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Diversified essential properties in halogenated graphenes | The significant halogenation effects on the essential properties of graphene
are investigated by the first-principles method. The geometric structures,
electronic properties, and magnetic configurations are greatly diversified
under the various halogen adsorptions. Fluorination, with the strong
multi-orbital chemical bondings, can create the buckled graphene structure,
while the other halogenations do not change the planar {\sigma} bonding in the
presence of single-orbital hybridization. Electronic structures consist of the
carbon-, adatom- and (carbon, adatom)-dominated energy bands. All halogenated
graphenes belong to hole-doped metals except that fluorinated systems are
middle-gap semiconductors at sufficiently high concentration. Moreover, the
metallic ferromagnetism is revealed in certain adatom distributions. The
unusual hybridization-induced features are clearly evidenced in many van Hove
singularities of the density of states. The structure- and adatom-enriched
essential properties are compared with the measured results, and potential
applications are also discussed.
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Quantifying genuine multipartite correlations and their pattern complexity | We propose an information-theoretic framework to quantify multipartite
correlations in classical and quantum systems, answering questions such as:
what is the amount of seven-partite correlations in a given state of ten
particles? We identify measures of genuine multipartite correlations, i.e.
statistical dependencies which cannot be ascribed to bipartite correlations,
satisfying a set of desirable properties. Inspired by ideas developed in
complexity science, we then introduce the concept of weaving to classify states
which display different correlation patterns, but cannot be distinguished by
correlation measures. The weaving of a state is defined as the weighted sum of
correlations of every order. Weaving measures are good descriptors of the
complexity of correlation structures in multipartite systems.
| 0 | 1 | 0 | 0 | 0 | 0 |
Pulsating low-mass white dwarfs in the frame of new evolutionary sequences: IV. The secular rate of period change | We present a theoretical assessment of the expected temporal rates of change
of periods ($\dot{\Pi}$) for low-mass ($M_{\star}/M_{\sun} \lesssim 0.45$) and
extremely low-mass (ELM, $M_{\star}/M_{\sun} \lesssim 0.18-0.20$) white-dwarf
stars, based on fully evolutionary low-mass He-core white dwarf and pre-white
dwarf models. Our analysis is based on a large set of adiabatic periods of
radial and nonradial pulsation modes computed on a suite of low-mass He-core
white dwarf and pre-white dwarf models with masses ranging from $0.1554$ to
$0.4352 M_{\sun}$. We compute the secular rates of period change of radial
($\ell= 0$) and nonradial ($\ell= 1, 2$) $g$ and $p$ modes for stellar models
representative of ELMV and pre-ELMV stars, as well as for stellar objects that
are evolving just before the occurrence of CNO flashes at the early cooling
branches. We found that the theoretically expected magnitude of $\dot{\Pi}$ of
$g$ modes for pre-ELMVs are by far larger than for ELMVs. In turn, $\dot{\Pi}$
of $g$ modes for models evolving before the occurrence of CNO flashes are
larger than the maximum values of the rates of period change predicted for
pre-ELMV stars. Regarding $p$ and radial modes, we found that the larger
absolute values of $\dot{\Pi}$ correspond to pre-ELMV models. We conclude that
any eventual measurement of a rate of period change for a given pulsating
low-mass pre-white dwarf or white dwarf star could shed light about its
evolutionary status. Also, in view of the systematic difficulties in the
spectroscopic classification of stars of the ELM Survey, an eventual
measurement of $\dot{\Pi}$ could help to confirm that a given pulsating star is
an authentic low-mass white dwarf and not a star from another stellar
population.
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Normalized Maximum Likelihood with Luckiness for Multivariate Normal Distributions | The normalized maximum likelihood (NML) is one of the most important
distribution in coding theory and statistics. NML is the unique solution (if
exists) to the pointwise minimax regret problem. However, NML is not defined
even for simple family of distributions such as the normal distributions. Since
there does not exist any meaningful minimax-regret distribution for such case,
it is pointed out that NML with luckiness (LNML) can be employed as an
alternative to NML. In this paper, we develop the closed form of LNMLs for
multivariate normal distributions.
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Tackling Diversity and Heterogeneity by Vertical Memory Management | Existing memory management mechanisms used in commodity computing machines
typically adopt hardware based address interleaving and OS directed random
memory allocation to service generic application requests. These conventional
memory management mechanisms are challenged by contention at multiple memory
levels, a daunting variety of workload behaviors, and an increasingly
complicated memory hierarchy. Our ISCA-41 paper proposes vertical partitioning
to eliminate shared resource contention at multiple levels in the memory
hierarchy. Combined with horizontal memory management policies, our framework
supports a flexible policy space for tackling diverse application needs in
production environment and is suitable for future heterogeneous memory systems.
| 1 | 0 | 0 | 0 | 0 | 0 |
From the Icosahedron to E8 | The regular icosahedron is connected to many exceptional objects in
mathematics. Here we describe two constructions of the $\mathrm{E}_8$ lattice
from the icosahedron. One uses a subring of the quaternions called the
"icosians", while the other uses du Val's work on the resolution of Kleinian
singularities. Together they link the golden ratio, the quaternions, the
quintic equation, the 600-cell, and the Poincare homology 3-sphere. We leave it
as a challenge to the reader to find the connection between these two
constructions.
| 0 | 0 | 1 | 0 | 0 | 0 |
Sequential Discrete Kalman Filter for Real-Time State Estimation in Power Distribution Systems: Theory and Implementation | This paper demonstrates the feasibility of implementing Real-Time State
Estimators (RTSEs) for Active Distribution Networks (ADNs) in
Field-Programmable Gate Arrays (FPGAs) by presenting an operational prototype.
The prototype is based on a Linear State Estimator (LSE) that uses
synchrophasor measurements from Phasor Measurement Units (PMUs). The underlying
algorithm is the Sequential Discrete Kalman Filter (SDKF), an equivalent
formulation of the Discrete Kalman Filter (DKF) for the case of uncorrelated
measurement noise. In this regard, this work formally proves the equivalence
the SDKF and the DKF, and highlights the suitability of the SDKF for an FPGA
implementation by means of a computational complexity analysis. The developed
prototype is validated using a case study adapted from the IEEE 34-node
distribution test feeder.
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Game-Theoretic Semantics for ATL+ with Applications to Model Checking | We develop game-theoretic semantics (GTS) for the fragment ATL+ of the full
Alternating-time Temporal Logic ATL*, essentially extending a recently
introduced GTS for ATL. We first show that the new game-theoretic semantics is
equivalent to the standard semantics of ATL+ (based on perfect recall
strategies). We then provide an analysis, based on the new semantics, of the
memory and time resources needed for model checking ATL+. Based on that, we
establish that strategies that use only a very limited amount of memory suffice
for ATL+. Furthermore, using the GTS we provide a new algorithm for model
checking of ATL+ and identify a natural hierarchy of tractable fragments of
ATL+ that extend ATL.
| 1 | 0 | 1 | 0 | 0 | 0 |
Killing Three Birds with one Gaussian Process: Analyzing Attack Vectors on Classification | The wide usage of Machine Learning (ML) has lead to research on the attack
vectors and vulnerability of these systems. The defenses in this area are
however still an open problem, and often lead to an arms race. We define a
naive, secure classifier at test time and show that a Gaussian Process (GP) is
an instance of this classifier given two assumptions: one concerns the
distances in the training data, the other rejection at test time. Using these
assumptions, we are able to show that a classifier is either secure, or
generalizes and thus learns. Our analysis also points towards another factor
influencing robustness, the curvature of the classifier. This connection is not
unknown for linear models, but GP offer an ideal framework to study this
relationship for nonlinear classifiers. We evaluate on five security and two
computer vision datasets applying test and training time attacks and membership
inference. We show that we only change which attacks are needed to succeed,
instead of alleviating the threat. Only for membership inference, there is a
setting in which attacks are unsuccessful (<10% increase in accuracy over
random guess). Given these results, we define a classification scheme based on
voting, ParGP. This allows us to decide how many points vote and how large the
agreement on a class has to be. This ensures a classification output only in
cases when there is evidence for a decision, where evidence is parametrized. We
evaluate this scheme and obtain promising results.
| 0 | 0 | 0 | 1 | 0 | 0 |
Approximate message passing for nonconvex sparse regularization with stability and asymptotic analysis | We analyse a linear regression problem with nonconvex regularization called
smoothly clipped absolute deviation (SCAD) under an overcomplete Gaussian basis
for Gaussian random data. We propose an approximate message passing (AMP)
algorithm considering nonconvex regularization, namely SCAD-AMP, and
analytically show that the stability condition corresponds to the de
Almeida--Thouless condition in spin glass literature. Through asymptotic
analysis, we show the correspondence between the density evolution of SCAD-AMP
and the replica symmetric solution. Numerical experiments confirm that for a
sufficiently large system size, SCAD-AMP achieves the optimal performance
predicted by the replica method. Through replica analysis, a phase transition
between replica symmetric (RS) and replica symmetry breaking (RSB) region is
found in the parameter space of SCAD. The appearance of the RS region for a
nonconvex penalty is a significant advantage that indicates the region of
smooth landscape of the optimization problem. Furthermore, we analytically show
that the statistical representation performance of the SCAD penalty is better
than that of L1-based methods, and the minimum representation error under RS
assumption is obtained at the edge of the RS/RSB phase. The correspondence
between the convergence of the existing coordinate descent algorithm and RS/RSB
transition is also indicated.
| 1 | 0 | 0 | 1 | 0 | 0 |
Concurrence Topology of Some Cancer Genomics Data | The topological data analysis method "concurrence topology" is applied to
mutation frequencies in 69 genes in glioblastoma data. In dimension 1 some
apparent "mutual exclusivity" is found. By simulation of data having
approximately the same second order dependence structure as that found in the
data, it appears that one triple of mutations, PTEN, RB1, TP53, exhibits mutual
exclusivity that depends on special features of the third order dependence and
may reflect global dependence among a larger group of genes. A bootstrap
analysis suggests that this form of mutual exclusivity is not uncommon in the
population from which the data were drawn.
| 0 | 0 | 0 | 1 | 0 | 0 |
Quasimomentum of an elementary excitation for a system of point bosons with zero boundary conditions | As is known, an elementary excitation of a many-particle system with
boundaries is not characterized by a definite momentum. Here, we obtain the
formula for the quasimomentum of an elementary excitation for a one-dimensional
system of $N$ spinless point bosons with zero boundary conditions (BCs). We
also find that the dispersion law $E(p)$ of the system with zero BCs coincides
with that of a system with periodic BCs. The elementary excitations are defined
within a new approach proposed earlier by the author. This approach is
mathematically equivalent to the traditional approach by Lieb, but differs from
it by a simpler way of enumeration of excited states and leads to a single
dispersion law (instead of two ones in the Lieb's approach).
