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Recommendation with k-anonymized Ratings | Recommender systems are widely used to predict personalized preferences of
goods or services using users' past activities, such as item ratings or
purchase histories. If collections of such personal activities were made
publicly available, they could be used to personalize a diverse range of
services, including targeted advertisement or recommendations. However, there
would be an accompanying risk of privacy violations. The pioneering work of
Narayanan et al.\ demonstrated that even if the identifiers are eliminated, the
public release of user ratings can allow for the identification of users by
those who have only a small amount of data on the users' past ratings.
In this paper, we assume the following setting. A collector collects user
ratings, then anonymizes and distributes them. A recommender constructs a
recommender system based on the anonymized ratings provided by the collector.
Based on this setting, we exhaustively list the models of recommender systems
that use anonymized ratings. For each model, we then present an item-based
collaborative filtering algorithm for making recommendations based on
anonymized ratings. Our experimental results show that an item-based
collaborative filtering based on anonymized ratings can perform better than
collaborative filterings based on 5--10 non-anonymized ratings. This surprising
result indicates that, in some settings, privacy protection does not
necessarily reduce the usefulness of recommendations. From the experimental
analysis of this counterintuitive result, we observed that the sparsity of the
ratings can be reduced by anonymization and the variance of the prediction can
be reduced if $k$, the anonymization parameter, is appropriately tuned. In this
way, the predictive performance of recommendations based on anonymized ratings
can be improved in some settings.
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Ro-vibrational states of H$_2^+$. Variational calculations | The nonrelativistic variational calculation of a complete set of
ro-vibrational states in the H$_2^+$ molecular ion supported by the ground
$1s\sigma$ adiabatic potential is presented. It includes both bound states and
resonances located above the $n=1$ threshold. In the latter case we also
evaluate a predissociation width of a state wherever it is significant.
Relativistic and radiative corrections are discussed and effective adiabatic
potentials of these corrections are included as supplementary files.
| 0 | 1 | 0 | 0 | 0 | 0 |
Control of automated guided vehicles without collision by quantum annealer and digital devices | We formulate an optimization problem to control a large number of automated
guided vehicles in a plant without collision. The formulation consists of
binary variables. A quadratic cost function over these variables enables us to
utilize certain solvers on digital computers and recently developed
purpose-specific hardware such as D-Wave 2000Q and the Fujitsu digital
annealer. In the present study, we consider an actual plant in Japan, in which
vehicles run, and assess efficiency of our formulation for optimizing the
vehicles via several solvers. We confirm that our formulation can be a powerful
approach for performing smooth control while avoiding collisions between
vehicles, as compared to a conventional method. In addition, comparative
experiments performed using several solvers reveal that D-Wave 2000Q can be
useful as a rapid solver for generating a plan for controlling the vehicles in
a short time although it deals only with a small number of vehicles, while a
digital computer can rapidly solve the corresponding optimization problem even
with a large number of binary variables.
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Meta-learning: searching in the model space | There is no free lunch, no single learning algorithm that will outperform
other algorithms on all data. In practice different approaches are tried and
the best algorithm selected. An alternative solution is to build new algorithms
on demand by creating a framework that accommodates many algorithms. The best
combination of parameters and procedures is searched here in the space of all
possible models belonging to the framework of Similarity-Based Methods (SBMs).
Such meta-learning approach gives a chance to find the best method in all
cases. Issues related to the meta-learning and first tests of this approach are
presented.
| 0 | 0 | 0 | 1 | 0 | 0 |
GIFT: Guided and Interpretable Factorization for Tensors - An Application to Large-Scale Multi-platform Cancer Analysis | Given multi-platform genome data with prior knowledge of functional gene
sets, how can we extract interpretable latent relationships between patients
and genes? More specifically, how can we devise a tensor factorization method
which produces an interpretable gene factor matrix based on gene set
information while maintaining the decomposition quality and speed? We propose
GIFT, a Guided and Interpretable Factorization for Tensors. GIFT provides
interpretable factor matrices by encoding prior knowledge as a regularization
term in its objective function. Experiment results demonstrate that GIFT
produces interpretable factorizations with high scalability and accuracy, while
other methods lack interpretability. We apply GIFT to the PanCan12 dataset, and
GIFT reveals significant relations between cancers, gene sets, and genes, such
as influential gene sets for specific cancer (e.g., interferon-gamma response
gene set for ovarian cancer) or relations between cancers and genes (e.g., BRCA
cancer - APOA1 gene and OV, UCEC cancers - BST2 gene).
| 1 | 0 | 0 | 0 | 1 | 0 |
Synchronization of spin torque oscillators through spin Hall magnetoresistance | Spin torque oscillators placed onto a nonmagnetic heavy metal show
synchronized auto-oscillations due to the coupling originating from spin Hall
magnetoresistance effect. Here, we study a system having two spin torque
oscillators under the effect of the spin Hall torque, and show that switching
the external current direction enables us to control the phase difference of
the synchronization between in-phase and antiphase.
| 0 | 1 | 0 | 0 | 0 | 0 |
Long quasi-polycyclic $t-$CIS codes | We study complementary information set codes of length $tn$ and dimension $n$
of order $t$ called ($t-$CIS code for short). Quasi-cyclic and quasi-twisted
$t$-CIS codes are enumerated by using their concatenated structure. Asymptotic
existence results are derived for one-generator and have co-index $n$ by
Artin's conjecture for quasi cyclic and special case for quasi twisted. This
shows that there are infinite families of long QC and QT $t$-CIS codes with
relative distance satisfying a modified Varshamov-Gilbert bound for rate $1/t$
codes.
Similar results are defined for the new and more general class of
quasi-polycyclic codes introduced recently by Berger and Amrani.
| 1 | 0 | 0 | 0 | 0 | 0 |
Fairness in Criminal Justice Risk Assessments: The State of the Art | Objectives: Discussions of fairness in criminal justice risk assessments
typically lack conceptual precision. Rhetoric too often substitutes for careful
analysis. In this paper, we seek to clarify the tradeoffs between different
kinds of fairness and between fairness and accuracy.
Methods: We draw on the existing literatures in criminology, computer science
and statistics to provide an integrated examination of fairness and accuracy in
criminal justice risk assessments. We also provide an empirical illustration
using data from arraignments.
Results: We show that there are at least six kinds of fairness, some of which
are incompatible with one another and with accuracy.
Conclusions: Except in trivial cases, it is impossible to maximize accuracy
and fairness at the same time, and impossible simultaneously to satisfy all
kinds of fairness. In practice, a major complication is different base rates
across different legally protected groups. There is a need to consider
challenging tradeoffs.
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Remarks to the article: New Light on the Invention of the Achromatic Telescope Objective | The article analysis was carried out within the confines of the replication
project of the telescope, which was used by Mikhail Lomonosov at observation
the transit of Venus in 1761. At that time he discovered the Venusian
atmosphere. It is known that Lomonosov used Dollond 4.5 feet long achromatic
telescope. The investigation revealed significant faults in the description of
the approximation method, which most likely was used by J. Dollond & Son during
manufacturing of the early achromatic lenses.
| 0 | 1 | 0 | 0 | 0 | 0 |
The spin-Brauer diagram algebra | We investigate the spin-Brauer diagram algebra, denoted ${\bf SB}_n(\delta)$,
that arises from studying an analogous form of Schur-Weyl duality for the
action of the pin group on ${\bf V}^{\otimes n} \otimes \Delta$. Here ${\bf V}$
is the standard $N$-dimensional complex representation of ${\bf Pin}(N)$ and
$\Delta$ is the spin representation. When $\delta = N$ is a positive integer,
we define a surjective map ${\bf SB}_n(N) \twoheadrightarrow {\rm End}_{{\bf
Pin}(N)}({\bf V}^{\otimes n} \otimes \Delta)$ and show it is an isomorphism for
$N \geq 2n$. We show ${\bf SB}_n(\delta)$ is a cellular algebra and use
cellularity to characterize its irreducible representations.
| 0 | 0 | 1 | 0 | 0 | 0 |
Superheating in coated niobium | Using muon spin rotation it is shown that the field of first flux penetration
H_entry in Nb is enhanced by about 30% if coated with an overlayer of Nb_3Sn or
MgB_2. This is consistent with an increase from the lower critical magnetic
field H_c1 up to the superheating field H_sh of the Nb substrate. In the
experiments presented here coatings of Nb_3Sn and MgB_2 with a thickness
between 50 and 2000nm have been tested. H_entry does not depend on material or
thickness. This suggests that the energy barrier at the boundary between the
two materials prevents flux entry up to H_sh of the substrate. A mechanism
consistent with these findings is that the proximity effect recovers the
stability of the energy barrier for flux penetration, which is suppressed by
defects for uncoated samples. Additionally, a low temperature baked Nb sample
has been tested. Here a 6% increase of H_entry was found, also pushing H_entry
beyond H_c1.
| 0 | 1 | 0 | 0 | 0 | 0 |
Contraction Analysis of Nonlinear DAE Systems | This paper studies the contraction properties of nonlinear
differential-algebraic equation (DAE) systems. Specifically we develop scalable
techniques for constructing the attraction regions associated with a particular
stable equilibrium, by establishing the relation between the contraction rates
of the original systems and the corresponding virtual extended systems. We show
that for a contracting DAE system, the reduced system always contracts faster
than the extended ones; furthermore, there always exists an extension with
contraction rate arbitrarily close to that of the original system. The proposed
construction technique is illustrated with a power system example in the
context of transient stability assessment.
| 0 | 0 | 1 | 0 | 0 | 0 |
New Braided $T$-Categories over Hopf (co)quasigroups | Let $H$ be a Hopf quasigroup with bijective antipode and let $Aut_{HQG}(H)$
be the set of all Hopf quasigroup automorphisms of $H$. We introduce a category
${_{H}\mathcal{YDQ}^{H}}(\alpha,\beta)$ with $\alpha,\beta\in Aut_{HQG}(H)$ and
construct a braided $T$-category $\mathcal{YDQ}(H)$ having all the categories
${_{H}\mathcal{YDQ}^{H}}(\alpha,\beta)$ as components.
| 0 | 0 | 1 | 0 | 0 | 0 |
On the Reconstruction Risk of Convolutional Sparse Dictionary Learning | Sparse dictionary learning (SDL) has become a popular method for adaptively
identifying parsimonious representations of a dataset, a fundamental problem in
machine learning and signal processing. While most work on SDL assumes a
training dataset of independent and identically distributed samples, a variant
known as convolutional sparse dictionary learning (CSDL) relaxes this
assumption, allowing more general sequential data sources, such as time series
or other dependent data. Although recent work has explored the statistical
properties of classical SDL, the statistical properties of CSDL remain
unstudied. This paper begins to study this by identifying the minimax
convergence rate of CSDL in terms of reconstruction risk, by both upper
bounding the risk of an established CSDL estimator and proving a matching
information-theoretic lower bound. Our results indicate that consistency in
reconstruction risk is possible precisely in the `ultra-sparse' setting, in
which the sparsity (i.e., the number of feature occurrences) is in $o(N)$ in
terms of the length N of the training sequence. Notably, our results make very
weak assumptions, allowing arbitrary dictionaries and dependent measurement
noise. Finally, we verify our theoretical results with numerical experiments on
synthetic data.
