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Collaborative Summarization of Topic-Related Videos | Large collections of videos are grouped into clusters by a topic keyword,
such as Eiffel Tower or Surfing, with many important visual concepts repeating
across them. Such a topically close set of videos have mutual influence on each
other, which could be used to summarize one of them by exploiting information
from others in the set. We build on this intuition to develop a novel approach
to extract a summary that simultaneously captures both important
particularities arising in the given video, as well as, generalities identified
from the set of videos. The topic-related videos provide visual context to
identify the important parts of the video being summarized. We achieve this by
developing a collaborative sparse optimization method which can be efficiently
solved by a half-quadratic minimization algorithm. Our work builds upon the
idea of collaborative techniques from information retrieval and natural
language processing, which typically use the attributes of other similar
objects to predict the attribute of a given object. Experiments on two
challenging and diverse datasets well demonstrate the efficacy of our approach
over state-of-the-art methods.
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ELICA: An Automated Tool for Dynamic Extraction of Requirements Relevant Information | Requirements elicitation requires extensive knowledge and deep understanding
of the problem domain where the final system will be situated. However, in many
software development projects, analysts are required to elicit the requirements
from an unfamiliar domain, which often causes communication barriers between
analysts and stakeholders. In this paper, we propose a requirements ELICitation
Aid tool (ELICA) to help analysts better understand the target application
domain by dynamic extraction and labeling of requirements-relevant knowledge.
To extract the relevant terms, we leverage the flexibility and power of
Weighted Finite State Transducers (WFSTs) in dynamic modeling of natural
language processing tasks. In addition to the information conveyed through
text, ELICA captures and processes non-linguistic information about the
intention of speakers such as their confidence level, analytical tone, and
emotions. The extracted information is made available to the analysts as a set
of labeled snippets with highlighted relevant terms which can also be exported
as an artifact of the Requirements Engineering (RE) process. The application
and usefulness of ELICA are demonstrated through a case study. This study shows
how pre-existing relevant information about the application domain and the
information captured during an elicitation meeting, such as the conversation
and stakeholders' intentions, can be captured and used to support analysts
achieving their tasks.
| 1 | 0 | 0 | 1 | 0 | 0 |
Crystal field excitations and magnons: their roles in oxyselenides Pr2O2M2OSe2 (M = Mn, Fe) | We present the results of neutron scattering experiments to study the crystal
and magnetic structures of the Mott-insulating transition metal oxyselenides
Pr2O2M2OSe2 (M = Mn, Fe). The structural role of the non-Kramers Pr3+ ion is
investigated and analysis of Pr3+ crystal field excitations performed.
Long-range order of Pr3+ moments in Pr2O2Fe2OSe2 can be induced by an applied
magnetic field.
| 0 | 1 | 0 | 0 | 0 | 0 |
Redistributing Funds across Charitable Crowdfunding Campaigns | On Kickstarter only 36% of crowdfunding campaigns successfully raise
sufficient funds for their projects. In this paper, we explore the possibility
of redistribution of crowdfunding donations to increase the chances of success.
We define several intuitive redistribution policies and, using data from a real
crowdfunding platform, LaunchGood, we assess the potential improvement in
campaign fundraising success rates. We find that an aggressive redistribution
scheme can boost campaign success rates from 37% to 79%, but such
choice-agnostic redistribution schemes come at the cost of disregarding donor
preferences. Taking inspiration from offline giving societies and donor clubs,
we build a case for choice preserving redistribution schemes that strike a
balance between increasing the number of successful campaigns and respecting
giving preference. We find that choice-preserving redistribution can easily
achieve campaign success rates of 48%. Finally, we discuss the implications of
these different redistribution schemes for the various stakeholders in the
crowdfunding ecosystem.
| 1 | 0 | 0 | 0 | 0 | 0 |
Far-HO: A Bilevel Programming Package for Hyperparameter Optimization and Meta-Learning | In (Franceschi et al., 2018) we proposed a unified mathematical framework,
grounded on bilevel programming, that encompasses gradient-based hyperparameter
optimization and meta-learning. We formulated an approximate version of the
problem where the inner objective is solved iteratively, and gave sufficient
conditions ensuring convergence to the exact problem. In this work we show how
to optimize learning rates, automatically weight the loss of single examples
and learn hyper-representations with Far-HO, a software package based on the
popular deep learning framework TensorFlow that allows to seamlessly tackle
both HO and ML problems.
| 0 | 0 | 0 | 1 | 0 | 0 |
Analysis of Coupled Scalar Systems by Displacement Convexity | Potential functionals have been introduced recently as an important tool for
the analysis of coupled scalar systems (e.g. density evolution equations). In
this contribution, we investigate interesting properties of this potential.
Using the tool of displacement convexity, we show that, under mild assumptions
on the system, the potential functional is displacement convex. Furthermore, we
give the conditions on the system such that the potential is strictly
displacement convex, in which case the minimizer is unique.
| 1 | 0 | 1 | 0 | 0 | 0 |
Deterministic subgraph detection in broadcast CONGEST | We present simple deterministic algorithms for subgraph finding and
enumeration in the broadcast CONGEST model of distributed computation:
-- For any constant $k$, detecting $k$-paths and trees on $k$ nodes can be
done in $O(1)$ rounds.
-- For any constant $k$, detecting $k$-cycles and pseudotrees on $k$ nodes
can be done in $O(n)$ rounds.
-- On $d$-degenerate graphs, cliques and $4$-cycles can be enumerated in $O(d
+ \log n)$ rounds, and $5$-cycles in $O(d^2 + \log n)$ rounds.
In many cases, these bounds are tight up to logarithmic factors. Moreover, we
show that the algorithms for $d$-degenerate graphs can be improved to optimal
complexity $O(d/\log n)$ and $O(d^2/\log n)$, respectively, in the supported
CONGEST model, which can be seen as an intermediate model between CONGEST and
the congested clique.
| 1 | 0 | 0 | 0 | 0 | 0 |
On Graded Lie Algebras of Characteristic Three With Classical Reductive Null Component | We consider finite-dimensional irreducible transitive graded Lie algebras $L
= \sum_{i=-q}^rL_i$ over algebraically closed fields of characteristic three.