| 0 | 1 | 0 | 0 | 0 | 0 |
Moving to VideoKifu: the last steps toward a fully automatic record-keeping of a Go game | In a previous paper [ arXiv:1508.03269 ] we described the techniques we
successfully employed for automatically reconstructing the whole move sequence
of a Go game by means of a set of pictures. Now we describe how it is possible
to reconstruct the move sequence by means of a video stream (which may be
provided by an unattended webcam), possibly in real-time. Although the basic
algorithms remain the same, we will discuss the new problems that arise when
dealing with videos, with special care for the ones that could block a
real-time analysis and require an improvement of our previous techniques or
even a completely brand new approach. Eventually we present a number of
preliminary but positive experimental results supporting the effectiveness of
the software we are developing, built on the ideas here outlined.
| 1 | 0 | 0 | 0 | 0 | 0 |
Malware Detection by Eating a Whole EXE | In this work we introduce malware detection from raw byte sequences as a
fruitful research area to the larger machine learning community. Building a
neural network for such a problem presents a number of interesting challenges
that have not occurred in tasks such as image processing or NLP. In particular,
we note that detection from raw bytes presents a sequence problem with over two
million time steps and a problem where batch normalization appear to hinder the
learning process. We present our initial work in building a solution to tackle
this problem, which has linear complexity dependence on the sequence length,
and allows for interpretable sub-regions of the binary to be identified. In
doing so we will discuss the many challenges in building a neural network to
process data at this scale, and the methods we used to work around them.
| 1 | 0 | 0 | 1 | 0 | 0 |
On the Complexity of Model Checking for Syntactically Maximal Fragments of the Interval Temporal Logic HS with Regular Expressions | In this paper, we investigate the model checking (MC) problem for Halpern and
Shoham's interval temporal logic HS. In the last years, interval temporal logic
MC has received an increasing attention as a viable alternative to the
traditional (point-based) temporal logic MC, which can be recovered as a
special case. Most results have been obtained under the homogeneity assumption,
that constrains a proposition letter to hold over an interval if and only if it
holds over each component state. Recently, Lomuscio and Michaliszyn proposed a
way to relax such an assumption by exploiting regular expressions to define the
behaviour of proposition letters over intervals in terms of their component
states. When homogeneity is assumed, the exact complexity of MC is a difficult
open question for full HS and for its two syntactically maximal fragments
AA'BB'E' and AA'EB'E'. In this paper, we provide an asymptotically optimal
bound to the complexity of these two fragments under the more expressive
semantic variant based on regular expressions by showing that their MC problem
is AEXP_pol-complete, where AEXP_pol denotes the complexity class of problems
decided by exponential-time bounded alternating Turing Machines making a
polynomially bounded number of alternations.
| 1 | 0 | 0 | 0 | 0 | 0 |
Deforming 3-manifolds of bounded geometry and uniformly positive scalar curvature | We prove that the moduli space of complete Riemannian metrics of bounded
geometry and uniformly positive scalar curvature on an orientable 3-manifold is
path-connected. This generalizes the main result of the fourth author [Mar12]
in the compact case. The proof uses Ricci flow with surgery as well as
arguments involving performing infinite connected sums with control on the
geometry.
| 0 | 0 | 1 | 0 | 0 | 0 |
Optimal and Myopic Information Acquisition | We consider the problem of optimal dynamic information acquisition from many
correlated information sources. Each period, the decision-maker jointly takes
an action and allocates a fixed number of observations across the available
sources. His payoff depends on the actions taken and on an unknown state. In
the canonical setting of jointly normal information sources, we show that the
optimal dynamic information acquisition rule proceeds myopically after finitely
many periods. If signals are acquired in large blocks each period, then the
optimal rule turns out to be myopic from period 1. These results demonstrate
the possibility of robust and "simple" optimal information acquisition, and
simplify the analysis of dynamic information acquisition in a widely used
informational environment.
| 1 | 0 | 1 | 1 | 0 | 0 |
Infinitely many minimal classes of graphs of unbounded clique-width | The celebrated theorem of Robertson and Seymour states that in the family of
minor-closed graph classes, there is a unique minimal class of graphs of
unbounded tree-width, namely, the class of planar graphs. In the case of
tree-width, the restriction to minor-closed classes is justified by the fact
that the tree-width of a graph is never smaller than the tree-width of any of
its minors. This, however, is not the case with respect to clique-width, as the
clique-width of a graph can be (much) smaller than the clique-width of its
minor. On the other hand, the clique-width of a graph is never smaller than the
clique-width of any of its induced subgraphs, which allows us to be restricted
to hereditary classes (that is, classes closed under taking induced subgraphs),
when we study clique-width. Up to date, only finitely many minimal hereditary
classes of graphs of unbounded clique-width have been discovered in the
literature. In the present paper, we prove that the family of such classes is
infinite. Moreover, we show that the same is true with respect to linear
clique-width.
| 1 | 0 | 1 | 0 | 0 | 0 |
A Passivity-Based Approach to Nash Equilibrium Seeking over Networks | In this paper we consider the problem of distributed Nash equilibrium (NE)
seeking over networks, a setting in which players have limited local
information. We start from a continuous-time gradient-play dynamics that
converges to an NE under strict monotonicity of the pseudo-gradient and assumes
perfect information, i.e., instantaneous all-to-all player communication. We
consider how to modify this gradient-play dynamics in the case of partial, or
networked information between players. We propose an augmented gradient-play
dynamics with correction in which players communicate locally only with their
neighbours to compute an estimate of the other players' actions. We derive the
new dynamics based on the reformulation as a multi-agent coordination problem
over an undirected graph. We exploit incremental passivity properties and show
that a synchronizing, distributed Laplacian feedback can be designed using
relative estimates of the neighbours. Under a strict monotonicity property of
the pseudo-gradient, we show that the augmented gradient-play dynamics
converges to consensus on the NE of the game. We further discuss two cases that
highlight the tradeoff between properties of the game and the communication
graph.
| 0 | 0 | 1 | 0 | 0 | 0 |
Magnetic and dielectric investigations of $γ$ - Fe${_2}$WO${_6}$ | The magnetic, thermodynamic and dielectric properties of the $\gamma$ -
Fe${_2}$WO${_6}$ system is reported. Crystallizing in the centrosymmetric
$Pbcn$ space group, this particular polymorph exhibits a number of different
magnetic transitions, all of which are seen to exhibit a finite
magneto-dielectric coupling. At the lowest measured temperatures, the magnetic
ground state appears to be glass-like, as evidenced by the waiting time
dependence of the magnetic relaxation. Also reflected in the frequency
dependent dielectric measurements, these signatures possibly arise as a
consequence of the oxygen non-stoichiometry, which promotes an inhomogeneous
magnetic and electronic ground state.
| 0 | 1 | 0 | 0 | 0 | 0 |
Linear centralization classifier | A classification algorithm, called the Linear Centralization Classifier
(LCC), is introduced. The algorithm seeks to find a transformation that best
maps instances from the feature space to a space where they concentrate towards
the center of their own classes, while maximimizing the distance between class
centers. We formulate the classifier as a quadratic program with quadratic
constraints. We then simplify this formulation to a linear program that can be
solved effectively using a linear programming solver (e.g., simplex-dual). We
extend the formulation for LCC to enable the use of kernel functions for
non-linear classification applications. We compare our method with two standard
classification methods (support vector machine and linear discriminant
analysis) and four state-of-the-art classification methods when they are
applied to eight standard classification datasets. Our experimental results
show that LCC is able to classify instances more accurately (based on the area
under the receiver operating characteristic) in comparison to other tested
methods on the chosen datasets. We also report the results for LCC with a
particular kernel to solve for synthetic non-linear classification problems.
| 1 | 0 | 0 | 1 | 0 | 0 |
Spatio-temporal Person Retrieval via Natural Language Queries | In this paper, we address the problem of spatio-temporal person retrieval
from multiple videos using a natural language query, in which we output a tube
(i.e., a sequence of bounding boxes) which encloses the person described by the
query. For this problem, we introduce a novel dataset consisting of videos
containing people annotated with bounding boxes for each second and with five
natural language descriptions. To retrieve the tube of the person described by
a given natural language query, we design a model that combines methods for
spatio-temporal human detection and multimodal retrieval. We conduct
comprehensive experiments to compare a variety of tube and text representations
and multimodal retrieval methods, and present a strong baseline in this task as
well as demonstrate the efficacy of our tube representation and multimodal
feature embedding technique. Finally, we demonstrate the versatility of our
model by applying it to two other important tasks.
| 1 | 0 | 0 | 0 | 0 | 0 |
Thermo-elasto-plastic simulations of femtosecond laser-induced multiple-cavity in fused silica | The formation and the interaction of multiple cavities, induced by tightly
focused femtosecond laser pulses, are studied by using a developed numerical
tool, including the thermo-elasto-plastic material response. Simulations are
performed in fused silica in cases of one, two, and four spots of laser energy
deposition. The relaxation of the heated matter, launching shock waves in the
surrounding cold material, leads to cavity formation and emergence of areas
where cracks may be induced. Results show that the laser-induced structure
shape depends on the energy deposition configuration and demonstrate the
potential of the used numerical tool to obtain the desired designed structure
or technological process.
| 0 | 1 | 0 | 0 | 0 | 0 |
Efficient and Accurate Machine-Learning Interpolation of Atomic Energies in Compositions with Many Species | Machine-learning potentials (MLPs) for atomistic simulations are a promising
alternative to conventional classical potentials. Current approaches rely on
descriptors of the local atomic environment with dimensions that increase
quadratically with the number of chemical species. In this article, we
demonstrate that such a scaling can be avoided in practice. We show that a
mathematically simple and computationally efficient descriptor with constant
complexity is sufficient to represent transition-metal oxide compositions and
biomolecules containing 11 chemical species with a precision of around 3
meV/atom. This insight removes a perceived bound on the utility of MLPs and
paves the way to investigate the physics of previously inaccessible materials
with more than ten chemical species.
| 0 | 1 | 0 | 0 | 0 | 0 |
Randomized Constraints Consensus for Distributed Robust Linear Programming | In this paper we consider a network of processors aiming at cooperatively
solving linear programming problems subject to uncertainty. Each node only
knows a common cost function and its local uncertain constraint set. We propose
a randomized, distributed algorithm working under time-varying, asynchronous
and directed communication topology. The algorithm is based on a local
computation and communication paradigm. At each communication round, nodes
perform two updates: (i) a verification in which they check-in a randomized
setup-the robust feasibility (and hence optimality) of the candidate optimal
point, and (ii) an optimization step in which they exchange their candidate
bases (minimal sets of active constraints) with neighbors and locally solve an
optimization problem whose constraint set includes: a sampled constraint
violating the candidate optimal point (if it exists), agent's current basis and
the collection of neighbor's basis. As main result, we show that if a processor
successfully performs the verification step for a sufficient number of
communication rounds, it can stop the algorithm since a consensus has been
reached. The common solution is-with high confidence-feasible (and hence
optimal) for the entire set of uncertainty except a subset having arbitrary
small probability measure. We show the effectiveness of the proposed
distributed algorithm on a multi-core platform in which the nodes communicate
asynchronously.
