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Generalized weighted Ostrowski and Ostrowski-Grüss type inequalities on time scales via a parameter function | We prove generalized weighted Ostrowski and Ostrowski--Grüss type
inequalities on time scales via a parameter function. In particular, our result
extends a result of Dragomir and Barnett. Furthermore, we apply our results to
the continuous, discrete, and quantum cases, to obtain some interesting new
inequalities.
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"Found in Translation": Predicting Outcomes of Complex Organic Chemistry Reactions using Neural Sequence-to-Sequence Models | There is an intuitive analogy of an organic chemist's understanding of a
compound and a language speaker's understanding of a word. Consequently, it is
possible to introduce the basic concepts and analyze potential impacts of
linguistic analysis to the world of organic chemistry. In this work, we cast
the reaction prediction task as a translation problem by introducing a
template-free sequence-to-sequence model, trained end-to-end and fully
data-driven. We propose a novel way of tokenization, which is arbitrarily
extensible with reaction information. With this approach, we demonstrate
results superior to the state-of-the-art solution by a significant margin on
the top-1 accuracy. Specifically, our approach achieves an accuracy of 80.1%
without relying on auxiliary knowledge such as reaction templates. Also, 66.4%
accuracy is reached on a larger and noisier dataset.
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Efficient Decomposition of High-Rank Tensors | Tensors are a natural way to express correlations among many physical
variables, but storing tensors in a computer naively requires memory which
scales exponentially in the rank of the tensor. This is not optimal, as the
required memory is actually set not by the rank but by the mutual information
amongst the variables in question. Representations such as the tensor tree
perform near-optimally when the tree decomposition is chosen to reflect the
correlation structure in question, but making such a choice is non-trivial and
good heuristics remain highly context-specific. In this work I present two new
algorithms for choosing efficient tree decompositions, independent of the
physical context of the tensor. The first is a brute-force algorithm which
produces optimal decompositions up to truncation error but is generally
impractical for high-rank tensors, as the number of possible choices grows
exponentially in rank. The second is a greedy algorithm, and while it is not
optimal it performs extremely well in numerical experiments while having
runtime which makes it practical even for tensors of very high rank.
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Universality in Chaos: Lyapunov Spectrum and Random Matrix Theory | We propose the existence of a new universality in classical chaotic systems
when the number of degrees of freedom is large: the statistical property of the
Lyapunov spectrum is described by Random Matrix Theory. We demonstrate it by
studying the finite-time Lyapunov exponents of the matrix model of a stringy
black hole and the mass deformed models. The massless limit, which has a dual
string theory interpretation, is special in that the universal behavior can be
seen already at t=0, while in other cases it sets in at late time. The same
pattern is demonstrated also in the product of random matrices.
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Fixed points of competitive threshold-linear networks | Threshold-linear networks (TLNs) are models of neural networks that consist
of simple, perceptron-like neurons and exhibit nonlinear dynamics that are
determined by the network's connectivity. The fixed points of a TLN, including
both stable and unstable equilibria, play a critical role in shaping its
emergent dynamics. In this work, we provide two novel characterizations for the
set of fixed points of a competitive TLN: the first is in terms of a simple
sign condition, while the second relies on the concept of domination. We apply
these results to a special family of TLNs, called combinatorial
threshold-linear networks (CTLNs), whose connectivity matrices are defined from
directed graphs. This leads us to prove a series of graph rules that enable one
to determine fixed points of a CTLN by analyzing the underlying graph.
Additionally, we study larger networks composed of smaller "building block"
subnetworks, and prove several theorems relating the fixed points of the full
network to those of its components. Our results provide the foundation for a
kind of "graphical calculus" to infer features of the dynamics from a network's
connectivity.
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A Decision Procedure for Herbrand Formulae without Skolemization | This paper describes a decision procedure for disjunctions of conjunctions of
anti-prenex normal forms of pure first-order logic (FOLDNFs) that do not
contain $\vee$ within the scope of quantifiers. The disjuncts of these FOLDNFs
are equivalent to prenex normal forms whose quantifier-free parts are
conjunctions of atomic and negated atomic formulae (= Herbrand formulae). In
contrast to the usual algorithms for Herbrand formulae, neither skolemization
nor unification algorithms with function symbols are applied. Instead, a
procedure is described that rests on nothing but equivalence transformations
within pure first-order logic (FOL). This procedure involves the application of
a calculus for negative normal forms (the NNF-calculus) with $A \dashv\vdash A
\wedge A$ (= $\wedge$I) as the sole rule that increases the complexity of given
FOLDNFs. The described algorithm illustrates how, in the case of Herbrand
formulae, decision problems can be solved through a systematic search for
proofs that reduce the number of applications of the rule $\wedge$I to a
minimum in the NNF-calculus. In the case of Herbrand formulae, it is even
possible to entirely abstain from applying $\wedge$I. Finally, it is shown how
the described procedure can be used within an optimized general search for
proofs of contradiction and what kind of questions arise for a
$\wedge$I-minimal proof strategy in the case of a general search for proofs of
contradiction.
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The role of relativistic many-body theory in probing new physics beyond the standard model via the electric dipole moments of diamagnetic atoms | The observation of electric dipole moments (EDMs) in atomic systems due to
parity and time-reversal violating (P,T-odd) interactions can probe new physics
beyond the standard model and also provide insights into the matter-antimatter
asymmetry in the Universe. The EDMs of open-shell atomic systems are sensitive
to the electron EDM and the P,T-odd scalar-pseudoscalar (S-PS) semi-leptonic
interaction, but the dominant contributions to the EDMs of diamagnetic atoms
come from the hadronic and tensor-pseudotensor (T-PT) semi-leptonic
interactions. Several diamagnetic atoms like $^{129}$Xe, $^{171}$Yb,
$^{199}$Hg, $^{223}$Rn, and $^{225}$Ra are candidates for the experimental
search for the possible existence of EDMs, and among these $^{199}$Hg has
yielded the lowest limit till date. The T or CP violating coupling constants of
the aforementioned interactions can be extracted from these measurements by
combining with atomic and nuclear calculations. In this work, we report the
calculations of the EDMs of the above atoms by including both the
electromagnetic and P,T-odd violating interactions simultaneously. These
calculations are performed by employing relativistic many-body methods based on
the random phase approximation (RPA) and the singles and doubles
coupled-cluster (CCSD) method starting with the Dirac-Hartree-Fock (DHF) wave
function in both cases. The differences in the results from both the methods
shed light on the importance of the non-core-polarization electron correlation
effects that are accounted for by the CCSD method. We also determine electric
dipole polarizabilities of these atoms, which have computational similarities
with EDMs and compare them with the available experimental and other
theoretical results to assess the accuracy of our calculations.
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Security Incident Recognition and Reporting (SIRR): An Industrial Perspective | Reports and press releases highlight that security incidents continue to
plague organizations. While researchers and practitioners' alike endeavor to
identify and implement realistic security solutions to prevent incidents from
occurring, the ability to initially identify a security incident is paramount
when researching a security incident lifecycle. Hence, this research
investigates the ability of employees in a Global Fortune 500 financial
organization, through internal electronic surveys, to recognize and report
security incidents to pursue a more holistic security posture. The research
contribution is an initial insight into security incident perceptions by
employees in the financial sector as well as serving as an initial guide for
future security incident recognition and reporting initiatives.
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On attainability of optimal controls in coefficients for system of Hammerstein type with anisotropic p-Laplacia | In this paper we consider an optimal control problem for the coupled system
of a nonlinear monotone Dirichlet problem with anisotropic p-Laplacian and
matrix-valued nonsmooth controls in its coefficients and a nonlinear equation
of Hammerstein type. Using the direct method in calculus of variations, we
prove the existence of an optimal control in considered problem and provide
sensitivity analysis for a specific case of considered problem with respect to
two-parameter regularization.
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Adversarial Variational Bayes Methods for Tweedie Compound Poisson Mixed Models | The Tweedie Compound Poisson-Gamma model is routinely used for modeling
non-negative continuous data with a discrete probability mass at zero. Mixed
models with random effects account for the covariance structure related to the
grouping hierarchy in the data. An important application of Tweedie mixed
models is pricing the insurance policies, e.g. car insurance. However, the
intractable likelihood function, the unknown variance function, and the
hierarchical structure of mixed effects have presented considerable challenges
for drawing inferences on Tweedie. In this study, we tackle the Bayesian
Tweedie mixed-effects models via variational inference approaches. In
particular, we empower the posterior approximation by implicit models trained
in an adversarial setting. To reduce the variance of gradients, we
reparameterize random effects, and integrate out one local latent variable of
Tweedie. We also employ a flexible hyper prior to ensure the richness of the
approximation. Our method is evaluated on both simulated and real-world data.
Results show that the proposed method has smaller estimation bias on the random
effects compared to traditional inference methods including MCMC; it also
achieves a state-of-the-art predictive performance, meanwhile offering a richer
estimation of the variance function.
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Sampling of Temporal Networks: Methods and Biases | Temporal networks have been increasingly used to model a diversity of systems
that evolve in time; for example human contact structures over which dynamic
processes such as epidemics take place. A fundamental aspect of real-life
networks is that they are sampled within temporal and spatial frames.
Furthermore, one might wish to subsample networks to reduce their size for
better visualization or to perform computationally intensive simulations. The
sampling method may affect the network structure and thus caution is necessary
to generalize results based on samples. In this paper, we study four sampling
strategies applied to a variety of real-life temporal networks. We quantify the
biases generated by each sampling strategy on a number of relevant statistics
such as link activity, temporal paths and epidemic spread. We find that some
biases are common in a variety of networks and statistics, but one strategy,
uniform sampling of nodes, shows improved performance in most scenarios. Our
results help researchers to better design network data collection protocols and
to understand the limitations of sampled temporal network data.
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Personalizing Path-Specific Effects | Unlike classical causal inference, which often has an average causal effect
of a treatment within a population as a target, in settings such as
personalized medicine, the goal is to map a given unit's characteristics to a
treatment tailored to maximize the expected outcome for that unit. Obtaining
high-quality mappings of this type is the goal of the dynamic regime literature
(Chakraborty and Moodie 2013), with connections to reinforcement learning and
experimental design. Aside from the average treatment effects, mechanisms
behind causal relationships are also of interest. A well-studied approach to
mechanism analysis is establishing average effects along with a particular set
of causal pathways, in the simplest case the direct and indirect effects.
Estimating such effects is the subject of the mediation analysis literature
(Robins and Greenland 1992; Pearl 2001).
In this paper, we consider how unit characteristics may be used to tailor a
treatment assignment strategy that maximizes a particular path-specific effect.
In healthcare applications, finding such a policy is of interest if, for
instance, we are interested in maximizing the chemical effect of a drug on an
outcome (corresponding to the direct effect), while assuming drug adherence
(corresponding to the indirect effect) is set to some reference level. To solve
our problem, we define counterfactuals associated with path-specific effects of
a policy, give a general identification algorithm for these counterfactuals,
give a proof of completeness, and show how classification algorithms in machine
learning (Chen, Zeng, and Kosorok 2016) may be used to find a high-quality
policy. We validate our approach via a simulation study.
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Bridge Programs as an approach to improving diversity in physics | In most physical sciences, students from underrepresented minority (URM)
groups constitute a small percentage of earned degrees at the undergraduate and
graduate levels. Bridge programs can serve as an initiative to increase the
number of URM students that gain access to graduate school and earn advanced
degrees in physics. This talk discussed levels of representation in physical
sciences as well as some results and best practices of current bridge programs
in physics. The APS Bridge Program has enabled over 100 students to be placed
into Bridge or graduate programs in physics, while retaining 88% of those
placed.
