space-ground interferometers. During 5 years of Radioastron space radio
telescope (SRT) successful operation, ASC correlator showed high potential of
satisfying steady growing needs of current and future ground and space VLBI
science. Results of ASC software correlator operation are demonstrated. | [
0,
1,
0,
0,
0,
0
] |
Title: Isogenies for point counting on genus two hyperelliptic curves with maximal real multiplication,
Abstract: Schoof's classic algorithm allows point-counting for elliptic curves over
finite fields in polynomial time. This algorithm was subsequently improved by
Atkin, using factorizations of modular polynomials, and by Elkies, using a
theory of explicit isogenies. Moving to Jacobians of genus-2 curves, the
current state of the art for point counting is a generalization of Schoof's
algorithm. While we are currently missing the tools we need to generalize
Elkies' methods to genus 2, recently Martindale and Milio have computed
analogues of modular polynomials for genus-2 curves whose Jacobians have real
multiplication by maximal orders of small discriminant. In this article, we
prove Atkin-style results for genus-2 Jacobians with real multiplication by
maximal orders, with a view to using these new modular polynomials to improve
the practicality of point-counting algorithms for these curves. | [
1,
0,
1,
0,
0,
0
] |
Title: Forecasting Transformative AI: An Expert Survey,
Abstract: Transformative AI technologies have the potential to reshape critical aspects
of society in the near future. However, in order to properly prepare policy
initiatives for the arrival of such technologies accurate forecasts and
timelines are necessary. A survey was administered to attendees of three AI
conferences during the summer of 2018 (ICML, IJCAI and the HLAI conference).
The survey included questions for estimating AI capabilities over the next
decade, questions for forecasting five scenarios of transformative AI and
questions concerning the impact of computational resources in AI research.
Respondents indicated a median of 21.5% of human tasks (i.e., all tasks that
humans are currently paid to do) can be feasibly automated now, and that this
figure would rise to 40% in 5 years and 60% in 10 years. Median forecasts
indicated a 50% probability of AI systems being capable of automating 90% of
current human tasks in 25 years and 99% of current human tasks in 50 years. The
conference of attendance was found to have a statistically significant impact
on all forecasts, with attendees of HLAI providing more optimistic timelines
with less uncertainty. These findings suggest that AI experts expect major
advances in AI technology to continue over the next decade to a degree that
will likely have profound transformative impacts on society. | [
1,
0,
0,
0,
0,
0
] |
Title: Change of grading, injective dimension and dualizing complexes,
Abstract: Let $G,H$ be groups, $\phi: G \rightarrow H$ a group morphism, and $A$ a
$G$-graded algebra. The morphism $\phi$ induces an $H$-grading on $A$, and on
any $G$-graded $A$-module, which thus becomes an $H$-graded $A$-module. Given
an injective $G$-graded $A$-module, we give bounds for its injective dimension
when seen as $H$-graded $A$-module. Following ideas by Van den Bergh, we give
an application of our results to the stability of dualizing complexes through
change of grading. | [
0,
0,
1,
0,
0,
0
] |
Title: Thermoelectric Cooperative Effect in Three-Terminal Elastic Transport through a Quantum Dot,
Abstract: The energy efficiency and power of a three-terminal thermoelectric nanodevice
are studied by considering elastic tunneling through a single quantum dot.
Facilitated by the three-terminal geometry, the nanodevice is able to generate
simultaneously two electrical powers by utilizing only one temperature bias.
These two electrical powers can add up constructively or destructively,
depending on their signs. It is demonstrated that the constructive addition
leads to the enhancement of both energy efficiency and output power for various
system parameters. In fact, such enhancement, dubbed as thermoelectric
cooperative effect, can lead to maximum efficiency and power no less than when
only one of the electrical power is harvested. | [
0,
1,
0,
0,
0,
0
] |
Title: DeepSaucer: Unified Environment for Verifying Deep Neural Networks,
Abstract: In recent years, a number of methods for verifying DNNs have been developed.
Because the approaches of the methods differ and have their own limitations, we
think that a number of verification methods should be applied to a developed
DNN. To apply a number of methods to the DNN, it is necessary to translate
either the implementation of the DNN or the verification method so that one
runs in the same environment as the other. Since those translations are
time-consuming, a utility tool, named DeepSaucer, which helps to retain and
reuse implementations of DNNs, verification methods, and their environments, is
proposed. In DeepSaucer, code snippets of loading DNNs, running verification
methods, and creating their environments are retained and reused as software
assets in order to reduce cost of verifying DNNs. The feasibility of DeepSaucer
is confirmed by implementing it on the basis of Anaconda, which provides
virtual environment for loading a DNN and running a verification method. In
addition, the effectiveness of DeepSaucer is demonstrated by usecase examples. | [
1,
0,
0,
0,
0,
0
] |
Title: Binaural Source Localization based on Modulation-Domain Features and Decision Pooling,
Abstract: In this work we apply Amplitude Modulation Spectrum (AMS) features to the
source localization problem. Our approach computes 36 bilateral features for 2s
long signal segments and estimates the azimuthal directions of a sound source
through a binaurally trained classifier. This directional information of a
sound source could be e.g. used to steer the beamformer in a hearing aid to the
source of interest in order to increase the SNR. We evaluated our approach on
the development set of the IEEE-AASP Challenge on sound source localization and
tracking (LOCATA) and achieved a 4.25° smaller MAE than the baseline
approach. Additionally, our approach is computationally less complex. | [
1,
0,
0,
0,
0,
0
] |
Title: Dynamic Shrinkage Processes,
Abstract: We propose a novel class of dynamic shrinkage processes for Bayesian time
series and regression analysis. Building upon a global-local framework of prior
construction, in which continuous scale mixtures of Gaussian distributions are
employed for both desirable shrinkage properties and computational
tractability, we model dependence among the local scale parameters. The
resulting processes inherit the desirable shrinkage behavior of popular
global-local priors, such as the horseshoe prior, but provide additional
localized adaptivity, which is important for modeling time series data or
regression functions with local features. We construct a computationally
efficient Gibbs sampling algorithm based on a Pólya-Gamma scale mixture
representation of the proposed process. Using dynamic shrinkage processes, we
develop a Bayesian trend filtering model that produces more accurate estimates
and tighter posterior credible intervals than competing methods, and apply the
model for irregular curve-fitting of minute-by-minute Twitter CPU usage data.
