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Title: Merging real and virtual worlds: An analysis of the state of the art and practical evaluation of Microsoft Hololens,
Abstract: Achieving a symbiotic blending between reality and virtuality is a dream that
has been lying in the minds of many people for a long time. Advances in various
domains constantly bring us closer to making that dream come true. Augmented
reality as well as virtual reality are in fact trending terms and are expected
to further progress in the years to come.
This master's thesis aims to explore these areas and starts by defining
necessary terms such as augmented reality (AR) or virtual reality (VR). Usual
taxonomies to classify and compare the corresponding experiences are then
discussed.
In order to enable those applications, many technical challenges need to be
tackled, such as accurate motion tracking with 6 degrees of freedom (positional
and rotational), that is necessary for compelling experiences and to prevent
user sickness. Additionally, augmented reality experiences typically rely on
image processing to position the superimposed content. To do so, "paper"
markers or features extracted from the environment are often employed. Both
sets of techniques are explored and common solutions and algorithms are
presented.
After investigating those technical aspects, I carry out an objective
comparison of the existing state-of-the-art and state-of-the-practice in those
domains, and I discuss present and potential applications in these areas. As a
practical validation, I present the results of an application that I have
developed using Microsoft HoloLens, one of the more advanced affordable
technologies for augmented reality that is available today. Based on the
experience and lessons learned during this development, I discuss the
limitations of current technologies and present some avenues of future
research. | [
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] |
Title: Injectivity of the connecting homomorphisms,
Abstract: Let $A$ be the inductive limit of a sequence $$A_1\, \xrightarrow{\phi_{1,2}}
\,A_2\,\xrightarrow{\phi_{2,3}} \,A_3\rightarrow\cdots$$ with
$A_n=\oplus_{i=1}^{n_i}A_{[n,i]}$, where all the $A_{[n,i]}$ are
Elliott-Thomsen algebras and $\phi_{n,n+1}$ are homomorphisms, in this paper,
we will prove that $A$ can be written as another inductive limit
$$B_1\,\xrightarrow{\psi_{1,2}} \,B_2\,\xrightarrow{\psi_{2,3}}
\,B_3\rightarrow\cdots$$ with $B_n=\oplus_{i=1}^{n_i}B_{[n,i]}$, where all the
$B_{[n,i]}$ are Elliott-Thomsen building blocks and with the extra condition
that all the $\phi_{n,n+1}$ are injective. | [
0,
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1,
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0
] |
Title: Selective Inference for Change Point Detection in Multi-dimensional Sequences,
Abstract: We study the problem of detecting change points (CPs) that are characterized
by a subset of dimensions in a multi-dimensional sequence. A method for
detecting those CPs can be formulated as a two-stage method: one for selecting
relevant dimensions, and another for selecting CPs. It has been difficult to
properly control the false detection probability of these CP detection methods
because selection bias in each stage must be properly corrected. Our main
contribution in this paper is to formulate a CP detection problem as a
selective inference problem, and show that exact (non-asymptotic) inference is
possible for a class of CP detection methods. We demonstrate the performances
of the proposed selective inference framework through numerical simulations and
its application to our motivating medical data analysis problem. | [
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0,
0,
1,
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0
] |
Title: Anyonic self-induced disorder in a stabilizer code: quasi-many body localization in a translational invariant model,
Abstract: We enquire into the quasi-many-body localization in topologically ordered
states of matter, revolving around the case of Kitaev toric code on ladder
geometry, where different types of anyonic defects carry different masses
induced by environmental errors. Our study verifies that random arrangement of
anyons generates a complex energy landscape solely through braiding statistics,
which suffices to suppress the diffusion of defects in such multi-component
anyonic liquid. This non-ergodic dynamic suggests a promising scenario for
investigation of quasi-many-body localization. Computing standard diagnostics
evidences that, in such disorder-free many-body system, a typical initial
inhomogeneity of anyons gives birth to a glassy dynamics with an exponentially
diverging time scale of the full relaxation. A by-product of this dynamical
effect is manifested by the slow growth of entanglement entropy, with
characteristic time scales bearing resemblance to those of inhomogeneity
relaxation. This setting provides a new platform which paves the way toward
impeding logical errors by self-localization of anyons in a generic, high
energy state, originated in their exotic statistics. | [
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1,
0,
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0,
0
] |
Title: Earthquake Early Warning and Beyond: Systems Challenges in Smartphone-based Seismic Network,
Abstract: Earthquake Early Warning (EEW) systems can effectively reduce fatalities,
injuries, and damages caused by earthquakes. Current EEW systems are mostly
based on traditional seismic and geodetic networks, and exist only in a few
countries due to the high cost of installing and maintaining such systems. The
MyShake system takes a different approach and turns people's smartphones into
portable seismic sensors to detect earthquake-like motions. However, to issue
EEW messages with high accuracy and low latency in the real world, we need to
address a number of challenges related to mobile computing. In this paper, we
first summarize our experience building and deploying the MyShake system, then
focus on two key challenges for smartphone-based EEW (sensing heterogeneity and
user/system dynamics) and some preliminary exploration. We also discuss other
challenges and new research directions associated with smartphone-based seismic
network. | [
1,
0,
0,
0,
0,
0
] |
Title: Gate-error analysis in simulations of quantum computers with transmon qubits,
Abstract: In the model of gate-based quantum computation, the qubits are controlled by
a sequence of quantum gates. In superconducting qubit systems, these gates can
be implemented by voltage pulses. The success of implementing a particular gate
can be expressed by various metrics such as the average gate fidelity, the
diamond distance, and the unitarity. We analyze these metrics of gate pulses
for a system of two superconducting transmon qubits coupled by a resonator, a
system inspired by the architecture of the IBM Quantum Experience. The metrics
are obtained by numerical solution of the time-dependent Schrödinger equation
of the transmon system. We find that the metrics reflect systematic errors that
are most pronounced for echoed cross-resonance gates, but that none of the
studied metrics can reliably predict the performance of a gate when used
repeatedly in a quantum algorithm. | [
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1,
0,
0,
0,
0
] |
Title: Common fixed point theorems under an implicit contractive condition on metric spaces endowed with an arbitrary binary relation and an application,
Abstract: The aim of this paper is to establish some metrical coincidence and common
fixed point theorems with an arbitrary relation under an implicit contractive
condition which is general enough to cover a multitude of well known
contraction conditions in one go besides yielding several new ones. We also
provide an example to demonstrate the generality of our results over several
well known corresponding results of the existing literature. Finally, we
utilize our results to prove an existence theorem for ensuring the solution of
an integral equation. | [
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0,
1,
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0,
0
] |
Title: Operationalizing Conflict and Cooperation between Automated Software Agents in Wikipedia: A Replication and Expansion of 'Even Good Bots Fight',
Abstract: This paper replicates, extends, and refutes conclusions made in a study
published in PLoS ONE ("Even Good Bots Fight"), which claimed to identify
substantial levels of conflict between automated software agents (or bots) in
Wikipedia using purely quantitative methods. By applying an integrative
mixed-methods approach drawing on trace ethnography, we place these alleged
cases of bot-bot conflict into context and arrive at a better understanding of
these interactions. We found that overwhelmingly, the interactions previously
characterized as problematic instances of conflict are typically better
characterized as routine, productive, even collaborative work. These results
challenge past work and show the importance of qualitative/quantitative
collaboration. In our paper, we present quantitative metrics and qualitative
heuristics for operationalizing bot-bot conflict. We give thick descriptions of
kinds of events that present as bot-bot reverts, helping distinguish conflict
from non-conflict. We computationally classify these kinds of events through
patterns in edit summaries. By interpreting found/trace data in the
socio-technical contexts in which people give that data meaning, we gain more
from quantitative measurements, drawing deeper understandings about the
governance of algorithmic systems in Wikipedia. We have also released our data
collection, processing, and analysis pipeline, to facilitate computational
reproducibility of our findings and to help other researchers interested in
conducting similar mixed-method scholarship in other platforms and contexts. | [
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] |
Title: Tuning the piezoelectric and mechanical properties of the AlN system via alloying with YN and BN,
Abstract: Recent advances in microelectromechanical systems often require
multifunctional materials, which are designed so as to optimize more than one
property. Using density functional theory calculations for alloyed nitride
systems, we illustrate how co-alloying a piezoelectric material (AlN) with
different nitrides helps tune both its piezoelectric and mechanical properties
simultaneously. Wurtzite AlN-YN alloys display increased piezoelectric response
with YN concentration, accompanied by mechanical softening along the
crystallographic c direction. Both effects increase the electromechanical
coupling coefficients relevant for transducers and actuators. Resonator
applications, however, require superior stiffness, thus leading to the need to
decouple the increased piezoelectric response from a softened lattice. We show
that co-alloying of AlN with YN and BN results in improved elastic properties
while retaining most of the piezoelectric enhancements from YN alloying. This
finding may lead to new avenues for tuning the design properties of
piezoelectrics through composition-property maps.
Keywords: piezoelectricity, electromechanical coupling, density functional
theory, co-alloying | [
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] |
Title: Simple Surveys: Response Retrieval Inspired by Recommendation Systems,
Abstract: In the last decade, the use of simple rating and comparison surveys has
proliferated on social and digital media platforms to fuel recommendations.
These simple surveys and their extrapolation with machine learning algorithms
shed light on user preferences over large and growing pools of items, such as
movies, songs and ads. Social scientists have a long history of measuring
perceptions, preferences and opinions, often over smaller, discrete item sets
with exhaustive rating or ranking surveys. This paper introduces simple surveys
for social science application. We ran experiments to compare the predictive
accuracy of both individual and aggregate comparative assessments using four
types of simple surveys: pairwise comparisons and ratings on 2, 5 and
continuous point scales in three distinct contexts: perceived Safety of Google
Streetview Images, Likeability of Artwork, and Hilarity of Animal GIFs. Across
contexts, we find that continuous scale ratings best predict individual
assessments but consume the most time and cognitive effort. Binary choice
surveys are quick and perform best to predict aggregate assessments, useful for
collective decision tasks, but poorly predict personalized preferences, for
which they are currently used by Netflix to recommend movies. Pairwise
comparisons, by contrast, perform well to predict personal assessments, but
poorly predict aggregate assessments despite being widely used to crowdsource
ideas and collective preferences. We demonstrate how findings from these
surveys can be visualized in a low-dimensional space that reveals distinct
respondent interpretations of questions asked in each context. We conclude by
reflecting on differences between sparse, incomplete simple surveys and their
traditional survey counterparts in terms of efficiency, information elicited
and settings in which knowing less about more may be critical for social
science. | [
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] |
Title: A Reduction for the Distinct Distances Problem in ${\mathbb R}^d$,
Abstract: We introduce a reduction from the distinct distances problem in ${\mathbb
R}^d$ to an incidence problem with $(d-1)$-flats in ${\mathbb R}^{2d-1}$.