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NEURAL: quantitative features for newborn EEG using Matlab | Background: For newborn infants in critical care, continuous monitoring of
brain function can help identify infants at-risk of brain injury. Quantitative
features allow a consistent and reproducible approach to EEG analysis, but only
when all implementation aspects are clearly defined.
Methods: We detail quantitative features frequently used in neonatal EEG
analysis and present a Matlab software package together with exact
implementation details for all features. The feature set includes stationary
features that capture amplitude and frequency characteristics and features of
inter-hemispheric connectivity. The software, a Neonatal Eeg featURe set in
mAtLab (NEURAL), is open source and freely available. The software also
includes a pre-processing stage with a basic artefact removal procedure.
Conclusions: NEURAL provides a common platform for quantitative analysis of
neonatal EEG. This will support reproducible research and enable comparisons
across independent studies. These features present summary measures of the EEG
that can also be used in automated methods to determine brain development and
health of the newborn in critical care.
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False Positive Reduction by Actively Mining Negative Samples for Pulmonary Nodule Detection in Chest Radiographs | Generating large quantities of quality labeled data in medical imaging is
very time consuming and expensive. The performance of supervised algorithms for
various tasks on imaging has improved drastically over the years, however the
availability of data to train these algorithms have become one of the main
bottlenecks for implementation. To address this, we propose a semi-supervised
learning method where pseudo-negative labels from unlabeled data are used to
further refine the performance of a pulmonary nodule detection network in chest
radiographs. After training with the proposed network, the false positive rate
was reduced to 0.1266 from 0.4864 while maintaining sensitivity at 0.89.
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WASP-12b: A Mass-Losing Extremely Hot Jupiter | WASP-12b is an extreme hot Jupiter in a 1 day orbit, suffering profound
irradiation from its F type host star. The planet is surrounded by a
translucent exosphere which overfills the Roche lobe and produces
line-blanketing absorption in the near-UV. The planet is losing mass. Another
unusual property of the WASP-12 system is that observed chromospheric emission
from the star is anomalously low: WASP-12 is an extreme outlier amongst
thousands of stars when the log $R^{'}_{HK}$ chromospheric activity indicator
is considered. Occam's razor suggests these two extremely rare properties
coincide in this system because they are causally related. The absence of the
expected chromospheric emission is attributable to absorption by a diffuse
circumstellar gas shroud which surrounds the entire planetary system and fills
our line of sight to the chromospherically active regions of the star. This
circumstellar gas shroud is probably fed by mass loss from WASP-12b. The
orbital eccentricity of WASP-12b is small but may be non-zero. The planet is
part of a hierarchical quadruple system; its current orbit is consistent with
prior secular dynamical evolution leading to a highly eccentric orbit followed
by tidal circularization. When compared with the Galaxy's population of
planets, WASP-12b lies on the upper boundary of the sub-Jovian desert in both
the $(M_{\rm P}, P)$ and $(R_{\rm P}, P)$ planes. Determining the mass loss
rate for WASP-12b will illuminate the mechanism(s) responsible for the
sub-Jovian desert.
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On the status of the Born-Oppenheimer expansion in molecular systems theory | It is shown that the adiabatic Born-Oppenheimer expansion does not satisfy
the necessary condition for the applicability of perturbation theory. A simple
example of an exact solution of a problem that can not be obtained from the
Born-Oppenheimer expansion is given. A new version of perturbation theory for
molecular systems is proposed.
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Computing Tropical Prevarieties in Parallel | The computation of the tropical prevariety is the first step in the
application of polyhedral methods to compute positive dimensional solution sets
of polynomial systems. In particular, pretropisms are candidate leading
exponents for the power series developments of the solutions. The computation
of the power series may start as soon as one pretropism is available, so our
parallel computation of the tropical prevariety has an application in a
pipelined solver.
We present a parallel implementation of dynamic enumeration. Our first
distributed memory implementation with forked processes achieved good speedups,
but quite often resulted in large variations in the execution times of the
processes. The shared memory multithreaded version applies work stealing to
reduce the variability of the run time. Our implementation applies the thread
safe Parma Polyhedral Library (PPL), in exact arithmetic with the GNU
Multiprecision Arithmetic Library (GMP), aided by the fast memory allocations
of TCMalloc.
Our parallel implementation is capable of computing the tropical prevariety
of the cyclic 16-roots problem. We also report on computational experiments on
the $n$-body and $n$-vortex problems; our computational results compare
favorably with Gfan.
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3k-4 theorem for ordered groups | Recently, G. A. Freiman, M. Herzog, P. Longobardi, M. Maj proved two
`structure theorems' for ordered groups \cite{FHLM}. We give elementary proof
of these two theorems.
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The Extinction Properties of and Distance to the Highly Reddened Type Ia Supernova SN 2012cu | Correction of Type Ia Supernova brightnesses for extinction by dust has
proven to be a vexing problem. Here we study the dust foreground to the highly
reddened SN 2012cu, which is projected onto a dust lane in the galaxy NGC 4772.
The analysis is based on multi-epoch, spectrophotometric observations spanning
3,300 - 9,200 {\AA}, obtained by the Nearby Supernova Factory. Phase-matched
comparison of the spectroscopically twinned SN 2012cu and SN 2011fe across 10
epochs results in the best-fit color excess of (E(B-V), RMS) = (1.00, 0.03) and
total-to-selective extinction ratio of (RV , RMS) = (2.95, 0.08) toward SN
2012cu within its host galaxy. We further identify several diffuse interstellar
bands, and compare the 5780 {\AA} band with the dust-to-band ratio for the
Milky Way. Overall, we find the foreground dust-extinction properties for SN
2012cu to be consistent with those of the Milky Way. Furthermore we find no
evidence for significant time variation in any of these extinction tracers. We
also compare the dust extinction curve models of Cardelli et al. (1989),
O'Donnell (1994), and Fitzpatrick (1999), and find the predictions of
Fitzpatrick (1999) fit SN 2012cu the best. Finally, the distance to NGC4772,
the host of SN 2012cu, at a redshift of z = 0.0035, often assigned to the Virgo
Southern Extension, is determined to be 16.6$\pm$1.1 Mpc. We compare this
result with distance measurements in the literature.
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On Whitham and related equations | The aim of this paper is to study, via theoretical analysis and numerical
simulations, the dynamics of Whitham and related equations. In particular we
establish rigorous bounds between solutions of the Whitham and KdV equations
and provide some insights into the dynamics of the Whitham equation in
different regimes, some of them being outside the range of validity of the
Whitham equation as a water waves model.
| 0 | 0 | 1 | 0 | 0 | 0 |
Analytic Connectivity in General Hypergraphs | In this paper we extend the known results of analytic connectivity to
non-uniform hypergraphs. We prove a modified Cheeger's inequality and also give
a bound on analytic connectivity with respect to the degree sequence and
diameter of a hypergraph.
| 1 | 0 | 0 | 0 | 0 | 0 |
Cosmological searches for a non-cold dark matter component | We explore an extended cosmological scenario where the dark matter is an
admixture of cold and additional non-cold species. The mass and temperature of
the non-cold dark matter particles are extracted from a number of cosmological
measurements. Among others, we consider tomographic weak lensing data and Milky
Way dwarf satellite galaxy counts. We also study the potential of these
scenarios in alleviating the existing tensions between local measurements and
Cosmic Microwave Background (CMB) estimates of the $S_8$ parameter, with
$S_8=\sigma_8\sqrt{\Omega_m}$, and of the Hubble constant $H_0$. In principle,
a sub-dominant, non-cold dark matter particle with a mass $m_X\sim$~keV, could
achieve the goals above. However, the preferred ranges for its temperature and
its mass are different when extracted from weak lensing observations and from
Milky Way dwarf satellite galaxy counts, since these two measurements require
suppressions of the matter power spectrum at different scales. Therefore,
solving simultaneously the CMB-weak lensing tensions and the small scale crisis
in the standard cold dark matter picture via only one non-cold dark matter
component seems to be challenging.
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F-pure threshold and height of quasi-homogeneous polynomials | We consider a quasi-homogeneous polynomial $f \in \mathbb{Z}[x_0, \ldots,
x_N]$ of degree $w$ equal to the degree of $x_0 \cdots x_N$ and show that the
$F$-pure threshold of the reduction $f_p \in \mathbb{F}_p[x_0, \ldots, x_N]$ is
equal to the log canonical threshold if and only if the height of the
Artin-Mazur formal group associated to $H^{N-1}\left( X, {\mathbb{G}}_{m,X}
\right)$, where $X$ is the hypersurface given by $f$, is equal to 1. We also
prove that a similar result holds for Fermat hypersurfaces of degree $>N+1$.
Furthermore, we give examples of weighted Delsarte surfaces which show that
other values of the $F$-pure threshold of a quasi-homogeneous polynomial of
degree $w$ cannot be characterized by the height.
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Optimal VWAP execution under transient price impact | We solve the problem of optimal liquidation with volume weighted average
price (VWAP) benchmark when the market impact is linear and transient. Our
setting is indeed more general as it considers the case when the trading
interval is not necessarily coincident with the benchmark interval:
Implementation Shortfall and Target Close execution are shown to be particular
cases of our setting. We find explicit solutions in continuous and discrete
time considering risk averse investors having a CARA utility function. Finally,
we show that, contrary to what is observed for Implementation Shortfall, the
optimal VWAP solution contains both buy and sell trades also when the decay
kernel is convex.
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Image-derived generative modeling of pseudo-macromolecular structures - towards the statistical assessment of Electron CryoTomography template matching | Cellular Electron CryoTomography (CECT) is a 3D imaging technique that
captures information about the structure and spatial organization of
macromolecular complexes within single cells, in near-native state and at
sub-molecular resolution. Although template matching is often used to locate
macromolecules in a CECT image, it is insufficient as it only measures the
relative structural similarity. Therefore, it is preferable to assess the
statistical credibility of the decision through hypothesis testing, requiring
many templates derived from a diverse population of macromolecular structures.
Due to the very limited number of known structures, we need a generative model
to efficiently and reliably sample pseudo-structures from the complex
distribution of macromolecular structures. To address this challenge, we
propose a novel image-derived approach for performing hypothesis testing for
template matching by constructing generative models using the generative
adversarial network. Finally, we conducted hypothesis testing experiments for
template matching on both simulated and experimental subtomograms, allowing us
to conclude the identity of subtomograms with high statistical credibility and
significantly reducing false positives.
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Bayesian Alignments of Warped Multi-Output Gaussian Processes | We propose a novel Bayesian approach to modelling nonlinear alignments of
time series based on latent shared information. We apply the method to the
real-world problem of finding common structure in the sensor data of wind
turbines introduced by the underlying latent and turbulent wind field. The
proposed model allows for both arbitrary alignments of the inputs and
non-parametric output warpings to transform the observations. This gives rise
to multiple deep Gaussian process models connected via latent generating
processes. We present an efficient variational approximation based on nested
variational compression and show how the model can be used to extract shared
information between dependent time series, recovering an interpretable
functional decomposition of the learning problem. We show results for an
artificial data set and real-world data of two wind turbines.
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Fast and unsupervised methods for multilingual cognate clustering | In this paper we explore the use of unsupervised methods for detecting
cognates in multilingual word lists. We use online EM to train sound segment
similarity weights for computing similarity between two words. We tested our
online systems on geographically spread sixteen different language groups of
the world and show that the Online PMI system (Pointwise Mutual Information)
outperforms a HMM based system and two linguistically motivated systems:
LexStat and ALINE. Our results suggest that a PMI system trained in an online
fashion can be used by historical linguists for fast and accurate
identification of cognates in not so well-studied language families.