In addition, we develop an adaptive time-varying parameter regression model to
assess the efficacy of the Fama-French five-factor asset pricing model with
momentum added as a sixth factor. Our dynamic analysis of manufacturing and
healthcare industry data shows that with the exception of the market risk, no
other risk factors are significant except for brief periods. | [
0,
0,
0,
1,
0,
0
] |
Title: Maximum likelihood estimators based on the block maxima method,
Abstract: The extreme value index is a fundamental parameter in univariate Extreme
Value Theory (EVT). It captures the tail behavior of a distribution and is
central in the extrapolation beyond observed data. Among other semi-parametric
methods (such as the popular Hill's estimator), the Block Maxima (BM) and
Peaks-Over-Threshold (POT) methods are widely used for assessing the extreme
value index and related normalizing constants. We provide asymptotic theory for
the maximum likelihood estimators (MLE) based on the BM method. Our main result
is the asymptotic normality of the MLE with a non-trivial bias depending on the
extreme value index and on the so-called second order parameter. Our approach
combines asymptotic expansions of the likelihood process and of the empirical
quantile process of block maxima. The results permit to complete the comparison
of most common semi-parametric estimators in EVT (MLE and probability weighted
moment estimators based on the POT or BM methods) through their asymptotic
variances, biases and optimal mean square errors. | [
0,
0,
1,
1,
0,
0
] |
Title: Handling state space explosion in verification of component-based systems: A review,
Abstract: Component-based design is a different way of constructing systems which
offers numerous benefits, in particular, decreasing the complexity of system
design. However, deploying components into a system is a challenging and
error-prone task. Model checking is one of the reliable methods that
automatically and systematically analyse the correctness of a given system. Its
brute-force check of the state space significantly expands the level of
confidence in the system. Nevertheless, model checking is limited by a critical
problem so-called State Space Explosion (SSE). To benefit from model checking,
appropriate methods to reduce SSE, is required. In two last decades, a great
number of methods to mitigate the state space explosion have been proposed
which have many similarities, dissimilarities, and unclear concepts in some
cases. This research, firstly, aims at present a review and brief discussion of
the methods of handling SSE problem and classify them based on their
similarities, principle and characteristics. Second, it investigates the
methods for handling SSE problem in verifying Component-based system (CBS) and
provides insight into CBS verification limitations that have not been addressed
yet. The analysis in this research has revealed the patterns, specific
features, and gaps in the state-of-the-art methods. In addition, we identified
and discussed suitable methods to soften SSE problem in CBS and underlined the
key challenges for future research efforts. | [
1,
0,
1,
0,
0,
0
] |
Title: A Framework for Relating the Structures and Recovery Statistics in Pressure Time-Series Surveys for Dust Devils,
Abstract: Dust devils are likely the dominant source of dust for the martian
atmosphere, but the amount and frequency of dust-lifting depend on the
statistical distribution of dust devil parameters. Dust devils exhibit pressure
perturbations and, if they pass near a barometric sensor, they may register as
a discernible dip in a pressure time-series. Leveraging this fact, several
surveys using barometric sensors on landed spacecraft have revealed dust devil
structures and occurrence rates. However powerful they are, though, such
surveys suffer from non-trivial biases that skew the inferred dust devil
properties. For example, such surveys are most sensitive to dust devils with
the widest and deepest pressure profiles, but the recovered profiles will be
distorted, broader and shallow than the actual profiles. In addition, such
surveys often do not provide wind speed measurements alongside the pressure
time series, and so the durations of the dust devil signals in the time series
cannot be directly converted to profile widths. Fortunately, simple statistical
and geometric considerations can de-bias these surveys, allowing conversion of
the duration of dust devil signals into physical widths, given only a
distribution of likely translation velocities, and the recovery of the
underlying distributions of physical parameters. In this study, we develop a
scheme for de-biasing such surveys. Applying our model to an in-situ survey
using data from the Phoenix lander suggests a larger dust flux and a dust devil
occurrence rate about ten times larger than previously inferred. Comparing our
results to dust devil track surveys suggests only about one in five
low-pressure cells lifts sufficient dust to leave a visible track. | [
0,
1,
0,
0,
0,
0
] |
Title: Optimal Envelope Approximation in Fourier Basis with Applications in TV White Space,
Abstract: Lowpass envelope approximation of smooth continuous-variable signals are
introduced in this work. Envelope approximations are necessary when a given
signal has to be approximated always to a larger value (such as in TV white
space protection regions). In this work, a near-optimal approximate algorithm
for finding a signal's envelope, while minimizing a mean-squared cost function,
is detailed. The sparse (lowpass) signal approximation is obtained in the
linear Fourier series basis. This approximate algorithm works by discretizing
the envelope property from an infinite number of points to a large (but finite)
number of points. It is shown that this approximate algorithm is near-optimal
and can be solved by using efficient convex optimization programs available in
the literature. Simulation results are provided towards the end to gain more
insights into the analytical results presented. | [
1,
0,
0,
0,
0,
0
] |
Title: Generalized Lambert series and arithmetic nature of odd zeta values,
Abstract: It is pointed out that the generalized Lambert series
$\displaystyle\sum_{n=1}^{\infty}\frac{n^{N-2h}}{e^{n^{N}x}-1}$ studied by
Kanemitsu, Tanigawa and Yoshimoto can be found on page $332$ of Ramanujan's
Lost Notebook in a slightly more general form. We extend an important
transformation of this series obtained by Kanemitsu, Tanigawa and Yoshimoto by
removing restrictions on the parameters $N$ and $h$ that they impose. From our
extension we deduce a beautiful new generalization of Ramanujan's famous
formula for odd zeta values which, for $N$ odd and $m>0$, gives a relation
between $\zeta(2m+1)$ and $\zeta(2Nm+1)$. A result complementary to the
aforementioned generalization is obtained for any even $N$ and
$m\in\mathbb{Z}$. It generalizes a transformation of Wigert and can be regarded
as a formula for $\zeta\left(2m+1-\frac{1}{N}\right)$. Applications of these
transformations include a generalization of the transformation for the
logarithm of Dedekind eta-function $\eta(z)$, Zudilin- and Rivoal-type results
on transcendence of certain values, and a transcendence criterion for Euler's
constant $\gamma$. | [
0,
0,
1,
0,
0,
0
] |
Title: Static Free Space Detection with Laser Scanner using Occupancy Grid Maps,
Abstract: Drivable free space information is vital for autonomous vehicles that have to
plan evasive maneuvers in real-time. In this paper, we present a new efficient
method for environmental free space detection with laser scanner based on 2D
occupancy grid maps (OGM) to be used for Advanced Driving Assistance Systems
(ADAS) and Collision Avoidance Systems (CAS). Firstly, we introduce an enhanced
