title
stringlengths 7
239
| abstract
stringlengths 7
2.76k
| cs
int64 0
1
| phy
int64 0
1
| math
int64 0
1
| stat
int64 0
1
| quantitative biology
int64 0
1
| quantitative finance
int64 0
1
|
|---|---|---|---|---|---|---|---|
Characterizing spectral continuity in SDSS u'g'r'i'z' asteroid photometry | Context. The 4th release of the SDSS Moving Object Catalog (SDSSMOC) is
presently the largest photometric dataset of asteroids. Up to this point, the
release of large asteroid datasets has always been followed by a redefinition
of asteroid taxonomy. In the years that followed the release of the first
SDSSMOC, several classification schemes using its data were proposed, all using
the taxonomic classes from previous taxonomies. However, no successful attempt
has been made to derive a new taxonomic system directly from the SDSS dataset.
Aims. The scope of the work is to propose a different interpretation scheme for
gauging u0g0r0i0z0 asteroid observations based on the continuity of spectral
features. The scheme is integrated into previous taxonomic labeling, but is not
dependent on them. Methods. We analyzed the behavior of asteroid sampling
through principal components analysis to understand the role of uncertainties
in the SDSSMOC. We identified that asteroids in this space follow two separate
linear trends using reflectances in the visible, which is characteristic of
their spectrophotometric features. Results. Introducing taxonomic classes, we
are able to interpret both trends as representative of featured and featureless
spectra. The evolution within the trend is connected mainly to the band depth
for featured asteroids and to the spectral slope for featureless ones. We
defined a different taxonomic system that allowed us to only classify asteroids
by two labels. Conclusions. We have classified 69% of all SDSSMOC sample, which
is a robustness higher than reached by previous SDSS classifications.
Furthermore, as an example, we present the behavior of asteroid (5129) Groom,
whose taxonomic labeling changes according to one of the trends owing to phase
reddening. Now, such behavior can be characterized by the variation of one
single parameter, its position in the trend.
| 0 | 1 | 0 | 0 | 0 | 0 |
Transverse-spin correlations of the random transverse-field Ising model | The critical behavior of the random transverse-field Ising model in finite
dimensional lattices is governed by infinite disorder fixed points, several
properties of which have already been calculated by the use of the strong
disorder renormalization group (SDRG) method. Here we extend these studies and
calculate the connected transverse-spin correlation function by a numerical
implementation of the SDRG method in $d=1,2$ and $3$ dimensions. At the
critical point an algebraic decay of the form $\sim r^{-\eta_t}$ is found, with
a decay exponent being approximately $\eta_t \approx 2+2d$. In $d=1$ the
results are related to dimer-dimer correlations in the random AF XX-chain and
have been tested by numerical calculations using free-fermionic techniques.
| 0 | 1 | 0 | 0 | 0 | 0 |
Multivariate stable distributions and their applications for modelling cryptocurrency-returns | In this paper we extend the known methodology for fitting stable
distributions to the multivariate case and apply the suggested method to the
modelling of daily cryptocurrency-return data. The investigated time period is
cut into 10 non-overlapping sections, thus the changes can also be observed. We
apply bootstrap tests for checking the models and compare our approach to the
more traditional extreme-value and copula models.
| 0 | 0 | 0 | 0 | 0 | 1 |
Beautiful and damned. Combined effect of content quality and social ties on user engagement | User participation in online communities is driven by the intertwinement of
the social network structure with the crowd-generated content that flows along
its links. These aspects are rarely explored jointly and at scale. By looking
at how users generate and access pictures of varying beauty on Flickr, we
investigate how the production of quality impacts the dynamics of online social
systems. We develop a deep learning computer vision model to score images
according to their aesthetic value and we validate its output through
crowdsourcing. By applying it to over 15B Flickr photos, we study for the first
time how image beauty is distributed over a large-scale social system.
Beautiful images are evenly distributed in the network, although only a small
core of people get social recognition for them. To study the impact of exposure
to quality on user engagement, we set up matching experiments aimed at
detecting causality from observational data. Exposure to beauty is
double-edged: following people who produce high-quality content increases one's
probability of uploading better photos; however, an excessive imbalance between
the quality generated by a user and the user's neighbors leads to a decline in
engagement. Our analysis has practical implications for improving link
recommender systems.
| 1 | 0 | 0 | 0 | 0 | 0 |
SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities | The detection of software vulnerabilities (or vulnerabilities for short) is
an important problem that has yet to be tackled, as manifested by many
vulnerabilities reported on a daily basis. This calls for machine learning
methods to automate vulnerability detection. Deep learning is attractive for
this purpose because it does not require human experts to manually define
features. Despite the tremendous success of deep learning in other domains, its
applicability to vulnerability detection is not systematically understood. In
order to fill this void, we propose the first systematic framework for using
deep learning to detect vulnerabilities. The framework, dubbed Syntax-based,
Semantics-based, and Vector Representations (SySeVR), focuses on obtaining
program representations that can accommodate syntax and semantic information
pertinent to vulnerabilities. Our experiments with 4 software products
demonstrate the usefulness of the framework: we detect 15 vulnerabilities that
are not reported in the National Vulnerability Database. Among these 15
vulnerabilities, 7 are unknown and have been reported to the vendors, and the
other 8 have been "silently" patched by the vendors when releasing newer
versions of the products.
| 0 | 0 | 0 | 1 | 0 | 0 |
Differentially Private Learning of Undirected Graphical Models using Collective Graphical Models | We investigate the problem of learning discrete, undirected graphical models
in a differentially private way. We show that the approach of releasing noisy
sufficient statistics using the Laplace mechanism achieves a good trade-off
between privacy, utility, and practicality. A naive learning algorithm that
uses the noisy sufficient statistics "as is" outperforms general-purpose
differentially private learning algorithms. However, it has three limitations:
it ignores knowledge about the data generating process, rests on uncertain
theoretical foundations, and exhibits certain pathologies. We develop a more
principled approach that applies the formalism of collective graphical models
to perform inference over the true sufficient statistics within an
expectation-maximization framework. We show that this learns better models than
competing approaches on both synthetic data and on real human mobility data
used as a case study.
| 1 | 0 | 0 | 1 | 0 | 0 |
Phase unwinding, or invariant subspace decompositions of Hardy spaces | We consider orthogonal decompositions of invariant subspaces of Hardy spaces,
these relate to the Blaschke based phase unwinding decompositions. We prove
convergence in Lp. In particular we build an explicit multiscale wavelet basis.
We also obtain an explicit unwindinig decomposition for the singular inner
function, exp 2i\pi/x.
| 0 | 0 | 1 | 0 | 0 | 0 |
SalProp: Salient object proposals via aggregated edge cues | In this paper, we propose a novel object proposal generation scheme by
formulating a graph-based salient edge classification framework that utilizes
the edge context. In the proposed method, we construct a Bayesian probabilistic
edge map to assign a saliency value to the edgelets by exploiting low level
edge features. A Conditional Random Field is then learned to effectively
combine these features for edge classification with object/non-object label. We
propose an objectness score for the generated windows by analyzing the salient
edge density inside the bounding box. Extensive experiments on PASCAL VOC 2007
dataset demonstrate that the proposed method gives competitive performance
against 10 popular generic object detection techniques while using fewer number
of proposals.
| 1 | 0 | 0 | 0 | 0 | 0 |
Quantum gap and spin-wave excitations in the Kitaev model on a triangular lattice | We study the effects of quantum fluctuations on the dynamical generation of a
gap and on the evolution of the spin-wave spectra of a frustrated magnet on a
triangular lattice with bond-dependent Ising couplings, analog of the Kitaev
honeycomb model. The quantum fluctuations lift the subextensive degeneracy of
the classical ground-state manifold by a quantum order-by-disorder mechanism.
Nearest-neighbor chains remain decoupled and the surviving discrete degeneracy
of the ground state is protected by a hidden model symmetry. We show how the
four-spin interaction, emergent from the fluctuations, generates a spin gap
shifting the nodal lines of the linear spin-wave spectrum to finite energies.
| 0 | 1 | 0 | 0 | 0 | 0 |
Sparse Gaussian ICA | Independent component analysis (ICA) is a cornerstone of modern data
analysis. Its goal is to recover a latent random vector S with independent
components from samples of X=AS where A is an unknown mixing matrix.
Critically, all existing methods for ICA rely on and exploit strongly the
assumption that S is not Gaussian as otherwise A becomes unidentifiable. In
this paper, we show that in fact one can handle the case of Gaussian components
by imposing structure on the matrix A. Specifically, we assume that A is sparse
and generic in the sense that it is generated from a sparse Bernoulli-Gaussian
ensemble. Under this condition, we give an efficient algorithm to recover the
columns of A given only the covariance matrix of X as input even when S has
several Gaussian components.
| 0 | 0 | 0 | 1 | 0 | 0 |
Supervisor Synthesis of POMDP based on Automata Learning | As a general and thus popular model for autonomous systems, partially
observable Markov decision process (POMDP) can capture uncertainties from
different sources like sensing noises, actuation errors, and uncertain
environments. However, its comprehensiveness makes the planning and control in
POMDP difficult. Traditional POMDP planning problems target to find the optimal
policy to maximize the expectation of accumulated rewards. But for safety
critical applications, guarantees of system performance described by formal
specifications are desired, which motivates us to consider formal methods to
synthesize supervisor for POMDP. With system specifications given by
Probabilistic Computation Tree Logic (PCTL), we propose a supervisory control
framework with a type of deterministic finite automata (DFA), za-DFA, as the
controller form. While the existing work mainly relies on optimization
techniques to learn fixed-size finite state controllers (FSCs), we develop an
$L^*$ learning based algorithm to determine both space and transitions of
za-DFA. Membership queries and different oracles for conjectures are defined.
The learning algorithm is sound and complete. An example is given in detailed
steps to illustrate the supervisor synthesis algorithm.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Pseudo Knockoff Filter for Correlated Features | In 2015, Barber and Candes introduced a new variable selection procedure
called the knockoff filter to control the false discovery rate (FDR) and prove
that this method achieves exact FDR control. Inspired by the work of Barber and
Candes (2015), we propose and analyze a pseudo-knockoff filter that inherits
some advantages of the original knockoff filter and has more flexibility in
constructing its knockoff matrix. Although we have not been able to obtain
exact FDR control of the pseudo knockoff filter, we show that it satisfies an
expectation inequality that offers some insight into FDR control. Moreover, we
provide some partial analysis of the pseudo knockoff filter for the half Lasso
and the least squares statistics. Our analysis indicates that the inverse of
the covariance matrix of the feature matrix plays an important role in
designing and analyzing the pseudo knockoff filter. Our preliminary numerical
experiments show that the pseudo knockoff filter with the half Lasso statistic
has FDR control. Moreover, our numerical experiments show that the
pseudo-knockoff filter could offer more power than the original knockoff filter
with the OMP or Lasso Path statistic when the features are correlated and
non-sparse.
| 0 | 0 | 1 | 1 | 0 | 0 |
$μ$-constant monodromy groups and Torelli results for the quadrangle singularities and the bimodal series | This paper is a sequel to [He11] and [GH17]. In [He11] a notion of marking of
isolated hypersurface singularities was defined, and a moduli space
$M_\mu^{mar}$ for marked singularities in one $\mu$-homotopy class of isolated
hypersurface singularities was established. It is an analogue of a
Teichmüller space. It comes together with a $\mu$-constant monodromy group
$G^{mar}\subset G_{\mathbb{Z}}$. Here $G_{\mathbb{Z}}$ is the group of
automorphisms of a Milnor lattice which respect the Seifert form. It was
conjectured that $M_\mu^{mar}$ is connected. This is equivalent to $G^{mar}=
G_{\mathbb{Z}}$. Also Torelli type conjectures were formulated. In [He11] and
[GH17] $M_\mu^{mar}, G_{\mathbb{Z}}$ and $G^{mar}$ were determined and all
conjectures were proved for the simple, the unimodal and the exceptional
bimodal singularities. In this paper the quadrangle singularities and the
bimodal series are treated. The Torelli type conjectures are true. But the
conjecture $G^{mar}= G_{\mathbb{Z}}$ and $M_\mu^{mar}$ connected does not hold
for certain subseries of the bimodal series.
