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Title: Refining Source Representations with Relation Networks for Neural Machine Translation,
Abstract: Although neural machine translation (NMT) with the encoder-decoder framework
has achieved great success in recent times, it still suffers from some
drawbacks: RNNs tend to forget old information which is often useful and the
encoder only operates through words without considering word relationship. To
solve these problems, we introduce a relation networks (RN) into NMT to refine
the encoding representations of the source. In our method, the RN first
augments the representation of each source word with its neighbors and reasons
all the possible pairwise relations between them. Then the source
representations and all the relations are fed to the attention module and the
decoder together, keeping the main encoder-decoder architecture unchanged.
Experiments on two Chinese-to-English data sets in different scales both show
that our method can outperform the competitive baselines significantly. | [
1,
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] |
Title: On the Statistical Efficiency of Optimal Kernel Sum Classifiers,
Abstract: We propose a novel combination of optimization tools with learning theory
bounds in order to analyze the sample complexity of optimal kernel sum
classifiers. This contrasts the typical learning theoretic results which hold
for all (potentially suboptimal) classifiers. Our work also justifies
assumptions made in prior work on multiple kernel learning. As a byproduct of
our analysis, we also provide a new form of Rademacher complexity for
hypothesis classes containing only optimal classifiers. | [
1,
0,
0,
1,
0,
0
] |
Title: Henkin constructions of models with size continuum,
Abstract: We survey the technique of constructing customized models of size continuum
in omega steps and illustrate the method by giving new proofs of mostly old
results within this rubric. One new theorem, which is joint with Saharon
Shelah, is that a pseudominimal theory has an atomic model of size continuum. | [
0,
0,
1,
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0,
0
] |
Title: Semi-supervised and Active-learning Scenarios: Efficient Acoustic Model Refinement for a Low Resource Indian Language,
Abstract: We address the problem of efficient acoustic-model refinement (continuous
retraining) using semi-supervised and active learning for a low resource Indian
language, wherein the low resource constraints are having i) a small labeled
corpus from which to train a baseline `seed' acoustic model and ii) a large
training corpus without orthographic labeling or from which to perform a data
selection for manual labeling at low costs. The proposed semi-supervised
learning decodes the unlabeled large training corpus using the seed model and
through various protocols, selects the decoded utterances with high reliability
using confidence levels (that correlate to the WER of the decoded utterances)
and iterative bootstrapping. The proposed active learning protocol uses
confidence level based metric to select the decoded utterances from the large
unlabeled corpus for further labeling. The semi-supervised learning protocols
can offer a WER reduction, from a poorly trained seed model, by as much as 50%
of the best WER-reduction realizable from the seed model's WER, if the large
corpus were labeled and used for acoustic-model training. The active learning
protocols allow that only 60% of the entire training corpus be manually
labeled, to reach the same performance as the entire data. | [
1,
0,
0,
0,
0,
0
] |
Title: Energy spectrum of cascade showers generated by cosmic ray muons in water,
Abstract: The spatial distribution of Cherenkov radiation from cascade showers
generated by muons in water has been measured with Cherenkov water calorimeter
(CWC) NEVOD. This result allowed to improve the techniques of treating cascade
showers with unknown axes by means of CWC response analysis. The techniques of
selecting the events with high energy cascade showers and reconstructing their
parameters are discussed. Preliminary results of measurements of the spectrum
of cascade showers in the energy range 100 GeV - 20 TeV generated by cosmic ray
muons at large zenith angles and their comparison with expectation are
presented. | [
0,
1,
0,
0,
0,
0
] |
Title: The limit point of the pentagram map,
Abstract: The pentagram map is a discrete dynamical system defined on the space of
polygons in the plane. In the first paper on the subject, R. Schwartz proved
that the pentagram map produces from each convex polygon a sequence of
successively smaller polygons that converges exponentially to a point. We
investigate the limit point itself, giving an explicit description of its
Cartesian coordinates as roots of certain degree three polynomials. | [
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0,
1,
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0,
0
] |
Title: Transfer Learning for Neural Semantic Parsing,
Abstract: The goal of semantic parsing is to map natural language to a machine
interpretable meaning representation language (MRL). One of the constraints
that limits full exploration of deep learning technologies for semantic parsing
is the lack of sufficient annotation training data. In this paper, we propose
using sequence-to-sequence in a multi-task setup for semantic parsing with a
focus on transfer learning. We explore three multi-task architectures for
sequence-to-sequence modeling and compare their performance with an
independently trained model. Our experiments show that the multi-task setup
aids transfer learning from an auxiliary task with large labeled data to a
target task with smaller labeled data. We see absolute accuracy gains ranging
from 1.0% to 4.4% in our in- house data set, and we also see good gains ranging
from 2.5% to 7.0% on the ATIS semantic parsing tasks with syntactic and
semantic auxiliary tasks. | [
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] |
Title: Definable Valuations induced by multiplicative subgroups and NIP Fields,
Abstract: We study the algebraic implications of the non-independence property (NIP)
and variants thereof (dp-minimality) on infinite fields, motivated by the
conjecture that all such fields which are neither real closed nor separably
closed admit a definable henselian valuation. Our results mainly focus on Hahn
fields and build up on Will Johnson's preprint "dp-minimal fields", arXiv:
1507.02745v1, July 2015. | [
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0,
1,
0,
0,
0
] |
Title: Bridging the Gap Between Layout Pattern Sampling and Hotspot Detection via Batch Active Learning,
Abstract: Layout hotpot detection is one of the main steps in modern VLSI design. A
typical hotspot detection flow is extremely time consuming due to the
computationally expensive mask optimization and lithographic simulation. Recent
researches try to facilitate the procedure with a reduced flow including
feature extraction, training set generation and hotspot detection, where
feature extraction methods and hotspot detection engines are deeply studied.
However, the performance of hotspot detectors relies highly on the quality of
reference layout libraries which are costly to obtain and usually predetermined
or randomly sampled in previous works. In this paper, we propose an active
learning-based layout pattern sampling and hotspot detection flow, which
simultaneously optimizes the machine learning model and the training set that
aims to achieve similar or better hotspot detection performance with much
smaller number of training instances. Experimental results show that our
proposed method can significantly reduce lithography simulation overhead while
attaining satisfactory detection accuracy on designs under both DUV and EUV
lithography technologies. | [
0,
0,
0,
1,
0,
0
] |
Title: On the Power Spectral Density Applied to the Analysis of Old Canvases,
Abstract: A routine task for art historians is painting diagnostics, such as dating or
attribution. Signal processing of the X-ray image of a canvas provides useful
information about its fabric. However, previous methods may fail when very old
and deteriorated artworks or simply canvases of small size are studied. We
present a new framework to analyze and further characterize the paintings from
their radiographs. First, we start from a general analysis of lattices and
provide new unifying results about the theoretical spectra of weaves. Then, we
use these results to infer the main structure of the fabric, like the type of
weave and the thread densities. We propose a practical estimation of these
theoretical results from paintings with the averaged power spectral density
(PSD), which provides a more robust tool. Furthermore, we found that the PSD
provides a fingerprint that characterizes the whole canvas. We search and
discuss some distinctive features we may find in that fingerprint. We apply
these results to several masterpieces of the 17th and 18th centuries from the
Museo Nacional del Prado to show that this approach yields accurate results in
thread counting and is very useful for paintings comparison, even in situations
where previous methods fail. | [
1,
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1,
0,
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0
] |
Title: Eco-evolutionary feedbacks - theoretical models and perspectives,
Abstract: 1. Theoretical models pertaining to feedbacks between ecological and
evolutionary processes are prevalent in multiple biological fields. An
integrative overview is currently lacking, due to little crosstalk between the
fields and the use of different methodological approaches.
2. Here we review a wide range of models of eco-evolutionary feedbacks and
highlight their underlying assumptions. We discuss models where feedbacks occur
both within and between hierarchical levels of ecosystems, including
populations, communities, and abiotic environments, and consider feedbacks
across spatial scales.
3. Identifying the commonalities among feedback models, and the underlying
assumptions, helps us better understand the mechanistic basis of
eco-evolutionary feedbacks. Eco-evolutionary feedbacks can be readily modelled
by coupling demographic and evolutionary formalisms. We provide an overview of
these approaches and suggest future integrative modelling avenues.
4. Our overview highlights that eco-evolutionary feedbacks have been
incorporated in theoretical work for nearly a century. Yet, this work does not
always include the notion of rapid evolution or concurrent ecological and
evolutionary time scales. We discuss the importance of density- and
frequency-dependent selection for feedbacks, as well as the importance of
dispersal as a central linking trait between ecology and evolution in a spatial
context. | [
0,
0,
0,
0,
1,
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] |
Title: Unusual behavior of cuprates explained by heterogeneous charge localization,
Abstract: The cuprate high-temperature superconductors are among the most intensively
studied materials, yet essential questions regarding their principal phases and
the transitions between them remain unanswered. Generally thought of as doped
charge-transfer insulators, these complex lamellar oxides exhibit pseudogap,
strange-metal, superconducting and Fermi-liquid behaviour with increasing
hole-dopant concentration. Here we propose a simple inhomogeneous Mott-like
(de)localization model wherein exactly one hole per copper-oxygen unit is
gradually delocalized with increasing doping and temperature. The model is
percolative in nature, with parameters that are experimentally constrained. It
comprehensively captures pivotal unconventional experimental results, including
the temperature and doping dependence of the pseudogap phenomenon, the
strange-metal linear temperature dependence of the planar resistivity, and the
doping dependence of the superfluid density. The success and simplicity of our
model greatly demystify the cuprate phase diagram and point to a local
superconducting pairing mechanism involving the (de)localized hole. | [
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] |
Title: Explicit construction of RIP matrices is Ramsey-hard,
Abstract: Matrices $\Phi\in\R^{n\times p}$ satisfying the Restricted Isometry Property
(RIP) are an important ingredient of the compressive sensing methods. While it
is known that random matrices satisfy the RIP with high probability even for
$n=\log^{O(1)}p$, the explicit construction of such matrices defied the
repeated efforts, and the most known approaches hit the so-called $\sqrt{n}$
sparsity bottleneck. The notable exception is the work by Bourgain et al
\cite{bourgain2011explicit} constructing an $n\times p$ RIP matrix with
sparsity $s=\Theta(n^{{1\over 2}+\epsilon})$, but in the regime
$n=\Omega(p^{1-\delta})$.
In this short note we resolve this open question in a sense by showing that
an explicit construction of a matrix satisfying the RIP in the regime
$n=O(\log^2 p)$ and $s=\Theta(n^{1\over 2})$ implies an explicit construction
of a three-colored Ramsey graph on $p$ nodes with clique sizes bounded by
$O(\log^2 p)$ -- a question in the extremal combinatorics which has been open
for decades. | [
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] |
Title: Rocket Launching: A Universal and Efficient Framework for Training Well-performing Light Net,
Abstract: Models applied on real time response task, like click-through rate (CTR)
prediction model, require high accuracy and rigorous response time. Therefore,
top-performing deep models of high depth and complexity are not well suited for
these applications with the limitations on the inference time. In order to
further improve the neural networks' performance given the time and
computational limitations, we propose an approach that exploits a cumbersome
net to help train the lightweight net for prediction. We dub the whole process
rocket launching, where the cumbersome booster net is used to guide the
learning of the target light net throughout the whole training process. We
analyze different loss functions aiming at pushing the light net to behave
similarly to the booster net, and adopt the loss with best performance in our
experiments. We use one technique called gradient block to improve the
performance of the light net and booster net further. Experiments on benchmark
datasets and real-life industrial advertisement data present that our light
model can get performance only previously achievable with more complex models. | [
1,
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] |
Title: Complete Classification of Generalized Santha-Vazirani Sources,
Abstract: Let $\mathcal{F}$ be a finite alphabet and $\mathcal{D}$ be a finite set of
distributions over $\mathcal{F}$. A Generalized Santha-Vazirani (GSV) source of
type $(\mathcal{F}, \mathcal{D})$, introduced by Beigi, Etesami and Gohari
(ICALP 2015, SICOMP 2017), is a random sequence $(F_1, \dots, F_n)$ in
$\mathcal{F}^n$, where $F_i$ is a sample from some distribution $d \in
\mathcal{D}$ whose choice may depend on $F_1, \dots, F_{i-1}$.
