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J0906+6930: a radio-loud quasar in the early Universe | Radio-loud high-redshift quasars (HRQs), although only a few of them are
known to date, are crucial for the studies of the growth of supermassive black
holes (SMBHs) and the evolution of active galactic nuclei (AGN) at early
cosmological epochs. Radio jets offer direct evidence of SMBHs, and their radio
structures can be studied with the highest angular resolution using Very Long
Baseline Interferometry (VLBI). Here we report on the observations of three
HRQs (J0131-0321, J0906+6930, J1026+2542) at z>5 using the Korean VLBI Network
and VLBI Exploration of Radio Astrometry Arrays (together known as KaVA) with
the purpose of studying their pc-scale jet properties. The observations were
carried out at 22 and 43 GHz in 2016 January among the first-batch open-use
experiments of KaVA. The quasar J0906+6930 was detected at 22 GHz but not at 43
GHz. The other two sources were not detected and upper limits to their compact
radio emission are given. Archival VLBI imaging data and single-dish 15-GHz
monitoring light curve of J0906+6930 were also acquired as complementary
information. J0906+6930 shows a moderate-level variability at 15 GHz. The radio
image is characterized by a core-jet structure with a total detectable size of
~5 pc in projection. The brightness temperature, 1.9x10^{11} K, indicates
relativistic beaming of the jet. The radio properties of J0906+6930 are
consistent with a blazar. Follow-up VLBI observations will be helpful for
determining its structural variation.
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Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields | This work investigates the training of conditional random fields (CRFs) via
the stochastic dual coordinate ascent (SDCA) algorithm of Shalev-Shwartz and
Zhang (2016). SDCA enjoys a linear convergence rate and a strong empirical
performance for binary classification problems. However, it has never been used
to train CRFs. Yet it benefits from an `exact' line search with a single
marginalization oracle call, unlike previous approaches. In this paper, we
adapt SDCA to train CRFs, and we enhance it with an adaptive non-uniform
sampling strategy based on block duality gaps. We perform experiments on four
standard sequence prediction tasks. SDCA demonstrates performances on par with
the state of the art, and improves over it on three of the four datasets, which
have in common the use of sparse features.
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Accelerating Innovation Through Analogy Mining | The availability of large idea repositories (e.g., the U.S. patent database)
could significantly accelerate innovation and discovery by providing people
with inspiration from solutions to analogous problems. However, finding useful
analogies in these large, messy, real-world repositories remains a persistent
challenge for either human or automated methods. Previous approaches include
costly hand-created databases that have high relational structure (e.g.,
predicate calculus representations) but are very sparse. Simpler
machine-learning/information-retrieval similarity metrics can scale to large,
natural-language datasets, but struggle to account for structural similarity,
which is central to analogy. In this paper we explore the viability and value
of learning simpler structural representations, specifically, "problem
schemas", which specify the purpose of a product and the mechanisms by which it
achieves that purpose. Our approach combines crowdsourcing and recurrent neural
networks to extract purpose and mechanism vector representations from product
descriptions. We demonstrate that these learned vectors allow us to find
analogies with higher precision and recall than traditional
information-retrieval methods. In an ideation experiment, analogies retrieved
by our models significantly increased people's likelihood of generating
creative ideas compared to analogies retrieved by traditional methods. Our
results suggest a promising approach to enabling computational analogy at scale
is to learn and leverage weaker structural representations.
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$η$-Ricci solitons in $(\varepsilon)$-almost paracontact metric manifolds | The object of this paper is to study $\eta$-Ricci solitons on
$(\varepsilon)$-almost paracontact metric manifolds. We investigate
$\eta$-Ricci solitons in the case when its potential vector field is exactly
the characteristic vector field $\xi$ of the $(\varepsilon)$-almost paracontact
metric manifold and when the potential vector field is torse-forming. We also
study Einstein-like and $(\varepsilon)$-para Sasakian manifolds admitting
$\eta$-Ricci solitons. Finally we obtain some results for $\eta$-Ricci solitons
on $(\varepsilon)$-almost paracontact metric manifolds with a special view
towards parallel symmetric (0,2)-tensor fields.
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Entropy facilitated active transport | We show how active transport of ions can be interpreted as an entropy
facilitated process. In this interpretation, the pore geometry through which
substrates are transported can give rise to a driving force. This gives a
direct link between the geometry and the changes in Gibbs energy required.
Quantifying the size of this effect for several proteins we find that the
entropic contribution from the pore geometry is significant and we discuss how
the effect can be used to interpret variations in the affinity at the binding
site.
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Consistency of the Predicative Calculus of Cumulative Inductive Constructions (pCuIC) | In order to avoid well-know paradoxes associated with self-referential
definitions, higher-order dependent type theories stratify the theory using a
countably infinite hierarchy of universes (also known as sorts), Type$_0$ :
Type$_1$ : $\cdots$ . Such type systems are called cumulative if for any type
$A$ we have that $A$ : Type$_{i}$ implies $A$ : Type$_{i+1}$. The predicative
calculus of inductive constructions (pCIC) which forms the basis of the Coq
proof assistant, is one such system.
In this paper we present and establish the soundness of the predicative
calculus of cumulative inductive constructions (pCuIC) which extends the
cumulativity relation to inductive types.
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Private Information Retrieval from MDS Coded Data with Colluding Servers: Settling a Conjecture by Freij-Hollanti et al. | A $(K, N, T, K_c)$ instance of the MDS-TPIR problem is comprised of $K$
messages and $N$ distributed servers. Each message is separately encoded
through a $(K_c, N)$ MDS storage code. A user wishes to retrieve one message,
as efficiently as possible, while revealing no information about the desired
message index to any colluding set of up to $T$ servers. The fundamental limit
on the efficiency of retrieval, i.e., the capacity of MDS-TPIR is known only at
the extremes where either $T$ or $K_c$ belongs to $\{1,N\}$. The focus of this
work is a recent conjecture by Freij-Hollanti, Gnilke, Hollanti and Karpuk
which offers a general capacity expression for MDS-TPIR. We prove that the
conjecture is false by presenting as a counterexample a PIR scheme for the
setting $(K, N, T, K_c) = (2,4,2,2)$, which achieves the rate $3/5$, exceeding
the conjectured capacity, $4/7$. Insights from the counterexample lead us to
capacity characterizations for various instances of MDS-TPIR including all
cases with $(K, N, T, K_c) = (2,N,T,N-1)$, where $N$ and $T$ can be arbitrary.
| 1 | 0 | 0 | 0 | 0 | 0 |
ADaPTION: Toolbox and Benchmark for Training Convolutional Neural Networks with Reduced Numerical Precision Weights and Activation | Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) are
useful for many practical tasks in machine learning. Synaptic weights, as well
as neuron activation functions within the deep network are typically stored
with high-precision formats, e.g. 32 bit floating point. However, since storage
capacity is limited and each memory access consumes power, both storage
capacity and memory access are two crucial factors in these networks. Here we
present a method and present the ADaPTION toolbox to extend the popular deep
learning library Caffe to support training of deep CNNs with reduced numerical
precision of weights and activations using fixed point notation. ADaPTION
includes tools to measure the dynamic range of weights and activations. Using
the ADaPTION tools, we quantized several CNNs including VGG16 down to 16-bit
weights and activations with only 0.8% drop in Top-1 accuracy. The
quantization, especially of the activations, leads to increase of up to 50% of
sparsity especially in early and intermediate layers, which we exploit to skip
multiplications with zero, thus performing faster and computationally cheaper
inference.
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Graph Attention Networks | We present graph attention networks (GATs), novel neural network
architectures that operate on graph-structured data, leveraging masked
self-attentional layers to address the shortcomings of prior methods based on
graph convolutions or their approximations. By stacking layers in which nodes
are able to attend over their neighborhoods' features, we enable (implicitly)
specifying different weights to different nodes in a neighborhood, without
requiring any kind of costly matrix operation (such as inversion) or depending
on knowing the graph structure upfront. In this way, we address several key
challenges of spectral-based graph neural networks simultaneously, and make our
model readily applicable to inductive as well as transductive problems. Our GAT
models have achieved or matched state-of-the-art results across four
established transductive and inductive graph benchmarks: the Cora, Citeseer and
Pubmed citation network datasets, as well as a protein-protein interaction
dataset (wherein test graphs remain unseen during training).
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Social Media Would Not Lie: Prediction of the 2016 Taiwan Election via Online Heterogeneous Data | The prevalence of online media has attracted researchers from various domains
to explore human behavior and make interesting predictions. In this research,
we leverage heterogeneous social media data collected from various online
platforms to predict Taiwan's 2016 presidential election. In contrast to most
existing research, we take a "signal" view of heterogeneous information and
adopt the Kalman filter to fuse multiple signals into daily vote predictions
for the candidates. We also consider events that influenced the election in a
quantitative manner based on the so-called event study model that originated in
the field of financial research. We obtained the following interesting
findings. First, public opinions in online media dominate traditional polls in
Taiwan election prediction in terms of both predictive power and timeliness.
But offline polls can still function on alleviating the sample bias of online
opinions. Second, although online signals converge as election day approaches,
the simple Facebook "Like" is consistently the strongest indicator of the
election result. Third, most influential events have a strong connection to
cross-strait relations, and the Chou Tzu-yu flag incident followed by the
apology video one day before the election increased the vote share of Tsai
Ing-Wen by 3.66%. This research justifies the predictive power of online media
in politics and the advantages of information fusion. The combined use of the
Kalman filter and the event study method contributes to the data-driven
political analytics paradigm for both prediction and attribution purposes.
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The stratified micro-randomized trial design: sample size considerations for testing nested causal effects of time-varying treatments | Technological advancements in the field of mobile devices and wearable
sensors have helped overcome obstacles in the delivery of care, making it
possible to deliver behavioral treatments anytime and anywhere. Increasingly
the delivery of these treatments is triggered by predictions of risk or
engagement which may have been impacted by prior treatments. Furthermore the
treatments are often designed to have an impact on individuals over a span of
time during which subsequent treatments may be provided.
Here we discuss our work on the design of a mobile health smoking cessation
experimental study in which two challenges arose. First the randomizations to
treatment should occur at times of stress and second the outcome of interest
accrues over a period that may include subsequent treatment. To address these
challenges we develop the "stratified micro-randomized trial," in which each
individual is randomized among treatments at times determined by predictions
constructed from outcomes to prior treatment and with randomization
probabilities depending on these outcomes. We define both conditional and
marginal proximal treatment effects. Depending on the scientific goal these
effects may be defined over a period of time during which subsequent treatments
may be provided. We develop a primary analysis method and associated sample
size formulae for testing these effects.
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Multiplicative Convolution of Real Asymmetric and Real Antisymmetric Matrices | The singular values of products of standard complex Gaussian random matrices,
or sub-blocks of Haar distributed unitary matrices, have the property that
their probability distribution has an explicit, structured form referred to as
a polynomial ensemble. It is furthermore the case that the corresponding
bi-orthogonal system can be determined in terms of Meijer G-functions, and the
correlation kernel given as an explicit double contour integral. It has
recently been shown that the Hermitised product $X_M \cdots X_2 X_1A X_1^T
X_2^T \cdots X_M^T$, where each $X_i$ is a standard real complex Gaussian
matrix, and $A$ is real anti-symmetric shares exhibits analogous properties.
Here we use the theory of spherical functions and transforms to present a
theory which, for even dimensions, includes these properties of the latter
product as a special case. As an example we show that the theory also allows
for a treatment of this class of Hermitised product when the $X_i$ are chosen
as sub-blocks of Haar distributed real orthogonal matrices.
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An approach to Griffiths conjecture | The Griffiths conjecture asserts that every ample vector bundle $E$ over a
compact complex manifold $S$ admits a hermitian metric with positive curvature
in the sense of Griffiths. In this article we give a sufficient condition for a
positive hermitian metric on $\mathcal{O}_{\mathbb{P}(E^*)}(1)$ to induce a
Griffiths positive $L^2$-metric on the vector bundle $E$. This result suggests
to study the relative Kähler-Ricci flow on $\mathcal{O}_{\mathbb{P}(E^*)}(1)$
for the fibration $\mathbb{P}(E^*)\to S$. We define a flow and give arguments
for the convergence.