| 1 | 0 | 1 | 1 | 0 | 0 |
Adaptive Questionnaires for Direct Identification of Optimal Product Design | We consider the problem of identifying the most profitable product design
from a finite set of candidates under unknown consumer preference. A standard
approach to this problem follows a two-step strategy: First, estimate the
preference of the consumer population, represented as a point in part-worth
space, using an adaptive discrete-choice questionnaire. Second, integrate the
estimated part-worth vector with engineering feasibility and cost models to
determine the optimal design. In this work, we (1) demonstrate that accurate
preference estimation is neither necessary nor sufficient for identifying the
optimal design, (2) introduce a novel adaptive questionnaire that leverages
knowledge about engineering feasibility and manufacturing costs to directly
determine the optimal design, and (3) interpret product design in terms of a
nonlinear segmentation of part-worth space, and use this interpretation to
illuminate the intrinsic difficulty of optimal design in the presence of noisy
questionnaire responses. We establish the superiority of the proposed approach
using a well-documented optimal product design task. This study demonstrates
how the identification of optimal product design can be accelerated by
integrating marketing and manufacturing knowledge into the adaptive
questionnaire.
| 1 | 0 | 0 | 1 | 0 | 0 |
Identities involving Bernoulli and Euler polynomials | We present various identities involving the classical Bernoulli and Euler
polynomials. Among others, we prove that $$ \sum_{k=0}^{[n/4]}(-1)^k {n\choose
4k}\frac{B_{n-4k}(z) }{2^{6k}} =\frac{1}{2^{n+1}}\sum_{k=0}^{n} (-1)^k
\frac{1+i^k}{(1+i)^k} {n\choose k}{B_{n-k}(2z)} $$ and $$ \sum_{k=1}^{n}
2^{2k-1} {2n\choose 2k-1} B_{2k-1}(z) = \sum_{k=1}^n k \, 2^{2k} {2n\choose 2k}
E_{2k-1}(z). $$ Applications of our results lead to formulas for Bernoulli and
Euler numbers, like, for instance, $$ n E_{n-1} =\sum_{k=1}^{[n/2]}
\frac{2^{2k}-1}{k} (2^{2k}-2^n){n\choose 2k-1} B_{2k}B_{n-2k}. $$
| 0 | 0 | 1 | 0 | 0 | 0 |
Gate Tunable Magneto-resistance of Ultra-Thin WTe2 Devices | In this work, the magneto-resistance (MR) of ultra-thin WTe2/BN
heterostructures far away from electron-hole equilibrium is measured. The
change of MR of such devices is found to be determined largely by a single
tunable parameter, i.e. the amount of imbalance between electrons and holes. We
also found that the magnetoresistive behavior of ultra-thin WTe2 devices is
well-captured by a two-fluid model. According to the model, the change of MR
could be as large as 400,000%, the largest potential change of MR among all
materials known, if the ultra-thin samples are tuned to neutrality when
preserving the mobility of 167,000 cm2V-1s-1 observed in bulk samples. Our
findings show the prospects of ultra-thin WTe2 as a variable magnetoresistance
material in future applications such as magnetic field sensors, information
storage and extraction devices, and galvanic isolators. The results also
provide important insight into the electronic structure and the origin of the
large MR in ultra-thin WTe2 samples.
| 0 | 1 | 0 | 0 | 0 | 0 |
Morpheo: Traceable Machine Learning on Hidden data | Morpheo is a transparent and secure machine learning platform collecting and
analysing large datasets. It aims at building state-of-the art prediction
models in various fields where data are sensitive. Indeed, it offers strong
privacy of data and algorithm, by preventing anyone to read the data, apart
from the owner and the chosen algorithms. Computations in Morpheo are
orchestrated by a blockchain infrastructure, thus offering total traceability
of operations. Morpheo aims at building an attractive economic ecosystem around
data prediction by channelling crypto-money from prediction requests to useful
data and algorithms providers. Morpheo is designed to handle multiple data
sources in a transfer learning approach in order to mutualize knowledge
acquired from large datasets for applications with smaller but similar
datasets.
| 1 | 0 | 0 | 1 | 0 | 0 |
Hölder regularity of the 2D dual semigeostrophic equations via analysis of linearized Monge-Ampère equations | We obtain the Hölder regularity of time derivative of solutions to the dual
semigeostrophic equations in two dimensions when the initial potential density
is bounded away from zero and infinity. Our main tool is an interior Hölder
estimate in two dimensions for an inhomogeneous linearized Monge-Ampère
equation with right hand side being the divergence of a bounded vector field.
As a further application of our Hölder estimate, we prove the Hölder
regularity of the polar factorization for time-dependent maps in two dimensions
with densities bounded away from zero and infinity. Our applications improve
previous work by G. Loeper who considered the cases of densities sufficiently
close to a positive constant.
| 0 | 0 | 1 | 0 | 0 | 0 |
New nanostructures of carbon: Quasifullerenes Cn-q (n=20,42,48,60) | Based on the third allotropic form of carbon (Fullerenes) through theoretical
study have been predicted structures described as non-classical fullerenes. We
have studied novel allotropic carbon structures with a closed cage
configuration that have been predicted for the first time, by using DFT at the
B3LYP level. Such carbon Cn-q structures (where, n=20, 42, 48 and 60), combine
states of hybridization sp1 and sp2, for the formation of bonds. A comparative
analysis of quasi-fullerenes with respect to their isomers of greater stability
was also performed. Chemical stability was evaluated with the criteria of
aromaticity through the different rings that build the systems. The results
show new isomerism of carbon nanostructures with interesting chemical
properties such as hardness, chemical potential and HOMO-LUMO gaps. We also
studied thermal stability with Lagrangian molecular dynamics method using Atom-
Center Density propagation (ADMP) method.
| 0 | 1 | 0 | 0 | 0 | 0 |
The Top 10 Topics in Machine Learning Revisited: A Quantitative Meta-Study | Which topics of machine learning are most commonly addressed in research?
This question was initially answered in 2007 by doing a qualitative survey
among distinguished researchers. In our study, we revisit this question from a
quantitative perspective. Concretely, we collect 54K abstracts of papers
published between 2007 and 2016 in leading machine learning journals and
conferences. We then use machine learning in order to determine the top 10
topics in machine learning. We not only include models, but provide a holistic
view across optimization, data, features, etc. This quantitative approach
allows reducing the bias of surveys. It reveals new and up-to-date insights
into what the 10 most prolific topics in machine learning research are. This
allows researchers to identify popular topics as well as new and rising topics
for their research.
| 1 | 0 | 0 | 0 | 0 | 0 |
Random taste heterogeneity in discrete choice models: Flexible nonparametric finite mixture distributions | This study proposes a mixed logit model with multivariate nonparametric
finite mixture distributions. The support of the distribution is specified as a
high-dimensional grid over the coefficient space, with equal or unequal
intervals between successive points along the same dimension; the location of
each point on the grid and the probability mass at that point are model
parameters that need to be estimated. The framework does not require the
analyst to specify the shape of the distribution prior to model estimation, but
can approximate any multivariate probability distribution function to any
arbitrary degree of accuracy. The grid with unequal intervals, in particular,
offers greater flexibility than existing multivariate nonparametric
specifications, while requiring the estimation of a small number of additional
parameters. An expectation maximization algorithm is developed for the
estimation of these models. Multiple synthetic datasets and a case study on
travel mode choice behavior are used to demonstrate the value of the model
framework and estimation algorithm. Compared to extant models that incorporate
random taste heterogeneity through continuous mixture distributions, the
proposed model provides better out-of-sample predictive ability. Findings
reveal significant differences in willingness to pay measures between the
proposed model and extant specifications. The case study further demonstrates
the ability of the proposed model to endogenously recover patterns of attribute
non-attendance and choice set formation.
| 0 | 0 | 0 | 1 | 0 | 0 |
Microscopic mechanism of tunable band gap in potassium doped few-layer black phosphorus | Tuning band gaps in two-dimensional (2D) materials is of great interest in
the fundamental and practical aspects of contemporary material sciences.
Recently, black phosphorus (BP) consisting of stacked layers of phosphorene was
experimentally observed to show a widely tunable band gap by means of the
deposition of potassium (K) atoms on the surface, thereby allowing great
flexibility in design and optimization of electronic and optoelectronic
devices. Here, based on the density-functional theory calculations, we
demonstrates that the donated electrons from K dopants are mostly localized at
the topmost BP layer and such a surface charging efficiently screens the K ion
potential. It is found that, as the K doping increases, the extreme surface
charging and its screening of K atoms shift the conduction bands down in
energy, i.e., towards higher binding energy, because they have more charge near
the surface, while it has little influence on the valence bands having more
charge in the deeper layers. This result provides a different explanation for
the observed tunable band gap compared to the previously proposed giant Stark
effect where a vertical electric field from the positively ionized K overlayer
to the negatively charged BP layers shifts the conduction band minimum
${\Gamma}_{\rm 1c}$ (valence band minimum ${\Gamma}_{\rm 8v}$) downwards
(upwards). The present prediction of ${\Gamma}_{\rm 1c}$ and ${\Gamma}_{\rm
8v}$ as a function of the K doping reproduces well the widely tunable band gap,
anisotropic Dirac semimetal state, and band-inverted semimetal state, as
observed by angle-resolved photoemission spectroscopy experiment. Our findings
shed new light on a route for tunable band gap engineering of 2D materials
through the surface doping of alkali metals.
| 0 | 1 | 0 | 0 | 0 | 0 |
Quasi-steady state reduction for the Michaelis-Menten reaction-diffusion system | The Michaelis-Menten mechanism is probably the best known model for an
enzyme-catalyzed reaction. For spatially homogeneous concentrations, QSS
reductions are well known, but this is not the case when chemical species are
allowed to diffuse. We will discuss QSS reductions for both the irreversible
and reversible Michaelis-Menten reaction in the latter case, given small
initial enzyme concentration and slow diffusion. Our work is based on a
heuristic method to obtain an ordinary differential equation which admits
reduction by Tikhonov-Fenichel theory. We will not give convergence proofs but
we provide numerical results that support the accuracy of the reductions.
| 0 | 0 | 1 | 0 | 0 | 0 |
All the people around me: face discovery in egocentric photo-streams | Given an unconstrained stream of images captured by a wearable photo-camera
(2fpm), we propose an unsupervised bottom-up approach for automatic clustering
appearing faces into the individual identities present in these data. The
problem is challenging since images are acquired under real world conditions;
hence the visible appearance of the people in the images undergoes intensive
variations. Our proposed pipeline consists of first arranging the photo-stream
into events, later, localizing the appearance of multiple people in them, and
finally, grouping various appearances of the same person across different
events. Experimental results performed on a dataset acquired by wearing a
photo-camera during one month, demonstrate the effectiveness of the proposed
approach for the considered purpose.
| 1 | 0 | 0 | 0 | 0 | 0 |
Graphene oxide nanosheets disrupt lipid composition, Ca2+ homeostasis and synaptic transmission in primary cortical neurons | Graphene has the potential to make a very significant impact on society, with
important applications in the biomedical field. The possibility to engineer
graphene-based medical devices at the neuronal interface is of particular
interest, making it imperative to determine the biocompatibility of graphene
materials with neuronal cells. Here we conducted a comprehensive analysis of
the effects of chronic and acute exposure of rat primary cortical neurons to
few-layers pristine graphene (GR) and monolayer graphene oxide (GO) flakes. By
combining a range of cell biology, microscopy, electrophysiology and omics
approaches we characterized the graphene neuron interaction from the first
steps of membrane contact and internalization to the long-term effects on cell
viability, synaptic transmission and cell metabolism. GR/GO flakes are found in
contact with the neuronal membrane, free in the cytoplasm and internalized
through the endolysosomal pathway, with no significant impact on neuron
viability. However, GO exposure selectively caused the inhibition of excitatory
transmission, paralleled by a reduction in the number of excitatory synaptic
contacts, and a concomitant enhancement of the inhibitory activity. This was
accompanied by induction of autophagy, altered Ca2+ dynamics and by a
downregulation of some of the main players in the regulation of Ca2+
homeostasis in both excitatory and inhibitory neurons. Our results show that,
although graphene exposure does not impact on neuron viability, it does
nevertheless have important effects on neuronal transmission and network
functionality, thus warranting caution when planning to employ this material
for neuro-biological applications.