| 0 | 0 | 1 | 0 | 0 | 0 |
Continuum limit of the vibrational properties of amorphous solids | The low-frequency vibrational and low-temperature thermal properties of
amorphous solids are markedly different from those of crystalline solids. This
situation is counter-intuitive because any solid material is expected to behave
as a homogeneous elastic body in the continuum limit, in which vibrational
modes are phonons following the Debye law. A number of phenomenological
explanations have been proposed, which assume elastic heterogeneities, soft
localized vibrations, and so on. Recently, the microscopic mean-field theories
have been developed to predict the universal non-Debye scaling law. Considering
these theoretical arguments, it is absolutely necessary to directly observe the
nature of the low-frequency vibrations of amorphous solids and determine the
laws that such vibrations obey. Here, we perform an extremely large-scale
vibrational mode analysis of a model amorphous solid. We find that the scaling
law predicted by the mean-field theory is violated at low frequency, and in the
continuum limit, the vibrational modes converge to a mixture of phonon modes
following the Debye law and soft localized modes following another universal
non-Debye scaling law.
| 0 | 1 | 0 | 0 | 0 | 0 |
Existence of smooth solutions of multi-term Caputo-type fractional differential equations | This paper deals with the initial value problem for the multi-term fractional
differential equation. The fractional derivative is defined in the Caputo
sense. Firstly the initial value problem is transformed into a equivalent
Volterra-type integral equation under appropriate assumptions. Then new
existence results for smooth solutions are established by using the Schauder
fixed point theorem.
| 0 | 0 | 1 | 0 | 0 | 0 |
Classes of elementary function solutions to the CEV model. I | The CEV model subsumes some of the previous option pricing models. An
important parameter in the model is the parameter b, the elasticity of
volatility. For b=0, b=-1/2, and b=-1 the CEV model reduces respectively to the
BSM model, the square-root model of Cox and Ross, and the Bachelier model. Both
in the case of the BSM model and in the case of the CEV model it has become
traditional to begin a discussion of option pricing by starting with the
vanilla European calls and puts. In the case of BSM model simpler solutions are
the log and power solutions. These contracts, despite the simplicity of their
mathematical description, are attracting increasing attention as a trading
instrument. Similar simple solutions have not been studied so far in a
systematic fashion for the CEV model. We use Kovacic's algorithm to derive, for
all half-integer values of b, all solutions "in quadratures" of the CEV
ordinary differential equation. These solutions give rise, by separation of
variables, to simple solutions to the CEV partial differential equation. In
particular, when b=...,-5/2,-2,-3/2,-1, 1, 3/2, 2, 5/2,..., we obtain four
classes of denumerably infinite elementary function solutions, when b=-1/2 and
b=1/2 we obtain two classes of denumerably infinite elementary function
solutions, whereas, when b=0 we find two elementary function solutions. In the
derived solutions we have also dispensed with the unnecessary assumption made
in the the BSM model asserting that the underlying asset pays no dividends
during the life of the option.
| 0 | 0 | 0 | 0 | 0 | 1 |
Congruent families and invariant tensors | Classical results of Chentsov and Campbell state that -- up to constant
multiples -- the only $2$-tensor field of a statistical model which is
invariant under congruent Markov morphisms is the Fisher metric and the only
invariant $3$-tensor field is the Amari-Chentsov tensor. We generalize this
result for arbitrary degree $n$, showing that any family of $n$-tensors which
is invariant under congruent Markov morphisms is algebraically generated by the
canonical tensor fields defined in an earlier paper.
| 0 | 0 | 1 | 1 | 0 | 0 |
Quality of Information in Mobile Crowdsensing: Survey and Research Challenges | Smartphones have become the most pervasive devices in people's lives, and are
clearly transforming the way we live and perceive technology. Today's
smartphones benefit from almost ubiquitous Internet connectivity and come
equipped with a plethora of inexpensive yet powerful embedded sensors, such as
accelerometer, gyroscope, microphone, and camera. This unique combination has
enabled revolutionary applications based on the mobile crowdsensing paradigm,
such as real-time road traffic monitoring, air and noise pollution, crime
control, and wildlife monitoring, just to name a few. Differently from prior
sensing paradigms, humans are now the primary actors of the sensing process,
since they become fundamental in retrieving reliable and up-to-date information
about the event being monitored. As humans may behave unreliably or
maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
more important than ever. In this paper, we provide a new framework for
defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
current state-of-the-art on the topic. We also outline novel research
challenges, along with possible directions of future work.
| 1 | 0 | 0 | 0 | 0 | 0 |
SimBlock: A Blockchain Network Simulator | Blockchain, which is a technology for distributedly managing ledger
information over multiple nodes without a centralized system, has elicited
increasing attention. Performing experiments on actual blockchains are
difficult because a large number of nodes in wide areas are necessary. In this
study, we developed a blockchain network simulator SimBlock for such
experiments. Unlike the existing simulators, SimBlock can easily change
behavior of node, so that it enables to investigate the influence of nodes'
behavior on blockchains. We compared some simulation results with the measured
values in actual blockchains to demonstrate the validity of this simulator.
Furthermore, to show practical usage, we conducted two experiments which
clarify the influence of neighbor node selection algorithms and relay networks
on the block propagation time. The simulator could depict the effects of the
two techniques on block propagation time. The simulator will be publicly
available in a few months.
| 1 | 0 | 0 | 0 | 0 | 0 |
Bayesian Fused Lasso regression for dynamic binary networks | We propose a multinomial logistic regression model for link prediction in a
time series of directed binary networks. To account for the dynamic nature of
the data we employ a dynamic model for the model parameters that is strongly
connected with the fused lasso penalty. In addition to promoting sparseness,
this prior allows us to explore the presence of change points in the structure
of the network. We introduce fast computational algorithms for estimation and
prediction using both optimization and Bayesian approaches. The performance of
the model is illustrated using simulated data and data from a financial trading
network in the NYMEX natural gas futures market. Supplementary material
containing the trading network data set and code to implement the algorithms is
available online.
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Indefinite Kernel Logistic Regression | Traditionally, kernel learning methods requires positive definitiveness on
the kernel, which is too strict and excludes many sophisticated similarities,
that are indefinite, in multimedia area. To utilize those indefinite kernels,
indefinite learning methods are of great interests. This paper aims at the
extension of the logistic regression from positive semi-definite kernels to
indefinite kernels. The model, called indefinite kernel logistic regression
(IKLR), keeps consistency to the regular KLR in formulation but it essentially
becomes non-convex. Thanks to the positive decomposition of an indefinite
matrix, IKLR can be transformed into a difference of two convex models, which
follows the use of concave-convex procedure. Moreover, we employ an inexact
solving scheme to speed up the sub-problem and develop a concave-inexact-convex
procedure (CCICP) algorithm with theoretical convergence analysis. Systematical
experiments on multi-modal datasets demonstrate the superiority of the proposed
IKLR method over kernel logistic regression with positive definite kernels and
other state-of-the-art indefinite learning based algorithms.
| 1 | 0 | 0 | 1 | 0 | 0 |
Microstructure and thickening of dense suspensions under extensional and shear flows | Dense suspensions are non-Newtonian fluids which exhibit strong shear
thickening and normal stress differences. Using numerical simulation of
extensional and shear flows, we investigate how rheological properties are
determined by the microstructure which is built under flows and by the
interactions between particles. By imposing extensional and shear flows, we can
assess the degree of flow-type dependence in regimes below and above
thickening. Even when the flow-type dependence is hindered, nondissipative
responses, such as normal stress differences, are present and characterise the
non-Newtonian behaviour of dense suspensions.
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Learning at the Ends: From Hand to Tool Affordances in Humanoid Robots | One of the open challenges in designing robots that operate successfully in
the unpredictable human environment is how to make them able to predict what
actions they can perform on objects, and what their effects will be, i.e., the
ability to perceive object affordances. Since modeling all the possible world
interactions is unfeasible, learning from experience is required, posing the
challenge of collecting a large amount of experiences (i.e., training data).
Typically, a manipulative robot operates on external objects by using its own
hands (or similar end-effectors), but in some cases the use of tools may be
desirable, nevertheless, it is reasonable to assume that while a robot can
collect many sensorimotor experiences using its own hands, this cannot happen
for all possible human-made tools.
Therefore, in this paper we investigate the developmental transition from
hand to tool affordances: what sensorimotor skills that a robot has acquired
with its bare hands can be employed for tool use? By employing a visual and
motor imagination mechanism to represent different hand postures compactly, we
propose a probabilistic model to learn hand affordances, and we show how this
model can generalize to estimate the affordances of previously unseen tools,
ultimately supporting planning, decision-making and tool selection tasks in
humanoid robots. We present experimental results with the iCub humanoid robot,
and we publicly release the collected sensorimotor data in the form of a hand
posture affordances dataset.
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Rewriting in Free Hypergraph Categories | We study rewriting for equational theories in the context of symmetric
monoidal categories where there is a separable Frobenius monoid on each object.
These categories, also called hypergraph categories, are increasingly relevant:
Frobenius structures recently appeared in cross-disciplinary applications,
including the study of quantum processes, dynamical systems and natural
language processing. In this work we give a combinatorial characterisation of
arrows of a free hypergraph category as cospans of labelled hypergraphs and
establish a precise correspondence between rewriting modulo Frobenius structure
on the one hand and double-pushout rewriting of hypergraphs on the other. This
interpretation allows to use results on hypergraphs to ensure decidability of
confluence for rewriting in a free hypergraph category. Our results generalise
previous approaches where only categories generated by a single object (props)
were considered.
| 1 | 0 | 0 | 0 | 0 | 0 |
Weighting Scheme for a Pairwise Multi-label Classifier Based on the Fuzzy Confusion Matrix | In this work we addressed the issue of applying a stochastic classifier and a
local, fuzzy confusion matrix under the framework of multi-label
classification. We proposed a novel solution to the problem of correcting label
pairwise ensembles. The main step of the correction procedure is to compute
classifier-specific competence and cross-competence measures, which estimates
error pattern of the underlying classifier. At the fusion phase we employed two
weighting approaches based on information theory. The classifier weights
promote base classifiers which are the most susceptible to the correction based
on the fuzzy confusion matrix. During the experimental study, the proposed
approach was compared against two reference methods. The comparison was made in
terms of six different quality criteria. The conducted experiments reveals that
the proposed approach eliminates one of main drawbacks of the original
FCM-based approach i.e. the original approach is vulnerable to the imbalanced
class/label distribution. What is more, the obtained results shows that the
introduced method achieves satisfying classification quality under all
considered quality criteria. Additionally, the impact of fluctuations of data
set characteristics is reduced.
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Wilcoxon Rank-Based Tests for Clustered Data with R Package clusrank | Wilcoxon Rank-based tests are distribution-free alternatives to the popular
two-sample and paired t-tests. For independent data, they are available in
several R packages such as stats and coin. For clustered data, in spite of the
recent methodological developments, there did not exist an R package that makes
them available at one place. We present a package clusrank where the latest
developments are implemented and wrapped under a unified user-friendly
interface. With different methods dispatched based on the inputs, this package
offers great flexibility in rank-based tests for various clustered data. Exact
tests based on permutations are also provided for some methods. Details of the
major schools of different methods are briefly reviewed. Usages of the package
clusrank are illustrated with simulated data as well as a real dataset from an
ophthalmological study. The package also enables convenient comparison between
selected methods under settings that have not been studied before and the
results are discussed.