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Structure, magnetic susceptibility and specific heat of the spin-orbital-liquid candidate FeSc2S4 : Influence of fe off-stoichiometry | We report structural, susceptibility and specific heat studies of
stoichiometric and off-stoichiometric poly- and single crystals of the A-site
spinel compound FeSc2S4. In stoichiometric samples no long-range magnetic order
is found down to 1.8 K. The magnetic susceptibility of these samples is field
independent in the temperature range 10 - 400 K and does not show irreversible
effects at low temperatures. In contrast, the magnetic susceptibility of
samples with iron excess shows substantial field dependence at high
temperatures and manifests a pronounced magnetic irreversibility at low
temperatures with a difference between ZFC and FC susceptibilities and a
maximum at 10 K reminiscent of a magnetic transition. Single crystal x-ray
diffraction of the stoichiometric samples revealed a single phase spinel
structure without site inversion. In single crystalline samples with Fe excess
besides the main spinel phase a second ordered single-crystal phase was
detected with the diffraction pattern of a vacancy-ordered superstructure of
iron sulfide, close to the 5C polytype Fe9S10. Specific heat studies reveal a
broad anomaly, which evolves below 20 K in both stoichiometric and
off-stoichiometric crystals. We show that the low-temperature specific heat can
be well described by considering the low-lying spin-orbital electronic levels
of Fe2+ ions. Our results demonstrate significant influence of excess Fe ions
on intrinsic magnetic behavior of FeSc2S4 and provide support for the
spin-orbital liquid scenario proposed in earlier studies for the stoichiometric
compound.
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Directed Information as Privacy Measure in Cloud-based Control | We consider cloud-based control scenarios in which clients with local control
tasks outsource their computational or physical duties to a cloud service
provider. In order to address privacy concerns in such a control architecture,
we first investigate the issue of finding an appropriate privacy measure for
clients who desire to keep local state information as private as possible
during the control operation. Specifically, we justify the use of Kramer's
notion of causally conditioned directed information as a measure of privacy
loss based on an axiomatic argument. Then we propose a methodology to design an
optimal "privacy filter" that minimizes privacy loss while a given level of
control performance is guaranteed. We show in particular that the optimal
privacy filter for cloud-based Linear-Quadratic-Gaussian (LQG) control can be
synthesized by a Linear-Matrix-Inequality (LMI) algorithm. The trade-off in the
design is illustrated by a numerical example.
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Classification of simple linearly compact Kantor triple systems over the complex numbers | Simple finite dimensional Kantor triple systems over the complex numbers are
classified in terms of Satake diagrams. We prove that every simple and linearly
compact Kantor triple system has finite dimension and give an explicit
presentation of all the classical and exceptional systems.
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Code-division multiplexed resistive pulse sensor networks for spatio-temporal detection of particles in microfluidic devices | Spatial separation of suspended particles based on contrast in their physical
or chemical properties forms the basis of various biological assays performed
on lab-on-achip devices. To electronically acquire this information, we have
recently introduced a microfluidic sensing platform, called Microfluidic CODES,
which combines the resistive pulse sensing with the code division multiple
access in multiplexing a network of integrated electrical sensors. In this
paper, we enhance the multiplexing capacity of the Microfluidic CODES by
employing sensors that generate non-orthogonal code waveforms and a new
decoding algorithm that combines machine learning techniques with minimum
mean-squared error estimation. As a proof of principle, we fabricated a
microfluidic device with a network of 10 code-multiplexed sensors and
characterized it using cells suspended in phosphate buffer saline solution.
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Navigability evaluation of complex networks by greedy routing efficiency | Network navigability is a key feature of complex networked systems. For a
network embedded in a geometrical space, maximization of greedy routing (GR)
measures based on the node geometrical coordinates can ensure efficient greedy
navigability. In PNAS, Seguin et al. (PNAS 2018, vol. 115, no. 24) define a
measure for quantifying the efficiency of brain network navigability in the
Euclidean space, referred to as the efficiency ratio, whose formula exactly
coincides with the GR-score (GR-efficiency) previously published by Muscoloni
et al. (Nature Communications 2017, vol. 8, no. 1615). In this Letter, we point
out potential flaws in the study of Seguin et al. regarding the discussion of
the GR evaluation. In particular, we revise the concept of GR navigability,
together with a careful discussion of the advantage offered by the new proposed
GR-efficiency measure in comparison to the main measures previously adopted in
literature. Finally, we clarify and standardize the GR-efficiency terminology
in order to simplify and facilitate the discussion in future studies.
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Fermion condensation and super pivotal categories | We study fermionic topological phases using the technique of fermion
condensation. We give a prescription for performing fermion condensation in
bosonic topological phases which contain a fermion. Our approach to fermion
condensation can roughly be understood as coupling the parent bosonic
topological phase to a phase of physical fermions, and condensing pairs of
physical and emergent fermions. There are two distinct types of objects in
fermionic theories, which we call "m-type" and "q-type" particles. The
endomorphism algebras of q-type particles are complex Clifford algebras, and
they have no analogues in bosonic theories. We construct a fermionic
generalization of the tube category, which allows us to compute the
quasiparticle excitations in fermionic topological phases. We then prove a
series of results relating data in condensed theories to data in their parent
theories; for example, if $\mathcal{C}$ is a modular tensor category containing
a fermion, then the tube category of the condensed theory satisfies
$\textbf{Tube}(\mathcal{C}/\psi) \cong \mathcal{C} \times (\mathcal{C}/\psi)$.
We also study how modular transformations, fusion rules, and coherence
relations are modified in the fermionic setting, prove a fermionic version of
the Verlinde dimension formula, construct a commuting projector lattice
Hamiltonian for fermionic theories, and write down a fermionic version of the
Turaev-Viro-Barrett-Westbury state sum. A large portion of this work is devoted
to three detailed examples of performing fermion condensation to produce
fermionic topological phases: we condense fermions in the Ising theory, the
$SO(3)_6$ theory, and the $\frac{1}{2}\text{E}_6$ theory, and compute the
quasiparticle excitation spectrum in each of these examples.
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PIMKL: Pathway Induced Multiple Kernel Learning | Reliable identification of molecular biomarkers is essential for accurate
patient stratification. While state-of-the-art machine learning approaches for
sample classification continue to push boundaries in terms of performance, most
of these methods are not able to integrate different data types and lack
generalization power, limiting their application in a clinical setting.
Furthermore, many methods behave as black boxes, and we have very little
understanding about the mechanisms that lead to the prediction. While
opaqueness concerning machine behaviour might not be a problem in deterministic
domains, in health care, providing explanations about the molecular factors and
phenotypes that are driving the classification is crucial to build trust in the
performance of the predictive system. We propose Pathway Induced Multiple
Kernel Learning (PIMKL), a novel methodology to reliably classify samples that
can also help gain insights into the molecular mechanisms that underlie the
classification. PIMKL exploits prior knowledge in the form of a molecular
interaction network and annotated gene sets, by optimizing a mixture of
pathway-induced kernels using a Multiple Kernel Learning (MKL) algorithm, an
approach that has demonstrated excellent performance in different machine
learning applications. After optimizing the combination of kernels for
prediction of a specific phenotype, the model provides a stable molecular
signature that can be interpreted in the light of the ingested prior knowledge
and that can be used in transfer learning tasks.
| 0 | 0 | 0 | 1 | 1 | 0 |
pyRecLab: A Software Library for Quick Prototyping of Recommender Systems | This paper introduces pyRecLab, a software library written in C++ with Python
bindings which allows to quickly train, test and develop recommender systems.
Although there are several software libraries for this purpose, only a few let
developers to get quickly started with the most traditional methods, permitting
them to try different parameters and approach several tasks without a
significant loss of performance. Among the few libraries that have all these
features, they are available in languages such as Java, Scala or C#, what is a
disadvantage for less experienced programmers more used to the popular Python
programming language. In this article we introduce details of pyRecLab, showing
as well performance analysis in terms of error metrics (MAE and RMSE) and
train/test time. We benchmark it against the popular Java-based library LibRec,
showing similar results. We expect programmers with little experience and
people interested in quickly prototyping recommender systems to be benefited
from pyRecLab.
| 1 | 0 | 0 | 0 | 0 | 0 |
A unified theory of adaptive stochastic gradient descent as Bayesian filtering | We formulate stochastic gradient descent (SGD) as a Bayesian filtering
problem. Inference in the Bayesian setting naturally gives rise to BRMSprop and
BAdam: Bayesian variants of RMSprop and Adam. Remarkably, the Bayesian approach
recovers many features of state-of-the-art adaptive SGD methods, including
amoungst others root-mean-square normalization, Nesterov acceleration and
AdamW. As such, the Bayesian approach provides one explanation for the
empirical effectiveness of state-of-the-art adaptive SGD algorithms.
Empirically comparing BRMSprop and BAdam with naive RMSprop and Adam on MNIST,
we find that Bayesian methods have the potential to considerably reduce test
loss and classification error.
| 0 | 0 | 0 | 1 | 0 | 0 |
Radiative effects during the assembly of direct collapse black holes | We perform a post-processing radiative feedback analysis on a 3D ab initio
cosmological simulation of an atomic cooling halo under the direct collapse
black hole (DCBH) scenario. We maintain the spatial resolution of the
simulation by incorporating native ray-tracing on unstructured mesh data,
including Monte Carlo Lyman-alpha (Ly{\alpha}) radiative transfer. DCBHs are
born in gas-rich, metal-poor environments with the possibility of Compton-thick
conditions, $N_H \gtrsim 10^{24} {\rm cm}^{-2}$. Therefore, the surrounding gas
is capable of experiencing the full impact of the bottled-up radiation
pressure. In particular, we find that multiple scattering of Ly{\alpha} photons
provides an important source of mechanical feedback after the gas in the
sub-parsec region becomes partially ionized, avoiding the bottleneck of
destruction via the two-photon emission mechanism. We provide detailed
discussion of the simulation environment, expansion of the ionization front,
emission and escape of Ly{\alpha} radiation, and Compton scattering. A sink
particle prescription allows us to extract approximate limits on the
post-formation evolution of the radiative feedback. Fully coupled Ly{\alpha}
radiation hydrodynamics will be crucial to consider in future DCBH simulations.
| 0 | 1 | 0 | 0 | 0 | 0 |
Spin-orbit interactions in optically active materials | We investigate the inherent influence of light polarization on the intensity
distribution in anisotropic media undergoing a local inhomogeneous rotation of
the principal axes. Whereas in general such configuration implies a complicated
interaction between geometric and dynamic phase, we show that, in a medium
showing an inhomogeneous circular birefringence, the geometric phase vanishes.