| 0 | 0 | 1 | 0 | 0 | 0 |
SoaAlloc: Accelerating Single-Method Multiple-Objects Applications on GPUs | We propose SoaAlloc, a dynamic object allocator for Single-Method
Multiple-Objects applications in CUDA. SoaAlloc is the first allocator for GPUs
that (a) arranges allocations in a SIMD-friendly Structure of Arrays (SOA) data
layout, (b) provides a do-all operation for maximizing the benefit of SOA, and
(c) is on par with state-of-the-art memory allocators for raw (de)allocation
time. Our benchmarks show that the SOA layout leads to significantly better
memory bandwidth utilization, resulting in a 2x speedup of application code.
| 1 | 0 | 0 | 0 | 0 | 0 |
Probabilistic risk bounds for the characterization of radiological contamination | The radiological characterization of contaminated elements (walls, grounds,
objects) from nuclear facilities often suffers from a too small number of
measurements. In order to determine risk prediction bounds on the level of
contamination, some classic statistical methods may then reveal unsuited as
they rely upon strong assumptions (e.g. that the underlying distribution is
Gaussian) which cannot be checked. Considering that a set of measurements or
their average value arise from a Gaussian distribution can sometimes lead to
erroneous conclusion, possibly underconservative. This paper presents several
alternative statistical approaches which are based on much weaker hypotheses
than Gaussianity. They result from general probabilistic inequalities and
order-statistics based formula. Given a data sample, these inequalities make it
possible to derive prediction intervals for a random variable, which can be
directly interpreted as probabilistic risk bounds. For the sake of validation,
they are first applied to synthetic data samples generated from several known
theoretical distributions. In a second time, the proposed methods are applied
to two data sets obtained from real radiological contamination measurements.
| 0 | 0 | 1 | 1 | 0 | 0 |
Automatic Backward Differentiation for American Monte-Carlo Algorithms (Conditional Expectation) | In this note we derive the backward (automatic) differentiation (adjoint
[automatic] differentiation) for an algorithm containing a conditional
expectation operator. As an example we consider the backward algorithm as it is
used in Bermudan product valuation, but the method is applicable in full
generality.
The method relies on three simple properties: 1) a forward or backward
(automatic) differentiation of an algorithm containing a conditional
expectation operator results in a linear combination of the conditional
expectation operators; 2) the differential of an expectation is the expectation
of the differential $\frac{d}{dx} E(Y) = E(\frac{d}{dx}Y)$; 3) if we are only
interested in the expectation of the final result (as we are in all valuation
problems), we may use $E(A \cdot E(B\vert\mathcal{F})) = E(E(A\vert\mathcal{F})
\cdot B)$, i.e., instead of applying the (conditional) expectation operator to
a function of the underlying random variable (continuation values), it may be
applied to the adjoint differential. \end{enumerate}
The methodology not only allows for a very clean and simple implementation,
but also offers the ability to use different conditional expectation estimators
in the valuation and the differentiation.
| 1 | 0 | 0 | 0 | 0 | 0 |
The Belgian repository of fundamental atomic data and stellar spectra (BRASS). I. Cross-matching atomic databases of astrophysical interest | Fundamental atomic parameters, such as oscillator strengths, play a key role
in modelling and understanding the chemical composition of stars in the
universe. Despite the significant work underway to produce these parameters for
many astrophysically important ions, uncertainties in these parameters remain
large and can propagate throughout the entire field of astronomy. The Belgian
repository of fundamental atomic data and stellar spectra (BRASS) aims to
provide the largest systematic and homogeneous quality assessment of atomic
data to date in terms of wavelength, atomic and stellar parameter coverage. To
prepare for it, we first compiled multiple literature occurrences of many
individual atomic transitions, from several atomic databases of astrophysical
interest, and assessed their agreement. Several atomic repositories were
searched and their data retrieved and formatted in a consistent manner. Data
entries from all repositories were cross-matched against our initial BRASS
atomic line list to find multiple occurrences of the same transition. Where
possible we used a non-parametric cross-match depending only on electronic
configurations and total angular momentum values. We also checked for duplicate
entries of the same physical transition, within each retrieved repository,
using the non-parametric cross-match. We report the cross-matched transitions
for each repository and compare their fundamental atomic parameters. We find
differences in log(gf) values of up to 2 dex or more. We also find and report
that ~2% of our line list and Vienna Atomic Line Database retrievals are
composed of duplicate transitions. Finally we provide a number of examples of
atomic spectral lines with different log(gf) values, and discuss the impact of
these uncertain log(gf) values on quantitative spectroscopy. All cross-matched
atomic data and duplicate transitions are available to download at
brass.sdf.org.
| 0 | 1 | 0 | 0 | 0 | 0 |
High-Precision Calculations in Strongly Coupled Quantum Field Theory with Next-to-Leading-Order Renormalized Hamiltonian Truncation | Hamiltonian Truncation (a.k.a. Truncated Spectrum Approach) is an efficient
numerical technique to solve strongly coupled QFTs in d=2 spacetime dimensions.
Further theoretical developments are needed to increase its accuracy and the
range of applicability. With this goal in mind, here we present a new variant
of Hamiltonian Truncation which exhibits smaller dependence on the UV cutoff
than other existing implementations, and yields more accurate spectra. The key
idea for achieving this consists in integrating out exactly a certain class of
high energy states, which corresponds to performing renormalization at the
cubic order in the interaction strength. We test the new method on the strongly
coupled two-dimensional quartic scalar theory. Our work will also be useful for
the future goal of extending Hamiltonian Truncation to higher dimensions d >=
3.
| 0 | 1 | 0 | 0 | 0 | 0 |
State Distribution-aware Sampling for Deep Q-learning | A critical and challenging problem in reinforcement learning is how to learn
the state-action value function from the experience replay buffer and
simultaneously keep sample efficiency and faster convergence to a high quality
solution. In prior works, transitions are uniformly sampled at random from the
replay buffer or sampled based on their priority measured by
temporal-difference (TD) error. However, these approaches do not fully take
into consideration the intrinsic characteristics of transition distribution in
the state space and could result in redundant and unnecessary TD updates,
slowing down the convergence of the learning procedure. To overcome this
problem, we propose a novel state distribution-aware sampling method to balance
the replay times for transitions with skew distribution, which takes into
account both the occurrence frequencies of transitions and the uncertainty of
state-action values. Consequently, our approach could reduce the unnecessary TD
updates and increase the TD updates for state-action value with more
uncertainty, making the experience replay more effective and efficient.
Extensive experiments are conducted on both classic control tasks and Atari
2600 games based on OpenAI gym platform and the experimental results
demonstrate the effectiveness of our approach in comparison with the standard
DQN approach.
| 0 | 0 | 0 | 1 | 0 | 0 |
Energy Trading between microgrids Individual Cost Minimization and Social Welfare Maximization | High penetration of renewable energy source makes microgrid (MGs) be
environment friendly. However, the stochastic input from renewable energy
resource brings difficulty in balancing the energy supply and demand.
Purchasing extra energy from macrogrid to deal with energy shortage will
increase MG energy cost. To mitigate intermittent nature of renewable energy,
energy trading and energy storage which can exploit diversity of renewable
energy generation across space and time are efficient and cost-effective
methods. But current energy storage control action will impact the future
control action which brings challenge to energy management. In addition, due to
MG participating energy trading as prosumer, it calls for an efficient trading
mechanism. Therefore, this paper focuses on the problem of MG energy management
and trading. Energy trading problem is formulated as a stochastic optimization
one with both individual profit and social welfare maximization. Firstly a
Lyapunov optimization based algorithm is developed to solve the stochastic
problem. Secondly the double-auction based mechanism is provided to attract MG
truthful bidding for buying and selling energy. Through theoretical analysis,
we demonstrate that individual MG can achieve a time average energy cost close
to offline optimum with tradeoff between storage capacity and energy trading
cost. Meanwhile the social welfare is also asymptotically maximized under
double auction. Simulation results based on real world data show the
effectiveness of our algorithm.
| 1 | 0 | 0 | 0 | 0 | 0 |
Learning Kolmogorov Models for Binary Random Variables | We summarize our recent findings, where we proposed a framework for learning
a Kolmogorov model, for a collection of binary random variables. More
specifically, we derive conditions that link outcomes of specific random
variables, and extract valuable relations from the data. We also propose an
algorithm for computing the model and show its first-order optimality, despite
the combinatorial nature of the learning problem. We apply the proposed
algorithm to recommendation systems, although it is applicable to other
scenarios. We believe that the work is a significant step toward interpretable
machine learning.
| 0 | 0 | 0 | 1 | 0 | 0 |
Möbius topological superconductivity in UPt$_3$ | Intensive studies for more than three decades have elucidated multiple
superconducting phases and odd-parity Cooper pairs in a heavy fermion
superconductor UPt$_3$. We identify a time-reversal invariant superconducting
phase of UPt$_3$ as a recently proposed topological nonsymmorphic
superconductivity. Combining the band structure of UPt$_3$, order parameter of
$E_{\rm 2u}$ representation allowed by $P6_3/mmc$ space group symmetry, and
topological classification by $K$-theory, we demonstrate the nontrivial
$Z_2$-invariant of three-dimensional DIII class enriched by glide symmetry.
Correspondingly, double Majorana cone surface states appear at the surface
Brillouin zone boundary. Furthermore, we show a variety of surface states and
clarify the topological protection by crystal symmetry. Majorana arcs
corresponding to tunable Weyl points appear in the time-reversal symmetry
broken B-phase. Majorana cone protected by mirror Chern number and Majorana
flat band by glide-winding number are also revealed.
| 0 | 1 | 0 | 0 | 0 | 0 |
Sparse Identification and Estimation of High-Dimensional Vector AutoRegressive Moving Averages | The Vector AutoRegressive Moving Average (VARMA) model is fundamental to the
study of multivariate time series. However, estimation becomes challenging in
even relatively low-dimensional VARMA models. With growing interest in the
simultaneous modeling of large numbers of marginal time series, many authors
have abandoned the VARMA model in favor of the Vector AutoRegressive (VAR)
model, which is seen as a simpler alternative, both in theory and practice, in
this high-dimensional context. However, even very simple VARMA models can be
very complicated to represent using only VAR modeling. In this paper, we
develop a new approach to VARMA identification and propose a two-phase method
for estimation. Our identification and estimation strategies are linked in
their use of sparsity-inducing convex regularizers, which favor VARMA models
that have only a small number of nonzero parameters. We establish sufficient
conditions for consistency of sparse infinite-order VAR estimates in high
dimensions, a key ingredient for our two-phase sparse VARMA estimation
strategy. The proposed framework has good estimation and forecast accuracy
under numerous simulation settings. We illustrate the forecast performance of
the sparse VARMA models for several application domains, including
macro-economic forecasting, demand forecasting, and volatility forecasting. The
proposed sparse VARMA estimator gives parsimonious forecast models that lead to
important gains in relative forecast accuracy.
| 0 | 0 | 0 | 1 | 0 | 0 |
Information Retrieval and Criticality in Parity-Time-Symmetric Systems | By investigating information flow between a general parity-time (PT)
-symmetric non-Hermitian system and an environment, we find that the complete
information retrieval from the environment can be achieved in the PT-unbroken
phase, whereas no information can be retrieved in the PT-broken phase. The
PT-transition point thus marks the reversible-irreversible criticality of
information flow, around which many physical quantities such as the recurrence
time and the distinguishability between quantum states exhibit power-law
behavior. Moreover, by embedding a PT-symmetric system into a larger Hilbert
space so that the entire system obeys unitary dynamics, we reveal that behind
the information retrieval lies a hidden entangled partner protected by PT
symmetry. Possible experimental situations are also discussed.
| 0 | 1 | 0 | 0 | 0 | 0 |
A practical guide and software for analysing pairwise comparison experiments | Most popular strategies to capture subjective judgments from humans involve
the construction of a unidimensional relative measurement scale, representing
order preferences or judgments about a set of objects or conditions. This
information is generally captured by means of direct scoring, either in the
form of a Likert or cardinal scale, or by comparative judgments in pairs or
sets. In this sense, the use of pairwise comparisons is becoming increasingly
popular because of the simplicity of this experimental procedure. However, this
strategy requires non-trivial data analysis to aggregate the comparison ranks
into a quality scale and analyse the results, in order to take full advantage
of the collected data. This paper explains the process of translating pairwise
comparison data into a measurement scale, discusses the benefits and
limitations of such scaling methods and introduces a publicly available
software in Matlab. We improve on existing scaling methods by introducing
outlier analysis, providing methods for computing confidence intervals and
statistical testing and introducing a prior, which reduces estimation error
when the number of observers is low. Most of our examples focus on image
quality assessment.
| 1 | 0 | 0 | 1 | 0 | 0 |
Web-Based Implementation of Travelling Salesperson Problem Using Genetic Algorithm | The world is connected through the Internet. As the abundance of Internet
users connected into the Web and the popularity of cloud computing research,
the need of Artificial Intelligence (AI) is demanding. In this research,
Genetic Algorithm (GA) as AI optimization method through natural selection and
genetic evolution is utilized. There are many applications of GA such as web
mining, load balancing, routing, and scheduling or web service selection.
