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
|
|---|---|---|---|---|---|---|---|
Tidal disruptions by rotating black holes: relativistic hydrodynamics with Newtonian codes | We propose an approximate approach for studying the relativistic regime of
stellar tidal disruptions by rotating massive black holes. It combines an exact
relativistic description of the hydrodynamical evolution of a test fluid in a
fixed curved spacetime with a Newtonian treatment of the fluid's self-gravity.
Explicit expressions for the equations of motion are derived for Kerr spacetime
using two different coordinate systems. We implement the new methodology within
an existing Newtonian Smoothed Particle Hydrodynamics code and show that
including the additional physics involves very little extra computational cost.
We carefully explore the validity of the novel approach by first testing its
ability to recover geodesic motion, and then by comparing the outcome of tidal
disruption simulations against previous relativistic studies. We further
compare simulations in Boyer--Lindquist and Kerr--Schild coordinates and
conclude that our approach allows accurate simulation even of tidal disruption
events where the star penetrates deeply inside the tidal radius of a rotating
black hole. Finally, we use the new method to study the effect of the black
hole spin on the morphology and fallback rate of the debris streams resulting
from tidal disruptions, finding that while the spin has little effect on the
fallback rate, it does imprint heavily on the stream morphology, and can even
be a determining factor in the survival or disruption of the star itself. Our
methodology is discussed in detail as a reference for future astrophysical
applications.
| 0 | 1 | 0 | 0 | 0 | 0 |
A promise checked is a promise kept: Inspection Testing | Occasionally, developers need to ensure that the compiler treats their code
in a specific way that is only visible by inspecting intermediate or final
compilation artifacts. This is particularly common with carefully crafted
compositional libraries, where certain usage patterns are expected to trigger
an intricate sequence of compiler optimizations -- stream fusion is a
well-known example.
The developer of such a library has to manually inspect build artifacts and
check for the expected properties. Because this is too tedious to do often, it
will likely go unnoticed if the property is broken by a change to the library
code, its dependencies or the compiler. The lack of automation has led to
released versions of such libraries breaking their documented promises.
This indicates that there is an unrecognized need for a new testing paradigm,
inspection testing, where the programmer declaratively describes non-functional
properties of an compilation artifact and the compiler checks these properties.
We define inspection testing abstractly, implement it in the context of Haskell
and show that it increases the quality of such libraries.
| 1 | 0 | 0 | 0 | 0 | 0 |
Linear-Time Sequence Classification using Restricted Boltzmann Machines | Classification of sequence data is the topic of interest for dynamic Bayesian
models and Recurrent Neural Networks (RNNs). While the former can explicitly
model the temporal dependencies between class variables, the latter have a
capability of learning representations. Several attempts have been made to
improve performance by combining these two approaches or increasing the
processing capability of the hidden units in RNNs. This often results in
complex models with a large number of learning parameters. In this paper, a
compact model is proposed which offers both representation learning and
temporal inference of class variables by rolling Restricted Boltzmann Machines
(RBMs) and class variables over time. We address the key issue of
intractability in this variant of RBMs by optimising a conditional
distribution, instead of a joint distribution. Experiments reported in the
paper on melody modelling and optical character recognition show that the
proposed model can outperform the state-of-the-art. Also, the experimental
results on optical character recognition, part-of-speech tagging and text
chunking demonstrate that our model is comparable to recurrent neural networks
with complex memory gates while requiring far fewer parameters.
| 1 | 0 | 0 | 1 | 0 | 0 |
A central $U(1)$-extension of a double Lie groupoid | In this paper, we introduce a notion of a central $U(1)$-extension of a
double Lie groupoid and show that it defines a cocycle in the certain triple
complex.
| 0 | 0 | 1 | 0 | 0 | 0 |
Common fixed point theorems under an implicit contractive condition on metric spaces endowed with an arbitrary binary relation and an application | The aim of this paper is to establish some metrical coincidence and common
fixed point theorems with an arbitrary relation under an implicit contractive
condition which is general enough to cover a multitude of well known
contraction conditions in one go besides yielding several new ones. We also
provide an example to demonstrate the generality of our results over several
well known corresponding results of the existing literature. Finally, we
utilize our results to prove an existence theorem for ensuring the solution of
an integral equation.
| 0 | 0 | 1 | 0 | 0 | 0 |
The NIEP | The nonnegative inverse eigenvalue problem (NIEP) asks which lists of $n$
complex numbers (counting multiplicity) occur as the eigenvalues of some
$n$-by-$n$ entry-wise nonnegative matrix. The NIEP has a long history and is a
known hard (perhaps the hardest in matrix analysis?) and sought after problem.
Thus, there are many subproblems and relevant results in a variety of
directions. We survey most work on the problem and its several variants, with
an emphasis on recent results, and include 130 references. The survey is
divided into: a) the single eigenvalue problems; b) necessary conditions; c)
low dimensional results; d) sufficient conditions; e) appending 0's to achieve
realizability; f) the graph NIEP's; g) Perron similarities; and h) the
relevance of Jordan structure.
| 0 | 0 | 1 | 0 | 0 | 0 |
Operationalizing Conflict and Cooperation between Automated Software Agents in Wikipedia: A Replication and Expansion of 'Even Good Bots Fight' | This paper replicates, extends, and refutes conclusions made in a study
published in PLoS ONE ("Even Good Bots Fight"), which claimed to identify
substantial levels of conflict between automated software agents (or bots) in
Wikipedia using purely quantitative methods. By applying an integrative
mixed-methods approach drawing on trace ethnography, we place these alleged
cases of bot-bot conflict into context and arrive at a better understanding of
these interactions. We found that overwhelmingly, the interactions previously
characterized as problematic instances of conflict are typically better
characterized as routine, productive, even collaborative work. These results
challenge past work and show the importance of qualitative/quantitative
collaboration. In our paper, we present quantitative metrics and qualitative
heuristics for operationalizing bot-bot conflict. We give thick descriptions of
kinds of events that present as bot-bot reverts, helping distinguish conflict
from non-conflict. We computationally classify these kinds of events through
patterns in edit summaries. By interpreting found/trace data in the
socio-technical contexts in which people give that data meaning, we gain more
from quantitative measurements, drawing deeper understandings about the
governance of algorithmic systems in Wikipedia. We have also released our data
collection, processing, and analysis pipeline, to facilitate computational
reproducibility of our findings and to help other researchers interested in
conducting similar mixed-method scholarship in other platforms and contexts.
| 1 | 0 | 0 | 0 | 0 | 0 |
Synthesizing Bijective Lenses | Bidirectional transformations between different data representations occur
frequently in modern software systems. They appear as serializers and
deserializers, as database views and view updaters, and more. Manually building
bidirectional transformations---by writing two separate functions that are
intended to be inverses---is tedious and error prone. A better approach is to
use a domain-specific language in which both directions can be written as a
single expression. However, these domain-specific languages can be difficult to
program in, requiring programmers to manage fiddly details while working in a
complex type system.
To solve this, we present Optician, a tool for type-directed synthesis of
bijective string transformers. The inputs to Optician are two ordinary regular
expressions representing two data formats and a few concrete examples for
disambiguation. The output is a well-typed program in Boomerang (a
bidirectional language based on the theory of lenses). The main technical
challenge involves navigating the vast program search space efficiently enough.
Unlike most prior work on type-directed synthesis, our system operates in the
context of a language with a rich equivalence relation on types (the theory of
regular expressions). We synthesize terms of a equivalent language and convert
those generated terms into our lens language. We prove the correctness of our
synthesis algorithm. We also demonstrate empirically that our new language
changes the synthesis problem from one that admits intractable solutions to one
that admits highly efficient solutions. We evaluate Optician on a benchmark
suite of 39 examples including both microbenchmarks and realistic examples
derived from other data management systems including Flash Fill, a tool for
synthesizing string transformations in spreadsheets, and Augeas, a tool for
bidirectional processing of Linux system configuration files.
| 1 | 0 | 0 | 0 | 0 | 0 |
Tuning the piezoelectric and mechanical properties of the AlN system via alloying with YN and BN | Recent advances in microelectromechanical systems often require
multifunctional materials, which are designed so as to optimize more than one
property. Using density functional theory calculations for alloyed nitride
systems, we illustrate how co-alloying a piezoelectric material (AlN) with
different nitrides helps tune both its piezoelectric and mechanical properties
simultaneously. Wurtzite AlN-YN alloys display increased piezoelectric response
with YN concentration, accompanied by mechanical softening along the
crystallographic c direction. Both effects increase the electromechanical
coupling coefficients relevant for transducers and actuators. Resonator
applications, however, require superior stiffness, thus leading to the need to
decouple the increased piezoelectric response from a softened lattice. We show
that co-alloying of AlN with YN and BN results in improved elastic properties
while retaining most of the piezoelectric enhancements from YN alloying. This
finding may lead to new avenues for tuning the design properties of
piezoelectrics through composition-property maps.
Keywords: piezoelectricity, electromechanical coupling, density functional
theory, co-alloying
| 0 | 1 | 0 | 0 | 0 | 0 |
Embedding for bulk systems using localized atomic orbitals | We present an embedding approach for semiconductors and insulators based on
or- bital rotations in the space of occupied Kohn-Sham orbitals. We have
implemented our approach in the popular VASP software package. We demonstrate
its power for defect structures in silicon and polaron formation in titania,
two challenging cases for conventional Kohn-Sham density functional theory.
| 0 | 1 | 0 | 0 | 0 | 0 |
Simple Surveys: Response Retrieval Inspired by Recommendation Systems | In the last decade, the use of simple rating and comparison surveys has
proliferated on social and digital media platforms to fuel recommendations.
These simple surveys and their extrapolation with machine learning algorithms
shed light on user preferences over large and growing pools of items, such as
movies, songs and ads. Social scientists have a long history of measuring
perceptions, preferences and opinions, often over smaller, discrete item sets
with exhaustive rating or ranking surveys. This paper introduces simple surveys
for social science application. We ran experiments to compare the predictive
accuracy of both individual and aggregate comparative assessments using four
types of simple surveys: pairwise comparisons and ratings on 2, 5 and
continuous point scales in three distinct contexts: perceived Safety of Google
Streetview Images, Likeability of Artwork, and Hilarity of Animal GIFs. Across
contexts, we find that continuous scale ratings best predict individual
assessments but consume the most time and cognitive effort. Binary choice
surveys are quick and perform best to predict aggregate assessments, useful for
collective decision tasks, but poorly predict personalized preferences, for
which they are currently used by Netflix to recommend movies. Pairwise
comparisons, by contrast, perform well to predict personal assessments, but
poorly predict aggregate assessments despite being widely used to crowdsource
ideas and collective preferences. We demonstrate how findings from these
surveys can be visualized in a low-dimensional space that reveals distinct
respondent interpretations of questions asked in each context. We conclude by
reflecting on differences between sparse, incomplete simple surveys and their
traditional survey counterparts in terms of efficiency, information elicited
and settings in which knowing less about more may be critical for social
science.
| 1 | 0 | 0 | 1 | 0 | 0 |
A Reduction for the Distinct Distances Problem in ${\mathbb R}^d$ | We introduce a reduction from the distinct distances problem in ${\mathbb
R}^d$ to an incidence problem with $(d-1)$-flats in ${\mathbb R}^{2d-1}$.
