title
stringlengths 7
239
| abstract
stringlengths 7
2.76k
| cs
int64 0
1
| phy
int64 0
1
| math
int64 0
1
| stat
int64 0
1
| quantitative biology
int64 0
1
| quantitative finance
int64 0
1
|
|---|---|---|---|---|---|---|---|
Massively parallel lattice-Boltzmann codes on large GPU clusters | This paper describes a massively parallel code for a state-of-the art thermal
lattice- Boltzmann method. Our code has been carefully optimized for
performance on one GPU and to have a good scaling behavior extending to a large
number of GPUs. Versions of this code have been already used for large-scale
studies of convective turbulence. GPUs are becoming increasingly popular in HPC
applications, as they are able to deliver higher performance than traditional
processors. Writing efficient programs for large clusters is not an easy task
as codes must adapt to increasingly parallel architectures, and the overheads
of node-to-node communications must be properly handled. We describe the
structure of our code, discussing several key design choices that were guided
by theoretical models of performance and experimental benchmarks. We present an
extensive set of performance measurements and identify the corresponding main
bot- tlenecks; finally we compare the results of our GPU code with those
measured on other currently available high performance processors. Our results
are a production-grade code able to deliver a sustained performance of several
tens of Tflops as well as a design and op- timization methodology that can be
used for the development of other high performance applications for
computational physics.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Large Dimensional Study of Regularized Discriminant Analysis Classifiers | This article carries out a large dimensional analysis of standard regularized
discriminant analysis classifiers designed on the assumption that data arise
from a Gaussian mixture model with different means and covariances. The
analysis relies on fundamental results from random matrix theory (RMT) when
both the number of features and the cardinality of the training data within
each class grow large at the same pace. Under mild assumptions, we show that
the asymptotic classification error approaches a deterministic quantity that
depends only on the means and covariances associated with each class as well as
the problem dimensions. Such a result permits a better understanding of the
performance of regularized discriminant analsysis, in practical large but
finite dimensions, and can be used to determine and pre-estimate the optimal
regularization parameter that minimizes the misclassification error
probability. Despite being theoretically valid only for Gaussian data, our
findings are shown to yield a high accuracy in predicting the performances
achieved with real data sets drawn from the popular USPS data base, thereby
making an interesting connection between theory and practice.
| 0 | 0 | 0 | 1 | 0 | 0 |
Non-Spherical Szekeres models in the language of Cosmological Perturbations | We study the differences and equivalences between the non-perturbative
description of the evolution of cosmic structure furnished by the Szekeres dust
models (a non-spherical exact solution of Einstein's equations) and the
dynamics of Cosmological Perturbation Theory (CPT) for dust sources in a
$\Lambda$CDM background. We show how the dynamics of Szekeres models can be
described by evolution equations given in terms of "exact fluctuations" that
identically reduce (at all orders) to evolution equations of CPT in the
comoving isochronous gauge. We explicitly show how Szekeres linearised exact
fluctuations are specific (deterministic) realisations of standard linear
perturbations of CPT given as random fields but, as opposed to the latter
perturbations, they can be evolved exactly into the full non-linear regime. We
prove two important results: (i) the conservation of the curvature perturbation
(at all scales) also holds for the appropriate approximation of the exact
Szekeres fluctuations in a $\Lambda$CDM background, and (ii) the different
collapse morphologies of Szekeres models yields, at nonlinear order, different
functional forms for the growth factor that follows from the study of redshift
space distortions. The metric based potentials used in linear CPT are computed
in terms of the parameters of the linearised Szekeres models, thus allowing us
to relate our results to linear CPT results in other gauges. We believe that
these results provide a solid starting stage to examine the role of
non-perturbative General Relativity in current cosmological research.
| 0 | 1 | 0 | 0 | 0 | 0 |
Smart materials and structures for energy harvesters | Vibrational energy harvesters capture mechanical energy from ambient
vibrations and convert the mechanical energy into electrical energy to power
wireless electronic systems. Challenges exist in the process of capturing
mechanical energy from ambient vibrations. For example, resonant harvesters may
be used to improve power output near their resonance, but their narrow
bandwidth makes them less suitable for applications with varying vibrational
frequencies. Higher operating frequencies can increase harvesters power output,
but many vibrational sources are characterized by lower frequencies, such as
human motions. This paper provides a thorough review of state of the art energy
harvesters based on various energy sources such as solar, thermal,
electromagnetic and mechanical energy, as well as smart materials including
piezoelectric materials and carbon nanotubes. The paper will then focus on
vibrational energy harvesters to review harvesters using typical transduction
mechanisms and various techniques to address the challenges in capturing
mechanical energy and delivering it to the transducers.
| 0 | 1 | 0 | 0 | 0 | 0 |
DropIn: Making Reservoir Computing Neural Networks Robust to Missing Inputs by Dropout | The paper presents a novel, principled approach to train recurrent neural
networks from the Reservoir Computing family that are robust to missing part of
the input features at prediction time. By building on the ensembling properties
of Dropout regularization, we propose a methodology, named DropIn, which
efficiently trains a neural model as a committee machine of subnetworks, each
capable of predicting with a subset of the original input features. We discuss
the application of the DropIn methodology in the context of Reservoir Computing
models and targeting applications characterized by input sources that are
unreliable or prone to be disconnected, such as in pervasive wireless sensor
networks and ambient intelligence. We provide an experimental assessment using
real-world data from such application domains, showing how the Dropin
methodology allows to maintain predictive performances comparable to those of a
model without missing features, even when 20\%-50\% of the inputs are not
available.
| 1 | 0 | 0 | 1 | 0 | 0 |
A Single-Channel Architecture for Algebraic Integer Based 8$\times$8 2-D DCT Computation | An area efficient row-parallel architecture is proposed for the real-time
implementation of bivariate algebraic integer (AI) encoded 2-D discrete cosine
transform (DCT) for image and video processing. The proposed architecture
computes 8$\times$8 2-D DCT transform based on the Arai DCT algorithm. An
improved fast algorithm for AI based 1-D DCT computation is proposed along with
a single channel 2-D DCT architecture. The design improves on the 4-channel AI
DCT architecture that was published recently by reducing the number of integer
channels to one and the number of 8-point 1-D DCT cores from 5 down to 2. The
architecture offers exact computation of 8$\times$8 blocks of the 2-D DCT
coefficients up to the FRS, which converts the coefficients from the AI
representation to fixed-point format using the method of expansion factors.
Prototype circuits corresponding to FRS blocks based on two expansion factors
are realized, tested, and verified on FPGA-chip, using a Xilinx Virtex-6
XC6VLX240T device. Post place-and-route results show a 20% reduction in terms
of area compared to the 2-D DCT architecture requiring five 1-D AI cores. The
area-time and area-time${}^2$ complexity metrics are also reduced by 23% and
22% respectively for designs with 8-bit input word length. The digital
realizations are simulated up to place and route for ASICs using 45 nm CMOS
standard cells. The maximum estimated clock rate is 951 MHz for the CMOS
realizations indicating 7.608$\cdot$10$^9$ pixels/seconds and a 8$\times$8
block rate of 118.875 MHz.
| 1 | 0 | 0 | 0 | 0 | 0 |
More lessons from the six box toy experiment | Following a paper in which the fundamental aspects of probabilistic inference
were introduced by means of a toy experiment, details of the analysis of
simulated long sequences of extractions are shown here. In fact, the striking
performance of probability-based inference and forecasting, compared to those
obtained by simple `rules', might impress those practitioners who are usually
underwhelmed by the philosophical foundation of the different methods. The
analysis of the sequences also shows how the smallness of the probability of
what has been actually observed, given the hypotheses of interest, is
irrelevant for the purpose of inference.
| 0 | 1 | 1 | 0 | 0 | 0 |
A Biomechanical Study on the Use of Curved Drilling Technique for Treatment of Osteonecrosis of Femoral Head | Osteonecrosis occurs due to the loss of blood supply to the bone, leading to
spontaneous death of the trabecular bone. Delayed treatment of the involved
patients results in collapse of the femoral head, which leads to a need for
total hip arthroplasty surgery. Core decompression, as the most popular
technique for treatment of the osteonecrosis, includes removal of the lesion
area by drilling a straight tunnel to the lesion, debriding the dead bone and
replacing it with bone substitutes. However, there are two drawbacks for this
treatment method. First, due to the rigidity of the instruments currently used
during core decompression, lesions cannot be completely removed and/or
excessive healthy bone may also be removed with the lesion. Second, the use of
bone substitutes, despite its biocompatibility and osteoconductivity, may not
provide sufficient mechanical strength and support for the bone. To address
these shortcomings, a novel robot-assisted curved core decompression (CCD)
technique is introduced to provide surgeons with direct access to the lesions
causing minimal damage to the healthy bone. In this study, with the aid of
finite element (FE) simulations, we investigate biomechanical performance of
core decompression using the curved drilling technique in the presence of
normal gait loading. In this regard, we compare the result of the CCD using
bone substitutes and flexible implants with other conventional core
decompression techniques. The study finding shows that the maximum principal
stress occurring at the superior domain of the neck is smaller in the CCD
techniques (i.e. 52.847 MPa) compared to the other core decompression methods.
| 0 | 0 | 0 | 0 | 1 | 0 |
Output feedback exponential stabilization for 1-D unstable wave equations with boundary control matched disturbance | We study the output feedback exponential stabilization of a one-dimensional
unstable wave equation, where the boundary input, given by the Neumann trace at
one end of the domain, is the sum of the control input and the total
disturbance. The latter is composed of a nonlinear uncertain feedback term and
an external bounded disturbance. Using the two boundary displacements as output
signals, we design a disturbance estimator that does not use high gain. It is
shown that the disturbance estimator can estimate the total disturbance in the
sense that the estimation error signal is in $L^2[0,\infty)$. Using the
estimated total disturbance, we design an observer whose state is exponentially
convergent to the state of original system. Finally, we design an
observer-based output feedback stabilizing controller. The total disturbance is
approximately canceled in the feedback loop by its estimate. The closed-loop
system is shown to be exponentially stable while guaranteeing that all the
internal signals are uniformly bounded.
| 0 | 0 | 1 | 0 | 0 | 0 |
Bio-inspired Tensegrity Soft Modular Robots | In this paper, we introduce a design principle to develop novel soft modular
robots based on tensegrity structures and inspired by the cytoskeleton of
living cells. We describe a novel strategy to realize tensegrity structures
using planar manufacturing techniques, such as 3D printing. We use this
strategy to develop icosahedron tensegrity structures with programmable
variable stiffness that can deform in a three-dimensional space. We also
describe a tendon-driven contraction mechanism to actively control the
deformation of the tensegrity mod-ules. Finally, we validate the approach in a
modular locomotory worm as a proof of concept.
| 1 | 0 | 0 | 0 | 0 | 0 |
How proper are Bayesian models in the astronomical literature? | The well-known Bayes theorem assumes that a posterior distribution is a
probability distribution. However, the posterior distribution may no longer be
a probability distribution if an improper prior distribution (non-probability
measure) such as an unbounded uniform prior is used. Improper priors are often
used in the astronomical literature to reflect a lack of prior knowledge, but
checking whether the resulting posterior is a probability distribution is
sometimes neglected. It turns out that 23 articles out of 75 articles (30.7%)
published online in two renowned astronomy journals (ApJ and MNRAS) between Jan
1, 2017 and Oct 15, 2017 make use of Bayesian analyses without rigorously
establishing posterior propriety. A disturbing aspect is that a Gibbs-type
Markov chain Monte Carlo (MCMC) method can produce a seemingly reasonable
posterior sample even when the posterior is not a probability distribution
(Hobert and Casella, 1996). In such cases, researchers may erroneously make
probabilistic inferences without noticing that the MCMC sample is from a
non-existing probability distribution. We review why checking posterior
propriety is fundamental in Bayesian analyses, and discuss how to set up
scientifically motivated proper priors.
| 0 | 1 | 0 | 0 | 0 | 0 |
Diffusive Tidal Evolution for Migrating hot Jupiters | I consider a Jovian planet on a highly eccentric orbit around its host star,
a situation produced by secular interactions with its planetary or stellar
companions. The tidal interactions at every periastron passage exchange energy
between the orbit and the planet's degree-2 fundamental-mode. Starting from
zero energy, the f-mode can diffusively grow to large amplitudes if its
one-kick energy gain > 10^-5 of the orbital energy. This requires a pericentre
distance of < 4 tidal radii (or 1.6 Roche radii). If the f-mode has a
non-negligible initial energy, diffusive evolution can occur at a lower
threshold. The first effect can stall the secular migration as the f-mode can
absorb orbital energy and decouple the planet from its secular perturbers,
parking all migrating jupiters safely outside the zone of tidal disruption. The
second effect leads to rapid orbit circularization as it allows an excited
f-mode to continuously absorb orbital energy as the orbit eccentricity
decreases. So without any explicit dissipation, other than the fact that the
f-mode will damp nonlinearly when its amplitude reaches unity, the planet can
be transported from a few AU to ~ 0.2 AU in ~ 10^4 yrs. Such a rapid
circularization is equivalent to a dissipation factor Q ~ 1, and it explains
the observed deficit of super-eccentric Jovian planets. Lastly, the repeated
f-mode breaking likely deposit energy and angular momentum in the outer
envelope, and avoid thermally ablating the planet.
