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Spectral Method and Regularized MLE Are Both Optimal for Top-$K$ Ranking | This paper is concerned with the problem of top-$K$ ranking from pairwise
comparisons. Given a collection of $n$ items and a few pairwise comparisons
across them, one wishes to identify the set of $K$ items that receive the
highest ranks. To tackle this problem, we adopt the logistic parametric model
--- the Bradley-Terry-Luce model, where each item is assigned a latent
preference score, and where the outcome of each pairwise comparison depends
solely on the relative scores of the two items involved. Recent works have made
significant progress towards characterizing the performance (e.g. the mean
square error for estimating the scores) of several classical methods, including
the spectral method and the maximum likelihood estimator (MLE). However, where
they stand regarding top-$K$ ranking remains unsettled.
We demonstrate that under a natural random sampling model, the spectral
method alone, or the regularized MLE alone, is minimax optimal in terms of the
sample complexity --- the number of paired comparisons needed to ensure exact
top-$K$ identification, for the fixed dynamic range regime. This is
accomplished via optimal control of the entrywise error of the score estimates.
We complement our theoretical studies by numerical experiments, confirming that
both methods yield low entrywise errors for estimating the underlying scores.
Our theory is established via a novel leave-one-out trick, which proves
effective for analyzing both iterative and non-iterative procedures. Along the
way, we derive an elementary eigenvector perturbation bound for probability
transition matrices, which parallels the Davis-Kahan $\sin\Theta$ theorem for
symmetric matrices. This also allows us to close the gap between the $\ell_2$
error upper bound for the spectral method and the minimax lower limit.
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One-Shot Reinforcement Learning for Robot Navigation with Interactive Replay | Recently, model-free reinforcement learning algorithms have been shown to
solve challenging problems by learning from extensive interaction with the
environment. A significant issue with transferring this success to the robotics
domain is that interaction with the real world is costly, but training on
limited experience is prone to overfitting. We present a method for learning to
navigate, to a fixed goal and in a known environment, on a mobile robot. The
robot leverages an interactive world model built from a single traversal of the
environment, a pre-trained visual feature encoder, and stochastic environmental
augmentation, to demonstrate successful zero-shot transfer under real-world
environmental variations without fine-tuning.
| 1 | 0 | 0 | 0 | 0 | 0 |
Data Race Detection on Compressed Traces | We consider the problem of detecting data races in program traces that have
been compressed using straight line programs (SLP), which are special
context-free grammars that generate exactly one string, namely the trace that
they represent. We consider two classical approaches to race detection ---
using the happens-before relation and the lockset discipline. We present
algorithms for both these methods that run in time that is linear in the size
of the compressed, SLP representation. Typical program executions almost always
exhibit patterns that lead to significant compression. Thus, our algorithms are
expected to result in large speedups when compared with analyzing the
uncompressed trace. Our experimental evaluation of these new algorithms on
standard benchmarks confirms this observation.
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Model and Integrate Medical Resource Availability into Verifiably Correct Executable Medical Guidelines - Technical Report | Improving effectiveness and safety of patient care is an ultimate objective
for medical cyber-physical systems. A recent study shows that the patients'
death rate can be reduced by computerizing medical guidelines. Most existing
medical guideline models are validated and/or verified based on the assumption
that all necessary medical resources needed for a patient care are always
available. However, the reality is that some medical resources, such as special
medical equipment or medical specialists, can be temporarily unavailable for an
individual patient. In such cases, safety properties validated and/or verified
in existing medical guideline models without considering medical resource
availability may not hold any more. The paper argues that considering medical
resource availability is essential in building verifiably correct executable
medical guidelines. We present an approach to explicitly and separately model
medical resource availability and automatically integrate resource availability
models into an existing statechart-based computerized medical guideline model.
This approach requires minimal change in existing medical guideline models to
take into consideration of medical resource availability in validating and
verifying medical guideline models. A simplified stroke scenario is used as a
case study to investigate the effectiveness and validity of our approach.
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Two-Party Function Computation on the Reconciled Data | In this paper, we initiate a study of a new problem termed function
computation on the reconciled data, which generalizes a set reconciliation
problem in the literature. Assume a distributed data storage system with two
users $A$ and $B$. The users possess a collection of binary vectors $S_{A}$ and
$S_{B}$, respectively. They are interested in computing a function $\phi$ of
the reconciled data $S_{A} \cup S_{B}$.
It is shown that any deterministic protocol, which computes a sum and a
product of reconciled sets of binary vectors represented as nonnegative
integers, has to communicate at least $2^n + n - 1$ and $2^n + n - 2$ bits in
the worst-case scenario, respectively, where $n$ is the length of the binary
vectors. Connections to other problems in computer science, such as set
disjointness and finding the intersection, are established, yielding a variety
of additional upper and lower bounds on the communication complexity. A
protocol for computation of a sum function, which is based on use of a family
of hash functions, is presented, and its characteristics are analyzed.
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On Calabi-Yau compactifications of toric Landau-Ginzburg models for Fano complete intersections | Toric Landau--Ginzburg models of Givental's type for Fano complete
intersections are known to have Calabi--Yau compactifications. We give an
alternative proof of this fact. As an output of our proof we get a description
of fibers over infinity for compactified toric Landau--Ginzburg models.
| 0 | 0 | 1 | 0 | 0 | 0 |
Halo assembly bias and the tidal anisotropy of the local halo environment | We study the role of the local tidal environment in determining the assembly
bias of dark matter haloes. Previous results suggest that the anisotropy of a
halo's environment (i.e, whether it lies in a filament or in a more isotropic
region) can play a significant role in determining the eventual mass and age of
the halo. We statistically isolate this effect using correlations between the
large-scale and small-scale environments of simulated haloes at $z=0$ with
masses between $10^{11.6}\lesssim (m/h^{-1}M_{\odot})\lesssim10^{14.9}$. We
probe the large-scale environment using a novel halo-by-halo estimator of
linear bias. For the small-scale environment, we identify a variable $\alpha_R$
that captures the $\textit{tidal anisotropy}$ in a region of radius
$R=4R_{\textrm{200b}}$ around the halo and correlates strongly with halo bias
at fixed mass. Segregating haloes by $\alpha_R$ reveals two distinct
populations. Haloes in highly isotropic local environments
($\alpha_R\lesssim0.2$) behave as expected from the simplest, spherically
averaged analytical models of structure formation, showing a
$\textit{negative}$ correlation between their concentration and large-scale
bias at $\textit{all}$ masses. In contrast, haloes in anisotropic,
filament-like environments ($\alpha_R\gtrsim0.5$) tend to show a
$\textit{positive}$ correlation between bias and concentration at any mass. Our
multi-scale analysis cleanly demonstrates how the overall assembly bias trend
across halo mass emerges as an average over these different halo populations,
and provides valuable insights towards building analytical models that
correctly incorporate assembly bias. We also discuss potential implications for
the nature and detectability of galaxy assembly bias.
| 0 | 1 | 0 | 0 | 0 | 0 |
Towards a general theory for non-linear locally stationary processes | In this paper some general theory is presented for locally stationary
processes based on the stationary approximation and the stationary derivative.
Laws of large numbers, central limit theorems as well as deterministic and
stochastic bias expansions are proved for processes obeying an expansion in
terms of the stationary approximation and derivative. In addition it is shown
that this applies to some general nonlinear non-stationary Markov-models. In
addition the results are applied to derive the asymptotic properties of maximum
likelihood estimates of parameter curves in such models.
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Self-Learning Monte Carlo Method: Continuous-Time Algorithm | The recently-introduced self-learning Monte Carlo method is a general-purpose
numerical method that speeds up Monte Carlo simulations by training an
effective model to propose uncorrelated configurations in the Markov chain. We
implement this method in the framework of continuous time Monte Carlo method
with auxiliary field in quantum impurity models. We introduce and train a
diagram generating function (DGF) to model the probability distribution of
auxiliary field configurations in continuous imaginary time, at all orders of
diagrammatic expansion. By using DGF to propose global moves in configuration
space, we show that the self-learning continuous-time Monte Carlo method can
significantly reduce the computational complexity of the simulation.
| 0 | 1 | 0 | 0 | 0 | 0 |
Single-cell diffraction tomography with optofluidic rotation about a tilted axis | Optical diffraction tomography (ODT) is a tomographic technique that can be
used to measure the three-dimensional (3D) refractive index distribution within
living cells without the requirement of any marker. In principle, ODT can be
regarded as a generalization of optical projection tomography which is
equivalent to computerized tomography (CT). Both optical tomographic techniques
require projection-phase images of cells measured at multiple angles. However,
the reconstruction of the 3D refractive index distribution post-measurement
differs for the two techniques. It is known that ODT yields better results than
projection tomography, because it takes into account diffraction of the imaging
light due to the refractive index structure of the sample. Here, we apply ODT
to biological cells in a microfluidic chip which combines optical trapping and
microfluidic flow to achieve an optofluidic single-cell rotation. In
particular, we address the problem that arises when the trapped cell is not
rotating about an axis perpendicular to the imaging plane, but instead about an
arbitrarily tilted axis. In this paper we show that the 3D reconstruction can
be improved by taking into account such a tilted rotational axis in the
reconstruction process.
| 0 | 0 | 0 | 0 | 1 | 0 |
Non-Linear Least-Squares Optimization of Rational Filters for the Solution of Interior Eigenvalue Problems | Rational filter functions can be used to improve convergence of contour-based
eigensolvers, a popular family of algorithms for the solution of the interior
eigenvalue problem. We present a framework for the optimization of rational
filters based on a non-convex weighted Least-Squares scheme. When used in
combination with the FEAST library, our filters out-perform existing ones on a
large and representative set of benchmark problems. This work provides a
detailed description of: (1) a set up of the optimization process that exploits
symmetries of the filter function for Hermitian eigenproblems, (2) a
formulation of the gradient descent and Levenberg-Marquardt algorithms that
exploits the symmetries, (3) a method to select the starting position for the
optimization algorithms that reliably produces effective filters, (4) a
constrained optimization scheme that produces filter functions with specific
properties that may be beneficial to the performance of the eigensolver that
employs them.
