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Communication-Efficient Algorithms for Decentralized and Stochastic Optimization | We present a new class of decentralized first-order methods for nonsmooth and
stochastic optimization problems defined over multiagent networks. Considering
that communication is a major bottleneck in decentralized optimization, our
main goal in this paper is to develop algorithmic frameworks which can
significantly reduce the number of inter-node communications. We first propose
a decentralized primal-dual method which can find an $\epsilon$-solution both
in terms of functional optimality gap and feasibility residual in
$O(1/\epsilon)$ inter-node communication rounds when the objective functions
are convex and the local primal subproblems are solved exactly. Our major
contribution is to present a new class of decentralized primal-dual type
algorithms, namely the decentralized communication sliding (DCS) methods, which
can skip the inter-node communications while agents solve the primal
subproblems iteratively through linearizations of their local objective
functions. By employing DCS, agents can still find an $\epsilon$-solution in
$O(1/\epsilon)$ (resp., $O(1/\sqrt{\epsilon})$) communication rounds for
general convex functions (resp., strongly convex functions), while maintaining
the $O(1/\epsilon^2)$ (resp., $O(1/\epsilon)$) bound on the total number of
intra-node subgradient evaluations. We also present a stochastic counterpart
for these algorithms, denoted by SDCS, for solving stochastic optimization
problems whose objective function cannot be evaluated exactly. In comparison
with existing results for decentralized nonsmooth and stochastic optimization,
we can reduce the total number of inter-node communication rounds by orders of
magnitude while still maintaining the optimal complexity bounds on intra-node
stochastic subgradient evaluations. The bounds on the subgradient evaluations
are actually comparable to those required for centralized nonsmooth and
stochastic optimization.
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A Machine Learning Approach to Shipping Box Design | Having the right assortment of shipping boxes in the fulfillment warehouse to
pack and ship customer's online orders is an indispensable and integral part of
nowadays eCommerce business, as it will not only help maintain a profitable
business but also create great experiences for customers. However, it is an
extremely challenging operations task to strategically select the best
combination of tens of box sizes from thousands of feasible ones to be
responsible for hundreds of thousands of orders daily placed on millions of
inventory products. In this paper, we present a machine learning approach to
tackle the task by formulating the box design problem prescriptively as a
generalized version of weighted $k$-medoids clustering problem, where the
parameters are estimated through a variety of descriptive analytics. We test
this machine learning approach on fulfillment data collected from Walmart U.S.
eCommerce, and our approach is shown to be capable of improving the box
utilization rate by more than $10\%$.
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Precise but Natural Specification for Robot Tasks | We present Flipper, a natural language interface for describing high-level
task specifications for robots that are compiled into robot actions. Flipper
starts with a formal core language for task planning that allows expressing
rich temporal specifications and uses a semantic parser to provide a natural
language interface. Flipper provides immediate visual feedback by executing an
automatically constructed plan of the task in a graphical user interface. This
allows the user to resolve potentially ambiguous interpretations. Flipper
extends itself via naturalization: its users can add definitions for
utterances, from which Flipper induces new rules and adds them to the core
language, gradually growing a more and more natural task specification
language. Flipper improves the naturalization by generalizing the definition
provided by users. Unlike other task-specification systems, Flipper enables
natural language interactions while maintaining the expressive power and formal
precision of a programming language. We show through an initial user study that
natural language interactions and generalization can considerably ease the
description of tasks. Moreover, over time, users employ more and more concepts
outside of the initial core language. Such extensions are available to the
Flipper community, and users can use concepts that others have defined.
| 1 | 0 | 0 | 0 | 0 | 0 |
Rigorous Analysis for Efficient Statistically Accurate Algorithms for Solving Fokker-Planck Equations in Large Dimensions | This article presents a rigorous analysis for efficient statistically
accurate algorithms for solving the Fokker-Planck equations associated with
high-dimensional nonlinear turbulent dynamical systems with conditional
Gaussian structures. Despite the conditional Gaussianity, these nonlinear
systems contain many strong non-Gaussian features such as intermittency and
fat-tailed probability density functions (PDFs). The algorithms involve a
hybrid strategy that requires only a small number of samples $L$ to capture
both the transient and the equilibrium non-Gaussian PDFs with high accuracy.
Here, a conditional Gaussian mixture in a high-dimensional subspace via an
extremely efficient parametric method is combined with a judicious Gaussian
kernel density estimation in the remaining low-dimensional subspace. Rigorous
analysis shows that the mean integrated squared error in the recovered PDFs in
the high-dimensional subspace is bounded by the inverse square root of the
determinant of the conditional covariance, where the conditional covariance is
completely determined by the underlying dynamics and is independent of $L$.
This is fundamentally different from a direct application of kernel methods to
solve the full PDF, where $L$ needs to increase exponentially with the
dimension of the system and the bandwidth shrinks. A detailed comparison
between different methods justifies that the efficient statistically accurate
algorithms are able to overcome the curse of dimensionality. It is also shown
with mathematical rigour that these algorithms are robust in long time provided
that the system is controllable and stochastically stable. Particularly,
dynamical systems with energy-conserving quadratic nonlinearity as in many
geophysical and engineering turbulence are proved to have these properties.
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Rapid processing of 85Kr/Kr ratios using Atom Trap Trace Analysis | We report a methodology for measuring 85Kr/Kr isotopic abundances using Atom
Trap Trace Analysis (ATTA) that increases sample measurement throughput by over
an order of magnitude to 6 samples per 24 hours. The noble gas isotope 85Kr
(half-life = 10.7 yr) is a useful tracer for young groundwater in the age range
of 5-50 years. ATTA, an efficient and selective laser-based atom counting
method, has recently been applied to 85Kr/Kr isotopic abundance measurements,
requiring 5-10 microliters of krypton gas at STP extracted from 50-100 L of
water. Previously a single such measurement required 48 hours. Our new method
demonstrates that we can measure 85Kr/Kr ratios with 3-5% relative uncertainty
every 4 hours, on average, with the same sample requirements.
| 0 | 1 | 0 | 0 | 0 | 0 |
Adapting Everyday Manipulation Skills to Varied Scenarios | We address the problem of executing tool-using manipulation skills in
scenarios where the objects to be used may vary. We assume that point clouds of
the tool and target object can be obtained, but no interpretation or further
knowledge about these objects is provided. The system must interpret the point
clouds and decide how to use the tool to complete a manipulation task with a
target object; this means it must adjust motion trajectories appropriately to
complete the task. We tackle three everyday manipulations: scraping material
from a tool into a container, cutting, and scooping from a container. Our
solution encodes these manipulation skills in a generic way, with parameters
that can be filled in at run-time via queries to a robot perception module; the
perception module abstracts the functional parts for the tool and extracts key
parameters that are needed for the task. The approach is evaluated in
simulation and with selected examples on a PR2 robot.
| 1 | 0 | 0 | 0 | 0 | 0 |
Effect of Particle Number Conservation on the Berry Phase Resulting from Transport of a Bound Quasiparticle around a Superfluid Vortex | Motivated by understanding Majorana zero modes in topological superfluids in
particle-number conserving framework beyond the present framework, we study the
effect of particle number conservation on the Berry phase resulting from
transport of a bound quasiparticle around a superfluid vortex. We find that
particle-number non-conserving calculations based on Bogoliubov-de Gennes (BdG)
equations are unable to capture the correct physics when the quasiparticle is
within the penetration depth of the vortex core where the superfluid velocity
is non-zero. Particle number conservation is crucial for deriving the correct
Berry phase in this context, and the Berry phase takes non-universal values
depending on the system parameters and the external trap imposed to bind the
quasiparticle. Of particular relevance to Majorana physics are the findings
that superfluid condensate affects the part of the Berry phase not accounted
for in the standard BdG framework, and that the superfluid many-body ground
state of odd number of fermions involves superfluid condensate deformation due
to the presence of the bound quasiparticle - an effect which is beyond the
description of the BdG equations.
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A new topological insulator - β-InTe strained in the layer plane | We have investigated the band structure of the bulk crystal and the (001)
surface of the \beta-InTe layered crystal subjected to biaxial stretching in
the layer plane. The calculation has been carried out using the full-potential
linearized augmented plane wave method (FP LAPW) implemented in WIEN2k. It has
been shown that at the strain \Deltaa/a=0.06, where a is the lattice parameter
in the layer plane, the band gap in the electronic spectrum collapses. With
further strain increase a band inversion occurs. The inclusion of the
spin-orbit interaction reopens the gap in the electronic spectrum of a bulk
crystal, and our calculations show that the spectrum of the surface states has
the form of a Dirac cone, typical for topological insulators.
| 0 | 1 | 0 | 0 | 0 | 0 |
Stock management (Gestão de estoques) | There is a great need to stock materials for production, but storing
materials comes at a cost. Lack of organization in the inventory can result in
a very high cost for the final product, in addition to generating other
problems in the production chain. In this work we present mathematical and
statistical methods applicable to stock management. The stock analysis using
ABC curves serves to identify which are the priority items, the most expensive
and with the highest turnover (demand), and thus determine, through stock
control models, the purchase lot size and the periodicity that minimize the
total costs of storing these materials. Using the Economic Order Quantity (EOQ)
model and the (Q,R) model, the inventory costs of a company were minimized. The
comparison of the results provided by the models was performed.
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From Principal Subspaces to Principal Components with Linear Autoencoders | The autoencoder is an effective unsupervised learning model which is widely
used in deep learning. It is well known that an autoencoder with a single
fully-connected hidden layer, a linear activation function and a squared error
cost function trains weights that span the same subspace as the one spanned by
the principal component loading vectors, but that they are not identical to the
loading vectors. In this paper, we show how to recover the loading vectors from
the autoencoder weights.
| 0 | 0 | 0 | 1 | 0 | 0 |
A critical nonlinear elliptic equation with non local regional diffusion | In this article we are interested in the nonlocal regional Schrödinger
equation with critical exponent \begin{eqnarray*} &\epsilon^{2\alpha}
(-\Delta)_{\rho}^{\alpha}u + u = \lambda u^q + u^{2_{\alpha}^{*}-1} \mbox{ in }
\mathbb{R}^{N}, \\ & u \in H^{\alpha}(\mathbb{R}^{N}), \end{eqnarray*} where
$\epsilon$ is a small positive parameter, $\alpha \in (0,1)$, $q\in
(1,2_{\alpha}^{*}-1)$, $2_{\alpha}^{*} = \frac{2N}{N-2\alpha}$ is the critical
Sobolev exponent, $\lambda >0$ is a parameter and $(-\Delta)_{\rho}^{\alpha}$
is a variational version of the regional laplacian, whose range of scope is a
ball with radius $\rho(x)>0$. We study the existence of a ground state and we
analyze the behavior of semi-classical solutions as $\varepsilon\to 0$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Don't Decay the Learning Rate, Increase the Batch Size | It is common practice to decay the learning rate. Here we show one can
usually obtain the same learning curve on both training and test sets by
instead increasing the batch size during training. This procedure is successful
for stochastic gradient descent (SGD), SGD with momentum, Nesterov momentum,
and Adam. It reaches equivalent test accuracies after the same number of
training epochs, but with fewer parameter updates, leading to greater
parallelism and shorter training times. We can further reduce the number of
parameter updates by increasing the learning rate $\epsilon$ and scaling the
batch size $B \propto \epsilon$. Finally, one can increase the momentum
coefficient $m$ and scale $B \propto 1/(1-m)$, although this tends to slightly
reduce the test accuracy. Crucially, our techniques allow us to repurpose
existing training schedules for large batch training with no hyper-parameter
tuning. We train ResNet-50 on ImageNet to $76.1\%$ validation accuracy in under
30 minutes.