Overall, this work boosts the case for forming hot Jupiters through
high-eccentricity secular migration.
| 0 | 1 | 0 | 0 | 0 | 0 |
Von Neumann dimension, Hodge index theorem and geometric applications | This note contains a reformulation of the Hodge index theorem within the
framework of Atiyah's $L^2$-index theory. More precisely, given a compact
Kähler manifold $(M,h)$ of even complex dimension $2m$, we prove that
$$\sigma(M)=\sum_{p,q=0}^{2m}(-1)^ph_{(2),\Gamma}^{p,q}(M)$$ where $\sigma(M)$
is the signature of $M$ and $h_{(2),\Gamma}^{p,q}(M)$ are the $L^2$-Hodge
numbers of $M$ with respect to a Galois covering having $\Gamma$ as group of
Deck transformations. Likewise we also prove an $L^2$-version of the
Frölicher index theorem. Afterwards we give some applications of these two
theorems and finally we conclude this paper by collecting other properties of
the $L^2$-Hodge numbers.
| 0 | 0 | 1 | 0 | 0 | 0 |
Relativistic asymmetries in the galaxy cross-correlation function | We study the asymmetry in the two-point cross-correlation function of two
populations of galaxies focusing in particular on the relativistic effects that
include the gravitational redshift. We derive the cross-correlation function on
small and large scales using two different approaches: General Relativistic and
Newtonian perturbation theory. Following recent work by Bonvin et al.,
Gaztanaga et al. and Croft, we calculate the dipole and the shell estimator
with the two procedures and we compare our results. We find that while General
Relativistic Perturbation Theory (GRPT) is able to make predictions of
relativistic effects on very large, obviously linear scales (r > 50 Mpc/h), the
presence of non-linearities physically occurring on much smaller scales (down
to those describing galactic potential wells) can strongly affect the asymmetry
estimators. These can lead to cancellations of the relativistic terms, and sign
changes in the estimators on scales up to r ~ 50 Mpc/h. On the other hand, with
an appropriate non-linear gravitational potential, the results obtained using
Newtonian theory can successfully describe the asymmetry on smaller, non-linear
scales (r < 20 Mpc/h) where gravitational redshift is the dominant term. On
larger scales the asymmetry is much smaller in magnitude, and measurement is
not within reach of current observations. This is in agreement with the
observational results obtained by Gaztnaga et al. and the first detection of
relativistic effects (on (r < 20 Mpc/h) scales) by Alam et al.
| 0 | 1 | 0 | 0 | 0 | 0 |
Hybrid CTC-Attention based End-to-End Speech Recognition using Subword Units | In this paper, we present an end-to-end automatic speech recognition system,
which successfully employs subword units in a hybrid CTC-Attention based
system. The subword units are obtained by the byte-pair encoding (BPE)
compression algorithm. Compared to using words as modeling units, using
characters or subword units does not suffer from the out-of-vocabulary (OOV)
problem. Furthermore, using subword units further offers a capability in
modeling longer context than using characters. We evaluate different systems
over the LibriSpeech 1000h dataset. The subword-based hybrid CTC-Attention
system obtains 6.8% word error rate (WER) on the test_clean subset without any
dictionary or external language model. This represents a significant
improvement (a 12.8% WER relative reduction) over the character-based hybrid
CTC-Attention system.
| 1 | 0 | 0 | 0 | 0 | 0 |
Harmonic analysis and distribution-free inference for spherical distributions | Fourier analysis and representation of circular distributions in terms of
their Fourier coefficients, is quite commonly discussed and used for model-free
inference such as testing uniformity and symmetry etc. in dealing with
2-dimensional directions. However a similar discussion for spherical
distributions, which are used to model 3-dimensional directional data, has not
been fully developed in the literature in terms of their harmonics. This paper,
in what we believe is the first such attempt, looks at the probability
distributions on a unit sphere, through the perspective of spherical harmonics,
analogous to the Fourier analysis for distributions on a unit circle. Harmonic
representations of many currently used spherical models are presented and
discussed. A very general family of spherical distributions is then introduced,
special cases of which yield many known spherical models. Through the prism of
harmonic analysis, one can look at the mean direction, dispersion, and various
forms of symmetry for these models in a generic setting. Aspects of
distribution free inference such as estimation and large-sample tests for these
symmetries, are provided. The paper concludes with a real-data example
analyzing the longitudinal sunspot activity.
| 0 | 0 | 0 | 1 | 0 | 0 |
Free-form modelling of galaxy clusters: a Bayesian and data-driven approach | A new method is presented for modelling the physical properties of galaxy
clusters. Our technique moves away from the traditional approach of assuming
specific parameterised functional forms for the variation of physical
quantities within the cluster, and instead allows for a 'free-form'
reconstruction, but one for which the level of complexity is determined
automatically by the observational data and may depend on position within the
cluster. This is achieved by representing each independent cluster property as
some interpolating or approximating function that is specified by a set of
control points, or 'nodes', for which the number of nodes, together with their
positions and amplitudes, are allowed to vary and are inferred in a Bayesian
manner from the data. We illustrate our nodal approach in the case of a
spherical cluster by modelling the electron pressure profile Pe(r) in analyses
both of simulated Sunyaev-Zel'dovich (SZ) data from the Arcminute MicroKelvin
Imager (AMI) and of real AMI observations of the cluster MACS J0744+3927 in the
CLASH sample. We demonstrate that one may indeed determine the complexity
supported by the data in the reconstructed Pe(r), and that one may constrain
two very important quantities in such an analysis: the cluster total volume
integrated Comptonisation parameter (Ytot) and the extent of the gas
distribution in the cluster (rmax). The approach is also well-suited to
detecting clusters in blind SZ surveys.
| 0 | 1 | 0 | 0 | 0 | 0 |
Multi-focus Attention Network for Efficient Deep Reinforcement Learning | Deep reinforcement learning (DRL) has shown incredible performance in
learning various tasks to the human level. However, unlike human perception,
current DRL models connect the entire low-level sensory input to the
state-action values rather than exploiting the relationship between and among
entities that constitute the sensory input. Because of this difference, DRL
needs vast amount of experience samples to learn. In this paper, we propose a
Multi-focus Attention Network (MANet) which mimics human ability to spatially
abstract the low-level sensory input into multiple entities and attend to them
simultaneously. The proposed method first divides the low-level input into
several segments which we refer to as partial states. After this segmentation,
parallel attention layers attend to the partial states relevant to solving the
task. Our model estimates state-action values using these attended partial
states. In our experiments, MANet attains highest scores with significantly
less experience samples. Additionally, the model shows higher performance
compared to the Deep Q-network and the single attention model as benchmarks.
Furthermore, we extend our model to attentive communication model for
performing multi-agent cooperative tasks. In multi-agent cooperative task
experiments, our model shows 20% faster learning than existing state-of-the-art
model.
| 1 | 0 | 0 | 1 | 0 | 0 |
Weak lensing power spectrum reconstruction by counting galaxies.-- I: the ABS method | We propose an Analytical method of Blind Separation (ABS) of cosmic
magnification from the intrinsic fluctuations of galaxy number density in the
observed galaxy number density distribution. The ABS method utilizes the
different dependences of the signal (cosmic magnification) and contamination
(galaxy intrinsic clustering) on galaxy flux, to separate the two. It works
directly on the measured cross galaxy angular power spectra between different
flux bins. It determines/reconstructs the lensing power spectrum analytically,
without assumptions of galaxy intrinsic clustering and cosmology. It is
unbiased in the limit of infinite number of galaxies. In reality the lensing
reconstruction accuracy depends on survey configurations, galaxy biases, and
other complexities, due to finite number of galaxies and the resulting shot
noise fluctuations in the cross galaxy power spectra. We estimate its
performance (systematic and statistical errors) in various cases. We find that,
stage IV dark energy surveys such as SKA and LSST are capable of reconstructing
the lensing power spectrum at $z\simeq 1$ and $\ell\la 5000$ accurately. This
lensing reconstruction only requires counting galaxies, and is therefore highly
complementary to the cosmic shear measurement by the same surveys.
| 0 | 1 | 0 | 0 | 0 | 0 |
Optimal Output Consensus of High-Order Multi-Agent Systems with Embedded Technique | In this paper, we study an optimal output consensus problem for a multi-agent
network with agents in the form of multi-input multi-output minimum-phase
dynamics. Optimal output consensus can be taken as an extended version of the
existing output consensus problem for higher-order agents with an optimization
requirement, where the output variables of agents are driven to achieve a
consensus on the optimal solution of a global cost function. To solve this
problem, we first construct an optimal signal generator, and then propose an
embedded control scheme by embedding the generator in the feedback loop. We
give two kinds of algorithms based on different available information along
with both state feedback and output feedback, and prove that these algorithms
with the embedded technique can guarantee the solvability of the problem for
high-order multi-agent systems under standard assumptions.
| 1 | 0 | 1 | 0 | 0 | 0 |
Equilibrium selection via Optimal transport | We propose a new dynamics for equilibrium selection of finite player discrete
strategy games. The dynamics is motivated by optimal transportation, and models
individual players' myopicity, greedy and uncertainty when making decisions.
The stationary measure of the dynamics provides each pure Nash equilibrium a
probability by which it is ranked. For potential games, its dynamical
properties are characterized by entropy and Fisher information.
| 0 | 0 | 1 | 0 | 0 | 0 |
Predicting Parkinson's Disease using Latent Information extracted from Deep Neural Networks | This paper presents a new method for medical diagnosis of neurodegenerative
diseases, such as Parkinson's, by extracting and using latent information from
trained Deep convolutional, or convolutional-recurrent Neural Networks (DNNs).