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Maps on statistical manifolds exactly reduced from the Perron-Frobenius equations for solvable chaotic maps | Maps on a parameter space for expressing distribution functions are exactly
derived from the Perron-Frobenius equations for a generalized Boole transform
family. Here the generalized Boole transform family is a one-parameter family
of maps where it is defined on a subset of the real line and its probability
distribution function is the Cauchy distribution with some parameters. With
this reduction, some relations between the statistical picture and the orbital
one are shown. From the viewpoint of information geometry, the parameter space
can be identified with a statistical manifold, and then it is shown that the
derived maps can be characterized. Also, with an induced symplectic structure
from a statistical structure, symplectic and information geometric aspects of
the derived maps are discussed.
| 0 | 1 | 0 | 0 | 0 | 0 |
Discontinuous classical ground state magnetic response as an even-odd effect in higher order rotationally invariant exchange interactions | The classical ground state magnetic response of the Heisenberg model when
rotationally invariant exchange interactions of integer order q>1 are added is
found to be discontinuous, even though the interactions lack magnetic
anisotropy. This holds even in the case of bipartite lattices which are not
frustrated, as well as for the frustrated triangular lattice. The total number
of discontinuities is associated with even-odd effects as it depends on the
parity of q via the relative strength of the bilinear and higher order exchange
interactions, and increases with q. These results demonstrate that the precise
form of the microscopic interactions is important for the ground state
magnetization response.
| 0 | 1 | 0 | 0 | 0 | 0 |
Sparse Matrix Multiplication On An Associative Processor | Sparse matrix multiplication is an important component of linear algebra
computations. Implementing sparse matrix multiplication on an associative
processor (AP) enables high level of parallelism, where a row of one matrix is
multiplied in parallel with the entire second matrix, and where the execution
time of vector dot product does not depend on the vector size. Four sparse
matrix multiplication algorithms are explored in this paper, combining AP and
baseline CPU processing to various levels. They are evaluated by simulation on
a large set of sparse matrices. The computational complexity of sparse matrix
multiplication on AP is shown to be an O(nnz) where nnz is the number of
nonzero elements. The AP is found to be especially efficient in binary sparse
matrix multiplication. AP outperforms conventional solutions in power
efficiency.
| 1 | 0 | 0 | 0 | 0 | 0 |
On the intersection of tame subgroups in groups acting on trees | Let $G$ be a group acting on a tree $T$ with finite edge stabilizers of
bounded order. We provide, in some very interesting cases, upper bounds for the
complexity of the intersection $H\cap K$ of two tame subgroups $H$ and $K$ of
$G$ in terms of the complexities of $H$ and $K$. In particular, we obtain
bounds for the Kurosh rank $Kr(H\cap K)$ of the intersection in terms of Kurosh
ranks $Kr(H)$ and $Kr(K)$, in the case where $H$ and $K$ act freely on the
edges of $T$.
| 0 | 0 | 1 | 0 | 0 | 0 |
A Lagrangian scheme for the solution of nonlinear diffusion equations using moving simplex meshes | A Lagrangian numerical scheme for solving nonlinear degenerate Fokker-Planck
equations in space dimensions $d\ge2$ is presented. It applies to a large class
of nonlinear diffusion equations, whose dynamics are driven by internal
energies and given external potentials, e.g. the porous medium equation and the
fast diffusion equation. The key ingredient in our approach is the gradient
flow structure of the dynamics. For discretization of the Lagrangian map, we
use a finite subspace of linear maps in space and a variational form of the
implicit Euler method in time. Thanks to that time discretisation, the fully
discrete solution inherits energy estimates from the original gradient flow,
and these lead to weak compactness of the trajectories in the continuous limit.
Consistency is analyzed in the planar situation, $d=2$. A variety of numerical
experiments for the porous medium equation indicates that the scheme is
well-adapted to track the growth of the solution's support.
| 0 | 0 | 1 | 0 | 0 | 0 |
Optimal Control for Multi-Mode Systems with Discrete Costs | This paper studies optimal time-bounded control in multi-mode systems with
discrete costs. Multi-mode systems are an important subclass of linear hybrid
systems, in which there are no guards on transitions and all invariants are
global. Each state has a continuous cost attached to it, which is linear in the
sojourn time, while a discrete cost is attached to each transition taken. We
show that an optimal control for this model can be computed in NEXPTIME and
approximated in PSPACE. We also show that the one-dimensional case is simpler:
although the problem is NP-complete (and in LOGSPACE for an infinite time
horizon), we develop an FPTAS for finding an approximate solution.
| 1 | 0 | 0 | 0 | 0 | 0 |
Digital Advertising Traffic Operation: Flow Management Analysis | In a Web Advertising Traffic Operation the Trafficking Routing Problem (TRP)
consists in scheduling the management of Web Advertising (Adv) campaign between
Trafficking campaigns in the most efficient way to oversee and manage
relationship with partners and internal teams, managing expectations through
integration and post-launch in order to ensure success for every stakeholders
involved. For our own interest we did that independent research projects also
through specific innovative tasks validate towards average working time
declared on "specification required" by the main worldwide industry leading
Advertising Agency. We present a Mixed Integer Linear Programming (MILP)
formulation for end-to-end management of campaign workflow along a
predetermined path and generalize it to include alternative path to oversee and
manage detail-oriented relationship with partners and internal teams to achieve
the goals above mentioned. To meet clients' KPIs, we consider an objective
function that includes the punctuality indicators (the average waiting time and
completion times) but also the main punctuality indicators (the average delay
and the on time performance). Then we investigate their analytical
relationships in the advertising domain through experiments based on real data
from a Traffic Office. We show that the classic punctuality indicators are in
contradiction with the task of reducing waiting times. We propose new
indicators used for a synthesize analysis on projects or process changes for
the wider team that are more sustainable, but also more relevant for
stakeholders. We also show that the flow of a campaign (adv-ways) is the main
bottleneck of a Traffic Office and that alternate paths cannot improve the
performance indicators.
| 1 | 0 | 1 | 0 | 0 | 0 |
Addressing Class Imbalance in Classification Problems of Noisy Signals by using Fourier Transform Surrogates | Randomizing the Fourier-transform (FT) phases of temporal-spatial data
generates surrogates that approximate examples from the data-generating
distribution. We propose such FT surrogates as a novel tool to augment and
analyze training of neural networks and explore the approach in the example of
sleep-stage classification. By computing FT surrogates of raw EEG, EOG, and EMG
signals of under-represented sleep stages, we balanced the CAPSLPDB sleep
database. We then trained and tested a convolutional neural network for sleep
stage classification, and found that our surrogate-based augmentation improved
the mean F1-score by 7%. As another application of FT surrogates, we formulated
an approach to compute saliency maps for individual sleep epochs. The
visualization is based on the response of inferred class probabilities under
replacement of short data segments by partial surrogates. To quantify how well
the distributions of the surrogates and the original data match, we evaluated a
trained classifier on surrogates of correctly classified examples, and
summarized these conditional predictions in a confusion matrix. We show how
such conditional confusion matrices can qualitatively explain the performance
of surrogates in class balancing. The FT-surrogate augmentation approach may
improve classification on noisy signals if carefully adapted to the data
distribution under analysis.
| 0 | 0 | 0 | 1 | 1 | 0 |
Generating global network structures by triad types | This paper addresses the question of whether it is possible to generate
networks with a given global structure (defined by selected blockmodels, i.e.,
cohesive, core-periphery, hierarchical and transitivity), considering only
different types of triads. Two methods are used to generate networks: (i) the
method of relocating links; and (ii) the Monte Carlo Multi Chain algorithm
implemented in the "ergm" package implemented in R. Although all types of
triads can generate networks with the selected blockmodel types, the selection
of only a subset of triads improves the generated networks' blockmodel
structure. However, in the case of a hierarchical blockmodel without complete
blocks on the diagonal, additional local structures are needed to achieve the
desired global structure of generated networks. This shows that blockmodels can
emerge based on only local processes that do not take attributes into account.
| 0 | 0 | 1 | 1 | 0 | 0 |
Simultaneous smoothness and simultaneous stability of a $C^\infty$ strictly convex integrand and its dual | In this paper, we investigate simultaneous properties of a convex integrand
$\gamma$ and its dual $\delta$. The main results are the following three.
(1) For a $C^\infty$ convex integrand $\gamma: S^n\to \mathbb{R}_+$, its dual
convex integrand $\delta: S^n\to \mathbb{R}_+$ is of class $C^\infty$ if and
only if $\gamma$ is a strictly convex integrand.
(2) Let $\gamma: S^n\to \mathbb{R}_+$ be a $C^\infty$ strictly convex
integrand. Then, $\gamma$ is stable if and only if its dual convex integrand
$\delta: S^n\to \mathbb{R}_+$ is stable.
(3) Let $\gamma: S^n\to \mathbb{R}_+$ be a $C^\infty$ strictly convex
integrand. Suppose that $\gamma$ is stable. Then, for any $i$ $(0\le i\le n)$,
a point $\theta_0\in S^n$ is a non-degenerate critical point of $\gamma$ with
Morse index $i$ if and only if its antipodal point $-\theta_0\in S^n$ is a
non-degenerate critical point of the dual convex integrand $\delta$ with Morse
index $(n-i)$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Adaptive quadrature by expansion for layer potential evaluation in two dimensions | When solving partial differential equations using boundary integral equation
methods, accurate evaluation of singular and nearly singular integrals in layer
potentials is crucial. A recent scheme for this is quadrature by expansion
(QBX), which solves the problem by locally approximating the potential using a
local expansion centered at some distance from the source boundary. In this
paper we introduce an extension of the QBX scheme in 2D denoted AQBX - adaptive
quadrature by expansion - which combines QBX with an algorithm for automated
selection of parameters, based on a target error tolerance. A key component in
this algorithm is the ability to accurately estimate the numerical errors in
the coefficients of the expansion. Combining previous results for flat panels
with a procedure for taking the panel shape into account, we derive such error
estimates for arbitrarily shaped boundaries in 2D that are discretized using
panel-based Gauss-Legendre quadrature. Applying our scheme to numerical
solutions of Dirichlet problems for the Laplace and Helmholtz equations, and
also for solving these equations, we find that the scheme is able to satisfy a
given target tolerance to within an order of magnitude, making it useful for
practical applications. This represents a significant simplification over the
original QBX algorithm, in which choosing a good set of parameters can be hard.
| 0 | 0 | 1 | 0 | 0 | 0 |
Anomalous transport effects on switching currents of graphene-based Josephson junctions | We explore the effect of noise on the ballistic graphene-based small
Josephson junctions in the framework of the resistively and capacitively
shunted model. We use the non-sinusoidal current-phase relation specific for
graphene layers partially covered by superconducting electrodes. The noise
induced escapes from the metastable states, when the external bias current is
ramped, give the switching current distribution, i.e. the probability
distribution of the passages to finite voltage from the superconducting state
as a function of the bias current, that is the information more promptly
available in the experiments. We consider a noise source that is a mixture of
two different types of processes: a Gaussian contribution to simulate an
uncorrelated ordinary thermal bath, and non-Gaussian, $\alpha$-stable (or
Lévy) term, generally associated to non-equilibrium transport phenomena. We
find that the analysis of the switching current distribution makes it possible
to efficiently detect a non-Gaussian noise component in a Gaussian background.
| 0 | 1 | 0 | 0 | 0 | 0 |
Fixed-Parameter Tractable Sampling for RNA Design with Multiple Target Structures | The design of multi-stable RNA molecules has important applications in
biology, medicine, and biotechnology. Synthetic design approaches profit
strongly from effective in-silico methods, which can tremendously impact their
cost and feasibility. We revisit a central ingredient of most in-silico design
methods: the sampling of sequences for the design of multi-target structures,
possibly including pseudoknots. For this task, we present the efficient, tree
decomposition-based algorithm. Our fixed parameter tractable approach is
underpinned by establishing the P-hardness of uniform sampling. Modeling the
problem as a constraint network, our program supports generic
Boltzmann-weighted sampling for arbitrary additive RNA energy models; this
enables the generation of RNA sequences meeting specific goals like expected
free energies or \GCb-content. Finally, we empirically study general properties
of the approach and generate biologically relevant multi-target
Boltzmann-weighted designs for a common design benchmark. Generating seed
sequences with our program, we demonstrate significant improvements over the
previously best multi-target sampling strategy (uniform sampling).Our software
is freely available at: this https URL .