We show that all GSV source types $(\mathcal{F}, \mathcal{D})$ fall into one
of three categories: (1) non-extractable; (2) extractable with error
$n^{-\Theta(1)}$; (3) extractable with error $2^{-\Omega(n)}$. This rules out
other error rates like $1/\log n$ or $2^{-\sqrt{n}}$.
We provide essentially randomness-optimal extraction algorithms for
extractable sources. Our algorithm for category (2) sources extracts with error
$\varepsilon$ from $n = \mathrm{poly}(1/\varepsilon)$ samples in time linear in
$n$. Our algorithm for category (3) sources extracts $m$ bits with error
$\varepsilon$ from $n = O(m + \log 1/\varepsilon)$ samples in time
$\min\{O(nm2^m),n^{O(\lvert\mathcal{F}\rvert)}\}$.
We also give algorithms for classifying a GSV source type $(\mathcal{F},
\mathcal{D})$: Membership in category (1) can be decided in $\mathrm{NP}$,
while membership in category (3) is polynomial-time decidable. | [
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] |
Title: Hermann Hankel's "On the general theory of motion of fluids", an essay including an English translation of the complete Preisschrift from 1861,
Abstract: The present is a companion paper to "A contemporary look at Hermann Hankel's
1861 pioneering work on Lagrangian fluid dynamics" by Frisch, Grimberg and
Villone (2017). Here we present the English translation of the 1861 prize
manuscript from Göttingen University "Zur allgemeinen Theorie der Bewegung
der Flüssigkeiten" (On the general theory of the motion of the fluids) of
Hermann Hankel (1839-1873), which was originally submitted in Latin and then
translated into German by the Author for publication. We also provide the
English translation of two important reports on the manuscript, one written by
Bernhard Riemann and the other by Wilhelm Eduard Weber, during the assessment
process for the prize. Finally we give a short biography of Hermann Hankel with
his complete bibliography. | [
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] |
Title: The limit of the Hermitian-Yang-Mills flow on reflexive sheaves,
Abstract: In this paper, we study the asymptotic behavior of the Hermitian-Yang-Mills
flow on a reflexive sheaf. We prove that the limiting reflexive sheaf is
isomorphic to the double dual of the graded sheaf associated to the
Harder-Narasimhan-Seshadri filtration, this answers a question by Bando and
Siu. | [
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] |
Title: Application of backpropagation neural networks to both stages of fingerprinting based WIPS,
Abstract: We propose a scheme to employ backpropagation neural networks (BPNNs) for
both stages of fingerprinting-based indoor positioning using WLAN/WiFi signal
strengths (FWIPS): radio map construction during the offline stage, and
localization during the online stage. Given a training radio map (TRM), i.e., a
set of coordinate vectors and associated WLAN/WiFi signal strengths of the
available access points, a BPNN can be trained to output the expected signal
strengths for any input position within the region of interest (BPNN-RM). This
can be used to provide a continuous representation of the radio map and to
filter, densify or decimate a discrete radio map. Correspondingly, the TRM can
also be used to train another BPNN to output the expected position within the
region of interest for any input vector of recorded signal strengths and thus
carry out localization (BPNN-LA).Key aspects of the design of such artificial
neural networks for a specific application are the selection of design
parameters like the number of hidden layers and nodes within the network, and
the training procedure. Summarizing extensive numerical simulations, based on
real measurements in a testbed, we analyze the impact of these design choices
on the performance of the BPNN and compare the results in particular to those
obtained using the $k$ nearest neighbors ($k$NN) and weighted $k$ nearest
neighbors approaches to FWIPS. | [
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] |
Title: Bayesian Bootstraps for Massive Data,
Abstract: Recently, two scalable adaptations of the bootstrap have been proposed: the
bag of little bootstraps (BLB; Kleiner et al., 2014) and the subsampled double
bootstrap (SDB; Sengupta et al., 2016). In this paper, we introduce Bayesian
bootstrap analogues to the BLB and SDB that have similar theoretical and
computational properties, a strategy to perform lossless inference for a class
of functionals of the Bayesian bootstrap, and briefly discuss extensions for
Dirichlet Processes. | [
0,
0,
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1,
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] |
Title: Show, Adapt and Tell: Adversarial Training of Cross-domain Image Captioner,
Abstract: Impressive image captioning results are achieved in domains with plenty of
training image and sentence pairs (e.g., MSCOCO). However, transferring to a
target domain with significant domain shifts but no paired training data
(referred to as cross-domain image captioning) remains largely unexplored. We
propose a novel adversarial training procedure to leverage unpaired data in the
target domain. Two critic networks are introduced to guide the captioner,
namely domain critic and multi-modal critic. The domain critic assesses whether
the generated sentences are indistinguishable from sentences in the target
domain. The multi-modal critic assesses whether an image and its generated
sentence are a valid pair. During training, the critics and captioner act as
adversaries -- captioner aims to generate indistinguishable sentences, whereas
critics aim at distinguishing them. The assessment improves the captioner
through policy gradient updates. During inference, we further propose a novel
critic-based planning method to select high-quality sentences without
additional supervision (e.g., tags). To evaluate, we use MSCOCO as the source
domain and four other datasets (CUB-200-2011, Oxford-102, TGIF, and Flickr30k)
as the target domains. Our method consistently performs well on all datasets.
In particular, on CUB-200-2011, we achieve 21.8% CIDEr-D improvement after
adaptation. Utilizing critics during inference further gives another 4.5%
boost. | [
1,
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] |
Title: Contego: An Adaptive Framework for Integrating Security Tasks in Real-Time Systems,
Abstract: Embedded real-time systems (RTS) are pervasive. Many modern RTS are exposed
to unknown security flaws, and threats to RTS are growing in both number and
sophistication. However, until recently, cyber-security considerations were an
afterthought in the design of such systems. Any security mechanisms integrated
into RTS must (a) co-exist with the real- time tasks in the system and (b)
operate without impacting the timing and safety constraints of the control
logic. We introduce Contego, an approach to integrating security tasks into RTS
without affecting temporal requirements. Contego is specifically designed for
legacy systems, viz., the real-time control systems in which major alterations
of the system parameters for constituent tasks is not always feasible. Contego
combines the concept of opportunistic execution with hierarchical scheduling to
maintain compatibility with legacy systems while still providing flexibility by
allowing security tasks to operate in different modes. We also define a metric
to measure the effectiveness of such integration. We evaluate Contego using
synthetic workloads as well as with an implementation on a realistic embedded
platform (an open- source ARM CPU running real-time Linux). | [
1,
0,
0,
0,
0,
0
] |
Title: Interface currents and magnetization in singlet-triplet superconducting heterostructures: Role of chiral and helical domains,
Abstract: Chiral and helical domain walls are generic defects of topological
spin-triplet superconductors. We study theoretically the magnetic and transport
properties of superconducting singlet-triplet-singlet heterostructure as a
function of the phase difference between the singlet leads in the presence of
chiral and helical domains inside the spin-triplet region. The local inversion
symmetry breaking at the singlet-triplet interface allows the emergence of a
static phase-controlled magnetization, and generally yields both spin and
charge currents flowing along the edges. The parity of the domain wall number
affects the relative orientation of the interface moments and currents, while
in some cases the domain walls themselves contribute to spin and charge
transport. We demonstrate that singlet-triplet heterostructures are a generic
prototype to generate and control non-dissipative spin and charge effects,
putting them in a broader class of systems exhibiting spin-Hall, anomalous Hall
effects and similar phenomena. Features of the electron transport and magnetic
effects at the interfaces can be employed to assess the presence of domains in
chiral/helical superconductors. | [
0,
1,
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] |
Title: (non)-automaticity of completely multiplicative sequences having negligible many non-trivial prime factors,
Abstract: In this article we consider the completely multiplicative sequences $(a_n)_{n
\in \mathbf{N}}$ defined on a field $\mathbf{K}$ and satisfying $$\sum_{p| p
\leq n, a_p \neq 1, p \in \mathbf{P}}\frac{1}{p}<\infty,$$ where $\mathbf{P}$
is the set of prime numbers. We prove that if such sequences are automatic then
they cannot have infinitely many prime numbers $p$ such that $a_{p}\neq 1$.
Using this fact, we prove that if a completely multiplicative sequence
$(a_n)_{n \in \mathbf{N}}$, vanishing or not, can be written in the form
$a_n=b_n\chi_n$ such that $(b_n)_{n \in \mathbf{N}}$ is a non ultimately
periodic, completely multiplicative automatic sequence satisfying the above
condition, and $(\chi_n)_{n \in \mathbf{N}}$ is a Dirichlet character or a
constant sequence, then there exists only one prime number $p$ such that $b_p
\neq 1$ or $0$. | [
0,
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] |
Title: Timing Aware Dummy Metal Fill Methodology,
Abstract: In this paper, we analyzed parasitic coupling capacitance coming from dummy
metal fill and its impact on timing. Based on the modeling, we proposed two
approaches to minimize the timing impact from dummy metal fill. The first
approach applies more spacing between critical nets and metal fill, while the
second approach leverages the shielding effects of reference nets. Experimental
results show consistent improvement compared to traditional metal fill method. | [
1,
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] |
Title: Some results on the existence of t-all-or-nothing transforms over arbitrary alphabets,
Abstract: A $(t, s, v)$-all-or-nothing transform is a bijective mapping defined on
$s$-tuples over an alphabet of size $v$, which satisfies the condition that the
values of any $t$ input co-ordinates are completely undetermined, given only
the values of any $s-t$ output co-ordinates. The main question we address in
this paper is: for which choices of parameters does a $(t, s,
v)$-all-or-nothing transform (AONT) exist? More specifically, if we fix $t$ and
$v$, we want to determine the maximum integer $s$ such that a $(t, s, v)$-AONT
exists. We mainly concentrate on the case $t=2$ for arbitrary values of $v$,
where we obtain various necessary as well as sufficient conditions for
existence of these objects. We consider both linear and general (linear or
nonlinear) AONT. We also show some connections between AONT, orthogonal arrays
and resilient functions. | [
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0,
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] |
Title: Exhaustive Exploration of the Failure-oblivious Computing Search Space,
Abstract: High-availability of software systems requires automated handling of crashes
in presence of errors. Failure-oblivious computing is one technique that aims
to achieve high availability. We note that failure-obliviousness has not been
studied in depth yet, and there is very few study that helps understand why
failure-oblivious techniques work. In order to make failure-oblivious computing
to have an impact in practice, we need to deeply understand failure-oblivious
behaviors in software. In this paper, we study, design and perform an
experiment that analyzes the size and the diversity of the failure-oblivious
behaviors. Our experiment consists of exhaustively computing the search space
of 16 field failures of large-scale open-source Java software. The outcome of
this experiment is a much better understanding of what really happens when
failure-oblivious computing is used, and this opens new promising research
directions. | [
1,
0,
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0,
0,
0
] |
Title: Theoretical Accuracy in Cosmological Growth Estimation,
Abstract: We elucidate the importance of the consistent treatment of gravity-model
specific non-linearities when estimating the growth of cosmological structures
from redshift space distortions (RSD). Within the context of standard
perturbation theory (SPT), we compare the predictions of two theoretical
templates with redshift space data from COLA (COmoving Lagrangian Acceleration)
simulations in the normal branch of DGP gravity (nDGP) and General Relativity
(GR). Using COLA for these comparisons is validated using a suite of full
N-body simulations for the same theories. The two theoretical templates
correspond to the standard general relativistic perturbation equations and
those same equations modelled within nDGP. Gravitational clustering non-linear
effects are accounted for by modelling the power spectrum up to one loop order
and redshift space clustering anisotropy is modelled using the Taruya,
Nishimichi and Saito (TNS) RSD model. Using this approach, we attempt to
recover the simulation's fiducial logarithmic growth parameter $f$. By
assigning the simulation data with errors representing an idealised survey with
a volume of $10\mbox{Gpc}^3/h^3$, we find the GR template is unable to recover
fiducial $f$ to within 1$\sigma$ at $z=1$ when we match the data up to $k_{\rm
max}=0.195h$/Mpc. On the other hand, the DGP template recovers the fiducial
value within $1\sigma$. Further, we conduct the same analysis for sets of mock
data generated for generalised models of modified gravity using SPT, where
again we analyse the GR template's ability to recover the fiducial value. We
find that for models with enhanced gravitational non-linearity, the theoretical
bias of the GR template becomes significant for stage IV surveys. Thus, we show
that for the future large data volume galaxy surveys, the self-consistent
modelling of non-GR gravity scenarios will be crucial in constraining theory
parameters. | [
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] |
Title: Model-Robust Counterfactual Prediction Method,
Abstract: We develop a novel method for counterfactual analysis based on observational
data using prediction intervals for units under different exposures. Unlike
methods that target heterogeneous or conditional average treatment effects of
an exposure, the proposed approach aims to take into account the irreducible
dispersions of counterfactual outcomes so as to quantify the relative impact of
different exposures. The prediction intervals are constructed in a
distribution-free and model-robust manner based on the conformal prediction
approach. The computational obstacles to this approach are circumvented by
leveraging properties of a tuning-free method that learns sparse additive
predictor models for counterfactual outcomes. The method is illustrated using
both real and synthetic data. | [
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] |
Title: Learning with Average Top-k Loss,
Abstract: In this work, we introduce the {\em average top-$k$} (\atk) loss as a new
aggregate loss for supervised learning, which is the average over the $k$
largest individual losses over a training dataset. We show that the \atk loss
is a natural generalization of the two widely used aggregate losses, namely the
average loss and the maximum loss, but can combine their advantages and
mitigate their drawbacks to better adapt to different data distributions.