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Latent heterogeneous multilayer community detection | We propose a method for simultaneously detecting shared and unshared
communities in heterogeneous multilayer weighted and undirected networks. The
multilayer network is assumed to follow a generative probabilistic model that
takes into account the similarities and dissimilarities between the
communities. We make use of a variational Bayes approach for jointly inferring
the shared and unshared hidden communities from multilayer network
observations. We show the robustness of our approach compared to state-of-the
art algorithms in detecting disparate (shared and private) communities on
synthetic data as well as on real genome-wide fibroblast proliferation dataset.
| 1 | 0 | 0 | 1 | 0 | 0 |
Understanding a Version of Multivariate Symmetric Uncertainty to assist in Feature Selection | In this paper, we analyze the behavior of the multivariate symmetric
uncertainty (MSU) measure through the use of statistical simulation techniques
under various mixes of informative and non-informative randomly generated
features. Experiments show how the number of attributes, their cardinalities,
and the sample size affect the MSU. We discovered a condition that preserves
good quality in the MSU under different combinations of these three factors,
providing a new useful criterion to help drive the process of dimension
reduction.
| 1 | 0 | 0 | 1 | 0 | 0 |
Approximation Dynamics | We describe the approximation of a continuous dynamical system on a p. l.
manifold or Cantor set by a tractable system. A system is tractable when it has
a finite number of chain components and, with respect to a given full
background measure, almost every point is generic for one of a finite number of
ergodic invariant measures with non-overlapping supports. The approximations
use non-degenerate simplicial dynamical systems for p. l. manifolds and
shift-like dynamical systems for Cantor Sets.
| 0 | 0 | 1 | 0 | 0 | 0 |
Sharp asymptotic and finite-sample rates of convergence of empirical measures in Wasserstein distance | The Wasserstein distance between two probability measures on a metric space
is a measure of closeness with applications in statistics, probability, and
machine learning. In this work, we consider the fundamental question of how
quickly the empirical measure obtained from $n$ independent samples from $\mu$
approaches $\mu$ in the Wasserstein distance of any order. We prove sharp
asymptotic and finite-sample results for this rate of convergence for general
measures on general compact metric spaces. Our finite-sample results show the
existence of multi-scale behavior, where measures can exhibit radically
different rates of convergence as $n$ grows.
| 0 | 0 | 1 | 1 | 0 | 0 |
Ensuring patients privacy in a cryptographic-based-electronic health records using bio-cryptography | Several recent works have proposed and implemented cryptography as a means to
preserve privacy and security of patients health data. Nevertheless, the
weakest point of electronic health record (EHR) systems that relied on these
cryptographic schemes is key management. Thus, this paper presents the
development of privacy and security system for cryptography-based-EHR by taking
advantage of the uniqueness of fingerprint and iris characteristic features to
secure cryptographic keys in a bio-cryptography framework. The results of the
system evaluation showed significant improvements in terms of time efficiency
of this approach to cryptographic-based-EHR. Both the fuzzy vault and fuzzy
commitment demonstrated false acceptance rate (FAR) of 0%, which reduces the
likelihood of imposters gaining successful access to the keys protecting
patients protected health information. This result also justifies the
feasibility of implementing fuzzy key binding scheme in real applications,
especially fuzzy vault which demonstrated a better performance during key
reconstruction.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Dynamically Reconfigurable Terahertz Array Antenna for Near-field Imaging Applications | A proof of concept for high speed near-field imaging with sub-wavelength
resolution using SLM is presented. An 8 channel THz detector array antenna with
an electrode gap of 100 um and length of 5 mm is fabricated using the
commercially available GaAs semiconductor substrate. Each array antenna can be
excited simultaneously by spatially reconfiguring the optical probe beam and
the THz electric field can be recorded using 8 channel lock-in amplifiers. By
scanning the probe beam along the length of the array antenna, a 2D image can
be obtained with amplitude, phase and frequency information.
| 0 | 1 | 0 | 0 | 0 | 0 |
Stationary C*-dynamical systems | We introduce the notion of stationary actions in the context of C*-algebras.
We develop the basics of the theory, and provide applications to several
ergodic theoretical and operator algebraic rigidity problems.
| 0 | 0 | 1 | 0 | 0 | 0 |
The impact of imbalanced training data on machine learning for author name disambiguation | In supervised machine learning for author name disambiguation, negative
training data are often dominantly larger than positive training data. This
paper examines how the ratios of negative to positive training data can affect
the performance of machine learning algorithms to disambiguate author names in
bibliographic records. On multiple labeled datasets, three classifiers -
Logistic Regression, Naïve Bayes, and Random Forest - are trained through
representative features such as coauthor names, and title words extracted from
the same training data but with various positive-negative training data ratios.
Results show that increasing negative training data can improve disambiguation
performance but with a few percent of performance gains and sometimes degrade
it. Logistic Regression and Naïve Bayes learn optimal disambiguation models
even with a base ratio (1:1) of positive and negative training data. Also, the
performance improvement by Random Forest tends to quickly saturate roughly
after 1:10 ~ 1:15. These findings imply that contrary to the common practice
using all training data, name disambiguation algorithms can be trained using
part of negative training data without degrading much disambiguation
performance while increasing computational efficiency. This study calls for
more attention from author name disambiguation scholars to methods for machine
learning from imbalanced data.
| 0 | 0 | 0 | 1 | 0 | 0 |
SIGNet: Scalable Embeddings for Signed Networks | Recent successes in word embedding and document embedding have motivated
researchers to explore similar representations for networks and to use such
representations for tasks such as edge prediction, node label prediction, and
community detection. Such network embedding methods are largely focused on
finding distributed representations for unsigned networks and are unable to
discover embeddings that respect polarities inherent in edges. We propose
SIGNet, a fast scalable embedding method suitable for signed networks. Our
proposed objective function aims to carefully model the social structure
implicit in signed networks by reinforcing the principles of social balance
theory. Our method builds upon the traditional word2vec family of embedding
approaches and adds a new targeted node sampling strategy to maintain
structural balance in higher-order neighborhoods. We demonstrate the
superiority of SIGNet over state-of-the-art methods proposed for both signed
and unsigned networks on several real world datasets from different domains. In
particular, SIGNet offers an approach to generate a richer vocabulary of
features of signed networks to support representation and reasoning.
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Spread of hate speech in online social media | The present online social media platform is afflicted with several issues,
with hate speech being on the predominant forefront. The prevalence of online
hate speech has fueled horrific real-world hate-crime such as the mass-genocide
of Rohingya Muslims, communal violence in Colombo and the recent massacre in
the Pittsburgh synagogue. Consequently, It is imperative to understand the
diffusion of such hateful content in an online setting. We conduct the first
study that analyses the flow and dynamics of posts generated by hateful and
non-hateful users on Gab (gab.com) over a massive dataset of 341K users and 21M
posts. Our observations confirms that hateful content diffuse farther, wider
and faster and have a greater outreach than those of non-hateful users. A
deeper inspection into the profiles and network of hateful and non-hateful
users reveals that the former are more influential, popular and cohesive. Thus,
our research explores the interesting facets of diffusion dynamics of hateful
users and broadens our understanding of hate speech in the online world.
| 1 | 0 | 0 | 0 | 0 | 0 |
GOOWE: Geometrically Optimum and Online-Weighted Ensemble Classifier for Evolving Data Streams | Designing adaptive classifiers for an evolving data stream is a challenging
task due to the data size and its dynamically changing nature. Combining
individual classifiers in an online setting, the ensemble approach, is a
well-known solution. It is possible that a subset of classifiers in the
ensemble outperforms others in a time-varying fashion. However, optimum weight
assignment for component classifiers is a problem which is not yet fully
addressed in online evolving environments. We propose a novel data stream
ensemble classifier, called Geometrically Optimum and Online-Weighted Ensemble
(GOOWE), which assigns optimum weights to the component classifiers using a
sliding window containing the most recent data instances. We map vote scores of
individual classifiers and true class labels into a spatial environment. Based
on the Euclidean distance between vote scores and ideal-points, and using the
linear least squares (LSQ) solution, we present a novel, dynamic, and online
weighting approach. While LSQ is used for batch mode ensemble classifiers, it
is the first time that we adapt and use it for online environments by providing
a spatial modeling of online ensembles. In order to show the robustness of the
proposed algorithm, we use real-world datasets and synthetic data generators
using the MOA libraries. First, we analyze the impact of our weighting system
on prediction accuracy through two scenarios. Second, we compare GOOWE with 8
state-of-the-art ensemble classifiers in a comprehensive experimental
environment. Our experiments show that GOOWE provides improved reactions to
different types of concept drift compared to our baselines. The statistical
tests indicate a significant improvement in accuracy, with conservative time
and memory requirements.
| 1 | 0 | 0 | 0 | 0 | 0 |
Graph Product Multilayer Networks: Spectral Properties and Applications | This paper aims to establish theoretical foundations of graph product
multilayer networks (GPMNs), a family of multilayer networks that can be
obtained as a graph product of two or more factor networks. Cartesian, direct
(tensor), and strong product operators are considered, and then generalized. We
first describe mathematical relationships between GPMNs and their factor
networks regarding their degree/strength, adjacency, and Laplacian spectra, and
then show that those relationships can still hold for nonsimple and generalized
GPMNs. Applications of GPMNs are discussed in three areas: predicting epidemic
thresholds, modeling propagation in nontrivial space and time, and analyzing
higher-order properties of self-similar networks. Directions of future research
are also discussed.
| 1 | 1 | 0 | 0 | 0 | 0 |
Multilayer flows in molecular networks identify biological modules in the human proteome | A variety of complex systems exhibit different types of relationships
simultaneously that can be modeled by multiplex networks. A typical problem is
to determine the community structure of such systems that, in general, depend
on one or more parameters to be tuned. In this study we propose one measure,
grounded on information theory, to find the optimal value of the relax rate
characterizing Multiplex Infomap, the generalization of the Infomap algorithm
to the realm of multilayer networks. We evaluate our methodology on synthetic
networks, to show that the most representative community structure can be
reliably identified when the most appropriate relax rate is used. Capitalizing
on these results, we use this measure to identify the most reliable meso-scale
functional organization in the human protein-protein interaction multiplex
network and compare the observed clusters against a collection of independently
annotated gene sets from the Molecular Signatures Database (MSigDB). Our
analysis reveals that modules obtained with the optimal value of the relax rate
are biologically significant and, remarkably, with higher functional content
than the ones obtained from the aggregate representation of the human proteome.
Our framework allows us to characterize the meso-scale structure of those
multilayer systems whose layers are not explicitly interconnected each other --
as in the case of edge-colored models -- the ones describing most biological
networks, from proteomes to connectomes.