We assume that the null component $L_0$ is classical and reductive. The adjoint
representation of $L$ on itself induces a representation of the commutator
subalgebra $L_0'$ of the null component on the minus-one component $L_{-1}.$ We
show that if the depth $q$ of $L$ is greater than one, then this representation
must be restricted.
| 0 | 0 | 1 | 0 | 0 | 0 |
Femtosecond mega-electron-volt electron microdiffraction | Instruments to visualize transient structural changes of inhomogeneous
materials on the nanometer scale with atomic spatial and temporal resolution
are demanded to advance materials science, bioscience, and fusion sciences. One
such technique is femtosecond electron microdiffraction, in which a short pulse
of electrons with femtosecond-scale duration is focused into a micron-scale
spot and used to obtain diffraction images to resolve ultrafast structural
dynamics over localized crystalline domain. In this letter, we report the
experimental demonstration of time-resolved mega-electron-volt electron
microdiffraction which achieves a 5 {\mu}m root-mean-square (rms) beam size on
the sample and a 100 fs rms temporal resolution. Using pulses of 10k electrons
at 4.2 MeV energy with a normalized emittance 3 nm-rad, we obtained high
quality diffraction from a single 10 {\mu}m paraffin (C_44 H_90) crystal. The
phonon softening mode in optical-pumped polycrystalline Bi was also
time-resolved, demonstrating the temporal resolution limits of our instrument
design. This new characterization capability will open many research
opportunities in material and biological sciences.
| 0 | 1 | 0 | 0 | 0 | 0 |
Deep Recurrent Neural Network for Protein Function Prediction from Sequence | As high-throughput biological sequencing becomes faster and cheaper, the need
to extract useful information from sequencing becomes ever more paramount,
often limited by low-throughput experimental characterizations. For proteins,
accurate prediction of their functions directly from their primary amino-acid
sequences has been a long standing challenge. Here, machine learning using
artificial recurrent neural networks (RNN) was applied towards classification
of protein function directly from primary sequence without sequence alignment,
heuristic scoring or feature engineering. The RNN models containing
long-short-term-memory (LSTM) units trained on public, annotated datasets from
UniProt achieved high performance for in-class prediction of four important
protein functions tested, particularly compared to other machine learning
algorithms using sequence-derived protein features. RNN models were used also
for out-of-class predictions of phylogenetically distinct protein families with
similar functions, including proteins of the CRISPR-associated nuclease,
ferritin-like iron storage and cytochrome P450 families. Applying the trained
RNN models on the partially unannotated UniRef100 database predicted not only
candidates validated by existing annotations but also currently unannotated
sequences. Some RNN predictions for the ferritin-like iron sequestering
function were experimentally validated, even though their sequences differ
significantly from known, characterized proteins and from each other and cannot
be easily predicted using popular bioinformatics methods. As sequencing and
experimental characterization data increases rapidly, the machine-learning
approach based on RNN could be useful for discovery and prediction of
homologues for a wide range of protein functions.
| 1 | 0 | 0 | 1 | 0 | 0 |
CubemapSLAM: A Piecewise-Pinhole Monocular Fisheye SLAM System | We present a real-time feature-based SLAM (Simultaneous Localization and
Mapping) system for fisheye cameras featured by a large field-of-view (FoV).
Large FoV cameras are beneficial for large-scale outdoor SLAM applications,
because they increase visual overlap between consecutive frames and capture
more pixels belonging to the static parts of the environment. However, current
feature-based SLAM systems such as PTAM and ORB-SLAM limit their camera model
to pinhole only. To compensate for the vacancy, we propose a novel SLAM system
with the cubemap model that utilizes the full FoV without introducing
distortion from the fisheye lens, which greatly benefits the feature matching
pipeline. In the initialization and point triangulation stages, we adopt a
unified vector-based representation to efficiently handle matches across
multiple faces, and based on this representation we propose and analyze a novel
inlier checking metric. In the optimization stage, we design and test a novel
multi-pinhole reprojection error metric that outperforms other metrics by a
large margin. We evaluate our system comprehensively on a public dataset as
well as a self-collected dataset that contains real-world challenging
sequences. The results suggest that our system is more robust and accurate than
other feature-based fisheye SLAM approaches. The CubemapSLAM system has been
released into the public domain.
| 1 | 0 | 0 | 0 | 0 | 0 |
$J$-holomorphic disks with pre-Lagrangian boundary conditions | The purpose of this paper is to carry out a classical construction of a
non-constant holomorphic disk with boundary on (the suspension of) a Lagrangian
submanifold in $\mathbb{R}^{2 n}$ in the case the Lagrangian is the lift of a
coisotropic (a.k.a. pre-Lagrangian) submanifold in (a subset $U$ of)
$\mathbb{R}^{2 n - 1}$. We show that the positive lower and finite upper bounds
for the area of such a disk (which are due to M. Gromov and J.-C. Sikorav and
F. Laudenbach-Sikorav for general Lagrangians) depend on the coisotropic
submanifold only but not on its lift to the symplectization. The main
application is to a $C^0$-characterization of contact embeddings in terms of
coisotropic embeddings in another paper by the present author. Moreover, we
prove a version of Gromov's non-existence of exact Lagrangian embeddings into
standard $\mathbb{R}^{2 n}$ for coisotropic embeddings into $S^1 \times
\mathbb{R}^{2 n}$. This allows us to distinguish different contact structures
on the latter by means of the (modified) contact shape invariant. As in the
general Lagrangian case, all of the existence results are based on Gromov's
theory of $J$-holomorphic curves and his compactness theorem (or persistence
principle). Analytical difficulties arise mainly at the ends of the cone
$\mathbb{R}_+ \times U$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Evolutionary sequences for hydrogen-deficient white dwarfs | We present a set of full evolutionary sequences for white dwarfs with
hydrogen-deficient atmospheres. We take into account the evolutionary history
of the progenitor stars, all the relevant energy sources involved in the
cooling, element diffusion in the very outer layers, and outer boundary
conditions provided by new and detailed non-gray white dwarf model atmospheres
for pure helium composition. These model atmospheres are based on the most
up-to-date physical inputs. Our calculations extend down to very low effective
temperatures, of $\sim 2\,500$~K, provide a homogeneous set of evolutionary
cooling tracks that are appropriate for mass and age determinations of old
hydrogen-deficient white dwarfs, and represent a clear improvement over
previous efforts, which were computed using gray atmospheres.
| 0 | 1 | 0 | 0 | 0 | 0 |
On the uniqueness of complete biconservative surfaces in $\mathbb{R}^3$ | We study the uniqueness of complete biconservative surfaces in the Euclidean
space $\mathbb{R}^3$, and prove that the only complete biconservative regular
surfaces in $\mathbb{R}^3$ are either $CMC$ or certain surfaces of revolution.