Furthermore, it remains a convex function over all individual losses, which can
lead to convex optimization problems that can be solved effectively with
conventional gradient-based methods. We provide an intuitive interpretation of
the \atk loss based on its equivalent effect on the continuous individual loss
functions, suggesting that it can reduce the penalty on correctly classified
data. We further give a learning theory analysis of \matk learning on the
classification calibration of the \atk loss and the error bounds of \atk-SVM.
We demonstrate the applicability of minimum average top-$k$ learning for binary
classification and regression using synthetic and real datasets. | [
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] |
Title: Variable selection for clustering with Gaussian mixture models: state of the art,
Abstract: The mixture models have become widely used in clustering, given its
probabilistic framework in which its based, however, for modern databases that
are characterized by their large size, these models behave disappointingly in
setting out the model, making essential the selection of relevant variables for
this type of clustering. After recalling the basics of clustering based on a
model, this article will examine the variable selection methods for model-based
clustering, as well as presenting opportunities for improvement of these
methods. | [
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] |
Title: On the essential self-adjointness of singular sub-Laplacians,
Abstract: We prove a general essential self-adjointness criterion for sub-Laplacians on
complete sub-Riemannian manifolds, defined with respect to singular measures.
As a consequence, we show that the intrinsic sub-Laplacian (i.e. defined w.r.t.
Popp's measure) is essentially self-adjoint on the equiregular connected
components of a sub-Riemannian manifold. This result holds under mild
regularity assumptions on the singular region, and when the latter does not
contain characteristic points. | [
0,
0,
1,
0,
0,
0
] |
Title: Tension and chemical efficiency of Myosin-II motors,
Abstract: Recent experiments demonstrate that molecular motors from the Myosin II
family serve as cross-links inducing active tension in the cytoskeletal
network. Here we revise the Brownian ratchet model, previously studied in the
context of active transport along polymer tracks, in setups resembling a motor
in a polymer network, also taking into account the effect of electrostatic
changes in the motor heads. We explore important mechanical quantities and show
that such a model is also capable of mechanosensing. Finally, we introduce a
novel efficiency based on excess heat production by the chemical cycle which is
directly related to the active tension the motor exerts. The chemical
efficiencies differ considerably for motors with a different number of heads,
while their mechanical properties remain qualitatively similar. For motors with
a small number of heads, the chemical efficiency is maximal when they are
frustrated, a trait that is not found in larger motors. | [
0,
1,
0,
0,
0,
0
] |
Title: Token-based Function Computation with Memory,
Abstract: In distributed function computation, each node has an initial value and the
goal is to compute a function of these values in a distributed manner. In this
paper, we propose a novel token-based approach to compute a wide class of
target functions to which we refer as "Token-based function Computation with
Memory" (TCM) algorithm. In this approach, node values are attached to tokens
and travel across the network. Each pair of travelling tokens would coalesce
when they meet, forming a token with a new value as a function of the original
token values. In contrast to the Coalescing Random Walk (CRW) algorithm, where
token movement is governed by random walk, meeting of tokens in our scheme is
accelerated by adopting a novel chasing mechanism. We proved that, compared to
the CRW algorithm, the TCM algorithm results in a reduction of time complexity
by a factor of at least $\sqrt{n/\log(n)}$ in Erdös-Renyi and complete
graphs, and by a factor of $\log(n)/\log(\log(n))$ in torus networks.
Simulation results show that there is at least a constant factor improvement in
the message complexity of TCM algorithm in all considered topologies.
Robustness of the CRW and TCM algorithms in the presence of node failure is
analyzed. We show that their robustness can be improved by running multiple
instances of the algorithms in parallel. | [
1,
0,
0,
1,
0,
0
] |
Title: Simple property of heterogeneous aspiration dynamics: Beyond weak selection,
Abstract: How individuals adapt their behavior in cultural evolution remains elusive.
Theoretical studies have shown that the update rules chosen to model individual
decision making can dramatically modify the evolutionary outcome of the
population as a whole. This hints at the complexities of considering the
personality of individuals in a population, where each one uses its own rule.
Here, we investigate whether and how heterogeneity in the rules of behavior
update alters the evolutionary outcome. We assume that individuals update
behaviors by aspiration-based self-evaluation and they do so in their own ways.
Under weak selection, we analytically reveal a simple property that holds for
any two-strategy multi-player games in well-mixed populations and on regular
graphs: the evolutionary outcome in a population with heterogeneous update
rules is the weighted average of the outcomes in the corresponding homogeneous
populations, and the associated weights are the frequencies of each update rule
in the heterogeneous population. Beyond weak selection, we show that this
property holds for public goods games. Our finding implies that heterogeneous
aspiration dynamics is additive. This additivity greatly reduces the complexity
induced by the underlying individual heterogeneity. Our work thus provides an
efficient method to calculate evolutionary outcomes under heterogeneous update
rules. | [
0,
0,
0,
0,
1,
0
] |
Title: High quality factor manganese-doped aluminum lumped-element kinetic inductance detectors sensitive to frequencies below 100 GHz,
Abstract: Aluminum lumped-element kinetic inductance detectors (LEKIDs) sensitive to
millimeter-wave photons have been shown to exhibit high quality factors, making
them highly sensitive and multiplexable. The superconducting gap of aluminum
limits aluminum LEKIDs to photon frequencies above 100 GHz. Manganese-doped
aluminum (Al-Mn) has a tunable critical temperature and could therefore be an
attractive material for LEKIDs sensitive to frequencies below 100 GHz if the
internal quality factor remains sufficiently high when manganese is added to
the film. To investigate, we measured some of the key properties of Al-Mn
LEKIDs. A prototype eight-element LEKID array was fabricated using a 40 nm
thick film of Al-Mn deposited on a 500 {\mu}m thick high-resistivity,
float-zone silicon substrate. The manganese content was 900 ppm, the measured
$T_c = 694\pm1$ mK, and the resonance frequencies were near 150 MHz. Using
measurements of the forward scattering parameter $S_{21}$ at various bath
temperatures between 65 and 250 mK, we determined that the Al-Mn LEKIDs we
fabricated have internal quality factors greater than $2 \times 10^5$, which is
high enough for millimeter-wave astrophysical observations. In the dark
conditions under which these devices were measured, the fractional frequency
noise spectrum shows a shallow slope that depends on bath temperature and probe
tone amplitude, which could be two-level system noise. The anticipated white
photon noise should dominate this level of low-frequency noise when the
detectors are illuminated with millimeter-waves in future measurements. The
LEKIDs responded to light pulses from a 1550 nm light-emitting diode, and we
used these light pulses to determine that the quasiparticle lifetime is 60
{\mu}s. | [
0,
1,
0,
0,
0,
0
] |
Title: Dark Energy Cosmological Models with General forms of Scale Factor,
Abstract: In this paper, we have constructed dark energy models in an anisotropic
Bianchi-V space-time and studied the role of anisotropy in the evolution of
dark energy. We have considered anisotropic dark energy fluid with different
pressure gradients along different spatial directions. In order to obtain a
deterministic solution, we have considered three general forms of scale factor.
The different forms of scale factors considered here produce time varying
deceleration parameters in all the cases that simulates the cosmic transition.
The variable equation of state (EoS) parameter, skewness parameters for all the
models are obtained and analyzed. The physical properties of the models are
also discussed. | [
0,
1,
0,
0,
0,
0
] |
Title: Quantum Klein Space and Superspace,
Abstract: We give an algebraic quantization, in the sense of quantum groups, of the
complex Minkowski space, and we examine the real forms corresponding to the
signatures $(3,1)$, $(2,2)$, $(4,0)$, constructing the corresponding quantum
metrics and providing an explicit presentation of the quantized coordinate
algebras. In particular, we focus on the Kleinian signature $(2,2)$. The
quantizations of the complex and real spaces come together with a coaction of
the quantizations of the respective symmetry groups. We also extend such
quantizations to the $\mathcal{N}=1$ supersetting. | [
0,
0,
1,
0,
0,
0
] |
Title: Bayesian Lasso Posterior Sampling via Parallelized Measure Transport,
Abstract: It is well known that the Lasso can be interpreted as a Bayesian posterior
mode estimate with a Laplacian prior. Obtaining samples from the full posterior
distribution, the Bayesian Lasso, confers major advantages in performance as
compared to having only the Lasso point estimate. Traditionally, the Bayesian
Lasso is implemented via Gibbs sampling methods which suffer from lack of
scalability, unknown convergence rates, and generation of samples that are
necessarily correlated. We provide a measure transport approach to generate
i.i.d samples from the posterior by constructing a transport map that
transforms a sample from the Laplacian prior into a sample from the posterior.
We show how the construction of this transport map can be parallelized into
modules that iteratively solve Lasso problems and perform closed-form linear
algebra updates. With this posterior sampling method, we perform maximum
likelihood estimation of the Lasso regularization parameter via the EM
algorithm. We provide comparisons to traditional Gibbs samplers using the
diabetes dataset of Efron et al. Lastly, we give an example implementation on a
computing system that leverages parallelization, a graphics processing unit,
whose execution time has much less dependence on dimension as compared to a
standard implementation. | [
0,
0,
0,
1,
0,
0
] |
Title: Endogeneous Dynamics of Intraday Liquidity,
Abstract: In this paper we investigate the endogenous information contained in four
liquidity variables at a five minutes time scale on equity markets around the
world: the traded volume, the bid-ask spread, the volatility and the volume at
first limits of the orderbook. In the spirit of Granger causality, we measure
the level of information by the level of accuracy of linear autoregressive
models. This empirical study is carried out on a dataset of more than 300
stocks from four different markets (US, UK, Japan and Hong Kong) from a period
of over five years. We discuss the obtained performances of autoregressive (AR)
models on stationarized versions of the variables, focusing on explaining the
observed differences between stocks.