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Using Artificial Neural Networks (ANN) to Control Chaos | Controlling Chaos could be a big factor in getting great stable amounts of
energy out of small amounts of not necessarily stable resources. By definition,
Chaos is getting huge changes in the system's output due to unpredictable small
changes in initial conditions, and that means we could take advantage of this
fact and select the proper control system to manipulate system's initial
conditions and inputs in general and get a desirable output out of otherwise a
Chaotic system. That was accomplished by first building some known chaotic
circuit (Chua circuit) and the NI's MultiSim was used to simulate the ANN
control system. It was shown that this technique can also be used to stabilize
some hard to stabilize electronic systems.
| 1 | 1 | 0 | 0 | 0 | 0 |
Glassy quantum dynamics in translation invariant fracton models | We investigate relaxation in the recently discovered "fracton" models and
discover that these models naturally host glassy quantum dynamics in the
absence of quenched disorder. We begin with a discussion of "type I" fracton
models, in the taxonomy of Vijay, Haah, and Fu. We demonstrate that in these
systems, the mobility of charges is suppressed exponentially in the inverse
temperature. We further demonstrate that when a zero temperature type I fracton
model is placed in contact with a finite temperature heat bath, the approach to
equilibrium is a logarithmic function of time over an exponentially wide window
of time scales. Generalizing to the more complex "type II" fracton models, we
find that the charges exhibit subdiffusion upto a relaxation time that diverges
at low temperatures as a super-exponential function of inverse temperature.
This behaviour is reminiscent of "nearly localized" disordered systems, but
occurs with a translation invariant three-dimensional Hamiltonian. We also
conjecture that fracton models with conserved charge may support a phase which
is a thermal metal but a charge insulator.
| 0 | 1 | 0 | 0 | 0 | 0 |
Saliency Guided Hierarchical Robust Visual Tracking | A saliency guided hierarchical visual tracking (SHT) algorithm containing
global and local search phases is proposed in this paper. In global search, a
top-down saliency model is novelly developed to handle abrupt motion and
appearance variation problems. Nineteen feature maps are extracted first and
combined with online learnt weights to produce the final saliency map and
estimated target locations. After the evaluation of integration mechanism, the
optimum candidate patch is passed to the local search. In local search, a
superpixel based HSV histogram matching is performed jointly with an L2-RLS
tracker to take both color distribution and holistic appearance feature of the
object into consideration. Furthermore, a linear refinement search process with
fast iterative solver is implemented to attenuate the possible negative
influence of dominant particles. Both qualitative and quantitative experiments
are conducted on a series of challenging image sequences. The superior
performance of the proposed method over other state-of-the-art algorithms is
demonstrated by comparative study.
| 1 | 0 | 0 | 0 | 0 | 0 |
Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images | The present study shows that the performance of CNN is not significantly
different from the best classical methods and human doctors for classifying
mediastinal lymph node metastasis of NSCLC from PET/CT images. Because CNN does
not need tumor segmentation or feature calculation, it is more convenient and
more objective than the classical methods. However, CNN does not make use of
the import diagnostic features, which have been proved more discriminative than
the texture features for classifying small-sized lymph nodes. Therefore,
incorporating the diagnostic features into CNN is a promising direction for
future research.
| 1 | 1 | 0 | 0 | 0 | 0 |
A Semantics for Probabilistic Control-Flow Graphs | This article develops a novel operational semantics for probabilistic
control-flow graphs (pCFGs) of probabilistic imperative programs with random
assignment and "observe" (or conditioning) statements. The semantics transforms
probability distributions (on stores) as control moves from one node to another
in pCFGs. We relate this semantics to a standard, expectation-transforming,
denotational semantics of structured probabilistic imperative programs, by
translating structured programs into (unstructured) pCFGs, and proving adequacy
of the translation. This shows that the operational semantics can be used
without loss of information, and is faithful to the "intended" semantics and
hence can be used to reason about, for example, the correctness of
transformations (as we do in a companion article).
| 1 | 0 | 0 | 0 | 0 | 0 |
Generalized Slow Roll in the Unified Effective Field Theory of Inflation | We provide a compact and unified treatment of power spectrum observables for
the effective field theory (EFT) of inflation with the complete set of
operators that lead to second-order equations of motion in metric perturbations
in both space and time derivatives, including Horndeski and GLPV theories. We
relate the EFT operators in ADM form to the four additional free functions of
time in the scalar and tensor equations. Using the generalized slow roll
formalism, we show that each power spectrum can be described by an integral
over a single source that is a function of its respective sound horizon. With
this correspondence, existing model independent constraints on the source
function can be simply reinterpreted in the more general inflationary context.
By expanding these sources around an optimized freeze-out epoch, we also
provide characterizations of these spectra in terms of five slow-roll
hierarchies whose leading order forms are compact and accurate as long as EFT
coefficients vary only on timescales greater than an efold. We also clarify the
relationship between the unitary gauge observables employed in the EFT and the
comoving gauge observables of the post-inflationary universe.
| 0 | 1 | 0 | 0 | 0 | 0 |
MgO thickness-induced spin reorientation transition in Co0.9Fe0.1/MgO/Co0.9Fe0.1 structure | The magnetic anisotropy (MA) of Mo/Au/Co0.9Fe0.1/Au/MgO(0.7 - 3
nm)/Au/Co0.9Fe0.1/Au heterostructure has been investigated at room temperature
as a function of MgO layer thickness (tMgO). Our studies show that while the MA
of the top layer does not change its character upon variation of tMgO, the
uniaxial out-of-plane MA of the bottom one undergoes a spin reorientation
transition at tMgO of about 0.8 nm, switching to the regime where the
coexistence of in- and out-of-plane magnetization alignments is observed. The
magnitudes of the magnetic anisotropy constants have been determined from
ferromagnetic resonance and dc-magnetometry measurements. The origin of MA
evolution has been attributed to a presence of an interlayer exchange coupling
(IEC) between Co0.9Fe0.1 layers through the thin MgO film.
| 0 | 1 | 0 | 0 | 0 | 0 |
Bjerrum Pairs in Ionic Solutions: a Poisson-Boltzmann Approach | Ionic solutions are often regarded as fully dissociated ions dispersed in a
polar solvent. While this picture holds for dilute solutions, at higher ionic
concentrations, oppositely charged ions can associate into dimers, referred to
as Bjerrum pairs. We consider the formation of such pairs within the nonlinear
Poisson-Boltzmann framework, and investigate their effects on bulk and
interfacial properties of electrolytes. Our findings show that pairs can reduce
the magnitude of the dielectric decrement of ionic solutions as the ionic
concentration increases. We describe the effect of pairs on the Debye screening
length, and relate our results to recent surface-force experiments.
Furthermore, we show that Bjerrum pairs reduce the ionic concentration in bulk
electrolyte and at the proximity of charged surfaces, while they enhance the
attraction between oppositely charged surfaces.
| 0 | 1 | 0 | 0 | 0 | 0 |
Neural-Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding | Network embedding methodologies, which learn a distributed vector
representation for each vertex in a network, have attracted considerable
interest in recent years. Existing works have demonstrated that vertex
representation learned through an embedding method provides superior
performance in many real-world applications, such as node classification, link
prediction, and community detection. However, most of the existing methods for
network embedding only utilize topological information of a vertex, ignoring a
rich set of nodal attributes (such as, user profiles of an online social
network, or textual contents of a citation network), which is abundant in all
real-life networks. A joint network embedding that takes into account both
attributional and relational information entails a complete network information
and could further enrich the learned vector representations. In this work, we
present Neural-Brane, a novel Neural Bayesian Personalized Ranking based
Attributed Network Embedding. For a given network, Neural-Brane extracts latent
feature representation of its vertices using a designed neural network model
that unifies network topological information and nodal attributes; Besides, it
utilizes Bayesian personalized ranking objective, which exploits the proximity
ordering between a similar node-pair and a dissimilar node-pair. We evaluate
the quality of vertex embedding produced by Neural-Brane by solving the node
classification and clustering tasks on four real-world datasets. Experimental
results demonstrate the superiority of our proposed method over the
state-of-the-art existing methods.
| 0 | 0 | 0 | 1 | 0 | 0 |
Pairwise $k$-Semi-Stratifiable Bispaces and Topological Ordered Spaces | In this paper, we continue to study pairwise ($k$-semi-)stratifiable
bitopological spaces. Some new characterizations of pairwise
$k$-semi-stratifiable bitopological spaces are provided. Relationships between
pairwise stratifiable and pairwise $k$-semi-stratifiable bitopological spaces
are further investigated, and an open question recently posed by Li and Lin in
\cite{LL} is completely solved. We also study the quasi-pseudo-metrizability of
a topological ordered space $(X, \tau, \preccurlyeq)$. It is shown that if $(X,
\tau, \preccurlyeq)$ is a ball transitive topological ordered $C$- and
$I$-space such that $\tau$ is metrizable, then its associated bitopological
space $(X,\tau^{\flat},\tau^{\natural})$ is quasi-pseudo-metrizable. This
result provides a partial affirmative answer to a problem in \cite{KM}.
| 0 | 0 | 1 | 0 | 0 | 0 |
Ontology based system to guide internship assignment process | Internship assignment is a complicated process for universities since it is
necessary to take into account a multiplicity of variables to establish a
compromise between companies' requirements and student competencies acquired
during the university training. These variables build up a complex relations
map that requires the formulation of an exhaustive and rigorous conceptual
scheme. In this research a domain ontological model is presented as support to
the student's decision making for opportunities of University studies level of
the University Lumiere Lyon 2 (ULL) education system. The ontology is designed
and created using methodological approach offering the possibility of improving
the progressive creation, capture and knowledge articulation. In this paper, we
draw a balance taking the demands of the companies across the capabilities of
the students. This will be done through the establishment of an ontological
model of an educational learners' profile and the internship postings which are
written in a free text and using uncontrolled vocabulary. Furthermore, we
outline the process of semantic matching which improves the quality of query
results.
| 1 | 0 | 0 | 0 | 0 | 0 |
Vacancy-driven extended stability of cubic metastable Ta-Al-N and Nb-Al-N phases | Quantum mechanical calculations had been previously applied to predict phase
stability in many ternary and multinary nitride systems. While the predictions
were very accurate for the Ti-Al-N system, some discrepancies between theory
and experiment were obtained in the case of other systems. Namely, in the case
of Ta-Al-N, the calculations tend to overestimate the minimum Al content
necessary to obtain a metastable solid solution with a cubic structure. In this
work, we present a comprehensive study of the impact of vacancies on the phase
fields in quasi-binary TaN-AlN and NbN-AlN systems. Our calculations clearly
show that presence of point defects strongly enlarges the cubic phase field in
the TaN-AlN system, while the effect is less pronounced in the NbN-AlN case.