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On the relevance of generalized disclinations in defect mechanics | The utility of the notion of generalized disclinations in materials science
is discussed within the physical context of modeling interfacial and bulk line
defects like defected grain and phase boundaries, dislocations and
disclinations. The Burgers vector of a disclination dipole in linear elasticity
is derived, clearly demonstrating the equivalence of its stress field to that
of an edge dislocation. We also prove that the inverse deformation/displacement
jump of a defect line is independent of the cut-surface when its g.disclination
strength vanishes. An explicit formula for the displacement jump of a single
localized composite defect line in terms of given g.disclination and
dislocation strengths is deduced based on the Weingarten theorem for
g.disclination theory (Weingarten-gd theorem) at finite deformation. The
Burgers vector of a g.disclination dipole at finite deformation is also
derived.
| 0 | 1 | 0 | 0 | 0 | 0 |
PbTe(111) Sub-Thermionic Photocathode: A Route to High-Quality Electron Pulses | The emission properties of PbTe(111) single crystal have been extensively
investigated to demonstrate that PbTe(111) is a promising low root mean square
transverse momentum ({\Delta}p$_T$) and high brightness photocathode. The
density functional theory (DFT) based photoemission analysis successfully
elucidates that the 'hole-like' {\Lambda}$^+_6$ energy band in the $L$ valley
with low effective mass $m^*$ results in low {\Delta}p$_T$. Especially, as a
300K solid planar photocathode, Te-terminated PbTe(111) single crystal is
expected to be a potential 50K electron source.
| 0 | 1 | 0 | 0 | 0 | 0 |
Envy-free Matchings with Lower Quotas | While every instance of the Hospitals/Residents problem admits a stable
matching, the problem with lower quotas (HR-LQ) has instances with no stable
matching. For such an instance, we expect the existence of an envy-free
matching, which is a relaxation of a stable matching preserving a kind of
fairness property. In this paper, we investigate the existence of an envy-free
matching in several settings, in which hospitals have lower quotas and not all
doctor-hospital pairs are acceptable. We first show that, for an HR-LQ
instance, we can efficiently decide the existence of an envy-free matching.
Then, we consider envy-freeness in the Classified Stable Matching model due to
Huang (2010), i.e., each hospital has lower and upper quotas on subsets of
doctors. We show that, for this model, deciding the existence of an envy-free
matching is NP-hard in general, but solvable in polynomial time if quotas are
paramodular.
| 1 | 0 | 0 | 0 | 0 | 0 |
Characterizing Feshbach resonances in ultracold scattering calculations | We describe procedures for converging on and characterizing zero-energy
Feshbach resonances that appear in scattering lengths as a function of an
external field. The elastic procedure is appropriate for purely elastic
scattering, where the scattering length is real and displays a true pole. The
regularized scattering length (RSL) procedure is appropriate when there is weak
background inelasticity, so that the scattering length is complex and displays
an oscillation rather than a pole, but the resonant scattering length $a_{\rm
res}$ is close to real. The fully complex procedure is appropriate when there
is substantial background inelasticity and the real and complex parts of
$a_{\rm res}$ are required. We demonstrate these procedures for scattering of
ultracold $^{85}$Rb in various initial states. All of them can converge on and
provide full characterization of resonances, from initial guesses many
thousands of widths away, using scattering calculations at only about 10 values
of the external field.
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Interacting Multi-particle Classical Szilard Engine | Szilard engine(SZE) is one of the best example of how information can be used
to extract work from a system. Initially, the working substance of SZE was
considered to be a single particle. Later on, researchers has extended the
studies of SZE to multi-particle systems and even to quantum regime. Here we
present a detailed study of classical SZE consisting of $N$ particles with
inter-particle interactions, i.e., the working substance is a low density
non-ideal gas and compare the work extraction with respect to SZE with
non-interacting multi particle system as working substance. We have considered
two cases of interactions namely: (i) hard core interactions and (ii) square
well interaction. Our study reveals that work extraction is less when more
particles are interacting through hard core interactions. More work is
extracted when the particles are interacting via square well interaction.
Another important result for the second case is that as we increase the
particle number the work extraction becomes independent of the initial position
of the partition, as opposed to the first case. Work extraction depends
crucially on the initial position of the partition. More work can be extracted
with larger number of particles when partition is inserted at positions near
the boundary walls.
| 0 | 1 | 0 | 0 | 0 | 0 |
Speeding-up Object Detection Training for Robotics with FALKON | Latest deep learning methods for object detection provide remarkable
performance, but have limits when used in robotic applications. One of the most
relevant issues is the long training time, which is due to the large size and
imbalance of the associated training sets, characterized by few positive and a
large number of negative examples (i.e. background). Proposed approaches are
based on end-to-end learning by back-propagation [22] or kernel methods trained
with Hard Negatives Mining on top of deep features [8]. These solutions are
effective, but prohibitively slow for on-line applications. In this paper we
propose a novel pipeline for object detection that overcomes this problem and
provides comparable performance, with a 60x training speedup. Our pipeline
combines (i) the Region Proposal Network and the deep feature extractor from
[22] to efficiently select candidate RoIs and encode them into powerful
representations, with (ii) the FALKON [23] algorithm, a novel kernel-based
method that allows fast training on large scale problems (millions of points).
We address the size and imbalance of training data by exploiting the stochastic
subsampling intrinsic into the method and a novel, fast, bootstrapping
approach. We assess the effectiveness of the approach on a standard Computer
Vision dataset (PASCAL VOC 2007 [5]) and demonstrate its applicability to a
real robotic scenario with the iCubWorld Transformations [18] dataset.
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Graph of Virtual Actors (GOVA): a Big Data Analytics Architecture for IoT | With the emergence of cloud computing and sensor technologies, Big Data
analytics for the Internet of Things (IoT) has become the main force behind
many innovative solutions for our society's problems. This paper provides
practical explanations for the question "why is the number of Big Data
applications that succeed and have an effect on our daily life so limited,
compared with all of the solutions proposed and tested in the literature?",
with examples taken from Smart Grids. We argue that "noninvariants" are the
most challenging issues in IoT applications, which can be easily revealed if we
use the term "invariant" to replace the more common terms such as
"information", "knowledge", or "insight" in any Big Data for IoT research. From
our experience with developing Smart Grid applications, we produced a list of
"noninvariants", which we believe to be the main causes of the gaps between Big
Data in a laboratory and in practice in IoT applications. This paper also
proposes Graph of Virtual Actors (GOVA) as a Big Data analytics architecture
for IoT applications, which not only can solve the noninvariants issues, but
can also quickly scale horizontally in terms of computation, data storage,
caching requirements, and programmability of the system.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Variation of the $q$-Painlevé System with Affine Weyl Group Symmetry of Type $E_7^{(1)}$ | Recently a certain $q$-Painlevé type system has been obtained from a
reduction of the $q$-Garnier system. In this paper it is shown that the
$q$-Painlevé type system is associated with another realization of the affine
Weyl group symmetry of type $E_7^{(1)}$ and is different from the well-known
$q$-Painlevé system of type $E_7^{(1)}$ from the point of view of evolution
directions. We also study a connection between the $q$-Painlevé type system
and the $q$-Painlevé system of type $E_7^{(1)}$. Furthermore determinant
formulas of particular solutions for the $q$-Painlevé type system are
constructed in terms of the terminating $q$-hypergeometric function.
| 0 | 1 | 1 | 0 | 0 | 0 |
Statics and dynamics of a self-bound dipolar matter-wave droplet | We study the statics and dynamics of a stable, mobile, self-bound
three-dimensional dipolar matter-wave droplet created in the presence of a tiny
repulsive three-body interaction. In frontal collision with an impact parameter
and in angular collision at large velocities {along all directions} two
droplets behave like quantum solitons. Such collision is found to be quasi
elastic and the droplets emerge undeformed after collision without any change
of velocity. However, in a collision at small velocities the axisymmeric
dipolar interaction plays a significant role and the collision dynamics is
sensitive to the direction of motion. For an encounter along the $z$ direction
at small velocities, two droplets, polarized along the $z$ direction, coalesce
to form a larger droplet $-$ a droplet molecule. For an encounter along the $x$
direction at small velocities, the same droplets stay apart and never meet each
other due to the dipolar repulsion. The present study is based on an analytic
variational approximation and a numerical solution of the mean-field
Gross-Pitaevskii equation using the parameters of $^{52}$Cr atoms.
| 0 | 1 | 0 | 0 | 0 | 0 |
Riemannian geometry in infinite dimensional spaces | We lay foundations of the subject in the title, on which we build in another
paper devoted to isometries in spaces of Kähler metrics.
| 0 | 0 | 1 | 0 | 0 | 0 |
Finite Blaschke products with prescribed critical points, Stieltjes polynomials, and moment problems | The determination of a finite Blaschke product from its critical points is a
well-known problem with interrelations to other topics. Though existence and
uniqueness of solutions are established for long, we present several new
aspects which have not yet been explored to their full extent. In particular,
we show that the following three problems are equivalent: (i) determining a
finite Blaschke product from its critical points, (ii) finding the equilibrium
position of moveable point charges interacting with a special configuration of
fixed charges, (iii) solving a moment problem for the canonical representation
of power moments on the real axis. These equivalences are not only of
theoretical interest, but also open up new perspectives for the design of
algorithms. For instance, the second problem is closely linked to the
determination of certain Stieltjes and Van Vleck polynomials for a second order
ODE and allows the description of solutions as global minimizers of an energy
functional.
| 0 | 0 | 1 | 0 | 0 | 0 |
The Stochastic Processes Generation in OpenModelica | Background: Component-based modeling language Modelica (OpenModelica is open
source implementation) is used for the numerical simulation of complex
processes of different nature represented by ODE system. However, in
OpenModelica standard library there is no routines for pseudo-random numbers
generation, which makes it impossible to use for stochastic modeling processes.
Purpose: The goal of this article is a brief overview of a number of algorithms
for generation a sequence of uniformly distributed pseudo random numbers and
quality assessment of the sequence given by them, as well as the ways to
implement some of these algorithms in OpenModelica system. Methods: All the
algorithms are implemented in C language, and the results of their work tested
using open source package DieHarder. For those algorithms that do not use bit
operations, we describe there realisation using OpwnModelica. The other
algorithms can be called in OpenModelica as C functions Results: We have
implemented and tested about nine algorithms. DieHarder testing revealed the
highest quality pseudo-random number generators. Also we have reviewed
libraries Noise and AdvancedNoise, who claim to be adding to the Modelica
Standard Library. Conclusions: In OpenModelica system can be implemented
generators of uniformly distributed pseudo-random numbers, which is the first
step towards to make OpenModelica suitable for simulation of stochastic
processes.
| 1 | 1 | 0 | 0 | 0 | 0 |
MIT SuperCloud Portal Workspace: Enabling HPC Web Application Deployment | The MIT SuperCloud Portal Workspace enables the secure exposure of web
services running on high performance computing (HPC) systems. The portal allows
users to run any web application as an HPC job and access it from their
workstation while providing authentication, encryption, and access control at
the system level to prevent unintended access. This capability permits users to
seamlessly utilize existing and emerging tools that present their user
interface as a website on an HPC system creating a portal workspace.