In particular, our approach adopts a combination of transfer learning, k-means
clustering and k-Nearest Neighbour classification of deep neural network
learned representations to provide enriched prediction of the disease based on
MRI and/or DaT Scan data. A new loss function is introduced and used in the
training of the DNNs, so as to perform adaptation of the generated learned
representations between data from different medical environments. Results are
presented using a recently published database of Parkinson's related
information, which was generated and evaluated in a hospital environment.
| 1 | 0 | 0 | 1 | 0 | 0 |
Representation Theorems for Solvable Sesquilinear Forms | New results are added to the paper [4] about q-closed and solvable
sesquilinear forms. The structure of the Banach space
$\mathcal{D}[||\cdot||_\Omega]$ defined on the domain $\mathcal{D}$ of a
q-closed sesquilinear form $\Omega$ is unique up to isomorphism, and the
adjoint of a sesquilinear form has the same property of q-closure or of
solvability. The operator associated to a solvable sesquilinear form is the
greatest which represents the form and it is self-adjoint if, and only if, the
form is symmetric. We give more criteria of solvability for q-closed
sesquilinear forms. Some of these criteria are related to the numerical range,
and we analyse in particular the forms which are solvable with respect to inner
products. The theory of solvable sesquilinear forms generalises those of many
known sesquilinear forms in literature.
| 0 | 0 | 1 | 0 | 0 | 0 |
Using Continuous Power Modulation for Exchanging Local Channel State Information | This letter provides a simple but efficient technique, which allows each
transmitter of an interference network, to exchange local channel state
information with the other transmitters. One salient feature of the proposed
technique is that a transmitter only needs measurements of the signal power at
its intended receiver to implement it, making direct inter-transmitter
signaling channels unnecessary. The key idea to achieve this is to use a
transient period during which the continuous power level of a transmitter is
taken to be the linear combination of the channel gains to be exchanged.
| 1 | 0 | 0 | 0 | 0 | 0 |
Spectral Efficient and Energy Aware Clustering in Cellular Networks | The current and envisaged increase of cellular traffic poses new challenges
to Mobile Network Operators (MNO), who must densify their Radio Access Networks
(RAN) while maintaining low Capital Expenditure and Operational Expenditure to
ensure long-term sustainability. In this context, this paper analyses optimal
clustering solutions based on Device-to-Device (D2D) communications to mitigate
partially or completely the need for MNOs to carry out extremely dense RAN
deployments. Specifically, a low complexity algorithm that enables the creation
of spectral efficient clusters among users from different cells, denoted as
enhanced Clustering Optimization for Resources' Efficiency (eCORE) is
presented. Due to the imbalance between uplink and downlink traffic, a
complementary algorithm, known as Clustering algorithm for Load Balancing
(CaLB), is also proposed to create non-spectral efficient clusters when they
result in a capacity increase. Finally, in order to alleviate the energy
overconsumption suffered by cluster heads, the Clustering Energy Efficient
algorithm (CEEa) is also designed to manage the trade-off between the capacity
enhancement and the early battery drain of some users. Results show that the
proposed algorithms increase the network capacity and outperform existing
solutions, while, at the same time, CEEa is able to handle the cluster heads
energy overconsumption.
| 1 | 0 | 0 | 0 | 0 | 0 |
Lattice Rescoring Strategies for Long Short Term Memory Language Models in Speech Recognition | Recurrent neural network (RNN) language models (LMs) and Long Short Term
Memory (LSTM) LMs, a variant of RNN LMs, have been shown to outperform
traditional N-gram LMs on speech recognition tasks. However, these models are
computationally more expensive than N-gram LMs for decoding, and thus,
challenging to integrate into speech recognizers. Recent research has proposed
the use of lattice-rescoring algorithms using RNNLMs and LSTMLMs as an
efficient strategy to integrate these models into a speech recognition system.
In this paper, we evaluate existing lattice rescoring algorithms along with new
variants on a YouTube speech recognition task. Lattice rescoring using LSTMLMs
reduces the word error rate (WER) for this task by 8\% relative to the WER
obtained using an N-gram LM.
| 1 | 0 | 0 | 1 | 0 | 0 |
Vanishing of Littlewood-Richardson polynomials is in P | J. DeLoera-T. McAllister and K. D. Mulmuley-H. Narayanan-M. Sohoni
independently proved that determining the vanishing of Littlewood-Richardson
coefficients has strongly polynomial time computational complexity. Viewing
these as Schubert calculus numbers, we prove the generalization to the
Littlewood-Richardson polynomials that control equivariant cohomology of
Grassmannians. We construct a polytope using the edge-labeled tableau rule of
H. Thomas-A. Yong. Our proof then combines a saturation theorem of D.
Anderson-E. Richmond-A. Yong, a reading order independence property, and E.
Tardos' algorithm for combinatorial linear programming.
| 1 | 0 | 1 | 0 | 0 | 0 |
Multi-task memory networks for category-specific aspect and opinion terms co-extraction | In aspect-based sentiment analysis, most existing methods either focus on
aspect/opinion terms extraction or aspect terms categorization. However, each
task by itself only provides partial information to end users. To generate more
detailed and structured opinion analysis, we propose a finer-grained problem,
which we call category-specific aspect and opinion terms extraction. This
problem involves the identification of aspect and opinion terms within each
sentence, as well as the categorization of the identified terms. To this end,
we propose an end-to-end multi-task attention model, where each task
corresponds to aspect/opinion terms extraction for a specific category. Our
model benefits from exploring the commonalities and relationships among
different tasks to address the data sparsity issue. We demonstrate its
state-of-the-art performance on three benchmark datasets.
| 1 | 0 | 0 | 0 | 0 | 0 |
Discussion on Computationally Efficient Multivariate Spatio-Temporal Models for High-Dimensional Count-Valued Data by Bradley et al | I begin my discussion by summarizing the methodology proposed and new
distributional results on multivariate log-Gamma derived in the paper. Then, I
draw an interesting connection between their work with mean field variational
Bayes. Lastly, I make some comments on the simulation results and the
performance of the proposed Poisson multivariate spatio-temporal mixed effects
model (P-MSTM).
| 0 | 0 | 1 | 1 | 0 | 0 |
Elucidation of the helical spin structure of FeAs | We present the results of resonant x-ray scattering measurements and
electronic structure calculations on the monoarsenide FeAs. We elucidate
details of the magnetic structure, showing the ratio of ellipticity of the spin
helix is larger than previously thought, at 2.58(3), and reveal both a
right-handed chirality and an out of plane component of the magnetic moments in
the spin helix. We find that electronic structure calculations and analysis of
the spin-orbit interaction are able to qualitatively account for this canting.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Brain-Inspired Trust Management Model to Assure Security in a Cloud based IoT Framework for Neuroscience Applications | Rapid popularity of Internet of Things (IoT) and cloud computing permits
neuroscientists to collect multilevel and multichannel brain data to better
understand brain functions, diagnose diseases, and devise treatments. To ensure
secure and reliable data communication between end-to-end (E2E) devices
supported by current IoT and cloud infrastructure, trust management is needed
at the IoT and user ends. This paper introduces a Neuro-Fuzzy based
Brain-inspired trust management model (TMM) to secure IoT devices and relay
nodes, and to ensure data reliability. The proposed TMM utilizes node
behavioral trust and data trust estimated using Adaptive Neuro-Fuzzy Inference
System and weighted-additive methods respectively to assess the nodes
trustworthiness. In contrast to the existing fuzzy based TMMs, the NS2
simulation results confirm the robustness and accuracy of the proposed TMM in
identifying malicious nodes in the communication network. With the growing
usage of cloud based IoT frameworks in Neuroscience research, integrating the
proposed TMM into the existing infrastructure will assure secure and reliable
data communication among the E2E devices.
| 0 | 0 | 0 | 0 | 1 | 0 |
The topography of the environment alters the optimal search strategy for active particles | In environments with scarce resources, adopting the right search strategy can
make the difference between succeeding and failing, even between life and
death. At different scales, this applies to molecular encounters in the cell
cytoplasm, to animals looking for food or mates in natural landscapes, to
rescuers during search-and-rescue operations in disaster zones, as well as to
genetic computer algorithms exploring parameter spaces. When looking for sparse
targets in a homogeneous environment, a combination of ballistic and diffusive
steps is considered optimal; in particular, more ballistic Lévy flights with
exponent {\alpha} <= 1 are generally believed to optimize the search process.
However, most search spaces present complex topographies, with boundaries,
barriers and obstacles. What is the best search strategy in these more
realistic scenarios? Here we show that the topography of the environment
significantly alters the optimal search strategy towards less ballistic and
more Brownian strategies. We consider an active particle performing a blind
search in a two-dimensional space with steps drawn from a Lévy distribution
with exponent varying from {\alpha} = 1 to {\alpha} = 2 (Brownian). We
demonstrate that the optimal search strategy depends on the topography of the
environment, with {\alpha} assuming intermediate values in the whole range
under consideration. We interpret these findings in terms of a simple
theoretical model, and discuss their robustness to the addition of Brownian
diffusion to the searcher's motion. Our results are relevant for search
problems at different length scales, from animal and human foraging to
microswimmers' taxis, to biochemical rates of reaction.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Neural Network Approach for Mixing Language Models | The performance of Neural Network (NN)-based language models is steadily
improving due to the emergence of new architectures, which are able to learn
different natural language characteristics. This paper presents a novel
framework, which shows that a significant improvement can be achieved by
combining different existing heterogeneous models in a single architecture.
This is done through 1) a feature layer, which separately learns different
NN-based models and 2) a mixture layer, which merges the resulting model
features. In doing so, this architecture benefits from the learning
capabilities of each model with no noticeable increase in the number of model
parameters or the training time. Extensive experiments conducted on the Penn
Treebank (PTB) and the Large Text Compression Benchmark (LTCB) corpus showed a
significant reduction of the perplexity when compared to state-of-the-art
feedforward as well as recurrent neural network architectures.
| 1 | 0 | 0 | 0 | 0 | 0 |
Iron Intercalated Covalent-Organic Frameworks: First Crystalline Porous Thermoelectric Materials | Covalent-organic frameworks (COFs) are intriguing platforms for designing
functional molecular materials. Here, we present a computational study based on
van der Waals dispersion-corrected hybrid density functional theory
calculations to analyze the material properties of boroxine-linked and
triazine-linked intercalated-COFs. The effect of Fe atoms on the electronic
band structures near the Fermi energy level of the intercalated-COFs have been
investigated. The density of states (DOSs) computations have been performed to
analyze the material properties of these kind of intercalated-COFs. We predict
that COFs's electronic properties can be fine tuned by adding Fe atoms between
two organic layers in their structures. The new COFs are predicted to be
thermoelectric materials. These intercalated-COFs provide a new strategy to
create thermoelectric materials within a rigid porous network in a highly
controlled and predictable manner.
| 0 | 1 | 0 | 0 | 0 | 0 |
DCFNet: Deep Neural Network with Decomposed Convolutional Filters | Filters in a Convolutional Neural Network (CNN) contain model parameters
learned from enormous amounts of data. In this paper, we suggest to decompose
convolutional filters in CNN as a truncated expansion with pre-fixed bases,
namely the Decomposed Convolutional Filters network (DCFNet), where the
expansion coefficients remain learned from data. Such a structure not only
reduces the number of trainable parameters and computation, but also imposes
filter regularity by bases truncation. Through extensive experiments, we
consistently observe that DCFNet maintains accuracy for image classification
tasks with a significant reduction of model parameters, particularly with
Fourier-Bessel (FB) bases, and even with random bases. Theoretically, we
analyze the representation stability of DCFNet with respect to input
variations, and prove representation stability under generic assumptions on the
expansion coefficients. The analysis is consistent with the empirical
observations.
| 0 | 0 | 0 | 1 | 0 | 0 |
Symmetries, Invariants and Generating Functions: Higher-order Statistics of Biased Tracers | Gravitationally collapsed objects are known to be biased tracers of an
underlying density contrast. Using symmetry arguments, generalised biasing
schemes have recently been developed to relate the halo density contrast
$\delta_h$ with the underlying density contrast $\delta$, divergence of
velocity $\theta$ and their higher-order derivatives. This is done by
constructing invariants such as $s, t, \psi,\eta$. We show how the generating
function formalism in Eulerian standard perturbation theory (SPT) can be used
to show that many of the additional terms based on extended Galilean and
Lifshitz symmetry actually do not make any contribution to the higher-order
statistics of biased tracers. Other terms can also be drastically simplified
allowing us to write the vertices associated with $\delta_h$ in terms of the
vertices of $\delta$ and $\theta$, the higher-order derivatives and the bias
coefficients. We also compute the cumulant correlators (CCs) for two different
tracer populations. These perturbative results are valid for tree-level
contributions but at an arbitrary order. We also take into account the
stochastic nature bias in our analysis. Extending previous results of a local
polynomial model of bias, we express the one-point cumulants ${\cal S}_N$ and
their two-point counterparts, the CCs i.e. ${\cal C}_{pq}$, of biased tracers
in terms of that of their underlying density contrast counterparts. As a
by-product of our calculation we also discuss the results using approximations
based on Lagrangian perturbation theory (LPT).