The present phase stability predictions agree better with experimental
observations of physical vapour deposited thin films reported in the literature
than that based on perfect, non-defected structures. This study shows that a
representative structural model is crucial for a meaningful comparison with
experimental data.
| 0 | 1 | 0 | 0 | 0 | 0 |
Why Do Neural Dialog Systems Generate Short and Meaningless Replies? A Comparison between Dialog and Translation | This paper addresses the question: Why do neural dialog systems generate
short and meaningless replies? We conjecture that, in a dialog system, an
utterance may have multiple equally plausible replies, causing the deficiency
of neural networks in the dialog application. We propose a systematic way to
mimic the dialog scenario in a machine translation system, and manage to
reproduce the phenomenon of generating short and less meaningful sentences in
the translation setting, showing evidence of our conjecture.
| 1 | 0 | 0 | 0 | 0 | 0 |
Material Recognition CNNs and Hierarchical Planning for Biped Robot Locomotion on Slippery Terrain | In this paper we tackle the problem of visually predicting surface friction
for environments with diverse surfaces, and integrating this knowledge into
biped robot locomotion planning. The problem is essential for autonomous robot
locomotion since diverse surfaces with varying friction abound in the real
world, from wood to ceramic tiles, grass or ice, which may cause difficulties
or huge energy costs for robot locomotion if not considered. We propose to
estimate friction and its uncertainty from visual estimation of material
classes using convolutional neural networks, together with probability
distribution functions of friction associated with each material. We then
robustly integrate the friction predictions into a hierarchical (footstep and
full-body) planning method using chance constraints, and optimize the same
trajectory costs at both levels of the planning method for consistency. Our
solution achieves fully autonomous perception and locomotion on slippery
terrain, which considers not only friction and its uncertainty, but also
collision, stability and trajectory cost. We show promising friction prediction
results in real pictures of outdoor scenarios, and planning experiments on a
real robot facing surfaces with different friction.
| 1 | 0 | 0 | 0 | 0 | 0 |
Non-Archimedean Replicator Dynamics and Eigen's Paradox | We present a new non-Archimedean model of evolutionary dynamics, in which the
genomes are represented by p-adic numbers. In this model the genomes have a
variable length, not necessarily bounded, in contrast with the classical models
where the length is fixed. The time evolution of the concentration of a given
genome is controlled by a p-adic evolution equation. This equation depends on a
fitness function f and on mutation measure Q. By choosing a mutation measure of
Gibbs type, and by using a p-adic version of the Maynard Smith Ansatz, we show
the existence of threshold function M_{c}(f,Q), such that the long term
survival of a genome requires that its length grows faster than M_{c}(f,Q).
This implies that Eigen's paradox does not occur if the complexity of genomes
grows at the right pace. About twenty years ago, Scheuring and Poole, Jeffares,
Penny proposed a hypothesis to explain Eigen's paradox. Our mathematical model
shows that this biological hypothesis is feasible, but it requires p-adic
analysis instead of real analysis. More exactly, the Darwin-Eigen cycle
proposed by Poole et al. takes place if the length of the genomes exceeds
M_{c}(f,Q).
| 0 | 0 | 0 | 0 | 1 | 0 |
D-optimal design for multivariate polynomial regression via the Christoffel function and semidefinite relaxations | We present a new approach to the design of D-optimal experiments with
multivariate polynomial regressions on compact semi-algebraic design spaces. We
apply the moment-sum-of-squares hierarchy of semidefinite programming problems
to solve numerically and approximately the optimal design problem. The geometry
of the design is recovered with semidefinite programming duality theory and the
Christoffel polynomial.
| 0 | 0 | 1 | 1 | 0 | 0 |
A dynamic network model to measure exposure diversification in the Austrian interbank market | We propose a statistical model for weighted temporal networks capable of
measuring the level of heterogeneity in a financial system. Our model focuses
on the level of diversification of financial institutions; that is, whether
they are more inclined to distribute their assets equally among partners, or if
they rather concentrate their commitment towards a limited number of
institutions. Crucially, a Markov property is introduced to capture time
dependencies and to make our measures comparable across time. We apply the
model on an original dataset of Austrian interbank exposures. The temporal span
encompasses the onset and development of the financial crisis in 2008 as well
as the beginnings of European sovereign debt crisis in 2011. Our analysis
highlights an overall increasing trend for network homogeneity, whereby core
banks have a tendency to distribute their market exposures more equally across
their partners.
| 0 | 0 | 0 | 1 | 0 | 1 |
Universal scaling in the Knight shift anomaly of doped periodic Anderson model | We report a Dynamical Cluster Approximation (DCA) investigation of the doped
periodic Anderson model (PAM) to explain the universal scaling in the Knight
shift anomaly predicted by the phenomenological two-fluid model and confirmed
in many heavy-fermion compounds. We calculate the quantitative evolution of the
orbital-dependent magnetic susceptibility and reproduce correctly the two-fluid
prediction in a large range of doping and hybridization. Our results confirm
the presence of a temperature/energy scale $T^{\ast}$ for the universal scaling
and show distinctive behavors of the Knight shift anomaly in response to other
"orders" at low temperatures. However, comparison with the temperature
evolution of the calculated resistivity and quasiparticle spectral peak
indicates a different characteristic temperature from $T^*$, in contradiction
with the experimental observation in CeCoIn$_5$ and other compounds. This
reveals a missing piece in the current model calculations in explaining the
two-fluid phenomenology.
| 0 | 1 | 0 | 0 | 0 | 0 |
Reinforcement Learning Algorithm Selection | This paper formalises the problem of online algorithm selection in the
context of Reinforcement Learning. The setup is as follows: given an episodic
task and a finite number of off-policy RL algorithms, a meta-algorithm has to
decide which RL algorithm is in control during the next episode so as to
maximize the expected return. The article presents a novel meta-algorithm,
called Epochal Stochastic Bandit Algorithm Selection (ESBAS). Its principle is
to freeze the policy updates at each epoch, and to leave a rebooted stochastic
bandit in charge of the algorithm selection. Under some assumptions, a thorough
theoretical analysis demonstrates its near-optimality considering the
structural sampling budget limitations. ESBAS is first empirically evaluated on
a dialogue task where it is shown to outperform each individual algorithm in
most configurations. ESBAS is then adapted to a true online setting where
algorithms update their policies after each transition, which we call SSBAS.
SSBAS is evaluated on a fruit collection task where it is shown to adapt the
stepsize parameter more efficiently than the classical hyperbolic decay, and on
an Atari game, where it improves the performance by a wide margin.
| 1 | 0 | 1 | 1 | 0 | 0 |
On periodic solutions of nonlinear wave equations, including Einstein equations with a negative cosmological constant | We construct periodic solutions of nonlinear wave equations using analytic
continuation. The construction applies in particular to Einstein equations,
leading to infinite-dimensional families of time-periodic solutions of the
vacuum, or of the Einstein-Maxwell-dilaton-scalar
fields-Yang-Mills-Higgs-Chern-Simons-$f(R)$ equations, with a negative
cosmological constant.
| 0 | 0 | 1 | 0 | 0 | 0 |
Automatic Discovery, Association Estimation and Learning of Semantic Attributes for a Thousand Categories | Attribute-based recognition models, due to their impressive performance and
their ability to generalize well on novel categories, have been widely adopted
for many computer vision applications. However, usually both the attribute
vocabulary and the class-attribute associations have to be provided manually by
domain experts or large number of annotators. This is very costly and not
necessarily optimal regarding recognition performance, and most importantly, it
limits the applicability of attribute-based models to large scale data sets. To
tackle this problem, we propose an end-to-end unsupervised attribute learning
approach. We utilize online text corpora to automatically discover a salient
and discriminative vocabulary that correlates well with the human concept of
semantic attributes. Moreover, we propose a deep convolutional model to
optimize class-attribute associations with a linguistic prior that accounts for
noise and missing data in text. In a thorough evaluation on ImageNet, we
demonstrate that our model is able to efficiently discover and learn semantic
attributes at a large scale. Furthermore, we demonstrate that our model
outperforms the state-of-the-art in zero-shot learning on three data sets:
ImageNet, Animals with Attributes and aPascal/aYahoo. Finally, we enable
attribute-based learning on ImageNet and will share the attributes and
associations for future research.
| 1 | 0 | 0 | 0 | 0 | 0 |
Variational Bayesian Inference For A Scale Mixture Of Normal Distributions Handling Missing Data | In this paper, a scale mixture of Normal distributions model is developed for
classification and clustering of data having outliers and missing values. The
classification method, based on a mixture model, focuses on the introduction of
latent variables that gives us the possibility to handle sensitivity of model
to outliers and to allow a less restrictive modelling of missing data.
Inference is processed through a Variational Bayesian Approximation and a
Bayesian treatment is adopted for model learning, supervised classification and
clustering.
| 0 | 0 | 0 | 1 | 0 | 0 |
Energy stable discretization of Allen-Cahn type problems modeling the motion of phase boundaries | We study the systematic numerical approximation of a class of Allen-Cahn type
problems modeling the motion of phase interfaces. The common feature of these
models is an underlying gradient flow structure which gives rise to a decay of
an associated energy functional along solution trajectories. We first study the
discretization in space by a conforming Galerkin approximation of a variational
principle which characterizes smooth solutions of the problem. Well-posedness
of the resulting semi-discretization is established and the energy decay along
discrete solution trajectories is proven. A problem adapted implicit
time-stepping scheme is then proposed and we establish its well-posed and decay
of the free energy for the fully discrete scheme. Some details about the
numerical realization by finite elements are discussed, in particular the
iterative solution of the nonlinear problems arising in every time-step. The
theoretical results are illustrated by numerical tests which also provide
further evidence for asymptotic expansions of the interface velocities derived
by Alber et al.
| 0 | 0 | 1 | 0 | 0 | 0 |
Hidden space reconstruction inspires link prediction in complex networks | As a fundamental challenge in vast disciplines, link prediction aims to
identify potential links in a network based on the incomplete observed
information, which has broad applications ranging from uncovering missing
protein-protein interaction to predicting the evolution of networks. One of the
most influential methods rely on similarity indices characterized by the common
neighbors or its variations. We construct a hidden space mapping a network into
Euclidean space based solely on the connection structures of a network.
Compared with real geographical locations of nodes, our reconstructed locations
are in conformity with those real ones. The distances between nodes in our
hidden space could serve as a novel similarity metric in link prediction. In
addition, we hybrid our hidden space method with other state-of-the-art
similarity methods which substantially outperforms the existing methods on the
prediction accuracy. Hence, our hidden space reconstruction model provides a
fresh perspective to understand the network structure, which in particular
casts a new light on link prediction.
| 1 | 1 | 0 | 0 | 0 | 0 |
Maximum principles for the fractional p-Laplacian and symmetry of solutions | In this paper, we consider nonlinear equations involving the fractional
p-Laplacian $$ (-\lap)_p^s u(x)) \equiv C_{n,s,p} PV \int_{\mathbb{R}^n}
\frac{|u(x)-u(y)|^{p-2}[u(x)-u(y)]}{|x-z|^{n+ps}} dz= f(x,u).$$
We prove a {\em maximum principle for anti-symmetric functions} and obtain
other key ingredients for carrying on the method of moving planes, such as {\em
a key boundary estimate lemma}. Then we establish radial symmetry and
monotonicity for positive solutions to semilinear equations involving the
fractional p-Laplacian in a unit ball and in the whole space. We believe that
the methods developed here can be applied to a variety of problems involving
nonlinear nonlocal operators.
| 0 | 0 | 1 | 0 | 0 | 0 |
On slowly rotating axisymmetric solutions of the Einstein-Euler equations | In recent works we have constructed axisymmetric solutions to the
Euler-Poisson equations which give mathematical models of slowly uniformly
rotating gaseous stars. We try to extend this result to the study of solutions
of the Einstein-Euler equations in the framework of the general theory of
relativity. Although many interesting studies have been done about axisymmetric
metric in the general theory of relativity, they are restricted to the region
of the vacuum. Mathematically rigorous existence theorem of the axisymmetric
interior solutions of the stationary metric corresponding to the
energy-momentum tensor of the perfect fluid with non-zero pressure may be not
yet established until now except only one found in the pioneering work by U.
