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Labeled Memory Networks for Online Model Adaptation | Augmenting a neural network with memory that can grow without growing the
number of trained parameters is a recent powerful concept with many exciting
applications. We propose a design of memory augmented neural networks (MANNs)
called Labeled Memory Networks (LMNs) suited for tasks requiring online
adaptation in classification models. LMNs organize the memory with classes as
the primary key.The memory acts as a second boosted stage following a regular
neural network thereby allowing the memory and the primary network to play
complementary roles. Unlike existing MANNs that write to memory for every
instance and use LRU based memory replacement, LMNs write only for instances
with non-zero loss and use label-based memory replacement. We demonstrate
significant accuracy gains on various tasks including word-modelling and
few-shot learning. In this paper, we establish their potential in online
adapting a batch trained neural network to domain-relevant labeled data at
deployment time. We show that LMNs are better than other MANNs designed for
meta-learning. We also found them to be more accurate and faster than
state-of-the-art methods of retuning model parameters for adapting to
domain-specific labeled data.
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Perturbative approach to weakly driven many-particle systems in the presence of approximate conservation laws | We develop a Liouville perturbation theory for weakly driven and weakly open
quantum systems in situations when the unperturbed system has a number of
conservations laws. If the perturbation violates the conservation laws, it
drives the system to a new steady state which can be approximately but
efficiently described by a (generalized) Gibbs ensemble characterized by one
Lagrange parameter for each conservation law. The value of those has to be
determined from rate equations for conserved quantities. Remarkably, even weak
perturbations can lead to large responses of conserved quantities. We present a
perturbative expansion of the steady state density matrix; first we give the
condition that fixes the zeroth order expression (Lagrange parameters) and then
determine the higher order corrections via projections of the Liouvillian. The
formalism can be applied to a wide range of problems including two-temperature
models for electron-phonon systems, Bose condensates of excitons or photons or
weakly perturbed integrable models. We test our formalism by studying
interacting fermions coupled to non-thermal reservoirs, approximately described
by a Boltzmann equation.
| 0 | 1 | 0 | 0 | 0 | 0 |
Smart Mining for Deep Metric Learning | To solve deep metric learning problems and producing feature embeddings,
current methodologies will commonly use a triplet model to minimise the
relative distance between samples from the same class and maximise the relative
distance between samples from different classes. Though successful, the
training convergence of this triplet model can be compromised by the fact that
the vast majority of the training samples will produce gradients with
magnitudes that are close to zero. This issue has motivated the development of
methods that explore the global structure of the embedding and other methods
that explore hard negative/positive mining. The effectiveness of such mining
methods is often associated with intractable computational requirements. In
this paper, we propose a novel deep metric learning method that combines the
triplet model and the global structure of the embedding space. We rely on a
smart mining procedure that produces effective training samples for a low
computational cost. In addition, we propose an adaptive controller that
automatically adjusts the smart mining hyper-parameters and speeds up the
convergence of the training process. We show empirically that our proposed
method allows for fast and more accurate training of triplet ConvNets than
other competing mining methods. Additionally, we show that our method achieves
new state-of-the-art embedding results for CUB-200-2011 and Cars196 datasets.
| 1 | 0 | 0 | 0 | 0 | 0 |
Quantum phase transitions of a generalized compass chain with staggered Dzyaloshinskii-Moriya interaction | We consider a class of one-dimensional compass models with staggered
Dzyaloshinskii-Moriya exchange interactions in an external transverse magnetic
field. Based on the exact solution derived from Jordan-Wigner approach, we
study the excitation gap, energy spectra, spin correlations and critical
properties at phase transitions. We explore mutual effects of the staggered
Dzyaloshinskii-Moriya interaction and the magnetic field on the energy spectra
and the ground-state phase diagram. Thermodynamic quantities including the
entropy and the specific heat are discussed, and their universal scalings at
low temperature are demonstrated.
| 0 | 1 | 0 | 0 | 0 | 0 |
A blowup algebra of hyperplane arrangements | It is shown that the Orlik-Terao algebra is graded isomorphic to the special
fiber of the ideal $I$ generated by the $(n-1)$-fold products of the members of
a central arrangement of size $n$. This momentum is carried over to the Rees
algebra (blowup) of $I$ and it is shown that this algebra is of fiber-type and
Cohen-Macaulay. It follows by a result of Simis-Vasconcelos that the special
fiber of $I$ is Cohen-Macaulay, thus giving another proof of a result of
Proudfoot-Speyer about the Cohen-Macauleyness of the Orlik-Terao algebra.
| 0 | 0 | 1 | 0 | 0 | 0 |
Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach | We investigate a principle way to progressively mine discriminative object
regions using classification networks to address the weakly-supervised semantic
segmentation problems. Classification networks are only responsive to small and
sparse discriminative regions from the object of interest, which deviates from
the requirement of the segmentation task that needs to localize dense, interior
and integral regions for pixel-wise inference. To mitigate this gap, we propose
a new adversarial erasing approach for localizing and expanding object regions
progressively. Starting with a single small object region, our proposed
approach drives the classification network to sequentially discover new and
complement object regions by erasing the current mined regions in an
adversarial manner. These localized regions eventually constitute a dense and
complete object region for learning semantic segmentation. To further enhance
the quality of the discovered regions by adversarial erasing, an online
prohibitive segmentation learning approach is developed to collaborate with
adversarial erasing by providing auxiliary segmentation supervision modulated
by the more reliable classification scores. Despite its apparent simplicity,
the proposed approach achieves 55.0% and 55.7% mean Intersection-over-Union
(mIoU) scores on PASCAL VOC 2012 val and test sets, which are the new
state-of-the-arts.
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Streaming Binary Sketching based on Subspace Tracking and Diagonal Uniformization | In this paper, we address the problem of learning compact
similarity-preserving embeddings for massive high-dimensional streams of data
in order to perform efficient similarity search. We present a new online method
for computing binary compressed representations -sketches- of high-dimensional
real feature vectors. Given an expected code length $c$ and high-dimensional
input data points, our algorithm provides a $c$-bits binary code for preserving
the distance between the points from the original high-dimensional space. Our
algorithm does not require neither the storage of the whole dataset nor a
chunk, thus it is fully adaptable to the streaming setting. It also provides
low time complexity and convergence guarantees. We demonstrate the quality of
our binary sketches through experiments on real data for the nearest neighbors
search task in the online setting.
| 1 | 0 | 0 | 0 | 0 | 0 |
Improving hot-spot pressure for ignition in high-adiabat Inertial Confinement Fusion implosion | A novel capsule target design to improve the hot-spot pressure in the
high-adiabat implosion for inertial confinement fusion is proposed, where a
layer of comparatively high-density material is used as a pusher between the
fuel and the ablator. This design is based on our theoretical finding of the
stagnation scaling laws, which indicates that the hot spot pressure can be
improved by increasing the kinetic energy density $\rho_d V_{imp}^2/2$
($\rho_d$ is the shell density when the maximum shell velocity is reached,
$V_{imp}$ is the implosion velocity.) of the shell. The proposed design uses
the high density pusher to enhance the shell density $\rho_d$ so that the hot
spot pressure is improved. Radio-hydrodynamic simulations show that the hot
spot pressure of the design reaches the requirement for ignition even driven by
a very high-adiabat short-duration two-shock pulse. The design is hopeful to
simultaneously overcome the two major obstacles to achieving ignition--ablative
instability and laser-plasma instability.
| 0 | 1 | 0 | 0 | 0 | 0 |
Cell-to-cell variation sets a tissue-rheology-dependent bound on collective gradient sensing | When a single cell senses a chemical gradient and chemotaxes, stochastic
receptor-ligand binding can be a fundamental limit to the cell's accuracy. For
clusters of cells responding to gradients, however, there is a critical
difference: even genetically identical cells have differing responses to
chemical signals. With theory and simulation, we show collective chemotaxis is
limited by cell-to-cell variation in signaling. We find that when different
cells cooperate the resulting bias can be much larger than the effects of
ligand-receptor binding. Specifically, when a strongly-responding cell is at
one end of a cell cluster, cluster motion is biased toward that cell. These
errors are mitigated if clusters average measurements over times long enough
for cells to rearrange. In consequence, fluid clusters are better able to sense
gradients: we derive a link between cluster accuracy, cell-to-cell variation,
and the cluster rheology. Because of this connection, increasing the noisiness
of individual cell motion can actually increase the collective accuracy of a
cluster by improving fluidity.
| 0 | 1 | 0 | 0 | 0 | 0 |
Languages of Play: Towards semantic foundations for game interfaces | Formal models of games help us account for and predict behavior, leading to
more robust and innovative designs. While the games research community has
proposed many formalisms for both the "game half" (game models, game
description languages) and the "human half" (player modeling) of a game
experience, little attention has been paid to the interface between the two,
particularly where it concerns the player expressing her intent toward the
game. We describe an analytical and computational toolbox based on programming
language theory to examine the phenomenon sitting between control schemes and
game rules, which we identify as a distinct player intent language for each
game.
| 1 | 0 | 0 | 0 | 0 | 0 |
Hybrid Normed Ideal Perturbations of n-tuples of Operators I | In hybrid normed ideal perturbations of $n$-tuples of operators, the normed
ideal is allowed to vary with the component operators. We begin extending to
this setting the machinery we developed for normed ideal perturbations based on
the modulus of quasicentral approximation and an adaptation of our
non-commutative generalization of the Weyl--von~Neumann theorem. For commuting
$n$-tuples of hermitian operators, the modulus of quasicentral approximation
remains essentially the same when $\cC_n^-$ is replaced by a hybrid $n$-tuple
$\cC_{p_1,\dots}^-,\dots,\cC^-_{p_n}$, $p_1^{-1} + \dots + p_n^{-1} = 1$. The
proof involves singular integrals of mixed homogeneity.
| 0 | 0 | 1 | 0 | 0 | 0 |
Global behaviour of radially symmetric solutions stable at infinity for gradient systems | This paper is concerned with radially symmetric solutions of systems of the
form \[ u_t = -\nabla V(u) + \Delta_x u \] where space variable $x$ and and
state-parameter $u$ are multidimensional, and the potential $V$ is coercive at
infinity. For such systems, under generic assumptions on the potential, the
asymptotic behaviour of solutions "stable at infinity", that is approaching a
spatially homogeneous equilibrium when $|x|$ approaches $+\infty$, is
investigated. It is proved that every such solutions approaches a stacked
family of radially symmetric bistable fronts travelling to infinity. This
behaviour is similar to the one of bistable solutions for gradient systems in
one unbounded spatial dimension, described in a companion paper. It is expected
(but unfortunately not proved at this stage) that behind these travelling
fronts the solution again behaves as in the one-dimensional case (that is, the
time derivative approaches zero and the solution approaches a pattern of
stationary solutions).
| 0 | 0 | 1 | 0 | 0 | 0 |
On the Total Forcing Number of a Graph | Let $G$ be a simple and finite graph without isolated vertices. In this paper
we study forcing sets (zero forcing sets) which induce a subgraph of $G$
without isolated vertices. Such a set is called a total forcing set, introduced
and first studied by Davila \cite{Davila}. The minimum cardinality of a total
forcing set in $G$ is the total forcing number of $G$, denoted $F_t(G)$. We
study basic properties of $F_t(G)$, relate $F_t(G)$ to various domination
parameters, and establish $NP$-completeness of the associated decision problem
for $F_t(G)$. We also prove that if $G$ is a connected graph of order $n \ge 3$
and maximum degree $\Delta$, then $F_t(G) \le ( \frac{\Delta}{\Delta +1} ) n$,
with equality if and only if $G$ is a complete graph $K_{\Delta + 1}$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Brief Notes on Hard Takeoff, Value Alignment, and Coherent Extrapolated Volition | I make some basic observations about hard takeoff, value alignment, and
coherent extrapolated volition, concepts which have been central in analyses of
superintelligent AI systems.
| 1 | 0 | 0 | 0 | 0 | 0 |
Convolutional Neural Networks In Classifying Cancer Through DNA Methylation | DNA Methylation has been the most extensively studied epigenetic mark.