| 0 | 1 | 0 | 0 | 0 | 0 |
Reversible Sequences of Cardinals, Reversible Equivalence Relations, and Similar Structures | A relational structure ${\mathbb X}$ is said to be reversible iff every
bijective endomorphism $f:X\rightarrow X$ is an automorphism. We define a
sequence of non-zero cardinals $\langle \kappa_i :i\in I\rangle$ to be
reversible iff each surjection $f :I\rightarrow I$ such that $\kappa_j
=\sum_{i\in f^{-1}[\{ j \}]}\kappa_i$, for all $j\in I $, is a bijection, and
characterize such sequences: either $\langle \kappa_i :i\in I\rangle$ is a
finite-to-one sequence, or $\kappa_i\in {\mathbb N}$, for all $i\in I$, $K:=\{
m\in {\mathbb N} : \kappa_i =m $, for infinitely many $i\in I \}$ is a
non-empty independent set, and $\gcd (K)$ divides at most finitely many
elements of the set $\{ \kappa_i :i\in I \}$. We isolate a class of binary
structures such that a structure from the class is reversible iff the sequence
of cardinalities of its connectivity components is reversible. In particular,
we characterize reversible equivalence relations, reversible posets which are
disjoint unions of cardinals $\leq \omega$, and some similar structures. In
addition, we show that a poset with linearly ordered connectivity components is
reversible, if the corresponding sequence of cardinalities is reversible and,
using this fact, detect a wide class of examples of reversible posets and
topological spaces.
| 0 | 0 | 1 | 0 | 0 | 0 |
A Longitudinal Study of Google Play | The difficulty of large scale monitoring of app markets affects our
understanding of their dynamics. This is particularly true for dimensions such
as app update frequency, control and pricing, the impact of developer actions
on app popularity, as well as coveted membership in top app lists. In this
paper we perform a detailed temporal analysis on two datasets we have collected
from the Google Play Store, one consisting of 160,000 apps and the other of
87,223 newly released apps. We have monitored and collected data about these
apps over more than 6 months. Our results show that a high number of these apps
have not been updated over the monitoring interval. Moreover, these apps are
controlled by a few developers that dominate the total number of app downloads.
We observe that infrequently updated apps significantly impact the median app
price. However, a changing app price does not correlate with the download
count. Furthermore, we show that apps that attain higher ranks have better
stability in top app lists. We show that app market analytics can help detect
emerging threat vectors, and identify search rank fraud and even malware.
Further, we discuss the research implications of app market analytics on
improving developer and user experiences.
| 1 | 0 | 0 | 0 | 0 | 0 |
Nonparametric Variational Auto-encoders for Hierarchical Representation Learning | The recently developed variational autoencoders (VAEs) have proved to be an
effective confluence of the rich representational power of neural networks with
Bayesian methods. However, most work on VAEs use a rather simple prior over the
latent variables such as standard normal distribution, thereby restricting its
applications to relatively simple phenomena. In this work, we propose
hierarchical nonparametric variational autoencoders, which combines
tree-structured Bayesian nonparametric priors with VAEs, to enable infinite
flexibility of the latent representation space. Both the neural parameters and
Bayesian priors are learned jointly using tailored variational inference. The
resulting model induces a hierarchical structure of latent semantic concepts
underlying the data corpus, and infers accurate representations of data
instances. We apply our model in video representation learning. Our method is
able to discover highly interpretable activity hierarchies, and obtain improved
clustering accuracy and generalization capacity based on the learned rich
representations.
| 1 | 0 | 0 | 1 | 0 | 0 |
Corpus-compressed Streaming and the Spotify Problem | In this work, we describe a problem which we refer to as the \textbf{Spotify
problem} and explore a potential solution in the form of what we call
corpus-compressed streaming schemes.
Inspired by the problem of constrained bandwidth during use of the popular
Spotify application on mobile networks, the Spotify problem applies in any
number of practical domains where devices may be periodically expected to
experience degraded communication or storage capacity. One obvious solution
candidate which comes to mind immediately is standard compression. Though
obviously applicable, standard compression does not in any way exploit all
characteristics of the problem; in particular, standard compression is
oblivious to the fact that a decoder has a period of virtually unrestrained
communication. Towards applying compression in a manner which attempts to
stretch the benefit of periods of higher communication capacity into periods of
restricted capacity, we introduce as a solution the idea of a corpus-compressed
streaming scheme.
This report begins with a formal definition of a corpus-compressed streaming
scheme. Following a discussion of how such schemes apply to the Spotify
problem, we then give a survey of specific corpus-compressed scheming schemes
guided by an exploration of different measures of description complexity within
the Chomsky hierarchy of languages.
| 1 | 0 | 0 | 0 | 0 | 0 |
An intrinsic parallel transport in Wasserstein space | If M is a smooth compact connected Riemannian manifold, let P(M) denote the
Wasserstein space of probability measures on M. We describe a geometric
construction of parallel transport of some tangent cones along geodesics in
P(M). We show that when everything is smooth, the geometric parallel transport
agrees with earlier formal calculations.
| 0 | 0 | 1 | 0 | 0 | 0 |
Factor Analysis for Spectral Estimation | Power spectrum estimation is an important tool in many applications, such as
the whitening of noise. The popular multitaper method enjoys significant
success, but fails for short signals with few samples. We propose a statistical
model where a signal is given by a random linear combination of fixed, yet
unknown, stochastic sources. Given multiple such signals, we estimate the
subspace spanned by the power spectra of these fixed sources. Projecting
individual power spectrum estimates onto this subspace increases estimation
accuracy. We provide accuracy guarantees for this method and demonstrate it on
simulated and experimental data from cryo-electron microscopy.
| 0 | 0 | 1 | 1 | 0 | 0 |
Effects of Images with Different Levels of Familiarity on EEG | Evaluating human brain potentials during watching different images can be
used for memory evaluation, information retrieving, guilty-innocent
identification and examining the brain response. In this study, the effects of
watching images, with different levels of familiarity, on subjects'
Electroencephalogram (EEG) have been studied. Three different groups of images
with three familiarity levels of "unfamiliar", "familiar" and "very familiar"
have been considered for this study. EEG signals of 21 subjects (14 men) were
recorded. After signal acquisition, pre-processing, including noise and
artifact removal, were performed on epochs of data. Features, including
spatial-statistical, wavelet, frequency and harmonic parameters, and also
correlation between recording channels, were extracted from the data. Then, we
evaluated the efficiency of the extracted features by using p-value and also an
orthogonal feature selection method (combination of Gram-Schmitt method and
Fisher discriminant ratio) for feature dimensional reduction. As the final step
of feature selection, we used 'add-r take-away l' method for choosing the most
discriminative features. For data classification, including all two-class and
three-class cases, we applied Support Vector Machine (SVM) on the extracted
features. The correct classification rates (CCR) for "unfamiliar-familiar",
"unfamiliar-very familiar" and "familiar-very familiar" cases were 85.6%,
92.6%, and 70.6%, respectively. The best results of classifications were
obtained in pre-frontal and frontal regions of brain. Also, wavelet, frequency
and harmonic features were among the most discriminative features. Finally, in
three-class case, the best CCR was 86.8%.
| 0 | 0 | 0 | 1 | 0 | 0 |
Image synthesis with graph cuts: a fast model proposal mechanism in probabilistic inversion | Geophysical inversion should ideally produce geologically realistic
subsurface models that explain the available data. Multiple-point statistics is
a geostatistical approach to construct subsurface models that are consistent
with site-specific data, but also display the same type of patterns as those
found in a training image. The training image can be seen as a conceptual model
of the subsurface and is used as a non-parametric model of spatial variability.
Inversion based on multiple-point statistics is challenging due to high
nonlinearity and time-consuming geostatistical resimulation steps that are
needed to create new model proposals. We propose an entirely new model proposal
mechanism for geophysical inversion that is inspired by texture synthesis in
computer vision. Instead of resimulating pixels based on higher-order patterns
in the training image, we identify a suitable patch of the training image that
replace a corresponding patch in the current model without breaking the
patterns found in the training image, that is, remaining consistent with the
given prior. We consider three cross-hole ground-penetrating radar examples in
which the new model proposal mechanism is employed within an extended
Metropolis Markov chain Monte Carlo (MCMC) inversion. The model proposal step
is about 40 times faster than state-of-the-art multiple-point statistics
resimulation techniques, the number of necessary MCMC steps is lower and the
quality of the final model realizations is of similar quality. The model
proposal mechanism is presently limited to 2-D fields, but the method is
general and can be applied to a wide range of subsurface settings and
geophysical data types.
| 0 | 1 | 0 | 0 | 0 | 0 |
Graded super duality for general linear Lie superalgebras | We provide a new proof of the super duality equivalence between infinite-rank
parabolic BGG categories of general linear Lie (super) algebras conjectured by
Cheng and Wang and first proved by Cheng and Lam. We do this by establishing a
new uniqueness theorem for tensor product categorifications motivated by work
of Brundan, Losev, and Webster. Moreover we show that these BGG categories have
Koszul graded lifts and super duality can be lifted to a graded equivalence.
| 0 | 0 | 1 | 0 | 0 | 0 |
On a Formal Model of Safe and Scalable Self-driving Cars | In recent years, car makers and tech companies have been racing towards self
driving cars. It seems that the main parameter in this race is who will have
the first car on the road. The goal of this paper is to add to the equation two
additional crucial parameters. The first is standardization of safety assurance
--- what are the minimal requirements that every self-driving car must satisfy,
and how can we verify these requirements. The second parameter is scalability
--- engineering solutions that lead to unleashed costs will not scale to
millions of cars, which will push interest in this field into a niche academic
corner, and drive the entire field into a "winter of autonomous driving". In
the first part of the paper we propose a white-box, interpretable, mathematical
model for safety assurance, which we call Responsibility-Sensitive Safety
(RSS). In the second part we describe a design of a system that adheres to our
safety assurance requirements and is scalable to millions of cars.
| 1 | 0 | 0 | 1 | 0 | 0 |
A Low-power Reversible Alkali Atom Source | An electrically-controllable, solid-state, reversible device for sourcing and
sinking alkali vapor is presented. When placed inside an alkali vapor cell,
both an increase and decrease of the rubidium vapor density by a factor of two
are demonstrated through laser absorption spectroscopy on 10 to 15 s time
scales. The device requires low voltage (5 V), low power (<3.4 mW peak power),
and low energy (<10.7 mJ per 10 s pulse). The absence of oxygen emission during
operation is shown through residual gas analysis, indicating Rb is not lost
through chemical reaction but rather by ion transport through the designed
channel. This device is of interest for atomic physics experiments and, in
particular, for portable cold-atom systems where dynamic control of alkali
vapor density can enable advances in science and technology.
| 0 | 1 | 0 | 0 | 0 | 0 |
GoT-WAVE: Temporal network alignment using graphlet-orbit transitions | Global pairwise network alignment (GPNA) aims to find a one-to-one node
mapping between two networks that identifies conserved network regions. GPNA
algorithms optimize node conservation (NC) and edge conservation (EC). NC
quantifies topological similarity between nodes. Graphlet-based degree vectors
(GDVs) are a state-of-the-art topological NC measure. Dynamic GDVs (DGDVs) were
used as a dynamic NC measure within the first-ever algorithms for GPNA of
temporal networks: DynaMAGNA++ and DynaWAVE. The latter is superior for larger
networks. We recently developed a different graphlet-based measure of temporal
node similarity, graphlet-orbit transitions (GoTs). Here, we use GoTs instead
of DGDVs as a new dynamic NC measure within DynaWAVE, resulting in a new
approach, GoT-WAVE.
On synthetic networks, GoT-WAVE improves DynaWAVE's accuracy by 25% and speed
by 64%. On real networks, when optimizing only dynamic NC, each method is
superior ~50% of the time. While DynaWAVE benefits more from also optimizing
dynamic EC, only GoT-WAVE can support directed edges. Hence, GoT-WAVE is a
promising new temporal GPNA algorithm, which efficiently optimizes dynamic NC.