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On Detecting Adversarial Perturbations | Machine learning and deep learning in particular has advanced tremendously on
perceptual tasks in recent years. However, it remains vulnerable against
adversarial perturbations of the input that have been crafted specifically to
fool the system while being quasi-imperceptible to a human. In this work, we
propose to augment deep neural networks with a small "detector" subnetwork
which is trained on the binary classification task of distinguishing genuine
data from data containing adversarial perturbations. Our method is orthogonal
to prior work on addressing adversarial perturbations, which has mostly focused
on making the classification network itself more robust. We show empirically
that adversarial perturbations can be detected surprisingly well even though
they are quasi-imperceptible to humans. Moreover, while the detectors have been
trained to detect only a specific adversary, they generalize to similar and
weaker adversaries. In addition, we propose an adversarial attack that fools
both the classifier and the detector and a novel training procedure for the
detector that counteracts this attack.
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It's Time to Consider "Time" when Evaluating Recommender-System Algorithms [Proposal] | In this position paper, we question the current practice of calculating
evaluation metrics for recommender systems as single numbers (e.g. precision
p=.28 or mean absolute error MAE = 1.21). We argue that single numbers express
only average effectiveness over a usually rather long period (e.g. a year or
even longer), which provides only a vague and static view of the data. We
propose that recommender-system researchers should instead calculate metrics
for time-series such as weeks or months, and plot the results in e.g. a line
chart. This way, results show how algorithms' effectiveness develops over time,
and hence the results allow drawing more meaningful conclusions about how an
algorithm will perform in the future. In this paper, we explain our reasoning,
provide an example to illustrate our reasoning and present suggestions for what
the community should do next.
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Anomalous metals -- failed superconductors | The observation of metallic ground states in a variety of two-dimensional
electronic systems poses a fundamental challenge for the theory of electron
fluids. Here, we analyze evidence for the existence of a regime, which we call
the "anomalous metal regime," in diverse 2D superconducting systems driven
through a quantum superconductor to metal transition (QSMT) by tuning physical
parameters such as the magnetic field, the gate voltage in the case of systems
with a MOSFET geometry, or the degree of disorder. The principal
phenomenological observation is that in the anomalous metal, as a function of
decreasing temperature, the resistivity first drops as if the system were
approaching a superconducting ground state, but then saturates at low
temperatures to a value that can be orders of magnitude smaller than the Drude
value. The anomalous metal also shows a giant positive magneto-resistance.
Thus, it behaves as if it were a "failed superconductor." This behavior is
observed in a broad range of parameters. We moreover exhibit, by theoretical
solution of a model of superconducting grains embedded in a metallic matrix,
that as a matter of principle such anomalous metallic behavior can occur in the
neighborhood of a QSMT. However, we also argue that the robustness and
ubiquitous nature of the observed phenomena are difficult to reconcile with any
existing theoretical treatment, and speculate about the character of a more
fundamental theoretical framework.
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Energy Optimization of Automatic Hybrid Sailboat | Autonomous Surface Vehicles (ASVs) provide an effective way to actualize
applications such as environment monitoring, search and rescue, and scientific
researches. However, the conventional ASVs depends overly on the stored energy.
Hybrid Sailboat, mainly powered by the wind, can solve this problem by using an
auxiliary propulsion system. The electric energy cost of Hybrid Sailboat needs
to be optimized to achieve the ocean automatic cruise mission. Based on
adjusted setting on sails and rudders, this paper seeks the optimal trajectory
for autonomic cruising to reduce the energy cost by changing the heading angle
of sailing upwind. The experiment results validate the heading angle accounts
for energy cost and the trajectory with the best heading angle saves up to
23.7% than other conditions. Furthermore, the energy-time line can be used to
predict the energy cost for long-time sailing.
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Estimating the Operating Characteristics of Ensemble Methods | In this paper we present a technique for using the bootstrap to estimate the
operating characteristics and their variability for certain types of ensemble
methods. Bootstrapping a model can require a huge amount of work if the
training data set is large. Fortunately in many cases the technique lets us
determine the effect of infinite resampling without actually refitting a single
model. We apply the technique to the study of meta-parameter selection for
random forests. We demonstrate that alternatives to bootstrap aggregation and
to considering \sqrt{d} features to split each node, where d is the number of
features, can produce improvements in predictive accuracy.
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Plasma turbulence at ion scales: a comparison between PIC and Eulerian hybrid-kinetic approaches | Kinetic-range turbulence in magnetized plasmas and, in particular, in the
context of solar-wind turbulence has been extensively investigated over the
past decades via numerical simulations. Among others, one of the widely adopted
reduced plasma model is the so-called hybrid-kinetic model, where the ions are
fully kinetic and the electrons are treated as a neutralizing (inertial or
massless) fluid. Within the same model, different numerical methods and/or
approaches to turbulence development have been employed. In the present work,
we present a comparison between two-dimensional hybrid-kinetic simulations of
plasma turbulence obtained with two complementary approaches spanning about two
decades in wavenumber - from MHD inertial range to scales well below the ion
gyroradius - with a state-of-the-art accuracy. One approach employs hybrid
particle-in-cell (HPIC) simulations of freely-decaying Alfvénic turbulence,
whereas the other consists of Eulerian hybrid Vlasov-Maxwell (HVM) simulations
of turbulence continuously driven with partially-compressible large-scale
fluctuations. Despite the completely different initialization and
injection/drive at large scales, the same properties of turbulent fluctuations
at $k_\perp\rho_i\gtrsim1$ are observed. The system indeed self-consistently
"reprocesses" the turbulent fluctuations while they are cascading towards
smaller and smaller scales, in a way which actually depends on the plasma beta
parameter. Small-scale turbulence has been found to be mainly populated by
kinetic Alfvén wave (KAW) fluctuations for $\beta\geq1$, whereas KAW
fluctuations are only sub-dominant for low-$\beta$.
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Computational determination of the largest lattice polytope diameter | A lattice (d, k)-polytope is the convex hull of a set of points in dimension
d whose coordinates are integers between 0 and k. Let {\delta}(d, k) be the
largest diameter over all lattice (d, k)-polytopes. We develop a computational
framework to determine {\delta}(d, k) for small instances. We show that
{\delta}(3, 4) = 7 and {\delta}(3, 5) = 9; that is, we verify for (d, k) = (3,
4) and (3, 5) the conjecture whereby {\delta}(d, k) is at most (k + 1)d/2 and
is achieved, up to translation, by a Minkowski sum of lattice vectors.
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A high resolution ion microscope for cold atoms | We report on an ion-optical system that serves as a microscope for ultracold
ground state and Rydberg atoms. The system is designed to achieve a
magnification of up to 1000 and a spatial resolution in the 100 nm range,
thereby surpassing many standard imaging techniques for cold atoms. The
microscope consists of four electrostatic lenses and a microchannel plate in
conjunction with a delay line detector in order to achieve single particle
sensitivity with high temporal and spatial resolution. We describe the design
process of the microscope including ion-optical simulations of the imaging
system and characterize aberrations and the resolution limit. Furthermore, we
present the experimental realization of the microscope in a cold atom setup and
investigate its performance by patterned ionization with a structure size down
to 2.7 {\mu}m. The microscope meets the requirements for studying various
many-body effects, ranging from correlations in cold quantum gases up to
Rydberg molecule formation.
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Lock-Free Parallel Perceptron for Graph-based Dependency Parsing | Dependency parsing is an important NLP task. A popular approach for
dependency parsing is structured perceptron. Still, graph-based dependency
parsing has the time complexity of $O(n^3)$, and it suffers from slow training.
To deal with this problem, we propose a parallel algorithm called parallel
perceptron. The parallel algorithm can make full use of a multi-core computer
which saves a lot of training time. Based on experiments we observe that
dependency parsing with parallel perceptron can achieve 8-fold faster training
speed than traditional structured perceptron methods when using 10 threads, and
with no loss at all in accuracy.
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Finite groups with systems of $K$-$\frak{F}$-subnormal subgroups | Let $\frak {F}$ be a class of group. A subgroup $A$ of a finite group $G$ is
said to be $K$-$\mathfrak{F}$-subnormal in $G$ if there is a subgroup chain
$$A=A_{0} \leq A_{1} \leq \cdots \leq A_{n}=G$$ such that either $A_{i-1}
\trianglelefteq A_{i}$ or $A_{i}/(A_{i-1})_{A_{i}} \in \mathfrak{F}$ for all
$i=1, \ldots , n$. A formation $\frak {F}$ is said to be $K$-lattice provided
in every finite group $G$ the set of all its $K$-$\mathfrak{F}$-subnormal
subgroups forms a sublattice of the lattice of all subgroups of $G$.
In this paper we consider some new applications of the theory of $K$-lattice
formations. In particular, we prove the following
Theorem A. Let $\mathfrak{F}$ be a hereditary $K$-lattice saturated formation
containing all nilpotent groups.
(i) If every $\mathfrak{F}$-critical subgroup $H$ of $G$ is
$K$-$\mathfrak{F}$-subnormal in $G$ with $H/F(H)\in {\mathfrak{F}}$, then
$G/F(G)\in {\mathfrak{F}}$.
(ii) If every Schmidt subgroup of $G$ is $K$-$\mathfrak{F}$-subnormal in $G$,
then $G/G_{\mathfrak{F}}$ is abelian.
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Actions Speak Louder Than Goals: Valuing Player Actions in Soccer | Assessing the impact of the individual actions performed by soccer players
during games is a crucial aspect of the player recruitment process.
Unfortunately, most traditional metrics fall short in addressing this task as
they either focus on rare events like shots and goals alone or fail to account
for the context in which the actions occurred. This paper introduces a novel
advanced soccer metric for valuing any type of individual player action on the
pitch, be it with or without the ball. Our metric values each player action
based on its impact on the game outcome while accounting for the circumstances
under which the action happened. When applied to on-the-ball actions like
passes, dribbles, and shots alone, our metric identifies Argentine forward
Lionel Messi, French teenage star Kylian Mbappé, and Belgian winger Eden
Hazard as the most effective players during the 2016/2017 season.
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Partitioning the Outburst Energy of a Low Eddington Accretion Rate AGN at the Center of an Elliptical Galaxy: the Recent 12 Myr History of the Supermassive Black Hole in M87 | M87, the active galaxy at the center of the Virgo cluster, is ideal for
studying the interaction of a supermassive black hole (SMBH) with a hot,
gas-rich environment. A deep Chandra observation of M87 exhibits an
approximately circular shock front (13 kpc radius, in projection) driven by the
expansion of the central cavity (filled by the SMBH with relativistic
radio-emitting plasma) with projected radius $\sim$1.9 kpc. We combine
constraints from X-ray and radio observations of M87 with a shock model to
derive the properties of the outburst that created the 13 kpc shock. Principal
constraints for the model are 1) the measured Mach number ($M$$\sim$1.2), 2)
the radius of the 13 kpc shock, and 3) the observed size of the central
cavity/bubble (the radio-bright cocoon) that serves as the piston to drive the
shock. We find an outburst of $\sim$5$\times$$10^{57}$ ergs that began about 12
Myr ago and lasted $\sim$2 Myr matches all the constraints. In this model,
$\sim$22% of the energy is carried by the shock as it expands. The remaining
$\sim$80% of the outburst energy is available to heat the core gas. More than
half the total outburst energy initially goes into the enthalpy of the central
bubble, the radio cocoon. As the buoyant bubble rises, much of its energy is
transferred to the ambient thermal gas. For an outburst repetition rate of
about 12 Myrs (the age of the outburst), 80% of the outburst energy is
sufficient to balance the radiative cooling.