In particular, any compact biconservative regular surface in $\mathbb{R}^3$ is
a round sphere.
| 0 | 0 | 1 | 0 | 0 | 0 |
Quantum Annealing Applied to De-Conflicting Optimal Trajectories for Air Traffic Management | We present the mapping of a class of simplified air traffic management (ATM)
problems (strategic conflict resolution) to quadratic unconstrained boolean
optimization (QUBO) problems. The mapping is performed through an original
representation of the conflict-resolution problem in terms of a conflict graph,
where nodes of the graph represent flights and edges represent a potential
conflict between flights. The representation allows a natural decomposition of
a real world instance related to wind- optimal trajectories over the Atlantic
ocean into smaller subproblems, that can be discretized and are amenable to be
programmed in quantum annealers. In the study, we tested the new programming
techniques and we benchmark the hardness of the instances using both classical
solvers and the D-Wave 2X and D-Wave 2000Q quantum chip. The preliminary
results show that for reasonable modeling choices the most challenging
subproblems which are programmable in the current devices are solved to
optimality with 99% of probability within a second of annealing time.
| 1 | 0 | 0 | 0 | 0 | 0 |
On rumour propagation among sceptics | Junior, Machado and Zuluaga (2011) studied a model to understand the spread
of a rumour. Their model consists of individuals situated at the integer points
of the line $\N$. An individual at the origin $0$ starts a rumour and passes it
to all individuals in the interval $[0,R_0]$, where $R_0$ is a non-negative
random variable. An individual located at $i$ in this interval receives the
rumour and transmits it further among individuals in $[i, i+R_i]$ where $R_0$
and $R_i$ are i.i.d. random variables. The rumour spreads in this manner. An
alternate model considers individuals seeking to find the rumour from
individuals who have already heard it. For this s/he asks individuals to the
left of her/him and lying in an interval of a random size. We study these two
models, when the individuals are more sceptical and they transmit or accept the
rumour only if they receive it from at least two different sources.
In stochastic geometry the equivalent of this rumour process is the study of
coverage of the space $\N^d$ by random sets. Our study here extends the study
of coverage of space and considers the case when each vertex of $\N^d$ is
covered by at least two distinct random sets.
| 0 | 0 | 1 | 1 | 0 | 0 |
Neutron activation and prompt gamma intensity in Ar/CO$_{2}$-filled neutron detectors at the European Spallation Source | Monte Carlo simulations using MCNP6.1 were performed to study the effect of
neutron activation in Ar/CO$_{2}$ neutron detector counting gas. A general MCNP
model was built and validated with simple analytical calculations. Simulations
and calculations agree that only the $^{40}$Ar activation can have a
considerable effect. It was shown that neither the prompt gamma intensity from
the $^{40}$Ar neutron capture nor the produced $^{41}$Ar activity have an
impact in terms of gamma dose rate around the detector and background level.
| 0 | 1 | 0 | 0 | 0 | 0 |
Solving 1ODEs with functions | Here we present a new approach to deal with first order ordinary differential
equations (1ODEs), presenting functions. This method is an alternative to the
one we have presented in [1]. In [2], we have establish the theoretical
background to deal, in the extended Prelle-Singer approach context, with
systems of 1ODEs. In this present paper, we will apply these results in order
to produce a method that is more efficient in a great number of cases.
Directly, the solving of 1ODEs is applicable to any problem presenting
parameters to which the rate of change is related to the parameter itself.
Apart from that, the solving of 1ODEs can be a part of larger mathematical
processes vital to dealing with many problems.
| 0 | 1 | 1 | 0 | 0 | 0 |
System Level Framework for Assessing the Accuracy of Neonatal EEG Acquisition | Significant research has been conducted in recent years to design low-cost
alternatives to the current EEG monitoring systems used in healthcare
facilities. Testing such systems on a vulnerable population such as newborns is
complicated due to ethical and regulatory considerations that slow down the
technical development. This paper presents and validates a method for
quantifying the accuracy of neonatal EEG acquisition systems and electrode
technologies via clinical data simulations that do not require neonatal
participants. The proposed method uses an extensive neonatal EEG database to
simulate analogue signals, which are subsequently passed through electrical
models of the skin-electrode interface, which are developed using wet and dry
EEG electrode designs. The signal losses in the system are quantified at each
stage of the acquisition process for electrode and acquisition board losses.
SNR, correlation and noise values were calculated. The results verify that
low-cost EEG acquisition systems are capable of obtaining clinical grade EEG.
Although dry electrodes result in a significant increase in the skin-electrode
impedance, accurate EEG recordings are still achievable.
| 0 | 0 | 0 | 1 | 0 | 0 |
Strong Consistency of Spectral Clustering for Stochastic Block Models | In this paper we prove the strong consistency of several methods based on the
spectral clustering techniques that are widely used to study the community
detection problem in stochastic block models (SBMs). We show that under some
weak conditions on the minimal degree, the number of communities, and the
eigenvalues of the probability block matrix, the K-means algorithm applied to
the eigenvectors of the graph Laplacian associated with its first few largest
eigenvalues can classify all individuals into the true community uniformly
correctly almost surely. Extensions to both regularized spectral clustering and
degree-corrected SBMs are also considered. We illustrate the performance of
different methods on simulated networks.
| 0 | 0 | 0 | 1 | 0 | 0 |
Extremely fast simulations of heat transfer in fluidized beds | Besides their huge technological importance, fluidized beds have attracted a
large amount of research because they are perfect playgrounds to investigate
highly dynamic particulate flows. Their over-all behavior is determined by
short-lasting particle collisions and the interaction between solid and gas
phase. Modern simulation techniques that combine computational fluid dynamics
(CFD) and discrete element methods (DEM) are capable of describing their
evolution and provide detailed information on what is happening on the particle
scale. However, these approaches are limited by small time steps and large
numerical costs, which inhibits the investigation of slower long-term processes
like heat transfer or chemical conversion.
In a recent study (Lichtenegger and Pirker, 2016), we have introduced
recurrence CFD (rCFD) as a way to decouple fast from slow degrees of freedom in
systems with recurring patterns: A conventional simulation is carried out to
capture such coherent structures. Their re-appearance is characterized with
recurrence plots that allow us to extrapolate their evolution far beyond the
simulated time. On top of these predicted flow fields, any passive or weakly
coupled process can then be investigated at fractions of the original
computational costs.