Future work on better incorporating dynamic EC may yield further improvements.
| 1 | 0 | 0 | 1 | 0 | 0 |
Randomness-induced quantum spin liquid on honeycomb lattice | We present a quantu spin liquid state in a spin-1/2 honeycomb lattice with
randomness in the exchange interaction. That is, we successfully introduce
randomness into the organic radial-based complex and realize a random-singlet
(RS) state. All magnetic and thermodynamic experimental results indicate the
liquid-like behaviors, which are consistent with those expected in the RS
state. These results demonstrate that the randomness or inhomogeneity in the
actual systems stabilize the RS state and yield liquid-like behavior.
| 0 | 1 | 0 | 0 | 0 | 0 |
Symmetric structure for the endomorphism algebra of projective-injective module in parabolic category | We show that for any singular dominant integral weight $\lambda$ of a complex
semisimple Lie algebra $\mathfrak{g}$, the endomorphism algebra $B$ of any
projective-injective module of the parabolic BGG category
$\mathcal{O}_\lambda^{\mathfrak{p}}$ is a symmetric algebra (as conjectured by
Khovanov) extending the results of Mazorchuk and Stroppel for the regular
dominant integral weight. Moreover, the endomorphism algebra $B$ is equipped
with a homogeneous (non-degenerate) symmetrizing form. In the appendix, there
is a short proof due to K. Coulembier and V. Mazorchuk showing that the
endomorphism algebra $B_\lambda^{\mathfrak{p}}$ of the basic
projective-injective module of $\mathcal{O}_\lambda^{\mathfrak{p}}$ is a
symmetric algebra.
| 0 | 0 | 1 | 0 | 0 | 0 |
Cantor series and rational numbers | The article is devoted to the investigation of representation of rational
numbers by Cantor series. Necessary and sufficient conditions for a rational
number to be representable by a positive Cantor series are formulated for the
case of an arbitrary sequence $(q_k)$ and some its corollaries are considered.
Results of this article were presented by the author of this article on the
International Conference on Algebra dedicated to 100th anniversary of S. M.
Chernikov (www.researchgate.net/publication/311415815,
www.researchgate.net/publication/301849984). This investigation was also
presented in some reports (links to the reports:
www.researchgate.net/publication/303736670,
www.researchgate.net/publication/303720573, etc.).
| 0 | 0 | 1 | 0 | 0 | 0 |
PAFit: an R Package for the Non-Parametric Estimation of Preferential Attachment and Node Fitness in Temporal Complex Networks | Many real-world systems are profitably described as complex networks that
grow over time. Preferential attachment and node fitness are two simple growth
mechanisms that not only explain certain structural properties commonly
observed in real-world systems, but are also tied to a number of applications
in modeling and inference. While there are statistical packages for estimating
various parametric forms of the preferential attachment function, there is no
such package implementing non-parametric estimation procedures. The
non-parametric approach to the estimation of the preferential attachment
function allows for comparatively finer-grained investigations of the
`rich-get-richer' phenomenon that could lead to novel insights in the search to
explain certain nonstandard structural properties observed in real-world
networks. This paper introduces the R package PAFit, which implements
non-parametric procedures for estimating the preferential attachment function
and node fitnesses in a growing network, as well as a number of functions for
generating complex networks from these two mechanisms. The main computational
part of the package is implemented in C++ with OpenMP to ensure scalability to
large-scale networks. We first introduce the main functionalities of PAFit
through simulated examples, and then use the package to analyze a collaboration
network between scientists in the field of complex networks. The results
indicate the joint presence of `rich-get-richer' and `fit-get-richer' phenomena
in the collaboration network. The estimated attachment function is observed to
be near-linear, which we interpret as meaning that the chance an author gets a
new collaborator is proportional to their current number of collaborators.
Furthermore, the estimated author fitnesses reveal a host of familiar faces
from the complex networks community among the field's topmost fittest network
scientists.
| 1 | 1 | 0 | 1 | 0 | 0 |
Kähler metrics via Lorentzian Geometry in dimension four | Given a semi-Riemannian $4$-manifold $(M,g)$ with two distinguished vector
fields satisfying properties determined by their shear, twist and various Lie
bracket relations, a family of Kähler metrics $g_K$ is constructed, defined
on an open set in $M$, which coincides with $M$ in many typical examples. Under
certain conditions $g$ and $g_K$ share various properties, such as a Killing
vector field or a vector field with a geodesic flow. In some cases the Kähler
metrics are complete. The Ricci and scalar curvatures of $g_K$ are computed
under certain assumptions in terms of data associated to $g$. Many examples are
described, including classical spacetimes in warped products, for instance de
Sitter spacetime, as well as gravitational plane waves, metrics of Petrov type
$D$ such as Kerr and NUT metrics, and metrics for which $g_K$ is an SKR metric.
For the latter an inverse ansatz is described, constructing $g$ from the SKR
metric.
| 0 | 0 | 1 | 0 | 0 | 0 |
z-Classes and Rational Conjugacy Classes in Alternating Groups | In this paper, we compute the number of z-classes (conjugacy classes of
centralizers of elements) in the symmetric group S_n, when n is greater or
equal to 3 and alternating group A_n, when n is greater or equal to 4. It turns
out that the difference between the number of conjugacy classes and the number
of z-classes for S_n is determined by those restricted partitions of n-2 in
which 1 and 2 do not appear as its part. And, in the case of alternating
groups, it is determined by those restricted partitions of n-3 which has all
its parts distinct, odd and in which 1 (and 2) does not appear as its part,
along with an error term. The error term is given by those partitions of n
which have each of its part distinct, odd and perfect square. Further, we prove
that the number of rational-valued irreducible complex characters for A_n is
same as the number of conjugacy classes which are rational.
| 0 | 0 | 1 | 0 | 0 | 0 |
Sleep Stage Classification Based on Multi-level Feature Learning and Recurrent Neural Networks via Wearable Device | This paper proposes a practical approach for automatic sleep stage
classification based on a multi-level feature learning framework and Recurrent
Neural Network (RNN) classifier using heart rate and wrist actigraphy derived
from a wearable device. The feature learning framework is designed to extract
low- and mid-level features. Low-level features capture temporal and frequency
domain properties and mid-level features learn compositions and structural
information of signals. Since sleep staging is a sequential problem with
long-term dependencies, we take advantage of RNNs with Bidirectional Long
Short-Term Memory (BLSTM) architectures for sequence data learning. To simulate
the actual situation of daily sleep, experiments are conducted with a resting
group in which sleep is recorded in resting state, and a comprehensive group in
which both resting sleep and non-resting sleep are included.We evaluate the
algorithm based on an eight-fold cross validation to classify five sleep stages
(W, N1, N2, N3, and REM). The proposed algorithm achieves weighted precision,
recall and F1 score of 58.0%, 60.3%, and 58.2% in the resting group and 58.5%,
61.1%, and 58.5% in the comprehensive group, respectively. Various comparison
experiments demonstrate the effectiveness of feature learning and BLSTM. We
further explore the influence of depth and width of RNNs on performance. Our
method is specially proposed for wearable devices and is expected to be
applicable for long-term sleep monitoring at home. Without using too much prior
domain knowledge, our method has the potential to generalize sleep disorder
detection.
| 1 | 0 | 0 | 1 | 0 | 0 |
Martin David Kruskal: a biographical memoir | Martin David Kruskal was one of the most versatile theoretical physicists of
his generation and is distinguished for his enduring work in several different
areas, most notably plasma physics, a memorable detour into relativity, and his
pioneering work in nonlinear waves. In the latter, together with Norman
Zabusky, he invented the concept of the soliton and, with others, developed its
application to classes of partial differential equations of physical
significance.
| 0 | 1 | 0 | 0 | 0 | 0 |
Unified theory for finite Markov chains | We provide a unified framework to compute the stationary distribution of any
finite irreducible Markov chain or equivalently of any irreducible random walk
on a finite semigroup $S$. Our methods use geometric finite semigroup theory
via the Karnofsky-Rhodes and the McCammond expansions of finite semigroups with
specified generators; this does not involve any linear algebra. The original
Tsetlin library is obtained by applying the expansions to $P(n)$, the set of
all subsets of an $n$ element set. Our set-up generalizes previous
groundbreaking work involving left-regular bands (or $\mathscr{R}$-trivial
bands) by Brown and Diaconis, extensions to $\mathscr{R}$-trivial semigroups by
Ayyer, Steinberg, Thiéry and the second author, and important recent work by
Chung and Graham. The Karnofsky-Rhodes expansion of the right Cayley graph of
$S$ in terms of generators yields again a right Cayley graph. The McCammond
expansion provides normal forms for elements in the expanded $S$. Using our
previous results with Silva based on work by Berstel, Perrin, Reutenauer, we
construct (infinite) semaphore codes on which we can define Markov chains.
These semaphore codes can be lumped using geometric semigroup theory. Using
normal forms and associated Kleene expressions, they yield formulas for the
stationary distribution of the finite Markov chain of the expanded $S$ and the
original $S$. Analyzing the normal forms also provides an estimate on the
mixing time.
| 0 | 0 | 1 | 0 | 0 | 0 |
A Simulator for Hedonic Games | Hedonic games are meant to model how coalitions of people form and break
apart in the real world. However, it is difficult to run simulations when
everything must be done by hand on paper. We present an online software that
allows fast and visual simulation of several types of hedonic games.
this http URL
| 1 | 0 | 0 | 0 | 0 | 0 |
Estimating Large Precision Matrices via Modified Cholesky Decomposition | We introduce the $k$-banded Cholesky prior for estimating a high-dimensional
bandable precision matrix via the modified Cholesky decomposition. The bandable
assumption is imposed on the Cholesky factor of the decomposition. We obtained
the P-loss convergence rate under the spectral norm and the matrix
$\ell_{\infty}$ norm and the minimax lower bounds. Since the P-loss convergence
rate (Lee and Lee (2017)) is stronger than the posterior convergence rate, the
rates obtained are also posterior convergence rates. Furthermore, when the true
precision matrix is a $k_0$-banded matrix with some finite $k_0$, the obtained
P-loss convergence rates coincide with the minimax rates. The established
convergence rates are slightly slower than the minimax lower bounds, but these
are the fastest rates for bandable precision matrices among the existing
Bayesian approaches. A simulation study is conducted to compare the performance
to the other competitive estimators in various scenarios.
| 0 | 0 | 1 | 1 | 0 | 0 |
High order conformal symplectic and ergodic schemes for stochastic Langevin equation via generating functions | In this paper, we consider the stochastic Langevin equation with additive
noises, which possesses both conformal symplectic geometric structure and
ergodicity. We propose a methodology of constructing high weak order conformal
symplectic schemes by converting the equation into an equivalent autonomous
stochastic Hamiltonian system and modifying the associated generating function.
To illustrate this approach, we construct a specific second order numerical
scheme, and prove that its symplectic form dissipates exponentially. Moreover,
for the linear case, the proposed scheme is also shown to inherit the
ergodicity of the original system, and the temporal average of the numerical
solution is a proper approximation of the ergodic limit over long time.
Numerical experiments are given to verify these theoretical results.
| 0 | 0 | 1 | 0 | 0 | 0 |
Crowd ideation of supervised learning problems | Crowdsourcing is an important avenue for collecting machine learning data,
but crowdsourcing can go beyond simple data collection by employing the
creativity and wisdom of crowd workers. Yet crowd participants are unlikely to
be experts in statistics or predictive modeling, and it is not clear how well
non-experts can contribute creatively to the process of machine learning. Here
we study an end-to-end crowdsourcing algorithm where groups of non-expert
workers propose supervised learning problems, rank and categorize those
problems, and then provide data to train predictive models on those problems.