inverse sensor model tailored for high-resolution laser scanners for building
OGM. It compensates the unreflected beams and deals with the ray casting to
grid cells accuracy and computational effort problems. Secondly, we introduce
the 'vehicle on a circle for grid maps' map alignment algorithm that allows
building more accurate local maps by avoiding the computationally expensive
inaccurate operations of image sub-pixel shifting and rotation. The resulted
grid map is more convenient for ADAS features than existing methods, as it
allows using less memory sizes, and hence, results into a better real-time
performance. Thirdly, we present an algorithm to detect what we call the
'in-sight edges'. These edges guarantee modeling the free space area with a
single polygon of a fixed number of vertices regardless the driving situation
and map complexity. The results from real world experiments show the
effectiveness of our approach. | [
1,
0,
0,
0,
0,
0
] |
Title: Deuterium fractionation and H2D+ evolution in turbulent and magnetized cloud cores,
Abstract: High-mass stars are expected to form from dense prestellar cores. Their
precise formation conditions are widely discussed, including their virial
condition, which results in slow collapse for super-virial cores with strong
support by turbulence or magnetic fields, or fast collapse for sub-virial
sources. To disentangle their formation processes, measurements of the
deuterium fractions are frequently employed to approximately estimate the ages
of these cores and to obtain constraints on their dynamical evolution. We here
present 3D magneto-hydrodynamical simulations including for the first time an
accurate non-equilibrium chemical network with 21 gas-phase species plus dust
grains and 213 reactions. With this network we model the deuteration process in
fully depleted prestellar cores in great detail and determine its response to
variations in the initial conditions. We explore the dependence on the initial
gas column density, the turbulent Mach number, the mass-to-magnetic flux ratio
and the distribution of the magnetic field, as well as the initial
ortho-to-para ratio of H2. We find excellent agreement with recent observations
of deuterium fractions in quiescent sources. Our results show that deuteration
is rather efficient, even when assuming a conservative ortho-to-para ratio of 3
and highly sub-virial initial conditions, leading to large deuterium fractions
already within roughly a free-fall time. We discuss the implications of our
results and give an outlook to relevant future investigations. | [
0,
1,
0,
0,
0,
0
] |
Title: Experimental data over quantum mechanics simulations for inferring the repulsive exponent of the Lennard-Jones potential in Molecular Dynamics,
Abstract: The Lennard-Jones (LJ) potential is a cornerstone of Molecular Dynamics (MD)
simulations and among the most widely used computational kernels in science.
The potential models atomistic attraction and repulsion with century old
prescribed parameters ($q=6, \; p=12$, respectively), originally related by a
factor of two for simplicity of calculations. We re-examine the value of the
repulsion exponent through data driven uncertainty quantification. We perform
Hierarchical Bayesian inference on MD simulations of argon using experimental
data of the radial distribution function (RDF) for a range of thermodynamic
conditions, as well as dimer interaction energies from quantum mechanics
simulations. The experimental data suggest a repulsion exponent ($p \approx
6.5$), in contrast to the quantum simulations data that support values closer
to the original ($p=12$) exponent. Most notably, we find that predictions of
RDF, diffusion coefficient and density of argon are more accurate and robust in
producing the correct argon phase around its triple point, when using the
values inferred from experimental data over those from quantum mechanics
simulations. The present results suggest the need for data driven recalibration
of the LJ potential across MD simulations. | [
0,
1,
0,
1,
0,
0
] |
Title: Ensemble Sampling,
Abstract: Thompson sampling has emerged as an effective heuristic for a broad range of
online decision problems. In its basic form, the algorithm requires computing
and sampling from a posterior distribution over models, which is tractable only
for simple special cases. This paper develops ensemble sampling, which aims to
approximate Thompson sampling while maintaining tractability even in the face
of complex models such as neural networks. Ensemble sampling dramatically
expands on the range of applications for which Thompson sampling is viable. We
establish a theoretical basis that supports the approach and present
computational results that offer further insight. | [
1,
0,
0,
1,
0,
0
] |
Title: A Data-Driven Sparse-Learning Approach to Model Reduction in Chemical Reaction Networks,
Abstract: In this paper, we propose an optimization-based sparse learning approach to
identify the set of most influential reactions in a chemical reaction network.
This reduced set of reactions is then employed to construct a reduced chemical
reaction mechanism, which is relevant to chemical interaction network modeling.
The problem of identifying influential reactions is first formulated as a
mixed-integer quadratic program, and then a relaxation method is leveraged to
reduce the computational complexity of our approach. Qualitative and
quantitative validation of the sparse encoding approach demonstrates that the
model captures important network structural properties with moderate
computational load. | [
1,
0,
0,
0,
0,
0
] |
Title: Parity-Forbidden Transitions and Their Impacts on the Optical Absorption Properties of Lead-Free Metal Halide Perovskites and Double Perovskites,
Abstract: Using density-functional theory calculations, we analyze the optical
absorption properties of lead (Pb)-free metal halide perovskites
(AB$^{2+}$X$_3$) and double perovskites (AB$^+$B$^{3+}$X$_6$) (A = Cs or
monovalent organic ion, B$^{2+}$ = non-Pb divalent metal, B$^+$ = monovalent
metal, B$^{3+}$ = trivalent metal, X = halogen). We show that, if B$^{2+}$ is
not Sn or Ge, Pb-free metal halide perovskites exhibit poor optical absorptions
because of their indirect bandgap nature. Among the nine possible types of
Pb-free metal halide double perovskites, six have direct bandgaps. Of these six
types, four show inversion symmetry-induced parity-forbidden or weak
transitions between band edges, making them not ideal for thin-film solar cell
application. Only one type of Pb-free double perovskite shows optical
absorption and electronic properties suitable for solar cell applications,
namely those with B$^+$ = In, Tl and B$^{3+}$ = Sb, Bi. Our results provide
important insights for designing new metal halide perovskites and double
perovskites for optoelectronic applications. | [
0,
1,
0,
0,
0,
0
] |
Title: Local methods for blocks of finite simple groups,
Abstract: This survey is about old and new results about the modular representation
theory of finite reductive groups with a strong emphasis on local methods. This
includes subpairs, Brauer's Main Theorems, fusion, Rickard equivalences. In the
defining characteristic we describe the relation between $p$-local subgroups
and parabolic subgroups, then give classical consequences on simple modules and
blocks, including the Alperin weight conjecture in that case. In the
non-defining characteristics, we sketch a picture of the local methods
pioneered by Fong-Srinivasan in the determination of blocks and their ordinary
characters. This includes the relationship with Lusztig's twisted induction and
the determination of defect groups. We conclude with a survey of the results
and methods by Bonnafé-Dat-Rouquier giving Morita equivalences between blocks
that preserve defect groups and the local structures.