Deriving the conjectured bound for this incidence problem (the bound predicted
by the polynomial partitioning technique) would lead to a tight bound for the
distinct distances problem in ${\mathbb R}^d$. The reduction provides a large
amount of information about the $(d-1)$-flats, and a framework for deriving
more restrictions that these satisfy. Our reduction is based on introducing a
Lie group that is a double cover of the special Euclidean group. This group can
be seen as a variant of the Spin group, and a large part of our analysis
involves studying its properties.
| 0 | 0 | 1 | 0 | 0 | 0 |
Local electronic properties of the graphene-protected giant Rashba-split BiAg$_2$ surface | We report the preparation of the interface between graphene and the strong
Rashba-split BiAg$_2$ surface alloy and investigatigation of its structure as
well as the electronic properties by means of scanning tunneling
microscopy/spectroscopy and density functional theory calculations. Upon
evaluation of the quasiparticle interference patterns the unpertrubated linear
dispersion for the $\pi$ band of $n$-doped graphene is observed. Our results
also reveal the intact nature of the giant Rashba-split surface states of the
BiAg$_2$ alloy, which demonstrate only a moderate downward energy shift upon
the presence of graphene. This effect is explained in the framework of density
functional theory by an inward relaxation of the Bi atoms at the interface and
subsequent delocalisation of the wave function of the surface states. Our
findings demonstrate a realistic pathway to prepare a graphene protected giant
Rashba-split BiAg$_2$ for possible spintronic applications.
| 0 | 1 | 0 | 0 | 0 | 0 |
Particle trapping and conveying using an optical Archimedes' screw | Trapping and manipulation of particles using laser beams has become an
important tool in diverse fields of research. In recent years, particular
interest is given to the problem of conveying optically trapped particles over
extended distances either down or upstream the direction of the photons
momentum flow. Here, we propose and demonstrate experimentally an optical
analogue of the famous Archimedes' screw where the rotation of a
helical-intensity beam is transferred to the axial motion of optically-trapped
micro-meter scale airborne carbon based particles. With this optical screw,
particles were easily conveyed with controlled velocity and direction, upstream
or downstream the optical flow, over a distance of half a centimeter. Our
results offer a very simple optical conveyor that could be adapted to a wide
range of optical trapping scenarios.
| 0 | 1 | 0 | 0 | 0 | 0 |
Scalable Inference for Nested Chinese Restaurant Process Topic Models | Nested Chinese Restaurant Process (nCRP) topic models are powerful
nonparametric Bayesian methods to extract a topic hierarchy from a given text
corpus, where the hierarchical structure is automatically determined by the
data. Hierarchical Latent Dirichlet Allocation (hLDA) is a popular instance of
nCRP topic models. However, hLDA has only been evaluated at small scale,
because the existing collapsed Gibbs sampling and instantiated weight
variational inference algorithms either are not scalable or sacrifice inference
quality with mean-field assumptions. Moreover, an efficient distributed
implementation of the data structures, such as dynamically growing count
matrices and trees, is challenging.
In this paper, we propose a novel partially collapsed Gibbs sampling (PCGS)
algorithm, which combines the advantages of collapsed and instantiated weight
algorithms to achieve good scalability as well as high model quality. An
initialization strategy is presented to further improve the model quality.
Finally, we propose an efficient distributed implementation of PCGS through
vectorization, pre-processing, and a careful design of the concurrent data
structures and communication strategy.
Empirical studies show that our algorithm is 111 times more efficient than
the previous open-source implementation for hLDA, with comparable or even
better model quality. Our distributed implementation can extract 1,722 topics
from a 131-million-document corpus with 28 billion tokens, which is 4-5 orders
of magnitude larger than the previous largest corpus, with 50 machines in 7
hours.
| 1 | 0 | 0 | 1 | 0 | 0 |
Equations of $\,\overline{M}_{0,n}$ | Following work of Keel and Tevelev, we give explicit polynomials in the Cox
ring of $\mathbb{P}^1\times\cdots\times\mathbb{P}^{n-3}$ that, conjecturally,
determine $\overline{M}_{0,n}$ as a subscheme. Using Macaulay2, we prove that
these equations generate the ideal for $n=5, 6, 7, 8$. For $n \leq 6$ we give a
cohomological proof that these polynomials realize $\overline{M}_{0,n}$ as a
projective variety, embedded in $\mathbb{P}^{(n-2)!-1}$ by the complete log
canonical linear system.
| 0 | 0 | 1 | 0 | 0 | 0 |
Lattice Boltzmann simulation of viscous fingering of immiscible displacement in a channel using an improved wetting scheme | An improved wetting boundary implementation strategy is proposed based on
lattice Boltzmann color-gradient model in this paper. In this strategy, an
extra interface force condition is demonstrated based on the diffuse interface
assumption and is employed in contact line region. It has been validated by
three benchmark problems: static droplet wetting on a flat surface and a curved
surface, and dynamic capillary filling. Good performances are shown in all
three cases. Relied on the strict validation to our scheme, the viscous
fingering phenomenon of immiscible fluids displacement in a two-dimensional
channel has been restudied in this paper. High viscosity ratio, wide range
contact angle, accurate moving contact line and mutual independence between
surface tension and viscosity are the obvious advantages of our model. We find
the linear relationship between the contact angle and displacement velocity or
variation of finger length. When the viscosity ratio is smaller than 20, the
displacement velocity is increasing with increasing viscosity ratio and
reducing capillary number, and when the viscosity ratio is larger than 20, the
displacement velocity tends to a specific constant. A similar conclusion is
obtained on the variation of finger length.
| 0 | 1 | 0 | 0 | 0 | 0 |
Implementation of Control Strategies for Sterile Insect Techniques | In this paper, we propose a sex-structured entomological model that serves as
a basis for design of control strategies relying on releases of sterile male
mosquitoes (Aedes spp) and aiming at elimination of the wild vector population
in some target locality. We consider different types of releases (constant and
periodic impulsive), providing necessary conditions to reach elimination.
However, the main part of the paper is focused on the study of the periodic
impulsive control in different situations. When the size of wild mosquito
population cannot be assessed in real time, we propose the so-called open-loop
control strategy that relies on periodic impulsive releases of sterile males
with constant release size. Under this control mode, global convergence towards
the mosquito-free equilibrium is proved on the grounds of sufficient condition
that relates the size and frequency of releases. If periodic assessments
(either synchronized with releases or more sparse) of the wild population size
are available in real time, we propose the so-called closed-loop control
strategy, which is adjustable in accordance with reliable estimations of the
wild population sizes. Under this control mode, global convergence to the
mosquito-free equilibrium is proved on the grounds of another sufficient
condition that relates not only the size and frequency of periodic releases but
also the frequency of sparse measurements taken on wild populations. Finally,
we propose a mixed control strategy that combines open-loop and closed-loop
strategies. This control mode renders the best result, in terms of overall time
needed to reach elimination and the number of releases to be effectively
carried out during the whole release campaign, while requiring for a reasonable
amount of released sterile insects.
| 0 | 0 | 0 | 0 | 1 | 0 |
Convergence and submeasures in Boolean algebras | A Boolean algebra carries a strictly positive exhaustive submeasure if and
only if it has a sequential topology that is uniformly Frechet.
| 0 | 0 | 1 | 0 | 0 | 0 |
Characterizing a CCD detector for astronomical purposes: OAUNI Project | This work verifies the instrumental characteristics of the CCD detector which
is part of the UNI astronomical observatory. We measured the linearity of the
CCD detector of the SBIG STXL6303E camera, along with the associated gain and
readout noise. The linear response to the incident light of the detector is
extremely linear (R2 =99.99%), its effective gain is 1.65 +/- 0.01 e-/ADU and
its readout noise is 12.2 e-. These values are in agreement with the
manufacturer. We confirm that this detector is extremely precise to make
measurements for astronomical purposes.
| 0 | 1 | 0 | 0 | 0 | 0 |
Genetic algorithm-based control of birefringent filtering for self-tuning, self-pulsing fiber lasers | Polarization-based filtering in fiber lasers is well-known to enable spectral
tunability and a wide range of dynamical operating states. This effect is
rarely exploited in practical systems, however, because optimization of cavity
parameters is non-trivial and evolves due to environmental sensitivity. Here,
we report a genetic algorithm-based approach, utilizing electronic control of
the cavity transfer function, to autonomously achieve broad wavelength tuning
and the generation of Q-switched pulses with variable repetition rate and
duration. The practicalities and limitations of simultaneous spectral and
temporal self-tuning from a simple fiber laser are discussed, paving the way to
on-demand laser properties through algorithmic control and machine learning
schemes.
| 0 | 1 | 0 | 0 | 0 | 0 |
Public discourse and news consumption on online social media: A quantitative, cross-platform analysis of the Italian Referendum | The rising attention to the spreading of fake news and unsubstantiated rumors
on online social media and the pivotal role played by confirmation bias led
researchers to investigate different aspects of the phenomenon. Experimental
evidence showed that confirmatory information gets accepted even if containing
deliberately false claims while dissenting information is mainly ignored or
might even increase group polarization. It seems reasonable that, to address
misinformation problem properly, we have to understand the main determinants
behind content consumption and the emergence of narratives on online social
media. In this paper we address such a challenge by focusing on the discussion
around the Italian Constitutional Referendum by conducting a quantitative,
cross-platform analysis on both Facebook public pages and Twitter accounts. We
observe the spontaneous emergence of well-separated communities on both
platforms. Such a segregation is completely spontaneous, since no
categorization of contents was performed a priori. By exploring the dynamics
behind the discussion, we find that users tend to restrict their attention to a
specific set of Facebook pages/Twitter accounts. Finally, taking advantage of
automatic topic extraction and sentiment analysis techniques, we are able to
identify the most controversial topics inside and across both platforms. We
measure the distance between how a certain topic is presented in the
posts/tweets and the related emotional response of users. Our results provide
interesting insights for the understanding of the evolution of the core
narratives behind different echo chambers and for the early detection of
massive viral phenomena around false claims.
| 1 | 1 | 0 | 0 | 0 | 0 |
Testing small scale gravitational wave detectors with dynamical mass distributions | The recent discovery of gravitational waves by the LIGO-Virgo collaboration
created renewed interest in the investigation of alternative gravitational
detector designs, such as small scale resonant detectors. In this article, it
is shown how proposed small scale detectors can be tested by generating
dynamical gravitational fields with appropriate distributions of moving masses.
A series of interesting experiments will be possible with this setup. In
particular, small scale detectors can be tested very early in the development
phase and tests can be used to progress quickly in their development. This
could contribute to the emerging field of gravitational wave astronomy.
| 0 | 1 | 0 | 0 | 0 | 0 |
AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms | Approximate probabilistic inference algorithms are central to many fields.
Examples include sequential Monte Carlo inference in robotics, variational
inference in machine learning, and Markov chain Monte Carlo inference in
statistics. A key problem faced by practitioners is measuring the accuracy of
an approximate inference algorithm on a specific data set. This paper
introduces the auxiliary inference divergence estimator (AIDE), an algorithm
for measuring the accuracy of approximate inference algorithms. AIDE is based
on the observation that inference algorithms can be treated as probabilistic
models and the random variables used within the inference algorithm can be
viewed as auxiliary variables. This view leads to a new estimator for the
symmetric KL divergence between the approximating distributions of two
inference algorithms. The paper illustrates application of AIDE to algorithms
for inference in regression, hidden Markov, and Dirichlet process mixture
models. The experiments show that AIDE captures the qualitative behavior of a
broad class of inference algorithms and can detect failure modes of inference
algorithms that are missed by standard heuristics.
| 1 | 0 | 0 | 1 | 0 | 0 |
Mining within-trial oscillatory brain dynamics to address the variability of optimized spatial filters | Data-driven spatial filtering algorithms optimize scores such as the contrast
between two conditions to extract oscillatory brain signal components. Most
machine learning approaches for filter estimation, however, disregard
within-trial temporal dynamics and are extremely sensitive to changes in
training data and involved hyperparameters. This leads to highly variable
solutions and impedes the selection of a suitable candidate for,
e.g.,~neurotechnological applications. Fostering component introspection, we
propose to embrace this variability by condensing the functional signatures of
a large set of oscillatory components into homogeneous clusters, each
representing specific within-trial envelope dynamics.