Performance measurements indicate that the MIT SuperCloud Portal Workspace
incurs marginal overhead when compared to a direct connection of the same
service.
| 1 | 0 | 0 | 0 | 0 | 0 |
Estimator of Prediction Error Based on Approximate Message Passing for Penalized Linear Regression | We propose an estimator of prediction error using an approximate message
passing (AMP) algorithm that can be applied to a broad range of sparse
penalties. Following Stein's lemma, the estimator of the generalized degrees of
freedom, which is a key quantity for the construction of the estimator of the
prediction error, is calculated at the AMP fixed point. The resulting form of
the AMP-based estimator does not depend on the penalty function, and its value
can be further improved by considering the correlation between predictors. The
proposed estimator is asymptotically unbiased when the components of the
predictors and response variables are independently generated according to a
Gaussian distribution. We examine the behaviour of the estimator for real data
under nonconvex sparse penalties, where Akaike's information criterion does not
correspond to an unbiased estimator of the prediction error. The model selected
by the proposed estimator is close to that which minimizes the true prediction
error.
| 0 | 0 | 0 | 1 | 0 | 0 |
Faster Multiplication for Long Binary Polynomials | We set new speed records for multiplying long polynomials over finite fields
of characteristic two. Our multiplication algorithm is based on an additive FFT
(Fast Fourier Transform) by Lin, Chung, and Huang in 2014 comparing to
previously best results based on multiplicative FFTs. Both methods have similar
complexity for arithmetic operations on underlying finite field; however, our
implementation shows that the additive FFT has less overhead. For further
optimization, we employ a tower field construction because the multipliers in
the additive FFT naturally fall into small subfields, which leads to speed-ups
using table-lookup instructions in modern CPUs. Benchmarks show that our method
saves about $40 \%$ computing time when multiplying polynomials of $2^{28}$ and
$2^{29}$ bits comparing to previous multiplicative FFT implementations.
| 1 | 0 | 1 | 0 | 0 | 0 |
Full-angle Negative Reflection with An Ultrathin Acoustic Gradient Metasurface: Floquet-Bloch Modes Perspective and Experimental Verification | Metasurface with gradient phase response offers new alternative for steering
the propagation of waves. Conventional Snell's law has been revised by taking
the contribution of local phase gradient into account. However, the requirement
of momentum matching along the metasurface sets its nontrivial beam
manipulation functionality within a limited-angle incidence. In this work, we
theoretically and experimentally demonstrate that the acoustic gradient
metasurface supports the negative reflection for full-angle incidence. The mode
expansion theory is developed to help understand how the gradient metasurface
tailors the incident beams, and the full-angle negative reflection occurs when
the first negative order Floquet-Bloch mode dominates. The coiling-up space
structures are utilized to build desired acoustic gradient metasurface and the
full-angle negative reflections have been perfectly verified by experimental
measurements. Our work offers the Floquet-Bloch modes perspective for
qualitatively understanding the reflection behaviors of the acoustic gradient
metasurface and enables a new degree of the acoustic wave manipulating.
| 0 | 1 | 0 | 0 | 0 | 0 |
Semigroup C*-algebras and toric varieties | Let S be a finitely generated subsemigroup of Z^2. We derive a general
formula for the K-theory of the left regular C*-algebra for S.
| 0 | 0 | 1 | 0 | 0 | 0 |
Error analysis for small-sample, high-variance data: Cautions for bootstrapping and Bayesian bootstrapping | Recent advances in molecular simulations allow the direct evaluation of
kinetic parameters such as rate constants for protein folding or unfolding.
However, these calculations are usually computationally expensive and even
significant computing resources may result in a small number of independent
rate estimates spread over many orders of magnitude. Such small, high-variance
samples are not readily amenable to analysis using the standard uncertainty
("standard error of the mean") because unphysical negative limits of confidence
intervals result. Bootstrapping, a natural alternative guaranteed to yield a
confidence interval within the minimum and maximum values, also exhibits a
striking systematic bias of the lower confidence limit. As we show,
bootstrapping artifactually assigns high probability to improbably low mean
values. A second alternative, the Bayesian bootstrap strategy, does not suffer
from the same deficit and is more logically consistent with the type of
confidence interval desired, but must be used with caution nevertheless.
Neither standard nor Bayesian bootstrapping can overcome the intrinsic
challenge of under-estimating the mean from small, high-variance samples. Our
report is based on extensive re-analysis of multiple estimates for rate
constants obtained from independent atomistic simulations. Although we only
analyze rate constants, similar considerations may apply to other types of
high-variance calculations, such as may occur in highly non-linear averages
like the Jarzynski relation.
| 0 | 0 | 0 | 1 | 0 | 0 |
Computing Influence of a Product through Uncertain Reverse Skyline | Understanding the influence of a product is crucially important for making
informed business decisions. This paper introduces a new type of skyline
queries, called uncertain reverse skyline, for measuring the influence of a
probabilistic product in uncertain data settings. More specifically, given a
dataset of probabilistic products P and a set of customers C, an uncertain
reverse skyline of a probabilistic product q retrieves all customers c in C
which include q as one of their preferred products. We present efficient
pruning ideas and techniques for processing the uncertain reverse skyline query
of a probabilistic product using R-Tree data index. We also present an
efficient parallel approach to compute the uncertain reverse skyline and
influence score of a probabilistic product. Our approach significantly
outperforms the baseline approach derived from the existing literature. The
efficiency of our approach is demonstrated by conducting extensive experiments
with both real and synthetic datasets.
| 1 | 0 | 0 | 0 | 0 | 0 |
Effect of Heterogeneity in Models of El-Niño Southern Oscillations | The emergence of oscillations in models of the El-Niño effect is of utmost
relevance. Here we investigate a coupled nonlinear delay differential system
modeling theEl-Niño/ Southern Oscillation (ENSO) phenomenon, which arises
through the strong coupling of the ocean-atmosphere system. In particular, we
study the temporal patterns of the sea surface temperature anomaly of the two
sub-regions. For identical sub-regions we typically observe a co-existence of
amplitude and oscillator death behavior for low delays, and heterogeneous
oscillations for high delays, when inter-region coupling is weak. For moderate
inter-region coupling strengths one obtains homogeneous oscillations for
sufficiently large delays and amplitude death for small delays. When the
inter-region coupling strength is large, oscillations are suppressed
altogether, implying that strongly coupled sub-regions do not exhibit ENSO-like
oscillations. Further we observe that larger strengths of self-delay coupling
favours oscillations, while oscillations die out when the delayed coupling is
weak. This indicates again that delayed feedback, incorporating oceanic wave
transit effects, is the principal cause of oscillatory behaviour. So the effect
of trapped ocean waves propagating in a basin with closed boundaries is crucial
for the emergence of ENSO. Further, we show how non-uniformity in delays, and
difference in the strengths of the self-delay coupling of the sub-regions,
affect the rise of oscillations. Interestingly we find that larger delays and
self-delay coupling strengths lead to oscillations, while strong inter-region
coupling kills oscillatory behaviour. Thus, we find that coupling sub-regions
has a very significant effect on the emergence of oscillations, and strong
coupling typically suppresses oscillations, while weak coupling of
non-identical sub-regions can induce oscillations, thereby favouring ENSO.
| 0 | 1 | 0 | 0 | 0 | 0 |
A branch-and-price approach with MILP formulation to modularity density maximization on graphs | For clustering of an undirected graph, this paper presents an exact algorithm
for the maximization of modularity density, a more complicated criterion to
overcome drawbacks of the well-known modularity. The problem can be interpreted
as the set-partitioning problem, which reminds us of its integer linear
programming (ILP) formulation. We provide a branch-and-price framework for
solving this ILP, or column generation combined with branch-and-bound. Above
all, we formulate the column generation subproblem to be solved repeatedly as a
simpler mixed integer linear programming (MILP) problem. Acceleration
techniques called the set-packing relaxation and the
multiple-cutting-planes-at-a-time combined with the MILP formulation enable us
to optimize the modularity density for famous test instances including ones
with over 100 vertices in around four minutes by a PC. Our solution method is
deterministic and the computation time is not affected by any stochastic
behavior. For one of them, column generation at the root node of the
branch-and-bound tree provides a fractional upper bound solution and our
algorithm finds an integral optimal solution after branching.
| 1 | 0 | 1 | 0 | 0 | 0 |
Survey of Gravitationally-lensed Objects in HSC Imaging (SuGOHI). I. Automatic search for galaxy-scale strong lenses | The Hyper Suprime-Cam Subaru Strategic Program (HSC SSP) is an excellent
survey for the search for strong lenses, thanks to its area, image quality and
depth. We use three different methods to look for lenses among 43,000 luminous
red galaxies from the Baryon Oscillation Spectroscopic Survey (BOSS) sample
with photometry from the S16A internal data release of the HSC SSP. The first
method is a newly developed algorithm, named YATTALENS, which looks for
arc-like features around massive galaxies and then estimates the likelihood of
an object being a lens by performing a lens model fit. The second method,
CHITAH, is a modeling-based algorithm originally developed to look for lensed
quasars. The third method makes use of spectroscopic data to look for emission
lines from objects at a different redshift from that of the main galaxy. We
find 15 definite lenses, 36 highly probable lenses and 282 possible lenses.
Among the three methods, YATTALENS, which was developed specifically for this
problem, performs best in terms of both completeness and purity. Nevertheless
five highly probable lenses were missed by YATTALENS but found by the other two
methods, indicating that the three methods are highly complementary. Based on
these numbers we expect to find $\sim$300 definite or probable lenses by the
end of the HSC SSP.
| 0 | 1 | 0 | 0 | 0 | 0 |
Learning Sparse Neural Networks through $L_0$ Regularization | We propose a practical method for $L_0$ norm regularization for neural
networks: pruning the network during training by encouraging weights to become
exactly zero. Such regularization is interesting since (1) it can greatly speed
up training and inference, and (2) it can improve generalization. AIC and BIC,
well-known model selection criteria, are special cases of $L_0$ regularization.
However, since the $L_0$ norm of weights is non-differentiable, we cannot
incorporate it directly as a regularization term in the objective function. We
propose a solution through the inclusion of a collection of non-negative
stochastic gates, which collectively determine which weights to set to zero. We
show that, somewhat surprisingly, for certain distributions over the gates, the
expected $L_0$ norm of the resulting gated weights is differentiable with
respect to the distribution parameters. We further propose the \emph{hard
concrete} distribution for the gates, which is obtained by "stretching" a
binary concrete distribution and then transforming its samples with a
hard-sigmoid. The parameters of the distribution over the gates can then be
jointly optimized with the original network parameters. As a result our method
allows for straightforward and efficient learning of model structures with
stochastic gradient descent and allows for conditional computation in a
principled way. We perform various experiments to demonstrate the effectiveness
of the resulting approach and regularizer.
| 1 | 0 | 0 | 1 | 0 | 0 |
The relationships between PM2.5 and meteorological factors in China: Seasonal and regional variations | The interactions between PM2.5 and meteorological factors play a crucial role
in air pollution analysis. However, previous studies that have researched the
relationships between PM2.5 concentration and meteorological conditions have
been mainly confined to a certain city or district, and the correlation over
the whole of China remains unclear. Whether or not spatial and seasonal
variations exit deserves further research. In this study, the relationships
between PM2.5 concentration and meteorological factors were investigated in 74
major cities in China for a continuous period of 22 months from February 2013
to November 2014, at season, year, city, and regional scales, and the spatial
and seasonal variations were analyzed. The meteorological factors were relative
humidity (RH), temperature (TEM), wind speed (WS), and surface pressure (PS).
We found that spatial and seasonal variations of their relationships with PM2.5
do exist. Spatially, RH is positively correlated with PM2.5 concentration in
North China and Urumqi, but the relationship turns to negative in other areas
of China. WS is negatively correlated with PM2.5 everywhere expect for Hainan
Island. PS has a strong positive relationship with PM2.5 concentration in
Northeast China and Mid-south China, and in other areas the correlation is
weak. Seasonally, the positive correlation between PM2.5 concentration and RH
is stronger in winter and spring. TEM has a negative relationship with PM2.5 in
autumn and the opposite in winter. PS is more positively correlated with PM2.5
in autumn than in other seasons. Our study investigated the relationships
between PM2.5 and meteorological factors in terms of spatial and seasonal
variations, and the conclusions about the relationships between PM2.5 and
meteorological factors are more comprehensive and precise than before.
| 0 | 1 | 0 | 0 | 0 | 0 |
Rank-two Milnor idempotents for the multipullback quantum complex projective plane | The $K_0$-group of the C*-algebra of multipullback quantum complex projective
plane is known to be $\mathbb{Z}^3$, with one generator given by the C*-algebra
itself, one given by the section module of the noncommutative (dual)
tautological line bundle, and one given by the Milnor module associated to a
generator of the $K_1$-group of the C*-algebra of Calow-Matthes quantum
3-sphere. Herein we prove that these Milnor modules are isomorphic either to
the section module of a noncommutative vector bundle associated to the
$SU_q(2)$-prolongation of the Heegaard quantum 5-sphere $S^5_H$ viewed as a
$U(1)$-quantum principal bundle, or to a complement of this module in the
rank-four free module. Finally, we demonstrate that one of the above Milnor
modules always splits into the direct sum of the rank-one free module and a
rank-one non-free projective module that is \emph{not} associated with $S^5_H$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Lost Relatives of the Gumbel Trick | The Gumbel trick is a method to sample from a discrete probability
distribution, or to estimate its normalizing partition function. The method
relies on repeatedly applying a random perturbation to the distribution in a
particular way, each time solving for the most likely configuration. We derive
an entire family of related methods, of which the Gumbel trick is one member,
and show that the new methods have superior properties in several settings with
minimal additional computational cost. In particular, for the Gumbel trick to
yield computational benefits for discrete graphical models, Gumbel
perturbations on all configurations are typically replaced with so-called
low-rank perturbations. We show how a subfamily of our new methods adapts to
this setting, proving new upper and lower bounds on the log partition function
and deriving a family of sequential samplers for the Gibbs distribution.