The text grew out of the course and talks given by the author in July and
September 2016 during the program "Local representation theory and simple
groups" at CIB Lausanne. Written Oct 2017, to appear in a proceedings volume
published by EMS. | [
0,
0,
1,
0,
0,
0
] |
Title: Motion Planning for a Humanoid Mobile Manipulator System,
Abstract: A high redundant non-holonomic humanoid mobile dual-arm manipulator system is
presented in this paper where the motion planning to realize "human-like"
autonomous navigation and manipulation tasks is studied. Firstly, an improved
MaxiMin NSGA-II algorithm, which optimizes five objective functions to solve
the problems of singularity, redundancy, and coupling between mobile base and
manipulator simultaneously, is proposed to design the optimal pose to
manipulate the target object. Then, in order to link the initial pose and that
optimal pose, an off-line motion planning algorithm is designed. In detail, an
efficient direct-connect bidirectional RRT and gradient descent algorithm is
proposed to reduce the sampled nodes largely, and a geometric optimization
method is proposed for path pruning. Besides, head forward behaviors are
realized by calculating the reasonable orientations and assigning them to the
mobile base to improve the quality of human-robot interaction. Thirdly, the
extension to on-line planning is done by introducing real-time sensing,
collision-test and control cycles to update robotic motion in dynamic
environments. Fourthly, an EEs' via-point-based multi-objective genetic
algorithm is proposed to design the "human-like" via-poses by optimizing four
objective functions. Finally, numerous simulations are presented to validate
the effectiveness of proposed algorithms. | [
1,
0,
0,
0,
0,
0
] |
Title: Gravitational radiation from compact binary systems in screened modified gravity,
Abstract: Screened modified gravity (SMG) is a kind of scalar-tensor theory with
screening mechanisms, which can suppress the fifth force in dense regions and
allow theories to evade the solar system and laboratory tests. In this paper,
we investigate how the screening mechanisms in SMG affect the gravitational
radiation damping effects, calculate in detail the rate of the energy loss due
to the emission of tensor and scalar gravitational radiations, and derive their
contributions to the change in the orbital period of the binary system. We find
that the scalar radiation depends on the screened parameters and the
propagation speed of scalar waves, and the scalar dipole radiation dominates
the orbital decay of the binary system. For strongly self-gravitating bodies,
all effects of scalar sector are strongly suppressed by the screening
mechanisms in SMG. By comparing our results to observations of binary system
PSR J1738+0333, we place the stringent constraints on the screening mechanisms
in SMG. As an application of these results, we focus on three specific models
of SMG (chameleon, symmetron, and dilaton), and derive the constraints on the
model parameters, respectively. | [
0,
1,
0,
0,
0,
0
] |
Title: weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming,
Abstract: Selective weed treatment is a critical step in autonomous crop management as
related to crop health and yield. However, a key challenge is reliable, and
accurate weed detection to minimize damage to surrounding plants. In this
paper, we present an approach for dense semantic weed classification with
multispectral images collected by a micro aerial vehicle (MAV). We use the
recently developed encoder-decoder cascaded Convolutional Neural Network (CNN),
Segnet, that infers dense semantic classes while allowing any number of input
image channels and class balancing with our sugar beet and weed datasets. To
obtain training datasets, we established an experimental field with varying
herbicide levels resulting in field plots containing only either crop or weed,
enabling us to use the Normalized Difference Vegetation Index (NDVI) as a
distinguishable feature for automatic ground truth generation. We train 6
models with different numbers of input channels and condition (fine-tune) it to
achieve about 0.8 F1-score and 0.78 Area Under the Curve (AUC) classification
metrics. For model deployment, an embedded GPU system (Jetson TX2) is tested
for MAV integration. Dataset used in this paper is released to support the
community and future work. | [
1,
0,
0,
0,
0,
0
] |
Title: An explicit determination of the $K$-theoretic structure constants of the affine Grassmannian associated to $SL_2$,
Abstract: Let $G:=\widehat{SL_2}$ denote the affine Kac-Moody group associated to
$SL_2$ and $\bar{\mathcal{X}}$ the associated affine Grassmannian. We determine
an inductive formula for the Schubert basis structure constants in the
torus-equivariant Grothendieck group of $\bar{\mathcal{X}}$. In the case of
ordinary (non-equivariant) $K$-theory we find an explicit closed form for the
structure constants. We also determine an inductive formula for the structure
constants in the torus-equivariant cohomology ring, and use this formula to
find closed forms for some of the structure constants. | [
0,
0,
1,
0,
0,
0
] |
Title: The Correct Application of Variance Concept in Measurement Theory,
Abstract: The existing measurement theory interprets the variance as the dispersion of
measured value, which is actually contrary to a general mathematical knowledge
that the variance of a constant is 0. This paper will fully demonstrate that
the variance in measurement theory is actually the evaluation of probability
interval of an error instead of the dispersion of a measured value, point out
the key point of mistake in the existing interpretation, and fully interpret a
series of changes in conceptual logic and processing method brought about by
this new concept. | [
0,
0,
1,
1,
0,
0
] |
Title: Modular curves with infinitely many cubic points,
Abstract: In this study, we determine all modular curves $X_0(N)$ that admit infinitely
many cubic points. | [
0,
0,
1,
0,
0,
0
] |
Title: Robust and Imperceptible Adversarial Attacks on Capsule Networks,
Abstract: Capsule Networks envision an innovative point of view about the
representation of the objects in the brain and preserve the hierarchical
spatial relationships between them. This type of networks exhibits a huge
potential for several Machine Learning tasks like image classification, while
outperforming Convolutional Neural Networks (CNNs). A large body of work has
explored adversarial examples for CNNs, but their efficacy to Capsule Networks
is not well explored. In our work, we study the vulnerabilities in Capsule
Networks to adversarial attacks. These perturbations, added to the test inputs,
are small and imperceptible to humans, but fool the network to mis-predict. We
propose a greedy algorithm to automatically generate targeted imperceptible
adversarial examples in a black-box attack scenario. We show that this kind of
attacks, when applied to the German Traffic Sign Recognition Benchmark (GTSRB),
mislead Capsule Networks. Moreover, we apply the same kind of adversarial
attacks to a 9-layer CNN and analyze the outcome, compared to the Capsule
Networks to study their differences / commonalities. | [
1,
0,
0,
1,
0,
0
] |
Title: Thermophoretic MHD Flow and Non-linear Radiative Heat Transfer with Convective Boundary Conditions over a Non-linearly Stretching Sheet,
Abstract: The effects of MHD boundary layer flow of non-linear thermal radiation with
convective heat transfer and non-uniform heat source/sink in presence of
thermophortic velocity and chemical reaction investigated in this study.