Hence, it is a challenging task to discover whether the code mainly server side
and web based language technology affects the performance of GA. Travelling
Salesperson Problem (TSP) as Non Polynomial-hard (NP-hard) problem is provided
to be a problem domain to be solved by GA. While many scientists prefer Python
in GA implementation, another popular high-level interpreter programming
language such as PHP (PHP Hypertext Preprocessor) and Ruby were benchmarked.
Line of codes, file sizes, and performances based on GA implementation and
runtime were found varies among these programming languages. Based on the
result, the use of Ruby in GA implementation is recommended.
| 1 | 0 | 0 | 0 | 0 | 0 |
Random Spatial Networks: Small Worlds without Clustering, Traveling Waves, and Hop-and-Spread Disease Dynamics | Random network models play a prominent role in modeling, analyzing and
understanding complex phenomena on real-life networks. However, a key property
of networks is often neglected: many real-world networks exhibit spatial
structure, the tendency of a node to select neighbors with a probability
depending on physical distance. Here, we introduce a class of random spatial
networks (RSNs) which generalizes many existing random network models but adds
spatial structure. In these networks, nodes are placed randomly in space and
joined in edges with a probability depending on their distance and their
individual expected degrees, in a manner that crucially remains analytically
tractable. We use this network class to propose a new generalization of
small-world networks, where the average shortest path lengths in the graph are
small, as in classical Watts-Strogatz small-world networks, but with close
spatial proximity of nodes that are neighbors in the network playing the role
of large clustering. Small-world effects are demonstrated on these spatial
small-world networks without clustering. We are able to derive partial
integro-differential equations governing susceptible-infectious-recovered
disease spreading through an RSN, and we demonstrate the existence of traveling
wave solutions. If the distance kernel governing edge placement decays slower
than exponential, the population-scale dynamics are dominated by long-range
hops followed by local spread of traveling waves. This provides a theoretical
modeling framework for recent observations of how epidemics like Ebola evolve
in modern connected societies, with long-range connections seeding new focal
points from which the epidemic locally spreads in a wavelike manner.
| 0 | 1 | 0 | 0 | 0 | 0 |
The evolution of magnetic hot massive stars: Implementation of the quantitative influence of surface magnetic fields in modern models of stellar evolution | Large-scale dipolar surface magnetic fields have been detected in a fraction
of OB stars, however only few stellar evolution models of massive stars have
considered the impact of these fossil fields. We are performing 1D
hydrodynamical model calculations taking into account evolutionary consequences
of the magnetospheric-wind interactions in a simplified parametric way. Two
effects are considered: i) the global mass-loss rates are reduced due to
mass-loss quenching, and ii) the surface angular momentum loss is enhanced due
to magnetic braking. As a result of the magnetic mass-loss quenching, the mass
of magnetic massive stars remains close to their initial masses. Thus magnetic
massive stars - even at Galactic metallicity - have the potential to be
progenitors of `heavy' stellar mass black holes. Similarly, at Galactic
metallicity, the formation of pair instability supernovae is plausible with a
magnetic progenitor.
| 0 | 1 | 0 | 0 | 0 | 0 |
The finite gap method and the analytic description of the exact rogue wave recurrence in the periodic NLS Cauchy problem. 1 | The focusing NLS equation is the simplest universal model describing the
modulation instability (MI) of quasi monochromatic waves in weakly nonlinear
media, considered the main physical mechanism for the appearance of rogue
(anomalous) waves (RWs) in Nature. In this paper we study, using the finite gap
method, the NLS Cauchy problem for periodic initial perturbations of the
unstable background solution of NLS exciting just one of the unstable modes. We
distinguish two cases. In the case in which only the corresponding unstable gap
is theoretically open, the solution describes an exact deterministic alternate
recurrence of linear and nonlinear stages of MI, and the nonlinear RW stages
are described by the 1-breather Akhmediev solution, whose parameters, different
at each RW appearance, are always given in terms of the initial data through
elementary functions. If the number of unstable modes is >1, this uniform in t
dynamics is sensibly affected by perturbations due to numerics and/or real
experiments, provoking O(1) corrections to the result. In the second case in
which more than one unstable gap is open, a detailed investigation of all these
gaps is necessary to get a uniform in $t$ dynamics, and this study is postponed
to a subsequent paper. It is however possible to obtain the elementary
description of the first nonlinear stage of MI, given again by the Akhmediev
1-breather solution, and how perturbations due to numerics and/or real
experiments can affect this result.
| 0 | 1 | 0 | 0 | 0 | 0 |
First Discoveries of z>6 Quasars with the DECam Legacy Survey and UKIRT Hemisphere Survey | We present the first discoveries from a survey of $z\gtrsim6$ quasars using
imaging data from the DECam Legacy Survey (DECaLS) in the optical, the UKIRT
Deep Infrared Sky Survey (UKIDSS) and a preliminary version of the UKIRT
Hemisphere Survey (UHS) in the near-IR, and ALLWISE in the mid-IR. DECaLS will
image 9000 deg$^2$ of sky down to $z_{\rm AB}\sim23.0$, and UKIDSS and UHS,
which will map the northern sky at $0<DEC<+60^{\circ}$, reaching $J_{\rm
VEGA}\sim19.6$ (5-$\sigma$). The combination of these datasets allows us to
discover quasars at redshift $z\gtrsim7$ and to conduct a complete census of
the faint quasar population at $z\gtrsim6$. In this paper, we report on the
selection method of our search, and on the initial discoveries of two new,
faint $z\gtrsim6$ quasars and one new $z=6.63$ quasar in our pilot
spectroscopic observations. The two new $z\sim6$ quasars are at $z=6.07$ and
$z=6.17$ with absolute magnitudes at rest-frame wavelength 1450 \AA\ being
$M_{1450}=-25.83$ and $M_{1450}=-25.76$, respectively. These discoveries
suggest that we can find quasars close to or fainter than the break magnitude
of the Quasar Luminosity Function (QLF) at $z\gtrsim6$. The new $z=6.63$ quasar
has an absolute magnitude of $M_{1450}=-25.95$. This demonstrates the potential
of using the combined DECaLS and UKIDSS/UHS datasets to find $z\gtrsim7$
quasars. Extrapolating from previous QLF measurements, we predict that these
combined datasets will yield $\sim200$ $z\sim6$ quasars to $z_{\rm AB} < 21.5$,
$\sim1{,}000$ $z\sim6$ quasars to $z_{\rm AB}<23$, and $\sim 30$ quasars at
$z>6.5$ to $J_{\rm VEGA}<19.5$.
| 0 | 1 | 0 | 0 | 0 | 0 |
On unique continuation for solutions of the Schr{ö}dinger equation on trees | We prove that if a solution of the time-dependent Schr{ö}dinger equation on
an homogeneous tree with bounded potential decays fast at two distinct times
then the solution is trivial. For the free Schr{ö}dinger operator, we use the
spectral theory of the Laplacian and complex analysis and obtain a
characterization of the initial conditions that lead to a sharp decay at any
time. We then use the recent spectral decomposition of the Schr{ö}dinger
operator with compactly supported potential due to Colin de Verdi{è}rre and
Turc to extend our results in the presence of such potentials. Finally, we use
real variable methods first introduced by Escauriaza, Kenig, Ponce and Vega to
establish a general sharp result in the case of bounded potentials.
| 0 | 0 | 1 | 0 | 0 | 0 |
Particle-hole Asymmetry in the Cuprate Pseudogap Measured with Time-Resolved Spectroscopy | One of the most puzzling features of high-temperature cuprate superconductors
is the pseudogap state, which appears above the temperature at which
superconductivity is destroyed. There remain fundamental questions regarding
its nature and its relation to superconductivity. But to address these
questions, we must first determine whether the pseudogap and superconducting
states share a common property: particle-hole symmetry. We introduce a new
technique to test particle-hole symmetry by using laser pulses to manipulate
and measure the chemical potential on picosecond time scales. The results
strongly suggest that the asymmetry in the density of states is inverted in the
pseudogap state, implying a particle-hole asymmetric gap. Independent of
interpretation, these results can test theoretical predictions of the density
of states in cuprates.
| 0 | 1 | 0 | 0 | 0 | 0 |
Numerically modeling Brownian thermal noise in amorphous and crystalline thin coatings | Thermal noise is expected to be one of the noise sources limiting the
astrophysical reach of Advanced LIGO (once commissioning is complete) and
third-generation detectors. Adopting crystalline materials for thin, reflecting
mirror coatings, rather than the amorphous coatings used in current-generation
detectors, could potentially reduce thermal noise. Understanding and reducing
thermal noise requires accurate theoretical models, but modeling thermal noise
analytically is especially challenging with crystalline materials. Thermal
noise models typically rely on the fluctuation-dissipation theorem, which
relates the power spectral density of the thermal noise to an auxiliary elastic
problem. In this paper, we present results from a new, open-source tool that
numerically solves the auxiliary elastic problem to compute the Brownian
thermal noise for both amorphous and crystalline coatings. We employ
open-source frameworks to solve the auxiliary elastic problem using a
finite-element method, adaptive mesh refinement, and parallel processing that
enables us to use high resolutions capable of resolving the thin reflective
coating. We compare with approximate analytic solutions for amorphous
materials, and we verify that our solutions scale as expected. Finally, we
model the crystalline coating thermal noise in an experiment reported by Cole
and collaborators (2013), comparing our results to a simpler numerical
calculation that treats the coating as an "effectively amorphous" material. We
find that treating the coating as a cubic crystal instead of as an effectively
amorphous material increases the thermal noise by about 3%. Our results are a
step toward better understanding and reducing thermal noise to increase the
reach of future gravitational-wave detectors. (Abstract abbreviated.)
| 0 | 1 | 0 | 0 | 0 | 0 |
Net2Vec: Quantifying and Explaining how Concepts are Encoded by Filters in Deep Neural Networks | In an effort to understand the meaning of the intermediate representations
captured by deep networks, recent papers have tried to associate specific
semantic concepts to individual neural network filter responses, where
interesting correlations are often found, largely by focusing on extremal
filter responses. In this paper, we show that this approach can favor
easy-to-interpret cases that are not necessarily representative of the average
behavior of a representation.
A more realistic but harder-to-study hypothesis is that semantic
representations are distributed, and thus filters must be studied in
conjunction. In order to investigate this idea while enabling systematic
visualization and quantification of multiple filter responses, we introduce the
Net2Vec framework, in which semantic concepts are mapped to vectorial
embeddings based on corresponding filter responses. By studying such
embeddings, we are able to show that 1., in most cases, multiple filters are
required to code for a concept, that 2., often filters are not concept specific
and help encode multiple concepts, and that 3., compared to single filter
activations, filter embeddings are able to better characterize the meaning of a
representation and its relationship to other concepts.
| 0 | 0 | 0 | 1 | 0 | 0 |
Can the removal of molecular cloud envelopes by external feedback affect the efficiency of star formation? | We investigate how star formation efficiency can be significantly decreased
by the removal of a molecular cloud's envelope by feedback from an external
source. Feedback from star formation has difficulties halting the process in
dense gas but can easily remove the less dense and warmer envelopes where star
formation does not occur. However, the envelopes can play an important role
keeping their host clouds bound by deepening the gravitational potential and
providing a constraining pressure boundary. We use numerical simulations to
show that removal of the cloud envelopes results in all cases in a fall in the
star formation efficiency (SFE). At 1.38 free-fall times our 4 pc cloud
simulation experienced a drop in the SFE from 16 to six percent, while our 5 pc
cloud fell from 27 to 16 per cent. At the same time, our 3 pc cloud (the least
bound) fell from an SFE of 5.67 per cent to zero when the envelope was lost.