Here, we present the application of rCFD to heat transfer in a lab-scale
fluidized bed. Initially hot particles are fluidized with cool air and their
temperature evolution is recorded. In comparison to conventional CFD-DEM, we
observe speed-up factors of about two orders of magnitude at very good accuracy
with regard to recent measurements.
| 0 | 1 | 0 | 0 | 0 | 0 |
Machine learning out-of-equilibrium phases of matter | Neural network based machine learning is emerging as a powerful tool for
obtaining phase diagrams when traditional regression schemes using local
equilibrium order parameters are not available, as in many-body localized or
topological phases. Nevertheless, instances of machine learning offering new
insights have been rare up to now. Here we show that a single feed-forward
neural network can decode the defining structures of two distinct MBL phases
and a thermalizing phase, using entanglement spectra obtained from individual
eigenstates. For this, we introduce a simplicial geometry based method for
extracting multi-partite phase boundaries. We find that this method outperforms
conventional metrics (like the entanglement entropy) for identifying MBL phase
transitions, revealing a sharper phase boundary and shedding new insight into
the topology of the phase diagram. Furthermore, the phase diagram we acquire
from a single disorder configuration confirms that the machine-learning based
approach we establish here can enable speedy exploration of large phase spaces
that can assist with the discovery of new MBL phases. To our knowledge this
work represents the first example of a machine learning approach revealing new
information beyond conventional knowledge.
| 0 | 1 | 0 | 0 | 0 | 0 |
Exact Tensor Completion from Sparsely Corrupted Observations via Convex Optimization | This paper conducts a rigorous analysis for provable estimation of
multidimensional arrays, in particular third-order tensors, from a random
subset of its corrupted entries. Our study rests heavily on a recently proposed
tensor algebraic framework in which we can obtain tensor singular value
decomposition (t-SVD) that is similar to the SVD for matrices, and define a new
notion of tensor rank referred to as the tubal rank. We prove that by simply
solving a convex program, which minimizes a weighted combination of tubal
nuclear norm, a convex surrogate for the tubal rank, and the $\ell_1$-norm, one
can recover an incoherent tensor exactly with overwhelming probability,
provided that its tubal rank is not too large and that the corruptions are
reasonably sparse. Interestingly, our result includes the recovery guarantees
for the problems of tensor completion (TC) and tensor principal component
analysis (TRPCA) under the same algebraic setup as special cases. An
alternating direction method of multipliers (ADMM) algorithm is presented to
solve this optimization problem. Numerical experiments verify our theory and
real-world applications demonstrate the effectiveness of our algorithm.
| 1 | 0 | 0 | 1 | 0 | 0 |
On Triangle Inequality Based Approximation Error Estimation | The distance between the true and numerical solutions in some metric is
considered as the discretization error magnitude. If error magnitude ranging is
known, the triangle inequality enables the estimation of the vicinity of the
approximate solution that contains the exact one (exact solution enclosure).
The analysis of distances between the numerical solutions enables
discretization error ranging, if solutions errors are significantly different.
Numerical tests conducted using the steady supersonic flows, governed by the
two dimensional Euler equations, demonstrate the properties of the exact
solution enclosure. The set of solutions generated by solvers of different
orders of approximation is used. The success of this approach depends on the
choice of metric.
| 0 | 1 | 0 | 0 | 0 | 0 |
Maximal solutions for the Infinity-eigenvalue problem | In this article we prove that the first eigenvalue of the $\infty-$Laplacian
$$ \left\{ \begin{array}{rclcl}
\min\{ -\Delta_\infty v,\, |\nabla v|-\lambda_{1, \infty}(\Omega) v \} & = &
0 & \text{in} & \Omega v & = & 0 & \text{on} & \partial \Omega, \end{array}
\right. $$ has a unique (up to scalar multiplication) maximal solution. This
maximal solution can be obtained as the limit as $\ell \nearrow 1$ of concave
problems of the form $$ \left\{ \begin{array}{rclcl}
\min\{ -\Delta_\infty v_{\ell},\, |\nabla v_{\ell}|-\lambda_{1,
\infty}(\Omega) v_{\ell}^{\ell} \} & = & 0 & \text{in} & \Omega v_{\ell} & = &
0 & \text{on} & \partial \Omega. \end{array} \right. $$ In this way we obtain
that the maximal eigenfunction is the unique one that is the limit of the
concave problems as happens for the usual eigenvalue problem for the
$p-$Laplacian for a fixed $1<p<\infty$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Algorithmic Decision Making in the Presence of Unmeasured Confounding | On a variety of complex decision-making tasks, from doctors prescribing
treatment to judges setting bail, machine learning algorithms have been shown
to outperform expert human judgments. One complication, however, is that it is
often difficult to anticipate the effects of algorithmic policies prior to
deployment, making the decision to adopt them risky. In particular, one
generally cannot use historical data to directly observe what would have
happened had the actions recommended by the algorithm been taken. One standard
strategy is to model potential outcomes for alternative decisions assuming that
there are no unmeasured confounders (i.e., to assume ignorability). But if this
ignorability assumption is violated, the predicted and actual effects of an
algorithmic policy can diverge sharply. In this paper we present a flexible,
Bayesian approach to gauge the sensitivity of predicted policy outcomes to
unmeasured confounders. We show that this policy evaluation problem is a
generalization of estimating heterogeneous treatment effects in observational
studies, and so our methods can immediately be applied to that setting.
Finally, we show, both theoretically and empirically, that under certain
conditions it is possible to construct near-optimal algorithmic policies even
when ignorability is violated. We demonstrate the efficacy of our methods on a
large dataset of judicial actions, in which one must decide whether defendants
awaiting trial should be required to pay bail or can be released without
payment.
| 0 | 0 | 0 | 1 | 0 | 0 |
BL-MNE: Emerging Heterogeneous Social Network Embedding through Broad Learning with Aligned Autoencoder | Network embedding aims at projecting the network data into a low-dimensional
feature space, where the nodes are represented as a unique feature vector and
network structure can be effectively preserved. In recent years, more and more
online application service sites can be represented as massive and complex
networks, which are extremely challenging for traditional machine learning
algorithms to deal with. Effective embedding of the complex network data into
low-dimension feature representation can both save data storage space and
enable traditional machine learning algorithms applicable to handle the network
data. Network embedding performance will degrade greatly if the networks are of
a sparse structure, like the emerging networks with few connections. In this
paper, we propose to learn the embedding representation for a target emerging
network based on the broad learning setting, where the emerging network is
aligned with other external mature networks at the same time. To solve the
problem, a new embedding framework, namely "Deep alIgned autoencoder based
eMbEdding" (DIME), is introduced in this paper. DIME handles the diverse link
and attribute in a unified analytic based on broad learning, and introduces the
multiple aligned attributed heterogeneous social network concept to model the
network structure. A set of meta paths are introduced in the paper, which
define various kinds of connections among users via the heterogeneous link and
attribute information. The closeness among users in the networks are defined as
the meta proximity scores, which will be fed into DIME to learn the embedding
vectors of users in the emerging network. Extensive experiments have been done
on real-world aligned social networks, which have demonstrated the
effectiveness of DIME in learning the emerging network embedding vectors.