Problem proposal includes and extends feature engineering because workers
propose the entire problem, not only the input features but also the target
variable. We show that workers without machine learning experience can
collectively construct useful datasets and that predictive models can be
learned on these datasets. In our experiments, the problems proposed by workers
covered a broad range of topics, from politics and current events to problems
capturing health behavior, demographics, and more. Workers also favored
questions showing positively correlated relationships, which has interesting
implications given many supervised learning methods perform as well with strong
negative correlations. Proper instructions are crucial for non-experts, so we
also conducted a randomized trial to understand how different instructions may
influence the types of problems proposed by workers. In general, shifting the
focus of machine learning tasks from designing and training individual
predictive models to problem proposal allows crowdsourcers to design
requirements for problems of interest and then guide workers towards
contributing to the most suitable problems.
| 0 | 0 | 0 | 1 | 0 | 0 |
Joins in the strong Weihrauch degrees | The Weihrauch degrees and strong Weihrauch degrees are partially ordered
structures representing degrees of unsolvability of various mathematical
problems. Their study has been widely applied in computable analysis,
complexity theory, and more recently, also in computable combinatorics. We
answer an open question about the algebraic structure of the strong Weihrauch
degrees, by exhibiting a join operation that turns these degrees into a
lattice. Previously, the strong Weihrauch degrees were only known to form a
lower semi-lattice. We then show that unlike the Weihrauch degrees, which are
known to form a distributive lattice, the lattice of strong Weihrauch degrees
is not distributive. Therefore, the two structures are not isomorphic.
| 1 | 0 | 1 | 0 | 0 | 0 |
Advanced LSTM: A Study about Better Time Dependency Modeling in Emotion Recognition | Long short-term memory (LSTM) is normally used in recurrent neural network
(RNN) as basic recurrent unit. However,conventional LSTM assumes that the state
at current time step depends on previous time step. This assumption constraints
the time dependency modeling capability. In this study, we propose a new
variation of LSTM, advanced LSTM (A-LSTM), for better temporal context
modeling. We employ A-LSTM in weighted pooling RNN for emotion recognition. The
A-LSTM outperforms the conventional LSTM by 5.5% relatively. The A-LSTM based
weighted pooling RNN can also complement the state-of-the-art emotion
classification framework. This shows the advantage of A-LSTM.
| 1 | 0 | 0 | 1 | 0 | 0 |
A combinatorial model for the free loop fibration | We introduce the abstract notion of a closed necklical set in order to
describe a functorial combinatorial model of the free loop fibration $\Omega
Y\rightarrow \Lambda Y\rightarrow Y$ over the geometric realization $Y=|X|$ of
a path connected simplicial set $X.$ In particular, to any path connected
simplicial set $X$ we associate a closed necklical set
$\widehat{\mathbf{\Lambda}}X$ such that its geometric realization
$|\widehat{\mathbf{\Lambda}}X|$, a space built out of gluing "freehedrical" and
"cubical" cells, is homotopy equivalent to the free loop space $\Lambda Y$ and
the differential graded module of chains $C_*(\widehat{\mathbf{\Lambda}}X)$
generalizes the coHochschild chain complex of the chain coalgebra $C_\ast(X).$
| 0 | 0 | 1 | 0 | 0 | 0 |
On the number of circular orders on a group | We give a classification and complete algebraic description of groups
allowing only finitely many (left multiplication invariant) circular orders. In
particular, they are all solvable groups with a specific semi-direct product
decomposition. This allows us to also show that the space of circular orders of
any group is either finite or uncountable. As a special case and first step, we
show that the space of circular orderings of an infinite Abelian group has no
isolated points, hence is homeomorphic to a cantor set.
| 0 | 0 | 1 | 0 | 0 | 0 |
Preparation and Measurement in Quantum Memory Models | Quantum Cognition has delivered a number of models for semantic memory, but
to date these have tended to assume pure states and projective measurement.
Here we relax these assumptions. A quantum inspired model of human word
association experiments will be extended using a density matrix representation
of human memory and a POVM based upon non-ideal measurements. Our formulation
allows for a consideration of key terms like measurement and contextuality
within a rigorous modern approach. This approach both provides new conceptual
advances and suggests new experimental protocols.
| 0 | 0 | 0 | 0 | 1 | 0 |
Detailed proof of Nazarov's inequality | The purpose of this note is to provide a detailed proof of Nazarov's
inequality stated in Lemma A.1 in Chernozhukov, Chetverikov, and Kato (2017,
Annals of Probability).
| 0 | 0 | 1 | 1 | 0 | 0 |
TRPL+K: Thick-Restart Preconditioned Lanczos+K Method for Large Symmetric Eigenvalue Problems | The Lanczos method is one of the standard approaches for computing a few
eigenpairs of a large, sparse, symmetric matrix. It is typically used with
restarting to avoid unbounded growth of memory and computational requirements.
Thick-restart Lanczos is a popular restarted variant because of its simplicity
and numerically robustness. However, convergence can be slow for highly
clustered eigenvalues so more effective restarting techniques and the use of
preconditioning is needed. In this paper, we present a thick-restart
preconditioned Lanczos method, TRPL+K, that combines the power of locally
optimal restarting (+K) and preconditioning techniques with the efficiency of
the thick-restart Lanczos method. TRPL+K employs an inner-outer scheme where
the inner loop applies Lanczos on a preconditioned operator while the outer
loop augments the resulting Lanczos subspace with certain vectors from the
previous restart cycle to obtain eigenvector approximations with which it thick
restarts the outer subspace. We first identify the differences from various
relevant methods in the literature. Then, based on an optimization perspective,
we show an asymptotic global quasi-optimality of a simplified TRPL+K method
compared to an unrestarted global optimal method. Finally, we present extensive
experiments showing that TRPL+K either outperforms or matches other
state-of-the-art eigenmethods in both matrix-vector multiplications and
computational time.
| 1 | 0 | 0 | 0 | 0 | 0 |
Behavior of l-bits near the many-body localization transition | Eigenstates of fully many-body localized (FMBL) systems are described by
quasilocal operators $\tau_i^z$ (l-bits), which are conserved exactly under
Hamiltonian time evolution. The algebra of the operators $\tau_i^z$ and
$\tau_i^x$ associated with l-bits ($\boldsymbol{\tau}_i$) completely defines
the eigenstates and the matrix elements of local operators between eigenstates
at all energies. We develop a non-perturbative construction of the full set of
l-bit algebras in the many-body localized phase for the canonical model of MBL.
Our algorithm to construct the Pauli-algebra of l-bits combines exact
diagonalization and a tensor network algorithm developed for efficient
diagonalization of large FMBL Hamiltonians. The distribution of localization
lengths of the l-bits is evaluated in the MBL phase and used to characterize
the MBL-to-thermal transition.
| 0 | 1 | 0 | 0 | 0 | 0 |
ROPE: high-dimensional network modeling with robust control of edge FDR | Network modeling has become increasingly popular for analyzing genomic data,
to aid in the interpretation and discovery of possible mechanistic components
and therapeutic targets. However, genomic-scale networks are high-dimensional
models and are usually estimated from a relatively small number of samples.
Therefore, their usefulness is hampered by estimation instability. In addition,
the complexity of the models is controlled by one or more penalization (tuning)
parameters where small changes to these can lead to vastly different networks,
thus making interpretation of models difficult. This necessitates the
development of techniques to produce robust network models accompanied by
estimation quality assessments.
We introduce Resampling of Penalized Estimates (ROPE): a novel statistical
method for robust network modeling. The method utilizes resampling-based
network estimation and integrates results from several levels of penalization
through a constrained, over-dispersed beta-binomial mixture model. ROPE
provides robust False Discovery Rate (FDR) control of network estimates and
each edge is assigned a measure of validity, the q-value, corresponding to the
FDR-level for which the edge would be included in the network model. We apply
ROPE to several simulated data sets as well as genomic data from The Cancer
Genome Atlas. We show that ROPE outperforms state-of-the-art methods in terms
of FDR control and robust performance across data sets. We illustrate how to
use ROPE to make a principled model selection for which genomic associations to
study further. ROPE is available as an R package on CRAN.
| 0 | 0 | 0 | 1 | 0 | 0 |
Developing Robot Driver Etiquette Based on Naturalistic Human Driving Behavior | Automated vehicles can change the society by improved safety, mobility and
fuel efficiency. However, due to the higher cost and change in business model,
over the coming decades, the highly automated vehicles likely will continue to
interact with many human-driven vehicles. In the past, the control/design of
the highly automated (robotic) vehicles mainly considers safety and efficiency
but failed to address the "driving culture" of surrounding human-driven
vehicles. Thus, the robotic vehicles may demonstrate behaviors very different
from other vehicles. We study this "driving etiquette" problem in this paper.
As the first step, we report the key behavior parameters of human driven
vehicles derived from a large naturalistic driving database. The results can be
used to guide future algorithm design of highly automated vehicles or to
develop realistic human-driven vehicle behavior model in simulations.
| 1 | 0 | 0 | 0 | 0 | 0 |
e-Fair: Aggregation in e-Commerce for Exploiting Economies of Scale | In recent years, many new and interesting models of successful online
business have been developed, including competitive models such as auctions,
where the product price tends to rise, and group-buying, where users cooperate
obtaining a dynamic price that tends to go down. We propose the e-fair as a
business model for social commerce, where both sellers and buyers are grouped
to maximize benefits. e-Fairs extend the group-buying model aggregating demand
and supply for price optimization as well as consolidating shipments and
optimize withdrawals for guaranteeing additional savings. e-Fairs work upon
multiple dimensions: time to aggregate buyers, their geographical distribution,
price/quantity curves provided by sellers, and location of withdrawal points.
We provide an analytical model for time and spatial optimization and simulate
realistic scenarios using both real purchase data from an Italian marketplace
and simulated ones. Experimental results demonstrate the potentials offered by
e-fairs and show benefits for all the involved actors.
| 1 | 0 | 0 | 0 | 0 | 0 |
Stable spike clusters for the precursor Gierer-Meinhardt system in R2 | We consider the Gierer-Meinhardt system with small inhibitor diffusivity,
very small activator diffusivity and a precursor inhomogeneity.
For any given positive integer k we construct a spike cluster consisting of
$k$ spikes which all approach the same nondegenerate local minimum point of the
precursor inhomogeneity. We show that this spike cluster can be linearly
stable. In particular, we show the existence of spike clusters for spikes
located at the vertices of a polygon with or without centre. Further, the
cluster without centre is stable for up to three spikes, whereas the cluster
with centre is stable for up to six spikes.
The main idea underpinning these stable spike clusters is the following: due
to the small inhibitor diffusivity the interaction between spikes is repulsive,
and the spikes are attracted towards the local minimum point of the precursor
inhomogeneity. Combining these two effects can lead to an equilibrium of spike
positions within the cluster such that the cluster is linearly stable.
| 0 | 0 | 1 | 0 | 0 | 0 |
Solvability of curves on surfaces | In this article, we study subloci of solvable curves in $\mathcal{M}_g$ which
are contained in either a K3-surface or a quadric or a cubic surface. We give a
bound on the dimension of such subloci. In the case of complete intersection
genus $g$ curves in a cubic surface, we show that a general such curve is
solvable.
| 0 | 0 | 1 | 0 | 0 | 0 |
Scaling Limits for Super--replication with Transient Price Impact | We prove limit theorems for the super-replication cost of European options in
a Binomial model with transient price impact. We show that if the time step
goes to zero and the effective resilience between consecutive trading times
remains constant then the limit of the super--replication prices coincide with
the scaling limit for temporary price impact with a modified market depth.
| 0 | 0 | 0 | 0 | 0 | 1 |
Kernel-based Inference of Functions over Graphs | The study of networks has witnessed an explosive growth over the past decades
with several ground-breaking methods introduced. A particularly interesting --
and prevalent in several fields of study -- problem is that of inferring a
function defined over the nodes of a network. This work presents a versatile
kernel-based framework for tackling this inference problem that naturally
subsumes and generalizes the reconstruction approaches put forth recently by
the signal processing on graphs community. Both the static and the dynamic
settings are considered along with effective modeling approaches for addressing
real-world problems. The herein analytical discussion is complemented by a set
of numerical examples, which showcase the effectiveness of the presented
techniques, as well as their merits related to state-of-the-art methods.
| 1 | 0 | 0 | 1 | 0 | 0 |
Noncommutative products of Euclidean spaces | We present natural families of coordinate algebras of noncommutative products
of Euclidean spaces. These coordinate algebras are quadratic ones associated
with an R-matrix which is involutive and satisfies the Yang-Baxter equations.