| 0 | 0 | 0 | 0 | 1 | 0 |
Physical insight into the thermodynamic uncertainty relation using Brownian motion in tilted periodic potentials | Using Brownian motion in periodic potentials $V(x)$ tilted by a force $f$, we
provide physical insight into the thermodynamic uncertainty relation, a
recently conjectured principle for statistical errors and irreversible heat
dissipation in nonequilibrium steady states. According to the relation,
nonequilibrium output generated from dissipative processes necessarily incurs
an energetic cost or heat dissipation $q$, and in order to limit the output
fluctuation within a relative uncertainty $\epsilon$, at least
$2k_BT/\epsilon^2$ of heat must be dissipated. Our model shows that this bound
is attained not only at near-equilibrium ($f\ll V'(x)$) but also at
far-from-equilibrium $(f\gg V'(x))$, more generally when the dissipated heat is
normally distributed. Furthermore, the energetic cost is maximized near the
critical force when the barrier separating the potential wells is about to
vanish and the fluctuation of Brownian particle is maximized. These findings
indicate that the deviation of heat distribution from Gaussianity gives rise to
the inequality of the uncertainty relation, further clarifying the meaning of
the uncertainty relation. Our derivation of the uncertainty relation also
recognizes a new bound of nonequilibrium fluctuations that the variance of
dissipated heat ($\sigma_q^2$) increases with its mean ($\mu_q$) and cannot be
smaller than $2k_BT\mu_q$.
| 0 | 1 | 0 | 0 | 0 | 0 |
Observation of a Modulational Instability in Bose-Einstein condensates | We observe the breakup dynamics of an elongated cloud of condensed $^{85}$Rb
atoms placed in an optical waveguide. The number of localized spatial
components observed in the breakup is compared with the number of solitons
predicted by a plane-wave stability analysis of the nonpolynomial nonlinear
Schrödinger equation, an effective one-dimensional approximation of the
Gross-Pitaevskii equation for cigar-shaped condensates. It is shown that the
numbers predicted from the fastest growing sidebands are consistent with the
experimental data, suggesting that modulational instability is the key
underlying physical mechanism driving the breakup.
| 0 | 1 | 0 | 0 | 0 | 0 |
Dynamics of the scenery flow and conical density theorems | Conical density theorems are used in the geometric measure theory to derive
geometric information from given metric information. The idea is to examine how
a measure is distributed in small balls. Finding conditions that guarantee the
measure to be effectively spread out in different directions is a classical
question going back to Besicovitch (1938) and Marstrand (1954). Classically,
conical density theorems deal with the distribution of the Hausdorff measure.
The process of taking blow-ups of a measure around a point induces a natural
dynamical system called the scenery flow. Relying on this dynamics makes it
possible to apply ergodic-theoretical methods to understand the statistical
behavior of tangent measures. This approach was initiated by Furstenberg (1970,
2008) and greatly developed by Hochman (2010). The scenery flow is a
well-suited tool to address problems concerning conical densities.
In this survey, we demonstrate how to develop the ergodic-theoretical
machinery around the scenery flow and use it to study conical density theorems.
| 0 | 0 | 1 | 0 | 0 | 0 |
Model-independent analyses of non-Gaussianity in Planck CMB maps using Minkowski Functionals | Despite the wealth of $Planck$ results, there are difficulties in
disentangling the primordial non-Gaussianity of the Cosmic Microwave Background
(CMB) from the secondary and the foreground non-Gaussianity (NG). For each of
these forms of NG the lack of complete data introduces model-dependencies.
Aiming at detecting the NGs of the CMB temperature anisotropy $\delta T$, while
paying particular attention to a model-independent quantification of NGs, our
analysis is based upon statistical and morphological univariate descriptors,
respectively: the probability density function $P(\delta T)$, related to
${\mathrm v}_{0}$, the first Minkowski Functional (MF), and the two other MFs,
${\mathrm v}_{1}$ and ${\mathrm v}_{2}$. From their analytical Gaussian
predictions we build the discrepancy functions $\Delta_{k}$ ($k=P,0,1,2$) which
are applied to an ensemble of $10^{5}$ CMB realization maps of the $\Lambda$CDM
model and to the $Planck$ CMB maps. In our analysis we use general Hermite
expansions of the $\Delta_{k}$ up to the $12^{th}$ order, where the
coefficients are explicitly given in terms of cumulants. Assuming hierarchical
ordering of the cumulants, we obtain the perturbative expansions generalizing
the $2^{nd}$ order expansions of Matsubara to arbitrary order in the standard
deviation $\sigma_0$ for $P(\delta T)$ and ${\mathrm v}_0$, where the
perturbative expansion coefficients are explicitly given in terms of complete
Bell polynomials. The comparison of the Hermite expansions and the perturbative
expansions is performed for the $\Lambda$CDM map sample and the $Planck$ data.
We confirm the weak level of non-Gaussianity ($1$-$2$)$\sigma$ of the
foreground corrected masked $Planck$ $2015$ maps.
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Enhanced mixing in giant impact simulations with a new Lagrangian method | Giant impacts (GIs) are common in the late stage of planet formation. The
Smoothed Particle Hydrodynamics (SPH) method is widely used for simulating the
outcome of such violent collisions, one prominent example being the formation
of the Moon. However, a decade of numerical studies in various areas of
computational astrophysics has shown that the standard formulation of SPH
suffers from several shortcomings such as artificial surface tension and its
tendency to promptly damp turbulent motions on scales much larger than the
physical dissipation scale, both resulting in the suppression of mixing. In
order to quantify how severe these limitations are when modeling GIs we carried
out a comparison of simulations with identical initial conditions performed
with the standard SPH as well as with the novel Lagrangian Meshless Finite Mass
(MFM) method in the GIZMO code. We confirm the lack of mixing between the
impactor and target when SPH is employed, while MFM is capable of driving
vigorous sub-sonic turbulence and leads to significant mixing between the two
bodies. Modern SPH variants with artificial conductivity, a different
formulation of the hydro force or reduced artificial viscosity, do not improve
mixing as significantly. Angular momentum is conserved similarly well in both
methods, but MFM does not suffer from spurious transport induced by artificial
viscosity, resulting in a slightly higher angular momentum of the proto-lunar
disk. Furthermore, SPH initial conditions exhibit an unphysical density
discontinuity at the core-mantle boundary which is easily removed in MFM.
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New Reinforcement Learning Using a Chaotic Neural Network for Emergence of "Thinking" - "Exploration" Grows into "Thinking" through Learning - | Expectation for the emergence of higher functions is getting larger in the
framework of end-to-end reinforcement learning using a recurrent neural
network. However, the emergence of "thinking" that is a typical higher function
is difficult to realize because "thinking" needs non fixed-point, flow-type
attractors with both convergence and transition dynamics. Furthermore, in order
to introduce "inspiration" or "discovery" in "thinking", not completely random
but unexpected transition should be also required.
By analogy to "chaotic itinerancy", we have hypothesized that "exploration"
grows into "thinking" through learning by forming flow-type attractors on
chaotic random-like dynamics. It is expected that if rational dynamics are
learned in a chaotic neural network (ChNN), coexistence of rational state
transition, inspiration-like state transition and also random-like exploration
for unknown situation can be realized.
Based on the above idea, we have proposed new reinforcement learning using a
ChNN as an actor. The positioning of exploration is completely different from
the conventional one. The chaotic dynamics inside the ChNN produces exploration
factors by itself. Since external random numbers for stochastic action
selection are not used, exploration factors cannot be isolated from the output.
Therefore, the learning method is also completely different from the
conventional one.
At each non-feedback connection, one variable named causality trace takes in
and maintains the input through the connection according to the change in its
output. Using the trace and TD error, the weight is updated.
In this paper, as the result of a recent simple task to see whether the new
learning works or not, it is shown that a robot with two wheels and two visual
sensors reaches a target while avoiding an obstacle after learning though there
are still many rooms for improvement.
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Tensorizing Generative Adversarial Nets | Generative Adversarial Network (GAN) and its variants exhibit
state-of-the-art performance in the class of generative models. To capture
higher-dimensional distributions, the common learning procedure requires high
computational complexity and a large number of parameters. The problem of
employing such massive framework arises when deploying it on a platform with
limited computational power such as mobile phones. In this paper, we present a
new generative adversarial framework by representing each layer as a tensor
structure connected by multilinear operations, aiming to reduce the number of
model parameters by a large factor while preserving the generative performance
and sample quality. To learn the model, we employ an efficient algorithm which
alternatively optimizes both discriminator and generator. Experimental outcomes
demonstrate that our model can achieve high compression rate for model
parameters up to $35$ times when compared to the original GAN for MNIST
dataset.
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Pretending Fair Decisions via Stealthily Biased Sampling | Fairness by decision-makers is believed to be auditable by third parties. In
this study, we show that this is not always true.
We consider the following scenario. Imagine a decision-maker who discloses a
subset of his dataset with decisions to make his decisions auditable. If he is
corrupt, and he deliberately selects a subset that looks fair even though the
overall decision is unfair, can we identify this decision-maker's fraud?
We answer this question negatively. We first propose a sampling method that
produces a subset whose distribution is biased from the original (to pretend to
be fair); however, its differentiation from uniform sampling is difficult. We
call such a sampling method as stealthily biased sampling, which is formulated
as a Wasserstein distance minimization problem, and is solved through a
minimum-cost flow computation. We proved that the stealthily biased sampling
minimizes an upper-bound of the indistinguishability. We conducted experiments
to see that the stealthily biased sampling is, in fact, difficult to detect.
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Design of a Time Delay Reservoir Using Stochastic Logic: A Feasibility Study | This paper presents a stochastic logic time delay reservoir design. The
reservoir is analyzed using a number of metrics, such as kernel quality,
generalization rank, performance on simple benchmarks, and is also compared to
a deterministic design. A novel re-seeding method is introduced to reduce the
adverse effects of stochastic noise, which may also be implemented in other
stochastic logic reservoir computing designs, such as echo state networks.
Benchmark results indicate that the proposed design performs well on
noise-tolerant classification problems, but more work needs to be done to
improve the stochastic logic time delay reservoir's robustness for regression
problems.
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Polaritons in Living Systems: Modifying Energy Landscapes in Photosynthetic Organisms Using a Photonic Structure | Photosynthetic organisms rely on a series of self-assembled nanostructures
with tuned electronic energy levels in order to transport energy from where it
is collected by photon absorption, to reaction centers where the energy is used
to drive chemical reactions. In the photosynthetic bacteria Chlorobaculum
tepidum (Cba. tepidum), a member of the green sulphur bacteria (GSB) family,
light is absorbed by large antenna complexes called chlorosomes. The exciton
generated is transferred to a protein baseplate attached to the chlorosome,
before traveling through the Fenna-Matthews-Olson (FMO) complex to the reaction
center. The energy levels of these systems are generally defined by their
chemical structure. Here we show that by placing bacteria within a photonic
microcavity, we can access the strong exciton-photon coupling regime between a
confined cavity mode and exciton states of the chlorosome, whereby a coherent
exchange of energy between the bacteria and cavity mode results in the
formation of polariton states. The polaritons have an energy distinct from that
of the exciton and photon, and can be tuned in situ via the microcavity length.
This results in real-time, non-invasive control over the relative energy levels
within the bacteria. This demonstrates the ability to strongly influence living
biological systems with photonic structures such as microcavities. We believe
that by creating polariton states, that are in this case a superposition of a
photon and excitons within a living bacteria, we can modify energy transfer
pathways and therefore study the importance of energy level alignment on the
efficiency of photosynthetic systems.