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Multi-channel discourse as an indicator for Bitcoin price and volume movements | This research aims to identify how Bitcoin-related news publications and
online discourse are expressed in Bitcoin exchange movements of price and
volume. Being inherently digital, all Bitcoin-related fundamental data (from
exchanges, as well as transactional data directly from the blockchain) is
available online, something that is not true for traditional businesses or
currencies traded on exchanges. This makes Bitcoin an interesting subject for
such research, as it enables the mapping of sentiment to fundamental events
that might otherwise be inaccessible. Furthermore, Bitcoin discussion largely
takes place on online forums and chat channels. In stock trading, the value of
sentiment data in trading decisions has been demonstrated numerous times [1]
[2] [3], and this research aims to determine whether there is value in such
data for Bitcoin trading models. To achieve this, data over the year 2015 has
been collected from Bitcointalk.org, (the biggest Bitcoin forum in post
volume), established news sources such as Bloomberg and the Wall Street
Journal, the complete /r/btc and /r/Bitcoin subreddits, and the bitcoin-otc and
bitcoin-dev IRC channels. By analyzing this data on sentiment and volume, we
find weak to moderate correlations between forum, news, and Reddit sentiment
and movements in price and volume from 1 to 5 days after the sentiment was
expressed. A Granger causality test confirms the predictive causality of the
sentiment on the daily percentage price and volume movements, and at the same
time underscores the predictive causality of market movements on sentiment
expressions in online communities
| 0 | 0 | 0 | 0 | 0 | 1 |
Fano Resonances in a Photonic Crystal Covered with a Perforated Gold Film and its Application to Biosensing | Optical properties of the photonic crystal covered with a perforated metal
film were investigated and the existence of the Fano-type resonances was shown.
The Fano resonances originate from the interaction between the optical Tamm
state and the waveguide modes of the photonic crystal. It manifests itself as a
narrow dip in a broad peak in the transmission spectrum related to the optical
Tamm state. The design of a sensor based on this Fano resonance that is
sensitive to the change of the environment refractive index is suggested.
| 0 | 1 | 0 | 0 | 0 | 0 |
Non-Stationary Bandits with Habituation and Recovery Dynamics | Many settings involve sequential decision-making where a set of actions can
be chosen at each time step, each action provides a stochastic reward, and the
distribution for the reward of each action is initially unknown. However,
frequent selection of a specific action may reduce its expected reward, while
abstaining from choosing an action may cause its expected reward to increase.
Such non-stationary phenomena are observed in many real world settings such as
personalized healthcare-adherence improving interventions and targeted online
advertising. Though finding an optimal policy for general models with
non-stationarity is PSPACE-complete, we propose and analyze a new class of
models called ROGUE (Reducing or Gaining Unknown Efficacy) bandits, which we
show in this paper can capture these phenomena and are amenable to the design
of effective policies. We first present a consistent maximum likelihood
estimator for the parameters of these models. Next, we construct finite sample
concentration bounds that lead to an upper confidence bound policy called the
ROGUE Upper Confidence Bound (ROGUE-UCB) algorithm. We prove that under proper
conditions the ROGUE-UCB algorithm achieves logarithmic in time regret, unlike
existing algorithms which result in linear regret. We conclude with a numerical
experiment using real data from a personalized healthcare-adherence improving
intervention to increase physical activity. In this intervention, the goal is
to optimize the selection of messages (e.g., confidence increasing vs.
knowledge increasing) to send to each individual each day to increase adherence
and physical activity. Our results show that ROGUE-UCB performs better in terms
of regret and average reward as compared to state of the art algorithms, and
the use of ROGUE-UCB increases daily step counts by roughly 1,000 steps a day
(about a half-mile more of walking) as compared to other algorithms.
| 1 | 0 | 1 | 0 | 0 | 0 |
Exception-Based Knowledge Updates | Existing methods for dealing with knowledge updates differ greatly depending
on the underlying knowledge representation formalism. When Classical Logic is
used, updates are typically performed by manipulating the knowledge base on the
model-theoretic level. On the opposite side of the spectrum stand the semantics
for updating Answer-Set Programs that need to rely on rule syntax. Yet, a
unifying perspective that could embrace both these branches of research is of
great importance as it enables a deeper understanding of all involved methods
and principles and creates room for their cross-fertilisation, ripening and
further development.
This paper bridges the seemingly irreconcilable approaches to updates. It
introduces a novel monotonic characterisation of rules, dubbed RE-models, and
shows it to be a more suitable semantic foundation for rule updates than
SE-models. Then it proposes a generic scheme for specifying semantic rule
update operators, based on the idea of viewing a program as the set of sets of
RE-models of its rules; updates are performed by introducing additional
interpretations - exceptions - to the sets of RE-models of rules in the
original program. The introduced scheme is used to define rule update operators
that are closely related to both classical update principles and traditional
approaches to rules updates, and serve as a basis for a solution to the
long-standing problem of state condensing, showing how they can be equivalently
defined as binary operators on some class of logic programs.