Usually a change in the genotype, DNA sequence, leads to a change in the
phenotype, observable characteristics of the individual. But DNA methylation,
which happens in the context of CpG (cytosine and guanine bases linked by
phosphate backbone) dinucleotides, does not lead to a change in the original
DNA sequence but has the potential to change the phenotype. DNA methylation is
implicated in various biological processes and diseases including cancer. Hence
there is a strong interest in understanding the DNA methylation patterns across
various epigenetic related ailments in order to distinguish and diagnose the
type of disease in its early stages. In this work, the relationship between
methylated versus unmethylated CpG regions and cancer types is explored using
Convolutional Neural Networks (CNNs). A CNN based Deep Learning model that can
classify the cancer of a new DNA methylation profile based on the learning from
publicly available DNA methylation datasets is then proposed.
| 0 | 0 | 0 | 1 | 1 | 0 |
Persistent Monitoring of Dynamically Changing Environments Using an Unmanned Vehicle | We consider the problem of planning a closed walk $\mathcal W$ for a UAV to
persistently monitor a finite number of stationary targets with equal
priorities and dynamically changing properties. A UAV must physically visit the
targets in order to monitor them and collect information therein. The frequency
of monitoring any given target is specified by a target revisit time, $i.e.$,
the maximum allowable time between any two successive visits to the target. The
problem considered in this paper is the following: Given $n$ targets and $k
\geq n$ allowed visits to them, find an optimal closed walk $\mathcal W^*(k)$
so that every target is visited at least once and the maximum revisit time over
all the targets, $\mathcal R(\mathcal W(k))$, is minimized. We prove the
following: If $k \geq n^2-n$, $\mathcal R(\mathcal W^*(k))$ (or simply,
$\mathcal R^*(k)$) takes only two values: $\mathcal R^*(n)$ when $k$ is an
integral multiple of $n$, and $\mathcal R^*(n+1)$ otherwise. This result
suggests significant computational savings - one only needs to determine
$\mathcal W^*(n)$ and $\mathcal W^*(n+1)$ to construct an optimal solution
$\mathcal W^*(k)$. We provide MILP formulations for computing $\mathcal W^*(n)$
and $\mathcal W^*(n+1)$. Furthermore, for {\it any} given $k$, we prove that
$\mathcal R^*(k) \geq \mathcal R^*(k+n)$.
| 1 | 0 | 0 | 0 | 0 | 0 |
Almost sharp nonlinear scattering in one-dimensional Born-Infeld equations arising in nonlinear Electrodynamics | We study decay of small solutions of the Born-Infeld equation in 1+1
dimensions, a quasilinear scalar field equation modeling nonlinear
electromagnetism, as well as branes in String theory and minimal surfaces in
Minkowski space-times. From the work of Whitham, it is well-known that there is
no decay because of arbitrary solutions traveling to the speed of light just as
linear wave equation. However, even if there is no global decay in 1+1
dimensions, we are able to show that all globally small $H^{s+1}\times H^s$,
$s>\frac12$ solutions do decay to the zero background state in space, inside a
strictly proper subset of the light cone. We prove this result by constructing
a Virial identity related to a momentum law, in the spirit of works
\cite{KMM,KMM1}, as well as a Lyapunov functional that controls the $\dot H^1
\times L^2$ energy.
| 0 | 0 | 1 | 0 | 0 | 0 |
Life efficiency does not always increase with the dissipation rate | There does not exist a general positive correlation between important
life-supporting properties and the entropy production rate. The simple reason
is that nondissipative and time-symmetric kinetic aspects are also relevant for
establishing optimal functioning. In fact those aspects are even crucial in the
nonlinear regimes around equilibrium where we find biological processing on
mesoscopic scales. We make these claims specific via examples of molecular
motors, of circadian cycles and of sensory adaptation, whose performance in
some regimes is indeed spoiled by increasing the dissipated power. We use the
relation between dissipation and the amount of time-reversal breaking to keep
the discussion quantitative also in effective models where the physical entropy
production is not clearly identifiable.
| 0 | 1 | 0 | 0 | 0 | 0 |
Molecular simulations of entangled defect structures around nanoparticles in nematic liquid crystals | We investigate the defect structures forming around two nanoparticles in a
Gay-Berne nematic liquid crystal using molecular simulations. For small
separations, disclinations entangle both particles forming the figure of eight,
the figure of omega and the figure of theta. These defect structures are
similar in shape and occur with a comparable frequency to micron-sized
particles studied in experiments. The simulations reveal fast transitions from
one defect structure to another suggesting that particles of nanometre size
cannot be bound together effectively. We identify the 'three-ring' structure
observed in previous molecular simulations as a superposition of the different
entangled and non-entangled states over time and conclude that it is not itself
a stable defect structure.
| 0 | 1 | 0 | 0 | 0 | 0 |
Quasi Maximum-Likelihood Estimation of Dynamic Panel Data Models | This paper establishes the almost sure convergence and asymptotic normality
of levels and differenced quasi maximum-likelihood (QML) estimators of dynamic
panel data models. The QML estimators are robust with respect to initial
conditions, conditional and time-series heteroskedasticity, and
misspecification of the log-likelihood. The paper also provides an ECME
algorithm for calculating levels QML estimates. Finally, it uses Monte Carlo
experiments to compare the finite sample performance of levels and differenced
QML estimators, the differenced GMM estimator, and the system GMM estimator. In
these experiments the QML estimators usually have smaller --- typically
substantially smaller --- bias and root mean squared errors than the panel data
GMM estimators.
| 0 | 0 | 1 | 1 | 0 | 0 |
Disunited Nations? A Multiplex Network Approach to Detecting Preference Affinity Blocs using Texts and Votes | This paper contributes to an emerging literature that models votes and text
in tandem to better understand polarization of expressed preferences. It
introduces a new approach to estimate preference polarization in
multidimensional settings, such as international relations, based on
developments in the natural language processing and network science literatures
-- namely word embeddings, which retain valuable syntactical qualities of human
language, and community detection in multilayer networks, which locates densely
connected actors across multiple, complex networks. We find that the employment
of these tools in tandem helps to better estimate states' foreign policy
preferences expressed in UN votes and speeches beyond that permitted by votes
alone. The utility of these located affinity blocs is demonstrated through an
application to conflict onset in International Relations, though these tools
will be of interest to all scholars faced with the measurement of preferences
and polarization in multidimensional settings.
| 1 | 0 | 0 | 0 | 0 | 0 |
Magnetic phase diagram of the iron pnictides in the presence of spin-orbit coupling: Frustration between $C_2$ and $C_4$ magnetic phases | We investigate the impact of spin anisotropic interactions, promoted by
spin-orbit coupling, on the magnetic phase diagram of the iron-based
superconductors. Three distinct magnetic phases with Bragg peaks at $(\pi,0)$
and $(0,\pi)$ are possible in these systems: one $C_2$ (i.e. orthorhombic)
symmetric stripe magnetic phase and two $C_4$ (i.e. tetragonal) symmetric
magnetic phases. While the spin anisotropic interactions allow the magnetic
moments to point in any direction in the $C_2$ phase, they restrict the
possible moment orientations in the $C_4$ phases. As a result, an interesting
scenario arises in which the spin anisotropic interactions favor a $C_2$ phase,
but the other spin isotropic interactions favor a $C_4$ phase. We study this
frustration via both mean-field and renormalization-group approaches. We find
that, to lift this frustration, a rich magnetic landscape emerges well below
the magnetic transition temperature, with novel $C_2$, $C_4$, and mixed
$C_2$-$C_4$ phases. Near the putative magnetic quantum critical point, spin
anisotropies promote a stable Gaussian fixed point in the renormalization-group
flow, which is absent in the spin isotropic case, and is associated with a
near-degeneracy between $C_2$ and $C_4$ phases. We argue that this frustration
is the reason why most $C_4$ phases in the iron pnictides only appear inside
the $C_2$ phase, and discuss additional manifestations of this frustration in
the phase diagrams of these materials.
| 0 | 1 | 0 | 0 | 0 | 0 |
Algorithms and Bounds for Very Strong Rainbow Coloring | A well-studied coloring problem is to assign colors to the edges of a graph
$G$ so that, for every pair of vertices, all edges of at least one shortest
path between them receive different colors. The minimum number of colors
necessary in such a coloring is the strong rainbow connection number
($\src(G)$) of the graph. When proving upper bounds on $\src(G)$, it is natural
to prove that a coloring exists where, for \emph{every} shortest path between
every pair of vertices in the graph, all edges of the path receive different
colors. Therefore, we introduce and formally define this more restricted edge
coloring number, which we call \emph{very strong rainbow connection number}
($\vsrc(G)$).
In this paper, we give upper bounds on $\vsrc(G)$ for several graph classes,
some of which are tight. These immediately imply new upper bounds on $\src(G)$
for these classes, showing that the study of $\vsrc(G)$ enables meaningful
progress on bounding $\src(G)$. Then we study the complexity of the problem to
compute $\vsrc(G)$, particularly for graphs of bounded treewidth, and show this
is an interesting problem in its own right. We prove that $\vsrc(G)$ can be
computed in polynomial time on cactus graphs; in contrast, this question is
still open for $\src(G)$. We also observe that deciding whether $\vsrc(G) = k$
is fixed-parameter tractable in $k$ and the treewidth of $G$. Finally, on
general graphs, we prove that there is no polynomial-time algorithm to decide
whether $\vsrc(G) \leq 3$ nor to approximate $\vsrc(G)$ within a factor
$n^{1-\varepsilon}$, unless P$=$NP.
| 1 | 0 | 0 | 0 | 0 | 0 |
Quantile function expansion using regularly varying functions | We present a simple result that allows us to evaluate the asymptotic order of
the remainder of a partial asymptotic expansion of the quantile function $h(u)$
as $u\to 0^+$ or $1^-$. This is focussed on important univariate distributions
when $h(\cdot)$ has no simple closed form, with a view to assessing asymptotic
rate of decay to zero of tail dependence in the context of bivariate copulas.
The Introduction motivates the study in terms of the standard Normal. The
Normal, Skew-Normal and Gamma are used as initial examples. Finally, we discuss
approximation to the lower quantile of the Variance-Gamma and Skew-Slash
distributions.
| 0 | 0 | 1 | 1 | 0 | 0 |
Identification of Key Proteins Involved in Axon Guidance Related Disorders: A Systems Biology Approach | Axon guidance is a crucial process for growth of the central and peripheral
nervous systems. In this study, 3 axon guidance related disorders, namely-
Duane Retraction Syndrome (DRS) , Horizontal Gaze Palsy with Progressive
Scoliosis (HGPPS) and Congenital fibrosis of the extraocular muscles type 3
(CFEOM3) were studied using various Systems Biology tools to identify the genes
and proteins involved with them to get a better idea about the underlying
molecular mechanisms including the regulatory mechanisms. Based on the analyses
carried out, 7 significant modules have been identified from the PPI network.
Five pathways/processes have been found to be significantly associated with
DRS, HGPPS and CFEOM3 associated genes. From the PPI network, 3 have been
identified as hub proteins- DRD2, UBC and CUL3.
| 0 | 0 | 0 | 0 | 1 | 0 |
Last-Iterate Convergence: Zero-Sum Games and Constrained Min-Max Optimization | Motivated by applications in Game Theory, Optimization, and Generative
Adversarial Networks, recent work of Daskalakis et al~\cite{DISZ17} and
follow-up work of Liang and Stokes~\cite{LiangS18} have established that a
variant of the widely used Gradient Descent/Ascent procedure, called
"Optimistic Gradient Descent/Ascent (OGDA)", exhibits last-iterate convergence
to saddle points in {\em unconstrained} convex-concave min-max optimization
problems. We show that the same holds true in the more general problem of {\em
constrained} min-max optimization under a variant of the no-regret
Multiplicative-Weights-Update method called "Optimistic Multiplicative-Weights
Update (OMWU)". This answers an open question of Syrgkanis et al~\cite{SALS15}.
The proof of our result requires fundamentally different techniques from
those that exist in no-regret learning literature and the aforementioned
papers. We show that OMWU monotonically improves the Kullback-Leibler
divergence of the current iterate to the (appropriately normalized) min-max
solution until it enters a neighborhood of the solution. Inside that
neighborhood we show that OMWU becomes a contracting map converging to the
exact solution. We believe that our techniques will be useful in the analysis
of the last iterate of other learning algorithms.
| 0 | 0 | 0 | 1 | 0 | 0 |
The Lifetimes of Phases in High-Mass Star-Forming Regions | High-mass stars form within star clusters from dense, molecular regions, but
is the process of cluster formation slow and hydrostatic or quick and dynamic?