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On the missing link between pressure drop, viscous dissipation, and the turbulent energy spectrum | After decades of experimental, theoretical, and numerical research in fluid
dynamics, many aspects of turbulence remain poorly understood. The main reason
for this is often attributed to the multiscale nature of turbulent flows, which
poses a formidable challenge. There are, however, properties of these flows
whose roles and inter-connections have never been clarified fully. In this
article, we present a new connection between the pressure drop, viscous
dissipation, and the turbulent energy spectrum, which, to the best of our
knowledge, has never been established prior to our work. We use this finding to
show analytically that viscous dissipation in laminar pipe flows cannot
increase the temperature of the fluid, and to also reproduce qualitatively
Nikuradse's experimental results involving pressure drops in turbulent flows in
rough pipes.
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Discrete Local Induction Equation | The local induction equation, or the binormal flow on space curves is a
well-known model of deformation of space curves as it describes the dynamics of
vortex filaments, and the complex curvature is governed by the nonlinear
Schrödinger equation. In this paper, we present its discrete analogue,
namely, a model of deformation of discrete space curves by the discrete
nonlinear Schrödinger equation. We also present explicit formulas for both
smooth and discrete curves in terms of tau functions of the two-component KP
hierarchy.
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A sharp lower bound for the lifespan of small solutions to the Schrödinger equation with a subcritical power nonlinearity | Let $T_{\epsilon}$ be the lifespan for the solution to the Schrödinger
equation on $\mathbb{R}^d$ with a power nonlinearity $\lambda |u|^{2\theta/d}u$
($\lambda \in \mathbb{C}$, $0<\theta<1$) and the initial data in the form
$\epsilon \varphi(x)$. We provide a sharp lower bound estimate for
$T_{\epsilon}$ as $\epsilon \to +0$ which can be written explicitly by
$\lambda$, $d$, $\theta$, $\varphi$ and $\epsilon$. This is an improvement of
the previous result by H.Sasaki [Adv. Diff. Eq. 14 (2009), 1021--1039].
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State Space Reduction for Reachability Graph of CSM Automata | Classical CTL temporal logics are built over systems with interleaving model
concurrency. Many attempts are made to fight a state space explosion problem
(for instance, compositional model checking). There are some methods of
reduction of a state space based on independence of actions. However, in CSM
model, which is based on coincidences rather than on interleaving, independence
of actions cannot be defined. Therefore a state space reduction basing on
identical temporal consequences rather than on independence of action is
proposed. The new reduction is not as good as for interleaving systems, because
all successors of a state (in depth of two levels) must be obtained before a
reduction may be applied. This leads to reduction of space required for
representation of a state space, but not in time of state space construction.
Yet much savings may occur in regular state spaces for CSM systems.
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Permission Inference for Array Programs | Information about the memory locations accessed by a program is, for
instance, required for program parallelisation and program verification.
Existing inference techniques for this information provide only partial
solutions for the important class of array-manipulating programs. In this
paper, we present a static analysis that infers the memory footprint of an
array program in terms of permission pre- and postconditions as used, for
example, in separation logic. This formulation allows our analysis to handle
concurrent programs and produces specifications that can be used by
verification tools. Our analysis expresses the permissions required by a loop
via maximum expressions over the individual loop iterations. These maximum
expressions are then solved by a novel maximum elimination algorithm, in the
spirit of quantifier elimination. Our approach is sound and is implemented; an
evaluation on existing benchmarks for memory safety of array programs
demonstrates accurate results, even for programs with complex access patterns
and nested loops.
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Generating Query Suggestions to Support Task-Based Search | We address the problem of generating query suggestions to support users in
completing their underlying tasks (which motivated them to search in the first
place). Given an initial query, these query suggestions should provide a
coverage of possible subtasks the user might be looking for. We propose a
probabilistic modeling framework that obtains keyphrases from multiple sources
and generates query suggestions from these keyphrases. Using the test suites of
the TREC Tasks track, we evaluate and analyze each component of our model.
| 1 | 0 | 0 | 0 | 0 | 0 |
Application of Spin-Exchange Relaxation-Free Magnetometry to the Cosmic Axion Spin Precession Experiment | The Cosmic Axion Spin Precession Experiment (CASPEr) seeks to measure
oscillating torques on nuclear spins caused by axion or axion-like-particle
(ALP) dark matter via nuclear magnetic resonance (NMR) techniques. A sample
spin-polarized along a leading magnetic field experiences a resonance when the
Larmor frequency matches the axion/ALP Compton frequency, generating precessing
transverse nuclear magnetization. Here we demonstrate a Spin-Exchange
Relaxation-Free (SERF) magnetometer with sensitivity $\approx 1~{\rm
fT/\sqrt{Hz}}$ and an effective sensing volume of 0.1 $\rm{cm^3}$ that may be
useful for NMR detection in CASPEr. A potential drawback of
SERF-magnetometer-based NMR detection is the SERF's limited dynamic range. Use
of a magnetic flux transformer to suppress the leading magnetic field is
considered as a potential method to expand the SERF's dynamic range in order to
probe higher axion/ALP Compton frequencies.
| 0 | 1 | 0 | 0 | 0 | 0 |
Symmetry and the Geometric Phase in Ultracold Hydrogen-Exchange Reactions | Quantum reactive scattering calculations are reported for the ultracold
hydrogen-exchange reaction and its non-reactive atom-exchange isotopic
counterparts, proceeding from excited rotational states. It is shown that while
the geometric phase (GP) does not necessarily control the reaction to all final
states one can always find final states where it does. For the isotopic
counterpart reactions these states can be used to make a measurement of the GP
effect by separately measuring the even and odd symmetry contributions, which
experimentally requires nuclear-spin final-state resolution. This follows from
symmetry considerations that make the even and odd identical-particle exchange
symmetry wavefunctions which include the GP locally equivalent to the opposite
symmetry wavefunctions which do not. This equivalence reflects the important
role discrete symmetries play in ultracold chemistry generally and highlights
the key role ultracold reactions can play in understanding fundamental aspects
of chemical reactivity.
| 0 | 1 | 0 | 0 | 0 | 0 |
Characterization and Photometric Performance of the Hyper Suprime-Cam Software Pipeline | The Subaru Strategic Program (SSP) is an ambitious multi-band survey using
the Hyper Suprime-Cam (HSC) on the Subaru telescope. The Wide layer of the SSP
is both wide and deep, reaching a detection limit of i~26.0 mag. At these
depths, it is challenging to achieve accurate, unbiased, and consistent
photometry across all five bands. The HSC data are reduced using a pipeline
that builds on the prototype pipeline for the Large Synoptic Survey Telescope.
We have developed a Python-based, flexible framework to inject synthetic
galaxies into real HSC images called SynPipe. Here we explain the design and
implementation of SynPipe and generate a sample of synthetic galaxies to
examine the photometric performance of the HSC pipeline. For stars, we achieve
1% photometric precision at i~19.0 mag and 6% precision at i~25.0 in the
i-band. For synthetic galaxies with single-Sersic profiles, forced CModel
photometry achieves 13% photometric precision at i~20.0 mag and 18% precision
at i~25.0 in the i-band. We show that both forced PSF and CModel photometry
yield unbiased color estimates that are robust to seeing conditions. We
identify several caveats that apply to the version of HSC pipeline used for the
first public HSC data release (DR1) that need to be taking into consideration.
First, the degree to which an object is blended with other objects impacts the
overall photometric performance. This is especially true for point sources.
Highly blended objects tend to have larger photometric uncertainties,
systematically underestimated fluxes and slightly biased colors. Second, >20%
of stars at 22.5< i < 25.0 mag can be misclassified as extended objects. Third,
the current CModel algorithm tends to strongly underestimate the half-light
radius and ellipticity of galaxy with i>21.5 mag.
| 0 | 1 | 0 | 0 | 0 | 0 |
Information Geometry Approach to Parameter Estimation in Hidden Markov Models | We consider the estimation of hidden Markovian process by using information
geometry with respect to transition matrices. We consider the case when we use
only the histogram of $k$-memory data. Firstly, we focus on a partial
observation model with Markovian process and we show that the asymptotic
estimation error of this model is given as the inverse of projective Fisher
information of transition matrices. Next, we apply this result to the
estimation of hidden Markovian process. We carefully discuss the equivalence
problem for hidden Markovian process on the tangent space. Then, we propose a
novel method to estimate hidden Markovian process.
| 0 | 0 | 1 | 1 | 0 | 0 |
Parallel transport in principal 2-bundles | A nice differential-geometric framework for (non-abelian) higher gauge theory
is provided by principal 2-bundles, i.e. categorified principal bundles. Their
total spaces are Lie groupoids, local trivializations are kinds of Morita
equivalences, and connections are Lie-2-algebra-valued 1-forms. In this
article, we construct explicitly the parallel transport of a connection on a
principal 2-bundle. Parallel transport along a path is a Morita equivalence
between the fibres over the end points, and parallel transport along a surface
is an intertwiner between Morita equivalences. We prove that our constructions
fit into the general axiomatic framework for categorified parallel transport
and surface holonomy.
| 0 | 0 | 1 | 0 | 0 | 0 |
Gotta Learn Fast: A New Benchmark for Generalization in RL | In this report, we present a new reinforcement learning (RL) benchmark based
on the Sonic the Hedgehog (TM) video game franchise. This benchmark is intended
to measure the performance of transfer learning and few-shot learning
algorithms in the RL domain. We also present and evaluate some baseline
algorithms on the new benchmark.
| 0 | 0 | 0 | 1 | 0 | 0 |
Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit | Observations of astrophysical objects such as galaxies are limited by various
sources of random and systematic noise from the sky background, the optical
system of the telescope and the detector used to record the data. Conventional
deconvolution techniques are limited in their ability to recover features in
imaging data by the Shannon-Nyquist sampling theorem. Here we train a
generative adversarial network (GAN) on a sample of $4,550$ images of nearby
galaxies at $0.01<z<0.02$ from the Sloan Digital Sky Survey and conduct
$10\times$ cross validation to evaluate the results. We present a method using
a GAN trained on galaxy images that can recover features from artificially
degraded images with worse seeing and higher noise than the original with a
performance which far exceeds simple deconvolution. The ability to better
recover detailed features such as galaxy morphology from low-signal-to-noise
and low angular resolution imaging data significantly increases our ability to
study existing data sets of astrophysical objects as well as future
observations with observatories such as the Large Synoptic Sky Telescope (LSST)
and the Hubble and James Webb space telescopes.
| 0 | 1 | 0 | 1 | 0 | 0 |
Trends in European flood risk over the past 150 years | Flood risk changes in time and is influenced by both natural and
socio-economic trends and interactions. In Europe, previous studies of
historical flood losses corrected for demographic and economic growth
("normalized") have been limited in temporal and spatial extent, leading to an
incomplete representation in trends of losses over time. In this study we
utilize a gridded reconstruction of flood exposure in 37 European countries and
a new database of damaging floods since 1870. Our results indicate that since
1870 there has been an increase in annually inundated area and number of
persons affected, contrasted by a substantial decrease in flood fatalities,
after correcting for change in flood exposure. For more recent decades we also
found a considerable decline in financial losses per year. We estimate,
however, that there is large underreporting of smaller floods beyond most
recent years, and show that underreporting has a substantial impact on observed
trends.
| 0 | 0 | 0 | 1 | 0 | 0 |
Kinetic Trans-assembly of DNA Nanostructures | The central dogma of molecular biology is the principal framework for
understanding how nucleic acid information is propagated and used by living
systems to create complex biomolecules. Here, by integrating the structural and
dynamic paradigms of DNA nanotechnology, we present a rationally designed
synthetic platform which functions in an analogous manner to create complex DNA
nanostructures. Starting from one type of DNA nanostructure, DNA strand
displacement circuits were designed to interact and pass along the information
encoded in the initial structure to mediate the self-assembly of a different
type of structure, the final output structure depending on the type of circuit
triggered. Using this concept of a DNA structure "trans-assembling" a different
DNA structure through non-local strand displacement circuitry, four different
schemes were implemented. Specifically, 1D ladder and 2D double-crossover (DX)
lattices were designed to kinetically trigger DNA circuits to activate
polymerization of either ring structures or another type of DX lattice under
enzyme-free, isothermal conditions. In each scheme, the desired multilayer
reaction pathway was activated, among multiple possible pathways, ultimately
leading to the downstream self-assembly of the correct output structure.
| 0 | 0 | 0 | 0 | 1 | 0 |
Synthesis and analysis in total variation regularization | We generalize the bridge between analysis and synthesis estimators by Elad,
Milanfar and Rubinstein (2007) to rank deficient cases. This is a starting
point for the study of the connection between analysis and synthesis for total
variation regularized estimators. In particular, the case of first order total
variation regularized estimators over general graphs and their synthesis form
are studied.