The star formation efficiency per free-fall time varied from zero to $\approx$
0.25 according to $\alpha$, defined to be the ratio of the kinetic plus thermal
to gravitational energy, and irrespective of the absolute star forming mass
available. Furthermore the fall in SFE associated with the loss of the envelope
is found to even occur at later times. We conclude that the SFE will always
fall should a star forming cloud lose its envelope due to stellar feedback,
with less bound clouds suffering the greatest decrease.
| 0 | 1 | 0 | 0 | 0 | 0 |
Joint Rate and Resource Allocation in Hybrid Digital-Analog Transmission over Fading Channels | In hybrid digital-analog (HDA) systems, resource allocation has been utilized
to achieve desired distortion performance. However, existing studies on this
issue assume error-free digital transmission, which is not valid for fading
channels. With time-varying channel fading, the exact channel state information
is not available at the transmitter. Thus, random outage and resulting digital
distortion cannot be ignored. Moreover, rate allocation should be considered in
resource allocation, since it not only determines the amount of information for
digital transmission and that for analog transmission, but also affects the
outage probability. Based on above observations, in this paper, we attempt to
perform joint rate and resource allocation strategies to optimize system
distortion in HDA systems over fading channels. Consider a bandwidth expansion
scenario where a memoryless Gaussian source is transmitted in an HDA system
with the entropy-constrained scalar quantizer (ECSQ). Firstly, we formulate the
joint allocation problem as an expected system distortion minimization problem
where both analog and digital distortion are considered. Then, in the limit of
low outage probability, we decompose the problem into two coupled sub-problems
based on the block coordinate descent method, and propose an iterative gradient
algorithm to approach the optimal solution. Furthermore, we extend our work to
the multivariate Gaussian source scenario where a two-stage fast algorithm
integrating rounding and greedy strategies is proposed to optimize the joint
rate and resource allocation problem. Finally, simulation results demonstrate
that the proposed algorithms can achieve up to 2.3dB gains in terms of
signal-to-distortion ratio over existing schemes under the single Gaussian
source scenario, and up to 3.5dB gains under the multivariate Gaussian source
scenario.
| 1 | 0 | 0 | 0 | 0 | 0 |
Relaxation to a Phase-locked Equilibrium State in a One-dimensional Bosonic Josephson Junction | We present an experimental study on the non-equilibrium tunnel dynamics of
two coupled one-dimensional Bose-Einstein quasi-condensates deep in the
Josephson regime. Josephson oscillations are initiated by splitting a single
one-dimensional condensate and imprinting a relative phase between the
superfluids. Regardless of the initial state and experimental parameters, the
dynamics of the relative phase and atom number imbalance shows a relaxation to
a phase-locked steady state. The latter is characterized by a high phase
coherence and reduced fluctuations with respect to the initial state. We
propose an empirical model based on the analogy with the anharmonic oscillator
to describe the effect of various experimental parameters. A microscopic theory
compatible with our observations is still missing.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Hybrid Model for Role-related User Classification on Twitter | To aid a variety of research studies, we propose TWIROLE, a hybrid model for
role-related user classification on Twitter, which detects male-related,
female-related, and brand-related (i.e., organization or institution) users.
TWIROLE leverages features from tweet contents, user profiles, and profile
images, and then applies our hybrid model to identify a user's role. To
evaluate it, we used two existing large datasets about Twitter users, and
conducted both intra- and inter-comparison experiments. TWIROLE outperforms
existing methods and obtains more balanced results over the several roles. We
also confirm that user names and profile images are good indicators for this
task. Our research extends prior work that does not consider brand-related
users, and is an aid to future evaluation efforts relative to investigations
that rely upon self-labeled datasets.
| 1 | 0 | 0 | 0 | 0 | 0 |
SecureBoost: A Lossless Federated Learning Framework | The protection of user privacy is an important concern in machine learning,
as evidenced by the rolling out of the General Data Protection Regulation
(GDPR) in the European Union (EU) in May 2018. The GDPR is designed to give
users more control over their personal data, which motivates us to explore
machine learning frameworks with data sharing without violating user privacy.
To meet this goal, in this paper, we propose a novel lossless
privacy-preserving tree-boosting system known as SecureBoost in the setting of
federated learning. This federated-learning system allows a learning process to
be jointly conducted over multiple parties with partially common user samples
but different feature sets, which corresponds to a vertically partitioned
virtual data set. An advantage of SecureBoost is that it provides the same
level of accuracy as the non-privacy-preserving approach while at the same
time, reveal no information of each private data provider. We theoretically
prove that the SecureBoost framework is as accurate as other non-federated
gradient tree-boosting algorithms that bring the data into one place. In
addition, along with a proof of security, we discuss what would be required to
make the protocols completely secure.
| 1 | 0 | 0 | 1 | 0 | 0 |
Selective reflection from Rb layer with thickness below $λ$/12 and applications | We have studied the peculiarities of selective reflection from Rb vapor cell
with thickness $L <$ 70 nm, which is over an order of magnitude smaller than
the resonant wavelength for Rb atomic D$_1$ line $\lambda$ = 795 nm. A huge
($\approx$ 240 MHz) red shift and spectral broadening of reflection signal is
recorded for $L =$ 40 nm caused by the atom-surface interaction. Also
completely frequency resolved hyperfine Paschen-Back splitting of atomic
transitions to four components for $^{87}$Rb and six components for $^{85}$Rb
is recorded in strong magnetic field ($B >$ 2 kG).
| 0 | 1 | 0 | 0 | 0 | 0 |
The Complexity of Abstract Machines | The lambda-calculus is a peculiar computational model whose definition does
not come with a notion of machine. Unsurprisingly, implementations of the
lambda-calculus have been studied for decades. Abstract machines are
implementations schema for fixed evaluation strategies that are a compromise
between theory and practice: they are concrete enough to provide a notion of
machine and abstract enough to avoid the many intricacies of actual
implementations. There is an extensive literature about abstract machines for
the lambda-calculus, and yet-quite mysteriously-the efficiency of these
machines with respect to the strategy that they implement has almost never been
studied.
This paper provides an unusual introduction to abstract machines, based on
the complexity of their overhead with respect to the length of the implemented
strategies. It is conceived to be a tutorial, focusing on the case study of
implementing the weak head (call-by-name) strategy, and yet it is an original
re-elaboration of known results. Moreover, some of the observation contained
here never appeared in print before.
| 1 | 0 | 0 | 0 | 0 | 0 |
Recent progress in the Zimmer program | This paper can be viewed as a sequel to the author's long survey on the
Zimmer program \cite{F11} published in 2011. The sequel focuses on recent rapid
progress on certain aspects of the program particularly concerning rigidity of
Anosov actions and Zimmer's conjecture that there are no actions in low
dimensions. Some emphasis is put on the surprising connections between these
two different sets of developments and also on the key connections and ideas
for future research that arise from these works taken together.
| 0 | 0 | 1 | 0 | 0 | 0 |
Restricted Causal Inference Algorithm | This paper proposes a new algorithm for recovery of belief network structure
from data handling hidden variables. It consists essentially in an extension of
the CI algorithm of Spirtes et al. by restricting the number of conditional
dependencies checked up to k variables and in an extension of the original CI
by additional steps transforming so called partial including path graph into a
belief network. Its correctness is demonstrated.
| 1 | 0 | 0 | 0 | 0 | 0 |
Fiber plucking by molecular motors yields large emergent contractility in stiff biopolymer networks | The mechanical properties of the cell depend crucially on the tension of its
cytoskeleton, a biopolymer network that is put under stress by active motor
proteins. While the fibrous nature of the network is known to strongly affect
the transmission of these forces to the cellular scale, our understanding of
this process remains incomplete. Here we investigate the transmission of forces
through the network at the individual filament level, and show that active
forces can be geometrically amplified as a transverse motor-generated force
force "plucks" the fiber and induces a nonlinear tension. In stiff and densely
connnected networks, this tension results in large network-wide tensile
stresses that far exceed the expectation drawn from a linear elastic theory.
This amplification mechanism competes with a recently characterized
network-level amplification due to fiber buckling, suggesting that that fiber
networks provide several distinct pathways for living systems to amplify their
molecular forces.
| 0 | 0 | 0 | 0 | 1 | 0 |
Demographics of News Sharing in the U.S. Twittersphere | The widespread adoption and dissemination of online news through social media
systems have been revolutionizing many segments of our society and ultimately
our daily lives. In these systems, users can play a central role as they share
content to their friends. Despite that, little is known about news spreaders in
social media. In this paper, we provide the first of its kind in-depth
characterization of news spreaders in social media. In particular, we
investigate their demographics, what kind of content they share, and the
audience they reach. Among our main findings, we show that males and white
users tend to be more active in terms of sharing news, biasing the news
audience to the interests of these demographic groups. Our results also
quantify differences in interests of news sharing across demographics, which
has implications for personalized news digests.
| 1 | 0 | 0 | 0 | 0 | 0 |
Dynamics and fragmentation mechanism of (CH3-C5H4)Pt(CH3)3 on SiO2 Surfaces | The interaction of (CH3-C5H4)Pt(CH3)3
((methylcyclopentadienyl)trimethylplatinum)) molecules on fully and partially
hydroxylated SiO2 surfaces, as well as the dynamics of this interaction were
investigated using density functional theory (DFT) and finite temperature
DFT-based molecular dynamics simulations. Fully and partially hydroxylated
surfaces represent substrates before and after electron beam treatment and this
study examines the role of electron beam pretreatment on the substrates in the
initial stages of precursor dissociation and formation of Pt deposits. Our
simulations show that on fully hydroxylated surfaces or untreated surfaces, the
precursor molecules remain inactivated while we observe fragmentation of
(CH3-C5H4)Pt(CH3)3 on partially hydroxylated surfaces. The behavior of
precursor molecules on the partially hydroxylated surfaces has been found to
depend on the initial orientation of the molecule and the distribution of
surface active sites. Based on the observations from the simulations and
available experiments, we discuss possible dissociation channels of the
precursor.
| 0 | 1 | 0 | 0 | 0 | 0 |
On the robustness of the H$β$ Lick index as a cosmic clock in passive early-type galaxies | We examine the H$\beta$ Lick index in a sample of $\sim 24000$ massive ($\rm
log(M/M_{\odot})>10.75$) and passive early-type galaxies extracted from SDSS at
z<0.3, in order to assess the reliability of this index to constrain the epoch
of formation and age evolution of these systems. We further investigate the
possibility of exploiting this index as "cosmic chronometer", i.e. to derive
the Hubble parameter from its differential evolution with redshift, hence
constraining cosmological models independently of other probes. We find that
the H$\beta$ strength increases with redshift as expected in passive evolution
models, and shows at each redshift weaker values in more massive galaxies.