We link the physical properties of high-mass star-forming regions with their
evolutionary stage in a systematic way, using Herschel and Spitzer data. In
order to produce a robust estimate of the relative lifetimes of these regions,
we compare the fraction of dense, molecular regions above a column density
associated with high-mass star formation, N(H2) > 0.4-2.5 x 10^22 cm^-2, in the
'starless (no signature of stars > 10 Msun forming) and star-forming phases in
a 2x2 degree region of the Galactic Plane centered at l=30deg. Of regions
capable of forming high-mass stars on ~1 pc scales, the starless (or embedded
beyond detection) phase occupies about 60-70% of the dense, molecular region
lifetime and the star-forming phase occupies about 30-40%. These relative
lifetimes are robust over a wide range of thresholds. We outline a method by
which relative lifetimes can be anchored to absolute lifetimes from large-scale
surveys of methanol masers and UCHII regions. A simplistic application of this
method estimates the absolute lifetimes of the starless phase to be 0.2-1.7 Myr
(about 0.6-4.1 fiducial cloud free-fall times) and the star-forming phase to be
0.1-0.7 Myr (about 0.4-2.4 free-fall times), but these are highly uncertain.
This work uniquely investigates the star-forming nature of high-column density
gas pixel-by-pixel and our results demonstrate that the majority of high-column
density gas is in a starless or embedded phase.
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Null controllability of a population dynamics with interior degeneracy | In this paper, we deal with the null controllability of a population dynamics
model with an interior degenerate diffusion. To this end, we proved first a new
Carleman estimate for the full adjoint system and afterwards we deduce a
suitable observability inequality which will be needed to establish the
existence of a control acting on a subset of the space which lead the
population to extinction in a finite time.
| 0 | 0 | 1 | 0 | 0 | 0 |
ICLR Reproducibility Challenge Report (Padam : Closing The Generalization Gap Of Adaptive Gradient Methods in Training Deep Neural Networks) | This work is a part of ICLR Reproducibility Challenge 2019, we try to
reproduce the results in the conference submission PADAM: Closing The
Generalization Gap of Adaptive Gradient Methods In Training Deep Neural
Networks. Adaptive gradient methods proposed in past demonstrate a degraded
generalization performance than the stochastic gradient descent (SGD) with
momentum. The authors try to address this problem by designing a new
optimization algorithm that bridges the gap between the space of Adaptive
Gradient algorithms and SGD with momentum. With this method a new tunable
hyperparameter called partially adaptive parameter p is introduced that varies
between [0, 0.5]. We build the proposed optimizer and use it to mirror the
experiments performed by the authors. We review and comment on the empirical
analysis performed by the authors. Finally, we also propose a future direction
for further study of Padam. Our code is available at:
this https URL
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Robust Bayes-Like Estimation: Rho-Bayes estimation | We consider the problem of estimating the joint distribution $P$ of $n$
independent random variables within the Bayes paradigm from a non-asymptotic
point of view. Assuming that $P$ admits some density $s$ with respect to a
given reference measure, we consider a density model $\overline S$ for $s$ that
we endow with a prior distribution $\pi$ (with support $\overline S$) and we
build a robust alternative to the classical Bayes posterior distribution which
possesses similar concentration properties around $s$ whenever it belongs to
the model $\overline S$. Furthermore, in density estimation, the Hellinger
distance between the classical and the robust posterior distributions tends to
0, as the number of observations tends to infinity, under suitable assumptions
on the model and the prior, provided that the model $\overline S$ contains the
true density $s$. However, unlike what happens with the classical Bayes
posterior distribution, we show that the concentration properties of this new
posterior distribution are still preserved in the case of a misspecification of
the model, that is when $s$ does not belong to $\overline S$ but is close
enough to it with respect to the Hellinger distance.
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Nonlinear demixed component analysis for neural population data as a low-rank kernel regression problem | Here I introduce an extension to demixed principal component analysis (dPCA),
a linear dimensionality reduction technique for analyzing the activity of
neural populations, to the case of nonlinear dimensions. This is accomplished
using kernel methods, resulting in kernel demixed principal component analysis
(kdPCA). This extension resembles kernel-based extensions to standard principal
component analysis and canonical correlation analysis. kdPCA includes dPCA as a
special case when the kernel is linear. I present examples of simulated neural
activity that follows different low dimensional configurations and compare the
results of kdPCA to dPCA. These simulations demonstrate that nonlinear
interactions can impede the ability of dPCA to demix neural activity
corresponding to experimental parameters, but kdPCA can still recover
interpretable components. Additionally, I compare kdPCA and dPCA to a neural
population from rat orbitofrontal cortex during an odor classification task in
recovering decision-related activity.
| 0 | 0 | 0 | 0 | 1 | 0 |
Stability of semi-wavefronts for delayed reaction-diffusion equations | This paper deals with the asymptotic behavior of solutions to the delayed
monostable equation: $(*)$ $u_{t}(t,x) = u_{xx}(t,x) - u(t,x) + g(u(t-h,x)),$
$x \in \mathbb{R},\ t >0,$ where $h>0$ and the reaction term $g: \mathbb{R}_+
\to \mathbb{R}_+$ has exactly two fixed points (zero and $\kappa >0$). Under
certain condition on the derivative of $g$ at $\kappa$, the global stability of
fast wavefronts is proved. Also, the stability of the $leading \ edge$ of
semi-wavefronts for $(*)$ with $g$ satisfying $g(u)\leq g'(0)u, u\in\R_+,$ is
established
| 0 | 0 | 1 | 0 | 0 | 0 |
An End-to-End Approach to Natural Language Object Retrieval via Context-Aware Deep Reinforcement Learning | We propose an end-to-end approach to the natural language object retrieval
task, which localizes an object within an image according to a natural language
description, i.e., referring expression. Previous works divide this problem
into two independent stages: first, compute region proposals from the image
without the exploration of the language description; second, score the object
proposals with regard to the referring expression and choose the top-ranked
proposals. The object proposals are generated independently from the referring
expression, which makes the proposal generation redundant and even irrelevant
to the referred object. In this work, we train an agent with deep reinforcement
learning, which learns to move and reshape a bounding box to localize the
object according to the referring expression. We incorporate both the spatial
and temporal context information into the training procedure. By simultaneously
exploiting local visual information, the spatial and temporal context and the
referring language a priori, the agent selects an appropriate action to take at
each time. A special action is defined to indicate when the agent finds the
referred object, and terminate the procedure. We evaluate our model on various
datasets, and our algorithm significantly outperforms the compared algorithms.
Notably, the accuracy improvement of our method over the recent method GroundeR
and SCRC on the ReferItGame dataset are 7.67% and 18.25%, respectively.
| 1 | 0 | 0 | 0 | 0 | 0 |
Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction | With access to large datasets, deep neural networks (DNN) have achieved
human-level accuracy in image and speech recognition tasks. However, in
chemistry, data is inherently small and fragmented. In this work, we develop an
approach of using rule-based knowledge for training ChemNet, a transferable and
generalizable deep neural network for chemical property prediction that learns
in a weak-supervised manner from large unlabeled chemical databases. When
coupled with transfer learning approaches to predict other smaller datasets for
chemical properties that it was not originally trained on, we show that
ChemNet's accuracy outperforms contemporary DNN models that were trained using
conventional supervised learning. Furthermore, we demonstrate that the ChemNet
pre-training approach is equally effective on both CNN (Chemception) and RNN
(SMILES2vec) models, indicating that this approach is network architecture
agnostic and is effective across multiple data modalities. Our results indicate
a pre-trained ChemNet that incorporates chemistry domain knowledge, enables the
development of generalizable neural networks for more accurate prediction of
novel chemical properties.
| 1 | 0 | 0 | 1 | 0 | 0 |
Intelligent Sensor Based Bayesian Neural Network for Combined Parameters and States Estimation of a Brushed DC Motor | The objective of this paper is to develop an Artificial Neural Network (ANN)
model to estimate simultaneously, parameters and state of a brushed DC machine.
The proposed ANN estimator is novel in the sense that his estimates
simultaneously temperature, speed and rotor resistance based only on the
measurement of the voltage and current inputs. Many types of ANN estimators
have been designed by a lot of researchers during the last two decades. Each
type is designed for a specific application. The thermal behavior of the motor
is very slow, which leads to large amounts of data sets. The standard ANN use
often Multi-Layer Perceptron (MLP) with Levenberg-Marquardt Backpropagation
(LMBP), among the limits of LMBP in the case of large number of data, so the
use of MLP based on LMBP is no longer valid in our case. As solution, we
propose the use of Cascade-Forward Neural Network (CFNN) based Bayesian
Regulation backpropagation (BRBP). To test our estimator robustness a random
white-Gaussian noise has been added to the sets. The proposed estimator is in
our viewpoint accurate and robust.
| 1 | 0 | 0 | 0 | 0 | 0 |
The infinite Fibonacci groups and relative asphericity | We prove that the generalised Fibonacci group F(r,n) is infinite for (r,n) in
{(7 + 5k,5), (8 + 5k,5)} where k is greater than or equal to 0. This together
with previously known results yields a complete classification of the finite
F(r,n), a problem that has its origins in a question by J H Conway in 1965. The
method is to show that a related relative presentation is aspherical from which
it can be deduced that the groups are infinite.
| 0 | 0 | 1 | 0 | 0 | 0 |
Representations of Polynomial Rota-Baxter Algebras | A Rota--Baxter operator is an algebraic abstraction of integration, which is
the typical example of a weight zero Rota-Baxter operator. We show that
studying the modules over the polynomial Rota--Baxter algebra $(k[x],P)$ is
equivalent to studying the modules over the Jordan plane, and we generalize the
direct decomposability results for the $(k[x],P)$-modules in [Iy] from
algebraically closed fields of characteristic zero to fields of characteristic
zero. Furthermore, we provide a classification of Rota--Baxter modules up to
isomorphism based on indecomposable $k[x]$-modules.
| 0 | 0 | 1 | 0 | 0 | 0 |
The extended ROSAT-ESO Flux-Limited X-ray Galaxy Cluster Survey (REFLEX II) VII The Mass Function of Galaxy Clusters | The mass function of galaxy clusters is a sensitive tracer of the
gravitational evolution of the cosmic large-scale structure and serves as an
important census of the fraction of matter bound in large structures. We obtain
the mass function by fitting the observed cluster X-ray luminosity distribution
from the REFLEX galaxy cluster survey to models of cosmological structure
formation. We marginalise over uncertainties in the cosmological parameters as
well as those of the relevant galaxy cluster scaling relations. The mass
function is determined with an uncertainty less than 10% in the mass range 3 x
10^12 to 5 x 10^14 M$_\odot$. For the cumulative mass function we find a slope
at the low mass end consistent with a value of -1, while the mass rich end
cut-off is milder than a Schechter function with an exponential term exp($-
M^\delta$) with $\delta$ smaller than 1. Changing the Hubble parameter in the
range $H_0 = 67 - 73 km s^-1 Mpc^{-1}$ or allowing the total neutrino mass to
have a value between 0 - 0.4 eV causes variations less than the uncertainties.
We estimate the fraction of mass locked up in galaxy clusters: about 4.4% of
the matter in the Universe is bound in clusters (inside $r_200$) with a mass
larger than 10^14 M$_\odot$ and 14% to clusters and groups with a mass larger
than 10^13 M$_\odot$ at the present Universe. We also discuss the evolution of
the galaxy cluster population with redshift. Our results imply that there is
hardly any clusters with a mass > 10^15 M$_\odot$ above a redshift of z = 1.
| 0 | 1 | 0 | 0 | 0 | 0 |
An Optimized Pattern Recognition Algorithm for Anomaly Detection in IoT Environment | With the advent of large-scale heterogeneous search engines comes the problem
of unified search control resulting in mismatches that could have otherwise
avoided. A mechanism is needed to determine exact patterns in web mining and
ubiquitous device searching. In this paper we demonstrate the use of an
optimized string searching algorithm to recognize exact patterns from a large
database. The underlying principle in designing the algorithm is that each
letter that maps to a fixed real values and some arithmetic operations which
are applied to compute corresponding pattern and substring values. We have
implemented this algorithm in C. We have tested the algorithm using a large
dataset. We created our own dataset using DNA sequences. The experimental
result shows the number of mismatch occurred in string search from a large
database. Furthermore, some of the inherent weaknesses in the use of this
algorithm are highlighted.