Heilig done in 1993. In this article, along a different approach to that of
Heilig's work, axisymmetric stationary solutions of the Einstein-Euler
equations are constructed near those of the Euler-Poisson equations when the
speed of light is sufficiently large in the considered system of units, or,
when the gravitational field is sufficiently weak.
| 0 | 0 | 1 | 0 | 0 | 0 |
Gapped paramagnetic state in a frustrated spin-$\frac{1}{2}$ Heisenberg antiferromagnet on the cross-striped square lattice | We implement the coupled cluster method to very high orders of approximation
to study the spin-$\frac{1}{2}$ $J_{1}$--$J_{2}$ Heisenberg model on a
cross-striped square lattice. Every nearest-neighbour pair of sites on the
square lattice has an isotropic antiferromagnetic exchange bond of strength
$J_{1}>0$, while the basic square plaquettes in alternate columns have either
both or neither next-nearest-neighbour (diagonal) pairs of sites connected by
an equivalent frustrating bond of strength $J_{2} \equiv \alpha J_{1} > 0$. By
studying the magnetic order parameter (i.e., the average local on-site
magnetization) in the range $0 \leq \alpha \leq 1$ of the frustration parameter
we find that the quasiclassical antiferromagnetic Néel and (so-called)
double Néel states form the stable ground-state phases in the respective
regions $\alpha < \alpha_{1a}^{c} = 0.46(1)$ and $\alpha > \alpha_{1b}^{c} =
0.615(5)$. The double Néel state has Néel
($\cdots\uparrow\downarrow\uparrow\downarrow\cdots$) ordering along the
(column) direction parallel to the stripes of squares with both or no $J_{2}$
bonds, and spins alternating in a pairwise
($\cdots\uparrow\uparrow\downarrow\downarrow\uparrow\uparrow\downarrow\downarrow\cdots$)
fashion along the perpendicular (row) direction, so that the parallel pairs
occur on squares with both $J_{2}$ bonds present. Further explicit calculations
of both the triplet spin gap and the zero-field uniform transverse magnetic
susceptibility provide compelling evidence that the ground-state phase over all
or most of the intermediate regime $\alpha_{1a}^{c} < \alpha < \alpha_{1b}^{c}$
is a gapped state with no discernible long-range magnetic order.
| 0 | 1 | 0 | 0 | 0 | 0 |
Clouds in the atmospheres of extrasolar planets. V. The impact of CO2 ice clouds on the outer boundary of the habitable zone | Clouds have a strong impact on the climate of planetary atmospheres. The
potential scattering greenhouse effect of CO2 ice clouds in the atmospheres of
terrestrial extrasolar planets is of particular interest because it might
influence the position and thus the extension of the outer boundary of the
classic habitable zone around main sequence stars. Here, the impact of CO2 ice
clouds on the surface temperatures of terrestrial planets with CO2 dominated
atmospheres, orbiting different types of stars is studied. Additionally, their
corresponding effect on the position of the outer habitable zone boundary is
evaluated. For this study, a radiative-convective atmospheric model is used the
calculate the surface temperatures influenced by CO2 ice particles. The clouds
are included using a parametrised cloud model. The atmospheric model includes a
general discrete ordinate radiative transfer that can describe the anisotropic
scattering by the cloud particles accurately. A net scattering greenhouse
effect caused by CO2 clouds is only obtained in a rather limited parameter
range which also strongly depends on the stellar effective temperature. For
cool M-stars, CO2 clouds only provide about 6 K of additional greenhouse
heating in the best case scenario. On the other hand, the surface temperature
for a planet around an F-type star can be increased by 30 K if carbon dioxide
clouds are present. Accordingly, the extension of the habitable zone due to
clouds is quite small for late-type stars. Higher stellar effective
temperatures, on the other hand, can lead to outer HZ boundaries about 0.5 au
farther out than the corresponding clear-sky values.
| 0 | 1 | 0 | 0 | 0 | 0 |
Segmentation of the Proximal Femur from MR Images using Deep Convolutional Neural Networks | Magnetic resonance imaging (MRI) has been proposed as a complimentary method
to measure bone quality and assess fracture risk. However, manual segmentation
of MR images of bone is time-consuming, limiting the use of MRI measurements in
the clinical practice. The purpose of this paper is to present an automatic
proximal femur segmentation method that is based on deep convolutional neural
networks (CNNs). This study had institutional review board approval and written
informed consent was obtained from all subjects. A dataset of volumetric
structural MR images of the proximal femur from 86 subject were
manually-segmented by an expert. We performed experiments by training two
different CNN architectures with multiple number of initial feature maps and
layers, and tested their segmentation performance against the gold standard of
manual segmentations using four-fold cross-validation. Automatic segmentation
of the proximal femur achieved a high dice similarity score of 0.94$\pm$0.05
with precision = 0.95$\pm$0.02, and recall = 0.94$\pm$0.08 using a CNN
architecture based on 3D convolution exceeding the performance of 2D CNNs. The
high segmentation accuracy provided by CNNs has the potential to help bring the
use of structural MRI measurements of bone quality into clinical practice for
management of osteoporosis.
| 1 | 0 | 0 | 1 | 0 | 0 |
Identifying On-time Reward Delivery Projects with Estimating Delivery Duration on Kickstarter | In Crowdfunding platforms, people turn their prototype ideas into real
products by raising money from the crowd, or invest in someone else's projects.
In reward-based crowdfunding platforms such as Kickstarter and Indiegogo,
selecting accurate reward delivery duration becomes crucial for creators,
backers, and platform providers to keep the trust between the creators and the
backers, and the trust between the platform providers and users. According to
Kickstarter, 35% backers did not receive rewards on time. Unfortunately, little
is known about on-time and late reward delivery projects, and there is no prior
work to estimate reward delivery duration. To fill the gap, in this paper, we
(i) extract novel features that reveal latent difficulty levels of project
rewards; (ii) build predictive models to identify whether a creator will
deliver all rewards in a project on time or not; and (iii) build a regression
model to estimate accurate reward delivery duration (i.e., how long it will
take to produce and deliver all the rewards). Experimental results show that
our models achieve good performance -- 82.5% accuracy, 78.1 RMSE, and 0.108
NRMSE at the first 5% of the longest reward delivery duration.
| 1 | 0 | 0 | 0 | 0 | 0 |
Bayesian Model-Agnostic Meta-Learning | Learning to infer Bayesian posterior from a few-shot dataset is an important
step towards robust meta-learning due to the model uncertainty inherent in the
problem. In this paper, we propose a novel Bayesian model-agnostic
meta-learning method. The proposed method combines scalable gradient-based
meta-learning with nonparametric variational inference in a principled
probabilistic framework. During fast adaptation, the method is capable of
learning complex uncertainty structure beyond a point estimate or a simple
Gaussian approximation. In addition, a robust Bayesian meta-update mechanism
with a new meta-loss prevents overfitting during meta-update. Remaining an
efficient gradient-based meta-learner, the method is also model-agnostic and
simple to implement. Experiment results show the accuracy and robustness of the
proposed method in various tasks: sinusoidal regression, image classification,
active learning, and reinforcement learning.
| 0 | 0 | 0 | 1 | 0 | 0 |
Distributed matching scheme and a flexible deterministic matching algorithm for arbitrary systems | We discuss the distributed matching scheme in accelerators where control of
transverse beam phase space, oscillation, and transport is accomplished by
flexible distribution of focusing elements beyond dedicated matching sections.
Besides freeing accelerator design from fixed matching sections, such a scheme
has many operational advantages, and enables fluid optics manipulation not
possible in conventional schemes. Combined with an interpolation scheme this
can bring about a new paradigm for efficient, flexible, and robust optics
control. A rigorous and deterministic algorithm is developed for its
realization. The algorithm is a matching tool in its own right with unique
characteristics in robustness and determinism. The beam phase space dynamics is
naturally integrated into the algorithm, instead of being treated as generic
numerical parameters as in traditional schemes. It is applicable to a wider
range of problems, such as trading-off between competing options for desired
machine states.
| 0 | 1 | 0 | 0 | 0 | 0 |
Group Metrics for Graph Products of Cyclic Groups | We complement the characterization of the graph products of cyclic groups
$G(\Gamma, \mathfrak{p})$ admitting a Polish group topology of [9] with the
following result. Let $G = G(\Gamma, \mathfrak{p})$, then the following are
equivalent: (i) there is a metric on $\Gamma$ which induces a separable
topology in which $E_{\Gamma}$ is closed; (ii) $G(\Gamma, \mathfrak{p})$ is
embeddable into a Polish group; (iii) $G(\Gamma, \mathfrak{p})$ is embeddable
into a non-Archimedean Polish group. We also construct left-invariant separable
group ultrametrics for $G = G(\Gamma, \mathfrak{p})$ and $\Gamma$ a closed
graph on the Baire space, which is of independent interest.
| 0 | 0 | 1 | 0 | 0 | 0 |
Robust Online Multi-Task Learning with Correlative and Personalized Structures | Multi-Task Learning (MTL) can enhance a classifier's generalization
performance by learning multiple related tasks simultaneously. Conventional MTL
works under the offline or batch setting, and suffers from expensive training
cost and poor scalability. To address such inefficiency issues, online learning
techniques have been applied to solve MTL problems. However, most existing
algorithms of online MTL constrain task relatedness into a presumed structure
via a single weight matrix, which is a strict restriction that does not always
hold in practice. In this paper, we propose a robust online MTL framework that
overcomes this restriction by decomposing the weight matrix into two
components: the first one captures the low-rank common structure among tasks
via a nuclear norm and the second one identifies the personalized patterns of
outlier tasks via a group lasso. Theoretical analysis shows the proposed
algorithm can achieve a sub-linear regret with respect to the best linear model
in hindsight. Even though the above framework achieves good performance, the
nuclear norm that simply adds all nonzero singular values together may not be a
good low-rank approximation. To improve the results, we use a log-determinant
function as a non-convex rank approximation. The gradient scheme is applied to
optimize log-determinant function and can obtain a closed-form solution for
this refined problem. Experimental results on a number of real-world
applications verify the efficacy of our method.
| 1 | 0 | 0 | 1 | 0 | 0 |
Cobordism maps on PFH induced by Lefschetz fibration over higher genus base | In this note, we discuss the cobordism maps on periodic Floer homology(PFH)
induced by Lefschetz fibration. In the first part of the note, we define the
cobordism maps on PFH induced by Lefschetz fibration via Seiberg Witten theory
and the isomorphism between PFH and Seiberg Witten cohomology. The second part
is to define the cobordism maps induced by Lefschetz fibration provided that
the cobordism satisfies certain conditions. Under certain monotone assumptions,
we show that these two definitions in fact are equivalent.