Suitable similarity transformation are used to solve the partial ordinary
differential equation of considered governing flow. Runge-Kutta fourth fifth
order Fehlberg method with shooting techniques are used to solved
non-dimensional governing equations. The variation of different parameters such
as thermophoretic parameter, chemical reaction parameter, non- uniform heat
source/sink parameters are studied on velocity, temperature and concentration
profiles, and are described by suitable graphs and tables. The obtained results
are in very well agreement with previous results. | [
0,
1,
0,
0,
0,
0
] |
Title: Resting-state ASL : Toward an optimal sequence duration,
Abstract: Resting-state functional Arterial Spin Labeling (rs-fASL) in clinical daily
practice and academic research stay discreet compared to resting-state BOLD.
However, by giving direct access to cerebral blood flow maps, rs-fASL leads to
significant clinical subject scaled application as CBF can be considered as a
biomarker in common neuropathology. Our work here focuses on the link between
overall quality of rs-fASL and duration of acquisition. To this end, we
consider subject self-Default Mode Network (DMN), and assess DMN quality
depletion compared to a gold standard DMN depending on the duration of
acquisition. | [
0,
0,
0,
0,
1,
0
] |
Title: Learning Neural Models for End-to-End Clustering,
Abstract: We propose a novel end-to-end neural network architecture that, once trained,
directly outputs a probabilistic clustering of a batch of input examples in one
pass. It estimates a distribution over the number of clusters $k$, and for each
$1 \leq k \leq k_\mathrm{max}$, a distribution over the individual cluster
assignment for each data point. The network is trained in advance in a
supervised fashion on separate data to learn grouping by any perceptual
similarity criterion based on pairwise labels (same/different group). It can
then be applied to different data containing different groups. We demonstrate
promising performance on high-dimensional data like images (COIL-100) and
speech (TIMIT). We call this ``learning to cluster'' and show its conceptual
difference to deep metric learning, semi-supervise clustering and other related
approaches while having the advantage of performing learnable clustering fully
end-to-end. | [
0,
0,
0,
1,
0,
0
] |
Title: Anomalous transport properties in Nb/Bi1.95Sb0.05Se3 hybrid structure,
Abstract: We report the proximity induced anomalous transport behavior in a Nb
Bi1.95Sb0.05Se3 heterostructure. Mechanically Exfoliated single crystal of
Bi1.95Sb0.05Se3 topological insulator (TI) is partially covered with a 100 nm
thick Niobium superconductor using DC magnetron sputtering by shadow masking
technique. The magnetotransport (MR) measurements have been performed
simultaneously on the TI sample with and without Nb top layer in the
temperature,T, range of 3 to 8 K, and a magnetic field B up to 15 T. MR on TI
region shows Subnikov de Haas oscillation at fields greater than 5 T. Anomalous
linear change in resistance is observed in the field range of negative 4T to
positive 4T at which Nb is superconducting. At 0 T field, the temperature
dependence of resistance on the Nb covered region revealed a superconducting
transition (TC) at 8.2 K, whereas TI area showed similar TC with the absence of
zero resistance states due to the additional resistance from superconductor
(SC) TI interface. Interestingly below the TC the R vs T measured on TI showed
an enhancement in resistance for positive field and prominent fall in
resistance for negative field direction. This indicates the directional
dependent scattering of the Cooper pairs on the surface of the TI due to the
superposition of spin singlet and triplet states in the superconductor and TI
respectively. | [
0,
1,
0,
0,
0,
0
] |
Title: Adaptive local surface refinement based on LR NURBS and its application to contact,
Abstract: A novel adaptive local surface refinement technique based on Locally Refined
Non-Uniform Rational B-Splines (LR NURBS) is presented. LR NURBS can model
complex geometries exactly and are the rational extension of LR B-splines. The
local representation of the parameter space overcomes the drawback of
non-existent local refinement in standard NURBS-based isogeometric analysis.
For a convenient embedding into general finite element code, the Bézier
extraction operator for LR NURBS is formulated. An automatic remeshing
technique is presented that allows adaptive local refinement and coarsening of
LR NURBS. In this work, LR NURBS are applied to contact computations of 3D
solids and membranes. For solids, LR NURBS-enriched finite elements are used to
discretize the contact surfaces with LR NURBS finite elements, while the rest
of the body is discretized by linear Lagrange finite elements. For membranes,
the entire surface is discretized by LR NURBS. Various numerical examples are
shown, and they demonstrate the benefit of using LR NURBS: Compared to uniform
refinement, LR NURBS can achieve high accuracy at lower computational cost. | [
1,
0,
0,
0,
0,
0
] |
Title: Trusted Multi-Party Computation and Verifiable Simulations: A Scalable Blockchain Approach,
Abstract: Large-scale computational experiments, often running over weeks and over
large datasets, are used extensively in fields such as epidemiology,
meteorology, computational biology, and healthcare to understand phenomena, and
design high-stakes policies affecting everyday health and economy. For
instance, the OpenMalaria framework is a computationally-intensive simulation
used by various non-governmental and governmental agencies to understand
malarial disease spread and effectiveness of intervention strategies, and
subsequently design healthcare policies. Given that such shared results form
the basis of inferences drawn, technological solutions designed, and day-to-day
policies drafted, it is essential that the computations are validated and
trusted. In particular, in a multi-agent environment involving several
independent computing agents, a notion of trust in results generated by peers
is critical in facilitating transparency, accountability, and collaboration.
Using a novel combination of distributed validation of atomic computation
blocks and a blockchain-based immutable audits mechanism, this work proposes a
universal framework for distributed trust in computations. In particular we
address the scalaibility problem by reducing the storage and communication
costs using a lossy compression scheme. This framework guarantees not only
verifiability of final results, but also the validity of local computations,
and its cost-benefit tradeoffs are studied using a synthetic example of
training a neural network. | [
1,
0,
0,
0,
0,
0
] |
Title: Design of the Artificial: lessons from the biological roots of general intelligence,
Abstract: Our desire and fascination with intelligent machines dates back to the
antiquity's mythical automaton Talos, Aristotle's mode of mechanical thought
(syllogism) and Heron of Alexandria's mechanical machines and automata.