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Uniform asymptotics as a stationary point approaches an endpoint | We obtain the rigorous uniform asymptotics of a particular integral where a
stationary point is close to an endpoint. There exists a general method
introduced by Bleistein for obtaining uniform asymptotics in this situation.
However, this method does not provide rigorous estimates for the error. Indeed,
the method of Bleistein starts with a change of variables, which implies that
the parameter governing how close the stationary point is to the endpoint
appears in several parts of the integrand, and this means that one cannot
obtain general error bounds. By adapting the above method to our particular
integral, we obtain rigorous uniform leading-order asymptotics. We also give a
rigorous derivation of the asymptotics to all orders of the same integral; the
novelty of this second approach is that it does not involve a global change of
variables.
| 0 | 0 | 1 | 0 | 0 | 0 |
An SDP-Based Algorithm for Linear-Sized Spectral Sparsification | For any undirected and weighted graph $G=(V,E,w)$ with $n$ vertices and $m$
edges, we call a sparse subgraph $H$ of $G$, with proper reweighting of the
edges, a $(1+\varepsilon)$-spectral sparsifier if \[
(1-\varepsilon)x^{\intercal}L_Gx\leq x^{\intercal} L_{H} x\leq (1+\varepsilon)
x^{\intercal} L_Gx \] holds for any $x\in\mathbb{R}^n$, where $L_G$ and $L_{H}$
are the respective Laplacian matrices of $G$ and $H$. Noticing that $\Omega(m)$
time is needed for any algorithm to construct a spectral sparsifier and a
spectral sparsifier of $G$ requires $\Omega(n)$ edges, a natural question is to
investigate, for any constant $\varepsilon$, if a $(1+\varepsilon)$-spectral
sparsifier of $G$ with $O(n)$ edges can be constructed in $\tilde{O}(m)$ time,
where the $\tilde{O}$ notation suppresses polylogarithmic factors. All previous
constructions on spectral sparsification require either super-linear number of
edges or $m^{1+\Omega(1)}$ time.
In this work we answer this question affirmatively by presenting an algorithm
that, for any undirected graph $G$ and $\varepsilon>0$, outputs a
$(1+\varepsilon)$-spectral sparsifier of $G$ with $O(n/\varepsilon^2)$ edges in
$\tilde{O}(m/\varepsilon^{O(1)})$ time. Our algorithm is based on three novel
techniques: (1) a new potential function which is much easier to compute yet
has similar guarantees as the potential functions used in previous references;
(2) an efficient reduction from a two-sided spectral sparsifier to a one-sided
spectral sparsifier; (3) constructing a one-sided spectral sparsifier by a
semi-definite program.
| 1 | 0 | 0 | 0 | 0 | 0 |
Self-Adjusting Threshold Mechanism for Pixel Detectors | Readout chips of hybrid pixel detectors use a low power amplifier and
threshold discrimination to process charge deposited in semiconductor sensors.
Due to transistor mismatch each pixel circuit needs to be calibrated
individually to achieve response uniformity. Traditionally this is addressed by
programmable threshold trimming in each pixel, but requires robustness against
radiation effects, temperature, and time. In this paper a self-adjusting
threshold mechanism is presented, which corrects the threshold for both spatial
inequality and time variation and maintains a constant response. It exploits
the electrical noise as relative measure for the threshold and automatically
adjust the threshold of each pixel to always achieve a uniform frequency of
noise hits. A digital implementation of the method in the form of an up/down
counter and combinatorial logic filter is presented. The behavior of this
circuit has been simulated to evaluate its performance and compare it to
traditional calibration results. The simulation results show that this
mechanism can perform equally well, but eliminates instability over time and is
immune to single event upsets.
| 0 | 1 | 0 | 0 | 0 | 0 |
Rotational inertia interface in a dynamic lattice of flexural beams | The paper presents a novel analysis of a transmission problem for a network
of flexural beams incorporating conventional Euler-Bernoulli beams as well as
Rayleigh beams with the enhanced rotational inertia. Although, in the
low-frequency regime, these beams have a similar dynamic response, we have
demonstrated novel features which occur in the transmission at higher
frequencies across the layer of the Rayleigh beams.
| 0 | 1 | 0 | 0 | 0 | 0 |
Low-frequency wide band-gap elastic/acoustic meta-materials using the K-damping concept | The terms "acoustic/elastic meta-materials" describe a class of periodic
structures with unit cells exhibiting local resonance. This localized resonant
structure has been shown to result in negative effective stiffness and/or mass
at frequency ranges close to these local resonances. As a result, these
structures present unusual wave propagation properties at wavelengths well
below the regime corresponding to band-gap generation based on spatial
periodicity, (i.e. "Bragg scattering"). Therefore, acoustic/elastic
meta-materials can lead to applications, especially suitable in the
low-frequency range. However, low frequency range applications of such
meta-materials require very heavy internal moving masses, as well as additional
constraints at the amplitudes of the internally oscillating locally resonating
structures, which may prohibit their practical implementation. In order to
resolve this disadvantage, the K-Damping concept will be analyzed. According to
this concept, the acoustic/elastic meta-materials are designed to include
negative stiffness elements instead or in addition to the internally resonating
added masses. This concept removes the need for the heavy locally added heavy
masses, while it simultaneously exploits the negative stiffness damping
phenomenon. Application of both Bloch's theory and the classical modal analysis
at the one-dimensional mass-in-mass lattice is analyzed and corresponding
dispersion relations are derived. The results indicate significant advantages
over the conventional mass-in-a mass lattice, such as broader band-gaps and
increased damping ratio and reveal significant potential in the proposed
solution. Preliminary feasibility analysis for seismic meta-structures and low
frequency acoustic isolation-damping confirm the strong potential and
applicability of this concept.
| 0 | 1 | 0 | 0 | 0 | 0 |
Inertia-Constrained Pixel-by-Pixel Nonnegative Matrix Factorisation: a Hyperspectral Unmixing Method Dealing with Intra-class Variability | Blind source separation is a common processing tool to analyse the
constitution of pixels of hyperspectral images. Such methods usually suppose
that pure pixel spectra (endmembers) are the same in all the image for each
class of materials. In the framework of remote sensing, such an assumption is
no more valid in the presence of intra-class variabilities due to illumination
conditions, weathering, slight variations of the pure materials, etc... In this
paper, we first describe the results of investigations highlighting intra-class
variability measured in real images. Considering these results, a new
formulation of the linear mixing model is presented leading to two new methods.
Unconstrained Pixel-by-pixel NMF (UP-NMF) is a new blind source separation
method based on the assumption of a linear mixing model, which can deal with
intra-class variability. To overcome UP-NMF limitations an extended method is
proposed, named Inertia-constrained Pixel-by-pixel NMF (IP-NMF). For each
sensed spectrum, these extended versions of NMF extract a corresponding set of
source spectra. A constraint is set to limit the spreading of each source's
estimates in IP-NMF. The methods are tested on a semi-synthetic data set built
with spectra extracted from a real hyperspectral image and then numerically
mixed. We thus demonstrate the interest of our methods for realistic source
variabilities. Finally, IP-NMF is tested on a real data set and it is shown to
yield better performance than state of the art methods.
| 1 | 1 | 0 | 1 | 0 | 0 |
Warped Product Pointwise Semi-slant Submanifolds of Sasakian Manifolds | Recently, B.-Y. Chen and O. J. Garay studied pointwise slant submanifolds of
almost Hermitian manifolds. By using this notion, we investigate pointwise
semi-slant submanifolds and their warped products in Sasakian manifolds. We
give non-trivial examples of such submanifolds and obtain several fundamental
results, including a characterization for warped product pointwise semi-slant
submanifolds of Sasakian manifolds.
| 0 | 0 | 1 | 0 | 0 | 0 |
Bayesian Semi-supervised Learning with Graph Gaussian Processes | We propose a data-efficient Gaussian process-based Bayesian approach to the
semi-supervised learning problem on graphs. The proposed model shows extremely
competitive performance when compared to the state-of-the-art graph neural
networks on semi-supervised learning benchmark experiments, and outperforms the
neural networks in active learning experiments where labels are scarce.
Furthermore, the model does not require a validation data set for early
stopping to control over-fitting. Our model can be viewed as an instance of
empirical distribution regression weighted locally by network connectivity. We
further motivate the intuitive construction of the model with a Bayesian linear
model interpretation where the node features are filtered by an operator
related to the graph Laplacian. The method can be easily implemented by
adapting off-the-shelf scalable variational inference algorithms for Gaussian
processes.
| 1 | 0 | 0 | 1 | 0 | 0 |
Knowledge Engineering for Hybrid Deductive Databases | Modern knowledge base systems frequently need to combine a collection of
databases in different formats: e.g., relational databases, XML databases, rule
bases, ontologies, etc. In the deductive database system DDBASE, we can manage
these different formats of knowledge and reason about them. Even the file
systems on different computers can be part of the knowledge base. Often, it is
necessary to handle different versions of a knowledge base. E.g., we might want
to find out common parts or differences of two versions of a relational
database.
We will examine the use of abstractions of rule bases by predicate dependency
and rule predicate graphs. Also the proof trees of derived atoms can help to
compare different versions of a rule base. Moreover, it might be possible to
have derivations joining rules with other formalisms of knowledge
representation.
Ontologies have shown their benefits in many applications of intelligent
systems, and there have been many proposals for rule languages compatible with
the semantic web stack, e.g., SWRL, the semantic web rule language. Recently,
ontologies are used in hybrid systems for specifying the provenance of the
different components.
| 1 | 0 | 0 | 0 | 0 | 0 |
Satisfiability Bounds for ω-regular Properties in Interval-valued Markov Chains | We derive an algorithm to compute satisfiability bounds for arbitrary
{\omega}-regular properties in an Interval-valued Markov Chain (IMC)
interpreted in the adversarial sense. IMCs generalize regular Markov Chains by
assigning a range of possible values to the transition probabilities between
states. In particular, we expand the automata-based theory of {\omega}-regular
property verification in Markov Chains to apply it to IMCs. Any
{\omega}-regular property can be represented by a Deterministic Rabin Automata
(DRA) with acceptance conditions expressed by Rabin pairs. Previous works on
Markov Chains have shown that computing the probability of satisfying a given
{\omega}-regular property reduces to a reachability problem in the product
between the Markov Chain and the corresponding DRA. We similarly define the
notion of a product between an IMC and a DRA. Then, we show that in a product
IMC, there exists a particular assignment of the transition values that
generates a largest set of non-accepting states. Subsequently, we prove that a
lower bound is found by solving a reachability problem in that refined version
of the original product IMC. We derive a similar approach for computing a
satisfiability upper bound in a product IMC with one Rabin pair. For product
IMCs with more than one Rabin pair, we establish that computing a
satisfiability upper bound is equivalent to lower-bounding the satisfiability
of the complement of the original property. A search algorithm for finding the
largest accepting and non-accepting sets of states in a product IMC is
proposed. Finally, we demonstrate our findings in a case study.
| 1 | 0 | 0 | 0 | 0 | 0 |
On the relaxed mean-field stochastic control problem | This paper is concerned with optimal control problems for systems governed by
mean-field stochastic differential equation, in which the control enters both
the drift and the diffusion coefficient. We prove that the relaxed state
process, associated with measure valued controls, is governed by an orthogonal
martingale measure rather that a Brownian motion. In particular, we show by a
counter example that replacing the drift and diffusion coefficient by their
relaxed counterparts does not define a true relaxed control problem. We
establish the existence of an optimal relaxed control, which can be
approximated by a sequence of strict controls. Moreover under some convexity
conditions, we show that the optimal control is realized by a strict control.
| 0 | 0 | 1 | 0 | 0 | 0 |
A New Point-set Registration Algorithm for Fingerprint Matching | A novel minutia-based fingerprint matching algorithm is proposed that employs
iterative global alignment on two minutia sets. The matcher considers all
possible minutia pairings and iteratively aligns the two sets until the number
of minutia pairs does not exceed the maximum number of allowable one-to-one
pairings. The optimal alignment parameters are derived analytically via linear
least squares. The first alignment establishes a region of overlap between the
two minutia sets, which is then (iteratively) refined by each successive
alignment. After each alignment, minutia pairs that exhibit weak correspondence
are discarded. The process is repeated until the number of remaining pairs no
longer exceeds the maximum number of allowable one-to-one pairings. The
proposed algorithm is tested on both the FVC2000 and FVC2002 databases, and the
results indicate that the proposed matcher is both effective and efficient for
fingerprint authentication; it is fast and does not utilize any computationally
expensive mathematical functions (e.g. trigonometric, exponential). In addition
to the proposed matcher, another contribution of the paper is the analytical
derivation of the least squares solution for the optimal alignment parameters
for two point-sets lacking exact correspondence.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Faster Solution to Smale's 17th Problem I: Real Binomial Systems | Suppose $F:=(f_1,\ldots,f_n)$ is a system of random $n$-variate polynomials
with $f_i$ having degree $\leq\!d_i$ and the coefficient of $x^{a_1}_1\cdots
x^{a_n}_n$ in $f_i$ being an independent complex Gaussian of mean $0$ and
variance $\frac{d_i!}{a_1!\cdots a_n!\left(d_i-\sum^n_{j=1}a_j \right)!}$.