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A Matrix Expander Chernoff Bound | We prove a Chernoff-type bound for sums of matrix-valued random variables
sampled via a random walk on an expander, confirming a conjecture due to
Wigderson and Xiao. Our proof is based on a new multi-matrix extension of the
Golden-Thompson inequality which improves in some ways the inequality of
Sutter, Berta, and Tomamichel, and may be of independent interest, as well as
an adaptation of an argument for the scalar case due to Healy. Secondarily, we
also provide a generic reduction showing that any concentration inequality for
vector-valued martingales implies a concentration inequality for the
corresponding expander walk, with a weakening of parameters proportional to the
squared mixing time.
| 1 | 0 | 0 | 0 | 0 | 0 |
Learning Neural Representations of Human Cognition across Many fMRI Studies | Cognitive neuroscience is enjoying rapid increase in extensive public
brain-imaging datasets. It opens the door to large-scale statistical models.
Finding a unified perspective for all available data calls for scalable and
automated solutions to an old challenge: how to aggregate heterogeneous
information on brain function into a universal cognitive system that relates
mental operations/cognitive processes/psychological tasks to brain networks? We
cast this challenge in a machine-learning approach to predict conditions from
statistical brain maps across different studies. For this, we leverage
multi-task learning and multi-scale dimension reduction to learn
low-dimensional representations of brain images that carry cognitive
information and can be robustly associated with psychological stimuli. Our
multi-dataset classification model achieves the best prediction performance on
several large reference datasets, compared to models without cognitive-aware
low-dimension representations, it brings a substantial performance boost to the
analysis of small datasets, and can be introspected to identify universal
template cognitive concepts.
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Unsupervised Body Part Regression via Spatially Self-ordering Convolutional Neural Networks | Automatic body part recognition for CT slices can benefit various medical
image applications. Recent deep learning methods demonstrate promising
performance, with the requirement of large amounts of labeled images for
training. The intrinsic structural or superior-inferior slice ordering
information in CT volumes is not fully exploited. In this paper, we propose a
convolutional neural network (CNN) based Unsupervised Body part Regression
(UBR) algorithm to address this problem. A novel unsupervised learning method
and two inter-sample CNN loss functions are presented. Distinct from previous
work, UBR builds a coordinate system for the human body and outputs a
continuous score for each axial slice, representing the normalized position of
the body part in the slice. The training process of UBR resembles a
self-organization process: slice scores are learned from inter-slice
relationships. The training samples are unlabeled CT volumes that are abundant,
thus no extra annotation effort is needed. UBR is simple, fast, and accurate.
Quantitative and qualitative experiments validate its effectiveness. In
addition, we show two applications of UBR in network initialization and anomaly
detection.
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Investigating the Application of Common-Sense Knowledge-Base for Identifying Term Obfuscation in Adversarial Communication | Word obfuscation or substitution means replacing one word with another word
in a sentence to conceal the textual content or communication. Word obfuscation
is used in adversarial communication by terrorist or criminals for conveying
their messages without getting red-flagged by security and intelligence
agencies intercepting or scanning messages (such as emails and telephone
conversations). ConceptNet is a freely available semantic network represented
as a directed graph consisting of nodes as concepts and edges as assertions of
common sense about these concepts. We present a solution approach exploiting
vast amount of semantic knowledge in ConceptNet for addressing the technically
challenging problem of word substitution in adversarial communication. We frame
the given problem as a textual reasoning and context inference task and utilize
ConceptNet's natural-language-processing tool-kit for determining word
substitution. We use ConceptNet to compute the conceptual similarity between
any two given terms and define a Mean Average Conceptual Similarity (MACS)
metric to identify out-of-context terms. The test-bed to evaluate our proposed
approach consists of Enron email dataset (having over 600000 emails generated
by 158 employees of Enron Corporation) and Brown corpus (totaling about a
million words drawn from a wide variety of sources). We implement word
substitution techniques used by previous researches to generate a test dataset.
We conduct a series of experiments consisting of word substitution methods used
in the past to evaluate our approach. Experimental results reveal that the
proposed approach is effective.
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Titanium dioxide hole-blocking layer in ultra-thin-film crystalline silicon solar cells | One of the remaining obstacles to approaching the theoretical efficiency
limit of crystalline silicon (c-Si) solar cells is the exceedingly high
interface recombination loss for minority carriers at the Ohmic contacts. In
ultra-thin-film c-Si solar cells, this contact recombination loss is far more
severe than for traditional thick cells due to the smaller volume and higher
minority carrier concentration of the former. This paper presents a novel
design of an electron passing (Ohmic) contact to n-type Si that is
hole-blocking with significantly reduced hole recombination. This contact is
formed by depositing a thin titanium dioxide (TiO2) layer to form a silicon
metal-insulator-semiconductor (MIS) contact. A 2 {\mu}m thick Si cell with this
TiO2 MIS contact achieved an open circuit voltage (Voc) of 645 mV, which is 10
mV higher than that of an ultra-thin cell with a metal contact. This MIS
contact demonstrates a new path for ultra-thin-film c-Si solar cells to achieve
high efficiencies as high as traditional thick cells, and enables the
fabrication of high-efficiency c-Si solar cells at a lower cost.
| 0 | 1 | 0 | 0 | 0 | 0 |
Phase Space Sketching for Crystal Image Analysis based on Synchrosqueezed Transforms | Recent developments of imaging techniques enable researchers to visualize
materials at the atomic resolution to better understand the microscopic
structures of materials. This paper aims at automatic and quantitative
characterization of potentially complicated microscopic crystal images,
providing feedback to tweak theories and improve synthesis in materials
science. As such, an efficient phase-space sketching method is proposed to
encode microscopic crystal images in a translation, rotation, illumination, and
scale invariant representation, which is also stable with respect to small
deformations. Based on the phase-space sketching, we generalize our previous
analysis framework for crystal images with simple structures to those with
complicated geometry.
| 0 | 1 | 0 | 0 | 0 | 0 |
Specification tests in semiparametric transformation models - a multiplier bootstrap approach | We consider semiparametric transformation models, where after pre-estimation
of a parametric transformation of the response the data are modeled by means of
nonparametric regression. We suggest subsequent procedures for testing
lack-of-fit of the regression function and for significance of covariables,
which - in contrast to procedures from the literature - are asymptotically not
influenced by the pre-estimation of the transformation. The test statistics are
asymptotically pivotal and have the same asymptotic distribution as in
regression models without transformation. We show validity of a multiplier
bootstrap procedure which is easier to implement and much less computationally
demanding than bootstrap procedures based on the transformation model. In a
simulation study we demonstrate the superior performance of the procedure in
comparison with the competitors from the literature.
| 0 | 0 | 1 | 1 | 0 | 0 |
Collusions in Teichmüller expansions | If $\mathfrak{p} \subseteq \mathbb{Z}[\zeta]$ is a prime ideal over $p$ in
the $(p^d - 1)$th cyclotomic extension of $\mathbb{Z}$, then every element
$\alpha$ of the completion $\mathbb{Z}[\zeta]_\mathfrak{p}$ has a unique
expansion as a power series in $p$ with coefficients in $\mu_{p^d -1} \cup
\{0\}$ called the Teichmüller expansion of $\alpha$ at $\mathfrak{p}$. We
observe three peculiar and seemingly unrelated patterns that frequently appear
in the computation of Teichmüller expansions, then develop a unifying theory
to explain these patterns in terms of the dynamics of an affine group action on
$\mathbb{Z}[\zeta]$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Driver Drowsiness Estimation from EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR) | One big challenge that hinders the transition of brain-computer interfaces
(BCIs) from laboratory settings to real-life applications is the availability
of high-performance and robust learning algorithms that can effectively handle
individual differences, i.e., algorithms that can be applied to a new subject
with zero or very little subject-specific calibration data. Transfer learning
and domain adaptation have been extensively used for this purpose. However,
most previous works focused on classification problems. This paper considers an
important regression problem in BCI, namely, online driver drowsiness
estimation from EEG signals. By integrating fuzzy sets with domain adaptation,
we propose a novel online weighted adaptation regularization for regression
(OwARR) algorithm to reduce the amount of subject-specific calibration data,
and also a source domain selection (SDS) approach to save about half of the
computational cost of OwARR. Using a simulated driving dataset with 15
subjects, we show that OwARR and OwARR-SDS can achieve significantly smaller
estimation errors than several other approaches. We also provide comprehensive
analyses on the robustness of OwARR and OwARR-SDS.
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The complex social network of surnames: A comparison between Brazil and Portugal | We present a study of social networks based on the analysis of Brazilian and
Portuguese family names (surnames). We construct networks whose nodes are names
of families and whose edges represent parental relations between two families.
From these networks we extract the connectivity distribution, clustering
coefficient, shortest path and centrality. We find that the connectivity
distribution follows an approximate power law. We associate the number of hubs,
centrality and entropy to the degree of miscegenation in the societies in both
countries. Our results show that Portuguese society has a higher miscegenation
degree than Brazilian society. All networks analyzed lead to approximate
inverse square power laws in the degree distribution. We conclude that the
thermodynamic limit is reached for small networks (3 or 4 thousand nodes). The
assortative mixing of all networks is negative, showing that the more connected
vertices are connected to vertices with lower connectivity. Finally, the
network of surnames presents some small world characteristics.
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Electrical characterization of structured platinum diselenide devices | Platinum diselenide (PtSe2) is an exciting new member of the two-dimensional
(2D) transition metal dichalcogenide (TMD) family. it has a semimetal to
semiconductor transition when approaching monolayer thickness and has already
shown significant potential for use in device applications. Notably, PtSe2 can
be grown at low temperature making it potentially suitable for industrial
usage. Here, we address thickness dependent transport properties and
investigate electrical contacts to PtSe2, a crucial and universal element of
TMD-based electronic devices. PtSe2 films have been synthesized at various
thicknesses and structured to allow contact engineering and the accurate
extraction of electrical properties. Contact resistivity and sheet resistance
extracted from transmission line method (TLM) measurements are compared for
different contact metals and different PtSe2 film thicknesses. Furthermore, the
transition from semimetal to semiconductor in PtSe2 has been indirectly
verified by electrical characterization of field-effect devices. Finally, the
influence of edge contacts at the metal - PtSe2 interface has been studied by
nanostructuring the contact area using electron beam lithography. By increasing
the edge contact length, the contact resistivity was improved by up to 70%
compared to devices with conventional top contacts. The results presented here
represent crucial steps towards realizing high-performance nanoelectronic
devices based on group-10 TMDs.
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Topological $\mathbb{Z}_2$ Resonating-Valence-Bond Spin Liquid on the Square Lattice | A one-parameter family of long-range resonating valence bond (RVB) state on
the square lattice was previously proposed to describe a critical spin liquid
(SL) phase of the spin-$1/2$ frustrated Heisenberg model. We provide evidence
that this RVB state in fact also realises a topological (long-range entangled)
$\mathbb{Z}_2$ SL, limited by two transitions to critical SL phases. The
topological phase is naturally connected to the $\mathbb{Z}_2$ gauge symmetry
of the local tensor. This work shows that, on one hand, spin-$1/2$ topological
SL with $C_{4v}$ point group symmetry and $SU(2)$ spin rotation symmetry exists
on the square lattice and, on the other hand, criticality and nonbipartiteness
are compatible. We also point out that, strong similarities between our phase
diagram and the ones of classical interacting dimer models suggest both can be
described by similar Kosterlitz-Thouless transitions. This scenario is further
supported by the analysis of the one-dimensional boundary state.
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Scalable Inference for Space-Time Gaussian Cox Processes | The log-Gaussian Cox process is a flexible and popular class of point pattern
models for capturing spatial and space-time dependence for point patterns.