Deriving the conjectured bound for this incidence problem (the bound predicted
by the polynomial partitioning technique) would lead to a tight bound for the
distinct distances problem in ${\mathbb R}^d$. The reduction provides a large
amount of information about the $(d-1)$-flats, and a framework for deriving
more restrictions that these satisfy. Our reduction is based on introducing a
Lie group that is a double cover of the special Euclidean group. This group can
be seen as a variant of the Spin group, and a large part of our analysis
involves studying its properties. | [
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] |
Title: Local electronic properties of the graphene-protected giant Rashba-split BiAg$_2$ surface,
Abstract: We report the preparation of the interface between graphene and the strong
Rashba-split BiAg$_2$ surface alloy and investigatigation of its structure as
well as the electronic properties by means of scanning tunneling
microscopy/spectroscopy and density functional theory calculations. Upon
evaluation of the quasiparticle interference patterns the unpertrubated linear
dispersion for the $\pi$ band of $n$-doped graphene is observed. Our results
also reveal the intact nature of the giant Rashba-split surface states of the
BiAg$_2$ alloy, which demonstrate only a moderate downward energy shift upon
the presence of graphene. This effect is explained in the framework of density
functional theory by an inward relaxation of the Bi atoms at the interface and
subsequent delocalisation of the wave function of the surface states. Our
findings demonstrate a realistic pathway to prepare a graphene protected giant
Rashba-split BiAg$_2$ for possible spintronic applications. | [
0,
1,
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0,
0,
0
] |
Title: Lattice Boltzmann simulation of viscous fingering of immiscible displacement in a channel using an improved wetting scheme,
Abstract: An improved wetting boundary implementation strategy is proposed based on
lattice Boltzmann color-gradient model in this paper. In this strategy, an
extra interface force condition is demonstrated based on the diffuse interface
assumption and is employed in contact line region. It has been validated by
three benchmark problems: static droplet wetting on a flat surface and a curved
surface, and dynamic capillary filling. Good performances are shown in all
three cases. Relied on the strict validation to our scheme, the viscous
fingering phenomenon of immiscible fluids displacement in a two-dimensional
channel has been restudied in this paper. High viscosity ratio, wide range
contact angle, accurate moving contact line and mutual independence between
surface tension and viscosity are the obvious advantages of our model. We find
the linear relationship between the contact angle and displacement velocity or
variation of finger length. When the viscosity ratio is smaller than 20, the
displacement velocity is increasing with increasing viscosity ratio and
reducing capillary number, and when the viscosity ratio is larger than 20, the
displacement velocity tends to a specific constant. A similar conclusion is
obtained on the variation of finger length. | [
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] |
Title: Implementation of Control Strategies for Sterile Insect Techniques,
Abstract: In this paper, we propose a sex-structured entomological model that serves as
a basis for design of control strategies relying on releases of sterile male
mosquitoes (Aedes spp) and aiming at elimination of the wild vector population
in some target locality. We consider different types of releases (constant and
periodic impulsive), providing necessary conditions to reach elimination.
However, the main part of the paper is focused on the study of the periodic
impulsive control in different situations. When the size of wild mosquito
population cannot be assessed in real time, we propose the so-called open-loop
control strategy that relies on periodic impulsive releases of sterile males
with constant release size. Under this control mode, global convergence towards
the mosquito-free equilibrium is proved on the grounds of sufficient condition
that relates the size and frequency of releases. If periodic assessments
(either synchronized with releases or more sparse) of the wild population size
are available in real time, we propose the so-called closed-loop control
strategy, which is adjustable in accordance with reliable estimations of the
wild population sizes. Under this control mode, global convergence to the
mosquito-free equilibrium is proved on the grounds of another sufficient
condition that relates not only the size and frequency of periodic releases but
also the frequency of sparse measurements taken on wild populations. Finally,
we propose a mixed control strategy that combines open-loop and closed-loop
strategies. This control mode renders the best result, in terms of overall time
needed to reach elimination and the number of releases to be effectively
carried out during the whole release campaign, while requiring for a reasonable
amount of released sterile insects. | [
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] |
Title: Convergence and submeasures in Boolean algebras,
Abstract: A Boolean algebra carries a strictly positive exhaustive submeasure if and
only if it has a sequential topology that is uniformly Frechet. | [
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] |
Title: Genetic algorithm-based control of birefringent filtering for self-tuning, self-pulsing fiber lasers,
Abstract: Polarization-based filtering in fiber lasers is well-known to enable spectral
tunability and a wide range of dynamical operating states. This effect is
rarely exploited in practical systems, however, because optimization of cavity
parameters is non-trivial and evolves due to environmental sensitivity. Here,
we report a genetic algorithm-based approach, utilizing electronic control of
the cavity transfer function, to autonomously achieve broad wavelength tuning
and the generation of Q-switched pulses with variable repetition rate and
duration. The practicalities and limitations of simultaneous spectral and
temporal self-tuning from a simple fiber laser are discussed, paving the way to
on-demand laser properties through algorithmic control and machine learning
schemes. | [
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] |
Title: Public discourse and news consumption on online social media: A quantitative, cross-platform analysis of the Italian Referendum,
Abstract: The rising attention to the spreading of fake news and unsubstantiated rumors
on online social media and the pivotal role played by confirmation bias led
researchers to investigate different aspects of the phenomenon. Experimental
evidence showed that confirmatory information gets accepted even if containing
deliberately false claims while dissenting information is mainly ignored or
might even increase group polarization. It seems reasonable that, to address
misinformation problem properly, we have to understand the main determinants
behind content consumption and the emergence of narratives on online social
media. In this paper we address such a challenge by focusing on the discussion
around the Italian Constitutional Referendum by conducting a quantitative,
cross-platform analysis on both Facebook public pages and Twitter accounts. We
observe the spontaneous emergence of well-separated communities on both
platforms. Such a segregation is completely spontaneous, since no
categorization of contents was performed a priori. By exploring the dynamics
behind the discussion, we find that users tend to restrict their attention to a
specific set of Facebook pages/Twitter accounts. Finally, taking advantage of
automatic topic extraction and sentiment analysis techniques, we are able to
identify the most controversial topics inside and across both platforms. We
measure the distance between how a certain topic is presented in the
posts/tweets and the related emotional response of users. Our results provide
interesting insights for the understanding of the evolution of the core
narratives behind different echo chambers and for the early detection of
massive viral phenomena around false claims. | [
1,
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] |
Title: AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms,
Abstract: Approximate probabilistic inference algorithms are central to many fields.
Examples include sequential Monte Carlo inference in robotics, variational
inference in machine learning, and Markov chain Monte Carlo inference in
statistics. A key problem faced by practitioners is measuring the accuracy of
an approximate inference algorithm on a specific data set. This paper
introduces the auxiliary inference divergence estimator (AIDE), an algorithm
for measuring the accuracy of approximate inference algorithms. AIDE is based
on the observation that inference algorithms can be treated as probabilistic
models and the random variables used within the inference algorithm can be
viewed as auxiliary variables. This view leads to a new estimator for the
symmetric KL divergence between the approximating distributions of two
inference algorithms. The paper illustrates application of AIDE to algorithms
for inference in regression, hidden Markov, and Dirichlet process mixture
models. The experiments show that AIDE captures the qualitative behavior of a
broad class of inference algorithms and can detect failure modes of inference
algorithms that are missed by standard heuristics. | [
1,
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1,
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0
] |
Title: Kidnapping Model: An Extension of Selten's Game,
Abstract: Selten's game is a kidnapping model where the probability of capturing the
kidnapper is independent of whether the hostage has been released or executed.
Most often, in view of the elevated sensitivities involved, authorities put
greater effort and resources into capturing the kidnapper if the hostage has
been executed, in contrast to the case when a ransom is paid to secure the
hostage's release. In this paper, we study the asymmetric game when the
probability of capturing the kidnapper depends on whether the hostage has been
executed or not and find a new uniquely determined perfect equilibrium point in
Selten's game. | [
1,
0,
0,
0,
0,
0
] |
Title: Semiparametric panel data models using neural networks,
Abstract: This paper presents an estimator for semiparametric models that uses a
feed-forward neural network to fit the nonparametric component. Unlike many
methodologies from the machine learning literature, this approach is suitable
for longitudinal/panel data. It provides unbiased estimation of the parametric
component of the model, with associated confidence intervals that have
near-nominal coverage rates. Simulations demonstrate (1) efficiency, (2) that
parametric estimates are unbiased, and (3) coverage properties of estimated
intervals. An application section demonstrates the method by predicting
county-level corn yield using daily weather data from the period 1981-2015,
along with parametric time trends representing technological change. The method
is shown to out-perform linear methods such as OLS and ridge/lasso, as well as
random forest. The procedures described in this paper are implemented in the R
package panelNNET. | [
0,
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1,
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0
] |
Title: Split and Rephrase,
Abstract: We propose a new sentence simplification task (Split-and-Rephrase) where the
aim is to split a complex sentence into a meaning preserving sequence of
shorter sentences. Like sentence simplification, splitting-and-rephrasing has
the potential of benefiting both natural language processing and societal
applications. Because shorter sentences are generally better processed by NLP
systems, it could be used as a preprocessing step which facilitates and
improves the performance of parsers, semantic role labellers and machine
translation systems. It should also be of use for people with reading
disabilities because it allows the conversion of longer sentences into shorter
ones. This paper makes two contributions towards this new task. First, we
create and make available a benchmark consisting of 1,066,115 tuples mapping a
single complex sentence to a sequence of sentences expressing the same meaning.
Second, we propose five models (vanilla sequence-to-sequence to
semantically-motivated models) to understand the difficulty of the proposed
task. | [
1,
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0,
0,
0,
0
] |
Title: Evaporation and scattering of momentum- and velocity-dependent dark matter in the Sun,
Abstract: Dark matter with momentum- or velocity-dependent interactions with nuclei has
shown significant promise for explaining the so-called Solar Abundance Problem,
a longstanding discrepancy between solar spectroscopy and helioseismology. The
best-fit models are all rather light, typically with masses in the range of 3-5
GeV. This is exactly the mass range where dark matter evaporation from the Sun
can be important, but to date no detailed calculation of the evaporation of
such models has been performed. Here we carry out this calculation, for the
first time including arbitrary velocity- and momentum-dependent interactions,
thermal effects, and a completely general treatment valid from the optically
thin limit all the way through to the optically thick regime. We find that
depending on the dark matter mass, interaction strength and type, the mass
below which evaporation is relevant can vary from 1 to 4 GeV. This has the
effect of weakening some of the better-fitting solutions to the Solar Abundance
Problem, but also improving a number of others. As a by-product, we also
provide an improved derivation of the capture rate that takes into account
thermal and optical depth effects, allowing the standard result to be smoothly
matched to the well-known saturation limit. | [
0,
1,
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0,
0,
0
] |
Title: ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features,
Abstract: With the rapid development of spaceborne imaging techniques, object detection
in optical remote sensing imagery has drawn much attention in recent decades.
While many advanced works have been developed with powerful learning
algorithms, the incomplete feature representation still cannot meet the demand
for effectively and efficiently handling image deformations, particularly
objective scaling and rotation. To this end, we propose a novel object
detection framework, called optical remote sensing imagery detector (ORSIm
detector), integrating diverse channel features extraction, feature learning,
fast image pyramid matching, and boosting strategy. ORSIm detector adopts a
novel spatial-frequency channel feature (SFCF) by jointly considering the
rotation-invariant channel features constructed in frequency domain and the
original spatial channel features (e.g., color channel, gradient magnitude).
Subsequently, we refine SFCF using learning-based strategy in order to obtain
the high-level or semantically meaningful features. In the test phase, we
achieve a fast and coarsely-scaled channel computation by mathematically
estimating a scaling factor in the image domain. Extensive experimental results
conducted on the two different airborne datasets are performed to demonstrate
the superiority and effectiveness in comparison with previous state-of-the-art
methods. | [
1,
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0,
0,
0
] |
Title: Synergies between Exoplanet Surveys and Variable Star Research,
Abstract: With the discovery of the first transiting extrasolar planetary system back
to 1999, a great number of projects started to hunt for other similar systems.