We give a definition of the discrete graph derivative operator based on the
notion of line graph and provide examples of the synthesis form of
$k^{\text{th}}$ order total variation regularized estimators over a range of
graphs.
| 0 | 0 | 1 | 1 | 0 | 0 |
The Leray transform: factorization, dual $CR$ structures and model hypersurfaces in $\mathbb{C}\mathbb{P}^2$ | We compute the exact norms of the Leray transforms for a family
$\mathcal{S}_{\beta}$ of unbounded hypersurfaces in two complex dimensions. The
$\mathcal{S}_{\beta}$ generalize the Heisenberg group, and provide local
projective approximations to any smooth, strongly $\mathbb{C}$-convex
hypersurface $\mathcal{S}_{\beta}$ to two orders of tangency. This work is then
examined in the context of projective dual $CR$-structures and the
corresponding pair of canonical dual Hardy spaces associated to
$\mathcal{S}_{\beta}$, leading to a universal description of the Leray
transform and a factorization of the transform through orthogonal projection
onto the conjugate dual Hardy space.
| 0 | 0 | 1 | 0 | 0 | 0 |
A Fast Quantum-safe Asymmetric Cryptosystem Using Extra Superincreasing Sequences | This paper gives the definitions of an extra superincreasing sequence and an
anomalous subset sum, and proposes a fast quantum-safe asymmetric cryptosystem
called JUOAN2. The new cryptosystem is based on an additive multivariate
permutation problem (AMPP) and an anomalous subset sum problem (ASSP) which
parallel a multivariate polynomial problem and a shortest vector problem
respectively, and composed of a key generator, an encryption algorithm, and a
decryption algorithm. The authors analyze the security of the new cryptosystem
against the Shamir minima accumulation point attack and the LLL lattice basis
reduction attack, and prove it to be semantically secure (namely IND-CPA) on
the assumption that AMPP and ASSP have no subexponential time solutions.
Particularly, the analysis shows that the new cryptosystem has the potential to
be resistant to quantum computing attack, and is especially suitable to the
secret communication between two mobile terminals in maneuvering field
operations under any weather. At last, an example explaining the correctness of
the new cryptosystem is given.
| 1 | 0 | 0 | 0 | 0 | 0 |
Knowledge Transfer for Melanoma Screening with Deep Learning | Knowledge transfer impacts the performance of deep learning -- the state of
the art for image classification tasks, including automated melanoma screening.
Deep learning's greed for large amounts of training data poses a challenge for
medical tasks, which we can alleviate by recycling knowledge from models
trained on different tasks, in a scheme called transfer learning. Although much
of the best art on automated melanoma screening employs some form of transfer
learning, a systematic evaluation was missing. Here we investigate the presence
of transfer, from which task the transfer is sourced, and the application of
fine tuning (i.e., retraining of the deep learning model after transfer). We
also test the impact of picking deeper (and more expensive) models. Our results
favor deeper models, pre-trained over ImageNet, with fine-tuning, reaching an
AUC of 80.7% and 84.5% for the two skin-lesion datasets evaluated.
| 1 | 0 | 0 | 0 | 0 | 0 |
Large odd order character sums and improvements of the Pólya-Vinogradov inequality | For a primitive Dirichlet character $\chi$ modulo $q$, we define
$M(\chi)=\max_{t } |\sum_{n \leq t} \chi(n)|$. In this paper, we study this
quantity for characters of a fixed odd order $g\geq 3$. Our main result
provides a further improvement of the classical Pólya-Vinogradov inequality
in this case. More specifically, we show that for any such character $\chi$ we
have $$M(\chi)\ll_{\varepsilon} \sqrt{q}(\log q)^{1-\delta_g}(\log\log
q)^{-1/4+\varepsilon},$$ where $\delta_g := 1-\frac{g}{\pi}\sin(\pi/g)$. This
improves upon the works of Granville and Soundararajan and of Goldmakher.
Furthermore, assuming the Generalized Riemann hypothesis (GRH) we prove that $$
M(\chi) \ll \sqrt{q} \left(\log_2 q\right)^{1-\delta_g} \left(\log_3
q\right)^{-\frac{1}{4}}\left(\log_4 q\right)^{O(1)}, $$ where $\log_j$ is the
$j$-th iterated logarithm. We also show unconditionally that this bound is best
possible (up to a power of $\log_4 q$). One of the key ingredients in the proof
of the upper bounds is a new Halász-type inequality for logarithmic mean
values of completely multiplicative functions, which might be of independent
interest.
| 0 | 0 | 1 | 0 | 0 | 0 |
Estimation under group actions: recovering orbits from invariants | Motivated by geometric problems in signal processing, computer vision, and
structural biology, we study a class of orbit recovery problems where we
observe very noisy copies of an unknown signal, each acted upon by a random
element of some group (such as Z/p or SO(3)). The goal is to recover the orbit
of the signal under the group action in the high-noise regime. This generalizes
problems of interest such as multi-reference alignment (MRA) and the
reconstruction problem in cryo-electron microscopy (cryo-EM). We obtain
matching lower and upper bounds on the sample complexity of these problems in
high generality, showing that the statistical difficulty is intricately
determined by the invariant theory of the underlying symmetry group.
In particular, we determine that for cryo-EM with noise variance $\sigma^2$
and uniform viewing directions, the number of samples required scales as
$\sigma^6$. We match this bound with a novel algorithm for ab initio
reconstruction in cryo-EM, based on invariant features of degree at most 3. We
further discuss how to recover multiple molecular structures from heterogeneous
cryo-EM samples.
| 1 | 0 | 1 | 0 | 0 | 0 |
Crystal field excitations from $\mathrm{Yb^{3+}}$ ions at defective sites in highly stuffed $\rm Yb_2Ti_2O_7$ | The pyrochlore magnet $\rm Yb_2Ti_2O_7$ has been proposed as a quantum spin
ice candidate, a spin liquid state expected to display emergent quantum
electrodynamics with gauge photons among its elementary excitations. However,
$\rm Yb_2Ti_2O_7$'s ground state is known to be very sensitive to its precise
stoichiometry. Powder samples, produced by solid state synthesis at relatively
low temperatures, tend to be stoichiometric, while single crystals grown from
the melt tend to display weak "stuffing" wherein $\mathrm{\sim 2\%}$ of the
$\mathrm{Yb^{3+}}$, normally at the $A$ site of the $A_2B_2O_7$ pyrochlore
structure, reside as well at the $B$ site. In such samples $\mathrm{Yb^{3+}}$
ions should exist in defective environments at low levels, and be subjected to
crystalline electric fields (CEFs) very different from those at the
stoichiometric $A$ sites. New neutron scattering measurements of
$\mathrm{Yb^{3+}}$ in four compositions of $\rm Yb_{2+x}Ti_{2-x}O_{7-y}$, show
the spectroscopic signatures for these defective $\mathrm{Yb^{3+}}$ ions and
explicitly demonstrate that the spin anisotropy of the $\mathrm{Yb^{3+}}$
moment changes from XY-like for stoichiometric $\mathrm{Yb^{3+}}$, to
Ising-like for "stuffed" $B$ site $\mathrm{Yb^{3+}}$, or for $A$ site
$\mathrm{Yb^{3+}}$ in the presence of an oxygen vacancy.
| 0 | 1 | 0 | 0 | 0 | 0 |
HOUDINI: Lifelong Learning as Program Synthesis | We present a neurosymbolic framework for the lifelong learning of algorithmic
tasks that mix perception and procedural reasoning. Reusing high-level concepts
across domains and learning complex procedures are key challenges in lifelong
learning. We show that a program synthesis approach that combines gradient
descent with combinatorial search over programs can be a more effective
response to these challenges than purely neural methods. Our framework, called
HOUDINI, represents neural networks as strongly typed, differentiable
functional programs that use symbolic higher-order combinators to compose a
library of neural functions. Our learning algorithm consists of: (1) a symbolic
program synthesizer that performs a type-directed search over parameterized
programs, and decides on the library functions to reuse, and the architectures
to combine them, while learning a sequence of tasks; and (2) a neural module
that trains these programs using stochastic gradient descent. We evaluate
HOUDINI on three benchmarks that combine perception with the algorithmic tasks
of counting, summing, and shortest-path computation. Our experiments show that
HOUDINI transfers high-level concepts more effectively than traditional
transfer learning and progressive neural networks, and that the typed
representation of networks significantly accelerates the search.
| 1 | 0 | 0 | 1 | 0 | 0 |
Detecting Adversarial Examples via Key-based Network | Though deep neural networks have achieved state-of-the-art performance in
visual classification, recent studies have shown that they are all vulnerable
to the attack of adversarial examples. Small and often imperceptible
perturbations to the input images are sufficient to fool the most powerful deep
neural networks. Various defense methods have been proposed to address this
issue. However, they either require knowledge on the process of generating
adversarial examples, or are not robust against new attacks specifically
designed to penetrate the existing defense. In this work, we introduce
key-based network, a new detection-based defense mechanism to distinguish
adversarial examples from normal ones based on error correcting output codes,
using the binary code vectors produced by multiple binary classifiers applied
to randomly chosen label-sets as signatures to match normal images and reject
adversarial examples. In contrast to existing defense methods, the proposed
method does not require knowledge of the process for generating adversarial
examples and can be applied to defend against different types of attacks. For
the practical black-box and gray-box scenarios, where the attacker does not
know the encoding scheme, we show empirically that key-based network can
effectively detect adversarial examples generated by several state-of-the-art
attacks.
| 0 | 0 | 0 | 1 | 0 | 0 |
Guessing Attacks on Distributed-Storage Systems | The secrecy of a distributed-storage system for passwords is studied. The
encoder, Alice, observes a length-n password and describes it using two hints,
which she stores in different locations. The legitimate receiver, Bob, observes
both hints. In one scenario the requirement is that the expected number of
guesses it takes Bob to guess the password approach one as n tends to infinity,
and in the other that the expected size of the shortest list that Bob must form
to guarantee that it contain the password approach one. The eavesdropper, Eve,
sees only one of the hints. Assuming that Alice cannot control which hints Eve
observes, the largest normalized (by n) exponent that can be guaranteed for the
expected number of guesses it takes Eve to guess the password is characterized
for each scenario. Key to the proof are new results on Arikan's guessing and
Bunte and Lapidoth's task-encoding problem; in particular, the paper
establishes a close relation between the two problems. A rate-distortion
version of the model is also discussed, as is a generalization that allows for
Alice to produce {\delta} (not necessarily two) hints, for Bob to observe {\nu}
(not necessarily two) of the hints, and for Eve to observe {\eta} (not
necessarily one) of the hints. The generalized model is robust against {\delta}
- {\nu} disk failures.
| 1 | 0 | 1 | 0 | 0 | 0 |
Numerical analysis of nonlocal fracture models in Hölder space | In this work, we calculate the convergence rate of the finite difference
approximation for a class of nonlocal fracture models. We consider two point
force interactions characterized by a double well potential. We show the
existence of a evolving displacement field in Hölder space with Hölder
exponent $\gamma \in (0,1]$. The rate of convergence of the finite difference
approximation depends on the factor $C_s h^\gamma/\epsilon^2$ where $\epsilon$
gives the length scale of nonlocal interaction, $h$ is the discretization
length and $C_s$ is the maximum of Hölder norm of the solution and its second
derivatives during the evolution. It is shown that the rate of convergence
holds for both the forward Euler scheme as well as general single step implicit
schemes. A stability result is established for the semi-discrete approximation.