Model fitting requires approximation of stochastic integrals which is
implemented through discretization over the domain of interest. With fine scale
discretization, inference based on Markov chain Monte Carlo is computationally
burdensome because of the cost of matrix decompositions and storage, such as
the Cholesky, for high dimensional covariance matrices associated with latent
Gaussian variables. This article addresses these computational bottlenecks by
combining two recent developments: (i) a data augmentation strategy that has
been proposed for space-time Gaussian Cox processes that is based on exact
Bayesian inference and does not require fine grid approximations for infinite
dimensional integrals, and (ii) a recently developed family of
sparsity-inducing Gaussian processes, called nearest-neighbor Gaussian
processes, to avoid expensive matrix computations. Our inference is delivered
within the fully model-based Bayesian paradigm and does not sacrifice the
richness of traditional log-Gaussian Cox processes. We apply our method to
crime event data in San Francisco and investigate the recovery of the intensity
surface.
| 0 | 0 | 0 | 1 | 0 | 0 |
The MISRA C Coding Standard and its Role in the Development and Analysis of Safety- and Security-Critical Embedded Software | The MISRA project started in 1990 with the mission of providing world-leading
best practice guidelines for the safe and secure application of both embedded
control systems and standalone software. MISRA C is a coding standard defining
a subset of the C language, initially targeted at the automotive sector, but
now adopted across all industry sectors that develop C software in safety-
and/or security-critical contexts. In this paper, we introduce MISRA C, its
role in the development of critical software, especially in embedded systems,
its relevance to industry safety standards, as well as the challenges of
working with a general-purpose programming language standard that is written in
natural language with a slow evolution over the last 40+ years. We also outline
the role of static analysis in the automatic checking of compliance with
respect to MISRA C, and the role of the MISRA C language subset in enabling a
wider application of formal methods to industrial software written in C.
| 1 | 0 | 0 | 0 | 0 | 0 |
Finger Grip Force Estimation from Video using Two Stream Approach | Estimation of a hand grip force is essential for the understanding of force
pattern during the execution of assembly or disassembly operations. Human
demonstration of a correct way of doing an operation is a powerful source of
information which can be used for guided robot teaching. Typically to assess
this problem instrumented approach is used, which requires hand or object
mounted devices and poses an inconvenience for an operator or limits the scope
of addressable objects. The work demonstrates that contact force may be
estimated using a noninvasive contactless method with the help of vision system
alone. We propose a two-stream approach for video processing, which utilizes
both spatial information of each frame and dynamic information of frame change.
In this work, image processing and machine learning techniques are used along
with dense optical flow for frame change tracking and Kalman filter is used for
stream fusion. Our studies show that the proposed method can successfully
estimate contact grip force with RMSE < 10% of sensor range (RMSE $\approx 0.2$
N), the performances of each stream and overall method performance are
reported. The proposed method has a wide range of applications, including robot
teaching through demonstration, haptic force feedback, and validation of human-
performed operations.
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Markov Properties for Graphical Models with Cycles and Latent Variables | We investigate probabilistic graphical models that allow for both cycles and
latent variables. For this we introduce directed graphs with hyperedges
(HEDGes), generalizing and combining both marginalized directed acyclic graphs
(mDAGs) that can model latent (dependent) variables, and directed mixed graphs
(DMGs) that can model cycles. We define and analyse several different Markov
properties that relate the graphical structure of a HEDG with a probability
distribution on a corresponding product space over the set of nodes, for
example factorization properties, structural equations properties,
ordered/local/global Markov properties, and marginal versions of these. The
various Markov properties for HEDGes are in general not equivalent to each
other when cycles or hyperedges are present, in contrast with the simpler case
of directed acyclic graphical (DAG) models (also known as Bayesian networks).
We show how the Markov properties for HEDGes - and thus the corresponding
graphical Markov models - are logically related to each other.
| 0 | 0 | 1 | 1 | 0 | 0 |
A Fast Algorithm for Solving Henderson's Mixed Model Equation | This article investigates a fast and stable method to solve Henderson's mixed
model equation. The proposed algorithm is stable in that it avoids inverting a
matrix of a large dimension and hence is free from the curse of dimensionality.
This tactic is enabled through row operations performed on the design matrix.
| 0 | 0 | 0 | 1 | 0 | 0 |
Regularity and stability results for the level set flow via the mean curvature flow with surgery | In this article we us the mean curvature flow with surgery to derive
regularity estimates going past Brakke regularity for the level set flow. We
also show a stability result for the plane under the level set flow.
| 0 | 0 | 1 | 0 | 0 | 0 |
Non-canonical Conformal Attractors for Single Field Inflation | We extend the idea of conformal attractors in inflation to non-canonical
sectors by developing a non-canonical conformally invariant theory from two
different approaches. In the first approach, namely, ${\cal N}=1$ supergravity,
the construction is more or less phenomenological, where the non-canonical
kinetic sector is derived from a particular form of the K$\ddot{a}$hler
potential respecting shift symmetry. In the second approach i.e.,
superconformal theory, we derive the form of the Lagrangian from a
superconformal action and it turns out to be exactly of the same form as in the
first approach. Conformal breaking of these theories results in a new class of
non-canonical models which can govern inflation with modulated shape of the
T-models. We further employ this framework to explore inflationary
phenomenology with a representative example and show how the form of the
K$\ddot{a}$hler potential can possibly be constrained in non-canonical models
using the latest confidence contour in the $n_s-r$ plane given by Planck.
| 0 | 1 | 0 | 0 | 0 | 0 |
Learning model-based planning from scratch | Conventional wisdom holds that model-based planning is a powerful approach to
sequential decision-making. It is often very challenging in practice, however,
because while a model can be used to evaluate a plan, it does not prescribe how
to construct a plan. Here we introduce the "Imagination-based Planner", the
first model-based, sequential decision-making agent that can learn to
construct, evaluate, and execute plans. Before any action, it can perform a
variable number of imagination steps, which involve proposing an imagined
action and evaluating it with its model-based imagination. All imagined actions
and outcomes are aggregated, iteratively, into a "plan context" which
conditions future real and imagined actions. The agent can even decide how to
imagine: testing out alternative imagined actions, chaining sequences of
actions together, or building a more complex "imagination tree" by navigating
flexibly among the previously imagined states using a learned policy. And our
agent can learn to plan economically, jointly optimizing for external rewards
and computational costs associated with using its imagination. We show that our
architecture can learn to solve a challenging continuous control problem, and
also learn elaborate planning strategies in a discrete maze-solving task. Our
work opens a new direction toward learning the components of a model-based
planning system and how to use them.
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A Simple Reservoir Model of Working Memory with Real Values | The prefrontal cortex is known to be involved in many high-level cognitive
functions, in particular, working memory. Here, we study to what extent a group
of randomly connected units (namely an Echo State Network, ESN) can store and
maintain (as output) an arbitrary real value from a streamed input, i.e. can
act as a sustained working memory unit. Furthermore, we explore to what extent
such an architecture can take advantage of the stored value in order to produce
non-linear computations. Comparison between different architectures (with and
without feedback, with and without a working memory unit) shows that an
explicit memory improves the performances.
| 0 | 0 | 0 | 0 | 1 | 0 |
Tailoring spin defects in diamond | Atomic-size spin defects in solids are unique quantum systems. Most
applications require nanometer positioning accuracy, which is typically
achieved by low energy ion implantation. So far, a drawback of this technique
is the significant residual implantation-induced damage to the lattice, which
strongly degrades the performance of spins in quantum applications. In this
letter we show that the charge state of implantation-induced defects
drastically influences the formation of lattice defects during thermal
annealing. We demonstrate that charging of vacancies localized at e.g.
individual nitrogen implantation sites suppresses the formation of vacancy
complexes, resulting in a tenfold-improved spin coherence time of single
nitrogen-vacancy (NV) centers in diamond. This has been achieved by confining
implantation defects into the space charge layer of free carriers generated by
a nanometer-thin boron-doped diamond structure. Besides, a twofold-improved
yield of formation of NV centers is observed. By combining these results with
numerical calculations, we arrive at a quantitative understanding of the
formation and dynamics of the implanted spin defects. The presented results
pave the way for improved engineering of diamond spin defect quantum devices
and other solid-state quantum systems.
| 0 | 1 | 0 | 0 | 0 | 0 |
Relaxed Oracles for Semi-Supervised Clustering | Pairwise "same-cluster" queries are one of the most widely used forms of
supervision in semi-supervised clustering. However, it is impractical to ask
human oracles to answer every query correctly. In this paper, we study the
influence of allowing "not-sure" answers from a weak oracle and propose an
effective algorithm to handle such uncertainties in query responses. Two
realistic weak oracle models are considered where ambiguity in answering
depends on the distance between two points. We show that a small query
complexity is adequate for effective clustering with high probability by
providing better pairs to the weak oracle. Experimental results on synthetic
and real data show the effectiveness of our approach in overcoming supervision
uncertainties and yielding high quality clusters.
| 1 | 0 | 0 | 1 | 0 | 0 |
Searching for axion stars and Q-balls with a terrestrial magnetometer network | Light (pseudo-)scalar fields are promising candidates to be the dark matter
in the Universe. Under certain initial conditions in the early Universe and/or
with certain types of self-interactions, they can form compact dark-matter
objects such as axion stars or Q-balls. Direct encounters with such objects can
be searched for by using a global network of atomic magnetometers. It is shown
that for a range of masses and radii not ruled out by existing observations,
the terrestrial encounter rate with axion stars or Q-balls can be sufficiently
high (at least once per year) for a detection. Furthermore, it is shown that a
global network of atomic magnetometers is sufficiently sensitive to
pseudoscalar couplings to atomic spins so that a transit through an axion star
or Q-ball could be detected over a broad range of unexplored parameter space.
| 0 | 1 | 0 | 0 | 0 | 0 |
Measuring Information Leakage in Website Fingerprinting Attacks | Tor is a low-latency anonymity system intended to provide low-latency
anonymous and uncensored network access against a local or network adversary.
Because of the design choice to minimize traffic overhead (and increase the
pool of potential users) Tor allows some information about the client's
connections to leak in the form of packet timing. Attacks that use (features
extracted from) this information to infer the website a user visits are
referred to as Website Fingerprinting (WF) attacks. We develop a methodology
and tools to directly measure the amount of information about a website leaked
by a given set of features. We apply this tool to a comprehensive set of
features extracted from a large set of websites and WF defense mechanisms,
allowing us to make more fine-grained observations about WF attack and defense
mechanisms.
| 1 | 0 | 0 | 0 | 0 | 0 |
Improving galaxy morphology with machine learning | This paper presents machine learning experiments performed over results of
galaxy classification into elliptical (E) and spiral (S) with morphological
parameters: concetration (CN), assimetry metrics (A3), smoothness metrics (S3),
entropy (H) and gradient pattern analysis parameter (GA). Except concentration,
all parameters performed a image segmentation pre-processing. For supervision
and to compute confusion matrices, we used as true label the galaxy
classification from GalaxyZoo. With a 48145 objects dataset after preprocessing
(44760 galaxies labeled as S and 3385 as E), we performed experiments with
Support Vector Machine (SVM) and Decision Tree (DT). Whit a 1962 objects
balanced dataset, we applied K- means and Agglomerative Hierarchical
Clustering. All experiments with supervision reached an Overall Accuracy OA >=
97%.
| 0 | 1 | 0 | 0 | 0 | 0 |
Magnetized strange quark model with Big Rip singularity in $f(R,T)$ gravity | LRS (Locally Rotationally symmetric) Bianchi type-I magnetized strange quark
matter cosmological model have been studied based on $f(R,T)$ gravity. The
exact solutions of the field equations are derived with linearly time varying
deceleration parameter which is consistent with observational data (from SNIa,
BAO and CMB) of standard cosmology. It is observed that the model start with
big bang and ends with a Big Rip. The transition of deceleration parameter from
decelerating phase to accelerating phase with respect to redshift obtained in
our model fits with the recent observational data obtained by Farook et al. in
2017. The well known Hubble parameter $H(z)$ and distance modulus $\mu(z)$ are
discussed with redshift.
| 0 | 1 | 0 | 0 | 0 | 0 |
NPC: Neighbors Progressive Competition Algorithm for Classification of Imbalanced Data Sets | Learning from many real-world datasets is limited by a problem called the
class imbalance problem. A dataset is imbalanced when one class (the majority
class) has significantly more samples than the other class (the minority
class). Such datasets cause typical machine learning algorithms to perform
poorly on the classification task. To overcome this issue, this paper proposes
a new approach Neighbors Progressive Competition (NPC) for classification of
imbalanced datasets. Whilst the proposed algorithm is inspired by weighted
k-Nearest Neighbor (k-NN) algorithms, it has major differences from them.