The proposed method is exemplified by and evaluated on a complex hand force
task with a rich within-trial structure. Based on electroencephalography data
of 18 healthy subjects, we found that the components' distinct temporal
envelope dynamics are highly subject-specific. On average, we obtained seven
clusters per subject, which were strictly confined regarding their underlying
frequency bands. As the analysis method is not limited to a specific spatial
filtering algorithm, it could be utilized for a wide range of
neurotechnological applications, e.g., to select and monitor functionally
relevant features for brain-computer interface protocols in stroke
rehabilitation.
| 0 | 0 | 0 | 1 | 1 | 0 |
Neural Architecture Search with Bayesian Optimisation and Optimal Transport | Bayesian Optimisation (BO) refers to a class of methods for global
optimisation of a function $f$ which is only accessible via point evaluations.
It is typically used in settings where $f$ is expensive to evaluate. A common
use case for BO in machine learning is model selection, where it is not
possible to analytically model the generalisation performance of a statistical
model, and we resort to noisy and expensive training and validation procedures
to choose the best model. Conventional BO methods have focused on Euclidean and
categorical domains, which, in the context of model selection, only permits
tuning scalar hyper-parameters of machine learning algorithms. However, with
the surge of interest in deep learning, there is an increasing demand to tune
neural network \emph{architectures}. In this work, we develop NASBOT, a
Gaussian process based BO framework for neural architecture search. To
accomplish this, we develop a distance metric in the space of neural network
architectures which can be computed efficiently via an optimal transport
program. This distance might be of independent interest to the deep learning
community as it may find applications outside of BO. We demonstrate that NASBOT
outperforms other alternatives for architecture search in several cross
validation based model selection tasks on multi-layer perceptrons and
convolutional neural networks.
| 0 | 0 | 0 | 1 | 0 | 0 |
An improved parametric model for hysteresis loop approximation | A number of improvements have been added to the existing analytical model of
hysteresis loop defined in parametric form. In particular, three phase shifts
are included in the model, which permits to tilt the hysteresis loop smoothly
by the required angle at the split point as well as to smoothly change the
curvature of the loop. As a result, the error of approximation of a hysteresis
loop by the improved model does not exceed 1%, which is several times less than
the error of the existing model. The improved model is capable of approximating
most of the known types of rate-independent symmetrical hysteresis loops
encountered in the practice of physical measurements. The model allows building
smooth, piecewise-linear, hybrid, minor, mirror-reflected, inverse, reverse,
double and triple loops. One of the possible applications of the model
developed is linearization of a probe microscope piezoscanner. The improved
model can be found useful for the tasks of simulation of scientific instruments
that contain hysteresis elements.
| 1 | 1 | 1 | 0 | 0 | 0 |
Kidnapping Model: An Extension of Selten's Game | Selten's game is a kidnapping model where the probability of capturing the
kidnapper is independent of whether the hostage has been released or executed.
Most often, in view of the elevated sensitivities involved, authorities put
greater effort and resources into capturing the kidnapper if the hostage has
been executed, in contrast to the case when a ransom is paid to secure the
hostage's release. In this paper, we study the asymmetric game when the
probability of capturing the kidnapper depends on whether the hostage has been
executed or not and find a new uniquely determined perfect equilibrium point in
Selten's game.
| 1 | 0 | 0 | 0 | 0 | 0 |
To prune, or not to prune: exploring the efficacy of pruning for model compression | Model pruning seeks to induce sparsity in a deep neural network's various
connection matrices, thereby reducing the number of nonzero-valued parameters
in the model. Recent reports (Han et al., 2015; Narang et al., 2017) prune deep
networks at the cost of only a marginal loss in accuracy and achieve a sizable
reduction in model size. This hints at the possibility that the baseline models
in these experiments are perhaps severely over-parameterized at the outset and
a viable alternative for model compression might be to simply reduce the number
of hidden units while maintaining the model's dense connection structure,
exposing a similar trade-off in model size and accuracy. We investigate these
two distinct paths for model compression within the context of energy-efficient
inference in resource-constrained environments and propose a new gradual
pruning technique that is simple and straightforward to apply across a variety
of models/datasets with minimal tuning and can be seamlessly incorporated
within the training process. We compare the accuracy of large, but pruned
models (large-sparse) and their smaller, but dense (small-dense) counterparts
with identical memory footprint. Across a broad range of neural network
architectures (deep CNNs, stacked LSTM, and seq2seq LSTM models), we find
large-sparse models to consistently outperform small-dense models and achieve
up to 10x reduction in number of non-zero parameters with minimal loss in
accuracy.
| 1 | 0 | 0 | 1 | 0 | 0 |
Connecting Software Metrics across Versions to Predict Defects | Accurate software defect prediction could help software practitioners
allocate test resources to defect-prone modules effectively and efficiently. In
the last decades, much effort has been devoted to build accurate defect
prediction models, including developing quality defect predictors and modeling
techniques. However, current widely used defect predictors such as code metrics
and process metrics could not well describe how software modules change over
the project evolution, which we believe is important for defect prediction. In
order to deal with this problem, in this paper, we propose to use the
Historical Version Sequence of Metrics (HVSM) in continuous software versions
as defect predictors. Furthermore, we leverage Recurrent Neural Network (RNN),
a popular modeling technique, to take HVSM as the input to build software
prediction models. The experimental results show that, in most cases, the
proposed HVSM-based RNN model has a significantly better effort-aware ranking
effectiveness than the commonly used baseline models.
| 1 | 0 | 0 | 0 | 0 | 0 |
Semiparametric panel data models using neural networks | This paper presents an estimator for semiparametric models that uses a
feed-forward neural network to fit the nonparametric component. Unlike many
methodologies from the machine learning literature, this approach is suitable
for longitudinal/panel data. It provides unbiased estimation of the parametric
component of the model, with associated confidence intervals that have
near-nominal coverage rates. Simulations demonstrate (1) efficiency, (2) that
parametric estimates are unbiased, and (3) coverage properties of estimated
intervals. An application section demonstrates the method by predicting
county-level corn yield using daily weather data from the period 1981-2015,
along with parametric time trends representing technological change. The method
is shown to out-perform linear methods such as OLS and ridge/lasso, as well as
random forest. The procedures described in this paper are implemented in the R
package panelNNET.
| 0 | 0 | 0 | 1 | 0 | 0 |
Split and Rephrase | We propose a new sentence simplification task (Split-and-Rephrase) where the
aim is to split a complex sentence into a meaning preserving sequence of
shorter sentences. Like sentence simplification, splitting-and-rephrasing has
the potential of benefiting both natural language processing and societal
applications. Because shorter sentences are generally better processed by NLP
systems, it could be used as a preprocessing step which facilitates and
improves the performance of parsers, semantic role labellers and machine
translation systems. It should also be of use for people with reading
disabilities because it allows the conversion of longer sentences into shorter
ones. This paper makes two contributions towards this new task. First, we
create and make available a benchmark consisting of 1,066,115 tuples mapping a
single complex sentence to a sequence of sentences expressing the same meaning.
Second, we propose five models (vanilla sequence-to-sequence to
semantically-motivated models) to understand the difficulty of the proposed
task.
| 1 | 0 | 0 | 0 | 0 | 0 |
Constructive Euler hydrodynamics for one-dimensional attractive particle systems | We review a (constructive) approach first introduced in [6] and further
developed in [7, 8, 38, 9] for hydrodynamic limits of asymmetric attractive
particle systems, in a weak or in a strong (that is, almost sure) sense, in an
homogeneous or in a quenched disordered setting.
| 0 | 0 | 1 | 0 | 0 | 0 |
Cyber Insurance for Heterogeneous Wireless Networks | Heterogeneous wireless networks (HWNs) composed of densely deployed base
stations of different types with various radio access technologies have become
a prevailing trend to accommodate ever-increasing traffic demand in enormous
volume. Nowadays, users rely heavily on HWNs for ubiquitous network access that
contains valuable and critical information such as financial transactions,
e-health, and public safety. Cyber risks, representing one of the most
significant threats to network security and reliability, are increasing in
severity. To address this problem, this article introduces the concept of cyber
insurance to transfer the cyber risk (i.e., service outage, as a consequence of
cyber risks in HWNs) to a third party insurer. Firstly, a review of the
enabling technologies for HWNs and their vulnerabilities to cyber risks is
presented. Then, the fundamentals of cyber insurance are introduced, and
subsequently, a cyber insurance framework for HWNs is presented. Finally, open
issues are discussed and the challenges are highlighted for integrating cyber
insurance as a service of next generation HWNs.
| 1 | 0 | 0 | 0 | 0 | 0 |
Combining Contrast Invariant L1 Data Fidelities with Nonlinear Spectral Image Decomposition | This paper focuses on multi-scale approaches for variational methods and
corresponding gradient flows. Recently, for convex regularization functionals
such as total variation, new theory and algorithms for nonlinear eigenvalue
problems via nonlinear spectral decompositions have been developed. Those
methods open new directions for advanced image filtering. However, for an
effective use in image segmentation and shape decomposition, a clear
interpretation of the spectral response regarding size and intensity scales is
needed but lacking in current approaches. In this context, $L^1$ data
fidelities are particularly helpful due to their interesting multi-scale
properties such as contrast invariance. Hence, the novelty of this work is the
combination of $L^1$-based multi-scale methods with nonlinear spectral
decompositions. We compare $L^1$ with $L^2$ scale-space methods in view of
spectral image representation and decomposition. We show that the contrast
invariant multi-scale behavior of $L^1-TV$ promotes sparsity in the spectral
response providing more informative decompositions. We provide a numerical
method and analyze synthetic and biomedical images at which decomposition leads
to improved segmentation.
| 1 | 0 | 1 | 0 | 0 | 0 |
PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks | We present a novel approach for the prediction of anticancer compound
sensitivity by means of multi-modal attention-based neural networks (PaccMann).
In our approach, we integrate three key pillars of drug sensitivity, namely,
the molecular structure of compounds, transcriptomic profiles of cancer cells
as well as prior knowledge about interactions among proteins within cells. Our
models ingest a drug-cell pair consisting of SMILES encoding of a compound and
the gene expression profile of a cancer cell and predicts an IC50 sensitivity
value. Gene expression profiles are encoded using an attention-based encoding
mechanism that assigns high weights to the most informative genes. We present
and study three encoders for SMILES string of compounds: 1) bidirectional
recurrent 2) convolutional 3) attention-based encoders. We compare our devised
models against a baseline model that ingests engineered fingerprints to
represent the molecular structure. We demonstrate that using our
attention-based encoders, we can surpass the baseline model. The use of
attention-based encoders enhance interpretability and enable us to identify
genes, bonds and atoms that were used by the network to make a prediction.
| 0 | 0 | 0 | 0 | 1 | 0 |
Evaporation and scattering of momentum- and velocity-dependent dark matter in the Sun | Dark matter with momentum- or velocity-dependent interactions with nuclei has
shown significant promise for explaining the so-called Solar Abundance Problem,
a longstanding discrepancy between solar spectroscopy and helioseismology. The
best-fit models are all rather light, typically with masses in the range of 3-5
GeV. This is exactly the mass range where dark matter evaporation from the Sun
can be important, but to date no detailed calculation of the evaporation of
such models has been performed. Here we carry out this calculation, for the
first time including arbitrary velocity- and momentum-dependent interactions,
thermal effects, and a completely general treatment valid from the optically
thin limit all the way through to the optically thick regime. We find that
depending on the dark matter mass, interaction strength and type, the mass
below which evaporation is relevant can vary from 1 to 4 GeV. This has the
effect of weakening some of the better-fitting solutions to the Solar Abundance
Problem, but also improving a number of others. As a by-product, we also
provide an improved derivation of the capture rate that takes into account
thermal and optical depth effects, allowing the standard result to be smoothly
matched to the well-known saturation limit.
| 0 | 1 | 0 | 0 | 0 | 0 |
ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features | With the rapid development of spaceborne imaging techniques, object detection
in optical remote sensing imagery has drawn much attention in recent decades.