However, a detailed comparison of the observed index with the predictions of
stellar population synthesis models highlights a significant tension, with the
observed index being systematically lower than expected. By analyzing the
stacked spectra, we find a weak [NII]$\lambda6584$ emission line (not
detectable in the single spectra) which anti-correlates with the mass, that can
be interpreted as a hint of the presence of ionized gas. We estimated the
correction of the H$\beta$ index by the residual emission component exploiting
different approaches, but find it very uncertain and model-dependent. We
conclude that, while the qualitative trends of the observed H$\beta$-z
relations are consistent with the expected passive and downsizing scenario, the
possible presence of ionized gas even in the most massive and passive galaxies
prevents to use this index for a quantitative estimate of the age evolution and
for cosmological applications.
| 0 | 1 | 0 | 0 | 0 | 0 |
Finite-size effects in a stochastic Kuramoto model | We present a collective coordinate approach to study the collective behaviour
of a finite ensemble of $N$ stochastic Kuramoto oscillators using two degrees
of freedom; one describing the shape dynamics of the oscillators and one
describing their mean phase. Contrary to the thermodynamic limit $N\to\infty$
in which the mean phase of the cluster of globally synchronized oscillators is
constant in time, the mean phase of a finite-size cluster experiences Brownian
diffusion with a variance proportional to $1/N$. This finite-size effect is
quantitatively well captured by our collective coordinate approach.
| 0 | 1 | 0 | 0 | 0 | 0 |
The normal distribution is freely selfdecomposable | The class of selfdecomposable distributions in free probability theory was
introduced by Barndorff-Nielsen and the third named author. It constitutes a
fairly large subclass of the freely infinitely divisible distributions, but so
far specific examples have been limited to Wigner's semicircle distributions,
the free stable distributions, two kinds of free gamma distributions and a few
other examples. In this paper, we prove that the (classical) normal
distributions are freely selfdecomposable. More generally it is established
that the Askey-Wimp-Kerov distribution $\mu_c$ is freely selfdecomposable for
any $c$ in $[-1,0]$. The main ingredient in the proof is a general
characterization of the freely selfdecomposable distributions in terms of the
derivative of their free cumulant transform.
| 0 | 0 | 1 | 0 | 0 | 0 |
Simplex Queues for Hot-Data Download | In cloud storage systems, hot data is usually replicated over multiple nodes
in order to accommodate simultaneous access by multiple users as well as
increase the fault tolerance of the system. Recent cloud storage research has
proposed using availability codes, which is a special class of erasure codes,
as a more storage efficient way to store hot data. These codes enable data
recovery from multiple, small disjoint groups of servers. The number of the
recovery groups is referred to as the availability and the size of each group
as the locality of the code. Until now, we have very limited knowledge on how
code locality and availability affect data access time. Data download from
these systems involves multiple fork-join queues operating in-parallel, making
the analysis of access time a very challenging problem. In this paper, we
present an approximate analysis of data access time in storage systems that
employ simplex codes, which are an important and in certain sense optimal class
of availability codes. We consider and compare three strategies in assigning
download requests to servers; first one aggressively exploits the storage
availability for faster download, second one implements only load balancing,
and the last one employs storage availability only for hot data download
without incurring any negative impact on the cold data download.
| 1 | 0 | 0 | 0 | 0 | 0 |
Voting power of political parties in the Senate of Chile during the whole binomial system period: 1990-2017 | The binomial system is an electoral system unique in the world. It was used
to elect the senators and deputies of Chile during 27 years, from the return of
democracy in 1990 until 2017. In this paper we study the real voting power of
the different political parties in the Senate of Chile during the whole
binomial period. We not only consider the different legislative periods, but
also any party changes between one period and the next. The real voting power
is measured by considering power indices from cooperative game theory, which
are based on the capability of the political parties to form winning
coalitions. With this approach, we can do an analysis that goes beyond the
simple count of parliamentary seats.
| 0 | 0 | 0 | 0 | 0 | 1 |
A uniform stability principle for dual lattices | We prove a highly uniform stability or "almost-near" theorem for dual
lattices of lattices $L \subseteq \Bbb R^n$. More precisely, we show that, for
a vector $x$ from the linear span of a lattice $L \subseteq \Bbb R^n$, subject
to $\lambda_1(L) \ge \lambda > 0$, to be $\varepsilon$-close to some vector
from the dual lattice $L'$ of $L$, it is enough that the inner products $u\,x$
are $\delta$-close (with $\delta < 1/3$) to some integers for all vectors $u
\in L$ satisfying $\| u \| \le r$, where $r > 0$ depends on $n$, $\lambda$,
$\delta$ and $\varepsilon$, only. This generalizes an earlier analogous result
proved for integral vector lattices by M. Mačaj and the second author. The
proof is nonconstructive, using the ultraproduct construction and a slight
portion of nonstandard analysis.
| 0 | 0 | 1 | 0 | 0 | 0 |
pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems | pandapower is a Python based, BSD-licensed power system analysis tool aimed
at automation of static and quasi-static analysis and optimization of balanced
power systems. It provides power flow, optimal power flow, state estimation,
topological graph searches and short circuit calculations according to IEC
60909. pandapower includes a Newton-Raphson power flow solver formerly based on
PYPOWER, which has been accelerated with just-in-time compilation. Additional
enhancements to the solver include the capability to model constant current
loads, grids with multiple reference nodes and a connectivity check. The
pandapower network model is based on electric elements, such as lines, two and
three-winding transformers or ideal switches. All elements can be defined with
nameplate parameters and are internally processed with equivalent circuit
models, which have been validated against industry standard software tools. The
tabular data structure used to define networks is based on the Python library
pandas, which allows comfortable handling of input and output parameters. The
implementation in Python makes pandapower easy to use and allows comfortable
extension with third-party libraries. pandapower has been successfully applied
in several grid studies as well as for educational purposes. A comprehensive,
publicly available case-study demonstrates a possible application of pandapower
in an automated time series calculation.
| 1 | 0 | 0 | 0 | 0 | 0 |
Temporal oscillations of light transmission through dielectric microparticles subjected to optically induced motion | We consider light-induced binding and motion of dielectric microparticles in
an optical waveguide that gives rise to a back-action effect such as light
transmission oscillating with time. Modeling the particles by dielectric slabs
allows us to solve the problem analytically and obtain a rich variety of
dynamical regimes both for Newtonian and damped motion. This variety is clearly
reflected in temporal oscillations of the light transmission. The
characteristic frequencies of the oscillations are within the ultrasound range
of the order of $10^{5}$ Hz for micron size particles and injected power of the
order of 100 mW. In addition, we consider driven by propagating light dynamics
of a dielectric particle inside a Fabry-Perot resonator. These phenomena pave a
way for optical driving and monitoring of motion of particles in waveguides and
resonators.
| 0 | 1 | 0 | 0 | 0 | 0 |
Berry-Esséen bounds for parameter estimation of general Gaussian processes | We study rates of convergence in central limit theorems for the partial sum
of squares of general Gaussian sequences, using tools from analysis on Wiener
space. No assumption of stationarity, asymptotically or otherwise, is made. The
main theoretical tool is the so-called Optimal Fourth Moment Theorem
\cite{NP2015}, which provides a sharp quantitative estimate of the total
variation distance on Wiener chaos to the normal law. The only assumptions made
on the sequence are the existence of an asymptotic variance, that a
least-squares-type estimator for this variance parameter has a bias and a
variance which can be controlled, and that the sequence's auto-correlation
function, which may exhibit long memory, has a no-worse memory than that of
fractional Brownian motion with Hurst parameter }$H<3/4$.{\ \ Our main result
is explicit, exhibiting the trade-off between bias, variance, and memory. We
apply our result to study drift parameter estimation problems for subfractional
Ornstein-Uhlenbeck and bifractional Ornstein-Uhlenbeck processes with
fixed-time-step observations. These are processes which fail to be stationary
or self-similar, but for which detailed calculations result in explicit
formulas for the estimators' asymptotic normality.
| 0 | 0 | 1 | 1 | 0 | 0 |
Anomaly Detection via Minimum Likelihood Generative Adversarial Networks | Anomaly detection aims to detect abnormal events by a model of normality. It
plays an important role in many domains such as network intrusion detection,
criminal activity identity and so on. With the rapidly growing size of
accessible training data and high computation capacities, deep learning based
anomaly detection has become more and more popular. In this paper, a new
domain-based anomaly detection method based on generative adversarial networks
(GAN) is proposed. Minimum likelihood regularization is proposed to make the
generator produce more anomalies and prevent it from converging to normal data
distribution. Proper ensemble of anomaly scores is shown to improve the
stability of discriminator effectively. The proposed method has achieved
significant improvement than other anomaly detection methods on Cifar10 and UCI
datasets.
| 0 | 0 | 0 | 1 | 0 | 0 |
Supercurrent as a Probe for Topological Superconductivity in Magnetic Adatom Chains | A magnetic adatom chain, proximity coupled to a conventional superconductor
with spin-orbit coupling, exhibits locally an odd-parity, spin-triplet pairing
amplitude. We show that the singlet-triplet junction, thus formed, leads to a
net spin accumulation in the near vicinity of the chain. The accumulated spins
are polarized along the direction of the local $\mathbf{d}$-vector for triplet
pairing and generate an enhanced persistent current flowing around the chain.
The spin polarization and the "supercurrent" reverse their directions beyond a
critical exchange coupling strength at which the singlet superconducting order
changes its sign on the chain. The current is strongly enhanced in the
topological superconducting regime where Majorana bound states appear at the
chain ends. The current and the spin profile offer alternative routes to
characterize the topological superconducting state in adatom chains and
islands.
| 0 | 1 | 0 | 0 | 0 | 0 |
Experimental statistics of veering triangulations | Certain fibered hyperbolic 3-manifolds admit a $\mathit{\text{layered veering
triangulation}}$, which can be constructed algorithmically given the stable
lamination of the monodromy. These triangulations were introduced by Agol in
2011, and have been further studied by several others in the years since. We
obtain experimental results which shed light on the combinatorial structure of
veering triangulations, and its relation to certain topological invariants of
the underlying manifold.
| 0 | 0 | 1 | 0 | 0 | 0 |
Concept Drift and Anomaly Detection in Graph Streams | Graph representations offer powerful and intuitive ways to describe data in a
multitude of application domains. Here, we consider stochastic processes
generating graphs and propose a methodology for detecting changes in
stationarity of such processes. The methodology is general and considers a
process generating attributed graphs with a variable number of vertices/edges,
without the need to assume one-to-one correspondence between vertices at
different time steps. The methodology acts by embedding every graph of the
stream into a vector domain, where a conventional multivariate change detection
procedure can be easily applied. We ground the soundness of our proposal by
proving several theoretical results. In addition, we provide a specific
implementation of the methodology and evaluate its effectiveness on several
detection problems involving attributed graphs representing biological
molecules and drawings. Experimental results are contrasted with respect to
suitable baseline methods, demonstrating the effectiveness of our approach.
| 1 | 0 | 0 | 1 | 0 | 0 |
Lat-Net: Compressing Lattice Boltzmann Flow Simulations using Deep Neural Networks | Computational Fluid Dynamics (CFD) is a hugely important subject with
applications in almost every engineering field, however, fluid simulations are
extremely computationally and memory demanding. Towards this end, we present
Lat-Net, a method for compressing both the computation time and memory usage of
Lattice Boltzmann flow simulations using deep neural networks. Lat-Net employs
convolutional autoencoders and residual connections in a fully differentiable
scheme to compress the state size of a simulation and learn the dynamics on
this compressed form. The result is a computationally and memory efficient
neural network that can be iterated and queried to reproduce a fluid
simulation. We show that once Lat-Net is trained, it can generalize to large
grid sizes and complex geometries while maintaining accuracy. We also show that
Lat-Net is a general method for compressing other Lattice Boltzmann based
simulations such as Electromagnetism.
| 0 | 1 | 0 | 1 | 0 | 0 |
Second-Order Optimization for Non-Convex Machine Learning: An Empirical Study | While first-order optimization methods such as stochastic gradient descent
(SGD) are popular in machine learning (ML), they come with well-known
deficiencies, including relatively-slow convergence, sensitivity to the
settings of hyper-parameters such as learning rate, stagnation at high training
errors, and difficulty in escaping flat regions and saddle points. These issues
are particularly acute in highly non-convex settings such as those arising in
neural networks. Motivated by this, there has been recent interest in
second-order methods that aim to alleviate these shortcomings by capturing
curvature information. In this paper, we report detailed empirical evaluations
of a class of Newton-type methods, namely sub-sampled variants of trust region
(TR) and adaptive regularization with cubics (ARC) algorithms, for non-convex
ML problems. In doing so, we demonstrate that these methods not only can be
computationally competitive with hand-tuned SGD with momentum, obtaining
comparable or better generalization performance, but also they are highly
robust to hyper-parameter settings. Further, in contrast to SGD with momentum,
we show that the manner in which these Newton-type methods employ curvature
information allows them to seamlessly escape flat regions and saddle points.
| 1 | 0 | 0 | 1 | 0 | 0 |
Computational Approaches for Stochastic Shortest Path on Succinct MDPs | We consider the stochastic shortest path (SSP) problem for succinct Markov
decision processes (MDPs), where the MDP consists of a set of variables, and a
set of nondeterministic rules that update the variables. First, we show that
several examples from the AI literature can be modeled as succinct MDPs. Then
we present computational approaches for upper and lower bounds for the SSP
problem: (a)~for computing upper bounds, our method is polynomial-time in the
implicit description of the MDP; (b)~for lower bounds, we present a
polynomial-time (in the size of the implicit description) reduction to
quadratic programming. Our approach is applicable even to infinite-state MDPs.