However, the quest for Artificial General Intelligence (AGI) is troubled with
repeated failures of strategies and approaches throughout the history. This
decade has seen a shift in interest towards bio-inspired software and hardware,
with the assumption that such mimicry entails intelligence. Though these steps
are fruitful in certain directions and have advanced automation, their singular
design focus renders them highly inefficient in achieving AGI. Which set of
requirements have to be met in the design of AGI? What are the limits in the
design of the artificial? Here, a careful examination of computation in
biological systems hints that evolutionary tinkering of contextual processing
of information enabled by a hierarchical architecture is the key to build AGI. | [
1,
1,
0,
0,
0,
0
] |
Title: SimProp v2r4: Monte Carlo simulation code for UHECR propagation,
Abstract: We introduce the new version of SimProp, a Monte Carlo code for simulating
the propagation of ultra-high energy cosmic rays in intergalactic space. This
version, SimProp v2r4, together with an overall improvement of the code
capabilities with a substantial reduction in the computation time, also
computes secondary cosmogenic particles such as electron-positron pairs and
gamma rays produced during the propagation of ultra-high energy cosmic rays. As
recently pointed out by several authors, the flux of this secondary radiation
and its products, within reach of the current observatories, provides useful
information about models of ultra-high energy cosmic ray sources which would be
hard to discriminate otherwise. | [
0,
1,
0,
0,
0,
0
] |
Title: Non-negative Matrix Factorization via Archetypal Analysis,
Abstract: Given a collection of data points, non-negative matrix factorization (NMF)
suggests to express them as convex combinations of a small set of `archetypes'
with non-negative entries. This decomposition is unique only if the true
archetypes are non-negative and sufficiently sparse (or the weights are
sufficiently sparse), a regime that is captured by the separability condition
and its generalizations.
In this paper, we study an approach to NMF that can be traced back to the
work of Cutler and Breiman (1994) and does not require the data to be
separable, while providing a generally unique decomposition. We optimize the
trade-off between two objectives: we minimize the distance of the data points
from the convex envelope of the archetypes (which can be interpreted as an
empirical risk), while minimizing the distance of the archetypes from the
convex envelope of the data (which can be interpreted as a data-dependent
regularization). The archetypal analysis method of (Cutler, Breiman, 1994) is
recovered as the limiting case in which the last term is given infinite weight.
We introduce a `uniqueness condition' on the data which is necessary for
exactly recovering the archetypes from noiseless data. We prove that, under
uniqueness (plus additional regularity conditions on the geometry of the
archetypes), our estimator is robust. While our approach requires solving a
non-convex optimization problem, we find that standard optimization methods
succeed in finding good solutions both for real and synthetic data. | [
1,
0,
0,
0,
0,
0
] |
Title: An Extended Low Fat Allocator API and Applications,
Abstract: The primary function of memory allocators is to allocate and deallocate
chunks of memory primarily through the malloc API. Many memory allocators also
implement other API extensions, such as deriving the size of an allocated
object from the object's pointer, or calculating the base address of an
allocation from an interior pointer. In this paper, we propose a general
purpose extended allocator API built around these common extensions. We argue
that such extended APIs have many applications and demonstrate several use
cases, such as (manual) memory error detection, meta data storage, typed
pointers and compact data-structures. Because most existing allocators were not
designed for the extended API, traditional implementations are expensive or not
possible.
Recently, the LowFat allocator for heap and stack objects has been developed.
The LowFat allocator is an implementation of the idea of low-fat pointers,
where object bounds information (size and base) are encoded into the native
machine pointer representation itself. The "killer app" for low-fat pointers is
automated bounds check instrumentation for program hardening and bug detection.
However, the LowFat allocator can also be used to implement highly optimized
version of the extended allocator API, which makes the new applications (listed
above) possible. In this paper, we implement and evaluate several applications
based efficient memory allocator API extensions using low-fat pointers. We also
extend the LowFat allocator to cover global objects for the first time. | [
1,
0,
0,
0,
0,
0
] |
Title: Permutation Tests for Infection Graphs,
Abstract: We formulate and analyze a novel hypothesis testing problem for inferring the
edge structure of an infection graph. In our model, a disease spreads over a
network via contagion or random infection, where the random variables governing
the rates of contracting the disease from neighbors or random infection are
independent exponential random variables with unknown rate parameters. A subset
of nodes is also censored uniformly at random. Given the statuses of nodes in
the network, the goal is to determine the underlying graph. We present a
procedure based on permutation testing, and we derive sufficient conditions for
the validity of our test in terms of automorphism groups of the graphs
corresponding to the null and alternative hypotheses. Further, the test is
valid more generally for infection processes satisfying a basic symmetry
condition. Our test is easy to compute and does not involve estimating unknown
parameters governing the process. We also derive risk bounds for our
permutation test in a variety of settings, and motivate our test statistic in
terms of approximate equivalence to likelihood ratio testing and maximin tests.
We conclude with an application to real data from an HIV infection network. | [
1,
0,
1,
1,
0,
0
] |
Title: Exploring deep learning as an event classification method for the Cherenkov Telescope Array,
Abstract: Telescopes based on the imaging atmospheric Cherenkov technique (IACTs)
detect images of the atmospheric showers generated by gamma rays and cosmic
rays as they are absorbed by the atmosphere. The much more frequent cosmic-ray
events form the main background when looking for gamma-ray sources, and
therefore IACT sensitivity is significantly driven by the capability to
distinguish between these two types of events. Supervised learning algorithms,
like random forests and boosted decision trees, have been shown to effectively
classify IACT events. In this contribution we present results from exploratory
work using deep learning as an event classification method for the Cherenkov