Because of the incidence rate of such systems was unknown and the length of the
shallow transit events is only a few percent of the orbital period, the goal
was to monitor continuously as many stars as possible for at least a period of
a few months. Small aperture, large field of view automated telescope systems
have been installed with a parallel development of new data reduction and
analysis methods, leading to better than 1% per data point precision for
thousands of stars. With the successful launch of the photometric satellites
CoRot and Kepler, the precision increased further by one-two orders of
magnitude. Millions of stars have been analyzed and searched for transits. In
the history of variable star astronomy this is the biggest undertaking so far,
resulting in photometric time series inventories immensely valuable for the
whole field. In this review we briefly discuss the methods of data analysis
that were inspired by the main science driver of these surveys and highlight
some of the most interesting variable star results that impact the field of
variable star astronomy. | [
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1,
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] |
Title: Improved Set-based Symbolic Algorithms for Parity Games,
Abstract: Graph games with {\omega}-regular winning conditions provide a mathematical
framework to analyze a wide range of problems in the analysis of reactive
systems and programs (such as the synthesis of reactive systems, program
repair, and the verification of branching time properties). Parity conditions
are canonical forms to specify {\omega}-regular winning conditions. Graph games
with parity conditions are equivalent to {\mu}-calculus model checking, and
thus a very important algorithmic problem. Symbolic algorithms are of great
significance because they provide scalable algorithms for the analysis of large
finite-state systems, as well as algorithms for the analysis of infinite-state
systems with finite quotient. A set-based symbolic algorithm uses the basic set
operations and the one-step predecessor operators. We consider graph games with
$n$ vertices and parity conditions with $c$ priorities. While many explicit
algorithms exist for graph games with parity conditions, for set-based symbolic
algorithms there are only two algorithms (notice that we use space to refer to
the number of sets stored by a symbolic algorithm): (a) the basic algorithm
that requires $O(n^c)$ symbolic operations and linear space; and (b) an
improved algorithm that requires $O(n^{c/2+1})$ symbolic operations but also
$O(n^{c/2+1})$ space (i.e., exponential space). In this work we present two
set-based symbolic algorithms for parity games: (a) our first algorithm
requires $O(n^{c/2+1})$ symbolic operations and only requires linear space; and
(b) developing on our first algorithm, we present an algorithm that requires
$O(n^{c/3+1})$ symbolic operations and only linear space. We also present the
first linear space set-based symbolic algorithm for parity games that requires
at most a sub-exponential number of symbolic operations. | [
1,
0,
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0
] |
Title: Existence of Evolutionarily Stable Strategies Remains Hard to Decide for a Wide Range of Payoff Values,
Abstract: The concept of an evolutionarily stable strategy (ESS), introduced by Smith
and Price, is a refinement of Nash equilibrium in 2-player symmetric games in
order to explain counter-intuitive natural phenomena, whose existence is not
guaranteed in every game. The problem of deciding whether a game possesses an
ESS has been shown to be $\Sigma_{2}^{P}$-complete by Conitzer using the
preceding important work by Etessami and Lochbihler. The latter, among other
results, proved that deciding the existence of ESS is both NP-hard and
coNP-hard. In this paper we introduce a "reduction robustness" notion and we
show that deciding the existence of an ESS remains coNP-hard for a wide range
of games even if we arbitrarily perturb within some intervals the payoff values
of the game under consideration. In contrast, ESS exist almost surely for large
games with random and independent payoffs chosen from the same distribution. | [
1,
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0,
0
] |
Title: Compound Poisson approximation to estimate the Lévy density,
Abstract: We construct an estimator of the Lévy density of a pure jump Lévy
process, possibly of infinite variation, from the discrete observation of one
trajectory at high frequency. The novelty of our procedure is that we directly
estimate the Lévy density relying on a pathwise strategy, whereas existing
procedures rely on spectral techniques. By taking advantage of a compound
Poisson approximation of the Lévy density, we circumvent the use of spectral
techniques and in particular of the Lévy-Khintchine formula. A linear wavelet
estimators is built and its performance is studied in terms of $L_p$ loss
functions, $p\geq 1$, over Besov balls. The resulting rates are minimax-optimal
for a large class of Lévy processes. We discuss the robustness of the
procedure to the presence of a Brownian part and to the estimation set getting
close to the critical value 0. | [
0,
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1,
0,
0
] |
Title: On the non-vanishing of certain Dirichlet series,
Abstract: Given $k\in\mathbb N$, we study the vanishing of the Dirichlet series
$$D_k(s,f):=\sum_{n\geq1} d_k(n)f(n)n^{-s}$$ at the point $s=1$, where $f$ is a
periodic function modulo a prime $p$. We show that if $(k,p-1)=1$ or
$(k,p-1)=2$ and $p\equiv 3\mod 4$, then there are no odd rational-valued
functions $f\not\equiv 0$ such that $D_k(1,f)=0$, whereas in all other cases
there are examples of odd functions $f$ such that $D_k(1,f)=0$.
As a consequence, we obtain, for example, that the set of values
$L(1,\chi)^2$, where $\chi$ ranges over odd characters mod $p$, are linearly
independent over $\mathbb Q$. | [
0,
0,
1,
0,
0,
0
] |
Title: Points2Pix: 3D Point-Cloud to Image Translation using conditional Generative Adversarial Networks,
Abstract: We present the first approach for 3D point-cloud to image translation based
on conditional Generative Adversarial Networks (cGAN). The model handles
multi-modal information sources from different domains, i.e. raw point-sets and
images. The generator is capable of processing three conditions, whereas the
point-cloud is encoded as raw point-set and camera projection. An image
background patch is used as constraint to bias environmental texturing. A
global approximation function within the generator is directly applied on the
point-cloud (Point-Net). Hence, the representative learning model incorporates
global 3D characteristics directly at the latent feature space. Conditions are
used to bias the background and the viewpoint of the generated image. This
opens up new ways in augmenting or texturing 3D data to aim the generation of
fully individual images. We successfully evaluated our method on the Kitti and
SunRGBD dataset with an outstanding object detection inception score. | [
1,
0,
0,
0,
0,
0
] |
Title: Parametrization and Generation of Geological Models with Generative Adversarial Networks,
Abstract: One of the main challenges in the parametrization of geological models is the
ability to capture complex geological structures often observed in subsurface
fields. In recent years, Generative Adversarial Networks (GAN) were proposed as
an efficient method for the generation and parametrization of complex data,
showing state-of-the-art performances in challenging computer vision tasks such
as reproducing natural images (handwritten digits, human faces, etc.). In this
work, we study the application of Wasserstein GAN for the parametrization of
geological models. The effectiveness of the method is assessed for uncertainty
propagation tasks using several test cases involving different permeability
patterns and subsurface flow problems. Results show that GANs are able to
generate samples that preserve the multipoint statistical features of the
geological models both visually and quantitatively. The generated samples
reproduce both the geological structures and the flow properties of the
reference data. | [
0,
1,
0,
1,
0,
0
] |
Title: Finite element procedures for computing normals and mean curvature on triangulated surfaces and their use for mesh refinement,
Abstract: In this paper we consider finite element approaches to computing the mean
curvature vector and normal at the vertices of piecewise linear triangulated
surfaces. In particular, we adopt a stabilization technique which allows for
first order $L^2$-convergence of the mean curvature vector and apply this
stabilization technique also to the computation of continuous, recovered,
normals using $L^2$-projections of the piecewise constant face normals.
Finally, we use our projected normals to define an adaptive mesh refinement
approach to geometry resolution where we also employ spline techniques to
reconstruct the surface before refinement. We compare or results to previously
proposed approaches. | [
0,
0,
1,
0,
0,
0
] |
Title: Maximum a posteriori estimation through simulated annealing for binary asteroid orbit determination,
Abstract: This paper considers a new method for the binary asteroid orbit determination
problem. The method is based on the Bayesian approach with a global
optimisation algorithm. The orbital parameters to be determined are modelled
through an a posteriori distribution made of a priori and likelihood terms. The
first term constrains the parameters space and it allows the introduction of
available knowledge about the orbit. The second term is based on given
observations and it allows us to use and compare different observational error
models. Once the a posteriori model is built, the estimator of the orbital
parameters is computed using a global optimisation procedure: the simulated
annealing algorithm. The maximum a posteriori (MAP) techniques are verified
using simulated and real data. The obtained results validate the proposed
method. The new approach guarantees independence of the initial parameters
estimation and theoretical convergence towards the global optimisation
solution. It is particularly useful in these situations, whenever a good
initial orbit estimation is difficult to get, whenever observations are not
well-sampled, and whenever the statistical behaviour of the observational
errors cannot be stated Gaussian like. | [
0,
1,
0,
1,
0,
0
] |
Title: Extrapolating Expected Accuracies for Large Multi-Class Problems,
Abstract: The difficulty of multi-class classification generally increases with the
number of classes. Using data from a subset of the classes, can we predict how
well a classifier will scale with an increased number of classes? Under the
assumptions that the classes are sampled identically and independently from a
population, and that the classifier is based on independently learned scoring
functions, we show that the expected accuracy when the classifier is trained on
k classes is the (k-1)st moment of a certain distribution that can be estimated
from data. We present an unbiased estimation method based on the theory, and
demonstrate its application on a facial recognition example. | [
1,
0,
0,
1,
0,
0
] |
Title: RSI-CB: A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data,
Abstract: Remote sensing image classification is a fundamental task in remote sensing
image processing. Remote sensing field still lacks of such a large-scale
benchmark compared to ImageNet, Place2. We propose a remote sensing image
classification benchmark (RSI-CB) based on crowd-source data which is massive,
scalable, and diversity. Using crowdsource data, we can efficiently annotate
ground objects in remotes sensing image by point of interests, vectors data
from OSM or other crowd-source data. Based on this method, we construct a
worldwide large-scale benchmark for remote sensing image classification. In
this benchmark, there are two sub datasets with 256 * 256 and 128 * 128 size
respectively since different convolution neural networks requirement different
image size. The former sub dataset contains 6 categories with 35 subclasses
with total of more than 24,000 images; the later one contains 6 categories with
45 subclasses with total of more than 36,000 images. The six categories are
agricultural land, construction land and facilities, transportation and
facilities, water and water conservancy facilities, woodland and other land,
and each category has several subclasses. This classification system is defined
according to the national standard of land use classification in China, and is
inspired by the hierarchy mechanism of ImageNet. Finally, we have done a large
number of experiments to compare RSI-CB with SAT-4, UC-Merced datasets on
handcrafted features, such as such as SIFT, and classical CNN models, such as
AlexNet, VGG, GoogleNet, and ResNet. We also show CNN models trained by RSI-CB
have good performance when transfer to other dataset, i.e. UC-Merced, and good
generalization ability. The experiments show that RSI-CB is more suitable as a
benchmark for remote sensing image classification task than other ones in big
data era, and can be potentially used in practical applications. | [
1,
0,
0,
0,
0,
0
] |
Title: Quantum groups, Yang-Baxter maps and quasi-determinants,
Abstract: For any quasi-triangular Hopf algebra, there exists the universal R-matrix,
which satisfies the Yang-Baxter equation. It is known that the adjoint action
of the universal R-matrix on the elements of the tensor square of the algebra
constitutes a quantum Yang-Baxter map, which satisfies the set-theoretic
Yang-Baxter equation. The map has a zero curvature representation among
L-operators defined as images of the universal R-matrix. We find that the zero
curvature representation can be solved by the Gauss decomposition of a product
of L-operators. Thereby obtained a quasi-determinant expression of the quantum
Yang-Baxter map associated with the quantum algebra $U_{q}(gl(n))$. Moreover,
the map is identified with products of quasi-Plücker coordinates over a
matrix composed of the L-operators. We also consider the quasi-classical limit,
where the underlying quantum algebra reduces to a Poisson algebra. The
quasi-determinant expression of the quantum Yang-Baxter map reduces to ratios
of determinants, which give a new expression of a classical Yang-Baxter map. | [
0,
1,
1,
0,
0,
0
] |
Title: A Brownian Motion Model and Extreme Belief Machine for Modeling Sensor Data Measurements,
Abstract: As the title suggests, we will describe (and justify through the presentation
of some of the relevant mathematics) prediction methodologies for sensor
measurements. This exposition will mainly be concerned with the mathematics
related to modeling the sensor measurements. | [
1,
0,
0,
0,
0,
0
] |
Title: On the nature of the magnetic phase transition in a Weyl semimetal,
Abstract: We investigate the nature of the magnetic phase transition induced by the
short-ranged electron-electron interactions in a Weyl semimetal by using the
perturbative renormalization-group method. We find that the critical point
associated with the quantum phase transition is characterized by a Gaussian
fixed point perturbed by a dangerously irrelevant operator. Although the
low-energy and long-distance physics is governed by a free theory, the
velocities of the fermionic quasiparticles and the magnetic excitations suffer
from nontrivial renormalization effects. In particular, their ratio approaches
one, which indicates an emergent Lorentz symmetry at low energies. We further
investigate the stability of the fixed point in the presence of weak disorder.