The Hölder continuous evolutions are seen to converge to a brittle fracture
evolution in the limit of vanishing nonlocality.
| 0 | 0 | 1 | 0 | 0 | 0 |
Iterative Collaborative Filtering for Sparse Matrix Estimation | The sparse matrix estimation problem consists of estimating the distribution
of an $n\times n$ matrix $Y$, from a sparsely observed single instance of this
matrix where the entries of $Y$ are independent random variables. This captures
a wide array of problems; special instances include matrix completion in the
context of recommendation systems, graphon estimation, and community detection
in (mixed membership) stochastic block models. Inspired by classical
collaborative filtering for recommendation systems, we propose a novel
iterative, collaborative filtering-style algorithm for matrix estimation in
this generic setting. We show that the mean squared error (MSE) of our
estimator converges to $0$ at the rate of $O(d^2 (pn)^{-2/5})$ as long as
$\omega(d^5 n)$ random entries from a total of $n^2$ entries of $Y$ are
observed (uniformly sampled), $\mathbb{E}[Y]$ has rank $d$, and the entries of
$Y$ have bounded support. The maximum squared error across all entries
converges to $0$ with high probability as long as we observe a little more,
$\Omega(d^5 n \ln^2(n))$ entries. Our results are the best known sample
complexity results in this generality.
| 0 | 0 | 1 | 1 | 0 | 0 |
Parameter Estimation in Finite Mixture Models by Regularized Optimal Transport: A Unified Framework for Hard and Soft Clustering | In this short paper, we formulate parameter estimation for finite mixture
models in the context of discrete optimal transportation with convex
regularization. The proposed framework unifies hard and soft clustering methods
for general mixture models. It also generalizes the celebrated
$k$\nobreakdash-means and expectation-maximization algorithms in relation to
associated Bregman divergences when applied to exponential family mixture
models.
| 1 | 0 | 0 | 1 | 0 | 0 |
Robust parameter determination in epidemic models with analytical descriptions of uncertainties | Compartmental equations are primary tools in disease spreading studies. Their
predictions are accurate for large populations but disagree with empirical and
simulated data for finite populations, where uncertainties become a relevant
factor. Starting from the agent-based approach, we investigate the role of
uncertainties and autocorrelation functions in SIS epidemic model, including
their relationship with epidemiological variables. We find new differential
equations that take uncertainties into account. The findings provide improved
predictions to the SIS model and it can offer new insights for emerging
diseases.
| 0 | 0 | 0 | 0 | 1 | 0 |
Direct observation of domain wall surface tension by deflating or inflating a magnetic bubble | The surface energy of a magnetic Domain Wall (DW) strongly affects its static
and dynamic behaviours. However, this effect was seldom directly observed and
many related phenomena have not been well understood. Moreover, a reliable
method to quantify the DW surface energy is still missing. Here, we report a
series of experiments in which the DW surface energy becomes a dominant
parameter. We observed that a semicircular magnetic domain bubble could
spontaneously collapse under the Laplace pressure induced by DW surface energy.
We further demonstrated that the surface energy could lead to a geometrically
induced pinning when the DW propagates in a Hall cross or from a nanowire into
a nucleation pad. Based on these observations, we developed two methods to
quantify the DW surface energy, which could be very helpful to estimate
intrinsic parameters such as Dzyaloshinskii-Moriya Interactions (DMI) or
exchange stiffness in magnetic ultra-thin films.
| 0 | 1 | 0 | 0 | 0 | 0 |
Unified Halo-Independent Formalism From Convex Hulls for Direct Dark Matter Searches | Using the Fenchel-Eggleston theorem for convex hulls (an extension of the
Caratheodory theorem), we prove that any likelihood can be maximized by either
a dark matter 1- speed distribution $F(v)$ in Earth's frame or 2- Galactic
velocity distribution $f^{\rm gal}(\vec{u})$, consisting of a sum of delta
functions. The former case applies only to time-averaged rate measurements and
the maximum number of delta functions is $({\mathcal N}-1)$, where ${\mathcal
N}$ is the total number of data entries. The second case applies to any
harmonic expansion coefficient of the time-dependent rate and the maximum
number of terms is ${\mathcal N}$. Using time-averaged rates, the
aforementioned form of $F(v)$ results in a piecewise constant unmodulated halo
function $\tilde\eta^0_{BF}(v_{\rm min})$ (which is an integral of the speed
distribution) with at most $({\mathcal N}-1)$ downward steps. The authors had
previously proven this result for likelihoods comprised of at least one
extended likelihood, and found the best-fit halo function to be unique. This
uniqueness, however, cannot be guaranteed in the more general analysis applied
to arbitrary likelihoods. Thus we introduce a method for determining whether
there exists a unique best-fit halo function, and provide a procedure for
constructing either a pointwise confidence band, if the best-fit halo function
is unique, or a degeneracy band, if it is not. Using measurements of modulation
amplitudes, the aforementioned form of $f^{\rm gal}(\vec{u})$, which is a sum
of Galactic streams, yields a periodic time-dependent halo function
$\tilde\eta_{BF}(v_{\rm min}, t)$ which at any fixed time is a piecewise
constant function of $v_{\rm min}$ with at most ${\mathcal N}$ downward steps.
In this case, we explain how to construct pointwise confidence and degeneracy
bands from the time-averaged halo function. Finally, we show that requiring an
isotropic ...
| 0 | 1 | 0 | 0 | 0 | 0 |
Finite size effects for spiking neural networks with spatially dependent coupling | We study finite-size fluctuations in a network of spiking deterministic
neurons coupled with non-uniform synaptic coupling. We generalize a previously
developed theory of finite size effects for uniform globally coupled neurons.
In the uniform case, mean field theory is well defined by averaging over the
network as the number of neurons in the network goes to infinity. However, for
nonuniform coupling it is no longer possible to average over the entire network
if we are interested in fluctuations at a particular location within the
network. We show that if the coupling function approaches a continuous function
in the infinite system size limit then an average over a local neighborhood can
be defined such that mean field theory is well defined for a spatially
dependent field. We then derive a perturbation expansion in the inverse system
size around the mean field limit for the covariance of the input to a neuron
(synaptic drive) and firing rate fluctuations due to dynamical deterministic
finite-size effects.
| 0 | 0 | 0 | 0 | 1 | 0 |
Shape and fission instabilities of ferrofluids in non-uniform magnetic fields | We study static distributions of ferrofluid submitted to non-uniform magnetic
fields. We show how the normal-field instability is modified in the presence of
a weak magnetic field gradient. Then we consider a ferrofluid droplet and show
how the gradient affects its shape. A rich phase transitions phenomenology is
found. We also investigate the creation of droplets by successive splits when a
magnet is vertically approached from below and derive theoretical expressions
which are solved numerically to obtain the number of droplets and their aspect
ratio as function of the field configuration. A quantitative comparison is
performed with previous experimental results, as well as with our own
experiments, and yields good agreement with the theoretical modeling.
| 0 | 1 | 0 | 0 | 0 | 0 |
Encrypted accelerated least squares regression | Information that is stored in an encrypted format is, by definition, usually
not amenable to statistical analysis or machine learning methods. In this paper
we present detailed analysis of coordinate and accelerated gradient descent
algorithms which are capable of fitting least squares and penalised ridge
regression models, using data encrypted under a fully homomorphic encryption
scheme. Gradient descent is shown to dominate in terms of encrypted
computational speed, and theoretical results are proven to give parameter
bounds which ensure correctness of decryption. The characteristics of encrypted
computation are empirically shown to favour a non-standard acceleration
technique. This demonstrates the possibility of approximating conventional
statistical regression methods using encrypted data without compromising
privacy.
| 1 | 0 | 0 | 1 | 0 | 0 |
Unified Model of Chaotic Inflation and Dynamical Supersymmetry Breaking | The large hierarchy between the Planck scale and the weak scale can be
explained by the dynamical breaking of supersymmetry in strongly coupled gauge
theories. Similarly, the hierarchy between the Planck scale and the energy
scale of inflation may also originate from strong dynamics, which dynamically
generate the inflaton potential. We present a model of the hidden sector which
unifies these two ideas, i.e., in which the scales of inflation and
supersymmetry breaking are provided by the dynamics of the same gauge group.
The resultant inflation model is chaotic inflation with a fractional power-law
potential in accord with the upper bound on the tensor-to-scalar ratio. The
supersymmetry breaking scale can be much smaller than the inflation scale, so
that the solution to the large hierarchy problem of the weak scale remains
intact. As an intrinsic feature of our model, we find that the sgoldstino,
which might disturb the inflationary dynamics, is automatically stabilized
during inflation by dynamically generated corrections in the strongly coupled
sector. This renders our model a field-theoretical realization of what is
sometimes referred to as sgoldstino-less inflation.
| 0 | 1 | 0 | 0 | 0 | 0 |
Tuplemax Loss for Language Identification | In many scenarios of a language identification task, the user will specify a
small set of languages which he/she can speak instead of a large set of all
possible languages. We want to model such prior knowledge into the way we train
our neural networks, by replacing the commonly used softmax loss function with
a novel loss function named tuplemax loss. As a matter of fact, a typical
language identification system launched in North America has about 95% users
who could speak no more than two languages. Using the tuplemax loss, our system
achieved a 2.33% error rate, which is a relative 39.4% improvement over the
3.85% error rate of standard softmax loss method.
| 1 | 0 | 0 | 0 | 0 | 0 |
Sparse Data Driven Mesh Deformation | Example-based mesh deformation methods are powerful tools for realistic shape
editing. However, existing techniques typically combine all the example
deformation modes, which can lead to overfitting, i.e. using a overly
complicated model to explain the user-specified deformation. This leads to
implausible or unstable deformation results, including unexpected global
changes outside the region of interest. To address this fundamental limitation,
we propose a sparse blending method that automatically selects a smaller number
of deformation modes to compactly describe the desired deformation. This along
with a suitably chosen deformation basis including spatially localized
deformation modes leads to significant advantages, including more meaningful,
reliable, and efficient deformations because fewer and localized deformation
modes are applied. To cope with large rotations, we develop a simple but
effective representation based on polar decomposition of deformation gradients,
which resolves the ambiguity of large global rotations using an
as-consistent-as-possible global optimization. This simple representation has a
closed form solution for derivatives, making it efficient for sparse localized
representation and thus ensuring interactive performance. Experimental results
show that our method outperforms state-of-the-art data-driven mesh deformation
methods, for both quality of results and efficiency.
| 1 | 0 | 0 | 0 | 0 | 0 |
Short Presburger arithmetic is hard | We study the computational complexity of short sentences in Presburger
arithmetic (Short-PA). Here by "short" we mean sentences with a bounded number
of variables, quantifiers, inequalities and Boolean operations; the input
consists only of the integer coefficients involved in the linear inequalities.
We prove that satisfiability of Short-PA sentences with $m+2$ alternating
quantifiers is $\Sigma_{P}^m$-complete or $\Pi_{P}^m$-complete, when the first
quantifier is $\exists$ or $\forall$, respectively. Counting versions and
restricted systems are also analyzed. Further application are given to hardness
of two natural problems in Integer Optimizations.
| 1 | 0 | 1 | 0 | 0 | 0 |
The Bias of the Log Power Spectrum for Discrete Surveys | A primary goal of galaxy surveys is to tighten constraints on cosmological
parameters, and the power spectrum $P(k)$ is the standard means of doing so.
However, at translinear scales $P(k)$ is blind to much of these surveys'
information---information which the log density power spectrum recovers. For
discrete fields (such as the galaxy density), $A^*$ denotes the statistic
analogous to the log density: $A^*$ is a "sufficient statistic" in that its
power spectrum (and mean) capture virtually all of a discrete survey's
information. However, the power spectrum of $A^*$ is biased with respect to the
corresponding log spectrum for continuous fields, and to use $P_{A^*}(k)$ to
constrain the values of cosmological parameters, we require some means of
predicting this bias. Here we present a prescription for doing so; for
Euclid-like surveys (with cubical cells 16$h^{-1}$ Mpc across) our bias
prescription's error is less than 3 per cent. This prediction will facilitate
optimal utilization of the information in future galaxy surveys.
| 0 | 1 | 0 | 0 | 0 | 0 |
Consistent nonparametric change point detection combining CUSUM and marked empirical processes | A weakly dependent time series regression model with multivariate covariates
and univariate observations is considered, for which we develop a procedure to
detect whether the nonparametric conditional mean function is stable in time
against change point alternatives. Our proposal is based on a modified CUSUM
type test procedure, which uses a sequential marked empirical process of
residuals. We show weak convergence of the considered process to a centered
Gaussian process under the null hypothesis of no change in the mean function
and a stationarity assumption. This requires some sophisticated arguments for
sequential empirical processes of weakly dependent variables. As a consequence
we obtain convergence of Kolmogorov-Smirnov and Cramér-von Mises type test
statistics. The proposed procedure acquires a very simple limiting distribution
and nice consistency properties, features from which related tests are lacking.