Finally, we present experimental results to demonstrate the effectiveness of
our approach on several classical examples from the AI literature.
| 1 | 0 | 0 | 0 | 0 | 0 |
One-dimensional in-plane edge domain walls in ultrathin ferromagnetic films | We study existence and properties of one-dimensional edge domain walls in
ultrathin ferromagnetic films with uniaxial in-plane magnetic anisotropy. In
these materials, the magnetization vector is constrained to lie entirely in the
film plane, with the preferred directions dictated by the magnetocrystalline
easy axis. We consider magnetization profiles in the vicinity of a straight
film edge oriented at an arbitrary angle with respect to the easy axis. To
minimize the micromagnetic energy, these profiles form transition layers in
which the magnetization vector rotates away from the direction of the easy axis
to align with the film edge. We prove existence of edge domain walls as
minimizers of the appropriate one-dimensional micromagnetic energy functional
and show that they are classical solutions of the associated Euler-Lagrange
equation with Dirichlet boundary condition at the edge. We also perform a
numerical study of these one-dimensional domain walls and uncover further
properties of these domain wall profiles.
| 0 | 1 | 1 | 0 | 0 | 0 |
Nontrivial Turmites are Turing-universal | A Turmit is a Turing machine that works over a two-dimensional grid, that is,
an agent that moves, reads and writes symbols over the cells of the grid. Its
state is an arrow and, depending on the symbol that it reads, it turns to the
left or to the right, switching the symbol at the same time. Several symbols
are admitted, and the rule is specified by the turning sense that the machine
has over each symbol. Turmites are a generalization of Langtons ant, and they
present very complex and diverse behaviors. We prove that any Turmite, except
for those whose rule does not depend on the symbol, can simulate any Turing
Machine. We also prove the P-completeness of prediction their future behavior
by explicitly giving a log-space reduction from the Topological Circuit Value
Problem. A similar result was already established for Langtons ant; here we use
a similar technique but prove a stronger notion of simulation, and for a more
general family.
| 1 | 1 | 0 | 0 | 0 | 0 |
Dandelion: Redesigning the Bitcoin Network for Anonymity | Bitcoin and other cryptocurrencies have surged in popularity over the last
decade. Although Bitcoin does not claim to provide anonymity for its users, it
enjoys a public perception of being a `privacy-preserving' financial system. In
reality, cryptocurrencies publish users' entire transaction histories in
plaintext, albeit under a pseudonym; this is required for transaction
validation. Therefore, if a user's pseudonym can be linked to their human
identity, the privacy fallout can be significant. Recently, researchers have
demonstrated deanonymization attacks that exploit weaknesses in the Bitcoin
network's peer-to-peer (P2P) networking protocols. In particular, the P2P
network currently forwards content in a structured way that allows observers to
deanonymize users. In this work, we redesign the P2P network from first
principles with the goal of providing strong, provable anonymity guarantees. We
propose a simple networking policy called Dandelion, which achieves
nearly-optimal anonymity guarantees at minimal cost to the network's utility.
We also provide a practical implementation of Dandelion.
| 1 | 0 | 0 | 0 | 0 | 0 |
A characterisation of Lie algebras amongst anti-commutative algebras | Let $\mathbb{K}$ be an infinite field. We prove that if a variety of
anti-commutative $\mathbb{K}$-algebras - not necessarily associative, where
$xx=0$ is an identity - is locally algebraically cartesian closed, then it must
be a variety of Lie algebras over $\mathbb{K}$. In particular,
$\mathsf{Lie}_{\mathbb{K}}$ is the largest such. Thus, for a given variety of
anti-commutative $\mathbb{K}$-algebras, the Jacobi identity becomes equivalent
to a categorical condition: it is an identity in~$\mathcal{V}$ if and only if
$\mathcal{V}$ is a subvariety of a locally algebraically cartesian closed
variety of anti-commutative $\mathbb{K}$-algebras. This is based on a result
saying that an algebraically coherent variety of anti-commutative
$\mathbb{K}$-algebras is either a variety of Lie algebras or a variety of
anti-associative algebras over $\mathbb{K}$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Characteristics of a magneto-optical trap of molecules | We present the properties of a magneto-optical trap (MOT) of CaF molecules.
We study the process of loading the MOT from a decelerated buffer-gas-cooled
beam, and how best to slow this molecular beam in order to capture the most
molecules. We determine how the number of molecules, the photon scattering
rate, the oscillation frequency, damping constant, temperature, cloud size and
lifetime depend on the key parameters of the MOT, especially the intensity and
detuning of the main cooling laser. We compare our results to analytical and
numerical models, to the properties of standard atomic MOTs, and to MOTs of SrF
molecules. We load up to $2 \times 10^4$ molecules, and measure a maximum
scattering rate of $2.5 \times 10^6$ s$^{-1}$ per molecule, a maximum
oscillation frequency of 100 Hz, a maximum damping constant of 500 s$^{-1}$,
and a minimum MOT rms radius of 1.5 mm. A minimum temperature of 730 $\mu$K is
obtained by ramping down the laser intensity to low values. The lifetime,
typically about 100 ms, is consistent with a leak out of the cooling cycle with
a branching ratio of about $6 \times 10^{-6}$. The MOT has a capture velocity
of about 11 m/s.
| 0 | 1 | 0 | 0 | 0 | 0 |
Reconstructing a Lattice Equation: a Non-Autonomous Approach to the Hietarinta Equation | In this paper we construct a non-autonomous version of the Hietarinta
equation [Hietarinta J., J. Phys. A: Math. Gen. 37 (2004), L67-L73] and study
its integrability properties. We show that this equation possess linear growth
of the degrees of iterates, generalized symmetries depending on arbitrary
functions, and that it is Darboux integrable. We use the first integrals to
provide a general solution of this equation. In particular we show that this
equation is a sub-case of the non-autonomous $Q_{\rm V}$ equation, and we
provide a non-autonomous Möbius transformation to another equation found in
[Hietarinta J., J. Nonlinear Math. Phys. 12 (2005), suppl. 2, 223-230] and
appearing also in Boll's classification [Boll R., Ph.D. Thesis, Technische
Universität Berlin, 2012].
| 0 | 1 | 0 | 0 | 0 | 0 |
Adversarial Training Versus Weight Decay | Performance-critical machine learning models should be robust to input
perturbations not seen during training. Adversarial training is a method for
improving a model's robustness to some perturbations by including them in the
training process, but this tends to exacerbate other vulnerabilities of the
model. The adversarial training framework has the effect of translating the
data with respect to the cost function, while weight decay has a scaling
effect. Although weight decay could be considered a crude regularization
technique, it appears superior to adversarial training as it remains stable
over a broader range of regimes and reduces all generalization errors. Equipped
with these abstractions, we provide key baseline results and methodology for
characterizing robustness. The two approaches can be combined to yield one
small model that demonstrates good robustness to several white-box attacks
associated with different metrics.
| 0 | 0 | 0 | 1 | 0 | 0 |
Ray tracing method for stereo image synthesis using CUDA | This paper presents a realization of the approach to spatial 3D stereo of
visualization of 3D images with use parallel Graphics processing unit (GPU).
The experiments of realization of synthesis of images of a 3D stage by a method
of trace of beams on GPU with Compute Unified Device Architecture (CUDA) have
shown that 60 % of the time is spent for the decision of a computing problem
approximately, the major part of time (40 %) is spent for transfer of data
between the central processing unit and GPU for computations and the
organization process of visualization. The study of the influence of increase
in the size of the GPU network at the speed of computations showed importance
of the correct task of structure of formation of the parallel computer network
and general mechanism of parallelization. Keywords: Volumetric 3D
visualization, stereo 3D visualization, ray tracing, parallel computing on GPU,
CUDA
| 1 | 0 | 0 | 0 | 0 | 0 |
Numerical investigation of gapped edge states in fractional quantum Hall-superconductor heterostructures | Fractional quantum Hall-superconductor heterostructures may provide a
platform towards non-abelian topological modes beyond Majoranas. However their
quantitative theoretical study remains extremely challenging. We propose and
implement a numerical setup for studying edge states of fractional quantum Hall
droplets with a superconducting instability. The fully gapped edges carry a
topological degree of freedom that can encode quantum information protected
against local perturbations. We simulate such a system numerically using exact
diagonalization by restricting the calculation to the quasihole-subspace of a
(time-reversal symmetric) bilayer fractional quantum Hall system of Laughlin
$\nu=1/3$ states. We show that the edge ground states are permuted by
spin-dependent flux insertion and demonstrate their fractional $6\pi$ Josephson
effect, evidencing their topological nature and the Cooper pairing of
fractionalized quasiparticles.
| 0 | 1 | 0 | 0 | 0 | 0 |
Multimodal Observation and Interpretation of Subjects Engaged in Problem Solving | In this paper we present the first results of a pilot experiment in the
capture and interpretation of multimodal signals of human experts engaged in
solving challenging chess problems. Our goal is to investigate the extent to
which observations of eye-gaze, posture, emotion and other physiological
signals can be used to model the cognitive state of subjects, and to explore
the integration of multiple sensor modalities to improve the reliability of
detection of human displays of awareness and emotion. We observed chess players
engaged in problems of increasing difficulty while recording their behavior.
Such recordings can be used to estimate a participant's awareness of the
current situation and to predict ability to respond effectively to challenging
situations. Results show that a multimodal approach is more accurate than a
unimodal one. By combining body posture, visual attention and emotion, the
multimodal approach can reach up to 93% of accuracy when determining player's
chess expertise while unimodal approach reaches 86%. Finally this experiment
validates the use of our equipment as a general and reproducible tool for the
study of participants engaged in screen-based interaction and/or problem
solving.
| 1 | 0 | 0 | 1 | 0 | 0 |
Theoretical limitations of Encoder-Decoder GAN architectures | Encoder-decoder GANs architectures (e.g., BiGAN and ALI) seek to add an
inference mechanism to the GANs setup, consisting of a small encoder deep net
that maps data-points to their succinct encodings. The intuition is that being
forced to train an encoder alongside the usual generator forces the system to
learn meaningful mappings from the code to the data-point and vice-versa, which
should improve the learning of the target distribution and ameliorate
mode-collapse. It should also yield meaningful codes that are useful as
features for downstream tasks. The current paper shows rigorously that even on
real-life distributions of images, the encode-decoder GAN training objectives
(a) cannot prevent mode collapse; i.e. the objective can be near-optimal even
when the generated distribution has low and finite support (b) cannot prevent
learning meaningless codes for data -- essentially white noise. Thus if
encoder-decoder GANs do indeed work then it must be due to reasons as yet not
understood, since the training objective can be low even for meaningless
solutions.
| 1 | 0 | 0 | 1 | 0 | 0 |
Ground state sign-changing solutions for a class of nonlinear fractional Schrödinger-Poisson system in $\mathbb{R}^{3}$ | In this paper, we are concerned with the existence of the least energy
sign-changing solutions for the following fractional Schrödinger-Poisson
system: \begin{align*}
\left\{ \begin{aligned} &(-\Delta)^{s} u+V(x)u+\lambda\phi(x)u=f(x, u),\quad
&\text{in}\, \ \mathbb{R}^{3},\\ &(-\Delta)^{t}\phi=u^{2},& \text{in}\,\
\mathbb{R}^{3}, \end{aligned} \right. \end{align*} where $\lambda\in
\mathbb{R}^{+}$ is a parameter, $s, t\in (0, 1)$ and $4s+2t>3$, $(-\Delta)^{s}$
stands for the fractional Laplacian. By constraint variational method and
quantitative deformation lemma, we prove that the above problem has one least
energy sign-changing solution. Moreover, for any $\lambda>0$, we show that the
energy of the least energy sign-changing solutions is strictly larger than two
times the ground state energy.