| 0 | 0 | 1 | 0 | 0 | 0 |
A short note on the order of the Zhang-Liu matrices over arbitrary fields | We give necessary and sufficient conditions for the Zhang-Liu matrices to be
diagonalizable over arbitrary fields and provide the eigen-decomposition when
it is possible. We use this result to calculate the order of these matrices
over any arbitrary field. This generalizes a result of the second author.
| 0 | 0 | 1 | 0 | 0 | 0 |
Proceedings of the 2017 ICML Workshop on Human Interpretability in Machine Learning (WHI 2017) | This is the Proceedings of the 2017 ICML Workshop on Human Interpretability
in Machine Learning (WHI 2017), which was held in Sydney, Australia, August 10,
2017. Invited speakers were Tony Jebara, Pang Wei Koh, and David Sontag.
| 1 | 0 | 0 | 1 | 0 | 0 |
Ferromagnetic transition in a one-dimensional spin-orbit-coupled metal and its mapping to a critical point in smectic liquid crystals | We study the quantum phase transition between a paramagnetic and
ferromagnetic metal in the presence of Rashba spin-orbit coupling in one
dimension. Using bosonization, we analyze the transition by means of
renormalization group, controlled by an $\varepsilon$-expansion around the
upper critical dimension of two. We show that the presence of Rashba spin-orbit
coupling allows for a new nonlinear term in the bosonized action, which
generically leads to a fluctuation driven first-order transition. We further
demonstrate that the Euclidean action of this system maps onto a classical
smectic-A -- C phase transition in a magnetic field in two dimensions. We show
that the smectic transition is second-order and is controlled by a new critical
point.
| 0 | 1 | 0 | 0 | 0 | 0 |
Riemannian almost product manifolds generated by a circulant structure | A 4-dimensional Riemannian manifold equipped with a circulant structure,
which is an isometry with respect to the metric and its fourth power is the
identity, is considered. The almost product manifold associated with the
considered manifold is studied. The relation between the covariant derivatives
of the almost product structure and the circulant structure is obtained. The
conditions for the covariant derivative of the circulant structure, which imply
that an almost product manifold belongs to each of the basic classes of the
Staikova-Gribachev classification, are given.
| 0 | 0 | 1 | 0 | 0 | 0 |
Transfer Learning for Performance Modeling of Configurable Systems: An Exploratory Analysis | Modern software systems provide many configuration options which
significantly influence their non-functional properties. To understand and
predict the effect of configuration options, several sampling and learning
strategies have been proposed, albeit often with significant cost to cover the
highly dimensional configuration space. Recently, transfer learning has been
applied to reduce the effort of constructing performance models by transferring
knowledge about performance behavior across environments. While this line of
research is promising to learn more accurate models at a lower cost, it is
unclear why and when transfer learning works for performance modeling. To shed
light on when it is beneficial to apply transfer learning, we conducted an
empirical study on four popular software systems, varying software
configurations and environmental conditions, such as hardware, workload, and
software versions, to identify the key knowledge pieces that can be exploited
for transfer learning. Our results show that in small environmental changes
(e.g., homogeneous workload change), by applying a linear transformation to the
performance model, we can understand the performance behavior of the target
environment, while for severe environmental changes (e.g., drastic workload
change) we can transfer only knowledge that makes sampling more efficient,
e.g., by reducing the dimensionality of the configuration space.
| 1 | 0 | 0 | 1 | 0 | 0 |
Critique of Barbosa's "P != NP Proof" | We review André Luiz Barbosa's paper "P != NP Proof," in which the classes
P and NP are generalized and claimed to be proven separate. We highlight
inherent ambiguities in Barbosa's definitions, and show that attempts to
resolve this ambiguity lead to flaws in the proof of his main result.
| 1 | 0 | 0 | 0 | 0 | 0 |
Two-Armed Bandit Problem, Data Processing, and Parallel Version of the Mirror Descent Algorithm | We consider the minimax setup for the two-armed bandit problem as applied to
data processing if there are two alternative processing methods available with
different a priori unknown efficiencies. One should determine the most
effective method and provide its predominant application. To this end we use
the mirror descent algorithm (MDA). It is well-known that corresponding minimax
risk has the order $N^{1/2}$ with $N$ being the number of processed data. We
improve significantly the theoretical estimate of the factor using Monte-Carlo
simulations. Then we propose a parallel version of the MDA which allows
processing of data by packets in a number of stages. The usage of parallel
version of the MDA ensures that total time of data processing depends mostly on
the number of packets but not on the total number of data. It is quite
unexpectedly that the parallel version behaves unlike the ordinary one even if
the number of packets is large. Moreover, the parallel version considerably
improves control performance because it provides significantly smaller value of
the minimax risk. We explain this result by considering another parallel
modification of the MDA which behavior is close to behavior of the ordinary
version. Our estimates are based on invariant descriptions of the algorithms.
All estimates are obtained by Monte-Carlo simulations. It's worth noting that
parallel version performs well only for methods with close efficiencies. If
efficiencies differ significantly then one should use the combined algorithm
which at initial sufficiently short control horizon uses ordinary version and
then switches to the parallel version of the MDA.
| 0 | 0 | 1 | 1 | 0 | 0 |
Bearing fault diagnosis under varying working condition based on domain adaptation | Traditional intelligent fault diagnosis of rolling bearings work well only
under a common assumption that the labeled training data (source domain) and
unlabeled testing data (target domain) are drawn from the same distribution.
When the distribution changes, most fault diagnosis models need to be rebuilt
from scratch using newly recollected labeled training data. However, it is
expensive or impossible to annotate huge amount of training data to rebuild
such new model. Meanwhile, large amounts of labeled training data have not been
fully utilized yet, which is apparently a waste of resources. As one of the
important research directions of transfer learning, domain adaptation (DA)
typically aims at minimizing the differences between distributions of different
domains in order to minimize the cross-domain prediction error by taking full
advantage of information coming from both source and target domains. In this
paper, we present one of the first studies on unsupervised DA in the field of
fault diagnosis of rolling bearings under varying working conditions and a
novel diagnosis strategy based on unsupervised DA using subspace alignment (SA)
is proposed. After processed by unsupervised DA with SA, the distributions of
training data and testing data become close and the classifier trained on
training data can be used to classify the testing data. Experimental results on
the 60 domain adaptation diagnosis problems under varying working condition in
Case Western Reserve benchmark data and 12 domain adaptation diagnosis problems
under varying working conditions in our new data are given to demonstrate the
effectiveness of the proposed method. The proposed methods can effectively
distinguish not only bearing faults categories but also fault severities.
| 1 | 0 | 0 | 0 | 0 | 0 |
Dialectical Rough Sets, Parthood and Figures of Opposition-1 | In one perspective, the main theme of this research revolves around the
inverse problem in the context of general rough sets that concerns the
existence of rough basis for given approximations in a context. Granular
operator spaces and variants were recently introduced by the present author as
an optimal framework for anti-chain based algebraic semantics of general rough
sets and the inverse problem. In the framework, various sub-types of crisp and
non-crisp objects are identifiable that may be missed in more restrictive
formalism. This is also because in the latter cases concepts of complementation
and negation are taken for granted - while in reality they have a complicated
dialectical basis. This motivates a general approach to dialectical rough sets
building on previous work of the present author and figures of opposition. In
this paper dialectical rough logics are invented from a semantic perspective, a
concept of dialectical predicates is formalised, connection with dialetheias
and glutty negation are established, parthood analyzed and studied from the
viewpoint of classical and dialectical figures of opposition by the present
author. Her methods become more geometrical and encompass parthood as a primary
relation (as opposed to roughly equivalent objects) for algebraic semantics.
| 1 | 0 | 1 | 0 | 0 | 0 |
On quasi-hereditary algebras | In this paper we introduce an easily verifiable sufficient condition to
determine whether an algebra is quasi-hereditary. In the case of monomial
algebras, we give conditions that are both necessary and sufficient to show
whether an algebra is quasi-hereditary.
| 0 | 0 | 1 | 0 | 0 | 0 |
Clustering Analysis on Locally Asymptotically Self-similar Processes with Known Number of Clusters | We study the problems of clustering locally asymptotically self-similar
stochastic processes, when the true number of clusters is priorly known. A new
covariance-based dissimilarity measure is introduced, from which the so-called
approximately asymptotically consistent clustering algorithms are obtained. In
a simulation study, clustering data sampled from multifractional Brownian
motions is performed to illustrate the approximated asymptotic consistency of
the proposed algorithms.
| 0 | 0 | 0 | 1 | 0 | 0 |
The OSIRIS-REx Visible and InfraRed Spectrometer (OVIRS): Spectral Maps of the Asteroid Bennu | The OSIRIS-REx Visible and Infrared Spectrometer (OVIRS) is a point
spectrometer covering the spectral range of 0.4 to 4.3 microns (25,000-2300
cm-1). Its primary purpose is to map the surface composition of the asteroid
Bennu, the target asteroid of the OSIRIS-REx asteroid sample return mission.
The information it returns will help guide the selection of the sample site. It
will also provide global context for the sample and high spatial resolution
spectra that can be related to spatially unresolved terrestrial observations of
asteroids. It is a compact, low-mass (17.8 kg), power efficient (8.8 W
average), and robust instrument with the sensitivity needed to detect a 5%
spectral absorption feature on a very dark surface (3% reflectance) in the
inner solar system (0.89-1.35 AU). It, in combination with the other
instruments on the OSIRIS-REx Mission, will provide an unprecedented view of an
asteroid's surface.
| 0 | 1 | 0 | 0 | 0 | 0 |
The effect of the smoothness of fractional type operators over their commutators with Lipschitz symbols on weighted spaces | We prove boundedness results for integral operators of fractional type and
their higher order commutators between weighted spaces, including $L^p$-$L^q$,
$L^p$-$BMO$ and $L^p$-Lipschitz estimates. The kernels of such operators
satisfy certain size condition and a Lipschitz type regularity, and the symbol
of the commutator belongs to a Lipschitz class. We also deal with commutators
of fractional type operators with less regular kernels satisfying a
Hörmander's type inequality. As far as we know, these last results are new
even in the unweighted case. Moreover, we give a characterization result
involving symbols of the commutators and continuity results for extreme values
of $p$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Fast mean-reversion asymptotics for large portfolios of stochastic volatility models | We consider a large portfolio limit where the asset prices evolve according
certain stochastic volatility models with default upon hitting a lower barrier.
When the asset prices and the volatilities are correlated via systemic Brownian
Motions, that limit exist and it is described by a SPDE on the positive
half-space with Dirichlet boundary conditions which has been studied in
\cite{HK17}. We study the convergence of the total mass of a solution to this
stochastic initial-boundary value problem when the mean-reversion coefficients
of the volatilities are multiples of a parameter that tends to infinity. When
the volatilities of the volatilities are multiples of the square root of the
same parameter, the convergence is extremely weak. On the other hand, when the
volatilities of the volatilities are independent of this exploding parameter,
the volatilities converge to their means and we can have much better
approximations. Our aim is to use such approximations to improve the accuracy
of certain risk-management methods in markets where fast volatility
mean-reversion is observed.
| 0 | 0 | 0 | 0 | 0 | 1 |
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