Recent progress on Smale's 17th Problem by Lairez --- building upon seminal
work of Shub, Beltran, Pardo, Bürgisser, and Cucker --- has resulted in a
deterministic algorithm that finds a single (complex) approximate root of $F$
using just $N^{O(1)}$ arithmetic operations on average, where
$N\!:=\!\sum^n_{i=1}\frac{(n+d_i)!}{n!d_i!}$ ($=n(n+\max_i
d_i)^{O(\min\{n,\max_i d_i)\}}$) is the maximum possible total number of
monomial terms for such an $F$. However, can one go faster when the number of
terms is smaller, and we restrict to real coefficient and real roots? And can
one still maintain average-case polynomial-time with more general probability
measures?
We show the answer is yes when $F$ is instead a binomial system --- a case
whose numerical solution is a key step in polyhedral homotopy algorithms for
solving arbitrary polynomial systems. We give a deterministic algorithm that
finds a real approximate root (or correctly decides there are none) using just
$O(n^2(\log(n)+\log\max_i d_i))$ arithmetic operations on average. Furthermore,
our approach allows Gaussians with arbitrary variance. We also discuss briefly
the obstructions to maintaining average-case time polynomial in $n\log \max_i
d_i$ when $F$ has more terms.
| 1 | 0 | 0 | 0 | 0 | 0 |
Making compression algorithms for Unicode text | The majority of online content is written in languages other than English,
and is most commonly encoded in UTF-8, the world's dominant Unicode character
encoding. Traditional compression algorithms typically operate on individual
bytes. While this approach works well for the single-byte ASCII encoding, it
works poorly for UTF-8, where characters often span multiple bytes. Previous
research has focused on developing Unicode compressors from scratch, which
often failed to outperform established algorithms such as bzip2. We develop a
technique to modify byte-based compressors to operate directly on Unicode
characters, and implement variants of LZW and PPM that apply this technique. We
find that our method substantially improves compression effectiveness on a
UTF-8 corpus, with our PPM variant outperforming the state-of-the-art PPMII
compressor. On ASCII and binary files, our variants perform similarly to the
original unmodified compressors.
| 1 | 0 | 1 | 0 | 0 | 0 |
Adaptive Exact Learning of Decision Trees from Membership Queries | In this paper we study the adaptive learnability of decision trees of depth
at most $d$ from membership queries. This has many applications in automated
scientific discovery such as drugs development and software update problem.
Feldman solves the problem in a randomized polynomial time algorithm that asks
$\tilde O(2^{2d})\log n$ queries and Kushilevitz-Mansour in a deterministic
polynomial time algorithm that asks $ 2^{18d+o(d)}\log n$ queries. We improve
the query complexity of both algorithms. We give a randomized polynomial time
algorithm that asks $\tilde O(2^{2d}) + 2^{d}\log n$ queries and a
deterministic polynomial time algorithm that asks $2^{5.83d}+2^{2d+o(d)}\log n$
queries.
| 1 | 0 | 0 | 1 | 0 | 0 |
Image denoising by median filter in wavelet domain | The details of an image with noise may be restored by removing noise through
a suitable image de-noising method. In this research, a new method of image
de-noising based on using median filter (MF) in the wavelet domain is proposed
and tested. Various types of wavelet transform filters are used in conjunction
with median filter in experimenting with the proposed approach in order to
obtain better results for image de-noising process, and, consequently to select
the best suited filter. Wavelet transform working on the frequencies of
sub-bands split from an image is a powerful method for analysis of images.
According to this experimental work, the proposed method presents better
results than using only wavelet transform or median filter alone. The MSE and
PSNR values are used for measuring the improvement in de-noised images.
| 1 | 0 | 0 | 0 | 0 | 0 |
Restoration of Images with Wavefront Aberrations | This contribution deals with image restoration in optical systems with
coherent illumination, which is an important topic in astronomy, coherent
microscopy and radar imaging. Such optical systems suffer from wavefront
distortions, which are caused by imperfect imaging components and conditions.
Known image restoration algorithms work well for incoherent imaging, they fail
in case of coherent images. In this paper a novel wavefront correction
algorithm is presented, which allows image restoration under coherent
conditions. In most coherent imaging systems, especially in astronomy, the
wavefront deformation is known. Using this information, the proposed algorithm
allows a high quality restoration even in case of severe wavefront distortions.
We present two versions of this algorithm, which are an evolution of the
Gerchberg-Saxton and the Hybrid-Input-Output algorithm. The algorithm is
verified on simulated and real microscopic images.
| 1 | 1 | 0 | 0 | 0 | 0 |
Controlling the shape of membrane protein polyhedra | Membrane proteins and lipids can self-assemble into membrane protein
polyhedral nanoparticles (MPPNs). MPPNs have a closed spherical surface and a
polyhedral protein arrangement, and may offer a new route for structure
determination of membrane proteins and targeted drug delivery. We develop here
a general analytic model of how MPPN self-assembly depends on bilayer-protein
interactions and lipid bilayer mechanical properties. We find that the
bilayer-protein hydrophobic thickness mismatch is a key molecular control
parameter for MPPN shape that can be used to bias MPPN self-assembly towards
highly symmetric and uniform MPPN shapes. Our results suggest strategies for
optimizing MPPN shape for structural studies of membrane proteins and targeted
drug delivery.
| 0 | 1 | 0 | 0 | 0 | 0 |
Realizing uniformly recurrent subgroups | We show that every uniformly recurrent subgroup of a locally compact group is
the family of stabilizers of a minimal action on a compact space. More
generally, every closed invariant subset of the Chabauty space is the family of
stabilizers of an action on a compact space on which the stabilizer map is
continuous everywhere. This answers a question of Glasner and Weiss. We also
introduce the notion of a universal minimal flow relative to a uniformly
recurrent subgroup and prove its existence and uniqueness.
| 0 | 0 | 1 | 0 | 0 | 0 |
A New Approximation Guarantee for Monotone Submodular Function Maximization via Discrete Convexity | In monotone submodular function maximization, approximation guarantees based
on the curvature of the objective function have been extensively studied in the
literature. However, the notion of curvature is often pessimistic, and we
rarely obtain improved approximation guarantees, even for very simple objective
functions.
In this paper, we provide a novel approximation guarantee by extracting an
M$^\natural$-concave function $h:2^E \to \mathbb R_+$, a notion in discrete
convex analysis, from the objective function $f:2^E \to \mathbb R_+$. We
introduce the notion of $h$-curvature, which measures how much $f$ deviates
from $h$, and show that we can obtain a $(1-\gamma/e-\epsilon)$-approximation
to the problem of maximizing $f$ under a cardinality constraint in polynomial
time for any constant $\epsilon > 0$. Then, we show that we can obtain
nontrivial approximation guarantees for various problems by applying the
proposed algorithm.
| 1 | 0 | 0 | 0 | 0 | 0 |
Electrode Reactions in Slowly Relaxing Media | Standard models of reaction kinetics in condensed materials rely on the
Boltzmann-Gibbs distribution for the population of reactants at the top of the
free energy barrier separating them from the products. While energy dissipation
and quantum effects at the barrier top can potentially affect the transmission
coefficient entering the rate preexponential factor, much stronger dynamical
effects on the reaction barrier are caused by the breakdown of ergodicity for
populating the reaction barrier (violation of the Boltzmann-Gibbs statistics).
When the spectrum of medium modes coupled to the reaction coordinate includes
fluctuations slower than the reaction rate, such nuclear motions dynamically
freeze on the reaction time-scale and do not contribute to the activation
barrier. Here we consider the consequences of this scenario for electrode
reactions in slowly relaxing media. Changing electrode overpotential speeds
electrode electron transfer up, potentially cutting through the spectrum of
nuclear modes coupled to the reaction coordinate. The reorganization energy of
electrochemical electron transfer becomes a function of the electrode
overpotential, switching between the thermodynamic value at low rates to the
nonergodic limit at higher rates. The sharpness of this transition depends of
the relaxation spectrum of the medium. The reorganization energy experiences a
sudden drop with increasing overpotential for a medium with a Debye relaxation,
but becomes a much shallower function of the overpotential for media with
stretched exponential dynamics. The latter scenario characterizes electron
transfer in ionic liquids. The analysis of electrode reactions in
room-temperature ionic liquids shows that the magnitude of the free energy of
nuclear solvation is significantly below its thermodynamic limit.
| 0 | 1 | 0 | 0 | 0 | 0 |
End-to-end 3D face reconstruction with deep neural networks | Monocular 3D facial shape reconstruction from a single 2D facial image has
been an active research area due to its wide applications. Inspired by the
success of deep neural networks (DNN), we propose a DNN-based approach for
End-to-End 3D FAce Reconstruction (UH-E2FAR) from a single 2D image. Different
from recent works that reconstruct and refine the 3D face in an iterative
manner using both an RGB image and an initial 3D facial shape rendering, our
DNN model is end-to-end, and thus the complicated 3D rendering process can be
avoided. Moreover, we integrate in the DNN architecture two components, namely
a multi-task loss function and a fusion convolutional neural network (CNN) to
improve facial expression reconstruction. With the multi-task loss function, 3D
face reconstruction is divided into neutral 3D facial shape reconstruction and
expressive 3D facial shape reconstruction. The neutral 3D facial shape is
class-specific. Therefore, higher layer features are useful. In comparison, the
expressive 3D facial shape favors lower or intermediate layer features. With
the fusion-CNN, features from different intermediate layers are fused and
transformed for predicting the 3D expressive facial shape. Through extensive
experiments, we demonstrate the superiority of our end-to-end framework in
improving the accuracy of 3D face reconstruction.
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Entanglement of photons in their dual wave-particle nature | Wave-particle duality is the most fundamental description of the nature of a
quantum object which behaves like a classical particle or wave depending on the
measurement apparatus. On the other hand, entanglement represents nonclassical
correlations of composite quantum systems, being also a key resource in quantum
information. Despite the very recent observations of wave-particle
superposition and entanglement, whether these two fundamental traits of quantum
mechanics can emerge simultaneously remains an open issue. Here we introduce
and experimentally realize a scheme that deterministically generates
wave-particle entanglement of two photons. The elementary tool allowing this
achievement is a scalable single-photon setup which can be in principle
extended to generate multiphoton wave-particle entanglement. Our study reveals
that photons can be entangled in their dual wave-particle nature and opens the
way to potential applications in quantum information protocols exploiting the
wave-particle degrees of freedom to encode qubits.
| 0 | 1 | 0 | 0 | 0 | 0 |
On Tensor Train Rank Minimization: Statistical Efficiency and Scalable Algorithm | Tensor train (TT) decomposition provides a space-efficient representation for
higher-order tensors. Despite its advantage, we face two crucial limitations
when we apply the TT decomposition to machine learning problems: the lack of
statistical theory and of scalable algorithms. In this paper, we address the
limitations. First, we introduce a convex relaxation of the TT decomposition
problem and derive its error bound for the tensor completion task. Next, we
develop an alternating optimization method with a randomization technique, in
which the time complexity is as efficient as the space complexity is. In
experiments, we numerically confirm the derived bounds and empirically
demonstrate the performance of our method with a real higher-order tensor.
| 0 | 0 | 0 | 1 | 0 | 0 |
Threshold fluctuations in a superconducting current-carrying bridge | We calculate the energy of threshold fluctuation $\delta F_{thr}$ which
triggers the transition of superconducting current-carrying bridge to resistive
state. We show that the dependence $\delta F_{thr}(I)\propto
I_{dep}\hbar(1-I/I_{dep})^{5/4}/e$, found by Langer and Ambegaokar for a long
bridge with length $L \gg \xi$, holds far below the critical temperature both
in dirty and clean limits (here $I_{dep}$ is the depairing current of the
bridge and $\xi$ is a coherence length). We also find that even 'weak' local
defect (leading to the small suppression of the critical current of the bridge
$I_c \lesssim I_{dep}$) provides $\delta F_{thr}\propto
I_c\hbar(1-I/I_c)^{3/2}/e$, typical for a short bridge with $L \ll \xi$ or a
Josephson junction.
| 0 | 1 | 0 | 0 | 0 | 0 |
Transient phenomena in a three-layer waveguide and the analytical structure of the dispersion diagram | Excitation of waves in a three-layer acoustic wavegide is studied. The wave
field is presented as a sum of integrals. The summation is held over all
waveguide modes. The integration is performed over the temporal frequency axis.