While many advanced works have been developed with powerful learning
algorithms, the incomplete feature representation still cannot meet the demand
for effectively and efficiently handling image deformations, particularly
objective scaling and rotation. To this end, we propose a novel object
detection framework, called optical remote sensing imagery detector (ORSIm
detector), integrating diverse channel features extraction, feature learning,
fast image pyramid matching, and boosting strategy. ORSIm detector adopts a
novel spatial-frequency channel feature (SFCF) by jointly considering the
rotation-invariant channel features constructed in frequency domain and the
original spatial channel features (e.g., color channel, gradient magnitude).
Subsequently, we refine SFCF using learning-based strategy in order to obtain
the high-level or semantically meaningful features. In the test phase, we
achieve a fast and coarsely-scaled channel computation by mathematically
estimating a scaling factor in the image domain. Extensive experimental results
conducted on the two different airborne datasets are performed to demonstrate
the superiority and effectiveness in comparison with previous state-of-the-art
methods.
| 1 | 0 | 0 | 0 | 0 | 0 |
Multi-Path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained "Hard Faces" | Large-scale variations still pose a challenge in unconstrained face
detection. To the best of our knowledge, no current face detection algorithm
can detect a face as large as 800 x 800 pixels while simultaneously detecting
another one as small as 8 x 8 pixels within a single image with equally high
accuracy. We propose a two-stage cascaded face detection framework, Multi-Path
Region-based Convolutional Neural Network (MP-RCNN), that seamlessly combines a
deep neural network with a classic learning strategy, to tackle this challenge.
The first stage is a Multi-Path Region Proposal Network (MP-RPN) that proposes
faces at three different scales. It simultaneously utilizes three parallel
outputs of the convolutional feature maps to predict multi-scale candidate face
regions. The "atrous" convolution trick (convolution with up-sampled filters)
and a newly proposed sampling layer for "hard" examples are embedded in MP-RPN
to further boost its performance. The second stage is a Boosted Forests
classifier, which utilizes deep facial features pooled from inside the
candidate face regions as well as deep contextual features pooled from a larger
region surrounding the candidate face regions. This step is included to further
remove hard negative samples. Experiments show that this approach achieves
state-of-the-art face detection performance on the WIDER FACE dataset "hard"
partition, outperforming the former best result by 9.6% for the Average
Precision.
| 1 | 0 | 0 | 0 | 0 | 0 |
Naturally occurring $^{32}$Si and low-background silicon dark matter detectors | The naturally occurring radioisotope $^{32}$Si represents a potentially
limiting background in future dark matter direct-detection experiments. We
investigate sources of $^{32}$Si and the vectors by which it comes to reside in
silicon crystals used for fabrication of radiation detectors. We infer that the
$^{32}$Si concentration in commercial single-crystal silicon is likely
variable, dependent upon the specific geologic and hydrologic history of the
source (or sources) of silicon "ore" and the details of the silicon-refinement
process. The silicon production industry is large, highly segmented by refining
step, and multifaceted in terms of final product type, from which we conclude
that production of $^{32}$Si-mitigated crystals requires both targeted silicon
material selection and a dedicated refinement-through-crystal-production
process. We review options for source material selection, including quartz from
an underground source and silicon isotopically reduced in $^{32}$Si. To
quantitatively evaluate the $^{32}$Si content in silicon metal and precursor
materials, we propose analytic methods employing chemical processing and
radiometric measurements. Ultimately, it appears feasible to produce silicon
detectors with low levels of $^{32}$Si, though significant assay method
development is required to validate this claim and thereby enable a quality
assurance program during an actual controlled silicon-detector production
cycle.
| 0 | 1 | 0 | 0 | 0 | 0 |
Synergies between Exoplanet Surveys and Variable Star Research | With the discovery of the first transiting extrasolar planetary system back
to 1999, a great number of projects started to hunt for other similar systems.
Because of the incidence rate of such systems was unknown and the length of the
shallow transit events is only a few percent of the orbital period, the goal
was to monitor continuously as many stars as possible for at least a period of
a few months. Small aperture, large field of view automated telescope systems
have been installed with a parallel development of new data reduction and
analysis methods, leading to better than 1% per data point precision for
thousands of stars. With the successful launch of the photometric satellites
CoRot and Kepler, the precision increased further by one-two orders of
magnitude. Millions of stars have been analyzed and searched for transits. In
the history of variable star astronomy this is the biggest undertaking so far,
resulting in photometric time series inventories immensely valuable for the
whole field. In this review we briefly discuss the methods of data analysis
that were inspired by the main science driver of these surveys and highlight
some of the most interesting variable star results that impact the field of
variable star astronomy.
| 0 | 1 | 0 | 0 | 0 | 0 |
Improved Set-based Symbolic Algorithms for Parity Games | Graph games with {\omega}-regular winning conditions provide a mathematical
framework to analyze a wide range of problems in the analysis of reactive
systems and programs (such as the synthesis of reactive systems, program
repair, and the verification of branching time properties). Parity conditions
are canonical forms to specify {\omega}-regular winning conditions. Graph games
with parity conditions are equivalent to {\mu}-calculus model checking, and
thus a very important algorithmic problem. Symbolic algorithms are of great
significance because they provide scalable algorithms for the analysis of large
finite-state systems, as well as algorithms for the analysis of infinite-state
systems with finite quotient. A set-based symbolic algorithm uses the basic set
operations and the one-step predecessor operators. We consider graph games with
$n$ vertices and parity conditions with $c$ priorities. While many explicit
algorithms exist for graph games with parity conditions, for set-based symbolic
algorithms there are only two algorithms (notice that we use space to refer to
the number of sets stored by a symbolic algorithm): (a) the basic algorithm
that requires $O(n^c)$ symbolic operations and linear space; and (b) an
improved algorithm that requires $O(n^{c/2+1})$ symbolic operations but also
$O(n^{c/2+1})$ space (i.e., exponential space). In this work we present two
set-based symbolic algorithms for parity games: (a) our first algorithm
requires $O(n^{c/2+1})$ symbolic operations and only requires linear space; and
(b) developing on our first algorithm, we present an algorithm that requires
$O(n^{c/3+1})$ symbolic operations and only linear space. We also present the
first linear space set-based symbolic algorithm for parity games that requires
at most a sub-exponential number of symbolic operations.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Syllable-based Technique for Word Embeddings of Korean Words | Word embedding has become a fundamental component to many NLP tasks such as
named entity recognition and machine translation. However, popular models that
learn such embeddings are unaware of the morphology of words, so it is not
directly applicable to highly agglutinative languages such as Korean. We
propose a syllable-based learning model for Korean using a convolutional neural
network, in which word representation is composed of trained syllable vectors.
Our model successfully produces morphologically meaningful representation of
Korean words compared to the original Skip-gram embeddings. The results also
show that it is quite robust to the Out-of-Vocabulary problem.
| 1 | 0 | 0 | 0 | 0 | 0 |
Existence of Evolutionarily Stable Strategies Remains Hard to Decide for a Wide Range of Payoff Values | The concept of an evolutionarily stable strategy (ESS), introduced by Smith
and Price, is a refinement of Nash equilibrium in 2-player symmetric games in
order to explain counter-intuitive natural phenomena, whose existence is not
guaranteed in every game. The problem of deciding whether a game possesses an
ESS has been shown to be $\Sigma_{2}^{P}$-complete by Conitzer using the
preceding important work by Etessami and Lochbihler. The latter, among other
results, proved that deciding the existence of ESS is both NP-hard and
coNP-hard. In this paper we introduce a "reduction robustness" notion and we
show that deciding the existence of an ESS remains coNP-hard for a wide range
of games even if we arbitrarily perturb within some intervals the payoff values
of the game under consideration. In contrast, ESS exist almost surely for large
games with random and independent payoffs chosen from the same distribution.
| 1 | 0 | 0 | 0 | 0 | 0 |
Compound Poisson approximation to estimate the Lévy density | We construct an estimator of the Lévy density of a pure jump Lévy
process, possibly of infinite variation, from the discrete observation of one
trajectory at high frequency. The novelty of our procedure is that we directly
estimate the Lévy density relying on a pathwise strategy, whereas existing
procedures rely on spectral techniques. By taking advantage of a compound
Poisson approximation of the Lévy density, we circumvent the use of spectral
techniques and in particular of the Lévy-Khintchine formula. A linear wavelet
estimators is built and its performance is studied in terms of $L_p$ loss
functions, $p\geq 1$, over Besov balls. The resulting rates are minimax-optimal
for a large class of Lévy processes. We discuss the robustness of the
procedure to the presence of a Brownian part and to the estimation set getting
close to the critical value 0.
| 0 | 0 | 1 | 1 | 0 | 0 |
On the non-vanishing of certain Dirichlet series | Given $k\in\mathbb N$, we study the vanishing of the Dirichlet series
$$D_k(s,f):=\sum_{n\geq1} d_k(n)f(n)n^{-s}$$ at the point $s=1$, where $f$ is a
periodic function modulo a prime $p$. We show that if $(k,p-1)=1$ or
$(k,p-1)=2$ and $p\equiv 3\mod 4$, then there are no odd rational-valued
functions $f\not\equiv 0$ such that $D_k(1,f)=0$, whereas in all other cases
there are examples of odd functions $f$ such that $D_k(1,f)=0$.
As a consequence, we obtain, for example, that the set of values
$L(1,\chi)^2$, where $\chi$ ranges over odd characters mod $p$, are linearly
independent over $\mathbb Q$.
| 0 | 0 | 1 | 0 | 0 | 0 |
On discrete homology of a free pro-$p$-group | For a prime $p$, let $\hat F_p$ be a finitely generated free pro-$p$-group of
rank $\geq 2$. We show that the second discrete homology group $H_2(\hat
F_p,\mathbb Z/p)$ is an uncountable $\mathbb Z/p$-vector space. This answers a
problem of A.K. Bousfield.
| 0 | 0 | 1 | 0 | 0 | 0 |
Conceptual Modeling of Inventory Management Processes as a Thinging Machine | A control model is typically classified into three forms: conceptual,
mathematical and simulation (computer). This paper analyzes a conceptual
modeling application with respect to an inventory management system. Today,
most organizations utilize computer systems for inventory control that provide
protection when interruptions or breakdowns occur within work processes.