We show that while the fixed point is generally stable against weak disorder,
among those disorders that are consistent with the emergent chiral symmetry of
the clean system, a moderately strong random chemical potential and/or random
vector potential may induce a quantum phase transition towards a
disorder-dominated phase. We propose a global phase diagram of the Weyl
semimetal in the presence of both electron-electron interactions and disorder
based on our results. | [
0,
1,
0,
0,
0,
0
] |
Title: Analysis of Peer Review Effectiveness for Academic Journals Based on Distributed Parallel System,
Abstract: A simulation model based on parallel systems is established, aiming to
explore the relation between the number of submissions and the overall quality
of academic journals within a similar discipline under peer review. The model
can effectively simulate the submission, review and acceptance behaviors of
academic journals, in a distributed manner. According to the simulation
experiments, it could possibly happen that the overall standard of academic
journals may deteriorate due to excessive submissions. | [
1,
0,
0,
0,
0,
0
] |
Title: Observational signatures of linear warps in circumbinary discs,
Abstract: In recent years an increasing number of observational studies have hinted at
the presence of warps in protoplanetary discs, however a general comprehensive
description of observational diagnostics of warped discs was missing. We
performed a series of 3D SPH hydrodynamic simulations and combined them with 3D
radiative transfer calculations to study the observability of warps in
circumbinary discs, whose plane is misaligned with respect to the orbital plane
of the central binary. Our numerical hydrodynamic simulations confirm previous
analytical results on the dependence of the warp structure on the viscosity and
the initial misalignment between the binary and the disc. To study the
observational signatures of warps we calculate images in the continuum at
near-infrared and sub-millimetre wavelengths and in the pure rotational
transition of CO in the sub-millimetre. Warped circumbinary discs show surface
brightness asymmetry in near-infrared scattered light images as well as in
optically thick gas lines at sub-millimetre wavelengths. The asymmetry is
caused by self-shadowing of the disc by the inner warped regions, thus the
strength of the asymmetry depends on the strength of the warp. The projected
velocity field, derived from line observations, shows characteristic
deviations, twists and a change in the slope of the rotation curve, from that
of an unperturbed disc. In extreme cases even the direction of rotation appears
to change in the disc inwards of a characteristic radius. The strength of the
kinematical signatures of warps decreases with increasing inclination. The
strength of all warp signatures decreases with decreasing viscosity. | [
0,
1,
0,
0,
0,
0
] |
Title: Multi-parameter One-Sided Monitoring Test,
Abstract: Multi-parameter one-sided hypothesis test problems arise naturally in many
applications. We are particularly interested in effective tests for monitoring
multiple quality indices in forestry products. Our search reveals that there
are many effective statistical methods in the literature for normal data, and
that they can easily be adapted for non-normal data. We find that the beautiful
likelihood ratio test is unsatisfactory, because in order to control the size,
it must cope with the least favorable distributions at the cost of power. In
this paper, we find a novel way to slightly ease the size control, obtaining a
much more powerful test. Simulation confirms that the new test retains good
control of the type I error and is markedly more powerful than the likelihood
ratio test as well as many competitors based on normal data. The new method
performs well in the context of monitoring multiple quality indices. | [
0,
0,
1,
1,
0,
0
] |
Title: Statistical mechanics of low-rank tensor decomposition,
Abstract: Often, large, high dimensional datasets collected across multiple modalities
can be organized as a higher order tensor. Low-rank tensor decomposition then
arises as a powerful and widely used tool to discover simple low dimensional
structures underlying such data. However, we currently lack a theoretical
understanding of the algorithmic behavior of low-rank tensor decompositions. We
derive Bayesian approximate message passing (AMP) algorithms for recovering
arbitrarily shaped low-rank tensors buried within noise, and we employ dynamic
mean field theory to precisely characterize their performance. Our theory
reveals the existence of phase transitions between easy, hard and impossible
inference regimes, and displays an excellent match with simulations. Moreover,
it reveals several qualitative surprises compared to the behavior of symmetric,
cubic tensor decomposition. Finally, we compare our AMP algorithm to the most
commonly used algorithm, alternating least squares (ALS), and demonstrate that
AMP significantly outperforms ALS in the presence of noise. | [
0,
0,
0,
0,
1,
0
] |
Title: A path integral based model for stocks and order dynamics,
Abstract: We introduce a model for the short-term dynamics of financial assets based on
an application to finance of quantum gauge theory, developing ideas of Ilinski.
We present a numerical algorithm for the computation of the probability
distribution of prices and compare the results with APPLE stocks prices and the
S&P500 index. | [
0,
0,
0,
0,
0,
1
] |
Title: A New Algorithm to Automate Inductive Learning of Default Theories,
Abstract: In inductive learning of a broad concept, an algorithm should be able to
distinguish concept examples from exceptions and noisy data. An approach
through recursively finding patterns in exceptions turns out to correspond to
the problem of learning default theories. Default logic is what humans employ
in common-sense reasoning. Therefore, learned default theories are better
understood by humans. In this paper, we present new algorithms to learn default
theories in the form of non-monotonic logic programs. Experiments reported in
this paper show that our algorithms are a significant improvement over
traditional approaches based on inductive logic programming. | [
1,
0,
0,
0,
0,
0
] |
Title: On a result of Fel'dman on linear forms in the values of some E-functions,
Abstract: We shall consider a result of Fel'dman, where a sharp Baker-type lower bound
is obtained for linear forms in the values of some E-functions. Fel'dman's
proof is based on an explicit construction of Padé approximations of the
first kind for these functions. In the present paper we introduce Padé
approximations of the second kind for the same functions and use these to
obtain a slightly improved version of Fel'dman's result. | [
0,
0,
1,
0,
0,
0
] |
Title: Learning Low-shot facial representations via 2D warping,
Abstract: In this work, we mainly study the influence of the 2D warping module for
one-shot face recognition. | [
1,
0,
0,
0,
0,
0
] |
Title: Catalyzed bimolecular reactions in responsive nanoreactors,
Abstract: We describe a general theory for surface-catalyzed bimolecular reactions in
responsive nanoreactors, catalytically active nanoparticles coated by a
stimuli-responsive 'gating' shell, whose permeability controls the activity of
the process. We address two archetypal scenarios encountered in this system:
The first, where two species diffusing from a bulk solution react at the
catalyst's surface; the second where only one of the reactants diffuses from
the bulk while the other one is produced at the nanoparticle surface, e.g., by
light conversion. We find that in both scenarios the total catalytic rate has
the same mathematical structure, once diffusion rates are properly redefined.
Moreover, the diffusional fluxes of the different reactants are strongly
coupled, providing a richer behavior than that arising in unimolecular
reactions. We also show that in stark contrast to bulk reactions, the
identification of a limiting reactant is not simply determined by the relative
bulk concentrations but controlled by the nanoreactor shell permeability.
Finally, we describe an application of our theory by analyzing experimental
data on the reaction between hexacyanoferrate (III) and borohydride ions in
responsive hydrogel-based core-shell nanoreactors. | [
0,
1,
0,
0,
0,
0
] |
Title: Hierarchical Learning for Modular Robots,
Abstract: We argue that hierarchical methods can become the key for modular robots
achieving reconfigurability. We present a hierarchical approach for modular
robots that allows a robot to simultaneously learn multiple tasks. Our
evaluation results present an environment composed of two different modular
robot configurations, namely 3 degrees-of-freedom (DoF) and 4DoF with two
corresponding targets. During the training, we switch between configurations
and targets aiming to evaluate the possibility of training a neural network
that is able to select appropriate motor primitives and robot configuration to
achieve the target. The trained neural network is then transferred and executed
on a real robot with 3DoF and 4DoF configurations. We demonstrate how this
technique generalizes to robots with different configurations and tasks. | [
1,
0,
0,
0,
0,
0
] |
Title: Online Learning with Diverse User Preferences,
Abstract: In this paper, we investigate the impact of diverse user preference on
learning under the stochastic multi-armed bandit (MAB) framework. We aim to
show that when the user preferences are sufficiently diverse and each arm can
be optimal for certain users, the O(log T) regret incurred by exploring the
sub-optimal arms under the standard stochastic MAB setting can be reduced to a
constant. Our intuition is that to achieve sub-linear regret, the number of
times an optimal arm being pulled should scale linearly in time; when all arms
are optimal for certain users and pulled frequently, the estimated arm
statistics can quickly converge to their true values, thus reducing the need of
exploration dramatically. We cast the problem into a stochastic linear bandits
model, where both the users preferences and the state of arms are modeled as
{independent and identical distributed (i.i.d)} d-dimensional random vectors.
After receiving the user preference vector at the beginning of each time slot,
the learner pulls an arm and receives a reward as the linear product of the
preference vector and the arm state vector. We also assume that the state of
the pulled arm is revealed to the learner once its pulled. We propose a
Weighted Upper Confidence Bound (W-UCB) algorithm and show that it can achieve
a constant regret when the user preferences are sufficiently diverse. The
performance of W-UCB under general setups is also completely characterized and
validated with synthetic data. | [
1,
0,
0,
1,
0,
0
] |
Title: The cohomology of rank two stable bundle moduli: mod two nilpotency & skew Schur polynomials,
Abstract: We compute cup product pairings in the integral cohomology ring of the moduli
space of rank two stable bundles with odd determinant over a Riemann surface
using methods of Zagier. The resulting formula is related to a generating
function for certain skew Schur polynomials. As an application, we compute the
nilpotency degree of a distinguished degree two generator in the mod two
cohomology ring. We then give descriptions of the mod two cohomology rings in
low genus, and describe the subrings invariant under the mapping class group
action. | [
0,
0,
1,
0,
0,
0
] |
Title: Automated Formal Synthesis of Digital Controllers for State-Space Physical Plants,
Abstract: We present a sound and automated approach to synthesize safe digital feedback
controllers for physical plants represented as linear, time invariant models.
Models are given as dynamical equations with inputs, evolving over a continuous
state space and accounting for errors due to the digitalization of signals by
the controller. Our approach has two stages, leveraging counterexample guided
inductive synthesis (CEGIS) and reachability analysis. CEGIS synthesizes a
static feedback controller that stabilizes the system under restrictions given
by the safety of the reach space. Safety is verified either via BMC or abstract
acceleration; if the verification step fails, we refine the controller by
generalizing the counterexample. We synthesize stable and safe controllers for
intricate physical plant models from the digital control literature. | [
1,
0,
0,
0,
0,
0
] |
Title: High efficiently numerical simulation of the TDGL equation with reticular free energy in hydrogel,
Abstract: In this paper, we focus on the numerical simulation of phase separation about
macromolecule microsphere composite (MMC) hydrogel. The model equation is based
on Time-Dependent Ginzburg-Landau (TDGL) equation with reticular free energy.