Finally, we consider $\lambda$ as a parameter and study the convergence
property of the least energy sign-changing solutions as $\lambda\searrow 0$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Monads on higher monoidal categories | We study the action of monads on categories equipped with several monoidal
structures. We identify the structure and conditions that guarantee that the
higher monoidal structure is inherited by the category of algebras over the
monad. Monoidal monads and comonoidal monads appear as the base cases in this
hierarchy. Monads acting on duoidal categories constitute the next case. We
cover the general case of $n$-monoidal categories and discuss several naturally
occurring examples in which $n\leq 3$.
| 0 | 0 | 1 | 0 | 0 | 0 |
A Review of Augmented Reality Applications for Building Evacuation | Evacuation is one of the main disaster management solutions to reduce the
impact of man-made and natural threats on building occupants. To date, several
modern technologies and gamification concepts, e.g. immersive virtual reality
and serious games, have been used to enhance building evacuation preparedness
and effectiveness. Those tools have been used both to investigate human
behavior during building emergencies and to train building occupants on how to
cope with building evacuations.
Augmented Reality (AR) is novel technology that can enhance this process
providing building occupants with virtual contents to improve their evacuation
performance. This work aims at reviewing existing AR applications developed for
building evacuation. This review identifies the disasters and types of building
those tools have been applied for. Moreover, the application goals, hardware
and evacuation stages affected by AR are also investigated in the review.
Finally, this review aims at identifying the challenges to face for further
development of AR evacuation tools.
| 1 | 0 | 0 | 0 | 0 | 0 |
Topology in time-reversal symmetric crystals | The discovery of topological insulators has reformed modern materials
science, promising to be a platform for tabletop relativistic physics,
electronic transport without scattering, and stable quantum computation.
Topological invariants are used to label distinct types of topological
insulators. But it is not generally known how many or which invariants can
exist in any given crystalline material. Using a new and efficient counting
algorithm, we study the topological invariants that arise in time-reversal
symmetric crystals. This results in a unified picture that explains the
relations between all known topological invariants in these systems. It also
predicts new topological phases and one entirely new topological invariant. We
present explicitly the classification of all two-dimensional crystalline
fermionic materials, and give a straightforward procedure for finding the
analogous result in any three-dimensional structure. Our study represents a
single, intuitive physical picture applicable to all topological invariants in
real materials, with crystal symmetries.
| 0 | 1 | 0 | 0 | 0 | 0 |
Deep Convolutional Neural Network Inference with Floating-point Weights and Fixed-point Activations | Deep convolutional neural network (CNN) inference requires significant amount
of memory and computation, which limits its deployment on embedded devices. To
alleviate these problems to some extent, prior research utilize low precision
fixed-point numbers to represent the CNN weights and activations. However, the
minimum required data precision of fixed-point weights varies across different
networks and also across different layers of the same network. In this work, we
propose using floating-point numbers for representing the weights and
fixed-point numbers for representing the activations. We show that using
floating-point representation for weights is more efficient than fixed-point
representation for the same bit-width and demonstrate it on popular large-scale
CNNs such as AlexNet, SqueezeNet, GoogLeNet and VGG-16. We also show that such
a representation scheme enables compact hardware multiply-and-accumulate (MAC)
unit design. Experimental results show that the proposed scheme reduces the
weight storage by up to 36% and power consumption of the hardware multiplier by
up to 50%.
| 1 | 0 | 0 | 0 | 0 | 0 |
Voids in the Cosmic Web as a probe of dark energy | The formation of large voids in the Cosmic Web from the initial adiabatic
cosmological perturbations of space-time metric, density and velocity of matter
is investigated in cosmological model with the dynamical dark energy
accelerating expansion of the Universe. It is shown that the negative density
perturbations with the initial radius of about 50 Mpc in comoving to the
cosmological background coordinates and the amplitude corresponding to the
r.m.s. temperature fluctuations of the cosmic microwave background lead to the
formation of voids with the density contrast up to $-$0.9, maximal peculiar
velocity about 400 km/s and the radius close to the initial one. An important
feature of voids formation from the analyzed initial amplitudes and profiles is
establishing the surrounding overdensity shell. We have shown that the ratio of
the peculiar velocity in units of the Hubble flow to the density contrast in
the central part of a void does not depend or weakly depends on the distance
from the center of the void. It is also shown that this ratio is sensitive to
the values of dark energy parameters and can be used to find them based on the
observational data on mass density and peculiar velocities of galaxies in the
voids.
| 0 | 1 | 0 | 0 | 0 | 0 |
Templated ligation can create a hypercycle replication network | The stability of sequence replication was crucial for the emergence of
molecular evolution and early life. Exponential replication with a first-order
growth dynamics show inherent instabilities such as the error catastrophe and
the dominance by the fastest replicators. This favors less structured and short
sequences. The theoretical concept of hypercycles has been proposed to solve
these problems. Their higher-order growth kinetics leads to frequency-dependent
selection and stabilizes the replication of majority molecules. However, many
implementations of hypercycles are unstable or require special sequences with
catalytic activity. Here, we demonstrate the spontaneous emergence of
higher-order cooperative replication from a network of simple ligation chain
reactions (LCR). We performed long-term LCR experiments from a mixture of
sequences under molecule degrading conditions with a ligase protein. At the
chosen temperature cycling, a network of positive feedback loops arose from
both the cooperative ligation of matching sequences and the emerging increase
in sequence length. It generated higher-order replication with
frequency-dependent selection. The experiments matched a complete simulation
using experimentally determined ligation rates and the hypercycle mechanism was
also confirmed by abstracted modeling. Since templated ligation is a most basic
reaction of oligonucleotides, the described mechanism could have been
implemented under microthermal convection on early Earth.
| 0 | 0 | 0 | 0 | 1 | 0 |
Variational characterization of H^p | In this paper we obtain the variational characterization of Hardy space $H^p$
for $p\in(\frac n{n+1},1]$ and get estimates for the oscillation operator and
the $\lambda$-jump operator associated with approximate identities acting on
$H^p$ for $p\in(\frac n{n+1},1]$. Moreover, we give counterexamples to show
that the oscillation and $\lambda$-jump associated with some approximate
identity can not be used to characterize $H^p$ for $p\in(\frac n{n+1},1]$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Towards Deep Learning Models Resistant to Adversarial Attacks | Recent work has demonstrated that neural networks are vulnerable to
adversarial examples, i.e., inputs that are almost indistinguishable from
natural data and yet classified incorrectly by the network. In fact, some of
the latest findings suggest that the existence of adversarial attacks may be an
inherent weakness of deep learning models. To address this problem, we study
the adversarial robustness of neural networks through the lens of robust
optimization. This approach provides us with a broad and unifying view on much
of the prior work on this topic. Its principled nature also enables us to
identify methods for both training and attacking neural networks that are
reliable and, in a certain sense, universal. In particular, they specify a
concrete security guarantee that would protect against any adversary. These
methods let us train networks with significantly improved resistance to a wide
range of adversarial attacks. They also suggest the notion of security against
a first-order adversary as a natural and broad security guarantee. We believe
that robustness against such well-defined classes of adversaries is an
important stepping stone towards fully resistant deep learning models.
| 1 | 0 | 0 | 1 | 0 | 0 |
Internal delensing of Planck CMB temperature and polarization | We present a first internal delensing of CMB maps, both in temperature and
polarization, using the public foreground-cleaned (SMICA) Planck 2015 maps.
After forming quadratic estimates of the lensing potential, we use the
corresponding displacement field to undo the lensing on the same data. We build
differences of the delensed spectra to the original data spectra specifically
to look for delensing signatures. After taking into account reconstruction
noise biases in the delensed spectra, we find an expected sharpening of the
power spectrum acoustic peaks with a delensing efficiency of $29\,\%$ ($TT$)
$25\,\%$ ($TE$) and $22\,\%$ ($EE$). The detection significance of the
delensing effects is very high in all spectra: $12\,\sigma$ in $EE$
polarization; $18\,\sigma$ in $TE$; and $20\,\sigma$ in $TT$. The null
hypothesis of no lensing in the maps is rejected at $26\,\sigma$. While direct
detection of the power in lensing $B$-modes themselves is not possible at high
significance at Planck noise levels, we do detect (at $4.5\,\sigma$ under the
null hypothesis) delensing effects in the $B$-mode map, with $7\,\%$ reduction
in lensing power. Our results provide a first demonstration of polarization
delensing, and generally of internal CMB delensing, and stand in agreement with
the baseline $\Lambda$CDM Planck 2015 cosmology expectations.
| 0 | 1 | 0 | 0 | 0 | 0 |
Renaissance: Self-Stabilizing Distributed SDN Control Plane | By introducing programmability, automated verification, and innovative
debugging tools, Software-Defined Networks (SDNs) are poised to meet the
increasingly stringent dependability requirements of today's communication
networks. However, the design of fault-tolerant SDNs remains an open challenge.
This paper considers the design of dependable SDNs through the lenses of
self-stabilization - a very strong notion of fault-tolerance. In particular, we
develop algorithms for an in-band and distributed control plane for SDNs,
called Renaissance, which tolerate a wide range of (concurrent) controller,
link, and communication failures. Our self-stabilizing algorithms ensure that
after the occurrence of an arbitrary combination of failures, (i) every
non-faulty SDN controller can eventually reach any switch in the network within
a bounded communication delay (in the presence of a bounded number of
concurrent failures) and (ii) every switch is managed by at least one
non-faulty controller. We evaluate Renaissance through a rigorous worst-case
analysis as well as a prototype implementation (based on OVS and Floodlight),
and we report on our experiments using Mininet.
| 1 | 0 | 0 | 0 | 0 | 0 |
Robust and Scalable Power System State Estimation via Composite Optimization | In today's cyber-enabled smart grids, high penetration of uncertain
renewables, purposeful manipulation of meter readings, and the need for
wide-area situational awareness, call for fast, accurate, and robust power
system state estimation. The least-absolute-value (LAV) estimator is known for
its robustness relative to the weighted least-squares (WLS) one. However, due
to nonconvexity and nonsmoothness, existing LAV solvers based on linear
programming are typically slow, hence inadequate for real-time system
monitoring. This paper develops two novel algorithms for efficient LAV
estimation, which draw from recent advances in composite optimization. The
first is a deterministic linear proximal scheme that handles a sequence of
convex quadratic problems, each efficiently solvable either via off-the-shelf
algorithms or through the alternating direction method of multipliers.
Leveraging the sparse connectivity inherent to power networks, the second
scheme is stochastic, and updates only \emph{a few} entries of the complex
voltage state vector per iteration. In particular, when voltage magnitude and
(re)active power flow measurements are used only, this number reduces to one or
two, \emph{regardless of} the number of buses in the network. This
computational complexity evidently scales well to large-size power systems.