| 1 | 0 | 0 | 1 | 0 | 0 |
Collisions of Dark Matter Axion Stars with Astrophysical Sources | If QCD axions form a large fraction of the total mass of dark matter, then
axion stars could be very abundant in galaxies. As a result, collisions with
each other, and with other astrophysical bodies, can occur. We calculate the
rate and analyze the consequences of three classes of collisions, those
occurring between a dilute axion star and: another dilute axion star, an
ordinary star, or a neutron star. In all cases we attempt to quantify the most
important astrophysical uncertainties; we also pay particular attention to
scenarios in which collisions lead to collapse of otherwise stable axion stars,
and possible subsequent decay through number changing interactions. Collisions
between two axion stars can occur with a high total rate, but the low relative
velocity required for collapse to occur leads to a very low total rate of
collapses. On the other hand, collisions between an axion star and an ordinary
star have a large rate, $\Gamma_\odot \sim 3000$ collisions/year/galaxy, and
for sufficiently heavy axion stars, it is plausible that most or all such
collisions lead to collapse. We identify in this case a parameter space which
has a stable region and a region in which collision triggers collapse, which
depend on the axion number ($N$) in the axion star, and a ratio of mass to
radius cubed characterizing the ordinary star ($M_s/R_s^3$). Finally, we
revisit the calculation of collision rates between axion stars and neutron
stars, improving on previous estimates by taking cylindrical symmetry of the
neutron star distribution into account. Collapse and subsequent decay through
collision processes, if occurring with a significant rate, can affect dark
matter phenomenology and the axion star mass distribution.
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Practical Algorithms for Best-K Identification in Multi-Armed Bandits | In the Best-$K$ identification problem (Best-$K$-Arm), we are given $N$
stochastic bandit arms with unknown reward distributions. Our goal is to
identify the $K$ arms with the largest means with high confidence, by drawing
samples from the arms adaptively. This problem is motivated by various
practical applications and has attracted considerable attention in the past
decade. In this paper, we propose new practical algorithms for the Best-$K$-Arm
problem, which have nearly optimal sample complexity bounds (matching the lower
bound up to logarithmic factors) and outperform the state-of-the-art algorithms
for the Best-$K$-Arm problem (even for $K=1$) in practice.
| 1 | 0 | 0 | 1 | 0 | 0 |
Hyperfield Grassmannians | In a recent paper Baker and Bowler introduced matroids over hyperfields,
offering a common generalization of matroids, oriented matroids, and linear
subspaces of based vector spaces. This paper introduces the notion of a
topological hyperfield and explores the generalization of Grassmannians and
realization spaces to this context, particularly in relating the (hyper)fields
R and C to hyperfields arising in matroid theory and in tropical geometry.
| 0 | 0 | 1 | 0 | 0 | 0 |
Statistical estimation in a randomly structured branching population | We consider a binary branching process structured by a stochastic trait that
evolves according to a diffusion process that triggers the branching events, in
the spirit of Kimmel's model of cell division with parasite infection. Based on
the observation of the trait at birth of the first n generations of the
process, we construct nonparametric estimator of the transition of the
associated bifurcating chain and study the parametric estimation of the
branching rate. In the limit, as n tends to infinity, we obtain asymptotic
efficiency in the parametric case and minimax optimality in the nonparametric
case.
| 0 | 0 | 1 | 0 | 0 | 0 |
Discovering Eastern European PCs by hacking them. Today | Computer science would not be the same without personal computers. In the
West the so called PC revolution started in the late '70s and has its roots in
hobbyists and do-it-yourself clubs. In the following years the diffusion of
home and personal computers has made the discipline closer to many people. A
bit later, to a lesser extent, yet in a similar way, the revolution took place
also in East European countries. Today, the scenario of personal computing has
completely changed, however the computers of the '80s are still objects of
fascination for a number of retrocomputing fans who enjoy using, programming
and hacking the old 8-bits. The paper highlights the continuity between
yesterday's hobbyists and today's retrocomputing enthusiasts, particularly
focusing on East European PCs. Besides the preservation of old hardware and
software, the community is engaged in the development of emulators and cross
compilers. Such tools can be used for historical investigation, for example to
trace the origins of the BASIC interpreters loaded in the ROMs of East European
PCs.
| 1 | 0 | 0 | 0 | 0 | 0 |
Neural Rating Regression with Abstractive Tips Generation for Recommendation | Recently, some E-commerce sites launch a new interaction box called Tips on
their mobile apps. Users can express their experience and feelings or provide
suggestions using short texts typically several words or one sentence. In
essence, writing some tips and giving a numerical rating are two facets of a
user's product assessment action, expressing the user experience and feelings.
Jointly modeling these two facets is helpful for designing a better
recommendation system. While some existing models integrate text information
such as item specifications or user reviews into user and item latent factors
for improving the rating prediction, no existing works consider tips for
improving recommendation quality. We propose a deep learning based framework
named NRT which can simultaneously predict precise ratings and generate
abstractive tips with good linguistic quality simulating user experience and
feelings. For abstractive tips generation, gated recurrent neural networks are
employed to "translate" user and item latent representations into a concise
sentence. Extensive experiments on benchmark datasets from different domains
show that NRT achieves significant improvements over the state-of-the-art
methods. Moreover, the generated tips can vividly predict the user experience
and feelings.
| 1 | 0 | 0 | 0 | 0 | 0 |
Calibration Uncertainty for Advanced LIGO's First and Second Observing Runs | Calibration of the Advanced LIGO detectors is the quantification of the
detectors' response to gravitational waves. Gravitational waves incident on the
detectors cause phase shifts in the interferometer laser light which are read
out as intensity fluctuations at the detector output. Understanding this
detector response to gravitational waves is crucial to producing accurate and
precise gravitational wave strain data. Estimates of binary black hole and
neutron star parameters and tests of general relativity require well-calibrated
data, as miscalibrations will lead to biased results. We describe the method of
producing calibration uncertainty estimates for both LIGO detectors in the
first and second observing runs.
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Entanglement transitions induced by large deviations | The probability of large deviations of the smallest Schmidt eigenvalue for
random pure states of bipartite systems, denoted as $A$ and $B$, is computed
analytically using a Coulomb gas method. It is shown that this probability, for
large $N$, goes as $\exp[-\beta N^2\Phi(\zeta)]$, where the parameter $\beta$
is the Dyson index of the ensemble, $\zeta$ is the large deviation parameter
while the rate function $\Phi(\zeta)$ is calculated exactly. Corresponding
equilibrium Coulomb charge density is derived for its large deviations. Effects
of the large deviations of the extreme (largest and smallest) Schmidt
eigenvalues on the bipartite entanglement are studied using the von Neumann
entropy. Effect of these deviations is also studied on the entanglement between
subsystems $1$ and $2$, obtained by further partitioning the subsystem $A$,
using the properties of the density matrix's partial transpose
$\rho_{12}^\Gamma$. The density of states of $\rho_{12}^\Gamma$ is found to be
close to the Wigner's semicircle law with these large deviations. The
entanglement properties are captured very well by a simple random matrix model
for the partial transpose. The model predicts the entanglement transition
across a critical large deviation parameter $\zeta$. Log negativity is used to
quantify the entanglement between subsystems $1$ and $2$. Analytical formulas
for it are derived using the simple model. Numerical simulations are in
excellent agreement with the analytical results.
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Understanding System Characteristics of Online Erasure Coding on Scalable, Distributed and Large-Scale SSD Array Systems | Large-scale systems with arrays of solid state disks (SSDs) have become
increasingly common in many computing segments. To make such systems resilient,
we can adopt erasure coding such as Reed-Solomon (RS) code as an alternative to
replication because erasure coding can offer a significantly lower storage cost
than replication. To understand the impact of using erasure coding on system
performance and other system aspects such as CPU utilization and network
traffic, we build a storage cluster consisting of approximately one hundred
processor cores with more than fifty high-performance SSDs, and evaluate the
cluster with a popular open-source distributed parallel file system, Ceph. Then
we analyze behaviors of systems adopting erasure coding from the following five
viewpoints, compared with those of systems using replication: (1) storage
system I/O performance; (2) computing and software overheads; (3) I/O
amplification; (4) network traffic among storage nodes; (5) the impact of
physical data layout on performance of RS-coded SSD arrays. For all these
analyses, we examine two representative RS configurations, which are used by
Google and Facebook file systems, and compare them with triple replication that
a typical parallel file system employs as a default fault tolerance mechanism.
Lastly, we collect 54 block-level traces from the cluster and make them
available for other researchers.
| 1 | 0 | 0 | 0 | 0 | 0 |
Three Questions on Special Homeomorphisms on Subgroups of $R$ and $R^\infty$ | We provide justifications for two questions on special maps on subgroups of
the reals. We will show that the questions can be treated from different points
of view. We also discuss two versions of Anderson's Involution Conjecture.
| 0 | 0 | 1 | 0 | 0 | 0 |
Nearly Semiparametric Efficient Estimation of Quantile Regression | As a competitive alternative to least squares regression, quantile regression
is popular in analyzing heterogenous data. For quantile regression model
specified for one single quantile level $\tau$, major difficulties of
semiparametric efficient estimation are the unavailability of a parametric
efficient score and the conditional density estimation. In this paper, with the
help of the least favorable submodel technique, we first derive the
semiparametric efficient scores for linear quantile regression models that are
assumed for a single quantile level, multiple quantile levels and all the
quantile levels in $(0,1)$ respectively. Our main discovery is a one-step
(nearly) semiparametric efficient estimation for the regression coefficients of
the quantile regression models assumed for multiple quantile levels, which has
several advantages: it could be regarded as an optimal way to pool information
across multiple/other quantiles for efficiency gain; it is computationally
feasible and easy to implement, as the initial estimator is easily available;
due to the nature of quantile regression models under investigation, the
conditional density estimation is straightforward by plugging in an initial
estimator. The resulting estimator is proved to achieve the corresponding
semiparametric efficiency lower bound under regularity conditions. Numerical
studies including simulations and an example of birth weight of children
confirms that the proposed estimator leads to higher efficiency compared with
the Koenker-Bassett quantile regression estimator for all quantiles of
interest.