Finally, we balance the discussion by showing how the simpler analytical form
of the Gumbel trick enables additional theoretical results.
| 1 | 0 | 0 | 1 | 0 | 0 |
Sequential Randomized Matrix Factorization for Gaussian Processes: Efficient Predictions and Hyper-parameter Optimization | This paper presents a sequential randomized lowrank matrix factorization
approach for incrementally predicting values of an unknown function at test
points using the Gaussian Processes framework. It is well-known that in the
Gaussian processes framework, the computational bottlenecks are the inversion
of the (regularized) kernel matrix and the computation of the hyper-parameters
defining the kernel. The main contributions of this paper are two-fold. First,
we formalize an approach to compute the inverse of the kernel matrix using
randomized matrix factorization algorithms in a streaming scenario, i.e., data
is generated incrementally over time. The metrics of accuracy and computational
efficiency of the proposed method are compared against a batch approach based
on use of randomized matrix factorization and an existing streaming approach
based on approximating the Gaussian process by a finite set of basis vectors.
Second, we extend the sequential factorization approach to a class of kernel
functions for which the hyperparameters can be efficiently optimized. All
results are demonstrated on two publicly available datasets.
| 1 | 0 | 0 | 1 | 0 | 0 |
Large-scale dynamos in rapidly rotating plane layer convection | Context: Convectively-driven flows play a crucial role in the dynamo
processes that are responsible for producing magnetic activity in stars and
planets. It is still not fully understood why many astrophysical magnetic
fields have a significant large-scale component. Aims: Our aim is to
investigate the dynamo properties of compressible convection in a rapidly
rotating Cartesian domain, focusing upon a parameter regime in which the
underlying hydrodynamic flow is known to be unstable to a large-scale vortex
instability. Methods: The governing equations of three-dimensional nonlinear
magnetohydrodynamics (MHD) are solved numerically. Different numerical schemes
are compared and we propose a possible benchmark case for other similar codes.
Results: In keeping with previous related studies, we find that convection in
this parameter regime can drive a large-scale dynamo. The components of the
mean horizontal magnetic field oscillate, leading to a continuous overall
rotation of the mean field. Whilst the large-scale vortex instability dominates
the early evolution of the system, it is suppressed by the magnetic field and
makes a negligible contribution to the mean electromotive force that is
responsible for driving the large-scale dynamo. The cycle period of the dynamo
is comparable to the ohmic decay time, with longer cycles for dynamos in
convective systems that are closer to onset. In these particular simulations,
large-scale dynamo action is found only when vertical magnetic field boundary
conditions are adopted at the upper and lower boundaries. Strongly modulated
large-scale dynamos are found at higher Rayleigh numbers, with periods of
reduced activity ("grand minima"-like events) occurring during transient phases
in which the large-scale vortex temporarily re-establishes itself, before being
suppressed again by the magnetic field.
| 0 | 1 | 0 | 0 | 0 | 0 |
Statistical inference for misspecified ergodic Lévy driven stochastic differential equation models | This paper deals with the estimation problem of misspecified ergodic Lévy
driven stochastic differential equation models based on high-frequency samples.
We utilize the widely applicable and tractable Gaussian quasi-likelihood
approach which focuses on (conditional) mean and variance structure. It is
shown that the corresponding Gaussian quasi-likelihood estimators of drift and
scale parameters satisfy tail probability estimates and asymptotic normality at
the same rate as correctly specified case. In this process, extended Poisson
equation for time-homogeneous Feller Markov processes plays an important role
to handle misspecification effect. Our result confirms the practical usefulness
of the Gaussian quasi-likelihood approach for SDE models, more firmly.
| 0 | 0 | 1 | 1 | 0 | 0 |
Bar formation in the Milky Way type galaxies | Many barred galaxies, possibly including the Milky Way, have cusps in the
centres. There is a widespread belief, however, that usual bar instability
taking place in bulgeless galaxy models is impossible for the cuspy models,
because of the presence of the inner Lindblad resonance for any pattern speed.
At the same time there are numerical evidences that the bar instability can
form a bar. We analyse this discrepancy, by accurate and diverse N-body
simulations and using the calculation of normal modes. We show that bar
formation in cuspy galaxies can be explained by taking into account the disc
thickness. The exponential growth time is moderate for typical current disc
masses (about 250 Myr), but considerably increases (factor 2 or more) upon
substitution of the live halo and bulge with a rigid halo/bulge potential;
meanwhile pattern speeds remain almost the same. Normal mode analysis with
different disc mass favours a young bar hypothesis, according to which the bar
instability saturated only recently.
| 0 | 1 | 0 | 0 | 0 | 0 |
TLR: Transfer Latent Representation for Unsupervised Domain Adaptation | Domain adaptation refers to the process of learning prediction models in a
target domain by making use of data from a source domain. Many classic methods
solve the domain adaptation problem by establishing a common latent space,
which may cause the loss of many important properties across both domains. In
this manuscript, we develop a novel method, transfer latent representation
(TLR), to learn a better latent space. Specifically, we design an objective
function based on a simple linear autoencoder to derive the latent
representations of both domains. The encoder in the autoencoder aims to project
the data of both domains into a robust latent space. Besides, the decoder
imposes an additional constraint to reconstruct the original data, which can
preserve the common properties of both domains and reduce the noise that causes
domain shift. Experiments on cross-domain tasks demonstrate the advantages of
TLR over competing methods.
| 0 | 0 | 0 | 1 | 0 | 0 |
Automated and Robust Quantification of Colocalization in Dual-Color Fluorescence Microscopy: A Nonparametric Statistical Approach | Colocalization is a powerful tool to study the interactions between
fluorescently labeled molecules in biological fluorescence microscopy. However,
existing techniques for colocalization analysis have not undergone continued
development especially in regards to robust statistical support. In this paper,
we examine two of the most popular quantification techniques for colocalization
and argue that they could be improved upon using ideas from nonparametric
statistics and scan statistics. In particular, we propose a new colocalization
metric that is robust, easily implementable, and optimal in a rigorous
statistical testing framework. Application to several benchmark datasets, as
well as biological examples, further demonstrates the usefulness of the
proposed technique.
| 0 | 0 | 0 | 1 | 0 | 0 |
On the MISO Channel with Feedback: Can Infinitely Massive Antennas Achieve Infinite Capacity? | We consider communication over a multiple-input single-output (MISO) block
fading channel in the presence of an independent noiseless feedback link. We
assume that the transmitter and receiver have no prior knowledge of the channel
state realizations, but the transmitter and receiver can acquire the channel
state information (CSIT/CSIR) via downlink training and feedback. For this
channel, we show that increasing the number of transmit antennas to infinity
will not achieve an infinite capacity, for a finite channel coherence length
and a finite input constraint on the second or fourth moment. This insight
follows from our new capacity bounds that hold for any linear and nonlinear
coding strategies, and any channel training schemes. In addition to the channel
capacity bounds, we also provide a characterization on the beamforming gain
that is also known as array gain or power gain, at the regime with a large
number of antennas.
| 1 | 0 | 0 | 0 | 0 | 0 |
Structural Analysis and Optimal Design of Distributed System Throttlers | In this paper, we investigate the performance analysis and synthesis of
distributed system throttlers (DST). A throttler is a mechanism that limits the
flow rate of incoming metrics, e.g., byte per second, network bandwidth usage,
capacity, traffic, etc. This can be used to protect a service's backend/clients
from getting overloaded, or to reduce the effects of uncertainties in demand
for shared services. We study performance deterioration of DSTs subject to
demand uncertainty. We then consider network synthesis problems that aim to
improve the performance of noisy DSTs via communication link modifications as
well as server update cycle modifications.
| 1 | 0 | 1 | 0 | 0 | 0 |
Subadditivity and additivity of the Yang-Mills action functional in Noncommutative Geometry | We formulate notions of subadditivity and additivity of the Yang-Mills action
functional in noncommutative geometry. We identify a suitable hypothesis on
spectral triples which proves that the Yang-Mills functional is always
subadditive, as per expectation. The additivity property is much stronger in
the sense that it implies the subadditivity property. Under this hypothesis we
obtain a necessary and sufficient condition for the additivity of the
Yang-Mills functional. An instance of additivity is shown for the case of
noncommutative $n$-tori. We also investigate the behaviour of critical points
of the Yang-Mills functional under additivity. At the end we discuss few
examples involving compact spin manifolds, matrix algebras, noncommutative
$n$-torus and the quantum Heisenberg manifolds which validate our hypothesis.
| 0 | 0 | 1 | 0 | 0 | 0 |
Sensivity of the Hermite rank | The Hermite rank appears in limit theorems involving long memory. We show
that an Hermite rank higher than one is unstable when the data is slightly
perturbed by transformations such as shift and scaling. We carry out a "near
higher order rank analysis" to illustrate how the limit theorems are affected
by a shift perturbation that is decreasing in size. As a byproduct of our
analysis, we also prove the coincidence of the Hermite rank and the power rank
in the Gaussian context. The paper is a technical companion of
\citet{bai:taqqu:2017:instability} which discusses the instability of the
Hermite rank in the statistical context. (Older title "Some properties of the
Hermite rank">)
| 0 | 0 | 1 | 1 | 0 | 0 |
Specifying a positive threshold function via extremal points | An extremal point of a positive threshold Boolean function $f$ is either a
maximal zero or a minimal one. It is known that if $f$ depends on all its
variables, then the set of its extremal points completely specifies $f$ within
the universe of threshold functions. However, in some cases, $f$ can be
specified by a smaller set. The minimum number of points in such a set is the
specification number of $f$. It was shown in [S.-T. Hu. Threshold Logic, 1965]
that the specification number of a threshold function of $n$ variables is at
least $n+1$. In [M. Anthony, G. Brightwell, and J. Shawe-Taylor. On specifying
Boolean functions by labelled examples. Discrete Applied Mathematics, 1995] it
was proved that this bound is attained for nested functions and conjectured
that for all other threshold functions the specification number is strictly
greater than $n+1$. In the present paper, we resolve this conjecture negatively
by exhibiting threshold Boolean functions of $n$ variables, which are
non-nested and for which the specification number is $n+1$. On the other hand,
we show that the set of extremal points satisfies the statement of the
conjecture, i.e., a positive threshold Boolean function depending on all its
$n$ variables has $n+1$ extremal points if and only if it is nested. To prove
this, we reveal an underlying structure of the set of extremal points.
| 1 | 0 | 0 | 0 | 0 | 0 |
Link Mining for Kernel-based Compound-Protein Interaction Predictions Using a Chemogenomics Approach | Virtual screening (VS) is widely used during computational drug discovery to
reduce costs. Chemogenomics-based virtual screening (CGBVS) can be used to
predict new compound-protein interactions (CPIs) from known CPI network data
using several methods, including machine learning and data mining. Although
CGBVS facilitates highly efficient and accurate CPI prediction, it has poor
performance for prediction of new compounds for which CPIs are unknown. The
pairwise kernel method (PKM) is a state-of-the-art CGBVS method and shows high
accuracy for prediction of new compounds. In this study, on the basis of link
mining, we improved the PKM by combining link indicator kernel (LIK) and
chemical similarity and evaluated the accuracy of these methods. The proposed
method obtained an average area under the precision-recall curve (AUPR) value
of 0.562, which was higher than that achieved by the conventional Gaussian
interaction profile (GIP) method (0.425), and the calculation time was only
increased by a few percent.
| 1 | 0 | 0 | 1 | 0 | 0 |
Using Stock Prices as Ground Truth in Sentiment Analysis to Generate Profitable Trading Signals | The increasing availability of "big" (large volume) social media data has
motivated a great deal of research in applying sentiment analysis to predict
the movement of prices within financial markets. Previous work in this field
investigates how the true sentiment of text (i.e. positive or negative
opinions) can be used for financial predictions, based on the assumption that
sentiments expressed online are representative of the true market sentiment.