We have put forward two $L^2$ stable schemes to simulate simplified TDGL
equation. In numerical experiments, we observe that simulating the whole
process of phase separation requires a considerably long time. We also notice
that the total free energy changes significantly in initial time and varies
slightly in the following time. Based on these properties, we introduce an
adaptive strategy based on one of stable scheme mentioned. It is found that the
introduction of the time adaptivity cannot only resolve the dynamical changes
of the solution accurately but also can significantly save CPU time for the
long time simulation. | [
0,
1,
0,
0,
0,
0
] |
Title: Entanglement verification protocols for distributed systems based on the Quantum Recursive Network Architecture,
Abstract: In distributed systems based on the Quantum Recursive Network Architecture,
quantum channels and quantum memories are used to establish entangled quantum
states between node pairs. Such systems are robust against attackers that
interact with the quantum channels. Conversely, weaknesses emerge when an
attacker takes full control of a node and alters the configuration of the local
quantum memory, either to make a denial-of-service attack or to reprogram the
node. In such a scenario, entanglement verification over quantum memories is a
means for detecting the intruder. Usually, entanglement verification approaches
focus either on untrusted sources of entangled qubits (photons, in most cases)
or on eavesdroppers that interfere with the quantum channel while entangled
qubits are transmitted. Instead, in this work we assume that the source of
entanglement is trusted, but parties may be dishonest. Looking for efficient
entanglement verification protocols that only require classical channels and
local quantum operations to work, we thoroughly analyze the one proposed by
Nagy and Akl, that we denote as NA2010 for simplicity, and we define and
analyze two entanglement verification protocols based on teleportation (denoted
as AC1 and AC2), characterized by increasing efficiency in terms of intrusion
detection probability versus sacrificed quantum resources. | [
1,
0,
0,
0,
0,
0
] |
Title: Testing Equality of Autocovariance Operators for Functional Time Series,
Abstract: We consider strictly stationary stochastic processes of Hilbert space-valued
random variables and focus on tests of the equality of the lag-zero
autocovariance operators of several independent functional time series. A
moving block bootstrap-based testing procedure is proposed which generates
pseudo random elements that satisfy the null hypothesis of interest. It is
based on directly bootstrapping the time series of tensor products which
overcomes some common difficulties associated with applications of the
bootstrap to related testing problems. The suggested methodology can be
potentially applied to a broad range of test statistics of the hypotheses of
interest. As an example, we establish validity for approximating the
distribution under the null of a fully functional test statistic based on the
Hilbert-Schmidt distance of the corresponding sample lag-zero autocovariance
operators, and show consistency under the alternative. As a prerequisite, we
prove a central limit theorem for the moving block bootstrap procedure applied
to the sample autocovariance operator which is of interest on its own. The
finite sample size and power performance of the suggested moving block
bootstrap-based testing procedure is illustrated through simulations and an
application to a real-life dataset is discussed. | [
0,
0,
1,
1,
0,
0
] |
Title: AdS4 backgrounds with N>16 supersymmetries in 10 and 11 dimensions,
Abstract: We explore all warped $AdS_4\times_w M^{D-4}$ backgrounds with the most
general allowed fluxes that preserve more than 16 supersymmetries in $D=10$-
and $11$-dimensional supergravities. After imposing the assumption that either
the internal space $M^{D-4}$ is compact without boundary or the isometry
algebra of the background decomposes into that of AdS$_4$ and that of
$M^{D-4}$, we find that there are no such backgrounds in IIB supergravity.
Similarly in IIA supergravity, there is a unique such background with 24
supersymmetries locally isometric to $AdS_4\times \mathbb{CP}^3$, and in $D=11$
supergravity all such backgrounds are locally isometric to the maximally
supersymmetric $AdS_4\times S^7$ solution. | [
0,
0,
1,
0,
0,
0
] |
Title: Room Temperature Polariton Lasing in All-Inorganic Perovskites,
Abstract: Polariton lasing is the coherent emission arising from a macroscopic
polariton condensate first proposed in 1996. Over the past two decades,
polariton lasing has been demonstrated in a few inorganic and organic
semiconductors in both low and room temperatures. Polariton lasing in inorganic
materials significantly relies on sophisticated epitaxial growth of crystalline
gain medium layers sandwiched by two distributed Bragg reflectors in which
combating the built-in strain and mismatched thermal properties is nontrivial.
On the other hand, organic active media usually suffer from large threshold
density and weak nonlinearity due to the Frenkel exciton nature. Further
development of polariton lasing towards technologically significant
applications demand more accessible materials, ease of device fabrication and
broadly tunable emission at room temperature. Herein, we report the
experimental realization of room-temperature polariton lasing based on an
epitaxy-free all-inorganic cesium lead chloride perovskite microcavity.
Polariton lasing is unambiguously evidenced by a superlinear power dependence,
macroscopic ground state occupation, blueshift of ground state emission,
narrowing of the linewidth and the build-up of long-range spatial coherence.
Our work suggests considerable promise of lead halide perovskites towards
large-area, low-cost, high performance room temperature polariton devices and
coherent light sources extending from the ultraviolet to near infrared range. | [
0,
1,
0,
0,
0,
0
] |
Title: Probabilistic Sensor Fusion for Ambient Assisted Living,
Abstract: There is a widely-accepted need to revise current forms of health-care
provision, with particular interest in sensing systems in the home. Given a
multiple-modality sensor platform with heterogeneous network connectivity, as
is under development in the Sensor Platform for HEalthcare in Residential
Environment (SPHERE) Interdisciplinary Research Collaboration (IRC), we face
specific challenges relating to the fusion of the heterogeneous sensor
modalities.
We introduce Bayesian models for sensor fusion, which aims to address the
challenges of fusion of heterogeneous sensor modalities. Using this approach we
are able to identify the modalities that have most utility for each particular
activity, and simultaneously identify which features within that activity are
most relevant for a given activity.
We further show how the two separate tasks of location prediction and
activity recognition can be fused into a single model, which allows for
simultaneous learning an prediction for both tasks.
We analyse the performance of this model on data collected in the SPHERE
house, and show its utility. We also compare against some benchmark models
which do not have the full structure,and show how the proposed model compares
favourably to these methods | [
1,
0,
0,
1,
0,
0
] |
Title: Bounded time computation on metric spaces and Banach spaces,
Abstract: We extend the framework by Kawamura and Cook for investigating computational
complexity for operators occurring in analysis. This model is based on
second-order complexity theory for functions on the Baire space, which is
lifted to metric spaces by means of representations. Time is measured in terms
of the length of the input encodings and the required output precision. We
propose the notions of a complete representation and of a regular
representation. We show that complete representations ensure that any
computable function has a time bound. Regular representations generalize
Kawamura and Cook's more restrictive notion of a second-order representation,
while still guaranteeing fast computability of the length of the encodings.
Applying these notions, we investigate the relationship between purely metric
properties of a metric space and the existence of a representation such that
the metric is computable within bounded time. We show that a bound on the
running time of the metric can be straightforwardly translated into size bounds
of compact subsets of the metric space. Conversely, for compact spaces and for
Banach spaces we construct a family of admissible, complete, regular
representations that allow for fast computation of the metric and provide short
encodings. Here it is necessary to trade the time bound off against the length
of encodings. | [
1,
0,
1,
0,
0,
0
] |
Title: Defect entropies and enthalpies in Barium Fluoride,
Abstract: Various experimental techniques, have revealed that the predominant intrinsic
point defects in BaF$_2$ are anion Frenkel defects. Their formation enthalpy
and entropy as well as the corresponding parameters for the fluorine vacancy
and fluorine interstitial motion have been determined. In addition, low
temperature dielectric relaxation measurements in BaF$_2$ doped with uranium
leads to the parameters {\tau}$_0$, E in the Arrhenius relation
{\tau}={\tau}$_0$exp(E/kBT) for the relaxation time {\tau}. For the relaxation
peak associated with a single tetravalent uranium, the migration entropy
deduced from the pre-exponential factor {\tau}$_0$, is smaller than the anion
Frenkel defect formation entropy by almost two orders of magnitude. We show
that, despite their great variation, the defect entropies and enthalpies are
interconnected through a model based on anharmonic properties of the bulk
material that have been recently studied by employing density-functional theory
and density-functional perturbation theory. | [
0,
1,
0,
0,
0,
0
] |
Title: Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning,
Abstract: Exploration in complex domains is a key challenge in reinforcement learning,
especially for tasks with very sparse rewards. Recent successes in deep
reinforcement learning have been achieved mostly using simple heuristic
exploration strategies such as $\epsilon$-greedy action selection or Gaussian
control noise, but there are many tasks where these methods are insufficient to
make any learning progress. Here, we consider more complex heuristics:
efficient and scalable exploration strategies that maximize a notion of an
agent's surprise about its experiences via intrinsic motivation. We propose to
learn a model of the MDP transition probabilities concurrently with the policy,
and to form intrinsic rewards that approximate the KL-divergence of the true
transition probabilities from the learned model. One of our approximations
results in using surprisal as intrinsic motivation, while the other gives the
$k$-step learning progress. We show that our incentives enable agents to
succeed in a wide range of environments with high-dimensional state spaces and
very sparse rewards, including continuous control tasks and games in the Atari
RAM domain, outperforming several other heuristic exploration techniques. | [
1,
0,
0,
0,
0,
0
] |
Title: Active model learning and diverse action sampling for task and motion planning,
Abstract: The objective of this work is to augment the basic abilities of a robot by
learning to use new sensorimotor primitives to enable the solution of complex
long-horizon problems. Solving long-horizon problems in complex domains
requires flexible generative planning that can combine primitive abilities in
novel combinations to solve problems as they arise in the world. In order to
plan to combine primitive actions, we must have models of the preconditions and
effects of those actions: under what circumstances will executing this
primitive achieve some particular effect in the world?
We use, and develop novel improvements on, state-of-the-art methods for
active learning and sampling. We use Gaussian process methods for learning the
conditions of operator effectiveness from small numbers of expensive training
examples collected by experimentation on a robot. We develop adaptive sampling
methods for generating diverse elements of continuous sets (such as robot
configurations and object poses) during planning for solving a new task, so
that planning is as efficient as possible. We demonstrate these methods in an
integrated system, combining newly learned models with an efficient
continuous-space robot task and motion planner to learn to solve long horizon
problems more efficiently than was previously possible. | [
1,
0,
0,
1,
0,
0
] |
Title: Four Fundamental Questions in Probability Theory and Statistics,
Abstract: This study has the purpose of addressing four questions that lie at the base
of the probability theory and statistics, and includes two main steps. As
first, we conduct the textual analysis of the most significant works written by
eminent probability theorists. The textual analysis turns out to be a rather
innovative method of study in this domain, and shows how the sampled writers,
no matter he is a frequentist or a subjectivist, share a similar approach. Each
author argues on the multifold aspects of probability then he establishes the
mathematical theory on the basis of his intellectual conclusions. It may be
said that mathematics ranks second. Hilbert foresees an approach far different
from that used by the sampled authors. He proposes to axiomatize the
probability calculus notably to describe the probability concepts using purely
mathematical criteria. In the second stage of the present research we address
the four issues of the probability theory and statistics following the
recommendations of Hilbert. Specifically, we use two theorems that prove how
the frequentist and the subjectivist models are not incompatible as many
believe. Probability has distinct meanings under different hypotheses, and in
turn classical statistics and Bayesian statistics are available for adoption in
different circumstances. Subsequently, these results are commented upon,
followed by our conclusions | [
0,
0,
0,
1,
0,
0
] |
Title: Connections on parahoric torsors over curves,
Abstract: We define parahoric $\cG$--torsors for certain Bruhat--Tits group scheme
$\cG$ on a smooth complex projective curve $X$ when the weights are real, and
also define connections on them. We prove that a $\cG$--torsor is given by a
homomorphism from $\pi_1(X\setminus D)$ to a maximal compact subgroup of $G$,
where $D\, \subset\, X$ is the parabolic divisor, if and only if the torsor is
polystable. | [
0,
0,
1,
0,
0,
0
] |
Title: On Reduced Input-Output Dynamic Mode Decomposition,
Abstract: The identification of reduced-order models from high-dimensional data is a
challenging task, and even more so if the identified system should not only be
suitable for a certain data set, but generally approximate the input-output
behavior of the data source. In this work, we consider the input-output dynamic
mode decomposition method for system identification. We compare excitation
approaches for the data-driven identification process and describe an
optimization-based stabilization strategy for the identified systems. | [
1,
0,
0,
0,
0,
0
] |
Title: An assessment of Fe XX - Fe XXII emission lines in SDO/EVE data as diagnostics for high density solar flare plasmas using EUVE stellar observations,
Abstract: The Extreme Ultraviolet Variability Experiment (EVE) on the Solar Dynamics
Observatory obtains extreme-ultraviolet (EUV) spectra of the full-disk Sun at a
spectral resolution of ~1 A and cadence of 10 s. Such a spectral resolution
would normally be considered to be too low for the reliable determination of
electron density (N_e) sensitive emission line intensity ratios, due to
blending. However, previous work has shown that a limited number of Fe XXI
features in the 90-60 A wavelength region of EVE do provide useful
N_e-diagnostics at relatively low flare densities (N_e ~ 10^11-10^12 cm^-3).