| 0 | 0 | 0 | 1 | 0 | 0 |
Relevance of backtracking paths in epidemic spreading on networks | The understanding of epidemics on networks has greatly benefited from the
recent application of message-passing approaches, which allow to derive exact
results for irreversible spreading (i.e. diseases with permanent acquired
immunity) in locally-tree like topologies. This success has suggested the
application of the same approach to reversible epidemics, for which an
individual can contract the epidemic and recover repeatedly. The underlying
assumption is that backtracking paths (i.e. an individual is reinfected by a
neighbor he/she previously infected) do not play a relevant role. In this paper
we show that this is not the case for reversible epidemics, since the neglect
of backtracking paths leads to a formula for the epidemic threshold that is
qualitatively incorrect in the large size limit. Moreover we define a modified
reversible dynamics which explicitly forbids direct backtracking events and
show that this modification completely upsets the phenomenology.
| 1 | 0 | 0 | 0 | 0 | 0 |
Hybrid graphene tunneling photoconductor with interface engineering towards fast photoresponse and high responsivity | Hybrid graphene photoconductor/phototransistor has achieved giant
photoresponsivity, but its response speed dramatically degrades as the expense
due to the long lifetime of trapped interfacial carriers. In this work, by
intercalating a large-area atomically thin MoS2 film into a hybrid graphene
photoconductor, we have developed a prototype tunneling photoconductor, which
exhibits a record-fast response (rising time ~17 ns) and a high responsivity
(~$3\times10^4$ A/W at 635 nm and 16.8 nW illumination) across the broad
spectral range. We demonstrate that the photo-excited carriers generated in
silicon are transferred into graphene through a tunneling process rather than
carrier drift. The atomically thin MoS2 film not only serves as tunneling layer
but also passivates surface states, which in combination delivers a superior
response speed (~3 order of magnitude improved than a device without MoS2
layer), while the responsivity remains high. This intriguing tunneling
photoconductor integrates both fast response and high responsivity and thus has
significant potential in practical applications of optoelectronic devices.
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Embedded Real-Time Fall Detection Using Deep Learning For Elderly Care | This paper proposes a real-time embedded fall detection system using a
DVS(Dynamic Vision Sensor) that has never been used for traditional fall
detection, a dataset for fall detection using that, and a DVS-TN(DVS-Temporal
Network). The first contribution is building a DVS Falls Dataset, which made
our network to recognize a much greater variety of falls than the existing
datasets that existed before and solved privacy issues using the DVS. Secondly,
we introduce the DVS-TN : optimized deep learning network to detect falls using
DVS. Finally, we implemented a fall detection system which can run on
low-computing H/W with real-time, and tested on DVS Falls Dataset that takes
into account various falls situations. Our approach achieved 95.5% on the
F1-score and operates at 31.25 FPS on NVIDIA Jetson TX1 board.
| 1 | 0 | 0 | 1 | 0 | 0 |
A Distributed Algorithm for Solving Linear Algebraic Equations Over Random Networks | In this paper, we consider the problem of solving linear algebraic equations
of the form $Ax=b$ among multi agents which seek a solution by using local
information in presence of random communication topologies. The equation is
solved by $m$ agents where each agent only knows a subset of rows of the
partitioned matrix $[A,b]$. We formulate the problem such that this formulation
does not need the distribution of random interconnection graphs. Therefore,
this framework includes asynchronous updates or unreliable communication
protocols without B-connectivity assumption. We apply the random
Krasnoselskii-Mann iterative algorithm which converges almost surely and in
mean square to a solution of the problem for any matrices $A$ and $b$ and any
initial conditions of agents' states. We demonestrate that the limit point to
which the agents' states converge is determined by the unique solution of a
convex optimization problem regardless of the distribution of random
communication graphs. Eventually, we show by two numerical examples that the
rate of convergence of the algorithm cannot be guaranteed.
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Doping-induced quantum cross-over in Er$_2$Ti$_{2-x}$Sn$_x$O$_7$ | We present the results of the investigation of magnetic properties of the
Er$_2$Ti$_{2-x}$Sn$_x$O$_7$ series. For small doping values the ordering
temperature decreases linearly with $x$ while the moment configuration remains
the same as in the $x = 0$ parent compound. Around $x = 1.7$ doping level we
observe a change in the behavior, where the ordering temperature starts to
increase and new magnetic Bragg peaks appear. For the first time we present
evidence of a long-range order (LRO) in Er$_2$Sn$_2$O$_7$ ($x = 2.0$) below
$T_N = 130$ mK. It is revealed that the moment configuration corresponds to a
Palmer-Chalker type with a value of the magnetic moment significantly
renormalized compared to $x = 0$. We discuss our results in the framework of a
possible quantum phase transition occurring close to $x = 1.7$.
| 0 | 1 | 0 | 0 | 0 | 0 |
Stochastic Block Models with Multiple Continuous Attributes | The stochastic block model (SBM) is a probabilistic model for community
structure in networks. Typically, only the adjacency matrix is used to perform
SBM parameter inference. In this paper, we consider circumstances in which
nodes have an associated vector of continuous attributes that are also used to
learn the node-to-community assignments and corresponding SBM parameters. While
this assumption is not realistic for every application, our model assumes that
the attributes associated with the nodes in a network's community can be
described by a common multivariate Gaussian model. In this augmented,
attributed SBM, the objective is to simultaneously learn the SBM connectivity
probabilities with the multivariate Gaussian parameters describing each
community. While there are recent examples in the literature that combine
connectivity and attribute information to inform community detection, our model
is the first augmented stochastic block model to handle multiple continuous
attributes. This provides the flexibility in biological data to, for example,
augment connectivity information with continuous measurements from multiple
experimental modalities. Because the lack of labeled network data often makes
community detection results difficult to validate, we highlight the usefulness
of our model for two network prediction tasks: link prediction and
collaborative filtering. As a result of fitting this attributed stochastic
block model, one can predict the attribute vector or connectivity patterns for
a new node in the event of the complementary source of information
(connectivity or attributes, respectively). We also highlight two biological
examples where the attributed stochastic block model provides satisfactory
performance in the link prediction and collaborative filtering tasks.
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Asymptotic Independence of Bivariate Order Statistics | It is well known that an extreme order statistic and a central order
statistic (os) as well as an intermediate os and a central os from a sample of
iid univariate random variables get asymptotically independent as the sample
size increases. We extend this result to bivariate random variables, where the
os are taken componentwise. An explicit representation of the conditional
distribution of bivariate os turns out to be a powerful tool.
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Semi-Supervised Haptic Material Recognition for Robots using Generative Adversarial Networks | Material recognition enables robots to incorporate knowledge of material
properties into their interactions with everyday objects. For example, material
recognition opens up opportunities for clearer communication with a robot, such
as "bring me the metal coffee mug", and recognizing plastic versus metal is
crucial when using a microwave or oven. However, collecting labeled training
data with a robot is often more difficult than unlabeled data. We present a
semi-supervised learning approach for material recognition that uses generative
adversarial networks (GANs) with haptic features such as force, temperature,
and vibration. Our approach achieves state-of-the-art results and enables a
robot to estimate the material class of household objects with ~90% accuracy
when 92% of the training data are unlabeled. We explore how well this approach
can recognize the material of new objects and we discuss challenges facing
generalization. To motivate learning from unlabeled training data, we also
compare results against several common supervised learning classifiers. In
addition, we have released the dataset used for this work which consists of
time-series haptic measurements from a robot that conducted thousands of
interactions with 72 household objects.
| 1 | 0 | 0 | 1 | 0 | 0 |
Level set shape and topology optimization of finite strain bilateral contact problems | This paper presents a method for the optimization of multi-component
structures comprised of two and three materials considering large motion
sliding contact and separation along interfaces. The structural geometry is
defined by an explicit level set method, which allows for both shape and
topology changes. The mechanical model assumes finite strains, a nonlinear
elastic material behavior, and a quasi-static response. Identification of
overlapping surface position is handled by a coupled parametric representation
of contact surfaces. A stabilized Lagrange method and an active set strategy
are used to model frictionless contact and separation. The mechanical model is
discretized by the extended finite element method which maintains a clear
definition of geometry. Face-oriented ghost penalization and dynamic relaxation
are implemented to improve the stability of the physical response prediction. A
nonlinear programming scheme is used to solve the optimization problem, which
is regularized by introducing a perimeter penalty into the objective function.
Sensitivities are determined by the adjoint method. The main characteristics of
the proposed method are studied by numerical examples in two dimensions. The
numerical results demonstrate improved design performance when compared to
models optimized with a small strain assumption. Additionally, examples with
load path dependent objectives display non-intuitive designs.
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Thermal distortions of non-Gaussian beams in Fabry-Perot cavities | Thermal effects are already important in currently operating interferometric
gravitational wave detectors. Planned upgrades of these detectors involve
increasing optical power to combat quantum shot noise. We consider the
ramifications of this increased power for one particular class of laser
beams--wide, flat-topped, mesa beams. In particular we model a single mesa beam
Fabry-Perot cavity having thermoelastically deformed mirrors. We calculate the
intensity profile of the fundamental cavity eigenmode in the presence of
thermal perturbations, and the associated changes in thermal noise. We also
outline an idealized method of correcting for such effects. At each stage we
contrast our results with those of a comparable Gaussian beam cavity. Although
we focus on mesa beams the techniques described are applicable to any
azimuthally symmetric system.
| 0 | 1 | 0 | 0 | 0 | 0 |
Rigidity and trace properties of divergence-measure vector fields | We show some rigidity properties of divergence-free vector fields defined on
half-spaces. As an application, we prove the existence of the classical trace
for a bounded, divergence-measure vector field $\xi$ defined on the Euclidean
plane, at almost every point of a locally oriented rectifiable set $S$, under
the assumption that its weak normal trace $[\xi\cdot \nu_S]$ attains a local
maximum for the norm of $\xi$ at the point.
| 0 | 0 | 1 | 0 | 0 | 0 |
A note on the Almansi property | The first goal of this note is to study the Almansi property on an
m-dimensional model in the sense of Greene and Wu and, more generally, in a
Riemannian geometric setting. In particular, we shall prove that the only model
on which the Almansi property is verified is the Euclidean space R^m. In the
second part of the paper we shall study Almansi's property and biharmonicity
for functions which depend on the distance from a given submanifold. Finally,
in the last section we provide an extension to the semi-Euclidean case R^{p,q}
which includes the proof of the classical Almansi property in R^m as a special
instance.
| 0 | 0 | 1 | 0 | 0 | 0 |
The Quantum Complexity of Computing Schatten $p$-norms | We consider the quantum complexity of computing Schatten $p$-norms and
related quantities, and find that the problem of estimating these quantities is
closely related to the one clean qubit model of computation. We show that the
problem of approximating $\text{Tr}\, (|A|^p)$ for a log-local $n$-qubit
Hamiltonian $A$ and $p=\text{poly}(n)$, up to a suitable level of accuracy, is
contained in DQC1; and that approximating this quantity up to a somewhat higher
level of accuracy is DQC1-hard. In some cases the level of accuracy achieved by
the quantum algorithm is substantially better than a natural classical
algorithm for the problem. The same problem can be solved for arbitrary sparse
matrices in BQP. One application of the algorithm is the approximate
computation of the energy of a graph.