Finally, the essence of these ideas is extracted to define an abstract
framework for exception-based update operators, viewing a knowledge base as the
set of sets of models of its elements, which can capture a wide range of both
model- and formula-based classical update operators, and thus serves as the
first firm formal ground connecting classical and rule updates.
| 1 | 0 | 0 | 0 | 0 | 0 |
Dynamics of observables in rank-based models and performance of functionally generated portfolios | In the seminal work [9], several macroscopic market observables have been
introduced, in an attempt to find characteristics capturing the diversity of a
financial market. Despite the crucial importance of such observables for
investment decisions, a concise mathematical description of their dynamics has
been missing. We fill this gap in the setting of rank-based models and expect
our ideas to extend to other models of large financial markets as well. The
results are then used to study the performance of multiplicatively and
additively functionally generated portfolios, in particular, over short-term
and medium-term horizons.
| 0 | 0 | 0 | 0 | 0 | 1 |
An Approach to Controller Design Based on the Generalized Cloud Model | In this paper, an approach to controller design based on the cloud models,
without using the analog plant model is presented.
| 1 | 0 | 0 | 0 | 0 | 0 |
A 3pi Search for Planet Nine at 3.4 microns with WISE and NEOWISE | The recent 'Planet Nine' hypothesis has led to many observational and
archival searches for this giant planet proposed to orbit the Sun at hundreds
of astronomical units. While trans-Neptunian object searches are typically
conducted in the optical, models suggest Planet Nine could be self-luminous and
potentially bright enough at ~3-5 microns to be detected by the Wide-field
Infrared Survey Explorer (WISE). We have previously demonstrated a Planet Nine
search methodology based on time-resolved WISE coadds, allowing us to detect
moving objects much fainter than would be possible using single-frame
extractions. In the present work, we extend our 3.4 micron (W1) search to cover
more than three quarters of the sky and incorporate four years of WISE
observations spanning a seven year time period. This represents the deepest and
widest-area WISE search for Planet Nine to date. We characterize the spatial
variation of our survey's sensitivity and rule out the presence of Planet Nine
in the parameter space searched at W1 < 16.7 in high Galactic latitude regions
(90% completeness).
| 0 | 1 | 0 | 0 | 0 | 0 |
Integrable 7-point discrete equations and evolution lattice equations of order 2 | We consider differential-difference equations that determine the continuous
symmetries of discrete equations on the triangular lattice. It is shown that a
certain combination of continuous flows can be represented as a scalar
evolution lattice equation of order 2. The general scheme is illustrated by a
number of examples, including an analog of the elliptic Yamilov lattice
equation.
| 0 | 1 | 0 | 0 | 0 | 0 |
Complete Analysis of a Random Forest Model | Random forests have become an important tool for improving accuracy in
regression problems since their popularization by [Breiman, 2001] and others.
In this paper, we revisit a random forest model originally proposed by
[Breiman, 2004] and later studied by [Biau, 2012], where a feature is selected
at random and the split occurs at the midpoint of the box containing the chosen
feature. If the Lipschitz regression function is sparse and only depends on a
small, unknown subset of $S$ out of $d$ features, we show that, given access to
$n$ observations, this random forest model outputs a predictor that has a
mean-squared prediction error $O((n(\sqrt{\log
n})^{S-1})^{-\frac{1}{S\log2+1}})$. This positively answers an outstanding
question of [Biau, 2012] about whether the rate of convergence therein could be
improved. The second part of this article shows that the aforementioned
prediction error cannot generally be improved, which we accomplish by
characterizing the variance and by showing that the bias is tight for any
linear model with nonzero parameter vector. As a striking consequence of our
analysis, we show the variance of this forest is similar in form to the
best-case variance lower bound of [Lin and Jeon, 2006] among all random forest
models with nonadaptive splitting schemes (i.e., where the split protocol is
independent of the training data).
| 0 | 0 | 0 | 1 | 0 | 0 |
Relative Chern character number and super-connection | For two complex vector bundles admitting a homomorphism, whose singularity
locates in the disjoint union of some odd--dimensional spheres, we give a
formula to compute the relative Chern characteristic number of these two
complex vector bundles. In particular, for a spin manifold admitting some
sphere bundle structure, we give a formula to express the index of a special
twisted Dirac operator.
| 0 | 0 | 1 | 0 | 0 | 0 |
Mental Sampling in Multimodal Representations | Both resources in the natural environment and concepts in a semantic space
are distributed "patchily", with large gaps in between the patches. To describe
people's internal and external foraging behavior, various random walk models
have been proposed. In particular, internal foraging has been modeled as
sampling: in order to gather relevant information for making a decision, people
draw samples from a mental representation using random-walk algorithms such as
Markov chain Monte Carlo (MCMC). However, two common empirical observations
argue against simple sampling algorithms such as MCMC. First, the spatial
structure is often best described by a Lévy flight distribution: the
probability of the distance between two successive locations follows a
power-law on the distances. Second, the temporal structure of the sampling that
humans and other animals produce have long-range, slowly decaying serial
correlations characterized as $1/f$-like fluctuations. We propose that mental
sampling is not done by simple MCMC, but is instead adapted to multimodal
representations and is implemented by Metropolis-coupled Markov chain Monte
Carlo (MC$^3$), one of the first algorithms developed for sampling from
multimodal distributions. MC$^3$ involves running multiple Markov chains in
parallel but with target distributions of different temperatures, and it swaps
the states of the chains whenever a better location is found. Heated chains
more readily traverse valleys in the probability landscape to propose moves to
far-away peaks, while the colder chains make the local steps that explore the
current peak or patch. We show that MC$^3$ generates distances between
successive samples that follow a Lévy flight distribution and $1/f$-like
serial correlations, providing a single mechanistic account of these two
puzzling empirical phenomena.
| 1 | 0 | 0 | 0 | 0 | 0 |
The Simulator: Understanding Adaptive Sampling in the Moderate-Confidence Regime | We propose a novel technique for analyzing adaptive sampling called the {\em
Simulator}. Our approach differs from the existing methods by considering not
how much information could be gathered by any fixed sampling strategy, but how
difficult it is to distinguish a good sampling strategy from a bad one given
the limited amount of data collected up to any given time. This change of
perspective allows us to match the strength of both Fano and change-of-measure
techniques, without succumbing to the limitations of either method. For
concreteness, we apply our techniques to a structured multi-arm bandit problem
in the fixed-confidence pure exploration setting, where we show that the
constraints on the means imply a substantial gap between the
moderate-confidence sample complexity, and the asymptotic sample complexity as
$\delta \to 0$ found in the literature. We also prove the first instance-based
lower bounds for the top-k problem which incorporate the appropriate
log-factors. Moreover, our lower bounds zero-in on the number of times each
\emph{individual} arm needs to be pulled, uncovering new phenomena which are
drowned out in the aggregate sample complexity. Our new analysis inspires a
simple and near-optimal algorithm for the best-arm and top-k identification,
the first {\em practical} algorithm of its kind for the latter problem which
removes extraneous log factors, and outperforms the state-of-the-art in
experiments.