Unlike k- NN, NPC does not limit its decision criteria to a preset number of
nearest neighbors. In contrast, NPC considers progressively more neighbors of
the query sample in its decision making until the sum of grades for one class
is much higher than the other classes. Furthermore, NPC uses a novel method for
grading the training samples to compensate for the imbalance issue. The grades
are calculated using both local and global information. In brief, the
contribution of this paper is an entirely new classifier for handling the
imbalance issue effectively without any manually-set parameters or any need for
expert knowledge. Experimental results compare the proposed approach with five
representative algorithms applied to fifteen imbalanced datasets and illustrate
this algorithms effectiveness.
| 1 | 0 | 0 | 1 | 0 | 0 |
On symmetric intersecting families | A family of sets is said to be \emph{symmetric} if its automorphism group is
transitive, and \emph{intersecting} if any two sets in the family have nonempty
intersection. Our purpose here is to study the following question: for $n, k\in
\mathbb{N}$ with $k \le n/2$, how large can a symmetric intersecting family of
$k$-element subsets of $\{1,2,\ldots,n\}$ be? As a first step towards a
complete answer, we prove that such a family has size at most
\[\exp\left(-\frac{c(n-2k)\log n}{k( \log n - \log k)} \right) \binom{n}{k},\]
where $c > 0$ is a universal constant. We also describe various combinatorial
and algebraic approaches to constructing such families.
| 0 | 0 | 1 | 0 | 0 | 0 |
The Geometry of Limit State Function Graphs and Subset Simulation | In the last fifteen the subset sampling method has often been used in
reliability problems as a tool for calculating small probabilities. This method
is extrapolating from an initial Monte Carlo estimate for the probability
content of a failure domain found by a suitable higher level of the original
limit state function. Then iteratively conditional probabilities are estimated
for failures domains decreasing to the original failure domain.
But there are assumptions not immediately obvious about the structure of the
failure domains which must be fulfilled that the method works properly. Here
examples are studied that show that at least in some cases if these premises
are not fulfilled, inaccurate results may be obtained. For the further
development of the subset sampling method it is certainly desirable to find
approaches where it is possible to check that these implicit assumptions are
not violated. Also it would be probably important to develop further
improvements of the concept to get rid of these limitations.
| 0 | 0 | 0 | 1 | 0 | 0 |
Decay of Solutions to the Maxwell Equations on Schwarzschild-de Sitter Spacetimes | In this work, we consider solutions of the Maxwell equations on the
Schwarzschild-de Sitter family of black hole spacetimes. We prove that, in the
static region bounded by black hole and cosmological horizons, solutions of the
Maxwell equations decay to stationary Coulomb solutions at a super-polynomial
rate, with decay measured according to ingoing and outgoing null coordinates.
Our method employs a differential transformation of Maxwell tensor components
to obtain higher-order quantities satisfying a Fackerell-Ipser equation, in the
style of Chandrasekhar and the more recent work of Pasqualotto. The analysis of
the Fackerell-Ipser equation is accomplished by means of the vector field
method, with decay estimates for the higher-order quantities leading to decay
estimates for components of the Maxwell tensor.
| 0 | 0 | 1 | 0 | 0 | 0 |
Machine Learning pipeline for discovering neuroimaging-based biomarkers in neurology and psychiatry | We consider a problem of diagnostic pattern recognition/classification from
neuroimaging data. We propose a common data analysis pipeline for
neuroimaging-based diagnostic classification problems using various ML
algorithms and processing toolboxes for brain imaging. We illustrate the
pipeline application by discovering new biomarkers for diagnostics of epilepsy
and depression based on clinical and MRI/fMRI data for patients and healthy
volunteers.
| 0 | 0 | 0 | 1 | 0 | 0 |
Covering and separation of Chebyshev points for non-integrable Riesz potentials | For Riesz $s$-potentials $K(x,y)=|x-y|^{-s}$, $s>0$, we investigate
separation and covering properties of $N$-point configurations
$\omega^*_N=\{x_1, \ldots, x_N\}$ on a $d$-dimensional compact set $A\subset
\mathbb{R}^\ell$ for which the minimum of $\sum_{j=1}^N K(x, x_j)$ is maximal.
Such configurations are called $N$-point optimal Riesz $s$-polarization (or
Chebyshev) configurations. For a large class of $d$-dimensional sets $A$ we
show that for $s>d$ the configurations $\omega^*_N$ have the optimal order of
covering. Furthermore, for these sets we investigate the asymptotics as $N\to
\infty$ of the best covering constant. For these purposes we compare
best-covering configurations with optimal Riesz $s$-polarization configurations
and determine the $s$-th root asymptotic behavior (as $s\to \infty$) of the
maximal $s$-polarization constants. In addition, we introduce the notion of
"weak separation" for point configurations and prove this property for optimal
Riesz $s$-polarization configurations on $A$ for $s>\text{dim}(A)$, and for
$d-1\leqslant s < d$ on the sphere $\mathbb{S}^d$.
| 0 | 0 | 1 | 0 | 0 | 0 |
The Strong Small Index Property for Free Homogeneous Structures | We show that in algebraically locally finite countable homogeneous structures
with a free stationary independence relation the small index property implies
the strong small index property. We use this and the main result of [15] to
deduce that countable free homogeneous structures in a locally finite
relational language have the strong small index property. We also exhibit new
continuum sized classes of $\aleph_0$-categorical structures with the strong
small index property whose automorphism groups are pairwise non-isomorphic.
| 0 | 0 | 1 | 0 | 0 | 0 |
The Unheralded Value of the Multiway Rendezvous: Illustration with the Production Cell Benchmark | The multiway rendezvous introduced in Theoretical CSP is a powerful paradigm
to achieve synchronization and communication among a group of (possibly more
than two) processes. We illustrate the advantages of this paradigm on the
production cell benchmark, a model of a real metal processing plant, for which
we propose a compositional software controller, which is written in LNT and
LOTOS, and makes intensive use of the multiway rendezvous.
| 1 | 0 | 0 | 0 | 0 | 0 |
Sequential Multiple Testing | We study an online multiple testing problem where the hypotheses arrive
sequentially in a stream. The test statistics are independent and assumed to
have the same distribution under their respective null hypotheses. We
investigate two procedures LORD and LOND, proposed by (Javanmard and Montanari,
2015), which are proved to control the FDR in an online manner. In some
(static) model, we show that LORD is optimal in some asymptotic sense, in
particular as powerful as the (static) Benjamini-Hochberg procedure to first
asymptotic order. We also quantify the performance of LOND. Some numerical
experiments complement our theory.
| 0 | 0 | 1 | 1 | 0 | 0 |
On the selection of polynomials for the DLP algorithm | In this paper we characterize the set of polynomials $f\in\mathbb F_q[X]$
satisfying the following property: there exists a positive integer $d$ such
that for any positive integer $\ell$ less or equal than the degree of $f$,
there exists $t_0$ in $\mathbb F_{q^d}$ such that the polynomial $f-t_0$ has an
irreducible factor of degree $\ell$ over $\mathbb F_{q^d}[X]$. This result is
then used to progress in the last step which is needed to remove the heuristic
from one of the quasi-polynomial time algorithms for discrete logarithm
problems (DLP) in small characteristic. Our characterization allows a
construction of polynomials satisfying the wanted property.
| 1 | 0 | 1 | 0 | 0 | 0 |
Existence of global weak solutions to the kinetic Peterlin model | We consider a class of kinetic models for polymeric fluids motivated by the
Peterlin dumbbell theories for dilute polymer solutions with a nonlinear spring
law for an infinitely extensible spring. The polymer molecules are suspended in
an incompressible viscous Newtonian fluid confined to a bounded domain in two
or three space dimensions. The unsteady motion of the solvent is described by
the incompressible Navier-Stokes equations with the elastic extra stress tensor
appearing as a forcing term in the momentum equation. The elastic stress tensor
is defined by the Kramers expression through the probability density function
that satisfies the corresponding Fokker-Planck equation. In this case, a
coefficient depending on the average length of polymer molecules appears in the
latter equation. Following the recent work of Barrett and Süli we prove the
existence of global-in-time weak solutions to the kinetic Peterlin model in two
space dimensions.
| 0 | 0 | 1 | 0 | 0 | 0 |
Passivation and Cooperative Control of Equilibrium-Independent Passivity-Short Systems | Maximal equilibrium-independent passivity (MEIP) is a recently introduced
system property which has acquired special attention in the study of networked
dynamical systems. MEIP requires a system to be passive with respect to any
forced equilibrium configuration and the associated steady-state input-output
map must be maximally monotone. In practice, however, most of the systems are
not well behaved and possess shortage of passivity or non-passiveness in their
operation. In this paper, we consider a class of passivity-short systems,
namely equilibrium-independent passivity-short (EIPS) systems, and presents an
input-output transformation based generalized passivation approach to ensure
their MEIP properties. We characterize the steady-state input-output relations
of the EIPS systems and establish their connection with that of the transformed
MEIP systems. We further study the diffusively-coupled networked interactions
of such EIPS systems and explore their connection to a pair of dual network
optimization problems, under the proposed matrix transformation. A simulation
example is given to illustrate the theoretical results.
| 1 | 0 | 0 | 0 | 0 | 0 |
OpenCluster: A Flexible Distributed Computing Framework for Astronomical Data Processing | The volume of data generated by modern astronomical telescopes is extremely
large and rapidly growing. However, current high-performance data processing
architectures/frameworks are not well suited for astronomers because of their
limitations and programming difficulties. In this paper, we therefore present
OpenCluster, an open-source distributed computing framework to support rapidly
developing high-performance processing pipelines of astronomical big data. We
first detail the OpenCluster design principles and implementations and present
the APIs facilitated by the framework. We then demonstrate a case in which
OpenCluster is used to resolve complex data processing problems for developing
a pipeline for the Mingantu Ultrawide Spectral Radioheliograph. Finally, we
present our OpenCluster performance evaluation. Overall, OpenCluster provides
not only high fault tolerance and simple programming interfaces, but also a
flexible means of scaling up the number of interacting entities. OpenCluster
thereby provides an easily integrated distributed computing framework for
quickly developing a high-performance data processing system of astronomical
telescopes and for significantly reducing software development expenses.
| 1 | 1 | 0 | 0 | 0 | 0 |
Magnetic order and spin dynamics across a ferromagnetic quantum critical point: $μ$SR investigations of YbNi$_4$(P$_{1-x}$As$_x$)$_2$ | In the quasi-1D heavy-fermion system YbNi$_4$(P$_{1-x}$As$_x$)$_2$ the
presence of a ferromagnetic (FM) quantum critical point (QCP) at $x_c$ $\approx
0.1$ with unconventional quantum critical exponents in the thermodynamic
properties has been recently reported. Here, we present muon-spin relaxation
($\mu$SR) experiments on polycrystals of this series to study the magnetic
order and the low energy 4$f$-electronic spin dynamics across the FM QCP. The
zero field $\mu$SR measurements on pure YbNi$_4$(P$_{2}$ proved static long
range magnetic order and suggested a strongly reduced ordered Yb moment of
about 0.04$\mu_B$. With increasing As substitution the ordered moment is
reduced by half at $x = 0.04$ and to less than 0.005 $\mu_B$ at $x=0.08$. The
dynamic behavior in the $\mu$SR response show that magnetism remains
homogeneous upon As substitution, without evidence for disorder effect. In the
paramagnetic state across the FM QCP the dynamic muon-spin relaxation rate
follows 1/$T_{1}T\propto T^{-n}$ with $1.01 \pm 0.04 \leq n \leq 1.13 \pm
0.06$. The critical fluctuations are very slow and are even becoming slower
when approaching the QCP.