| 1 | 0 | 0 | 0 | 0 | 0 |
Bounds for fidelity of semiclassical Lagrangian states in K{ä}hler quantization | We define mixed states associated with submanifolds with probability
densities in quantizable closed K{ä}hler manifolds. Then, we address the
problem of comparing two such states via their fidelity. Firstly, we estimate
the sub-fidelity and super-fidelity of two such states, giving lower and upper
bounds for their fidelity, when the underlying submanifolds are two Lagrangian
submanifolds intersecting transversally at a finite number of points, in the
semiclassical limit. Secondly, we investigate a family of examples on the
sphere, for which we manage to obtain a better upper bound for the fidelity. We
conclude by stating a conjecture regarding the fidelity in the general case.
| 0 | 0 | 1 | 0 | 0 | 0 |
Precision measurement of antiproton to proton ratio with the Alpha Magnetic Spectrometer on the International Space Station | A precision measurement by AMS of the antiproton-to-proton flux ratio in
primary cosmic rays in the absolute rigidity range from 1 to 450 GV is
presented based on $3.49\times10^5$ antiproton events and $2.42\times10^9$
proton events. Above $\sim60$ GV the antiproton to proton flux ratio is
consistent with being rigidity independent. A decreasing behaviour is expected
for this ratio considering the traditional models for the secondary antiproton
flux.
| 0 | 1 | 0 | 0 | 0 | 0 |
Stability criteria for the 2D $α$-Euler equations | We derive analogues of the classical Rayleigh, Fjortoft and Arnold stability
and instability theorems in the context of the 2D $\alpha$-Euler equations.
| 0 | 1 | 0 | 0 | 0 | 0 |
Superconducting spin valves controlled by spiral re-orientation in B20-family magnets | We propose a superconducting spin-triplet valve, which consists of a
superconductor and an itinerant magnetic material, with the magnet showing an
intrinsic non-collinear order characterized by a wave vector that may be
aligned in a few equivalent preferred directions under control of a weak
external magnetic field. Re-orienting the spiral direction allows one to
controllably modify long-range spin-triplet superconducting correlations,
leading to spin-valve switching behavior. Our results indicate that the
spin-valve effect may be noticeable. This bilayer may be used as a magnetic
memory element for cryogenic nanoelectronics. It has the following advantages
in comparison to superconducting spin valves proposed previously: (i) it
contains only one magnetic layer, which may be more easily fabricated and
controlled, (ii) its ground states are separated by a potential barrier, which
solves the "half-select" problem of the addressed switch of memory elements.
| 0 | 1 | 0 | 0 | 0 | 0 |
Experience Recommendation for Long Term Safe Learning-based Model Predictive Control in Changing Operating Conditions | Learning has propelled the cutting edge of performance in robotic control to
new heights, allowing robots to operate with high performance in conditions
that were previously unimaginable. The majority of the work, however, assumes
that the unknown parts are static or slowly changing. This limits them to
static or slowly changing environments. However, in the real world, a robot may
experience various unknown conditions. This paper presents a method to extend
an existing single mode GP-based safe learning controller to learn an
increasing number of non-linear models for the robot dynamics. We show that
this approach enables a robot to re-use past experience from a large number of
previously visited operating conditions, and to safely adapt when a new and
distinct operating condition is encountered. This allows the robot to achieve
safety and high performance in an large number of operating conditions that do
not have to be specified ahead of time. Our approach runs independently from
the controller, imposing no additional computation time on the control loop
regardless of the number of previous operating conditions considered. We
demonstrate the effectiveness of our approach in experiment on a 900\,kg ground
robot with both physical and artificial changes to its dynamics. All of our
experiments are conducted using vision for localization.
| 1 | 0 | 0 | 0 | 0 | 0 |
Calderón-type inequalities for affine frames | We prove sharp upper and lower bounds for generalized Calderón's sums
associated to frames on LCA groups generated by affine actions of cocompact
subgroup translations and general measurable families of automorphisms. The
proof makes use of techniques of analysis on metric spaces, and relies on a
counting estimate of lattice points inside metric balls. We will deduce as
special cases Calderón-type inequalities for families of expanding
automorphisms as well as for LCA-Gabor systems.
| 0 | 0 | 1 | 0 | 0 | 0 |
Magnetism in Semiconducting Molybdenum Dichalcogenides | Transition metal dichalcogenides (TMDs) are interesting for understanding
fundamental physics of two-dimensional materials (2D) as well as for many
emerging technologies, including spin electronics. Here, we report the
discovery of long-range magnetic order below TM = 40 K and 100 K in bulk
semiconducting TMDs 2H-MoTe2 and 2H-MoSe2, respectively, by means of muon
spin-rotation (muSR), scanning tunneling microscopy (STM), as well as density
functional theory (DFT) calculations. The muon spin rotation measurements show
the presence of a large and homogeneous internal magnetic fields at low
temperatures in both compounds indicative of long-range magnetic order. DFT
calculations show that this magnetism is promoted by the presence of defects in
the crystal. The STM measurements show that the vast majority of defects in
these materials are metal vacancies and chalcogen-metal antisites which are
randomly distributed in the lattice at the sub-percent level. DFT indicates
that the antisite defects are magnetic with a magnetic moment in the range of
0.9-2.8 mu_B. Further, we find that the magnetic order stabilized in 2H-MoTe2
and 2H-MoSe2 is highly sensitive to hydrostatic pressure. These observations
establish 2H-MoTe2 and 2H-MoSe2 as a new class of magnetic semiconductors and
opens a path to studying the interplay of 2D physics and magnetism in these
interesting semiconductors.
| 0 | 1 | 0 | 0 | 0 | 0 |
Characteristic Polynomial of Certain Hyperplane Arrangements through Graph Theory | We give a formula for computing the characteristic polynomial for certain
hyperplane arrangements in terms of the number of bipartite graphs of given
rank and cardinality.
| 0 | 0 | 1 | 0 | 0 | 0 |
Accelerated Evaluation of Automated Vehicles Using Piecewise Mixture Models | The process to certify highly Automated Vehicles has not yet been defined by
any country in the world. Currently, companies test Automated Vehicles on
public roads, which is time-consuming and inefficient. We proposed the
Accelerated Evaluation concept, which uses a modified statistics of the
surrounding vehicles and the Importance Sampling theory to reduce the
evaluation time by several orders of magnitude, while ensuring the evaluation
results are statistically accurate. In this paper, we further improve the
accelerated evaluation concept by using Piecewise Mixture Distribution models,
instead of Single Parametric Distribution models. We developed and applied this
idea to forward collision control system reacting to vehicles making cut-in
lane changes. The behavior of the cut-in vehicles was modeled based on more
than 403,581 lane changes collected by the University of Michigan Safety Pilot
Model Deployment Program. Simulation results confirm that the accuracy and
efficiency of the Piecewise Mixture Distribution method outperformed single
parametric distribution methods in accuracy and efficiency, and accelerated the
evaluation process by almost four orders of magnitude.
| 1 | 0 | 0 | 0 | 0 | 0 |
Phase shift's influence of two strong pulsed laser waves on effective interaction of electrons | The phase shift's influence of two strong pulsed laser waves on effective
interaction of electrons was studied. Considerable amplification of electrons
repulsion in the certain range of phase shifts and waves intensities is shown.
That leads to electrons scatter on greater distances than without an external
field. The value of the distance can be greater on 2-3 order of magnitude. Also
considerable influence of the phase shift of pulses of waves on the possibility
of effective attraction of electrons is shown.
| 0 | 1 | 0 | 0 | 0 | 0 |
Topological Sieving of Rings According to their Rigidity | We present a novel mechanism for resolving the mechanical rigidity of
nanoscopic circular polymers that flow in a complex environment. The emergence
of a regime of negative differential mobility induced by topological
interactions between the rings and the substrate is the key mechanism for
selective sieving of circular polymers with distinct flexibilities. A simple
model accurately describes the sieving process observed in molecular dynamics
simulations and yields experimentally verifiable analytical predictions, which
can be used as a reference guide for improving filtration procedures of
circular filaments. The topological sieving mechanism we propose ought to be
relevant also in probing the microscopic details of complex substrates.
| 0 | 0 | 0 | 0 | 1 | 0 |
A structural Markov property for decomposable graph laws that allows control of clique intersections | We present a new kind of structural Markov property for probabilistic laws on
decomposable graphs, which allows the explicit control of interactions between
cliques, so is capable of encoding some interesting structure. We prove the
equivalence of this property to an exponential family assumption, and discuss
identifiability, modelling, inferential and computational implications.
| 0 | 0 | 0 | 1 | 0 | 0 |
Fisher consistency for prior probability shift | We introduce Fisher consistency in the sense of unbiasedness as a desirable
property for estimators of class prior probabilities. Lack of Fisher
consistency could be used as a criterion to dismiss estimators that are
unlikely to deliver precise estimates in test datasets under prior probability
and more general dataset shift. The usefulness of this unbiasedness concept is
demonstrated with three examples of classifiers used for quantification:
Adjusted Classify & Count, EM-algorithm and CDE-Iterate. We find that Adjusted
Classify & Count and EM-algorithm are Fisher consistent. A counter-example
shows that CDE-Iterate is not Fisher consistent and, therefore, cannot be
trusted to deliver reliable estimates of class probabilities.
| 1 | 0 | 0 | 1 | 0 | 0 |
Quantifying the Effects of Enforcing Disentanglement on Variational Autoencoders | The notion of disentangled autoencoders was proposed as an extension to the
variational autoencoder by introducing a disentanglement parameter $\beta$,
controlling the learning pressure put on the possible underlying latent
representations. For certain values of $\beta$ this kind of autoencoders is
capable of encoding independent input generative factors in separate elements
of the code, leading to a more interpretable and predictable model behaviour.
In this paper we quantify the effects of the parameter $\beta$ on the model
performance and disentanglement. After training multiple models with the same
value of $\beta$, we establish the existence of consistent variance in one of
the disentanglement measures, proposed in literature. The negative consequences
of the disentanglement to the autoencoder's discriminative ability are also
asserted while varying the amount of examples available during training.
| 1 | 0 | 0 | 1 | 0 | 0 |
An omnibus test for the global null hypothesis | Global hypothesis tests are a useful tool in the context of, e.g, clinical
trials, genetic studies or meta analyses, when researchers are not interested
in testing individual hypotheses, but in testing whether none of the hypotheses
is false. There are several possibilities how to test the global null
hypothesis when the individual null hypotheses are independent. If it is
assumed that many of the individual null hypotheses are false, combinations
tests have been recommended to maximise power. If, however, it is assumed that
only one or a few null hypotheses are false, global tests based on individual
test statistics are more powerful (e.g., Bonferroni or Simes test). However,
usually there is no a-priori knowledge on the number of false individual null
hypotheses. We therefore propose an omnibus test based on the combination of
p-values. We show that this test yields an impressive overall performance. The
proposed method is implemented in the R-package omnibus.
| 0 | 0 | 0 | 1 | 0 | 0 |
Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification | We prove that the ordinary least-squares (OLS) estimator attains nearly
minimax optimal performance for the identification of linear dynamical systems
from a single observed trajectory. Our upper bound relies on a generalization
of Mendelson's small-ball method to dependent data, eschewing the use of
standard mixing-time arguments. Our lower bounds reveal that these upper bounds
match up to logarithmic factors. In particular, we capture the correct
signal-to-noise behavior of the problem, showing that more unstable linear
systems are easier to estimate. This behavior is qualitatively different from
arguments which rely on mixing-time calculations that suggest that unstable
systems are more difficult to estimate. We generalize our technique to provide
bounds for a more general class of linear response time-series.
| 0 | 0 | 0 | 1 | 0 | 0 |
Positive and Unlabeled Learning through Negative Selection and Imbalance-aware Classification | Motivated by applications in protein function prediction, we consider a
challenging supervised classification setting in which positive labels are
scarce and there are no explicit negative labels. The learning algorithm must
thus select which unlabeled examples to use as negative training points,
possibly ending up with an unbalanced learning problem. We address these issues
by proposing an algorithm that combines active learning (for selecting negative
examples) with imbalance-aware learning (for mitigating the label imbalance).