We moreover suggest a bootstrap version of the procedure and discuss its
applicability in the case of unstable variances.
| 0 | 0 | 1 | 1 | 0 | 0 |
Nonlinear electric field effect on perpendicular magnetic anisotropy in Fe/MgO interfaces | The electric field effect on magnetic anisotropy was studied in an ultrathin
Fe(001) monocrystalline layer sandwiched between Cr buffer and MgO tunnel
barrier layers, mainly through post-annealing temperature and measurement
temperature dependences. A large coefficient of the electric field effect of
more than 200 fJ/Vm was observed in the negative range of electric field, as
well as an areal energy density of perpendicular magnetic anisotropy (PMA) of
around 600 uJ/m2. More interestingly, nonlinear behavior, giving rise to a
local minimum around +100 mV/nm, was observed in the electric field dependence
of magnetic anisotropy, being independent of the post-annealing and measurement
temperatures. The insensitivity to both the interface conditions and the
temperature of the system suggests that the nonlinear behavior is attributed to
an intrinsic origin such as an inherent electronic structure in the Fe/MgO
interface. The present study can contribute to the progress in theoretical
studies, such as ab initio calculations, on the mechanism of the electric field
effect on PMA.
| 0 | 1 | 0 | 0 | 0 | 0 |
Local Symmetry and Global Structure in Adaptive Voter Models | "Coevolving" or "adaptive" voter models (AVMs) are natural systems for
modeling the emergence of mesoscopic structure from local networked processes
driven by conflict and homophily. Because of this, many methods for
approximating the long-run behavior of AVMs have been proposed over the last
decade. However, most such methods are either restricted in scope, expensive in
computation, or inaccurate in predicting important statistics. In this work, we
develop a novel, second-order moment closure approximation method for studying
the equilibrium mesoscopic structure of AVMs and apply it to binary-state
rewire-to-random and rewire-to-same model variants with random state-switching.
This framework exploits an asymmetry in voting events that enables us to derive
analytic approximations for the fast-timescale dynamics. The resulting
numerical approximations enable the computation of key properties of the model
behavior, such as the location of the fragmentation transition and the
equilibrium active edge density, across the entire range of state densities.
Numerically, they are nearly exact for the rewire-to-random model, and
competitive with other current approaches for the rewire-to-same model. We
conclude with suggestions for model refinement and extensions to more complex
models.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Weighted Model Confidence Set: Applications to Local and Mixture Model Confidence Sets | This article provides a weighted model confidence set, whenever underling
model has been misspecified and some part of support of random variable $X$
conveys some important information about underling true model. Application of
such weighted model confidence set for local and mixture model confidence sets
have been given. Two simulation studies have been conducted to show practical
application of our findings.
| 0 | 0 | 0 | 1 | 0 | 0 |
Poverty Mapping Using Convolutional Neural Networks Trained on High and Medium Resolution Satellite Images, With an Application in Mexico | Mapping the spatial distribution of poverty in developing countries remains
an important and costly challenge. These "poverty maps" are key inputs for
poverty targeting, public goods provision, political accountability, and impact
evaluation, that are all the more important given the geographic dispersion of
the remaining bottom billion severely poor individuals. In this paper we train
Convolutional Neural Networks (CNNs) to estimate poverty directly from high and
medium resolution satellite images. We use both Planet and Digital Globe
imagery with spatial resolutions of 3-5 sq. m. and 50 sq. cm. respectively,
covering all 2 million sq. km. of Mexico. Benchmark poverty estimates come from
the 2014 MCS-ENIGH combined with the 2015 Intercensus and are used to estimate
poverty rates for 2,456 Mexican municipalities. CNNs are trained using the 896
municipalities in the 2014 MCS-ENIGH. We experiment with several architectures
(GoogleNet, VGG) and use GoogleNet as a final architecture where weights are
fine-tuned from ImageNet. We find that 1) the best models, which incorporate
satellite-estimated land use as a predictor, explain approximately 57% of the
variation in poverty in a validation sample of 10 percent of MCS-ENIGH
municipalities; 2) Across all MCS-ENIGH municipalities explanatory power
reduces to 44% in a CNN prediction and landcover model; 3) Predicted poverty
from the CNN predictions alone explains 47% of the variation in poverty in the
validation sample, and 37% over all MCS-ENIGH municipalities; 4) In urban areas
we see slight improvements from using Digital Globe versus Planet imagery,
which explain 61% and 54% of poverty variation respectively. We conclude that
CNNs can be trained end-to-end on satellite imagery to estimate poverty,
although there is much work to be done to understand how the training process
influences out of sample validation.
| 1 | 0 | 0 | 1 | 0 | 0 |
Network of sensitive magnetometers for urban studies | The magnetic signature of an urban environment is investigated using a
geographically distributed network of fluxgate magnetometers deployed in and
around Berkeley, California. The system hardware and software are described and
results from initial operation of the network are reported. The sensors sample
the vector magnetic field with a 4 kHz resolution and are sensitive to
fluctuations below 0.1 $\textrm{nT}/\sqrt{\textrm{Hz}}$. Data from separate
stations are synchronized to around $\pm100$ $\mu{s}$ using GPS and computer
system clocks. Data from all sensors are automatically uploaded to a central
server. Anomalous events, such as lightning strikes, have been observed. A
wavelet analysis is used to study observations over a wide range of temporal
scales up to daily variations that show strong differences between weekend and
weekdays. The Bay Area Rapid Transit (BART) is identified as the most dominant
signal from these observations and a superposed epoch analysis is used to study
and extract the BART signal. Initial results of the correlation between sensors
are also presented.
| 0 | 1 | 0 | 0 | 0 | 0 |
Cooperative Hierarchical Dirichlet Processes: Superposition vs. Maximization | The cooperative hierarchical structure is a common and significant data
structure observed in, or adopted by, many research areas, such as: text mining
(author-paper-word) and multi-label classification (label-instance-feature).
Renowned Bayesian approaches for cooperative hierarchical structure modeling
are mostly based on topic models. However, these approaches suffer from a
serious issue in that the number of hidden topics/factors needs to be fixed in
advance and an inappropriate number may lead to overfitting or underfitting.
One elegant way to resolve this issue is Bayesian nonparametric learning, but
existing work in this area still cannot be applied to cooperative hierarchical
structure modeling.
In this paper, we propose a cooperative hierarchical Dirichlet process (CHDP)
to fill this gap. Each node in a cooperative hierarchical structure is assigned
a Dirichlet process to model its weights on the infinite hidden factors/topics.
Together with measure inheritance from hierarchical Dirichlet process, two
kinds of measure cooperation, i.e., superposition and maximization, are defined
to capture the many-to-many relationships in the cooperative hierarchical
structure. Furthermore, two constructive representations for CHDP, i.e.,
stick-breaking and international restaurant process, are designed to facilitate
the model inference. Experiments on synthetic and real-world data with
cooperative hierarchical structures demonstrate the properties and the ability
of CHDP for cooperative hierarchical structure modeling and its potential for
practical application scenarios.
| 1 | 0 | 0 | 1 | 0 | 0 |
The extended law of star formation: the combined role of gas and stars | We present a model for the origin of the extended law of star formation in
which the surface density of star formation ($\Sigma_{\rm SFR}$) depends not
only on the local surface density of the gas ($\Sigma_{g}$), but also on the
stellar surface density ($\Sigma_{*}$), the velocity dispersion of the stars,
and on the scaling laws of turbulence in the gas. We compare our model with the
spiral, face-on galaxy NGC 628 and show that the dependence of the star
formation rate on the entire set of physical quantities for both gas and stars
can help explain both the observed general trends in the
$\Sigma_{g}-\Sigma_{\rm SFR}$ and $\Sigma_{*}-\Sigma_{\rm SFR}$ relations, but
also, and equally important, the scatter in these relations at any value of
$\Sigma_{g}$ and $\Sigma_{*}$. Our results point out to the crucial role played
by existing stars along with the gaseous component in setting the conditions
for large scale gravitational instabilities and star formation in galactic
disks.
| 0 | 1 | 0 | 0 | 0 | 0 |
Local and global similarity of holomorphic matrices | R. Guralnick (Linear Algebra Appl. 99, 85-96, 1988) proved that two
holomorphic matrices on a noncompact connected Riemann surface, which are
locally holomorphically similar, are globally holomorphically similar. We
generalize this to (possibly, non-smooth) one-dimensional Stein spaces. For
Stein spaces of arbitrary dimension, we prove that global $\mathcal C^\infty$
similarity implies global holomorphic similarity, whereas global continuous
similarity is not sufficient.
| 0 | 0 | 1 | 0 | 0 | 0 |
WHInter: A Working set algorithm for High-dimensional sparse second order Interaction models | Learning sparse linear models with two-way interactions is desirable in many
application domains such as genomics. l1-regularised linear models are popular
to estimate sparse models, yet standard implementations fail to address
specifically the quadratic explosion of candidate two-way interactions in high
dimensions, and typically do not scale to genetic data with hundreds of
thousands of features. Here we present WHInter, a working set algorithm to
solve large l1-regularised problems with two-way interactions for binary design
matrices. The novelty of WHInter stems from a new bound to efficiently identify
working sets while avoiding to scan all features, and on fast computations
inspired from solutions to the maximum inner product search problem. We apply
WHInter to simulated and real genetic data and show that it is more scalable
and two orders of magnitude faster than the state of the art.
| 0 | 0 | 0 | 1 | 1 | 0 |
On the Limitation of Convolutional Neural Networks in Recognizing Negative Images | Convolutional Neural Networks (CNNs) have achieved state-of-the-art
performance on a variety of computer vision tasks, particularly visual
classification problems, where new algorithms reported to achieve or even
surpass the human performance. In this paper, we examine whether CNNs are
capable of learning the semantics of training data. To this end, we evaluate
CNNs on negative images, since they share the same structure and semantics as
regular images and humans can classify them correctly. Our experimental results
indicate that when training on regular images and testing on negative images,
the model accuracy is significantly lower than when it is tested on regular
images. This leads us to the conjecture that current training methods do not
effectively train models to generalize the concepts. We then introduce the
notion of semantic adversarial examples - transformed inputs that semantically
represent the same objects, but the model does not classify them correctly -
and present negative images as one class of such inputs.
| 1 | 0 | 0 | 1 | 0 | 0 |
$\aleph_1$ and the modal $μ$-calculus | For a regular cardinal $\kappa$, a formula of the modal $\mu$-calculus is
$\kappa$-continuous in a variable x if, on every model, its interpretation as a
unary function of x is monotone and preserves unions of $\kappa$-directed sets.