The dispersion diagram of the waveguide is analytically continued, and the
integral is transformed by deformation of the integration contour into the
domain of complex frequencies. As the result, the expression for the fast
components of the signal (i.e. for the transient fields) is simplified.
The structure of the Riemann surface of the dispersion diagram of the
waveguide is studied. For this, a family of auxiliary problems indexed by the
parameters describing the links between layers is introduced. The family
depends on the linking parameters analytically, and the limiting case of weak
links can be solved analytically.
| 0 | 0 | 1 | 0 | 0 | 0 |
Introducing symplectic billiards | In this article we introduce a simple dynamical system called symplectic
billiards. As opposed to usual/Birkhoff billiards, where length is the
generating function, for symplectic billiards symplectic area is the generating
function. We explore basic properties and exhibit several similarities, but
also differences of symplectic billiards to Birkhoff billiards.
| 0 | 0 | 1 | 0 | 0 | 0 |
Banchoff's sphere and branched covers over the trefoil | A filling Dehn surface in a $3$-manifold $M$ is a generically immersed
surface in $M$ that induces a cellular decomposition of $M$. Given a tame link
$L$ in $M$ there is a filling Dehn sphere of $M$ that "trivializes"
(\emph{diametrically splits}) it. This allows to construct filling Dehn
surfaces in the coverings of $M$ branched over $L$. It is shown that one of the
simplest filling Dehn spheres of $S^3$ (Banchoff's sphere) diametrically splits
the trefoil knot. Filling Dehn spheres, and their Johansson diagrams, are
constructed for the coverings of $S^3$ branched over the trefoil. The
construction is explained in detail. Johansson diagrams for generic cyclic
coverings and for the simplest locally cyclic and irregular ones are
constructed explicitly, providing new proofs of known results about cyclic
coverings and the $3$-fold irregular covering over the trefoil.
| 0 | 0 | 1 | 0 | 0 | 0 |
The Impact of Social Curiosity on Information Spreading on Networks | Most information spreading models consider that all individuals are identical
psychologically. They ignore, for instance, the curiosity level of people,
which may indicate that they can be influenced to seek for information given
their interest. For example, the game Pokémon GO spread rapidly because of
the aroused curiosity among users. This paper proposes an information
propagation model considering the curiosity level of each individual, which is
a dynamical parameter that evolves over time. We evaluate the efficiency of our
model in contrast to traditional information propagation models, like SIR or
IC, and perform analysis on different types of artificial and real-world
networks, like Google+, Facebook, and the United States roads map. We present a
mean-field approach that reproduces with a good accuracy the evolution of
macroscopic quantities, such as the density of stiflers, for the system's
behavior with the curiosity. We also obtain an analytical solution of the
mean-field equations that allows to predicts a transition from a phase where
the information remains confined to a small number of users to a phase where it
spreads over a large fraction of the population. The results indicate that the
curiosity increases the information spreading in all networks as compared with
the spreading without curiosity, and that this increase is larger in spatial
networks than in social networks. When the curiosity is taken into account, the
maximum number of informed individuals is reached close to the transition
point. Since curious people are more open to a new product, concepts, and
ideas, this is an important factor to be considered in propagation modeling.
Our results contribute to the understanding of the interplay between diffusion
process and dynamical heterogeneous transmission in social networks.
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Decomposition of mean-field Gibbs distributions into product measures | We show that under a low complexity condition on the gradient of a
Hamiltonian, Gibbs distributions on the Boolean hypercube are approximate
mixtures of product measures whose probability vectors are critical points of
an associated mean-field functional. This extends a previous work by the first
author. As an application, we demonstrate how this framework helps characterize
both Ising models satisfying a mean-field condition and the conditional
distributions which arise in the emerging theory of nonlinear large deviations,
both in the dense case and in the polynomially-sparse case.
| 0 | 0 | 1 | 1 | 0 | 0 |
Analytic Discs and Uniform Algebras on Real-Analytic Varieties | Under very general conditions it is shown that if $A$ is a uniform algebra
generated by real-analytic functions, then either $A$ consists of all
continuous functions or else there exists a disc on which every function in $A$
is holomorphic. This strengthens several earlier results concerning uniform
algebras generated by real-analytic functions.
| 0 | 0 | 1 | 0 | 0 | 0 |
Novel solid state vacuum quartz encapsulated growth of p-Terphenyl: the parent High Tc Oraganic Superconductor (HTOS) | We report an easy and versatile route for the synthesis of the parent phase
of newest superconducting wonder material i.e. p-Terphenyl. Doped p-terphenyl
has recently shown superconductivity with transition temperature as high as
120K. For crystal growth, the commercially available p-Terphenyl powder is
pelletized, encapsulated in evacuated (10-4 Torr) quartz tube and subjected to
high temperature (260C) melt followed by slow cooling at 5C/hour. Simple
temperature controlled heating furnace is used during the process. The obtained
crystal is one piece, shiny and plate like. Single crystal surface XRD (X-ray
Diffraction) showed unidirectional (00l) lines, indicating that the crystal is
grown along c-direction. Powder XRD of the specimen showed that as grown
p-Terphenyl is crystallized in monoclinic structure with space group P21/a
space group, having lattice parameters a = 8.08(2) A, b = 5.62(5) A and c=
13.58(3) A. Scanning electron microscopy (SEM) pictures of the crystal showed
clear layered slab like growth without any visible contamination from oxygen.
Characteristic reported Raman active modes related to C-C-C bending, C-H
bending, C-C stretching and C-H stretching vibrations are seen clearly for the
studied p-Terphenyl crystal. The physical properties of crystal are yet
underway. The short letter reports an easy and versatile crystal growth method
for obtaining quality p-terphenyl. The same growth method may probably be
applied to doped p-terphenyl and to subsequently achieve superconductivity to
the tune of as high 120K for the newest superconductivity wonder i.e., High Tc
Oraganic Superconductor (HTOS).
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Twistors from Killing Spinors alias Radiation from Pair Annihilation I: Theoretical Considerations | This paper is intended to be a further step through our Killing spinor
programme started with Class. Quantum Grav. \textbf{32}, 175007 (2015), and we
will advance our programme in accordance with the road map recently given in
arXiv:1611.04424v2. In the latter reference many open problems were declared,
one of which contained the uncovered relations between specific spinors in
spacetime represented by an arrow diagram built upon them. This work deals with
one of the arrows with almost all of its details and ends up with an important
physical interpretation of this setup in terms of the quantum electrodynamical
pair annihilation process. This method will shed light on the classification of
pseudo-Riemannian manifolds admitting twistors in connection with the
classification problem related to Killing spinors. Many physical
interpretations are given during the text some of which include dynamics of
brane immersions, quantum field theoretical considerations and black hole
evaporation.
| 0 | 0 | 1 | 0 | 0 | 0 |
Table Space Designs For Implicit and Explicit Concurrent Tabled Evaluation | One of the main advantages of Prolog is its potential for the implicit
exploitation of parallelism and, as a high-level language, Prolog is also often
used as a means to explicitly control concurrent tasks. Tabling is a powerful
implementation technique that overcomes some limitations of traditional Prolog
systems in dealing with recursion and redundant sub-computations. Given these
advantages, the question that arises is if tabling has also the potential for
the exploitation of concurrency/parallelism. On one hand, tabling still
exploits a search space as traditional Prolog but, on the other hand, the
concurrent model of tabling is necessarily far more complex since it also
introduces concurrency on the access to the tables. In this paper, we summarize
Yap's main contributions to concurrent tabled evaluation and we describe the
design and implementation challenges of several alternative table space designs
for implicit and explicit concurrent tabled evaluation which represent
different trade-offs between concurrency and memory usage. We also motivate for
the advantages of using fixed-size and lock-free data structures, elaborate on
the key role that the engine's memory allocator plays on such environments, and
discuss how Yap's mode-directed tabling support can be extended to concurrent
evaluation. Finally, we present our future perspectives towards an efficient
and novel concurrent framework which integrates both implicit and explicit
concurrent tabled evaluation in a single Prolog engine. Under consideration in
Theory and Practice of Logic Programming (TPLP).
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Evidence for triplet superconductivity near an antiferromagnetic instability in CrAs | Superconductivity was recently observed in CrAs as the helimagnetic order is
suppressed by applying pressure, suggesting possible unconventional
superconductivity. To reveal the nature of the superconducting order parameter
of CrAs, here we report the angular dependence of the upper critical field
under pressure. Upon rotating the field by 360 degrees in the $bc$-plane, six
maxima are observed in the upper critical field, where the oscillations have
both six-fold and two-fold symmetric components. Our analysis suggests the
presence of an unconventional odd-parity spin triplet state.
| 0 | 1 | 0 | 0 | 0 | 0 |
Singlet ground state in the spin-$1/2$ weakly coupled dimer compound NH$_4$[(V$_2$O$_3$)$_2$(4,4$^\prime$-$bpy$)$_2$(H$_2$PO$_4$)(PO$_4$)$_2$]$\cdot$0.5H$_2$O | We present the synthesis and a detailed investigation of structural and
magnetic properties of polycrystalline
NH$_4$[(V$_2$O$_3$)$_2$(4,4$^\prime$-$bpy$)$_2$(H$_2$PO$_4$)(PO$_4$)$_2$]$\cdot$0.5H$_2$O
by means of x-ray diffraction, magnetic susceptibility, electron spin
resonance, and $^{31}$P nuclear magnetic resonance measurements. Temperature
dependent magnetic susceptibility could be described well using a weakly
coupled spin-$1/2$ dimer model with an excitation gap $\Delta/k_{\rm B}\simeq
26.1$ K between the singlet ground state and triplet excited states and a weak
inter-dimer exchange coupling $J^\prime/k_{\rm B} \simeq 4.6$ K. A gapped chain
model also describes the data well with a gap of about 20 K. The ESR intensity
as a function of temperature traces the bulk susceptibility nicely. The
isotropic Land$\acute{\rm e}$ $g$-factor is estimated to be about $g \simeq
1.97$, at room temperature. We are able to resolve the $^{31}$P NMR signal as
coming from two inequivalent P-sites in the crystal structure. The hyperfine
coupling constant between $^{31}$P nucleus and V$^{4+}$ spins is calculated to
be $A_{\rm hf}(1) \simeq 2963$ Oe/$\mu_{\rm B}$ and $A_{\rm hf}(2) \simeq 1466$
Oe/$\mu_{\rm B}$ for the P(1) and P(2) sites, respectively. Our NMR shift and
spin-lattice relaxation rate for both the $^{31}$P sites show an activated
behaviour at low temperatures, further confirming the singlet ground state. The
estimated value of the spin gap from the NMR data measured in an applied field
of $H = 9.394$ T is consistent with the gap obtained from the magnetic
susceptibility analysis using the dimer model. Because of a relatively small
spin gap,
NH$_4$[(V$_2$O$_3$)$_2$(4,4$^\prime$-$bpy$)$_2$(H$_2$PO$_4$)(PO$_4$)$_2$]$\cdot$0.5H$_2$O
is a promising compound for further experimental studies under high magnetic
fields.
| 0 | 1 | 0 | 0 | 0 | 0 |
Deep learning bank distress from news and numerical financial data | In this paper we focus our attention on the exploitation of the information
contained in financial news to enhance the performance of a classifier of bank
distress. Such information should be analyzed and inserted into the predictive
model in the most efficient way and this task deals with all the issues related
to text analysis and specifically analysis of news media. Among the different
models proposed for such purpose, we investigate one of the possible deep
learning approaches, based on a doc2vec representation of the textual data, a
kind of neural network able to map the sequential and symbolic text input onto
a reduced latent semantic space. Afterwards, a second supervised neural network
is trained combining news data with standard financial figures to classify
banks whether in distressed or tranquil states, based on a small set of known
distress events. Then the final aim is not only the improvement of the
predictive performance of the classifier but also to assess the importance of
news data in the classification process. Does news data really bring more
useful information not contained in standard financial variables? Our results
seem to confirm such hypothesis.
| 1 | 0 | 0 | 1 | 0 | 0 |
Theoretical studies of superconductivity in doped BaCoSO | We investigate superconductivity that may exist in the doped BaCoSO, a
multi-orbital Mott insulator with a strong antiferromagnetic ground state. The
superconductivity is studied in both t-J type and Hubbard type multi-orbital
models by mean field approach and random phase approximation (RPA) analysis.