| 1 | 0 | 0 | 1 | 0 | 0 |
A new method to suppress the bias in polarized intensity | Computing polarised intensities from noisy data in Stokes U and Q suffers
from a positive bias that should be suppressed. To develop a correction method
that, when applied to maps, should provide a distribution of polarised
intensity that closely follows the signal from the source. We propose a new
method to suppress the bias by estimating the polarisation angle of the source
signal in a noisy environment with help of a modified median filter. We then
determine the polarised intensity, including the noise, by projection of the
observed values of Stokes U and Q onto the direction of this polarisation
angle. We show that our new method represents the true signal very well. If the
noise distribution in the maps of U and Q is Gaussian, then in the corrected
map of polarised intensity it is also Gaussian. Smoothing to larger Gaussian
beamsizes, to improve the signal-to-noise ratio, can be done directly with our
method in the map of the polarised intensity. Our method also works in case of
non-Gaussian noise distributions. The maps of the corrected polarised
intensities and polarisation angles are reliable even in regions with weak
signals and provide integrated flux densities and degrees of polarisation
without the cumulative effect of the bias, which especially affects faint
sources. Features at low intensity levels like 'depolarisation canals' are
smoother than in the maps using the previous methods, which has broader
implications, for example on the interpretation of interstellar turbulence.
| 0 | 1 | 0 | 0 | 0 | 0 |
Bootstrapping kernel intensity estimation for nonhomogeneous point processes depending on spatial covariates | In the spatial point process context, kernel intensity estimation has been
mainly restricted to exploratory analysis due to its lack of consistency.
Different methods have been analysed to overcome this problem, and the
inclusion of covariates resulted to be one possible solution. In this paper we
focus on de\-fi\-ning a theoretical framework to derive a consistent kernel
intensity estimator using covariates, as well as a consistent smooth bootstrap
procedure. We define two new data-driven bandwidth selectors specifically
designed for our estimator: a rule-of-thumb and a plug-in bandwidth based on
our consistent bootstrap method. A simulation study is accomplished to
understand the performance of our proposals in finite samples. Finally, we
describe an application to a real data set consisting of the wildfires in
Canada during June 2015, using meteorological information as covariates.
| 0 | 0 | 0 | 1 | 0 | 0 |
Levi-Kahler reduction of CR structures, products of spheres, and toric geometry | We study CR geometry in arbitrary codimension, and introduce a process, which
we call the Levi-Kahler quotient, for constructing Kahler metrics from CR
structures with a transverse torus action. Most of the paper is devoted to the
study of Levi-Kahler quotients of toric CR manifolds, and in particular,
products of odd dimensional spheres. We obtain explicit descriptions and
characterizations of such quotients, and find Levi-Kahler quotients of products
of 3-spheres which are extremal in a weighted sense introduced by G. Maschler
and the first author.
| 0 | 0 | 1 | 0 | 0 | 0 |
Network Representation Learning: A Survey | With the widespread use of information technologies, information networks are
becoming increasingly popular to capture complex relationships across various
disciplines, such as social networks, citation networks, telecommunication
networks, and biological networks. Analyzing these networks sheds light on
different aspects of social life such as the structure of societies,
information diffusion, and communication patterns. In reality, however, the
large scale of information networks often makes network analytic tasks
computationally expensive or intractable. Network representation learning has
been recently proposed as a new learning paradigm to embed network vertices
into a low-dimensional vector space, by preserving network topology structure,
vertex content, and other side information. This facilitates the original
network to be easily handled in the new vector space for further analysis. In
this survey, we perform a comprehensive review of the current literature on
network representation learning in the data mining and machine learning field.
We propose new taxonomies to categorize and summarize the state-of-the-art
network representation learning techniques according to the underlying learning
mechanisms, the network information intended to preserve, as well as the
algorithmic designs and methodologies. We summarize evaluation protocols used
for validating network representation learning including published benchmark
datasets, evaluation methods, and open source algorithms. We also perform
empirical studies to compare the performance of representative algorithms on
common datasets, and analyze their computational complexity. Finally, we
suggest promising research directions to facilitate future study.
| 1 | 0 | 0 | 1 | 0 | 0 |
Confidence intervals for the area under the receiver operating characteristic curve in the presence of ignorable missing data | Receiver operating characteristic (ROC) curves are widely used as a measure
of accuracy of diagnostic tests and can be summarized using the area under the
ROC curve (AUC). Often, it is useful to construct a confidence intervals for
the AUC, however, since there are a number of different proposed methods to
measure variance of the AUC, there are thus many different resulting methods
for constructing these intervals. In this manuscript, we compare different
methods of constructing Wald-type confidence interval in the presence of
missing data where the missingness mechanism is ignorable. We find that
constructing confidence intervals using multiple imputation (MI) based on
logistic regression (LR) gives the most robust coverage probability and the
choice of CI method is less important. However, when missingness rate is less
severe (e.g. less than 70%), we recommend using Newcombe's Wald method for
constructing confidence intervals along with multiple imputation using
predictive mean matching (PMM).
| 0 | 0 | 0 | 1 | 0 | 0 |
Maximum Entropy Flow Networks | Maximum entropy modeling is a flexible and popular framework for formulating
statistical models given partial knowledge. In this paper, rather than the
traditional method of optimizing over the continuous density directly, we learn
a smooth and invertible transformation that maps a simple distribution to the
desired maximum entropy distribution. Doing so is nontrivial in that the
objective being maximized (entropy) is a function of the density itself. By
exploiting recent developments in normalizing flow networks, we cast the
maximum entropy problem into a finite-dimensional constrained optimization, and
solve the problem by combining stochastic optimization with the augmented
Lagrangian method. Simulation results demonstrate the effectiveness of our
method, and applications to finance and computer vision show the flexibility
and accuracy of using maximum entropy flow networks.
| 0 | 0 | 0 | 1 | 0 | 0 |
Overcoming the Sign Problem at Finite Temperature: Quantum Tensor Network for the Orbital $e_g$ Model on an Infinite Square Lattice | The variational tensor network renormalization approach to two-dimensional
(2D) quantum systems at finite temperature is applied for the first time to a
model suffering the notorious quantum Monte Carlo sign problem --- the orbital
$e_g$ model with spatially highly anisotropic orbital interactions.
Coarse-graining of the tensor network along the inverse temperature $\beta$
yields a numerically tractable 2D tensor network representing the Gibbs state.