| 0 | 1 | 0 | 0 | 0 | 0 |
Pixel-Level Statistical Analyses of Prescribed Fire Spread | Wildland fire dynamics is a complex turbulent dimensional process. Cellular
automata (CA) is an efficient tool to predict fire dynamics, but the main
parameters of the method are challenging to estimate. To overcome this
challenge, we compute statistical distributions of the key parameters of a CA
model using infrared images from controlled burns. Moreover, we apply this
analysis to different spatial scales and compare the experimental results to a
simple statistical model. By performing this analysis and making this
comparison, several capabilities and limitations of CA are revealed.
| 0 | 1 | 0 | 0 | 0 | 0 |
Deep Reinforcement Learning for Swarm Systems | Recently, deep reinforcement learning (RL) methods have been applied
successfully to multi-agent scenarios. Typically, these methods rely on a
concatenation of agent states to represent the information content required for
decentralized decision making. However, concatenation scales poorly to swarm
systems with a large number of homogeneous agents as it does not exploit the
fundamental properties inherent to these systems: (i) the agents in the swarm
are interchangeable and (ii) the exact number of agents in the swarm is
irrelevant. Therefore, we propose a new state representation for deep
multi-agent RL based on mean embeddings of distributions. We treat the agents
as samples of a distribution and use the empirical mean embedding as input for
a decentralized policy. We define different feature spaces of the mean
embedding using histograms, radial basis functions and a neural network learned
end-to-end. We evaluate the representation on two well known problems from the
swarm literature (rendezvous and pursuit evasion), in a globally and locally
observable setup. For the local setup we furthermore introduce simple
communication protocols. Of all approaches, the mean embedding representation
using neural network features enables the richest information exchange between
neighboring agents facilitating the development of more complex collective
strategies.
| 1 | 0 | 0 | 1 | 0 | 0 |
Predicting computational reproducibility of data analysis pipelines in large population studies using collaborative filtering | Evaluating the computational reproducibility of data analysis pipelines has
become a critical issue. It is, however, a cumbersome process for analyses that
involve data from large populations of subjects, due to their computational and
storage requirements. We present a method to predict the computational
reproducibility of data analysis pipelines in large population studies. We
formulate the problem as a collaborative filtering process, with constraints on
the construction of the training set. We propose 6 different strategies to
build the training set, which we evaluate on 2 datasets, a synthetic one
modeling a population with a growing number of subject types, and a real one
obtained with neuroinformatics pipelines. Results show that one sampling
method, "Random File Numbers (Uniform)" is able to predict computational
reproducibility with a good accuracy. We also analyze the relevance of
including file and subject biases in the collaborative filtering model. We
conclude that the proposed method is able to speedup reproducibility
evaluations substantially, with a reduced accuracy loss.
| 0 | 0 | 0 | 1 | 0 | 0 |
Experiment Segmentation in Scientific Discourse as Clause-level Structured Prediction using Recurrent Neural Networks | We propose a deep learning model for identifying structure within experiment
narratives in scientific literature. We take a sequence labeling approach to
this problem, and label clauses within experiment narratives to identify the
different parts of the experiment. Our dataset consists of paragraphs taken
from open access PubMed papers labeled with rhetorical information as a result
of our pilot annotation. Our model is a Recurrent Neural Network (RNN) with
Long Short-Term Memory (LSTM) cells that labels clauses. The clause
representations are computed by combining word representations using a novel
attention mechanism that involves a separate RNN. We compare this model against
LSTMs where the input layer has simple or no attention and a feature rich CRF
model. Furthermore, we describe how our work could be useful for information
extraction from scientific literature.
| 1 | 0 | 0 | 0 | 0 | 0 |
Information-Theoretic Analysis of Refractory Effects in the P300 Speller | The P300 speller is a brain-computer interface that enables people with
neuromuscular disorders to communicate based on eliciting event-related
potentials (ERP) in electroencephalography (EEG) measurements. One challenge to
reliable communication is the presence of refractory effects in the P300 ERP
that induces temporal dependence in the user's EEG responses. We propose a
model for the P300 speller as a communication channel with memory. By studying
the maximum information rate on this channel, we gain insight into the
fundamental constraints imposed by refractory effects. We construct codebooks
based on the optimal input distribution, and compare them to existing codebooks
in literature.
| 1 | 0 | 1 | 0 | 0 | 0 |
Growth, Industrial Externality, Prospect Dynamics and Well-being on Markets | Functions or 'functionnings' enable to give a structure to any economic
activity whether they are used to describe a good or a service that is
exchanged on a market or they constitute the capability of an agent to provide
the labor market with specific work and skills. That structure encompasses the
basic law of supply and demand and the conditions of growth within a
transaction and of the inflation control. Functional requirements can be
followed from the design of a product to the delivery of a solution to a
customer needs with different levels of externalities while value is created
integrating organizational and technical constraints whereas a budget is
allocated to the various entities of the firm involved in the production.
Entering the market through that structure leads to designing basic equations
of its dynamics and to finding canonical solutions out of particular
equilibria. This approach enables to tackle behavioral foundations of Prospect
Theory within a generalization of its probability weighting function turned
into an operator which applies to Western, Educated, Industrialized, Rich, and
Democratic societies as well as to the poorest ones. The nature of reality and
well-being appears then as closely related to the relative satisfaction reached
on the market, as it can be conceived by an agent, according to business
cycles. This reality being the result of the complementary systems that govern
human mind as structured by rational psychologists.
| 0 | 0 | 0 | 0 | 0 | 1 |
Structure Preserving Model Reduction of Parametric Hamiltonian Systems | While reduced-order models (ROMs) have been popular for efficiently solving
large systems of differential equations, the stability of reduced models over
long-time integration is of present challenges. We present a greedy approach
for ROM generation of parametric Hamiltonian systems that captures the
symplectic structure of Hamiltonian systems to ensure stability of the reduced
model. Through the greedy selection of basis vectors, two new vectors are added
at each iteration to the linear vector space to increase the accuracy of the
reduced basis. We use the error in the Hamiltonian due to model reduction as an
error indicator to search the parameter space and identify the next best basis
vectors. Under natural assumptions on the set of all solutions of the
Hamiltonian system under variation of the parameters, we show that the greedy
algorithm converges with exponential rate. Moreover, we demonstrate that
combining the greedy basis with the discrete empirical interpolation method
also preserves the symplectic structure. This enables the reduction of the
computational cost for nonlinear Hamiltonian systems. The efficiency, accuracy,
and stability of this model reduction technique is illustrated through
simulations of the parametric wave equation and the parametric Schrodinger
equation.
| 0 | 0 | 1 | 0 | 0 | 0 |
Consensus measure of rankings | A ranking is an ordered sequence of items, in which an item with higher
ranking score is more preferred than the items with lower ranking scores. In
many information systems, rankings are widely used to represent the preferences
over a set of items or candidates. The consensus measure of rankings is the
problem of how to evaluate the degree to which the rankings agree. The
consensus measure can be used to evaluate rankings in many information systems,
as quite often there is not ground truth available for evaluation.
This paper introduces a novel approach for consensus measure of rankings by
using graph representation, in which the vertices or nodes are the items and
the edges are the relationship of items in the rankings. Such representation
leads to various algorithms for consensus measure in terms of different aspects
of rankings, including the number of common patterns, the number of common
patterns with fixed length and the length of the longest common patterns. The
proposed measure can be adopted for various types of rankings, such as full
rankings, partial rankings and rankings with ties. This paper demonstrates how
the proposed approaches can be used to evaluate the quality of rank aggregation
and the quality of top-$k$ rankings from Google and Bing search engines.
| 1 | 0 | 0 | 0 | 0 | 0 |
Possible resonance effect of dark matter axions in SNS Josephson junctions | Dark matter axions can generate peculiar effects in special types of
Josephson junctions, so-called SNS junctions. One can show that the axion field
equations in a Josephson environment allow for very small oscillating
supercurrents, which manifest themselves as a tiny wiggle in the I-V curve, a
so-called Shapiro step, which occurs at a frequency given by the axion mass.
The effect is very small but perfectly measurable in modern nanotechnological
devices. In this paper I will summarize the theory and then present evidence
that candidate Shapiro steps of this type have indeed been seen in several
independent condensed matter experiments. Assuming the observed tiny Shapiro
steps are due to axion flow then these data point to an axion mass of $(106 \pm
6)\mu$eV, consistent with what is expected for the QCD axion. In addition to
the above small Shapiro resonance effects at frequencies in the GHz region one
also expects to see broad-band noise effects at much lower frequencies. Overall
this approach provides a novel pathway for the future design of new types of
axionic dark matter detectors. The resonant Josephson data summarized in this
paper are consistent with a 'vanilla' axion with a coupling constant
$f_a=\sqrt{v_{EW}m_{Pl}}=5.48 \cdot 10^{10}$GeV given by the geometric average
of the electroweak symmetry breaking scale $v_{EW}$ and the Planck mass
$m_{Pl}$.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Composition Theorem for Randomized Query Complexity | Let the randomized query complexity of a relation for error probability
$\epsilon$ be denoted by $R_\epsilon(\cdot)$. We prove that for any relation $f
\subseteq \{0,1\}^n \times \mathcal{R}$ and Boolean function $g:\{0,1\}^m
\rightarrow \{0,1\}$, $R_{1/3}(f\circ g^n) = \Omega(R_{4/9}(f)\cdot
R_{1/2-1/n^4}(g))$, where $f \circ g^n$ is the relation obtained by composing
$f$ and $g$. We also show that $R_{1/3}\left(f \circ \left(g^\oplus_{O(\log
n)}\right)^n\right)=\Omega(\log n \cdot R_{4/9}(f) \cdot R_{1/3}(g))$, where
$g^\oplus_{O(\log n)}$ is the function obtained by composing the xor function
on $O(\log n)$ bits and $g^t$.
| 1 | 0 | 0 | 0 | 0 | 0 |
Soliton solutions for the elastic metric on spaces of curves | In this article we investigate a first order reparametrization-invariant
Sobolev metric on the space of immersed curves. Motivated by applications in
shape analysis where discretizations of this infinite-dimensional space are
needed, we extend this metric to the space of Lipschitz curves, establish the
wellposedness of the geodesic equation thereon, and show that the space of
piecewise linear curves is a totally geodesic submanifold. Thus, piecewise
linear curves are natural finite elements for the discretization of the
geodesic equation. Interestingly, geodesics in this space can be seen as
soliton solutions of the geodesic equation, which were not known to exist for
reparametrization-invariant Sobolev metrics on spaces of curves.
| 0 | 0 | 1 | 0 | 0 | 0 |
Influence of Personal Preferences on Link Dynamics in Social Networks | We study a unique network dataset including periodic surveys and electronic
logs of dyadic contacts via smartphones. The participants were a sample of
freshmen entering university in the Fall 2011. Their opinions on a variety of
political and social issues and lists of activities on campus were regularly
recorded at the beginning and end of each semester for the first three years of
study. We identify a behavioral network defined by call and text data, and a
cognitive network based on friendship nominations in ego-network surveys. Both
networks are limited to study participants. Since a wide range of attributes on
each node were collected in self-reports, we refer to these networks as
attribute-rich networks. We study whether student preferences for certain
attributes of friends can predict formation and dissolution of edges in both
networks. We introduce a method for computing student preferences for different
attributes which we use to predict link formation and dissolution. We then rank
these attributes according to their importance for making predictions. We find
that personal preferences, in particular political views, and preferences for
common activities help predict link formation and dissolution in both the
behavioral and cognitive networks.