In our experiments we observe that these two techniques operate
synergistically, outperforming state-of-the-art methods on standard protein
function prediction benchmarks.
| 0 | 0 | 0 | 0 | 1 | 0 |
Superconductivity in quantum wires: A symmetry analysis | We study properties of quantim wires with spin-orbit coupling and time
reversal symmetry breaking, in normal and superconducting states. Electronic
band structures are classified according to quasi-one-dimensional magnetic
point groups, or magnetic classes. The latter belong to one of three distinct
types, depending on the way the time reversal operation appears in the group
elements. The superconducting gap functions are constructed using antiunitary
operations and have different symmetry properties depending on the type of the
magnetic point group. We obtain the spectrum of the Andreev boundary modes near
the end of the wire in a model-independent way, using the semiclassical
approach with the boundary conditions described by a phenomenological
scattering matrix. Explicit expressions for the bulk topological invariants
controlling the number of the boundary zero modes are presented in the general
multiband case for two types of the magnetic point groups, corresponding to
DIII and BDI symmetry classes.
| 0 | 1 | 0 | 0 | 0 | 0 |
Exponentially convergent data assimilation algorithm for Navier-Stokes equations | The paper presents a new state estimation algorithm for a bilinear equation
representing the Fourier- Galerkin (FG) approximation of the Navier-Stokes (NS)
equations on a torus in R2. This state equation is subject to uncertain but
bounded noise in the input (Kolmogorov forcing) and initial conditions, and its
output is incomplete and contains bounded noise. The algorithm designs a
time-dependent gain such that the estimation error converges to zero
exponentially. The sufficient condition for the existence of the gain are
formulated in the form of algebraic Riccati equations. To demonstrate the
results we apply the proposed algorithm to the reconstruction a chaotic fluid
flow from incomplete and noisy data.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Multi-Modal Approach to Infer Image Affect | The group affect or emotion in an image of people can be inferred by
extracting features about both the people in the picture and the overall makeup
of the scene. The state-of-the-art on this problem investigates a combination
of facial features, scene extraction and even audio tonality. This paper
combines three additional modalities, namely, human pose, text-based tagging
and CNN extracted features / predictions. To the best of our knowledge, this is
the first time all of the modalities were extracted using deep neural networks.
We evaluate the performance of our approach against baselines and identify
insights throughout this paper.
| 0 | 0 | 0 | 1 | 0 | 0 |
Joint Tilt Angle Adaptation and Beamforming in Multicell Multiuser Cellular Networks | 3D beamforming is a promising approach for interference coordination in
cellular networks which brings significant improvements in comparison with
conventional 2D beamforming techniques. This paper investigates the problem of
joint beamforming design and tilt angle adaptation of the BS antenna array for
maximizing energy efficiency (EE) in downlink of multi-cell multi-user
coordinated cellular networks. An iterative algorithm based on fractional
programming approach is introduced to solve the resulting non-convex
optimization problem. In each iteration, users are clustered based on their
elevation angle. Then, optimization of the tilt angle is carried out through a
reduced complexity greedy search to find the best tilt angle for a given
placement of the users. Numerical results show that the proposed algorithm
achieves higher EE compared to the 2D beamforming techniques.
| 1 | 0 | 1 | 0 | 0 | 0 |
Testing Degree Corrections in Stochastic Block Models | We study sharp detection thresholds for degree corrections in Stochastic
Block Models in the context of a goodness of fit problem. When degree
corrections are relatively dense, a simple test based on the total number of
edges is asymptotically optimal. For sparse degree corrections in non-dense
graphs, simple degree based Higher Criticism Test (Mukherjee, Mukherjee, Sen
2016) is optimal with sharp constants. In contrast, for dense graphs, the
optimal procedure runs in two stages. It involves running a suitable community
recovery algorithm in stage 1, followed by a Higher Criticism Test based on a
linear combination of within and across (estimated) community degrees in stage
2. The necessity of the two step procedure is demonstrated by the failure of
the ordinary Maximum Degree Test in achieving sharp constants. As necessary
tools we also derive asymptotic distribution of the Maximum Degree in
Stochastic Block Models along with moderate deviation and local central limit
type asymptotics of positive linear combinations of independent Binomial random
variables.
| 0 | 0 | 1 | 1 | 0 | 0 |
A Survey on Mobile Edge Computing: The Communication Perspective | Driven by the visions of Internet of Things and 5G communications, recent
years have seen a paradigm shift in mobile computing, from the centralized
Mobile Cloud Computing towards Mobile Edge Computing (MEC). The main feature of
MEC is to push mobile computing, network control and storage to the network
edges (e.g., base stations and access points) so as to enable
computation-intensive and latency-critical applications at the resource-limited
mobile devices. MEC promises dramatic reduction in latency and mobile energy
consumption, tackling the key challenges for materializing 5G vision. The
promised gains of MEC have motivated extensive efforts in both academia and
industry on developing the technology. A main thrust of MEC research is to
seamlessly merge the two disciplines of wireless communications and mobile
computing, resulting in a wide-range of new designs ranging from techniques for
computation offloading to network architectures. This paper provides a
comprehensive survey of the state-of-the-art MEC research with a focus on joint
radio-and-computational resource management. We also present a research outlook
consisting of a set of promising directions for MEC research, including MEC
system deployment, cache-enabled MEC, mobility management for MEC, green MEC,
as well as privacy-aware MEC. Advancements in these directions will facilitate
the transformation of MEC from theory to practice. Finally, we introduce recent
standardization efforts on MEC as well as some typical MEC application
scenarios.
| 1 | 0 | 1 | 0 | 0 | 0 |
Deep Learning Approximation: Zero-Shot Neural Network Speedup | Neural networks offer high-accuracy solutions to a range of problems, but are
costly to run in production systems because of computational and memory
requirements during a forward pass. Given a trained network, we propose a
techique called Deep Learning Approximation to build a faster network in a tiny
fraction of the time required for training by only manipulating the network
structure and coefficients without requiring re-training or access to the
training data. Speedup is achieved by by applying a sequential series of
independent optimizations that reduce the floating-point operations (FLOPs)
required to perform a forward pass. First, lossless optimizations are applied,
followed by lossy approximations using singular value decomposition (SVD) and
low-rank matrix decomposition. The optimal approximation is chosen by weighing
the relative accuracy loss and FLOP reduction according to a single parameter
specified by the user. On PASCAL VOC 2007 with the YOLO network, we show an
end-to-end 2x speedup in a network forward pass with a 5% drop in mAP that can
be re-gained by finetuning.
| 0 | 0 | 0 | 1 | 0 | 0 |
Frequency truncated discrete-time system norm | Multirate digital signal processing and model reduction applications require
computation of the frequency truncated norm of a discrete-time system. This
paper explains how to compute the frequency truncated norm of a discrete-time
system. To this end, a much-generalized problem of integrating a transfer
function of a discrete-time system given in the descriptor form over an
interval of limited frequencies is also discussed along with its computation.
| 1 | 0 | 0 | 0 | 0 | 0 |
Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation | Recent work has shown that state-of-the-art classifiers are quite brittle, in
the sense that a small adversarial change of an originally with high confidence
correctly classified input leads to a wrong classification again with high
confidence. This raises concerns that such classifiers are vulnerable to
attacks and calls into question their usage in safety-critical systems. We show
in this paper for the first time formal guarantees on the robustness of a
classifier by giving instance-specific lower bounds on the norm of the input
manipulation required to change the classifier decision. Based on this analysis
we propose the Cross-Lipschitz regularization functional. We show that using
this form of regularization in kernel methods resp. neural networks improves
the robustness of the classifier without any loss in prediction performance.
| 1 | 0 | 0 | 1 | 0 | 0 |
Complete DFM Model for High-Performance Computing SoCs with Guard Ring and Dummy Fill Effect | For nanotechnology, the semiconductor device is scaled down dramatically with
additional strain engineering for device enhancement, the overall device
characteristic is no longer dominated by the device size but also circuit
layout. The higher order layout effects, such as well proximity effect (WPE),
oxide spacing effect (OSE) and poly spacing effect (PSE), play an important
role for the device performance, it is critical to understand Design for
Manufacturability (DFM) impacts with various layout topology toward the overall
circuit performance. Currently, the layout effects (WPE, OSE and PSE) are
validated through digital standard cell and analog differential pair test
structure. However, two analog layout structures: the guard ring and dummy fill
impact are not well studied yet, then, this paper describes the current mirror
test circuit to examine the guard ring and dummy fills DFM impacts using TSMC
28nm HPM process.
| 1 | 0 | 0 | 0 | 0 | 0 |
Investigating the potential of social network data for transport demand models | Location-based social network data offers the promise of collecting the data
from a large base of users over a longer span of time at negligible cost. While
several studies have applied social network data to activity and mobility
analysis, a comparison with travel diaries and general statistics has been
lacking. In this paper, we analysed geo-referenced Twitter activities from a
large number of users in Singapore and neighbouring countries. By combining
this data, population statistics and travel diaries and applying clustering
techniques, we addressed detection of activity locations, as well as spatial
separation and transitions between these locations. Kernel density estimation
performs best to detect activity locations due to the scattered nature of the
twitter data; more activity locations are detected per user than reported in
the travel survey. The descriptive analysis shows that determining home
locations is more difficult than detecting work locations for most planning
zones. Spatial separations between detected activity locations from Twitter
data - as reported in a travel survey and captured by public transport smart
card data - are mostly similarly distributed, but also show relevant
differences for very short and very long distances. This also holds for the
transitions between zones. Whether the differences between Twitter data and
other data sources stem from differences in the population sub-sample,
clustering methodology, or whether social networks are being used significantly
more at specific locations must be determined by further research. Despite
these shortcomings, location-based social network data offers a promising data
source for insights into activity locations and mobility patterns, especially
for regions where travel survey data is not readily available.