Since empirical studies are often conducted at this time scale, we believe it
is of paramount importance to document endogenous dynamics in a simple
framework with no addition of supplemental information. Our study can hence be
used as a benchmark to identify exogenous effects. On the other hand, most
optimal trading frameworks (like the celebrated Almgren and Chriss one), focus
on computing an optimal trading speed at a frequency close to the one we
consider. Such frameworks very often take i.i.d. assumptions on liquidity
variables; this paper document the auto-correlations emerging from real data,
opening the door to new developments in optimal trading. | [
0,
0,
0,
0,
0,
1
] |
Title: An analysis of the SPARSEVA estimate for the finite sample data case,
Abstract: In this paper, we develop an upper bound for the SPARSEVA (SPARSe Estimation
based on a VAlidation criterion) estimation error in a general scheme, i.e.,
when the cost function is strongly convex and the regularized norm is
decomposable for a pair of subspaces. We show how this general bound can be
applied to a sparse regression problem to obtain an upper bound for the
traditional SPARSEVA problem. Numerical results are used to illustrate the
effectiveness of the suggested bound. | [
0,
0,
1,
1,
0,
0
] |
Title: Rigorous estimates for the relegation algorithm,
Abstract: We revisit the relegation algorithm by Deprit et al. (Celest. Mech. Dyn.
Astron. 79:157-182, 2001) in the light of the rigorous Nekhoroshev's like
theory. This relatively recent algorithm is nowadays widely used for
implementing closed form analytic perturbation theories, as it generalises the
classical Birkhoff normalisation algorithm. The algorithm, here briefly
explained by means of Lie transformations, has been so far introduced and used
in a formal way, i.e. without providing any rigorous convergence or asymptotic
estimates. The overall aim of this paper is to find such quantitative estimates
and to show how the results about stability over exponentially long times can
be recovered in a simple and effective way, at least in the non-resonant case. | [
0,
1,
0,
0,
0,
0
] |
Title: Linear Pentapods with a Simple Singularity Variety,
Abstract: There exists a bijection between the configuration space of a linear pentapod
and all points $(u,v,w,p_x,p_y,p_z)\in\mathbb{R}^{6}$ located on the singular
quadric $\Gamma: u^2+v^2+w^2=1$, where $(u,v,w)$ determines the orientation of
the linear platform and $(p_x,p_y,p_z)$ its position. Then the set of all
singular robot configurations is obtained by intersecting $\Gamma$ with a cubic
hypersurface $\Sigma$ in $\mathbb{R}^{6}$, which is only quadratic in the
orientation variables and position variables, respectively. This article
investigates the restrictions to be imposed on the design of this mechanism in
order to obtain a reduction in degree. In detail we study the cases where
$\Sigma$ is (1) linear in position variables, (2) linear in orientation
variables and (3) quadratic in total. The resulting designs of linear pentapods
have the advantage of considerably simplified computation of singularity-free
spheres in the configuration space. Finally we propose three kinematically
redundant designs of linear pentapods with a simple singularity surface. | [
1,
0,
0,
0,
0,
0
] |
Title: Neural Networks as Interacting Particle Systems: Asymptotic Convexity of the Loss Landscape and Universal Scaling of the Approximation Error,
Abstract: Neural networks, a central tool in machine learning, have demonstrated
remarkable, high fidelity performance on image recognition and classification
tasks. These successes evince an ability to accurately represent high
dimensional functions, potentially of great use in computational and applied
mathematics. That said, there are few rigorous results about the representation
error and trainability of neural networks. Here we characterize both the error
and the scaling of the error with the size of the network by reinterpreting the
standard optimization algorithm used in machine learning applications,
stochastic gradient descent, as the evolution of a particle system with
interactions governed by a potential related to the objective or "loss"
function used to train the network. We show that, when the number $n$ of
parameters is large, the empirical distribution of the particles descends on a
convex landscape towards a minimizer at a rate independent of $n$. We establish
a Law of Large Numbers and a Central Limit Theorem for the empirical
distribution, which together show that the approximation error of the network
universally scales as $O(n^{-1})$. Remarkably, these properties do not depend
on the dimensionality of the domain of the function that we seek to represent.
Our analysis also quantifies the scale and nature of the noise introduced by
stochastic gradient descent and provides guidelines for the step size and batch
size to use when training a neural network. We illustrate our findings on
examples in which we train neural network to learn the energy function of the
continuous 3-spin model on the sphere. The approximation error scales as our
analysis predicts in as high a dimension as $d=25$. | [
0,
0,
0,
1,
0,
0
] |
Title: Images of Ideals under Derivations and $\mathcal E$-Derivations of Univariate Polynomial Algebras over a Field of Characteristic Zero,
Abstract: Let $K$ be a field of characteristic zero and $x$ a free variable. A
$K$-$\mathcal E$-derivation of $K[x]$ is a $K$-linear map of the form
$\operatorname{I}-\phi$ for some $K$-algebra endomorphism $\phi$ of $K[x]$,
where $\operatorname{I}$ denotes the identity map of $K[x]$. In this paper we
study the image of an ideal of $K[x]$ under some $K$-derivations and
$K$-$\mathcal E$-derivations of $K[x]$. We show that the LFED conjecture
proposed in [Z4] holds for all $K$-$\mathcal E$-derivations and all locally
finite $K$-derivations of $K[x]$. We also show that the LNED conjecture
proposed in [Z4] holds for all locally nilpotent $K$-derivations of $K[x]$, and
also for all locally nilpotent $K$-$\mathcal E$-derivations of $K[x]$ and the
ideals $uK[x]$ such that either $u=0$, or $\operatorname{deg}\, u\le 1$, or $u$
has at least one repeated root in the algebraic closure of $K$. As a
bi-product, the homogeneous Mathieu subspaces (Mathieu-Zhao spaces) of the
univariate polynomial algebra over an arbitrary field have also been
classified. | [
0,
0,
1,
0,
0,
0
] |
Title: Cloud-based Deep Learning of Big EEG Data for Epileptic Seizure Prediction,
Abstract: Developing a Brain-Computer Interface~(BCI) for seizure prediction can help
epileptic patients have a better quality of life. However, there are many
difficulties and challenges in developing such a system as a real-life support
for patients. Because of the nonstationary nature of EEG signals, normal and
seizure patterns vary across different patients. Thus, finding a group of
manually extracted features for the prediction task is not practical. Moreover,
when using implanted electrodes for brain recording massive amounts of data are
produced. This big data calls for the need for safe storage and high
computational resources for real-time processing. To address these challenges,
a cloud-based BCI system for the analysis of this big EEG data is presented.
First, a dimensionality-reduction technique is developed to increase
classification accuracy as well as to decrease the communication bandwidth and
computation time. Second, following a deep-learning approach, a stacked
autoencoder is trained in two steps for unsupervised feature extraction and
classification. Third, a cloud-computing solution is proposed for real-time
analysis of big EEG data. The results on a benchmark clinical dataset
illustrate the superiority of the proposed patient-specific BCI as an
alternative method and its expected usefulness in real-life support of epilepsy
patients. | [
1,
0,
0,
1,
0,
0
] |
Title: Anharmonicity and the isotope effect in superconducting lithium at high pressures: a first-principles approach,
Abstract: Recent experiments [Schaeffer 2015] have shown that lithium presents an
extremely anomalous isotope effect in the 15-25 GPa pressure range. In this
article we have calculated the anharmonic phonon dispersion of $\mathrm{^7Li}$
and $\mathrm{^6Li}$ under pressure, their superconducting transition
temperatures, and the associated isotope effect. We have found a huge
anharmonic renormalization of a transverse acoustic soft mode along $\Gamma$K
in the fcc phase, the expected structure at the pressure range of interest. In
fact, the anharmonic correction dynamically stabilizes the fcc phase above 25
GPa. However, we have not found any anomalous scaling of the superconducting
temperature with the isotopic mass. Additionally, we have also analyzed whether
the two lithium isotopes adopting different structures could explain the
observed anomalous behavior. According to our enthalpy calculations including
zero-point motion and anharmonicity it would not be possible in a stable
regime. | [
0,
1,
0,
0,
0,
0
] |
Title: Deep Bayesian Active Learning with Image Data,
Abstract: Even though active learning forms an important pillar of machine learning,
deep learning tools are not prevalent within it. Deep learning poses several
difficulties when used in an active learning setting. First, active learning
(AL) methods generally rely on being able to learn and update models from small
amounts of data. Recent advances in deep learning, on the other hand, are
notorious for their dependence on large amounts of data. Second, many AL
acquisition functions rely on model uncertainty, yet deep learning methods
rarely represent such model uncertainty. In this paper we combine recent
advances in Bayesian deep learning into the active learning framework in a
practical way. We develop an active learning framework for high dimensional
data, a task which has been extremely challenging so far, with very sparse
existing literature. Taking advantage of specialised models such as Bayesian
convolutional neural networks, we demonstrate our active learning techniques
with image data, obtaining a significant improvement on existing active
learning approaches. We demonstrate this on both the MNIST dataset, as well as
for skin cancer diagnosis from lesion images (ISIC2016 task). | [
1,
0,
0,
1,
0,
0
] |
Title: Robust Optical Flow Estimation in Rainy Scenes,
Abstract: Optical flow estimation in the rainy scenes is challenging due to background
degradation introduced by rain streaks and rain accumulation effects in the
scene. Rain accumulation effect refers to poor visibility of remote objects due
to the intense rainfall. Most existing optical flow methods are erroneous when
applied to rain sequences because the conventional brightness constancy
constraint (BCC) and gradient constancy constraint (GCC) generally break down
in this situation. Based on the observation that the RGB color channels receive
raindrop radiance equally, we introduce a residue channel as a new data
constraint to reduce the effect of rain streaks. To handle rain accumulation,
our method decomposes the image into a piecewise-smooth background layer and a
high-frequency detail layer. It also enforces the BCC on the background layer
only. Results on both synthetic dataset and real images show that our algorithm
outperforms existing methods on different types of rain sequences. To our
knowledge, this is the first optical flow method specifically dealing with
rain. | [
1,
0,
0,
0,
0,
0
] |
Title: Thermophysical Phenomena in Metal Additive Manufacturing by Selective Laser Melting: Fundamentals, Modeling, Simulation and Experimentation,
Abstract: Among the many additive manufacturing (AM) processes for metallic materials,
selective laser melting (SLM) is arguably the most versatile in terms of its
potential to realize complex geometries along with tailored microstructure.
However, the complexity of the SLM process, and the need for predictive
relation of powder and process parameters to the part properties, demands
further development of computational and experimental methods. This review
addresses the fundamental physical phenomena of SLM, with a special emphasis on
the associated thermal behavior. Simulation and experimental methods are
discussed according to three primary categories. First, macroscopic approaches
aim to answer questions at the component level and consider for example the
determination of residual stresses or dimensional distortion effects prevalent
in SLM. Second, mesoscopic approaches focus on the detection of defects such as
excessive surface roughness, residual porosity or inclusions that occur at the
mesoscopic length scale of individual powder particles. Third, microscopic
approaches investigate the metallurgical microstructure evolution resulting
from the high temperature gradients and extreme heating and cooling rates
induced by the SLM process. Consideration of physical phenomena on all of these
three length scales is mandatory to establish the understanding needed to
realize high part quality in many applications, and to fully exploit the
potential of SLM and related metal AM processes. | [
1,
1,
0,
0,
0,
0
] |
Title: Numerical Methods for Pulmonary Image Registration,
Abstract: Due to complexity and invisibility of human organs, diagnosticians need to
analyze medical images to determine where the lesion region is, and which kind
of disease is, in order to make precise diagnoses. For satisfying clinical
purposes through analyzing medical images, registration plays an essential
role. For instance, in Image-Guided Interventions (IGI) and computer-aided
surgeries, patient anatomy is registered to preoperative images to guide
surgeons complete procedures. Medical image registration is also very useful in
surgical planning, monitoring disease progression and for atlas construction.