Here we investigate if additional highly ionised Fe line ratios in the EVE
90-160 A range may be reliably employed as N_e-diagnostics. In particular, the
potential for such diagnostics to provide density estimates for high N_e
(~10^13 cm^-3) flare plasmas is assessed. Our study employs EVE spectra for
X-class flares, combined with observations of highly active late-type stars
from the Extreme Ultraviolet Explorer (EUVE) satellite plus experimental data
for well-diagnosed tokamak plasmas, both of which are similar in wavelength
coverage and spectral resolution to those from EVE. Several ratios are
identified in EVE data which yield consistent values of electron density,
including Fe XX 113.35/121.85 and Fe XXII 114.41/135.79, with confidence in
their reliability as N_e-diagnostics provided by the EUVE and tokamak results.
These ratios also allow the determination of density in solar flare plasmas up
to values of ~10^13 cm^-3. | [
0,
1,
0,
0,
0,
0
] |
Title: Multi-Scale Spatially Weighted Local Histograms in O(1),
Abstract: Weighting pixel contribution considering its location is a key feature in
many fundamental image processing tasks including filtering, object modeling
and distance matching. Several techniques have been proposed that incorporate
Spatial information to increase the accuracy and boost the performance of
detection, tracking and recognition systems at the cost of speed. But, it is
still not clear how to efficiently ex- tract weighted local histograms in
constant time using integral histogram. This paper presents a novel algorithm
to compute accurately multi-scale Spatially weighted local histograms in
constant time using Weighted Integral Histogram (SWIH) for fast search. We
applied our spatially weighted integral histogram approach for fast tracking
and obtained more accurate and robust target localization result in comparison
with using plain histogram. | [
1,
0,
0,
0,
0,
0
] |
Title: Limit theorems in bi-free probability theory,
Abstract: In this paper additive bi-free convolution is defined for general Borel
probability measures, and the limiting distributions for sums of bi-free pairs
of selfadjoint commuting random variables in an infinitesimal triangular array
are determined. These distributions are characterized by their bi-freely
infinite divisibility, and moreover, a transfer principle is established for
limit theorems in classical probability theory and Voiculescu's bi-free
probability theory. Complete descriptions of bi-free stability and fullness of
planar probability distributions are also set down. All these results reveal
one important feature about the theory of bi-free probability that it parallels
the classical theory perfectly well. The emphasis in the whole work is not on
the tool of bi-free combinatorics but only on the analytic machinery. | [
0,
0,
1,
0,
0,
0
] |
Title: Axiomatisability and hardness for universal Horn classes of hypergraphs,
Abstract: We characterise finite axiomatisability and intractability of deciding
membership for universal Horn classes generated by finite loop-free
hypergraphs. | [
1,
0,
1,
0,
0,
0
] |
Title: Dissipatively Coupled Waveguide Networks for Coherent Diffusive Photonics,
Abstract: A photonic circuit is generally described as a structure in which light
propagates by unitary exchange and transfers reversibly between channels. In
contrast, the term `diffusive' is more akin to a chaotic propagation in
scattering media, where light is driven out of coherence towards a thermal
mixture. Based on the dynamics of open quantum systems, the combination of
these two opposites can result in novel techniques for coherent light control.
The crucial feature of these photonic structures is dissipative coupling
between modes, via an interaction with a common reservoir. Here, we demonstrate
experimentally that such systems can perform optical equalisation to smooth
multimode light, or act as a distributor, guiding it into selected channels.
Quantum thermodynamically, these systems can act as catalytic coherent
reservoirs by performing perfect non-Landauer erasure. For lattice structures,
localised stationary states can be supported in the continuum, similar to
compacton-like states in conventional flat band lattices. | [
0,
1,
0,
0,
0,
0
] |
Title: Inadequate Risk Analysis Might Jeopardize The Functional Safety of Modern Systems,
Abstract: In the early 90s, researchers began to focus on security as an important
property to address in combination with safety. Over the years, researchers
have proposed approaches to harmonize activities within the safety and security
disciplines. Despite the academic efforts to identify interdependencies and to
propose combined approaches for safety and security, there is still a lack of
integration between safety and security practices in the industrial context, as
they have separate standards and independent processes often addressed and
assessed by different organizational teams and authorities. Specifically,
security concerns are generally not covered in any detail in safety standards
potentially resulting in successfully safety-certified systems that still are
open for security threats from e.g., malicious intents from internal and
external personnel and hackers that may jeopardize safety. In recent years
security has again received an increasing attention of being an important issue
also in safety assurance, as the open interconnected nature of emerging systems
makes them susceptible to security threats at a much higher degree than
existing more confined products.This article presents initial ideas on how to
extend safety work to include aspects of security during the context
establishment and initial risk assessment procedures. The ambition of our
proposal is to improve safety and increase efficiency and effectiveness of the
safety work within the frames of the current safety standards, i.e., raised
security awareness in compliance with the current safety standards. We believe
that our proposal is useful to raise the security awareness in industrial
contexts, although it is not a complete harmonization of safety and security
disciplines, as it merely provides applicable guidance to increase security
awareness in a safety context. | [
1,
0,
0,
0,
0,
0
] |
Title: A compactness theorem for four-dimensional shrinking gradient Ricci solitons,
Abstract: Haslhofer and Müller proved a compactness Theorem for four-dimensional
shrinking gradient Ricci solitons, with the only assumption being that the
entropy is uniformly bounded from below. However, the limit in their result
could possibly be an orbifold Ricci shrinker. In this paper we prove a
compactness theorem for noncompact four-dimensional shrinking gradient Ricci
solitons with a topological restriction and a noncollapsing assumption, that
is, we consider Ricci shrinkers that can be embedded in a closed four-manifold
with vanishing second homology group over every field and are strongly
$\kappa$-noncollapsed with respect to a universal $\kappa$. In particular, we
do not need any curvature assumption and the limit is still a smooth nonflat
shrinking gradient Ricci soliton. | [
0,
0,
1,
0,
0,
0
] |
Title: An analytic formulation for positive-unlabeled learning via weighted integral probability metric,
Abstract: We consider the problem of learning a binary classifier from only positive
and unlabeled observations (PU learning). Although recent research in PU
learning has succeeded in showing theoretical and empirical performance, most
existing algorithms need to solve either a convex or a non-convex optimization
problem and thus are not suitable for large-scale datasets. In this paper, we
propose a simple yet theoretically grounded PU learning algorithm by extending
the previous work proposed for supervised binary classification (Sriperumbudur
et al., 2012). The proposed PU learning algorithm produces a closed-form
classifier when the hypothesis space is a closed ball in reproducing kernel
Hilbert space. In addition, we establish upper bounds of the estimation error
and the excess risk. The obtained estimation error bound is sharper than
existing results and the excess risk bound does not rely on an approximation
error term. To the best of our knowledge, we are the first to explicitly derive
the excess risk bound in the field of PU learning. Finally, we conduct
extensive numerical experiments using both synthetic and real datasets,
demonstrating improved accuracy, scalability, and robustness of the proposed
algorithm. | [
1,
0,
0,
1,
0,
0
] |
Title: Benchmarking Data Analysis and Machine Learning Applications on the Intel KNL Many-Core Processor,
Abstract: Knights Landing (KNL) is the code name for the second-generation Intel Xeon
Phi product family. KNL has generated significant interest in the data analysis
and machine learning communities because its new many-core architecture targets
both of these workloads. The KNL many-core vector processor design enables it
to exploit much higher levels of parallelism. At the Lincoln Laboratory
Supercomputing Center (LLSC), the majority of users are running data analysis
applications such as MATLAB and Octave. More recently, machine learning
applications, such as the UC Berkeley Caffe deep learning framework, have
become increasingly important to LLSC users. Thus, the performance of these
applications on KNL systems is of high interest to LLSC users and the broader
data analysis and machine learning communities. Our data analysis benchmarks of
these application on the Intel KNL processor indicate that single-core
double-precision generalized matrix multiply (DGEMM) performance on KNL systems
has improved by ~3.5x compared to prior Intel Xeon technologies. Our data
analysis applications also achieved ~60% of the theoretical peak performance.