Modeling the inventory processes is an active area of research that utilizes
many diagrammatic techniques, including data flow diagrams, Universal Modeling
Language (UML) diagrams and Integration DEFinition (IDEF). We claim that
current conceptual modeling frameworks lack uniform notions and have inability
to appeal to designers and analysts. We propose modeling an inventory system as
an abstract machine, called a Thinging Machine (TM), with five operations:
creation, processing, receiving, releasing and transferring. The paper provides
side-by-side contrasts of some existing examples of conceptual modeling
methodologies that apply to TM. Additionally, TM is applied in a case study of
an actual inventory system that uses IBM Maximo. The resulting conceptual
depictions point to the viability of FM as a valuable tool for developing a
high-level representation of inventory processes.
| 1 | 0 | 0 | 0 | 0 | 0 |
Points2Pix: 3D Point-Cloud to Image Translation using conditional Generative Adversarial Networks | We present the first approach for 3D point-cloud to image translation based
on conditional Generative Adversarial Networks (cGAN). The model handles
multi-modal information sources from different domains, i.e. raw point-sets and
images. The generator is capable of processing three conditions, whereas the
point-cloud is encoded as raw point-set and camera projection. An image
background patch is used as constraint to bias environmental texturing. A
global approximation function within the generator is directly applied on the
point-cloud (Point-Net). Hence, the representative learning model incorporates
global 3D characteristics directly at the latent feature space. Conditions are
used to bias the background and the viewpoint of the generated image. This
opens up new ways in augmenting or texturing 3D data to aim the generation of
fully individual images. We successfully evaluated our method on the Kitti and
SunRGBD dataset with an outstanding object detection inception score.
| 1 | 0 | 0 | 0 | 0 | 0 |
11 T Dipole for the Dispersion Suppressor Collimators | Chapter 11 in High-Luminosity Large Hadron Collider (HL-LHC) : Preliminary
Design Report. The Large Hadron Collider (LHC) is one of the largest scientific
instruments ever built. Since opening up a new energy frontier for exploration
in 2010, it has gathered a global user community of about 7,000 scientists
working in fundamental particle physics and the physics of hadronic matter at
extreme temperature and density. To sustain and extend its discovery potential,
the LHC will need a major upgrade in the 2020s. This will increase its
luminosity (rate of collisions) by a factor of five beyond the original design
value and the integrated luminosity (total collisions created) by a factor ten.
The LHC is already a highly complex and exquisitely optimised machine so this
upgrade must be carefully conceived and will require about ten years to
implement. The new configuration, known as High Luminosity LHC (HL-LHC), will
rely on a number of key innovations that push accelerator technology beyond its
present limits. Among these are cutting-edge 11-12 tesla superconducting
magnets, compact superconducting cavities for beam rotation with ultra-precise
phase control, new technology and physical processes for beam collimation and
300 metre-long high-power superconducting links with negligible energy
dissipation. The present document describes the technologies and components
that will be used to realise the project and is intended to serve as the basis
for the detailed engineering design of HL-LHC.
| 0 | 1 | 0 | 0 | 0 | 0 |
Spectral Analysis of Jet Substructure with Neural Networks: Boosted Higgs Case | Jets from boosted heavy particles have a typical angular scale which can be
used to distinguish them from QCD jets. We introduce a machine learning
strategy for jet substructure analysis using a spectral function on the angular
scale. The angular spectrum allows us to scan energy deposits over the angle
between a pair of particles in a highly visual way. We set up an artificial
neural network (ANN) to find out characteristic shapes of the spectra of the
jets from heavy particle decays. By taking the Higgs jets and QCD jets as
examples, we show that the ANN of the angular spectrum input has similar
performance to existing taggers. In addition, some improvement is seen when
additional extra radiations occur. Notably, the new algorithm automatically
combines the information of the multi-point correlations in the jet.
| 0 | 0 | 0 | 1 | 0 | 0 |
UTD-CRSS Submission for MGB-3 Arabic Dialect Identification: Front-end and Back-end Advancements on Broadcast Speech | This study presents systems submitted by the University of Texas at Dallas,
Center for Robust Speech Systems (UTD-CRSS) to the MGB-3 Arabic Dialect
Identification (ADI) subtask. This task is defined to discriminate between five
dialects of Arabic, including Egyptian, Gulf, Levantine, North African, and
Modern Standard Arabic. We develop multiple single systems with different
front-end representations and back-end classifiers. At the front-end level,
feature extraction methods such as Mel-frequency cepstral coefficients (MFCCs)
and two types of bottleneck features (BNF) are studied for an i-Vector
framework. As for the back-end level, Gaussian back-end (GB), and Generative
Adversarial Networks (GANs) classifiers are applied alternately. The best
submission (contrastive) is achieved for the ADI subtask with an accuracy of
76.94% by augmenting the randomly chosen part of the development dataset.
Further, with a post evaluation correction in the submitted system, final
accuracy is increased to 79.76%, which represents the best performance achieved
so far for the challenge on the test dataset.
| 1 | 0 | 0 | 0 | 0 | 0 |
Parametrization and Generation of Geological Models with Generative Adversarial Networks | One of the main challenges in the parametrization of geological models is the
ability to capture complex geological structures often observed in subsurface
fields. In recent years, Generative Adversarial Networks (GAN) were proposed as
an efficient method for the generation and parametrization of complex data,
showing state-of-the-art performances in challenging computer vision tasks such
as reproducing natural images (handwritten digits, human faces, etc.). In this
work, we study the application of Wasserstein GAN for the parametrization of
geological models. The effectiveness of the method is assessed for uncertainty
propagation tasks using several test cases involving different permeability
patterns and subsurface flow problems. Results show that GANs are able to
generate samples that preserve the multipoint statistical features of the
geological models both visually and quantitatively. The generated samples
reproduce both the geological structures and the flow properties of the
reference data.
| 0 | 1 | 0 | 1 | 0 | 0 |
Finite element procedures for computing normals and mean curvature on triangulated surfaces and their use for mesh refinement | In this paper we consider finite element approaches to computing the mean
curvature vector and normal at the vertices of piecewise linear triangulated
surfaces. In particular, we adopt a stabilization technique which allows for
first order $L^2$-convergence of the mean curvature vector and apply this
stabilization technique also to the computation of continuous, recovered,
normals using $L^2$-projections of the piecewise constant face normals.
Finally, we use our projected normals to define an adaptive mesh refinement
approach to geometry resolution where we also employ spline techniques to
reconstruct the surface before refinement. We compare or results to previously
proposed approaches.
| 0 | 0 | 1 | 0 | 0 | 0 |
Basis Adaptive Sample Efficient Polynomial Chaos (BASE-PC) | For a large class of orthogonal basis functions, there has been a recent
identification of expansion methods for computing accurate, stable
approximations of a quantity of interest. This paper presents, within the
context of uncertainty quantification, a practical implementation using basis
adaptation, and coherence motivated sampling, which under assumptions has
satisfying guarantees. This implementation is referred to as Basis Adaptive
Sample Efficient Polynomial Chaos (BASE-PC). A key component of this is the use
of anisotropic polynomial order which admits evolving global bases for
approximation in an efficient manner, leading to consistently stable
approximation for a practical class of smooth functionals. This fully adaptive,
non-intrusive method, requires no a priori information of the solution, and has
satisfying theoretical guarantees of recovery. A key contribution to stability
is the use of a presented correction sampling for coherence-optimal sampling in
order to improve stability and accuracy within the adaptive basis scheme.
Theoretically, the method may dramatically reduce the impact of dimensionality
in function approximation, and numerically the method is demonstrated to
perform well on problems with dimension up to 1000.
| 0 | 0 | 1 | 1 | 0 | 0 |
Maximum a posteriori estimation through simulated annealing for binary asteroid orbit determination | This paper considers a new method for the binary asteroid orbit determination
problem. The method is based on the Bayesian approach with a global
optimisation algorithm. The orbital parameters to be determined are modelled
through an a posteriori distribution made of a priori and likelihood terms. The
first term constrains the parameters space and it allows the introduction of
available knowledge about the orbit. The second term is based on given
observations and it allows us to use and compare different observational error
models. Once the a posteriori model is built, the estimator of the orbital
parameters is computed using a global optimisation procedure: the simulated
annealing algorithm. The maximum a posteriori (MAP) techniques are verified
using simulated and real data. The obtained results validate the proposed
method. The new approach guarantees independence of the initial parameters
estimation and theoretical convergence towards the global optimisation
solution. It is particularly useful in these situations, whenever a good
initial orbit estimation is difficult to get, whenever observations are not
well-sampled, and whenever the statistical behaviour of the observational
errors cannot be stated Gaussian like.
| 0 | 1 | 0 | 1 | 0 | 0 |
Extrapolating Expected Accuracies for Large Multi-Class Problems | The difficulty of multi-class classification generally increases with the
number of classes. Using data from a subset of the classes, can we predict how
well a classifier will scale with an increased number of classes? Under the
assumptions that the classes are sampled identically and independently from a
population, and that the classifier is based on independently learned scoring
functions, we show that the expected accuracy when the classifier is trained on
k classes is the (k-1)st moment of a certain distribution that can be estimated
from data. We present an unbiased estimation method based on the theory, and
demonstrate its application on a facial recognition example.
| 1 | 0 | 0 | 1 | 0 | 0 |
Deep Learning Methods for Efficient Large Scale Video Labeling | We present a solution to "Google Cloud and YouTube-8M Video Understanding
Challenge" that ranked 5th place. The proposed model is an ensemble of three
model families, two frame level and one video level. The training was performed
on augmented dataset, with cross validation.
| 1 | 0 | 0 | 1 | 0 | 0 |
RSI-CB: A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data | Remote sensing image classification is a fundamental task in remote sensing
image processing. Remote sensing field still lacks of such a large-scale
benchmark compared to ImageNet, Place2. We propose a remote sensing image
classification benchmark (RSI-CB) based on crowd-source data which is massive,
scalable, and diversity. Using crowdsource data, we can efficiently annotate
ground objects in remotes sensing image by point of interests, vectors data
from OSM or other crowd-source data. Based on this method, we construct a
worldwide large-scale benchmark for remote sensing image classification. In
this benchmark, there are two sub datasets with 256 * 256 and 128 * 128 size
respectively since different convolution neural networks requirement different
image size. The former sub dataset contains 6 categories with 35 subclasses
with total of more than 24,000 images; the later one contains 6 categories with
45 subclasses with total of more than 36,000 images. The six categories are
agricultural land, construction land and facilities, transportation and
facilities, water and water conservancy facilities, woodland and other land,
and each category has several subclasses. This classification system is defined
according to the national standard of land use classification in China, and is
inspired by the hierarchy mechanism of ImageNet. Finally, we have done a large
number of experiments to compare RSI-CB with SAT-4, UC-Merced datasets on
handcrafted features, such as such as SIFT, and classical CNN models, such as
AlexNet, VGG, GoogleNet, and ResNet. We also show CNN models trained by RSI-CB
have good performance when transfer to other dataset, i.e. UC-Merced, and good
generalization ability. The experiments show that RSI-CB is more suitable as a
benchmark for remote sensing image classification task than other ones in big
data era, and can be potentially used in practical applications.
| 1 | 0 | 0 | 0 | 0 | 0 |
Quantum groups, Yang-Baxter maps and quasi-determinants | For any quasi-triangular Hopf algebra, there exists the universal R-matrix,
which satisfies the Yang-Baxter equation. It is known that the adjoint action
of the universal R-matrix on the elements of the tensor square of the algebra
constitutes a quantum Yang-Baxter map, which satisfies the set-theoretic
Yang-Baxter equation. The map has a zero curvature representation among
L-operators defined as images of the universal R-matrix. We find that the zero
curvature representation can be solved by the Gauss decomposition of a product
of L-operators. Thereby obtained a quasi-determinant expression of the quantum
Yang-Baxter map associated with the quantum algebra $U_{q}(gl(n))$. Moreover,
the map is identified with products of quasi-Plücker coordinates over a
matrix composed of the L-operators. We also consider the quasi-classical limit,
where the underlying quantum algebra reduces to a Poisson algebra. The
quasi-determinant expression of the quantum Yang-Baxter map reduces to ratios
of determinants, which give a new expression of a classical Yang-Baxter map.
| 0 | 1 | 1 | 0 | 0 | 0 |
Low-level Active Visual Navigation: Increasing robustness of vision-based localization using potential fields | This paper proposes a low-level visual navigation algorithm to improve visual
localization of a mobile robot. The algorithm, based on artificial potential
fields, associates each feature in the current image frame with an attractive
or neutral potential energy, with the objective of generating a control action
that drives the vehicle towards the goal, while still favoring feature rich
areas within a local scope, thus improving the localization performance. One
key property of the proposed method is that it does not rely on mapping, and
therefore it is a lightweight solution that can be deployed on miniaturized
aerial robots, in which memory and computational power are major constraints.