Due to the significance, the theories, methods, and implementation method of
image registration constitute fundamental knowledge in educational training for
medical specialists. In this chapter, we focus on image registration of a
specific human organ, i.e. the lung, which is prone to be lesioned. For
pulmonary image registration, the improvement of the accuracy and how to obtain
it in order to achieve clinical purposes represents an important problem which
should seriously be addressed. In this chapter, we provide a survey which
focuses on the role of image registration in educational training together with
the state-of-the-art of pulmonary image registration. In the first part, we
describe clinical applications of image registration introducing artificial
organs in Simulation-based Education. In the second part, we summarize the
common methods used in pulmonary image registration and analyze popular papers
to obtain a survey of pulmonary image registration. | [
0,
1,
0,
0,
0,
0
] |
Title: Topology Adaptive Graph Convolutional Networks,
Abstract: Spectral graph convolutional neural networks (CNNs) require approximation to
the convolution to alleviate the computational complexity, resulting in
performance loss. This paper proposes the topology adaptive graph convolutional
network (TAGCN), a novel graph convolutional network defined in the vertex
domain. We provide a systematic way to design a set of fixed-size learnable
filters to perform convolutions on graphs. The topologies of these filters are
adaptive to the topology of the graph when they scan the graph to perform
convolution. The TAGCN not only inherits the properties of convolutions in CNN
for grid-structured data, but it is also consistent with convolution as defined
in graph signal processing. Since no approximation to the convolution is
needed, TAGCN exhibits better performance than existing spectral CNNs on a
number of data sets and is also computationally simpler than other recent
methods. | [
1,
0,
0,
1,
0,
0
] |
Title: Far-from-equilibrium transport of excited carriers in nanostructures,
Abstract: Transport of charged carriers in regimes of strong non-equilibrium is
critical in a wide array of applications ranging from solar energy conversion
and semiconductor devices to quantum information. Plasmonic hot-carrier science
brings this regime of transport physics to the forefront since photo-excited
carriers must be extracted far from equilibrium to harvest their energy
efficiently. Here, we present a theoretical and computational framework,
Non-Equilibrium Scattering in Space and Energy (NESSE), to predict the spatial
evolution of carrier energy distributions that combines the best features of
phase-space (Boltzmann) and particle-based (Monte Carlo) methods. Within the
NESSE framework, we bridge first-principles electronic structure predictions of
plasmon decay and carrier collision integrals at the atomic scale, with
electromagnetic field simulations at the nano- to mesoscale. Finally, we apply
NESSE to predict spatially-resolved energy distributions of photo-excited
carriers that impact the surface of experimentally realizable plasmonic
nanostructures, enabling first-principles design of hot carrier devices. | [
0,
1,
0,
0,
0,
0
] |
Title: On annihilators of bounded $(\frak g, \frak k)$-modules,
Abstract: Let $\frak g$ be a semisimple Lie algebra and $\frak k\subset\frak g$ be a
reductive subalgebra. We say that a $\frak g$-module $M$ is a bounded $(\frak
g, \frak k)$-module if $M$ is a direct sum of simple finite-dimensional $\frak
k$-modules and the multiplicities of all simple $\frak k$-modules in that
direct sum are universally bounded.
The goal of this article is to show that the "boundedness" property for a
simple $(\frak g, \frak k)$-module $M$ is equivalent to a property of the
associated variety of the annihilator of $M$ (this is the closure of a
nilpotent coadjoint orbit inside $\frak g^*$) under the assumption that the
main field is algebraically closed and of characteristic 0. In particular this
implies that if $M_1, M_2$ are simple $(\frak g, \frak k)$-modules such that
$M_1$ is bounded and the associated varieties of the annihilators of $M_1$ and
$M_2$ coincide then $M_2$ is also bounded. This statement is a geometric
analogue of a purely algebraic fact due to I. Penkov and V. Serganova and it
was posed as a conjecture in my Ph.D. thesis. | [
0,
0,
1,
0,
0,
0
] |
Title: Discrete Time-Crystalline Order in Cavity and Circuit QED Systems,
Abstract: Discrete time crystals are a recently proposed and experimentally observed
out-of-equilibrium dynamical phase of Floquet systems, where the stroboscopic
evolution of a local observable repeats itself at an integer multiple of the
driving period. We address this issue in a driven-dissipative setup, focusing
on the modulated open Dicke model, which can be implemented by cavity or
circuit QED systems. In the thermodynamic limit, we employ semiclassical
approaches and find rich dynamical phases on top of the discrete
time-crystalline order. In a deep quantum regime with few qubits, we find clear
signatures of a transient discrete time-crystalline behavior, which is absent
in the isolated counterpart. We establish a phenomenology of dissipative
discrete time crystals by generalizing the Landau theory of phase transitions
to Floquet open systems. | [
0,
1,
0,
0,
0,
0
] |
Title: Relaxation of p-growth integral functionals under space-dependent differential constraints,
Abstract: A representation formula for the relaxation of integral energies
$$(u,v)\mapsto\int_{\Omega} f(x,u(x),v(x))\,dx,$$ is obtained, where $f$
satisfies $p$-growth assumptions, $1<p<+\infty$, and the fields $v$ are
subjected to space-dependent first order linear differential constraints in the
framework of $\mathscr{A}$-quasiconvexity with variable coefficients. | [
0,
0,
1,
0,
0,
0
] |
Title: Magma oceans and enhanced volcanism on TRAPPIST-1 planets due to induction heating,
Abstract: Low-mass M stars are plentiful in the Universe and often host small, rocky
planets detectable with the current instrumentation. Recently, seven small
planets have been discovered orbiting the ultracool dwarf
TRAPPIST-1\cite{Gillon16,Gillon17}. We examine the role of electromagnetic
induction heating of these planets, caused by the star's rotation and the
planet's orbital motion. If the stellar rotation and magnetic dipole axes are
inclined with respect to each other, induction heating can melt the upper
mantle and enormously increase volcanic activity, sometimes producing a magma
ocean below the planetary surface. We show that induction heating leads the
three innermost planets, one of which is in the habitable zone, to either
evolve towards a molten mantle planet, or to experience increased outgassing
and volcanic activity, while the four outermost planets remain mostly
unaffected. | [
0,
1,
0,
0,
0,
0
] |
Title: Buildup of Speaking Skills in an Online Learning Community: A Network-Analytic Exploration,
Abstract: In this study, we explore peer-interaction effects in online networks on
speaking skill development. In particular, we present an evidence for gradual
buildup of skills in a small-group setting that has not been reported in the
literature. We introduce a novel dataset of six online communities consisting
of 158 participants focusing on improving their speaking skills. They
video-record speeches for 5 prompts in 10 days and exchange comments and
performance-ratings with their peers. We ask (i) whether the participants'
ratings are affected by their interaction patterns with peers, and (ii) whether
there is any gradual buildup of speaking skills in the communities towards
homogeneity. To analyze the data, we employ tools from the emerging field of
Graph Signal Processing (GSP). GSP enjoys a distinction from Social Network
Analysis in that the latter is concerned primarily with the connection
structures of graphs, while the former studies signals on top of graphs. We
study the performance ratings of the participants as graph signals atop
underlying interaction topologies. Total variation analysis of the graph
signals show that the participants' rating differences decrease with time
(slope=-0.04, p<0.01), while average ratings increase (slope=0.07,
p<0.05)--thereby gradually building up the ratings towards community-wide
homogeneity. We provide evidence for peer-influence through a prediction
formulation. Our consensus-based prediction model outperforms baseline
network-agnostic regression models by about 23% in predicting performance
ratings. This, in turn, shows that participants' ratings are affected by their
peers' ratings and the associated interaction patterns, corroborating previous
findings. Then, we formulate a consensus-based diffusion model that captures
these observations of peer-influence from our analyses. | [
1,
0,
0,
0,
0,
0
] |
Title: A Note on Kaldi's PLDA Implementation,
Abstract: Some explanations to Kaldi's PLDA implementation to make formula derivation
easier to catch. | [
0,
0,
0,
1,
0,
0
] |
Title: Magnetic field--induced modification of selection rules for Rb D$_2$ line monitored by selective reflection from a vapor nanocell,
Abstract: Magnetic field-induced giant modification of the probabilities of five
transitions of $5S_{1/2}, F_g=2 \rightarrow 5P_{3/2}, F_e=4$ of $^{85}$Rb and
three transitions of $5S_{1/2}, F_g=1 \rightarrow 5P_{3/2}, F_e=3$ of $^{87}$Rb
forbidden by selection rules for zero magnetic field has been observed
experimentally and described theoretically for the first time. For the case of
excitation with circularly-polarized ($\sigma^+$) laser radiation, the
probability of $F_g=2, ~m_F=-2 \rightarrow F_e=4, ~m_F=-1$ transition becomes
the largest among the seventeen transitions of $^{85}$Rb $F_g=2 \rightarrow
F_e=1,2,3,4$ group, and the probability of $F_g=1,~m_F=-1 \rightarrow
F_e=3,~m_F=0$ transition becomes the largest among the nine transitions of
$^{87}$Rb $F_g=1 \rightarrow F_e=0,1,2,3$ group, in a wide range of magnetic
field 200 -- 1000 G. Complete frequency separation of individual Zeeman
components was obtained by implementation of derivative selective reflection
technique with a 300 nm-thick nanocell filled with Rb, allowing formation of
narrow optical resonances. Possible applications are addressed. The theoretical
model is perfectly consistent with the experimental results. | [
0,
1,
0,
0,
0,
0
] |
Title: Twists of quantum Borel algebras,
Abstract: We classify Drinfeld twists for the quantum Borel subalgebra u_q(b) in the
Frobenius-Lusztig kernel u_q(g), where g is a simple Lie algebra over C and q
an odd root of unity. More specifically, we show that alternating forms on the
character group of the group of grouplikes for u_q(b) generate all twists for
u_q(b), under a certain algebraic group action. This implies a simple
classification of Hopf algebras whose categories of representations are tensor
equivalent to that of u_q(b). We also show that cocycle twists for the
corresponding De Concini-Kac algebra are in bijection with alternating forms on
the aforementioned character group. | [
0,
0,
1,
0,
0,
0
] |
Title: Uniqueness of the von Neumann continuous factor,
Abstract: For a division ring $D$, denote by $\mathcal M_D$ the $D$-ring obtained as
the completion of the direct limit $\varinjlim_n M_{2^n}(D)$ with respect to
the metric induced by its unique rank function. We prove that, for any
ultramatricial $D$-ring $\mathcal B$ and any non-discrete extremal pseudo-rank
function $N$ on $\mathcal B$, there is an isomorphism of $D$-rings
$\overline{\mathcal B} \cong \mathcal M_D$, where $\overline{\mathcal B}$
stands for the completion of $\mathcal B$ with respect to the pseudo-metric
induced by $N$. This generalizes a result of von Neumann. We also show a
corresponding uniqueness result for $*$-algebras over fields $F$ with positive
definite involution, where the algebra $\mathcal M_F$ is endowed with its
natural involution coming from the $*$-transpose involution on each of the
factors $M_{2^n}(F)$. | [
0,
0,
1,
0,
0,
0
] |
Title: A hybrid finite volume -- finite element method for bulk--surface coupled problems,
Abstract: The paper develops a hybrid method for solving a system of
advection--diffusion equations in a bulk domain coupled to advection--diffusion
equations on an embedded surface. A monotone nonlinear finite volume method for
equations posed in the bulk is combined with a trace finite element method for
equations posed on the surface. In our approach, the surface is not fitted by
the mesh and is allowed to cut through the background mesh in an arbitrary way.