Due to the spin-orbit interaction, the two circular polarizations perceive
reversed spatial distribution of the dynamic phase. Based upon this effect,
polarization-selective lens, waveguides and beam deflectors are proposed.
| 0 | 1 | 0 | 0 | 0 | 0 |
Automatic White-Box Testing of First-Order Logic Ontologies | Formal ontologies are axiomatizations in a logic-based formalism. The
development of formal ontologies, and their important role in the Semantic Web
area, is generating considerable research on the use of automated reasoning
techniques and tools that help in ontology engineering. One of the main aims is
to refine and to improve axiomatizations for enabling automated reasoning tools
to efficiently infer reliable information. Defects in the axiomatization can
not only cause wrong inferences, but can also hinder the inference of expected
information, either by increasing the computational cost of, or even
preventing, the inference. In this paper, we introduce a novel, fully automatic
white-box testing framework for first-order logic ontologies. Our methodology
is based on the detection of inference-based redundancies in the given
axiomatization. The application of the proposed testing method is fully
automatic since a) the automated generation of tests is guided only by the
syntax of axioms and b) the evaluation of tests is performed by automated
theorem provers. Our proposal enables the detection of defects and serves to
certify the grade of suitability --for reasoning purposes-- of every axiom. We
formally define the set of tests that are generated from any axiom and prove
that every test is logically related to redundancies in the axiom from which
the test has been generated. We have implemented our method and used this
implementation to automatically detect several non-trivial defects that were
hidden in various first-order logic ontologies. Throughout the paper we provide
illustrative examples of these defects, explain how they were found, and how
each proof --given by an automated theorem-prover-- provides useful hints on
the nature of each defect. Additionally, by correcting all the detected
defects, we have obtained an improved version of one of the tested ontologies:
Adimen-SUMO.
| 1 | 0 | 0 | 0 | 0 | 0 |
Critical values in Bak-Sneppen type models | In the Bak-Sneppen model, the lowest fitness particle and its two nearest
neighbors are renewed at each temporal step with a uniform (0,1) fitness
distribution. The model presents a critical value that depends on the
interaction criteria (two nearest neighbors) and on the update procedure
(uniform). Here we calculate the critical value for models where one or both
properties are changed. We study models with non-uniform updates, models with
random neighbors and models with binary fitness and obtain exact results for
the average fitness and for $p_c$.
| 0 | 1 | 0 | 0 | 0 | 0 |
On the correspondence of deviances and maximum likelihood and interval estimates from log-linear to logistic regression modelling | Consider a set of categorical variables $\mathcal{P}$ where at least one,
denoted by $Y$, is binary. The log-linear model that describes the counts in
the resulting contingency table implies a specific logistic regression model,
with the binary variable as the outcome. Extending results in Christensen
(1997), by also considering the case where factors present in the contingency
table disappear from the logistic regression model, we prove that the Maximum
Likelihood Estimate (MLE) for the parameters of the logistic regression equals
the MLE for the corresponding parameters of the log-linear model. We prove
that, asymptotically, standard errors for the two sets of parameters are also
equal. Subsequently, Wald confidence intervals are asymptotically equal. These
results demonstrate the extent to which inferences from the log-linear
framework can be translated to inferences within the logistic regression
framework, on the magnitude of main effects and interactions. Finally, we prove
that the deviance of the log-linear model is equal to the deviance of the
corresponding logistic regression, provided that the latter is fitted to a
dataset where no cell observations are merged when one or more factors in
$\mathcal{P} \setminus \{ Y \}$ become obsolete. We illustrate the derived
results with the analysis of a real dataset.
| 0 | 0 | 0 | 1 | 0 | 0 |
ASIC Implementation of Time-Domain Digital Backpropagation with Deep-Learned Chromatic Dispersion Filters | We consider time-domain digital backpropagation with chromatic dispersion
filters jointly optimized and quantized using machine-learning techniques.
Compared to the baseline implementations, we show improved BER performance and
>40% power dissipation reductions in 28-nm CMOS.
| 0 | 0 | 0 | 1 | 0 | 0 |
A formalization of convex polyhedra based on the simplex method | We present a formalization of convex polyhedra in the proof assistant Coq.
The cornerstone of our work is a complete implementation of the simplex method,
together with the proof of its correctness and termination. This allows us to
define the basic predicates over polyhedra in an effective way (i.e., as
programs), and relate them with the corresponding usual logical counterparts.
To this end, we make an extensive use of the Boolean reflection methodology.
The benefit of this approach is that we can easily derive the proof of several
fundamental results on polyhedra, such as Farkas' Lemma, the duality theorem of
linear programming, and Minkowski's Theorem.
| 1 | 0 | 1 | 0 | 0 | 0 |
Weight Spectrum of Quasi-Perfect Binary Codes with Distance 4 | We consider the weight spectrum of a class of quasi-perfect binary linear
codes with code distance 4. For example, extended Hamming code and Panchenko
code are the known members of this class. Also, it is known that in many cases
Panchenko code has the minimal number of weight 4 codewords. We give exact
recursive formulas for the weight spectrum of quasi-perfect codes and their
dual codes. As an example of application of the weight spectrum we derive a
lower estimate for the conditional probability of correction of erasure
patterns of high weights (equal to or greater than code distance).
| 1 | 0 | 0 | 0 | 0 | 0 |
Ultra-High Electro-Optic Activity Demonstrated in a Silicon-Organic Hybrid (SOH) Modulator | Efficient electro-optic (EO) modulators crucially rely on advanced materials
that exhibit strong electro-optic activity and that can be integrated into
high-speed and efficient phase shifter structures. In this paper, we
demonstrate ultra-high in-device EO figures of merit of up to n3r33 = 2300 pm/V
achieved in a silicon-organic hybrid (SOH) Mach-Zehnder Modulator (MZM) using
the EO chromophore JRD1. This is the highest material-related in-device EO
figure of merit hitherto achieved in a high-speed modulator at any operating
wavelength. The {\pi}-voltage of the 1.5 mm-long device amounts to 210 mV,
leading to a voltage-length product of U{\pi}L = 320 V{\mu}m - the lowest value
reported for MZM that are based on low-loss dielectric waveguides. The
viability of the devices is demonstrated by generating high-quality
on-off-keying (OOK) signals at 40 Gbit/s with Q factors in excess of 8 at a
drive voltage as low as 140 mVpp. We expect that efficient high-speed EO
modulators will not only have major impact in the field of optical
communications, but will also open new avenues towards ultra-fast
photonic-electronic signal processing.
| 0 | 1 | 0 | 0 | 0 | 0 |
How the Experts Do It: Assessing and Explaining Agent Behaviors in Real-Time Strategy Games | How should an AI-based explanation system explain an agent's complex behavior
to ordinary end users who have no background in AI? Answering this question is
an active research area, for if an AI-based explanation system could
effectively explain intelligent agents' behavior, it could enable the end users
to understand, assess, and appropriately trust (or distrust) the agents
attempting to help them. To provide insights into this question, we turned to
human expert explainers in the real-time strategy domain, "shoutcaster", to
understand (1) how they foraged in an evolving strategy game in real time, (2)
how they assessed the players' behaviors, and (3) how they constructed
pertinent and timely explanations out of their insights and delivered them to
their audience. The results provided insights into shoutcasters' foraging
strategies for gleaning information necessary to assess and explain the
players; a characterization of the types of implicit questions shoutcasters
answered; and implications for creating explanations by using the patterns
| 1 | 0 | 0 | 0 | 0 | 0 |
Entanglement Entropy of Eigenstates of Quadratic Fermionic Hamiltonians | In a seminal paper [D. N. Page, Phys. Rev. Lett. 71, 1291 (1993)], Page
proved that the average entanglement entropy of subsystems of random pure
states is $S_{\rm ave}\simeq\ln{\cal D}_{\rm A} - (1/2) {\cal D}_{\rm
A}^2/{\cal D}$ for $1\ll{\cal D}_{\rm A}\leq\sqrt{\cal D}$, where ${\cal
D}_{\rm A}$ and ${\cal D}$ are the Hilbert space dimensions of the subsystem
and the system, respectively. Hence, typical pure states are (nearly) maximally
entangled. We develop tools to compute the average entanglement entropy
$\langle S\rangle$ of all eigenstates of quadratic fermionic Hamiltonians. In
particular, we derive exact bounds for the most general translationally
invariant models $\ln{\cal D}_{\rm A} - (\ln{\cal D}_{\rm A})^2/\ln{\cal D}
\leq \langle S \rangle \leq \ln{\cal D}_{\rm A} - [1/(2\ln2)] (\ln{\cal D}_{\rm
A})^2/\ln{\cal D}$. Consequently we prove that: (i) if the subsystem size is a
finite fraction of the system size then $\langle S\rangle<\ln{\cal D}_{\rm A}$
in the thermodynamic limit, i.e., the average over eigenstates of the
Hamiltonian departs from the result for typical pure states, and (ii) in the
limit in which the subsystem size is a vanishing fraction of the system size,
the average entanglement entropy is maximal, i.e., typical eigenstates of such
Hamiltonians exhibit eigenstate thermalization.
| 0 | 1 | 0 | 0 | 0 | 0 |
Focusing light through dynamical samples using fast closed-loop wavefront optimization | We describe a fast closed-loop optimization wavefront shaping system able to
focus light through dynamic scattering media. A MEMS-based spatial light
modulator (SLM), a fast photodetector and FPGA electronics are combined to
implement a closed-loop optimization of a wavefront with a single mode
optimization rate of 4.1 kHz. The system performances are demonstrated by
focusing light through colloidal solutions of TiO2 particles in glycerol with
tunable temporal stability.
| 0 | 1 | 0 | 0 | 0 | 0 |
The curvature estimates for convex solutions of some fully nonlinear Hessian type equations | The curvature estimates of quotient curvature equation do not always exist
even for convex setting \cite{GRW}. Thus it is natural question to find other
type of elliptic equations possessing curvature estimates. In this paper, we
discuss the existence of curvature estimate for fully nonlinear elliptic
equations defined by symmetric polynomials, mainlly, the linear combination of
elementary symmetric polynomials.
| 0 | 0 | 1 | 0 | 0 | 0 |
The Compressed Model of Residual CNDS | Convolutional neural networks have achieved a great success in the recent
years. Although, the way to maximize the performance of the convolutional
neural networks still in the beginning. Furthermore, the optimization of the
size and the time that need to train the convolutional neural networks is very
far away from reaching the researcher's ambition. In this paper, we proposed a
new convolutional neural network that combined several techniques to boost the
optimization of the convolutional neural network in the aspects of speed and
size. As we used our previous model Residual-CNDS (ResCNDS), which solved the
problems of slower convergence, overfitting, and degradation, and compressed
it. The outcome model called Residual-Squeeze-CNDS (ResSquCNDS), which we
demonstrated on our sold technique to add residual learning and our model of
compressing the convolutional neural networks. Our model of compressing adapted
from the SQUEEZENET model, but our model is more generalizable, which can be
applied almost to any neural network model, and fully integrated into the
residual learning, which addresses the problem of the degradation very
successfully. Our proposed model trained on very large-scale MIT
Places365-Standard scene datasets, which backing our hypothesis that the new
compressed model inherited the best of the previous ResCNDS8 model, and almost
get the same accuracy in the validation Top-1 and Top-5 with 87.64% smaller in
size and 13.33% faster in the training time.
| 1 | 0 | 0 | 0 | 0 | 0 |
Chow Rings of Mp_{0,2}(N,d) and Mbar_{0,2}(P^{N-1},d) and Gromov-Witten Invariants of Projective Hypersurfaces of Degree 1 and 2 | In this paper, we prove formulas that represent two-pointed Gromov-Witten
invariant <O_{h^a}O_{h^b}>_{0,d} of projective hypersurfaces with d=1,2 in
terms of Chow ring of Mbar_{0,2}(P^{N-1},d), the moduli spaces of stable maps
from genus 0 stable curves to projective space P^{N-1}. Our formulas are based
on representation of the intersection number w(O_{h^a}O_{h^b})_{0,d}, which was
introduced by Jinzenji, in terms of Chow ring of Mp_{0,2}(N,d), the moduli
space of quasi maps from P^1 to P^{N-1} with two marked points. In order to
prove our formulas, we use the results on Chow ring of Mbar_{0,2}(P^{N-1},d),
that were derived by A. Mustata and M. Mustata. We also present explicit toric
data of Mp_{0,2}(N,d) and prove relations of Chow ring of Mp_{0,2}(N,d).
| 0 | 0 | 1 | 0 | 0 | 0 |
A Real-Time Autonomous Highway Accident Detection Model Based on Big Data Processing and Computational Intelligence | Due to increasing urban population and growing number of motor vehicles,
traffic congestion is becoming a major problem of the 21st century. One of the
main reasons behind traffic congestion is accidents which can not only result
in casualties and losses for the participants, but also in wasted and lost time
for the others that are stuck behind the wheels. Early detection of an accident
can save lives, provides quicker road openings, hence decreases wasted time and
resources, and increases efficiency. In this study, we propose a preliminary
real-time autonomous accident-detection system based on computational
intelligence techniques. Istanbul City traffic-flow data for the year 2015 from
various sensor locations are populated using big data processing methodologies.