Even if there is no C4 rotational symmetry, it is found that the system still
carries a d-wave like pairing symmetry state with gapless nodes and sign
changed superconducting order parameters on Fermi surfaces. The results are
largely doping insensitive. In this superconducting state, the three t2g
orbitals have very different superconducting form factors in momentum space. In
particular, the intra-orbital pairing of the dx2-y2 orbital has a s-wave like
pairing form factor. The two methods also predict very different pairing
strength on different parts of Fermi surfaces.These results suggest that BaCoSO
and related materials can be a new ground to test and establish fundamental
principles for unconventional high temperature superconductivity.
| 0 | 1 | 0 | 0 | 0 | 0 |
On low for speed oracles | Relativizing computations of Turing machines to an oracle is a central
concept in the theory of computation, both in complexity theory and in
computability theory(!). Inspired by lowness notions from computability theory,
Allender introduced the concept of "low for speed" oracles. An oracle A is low
for speed if relativizing to A has essentially no effect on computational
complexity, meaning that if a decidable language can be decided in time $f(n)$
with access to oracle A, then it can be decided in time poly(f(n)) without any
oracle. The existence of non-computable such A's was later proven by Bayer and
Slaman, who even constructed a computably enumerable one, and exhibited a
number of properties of these oracles as well as interesting connections with
computability theory. In this paper, we pursue this line of research, answering
the questions left by Bayer and Slaman and give further evidence that the
structure of the class of low for speed oracles is a very rich one.
| 1 | 0 | 1 | 0 | 0 | 0 |
$\mbox{Rb}_{2}\mbox{Ti}_2\mbox{O}_{5-δ}$: A superionic conductor with colossal dielectric constant | Electrical conductivity and high dielectric constant are in principle
self-excluding, which makes the terms insulator and dielectric usually
synonymous. This is certainly true when the electrical carriers are electrons,
but not necessarily in a material where ions are extremely mobile, electronic
conduction is negligible and the charge transfer at the interface is
immaterial. Here we demonstrate in a perovskite-derived structure containing
five-coordinated Ti atoms, a colossal dielectric constant (up to $\mbox{10}^9$)
together with very high ionic conduction $\mbox{10}^{-3}\mbox{S.cm}^{-1}$ at
room temperature. Coupled investigations of I-V and dielectric constant
behavior allow to demonstrate that, due to ion migration and accumulation, this
material behaves like a giant dipole, exhibiting colossal electrical
polarization (of the order of $\mbox{0.1\,C.cm}^{-2}$). Therefore, it may be
considered as a "ferro-ionet" and is extremely promising in terms of
applications.
| 0 | 1 | 0 | 0 | 0 | 0 |
LinNet: Probabilistic Lineup Evaluation Through Network Embedding | Which of your team's possible lineups has the best chances against each of
your opponents possible lineups? In order to answer this question we develop
LinNet. LinNet exploits the dynamics of a directed network that captures the
performance of lineups at their matchups. The nodes of this network represent
the different lineups, while an edge from node j to node i exists if lineup i
has outperformed lineup j. We further annotate each edge with the corresponding
performance margin (point margin per minute). We then utilize this structure to
learn a set of latent features for each node (i.e., lineup) using the node2vec
framework. Consequently, LinNet builds a model on this latent space for the
probability of lineup A beating lineup B. We evaluate LinNet using NBA lineup
data from the five seasons between 2007-08 and 2011-12. Our results indicate
that our method has an out-of-sample accuracy of 69%. In comparison, utilizing
the adjusted plus-minus of the players within a lineup for the same prediction
problem provides an accuracy of 56%. More importantly, the probabilities are
well-calibrated as shown by the probability validation curves. One of the
benefits of LinNet - apart from its accuracy - is that it is generic and can be
applied in different sports since the only input required is the lineups'
matchup performances, i.e., not sport-specific features are needed.
| 0 | 0 | 0 | 1 | 0 | 0 |
Observational Equivalence in System Estimation: Contractions in Complex Networks | Observability of complex systems/networks is the focus of this paper, which
is shown to be closely related to the concept of contraction. Indeed, for
observable network tracking it is necessary/sufficient to have one node in each
contraction measured. Therefore, nodes in a contraction are equivalent to
recover for loss of observability, implying that contraction size is a key
factor for observability recovery. Here, using a polynomial order contraction
detection algorithm, we analyze the distribution of contractions, studying its
relation with key network properties. Our results show that contraction size is
related to network clustering coefficient and degree heterogeneity.
Particularly, in networks with power-law degree distribution, if the clustering
coefficient is high there are less contractions with smaller size on average.
The implication is that estimation/tracking of such systems requires less
number of measurements, while their observational recovery is more restrictive
in case of sensor failure. Further, in Small-World networks higher degree
heterogeneity implies that there are more contractions with smaller size on
average. Therefore, the estimation of representing system requires more
measurements, and also the recovery of measurement failure is more limited.
These results imply that one can tune the properties of synthetic networks to
alleviate their estimation/observability recovery.
| 1 | 0 | 0 | 0 | 0 | 0 |
Incremental Adversarial Domain Adaptation for Continually Changing Environments | Continuous appearance shifts such as changes in weather and lighting
conditions can impact the performance of deployed machine learning models.
While unsupervised domain adaptation aims to address this challenge, current
approaches do not utilise the continuity of the occurring shifts. In
particular, many robotics applications exhibit these conditions and thus
facilitate the potential to incrementally adapt a learnt model over minor
shifts which integrate to massive differences over time. Our work presents an
adversarial approach for lifelong, incremental domain adaptation which benefits
from unsupervised alignment to a series of intermediate domains which
successively diverge from the labelled source domain. We empirically
demonstrate that our incremental approach improves handling of large appearance
changes, e.g. day to night, on a traversable-path segmentation task compared
with a direct, single alignment step approach. Furthermore, by approximating
the feature distribution for the source domain with a generative adversarial
network, the deployment module can be rendered fully independent of retaining
potentially large amounts of the related source training data for only a minor
reduction in performance.
| 1 | 0 | 0 | 1 | 0 | 0 |
SAND: An automated VLBI imaging and analysing pipeline - I. Stripping component trajectories | We present our implementation of an automated VLBI data reduction pipeline
dedicated to interferometric data imaging and analysis. The pipeline can handle
massive VLBI data efficiently which makes it an appropriate tool to investigate
multi-epoch multiband VLBI data. Compared to traditional manual data reduction,
our pipeline provides more objective results since less human interference is
involved. Source extraction is done in the image plane, while deconvolution and
model fitting are done in both the image plane and the uv plane for parallel
comparison. The output from the pipeline includes catalogues of CLEANed images
and reconstructed models, polarisation maps, proper motion estimates, core
light curves and multi-band spectra. We have developed a regression strip
algorithm to automatically detect linear or non-linear patterns in the jet
component trajectories. This algorithm offers an objective method to match jet
components at different epochs and determine their proper motions.
| 0 | 1 | 0 | 0 | 0 | 0 |
Quench-induced entanglement and relaxation dynamics in Luttinger liquids | We investigate the time evolution towards the asymptotic steady state of a
one dimensional interacting system after a quantum quench. We show that at
finite time the latter induces entanglement between right- and left- moving
density excitations, encoded in their cross-correlators, which vanishes in the
long-time limit. This behavior results in a universal time-decay in system
spectral properties $ \propto t^{-2} $, in addition to non-universal power-law
contributions typical of Luttinger liquids. Importantly, we argue that the
presence of quench-induced entanglement clearly emerges in transport
properties, such as charge and energy currents injected in the system from a
biased probe, and determines their long-time dynamics. In particular, energy
fractionalization phenomenon turns out to be a promising platform to observe
the universal power-law decay $ \propto t^{-2} $ induced by entanglement and
represents a novel way to study the corresponding relaxation mechanism.
| 0 | 1 | 0 | 0 | 0 | 0 |
Coded Caching Schemes with Low Rate and Subpacketizations | Coded caching scheme, which is an effective technique to increase the
transmission efficiency during peak traffic times, has recently become quite
popular among the coding community. Generally rate can be measured to the
transmission in the peak traffic times, i.e., this efficiency increases with
the decreasing of rate. In order to implement a coded caching scheme, each file
in the library must be split in a certain number of packets. And this number
directly reflects the complexity of a coded caching scheme, i.e., the
complexity increases with the increasing of the packet number. However there
exists a tradeoff between the rate and packet number. So it is meaningful to
characterize this tradeoff and design the related Pareto-optimal coded caching
schemes with respect to both parameters.
Recently, a new concept called placement delivery array (PDA) was proposed to
characterize the coded caching scheme. However as far as we know no one has yet
proved that one of the previously known PDAs is Pareto-optimal. In this paper,
we first derive two lower bounds on the rate under the framework of PDA.
Consequently, the PDA proposed by Maddah-Ali and Niesen is Pareto-optimal, and
a tradeoff between rate and packet number is obtained for some parameters.
Then, from the above observations and the view point of combinatorial design,
two new classes of Pareto-optimal PDAs are obtained. Based on these PDAs, the
schemes with low rate and packet number are obtained. Finally the performance
of some previously known PDAs are estimated by comparing with these two classes
of schemes.
| 1 | 0 | 0 | 0 | 0 | 0 |
Multitask diffusion adaptation over networks with common latent representations | Online learning with streaming data in a distributed and collaborative manner
can be useful in a wide range of applications. This topic has been receiving
considerable attention in recent years with emphasis on both single-task and
multitask scenarios. In single-task adaptation, agents cooperate to track an
objective of common interest, while in multitask adaptation agents track
multiple objectives simultaneously. Regularization is one useful technique to
promote and exploit similarity among tasks in the latter scenario. This work
examines an alternative way to model relations among tasks by assuming that
they all share a common latent feature representation. As a result, a new
multitask learning formulation is presented and algorithms are developed for
its solution in a distributed online manner. We present a unified framework to
analyze the mean-square-error performance of the adaptive strategies, and
conduct simulations to illustrate the theoretical findings and potential
applications.
| 1 | 0 | 0 | 1 | 0 | 0 |
Strong perpendicular magnetic anisotropy energy density at Fe alloy/HfO2 interfaces | We report on the perpendicular magnetic anisotropy (PMA) behavior of heavy
metal (HM)/ Fe alloy/MgO thin film heterostructures after an ultrathin HfO2
passivation layer is inserted between the Fe alloy and the MgO. This is
accomplished by depositing one to two atomic layers of Hf onto the Fe alloy
before the subsequent rf sputter deposition of the MgO layer. This Hf layer is
fully oxidized during the subsequent deposition of the MgO layer, as confirmed
by X-ray photoelectron spectroscopy measurements. As the result a strong
interfacial perpendicular anisotropy energy density can be achieved without any
post-fabrication annealing treatment, for example 1.7 erg/cm^2 for the
Ta/Fe60Co20B20/HfO2/MgO heterostructure. Depending on the HM, further
enhancements of the PMA can be realized by thermal annealing to at least 400C.