| 1 | 1 | 0 | 0 | 0 | 0 |
Generalized Internal Boundaries (GIB) | Representing large-scale motions and topological changes in the finite volume
(FV) framework, while at the same time preserving the accuracy of the numerical
solution, is difficult. In this paper, we present a robust, highly efficient
method designed to achieve this capability. The proposed approach conceptually
shares many of the characteristics of the cut-cell interface tracking method,
but without the need for complex cell splitting/merging operations. The heart
of the new technique is to align existing mesh facets with the geometry to be
represented. We then modify the matrix contributions from these facets such
that they are represented in an identical fashion to traditional boundary
conditions. The collection of such faces is named a Generalised Internal
Boundary (GIB). In order to introduce motion into the system, we rely on the
classical ALE (Arbitrary Lagrangian-Eulerian) approach, but with the caveat
that the non-time-dependent motion of elements instantaneously crossing the
interface is handled separately from the time dependent component. The new
methodology is validated through comparison with: a) a body-fitted grid
simulation of an oscillating two dimensional cylinder and b) experimental
results of a butterfly valve.
| 1 | 1 | 0 | 0 | 0 | 0 |
Reducing Certification Granularity to Increase Adaptability of Avionics Software | A strong certification process is required to insure the safety of airplanes,
and more specifically the robustness of avionics applications. To implement
this process, the development of avionics software must follow long and costly
procedures. Most of these procedures have to be reexecuted each time the
software is modified. In this paper, we propose a framework to reduce the cost
and time impact of a software modification. With this new approach, the piece
of software likely to change is isolated from the rest of the application, so
it can be certified independently. This helps the system integrator to adapt an
avionics application to the specificities of the target airplane, without the
need for a new certification of the application.
| 1 | 0 | 0 | 0 | 0 | 0 |
Evolutionary multiplayer games on graphs with edge diversity | Evolutionary game dynamics in structured populations has been extensively
explored in past decades. However, most previous studies assume that payoffs of
individuals are fully determined by the strategic behaviors of interacting
parties and social ties between them only serve as the indicator of the
existence of interactions. This assumption neglects important information
carried by inter-personal social ties such as genetic similarity, geographic
proximity, and social closeness, which may crucially affect the outcome of
interactions. To model these situations, we present a framework of evolutionary
multiplayer games on graphs with edge diversity, where different types of edges
describe diverse social ties. Strategic behaviors together with social ties
determine the resulting payoffs of interactants. Under weak selection, we
provide a general formula to predict the success of one behavior over the
other. We apply this formula to various examples which cannot be dealt with
using previous models, including the division of labor and relationship- or
edge-dependent games. We find that labor division facilitates collective
cooperation by decomposing a many-player game into several games of smaller
sizes. The evolutionary process based on relationship-dependent games can be
approximated by interactions under a transformed and unified game. Our work
stresses the importance of social ties and provides effective methods to reduce
the calculating complexity in analyzing the evolution of realistic systems.
| 0 | 0 | 0 | 0 | 1 | 0 |
Robust Consensus for Multi-Agent Systems Communicating over Stochastic Uncertain Networks | In this paper, we study the robust consensus problem for a set of
discrete-time linear agents to coordinate over an uncertain communication
network, which is to achieve consensus against the transmission errors and
noises resulted from the information exchange between the agents. We model the
network by means of communication links subject to multiplicative stochastic
uncertainties, which are susceptible to describing packet dropout, random
delay, and fading phenomena. Different communication topologies, such as
undirected graphs and leader-follower graphs, are considered. We derive
sufficient conditions for robust consensus in the mean square sense. This
results unveil intrinsic constraints on consensus attainment imposed by the
network synchronizability, the unstable agent dynamics, and the channel
uncertainty variances. Consensus protocols are designed based on the state
information transmitted over the uncertain channels, by solving a modified
algebraic Riccati equation.
| 1 | 0 | 0 | 0 | 0 | 0 |
Fractional Topological Elasticity and Fracton Order | We analyze the "higher rank" gauge theories, that capture some of the
phenomenology of the Fracton order. It is shown that these theories loose gauge
invariance when arbitrarily weak and smooth curvature is introduced. We propose
a resolution to this problem by introducing a theory invariant under
area-preserving diffeomorphisms, which reduce to the "higher rank" gauge
transformations upon linearization around a flat background. The proposed
theory is \emph{geometric} in nature and is interpreted as a theory of
\emph{fractional topological elasticity}. This theory exhibits the Fracton
phenomenology. We explore the conservation laws, topological excitations,
linear response, various kinematical constraints, and canonical structure of
the theory. Finally, we emphasize that the very structure of Riemann-Cartan
geometry, which we use to formulate the theory, encodes the Fracton
phenomenology, suggesting that the Fracton order itself is \emph{geometric} in
nature.
| 0 | 1 | 0 | 0 | 0 | 0 |
Perturbing Eisenstein polynomials over local fields | Let $K$ be a local field whose residue field has characteristic $p$ and let
$L/K$ be a finite separable totally ramified extension. Let $\pi_L$ be a
uniformizer for $L$ and let $f(X)$ be the minimum polynomial for $\pi_L$ over
$K$. Suppose $\tilde{\pi}_L$ is another uniformizer for $L$ such that
$\tilde{\pi}_L\equiv\pi_L+r\pi_L^{\ell+1} \pmod{\pi_L^{\ell+2}}$ for some
$\ell\ge1$ and $r\in O_K$. Let $\tilde{f}(X)$ be the minimum polynomial for
$\tilde{\pi}_L$ over $K$. In this paper we give congruences for the
coefficients of $\tilde{f}(X)$ in terms of $r$ and the coefficients of $f(X)$.
These congruences improve and extend work of Krasner.
| 0 | 0 | 1 | 0 | 0 | 0 |
Galactic Orbits of Globular Clusters in the Region of the Galactic Bulge | Galactic orbits have been constructed over long time intervals for ten
globular clusters located near the Galactic center. A model with an axially
symmetric gravitational potential for the Galaxy was initially applied, after
which a non-axially symmetric potential corresponding to the central bar was
added. Variations in the trajectories of all these globular clusters in the XY
plane due to the influence of the bar were detected. These were greatest for
the cluster Terzan 4 in the meridional (RZ) plane. The globular clusters Terzan
1, Terzan 2, Terzan 4, Terzan 9, NGC 6522, and NGC 6558 always remained within
the Galactic bulge, no farther than 4 kpc from the Galactic center.
| 0 | 1 | 0 | 0 | 0 | 0 |
Streamlines for Motion Planning in Underwater Currents | Motion planning for underwater vehicles must consider the effect of ocean
currents. We present an efficient method to compute reachability and cost
between sample points in sampling-based motion planning that supports
long-range planning over hundreds of kilometres in complicated flows. The idea
is to search a reduced space of control inputs that consists of stream
functions whose level sets, or streamlines, optimally connect two given points.
Such stream functions are generated by superimposing a control input onto the
underlying current flow. A streamline represents the resulting path that a
vehicle would follow as it is carried along by the current given that control
input. We provide rigorous analysis that shows how our method avoids exhaustive
search of the control space, and demonstrate simulated examples in complicated
flows including a traversal along the east coast of Australia, using actual
current predictions, between Sydney and Brisbane.
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Eddington-Limited Accretion in z~2 WISE-selected Hot, Dust-Obscured Galaxies | Hot, Dust-Obscured Galaxies, or "Hot DOGs", are a rare, dusty, hyperluminous
galaxy population discovered by the WISE mission. Predominantly at redshifts
2-3, they include the most luminous known galaxies in the universe. Their high
luminosities likely come from accretion onto highly obscured super massive
black holes (SMBHs). We have conducted a pilot survey to measure the SMBH
masses of five z~2 Hot DOGs via broad H_alpha emission lines, using
Keck/MOSFIRE and Gemini/FLAMINGOS-2. We detect broad H_alpha emission in all
five Hot DOGs. We find substantial corresponding SMBH masses for these Hot DOGs
(~ 10^{9} M_sun), and their derived Eddington ratios are close to unity. These
z~2 Hot DOGs are the most luminous AGNs at given BH masses, suggesting they are
accreting at the maximum rates for their BHs. A similar property is found for
known z~6 quasars. Our results are consistent with scenarios in which Hot DOGs
represent a transitional, high-accretion phase between obscured and unobscured
quasars. Hot DOGs may mark a special evolutionary stage before the red quasar
and optical quasar phases, and they may be present at other cosmic epochs.
| 0 | 1 | 0 | 0 | 0 | 0 |
Boltzmann Transport in Nanostructures as a Friction Effect | Surface scattering is the key limiting factor to thermal transport in
dielectric crystals as the length scales are reduced or when temperature is
lowered. To explain this phenomenon, it is commonly assumed that the mean free
paths of heat carriers are bound by the crystal size and that thermal
conductivity is reduced in a manner proportional to such mean free paths. We
show here that these conclusions rely on simplifying assumptions and
approximated transport models. Instead, starting from the linearized Boltzmann
transport equation in the relaxon basis, we show how the problem can be reduced
to a set of decoupled linear differential equations. Then, the heat flow can be
interpreted as a hydrodynamic phenomenon, with the relaxon gas being slowed
down in proximity of a surface by friction effects, similar to the flux of a
viscous fluid in a pipe. As an example, we study a ribbon and a trench of
monolayer molybdenum disulphide, describing the procedure to reconstruct the
temperature and thermal conductivity profile in the sample interior and showing
how to estimate the effect of nanostructuring. The approach is general and
could be extended to other transport carriers, such as electrons, or extended
to materials of higher dimensionality and to different geometries, such as thin
films.
| 0 | 1 | 0 | 0 | 0 | 0 |
Agile Software Engineering and Systems Engineering at SKA Scale | Systems Engineering (SE) is the set of processes and documentation required
for successfully realising large-scale engineering projects, but the classical
approach is not a good fit for software-intensive projects, especially when the
needs of the different stakeholders are not fully known from the beginning, and
requirement priorities might change. The SKA is the ultimate software-enabled
telescope, with enormous amounts of computing hardware and software required to
perform its data reduction. We give an overview of the system and software
engineering processes in the SKA1 development, and the tension between
classical and agile SE.
| 1 | 1 | 0 | 0 | 0 | 0 |
Prime geodesic theorem of Gallagher type | We reduce the exponent in the error term of the prime geodesic theorem for
compact Riemann surfaces from $\frac{3}{4}$ to $\frac{7}{10}$ outside a set of
finite logarithmic measure.
| 0 | 0 | 1 | 0 | 0 | 0 |
PriMaL: A Privacy-Preserving Machine Learning Method for Event Detection in Distributed Sensor Networks | This paper introduces PriMaL, a general PRIvacy-preserving MAchine-Learning
method for reducing the privacy cost of information transmitted through a
network. Distributed sensor networks are often used for automated
classification and detection of abnormal events in high-stakes situations, e.g.
fire in buildings, earthquakes, or crowd disasters. Such networks might
transmit privacy-sensitive information, e.g. GPS location of smartphones, which
might be disclosed if the network is compromised. Privacy concerns might slow
down the adoption of the technology, in particular in the scenario of social
sensing where participation is voluntary, thus solutions are needed which
improve privacy without compromising on the event detection accuracy. PriMaL is
implemented as a machine-learning layer that works on top of an existing event
detection algorithm. Experiments are run in a general simulation framework, for
several network topologies and parameter values. The privacy footprint of
state-of-the-art event detection algorithms is compared within the proposed
framework. Results show that PriMaL is able to reduce the privacy cost of a
distributed event detection algorithm below that of the corresponding
centralized algorithm, within the bounds of some assumptions about the
protocol. Moreover the performance of the distributed algorithm is not
statistically worse than that of the centralized algorithm.
| 1 | 0 | 0 | 0 | 0 | 0 |
Follow the Compressed Leader: Faster Online Learning of Eigenvectors and Faster MMWU | The online problem of computing the top eigenvector is fundamental to machine
learning. In both adversarial and stochastic settings, previous results (such
as matrix multiplicative weight update, follow the regularized leader, follow
the compressed leader, block power method) either achieve optimal regret but
run slow, or run fast at the expense of loosing a $\sqrt{d}$ factor in total
regret where $d$ is the matrix dimension.