Furthermore, by carefully \emph{mini-batching} the voltage and power flow
measurements, accelerated implementation of the stochastic iterations becomes
possible. The developed algorithms are numerically evaluated using a variety of
benchmark power networks. Simulated tests corroborate that improved robustness
can be attained at comparable or markedly reduced computation times for medium-
or large-size networks relative to the "workhorse" WLS-based Gauss-Newton
iterations.
| 1 | 0 | 0 | 0 | 0 | 0 |
A new algorithm for constraint satisfaction problems with few subpowers templates | In this article, we provide a new algorithm for solving constraint
satisfaction problems over templates with few subpowers, by reducing the
problem to the combination of solvability of a polynomial number of systems of
linear equations over finite fields and reductions via absorbing subuniverses.
| 0 | 0 | 1 | 0 | 0 | 0 |
Testing of General Relativity with Geodetic VLBI | The geodetic VLBI technique is capable of measuring the Sun's gravity light
deflection from distant radio sources around the whole sky. This light
deflection is equivalent to the conventional gravitational delay used for the
reduction of geodetic VLBI data. While numerous tests based on a global set of
VLBI data have shown that the parameter 'gamma' of the post-Newtonian
approximation is equal to unity with a precision of about 0.02 percent, more
detailed analysis reveals some systematic deviations depending on the angular
elongation from the Sun. In this paper a limited set of VLBI observations near
the Sun were adjusted to obtain the estimate of the parameter 'gamma' free of
the elongation angle impact. The parameter 'gamma' is still found to be close
to unity with precision of 0.06 percent, two subsets of VLBI data measured at
short and long baselines produce some statistical inconsistency.
| 0 | 1 | 0 | 0 | 0 | 0 |
An unbiased estimator for the ellipticity from image moments | An unbiased estimator for the ellipticity of an object in a noisy image is
given in terms of the image moments. Three assumptions are made: i) the pixel
noise is normally distributed, although with arbitrary covariance matrix, ii)
the image moments are taken about a fixed centre, and iii) the point-spread
function is known. The relevant combinations of image moments are then jointly
normal and their covariance matrix can be computed. A particular estimator for
the ratio of the means of jointly normal variates is constructed and used to
provide the unbiased estimator for the ellipticity. Furthermore, an unbiased
estimate of the covariance of the new estimator is also given.
| 0 | 1 | 1 | 1 | 0 | 0 |
Spectral estimation of the percolation transition in clustered networks | There have been several spectral bounds for the percolation transition in
networks, using spectrum of matrices associated with the network such as the
adjacency matrix and the non-backtracking matrix. However they are far from
being tight when the network is sparse and displays clustering or transitivity,
which is represented by existence of short loops e.g. triangles. In this work,
for the bond percolation, we first propose a message passing algorithm for
calculating size of percolating clusters considering effects of triangles, then
relate the percolation transition to the leading eigenvalue of a matrix that we
name the triangle-non-backtracking matrix, by analyzing stability of the
message passing equations. We establish that our method gives a tighter
lower-bound to the bond percolation transition than previous spectral bounds,
and it becomes exact for an infinite network with no loops longer than 3. We
evaluate numerically our methods on synthetic and real-world networks, and
discuss further generalizations of our approach to include higher-order
sub-structures.
| 1 | 1 | 0 | 1 | 0 | 0 |
Collapsibility to a subcomplex of a given dimension is NP-complete | In this paper we extend the works of Tancer and of Malgouyres and Francés,
showing that $(d,k)$-collapsibility is NP-complete for $d\geq k+2$ except
$(2,0)$. By $(d,k)$-collapsibility we mean the following problem: determine
whether a given $d$-dimensional simplicial complex can be collapsed to some
$k$-dimensional subcomplex. The question of establishing the complexity status
of $(d,k)$-collapsibility was asked by Tancer, who proved NP-completeness of
$(d,0)$ and $(d,1)$-collapsibility (for $d\geq 3$). Our extended result,
together with the known polynomial-time algorithms for $(2,0)$ and $d=k+1$,
answers the question completely.
| 1 | 0 | 1 | 0 | 0 | 0 |
Run, skeleton, run: skeletal model in a physics-based simulation | In this paper, we present our approach to solve a physics-based reinforcement
learning challenge "Learning to Run" with objective to train
physiologically-based human model to navigate a complex obstacle course as
quickly as possible. The environment is computationally expensive, has a
high-dimensional continuous action space and is stochastic. We benchmark state
of the art policy-gradient methods and test several improvements, such as layer
normalization, parameter noise, action and state reflecting, to stabilize
training and improve its sample-efficiency. We found that the Deep
Deterministic Policy Gradient method is the most efficient method for this
environment and the improvements we have introduced help to stabilize training.
Learned models are able to generalize to new physical scenarios, e.g. different
obstacle courses.
| 1 | 0 | 0 | 1 | 0 | 0 |
Affect Estimation in 3D Space Using Multi-Task Active Learning for Regression | Acquisition of labeled training samples for affective computing is usually
costly and time-consuming, as affects are intrinsically subjective, subtle and
uncertain, and hence multiple human assessors are needed to evaluate each
affective sample. Particularly, for affect estimation in the 3D space of
valence, arousal and dominance, each assessor has to perform the evaluations in
three dimensions, which makes the labeling problem even more challenging. Many
sophisticated machine learning approaches have been proposed to reduce the data
labeling requirement in various other domains, but so far few have considered
affective computing. This paper proposes two multi-task active learning for
regression approaches, which select the most beneficial samples to label, by
considering the three affect primitives simultaneously. Experimental results on
the VAM corpus demonstrated that our optimal sample selection approaches can
result in better estimation performance than random selection and several
traditional single-task active learning approaches. Thus, they can help
alleviate the data labeling problem in affective computing, i.e., better
estimation performance can be obtained from fewer labeling queries.
| 1 | 0 | 0 | 1 | 0 | 0 |
Sparse Markov Decision Processes with Causal Sparse Tsallis Entropy Regularization for Reinforcement Learning | In this paper, a sparse Markov decision process (MDP) with novel causal
sparse Tsallis entropy regularization is proposed.The proposed policy
regularization induces a sparse and multi-modal optimal policy distribution of
a sparse MDP. The full mathematical analysis of the proposed sparse MDP is
provided.We first analyze the optimality condition of a sparse MDP. Then, we
propose a sparse value iteration method which solves a sparse MDP and then
prove the convergence and optimality of sparse value iteration using the Banach
fixed point theorem. The proposed sparse MDP is compared to soft MDPs which
utilize causal entropy regularization. We show that the performance error of a
sparse MDP has a constant bound, while the error of a soft MDP increases
logarithmically with respect to the number of actions, where this performance
error is caused by the introduced regularization term. In experiments, we apply
sparse MDPs to reinforcement learning problems. The proposed method outperforms
existing methods in terms of the convergence speed and performance.
| 1 | 0 | 0 | 1 | 0 | 0 |
Mid-infrared Spectroscopic Observations of the Dust-forming Classical Nova V2676 Oph | The dust-forming nova V2676 Oph is unique in that it was the first nova to
provide evidence of C_2 and CN molecules during its near-maximum phase and
evidence of CO molecules during its early decline phase. Observations of this
nova have revealed the slow evolution of its lightcurves and have also shown
low isotopic ratios of carbon (12C/13C) and nitrogen (14N/15N) in its nova
envelope. These behaviors indicate that the white dwarf (WD) star hosting V2676
Oph is a CO-rich WD rather than an ONe-rich WD (typically larger in mass than
the former). We performed mid-infrared spectroscopic and photometric
observations of V2676 Oph in 2013 and 2014 (respectively 452 and 782 days after
its discovery). No significant [Ne II] emission at 12.8 micron was detected at
either epoch. These provided evidence for a CO-rich WD star hosting V2676 Oph.
Both carbon-rich and oxygen-rich grains were detected in addition to an
unidentified infrared feature at 11.4 micron originating from polycyclic
aromatic hydrocarbon molecules or hydrogenated amorphous carbon grains in the
envelope of V2676 Oph.
| 0 | 1 | 0 | 0 | 0 | 0 |
An invitation to 2D TQFT and quantization of Hitchin spectral curves | This article consists of two parts. In Part 1, we present a formulation of
two-dimensional topological quantum field theories in terms of a functor from a
category of Ribbon graphs to the endofuntor category of a monoidal category.
The key point is that the category of ribbon graphs produces all Frobenius
objects. Necessary backgrounds from Frobenius algebras, topological quantum
field theories, and cohomological field theories are reviewed. A result on
Frobenius algebra twisted topological recursion is included at the end of Part
1.
In Part 2, we explain a geometric theory of quantum curves. The focus is
placed on the process of quantization as a passage from families of Hitchin
spectral curves to families of opers. To make the presentation simpler, we
unfold the story using SL_2(\mathbb{C})-opers and rank 2 Higgs bundles defined
on a compact Riemann surface $C$ of genus greater than $1$. In this case,
quantum curves, opers, and projective structures in $C$ all become the same
notion. Background materials on projective coordinate systems, Higgs bundles,
opers, and non-Abelian Hodge correspondence are explained.
| 0 | 0 | 1 | 0 | 0 | 0 |
An Ensemble Classification Algorithm Based on Information Entropy for Data Streams | Data stream mining problem has caused widely concerns in the area of machine
learning and data mining. In some recent studies, ensemble classification has
been widely used in concept drift detection, however, most of them regard
classification accuracy as a criterion for judging whether concept drift
happening or not. Information entropy is an important and effective method for
measuring uncertainty. Based on the information entropy theory, a new algorithm
using information entropy to evaluate a classification result is developed. It
uses ensemble classification techniques, and the weight of each classifier is
decided through the entropy of the result produced by an ensemble classifiers
system. When the concept in data streams changing, the classifiers' weight
below a threshold value will be abandoned to adapt to a new concept in one
time. In the experimental analysis section, six databases and four proposed
algorithms are executed. The results show that the proposed method can not only
handle concept drift effectively, but also have a better classification
accuracy and time performance than the contrastive algorithms.
| 1 | 0 | 0 | 0 | 0 | 0 |
Competition between disorder and interaction effects in 3D Weyl semimetals | We investigate the low-energy scaling behavior of an interacting 3D Weyl
semimetal in the presence of disorder. In order to achieve a renormalization
group analysis of the theory, we focus on the effects of a
short-ranged-correlated disorder potential, checking nevertheless that this
choice is not essential to locate the different phases of the Weyl semimetal.
We show that there is a line of fixed-points in the renormalization group flow
of the interacting theory, corresponding to the disorder-driven transition to a
diffusive metal phase. Along that boundary, the critical disorder strength
undergoes a strong increase with respect to the noninteracting theory, as a
consequence of the unconventional screening of the Coulomb and disorder-induced
interactions. A complementary resolution of the Schwinger-Dyson equations
allows us to determine the full phase diagram of the system, showing the
prevalence of a renormalized semimetallic phase in the regime of intermediate
interaction strength, and adjacent to the non-Fermi liquid phase characteristic
of the strong interaction regime of 3D Weyl semimetals.
| 0 | 1 | 0 | 0 | 0 | 0 |
Mixing properties and central limit theorem for associated point processes | Positively (resp. negatively) associated point processes are a class of point
processes that induce attraction (resp. inhibition) between the points. As an
important example, determinantal point processes (DPPs) are negatively
associated. We prove $\alpha$-mixing properties for associated spatial point
processes by controlling their $\alpha$-coefficients in terms of the first two
intensity functions. A central limit theorem for functionals of associated
point processes is deduced, using both the association and the $\alpha$-mixing
properties. We discuss in detail the case of DPPs, for which we obtain the
limiting distribution of sums, over subsets of close enough points of the
process, of any bounded function of the DPP. As an application, we get the
asymptotic properties of the parametric two-step estimator of some
inhomogeneous DPPs.
| 0 | 0 | 1 | 1 | 0 | 0 |
Computational landscape of user behavior on social media | With the increasing abundance of 'digital footprints' left by human
interactions in online environments, e.g., social media and app use, the
ability to model complex human behavior has become increasingly possible. Many
approaches have been proposed, however, most previous model frameworks are
fairly restrictive. We introduce a new social modeling approach that enables
the creation of models directly from data with minimal a priori restrictions on
the model class. In particular, we infer the minimally complex, maximally
predictive representation of an individual's behavior when viewed in isolation
and as driven by a social input. We then apply this framework to a
heterogeneous catalog of human behavior collected from fifteen thousand users
on the microblogging platform Twitter. The models allow us to describe how a
user processes their past behavior and their social inputs. Despite the
diversity of observed user behavior, most models inferred fall into a small
subclass of all possible finite-state processes. Thus, our work demonstrates
that user behavior, while quite complex, belies simple underlying computational
structures.
| 1 | 0 | 0 | 1 | 0 | 0 |
Simulating optical coherence tomography for observing nerve activity: a finite difference time domain bi-dimensional model | We present a finite difference time domain (FDTD) model for computation of A
line scans in time domain optical coherence tomography (OCT). By simulating
only the end of the two arms of the interferometer and computing the
interference signal in post processing, it is possible to reduce the
computational time required by the simulations and, thus, to simulate much
bigger environments. Moreover, it is possible to simulate successive A lines
and thus obtaining a cross section of the sample considered. In this paper we
present the model applied to two different samples: a glass rod filled with
water-sucrose solution at different concentrations and a peripheral nerve. This
work demonstrates the feasibility of using OCT for non-invasive, direct optical
monitoring of peripheral nerve activity, which is a long-sought goal of
neuroscience.
| 0 | 1 | 0 | 0 | 0 | 0 |
Subsets and Splits
No saved queries yet
Save your SQL queries to embed, download, and access them later. Queries will appear here once saved.