Simulations and real experimental results using a mini quadrotor equipped with
a downward looking camera demonstrate that the proposed method can effectively
drive the vehicle to a designated goal through a path that prevents
localization failure.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Brownian Motion Model and Extreme Belief Machine for Modeling Sensor Data Measurements | As the title suggests, we will describe (and justify through the presentation
of some of the relevant mathematics) prediction methodologies for sensor
measurements. This exposition will mainly be concerned with the mathematics
related to modeling the sensor measurements.
| 1 | 0 | 0 | 0 | 0 | 0 |
On the nature of the magnetic phase transition in a Weyl semimetal | We investigate the nature of the magnetic phase transition induced by the
short-ranged electron-electron interactions in a Weyl semimetal by using the
perturbative renormalization-group method. We find that the critical point
associated with the quantum phase transition is characterized by a Gaussian
fixed point perturbed by a dangerously irrelevant operator. Although the
low-energy and long-distance physics is governed by a free theory, the
velocities of the fermionic quasiparticles and the magnetic excitations suffer
from nontrivial renormalization effects. In particular, their ratio approaches
one, which indicates an emergent Lorentz symmetry at low energies. We further
investigate the stability of the fixed point in the presence of weak disorder.
We show that while the fixed point is generally stable against weak disorder,
among those disorders that are consistent with the emergent chiral symmetry of
the clean system, a moderately strong random chemical potential and/or random
vector potential may induce a quantum phase transition towards a
disorder-dominated phase. We propose a global phase diagram of the Weyl
semimetal in the presence of both electron-electron interactions and disorder
based on our results.
| 0 | 1 | 0 | 0 | 0 | 0 |
Detecting Cyber-Physical Attacks in Additive Manufacturing using Digital Audio Signing | Additive Manufacturing (AM, or 3D printing) is a novel manufacturing
technology that is being adopted in industrial and consumer settings. However,
the reliance of this technology on computerization has raised various security
concerns. In this paper we address sabotage via tampering with the 3D printing
process. We present an object verification system using side-channel
emanations: sound generated by onboard stepper motors. The contributions of
this paper are following. We present two algorithms: one which generates a
master audio fingerprint for the unmodified printing process, and one which
computes the similarity between other print recordings and the master audio
fingerprint. We then evaluate the deviation due to tampering, focusing on the
detection of minimal tampering primitives. By detecting the deviation at the
time of its occurrence, we can stop the printing process for compromised
objects, thus save time and prevent material waste. We discuss impacts on the
method by aspects like background noise, or different audio recorder positions.
We further outline our vision with use cases incorporating our approach.
| 1 | 0 | 0 | 0 | 0 | 0 |
Short-wavelength out-of-band EUV emission from Sn laser-produced plasma | We present the results of spectroscopic measurements in the extreme
ultraviolet (EUV) regime (7-17 nm) of molten tin microdroplets illuminated by a
high-intensity 3-J, 60-ns Nd:YAG laser pulse. The strong 13.5 nm emission from
this laser-produced plasma is of relevance for next-generation nanolithography
machines. Here, we focus on the shorter wavelength features between 7 and 12 nm
which have so far remained poorly investigated despite their diagnostic
relevance. Using flexible atomic code calculations and local thermodynamic
equilibrium arguments, we show that the line features in this region of the
spectrum can be explained by transitions from high-lying configurations within
the Sn$^{8+}$-Sn$^{15+}$ ions. The dominant transitions for all ions but
Sn$^{8+}$ are found to be electric-dipole transitions towards the $n$=4 ground
state from the core-excited configuration in which a 4$p$ electron is promoted
to the 5$s$ sub-shell. Our results resolve some long-standing spectroscopic
issues and provide reliable charge state identification for Sn laser-produced
plasma, which could be employed as a useful tool for diagnostic purposes.
| 0 | 1 | 0 | 0 | 0 | 0 |
Compact Cardinals and Eight Values in Cichoń's Diagram | Assuming three strongly compact cardinals, it is consistent that \[ \aleph_1
< \mathrm{add}(\mathrm{null}) < \mathrm{cov}(\mathrm{null}) < \mathfrak{b} <
\mathfrak{d} < \mathrm{non}(\mathrm{null}) < \mathrm{cof}(\mathrm{null}) <
2^{\aleph_0}.\] Under the same assumption, it is consistent that \[ \aleph_1 <
\mathrm{add}(\mathrm{null}) < \mathrm{cov}(\mathrm{null}) <
\mathrm{non}(\mathrm{meager}) < \mathrm{cov}(\mathrm{meager}) <
\mathrm{non}(\mathrm{null}) < \mathrm{cof}(\mathrm{null}) < 2^{\aleph_0}.\]
| 0 | 0 | 1 | 0 | 0 | 0 |
Analysis of Peer Review Effectiveness for Academic Journals Based on Distributed Parallel System | A simulation model based on parallel systems is established, aiming to
explore the relation between the number of submissions and the overall quality
of academic journals within a similar discipline under peer review. The model
can effectively simulate the submission, review and acceptance behaviors of
academic journals, in a distributed manner. According to the simulation
experiments, it could possibly happen that the overall standard of academic
journals may deteriorate due to excessive submissions.
| 1 | 0 | 0 | 0 | 0 | 0 |
Observational signatures of linear warps in circumbinary discs | In recent years an increasing number of observational studies have hinted at
the presence of warps in protoplanetary discs, however a general comprehensive
description of observational diagnostics of warped discs was missing. We
performed a series of 3D SPH hydrodynamic simulations and combined them with 3D
radiative transfer calculations to study the observability of warps in
circumbinary discs, whose plane is misaligned with respect to the orbital plane
of the central binary. Our numerical hydrodynamic simulations confirm previous
analytical results on the dependence of the warp structure on the viscosity and
the initial misalignment between the binary and the disc. To study the
observational signatures of warps we calculate images in the continuum at
near-infrared and sub-millimetre wavelengths and in the pure rotational
transition of CO in the sub-millimetre. Warped circumbinary discs show surface
brightness asymmetry in near-infrared scattered light images as well as in
optically thick gas lines at sub-millimetre wavelengths. The asymmetry is
caused by self-shadowing of the disc by the inner warped regions, thus the
strength of the asymmetry depends on the strength of the warp. The projected
velocity field, derived from line observations, shows characteristic
deviations, twists and a change in the slope of the rotation curve, from that
of an unperturbed disc. In extreme cases even the direction of rotation appears
to change in the disc inwards of a characteristic radius. The strength of the
kinematical signatures of warps decreases with increasing inclination. The
strength of all warp signatures decreases with decreasing viscosity.
| 0 | 1 | 0 | 0 | 0 | 0 |
Bootstrapping single-channel source separation via unsupervised spatial clustering on stereo mixtures | Separating an audio scene into isolated sources is a fundamental problem in
computer audition, analogous to image segmentation in visual scene analysis.
Source separation systems based on deep learning are currently the most
successful approaches for solving the underdetermined separation problem, where
there are more sources than channels. Traditionally, such systems are trained
on sound mixtures where the ground truth decomposition is already known. Since
most real-world recordings do not have such a decomposition available, this
limits the range of mixtures one can train on, and the range of mixtures the
learned models may successfully separate. In this work, we use a simple blind
spatial source separation algorithm to generate estimated decompositions of
stereo mixtures. These estimates, together with a weighting scheme in the
time-frequency domain, based on confidence in the separation quality, are used
to train a deep learning model that can be used for single-channel separation,
where no source direction information is available. This demonstrates how a
simple cue such as the direction of origin of source can be used to bootstrap a
model for source separation that can be used in situations where that cue is
not available.
| 1 | 0 | 0 | 0 | 0 | 0 |
Multi-parameter One-Sided Monitoring Test | Multi-parameter one-sided hypothesis test problems arise naturally in many
applications. We are particularly interested in effective tests for monitoring
multiple quality indices in forestry products. Our search reveals that there
are many effective statistical methods in the literature for normal data, and
that they can easily be adapted for non-normal data. We find that the beautiful
likelihood ratio test is unsatisfactory, because in order to control the size,
it must cope with the least favorable distributions at the cost of power. In
this paper, we find a novel way to slightly ease the size control, obtaining a
much more powerful test. Simulation confirms that the new test retains good
control of the type I error and is markedly more powerful than the likelihood
ratio test as well as many competitors based on normal data. The new method
performs well in the context of monitoring multiple quality indices.
| 0 | 0 | 1 | 1 | 0 | 0 |
Statistical mechanics of low-rank tensor decomposition | Often, large, high dimensional datasets collected across multiple modalities
can be organized as a higher order tensor. Low-rank tensor decomposition then
arises as a powerful and widely used tool to discover simple low dimensional
structures underlying such data. However, we currently lack a theoretical
understanding of the algorithmic behavior of low-rank tensor decompositions. We
derive Bayesian approximate message passing (AMP) algorithms for recovering
arbitrarily shaped low-rank tensors buried within noise, and we employ dynamic
mean field theory to precisely characterize their performance. Our theory
reveals the existence of phase transitions between easy, hard and impossible
inference regimes, and displays an excellent match with simulations. Moreover,
it reveals several qualitative surprises compared to the behavior of symmetric,
cubic tensor decomposition. Finally, we compare our AMP algorithm to the most
commonly used algorithm, alternating least squares (ALS), and demonstrate that
AMP significantly outperforms ALS in the presence of noise.
| 0 | 0 | 0 | 0 | 1 | 0 |
A path integral based model for stocks and order dynamics | We introduce a model for the short-term dynamics of financial assets based on
an application to finance of quantum gauge theory, developing ideas of Ilinski.
We present a numerical algorithm for the computation of the probability
distribution of prices and compare the results with APPLE stocks prices and the
S&P500 index.
| 0 | 0 | 0 | 0 | 0 | 1 |
A New Algorithm to Automate Inductive Learning of Default Theories | In inductive learning of a broad concept, an algorithm should be able to
distinguish concept examples from exceptions and noisy data. An approach
through recursively finding patterns in exceptions turns out to correspond to
the problem of learning default theories. Default logic is what humans employ
in common-sense reasoning. Therefore, learned default theories are better
understood by humans. In this paper, we present new algorithms to learn default
theories in the form of non-monotonic logic programs. Experiments reported in
this paper show that our algorithms are a significant improvement over
traditional approaches based on inductive logic programming.
| 1 | 0 | 0 | 0 | 0 | 0 |
Origin of the pressure-dependent T$_c$ valley in superconducting simple cubic phosphorus | Motivated by recent experiments, we investigate the pressure-dependent
electronic structure and electron-phonon (\emph{e-ph}) coupling for simple
cubic phosphorus by performing first-principle calculations within the full
potential linearized augmented plane wave method. As a function of increasing
pressure, our calculations show a valley feature in T$_c$, followed by an
eventual decrease for higher pressures. We demonstrate that this T$_c$ valley
at low pressures is due to two nearby Lifshitz transitions, as we analyze the
band-resolved contributions to the \emph{e-ph} coupling. Below the first
Lifshitz transition, the phonon hardening and shrinking of the $\gamma$ Fermi
surface with $s$ orbital character results in a decreased T$_c$ with increasing
pressure. After the second Lifshitz transition, the appearance of $\delta$
Fermi surfaces with $3d$ orbital character generate strong \emph{e-ph}
inter-band couplings in $\alpha\delta$ and $\beta\delta$ channels, and hence
lead to an increase of T$_c$. For higher pressures, the phonon hardening
finally dominates, and T$_c$ decreases again. Our study reveals that the
intriguing T$_c$} valley discovered in experiment can be attributed to Lifshitz
transitions, while the plateau of T$_c$ detected at intermediate pressures
appears to be beyond the scope of our analysis. This strongly suggests that
besides \emph{e-ph} coupling, electronic correlations along with plasmonic
contributions may be relevant for simple cubic phosphorous. Our findings hint
at the notion that increasing pressure can shift the low-energy orbital weight
towards $d$ character, and as such even trigger an enhanced importance of
orbital-selective electronic correlations despite an increase of the overall
bandwidth.
| 0 | 1 | 0 | 0 | 0 | 0 |
V-cycle multigrid algorithms for discontinuous Galerkin methods on non-nested polytopic meshes | In this paper we analyse the convergence properties of V-cycle multigrid
algorithms for the numerical solution of the linear system of equations arising
from discontinuous Galerkin discretization of second-order elliptic partial
differential equations on polytopal meshes. Here, the sequence of spaces that
stands at the basis of the multigrid scheme is possibly non nested and is
obtained based on employing agglomeration with possible edge/face coarsening.