The extracted features are then fed into a nearest neighbor model, a regression
tree, and a feed-forward neural network model. For the output, the possibility
of an occurrence of an accident is predicted. The results indicate that even
though the number of false alarms dominates the real accident cases, the system
can still provide useful information that can be used for status verification
and early reaction to possible accidents.
| 1 | 0 | 0 | 1 | 0 | 0 |
Asymptotic Eigenfunctions for a class of Difference Operators | We analyze a general class of difference operators $H_\varepsilon =
T_\varepsilon + V_\varepsilon$ on $\ell^2(\varepsilon \mathbb{Z}^d)$, where
$V_\varepsilon$ is a one-well potential and $\varepsilon$ is a small parameter.
We construct formal asymptotic expansions of WKB-type for eigenfunctions
associated with the low lying eigenvalues of $H_\varepsilon$. These are
obtained from eigenfunctions or quasimodes for the operator $H_\varepsilon$,
acting on $L^2(\mathbb{R}^d)$, via restriction to the lattice
$\varepsilon\mathbb{Z}^d$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Common Glass-Forming Spin-Liquid State in the Pyrochlore Magnets Dy$_2$Ti$_2$O$_7$ and Ho$_2$Ti$_2$O$_7$ | Despite a well-ordered pyrochlore crystal structure and strong magnetic
interactions between the Dy$^{3+}$ or Ho$^{3+}$ ions, no long range magnetic
order has been detected in the pyrochlore titanates Ho$_2$Ti$_2$O$_7$ and
Dy$_2$Ti$_2$O$_7$. To explore the actual magnetic phase formed by cooling these
materials, we measure their magnetization dynamics using toroidal,
boundary-free magnetization transport techniques. We find that the dynamical
magnetic susceptibility of both compounds has the same distinctive
phenomenology, that is indistinguishable in form from that of the dielectric
permittivity of dipolar glass-forming liquids. Moreover, Ho$_2$Ti$_2$O$_7$ and
Dy$_2$Ti$_2$O$_7$ both exhibit microscopic magnetic relaxation times that
increase along the super-Arrhenius trajectories analogous to those observed in
glass-forming dipolar liquids. Thus, upon cooling below about 2K,
Dy$_2$Ti$_2$O$_7$ and Ho$_2$Ti$_2$O$_7$ both appear to enter the same magnetic
state exhibiting the characteristics of a glass-forming spin-liquid.
| 0 | 1 | 0 | 0 | 0 | 0 |
Britannia Rule the Waves | The students are introduced to navigation in general and the longitude
problem in particular. A few videos provide insight into scientific and
historical facts related to the issue. Then, the students learn in two steps
how longitude can be derived from time measurements. They first build a
Longitude Clock that visualises the math behind the concept. They use it to
determine the longitudes corresponding to five time measurements. In the second
step, they assume the position of James Cook's navigator and plot the location
of seven destinations on Cook's second voyage between 1772 and 1775.
| 0 | 1 | 0 | 0 | 0 | 0 |
Error Forward-Propagation: Reusing Feedforward Connections to Propagate Errors in Deep Learning | We introduce Error Forward-Propagation, a biologically plausible mechanism to
propagate error feedback forward through the network. Architectural constraints
on connectivity are virtually eliminated for error feedback in the brain;
systematic backward connectivity is not used or needed to deliver error
feedback. Feedback as a means of assigning credit to neurons earlier in the
forward pathway for their contribution to the final output is thought to be
used in learning in the brain. How the brain solves the credit assignment
problem is unclear. In machine learning, error backpropagation is a highly
successful mechanism for credit assignment in deep multilayered networks.
Backpropagation requires symmetric reciprocal connectivity for every neuron.
From a biological perspective, there is no evidence of such an architectural
constraint, which makes backpropagation implausible for learning in the brain.
This architectural constraint is reduced with the use of random feedback
weights. Models using random feedback weights require backward connectivity
patterns for every neuron, but avoid symmetric weights and reciprocal
connections. In this paper, we practically remove this architectural
constraint, requiring only a backward loop connection for effective error
feedback. We propose reusing the forward connections to deliver the error
feedback by feeding the outputs into the input receiving layer. This mechanism,
Error Forward-Propagation, is a plausible basis for how error feedback occurs
deep in the brain independent of and yet in support of the functionality
underlying intricate network architectures. We show experimentally that
recurrent neural networks with two and three hidden layers can be trained using
Error Forward-Propagation on the MNIST and Fashion MNIST datasets, achieving
$1.90\%$ and $11\%$ generalization errors respectively.
| 0 | 0 | 0 | 0 | 1 | 0 |
CANA: A python package for quantifying control and canalization in Boolean Networks | Logical models offer a simple but powerful means to understand the complex
dynamics of biochemical regulation, without the need to estimate kinetic
parameters. However, even simple automata components can lead to collective
dynamics that are computationally intractable when aggregated into networks. In
previous work we demonstrated that automata network models of biochemical
regulation are highly canalizing, whereby many variable states and their
groupings are redundant (Marques-Pita and Rocha, 2013). The precise charting
and measurement of such canalization simplifies these models, making even very
large networks amenable to analysis. Moreover, canalization plays an important
role in the control, robustness, modularity and criticality of Boolean network
dynamics, especially those used to model biochemical regulation (Gates and
Rocha, 2016; Gates et al., 2016; Manicka, 2017). Here we describe a new
publicly-available Python package that provides the necessary tools to extract,
measure, and visualize canalizing redundancy present in Boolean network models.
It extracts the pathways most effective in controlling dynamics in these
models, including their effective graph and dynamics canalizing map, as well as
other tools to uncover minimum sets of control variables.
| 1 | 0 | 0 | 0 | 1 | 0 |
$α$-Variational Inference with Statistical Guarantees | We propose a family of variational approximations to Bayesian posterior
distributions, called $\alpha$-VB, with provable statistical guarantees. The
standard variational approximation is a special case of $\alpha$-VB with
$\alpha=1$. When $\alpha \in(0,1]$, a novel class of variational inequalities
are developed for linking the Bayes risk under the variational approximation to
the objective function in the variational optimization problem, implying that
maximizing the evidence lower bound in variational inference has the effect of
minimizing the Bayes risk within the variational density family. Operating in a
frequentist setup, the variational inequalities imply that point estimates
constructed from the $\alpha$-VB procedure converge at an optimal rate to the
true parameter in a wide range of problems. We illustrate our general theory
with a number of examples, including the mean-field variational approximation
to (low)-high-dimensional Bayesian linear regression with spike and slab
priors, mixture of Gaussian models, latent Dirichlet allocation, and (mixture
of) Gaussian variational approximation in regular parametric models.
| 0 | 0 | 1 | 1 | 0 | 0 |
Stable monoenergetic ion acceleration by a two color laser tweezer | In the past decades, the phenomenal progress in the development of
ultraintense lasers has opened up many exciting new frontiers in laser matter
physics, including laser plasma ion acceleration. Currently a major challenge
in this frontier is to find simple methods to stably produce monoenergetic ion
beams with sufficient charge for real applications. Here, we propose a novel
scheme using a two color laser tweezer to fulfill this goal. In this scheme,
two circularly polarized lasers with different wavelengths collide right on a
thin nano-foil target containing mixed ion species. The radiation pressure of
this laser pair acts like a tweezer to pinch and fully drag the electrons out,
forming a stable uniform accelerating field for the ions. Scaling laws and
three-dimensional particle-in-cell simulations confirm that high energy
(10-1000 MeV) high charge ($\sim 10^{10}$) proton beams with narrow energy
spread ($\sim4\%-20\%$) can be obtained by commercially available lasers. Such
a scheme may open up a new route for compact high quality ion sources for
various applications.
| 0 | 1 | 0 | 0 | 0 | 0 |
Experimental demonstration of an ultra-compact on-chip polarization controlling structure | We demonstrated a novel on-chip polarization controlling structure,
fabricated by standard 0.18-um foundry technology. It achieved polarization
rotation with a size of 0.726 um * 5.27 um and can be easily extended into
dynamic polarization controllers.
| 0 | 1 | 0 | 0 | 0 | 0 |
Letter-Based Speech Recognition with Gated ConvNets | In the recent literature, "end-to-end" speech systems often refer to
letter-based acoustic models trained in a sequence-to-sequence manner, either
via a recurrent model or via a structured output learning approach (such as
CTC). In contrast to traditional phone (or senone)-based approaches, these
"end-to-end'' approaches alleviate the need of word pronunciation modeling, and
do not require a "forced alignment" step at training time. Phone-based
approaches remain however state of the art on classical benchmarks. In this
paper, we propose a letter-based speech recognition system, leveraging a
ConvNet acoustic model. Key ingredients of the ConvNet are Gated Linear Units
and high dropout. The ConvNet is trained to map audio sequences to their
corresponding letter transcriptions, either via a classical CTC approach, or
via a recent variant called ASG. Coupled with a simple decoder at inference
time, our system matches the best existing letter-based systems on WSJ (in word
error rate), and shows near state of the art performance on LibriSpeech.
| 1 | 0 | 0 | 0 | 0 | 0 |
The Frequent Network Neighborhood Mapping of the Human Hippocampus Shows Much More Frequent Neighbor Sets in Males Than in Females | In the study of the human connectome, the vertices and the edges of the
network of the human brain are analyzed: the vertices of the graphs are the
anatomically identified gray matter areas of the subjects; this set is exactly
the same for all the subjects. The edges of the graphs correspond to the axonal
fibers, connecting these areas. In the biological applications of graph theory,
it happens very rarely that scientists examine numerous large graphs on the
very same, labeled vertex set. Exactly this is the case in the study of the
connectomes. Because of the particularity of these sets of graphs, novel,
robust methods need to be developed for their analysis. Here we introduce the
new method of the Frequent Network Neighborhood Mapping for the connectome,
which serves as a robust identification of the neighborhoods of given vertices
of special interest in the graph. We apply the novel method for mapping the
neighborhoods of the human hippocampus and discover strong statistical
asymmetries between the connectomes of the sexes, computed from the Human
Connectome Project. We analyze 413 braingraphs, each with 463 nodes. We show
that the hippocampi of men have much more significantly frequent neighbor sets
than women; therefore, in a sense, the connections of the hippocampi are more
regularly distributed in men and more varied in women. Our results are in
contrast to the volumetric studies of the human hippocampus, where it was shown
that the relative volume of the hippocampus is the same in men and women.
| 0 | 0 | 0 | 0 | 1 | 0 |
Recurrent Neural Networks as Weighted Language Recognizers | We investigate the computational complexity of various problems for simple
recurrent neural networks (RNNs) as formal models for recognizing weighted
languages. We focus on the single-layer, ReLU-activation, rational-weight RNNs
with softmax, which are commonly used in natural language processing
applications. We show that most problems for such RNNs are undecidable,
including consistency, equivalence, minimization, and the determination of the
highest-weighted string. However, for consistent RNNs the last problem becomes
decidable, although the solution length can surpass all computable bounds. If
additionally the string is limited to polynomial length, the problem becomes
NP-complete and APX-hard. In summary, this shows that approximations and
heuristic algorithms are necessary in practical applications of those RNNs.