Also a performance comparison of a machine learning application, Caffe, between
the two different Intel CPUs, Xeon E5 v3 and Xeon Phi 7210, demonstrated a 2.7x
improvement on a KNL node. | [
1,
1,
0,
0,
0,
0
] |
Title: Pseudo asymptotically periodic solutions for fractional integro-differential neutral equations,
Abstract: In this paper, we study the existence and uniqueness of pseudo
$S$-asymptotically $\omega$-periodic mild solutions of class $r$ for fractional
integro-differential neutral equations. An example is presented to illustrate
the application of the abstract results. | [
0,
0,
1,
0,
0,
0
] |
Title: Updating the silent speech challenge benchmark with deep learning,
Abstract: The 2010 Silent Speech Challenge benchmark is updated with new results
obtained in a Deep Learning strategy, using the same input features and
decoding strategy as in the original article. A Word Error Rate of 6.4% is
obtained, compared to the published value of 17.4%. Additional results
comparing new auto-encoder-based features with the original features at reduced
dimensionality, as well as decoding scenarios on two different language models,
are also presented. The Silent Speech Challenge archive has been updated to
contain both the original and the new auto-encoder features, in addition to the
original raw data. | [
1,
0,
0,
0,
0,
0
] |
Title: On Optimization of Radiative Dipole Body Array Coils for 7 Tesla MRI,
Abstract: In this contribution we present numerical and experimental results of a
parametric study of radiative dipole antennas in a phased array configuration
for efficient body magnetic resonance imaging at 7T via parallel transmit. For
magnetic resonance imaging (MRI) at ultrahigh fields (7T and higher) dipole
antennas are commonly used in phased arrays, particularly for body imaging
targets. This study reveals the effects of dipole positioning in the array
(elevation of dipoles above the subject and inter-dipole spacing) on their
mutual coupling, $B_1^{+}$ per unit power and $B_1^{+}$ per maximum local SAR
efficiencies as well as the RF-shimming capability. The results demonstrate the
trade-off between low maximum local SAR and sensitivity to the subject
variation and provide the working parameter range for practical body arrays
composed of recently suggested fractionated dipoles. | [
0,
1,
0,
0,
0,
0
] |
Title: Speaking Style Authentication Using Suprasegmental Hidden Markov Models,
Abstract: The importance of speaking style authentication from human speech is gaining
an increasing attention and concern from the engineering community. The
importance comes from the demand to enhance both the naturalness and efficiency
of spoken language human-machine interface. Our work in this research focuses
on proposing, implementing, and testing speaker-dependent and text-dependent
speaking style authentication (verification) systems that accept or reject the
identity claim of a speaking style based on suprasegmental hidden Markov models
(SPHMMs). Based on using SPHMMs, our results show that the average speaking
style authentication performance is: 99%, 37%, 85%, 60%, 61%, 59%, 41%, 61%,
and 57% belonging respectively to the speaking styles: neutral, shouted, slow,
loud, soft, fast, angry, happy, and fearful. | [
1,
0,
0,
0,
0,
0
] |
Title: Decomposing manifolds into Cartesian products,
Abstract: The decomposability of a Cartesian product of two nondecomposable manifolds
into products of lower dimensional manifolds is studied. For 3-manifolds we
obtain an analog of a result due to Borsuk for surfaces, and in higher
dimensions we show that similar analogs do not exist unless one imposes further
restrictions such as simple connectivity. | [
0,
0,
1,
0,
0,
0
] |
Title: Performance of the MAGIC telescopes under moonlight,
Abstract: MAGIC, a system of two imaging atmospheric Cherenkov telescopes, achieves its
best performance under dark conditions, i.e. in absence of moonlight or
twilight. Since operating the telescopes only during dark time would severely
limit the duty cycle, observations are also performed when the Moon is present
in the sky. Here we present a dedicated Moon-adapted analysis and characterize
the performance of MAGIC under moonlight. We evaluate energy threshold, angular
resolution and sensitivity of MAGIC under different background light levels,
based on Crab Nebula observations and tuned Monte Carlo simulations. This study
includes observations taken under non-standard hardware configurations, such as
reducing the camera photomultiplier tubes gain by a factor $\sim$1.7 (reduced
HV settings) with respect to standard settings (nominal HV) or using UV-pass
filters to strongly reduce the amount of moonlight reaching the telescopes
cameras. The Crab Nebula spectrum is correctly reconstructed in all the studied
illumination levels, that reach up to 30 times brighter than under dark
conditions. The main effect of moonlight is an increase in the analysis energy
threshold and in the systematic uncertainties on the flux normalization. The
sensitivity degradation is constrained to be below 10\%, within 15-30\% and
between 60 and 80\% for nominal HV, reduced HV and UV-pass filter observations,
respectively. No worsening of the angular resolution was found. Thanks to
observations during moonlight, the duty cycle can be doubled, suppressing the
need to stop observations around full Moon. | [
0,
1,
0,
0,
0,
0
] |
Title: Driver Distraction Identification with an Ensemble of Convolutional Neural Networks,
Abstract: The World Health Organization (WHO) reported 1.25 million deaths yearly due
to road traffic accidents worldwide and the number has been continuously
increasing over the last few years. Nearly fifth of these accidents are caused
by distracted drivers. Existing work of distracted driver detection is
concerned with a small set of distractions (mostly, cell phone usage).
Unreliable ad-hoc methods are often used.In this paper, we present the first
publicly available dataset for driver distraction identification with more
distraction postures than existing alternatives. In addition, we propose a
reliable deep learning-based solution that achieves a 90% accuracy. The system
consists of a genetically-weighted ensemble of convolutional neural networks,
we show that a weighted ensemble of classifiers using a genetic algorithm
yields in a better classification confidence. We also study the effect of
different visual elements in distraction detection by means of face and hand
localizations, and skin segmentation. Finally, we present a thinned version of
our ensemble that could achieve 84.64% classification accuracy and operate in a
real-time environment. | [
1,
0,
0,
1,
0,
0
] |
Title: A simple neural network module for relational reasoning,
Abstract: Relational reasoning is a central component of generally intelligent
behavior, but has proven difficult for neural networks to learn. In this paper
we describe how to use Relation Networks (RNs) as a simple plug-and-play module
to solve problems that fundamentally hinge on relational reasoning. We tested
RN-augmented networks on three tasks: visual question answering using a
challenging dataset called CLEVR, on which we achieve state-of-the-art,
super-human performance; text-based question answering using the bAbI suite of
tasks; and complex reasoning about dynamic physical systems. Then, using a
curated dataset called Sort-of-CLEVR we show that powerful convolutional
networks do not have a general capacity to solve relational questions, but can
gain this capacity when augmented with RNs. Our work shows how a deep learning
architecture equipped with an RN module can implicitly discover and learn to
reason about entities and their relations. | [
1,
0,
0,
0,
0,
0
] |
Title: Bridging the Gap Between Value and Policy Based Reinforcement Learning,
Abstract: We establish a new connection between value and policy based reinforcement
learning (RL) based on a relationship between softmax temporal value
consistency and policy optimality under entropy regularization. Specifically,
we show that softmax consistent action values correspond to optimal entropy
regularized policy probabilities along any action sequence, regardless of
provenance. From this observation, we develop a new RL algorithm, Path
Consistency Learning (PCL), that minimizes a notion of soft consistency error
along multi-step action sequences extracted from both on- and off-policy
traces. We examine the behavior of PCL in different scenarios and show that PCL
can be interpreted as generalizing both actor-critic and Q-learning algorithms.
We subsequently deepen the relationship by showing how a single model can be
used to represent both a policy and the corresponding softmax state values,
eliminating the need for a separate critic. The experimental evaluation
demonstrates that PCL significantly outperforms strong actor-critic and
Q-learning baselines across several benchmarks. | [
1,
0,
0,
1,
0,
0
] |
Title: Speaker verification using end-to-end adversarial language adaptation,
Abstract: In this paper we investigate the use of adversarial domain adaptation for
addressing the problem of language mismatch between speaker recognition
corpora. In the context of speaker verification, adversarial domain adaptation
methods aim at minimizing certain divergences between the distribution that the
utterance-level features follow (i.e. speaker embeddings) when drawn from
source and target domains (i.e. languages), while preserving their capacity in
recognizing speakers. Neural architectures for extracting utterance-level
representations enable us to apply adversarial adaptation methods in an
end-to-end fashion and train the network jointly with the standard
cross-entropy loss. We examine several configurations, such as the use of
(pseudo-)labels on the target domain as well as domain labels in the feature
extractor, and we demonstrate the effectiveness of our method on the
challenging NIST SRE16 and SRE18 benchmarks. | [
1,
0,
0,
0,
0,
0
] |
Title: Omni $n$-Lie algebras and linearization of higher analogues of Courant algebroids,
Abstract: In this paper, we introduce the notion of an omni $n$-Lie algebra and show
that they are linearization of higher analogues of standard Courant algebroids.
We also introduce the notion of a nonabelian omni $n$-Lie algebra and show that
they are linearization of higher analogues of Courant algebroids associated to
Nambu-Poisson manifolds. | [
0,
0,
1,
0,
0,
0
] |
Title: Asymptotics of Hankel determinants with a one-cut regular potential and Fisher-Hartwig singularities,
Abstract: We obtain asymptotics of large Hankel determinants whose weight depends on a
one-cut regular potential and any number of Fisher-Hartwig singularities. This
generalises two results: 1) a result of Berestycki, Webb and Wong [5] for
root-type singularities, and 2) a result of Its and Krasovsky [37] for a
Gaussian weight with a single jump-type singularity. We show that when we apply
a piecewise constant thinning on the eigenvalues of a random Hermitian matrix
drawn from a one-cut regular ensemble, the gap probability in the thinned
spectrum, as well as correlations of the characteristic polynomial of the
associated conditional point process, can be expressed in terms of these
determinants. | [
0,
0,
1,
0,
0,
0
] |
Title: Beyond linear galaxy alignments,
Abstract: Galaxy intrinsic alignments (IA) are a critical uncertainty for current and
future weak lensing measurements. We describe a perturbative expansion of IA,
analogous to the treatment of galaxy biasing. From an astrophysical
perspective, this model includes the expected large-scale alignment mechanisms
for galaxies that are pressure-supported (tidal alignment) and
rotation-supported (tidal torquing) as well as the cross-correlation between
the two. Alternatively, this expansion can be viewed as an effective model
capturing all relevant effects up to the given order. We include terms up to
second order in the density and tidal fields and calculate the resulting IA
contributions to two-point statistics at one-loop order. For fiducial
amplitudes of the IA parameters, we find the quadratic alignment and
linear-quadratic cross terms can contribute order-unity corrections to the
total intrinsic alignment signal at $k\sim0.1\,h^{-1}{\rm Mpc}$, depending on
the source redshift distribution. These contributions can lead to significant
biases on inferred cosmological parameters in Stage IV photometric weak lensing
surveys. We perform forecasts for an LSST-like survey, finding that use of the
standard "NLA" model for intrinsic alignments cannot remove these large
parameter biases, even when allowing for a more general redshift dependence.