We prove that the method converges uniformly with respect to the granularity of
the grid and the polynomial approximation degree p, provided that the number of
smoothing steps, which depends on p, is chosen sufficiently large.
| 1 | 0 | 0 | 0 | 0 | 0 |
On a result of Fel'dman on linear forms in the values of some E-functions | We shall consider a result of Fel'dman, where a sharp Baker-type lower bound
is obtained for linear forms in the values of some E-functions. Fel'dman's
proof is based on an explicit construction of Padé approximations of the
first kind for these functions. In the present paper we introduce Padé
approximations of the second kind for the same functions and use these to
obtain a slightly improved version of Fel'dman's result.
| 0 | 0 | 1 | 0 | 0 | 0 |
Learning Low-shot facial representations via 2D warping | In this work, we mainly study the influence of the 2D warping module for
one-shot face recognition.
| 1 | 0 | 0 | 0 | 0 | 0 |
Catalyzed bimolecular reactions in responsive nanoreactors | We describe a general theory for surface-catalyzed bimolecular reactions in
responsive nanoreactors, catalytically active nanoparticles coated by a
stimuli-responsive 'gating' shell, whose permeability controls the activity of
the process. We address two archetypal scenarios encountered in this system:
The first, where two species diffusing from a bulk solution react at the
catalyst's surface; the second where only one of the reactants diffuses from
the bulk while the other one is produced at the nanoparticle surface, e.g., by
light conversion. We find that in both scenarios the total catalytic rate has
the same mathematical structure, once diffusion rates are properly redefined.
Moreover, the diffusional fluxes of the different reactants are strongly
coupled, providing a richer behavior than that arising in unimolecular
reactions. We also show that in stark contrast to bulk reactions, the
identification of a limiting reactant is not simply determined by the relative
bulk concentrations but controlled by the nanoreactor shell permeability.
Finally, we describe an application of our theory by analyzing experimental
data on the reaction between hexacyanoferrate (III) and borohydride ions in
responsive hydrogel-based core-shell nanoreactors.
| 0 | 1 | 0 | 0 | 0 | 0 |
Average Case Constant Factor Time and Distance Optimal Multi-Robot Path Planning in Well-Connected Environments | Fast algorithms for optimal multi-robot path planning are sought after in
many real-world applications. Known methods, however, generally do not
simultaneously guarantee good solution optimality and fast run time (e.g.,
polynomial). In this work, we develop a low-polynomial running time algorithm,
called SplitAndGroup (SAG),that solves the multi-robot path planning problem on
grids and grid-like environments and produces constant factor makespan-optimal
solutions in the average case. That is, SAG is an average case
O(1)-approximation algorithm. SAG computes solutions with sub-linear makespan
and is capable of handling cases when the density of robots is extremely high -
in a graph-theoretic setting, the algorithm supports cases where all vertices
of the underlying graph are occupied by robots. SAG attains its desirable
properties through a careful combination of divide-and-conquer technique and
network flow based methods for routing the robots. Solutions from SAG, in a
weaker sense, is also a constant factor approximation on total distance
optimality.
| 1 | 0 | 0 | 0 | 0 | 0 |
Hierarchical Learning for Modular Robots | We argue that hierarchical methods can become the key for modular robots
achieving reconfigurability. We present a hierarchical approach for modular
robots that allows a robot to simultaneously learn multiple tasks. Our
evaluation results present an environment composed of two different modular
robot configurations, namely 3 degrees-of-freedom (DoF) and 4DoF with two
corresponding targets. During the training, we switch between configurations
and targets aiming to evaluate the possibility of training a neural network
that is able to select appropriate motor primitives and robot configuration to
achieve the target. The trained neural network is then transferred and executed
on a real robot with 3DoF and 4DoF configurations. We demonstrate how this
technique generalizes to robots with different configurations and tasks.
| 1 | 0 | 0 | 0 | 0 | 0 |
Online Learning with Diverse User Preferences | In this paper, we investigate the impact of diverse user preference on
learning under the stochastic multi-armed bandit (MAB) framework. We aim to
show that when the user preferences are sufficiently diverse and each arm can
be optimal for certain users, the O(log T) regret incurred by exploring the
sub-optimal arms under the standard stochastic MAB setting can be reduced to a
constant. Our intuition is that to achieve sub-linear regret, the number of
times an optimal arm being pulled should scale linearly in time; when all arms
are optimal for certain users and pulled frequently, the estimated arm
statistics can quickly converge to their true values, thus reducing the need of
exploration dramatically. We cast the problem into a stochastic linear bandits
model, where both the users preferences and the state of arms are modeled as
{independent and identical distributed (i.i.d)} d-dimensional random vectors.
After receiving the user preference vector at the beginning of each time slot,
the learner pulls an arm and receives a reward as the linear product of the
preference vector and the arm state vector. We also assume that the state of
the pulled arm is revealed to the learner once its pulled. We propose a
Weighted Upper Confidence Bound (W-UCB) algorithm and show that it can achieve
a constant regret when the user preferences are sufficiently diverse. The
performance of W-UCB under general setups is also completely characterized and
validated with synthetic data.
| 1 | 0 | 0 | 1 | 0 | 0 |
Gaussian Process based Passivation of a Class of Nonlinear Systems with Unknown Dynamics | The paper addresses the problem of passivation of a class of nonlinear
systems where the dynamics are unknown. For this purpose, we use the highly
flexible, data-driven Gaussian process regression for the identification of the
unknown dynamics for feed-forward compensation. The closed loop system of the
nonlinear system, the Gaussian process model and a feedback control law is
guaranteed to be semi-passive with a specific probability. The predicted
variance of the Gaussian process regression is used to bound the model error
which additionally allows to specify the state space region where the
closed-loop system behaves passive. Finally, the theoretical results are
illustrated by a simulation.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Bayesian Method for Joint Clustering of Vectorial Data and Network Data | We present a new model-based integrative method for clustering objects given
both vectorial data, which describes the feature of each object, and network
data, which indicates the similarity of connected objects. The proposed general
model is able to cluster the two types of data simultaneously within one
integrative probabilistic model, while traditional methods can only handle one
data type or depend on transforming one data type to another. Bayesian
inference of the clustering is conducted based on a Markov chain Monte Carlo
algorithm. A special case of the general model combining the Gaussian mixture
model and the stochastic block model is extensively studied. We used both
synthetic data and real data to evaluate this new method and compare it with
alternative methods. The results show that our simultaneous clustering method
performs much better. This improvement is due to the power of the model-based
probabilistic approach for efficiently integrating information.
| 1 | 0 | 0 | 1 | 0 | 0 |
Pointwise-generalized-inverses of linear maps between C$^*$-algebras and JB$^*$-triples | We study pointwise-generalized-inverses of linear maps between
C$^*$-algebras. Let $\Phi$ and $\Psi$ be linear maps between complex Banach
algebras $A$ and $B$. We say that $\Psi$ is a pointwise-generalized-inverse of
$\Phi$ if $\Phi(aba)=\Phi(a)\Psi(b)\Phi(a),$ for every $a,b\in A$. The pair
$(\Phi,\Psi)$ is Jordan-triple multiplicative if $\Phi$ is a
pointwise-generalized-inverse of $\Psi$ and the latter is a
pointwise-generalized-inverse of $\Phi$. We study the basic properties of this
maps in connection with Jordan homomorphism, triple homomorphisms and strongly
preservers. We also determine conditions to guarantee the automatic continuity
of the pointwise-generalized-inverse of continuous operator between
C$^*$-algebras. An appropriate generalization is introduced in the setting of
JB$^*$-triples.
| 0 | 0 | 1 | 0 | 0 | 0 |
The cohomology of rank two stable bundle moduli: mod two nilpotency & skew Schur polynomials | We compute cup product pairings in the integral cohomology ring of the moduli
space of rank two stable bundles with odd determinant over a Riemann surface
using methods of Zagier. The resulting formula is related to a generating
function for certain skew Schur polynomials. As an application, we compute the
nilpotency degree of a distinguished degree two generator in the mod two
cohomology ring. We then give descriptions of the mod two cohomology rings in
low genus, and describe the subrings invariant under the mapping class group
action.
| 0 | 0 | 1 | 0 | 0 | 0 |
Automated Formal Synthesis of Digital Controllers for State-Space Physical Plants | We present a sound and automated approach to synthesize safe digital feedback
controllers for physical plants represented as linear, time invariant models.
Models are given as dynamical equations with inputs, evolving over a continuous
state space and accounting for errors due to the digitalization of signals by
the controller. Our approach has two stages, leveraging counterexample guided
inductive synthesis (CEGIS) and reachability analysis. CEGIS synthesizes a
static feedback controller that stabilizes the system under restrictions given
by the safety of the reach space. Safety is verified either via BMC or abstract
acceleration; if the verification step fails, we refine the controller by
generalizing the counterexample. We synthesize stable and safe controllers for
intricate physical plant models from the digital control literature.
| 1 | 0 | 0 | 0 | 0 | 0 |
A constrained control-planning strategy for redundant manipulators | This paper presents an interconnected control-planning strategy for redundant
manipulators, subject to system and environmental constraints. The method
incorporates low-level control characteristics and high-level planning
components into a robust strategy for manipulators acting in complex
environments, subject to joint limits. This strategy is formulated using an
adaptive control rule, the estimated dynamic model of the robotic system and
the nullspace of the linearized constraints. A path is generated that takes
into account the capabilities of the platform. The proposed method is
computationally efficient, enabling its implementation on a real multi-body
robotic system. Through experimental results with a 7 DOF manipulator, we
demonstrate the performance of the method in real-world scenarios.
| 1 | 0 | 0 | 0 | 0 | 0 |
Lagrangian for RLC circuits using analogy with the classical mechanics concepts | We study and formulate the Lagrangian for the LC, RC, RL, and RLC circuits by
using the analogy concept with the mechanical problem in classical mechanics
formulations. We found that the Lagrangian for the LC and RLC circuits are
governed by two terms i. e. kinetic energy-like and potential energy-like
terms. The Lagrangian for the RC circuit is only a contribution from the
potential energy-like term and the Lagrangian for the RL circuit is only from
the kinetic energy-like term.
| 0 | 1 | 0 | 0 | 0 | 0 |
A note on recent criticisms to Birnbaum's theorem | In this note, we provide critical commentary on two articles that cast doubt
on the validity and implications of Birnbaum's theorem: Evans (2013) and Mayo
(2014). In our view, the proof is correct and the consequences of the theorem
are alive and well.
| 0 | 0 | 1 | 1 | 0 | 0 |
High efficiently numerical simulation of the TDGL equation with reticular free energy in hydrogel | In this paper, we focus on the numerical simulation of phase separation about
macromolecule microsphere composite (MMC) hydrogel. The model equation is based
on Time-Dependent Ginzburg-Landau (TDGL) equation with reticular free energy.