| 1 | 0 | 0 | 0 | 0 | 0 |
Classifying and Qualifying GUI Defects | Graphical user interfaces (GUIs) are integral parts of software systems that
require interactions from their users. Software testers have paid special
attention to GUI testing in the last decade, and have devised techniques that
are effective in finding several kinds of GUI errors. However, the introduction
of new types of interactions in GUIs (e.g., direct manipulation) presents new
kinds of errors that are not targeted by current testing techniques. We believe
that to advance GUI testing, the community needs a comprehensive and high level
GUI fault model, which incorporates all types of interactions. The work
detailed in this paper establishes 4 contributions: 1) A GUI fault model
designed to identify and classify GUI faults. 2) An empirical analysis for
assessing the relevance of the proposed fault model against failures found in
real GUIs. 3) An empirical assessment of two GUI testing tools (i.e. GUITAR and
Jubula) against those failures. 4) GUI mutants we've developed according to our
fault model. These mutants are freely available and can be reused by developers
for benchmarking their GUI testing tools.
| 1 | 0 | 0 | 0 | 0 | 0 |
A mechanistic model of connector hubs, modularity, and cognition | The human brain network is modular--comprised of communities of tightly
interconnected nodes. This network contains local hubs, which have many
connections within their own communities, and connector hubs, which have
connections diversely distributed across communities. A mechanistic
understanding of these hubs and how they support cognition has not been
demonstrated. Here, we leveraged individual differences in hub connectivity and
cognition. We show that a model of hub connectivity accurately predicts the
cognitive performance of 476 individuals in four distinct tasks. Moreover,
there is a general optimal network structure for cognitive
performance--individuals with diversely connected hubs and consequent modular
brain networks exhibit increased cognitive performance, regardless of the task.
Critically, we find evidence consistent with a mechanistic model in which
connector hubs tune the connectivity of their neighbors to be more modular
while allowing for task appropriate information integration across communities,
which increases global modularity and cognitive performance.
| 0 | 0 | 0 | 0 | 1 | 0 |
The OGLE Collection of Variable Stars. Over 450 000 Eclipsing and Ellipsoidal Binary Systems Toward the Galactic Bulge | We present a collection of 450 598 eclipsing and ellipsoidal binary systems
detected in the OGLE fields toward the Galactic bulge. The collection consists
of binary systems of all types: detached, semi-detached, and contact eclipsing
binaries, RS CVn stars, cataclysmic variables, HW Vir binaries, double periodic
variables, and even planetary transits. For all stars we provide the I- and
V-band time-series photometry obtained during the OGLE-II, OGLE-III, and
OGLE-IV surveys. We discuss methods used to identify binary systems in the OGLE
data and present several objects of particular interest.
| 0 | 1 | 0 | 0 | 0 | 0 |
The discrete moment problem with nonconvex shape constraints | The discrete moment problem is a foundational problem in distribution-free
robust optimization, where the goal is to find a worst-case distribution that
satisfies a given set of moments. This paper studies the discrete moment
problems with additional "shape constraints" that guarantee the worst case
distribution is either log-concave or has an increasing failure rate. These
classes of shape constraints have not previously been studied in the
literature, in part due to their inherent nonconvexities. Nonetheless, these
classes of distributions are useful in practice. We characterize the structure
of optimal extreme point distributions by developing new results in reverse
convex optimization, a lesser-known tool previously employed in designing
global optimization algorithms. We are able to show, for example, that an
optimal extreme point solution to a moment problem with $m$ moments and
log-concave shape constraints is piecewise geometric with at most $m$ pieces.
Moreover, this structure allows us to design an exact algorithm for computing
optimal solutions in a low-dimensional space of parameters. Moreover, We
describe a computational approach to solving these low-dimensional problems,
including numerical results for a representative set of instances.
| 0 | 0 | 1 | 1 | 0 | 0 |
Some studies using capillary for flow control in a closed loop gas recirculation system | A Pilot unit of a closed loop gas (CLS) mixing and distribution system for
the INO project was designed and is being operated with (1.8 x 1.9) m^2 glass
RPCs (Resistive Plate Chamber). The performance of an RPC depends on the
quality and quantity of gas mixture being used, a number of studies on
controlling the flow and optimization of the gas mixture is being carried out.
In this paper the effect of capillary as a dynamic impedance element on the
differential pressure across RPC detector in a closed loop gas system is being
highlighted. The flow versus the pressure variation with different types of
capillaries and also with different types of gasses that are being used in an
RPC is presented. An attempt is also made to measure the transient time of the
gas flow through the capillary.
| 0 | 1 | 0 | 0 | 0 | 0 |
Optimal Tuning of Two-Dimensional Keyboards | We give a new analysis of a tuning problem in music theory, pertaining
specifically to the approximation of harmonics on a two-dimensional keyboard.
We formulate the question as a linear programming problem on families of
constraints and provide exact solutions for many new keyboard dimensions. We
also show that an optimal tuning for harmonic approximation can be obtained for
any keyboard of given width, provided sufficiently many rows of octaves.
| 1 | 0 | 0 | 0 | 0 | 0 |
Ultra Reliable Short Message Relaying with Wireless Power Transfer | We consider a dual-hop wireless network where an energy constrained relay
node first harvests energy through the received radio-frequency signal from the
source, and then uses the harvested energy to forward the source's information
to the destination node. The throughput and delay metrics are investigated for
a decode-and-forward relaying mechanism at finite blocklength regime and
delay-limited transmission mode. We consider ultra-reliable communication
scenarios under discussion for the next fifth-generation of wireless systems,
with error and latency constraints. The impact on these metrics of the
blocklength, information bits, and relay position is investigated.
| 1 | 0 | 0 | 1 | 0 | 0 |
Verifiable Light-Weight Monitoring for Certificate Transparency Logs | Trust in publicly verifiable Certificate Transparency (CT) logs is reduced
through cryptography, gossip, auditing, and monitoring. The role of a monitor
is to observe each and every log entry, looking for suspicious certificates
that interest the entity running the monitor. While anyone can run a monitor,
it requires continuous operation and copies of the logs to be inspected. This
has lead to the emergence of monitoring-as-a-service: a trusted party runs the
monitor and provides registered subjects with selective certificate
notifications, e.g., "notify me of all foo.com certificates". We present a
CT/bis extension for verifiable light-weight monitoring that enables subjects
to verify the correctness of such notifications, reducing the trust that is
placed in these monitors. Our extension supports verifiable monitoring of
wild-card domains and piggybacks on CT's existing gossip-audit security model.
| 1 | 0 | 0 | 0 | 0 | 0 |
Chainspace: A Sharded Smart Contracts Platform | Chainspace is a decentralized infrastructure, known as a distributed ledger,
that supports user defined smart contracts and executes user-supplied
transactions on their objects. The correct execution of smart contract
transactions is verifiable by all. The system is scalable, by sharding state
and the execution of transactions, and using S-BAC, a distributed commit
protocol, to guarantee consistency. Chainspace is secure against subsets of
nodes trying to compromise its integrity or availability properties through
Byzantine Fault Tolerance (BFT), and extremely high-auditability,
non-repudiation and `blockchain' techniques. Even when BFT fails, auditing
mechanisms are in place to trace malicious participants. We present the design,
rationale, and details of Chainspace; we argue through evaluating an
implementation of the system about its scaling and other features; we
illustrate a number of privacy-friendly smart contracts for smart metering,
polling and banking and measure their performance.
| 1 | 0 | 0 | 0 | 0 | 0 |
On Certain Properties of Convex Functions | This note deals with certain properties of convex functions. We provide
results on the convexity of the set of minima of these functions, the behaviour
of their subgradient set under restriction, and optimization of these functions
over an affine subspace.
| 0 | 0 | 1 | 0 | 0 | 0 |
The detection of variable radio emission from the fast rotating magnetic hot B-star HR7355 and evidence for its X-ray aurorae | In this paper we investigate the multiwavelengths properties of the magnetic
early B-type star HR7355. We present its radio light curves at several
frequencies, taken with the Jansky Very Large Array, and X-ray spectra, taken
with the XMM X-ray telescope. Modeling of the radio light curves for the Stokes
I and V provides a quantitative analysis of the HR7355 magnetosphere. A
comparison between HR7355 and a similar analysis for the Ap star CUVir, allows
us to study how the different physical parameters of the two stars affect the
structure of the respective magnetospheres where the non-thermal electrons
originate. Our model includes a cold thermal plasma component that accumulates
at high magnetic latitudes that influences the radio regime, but does not give
rise to X-ray emission. Instead, the thermal X-ray emission arises from shocks
generated by wind stream collisions close to the magnetic equatorial plane. The
analysis of the X-ray spectrum of HR7355 also suggests the presence of a
non-thermal radiation. Comparison between the spectral index of the power-law
X-ray energy distribution with the non-thermal electron energy distribution
indicates that the non-thermal X-ray component could be the auroral signature
of the non-thermal electrons that impact the stellar surface, the same
non-thermal electrons that are responsible for the observed radio emission. On
the basis of our analysis, we suggest a novel model that simultaneously
explains the X-ray and the radio features of HR7355 and is likely relevant for
magnetospheres of other magnetic early type stars.
| 0 | 1 | 0 | 0 | 0 | 0 |
In silico optimization of critical currents in superconductors | For many technological applications of superconductors the performance of a
material is determined by the highest current it can carry losslessly - the
critical current. In turn, the critical current can be controlled by adding
non-superconducting defects in the superconductor matrix. Here we report on
systematic comparison of different local and global optimization strategies to
predict optimal structures of pinning centers leading to the highest possible
critical currents. We demonstrate performance of these methods for a
superconductor with randomly placed spherical, elliptical, and columnar
defects.
| 0 | 1 | 0 | 0 | 0 | 0 |
Attracting sequences of holomorphic automorphisms that agree to a certain order | The basin of attraction of a uniformly attracting sequence of holomorphic
automorphisms that agree to a certain order in the common fixed point, is
biholomorphic to $\mathbb{C}^n$. We also give sufficient estimates how large
this order has to be.
| 0 | 0 | 1 | 0 | 0 | 0 |
Repulsive Fermi polarons with negative effective mass | Recent LENS experiment on a 3D Fermi gas has reported a negative effective
mass ($m^*<0$) of Fermi polarons in the strongly repulsive regime. There
naturally arise a question whether the negative $m^*$ is a precursor of the
instability towards phase separation (or itinerant ferromagnetism). In this
work, we make use of the exact solutions to study the ground state and
excitation properties of repulsive Fermi polarons in 1D, which can also exhibit
a negative $m^*$ in the super Tonks-Girardeau regime. By analyzing the total
spin, quasi-momentum distribution and pair correlations, we conclude that the
negative $m^*$ is irrelevant to the instability towards ferromagnetism or phase
separation, but rather an intrinsic feature of collective excitations for
fermions in the strongly repulsive regime. Surprisingly, for large and negative
$m^*$, such excitation is accompanied with a spin density modulation when the
majority fermions move closer to the impurity rather than being repelled far
away, contrary to the picture of phase separation. These results suggest an
alternative interpretation of negative $m^*$ as observed in recent LENS
experiment.