As a consequence they enjoy a list of nice properties, being regular of finite
global dimension. Notably, we have eight-dimensional noncommutative euclidean
spaces which are particularly well behaved and are deformations parametrised by
a two-dimensional sphere. Quotients include noncommutative seven-spheres as
well as noncommutative "quaternionic tori". There is invariance for an action
of $SU(2) \times SU(2)$ in parallel with the action of $U(1) \times U(1)$ on a
"complex" noncommutative torus which allows one to construct quaternionic toric
noncommutative manifolds. Additional classes of solutions are disjoint from the
classical case.
| 0 | 0 | 1 | 0 | 0 | 0 |
X-ray and Optical Study of the Gamma-ray Source 3FGL J0838.8$-$2829: Identification of a Candidate Millisecond Pulsar Binary and an Asynchronous Polar | We observed the field of the Fermi source 3FGL J0838.8-2829 in optical and
X-rays, initially motivated by the cataclysmic variable (CV) 1RXS
J083842.1-282723 that lies within its error circle. Several X-ray sources first
classified as CVs have turned out to be gamma-ray emitting millisecond pulsars
(MSPs). We find that 1RXS J083842.1-282723 is in fact an unusual CV, a
stream-fed asynchronous polar in which accretion switches between magnetic
poles (that are $\approx$120$^{\circ}$ apart) when the accretion rate is at
minimum. High-amplitude X-ray modulation at periods of 94.8$\pm$0.4 minutes and
14.7$\pm$1.2 hr are seen. The former appears to be the spin period, while
latter is inferred to be one-third of the beat period between the spin and the
orbit, implying an orbital period of 98.3$\pm$0.5 minutes. We also measure an
optical emission-line spectroscopic period of 98.413$\pm$0.004 minutes which is
consistent with the orbital period inferred from the X-rays. In any case, this
system is unlikely to be the gamma-ray source. Instead, we find a fainter
variable X-ray and optical source, XMMU J083850.38-282756.8, that is modulated
on a time scale of hours in addition to exhibiting occasional sharp flares. It
resembles the black widow or redback pulsars that have been discovered as
counterparts of Fermi sources, with the optical modulation due to heating of
the photosphere of a low-mass companion star by, in this case, an as-yet
undetected MSP. We propose XMMU J083850.38-282756.8 as the MSP counterpart of
3FGL J0838.8-2829.
| 0 | 1 | 0 | 0 | 0 | 0 |
Adversarial Variational Inference and Learning in Markov Random Fields | Markov random fields (MRFs) find applications in a variety of machine
learning areas, while the inference and learning of such models are challenging
in general. In this paper, we propose the Adversarial Variational Inference and
Learning (AVIL) algorithm to solve the problems with a minimal assumption about
the model structure of an MRF. AVIL employs two variational distributions to
approximately infer the latent variables and estimate the partition function,
respectively. The variational distributions, which are parameterized as neural
networks, provide an estimate of the negative log likelihood of the MRF. On one
hand, the estimate is in an intuitive form of approximate contrastive free
energy. On the other hand, the estimate is a minimax optimization problem,
which is solved by stochastic gradient descent in an alternating manner. We
apply AVIL to various undirected generative models in a fully black-box manner
and obtain better results than existing competitors on several real datasets.
| 1 | 0 | 0 | 1 | 0 | 0 |
Geometric k-nearest neighbor estimation of entropy and mutual information | Nonparametric estimation of mutual information is used in a wide range of
scientific problems to quantify dependence between variables. The k-nearest
neighbor (knn) methods are consistent, and therefore expected to work well for
large sample size. These methods use geometrically regular local volume
elements. This practice allows maximum localization of the volume elements, but
can also induce a bias due to a poor description of the local geometry of the
underlying probability measure. We introduce a new class of knn estimators that
we call geometric knn estimators (g-knn), which use more complex local volume
elements to better model the local geometry of the probability measures. As an
example of this class of estimators, we develop a g-knn estimator of entropy
and mutual information based on elliptical volume elements, capturing the local
stretching and compression common to a wide range of dynamical systems
attractors. A series of numerical examples in which the thickness of the
underlying distribution and the sample sizes are varied suggest that local
geometry is a source of problems for knn methods such as the
Kraskov-Stögbauer-Grassberger (KSG) estimator when local geometric effects
cannot be removed by global preprocessing of the data. The g-knn method
performs well despite the manipulation of the local geometry. In addition, the
examples suggest that the g-knn estimators can be of particular relevance to
applications in which the system is large, but data size is limited.
| 0 | 0 | 1 | 1 | 0 | 0 |
Resonant Electron Impact Excitation of 3d levels in Fe$^{14+}$ and Fe$^{15+}$ | We present laboratory spectra of the $3p$--$3d$ transitions in Fe$^{14+}$ and
Fe$^{15+}$ excited with a mono-energetic electron beam. In the energy dependent
spectra obtained by sweeping the electron energy, resonant excitation is
confirmed as an intensity enhancement at specific electron energies. The
experimental results are compared with theoretical cross sections calculated
based on fully relativistic wave functions and the distorted-wave
approximation. Comparisons between the experimental and theoretical results
show good agreement for the resonance strength. A significant discrepancy is,
however, found for the non-resonant cross section in Fe$^{14+}$. %, which can
be considered as a fundamental cause of the line intensity ratio problem that
has often been found in both observatory and laboratory measurements. This
discrepancy is considered to be the fundamental cause of the previously
reported inconsistency of the model with the observed intensity ratio between
the $^3\!P_2$ -- $^3\!D_3$ and $^1\!P_1$ -- $^1\!D_2$ transitions.
| 0 | 1 | 0 | 0 | 0 | 0 |
Automated Top View Registration of Broadcast Football Videos | In this paper, we propose a novel method to register football broadcast video
frames on the static top view model of the playing surface. The proposed method
is fully automatic in contrast to the current state of the art which requires
manual initialization of point correspondences between the image and the static
model. Automatic registration using existing approaches has been difficult due
to the lack of sufficient point correspondences. We investigate an alternate
approach exploiting the edge information from the line markings on the field.
We formulate the registration problem as a nearest neighbour search over a
synthetically generated dictionary of edge map and homography pairs. The
synthetic dictionary generation allows us to exhaustively cover a wide variety
of camera angles and positions and reduce this problem to a minimal per-frame
edge map matching procedure. We show that the per-frame results can be improved
in videos using an optimization framework for temporal camera stabilization. We
demonstrate the efficacy of our approach by presenting extensive results on a
dataset collected from matches of football World Cup 2014.
| 1 | 0 | 0 | 0 | 0 | 0 |
Reservoir Computing for Detection of Steady State in Performance Tests of Compressors | Fabrication of devices in industrial plants often includes undergoing quality
assurance tests or tests that seek to determine some attributes or capacities
of the device. For instance, in testing refrigeration compressors, we want to
find the true refrigeration capacity of the compressor being tested. Such test
(also called an episode) may take up to four hours, being an actual hindrance
to applying it to the total number of compressors produced. This work seeks to
reduce the time spent on such industrial trials by employing Recurrent Neural
Networks (RNNs) as dynamical models for detecting when a test is entering the
so-called steady-state region. Specifically, we use Reservoir Computing (RC)
networks which simplify the learning of RNNs by speeding up training time and
showing convergence to a global optimum. Also, this work proposes a
self-organized subspace projection method for RC networks which uses
information from the beginning of the episode to define a cluster to which the
episode belongs to. This assigned cluster defines a particular binary input
that shifts the operating point of the reservoir to a subspace of trajectories
for the duration of the episode. This new method is shown to turn the RC model
robust in performance with respect to varying combination of reservoir
parameters, such as spectral radius and leak rate, when compared to a standard
RC network.
| 1 | 0 | 0 | 0 | 0 | 0 |
Fast swaption pricing in Gaussian term structure models | We propose a fast and accurate numerical method for pricing European
swaptions in multi-factor Gaussian term structure models. Our method can be
used to accelerate the calibration of such models to the volatility surface.
The pricing of an interest rate option in such a model involves evaluating a
multi-dimensional integral of the payoff of the claim on a domain where the
payoff is positive. In our method, we approximate the exercise boundary of the
state space by a hyperplane tangent to the maximum probability point on the
boundary and simplify the multi-dimensional integration into an analytical
form. The maximum probability point can be determined using the gradient
descent method. We demonstrate that our method is superior to previous methods
by comparing the results to the price obtained by numerical integration.
| 0 | 0 | 0 | 0 | 0 | 1 |
Temporal-related Convolutional-Restricted-Boltzmann-Machine capable of learning relational order via reinforcement learning procedure? | In this article, we extend the conventional framework of
convolutional-Restricted-Boltzmann-Machine to learn highly abstract features
among abitrary number of time related input maps by constructing a layer of
multiplicative units, which capture the relations among inputs. In many cases,
more than two maps are strongly related, so it is wise to make multiplicative
unit learn relations among more input maps, in other words, to find the optimal
relational-order of each unit. In order to enable our machine to learn
relational order, we developed a reinforcement-learning method whose optimality
is proven to train the network.
| 1 | 0 | 0 | 1 | 0 | 0 |
Predicting how and when hidden neurons skew measured synaptic interactions | A major obstacle to understanding neural coding and computation is the fact
that experimental recordings typically sample only a small fraction of the
neurons in a circuit. Measured neural properties are skewed by interactions
between recorded neurons and the "hidden" portion of the network. To properly
interpret neural data and determine how biological structure gives rise to
neural circuit function, we thus need a better understanding of the
relationships between measured effective neural properties and the true
underlying physiological properties. Here, we focus on how the effective
spatiotemporal dynamics of the synaptic interactions between neurons are
reshaped by coupling to unobserved neurons. We find that the effective
interactions from a pre-synaptic neuron $r'$ to a post-synaptic neuron $r$ can
be decomposed into a sum of the true interaction from $r'$ to $r$ plus
corrections from every directed path from $r'$ to $r$ through unobserved
neurons. Importantly, the resulting formula reveals when the hidden units
have---or do not have---major effects on reshaping the interactions among
observed neurons. As a particular example of interest, we derive a formula for
the impact of hidden units in random networks with "strong"
coupling---connection weights that scale with $1/\sqrt{N}$, where $N$ is the
network size, precisely the scaling observed in recent experiments. With this
quantitative relationship between measured and true interactions, we can study
how network properties shape effective interactions, which properties are
relevant for neural computations, and how to manipulate effective interactions.
| 0 | 1 | 0 | 0 | 0 | 0 |
Humanoid Robots as Agents of Human Consciousness Expansion | The "Loving AI" project involves developing software enabling humanoid robots
to interact with people in loving and compassionate ways, and to promote
people' self-understanding and self-transcendence. Currently the project
centers on the Hanson Robotics robot "Sophia" -- specifically, on supplying
Sophia with personality content and cognitive, linguistic, perceptual and
behavioral content aimed at enabling loving interactions supportive of human
self-transcendence. In September 2017 a small pilot study was conducted,
involving the Sophia robot leading human subjects through dialogues and
exercises focused on meditation, visualization and relaxation. The pilot was an
apparent success, qualitatively demonstrating the viability of the approach and
the ability of appropriate human-robot interaction to increase human well-being
and advance human consciousness.
| 1 | 0 | 0 | 0 | 0 | 0 |
Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training | In this paper we study leveraging confidence information induced by
adversarial training to reinforce adversarial robustness of a given
adversarially trained model. A natural measure of confidence is $\|F({\bf
x})\|_\infty$ (i.e. how confident $F$ is about its prediction?). We start by
analyzing an adversarial training formulation proposed by Madry et al.. We
demonstrate that, under a variety of instantiations, an only somewhat good
solution to their objective induces confidence to be a discriminator, which can
distinguish between right and wrong model predictions in a neighborhood of a
point sampled from the underlying distribution. Based on this, we propose
Highly Confident Near Neighbor (${\tt HCNN}$), a framework that combines
confidence information and nearest neighbor search, to reinforce adversarial
robustness of a base model. We give algorithms in this framework and perform a
detailed empirical study. We report encouraging experimental results that
support our analysis, and also discuss problems we observed with existing
adversarial training.
| 1 | 0 | 0 | 1 | 0 | 0 |
Stateless Puzzles for Real Time Online Fraud Preemption | The profitability of fraud in online systems such as app markets and social
networks marks the failure of existing defense mechanisms. In this paper, we
propose FraudSys, a real-time fraud preemption approach that imposes
Bitcoin-inspired computational puzzles on the devices that post online system
activities, such as reviews and likes. We introduce and leverage several novel
concepts that include (i) stateless, verifiable computational puzzles, that
impose minimal performance overhead, but enable the efficient verification of
their authenticity, (ii) a real-time, graph-based solution to assign fraud
scores to user activities, and (iii) mechanisms to dynamically adjust puzzle
difficulty levels based on fraud scores and the computational capabilities of
devices. FraudSys does not alter the experience of users in online systems, but
delays fraudulent actions and consumes significant computational resources of
the fraudsters. Using real datasets from Google Play and Facebook, we
demonstrate the feasibility of FraudSys by showing that the devices of honest
users are minimally impacted, while fraudster controlled devices receive daily
computational penalties of up to 3,079 hours. In addition, we show that with
FraudSys, fraud does not pay off, as a user equipped with mining hardware
(e.g., AntMiner S7) will earn less than half through fraud than from honest
Bitcoin mining.
| 1 | 0 | 0 | 0 | 0 | 0 |
Multi-Period Flexibility Forecast for Low Voltage Prosumers | Near-future electric distribution grids operation will have to rely on
demand-side flexibility, both by implementation of demand response strategies
and by taking advantage of the intelligent management of increasingly common
small-scale energy storage. The Home energy management system (HEMS), installed
at low voltage residential clients, will play a crucial role on the flexibility
provision to both system operators and market players like aggregators.