| 1 | 0 | 0 | 0 | 0 | 0 |
PBW bases and marginally large tableaux in types B and C | We explicitly describe the isomorphism between two combinatorial realizations
of Kashiwara's infinity crystal in types B and C. The first realization is in
terms of marginally large tableaux and the other is in terms of Kostant
partitions coming from PBW bases. We also discuss a stack notation for Kostant
partitions which simplifies that realization.
| 0 | 0 | 1 | 0 | 0 | 0 |
Ensemble Clustering for Graphs | We propose an ensemble clustering algorithm for graphs (ECG), which is based
on the Louvain algorithm and the concept of consensus clustering. We validate
our approach by replicating a recently published study comparing graph
clustering algorithms over artificial networks, showing that ECG outperforms
the leading algorithms from that study. We also illustrate how the ensemble
obtained with ECG can be used to quantify the presence of community structure
in the graph.
| 1 | 0 | 0 | 1 | 0 | 0 |
Testing for Feature Relevance: The HARVEST Algorithm | Feature selection with high-dimensional data and a very small proportion of
relevant features poses a severe challenge to standard statistical methods. We
have developed a new approach (HARVEST) that is straightforward to apply,
albeit somewhat computer-intensive. This algorithm can be used to pre-screen a
large number of features to identify those that are potentially useful. The
basic idea is to evaluate each feature in the context of many random subsets of
other features. HARVEST is predicated on the assumption that an irrelevant
feature can add no real predictive value, regardless of which other features
are included in the subset. Motivated by this idea, we have derived a simple
statistical test for feature relevance. Empirical analyses and simulations
produced so far indicate that the HARVEST algorithm is highly effective in
predictive analytics, both in science and business.
| 0 | 0 | 0 | 1 | 0 | 0 |
Energy Harvesting Enabled MIMO Relaying through PS | This paper considers a multiple-input multiple-output (MIMO) relay system
with an energy harvesting relay node. All nodes are equipped with multiple
antennas, and the relay node depends on the harvested energy from the received
signal to support information forwarding. In particular, the relay node deploys
power splitting based energy harvesting scheme. The capacity maximization
problem subject to power constraints at both the source and relay nodes is
considered for both fixed source covariance matrix and optimal source
covariance matrix cases. Instead of using existing software solvers, iterative
approaches using dual decomposition technique are developed based on the
structures of the optimal relay precoding and source covariance matrices.
Simulation results demonstrate the performance gain of the joint optimization
against the fixed source covariance matrix case.
| 1 | 0 | 0 | 0 | 0 | 0 |
X-ray diagnostics of massive star winds | Observations with powerful X-ray telescopes, such as XMM-Newton and Chandra,
significantly advance our understanding of massive stars. Nearly all early-type
stars are X-ray sources. Studies of their X-ray emission provide important
diagnostics of stellar winds. High-resolution X-ray spectra of O-type stars are
well explained when stellar wind clumping is taking into account, providing
further support to a modern picture of stellar winds as non-stationary,
inhomogeneous outflows. X-ray variability is detected from such winds, on time
scales likely associated with stellar rotation. High-resolution X-ray
spectroscopy indicates that the winds of late O-type stars are predominantly in
a hot phase. Consequently, X-rays provide the best observational window to
study these winds. X-ray spectroscopy of evolved, Wolf-Rayet type, stars allows
to probe their powerful metal enhanced winds, while the mechanisms responsible
for the X-ray emission of these stars are not yet understood.
| 0 | 1 | 0 | 0 | 0 | 0 |
Local asymptotic properties for Cox-Ingersoll-Ross process with discrete observations | In this paper, we consider a one-dimensional Cox-Ingersoll-Ross (CIR) process
whose drift coefficient depends on unknown parameters. Considering the process
discretely observed at high frequency, we prove the local asymptotic normality
property in the subcritical case, the local asymptotic quadraticity in the
critical case, and the local asymptotic mixed normality property in the
supercritical case. To obtain these results, we use the Malliavin calculus
techniques developed recently for CIR process together with the $L^p$-norm
estimation for positive and negative moments of the CIR process. In this study,
we require the same conditions of high frequency $\Delta_n\rightarrow 0$ and
infinite horizon $n\Delta_n\rightarrow\infty$ as in the case of ergodic
diffusions with globally Lipschitz coefficients studied earlier by Gobet
\cite{G02}. However, in the non-ergodic cases, additional assumptions on the
decreasing rate of $\Delta_n$ are required due to the fact that the square root
diffusion coefficient of the CIR process is not regular enough. Indeed, we
assume $\frac{n\Delta_n^{\frac{3}{2}}}{\log(n\Delta_n)}\to 0$ for the critical
case and $n\Delta_n^2\to 0$ for the supercritical case.
| 0 | 0 | 1 | 1 | 0 | 0 |
JHelioviewer - Time-dependent 3D visualisation of solar and heliospheric data | Context. Solar observatories are providing the world-wide community with a
wealth of data, covering large time ranges, multiple viewpoints, and returning
large amounts of data. In particular, the large volume of SDO data presents
challenges: it is available only from a few repositories, and full-disk,
full-cadence data for reasonable durations of scientific interest are difficult
to download practically due to their size and download data rates available to
most users. From a scientist's perspective this poses three problems:
accessing, browsing and finding interesting data as efficiently as possible.
Aims. To address these challenges, we have developed JHelioviewer, a
visualisation tool for solar data based on the JPEG2000 compression standard
and part of the open source ESA/NASA Helioviewer Project. Since the first
release of JHelioviewer, the scientific functionality of the software has been
extended significantly, and the objective of this paper is to highlight these
improvements.
Methods. The JPEG2000 standard offers useful new features that facilitate the
dissemination and analysis of high-resolution image data and offers a solution
to the challenge of efficiently browsing petabyte-scale image archives. The
JHelioviewer software is open source, platform independent and extendable via a
plug-in architecture.
Results. With JHelioviewer, users can visualise the Sun for any time period
between September 1991 and today. They can perform basic image processing in
real time, track features on the Sun and interactively overlay magnetic field
extrapolations. The software integrates solar event data and a time line
display. As a first step towards supporting science planning of the upcoming
Solar Orbiter mission, JHelioviewer offers a virtual camera model that enables
users to set the vantage point to the location of a spacecraft or celestial
body at any given time.
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Topological Dirac Nodal-net Fermions in AlB$_2$-type TiB$_2$ and ZrB$_2$ | Based on first-principles calculations and effective model analysis, a Dirac
nodal-net semimetal state is recognized in AlB$_2$-type TiB$_2$ and ZrB$_2$
when spin-orbit coupling (SOC) is ignored. Taking TiB$_2$ as an example, there
are several topological excitations in this nodal-net structure including
triple point, nexus, and nodal link, which are protected by coexistence of
spatial-inversion symmetry and time reversal symmetry. This nodal-net state is
remarkably different from that of IrF$_4$, which requires sublattice chiral
symmetry. In addition, linearly and quadratically dispersed two-dimensional
surface Dirac points are identified as having emerged on the B-terminated and
Ti-terminated (001) surfaces of TiB$_2$ respectively, which are analogous to
those of monolayer and bilayer graphene.
| 0 | 1 | 0 | 0 | 0 | 0 |
Model-based Design Evaluation of a Compact, High-Efficiency Neutron Scatter Camera | This paper presents the model-based design and evaluation of an instrument
that estimates incident neutron direction using the kinematics of neutron
scattering by hydrogen-1 nuclei in an organic scintillator. The instrument
design uses a single, nearly contiguous volume of organic scintillator that is
internally subdivided only as necessary to create optically isolated pillars.
Scintillation light emitted in a given pillar is confined to that pillar by a
combination of total internal reflection and a specular reflector applied to
the four sides of the pillar transverse to its long axis. The scintillation
light is collected at each end of the pillar using a photodetector. In this
optically segmented design, the (x, y) position of scintillation light emission
(where the x and y coordinates are transverse to the long axis of the pillars)
is estimated as the pillar's (x, y) position in the scintillator "block", and
the z-position (the position along the pillar's long axis) is estimated from
the amplitude and relative timing of the signals produced by the photodetectors
at each end of the pillar. For proton recoils greater than 1 MeV, we show that
the (x, y, z)-position of neutron-proton scattering can be estimated with < 1
cm root-mean-squared [RMS] error and the proton recoil energy can be estimated
with < 50 keV RMS error by fitting the photodetectors' response time history to
models of optical photon transport within the scintillator pillars. Finally, we
evaluate several alternative designs of this proposed single-volume scatter
camera made of pillars of plastic scintillator (SVSC-PiPS), studying the effect
of pillar dimensions, scintillator material, and photodetector response vs.
time. Specifically, we conclude that an SVSC-PiPS constructed using EJ-204 and
an MCP-PM will produce the most precise estimates of incident neutron direction
and energy.
| 0 | 1 | 0 | 0 | 0 | 0 |
Galactic Pal-eontology: Abundance Analysis of the Disrupting Globular Cluster Palomar 5 | We present a chemical abundance analysis of the tidally disrupted globular
cluster (GC) Palomar 5. By co-adding high-resolution spectra of 15 member stars
from the cluster's main body, taken at low signal-to-noise with the Keck/HIRES
spectrograph, we were able to measure integrated abundance ratios of 24 species
of 20 elements including all major nucleosynthetic channels (namely the light
element Na; $\alpha$-elements Mg, Si, Ca, Ti; Fe-peak and heavy elements Sc, V,
Cr, Mn, Co, Ni, Cu, Zn; and the neutron-capture elements Y, Zr, Ba, La, Nd, Sm,
Eu). The mean metallicity of $-1.56\pm0.02\pm0.06$ dex (statistical and
systematic errors) agrees well with the values from individual, low-resolution
measurements of individual stars, but it is lower than previous high-resolution
results of a small number of stars in the literature. Comparison with Galactic
halo stars and other disrupted and unperturbed GCs renders Pal~5 a typical
representative of the Milky Way halo population, as has been noted before,
emphasizing that the early chemical evolution of such clusters is decoupled
from their later dynamical history. We also performed a test as to the
detectability of light element variations in this co-added abundance analysis
technique and found that this approach is not sensitive even in the presence of
a broad range in sodium of $\sim$0.6 dex, a value typically found in the old
halo GCs. Thus, while methods of determining the global abundance patterns of
such objects are well suited to study their overall enrichment histories,
chemical distinctions of their multiple stellar populations is still best
obtained from measurements of individual stars.
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Anisotropic mechanical and optical response and negative Poissons ratio in Mo2C nanomembranes revealed by first-principles simulations | Transition metal carbides include a wide variety of materials with attractive
properties that are suitable for numerous and diverse applications. Most recent
experimental advance could provide a path toward successful synthesis of
large-area and high-quality ultrathin Mo2C membranes with superconducting
properties. In the present study, we used first-principles density functional
theory calculations to explore the mechanical and optical response of
single-layer and free-standing Mo2C. Uniaxial tensile simulations along the
armchair and zigzag directions were conducted and we found that while the
elastic properties are close along various loading directions, nonlinear
regimes in stress-strain curves are considerably different. We found that Mo2C
sheets present negative Poisson's ratio and thus can be categorized as an
auxetic material. Our simulations also reveal that Mo2C films retain their
metallic electronic characteristic upon the uniaxial loading. We found that for
Mo2C nanomembranes the dielectric function becomes anisotropic along in-plane
and out-of plane directions. Our findings can be useful for the practical
application of Mo2C sheets in nanodevices.
| 0 | 1 | 0 | 0 | 0 | 0 |
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