Moreover, a triangulation of the surface into regular shaped elements is not
required. The background mesh is an octree grid with cubic cells. As an example
of an application, we consider the modeling of contaminant transport in
fractured porous media. One standard model leads to a coupled system of
advection--diffusion equations in a bulk (matrix) and along a surface
(fracture). A series of numerical experiments with both steady and unsteady
problems and different embedded geometries illustrate the numerical properties
of the hybrid approach. The method demonstrates great flexibility in handling
curvilinear or branching lower dimensional embedded structures. | [
0,
1,
1,
0,
0,
0
] |
Title: From Quenched Disorder to Continuous Time Random Walk,
Abstract: This work focuses on quantitative representation of transport in systems with
quenched disorder. Explicit mapping of the quenched trap model to continuous
time random walk is presented. Linear temporal transformation: $t\to
t/\Lambda^{1/\alpha}$ for transient process on translationally invariant
lattice, in the sub-diffusive regime, is sufficient for asymptotic mapping.
Exact form of the constant $\Lambda^{1/\alpha}$ is established. Disorder
averaged position probability density function for quenched trap model is
obtained and analytic expressions for the diffusion coefficient and drift are
provided. | [
0,
1,
0,
0,
0,
0
] |
Title: Network Flows that Solve Least Squares for Linear Equations,
Abstract: This paper presents a first-order {distributed continuous-time algorithm} for
computing the least-squares solution to a linear equation over networks. Given
the uniqueness of the solution, with nonintegrable and diminishing step size,
convergence results are provided for fixed graphs. The exact rate of
convergence is also established for various types of step size choices falling
into that category. For the case where non-unique solutions exist, convergence
to one such solution is proved for constantly connected switching graphs with
square integrable step size, and for uniformly jointly connected switching
graphs under the boundedness assumption on system states. Validation of the
results and illustration of the impact of step size on the convergence speed
are made using a few numerical examples. | [
1,
0,
0,
0,
0,
0
] |
Title: Bipartite Envy-Free Matching,
Abstract: Bipartite Envy-Free Matching (BEFM) is a relaxation of perfect matching. In a
bipartite graph with parts X and Y, a BEFM is a matching of some vertices in X
to some vertices in Y, such that each unmatched vertex in X is not adjacent to
any matched vertex in Y (so the unmatched vertices do not "envy" the matched
ones). The empty matching is always a BEFM. This paper presents sufficient and
necessary conditions for the existence of a non-empty BEFM. These conditions
are based on cardinality of neighbor-sets, similarly to Hall's condition for
the existence of a perfect matching. The conditions can be verified in
polynomial time, and in case they are satisfied, a non-empty BEFM can be found
by a polynomial-time algorithm. The paper presents some applications of BEFM as
a subroutine in fair division algorithms. | [
1,
0,
0,
0,
0,
0
] |
Title: Hierarchical star formation across the grand design spiral NGC1566,
Abstract: We investigate how star formation is spatially organized in the grand-design
spiral NGC 1566 from deep HST photometry with the Legacy ExtraGalactic UV
Survey (LEGUS). Our contour-based clustering analysis reveals 890 distinct
stellar conglomerations at various levels of significance. These star-forming
complexes are organized in a hierarchical fashion with the larger congregations
consisting of smaller structures, which themselves fragment into even smaller
and more compact stellar groupings. Their size distribution, covering a wide
range in length-scales, shows a power-law as expected from scale-free
processes. We explain this shape with a simple "fragmentation and enrichment"
model. The hierarchical morphology of the complexes is confirmed by their
mass--size relation which can be represented by a power-law with a fractional
exponent, analogous to that determined for fractal molecular clouds. The
surface stellar density distribution of the complexes shows a log-normal shape
similar to that for supersonic non-gravitating turbulent gas. Between 50 and 65
per cent of the recently-formed stars, as well as about 90 per cent of the
young star clusters, are found inside the stellar complexes, located along the
spiral arms. We find an age-difference between young stars inside the complexes
and those in their direct vicinity in the arms of at least 10 Myr. This
timescale may relate to the minimum time for stellar evaporation, although we
cannot exclude the in situ formation of stars. As expected, star formation
preferentially occurs in spiral arms. Our findings reveal turbulent-driven
hierarchical star formation along the arms of a grand-design galaxy. | [
0,
1,
0,
0,
0,
0
] |
Title: Certifying coloring algorithms for graphs without long induced paths,
Abstract: Let $P_k$ be a path, $C_k$ a cycle on $k$ vertices, and $K_{k,k}$ a complete
bipartite graph with $k$ vertices on each side of the bipartition. We prove
that (1) for any integers $k, t>0$ and a graph $H$ there are finitely many
subgraph minimal graphs with no induced $P_k$ and $K_{t,t}$ that are not
$H$-colorable and (2) for any integer $k>4$ there are finitely many subgraph
minimal graphs with no induced $P_k$ that are not $C_{k-2}$-colorable.
The former generalizes the result of Hell and Huang [Complexity of coloring
graphs without paths and cycles, Discrete Appl. Math. 216: 211--232 (2017)] and
the latter extends a result of Bruce, Hoang, and Sawada [A certifying algorithm
for 3-colorability of $P_5$-Free Graphs, ISAAC 2009: 594--604]. Both our
results lead to polynomial-time certifying algorithms for the corresponding
coloring problems. | [
1,
0,
0,
0,
0,
0
] |
Title: Efficient Estimation for Dimension Reduction with Censored Data,
Abstract: We propose a general index model for survival data, which generalizes many
commonly used semiparametric survival models and belongs to the framework of
dimension reduction. Using a combination of geometric approach in
semiparametrics and martingale treatment in survival data analysis, we devise
estimation procedures that are feasible and do not require
covariate-independent censoring as assumed in many dimension reduction methods
for censored survival data. We establish the root-$n$ consistency and
asymptotic normality of the proposed estimators and derive the most efficient
estimator in this class for the general index model. Numerical experiments are
carried out to demonstrate the empirical performance of the proposed estimators
and an application to an AIDS data further illustrates the usefulness of the
work. | [
0,
0,
1,
1,
0,
0
] |
Title: Subdifferential characterization of probability functions under Gaussian distribution,
Abstract: Probability functions figure prominently in optimization problems of
engineering. They may be nonsmooth even if all input data are smooth.This fact
motivates the consideration of subdifferentials for such typically just
continuous functions. The aim of this paper is to provide subdifferential
formulae in the case of Gaussian distributions for possibly
infinite-dimensional decision variables and nonsmooth (locally Lipschitzian)
input data. These formulae are based on the spheric-radial decomposition of
Gaussian random vectors on the one hand and on a cone of directions of moderate
growth on the other. By successively adding additional hypotheses, conditions
are satisfied under which the probability function is locally Lipschitzian or
even differentiable. | [
0,
0,
1,
0,
0,
0
] |
Title: EPTL - A temporal logic for weakly consistent systems,
Abstract: The high availability and scalability of weakly-consistent systems attracts
system designers. Yet, writing correct application code for this type of
systems is difficult; even how to specify the intended behavior of such systems
is still an open question. There has not been established any standard method
to specify the intended dynamic behavior of a weakly consistent system. There
exist specifications of various consistency models for distributed and
concurrent systems; and the semantics of replicated datatypes like CRDTs have
been specified in axiomatic and operational models based on visibility
relations.
In this paper, we present a temporal logic, EPTL, that is tailored to specify
properties of weakly consistent systems. In contrast to LTL and CTL, EPTL takes
into account that operations of weakly consistent systems are in many cases not
serializable and have to be treated respectively to capture the behavior. We
embed our temporal logic in Isabelle/HOL and can thereby leverage strong
semi-automatic proving capabilities. | [
1,
0,
0,
0,
0,
0
] |
Title: Absence of chaos in Digital Memcomputing Machines with solutions,
Abstract: Digital memcomputing machines (DMMs) are non-linear dynamical systems
designed so that their equilibrium points are solutions of the Boolean problem
they solve. In a previous work [Chaos 27, 023107 (2017)] it was argued that
when DMMs support solutions of the associated Boolean problem then strange
attractors cannot coexist with such equilibria. In this work, we demonstrate
such conjecture. In particular, we show that both topological transitivity and
the strongest property of topological mixing are inconsistent with the point
dissipative property of DMMs when equilibrium points are present. This is true
for both the whole phase space and the global attractor. Absence of topological
transitivity is enough to imply absence of chaotic behavior. In a similar vein,
we prove that if DMMs do not have equilibrium points, the only attractors
present are invariant tori/periodic orbits with periods that may possibly
increase with system size (quasi-attractors). | [
1,
1,
0,
0,
0,
0
] |
Title: Fine-resolution analysis of exoplanetary distributions by wavelets: hints of an overshooting iceline accumulation,
Abstract: We investigate 1D exoplanetary distributions using a novel analysis algorithm
based on the continuous wavelet transform. The analysis pipeline includes an
estimation of the wavelet transform of the probability density function
(p.d.f.) without pre-binning, use of optimized wavelets, a rigorous
significance testing of the patterns revealed in the p.d.f., and an optimized
minimum-noise reconstruction of the p.d.f. via matching pursuit iterations.
In the distribution of orbital periods, $P$, our analysis revealed a narrow
subfamily of exoplanets within the broad family of "warm jupiters", or massive
giants with $P\gtrsim 300$~d, which are often deemed to be related with the
iceline accumulation in a protoplanetary disk. We detected a p.d.f. pattern
that represents an upturn followed by an overshooting peak spanning $P\sim
300-600$~d, right beyond the "period valley". It is separated from the other
planets by p.d.f. concavities from both sides. It has at least two-sigma
significance.
In the distribution of planet radii, $R$, and using the California Kepler
Survey sample properly cleaned, we confirm the hints of a bimodality with two
peaks about $R=1.3 R_\oplus$ and $R=2.4 R_\oplus$, and the "evaporation valley"
between them. However, we obtain just a modest significance for this pattern,
two-sigma only at the best. Besides, our follow-up application of the Hartigan
& Hartigan dip test for unimodality returns $3$ per cent false alarm
probability (merely $2.2$-sigma significance), contrary to $0.14$ per cent (or
$3.2$-sigma), as claimed by Fulton et al. (2017). | [
0,
1,
0,
0,
0,
0
] |
Title: Meta-Learning MCMC Proposals,
Abstract: Effective implementations of sampling-based probabilistic inference often
require manually constructed, model-specific proposals. Inspired by recent
progresses in meta-learning for training learning agents that can generalize to
unseen environments, we propose a meta-learning approach to building effective
and generalizable MCMC proposals. We parametrize the proposal as a neural
network to provide fast approximations to block Gibbs conditionals. The learned
neural proposals generalize to occurrences of common structural motifs across
different models, allowing for the construction of a library of learned
inference primitives that can accelerate inference on unseen models with no
model-specific training required. We explore several applications including
open-universe Gaussian mixture models, in which our learned proposals
outperform a hand-tuned sampler, and a real-world named entity recognition
task, in which our sampler yields higher final F1 scores than classical
single-site Gibbs sampling. | [
1,
0,
0,
1,
0,
0
] |
Title: Putting Self-Supervised Token Embedding on the Tables,
Abstract: Information distribution by electronic messages is a privileged means of
transmission for many businesses and individuals, often under the form of
plain-text tables. As their number grows, it becomes necessary to use an
algorithm to extract text and numbers instead of a human. Usual methods are
focused on regular expressions or on a strict structure in the data, but are
not efficient when we have many variations, fuzzy structure or implicit labels.