Here we consider the converse idea, that using the stock price as the
ground-truth in the system may be a better indication of sentiment. Tweets are
labelled as Buy or Sell dependent on whether the stock price discussed rose or
fell over the following hour, and from this, stock-specific dictionaries are
built for individual companies. A Bayesian classifier is used to generate stock
predictions, which are input to an automated trading algorithm. Placing 468
trades over a 1 month period yields a return rate of 5.18%, which annualises to
approximately 83% per annum. This approach performs significantly better than
random chance and outperforms two baseline sentiment analysis methods tested.
| 0 | 0 | 0 | 0 | 0 | 1 |
A novel approach to fractional calculus: utilizing fractional integrals and derivatives of the Dirac delta function | While the definition of a fractional integral may be codified by Riemann and
Liouville, an agreed-upon fractional derivative has eluded discovery for many
years. This is likely a result of integral definitions including numerous
constants of integration in their results. An elimination of constants of
integration opens the door to an operator that reconciles all known fractional
derivatives and shows surprising results in areas unobserved before, including
the appearance of the Riemann Zeta Function and fractional Laplace and Fourier
Transforms. A new class of functions, known as Zero Functions and closely
related to the Dirac Delta Function, are necessary for one to perform
elementary operations of functions without using constants. The operator also
allows for a generalization of the Volterra integral equation, and provides a
method of solving for Riemann's "complimentary" function introduced during his
research on fractional derivatives.
| 0 | 0 | 1 | 0 | 0 | 0 |
When does every definable nonempty set have a definable element? | The assertion that every definable set has a definable element is equivalent
over ZF to the principle $V=\text{HOD}$, and indeed, we prove, so is the
assertion merely that every $\Pi_2$-definable set has an ordinal-definable
element. Meanwhile, every model of ZFC has a forcing extension satisfying
$V\neq\text{HOD}$ in which every $\Sigma_2$-definable set has an
ordinal-definable element. Similar results hold for $\text{HOD}(\mathbb{R})$
and $\text{HOD}(\text{Ord}^\omega)$ and other natural instances of
$\text{HOD}(X)$.
| 0 | 0 | 1 | 0 | 0 | 0 |
An exploration to visualize finite element data with a DSL | The scientific community use PDEs to model a range of problems. The people in
this domain are interested in visualizing their results, but existing
mechanisms for visualization can not handle the full richness of computations
in the domain. We did an exploration to see how Diderot, a domain specific
language for scientific visualization and image analysis, could be used to
solve this problem.
We demonstrate our first and modest approach of visualizing FE data with
Diderot and provide examples. Using Diderot, we do a simple sampling and a
volume rendering of a FE field. These examples showcase Diderot's ability to
provide a visualization result for Firedrake. This paper describes the
extension of the Diderot language to include FE data.
| 1 | 0 | 0 | 0 | 0 | 0 |
Measurement and Analysis of Quality of Service of Mobile Networks in Afghanistan End User Perspective | Enhanced Quality of Service (QoS) and satisfaction of mobile phone user are
major concerns of a service provider. In order to manage network efficiently
and to provide enhanced end to end Quality of Experience (QoE), operator is
expected to measure and analyze QoS from various perspectives and at different
relevant points of network. The scope of this paper is measurement and
statistically analysis of QoS of mobile networks from end user perspective in
Afghanistan. The study is based on primary data collected on random basis from
1,515 mobile phone users of five cellular operators. The paper furthermore
proposes adequate technical solutions to mobile operators in order to address
existing challenges in the area of QoS and to remain competitive in the market.
Based on the result of processed data, considering geographical locations,
population and telecom regulations of the government, authors recommend
deployment of small cells (SCs), increasing number of regular performance
tests, optimal placement of base stations, increasing number of carriers, and
high order sectorization as proposed technical solutions.
| 1 | 0 | 0 | 0 | 0 | 0 |
On the uncertainty of temperature estimation in a rapid compression machine | Rapid compression machines (RCMs) have been widely used in the combustion
literature to study the low-to-intermediate temperature ignition of many fuels.
In a typical RCM, the pressure during and after the compression stroke is
measured. However, measurement of the temperature history in the RCM reaction
chamber is challenging. Thus, the temperature is generally calculated by the
isentropic relations between pressure and temperature, assuming that the
adiabatic core hypothesis holds. To estimate the uncertainty in the calculated
temperature, an uncertainty propagation analysis must be carried out. Our
previous analyses assumed that the uncertainties of the parameters in the
equation to calculate the temperature were normally distributed and
independent, but these assumptions do not hold for typical RCM operating
procedures. In this work, a Monte Carlo method is developed to estimate the
uncertainty in the calculated temperature, while taking into account the
correlation between parameters and the possibility of non-normal probability
distributions. In addition, the Monte Carlo method is compared to an analysis
that assumes normally distributed, independent parameters. Both analysis
methods show that the magnitude of the initial pressure and the uncertainty of
the initial temperature have strong influences on the magnitude of the
uncertainty. Finally, the uncertainty estimation methods studied here provide a
reference value for the uncertainty of the reference temperature in an RCM and
can be generalized to other similar facilities.
| 0 | 1 | 0 | 0 | 0 | 0 |
Abelian varieties isogenous to a power of an elliptic curve over a Galois extension | Given an elliptic curve $E/k$ and a Galois extension $k'/k$, we construct an
exact functor from torsion-free modules over the endomorphism ring ${\rm
End}(E_{k'})$ with a semilinear ${\rm Gal}(k'/k)$ action to abelian varieties
over $k$ that are $k'$-isogenous to a power of $E$. As an application, we show
that every elliptic curve with complex multiplication geometrically is
isogenous over the ground field to one with complex multiplication by a maximal
order.
| 0 | 0 | 1 | 0 | 0 | 0 |
Radiative nonrecoil nuclear finite size corrections of order $α(Z α)^5$ to the Lamb shift in light muonic atoms | On the basis of quasipotential method in quantum electrodynamics we calculate
nuclear finite size radiative corrections of order $\alpha(Z \alpha)^5$ to the
Lamb shift in muonic hydrogen and helium. To construct the interaction
potential of particles, which gives the necessary contributions to the energy
spectrum, we use the method of projection operators to states with a definite
spin. Separate analytic expressions for the contributions of the muon
self-energy, the muon vertex operator and the amplitude with spanning photon
are obtained. We present also numerical results for these contributions using
modern experimental data on the electromagnetic form factors of light nuclei.
| 0 | 1 | 0 | 0 | 0 | 0 |
Improved Training of Wasserstein GANs | Generative Adversarial Networks (GANs) are powerful generative models, but
suffer from training instability. The recently proposed Wasserstein GAN (WGAN)
makes progress toward stable training of GANs, but sometimes can still generate
only low-quality samples or fail to converge. We find that these problems are
often due to the use of weight clipping in WGAN to enforce a Lipschitz
constraint on the critic, which can lead to undesired behavior. We propose an
alternative to clipping weights: penalize the norm of gradient of the critic
with respect to its input. Our proposed method performs better than standard
WGAN and enables stable training of a wide variety of GAN architectures with
almost no hyperparameter tuning, including 101-layer ResNets and language
models over discrete data. We also achieve high quality generations on CIFAR-10
and LSUN bedrooms.
| 1 | 0 | 0 | 1 | 0 | 0 |
A cancellation theorem for Milnor-Witt correspondences | We show that finite Milnor-Witt correspondences satisfy a cancellation
theorem with respect to the pointed multiplicative group scheme. This has
several notable applications in the theory of Milnor-Witt motives and
Milnor-Witt motivic cohomology.
| 0 | 0 | 1 | 0 | 0 | 0 |
Wind Riemannian spaceforms and Randers metrics of constant flag curvature | Recently, wind Riemannian structures (WRS) have been introduced as a
generalization of Randers and Kropina metrics. They are constructed from the
natural data for Zermelo navigation problem, namely, a Riemannian metric $g_R$
and a vector field $W$ (the wind), where, now, the restriction of mild wind
$g_R(W,W)<1$ is dropped.
Here, the models of WRS spaceforms of constant flag curvature are determined.
Indeed, the celebrated classification of Randers metrics of constant flag
curvature by Bao, Robles and Shen, extended to the Kropina case in the works by
Yoshikawa, Okubo and Sabau, can be used to obtain the local classification. For
the global one, a suitable result on completeness for WRS yields the complete
simply connected models. In particular, any of the local models in the Randers
classification does admit an extension to a unique model of wind Riemannian
structure, even if it cannot be extended as a complete Finslerian manifold.
Thus, WRS's emerge as the natural framework for the analysis of Randers
spaceforms and, prospectively, wind Finslerian structures would become
important for other global problems too. For the sake of completeness, a brief
overview about WRS (including a useful link with the conformal geometry of a
class of relativistic spacetimes) is also provided.
| 0 | 0 | 1 | 0 | 0 | 0 |
Developing a Purely Visual Based Obstacle Detection using Inverse Perspective Mapping | Our solution is implemented in and for the frame of Duckietown. The goal of
Duckietown is to provide a relatively simple platform to explore, tackle and
solve many problems linked to autonomous driving. "Duckietown" is simple in the
basics, but an infinitely expandable environment. From controlling single
driving Duckiebots until complete fleet management, every scenario is possible
and can be put into practice. So far, none of the existing modules was capable
of reliably detecting obstacles and reacting to them in real time. We faced the
general problem of detecting obstacles given images from a monocular RGB camera
mounted at the front of our Duckiebot and reacting to them properly without
crashing or erroneously stopping the Duckiebot. Both, the detection as well as
the reaction have to be implemented and have to run on a Raspberry Pi in real
time. Due to the strong hardware limitations, we decided to not use any
learning algorithms for the obstacle detection part. As it later transpired, a
working "hard coded" software needs thorough analysis and understanding of the
given problem. In layman's terms, we simply seek to make Duckietown a safer
place.
| 1 | 0 | 0 | 0 | 0 | 0 |
Weak Keys and Cryptanalysis of a Cold War Block Cipher | T-310 is a cipher that was used for encryption of governmental communications
in East Germany during the final years of the Cold War. Due to its complexity
and the encryption process,there was no published attack for a period of more
than 40 years until 2018 by Nicolas T. Courtois et al. in [10]. In this thesis
we study the so called 'long term keys' that were used in the cipher, in order
to expose weaknesses which will assist the design of various attacks on T-310.
| 1 | 0 | 0 | 0 | 0 | 0 |
Dynamic Optimization of Neural Network Structures Using Probabilistic Modeling | Deep neural networks (DNNs) are powerful machine learning models and have
succeeded in various artificial intelligence tasks. Although various
architectures and modules for the DNNs have been proposed, selecting and
designing the appropriate network structure for a target problem is a
challenging task. In this paper, we propose a method to simultaneously optimize
the network structure and weight parameters during neural network training. We
consider a probability distribution that generates network structures, and
optimize the parameters of the distribution instead of directly optimizing the
network structure. The proposed method can apply to the various network
structure optimization problems under the same framework. We apply the proposed
method to several structure optimization problems such as selection of layers,
selection of unit types, and selection of connections using the MNIST,
CIFAR-10, and CIFAR-100 datasets. The experimental results show that the
proposed method can find the appropriate and competitive network structures.