We propose a $\textit{follow-the-compressed-leader (FTCL)}$ framework which
achieves optimal regret without sacrificing the running time. Our idea is to
"compress" the matrix strategy to dimension 3 in the adversarial setting, or
dimension 1 in the stochastic setting. These respectively resolve two open
questions regarding the design of optimal and efficient algorithms for the
online eigenvector problem.
| 1 | 0 | 1 | 1 | 0 | 0 |
Solving satisfiability using inclusion-exclusion | Using Maple, we implement a SAT solver based on the principle of
inclusion-exclusion and the Bonferroni inequalities. Using randomly generated
input, we investigate the performance of our solver as a function of the number
of variables and number of clauses. We also test it against Maple's built-in
tautology procedure. Finally, we implement the Lovász local lemma with Maple
and discuss its applicability to SAT.
| 1 | 0 | 0 | 0 | 0 | 0 |
Isometric immersions into manifolds with metallic structures | We consider submanifolds into Riemannian manifold with metallic structures.
We obtain some new results for hypersurfaces in these spaces and we express the
fundamental theorem of submanifolds into products spaces in terms of metallic
structures. Moreover, we define new structures called complex metallic
structures. We show that these structures are linked with complex structures.
Then, we consider submanifolds into Riemannian manifold with such structures
with a focus on invariant submanifolds and hypersurfaces. We also express in
particular the fundamental theorem of submanifolds of complex space form in
terms of complex metallic structures.
| 0 | 0 | 1 | 0 | 0 | 0 |
Demonstration of an ac Josephson junction laser | Superconducting electronic devices have re-emerged as contenders for both
classical and quantum computing due to their fast operation speeds, low
dissipation and long coherence times. An ultimate demonstration of coherence is
lasing. We use one of the fundamental aspects of superconductivity, the ac
Josephson effect, to demonstrate a laser made from a Josephson junction
strongly coupled to a multi-mode superconducting cavity. A dc voltage bias to
the junction provides a source of microwave photons, while the circuit's
nonlinearity allows for efficient down-conversion of higher order Josephson
frequencies down to the cavity's fundamental mode. The simple fabrication and
operation allows for easy integration with a range of quantum devices, allowing
for efficient on-chip generation of coherent microwave photons at low
temperatures.
| 0 | 1 | 0 | 0 | 0 | 0 |
Integrable systems, symmetries and quantization | These notes correspond to a mini-course given at the Poisson 2016 conference
in Geneva. Starting from classical integrable systems in the sense of
Liouville, we explore the notion of non-degenerate singularity and expose
recent research in connection with semi-toric systems. The quantum and
semiclassical counterpart will also be presented, in the viewpoint of the
inverse question: from the quantum mechanical spectrum, can you recover the
classical system?
| 0 | 1 | 1 | 0 | 0 | 0 |
Detecting Friedel oscillations in ultracold Fermi gases | Investigating Friedel oscillations in ultracold gases would complement the
studies performed on solid state samples with scanning-tunneling microscopes.
In atomic quantum gases interactions and external potentials can be tuned
freely and the inherently slower dynamics allow to access non-equilibrium
dynamics following a potential or interaction quench. Here, we examine how
Friedel oscillations can be observed in current ultracold gas experiments under
realistic conditions. To this aim we numerically calculate the amplitude of the
Friedel oscillations which a potential barrier provokes in a 1D Fermi gas and
compare it to the expected atomic and photonic shot noise in a density
measurement. We find that to detect Friedel oscillations the signal from
several thousand one-dimensional systems has to be averaged. However, as up to
100 parallel one-dimensional systems can be prepared in a single run with
present experiments, averaging over about 100 images is sufficient.
| 0 | 1 | 0 | 0 | 0 | 0 |
Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems | While variational methods have been among the most powerful tools for solving
linear inverse problems in imaging, deep (convolutional) neural networks have
recently taken the lead in many challenging benchmarks. A remaining drawback of
deep learning approaches is their requirement for an expensive retraining
whenever the specific problem, the noise level, noise type, or desired measure
of fidelity changes. On the contrary, variational methods have a plug-and-play
nature as they usually consist of separate data fidelity and regularization
terms.
In this paper we study the possibility of replacing the proximal operator of
the regularization used in many convex energy minimization algorithms by a
denoising neural network. The latter therefore serves as an implicit natural
image prior, while the data term can still be chosen independently. Using a
fixed denoising neural network in exemplary problems of image deconvolution
with different blur kernels and image demosaicking, we obtain state-of-the-art
reconstruction results. These indicate the high generalizability of our
approach and a reduction of the need for problem-specific training.
Additionally, we discuss novel results on the analysis of possible optimization
algorithms to incorporate the network into, as well as the choices of algorithm
parameters and their relation to the noise level the neural network is trained
on.
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Heated-Up Softmax Embedding | Metric learning aims at learning a distance which is consistent with the
semantic meaning of the samples. The problem is generally solved by learning an
embedding for each sample such that the embeddings of samples of the same
category are compact while the embeddings of samples of different categories
are spread-out in the feature space. We study the features extracted from the
second last layer of a deep neural network based classifier trained with the
cross entropy loss on top of the softmax layer. We show that training
classifiers with different temperature values of softmax function leads to
features with different levels of compactness. Leveraging these insights, we
propose a "heating-up" strategy to train a classifier with increasing
temperatures, leading the corresponding embeddings to achieve state-of-the-art
performance on a variety of metric learning benchmarks.
| 0 | 0 | 0 | 1 | 0 | 0 |
Fast Matrix Inversion and Determinant Computation for Polarimetric Synthetic Aperture Radar | This paper introduces a fast algorithm for simultaneous inversion and
determinant computation of small sized matrices in the context of fully
Polarimetric Synthetic Aperture Radar (PolSAR) image processing and analysis.
The proposed fast algorithm is based on the computation of the adjoint matrix
and the symmetry of the input matrix. The algorithm is implemented in a general
purpose graphical processing unit (GPGPU) and compared to the usual approach
based on Cholesky factorization. The assessment with simulated observations and
data from an actual PolSAR sensor show a speedup factor of about two when
compared to the usual Cholesky factorization. Moreover, the expressions
provided here can be implemented in any platform.
| 1 | 0 | 0 | 1 | 0 | 0 |
The imprint of neutrinos on clustering in redshift-space | (abridged) We investigate the signatures left by the cosmic neutrino
background on the clustering of matter, CDM+baryons and halos in redshift-space
using a set of more than 1000 N-body and hydrodynamical simulations with
massless and massive neutrinos. We find that the effect neutrinos induce on the
clustering of CDM+baryons in redshift-space on small scales is almost entirely
due to the change in $\sigma_8$. Neutrinos imprint a characteristic signature
in the quadrupole of the matter (CDM+baryons+neutrinos) field on small scales,
that can be used to disentangle the effect of $\sigma_8$ and $M_\nu$. We show
that the effect of neutrinos on the clustering of halos is very different, on
all scales, to the one induced by $\sigma_8$. We find that the effects of
neutrinos of the growth rate of CDM+baryons ranges from $\sim0.3\%$ to $2\%$ on
scales $k\in[0.01, 0.5]~h{\rm Mpc}^{-1}$ for neutrinos with masses $M_\nu
\leqslant 0.15$ eV. We compute the bias between the momentum of halos and the
momentum of CDM+baryon and find it to be 1 on large scales for all models with
massless and massive neutrinos considered. This point towards a velocity bias
between halos and total matter on large scales that it is important to account
for in order to extract unbiased neutrino information from velocity/momentum
surveys such as kSZ observations. We show that baryonic effects can affect the
clustering of matter and CDM+baryons in redshift-space by up to a few percent
down to $k=0.5~h{\rm Mpc}^{-1}$. We find that hydrodynamics and astrophysical
processes, as implemented in our simulations, only distort the relative effect
that neutrinos induce on the anisotropic clustering of matter, CDM+baryons and
halos in redshift-space by less than $1\%$. Thus, the effect of neutrinos in
the fully non-linear regime can be written as a transfer function with very
weak dependence on astrophysics.
| 0 | 1 | 0 | 0 | 0 | 0 |
Quantum Privacy-Preserving Data Analytics | Data analytics (such as association rule mining and decision tree mining) can
discover useful statistical knowledge from a big data set. But protecting the
privacy of the data provider and the data user in the process of analytics is a
serious issue. Usually, the privacy of both parties cannot be fully protected
simultaneously by a classical algorithm. In this paper, we present a quantum
protocol for data mining that can much better protect privacy than the known
classical algorithms: (1) if both the data provider and the data user are
honest, the data user can know nothing about the database except the
statistical results, and the data provider can get nearly no information about
the results mined by the data user; (2) if the data user is dishonest and tries
to disclose private information of the other, she/he will be detected with a
high probability; (3) if the data provider tries to disclose the privacy of the
data user, she/he cannot get any useful information since the data user hides
his privacy among noises.
| 1 | 0 | 0 | 0 | 0 | 0 |
Magnetic Properties of Transition-Metal Adsorbed ot-Phosphorus Monolayer: A First-principles and Monte Carlo Study | Using the first-principles and Monte Carlo methods, here we systematically
study magnetic properties of monolayer octagonal-tetragonal phosphorus with 3d
transition-metal (TM) adatoms. Different from the puckered hexagonal black
phosphorus monolayer (phosphorene or $\alpha$-P), the octagonal-tetragonal
phase of 2D phosphorus (named as ot-P or $\epsilon$-P in this article) is
buckled with octagon-tetragon structure. Our calculations show that all TMs,
except the closed-shell Zn atom, are able to strongly bind onto monolayer
$ot$-P with significant binding energies. Local magnetic moments (up to 6
$\mu$B) on adatoms of Sc, Ti, V, Cr, Mn, Fe and Co originate from the exchange
and crystal-field splitting of TM 3d orbitals. The magnetic coupling between
localized magnetic states of adatoms is dependent on adatomic distances and
directions. Lastly, the uniformly magnetic order is investigated to screening
two-dimensional dilute ferromagnets with high Curie temperature for
applications of spintronics. It is found that ot-P with V atoms homogeneously
adsorbed at the centre of octagons with a concentration of 5% has the most
stable ferromagnetic ground state. Its Curie temperature is estimated to be 173
K using the Monte Carlo method.