| 0 | 1 | 0 | 0 | 0 | 0 |
Entropy Formula for Random $\mathbb{Z}^k$-actions | In this paper, entropies, including measure-theoretic entropy and topological
entropy, are considered for random $\mathbb{Z}^k$-actions which are generated
by random compositions of the generators of $\mathbb{Z}^k$-actions. Applying
Pesin's theory for commutative diffeomorphisms we obtain a measure-theoretic
entropy formula of $C^{2}$ random $\mathbb{Z}^k$-actions via the Lyapunov
spectra of the generators. Some formulas and bounds of topological entropy for
certain random $\mathbb{Z}^k$(or $\mathbb{Z}_+^k$ )-actions generated by more
general maps, such as Lipschitz maps, continuous maps on finite graphs and
$C^{1}$ expanding maps, are also obtained. Moreover, as an application, we give
a formula of Friedland's entropy for certain $C^{2}$ $\mathbb{Z}^k$-actions.
| 0 | 0 | 1 | 0 | 0 | 0 |
Kohn anomalies in momentum dependence of magnetic susceptibility of some three-dimensional systems | We study a question of presence of Kohn points, yielding at low temperatures
non-analytic momentum dependence of magnetic susceptibility near its maximum,
in electronic spectum of some three-dimensional systems. In particular, we
consider one-band model on face centered cubic lattice with hopping between
nearest and next-nearest neighbors, which models some aspects of the dispersion
of ZrZn$_2$, and the two-band model on body centered cubic lattice, modeling
the dispersion of chromium. For the former model it is shown that Kohn points
yielding maxima of susceptibility exist in a certain (sufficiently wide) region
of electronic concentrations; the dependence of the wave vectors, corresponding
to the maxima, on the chemical potential is investigated. For the two-band
model we show existence of the lines of Kohn points, yielding maximum of the
susceptibility, which position agrees with the results of band structure
calculations and experimental data on the wave vector of antiferromagnetism of
chromium.
| 0 | 1 | 0 | 0 | 0 | 0 |
Stability conditions, $τ$-tilting Theory and Maximal Green Sequences | Extending the notion of maximal green sequences to an abelian category, we
characterize the stability functions, as defined by Rudakov, that induce a
maximal green sequence in an abelian length category. Furthermore, we use
$\tau$-tilting theory to give a description of the wall and chamber structure
of any finite dimensional algebra. Finally we introduce the notion of green
paths in the wall and chamber structure of an algebra and show that green paths
serve as geometrical generalization of maximal green sequences in this context.
| 0 | 0 | 1 | 0 | 0 | 0 |
The thermal phase curve offset on tidally- and non-tidally-locked exoplanets: A shallow water model | Using a shallow water model with time-dependent forcing we show that the peak
of an exoplanet thermal phase curve is, in general, offset from secondary
eclipse when the planet is rotating. That is, the planetary hot-spot is offset
from the point of maximal heating (the substellar point) and may lead or lag
the forcing; the extent and sign of the offset is a function of both the
rotation rate and orbital period of the planet. We also find that the system
reaches a steady-state in the reference frame of the moving forcing. The model
is an extension of the well studied Matsuno-Gill model into a full spherical
geometry and with a planetary-scale translating forcing representing the
insolation received on an exoplanet from a host star.
The speed of the gravity waves in the model is shown to be a key metric in
evaluating the phase curve offset. If the velocity of the substellar point
(relative to the planet's surface) exceeds that of the gravity waves then the
hotspot will lag the substellar point, as might be expected by consideration of
forced gravity wave dynamics. However, when the substellar point is moving
slower than the internal wavespeed of the system the hottest point can lead the
passage of the forcing. We provide an interpretation of this result by
consideration of the Rossby and Kelvin wave dynamics as well as, in the very
slowly rotating case, a one-dimensional model that yields an analytic solution.
Finally, we consider the inverse problem of constraining planetary rotation
rate from an observed phase curve.
| 0 | 1 | 0 | 0 | 0 | 0 |
On Asymptotic Standard Normality of the Two Sample Pivot | The asymptotic solution to the problem of comparing the means of two
heteroscedastic populations, based on two random samples from the populations,
hinges on the pivot underpinning the construction of the confidence interval
and the test statistic being asymptotically standard Normal, which is known to
happen if the two samples are independent and the ratio of the sample sizes
converges to a finite positive number. This restriction on the asymptotic
behavior of the ratio of the sample sizes carries the risk of rendering the
asymptotic justification of the finite sample approximation invalid. It turns
out that neither the restriction on the asymptotic behavior of the ratio of the
sample sizes nor the assumption of cross sample independence is necessary for
the pivotal convergence in question to take place. If the joint distribution of
the standardized sample means converges to a spherically symmetric
distribution, then that distribution must be bivariate standard Normal (which
can happen without the assumption of cross sample independence), and the
aforesaid pivotal convergence holds.
| 0 | 0 | 1 | 1 | 0 | 0 |
Breakthrough revisited: investigating the requirements for growth of dust beyond the bouncing barrier | For grain growth to proceed effectively and lead to planet formation a number
of barriers to growth must be overcome. One such barrier, relevant for compact
grains in the inner regions of the disc, is the `bouncing barrier' in which
large grains ($\sim$ mm size) tend to bounce off each other rather than
sticking. However, by maintaining a population of small grains it has been
suggested that cm-size particles may grow rapidly by sweeping up these small
grains. We present the first numerically resolved investigation into the
conditions under which grains may be lucky enough to grow beyond the bouncing
barrier by a series of rare collisions leading to growth (so-called
`breakthrough'). Our models support previous results, and show that in simple
models breakthrough requires the mass ratio at which high velocity collisions
transition to growth instead of causing fragmentation to be low, $\phi \lesssim
50$. However, in models that take into account the dependence of the
fragmentation threshold on mass-ratio, we find breakthrough occurs more
readily, even if mass transfer is relatively inefficient. This suggests that
bouncing may only slow down growth, rather than preventing growth beyond a
threshold barrier. However, even when growth beyond the bouncing barrier is
possible, radial drift will usually prevent growth to arbitrarily large sizes.
| 0 | 1 | 0 | 0 | 0 | 0 |
Extremely high magnetoresistance and conductivity in the type-II Weyl semimetals WP2 and MoP2 | The peculiar band structure of semimetals exhibiting Dirac and Weyl crossings
can lead to spectacular electronic properties such as large mobilities
accompanied by extremely high magnetoresistance. In particular, two closely
neighbouring Weyl points of the same chirality are protected from annihilation
by structural distortions or defects, thereby significantly reducing the
scattering probability between them. Here we present the electronic properties
of the transition metal diphosphides, WP2 and MoP2, that are type-II Weyl
semimetals with robust Weyl points. We present transport and angle resolved
photoemission spectroscopy measurements, and first principles calculations. Our
single crystals of WP2 display an extremely low residual low-temperature
resistivity of 3 nohm-cm accompanied by an enormous and highly anisotropic
magnetoresistance above 200 million % at 63 T and 2.5 K. These properties are
likely a consequence of the novel Weyl fermions expressed in this compound. We
observe a large suppression of charge carrier backscattering in WP2 from
transport measurements.
| 0 | 1 | 0 | 0 | 0 | 0 |
Density and current profiles in $U_q(A^{(1)}_2)$ zero range process | The stochastic $R$ matrix for $U_q(A^{(1)}_n)$ introduced recently gives rise
to an integrable zero range process of $n$ classes of particles in one
dimension. For $n=2$ we investigate how finitely many first class particles
fixed as defects influence the grand canonical ensemble of the second class
particles. By using the matrix product stationary probabilities involving
infinite products of $q$-bosons, exact formulas are derived for the local
density and current of the second class particles in the large volume limit.
| 0 | 1 | 0 | 0 | 0 | 0 |
Crystal structure, site selectivity, and electronic structure of layered chalcogenide LaOBiPbS3 | We have investigated the crystal structure of LaOBiPbS3 using neutron
diffraction and synchrotron X-ray diffraction. From structural refinements, we
found that the two metal sites, occupied by Bi and Pb, were differently
surrounded by the sulfur atoms. Calculated bond valence sum suggested that one
metal site was nearly trivalent and the other was nearly divalent. Neutron
diffraction also revealed site selectivity of Bi and Pb in the LaOBiPbS3
structure. These results suggested that the crystal structure of LaOBiPbS3 can
be regarded as alternate stacks of the rock-salt-type Pb-rich sulfide layers
and the LaOBiS2-type Bi-rich layers. From band calculations for an ideal
(LaOBiS2)(PbS) system, we found that the S bands of the PbS layer were
hybridized with the Bi bands of the BiS plane at around the Fermi energy, which
resulted in the electronic characteristics different from that of LaOBiS2.
Stacking the rock-salt type sulfide (chalcogenide) layers and the BiS2-based
layered structure could be a new strategy to exploration of new BiS2-based
layered compounds, exotic two-dimensional electronic states, or novel
functionality.
| 0 | 1 | 0 | 0 | 0 | 0 |
Responses of Pre-transitional Materials with Stress-Generating Defects to External Stimuli: Superelasticity, Supermagnetostriction, Invar and Elinvar Effects | We considered a generic case of pre-transitional materials with static
stress-generating defects, dislocations and coherent nano-precipitates, at
temperatures close but above the starting temperature of martensitic
transformation, Ms. Using the Phase Field Microelasticity theory and 3D
simulation, we demonstrated that the local stress generated by these defects
produces equilibrium nano-size martensitic embryos (MEs) in pre-transitional
state, these embryos being orientation variants of martensite. This is a new
type of equilibrium: the thermoelastic equilibrium between the MEs and parent
phase in which the total volume of MEs and their size are equilibrium internal
thermodynamic parameters. This thermoelastic equilibrium exists only in
presence of the stress-generating defects. Cooling the pre-transitional state
towards Ms or applying the external stimuli, stress or magnetic field, results
in a shift of the thermoelastic equilibrium provided by a reversible
anhysteretic growth of MEs that results in a giant ME-generated macroscopic
strain. In particular, this effect can be associated with the diffuse phase
transformations observed in some ferroelectrics above the Curie point. It is
shown that the ME-generated strain is giant and describes a superelasticity if
the applied field is stress. It describes a super magnetostriction if the
martensite (or austenite) are ferromagnetic and the applied field is a magnetic
field. In general, the material with defects can be a multiferroic with a giant
multiferroic response if the parent and martensitic phase have different
ferroic properties. Finally the ME-generated strain may explain or, at least,
contribute to the Invar and Elinvar effects that are typically observed in
pre-transitional austenite. The thermoelastic equilibrium and all these effects
exist only if the interaction between the defects and MEs is infinite-range.
| 0 | 1 | 0 | 0 | 0 | 0 |
On The Robustness of Epsilon Skew Extension for Burr III Distribution on Real Line | The Burr III distribution is used in a wide variety of fields of lifetime
data analysis, reliability theory, and financial literature, etc. It is defined
on the positive axis and has two shape parameters, say $c$ and $k$. These shape
parameters make the distribution quite flexible. They also control the tail
behavior of the distribution. In this study, we extent the Burr III
distribution to the real axis and also add a skewness parameter, say
$\varepsilon$, with epsilon-skew extension approach. When the parameters $c$
and $k$ have a relation such that $ck \approx 1 $ or $ck < 1 $, it is skewed
unimodal. Otherwise, it is skewed bimodal with the same level of peaks on the
negative and positive sides of real line. Thus, ESBIII distribution can capture
fitting the various data sets even when the number of parameters are three.
Location and scale form of this distribution are also given. Some
distributional properties of the new distribution are investigated. The maximum
likelihood (ML) estimation method for the parameters of ESBIII is considered.