Modeling and forecasting multi-period flexibility from residential prosumers,
such as battery storage and electric water heater, while complying with
internal constraints (comfort levels, data privacy) and uncertainty is a
complex task. This papers describes a computational method that is capable of
efficiently learn and define the feasibility flexibility space from
controllable resources connected to a HEMS. An Evolutionary Particle Swarm
Optimization (EPSO) algorithm is adopted and reshaped to derive a set of
feasible temporal trajectories for the residential net-load, considering
storage, flexible appliances, and predefined costumer preferences, as well as
load and photovoltaic (PV) forecast uncertainty. A support vector data
description (SVDD) algorithm is used to build models capable of classifying
feasible and non-feasible HEMS operating trajectories upon request from an
optimization/control algorithm operated by a DSO or market player.
| 1 | 0 | 0 | 0 | 0 | 0 |
Assessing the Economics of Customer-Sited Multi-Use Energy Storage | This paper presents an approach to assess the economics of customer-sited
energy storage systems (ESSs) which are owned and operated by a customer. The
ESSs can participate in frequency regulation and spinning reserve markets, and
are used to help the customer consume available renewable energy and reduce
electricity bill. A rolling-horizon approach is developed to optimize the
service schedule, and the resulting costs and revenues are used to assess
economics of the ESSs. The economic assessment approach is illustrated with
case studies, from which we obtain some new observations on profitability of
the customer- sited multi-use ESSs.
| 0 | 0 | 1 | 0 | 0 | 0 |
A Hamiltonian approach for the Thermodynamics of AdS black holes | In this work we study the Thermodynamics of D-dimensional Schwarzschild-anti
de Sitter (SAdS) black holes. The minimal Thermodynamics of the SAdS spacetime
is briefly discussed, highlighting some of its strong points and shortcomings.
The minimal SAdS Thermodynamics is extended within a Hamiltonian approach, by
means of the introduction of an additional degree of freedom. We demonstrate
that the cosmological constant can be introduced in the thermodynamic
description of the SAdS black hole with a canonical transformation of the
Schwarzschild problem, closely related to the introduction of an anti-de Sitter
thermodynamic volume. The treatment presented is consistent, in the sense that
it is compatible with the introduction of new thermodynamic potentials, and
respects the laws of black hole Thermodynamics. By demanding homogeneity of the
thermodynamic variables, we are able to construct a new equation of state that
completely characterizes the Thermodynamics of SAdS black holes. The treatment
naturally generates phenomenological constants that can be associated with
different boundary conditions in underlying microscopic theories. A whole new
set of phenomena can be expected from the proposed generalization of SAdS
Thermodynamics.
| 0 | 1 | 1 | 0 | 0 | 0 |
Dzyaloshinskii Moriya interaction across antiferromagnet / ferromagnet interface | The antiferromagnet (AFM) / ferromagnet (FM) interfaces are of central
importance in recently developed pure electric or ultrafast control of FM
spins, where the underlying mechanisms remain unresolved. Here we report the
direct observation of Dzyaloshinskii Moriya interaction (DMI) across the AFM/FM
interface of IrMn/CoFeB thin films. The interfacial DMI is quantitatively
measured from the asymmetric spin wave dispersion in the FM layer using
Brillouin light scattering. The DMI strength is enhanced by a factor of 7 with
increasing IrMn layer thickness in the range of 1- 7.5 nm. Our findings provide
deeper insight into the coupling at AFM/FM interface and may stimulate new
device concepts utilizing chiral spin textures such as magnetic skyrmions in
AFM/FM heterostructures.
| 0 | 1 | 0 | 0 | 0 | 0 |
Pore cross-talk in colloidal filtration | Blockage of pores by particles is found in many processes, including
filtration and oil extraction. We present filtration experiments through a
linear array of ten channels with one dimension which is sub-micron, through
which a dilute dispersion of Brownian polystyrene spheres flows under the
action of a fixed pressure drop. The growth rate of a clog formed by particles
at a pore entrance systematically increases with the number of already
saturated (entirely clogged) pores, indicating that there is an interaction or
"cross-talk" between the pores. This observation is interpreted based on a
phenomenological model, stating that a diffusive redistribution of particles
occurs along the membrane, from clogged to free pores. This one-dimensional
model could be extended to two-dimensional membranes.
| 0 | 1 | 0 | 0 | 0 | 0 |
Temporal Markov Processes for Transport in Porous Media: Random Lattice Networks | Monte Carlo (MC) simulations of transport in random porous networks indicate
that for high variances of the log-normal permeability distribution, the
transport of a passive tracer is non-Fickian. Here we model this non-Fickian
dispersion in random porous networks using discrete temporal Markov models. We
show that such temporal models capture the spreading behavior accurately. This
is true despite the fact that the slow velocities are strongly correlated in
time, and some studies have suggested that the persistence of low velocities
would render the temporal Markovian model inapplicable. Compared to previously
proposed temporal stochastic differential equations with case specific drift
and diffusion terms, the models presented here require fewer modeling
assumptions. Moreover, we show that discrete temporal Markov models can be used
to represent dispersion in unstructured networks, which are widely used to
model porous media. A new method is proposed to extend the state space of
temporal Markov models to improve the model predictions in the presence of
extremely low velocities in particle trajectories and extend the applicability
of the model to higher temporal resolutions. Finally, it is shown that by
combining multiple transitions, temporal models are more efficient for
computing particle evolution compared to correlated CTRW with spatial
increments that are equal to the lengths of the links in the network.
| 1 | 1 | 0 | 0 | 0 | 0 |
Defect Properties of Na and K in Cu2ZnSnS4 from Hybrid Functional Calculation | In-growth or post-deposition treatment of $Cu_{2}ZnSnS_{4}$ (CZTS) absorber
layer had led to improved photovoltaic efficiency, however, the underlying
physical mechanism of such improvements are less studied. In this study, the
thermodynamics of Na and K related defects in CZTS are investigated from first
principle approach using hybrid functional, with chemical potential of Na and K
established from various phases of their polysulphides. Both Na and K
predominantly substitute on Cu sites similar to their behavior in
$Cu(In,Ga)Se_{2}$, in contrast to previous results using the generalized
gradient approximation (GGA). All substitutional and interstitial defects are
shown to be either shallow levels or highly energetically unfavorable. Defect
complexing between Na and abundant intrinsic defects did not show possibility
of significant incorporation enhancement or introducing deep n-type levels. The
possible benefit of Na incorporation on enhancing photovoltaic efficiency is
discussed. The negligible defect solubility of K in CZTS also suggests possible
surfactant candidate.
| 0 | 1 | 0 | 0 | 0 | 0 |
Explicit minimisation of a convex quadratic under a general quadratic constraint: a global, analytic approach | A novel approach is introduced to a very widely occurring problem, providing
a complete, explicit resolution of it: minimisation of a convex quadratic under
a general quadratic, equality or inequality, constraint. Completeness comes via
identification of a set of mutually exclusive and exhaustive special cases.
Explicitness, via algebraic expressions for each solution set. Throughout,
underlying geometry illuminates and informs algebraic development. In
particular, centrally to this new approach, affine equivalence is exploited to
re-express the same problem in simpler coordinate systems. Overall, the
analysis presented provides insight into the diverse forms taken both by the
problem itself and its solution set, showing how each may be intrinsically
unstable. Comparisons of this global, analytic approach with the, intrinsically
complementary, local, computational approach of (generalised) trust region
methods point to potential synergies between them. Points of contact with
simultaneous diagonalisation results are noted.
| 0 | 0 | 1 | 1 | 0 | 0 |
Morphisms of open games | We define a notion of morphisms between open games, exploiting a surprising
connection between lenses in computer science and compositional game theory.
This extends the more intuitively obvious definition of globular morphisms as
mappings between strategy profiles that preserve best responses, and hence in
particular preserve Nash equilibria. We construct a symmetric monoidal double
category in which the horizontal 1-cells are open games, vertical 1-morphisms
are lenses, and 2-cells are morphisms of open games. States (morphisms out of
the monoidal unit) in the vertical category give a flexible solution concept
that includes both Nash and subgame perfect equilibria. Products in the
vertical category give an external choice operator that is reminiscent of
products in game semantics, and is useful in practical examples. We illustrate
the above two features with a simple worked example from microeconomics, the
market entry game.
| 1 | 0 | 0 | 0 | 0 | 0 |
U(1)$\times$SU(2) Gauge Invariance Made Simple for Density Functional Approximations | A semi-relativistic density-functional theory that includes spin-orbit
couplings and Zeeman fields on equal footing with the electromagnetic
potentials, is an appealing framework to develop a unified first-principles
computational approach for non-collinear magnetism, spintronics, orbitronics,
and topological states. The basic variables of this theory include the
paramagnetic current and the spin-current density, besides the particle and the
spin density, and the corresponding exchange-correlation (xc) energy functional
is invariant under local U(1)$\times$SU(2) gauge transformations. The xc-energy
functional must be approximated to enable practical applications, but, contrary
to the case of the standard density functional theory, finding simple
approximations suited to deal with realistic atomistic inhomogeneities has been
a long-standing challenge. Here, we propose a way out of this impasse by
showing that approximate gauge-invariant functionals can be easily generated
from existing approximate functionals of ordinary density-functional theory by
applying a simple {\it minimal substitution} on the kinetic energy density,
which controls the short-range behavior of the exchange hole. Our proposal
opens the way to the construction of approximate, yet non-empirical
functionals, which do not assume weak inhomogeneity and should therefore have a
wide range of applicability in atomic, molecular and condensed matter physics.
| 0 | 1 | 0 | 0 | 0 | 0 |
L^2-Betti numbers of rigid C*-tensor categories and discrete quantum groups | We compute the $L^2$-Betti numbers of the free $C^*$-tensor categories, which
are the representation categories of the universal unitary quantum groups
$A_u(F)$. We show that the $L^2$-Betti numbers of the dual of a compact quantum
group $G$ are equal to the $L^2$-Betti numbers of the representation category
$Rep(G)$ and thus, in particular, invariant under monoidal equivalence. As an
application, we obtain several new computations of $L^2$-Betti numbers for
discrete quantum groups, including the quantum permutation groups and the free
wreath product groups. Finally, we obtain upper bounds for the first
$L^2$-Betti number in terms of a generating set of a $C^*$-tensor category.
| 0 | 0 | 1 | 0 | 0 | 0 |
Subsets and Splits
No saved queries yet
Save your SQL queries to embed, download, and access them later. Queries will appear here once saved.