The model presented here will allow for more accurate and flexible IA treatment
in weak lensing and combined probes analyses, and an implementation is made
available as part of the public FAST-PT code. The model also provides a more
advanced framework for understanding the underlying IA processes and their
relationship to fundamental physics. | [
0,
1,
0,
0,
0,
0
] |
Title: Modelling thermo-electro-mechanical effects in orthotropic cardiac tissue,
Abstract: In this paper we introduce a new mathematical model for the active
contraction of cardiac muscle, featuring different thermo-electric and
nonlinear conductivity properties. The passive hyperelastic response of the
tissue is described by an orthotropic exponential model, whereas the ionic
activity dictates active contraction incorporated through the concept of
orthotropic active strain. We use a fully incompressible formulation, and the
generated strain modifies directly the conductivity mechanisms in the medium
through the pull-back transformation. We also investigate the influence of
thermo-electric effects in the onset of multiphysics emergent spatiotemporal
dynamics, using nonlinear diffusion. It turns out that these ingredients have a
key role in reproducing pathological chaotic dynamics such as ventricular
fibrillation during inflammatory events, for instance. The specific structure
of the governing equations suggests to cast the problem in mixed-primal form
and we write it in terms of Kirchhoff stress, displacements, solid pressure,
electric potential, activation generation, and ionic variables. We also propose
a new mixed-primal finite element method for its numerical approximation, and
we use it to explore the properties of the model and to assess the importance
of coupling terms, by means of a few computational experiments in 3D. | [
0,
0,
0,
0,
1,
0
] |
Title: Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions,
Abstract: By building up on the recent theory that established the connection between
implicit generative modeling and optimal transport, in this study, we propose a
novel parameter-free algorithm for learning the underlying distributions of
complicated datasets and sampling from them. The proposed algorithm is based on
a functional optimization problem, which aims at finding a measure that is
close to the data distribution as much as possible and also expressive enough
for generative modeling purposes. We formulate the problem as a gradient flow
in the space of probability measures. The connections between gradient flows
and stochastic differential equations let us develop a computationally
efficient algorithm for solving the optimization problem, where the resulting
algorithm resembles the recent dynamics-based Markov Chain Monte Carlo
algorithms. We provide formal theoretical analysis where we prove finite-time
error guarantees for the proposed algorithm. Our experimental results support
our theory and shows that our algorithm is able to capture the structure of
challenging distributions. | [
0,
0,
0,
1,
0,
0
] |
Title: Dual Iterative Hard Thresholding: From Non-convex Sparse Minimization to Non-smooth Concave Maximization,
Abstract: Iterative Hard Thresholding (IHT) is a class of projected gradient descent
methods for optimizing sparsity-constrained minimization models, with the best
known efficiency and scalability in practice. As far as we know, the existing
IHT-style methods are designed for sparse minimization in primal form. It
remains open to explore duality theory and algorithms in such a non-convex and
NP-hard problem setting. In this paper, we bridge this gap by establishing a
duality theory for sparsity-constrained minimization with $\ell_2$-regularized
loss function and proposing an IHT-style algorithm for dual maximization. Our
sparse duality theory provides a set of sufficient and necessary conditions
under which the original NP-hard/non-convex problem can be equivalently solved
in a dual formulation. The proposed dual IHT algorithm is a super-gradient
method for maximizing the non-smooth dual objective. An interesting finding is
that the sparse recovery performance of dual IHT is invariant to the Restricted
Isometry Property (RIP), which is required by virtually all the existing primal
IHT algorithms without sparsity relaxation. Moreover, a stochastic variant of
dual IHT is proposed for large-scale stochastic optimization. Numerical results
demonstrate the superiority of dual IHT algorithms to the state-of-the-art
primal IHT-style algorithms in model estimation accuracy and computational
efficiency. | [
1,
0,
0,
1,
0,
0
] |
Title: Generalized connected sum formula for the Arnold invariants of generic plane curves,
Abstract: We define the generalized connected sum for generic closed plane curves,
generalizing the strange sum defined by Arnold, and completely describe how the
Arnold invariants $J^{\pm}$ and $\mathit{St}$ behave under the generalized
connected sums. | [
0,
0,
1,
0,
0,
0
] |
Title: STAR: Spatio-Temporal Altimeter Waveform Retracking using Sparse Representation and Conditional Random Fields,
Abstract: Satellite radar altimetry is one of the most powerful techniques for
measuring sea surface height variations, with applications ranging from
operational oceanography to climate research. Over open oceans, altimeter
return waveforms generally correspond to the Brown model, and by inversion,
estimated shape parameters provide mean surface height and wind speed. However,
in coastal areas or over inland waters, the waveform shape is often distorted
by land influence, resulting in peaks or fast decaying trailing edges. As a
result, derived sea surface heights are then less accurate and waveforms need
to be reprocessed by sophisticated algorithms. To this end, this work suggests
a novel Spatio-Temporal Altimetry Retracking (STAR) technique. We show that
STAR enables the derivation of sea surface heights over the open ocean as well
as over coastal regions of at least the same quality as compared to existing
retracking methods, but for a larger number of cycles and thus retaining more
useful data. Novel elements of our method are (a) integrating information from
spatially and temporally neighboring waveforms through a conditional random
field approach, (b) sub-waveform detection, where relevant sub-waveforms are
separated from corrupted or non-relevant parts through a sparse representation
approach, and (c) identifying the final best set of sea surfaces heights from
multiple likely heights using Dijkstra's algorithm. We apply STAR to data from
the Jason-1, Jason-2 and Envisat missions for study sites in the Gulf of
Trieste, Italy and in the coastal region of the Ganges-Brahmaputra-Meghna
estuary, Bangladesh. We compare to several established and recent retracking
methods, as well as to tide gauge data. Our experiments suggest that the
obtained sea surface heights are significantly less affected by outliers when
compared to results obtained by other approaches. | [
0,
1,
0,
0,
0,
0
] |
Title: CoMID: Context-based Multi-Invariant Detection for Monitoring Cyber-Physical Software,
Abstract: Cyber-physical software continually interacts with its physical environment
for adaptation in order to deliver smart services. However, the interactions
can be subject to various errors when the software's assumption on its
environment no longer holds, thus leading to unexpected misbehavior or even
failure. To address this problem, one promising way is to conduct runtime
monitoring of invariants, so as to prevent cyber-physical software from
entering such errors (a.k.a. abnormal states). To effectively detect abnormal
states, we in this article present an approach, named Context-based
Multi-Invariant Detection (CoMID), which consists of two techniques:
context-based trace grouping and multi-invariant detection. The former infers
contexts to distinguish different effective scopes for CoMID's derived
invariants, and the latter conducts ensemble evaluation of multiple invariants
to detect abnormal states. We experimentally evaluate CoMID on real-world
cyber-physical software. The results show that CoMID achieves a 5.7-28.2%
higher true-positive rate and a 6.8-37.6% lower false-positive rate in
detecting abnormal states, as compared with state-of-the-art approaches (i.e.,
Daikon and ZoomIn). When deployed in field tests, CoMID's runtime monitoring
improves the success rate of cyber-physical software in its task executions by
15.3-31.7%. | [
1,
0,
0,
0,
0,
0
] |
Title: Asymptotics of multivariate contingency tables with fixed marginals,
Abstract: We consider the asymptotic distribution of a cell in a 2 x ... x 2
contingency table as the fixed marginal totals tend to infinity. The asymptotic
order of the cell variance is derived and a useful diagnostic is given for
determining whether the cell has a Poisson limit or a Gaussian limit. There are
three forms of Poisson convergence. The exact form is shown to be determined by
the growth rates of the two smallest marginal totals. The results are
generalized to contingency tables with arbitrary sizes and are further
complemented with concrete examples. | [
0,
0,
1,
1,
0,
0
] |
Title: Distribution of the periodic points of the Farey map,
Abstract: We expand the cross section of the geodesic flow in the tangent bundle of the
modular surface given by Series to produce another section whose return map
under the geodesic flow is a double cover of the natural extension of the Farey
map. We use this cross section to extend the correspondence between the closed
geodesics on the modular surface and the periodic points of the Gauss map to
include the periodic points of the Farey map. Then, analogous to the work of
Pollicott, we prove an equidistribution result for the periodic points of the
Farey map when they are ordered according to the length of their corresponding
closed geodesics. | [
0,
0,
1,
0,
0,
0
] |
Title: Marangoni effects on a thin liquid film coating a sphere with axial or radial thermal gradients,
Abstract: We study the time evolution of a thin liquid film coating the outer surface
of a sphere in the presence of gravity, surface tension and thermal gradients.
We derive the fourth-order nonlinear partial differential equation that models
the thin film dynamics, including Marangoni terms arising from the dependence
of surface tension on temperature. We consider two different imposed
temperature distributions with axial or radial thermal gradients. We analyze
the stability of a uniform coating under small perturbations and carry out
numerical simulations in COMSOL for a range of parameter values. In the case of
an axial temperature gradient, we find steady states with either uniform film
thickness, or with the fluid accumulating at the bottom or near the top of the
sphere, depending on the total volume of liquid in the film, dictating whether
gravity or Marangoni effects dominate. In the case of a radial temperature
gradient, a stability analysis reveals the most unstable non-axisymmetric modes
on an initially uniform coating film. | [
0,
1,
0,
0,
0,
0
] |
Title: Fooling the classifier: Ligand antagonism and adversarial examples,
Abstract: Machine learning algorithms are sensitive to so-called adversarial
perturbations. This is reminiscent of cellular decision-making where antagonist
ligands may prevent correct signaling, like during the early immune response.
We draw a formal analogy between neural networks used in machine learning and
the general class of adaptive proofreading networks. We then apply simple
adversarial strategies from machine learning to models of ligand
discrimination. We show how kinetic proofreading leads to "boundary tilting"
and identify three types of perturbation (adversarial, non adversarial and
ambiguous). We then use a gradient-descent approach to compare different
adaptive proofreading models, and we reveal the existence of two qualitatively
different regimes characterized by the presence or absence of a critical point.
These regimes are reminiscent of the "feature-to-prototype" transition
identified in machine learning, corresponding to two strategies in ligand
antagonism (broad vs. specialized). Overall, our work connects evolved cellular
decision-making to classification in machine learning, showing that behaviours
close to the decision boundary can be understood through the same mechanisms. | [
0,
0,
0,
1,
1,
0
] |
Title: Comparing Graph Clusterings: Set partition measures vs. Graph-aware measures,
Abstract: In this paper, we propose a family of graph partition similarity measures
that take the topology of the graph into account. These graph-aware measures
are alternatives to using set partition similarity measures that are not
specifically designed for graph partitions. The two types of measures,
graph-aware and set partition measures, are shown to have opposite behaviors
with respect to resolution issues and provide complementary information
necessary to assess that two graph partitions are similar. | [
0,
0,
0,
1,
0,
0
] |
Title: Competitive Resource Allocation in HetNets: the Impact of Small-cell Spectrum Constraints and Investment Costs,
Abstract: Heterogeneous wireless networks with small-cell deployments in licensed and
unlicensed spectrum bands are a promising approach for expanding wireless
connectivity and service. As a result, wireless service providers (SPs) are
adding small-cells to augment their existing macro-cell deployments. This added
flexibility complicates network management, in particular, service pricing and
spectrum allocations across macro- and small-cells. Further, these decisions
depend on the degree of competition among SPs. Restrictions on shared spectrum
access imposed by regulators, such as low power constraints that lead to
small-cell deployments, along with the investment cost needed to add small
cells to an existing network, also impact strategic decisions and market
efficiency. If the revenue generated by small-cells does not cover the
investment cost, then there will be no deployment even if it increases social
welfare. We study the implications of such spectrum constraints and investment
costs on resource allocation and pricing decisions by competitive SPs, along
with the associated social welfare. Our results show that while the optimal
resource allocation taking constraints and investment into account can be
uniquely determined, adding those features with strategic SPs can have a
substantial effect on the equilibrium market structure. | [
1,
0,
0,
0,
0,
0
] |
Title: Untangling the hairball: fitness based asymptotic reduction of biological networks,
Abstract: Complex mathematical models of interaction networks are routinely used for
prediction in systems biology. However, it is difficult to reconcile network
complexities with a formal understanding of their behavior. Here, we propose a
simple procedure (called $\bar \varphi$) to reduce biological models to
functional submodules, using statistical mechanics of complex systems combined
with a fitness-based approach inspired by $\textit{in silico}$ evolution. $\bar
\varphi$ works by putting parameters or combination of parameters to some
asymptotic limit, while keeping (or slightly improving) the model performance,
and requires parameter symmetry breaking for more complex models. We illustrate
$\bar \varphi$ on biochemical adaptation and on different models of immune
recognition by T cells. An intractable model of immune recognition with close
to a hundred individual transition rates is reduced to a simple two-parameter
model. $\bar \varphi$ extracts three different mechanisms for early immune
recognition, and automatically discovers similar functional modules in
different models of the same process, allowing for model classification and
comparison. Our procedure can be applied to biological networks based on rate
equations using a fitness function that quantifies phenotypic performance. | [
0,
1,
0,
0,
0,
0
] |
Title: Exotica and the status of the strong cosmic censor conjecture in four dimensions,
Abstract: An immense class of physical counterexamples to the four dimensional strong
cosmic censor conjecture---in its usual broad formulation---is exhibited. More
precisely, out of any closed and simply connected 4-manifold an open Ricci-flat
Lorentzian 4-manifold is constructed which is not globally hyperbolic and no
perturbation of it, in any sense, can be globally hyperbolic. This very stable
non-global-hyperbolicity is the consequence of our open spaces having a
"creased end" i.e., an end diffeomorphic to an exotic ${\mathbb R}^4$. Open
manifolds having an end like this is a typical phenomenon in four dimensions.
The construction is based on a collection of results of Gompf and Taubes on
exotic and self-dual spaces, respectively, as well as applying Penrose'
non-linear graviton construction (i.e., twistor theory) to solve the Riemannian
Einstein's equation. These solutions then are converted into stably
non-globally-hyperbolic Lorentzian vacuum solutions. It follows that the
plethora of vacuum solutions we found cannot be obtained via the initial value
formulation of the Einstein's equation because they are "too long" in a certain
sense (explained in the text). This different (i.e., not based on the initial
value formulation but twistorial) technical background might partially explain
why the existence of vacuum solutions of this kind have not been realized so
far in spite of the fact that, apparently, their superabundance compared to the
well-known globally hyperbolic vacuum solutions is overwhelming. | [
0,
0,
1,
0,
0,
0
] |
Title: The $2$-nd Hessian type equation on almost Hermitian manifolds,
Abstract: In this paper, we derive the second order estimate to the $2$-nd Hessian type
equation on a compact almost Hermitian manifold. | [
0,
0,
1,
0,
0,
0
] |
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