We show that ultra-thin HfO2 layers offer a range of options for enhancing the
magnetic properties of magnetic heterostructures for spintronics applications.
| 0 | 1 | 0 | 0 | 0 | 0 |
Covering Groups of Nonconnected Topological Groups and 2-Groups | We investigate the universal cover of a topological group that is not
necessarily connected. Its existence as a topological group is governed by a
Taylor cocycle, an obstruction in 3-cohomology. Alternatively, it always exists
as a topological 2-group. The splitness of this 2-group is also governed by an
obstruction in 3-cohomology, a Sinh cocycle. We give explicit formulas for both
obstructions and show that they are inverse of each other.
| 0 | 0 | 1 | 0 | 0 | 0 |
Polarization properties of turbulent synchrotron bubbles: an approach based on Chandrasekhar-Kendall functions | Synchrotron emitting bubbles arise when the outflow from a compact
relativistic engine, either a Black Hole or a Neutron Star, impacts on the
environment. The emission properties of synchrotron radiation are widely used
to infer the dynamical properties of these bubbles, and from them the injection
conditions of the engine. Radio polarization offers an important tool to
investigate the level and spectrum of turbulence, the magnetic field
configuration, and possibly the degree of mixing. Here we introduce a formalism
based on Chandrasekhar-Kendall functions that allows us to properly take into
account the geometry of the bubble, going beyond standard analysis based on
periodic cartesian domains. We investigate how different turbulent spectra,
magnetic helicity and particle distribution function, impact on global
properties that are easily accessible to observations, even at low resolution,
and we provide fitting formulae to relate observed quantities to the underlying
magnetic field structure.
| 0 | 1 | 0 | 0 | 0 | 0 |
Efimov Effect in the Dirac Semi-metals | Efimov effect refers to quantum states with discrete scaling symmetry and a
universal scaling factor, and has attracted considerable interests from nuclear
to atomic physics communities. In a Dirac semi-metal, when an electron
interacts with a static impurity though a Coulomb interaction, the same scaling
of the kinetic and interaction energies also gives rise to such a Efimov
effect. However, even when the Fermi energy exactly lies at the Dirac point,
the vacuum polarization of electron-hole pair fluctuation can still screen the
Coulomb interaction, which leads to derivation from this scaling symmetry and
eventually breakdown of the Efimov effect. This distortion of the Efimov bound
state energy due to vacuum polarization is a relativistic electron analogy of
the Lamb shift for the hydrogen atom. Motivated by recent experimental
observations in two- and three-dimensional Dirac semi-metals, in this paper we
investigate this many-body correction to the Efimov effect, and answer the
question that under what condition a good number of Efimov-like bound states
can still be observed in these condensed matter experiments.
| 0 | 1 | 0 | 0 | 0 | 0 |
Logic Lectures: Gödel's Basic Logic Course at Notre Dame | An edited version is given of the text of Gödel's unpublished manuscript of
the notes for a course in basic logic he delivered at the University of Notre
Dame in 1939. Gödel's notes deal with what is today considered as important
logical problems par excellence, completeness, decidability, independence of
axioms, and with natural deduction too, which was all still a novelty at the
time the course was delivered. Full of regards towards beginners, the notes are
not excessively formalistic. Gödel presumably intended them just for himself,
and they are full of abbreviations. This together with some other matters (like
two versions of the same topic, and guessing the right order of the pages)
required additional effort to obtain a readable edited version. Because of the
quality of the material provided by Gödel, including also important
philosophical points, this effort should however be worthwhile. The edited
version of the text is accompanied by another version, called the source
version, which is quite close to Gödel's manuscript. It is meant to be a
record of the editorial interventions involved in producing the edited version
(in particular, how the abbreviations were disabridged), and a justification of
that later version.
| 0 | 0 | 1 | 0 | 0 | 0 |
Field dependent neutron diffraction study in Ni50Mn38Sb12 Heusler alloy | In this paper, we present temperature and field dependent neutron diffraction
(ND) study to unravel the structural and the magnetic properties in
Ni50Mn38Sb12 Heusler system. This alloy shows martensitic transition from high
temperature austenite cubic phase to low temperature martensite orthorhombic
phase on cooling. At 3 K, the lattice parameters and magnetic moments are found
to be almost insensitive to field. Just below the martensitic transition
temperature, the martensite phase fraction is found to be 85%. Upon applying
the field, the austenite phase becomes dominant, and the field induced reverse
martensitic transition is clearly observed in the ND data. Therefore, the
present study gives an estimate of the strength of the martensite phase or the
sharpness of the martensitic transition. Variation of individual moments and
the change in the phase fraction obtained from the analysis of the ND data
vividly show the change in the magneto-structural state of the material across
the transition.
| 0 | 1 | 0 | 0 | 0 | 0 |
Optimistic lower bounds for convex regularized least-squares | Minimax lower bounds are pessimistic in nature: for any given estimator,
minimax lower bounds yield the existence of a worst-case target vector
$\beta^*_{worst}$ for which the prediction error of the given estimator is
bounded from below. However, minimax lower bounds shed no light on the
prediction error of the given estimator for target vectors different than
$\beta^*_{worst}$. A characterization of the prediction error of any convex
regularized least-squares is given. This characterization provide both a lower
bound and an upper bound on the prediction error. This produces lower bounds
that are applicable for any target vector and not only for a single, worst-case
$\beta^*_{worst}$. Finally, these lower and upper bounds on the prediction
error are applied to the Lasso is sparse linear regression. We obtain a lower
bound involving the compatibility constant for any tuning parameter, matching
upper and lower bounds for the universal choice of the tuning parameter, and a
lower bound for the Lasso with small tuning parameter.
| 0 | 0 | 1 | 1 | 0 | 0 |
Continual Lifelong Learning with Neural Networks: A Review | Humans and animals have the ability to continually acquire, fine-tune, and
transfer knowledge and skills throughout their lifespan. This ability, referred
to as lifelong learning, is mediated by a rich set of neurocognitive mechanisms
that together contribute to the development and specialization of our
sensorimotor skills as well as to the long-term memory consolidation and
retrieval without catastrophic forgetting. Consequently, lifelong learning
capabilities are crucial for autonomous agents interacting in the real world
and processing continuous streams of information. However, lifelong learning
remains a long-standing challenge for machine learning and neural network
models since the continual acquisition of incrementally available information
from non-stationary data distributions generally leads to catastrophic
forgetting or interference. This limitation represents a major drawback for
state-of-the-art deep neural network models that typically learn
representations from stationary batches of training data, thus without
accounting for situations in which information becomes incrementally available
over time. In this review, we critically summarize the main challenges linked
to lifelong learning for artificial learning systems and compare existing
neural network approaches that alleviate, to different extents, catastrophic
forgetting. We discuss well-established and emerging research motivated by
lifelong learning factors in biological systems such as structural plasticity,
memory replay, curriculum and transfer learning, intrinsic motivation, and
multisensory integration.
| 0 | 0 | 0 | 1 | 1 | 0 |
Potential kernel, hitting probabilities and distributional asymptotics | Z^d-extensions of probability-preserving dynamical systems are themselves
dynamical systems preserving an infinite measure, and generalize random walks.
Using the method of moments, we prove a generalized central limit theorem for
additive functionals of the extension of integral zero, under spectral
assumptions. As a corollary, we get the fact that Green-Kubo's formula is
invariant under induction. This allows us to relate the hitting probability of
sites with the symmetrized potential kernel, giving an alternative proof and
generalizing a theorem of Spitzer. Finally, this relation is used to improve in
turn the asumptions of the generalized central limit theorem. Applications to
Lorentz gases in finite horizon and to the geodesic flow on abelian covers of
compact manifolds of negative curvature are discussed.
| 0 | 0 | 1 | 0 | 0 | 0 |
On the economics of electrical storage for variable renewable energy sources | The use of renewable energy sources is a major strategy to mitigate climate
change. Yet Sinn (2017) argues that excessive electrical storage requirements
limit the further expansion of variable wind and solar energy. We question, and
alter, strong implicit assumptions of Sinn's approach and find that storage
needs are considerably lower, up to two orders of magnitude. First, we move
away from corner solutions by allowing for combinations of storage and
renewable curtailment. Second, we specify a parsimonious optimization model
that explicitly considers an economic efficiency perspective. We conclude that
electrical storage is unlikely to limit the transition to renewable energy.
| 1 | 0 | 0 | 0 | 0 | 0 |
Deep Learning in Customer Churn Prediction: Unsupervised Feature Learning on Abstract Company Independent Feature Vectors | As companies increase their efforts in retaining customers, being able to
predict accurately ahead of time, whether a customer will churn in the
foreseeable future is an extremely powerful tool for any marketing team. The
paper describes in depth the application of Deep Learning in the problem of
churn prediction. Using abstract feature vectors, that can generated on any
subscription based company's user event logs, the paper proves that through the
use of the intrinsic property of Deep Neural Networks (learning secondary
features in an unsupervised manner), the complete pipeline can be applied to
any subscription based company with extremely good churn predictive
performance. Furthermore the research documented in the paper was performed for
Framed Data (a company that sells churn prediction as a service for other
companies) in conjunction with the Data Science Institute at Lancaster
University, UK. This paper is the intellectual property of Framed Data.
| 1 | 0 | 0 | 1 | 0 | 0 |
Predicting and Discovering True Muonium | The recent observation of discrepancies in the muonic sector motivates
searches for the yet undiscovered atom true muonium $(\mu^+\mu^-)$. To leverage
potential experimental signals, precise theoretical calculations are required.
I will present the on-going work to compute higher-order corrections to the
hyperfine splitting and the Lamb shift. Further, possible detection in rare
meson decay experiments like REDTOP and using true muonium production to
constrain mesonic form factors will be discussed.
| 0 | 1 | 0 | 0 | 0 | 0 |
Prediction of half-metallic properties in TlCrS2 and TlCrSe2 based on density functional theory | Half-metallic properties of TlCrS2, TlCrSe2 and hypothetical TlCrSSe have
been investigated by first-principles all-electron full-potential linearized
augmented plane wave plus local orbital (FP-LAPW+lo) method based on density
functional theory (DFT). The results of calculations show that TlCrS2 and
TlCrSSe are half-metals with energy gap (Eg ) ~0.12 ev for spin-down channel.
Strong hybridization of p-state of chalchogen and d-state of Cr leads to
bonding and antibonding states and subsequently to the appearance of a gap in
spin-down channel of TlCrS2 and TlCrSSe. In the case of TlCrSe2, there is a
partial hybridization and p-state is partially present in the DOS at Fermi
level making this compound nearly half- metallic. The present calculations
revealed that total magnetic moment keeps its integer value on a relatively
wide range of changes in volume (-10% 10%) for TlCrS2 and TlCrSSe, while total
magnetic moment of TlCrSe2 decreases with increasing volume approaching to
integer value 3{\mu}B.
| 0 | 1 | 0 | 0 | 0 | 0 |
Giant interfacial perpendicular magnetic anisotropy in Fe/CuIn$_{1-x}$Ga$_x$Se$_2$ beyond Fe/MgO | We study interfacial magnetocrystalline anisotropies in various
Fe/semiconductor heterostructures by means of first-principles calculations. We
find that many of those systems show perpendicular magnetic anisotropy (PMA)
with a positive value of the interfacial anisotropy constant $K_{\rm i}$. In
particular, the Fe/CuInSe$_2$ interface has a large $K_{\rm i}$ of $\sim
2.3\,{\rm mJ/m^2}$, which is about 1.6 times larger than that of Fe/MgO known
as a typical system with relatively large PMA. We also find that the values of
$K_{\rm i}$ in almost all the systems studied in this work follow the
well-known Bruno's relation, which indicates that minority-spin states around
the Fermi level provide dominant contributions to the interfacial
magnetocrystalline anisotropies. Detailed analyses of the local density of
states and wave-vector-resolved anisotropy energy clarify that the large
$K_{\rm i}$ in Fe/CuInSe$_2$ is attributed to the preferable $3d$-orbital
configurations around the Fermi level in the minority-spin states of the
interfacial Fe atoms. Moreover, we have shown that the locations of interfacial
Se atoms are the key for such orbital configurations of the interfacial Fe
atoms.
| 0 | 1 | 0 | 0 | 0 | 0 |
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