We define the fragment $C_{\aleph_1}(x)$ of the modal $\mu$-calculus and prove
that all the formulas in this fragment are $\aleph_1$-continuous. For each
formula $\phi(x)$ of the modal $\mu$-calculus, we construct a formula $\psi(x)
\in C_{\aleph_1 }(x)$ such that $\phi(x)$ is $\kappa$-continuous, for some
$\kappa$, if and only if $\phi(x)$ is equivalent to $\psi(x)$. Consequently, we
prove that (i) the problem whether a formula is $\kappa$-continuous for some
$\kappa$ is decidable, (ii) up to equivalence, there are only two fragments
determined by continuity at some regular cardinal: the fragment
$C_{\aleph_0}(x)$ studied by Fontaine and the fragment $C_{\aleph_1}(x)$. We
apply our considerations to the problem of characterizing closure ordinals of
formulas of the modal $\mu$-calculus. An ordinal $\alpha$ is the closure
ordinal of a formula $\phi(x)$ if its interpretation on every model converges
to its least fixed-point in at most $\alpha$ steps and if there is a model
where the convergence occurs exactly in $\alpha$ steps. We prove that
$\omega_1$, the least uncountable ordinal, is such a closure ordinal. Moreover
we prove that closure ordinals are closed under ordinal sum. Thus, any formal
expression built from 0, 1, $\omega$, $\omega_1$ by using the binary operator
symbol + gives rise to a closure ordinal.
| 1 | 0 | 1 | 0 | 0 | 0 |
Optimal Service Elasticity in Large-Scale Distributed Systems | A fundamental challenge in large-scale cloud networks and data centers is to
achieve highly efficient server utilization and limit energy consumption, while
providing excellent user-perceived performance in the presence of uncertain and
time-varying demand patterns. Auto-scaling provides a popular paradigm for
automatically adjusting service capacity in response to demand while meeting
performance targets, and queue-driven auto-scaling techniques have been widely
investigated in the literature. In typical data center architectures and cloud
environments however, no centralized queue is maintained, and load balancing
algorithms immediately distribute incoming tasks among parallel queues. In
these distributed settings with vast numbers of servers, centralized
queue-driven auto-scaling techniques involve a substantial communication
overhead and major implementation burden, or may not even be viable at all.
Motivated by the above issues, we propose a joint auto-scaling and load
balancing scheme which does not require any global queue length information or
explicit knowledge of system parameters, and yet provides provably near-optimal
service elasticity. We establish the fluid-level dynamics for the proposed
scheme in a regime where the total traffic volume and nominal service capacity
grow large in proportion. The fluid-limit results show that the proposed scheme
achieves asymptotic optimality in terms of user-perceived delay performance as
well as energy consumption. Specifically, we prove that both the waiting time
of tasks and the relative energy portion consumed by idle servers vanish in the
limit. At the same time, the proposed scheme operates in a distributed fashion
and involves only constant communication overhead per task, thus ensuring
scalability in massive data center operations.
| 1 | 0 | 1 | 0 | 0 | 0 |
Variational Monte Carlo study of spin dynamics in underdoped cuprates | The hour-glass-like dispersion of spin excitations is a common feature of
underdoped cuprates. It was qualitatively explained by the random phase
approximation based on various ordered states with some phenomenological
parameters; however, its origin remains elusive. Here, we present a numerical
study of spin dynamics in the $t$-$J$ model using the variational Monte Carlo
method. This parameter-free method satisfies the no double-occupancy constraint
of the model and thus provides a better evaluation on the spin dynamics with
respect to various mean-field trial states. We conclude that the lower branch
of the hour-glass dispersion is a collective mode and the upper branch is more
likely the consequence of the stripe state than the other candidates.
| 0 | 1 | 0 | 0 | 0 | 0 |
High brightness electron beam for radiation therapy: A new approach | I propose to use high brightness electron beam with 1 to 100 MeV energy as
tool to combat tumor or cancerous tissues in deep part of body. The method is
to directly deliver the electron beam to the tumor site via a small tube that
connected to a high brightness electron beam accelerator that is commonly
available around the world. Here I gave a basic scheme on the principle, I
believe other issues people raises will be solved easily for those who are
interested in solving the problems.
| 0 | 1 | 0 | 0 | 0 | 0 |
Parabolic equations with divergence-free drift in space $L_{t}^{l}L_{x}^{q}$ | In this paper we study the fundamental solution $\varGamma(t,x;\tau,\xi)$ of
the parabolic operator $L_{t}=\partial_{t}-\Delta+b(t,x)\cdot\nabla$, where for
every $t$, $b(t,\cdot)$ is a divergence-free vector field, and we consider the
case that $b$ belongs to the Lebesgue space
$L^{l}\left(0,T;L^{q}\left(\mathbb{R}^{n}\right)\right)$. The regularity of
weak solutions to the parabolic equation $L_{t}u=0$ depends critically on the
value of the parabolic exponent $\gamma=\frac{2}{l}+\frac{n}{q}$. Without the
divergence-free condition on $b$, the regularity of weak solutions has been
established when $\gamma\leq1$, and the heat kernel estimate has been obtained
as well, except for the case that $l=\infty,q=n$. The regularity of weak
solutions was deemed not true for the critical case
$L^{\infty}\left(0,T;L^{n}\left(\mathbb{R}^{n}\right)\right)$ for a general
$b$, while it is true for the divergence-free case, and a written proof can be
deduced from the results in [Semenov, 2006]. One of the results obtained in the
present paper establishes the Aronson type estimate for critical and
supercritical cases and for vector fields $b$ which are divergence-free. We
will prove the best possible lower and upper bounds for the fundamental
solution one can derive under the current approach. The significance of the
divergence-free condition enters the study of parabolic equations rather
recently, mainly due to the discovery of the compensated compactness. The
interest for the study of such parabolic equations comes from its connections
with Leray's weak solutions of the Navier-Stokes equations and the Taylor
diffusion associated with a vector field where the heat operator $L_{t}$
appears naturally.
| 0 | 0 | 1 | 0 | 0 | 0 |
Translating Terminological Expressions in Knowledge Bases with Neural Machine Translation | Our work presented in this paper focuses on the translation of terminological
expressions represented in semantically structured resources, like ontologies
or knowledge graphs. The challenge of translating ontology labels or
terminological expressions represented in knowledge bases lies in the highly
specific vocabulary and the lack of contextual information, which can guide a
machine translation system to translate ambiguous words into the targeted
domain. Due to these challenges, we evaluate the translation quality of
domain-specific expressions in the medical and financial domain with
statistical (SMT) as well as with neural machine translation (NMT) methods and
experiment domain adaptation of the translation models with terminological
expressions only. Furthermore, we perform experiments on the injection of
external terminological expressions into the translation systems. Through these
experiments, we observed a significant advantage in domain adaptation for the
domain-specific resource in the medical and financial domain and the benefit of
subword models over word-based NMT models for terminology translation.
| 1 | 0 | 0 | 0 | 0 | 0 |
Detecting Arbitrary Attacks Using Continuous Secured Side Information in Wireless Networks | This paper focuses on Byzantine attack detection for Gaussian two-hop one-way
relay network, where an amplify-and-forward relay may conduct Byzantine attacks
by forwarding altered symbols to the destination. For facilitating attack
detection, we utilize the openness of wireless medium to make the destination
observe some secured signals that are not attacked. Then, a detection scheme is
developed for the destination by using its secured observations to
statistically check other observations from the relay. On the other hand,
notice the Gaussian channel is continuous, which allows the possible Byzantine
attacks to be conducted within continuous alphabet(s). The existing work on
discrete channel is not applicable for investigating the performance of the
proposed scheme. The main contribution of this paper is to prove that if and
only if the wireless relay network satisfies a non-manipulable channel
condition, the proposed detection scheme achieves asymptotic errorless
performance against arbitrary attacks that allow the stochastic distributions
of altered symbols to vary arbitrarily and depend on each other. No pre-shared
secret or secret transmission is needed for the detection. Furthermore, we also
prove that the relay network is non-manipulable as long as all channel
coefficients are non-zero, which is not essential restrict for many practical
systems.
| 1 | 0 | 0 | 0 | 0 | 0 |
Phase Transitions in the Pooled Data Problem | In this paper, we study the pooled data problem of identifying the labels
associated with a large collection of items, based on a sequence of pooled
tests revealing the counts of each label within the pool. In the noiseless
setting, we identify an exact asymptotic threshold on the required number of
tests with optimal decoding, and prove a phase transition between complete
success and complete failure. In addition, we present a novel noisy variation
of the problem, and provide an information-theoretic framework for
characterizing the required number of tests for general random noise models.
Our results reveal that noise can make the problem considerably more difficult,
with strict increases in the scaling laws even at low noise levels. Finally, we
demonstrate similar behavior in an approximate recovery setting, where a given
number of errors is allowed in the decoded labels.
| 1 | 0 | 0 | 1 | 0 | 0 |
Graph Convolutional Networks for Classification with a Structured Label Space | It is a usual practice to ignore any structural information underlying
classes in multi-class classification. In this paper, we propose a graph
convolutional network (GCN) augmented neural network classifier to exploit a
known, underlying graph structure of labels. The proposed approach resembles an
(approximate) inference procedure in, for instance, a conditional random field
(CRF). We evaluate the proposed approach on document classification and object
recognition and report both accuracies and graph-theoretic metrics that
correspond to the consistency of the model's prediction. The experiment results
reveal that the proposed model outperforms a baseline method which ignores the
graph structures of a label space in terms of graph-theoretic metrics.
| 1 | 0 | 0 | 1 | 0 | 0 |
Decoupling of graphene from Ni(111) via oxygen intercalation | The combination of the surface science techniques (STM, XPS, ARPES) and
density-functional theory calculations was used to study the decoupling of
graphene from Ni(111) by oxygen intercalation. The formation of the
antiferromagnetic (AFM) NiO layer at the interface between graphene and
ferromagnetic (FM) Ni is found, where graphene protects the underlying AFM/FM
sandwich system. It is found that graphene is fully decoupled in this system
and strongly $p$-doped via charge transfer with a position of the Dirac point
of $(0.69\pm0.02)$ eV above the Fermi level. Our theoretical analysis confirms
all experimental findings, addressing also the interface properties between
graphene and AFM NiO.
| 0 | 1 | 0 | 0 | 0 | 0 |
Model Risk Measurement under Wasserstein Distance | The paper proposes a new approach to model risk measurement based on the
Wasserstein distance between two probability measures. It formulates the
theoretical motivation resulting from the interpretation of fictitious
adversary of robust risk management. The proposed approach accounts for all
alternative models and incorporates the economic reality of the fictitious
adversary. It provides practically feasible results that overcome the
restriction and the integrability issue imposed by the nominal model. The
Wasserstein approach suits for all types of model risk problems, ranging from
the single-asset hedging risk problem to the multi-asset allocation problem.
The robust capital allocation line, accounting for the correlation risk, is not
achievable with other non-parametric approaches.
| 0 | 0 | 0 | 0 | 0 | 1 |
Fast kNN mode seeking clustering applied to active learning | A significantly faster algorithm is presented for the original kNN mode
seeking procedure. It has the advantages over the well-known mean shift
algorithm that it is feasible in high-dimensional vector spaces and results in
uniquely, well defined modes. Moreover, without any additional computational
effort it may yield a multi-scale hierarchy of clusterings. The time complexity
is just O(n^1.5). resulting computing times range from seconds for 10^4 objects
to minutes for 10^5 objects and to less than an hour for 10^6 objects. The
space complexity is just O(n). The procedure is well suited for finding large
sets of small clusters and is thereby a candidate to analyze thousands of
clusters in millions of objects.