In this paper we introduce SC2T, a totally self-supervised model for
constructing vector representations of tokens in semi-structured messages by
using characters and context levels that address these issues. It can then be
used for an unsupervised labeling of tokens, or be the basis for a
semi-supervised information extraction system. | [
1,
0,
0,
0,
0,
0
] |
Title: Getting the public involved in Quantum Error Correction,
Abstract: The Decodoku project seeks to let users get hands-on with cutting-edge
quantum research through a set of simple puzzle games. The design of these
games is explicitly based on the problem of decoding qudit variants of surface
codes. This problem is presented such that it can be tackled by players with no
prior knowledge of quantum information theory, or any other high-level physics
or mathematics. Methods devised by the players to solve the puzzles can then
directly be incorporated into decoding algorithms for quantum computation. In
this paper we give a brief overview of the novel decoding methods devised by
players, and provide short postmortem for Decodoku v1.0-v4.1. | [
0,
1,
0,
0,
0,
0
] |
Title: Second-grade fluids in curved pipes,
Abstract: This paper is concerned with the application of finite element methods to
obtain solutions for steady fully developed second-grade flows in a curved pipe
of circular cross-section and arbitrary curvature ratio, under a given axial
pressure gradient. The qualitative and quantitative behavior of the secondary
flows is analyzed with respect to inertia and viscoelasticity. | [
0,
1,
1,
0,
0,
0
] |
Title: Designing nearly tight window for improving time-frequency masking,
Abstract: Many audio signal processing methods are formulated in the time-frequency
(T-F) domain which is obtained by the short-time Fourier transform (STFT). The
property of STFT is fully characterized by window function, and thus designing
a better window is important for improving the performance of the processing
especially when a less redundant T-F representation is desirable. While many
window functions have been proposed in the literature, they are designed to
have a good frequency response for analysis, which may not perform well in
terms of signal processing. The window design must take the effect of the
reconstruction (from the T-F domain into the time domain) into account for
improving the performance. In this paper, an optimization-based design method
of a nearly tight window is proposed to obtain a window performing well for the
T-F domain signal processing. | [
1,
0,
0,
0,
0,
0
] |
Title: Asymptotic Properties of Recursive Maximum Likelihood Estimation in Non-Linear State-Space Models,
Abstract: Using stochastic gradient search and the optimal filter derivative, it is
possible to perform recursive (i.e., online) maximum likelihood estimation in a
non-linear state-space model. As the optimal filter and its derivative are
analytically intractable for such a model, they need to be approximated
numerically. In [Poyiadjis, Doucet and Singh, Biometrika 2018], a recursive
maximum likelihood algorithm based on a particle approximation to the optimal
filter derivative has been proposed and studied through numerical simulations.
Here, this algorithm and its asymptotic behavior are analyzed theoretically. We
show that the algorithm accurately estimates maxima to the underlying (average)
log-likelihood when the number of particles is sufficiently large. We also
derive (relatively) tight bounds on the estimation error. The obtained results
hold under (relatively) mild conditions and cover several classes of non-linear
state-space models met in practice. | [
0,
0,
0,
1,
0,
0
] |
Title: Microservices in Practice: A Survey Study,
Abstract: Microservices architectures have become largely popular in the last years.
However, we still lack empirical evidence about the use of microservices and
the practices followed by practitioners. Thereupon, in this paper, we report
the results of a survey with 122 professionals who work with microservices. We
report how the industry is using this architectural style and whether the
perception of practitioners regarding the advantages and challenges of
microservices is according to the literature. | [
1,
0,
0,
0,
0,
0
] |
Title: The relationship between $k$-forcing and $k$-power domination,
Abstract: Zero forcing and power domination are iterative processes on graphs where an
initial set of vertices are observed, and additional vertices become observed
based on some rules. In both cases, the goal is to eventually observe the
entire graph using the fewest number of initial vertices. Chang et al.
introduced $k$-power domination in [Generalized power domination in graphs,
{\it Discrete Applied Math.} 160 (2012) 1691-1698] as a generalization of power
domination and standard graph domination. Independently, Amos et al. defined
$k$-forcing in [Upper bounds on the $k$-forcing number of a graph, {\it
Discrete Applied Math.} 181 (2015) 1-10] to generalize zero forcing. In this
paper, we combine the study of $k$-forcing and $k$-power domination, providing
a new approach to analyze both processes. We give a relationship between the
$k$-forcing and the $k$-power domination numbers of a graph that bounds one in
terms of the other. We also obtain results using the contraction of subgraphs
that allow the parallel computation of $k$-forcing and $k$-power dominating
sets. | [
0,
0,
1,
0,
0,
0
] |
Title: Finding Root Causes of Floating Point Error with Herbgrind,
Abstract: Floating-point arithmetic plays a central role in science, engineering, and
finance by enabling developers to approximate real arithmetic. To address
numerical issues in large floating-point applications, developers must identify
root causes, which is difficult because floating-point errors are generally
non-local, non-compositional, and non-uniform.
This paper presents Herbgrind, a tool to help developers identify and address
root causes in numerical code written in low-level C/C++ and Fortran. Herbgrind
dynamically tracks dependencies between operations and program outputs to avoid
false positives and abstracts erroneous computations to a simplified program
fragment whose improvement can reduce output error. We perform several case
studies applying Herbgrind to large, expert-crafted numerical programs and show
that it scales to applications spanning hundreds of thousands of lines,
correctly handling the low-level details of modern floating point hardware and
mathematical libraries, and tracking error across function boundaries and
through the heap. | [
1,
0,
0,
0,
0,
0
] |
Title: Catalog of Candidates for Quasars at 3 < z < 5.5 Selected among X-Ray Sources from the 3XMM-DR4 Survey of the XMM-Newton Observatory,
Abstract: We have compiled a catalog of 903 candidates for type 1 quasars at redshifts
3<z<5.5 selected among the X-ray sources of the serendipitous XMM-Newton survey
presented in the 3XMM-DR4 catalog (the median X-ray flux is 5x10^{-15}
erg/s/cm^2 the 0.5-2 keV energy band) and located at high Galactic latitudes
>20 deg in Sloan Digital Sky Survey (SDSS) fields with a total area of about
300 deg^2. Photometric SDSS data as well infrared 2MASS and WISE data were used
to select the objects. We selected the point sources from the photometric SDSS
catalog with a magnitude error Delta z<0.2 and a color i-z<0.6 (to first
eliminate the M-type stars). For the selected sources, we have calculated the
dependences chi^2(z) for various spectral templates from the library that we
compiled for these purposes using the EAZY software. Based on these data, we
have rejected the objects whose spectral energy distributions are better
described by the templates of stars at z=0 and obtained a sample of quasars
with photometric redshift estimates 2.75<zphot<5.5. The selection completeness
of known quasars at z>3 in the investigated fields is shown to be about 80%.
The normalized median absolute deviation is 0.07, while the outlier fraction is
eta= 9. The number of objects per unit area in our sample exceeds the number of
quasars in the spectroscopic SDSS sample at the same redshifts approximately by
a factor of 1.5. The subsequent spectroscopic testing of the redshifts of our
selected candidates for quasars at 3<z<5.5 will allow the purity of this sample
to be estimated more accurately. | [
0,
1,
0,
0,
0,
0
] |
Title: The Theta Number of Simplicial Complexes,
Abstract: We introduce a generalization of the celebrated Lovász theta number of a
graph to simplicial complexes of arbitrary dimension. Our generalization takes
advantage of real simplicial cohomology theory, in particular combinatorial
Laplacians, and provides a semidefinite programming upper bound of the
independence number of a simplicial complex. We consider properties of the
graph theta number such as the relationship to Hoffman's ratio bound and to the
chromatic number and study how they extend to higher dimensions. Like in the
case of graphs, the higher dimensional theta number can be extended to a
hierarchy of semidefinite programming upper bounds reaching the independence
number. We analyze the value of the theta number and of the hierarchy for dense
random simplicial complexes. | [
1,
0,
1,
0,
0,
0
] |
Title: Prediction Scores as a Window into Classifier Behavior,
Abstract: Most multi-class classifiers make their prediction for a test sample by
scoring the classes and selecting the one with the highest score. Analyzing
these prediction scores is useful to understand the classifier behavior and to
assess its reliability. We present an interactive visualization that
facilitates per-class analysis of these scores. Our system, called Classilist,
enables relating these scores to the classification correctness and to the
underlying samples and their features. We illustrate how such analysis reveals
varying behavior of different classifiers. Classilist is available for use
online, along with source code, video tutorials, and plugins for R, RapidMiner,
and KNIME at this https URL. | [
1,
0,
0,
1,
0,
0
] |
Title: Effective perturbation theory for linear operators,
Abstract: We propose a new approach to the spectral theory of perturbed linear
operators , in the case of a simple isolated eigenvalue. We obtain two kind of
results: "radius bounds" which ensure perturbation theory applies for
perturbations up to an explicit size, and "regularity bounds" which control the
variations of eigendata to any order. Our method is based on the Implicit
Function Theorem and proceeds by establishing differential inequalities on two
natural quantities: the norm of the projection to the eigendirection, and the
norm of the reduced resolvent. We obtain completely explicit results without
any assumption on the underlying Banach space. In companion articles, on the
one hand we apply the regularity bounds to Markov chains, obtaining
non-asymptotic concentration and Berry-Ess{é}en inequalities with explicit
constants, and on the other hand we apply the radius bounds to transfer
operator of intermittent maps, obtaining explicit high-temperature regimes
where a spectral gap occurs. | [
0,
0,
1,
0,
0,
0
] |
Title: I-MMSE relations in random linear estimation and a sub-extensive interpolation method,
Abstract: Consider random linear estimation with Gaussian measurement matrices and
noise. One can compute infinitesimal variations of the mutual information under
infinitesimal variations of the signal-to-noise ratio or of the measurement
rate. We discuss how each variation is related to the minimum mean-square error
and deduce that the two variations are directly connected through a very simple
identity. The main technical ingredient is a new interpolation method called
"sub-extensive interpolation method". We use it to provide a new proof of an
I-MMSE relation recently found by Reeves and Pfister [1] when the measurement
rate is varied. Our proof makes it clear that this relation is intimately
related to another I-MMSE relation also recently proved in [2]. One can
directly verify that the identity relating the two types of variation of mutual
information is indeed consistent with the one letter replica symmetric formula
for the mutual information, first derived by Tanaka [3] for binary signals, and
recently proved in more generality in [1,2,4,5] (by independent methods).
However our proof is independent of any knowledge of Tanaka's formula. | [
1,
1,
0,
0,
0,
0
] |
Title: Layered semi-convection and tides in giant planet interiors - I. Propagation of internal waves,
Abstract: Layered semi-convection is a possible candidate to explain Saturn's
luminosity excess and the abnormally large radius of some hot Jupiters. In
giant planet interiors, it could lead to the creation of density staircases,
which are convective layers separated by thin stably stratified interfaces. We
study the propagation of internal waves in a region of layered semi-convection,
with the aim to predict energy transport by internal waves incident upon a
density staircase. The goal is then to understand the resulting tidal
dissipation when these waves are excited by other bodies such as moons in giant
planets systems. We use a local Cartesian analytical model, taking into account
the complete Coriolis acceleration at any latitude, thus generalizing previous
works. We find transmission of incident internal waves to be strongly affected
by the presence of a density staircase, even if these waves are initially pure
inertial waves (which are restored by the Coriolis acceleration). In
particular, low-frequency waves of all wavelengths are perfectly transmitted
near the critical latitude. Otherwise, short-wavelength waves are only
efficiently transmitted if they are resonant with a free mode (interfacial
gravity wave or short-wavelength inertial mode) of the staircase. In all other
cases, waves are primarily reflected unless their wavelengths are longer than
the vertical extent of the entire staircase (not just a single step). We expect
incident internal waves to be strongly affected by the presence of a density
staircase in a frequency-, latitude- and wavelength-dependent manner. First,
this could lead to new criteria to probe the interior of giant planets by
seismology; and second, this may have important consequences for tidal
dissipation and our understanding of the evolution of giant planet systems. | [
0,
1,
0,
0,
0,
0
] |
Title: Non-commutative Discretize-then-Optimize Algorithms for Elliptic PDE-Constrained Optimal Control Problems,
Abstract: In this paper, we analyze the convergence of several discretize-then-optimize
algorithms, based on either a second-order or a fourth-order finite difference
discretization, for solving elliptic PDE-constrained optimization or optimal
control problems. To ensure the convergence of a discretize-then-optimize
algorithm, one well-accepted criterion is to choose or redesign the
discretization scheme such that the resultant discretize-then-optimize
algorithm commutes with the corresponding optimize-then-discretize algorithm.