Telescope Array (CTA). CTA, conceived as an array of tens of IACTs, is an
international project for a next-generation ground-based gamma-ray observatory,
aiming to improve on the sensitivity of current-generation experiments by an
order of magnitude and provide energy coverage from 20 GeV to more than 300
TeV. | [
0,
1,
0,
0,
0,
0
] |
Title: Discriminant circle bundles over local models of Strebel graphs and Boutroux curves,
Abstract: We study special circle bundles over two elementary moduli spaces of
meromorphic quadratic differentials with real periods denoted by $\mathcal
Q_0^{\mathbb R}(-7)$ and $\mathcal Q^{\mathbb R}_0([-3]^2)$. The space
$\mathcal Q_0^{\mathbb R}(-7)$ is the moduli space of meromorphic quadratic
differentials on the Riemann sphere with one pole of order 7 with real periods;
it appears naturally in the study of a neighbourhood of the Witten's cycle
$W_1$ in the combinatorial model based on Jenkins-Strebel quadratic
differentials of $\mathcal M_{g,n}$. The space $\mathcal Q^{\mathbb
R}_0([-3]^2)$ is the moduli space of meromorphic quadratic differentials on the
Riemann sphere with two poles of order at most 3 with real periods; it appears
in description of a neighbourhood of Kontsevich's boundary $W_{-1,-1}$ of the
combinatorial model. The application of the formalism of the Bergman
tau-function to the combinatorial model (with the goal of computing
analytically Poincare dual cycles to certain combinations of tautological
classes) requires the study of special sections of circle bundles over
$\mathcal Q_0^{\mathbb R}(-7)$ and $\mathcal Q^{\mathbb R}_0([-3]^2)$; in the
case of the space $\mathcal Q_0^{\mathbb R}(-7)$ a section of this circle
bundle is given by the argument of the modular discriminant. We study the
spaces $\mathcal Q_0^{\mathbb R}(-7)$ and $\mathcal Q^{\mathbb R}_0([-3]^2)$,
also called the spaces of Boutroux curves, in detail, together with
corresponding circle bundles. | [
0,
1,
1,
0,
0,
0
] |
Title: Far-field theory for trajectories of magnetic ellipsoids in rectangular and circular channels,
Abstract: We report a method to control the positions of ellipsoidal magnets in flowing
channels of rectangular or circular cross section at low Reynolds number.A
static uniform magnetic field is used to pin the particle orientation, and the
particles move with translational drift velocities resulting from hydrodynamic
interactions with the channel walls which can be described using Blake's image
tensor.Building on his insights, we are able to present a far-field theory
predicting the particle motion in rectangular channels, and validate the
accuracy of the theory by comparing to numerical solutions using the boundary
element method.We find that, by changing the direction of the applied magnetic
field, the motion can be controlled so that particles move either to a curved
focusing region or to the channel walls.We also use simulations to show that
the particles are focused to a single line in a circular channel.Our results
suggest ways to focus and segregate magnetic particles in lab-on-a-chip
devices. | [
0,
1,
0,
0,
0,
0
] |
Title: On M-functions associated with modular forms,
Abstract: Let $f$ be a primitive cusp form of weight $k$ and level $N,$ let $\chi$ be a
Dirichlet character of conductor coprime with $N,$ and let
$\mathfrak{L}(f\otimes \chi, s)$ denote either $\log L(f\otimes \chi, s)$ or
$(L'/L)(f\otimes \chi, s).$ In this article we study the distribution of the
values of $\mathfrak{L}$ when either $\chi$ or $f$ vary. First, for a
quasi-character $\psi\colon \mathbb{C} \to \mathbb{C}^\times$ we find the limit
for the average $\mathrm{Avg}\_\chi \psi(L(f\otimes\chi, s)),$ when $f$ is
fixed and $\chi$ varies through the set of characters with prime conductor that
tends to infinity. Second, we prove an equidistribution result for the values
of $\mathfrak{L}(f\otimes \chi,s)$ by establishing analytic properties of the
above limit function. Third, we study the limit of the harmonic average
$\mathrm{Avg}^h\_f \psi(L(f, s)),$ when $f$ runs through the set of primitive
cusp forms of given weight $k$ and level $N\to \infty.$ Most of the results are
obtained conditionally on the Generalized Riemann Hypothesis for
$L(f\otimes\chi, s).$ | [
0,
0,
1,
0,
0,
0
] |
Title: Stability of casein micelles cross-linked with genipin: a physicochemical study as a function of pH,
Abstract: Chemical or enzymatic cross-linking of casein micelles (CMs) increases their
stability against dissociating agents. In this paper, a comparative study of
stability between native CMs and CMs cross-linked with genipin (CMs-GP) as a
function of pH is described. Stability to temperature and ethanol were
investigated in the pH range 2.0-7.0. The size and the charge
($\zeta$-potential) of the particles were determined by dynamic light
scattering. Native CMs precipitated below pH 5.5, CMs-GP precipitated from pH
3.5 to 4.5, whereas no precipitation was observed at pH 2.0-3.0 or pH 4.5-7.0.
The isoelectric point of CMs-GP was determined to be pH 3.7. Highest stability
against heat and ethanol was observed for CMs-GP at pH 2, where visible
coagulation was determined only after 800 s at 140 $^\circ$C or 87.5% (v/v) of
ethanol. These results confirmed the hypothesis that cross-linking by GP
increased the stability of CMs. | [
0,
1,
0,
0,
0,
0
] |
Title: Kites and Residuated Lattices,
Abstract: We investigate a construction of an integral residuated lattice starting from
an integral residuated lattice and two sets with an injective mapping from one
set into the second one. The resulting algebra has a shape of a Chinese cascade
kite, therefore, we call this algebra simply a kite. We describe subdirectly
irreducible kites and we classify them. We show that the variety of integral
residuated lattices generated by kites is generated by all finite-dimensional
kites. In particular, we describe some homomorphisms among kites. | [
0,
0,
1,
0,
0,
0
] |
Title: TED Talk Recommender Using Speech Transcripts,
Abstract: Nowadays, online video platforms mostly recommend related videos by analyzing
user-driven data such as viewing patterns, rather than the content of the
videos. However, content is more important than any other element when videos
aim to deliver knowledge. Therefore, we have developed a web application which
recommends related TED lecture videos to the users, considering the content of
the videos from the transcripts. TED Talk Recommender constructs a network for
recommending videos that are similar content-wise and providing a user
interface. | [
1,
0,
0,
0,
0,
0
] |
Title: Triplet Network with Attention for Speaker Diarization,
Abstract: In automatic speech processing systems, speaker diarization is a crucial
front-end component to separate segments from different speakers. Inspired by
the recent success of deep neural networks (DNNs) in semantic inferencing,
triplet loss-based architectures have been successfully used for this problem.