The robustness properties of ML estimators are studied and also tail behaviour
of ESBIII distribution is examined. The applications on real data are
considered to illustrate the modeling capacity of this distribution in the
class of bimodal distributions.
| 0 | 0 | 1 | 1 | 0 | 0 |
Logics for Word Transductions with Synthesis | We introduce a logic, called LT, to express properties of transductions, i.e.
binary relations from input to output (finite) words. In LT, the input/output
dependencies are modelled via an origin function which associates to any
position of the output word, the input position from which it originates. LT is
well-suited to express relations (which are not necessarily functional), and
can express all regular functional transductions, i.e. transductions definable
for instance by deterministic two-way transducers. Despite its high expressive
power, LT has decidable satisfiability and equivalence problems, with tight
non-elementary and elementary complexities, depending on specific
representation of LT-formulas. Our main contribution is a synthesis result:
from any transduction R defined in LT , it is possible to synthesise a regular
functional transduction f such that for all input words u in the domain of R, f
is defined and (u,f(u)) belongs to R. As a consequence, we obtain that any
functional transduction is regular iff it is LT-definable. We also investigate
the algorithmic and expressiveness properties of several extensions of LT, and
explicit a correspondence between transductions and data words. As a
side-result, we obtain a new decidable logic for data words.
| 1 | 0 | 0 | 0 | 0 | 0 |
Topology and strong four fermion interactions in four dimensions | We study massless fermions interacting through a particular four fermion term
in four dimensions. Exact symmetries prevent the generation of bilinear fermion
mass terms. We determine the structure of the low energy effective action for
the auxiliary field needed to generate the four fermion term and find it has an
novel structure that admits topologically non-trivial defects with non-zero
Hopf invariant. We show that fermions propagating in such a background pick up
a mass without breaking symmetries. Furthermore pairs of such defects
experience a logarithmic interaction. We argue that a phase transition
separates a phase where these defects proliferate from a broken phase where
they are bound tightly. We conjecture that by tuning one additional operator
the broken phase can be eliminated with a single BKT-like phase transition
separating the massless from massive phases.
| 0 | 1 | 0 | 0 | 0 | 0 |
Augmentor: An Image Augmentation Library for Machine Learning | The generation of artificial data based on existing observations, known as
data augmentation, is a technique used in machine learning to improve model
accuracy, generalisation, and to control overfitting. Augmentor is a software
package, available in both Python and Julia versions, that provides a high
level API for the expansion of image data using a stochastic, pipeline-based
approach which effectively allows for images to be sampled from a distribution
of augmented images at runtime. Augmentor provides methods for most standard
augmentation practices as well as several advanced features such as
label-preserving, randomised elastic distortions, and provides many helper
functions for typical augmentation tasks used in machine learning.
| 1 | 0 | 0 | 1 | 0 | 0 |
Multilevel Sequential${}^2$ Monte Carlo for Bayesian Inverse Problems | The identification of parameters in mathematical models using noisy
observations is a common task in uncertainty quantification. We employ the
framework of Bayesian inversion: we combine monitoring and observational data
with prior information to estimate the posterior distribution of a parameter.
Specifically, we are interested in the distribution of a diffusion coefficient
of an elliptic PDE. In this setting, the sample space is high-dimensional, and
each sample of the PDE solution is expensive. To address these issues we
propose and analyse a novel Sequential Monte Carlo (SMC) sampler for the
approximation of the posterior distribution. Classical, single-level SMC
constructs a sequence of measures, starting with the prior distribution, and
finishing with the posterior distribution. The intermediate measures arise from
a tempering of the likelihood, or, equivalently, a rescaling of the noise. The
resolution of the PDE discretisation is fixed. In contrast, our estimator
employs a hierarchy of PDE discretisations to decrease the computational cost.
We construct a sequence of intermediate measures by decreasing the temperature
or by increasing the discretisation level at the same time. This idea builds on
and generalises the multi-resolution sampler proposed in [P.S. Koutsourelakis,
J. Comput. Phys., 228 (2009), pp. 6184-6211] where a bridging scheme is used to
transfer samples from coarse to fine discretisation levels. Importantly, our
choice between tempering and bridging is fully adaptive. We present numerical
experiments in 2D space, comparing our estimator to single-level SMC and the
multi-resolution sampler.
| 0 | 0 | 0 | 1 | 0 | 0 |
Multiparameter actuation of a neutrally-stable shell: a flexible gear-less motor | We have designed and tested experimentally a morphing structure consisting of
a neutrally stable thin cylindrical shell driven by a multiparameter
piezoelectric actuation. The shell is obtained by plastically deforming an
initially flat copper disk, so as to induce large isotropic and almost uniform
inelastic curvatures. Following the plastic deformation, in a perfectly
isotropic system, the shell is theoretically neutrally stable, owning a
continuous manifold of stable cylindrical shapes corresponding to the rotation
of the axis of maximal curvature. Small imperfections render the actual
structure bistable, giving preferred orientations. A three-parameter
piezoelectric actuation, exerted through micro-fiber-composite actuators,
allows us to add a small perturbation to the plastic inelastic curvature and to
control the direction of maximal curvature. This actuation law is designed
through a geometrical analogy based on a fully non-linear inextensible
uniform-curvature shell model. We report on the fabrication, identification,
and experimental testing of a prototype and demonstrate the effectiveness of
the piezoelectric actuators in controlling its shape. The resulting motion is
an apparent rotation of the shell, controlled by the voltages as in a
"gear-less motor", which is, in reality, a precession of the axis of principal
curvature.
| 0 | 1 | 0 | 0 | 0 | 0 |
An Evolutionary Game for User Access Mode Selection in Fog Radio Access Networks | The fog radio access network (F-RAN) is a promising paradigm for the fifth
generation wireless communication systems to provide high spectral efficiency
and energy efficiency. Characterizing users to select an appropriate
communication mode among fog access point (F-AP), and device-to-device (D2D) in
F-RANs is critical for performance optimization. Using evolutionary game
theory, we investigate the dynamics of user access mode selection in F-RANs.
Specifically, the competition among groups of potential users space is
formulated as a dynamic evolutionary game, and the evolutionary equilibrium is
the solution to this game. Stochastic geometry tool is used to derive the
proposals' payoff expressions for both F-AP and D2D users by taking into
account the different nodes locations, cache sizes as well as the delay cost.
The analytical results obtained from the game model are evaluated via
simulations, which show that the evolutionary game based access mode selection
algorithm can reach a much higher payoff than the max rate based algorithm.
| 1 | 0 | 0 | 0 | 0 | 0 |
Latent Estimation of GDP, GDP per capita, and Population from Historic and Contemporary Sources | The concepts of Gross Domestic Product (GDP), GDP per capita, and population
are central to the study of political science and economics. However, a growing
literature suggests that existing measures of these concepts contain
considerable error or are based on overly simplistic modeling choices. We
address these problems by creating a dynamic, three-dimensional latent trait
model, which uses observed information about GDP, GDP per capita, and
population to estimate posterior prediction intervals for each of these
important concepts. By combining historical and contemporary sources of
information, we are able to extend the temporal and spatial coverage of
existing datasets for country-year units back to 1500 A.D through 2015 A.D.
and, because the model makes use of multiple indicators of the underlying
concepts, we are able to estimate the relative precision of the different
country-year estimates. Overall, our latent variable model offers a principled
method for incorporating information from different historic and contemporary
data sources. It can be expanded or refined as researchers discover new or
alternative sources of information about these concepts.
| 0 | 0 | 0 | 1 | 0 | 0 |
Portfolio Optimization under Fast Mean-reverting and Rough Fractional Stochastic Environment | Fractional stochastic volatility models have been widely used to capture the
non-Markovian structure revealed from financial time series of realized
volatility. On the other hand, empirical studies have identified scales in
stock price volatility: both fast-time scale on the order of days and
slow-scale on the order of months. So, it is natural to study the portfolio
optimization problem under the effects of dependence behavior which we will
model by fractional Brownian motions with Hurst index $H$, and in the fast or
slow regimes characterized by small parameters $\eps$ or $\delta$. For the
slowly varying volatility with $H \in (0,1)$, it was shown that the first order
correction to the problem value contains two terms of order $\delta^H$, one
random component and one deterministic function of state processes, while for
the fast varying case with $H > \half$, the same form holds at order
$\eps^{1-H}$. This paper is dedicated to the remaining case of a fast-varying
rough environment ($H < \half$) which exhibits a different behavior. We show
that, in the expansion, only one deterministic term of order $\sqrt{\eps}$
appears in the first order correction.
| 0 | 0 | 0 | 0 | 0 | 1 |
Triangle Generative Adversarial Networks | A Triangle Generative Adversarial Network ($\Delta$-GAN) is developed for
semi-supervised cross-domain joint distribution matching, where the training
data consists of samples from each domain, and supervision of domain
correspondence is provided by only a few paired samples. $\Delta$-GAN consists
of four neural networks, two generators and two discriminators. The generators
are designed to learn the two-way conditional distributions between the two
domains, while the discriminators implicitly define a ternary discriminative
function, which is trained to distinguish real data pairs and two kinds of fake
data pairs. The generators and discriminators are trained together using
adversarial learning. Under mild assumptions, in theory the joint distributions
characterized by the two generators concentrate to the data distribution. In
experiments, three different kinds of domain pairs are considered, image-label,
image-image and image-attribute pairs. Experiments on semi-supervised image
classification, image-to-image translation and attribute-based image generation
demonstrate the superiority of the proposed approach.
| 1 | 0 | 0 | 1 | 0 | 0 |
Learning Distributions of Meant Color | When a speaker says the name of a color, the color that they picture is not
necessarily the same as the listener imagines. Color is a grounded semantic
task, but that grounding is not a mapping of a single word (or phrase) to a
single point in color-space. Proper understanding of color language requires
the capacity to map a sequence of words to a probability distribution in
color-space. A distribution is required as there is no clear agreement between
people as to what a particular color describes -- different people have a
different idea of what it means to be `very dark orange'. We propose a novel
GRU-based model to handle this case. Learning how each word in a color name
contributes to the color described, allows for knowledge sharing between uses
of the words in different color names. This knowledge sharing significantly
improves predicative capacity for color names with sparse training data. The
extreme case of this challenge in data sparsity is for color names without any
direct training data. Our model is able to predict reasonable distributions for
these cases, as evaluated on a held-out dataset consisting only of such terms.
| 1 | 0 | 0 | 0 | 0 | 0 |
Bose - Einstein condensation of triplons with a weakly broken U(1) symmetry | The low-temperature properties of certain quantum magnets can be described in
terms of a Bose-Einstein condensation (BEC) of magnetic quasiparticles
(triplons). Some mean-field approaches (MFA) to describe these systems, based
on the standard grand canonical ensemble, do not take the anomalous density
into account and leads to an internal inconsistency, as it has been shown by
Hohenberg and Martin, and may therefore produce unphysical results. Moreover,
an explicit breaking of the U(1) symmetry as observed, for example, in TlCuCl3
makes the application of MFA more complicated. In the present work, we develop
a self-consistent MFA approach, similar to the Hartree-Fock-Bogolyubov
approximation in the notion of representative statistical ensembles, including
the effect of a weakly broken U(1) symmetry. We apply our results on
experimental data of the quantum magnet TlCuCl3 and show that magnetization
curves and the energy dispersion can be well described within this
approximation assuming that the BEC scenario is still valid. We predict that
the shift of the critical temperature Tc due to a finite exchange anisotropy is
rather substantial even when the anisotropy parameter \gamma is small, e.g.,
\Delta T_c \approx 10%$ of Tc in H = 6T and for \gamma\approx 4 \mu eV.
| 0 | 1 | 0 | 0 | 0 | 0 |
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