In other words, both types of algorithms would give rise to exactly the same
discrete optimality system. However, such an approach is not trivial. In this
work, by investigating a simple distributed elliptic optimal control problem,
we first show that enforcing such a stringent condition of commutative property
is only sufficient but not necessary for achieving the desired convergence. We
then propose to add some suitable $H_1$ semi-norm penalty/regularization terms
to recover the lost convergence due to the inconsistency caused by the loss of
commutativity. Numerical experiments are carried out to verify our theoretical
analysis and also validate the effectiveness of our proposed regularization
techniques. | [
0,
0,
1,
0,
0,
0
] |
Title: Translation matrix elements for spherical Gauss-Laguerre basis functions,
Abstract: Spherical Gauss-Laguerre (SGL) basis functions, i.e., normalized functions of
the type $L_{n-l-1}^{(l + 1/2)}(r^2) r^{l} Y_{lm}(\vartheta,\varphi)$, $|m|
\leq l < n \in \mathbb{N}$, constitute an orthonormal polynomial basis of the
space $L^{2}$ on $\mathbb{R}^{3}$ with radial Gaussian weight $\exp(-r^{2})$.
We have recently described reliable fast Fourier transforms for the SGL basis
functions. The main application of the SGL basis functions and our fast
algorithms is in solving certain three-dimensional rigid matching problems,
where the center is prioritized over the periphery. For this purpose, so-called
SGL translation matrix elements are required, which describe the spectral
behavior of the SGL basis functions under translations. In this paper, we
derive a closed-form expression of these translation matrix elements, allowing
for a direct computation of these quantities in practice. | [
0,
0,
1,
0,
0,
0
] |
Title: Parallel Concatenation of Bayesian Filters: Turbo Filtering,
Abstract: In this manuscript a method for developing novel filtering algorithms through
the parallel concatenation of two Bayesian filters is illustrated. Our
description of this method, called turbo filtering, is based on a new graphical
model; this allows us to efficiently describe both the processing accomplished
inside each of the constituent filter and the interactions between them. This
model is exploited to develop two new filtering algorithms for conditionally
linear Gaussian systems. Numerical results for a specific dynamic system
evidence that such filters can achieve a better complexity-accuracy tradeoff
than marginalized particle filtering. | [
0,
0,
0,
1,
0,
0
] |
Title: Shot noise in ultrathin superconducting wires,
Abstract: Quantum phase slips (QPS) may produce non-equilibrium voltage fluctuations in
current-biased superconducting nanowires. Making use of the Keldysh technique
and employing the phase-charge duality arguments we investigate such
fluctuations within the four-point measurement scheme and demonstrate that shot
noise of the voltage detected in such nanowires may essentially depend on the
particular measurement setup. In long wires the shot noise power decreases with
increasing frequency $\Omega$ and vanishes beyond a threshold value of $\Omega$
at $T \to 0$ | [
0,
1,
0,
0,
0,
0
] |
Title: Urban Data Streams and Machine Learning: A Case of Swiss Real Estate Market,
Abstract: In this paper, we show how using publicly available data streams and machine
learning algorithms one can develop practical data driven services with no
input from domain experts as a form of prior knowledge. We report the initial
steps toward development of a real estate portal in Switzerland. Based on
continuous web crawling of publicly available real estate advertisements and
using building data from Open Street Map, we developed a system, where we
roughly estimate the rental and sale price indexes of 1.7 million buildings
across the country. In addition to these rough estimates, we developed a web
based API for accurate automated valuation of rental prices of individual
properties and spatial sensitivity analysis of rental market. We tested several
established function approximation methods against the test data to check the
quality of the rental price estimations and based on our experiments, Random
Forest gives very reasonable results with the median absolute relative error of
6.57 percent, which is comparable with the state of the art in the industry. We
argue that while recently there have been successful cases of real estate
portals, which are based on Big Data, majority of the existing solutions are
expensive, limited to certain users and mostly with non-transparent underlying
systems. As an alternative we discuss, how using the crawled data sets and
other open data sets provided from different institutes it is easily possible
to develop data driven services for spatial and temporal sensitivity analysis
in the real estate market to be used for different stakeholders. We believe
that this kind of digital literacy can disrupt many other existing business
concepts across many domains. | [
1,
0,
0,
1,
0,
0
] |
Title: Unsupervised Learning of Neural Networks to Explain Neural Networks (extended abstract),
Abstract: This paper presents an unsupervised method to learn a neural network, namely
an explainer, to interpret a pre-trained convolutional neural network (CNN),
i.e., the explainer uses interpretable visual concepts to explain features in
middle conv-layers of a CNN. Given feature maps of a conv-layer of the CNN, the
explainer performs like an auto-encoder, which decomposes the feature maps into
object-part features. The object-part features are learned to reconstruct CNN
features without much loss of information. We can consider the disentangled
representations of object parts a paraphrase of CNN features, which help people
understand the knowledge encoded by the CNN. More crucially, we learn the
explainer via knowledge distillation without using any annotations of object
parts or textures for supervision. In experiments, our method was widely used
to interpret features of different benchmark CNNs, and explainers significantly
boosted the feature interpretability without hurting the discrimination power
of the CNNs. | [
1,
0,
0,
1,
0,
0
] |
Title: Measuring Cognitive Conflict in Virtual Reality with Feedback-Related Negativity,
Abstract: As virtual reality (VR) emerges as a mainstream platform, designers have
started to experiment new interaction techniques to enhance the user
experience. This is a challenging task because designers not only strive to
provide designs with good performance but also carefully ensure not to disrupt
users' immersive experience. There is a dire need for a new evaluation tool
that extends beyond traditional quantitative measurements to assist designers
in the design process. We propose an EEG-based experiment framework that
evaluates interaction techniques in VR by measuring intentionally elicited
cognitive conflict. Through the analysis of the feedback-related negativity
(FRN) as well as other quantitative measurements, this framework allows
designers to evaluate the effect of the variables of interest. We studied the
framework by applying it to the fundamental task of 3D object selection using
direct 3D input, i.e. tracked hand in VR. The cognitive conflict is
intentionally elicited by manipulating the selection radius of the target
object. Our first behavior experiment validated the framework in line with the
findings of conflict-induced behavior adjustments like those reported in other
classical psychology experiment paradigms. Our second EEG-based experiment
examines the effect of the appearance of virtual hands. We found that the
amplitude of FRN correlates with the level of realism of the virtual hands,
which concurs with the Uncanny Valley theory. | [
1,
0,
0,
0,
0,
0
] |
Title: Strong deformations of DNA: Effect on the persistence length,
Abstract: Extreme deformations of the DNA double helix attracted a lot of attention
during the past decades. Particularly, the determination of the persistence
length of DNA with extreme local disruptions, or kinks, has become a crucial
problem in the studies of many important biological processes. In this paper we
review an approach to calculate the persistence length of the double helix by
taking into account the formation of kinks of arbitrary configuration. The
reviewed approach improves the Kratky--Porod model to determine the type and
nature of kinks that occur in the double helix, by measuring a reduction of the
persistence length of the kinkable DNA. | [
0,
0,
0,
0,
1,
0
] |
Title: Dust in the reionization era: ALMA observations of a $z$=8.38 Galaxy,
Abstract: We report on the detailed analysis of a gravitationally-lensed Y-band
dropout, A2744_YD4, selected from deep Hubble Space Telescope imaging in the
Frontier Field cluster Abell 2744. Band 7 observations with the Atacama Large
Millimeter Array (ALMA) indicate the proximate detection of a significant 1mm
continuum flux suggesting the presence of dust for a star-forming galaxy with a
photometric redshift of $z\simeq8$. Deep X-SHOOTER spectra confirms the high
redshift identity of A2744_YD4 via the detection of Lyman $\alpha$ emission at
a redshift $z$=8.38. The association with the ALMA detection is confirmed by
the presence of [OIII] 88$\mu$m emission at the same redshift. Although both
emission features are only significant at the 4 $\sigma$ level, we argue their
joint detection and the positional coincidence with a high redshift dropout in
the HST images confirms the physical association. Analysis of the available
photometric data and the modest gravitational magnification ($\mu\simeq2$)
indicates A2744_YD4 has a stellar mass of $\sim$ 2$\times$10$^9$ M$_{\odot}$, a
star formation rate of $\sim20$ M$_{\odot}$/yr and a dust mass of
$\sim$6$\times$10$^{6}$ M$_{\odot}$. We discuss the implications of the
formation of such a dust mass only $\simeq$200 Myr after the onset of cosmic
reionisation. | [
0,
1,
0,
0,
0,
0
] |
Title: It Takes (Only) Two: Adversarial Generator-Encoder Networks,
Abstract: We present a new autoencoder-type architecture that is trainable in an
unsupervised mode, sustains both generation and inference, and has the quality
of conditional and unconditional samples boosted by adversarial learning.
Unlike previous hybrids of autoencoders and adversarial networks, the
adversarial game in our approach is set up directly between the encoder and the
generator, and no external mappings are trained in the process of learning. The
game objective compares the divergences of each of the real and the generated
data distributions with the prior distribution in the latent space. We show
that direct generator-vs-encoder game leads to a tight coupling of the two
components, resulting in samples and reconstructions of a comparable quality to
some recently-proposed more complex architectures. | [
1,
0,
0,
1,
0,
0
] |
Title: The Montecinos-Balsara ADER-FV Polynomial Basis: Convergence Properties & Extension to Non-Conservative Multidimensional Systems,
Abstract: Hyperbolic systems of PDEs can be solved to arbitrary orders of accuracy by
using the ADER Finite Volume method. These PDE systems may be non-conservative
and non-homogeneous, and contain stiff source terms. ADER-FV requires a
spatio-temporal polynomial reconstruction of the data in each spacetime cell,
at each time step. This reconstruction is obtained as the root of a nonlinear
system, resulting from the use of a Galerkin method. It was proved in Jackson
[7] that for traditional choices of basis polynomials, the eigenvalues of
certain matrices appearing in these nonlinear systems are always 0, regardless
of the number of spatial dimensions of the PDEs or the chosen order of accuracy
of the ADER-FV method. This guarantees fast convergence to the Galerkin root
for certain classes of PDEs.
In Montecinos and Balsara [9] a new, more efficient class of basis
polynomials for the one-dimensional ADER-FV method was presented. This new
class of basis polynomials, originally presented for conservative systems, is
extended to multidimensional, non-conservative systems here, and the
corresponding property regarding the eigenvalues of the Galerkin matrices is
proved. | [
0,
1,
0,
0,
0,
0
] |
Title: Reducibility of the Quantum Harmonic Oscillator in $d$-dimensions with Polynomial Time Dependent Perturbation,
Abstract: We prove a reducibility result for a quantum harmonic oscillator in arbitrary
dimensions with arbitrary frequencies perturbed by a linear operator which is a
polynomial of degree two in $x_j$, $-i \partial_j$ with coefficients which
depend quasiperiodically on time. | [
0,
0,
1,
0,
0,
0
] |
Title: Model Order Selection Rules For Covariance Structure Classification,
Abstract: The adaptive classification of the interference covariance matrix structure
for radar signal processing applications is addressed in this paper. This
represents a key issue because many detection architectures are synthesized
assuming a specific covariance structure which may not necessarily coincide
with the actual one due to the joint action of the system and environment
uncertainties. The considered classification problem is cast in terms of a
multiple hypotheses test with some nested alternatives and the theory of Model
Order Selection (MOS) is exploited to devise suitable decision rules. Several
MOS techniques, such as the Akaike, Takeuchi, and Bayesian information criteria
are adopted and the corresponding merits and drawbacks are discussed. At the
analysis stage, illustrating examples for the probability of correct model
selection are presented showing the effectiveness of the proposed rules. | [
0,
0,
1,
1,
0,
0
] |
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