We have put forward two $L^2$ stable schemes to simulate simplified TDGL
equation. In numerical experiments, we observe that simulating the whole
process of phase separation requires a considerably long time. We also notice
that the total free energy changes significantly in initial time and varies
slightly in the following time. Based on these properties, we introduce an
adaptive strategy based on one of stable scheme mentioned. It is found that the
introduction of the time adaptivity cannot only resolve the dynamical changes
of the solution accurately but also can significantly save CPU time for the
long time simulation.
| 0 | 1 | 0 | 0 | 0 | 0 |
Entanglement verification protocols for distributed systems based on the Quantum Recursive Network Architecture | In distributed systems based on the Quantum Recursive Network Architecture,
quantum channels and quantum memories are used to establish entangled quantum
states between node pairs. Such systems are robust against attackers that
interact with the quantum channels. Conversely, weaknesses emerge when an
attacker takes full control of a node and alters the configuration of the local
quantum memory, either to make a denial-of-service attack or to reprogram the
node. In such a scenario, entanglement verification over quantum memories is a
means for detecting the intruder. Usually, entanglement verification approaches
focus either on untrusted sources of entangled qubits (photons, in most cases)
or on eavesdroppers that interfere with the quantum channel while entangled
qubits are transmitted. Instead, in this work we assume that the source of
entanglement is trusted, but parties may be dishonest. Looking for efficient
entanglement verification protocols that only require classical channels and
local quantum operations to work, we thoroughly analyze the one proposed by
Nagy and Akl, that we denote as NA2010 for simplicity, and we define and
analyze two entanglement verification protocols based on teleportation (denoted
as AC1 and AC2), characterized by increasing efficiency in terms of intrusion
detection probability versus sacrificed quantum resources.
| 1 | 0 | 0 | 0 | 0 | 0 |
On the radius of spatial analyticity for the quartic generalized KdV equation | Lower bound on the rate of decrease in time of the uniform radius of spatial
analyticity of solutions to the quartic generalized KdV equation is derived,
which improves an earlier result by Bona, Grujić and Kalisch.
| 0 | 0 | 1 | 0 | 0 | 0 |
Calibrating Noise to Variance in Adaptive Data Analysis | Datasets are often used multiple times and each successive analysis may
depend on the outcome of previous analyses. Standard techniques for ensuring
generalization and statistical validity do not account for this adaptive
dependence. A recent line of work studies the challenges that arise from such
adaptive data reuse by considering the problem of answering a sequence of
"queries" about the data distribution where each query may depend arbitrarily
on answers to previous queries.
The strongest results obtained for this problem rely on differential privacy
-- a strong notion of algorithmic stability with the important property that it
"composes" well when data is reused. However the notion is rather strict, as it
requires stability under replacement of an arbitrary data element. The simplest
algorithm is to add Gaussian (or Laplace) noise to distort the empirical
answers. However, analysing this technique using differential privacy yields
suboptimal accuracy guarantees when the queries have low variance. Here we
propose a relaxed notion of stability that also composes adaptively. We
demonstrate that a simple and natural algorithm based on adding noise scaled to
the standard deviation of the query provides our notion of stability. This
implies an algorithm that can answer statistical queries about the dataset with
substantially improved accuracy guarantees for low-variance queries. The only
previous approach that provides such accuracy guarantees is based on a more
involved differentially private median-of-means algorithm and its analysis
exploits stronger "group" stability of the algorithm.
| 1 | 0 | 0 | 0 | 0 | 0 |
Testing Equality of Autocovariance Operators for Functional Time Series | We consider strictly stationary stochastic processes of Hilbert space-valued
random variables and focus on tests of the equality of the lag-zero
autocovariance operators of several independent functional time series. A
moving block bootstrap-based testing procedure is proposed which generates
pseudo random elements that satisfy the null hypothesis of interest. It is
based on directly bootstrapping the time series of tensor products which
overcomes some common difficulties associated with applications of the
bootstrap to related testing problems. The suggested methodology can be
potentially applied to a broad range of test statistics of the hypotheses of
interest. As an example, we establish validity for approximating the
distribution under the null of a fully functional test statistic based on the
Hilbert-Schmidt distance of the corresponding sample lag-zero autocovariance
operators, and show consistency under the alternative. As a prerequisite, we
prove a central limit theorem for the moving block bootstrap procedure applied
to the sample autocovariance operator which is of interest on its own. The
finite sample size and power performance of the suggested moving block
bootstrap-based testing procedure is illustrated through simulations and an
application to a real-life dataset is discussed.
| 0 | 0 | 1 | 1 | 0 | 0 |
The Lyman-alpha forest power spectrum from the XQ-100 Legacy Survey | We present the Lyman-$\alpha$ flux power spectrum measurements of the XQ-100
sample of quasar spectra obtained in the context of the European Southern
Observatory Large Programme "Quasars and their absorption lines: a legacy
survey of the high redshift universe with VLT/XSHOOTER". Using $100$ quasar
spectra with medium resolution and signal-to-noise ratio we measure the power
spectrum over a range of redshifts $z = 3 - 4.2$ and over a range of scales $k
= 0.003 - 0.06\,\mathrm{s\,km^{-1}}$. The results agree well with the
measurements of the one-dimensional power spectrum found in the literature. The
data analysis used in this paper is based on the Fourier transform and has been
tested on synthetic data. Systematic and statistical uncertainties of our
measurements are estimated, with a total error (statistical and systematic)
comparable to the one of the BOSS data in the overlapping range of scales, and
smaller by more than $50\%$ for higher redshift bins ($z>3.6$) and small scales
($k > 0.01\,\mathrm{s\,km^{-1}}$). The XQ-100 data set has the unique feature
of having signal-to-noise ratios and resolution intermediate between the two
data sets that are typically used to perform cosmological studies, i.e. BOSS
and high-resolution spectra (e.g. UVES/VLT or HIRES). More importantly, the
measured flux power spectra span the high redshift regime which is usually more
constraining for structure formation models.
| 0 | 1 | 0 | 0 | 0 | 0 |
AdS4 backgrounds with N>16 supersymmetries in 10 and 11 dimensions | We explore all warped $AdS_4\times_w M^{D-4}$ backgrounds with the most
general allowed fluxes that preserve more than 16 supersymmetries in $D=10$-
and $11$-dimensional supergravities. After imposing the assumption that either
the internal space $M^{D-4}$ is compact without boundary or the isometry
algebra of the background decomposes into that of AdS$_4$ and that of
$M^{D-4}$, we find that there are no such backgrounds in IIB supergravity.
Similarly in IIA supergravity, there is a unique such background with 24
supersymmetries locally isometric to $AdS_4\times \mathbb{CP}^3$, and in $D=11$
supergravity all such backgrounds are locally isometric to the maximally
supersymmetric $AdS_4\times S^7$ solution.
| 0 | 0 | 1 | 0 | 0 | 0 |
Room Temperature Polariton Lasing in All-Inorganic Perovskites | Polariton lasing is the coherent emission arising from a macroscopic
polariton condensate first proposed in 1996. Over the past two decades,
polariton lasing has been demonstrated in a few inorganic and organic
semiconductors in both low and room temperatures. Polariton lasing in inorganic
materials significantly relies on sophisticated epitaxial growth of crystalline
gain medium layers sandwiched by two distributed Bragg reflectors in which
combating the built-in strain and mismatched thermal properties is nontrivial.
On the other hand, organic active media usually suffer from large threshold
density and weak nonlinearity due to the Frenkel exciton nature. Further
development of polariton lasing towards technologically significant
applications demand more accessible materials, ease of device fabrication and
broadly tunable emission at room temperature. Herein, we report the
experimental realization of room-temperature polariton lasing based on an
epitaxy-free all-inorganic cesium lead chloride perovskite microcavity.
Polariton lasing is unambiguously evidenced by a superlinear power dependence,
macroscopic ground state occupation, blueshift of ground state emission,
narrowing of the linewidth and the build-up of long-range spatial coherence.
Our work suggests considerable promise of lead halide perovskites towards
large-area, low-cost, high performance room temperature polariton devices and
coherent light sources extending from the ultraviolet to near infrared range.
| 0 | 1 | 0 | 0 | 0 | 0 |
Conditional Independence, Conditional Mean Independence, and Zero Conditional Covariance | Investigation of the reversibility of the directional hierarchy in the
interdependency among the notions of conditional independence, conditional mean
independence, and zero conditional covariance, for two random variables X and Y
given a conditioning element Z which is not constrained by any topological
restriction on its range, reveals that if the first moments of X, Y, and XY
exist, then conditional independence implies conditional mean independence and
conditional mean independence implies zero conditional covariance, but the
direction of the hierarchy is not reversible in general. If the conditional
expectation of Y given X and Z is "affine in X," which happens when X is
Bernoulli, then the "intercept" and "slope" of the conditional expectation
(that is, the nonparametric regression function) equal the "intercept" and
"slope" of the "least-squares linear regression function", as a result of which
zero conditional covariance implies conditional mean independence.
| 0 | 0 | 1 | 1 | 0 | 0 |
Probabilistic Sensor Fusion for Ambient Assisted Living | There is a widely-accepted need to revise current forms of health-care
provision, with particular interest in sensing systems in the home. Given a
multiple-modality sensor platform with heterogeneous network connectivity, as
is under development in the Sensor Platform for HEalthcare in Residential
Environment (SPHERE) Interdisciplinary Research Collaboration (IRC), we face
specific challenges relating to the fusion of the heterogeneous sensor
modalities.
We introduce Bayesian models for sensor fusion, which aims to address the
challenges of fusion of heterogeneous sensor modalities. Using this approach we
are able to identify the modalities that have most utility for each particular
activity, and simultaneously identify which features within that activity are
most relevant for a given activity.
We further show how the two separate tasks of location prediction and
activity recognition can be fused into a single model, which allows for
simultaneous learning an prediction for both tasks.
We analyse the performance of this model on data collected in the SPHERE
house, and show its utility. We also compare against some benchmark models
which do not have the full structure,and show how the proposed model compares
favourably to these methods
| 1 | 0 | 0 | 1 | 0 | 0 |
Bounded time computation on metric spaces and Banach spaces | We extend the framework by Kawamura and Cook for investigating computational
complexity for operators occurring in analysis. This model is based on
second-order complexity theory for functions on the Baire space, which is
lifted to metric spaces by means of representations. Time is measured in terms
of the length of the input encodings and the required output precision. We
propose the notions of a complete representation and of a regular
representation. We show that complete representations ensure that any
computable function has a time bound. Regular representations generalize
Kawamura and Cook's more restrictive notion of a second-order representation,
while still guaranteeing fast computability of the length of the encodings.
Applying these notions, we investigate the relationship between purely metric
properties of a metric space and the existence of a representation such that
the metric is computable within bounded time. We show that a bound on the
running time of the metric can be straightforwardly translated into size bounds
of compact subsets of the metric space. Conversely, for compact spaces and for
Banach spaces we construct a family of admissible, complete, regular
representations that allow for fast computation of the metric and provide short
encodings. Here it is necessary to trade the time bound off against the length
of encodings.
| 1 | 0 | 1 | 0 | 0 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.