| 0 | 0 | 0 | 1 | 0 | 0 |
Spectroscopy of Ultra-diffuse Galaxies in the Coma Cluster | We present spectra of 5 ultra-diffuse galaxies (UDGs) in the vicinity of the
Coma Cluster obtained with the Multi-Object Double Spectrograph on the Large
Binocular Telescope. We confirm 4 of these as members of the cluster,
quintupling the number of spectroscopically confirmed systems. Like the
previously confirmed large (projected half light radius $>$ 4.6 kpc) UDG, DF44,
the systems we targeted all have projected half light radii $> 2.9$ kpc. As
such, we spectroscopically confirm a population of physically large UDGs in the
Coma cluster. The remaining UDG is located in the field, about $45$ Mpc behind
the cluster. We observe Balmer and Ca II H \& K absorption lines in all of our
UDG spectra. By comparing the stacked UDG spectrum against stellar population
synthesis models, we conclude that, on average, these UDGs are composed of
metal-poor stars ([Fe/H] $\lesssim -1.5$). We also discover the first UDG with
[OII] and [OIII] emission lines within a clustered environment, demonstrating
that not all cluster UDGs are devoid of gas and sources of ionizing radiation.
| 0 | 1 | 0 | 0 | 0 | 0 |
On Robust Tie-line Scheduling in Multi-Area Power Systems | The tie-line scheduling problem in a multi-area power system seeks to
optimize tie-line power flows across areas that are independently operated by
different system operators (SOs). In this paper, we leverage the theory of
multi-parametric linear programming to propose algorithms for optimal tie-line
scheduling within a deterministic and a robust optimization framework. Through
a coordinator, the proposed algorithms are proved to converge to the optimal
schedule within a finite number of iterations. A key feature of the proposed
algorithms, besides their finite step convergence, is the privacy of the
information exchanges; the SO in an area does not need to reveal its dispatch
cost structure, network constraints, or the nature of the uncertainty set to
the coordinator. The performance of the algorithms is evaluated using several
power system examples.
| 0 | 0 | 1 | 0 | 0 | 0 |
Novel Phases of Semi-Conducting Silicon Nitride Bilayer: A First-Principle Study | In this paper, we have predicted the stabilities of several two-dimensional
phases of silicon nitride, which we name as \alpha-phase, \beta-phase, and
\gamma-phase, respectively. Both \alpha- and \beta-phases has formula
Si$_{2}$N$_{2}$, and are consisted of two similar layer of buckled SiN sheet.
Similarly, \gamma-phase is consisted of two puckered SiN sheets. For these
phases, the two layers are connected with Si-Si covalent bonds. Transformation
between \alpha- and \beta-phases is difficult because of the high energy
barrier. Phonon spectra of both \alpha- and \beta-phase suggest their
thermodynamic stabilities, because no phonon mode with imaginary frequency is
present. By Contrast, \gamma-phase is unstable because phonon modes with
imaginary frequencies are found along \Gamma-Y path in the Brilliouin zone.
Both \alpha- and \beta-phase are semiconductor with narrow fundamental indirect
band gap of 1.7eV and 1.9eV, respectively. As expected, only s and p orbitals
in the outermost shells contribute the band structures. The p$_{z}$ orbitals
have greater contribution near the Fermi level. These materials can easily
exfoliate to form 2D structures, and may have potential electronic
applications.
| 0 | 1 | 0 | 0 | 0 | 0 |
Schwarz-Christoffel: piliero en rivero (a pillar on a river) | La transformoj de Schwarz-Christoffel mapas, konforme, la superan kompleksan
duon-ebenon al regiono limigita per rektaj segmentoj. Cxi tie ni priskribas
kiel konvene kunigi mapon de la suba duon-ebeno al mapo de la supera
duon-ebeno. Ni emfazas la bezonon de klara difino de angulo de kompleksa
nombro, por tiu kunigo. Ni diskutas kelkajn ekzemplojn kaj donas interesan
aplikon pri movado de fluido.
-------
Schwarz-Christoffel transformations map, conformally, the complex upper half
plane into a region bounded by right segments. Here we describe how to couple
conveniently a map of the lower half plane to the map of the upper half plane.
We emphasize the need of a clear definition of angle of a complex, to that
coupling. We discuss some examples and give an interesting application for
motion of fluid.
| 0 | 0 | 1 | 0 | 0 | 0 |
Towards Audio to Scene Image Synthesis using Generative Adversarial Network | Humans can imagine a scene from a sound. We want machines to do so by using
conditional generative adversarial networks (GANs). By applying the techniques
including spectral norm, projection discriminator and auxiliary classifier,
compared with naive conditional GAN, the model can generate images with better
quality in terms of both subjective and objective evaluations. Almost
three-fourth of people agree that our model have the ability to generate images
related to sounds. By inputting different volumes of the same sound, our model
output different scales of changes based on the volumes, showing that our model
truly knows the relationship between sounds and images to some extent.
| 1 | 0 | 0 | 0 | 0 | 0 |
Data-Mining Research in Education | As an interdisciplinary discipline, data mining (DM) is popular in education
area especially when examining students' learning performances. It focuses on
analyzing educational related data to develop models for improving learners'
learning experiences and enhancing institutional effectiveness. Therefore, DM
does help education institutions provide high-quality education for its
learners. Applying data mining in education also known as educational data
mining (EDM), which enables to better understand how students learn and
identify how improve educational outcomes. Present paper is designed to justify
the capabilities of data mining approaches in the filed of education. The
latest trends on EDM research are introduced in this review. Several specific
algorithms, methods, applications and gaps in the current literature and future
insights are discussed here.
| 1 | 0 | 0 | 1 | 0 | 0 |
Stochastic Input Models in Online Computing | In this paper, we study twelve stochastic input models for online problems
and reveal the relationships among the competitive ratios for the models. The
competitive ratio is defined as the worst ratio between the expected optimal
value and the expected profit of the solution obtained by the online algorithm
where the input distribution is restricted according to the model. To handle a
broad class of online problems, we use a framework called request-answer games
that is introduced by Ben-David et al. The stochastic input models consist of
two types: known distribution and unknown distribution. For each type, we
consider six classes of distributions: dependent distributions, deterministic
input, independent distributions, identical independent distribution, random
order of a deterministic input, and random order of independent distributions.
As an application of the models, we consider two basic online problems, which
are variants of the secretary problem and the prophet inequality problem, under
the twelve stochastic input models. We see the difference of the competitive
ratios through these problems.
| 1 | 0 | 1 | 1 | 0 | 0 |
Generating Visual Representations for Zero-Shot Classification | This paper addresses the task of learning an image clas-sifier when some
categories are defined by semantic descriptions only (e.g. visual attributes)
while the others are defined by exemplar images as well. This task is often
referred to as the Zero-Shot classification task (ZSC). Most of the previous
methods rely on learning a common embedding space allowing to compare visual
features of unknown categories with semantic descriptions. This paper argues
that these approaches are limited as i) efficient discrimi-native classifiers
can't be used ii) classification tasks with seen and unseen categories
(Generalized Zero-Shot Classification or GZSC) can't be addressed efficiently.
In contrast , this paper suggests to address ZSC and GZSC by i) learning a
conditional generator using seen classes ii) generate artificial training
examples for the categories without exemplars. ZSC is then turned into a
standard supervised learning problem. Experiments with 4 generative models and
5 datasets experimentally validate the approach, giving state-of-the-art
results on both ZSC and GZSC.
| 1 | 0 | 0 | 0 | 0 | 0 |
Online Learning Rate Adaptation with Hypergradient Descent | We introduce a general method for improving the convergence rate of
gradient-based optimizers that is easy to implement and works well in practice.
We demonstrate the effectiveness of the method in a range of optimization
problems by applying it to stochastic gradient descent, stochastic gradient
descent with Nesterov momentum, and Adam, showing that it significantly reduces
the need for the manual tuning of the initial learning rate for these commonly
used algorithms. Our method works by dynamically updating the learning rate
during optimization using the gradient with respect to the learning rate of the
update rule itself. Computing this "hypergradient" needs little additional
computation, requires only one extra copy of the original gradient to be stored
in memory, and relies upon nothing more than what is provided by reverse-mode
automatic differentiation.
| 1 | 0 | 0 | 1 | 0 | 0 |
Mitigating radiation damage of single photon detectors for space applications | Single-photon detectors in space must retain useful performance
characteristics despite being bombarded with sub-atomic particles. Mitigating
the effects of this space radiation is vital to enabling new space applications
which require high-fidelity single-photon detection. To this end, we conducted
proton radiation tests of various models of avalanche photodiodes (APDs) and
one model of photomultiplier tube potentially suitable for satellite-based
quantum communications. The samples were irradiated with 106 MeV protons at
doses approximately equivalent to lifetimes of 0.6 , 6, 12 and 24 months in a
low-Earth polar orbit. Although most detection properties were preserved,
including efficiency, timing jitter and afterpulsing probability, all APD
samples demonstrated significant increases in dark count rate (DCR) due to
radiation-induced damage, many orders of magnitude higher than the 200 counts
per second (cps) required for ground-to-satellite quantum communications. We
then successfully demonstrated the mitigation of this DCR degradation through
the use of deep cooling, to as low as -86 degrees C. This achieved DCR below
the required 200 cps over the 24 months orbit duration. DCR was further reduced
by thermal annealing at temperatures of +50 to +100 degrees C.
| 0 | 1 | 0 | 0 | 0 | 0 |
The Parameterized Complexity of Positional Games | We study the parameterized complexity of several positional games. Our main
result is that Short Generalized Hex is W[1]-complete parameterized by the
number of moves. This solves an open problem from Downey and Fellows'
influential list of open problems from 1999. Previously, the problem was
thought of as a natural candidate for AW[*]-completeness. Our main tool is a
new fragment of first-order logic where universally quantified variables only
occur in inequalities. We show that model-checking on arbitrary relational
structures for a formula in this fragment is W[1]-complete when parameterized
by formula size. We also consider a general framework where a positional game
is represented as a hypergraph and two players alternately pick vertices. In a
Maker-Maker game, the first player to have picked all the vertices of some
hyperedge wins the game. In a Maker-Breaker game, the first player wins if she
picks all the vertices of some hyperedge, and the second player wins otherwise.
In an Enforcer-Avoider game, the first player wins if the second player picks
all the vertices of some hyperedge, and the second player wins otherwise. Short
Maker-Maker is AW[*]-complete, whereas Short Maker-Breaker is W[1]-complete and
Short Enforcer-Avoider co-W[1]-complete parameterized by the number of moves.
This suggests a rough parameterized complexity categorization into positional
games that are complete for the first level of the W-hierarchy when the winning
configurations only depend on which vertices one player has been able to pick,
but AW[*]-completeness when the winning condition depends on which vertices
both players have picked. However, some positional games where the board and
the winning configurations are highly structured are fixed-parameter tractable.