The kNN mode seeking procedure can be used for active learning by assigning
the clusters to the class of the modal objects of the clusters. Its feasibility
is shown by some examples with up to 1.5 million handwritten digits. The
obtained classification results based on the clusterings are compared with
those obtained by the nearest neighbor rule and the support vector classifier
based on the same labeled objects for training. It can be concluded that using
the clustering structure for classification can be significantly better than
using the trained classifiers. A drawback of using the clustering for
classification, however, is that no classifier is obtained that may be used for
out-of-sample objects.
| 1 | 0 | 0 | 1 | 0 | 0 |
Towards a Flow- and Path-Sensitive Information Flow Analysis: Technical Report | This paper investigates a flow- and path-sensitive static information flow
analysis. Compared with security type systems with fixed labels, it has been
shown that flow-sensitive type systems accept more secure programs. We show
that an information flow analysis with fixed labels can be both flow- and
path-sensitive. The novel analysis has two major components: 1) a
general-purpose program transformation that removes false dataflow dependencies
in a program that confuse a fixed-label type system, and 2) a fixed-label type
system that allows security types to depend on path conditions. We formally
prove that the proposed analysis enforces a rigorous security property:
noninterference. Moreover, we show that the analysis is strictly more precise
than a classic flow-sensitive type system, and it allows sound control of
information flow in the presence of mutable variables without resorting to
run-time mechanisms.
| 1 | 0 | 0 | 0 | 0 | 0 |
Topic supervised non-negative matrix factorization | Topic models have been extensively used to organize and interpret the
contents of large, unstructured corpora of text documents. Although topic
models often perform well on traditional training vs. test set evaluations, it
is often the case that the results of a topic model do not align with human
interpretation. This interpretability fallacy is largely due to the
unsupervised nature of topic models, which prohibits any user guidance on the
results of a model. In this paper, we introduce a semi-supervised method called
topic supervised non-negative matrix factorization (TS-NMF) that enables the
user to provide labeled example documents to promote the discovery of more
meaningful semantic structure of a corpus. In this way, the results of TS-NMF
better match the intuition and desired labeling of the user. The core of TS-NMF
relies on solving a non-convex optimization problem for which we derive an
iterative algorithm that is shown to be monotonic and convergent to a local
optimum. We demonstrate the practical utility of TS-NMF on the Reuters and
PubMed corpora, and find that TS-NMF is especially useful for conceptual or
broad topics, where topic key terms are not well understood. Although
identifying an optimal latent structure for the data is not a primary objective
of the proposed approach, we find that TS-NMF achieves higher weighted Jaccard
similarity scores than the contemporary methods, (unsupervised) NMF and latent
Dirichlet allocation, at supervision rates as low as 10% to 20%.
| 1 | 0 | 0 | 1 | 0 | 0 |
Low-temperature lattice effects in the spin-liquid candidate $κ$-(BEDT-TTF)$_2$Cu$_2$(CN)$_3$ | The quasi-two-dimensional organic charge-transfer salt
$\kappa$-(BEDT-TTF)$_2$Cu$_2$(CN)$_3$ is one of the prime candidates for a
quantum spin-liquid due the strong spin frustration of its anisotropic
triangular lattice in combination with its proximity to the Mott transition.
Despite intensive investigations of the material's low-temperature properties,
several important questions remain to be answered. Particularly puzzling are
the 6\,K anomaly and the enigmatic effects observed in magnetic fields. Here we
report on low-temperature measurements of lattice effects which were shown to
be particularly strongly pronounced in this material (R. S. Manna \emph{et
al.}, Phys. Rev. Lett. \textbf{104}, 016403 (2010)). A special focus of our
study lies on sample-to-sample variations of these effects and their
implications on the interpretation of experimental data. By investigating
overall nine single crystals from two different batches, we can state that
there are considerable differences in the size of the second-order phase
transition anomaly around 6\,K, varying within a factor of 3. In addition, we
find field-induced anomalies giving rise to pronounced features in the sample
length for two out of these nine crystals for temperatures $T <$ 9 K. We
tentatively assign the latter effects to $B$-induced magnetic clusters
suspected to nucleate around crystal imperfections. These $B$-induced effects
are absent for the crystals where the 6\,K anomaly is most strongly pronounced.
The large lattice effects observed at 6\,K are consistent with proposed pairing
instabilities of fermionic excitations breaking the lattice symmetry. The
strong sample-to-sample variation in the size of the phase transition anomaly
suggests that the conversion of the fermions to bosons at the instability is
only partial and to some extent influenced by not yet identified
sample-specific parameters.
| 0 | 1 | 0 | 0 | 0 | 0 |
ECO-AMLP: A Decision Support System using an Enhanced Class Outlier with Automatic Multilayer Perceptron for Diabetes Prediction | With advanced data analytical techniques, efforts for more accurate decision
support systems for disease prediction are on rise. Surveys by World Health
Organization (WHO) indicate a great increase in number of diabetic patients and
related deaths each year. Early diagnosis of diabetes is a major concern among
researchers and practitioners. The paper presents an application of
\textit{Automatic Multilayer Perceptron }which\textit{ }is combined with an
outlier detection method \textit{Enhanced Class Outlier Detection using
distance based algorithm }to create a prediction framework named as Enhanced
Class Outlier with Automatic Multi layer Perceptron (ECO-AMLP). A series of
experiments are performed on publicly available Pima Indian Diabetes Dataset to
compare ECO-AMLP with other individual classifiers as well as ensemble based
methods. The outlier technique used in our framework gave better results as
compared to other pre-processing and classification techniques. Finally, the
results are compared with other state-of-the-art methods reported in literature
for diabetes prediction on PIDD and achieved accuracy of 88.7\% bests all other
reported studies.
| 1 | 0 | 0 | 0 | 0 | 0 |
Local approximation of non-holomorphic discs in almost complex manifolds | We provide a local approximation result of non-holomorphic discs with small
d-bar by pseudoholomorphic ones. As an application, we provide a certain gluing
construction.
| 0 | 0 | 1 | 0 | 0 | 0 |
A Tutorial on Deep Learning for Music Information Retrieval | Following their success in Computer Vision and other areas, deep learning
techniques have recently become widely adopted in Music Information Retrieval
(MIR) research. However, the majority of works aim to adopt and assess methods
that have been shown to be effective in other domains, while there is still a
great need for more original research focusing on music primarily and utilising
musical knowledge and insight. The goal of this paper is to boost the interest
of beginners by providing a comprehensive tutorial and reducing the barriers to
entry into deep learning for MIR. We lay out the basic principles and review
prominent works in this hard to navigate the field. We then outline the network
structures that have been successful in MIR problems and facilitate the
selection of building blocks for the problems at hand. Finally, guidelines for
new tasks and some advanced topics in deep learning are discussed to stimulate
new research in this fascinating field.
| 1 | 0 | 0 | 0 | 0 | 0 |
Modeling and control of modern wind turbine systems: An introduction | This chapter provides an introduction to the modeling and control of power
generation from wind turbine systems. In modeling, the focus is on the
electrical components: electrical machine (e.g. permanent-magnet synchronous
generators), back-to-back converter (consisting of machine-side and grid-side
converter sharing a common DC-link), mains filters and ideal (balanced) power
grid. The aerodynamics and the torque generation of the wind turbine are
explained in simplified terms using a so-called power coefficient. The overall
control system is considered. In particular, the phase-locked loop system for
grid-side voltage orientation, the nonlinear speed control system for the
generator (and turbine), and the non-minimum phase DC-link voltage control
system are discussed in detail; based on a brief derivation of the underlying
machine-side and grid-side current control systems. With the help of the power
balance of the wind turbine, the operation management and the control of the
power flow are explained. Concluding simulation results illustrate the overall
system behavior of a controlled wind turbine with a permanent-magnet
synchronous generator.
| 1 | 0 | 0 | 0 | 0 | 0 |
Linear algebraic analogues of the graph isomorphism problem and the Erdős-Rényi model | A classical difficult isomorphism testing problem is to test isomorphism of
p-groups of class 2 and exponent p in time polynomial in the group order. It is
known that this problem can be reduced to solving the alternating matrix space
isometry problem over a finite field in time polynomial in the underlying
vector space size. We propose a venue of attack for the latter problem by
viewing it as a linear algebraic analogue of the graph isomorphism problem.
This viewpoint leads us to explore the possibility of transferring techniques
for graph isomorphism to this long-believed bottleneck case of group
isomorphism.
In 1970's, Babai, Erdős, and Selkow presented the first average-case
efficient graph isomorphism testing algorithm (SIAM J Computing, 1980).
Inspired by that algorithm, we devise an average-case efficient algorithm for
the alternating matrix space isometry problem over a key range of parameters,
in a random model of alternating matrix spaces in vein of the Erdős-Rényi
model of random graphs. For this, we develop a linear algebraic analogue of the
classical individualisation technique, a technique belonging to a set of
combinatorial techniques that has been critical for the progress on the
worst-case time complexity for graph isomorphism, but was missing in the group
isomorphism context. As a consequence of the main algorithm, we establish a
weaker linear algebraic analogue of Erdős and Rényi's classical result
that most graphs have the trivial automorphism group. We finally show that
Luks' dynamic programming technique for graph isomorphism (STOC 1999) can be
adapted to slightly improve the worst-case time complexity of the alternating
matrix space isometry problem in a certain range of parameters.
| 1 | 0 | 1 | 0 | 0 | 0 |
A Family of Metrics for Clustering Algorithms | We give the motivation for scoring clustering algorithms and a metric $M : A
\rightarrow \mathbb{N}$ from the set of clustering algorithms to the natural
numbers which we realize as \begin{equation} M(A) = \sum_i \alpha_i |f_i -
\beta_i|^{w_i} \end{equation} where $\alpha_i,\beta_i,w_i$ are parameters used
for scoring the feature $f_i$, which is computed empirically.. We give a method
by which one can score features such as stability, noise sensitivity, etc and
derive the necessary parameters. We conclude by giving a sample set of scores.
| 1 | 0 | 0 | 0 | 0 | 0 |
A numerical scheme for an improved Green-Naghdi model in the Camassa-Holm regime for the propagation of internal waves | In this paper we introduce a new reformulation of the Green-Naghdi model in
the Camassa-Holm regime for the propagation of internal waves over a flat
topography derived by Duchêne, Israwi and Talhouk. These new Green-Naghdi
systems are adapted to improve the frequency dispersion of the original model,
they share the same order of precision as the standard one but have an
appropriate structure which makes them much more suitable for the numerical
resolution. We develop a second order splitting scheme where the hyperbolic
part of the system is treated with a high-order finite volume scheme and the
dispersive part is treated with a finite difference approach. Numerical
simulations are then performed to validate the model and the numerical methods.
| 0 | 0 | 1 | 0 | 0 | 0 |
General Bayesian Updating and the Loss-Likelihood Bootstrap | In this paper we revisit the weighted likelihood bootstrap, a method that
generates samples from an approximate Bayesian posterior of a parametric model.
We show that the same method can be derived, without approximation, under a
Bayesian nonparametric model with the parameter of interest defined as
minimising an expected negative log-likelihood under an unknown sampling
distribution. This interpretation enables us to extend the weighted likelihood
bootstrap to posterior sampling for parameters minimizing an expected loss. We
call this method the loss-likelihood bootstrap. We make a connection between
this and general Bayesian updating, which is a way of updating prior belief
distributions without needing to construct a global probability model, yet
requires the calibration of two forms of loss function. The loss-likelihood
bootstrap is used to calibrate the general Bayesian posterior by matching
asymptotic Fisher information. We demonstrate the methodology on a number of
examples.
| 0 | 0 | 0 | 1 | 0 | 0 |
Efficient Estimation of Generalization Error and Bias-Variance Components of Ensembles | For many applications, an ensemble of base classifiers is an effective
solution. The tuning of its parameters(number of classes, amount of data on
which each classifier is to be trained on, etc.) requires G, the generalization
error of a given ensemble. The efficient estimation of G is the focus of this
paper. The key idea is to approximate the variance of the class
scores/probabilities of the base classifiers over the randomness imposed by the
training subset by normal/beta distribution at each point x in the input
feature space. We estimate the parameters of the distribution using a small set
of randomly chosen base classifiers and use those parameters to give efficient
estimation schemes for G. We give empirical evidence for the quality of the
various estimators. We also demonstrate their usefulness in making design
choices such as the number of classifiers in the ensemble and the size of a
subset of data used for training that is needed to achieve a certain value of
generalization error. Our approach also has great potential for designing
distributed ensemble classifiers.
| 1 | 0 | 0 | 1 | 0 | 0 |
Self-consistent calculation of the flux-flow conductivity in diffusive superconductors | In the framework of Keldysh-Usadel kinetic theory, we study the temperature
dependence of flux-flow conductivity (FFC) in diffusive superconductors. By
using self-consistent vortex solutions we find the exact values of
dimensionless parameters that determine the diffusion-controlled FFC both in
the limit of the low temperatures and close to the critical one. Taking into
account the electron-phonon scattering we study the transition between
flux-flow regimes controlled either by the diffusion or the inelastic
relaxation of non-equilibrium quasiparticles. We demonstrate that the inelastic
electron-phonon relaxation leads to the strong suppression of FFC as compared
to the previous estimates making it possible to obtain the numerical agreement
with experimental results.
| 0 | 1 | 0 | 0 | 0 | 0 |
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