| 0 | 1 | 0 | 0 | 0 | 0 |
A bootstrap for the number of $\mathbb{F}_{q^r}$-rational points on a curve over $\mathbb{F}_q$ | In this note we present a fast algorithm that finds for any $r$ the number
$N_r$ of $\mathbb{F}_{q^r}$ rational points on a smooth absolutely irreducible
curve $C$ defined over $\mathbb{F}_{q}$ assuming that we know $N_1,\cdots,N_g$,
where $g$ is the genus of $C$. The proof of its validity is given in detail and
its working are illustrated with several examples. In an Appendix we list the
Python function in which we have implemented the algorithm together with other
routines used in the examples.
| 0 | 0 | 1 | 0 | 0 | 0 |
Doping anatase TiO2 with group V-b and VI-b transition metal atoms: a hybrid functional first-principles study | We investigate the role of transition metal atoms of group V-b (V, Nb, and
Ta) and VI-b (Cr, Mo, and W) as n- or p-type dopants in anatase TiO2 using
thermodynamic principles and density functional theory with the
Heyd-Scuseria-Ernzerhof HSE06 hybrid functional. The HSE06 functional provides
a realistic value for the band gap, which ensures a correct classification of
dopants as shallow or deep donors or acceptors. Defect formation energies and
thermodynamic transition levels are calculated taking into account the
constraints imposed by the stability of TiO2 and the solubility limit of the
impurities. Nb, Ta, W and Mo are identified as shallow donors. Although W
provides two electrons, Nb and Ta show a considerably lower formation energy,
in particular under O-poor conditions. Mo donates in principle one electron,
but under specific conditions can turn into a double donor. V impurities are
deep donors and Cr shows up as an amphoteric defect, thereby acting as an
electron trapping center in n-type TiO2 especially under O-rich conditions. A
comparison with the available experimental data yields excellent agreement.
| 0 | 1 | 0 | 0 | 0 | 0 |
Patch-planting spin-glass solution for benchmarking | We introduce an algorithm to generate (not solve) spin-glass instances with
planted solutions of arbitrary size and structure. First, a set of small
problem patches with open boundaries is solved either exactly or with a
heuristic, and then the individual patches are stitched together to create a
large problem with a known planted solution. Because in these problems
frustration is typically smaller than in random problems, we first assess the
typical computational complexity of the individual patches using population
annealing Monte Carlo, and introduce an approach that allows one to fine-tune
the typical computational complexity of the patch-planted system. The scaling
of the typical computational complexity of these planted instances with various
numbers of patches and patch sizes is investigated and compared to random
instances.
| 0 | 1 | 0 | 0 | 0 | 0 |
Evidence for the formation of comet 67P/Churyumov-Gerasimenko through gravitational collapse of a bound clump of pebbles | The processes that led to the formation of the planetary bodies in the Solar
System are still not fully understood. Using the results obtained with the
comprehensive suite of instruments on-board ESA's Rosetta mission, we present
evidence that comet 67P/Churyumov-Gerasimenko likely formed through the gentle
gravitational collapse of a bound clump of mm-sized dust aggregates
("pebbles"), intermixed with microscopic ice particles. This formation scenario
leads to a cometary make-up that is simultaneously compatible with the global
porosity, homogeneity, tensile strength, thermal inertia, vertical temperature
profiles, sizes and porosities of emitted dust, and the steep increase in
water-vapour production rate with decreasing heliocentric distance, measured by
the instruments on-board the Rosetta spacecraft and the Philae lander. Our
findings suggest that the pebbles observed to be abundant in protoplanetary
discs around young stars provide the building material for comets and other
minor bodies.
| 0 | 1 | 0 | 0 | 0 | 0 |
Detection of virial shocks in stacked Fermi-LAT clusters | Galaxy clusters are thought to grow by accreting mass through large-scale,
strong, yet elusive, virial shocks. Such a shock is expected to accelerate
relativistic electrons, thus generating a spectrally-flat leptonic virial-ring.
However, until now, only the nearby Coma cluster has shown evidence for a
$\gamma$-ray virial ring. We stack Fermi-LAT data for the 112 most massive,
high latitude, extended clusters, enhancing the ring sensitivity by rescaling
clusters to their virial radii and utilizing the expected flat energy spectrum.
In addition to a central unresolved, hard signal (detected at the $\sim
5.8\sigma$ confidence level), probably dominated by AGN, we identify (at the
$5.8\sigma$ confidence level) a bright, spectrally-flat $\gamma$-ray ring at
the expected virial shock position. The ring signal implies that the shock
deposits $\sim 0.6\%$ (with an interpretation uncertainty factor $\sim2$) of
the thermal energy in relativistic electrons over a Hubble time. This result,
consistent with the Coma signal, validates and calibrates the virial shock
model, and indicates that the cumulative emission from such shocks
significantly contributes to the diffuse extragalactic $\gamma$-ray and
low-frequency radio backgrounds.
| 0 | 1 | 0 | 0 | 0 | 0 |
Closed-form formulae of hyperbolic metamaterial made by stacked hole-array layers working at terahertz or microwave radiation | A metamaterial made by stacked hole-array layers known as a fishnet
metamaterial behaves as a hyperbolic metamaterial at wavelength much longer
than hole-array period. However, the analytical formulae of effective
parameters of a fishnet metamaterial have not been reported hindering the
design of deep-subwavelength imaging devices using this structure. We report
the new closed-form formulae of effective parameters comprising anisotropic
dispersion relation of a fishnet metamaterial working at terahertz or microwave
frequency. These effective parameters of a fishnet metamaterial are consistent
with those obtained by quasi-full solutions using known effective parameters of
a hole-array layer working at frequency below its spoof plasma frequency with
the superlattice period much smaller than the hole-array period. We also
theoretically demonstrate the deep-subwavelength focusing at {\lambda}/83 using
the composite structure of a slit-array layer and a fishnet metamaterial. It is
found that the focused intensity inside a fishnet metamaterial is several times
larger than that without the fishnet metamaterial, but the transmitted
intensity is still restricted by large-wavevector difference in air and a
fishnet metamaterial. Our effective parameters may aid the next-generation
deep-subwavelength imaging devices working at terahertz or microwave radiation.
| 0 | 1 | 0 | 0 | 0 | 0 |
RMPflow: A Computational Graph for Automatic Motion Policy Generation | We develop a novel policy synthesis algorithm, RMPflow, based on
geometrically consistent transformations of Riemannian Motion Policies (RMPs).
RMPs are a class of reactive motion policies designed to parameterize
non-Euclidean behaviors as dynamical systems in intrinsically nonlinear task
spaces. Given a set of RMPs designed for individual tasks, RMPflow can
consistently combine these local policies to generate an expressive global
policy, while simultaneously exploiting sparse structure for computational
efficiency. We study the geometric properties of RMPflow and provide sufficient
conditions for stability. Finally, we experimentally demonstrate that
accounting for the geometry of task policies can simplify classically difficult
problems, such as planning through clutter on high-DOF manipulation systems.
| 1 | 0 | 0 | 0 | 0 | 0 |
Long-range correlations and fractal dynamics in C. elegans: changes with aging and stress | Reduced motor control is one of the most frequent features associated with
aging and disease. Nonlinear and fractal analyses have proved to be useful in
investigating human physiological alterations with age and disease. Similar
findings have not been established for any of the model organisms typically
studied by biologists, though. If the physiology of a simpler model organism
displays the same characteristics, this fact would open a new research window
on the control mechanisms that organisms use to regulate physiological
processes during aging and stress. Here, we use a recently introduced animal
tracking technology to simultaneously follow tens of Caenorhabdits elegans for
several hours and use tools from fractal physiology to quantitatively evaluate
the effects of aging and temperature stress on nematode motility. Similarly to
human physiological signals, scaling analysis reveals long-range correlations
in numerous motility variables, fractal properties in behavioral shifts, and
fluctuation dynamics over a wide range of timescales. These properties change
as a result of a superposition of age and stress-related adaptive mechanisms
that regulate motility.
| 0 | 1 | 0 | 1 | 0 | 0 |
A Computational Approach to Extinction Events in Chemical Reaction Networks with Discrete State Spaces | Recent work of M.D. Johnston et al. has produced sufficient conditions on the
structure of a chemical reaction network which guarantee that the corresponding
discrete state space system exhibits an extinction event. The conditions
consist of a series of systems of equalities and inequalities on the edges of a
modified reaction network called a domination-expanded reaction network. In
this paper, we present a computational implementation of these conditions
written in Python and apply the program on examples drawn from the biochemical
literature, including a model of polyamine metabolism in mammals and a model of
the pentose phosphate pathway in Trypanosoma brucei. We also run the program on
458 models from the European Bioinformatics Institute's BioModels Database and
report our results.
| 0 | 0 | 1 | 0 | 0 | 0 |
Face-to-BMI: Using Computer Vision to Infer Body Mass Index on Social Media | A person's weight status can have profound implications on their life,
ranging from mental health, to longevity, to financial income. At the societal
level, "fat shaming" and other forms of "sizeism" are a growing concern, while
increasing obesity rates are linked to ever raising healthcare costs. For these
reasons, researchers from a variety of backgrounds are interested in studying
obesity from all angles. To obtain data, traditionally, a person would have to
accurately self-report their body-mass index (BMI) or would have to see a
doctor to have it measured. In this paper, we show how computer vision can be
used to infer a person's BMI from social media images. We hope that our tool,
which we release, helps to advance the study of social aspects related to body
weight.
| 1 | 0 | 0 | 0 | 0 | 0 |
Recursive simplex stars | This paper proposes a new method which builds a simplex based approximation
of a $d-1$-dimensional manifold $M$ separating a $d$-dimensional compact set
into two parts, and an efficient algorithm classifying points according to this
approximation. In a first variant, the approximation is made of simplices that
are defined in the cubes of a regular grid covering the compact set, from
boundary points that approximate the intersection between $M$ and the edges of
the cubes. All the simplices defined in a cube share the barycentre of the
boundary points located in the cube and include simplices similarly defined in
cube facets, and so on recursively. In a second variant, the Kuhn triangulation
is used to break the cubes into simplices and the approximation is defined in
these simplices from the boundary points computed on their edges, with the same
principle. Both the approximation in cubes and in simplices define a separating
surface on the whole grid and classifying a point on one side or the other of
this surface requires only a small number (at most $d$) of simple tests. Under
some conditions on the definition of the boundary points and on the reach of
$M$, for both variants the Hausdorff distance between $M$ and its approximation
decreases like $\mathcal{O}(d n_G^{-2})$, where $n_G$ is the number of points
on each axis of the grid. The approximation in cubes requires computing less
boundary points than the approximation in simplices but the latter is always a
manifold and is more accurate for a given value of $n_G$. The paper reports
tests of the method when varying $n_G$ and the dimensionality of the space (up
to 9).
| 1 | 0 | 0 | 0 | 0 | 0 |
Subsets and Splits