We give another example of such a game, Short k-Connect, which is
fixed-parameter tractable when parameterized by the number of moves.
| 1 | 0 | 0 | 0 | 0 | 0 |
Lunar laser ranging in infrfared at hte Grasse laser station | For many years, lunar laser ranging (LLR) observations using a green
wavelength have suffered an inhomogeneity problem both temporally and
spatially. This paper reports on the implementation of a new infrared detection
at the Grasse LLR station and describes how infrared telemetry improves this
situation. Our first results show that infrared detection permits us to densify
the observations and allows measurements during the new and the full Moon
periods. The link budget improvement leads to homogeneous telemetric
measurements on each lunar retro-reflector. Finally, a surprising result is
obtained on the Lunokhod 2 array which attains the same efficiency as Lunokhod
1 with an infrared laser link, although those two targets exhibit a
differential efficiency of six with a green laser link.
| 0 | 1 | 0 | 0 | 0 | 0 |
An exact solution to a Stefan problem with variable thermal conductivity and a Robin boundary condition | In this article it is proved the existence of similarity solutions for a
one-phase Stefan problem with temperature-dependent thermal conductivity and a
Robin condition at the fixed face. The temperature distribution is obtained
through a generalized modified error function which is defined as the solution
to a nonlinear ordinary differential problem of second order. It is proved that
the latter has a unique non-negative bounded analytic solution when the
parameter on which it depends assumes small positive values. Moreover, it is
shown that the generalized modified error function is concave and increasing,
and explicit approximations are proposed for it. Relation between the Stefan
problem considered in this article with those with either constant thermal
conductivity or a temperature boundary condition is also analysed.
| 0 | 0 | 1 | 0 | 0 | 0 |
State-of-the-art Speech Recognition With Sequence-to-Sequence Models | Attention-based encoder-decoder architectures such as Listen, Attend, and
Spell (LAS), subsume the acoustic, pronunciation and language model components
of a traditional automatic speech recognition (ASR) system into a single neural
network. In previous work, we have shown that such architectures are comparable
to state-of-theart ASR systems on dictation tasks, but it was not clear if such
architectures would be practical for more challenging tasks such as voice
search. In this work, we explore a variety of structural and optimization
improvements to our LAS model which significantly improve performance. On the
structural side, we show that word piece models can be used instead of
graphemes. We also introduce a multi-head attention architecture, which offers
improvements over the commonly-used single-head attention. On the optimization
side, we explore synchronous training, scheduled sampling, label smoothing, and
minimum word error rate optimization, which are all shown to improve accuracy.
We present results with a unidirectional LSTM encoder for streaming
recognition. On a 12, 500 hour voice search task, we find that the proposed
changes improve the WER from 9.2% to 5.6%, while the best conventional system
achieves 6.7%; on a dictation task our model achieves a WER of 4.1% compared to
5% for the conventional system.
| 1 | 0 | 0 | 1 | 0 | 0 |
Bayesian inference for Stable Levy driven Stochastic Differential Equations with high-frequency data | In this article we consider parametric Bayesian inference for stochastic
differential equations (SDE) driven by a pure-jump stable Levy process, which
is observed at high frequency. In most cases of practical interest, the
likelihood function is not available, so we use a quasi-likelihood and place an
associated prior on the unknown parameters. It is shown under regularity
conditions that there is a Bernstein-von Mises theorem associated to the
posterior. We then develop a Markov chain Monte Carlo (MCMC) algorithm for
Bayesian inference and assisted by our theoretical results, we show how to
scale Metropolis-Hastings proposals when the frequency of the data grows, in
order to prevent the acceptance ratio going to zero in the large data limit.
Our algorithm is presented on numerical examples that help to verify our
theoretical findings.
| 0 | 0 | 1 | 1 | 0 | 0 |
The Evolution of Reputation-Based Cooperation in Regular Networks | Despite recent advances in reputation technologies, it is not clear how
reputation systems can affect human cooperation in social networks. Although it
is known that two of the major mechanisms in the evolution of cooperation are
spatial selection and reputation-based reciprocity, theoretical study of the
interplay between both mechanisms remains almost uncharted. Here, we present a
new individual-based model for the evolution of reciprocal cooperation between
reputation and networks. We comparatively analyze four of the leading moral
assessment rules---shunning, image scoring, stern judging, and simple
standing---and base the model on the giving game in regular networks for
Cooperators, Defectors, and Discriminators. Discriminators rely on a proper
moral assessment rule. By using individual-based models, we show that the four
assessment rules are differently characterized in terms of how cooperation
evolves, depending on the benefit-to-cost ratio, the network-node degree, and
the observation and error conditions. Our findings show that the most tolerant
rule---simple standing---is the most robust among the four assessment rules in
promoting cooperation in regular networks.
| 1 | 1 | 0 | 0 | 0 | 0 |
Cocycles of nilpotent quotients of free groups | We focus on the cohomology of the $k$-th nilpotent quotient of the free
group, $F/F_k$. This paper describes all the group 2-, 3-cocycles in terms of
Massey products, and gives expressions for some of the 3-cocycles. We also give
simple proofs of some of the results on Milnor invariants and the
Johnson-Morita homomorphisms.
| 0 | 0 | 1 | 0 | 0 | 0 |
The ABCD of topological recursion | Kontsevich and Soibelman reformulated and slightly generalised the
topological recursion of math-ph/0702045, seeing it as a quantization of
certain quadratic Lagrangians in $T^*V$ for some vector space $V$. KS
topological recursion is a procedure which takes as initial data a quantum Airy
structure -- a family of at most quadratic differential operators on $V$
satisfying some axioms -- and gives as outcome a formal series of functions in
$V$ (the partition function) simultaneously annihilated by these operators.
Finding and classifying quantum Airy structures modulo gauge group action, is
by itself an interesting problem which we study here. We provide some
elementary, Lie-algebraic tools to address this problem, and give some elements
of classification for ${\rm dim}\,V = 2$. We also describe four more
interesting classes of quantum Airy structures, coming from respectively
Frobenius algebras (here we retrieve the 2d TQFT partition function as a
special case), non-commutative Frobenius algebras, loop spaces of Frobenius
algebras and a $\mathbb{Z}_{2}$-invariant version of the latter. This
$\mathbb{Z}_{2}$-invariant version in the case of a semi-simple Frobenius
algebra corresponds to the topological recursion of math-ph/0702045.
| 0 | 0 | 1 | 0 | 0 | 0 |
Evaluating Compositionality in Sentence Embeddings | An important challenge for human-like AI is compositional semantics. Recent
research has attempted to address this by using deep neural networks to learn
vector space embeddings of sentences, which then serve as input to other tasks.
We present a new dataset for one such task, `natural language inference' (NLI),
that cannot be solved using only word-level knowledge and requires some
compositionality. We find that the performance of state of the art sentence
embeddings (InferSent; Conneau et al., 2017) on our new dataset is poor. We
analyze the decision rules learned by InferSent and find that they are
consistent with simple heuristics that are ecologically valid in its training
dataset. Further, we find that augmenting training with our dataset improves
test performance on our dataset without loss of performance on the original
training dataset. This highlights the importance of structured datasets in
better understanding and improving AI systems.
| 0 | 0 | 0 | 1 | 0 | 0 |
A rigourous demonstration of the validity of Boltzmann's scenario for the spatial homogenization of a freely expanding gas and the equilibration of the Kac ring | Boltzmann provided a scenario to explain why individual macroscopic systems
composed of a large number $N$ of microscopic constituents are inevitably
(i.e., with overwhelming probability) observed to approach a unique macroscopic
state of thermodynamic equilibrium, and why after having done so, they are then
observed to remain in that state, apparently forever. We provide here rigourous
new results that mathematically prove the basic features of Boltzmann's
scenario for two classical models: a simple boundary-free model for the spatial
homogenization of a non-interacting gas of point particles, and the well-known
Kac ring model. Our results, based on concentration inequalities that go back
to Hoeffding, and which focus on the typical behavior of individual macroscopic
systems, improve upon previous results by providing estimates, exponential in
$N$, of probabilities and time scales involved.
| 0 | 1 | 1 | 0 | 0 | 0 |
Half-range lattice Boltzmann models for the simulation of Couette flow using the Shakhov collision term | The three-dimensional Couette flow between parallel plates is addressed using
mixed lattice Boltzmann models which implement the half-range and the
full-range Gauss-Hermite quadratures on the Cartesian axes perpendicular and
parallel to the walls, respectively. The ability of our models to simulate
rarefied flows are validated through comparison against previously reported
results obtained using the linearized Boltzmann-BGK equation for values of the
Knudsen number (Kn) up to $100$. We find that recovering the non-linear part of
the velocity profile (i.e., its deviation from a linear function) at ${\rm Kn}
\gtrsim 1$ requires high quadrature orders. We then employ the Shakhov model
for the collision term to obtain macroscopic profiles for Maxwell molecules
using the standard $\mu \sim T^\omega$ law, as well as for monatomic Helium and
Argon gases, modeled through ab-initio potentials, where the viscosity is
recovered using the Sutherland model. We validate our implementation by
comparison with DSMC results and find excellent match for all macroscopic
quantities for ${\rm Kn} \lesssim 0.1$. At ${\rm Kn} \gtrsim 0.1$, small
deviations can be seen in the profiles of the diagonal components of the
pressure tensor, the heat flux parallel to the plates, and the velocity
profile, as well as in the values of the velocity gradient at the channel
center. We attribute these deviations to the limited applicability of the
Shakhov collision model for highly out of equilibrium flows.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Nonlinear Dimensionality Reduction Framework Using Smooth Geodesics | Existing dimensionality reduction methods are adept at revealing hidden
underlying manifolds arising from high-dimensional data and thereby producing a
low-dimensional representation. However, the smoothness of the manifolds
produced by classic techniques over sparse and noisy data is not guaranteed. In
fact, the embedding generated using such data may distort the geometry of the
manifold and thereby produce an unfaithful embedding. Herein, we propose a
framework for nonlinear dimensionality reduction that generates a manifold in
terms of smooth geodesics that is designed to treat problems in which manifold
measurements are either sparse or corrupted by noise. Our method generates a
network structure for given high-dimensional data using a nearest neighbors
search and then produces piecewise linear shortest paths that are defined as
geodesics. Then, we fit points in each geodesic by a smoothing spline to
emphasize the smoothness. The robustness of this approach for sparse and noisy
datasets is demonstrated by the implementation of the method on synthetic and
real-world datasets.
| 1 | 0 | 0 | 1 | 0 | 0 |
Analyzing and improving maximal attainable accuracy in the communication hiding pipelined BiCGStab method | Pipelined Krylov subspace methods avoid communication latency by reducing the
number of global synchronization bottlenecks and by hiding global communication
behind useful computational work. In exact arithmetic pipelined Krylov subspace
algorithms are equivalent to classic Krylov subspace methods and generate
identical series of iterates. However, as a consequence of the reformulation of
the algorithm to improve parallelism, pipelined methods may suffer from
severely reduced attainable accuracy in a practical finite precision setting.
This work presents a numerical stability analysis that describes and quantifies
the impact of local rounding error propagation on the maximal attainable
accuracy of the multi-term recurrences in the preconditioned pipelined BiCGStab
method. Theoretical expressions for the gaps between the true and computed
residual as well as other auxiliary variables used in the algorithm are
derived, and the elementary dependencies between the gaps on the various
recursively computed vector variables are analyzed. The norms of the
corresponding propagation matrices and vectors provide insights in the possible
amplification of local rounding errors throughout the algorithm. Stability of
the pipelined BiCGStab method is compared numerically to that of pipelined CG
on a symmetric benchmark problem. Furthermore, numerical evidence supporting
the effectiveness of employing a residual replacement type strategy to improve
the maximal attainable accuracy for the pipelined BiCGStab method is provided.
| 1 | 0 | 0 | 0 | 0 | 0 |
Associated Graded Rings and Connected Sums | In 2012, Ananthnarayan, Avramov and Moore gave a new construction of
Gorenstein rings from two Gorenstein local rings, called their connected sum.
In this article, we investigate conditions on the associated graded ring of a
Gorenstein Artin local ring Q, which force it to be a connected sum over its
residue field. In particular, we recover some results regarding short, and
stretched, Gorenstein Artin rings. Finally, using these decompositions, we
obtain results about the rationality of the Poincare series of Q.
| 0 | 0 | 1 | 0 | 0 | 0 |
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