However, existing work utilizes conventional i-vectors as the input
representation and builds simple fully connected networks for metric learning,
thus not fully leveraging the modeling power of DNN architectures. This paper
investigates the importance of learning effective representations from the
sequences directly in metric learning pipelines for speaker diarization. More
specifically, we propose to employ attention models to learn embeddings and the
metric jointly in an end-to-end fashion. Experiments are conducted on the
CALLHOME conversational speech corpus. The diarization results demonstrate
that, besides providing a unified model, the proposed approach achieves
improved performance when compared against existing approaches. | [
0,
0,
0,
1,
0,
0
] |
Title: Adaptive Path-Integral Autoencoder: Representation Learning and Planning for Dynamical Systems,
Abstract: We present a representation learning algorithm that learns a low-dimensional
latent dynamical system from high-dimensional \textit{sequential} raw data,
e.g., video. The framework builds upon recent advances in amortized inference
methods that use both an inference network and a refinement procedure to output
samples from a variational distribution given an observation sequence, and
takes advantage of the duality between control and inference to approximately
solve the intractable inference problem using the path integral control
approach. The learned dynamical model can be used to predict and plan the
future states; we also present the efficient planning method that exploits the
learned low-dimensional latent dynamics. Numerical experiments show that the
proposed path-integral control based variational inference method leads to
tighter lower bounds in statistical model learning of sequential data. The
supplementary video: this https URL | [
1,
0,
0,
1,
0,
0
] |
Title: Hybrid Collaborative Recommendation via Semi-AutoEncoder,
Abstract: In this paper, we present a novel structure, Semi-AutoEncoder, based on
AutoEncoder. We generalize it into a hybrid collaborative filtering model for
rating prediction as well as personalized top-n recommendations. Experimental
results on two real-world datasets demonstrate its state-of-the-art
performances. | [
1,
0,
0,
0,
0,
0
] |
Title: The Linear Point: A cleaner cosmological standard ruler,
Abstract: We show how a characteristic length scale imprinted in the galaxy two-point
correlation function, dubbed the "linear point", can serve as a comoving
cosmological standard ruler. In contrast to the Baryon Acoustic Oscillation
peak location, this scale is constant in redshift and is unaffected by
non-linear effects to within $0.5$ percent precision. We measure the location
of the linear point in the galaxy correlation function of the LOWZ and CMASS
samples from the Twelfth Data Release (DR12) of the Baryon Oscillation
Spectroscopic Survey (BOSS) collaboration. We combine our linear-point
measurement with cosmic-microwave-background constraints from the Planck
satellite to estimate the isotropic-volume distance $D_{V}(z)$, without relying
on a model-template or reconstruction method. We find $D_V(0.32)=1264\pm 28$
Mpc and $D_V(0.57)=2056\pm 22$ Mpc respectively, consistent with the quoted
values from the BOSS collaboration. This remarkable result suggests that all
the distance information contained in the baryon acoustic oscillations can be
conveniently compressed into the single length associated with the linear
point. | [
0,
1,
0,
0,
0,
0
] |
Title: Stochastic Primal-Dual Method on Riemannian Manifolds with Bounded Sectional Curvature,
Abstract: We study a stochastic primal-dual method for constrained optimization over
Riemannian manifolds with bounded sectional curvature. We prove non-asymptotic
convergence to the optimal objective value. More precisely, for the class of
hyperbolic manifolds, we establish a convergence rate that is related to the
sectional curvature lower bound. To prove a convergence rate in terms of
sectional curvature for the elliptic manifolds, we leverage Toponogov's
comparison theorem. In addition, we provide convergence analysis for the
asymptotically elliptic manifolds, where the sectional curvature at each given
point on manifold is locally bounded from below by the distance function. We
demonstrate the performance of the primal-dual algorithm on the sphere for the
non-negative principle component analysis (PCA). In particular, under the
non-negativity constraint on the principle component and for the symmetric
spiked covariance model, we empirically show that the primal-dual approach
outperforms the spectral method. We also examine the performance of the
primal-dual method for the anchored synchronization from partial noisy
measurements of relative rotations on the Lie group SO(3). Lastly, we show that
the primal-dual algorithm can be applied to the weighted MAX-CUT problem under
constraints on the admissible cut. Specifically, we propose different
approximation algorithms for the weighted MAX-CUT problem based on optimizing a
function on the manifold of direct products of the unit spheres as well as the
manifold of direct products of the rotation groups. | [
0,
0,
1,
0,
0,
0
] |
Title: ExSIS: Extended Sure Independence Screening for Ultrahigh-dimensional Linear Models,
Abstract: Statistical inference can be computationally prohibitive in
ultrahigh-dimensional linear models. Correlation-based variable screening, in
which one leverages marginal correlations for removal of irrelevant variables
from the model prior to statistical inference, can be used to overcome this
challenge. Prior works on correlation-based variable screening either impose
strong statistical priors on the linear model or assume specific post-screening
inference methods. This paper first extends the analysis of correlation-based
variable screening to arbitrary linear models and post-screening inference
techniques. In particular, ($i$) it shows that a condition---termed the
screening condition---is sufficient for successful correlation-based screening
of linear models, and ($ii$) it provides insights into the dependence of
marginal correlation-based screening on different problem parameters. Numerical
experiments confirm that these insights are not mere artifacts of analysis;
rather, they are reflective of the challenges associated with marginal
correlation-based variable screening. Second, the paper explicitly derives the
screening condition for two families of linear models, namely, sub-Gaussian
linear models and arbitrary (random or deterministic) linear models. In the
process, it establishes that---under appropriate conditions---it is possible to
reduce the dimension of an ultrahigh-dimensional, arbitrary linear model to
almost the sample size even when the number of active variables scales almost
linearly with the sample size. | [
0,
0,
1,
1,
0,
0
] |
Title: Pressure tuning of structure, superconductivity and novel magnetic order in the Ce-underdoped electron-doped cuprate T'-Pr_1.3-xLa_0.7Ce_xCuO_4 (x = 0.1),
Abstract: High-pressure neutron powder diffraction, muon-spin rotation and
magnetization studies of the structural, magnetic and the superconducting
properties of the Ce-underdoped superconducting (SC) electron-doped cuprate
system T'-Pr_1.3-xLa_0.7Ce_xCuO_4 with x = 0.1 are reported. A strong reduction
of the lattice constants a and c is observed under pressure. However, no
indication of any pressure induced phase transition from T' to T structure is
observed up to the maximum applied pressure of p = 11 GPa. Large and non-linear
increase of the short-range magnetic order temperature T_so in
T'-Pr_1.3-xLa_0.7Ce_xCuO_4 (x = 0.1) was observed under pressure.
Simultaneously pressure causes a non-linear decrease of the SC transition
temperature T_c. All these experiments establish the short-range magnetic order
as an intrinsic and a new competing phase in SC T'-Pr_1.2La_0.7Ce_0.1CuO_4. The
observed pressure effects may be interpreted in terms of the improved nesting
conditions through the reduction of the in-plane and out-of-plane lattice
constants upon hydrostatic pressure. | [
0,
1,
0,
0,
0,
0
] |
Title: Subset Labeled LDA for Large-Scale Multi-Label Classification,
Abstract: Labeled Latent Dirichlet Allocation (LLDA) is an extension of the standard
unsupervised Latent Dirichlet Allocation (LDA) algorithm, to address
multi-label learning tasks. Previous work has shown it to perform in par with
other state-of-the-art multi-label methods. Nonetheless, with increasing label
sets sizes LLDA encounters scalability issues. In this work, we introduce
Subset LLDA, a simple variant of the standard LLDA algorithm, that not only can
effectively scale up to problems with hundreds of thousands of labels but also
improves over the LLDA state-of-the-art. We conduct extensive experiments on
eight data sets, with label sets sizes ranging from hundreds to hundreds of
thousands, comparing our proposed algorithm with the previously proposed LLDA
algorithms (Prior--LDA, Dep--LDA), as well as the state of the art in extreme
multi-label classification. The results show a steady advantage of our method
over the other LLDA algorithms and competitive results compared to the extreme
multi-label classification algorithms. | [
0,
0,
0,
1,
0,
0
] |
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