Its bond dimension $D$ --- limiting the amount of entanglement --- is a natural
refinement parameter. Increasing $D$ we obtain a converged order parameter and
its linear susceptibility close to the critical point. They confirm the
existence of finite order parameter below the critical temperature $T_c$,
provide a numerically exact estimate of~$T_c$, and give the critical exponents
within $1\%$ of the 2D Ising universality class.
| 0 | 1 | 0 | 0 | 0 | 0 |
Nonasymptotic estimation and support recovery for high dimensional sparse covariance matrices | We propose a general framework for nonasymptotic covariance matrix estimation
making use of concentration inequality-based confidence sets. We specify this
framework for the estimation of large sparse covariance matrices through
incorporation of past thresholding estimators with key emphasis on support
recovery. This technique goes beyond past results for thresholding estimators
by allowing for a wide range of distributional assumptions beyond merely
sub-Gaussian tails. This methodology can furthermore be adapted to a wide range
of other estimators and settings. The usage of nonasymptotic dimension-free
confidence sets yields good theoretical performance. Through extensive
simulations, it is demonstrated to have superior performance when compared with
other such methods. In the context of support recovery, we are able to specify
a false positive rate and optimize to maximize the true recoveries.
| 0 | 0 | 1 | 1 | 0 | 0 |
Most Complex Deterministic Union-Free Regular Languages | A regular language $L$ is union-free if it can be represented by a regular
expression without the union operation. A union-free language is deterministic
if it can be accepted by a deterministic one-cycle-free-path finite automaton;
this is an automaton which has one final state and exactly one cycle-free path
from any state to the final state. Jirásková and Masopust proved that the
state complexities of the basic operations reversal, star, product, and boolean
operations in deterministic union-free languages are exactly the same as those
in the class of all regular languages. To prove that the bounds are met they
used five types of automata, involving eight types of transformations of the
set of states of the automata. We show that for each $n\ge 3$ there exists one
ternary witness of state complexity $n$ that meets the bound for reversal and
product. Moreover, the restrictions of this witness to binary alphabets meet
the bounds for star and boolean operations. We also show that the tight upper
bounds on the state complexity of binary operations that take arguments over
different alphabets are the same as those for arbitrary regular languages.
Furthermore, we prove that the maximal syntactic semigroup of a union-free
language has $n^n$ elements, as in the case of regular languages, and that the
maximal state complexities of atoms of union-free languages are the same as
those for regular languages. Finally, we prove that there exists a most complex
union-free language that meets the bounds for all these complexity measures.
Altogether this proves that the complexity measures above cannot distinguish
union-free languages from regular languages.
| 1 | 0 | 0 | 0 | 0 | 0 |
Piecewise Deterministic Markov Processes and their invariant measure | Piecewise Deterministic Markov Processes (PDMPs) are studied in a general
framework. First, different constructions are proven to be equivalent. Second,
we introduce a coupling between two PDMPs following the same differential flow
which implies quantitative bounds on the total variation between the marginal
distributions of the two processes. Finally two results are established
regarding the invariant measures of PDMPs. A practical condition to show that a
probability measure is invariant for the associated PDMP semi-group is
presented. In a second time, a bound on the invariant probability measures in
$V$-norm of two PDMPs following the same differential flow is established. This
last result is then applied to study the asymptotic bias of some non-exact PDMP
MCMC methods.
| 0 | 0 | 0 | 1 | 0 | 0 |
Visual Speech Language Models | Language models (LM) are very powerful in lipreading systems. Language models
built upon the ground truth utterances of datasets learn grammar and structure
rules of words and sentences (the latter in the case of continuous speech).
However, visual co-articulation effects in visual speech signals damage the
performance of visual speech LM's as visually, people do not utter what the
language model expects. These models are commonplace but while higher-order
N-gram LM's may improve classification rates, the cost of this model is
disproportionate to the common goal of developing more accurate classifiers. So
we compare which unit would best optimize a lipreading (visual speech) LM to
observe their limitations. We compare three units; visemes (visual speech
units) \cite{lan2010improving}, phonemes (audible speech units), and words.
| 1 | 0 | 0 | 0 | 0 | 0 |
Millisecond Pulsars as Standards: Timing, positioning and communication | Millisecond pulsars (MSPs) have a great potential to set standards in
timekeeping, positioning and metadata communication.
| 0 | 1 | 0 | 0 | 0 | 0 |
Multipole resonances and directional scattering by hyperbolic-media antennas | We propose to use optical antennas made out of natural hyperbolic material
hexagonal boron nitride (hBN), and we demonstrate that this medium is a
promising alternative to plasmonic and all-dielectric materials for realizing
efficient subwavelength scatterers and metasurfaces based on them. We
theoretically show that particles out of hyperbolic medium possess different
resonances enabled by the support of high-k waves and their reflection from the
particle boundaries. Among those resonances, there are electric quadrupole
excitations, which cause magnetic resonance of the particle similar to what
occurs in high-refractive-index particles. Excitations of the particle
resonances are accompanied by the drop in the reflection from nanoparticle
array to near-zero value, which can be ascribed to resonant Kerker effect. If
particles are arranged in the spacer array with period d, narrow lattice
resonances are possible at wavelength d, d/2, d/3 etc. This provides an
additional degree of control and possibility to excite resonances at the
wavelength defined by the array spacing. For the hBN particle with hyperbolic
dispersion, we show that the full range of the resonances, including magnetic
resonance and a decrease of reflection, is possible.
| 0 | 1 | 0 | 0 | 0 | 0 |
Discerning Dark Energy Models with High-Redshift Standard Candles | Following the success of type Ia supernovae in constraining cosmologies at
lower redshift $(z\lesssim2)$, effort has been spent determining if a similarly
useful standardisable candle can be found at higher redshift. {In this work we
determine the largest possible magnitude discrepancy between a constant dark
energy $\Lambda$CDM cosmology and a cosmology in which the equation of state
$w(z)$ of dark energy is a function of redshift for high redshift standard
candles $(z\gtrsim2)$}. We discuss a number of popular parametrisations of
$w(z)$ with two free parameters, $w_z$CDM cosmologies, including the
Chevallier-Polarski-Linder and generalisation thereof, $n$CPL, as well as the
Jassal-Bagla-Padmanabhan parametrisation. For each of these parametrisations we
calculate and find extrema of $\Delta \mu$, the difference between the distance
modulus of a $w_z$CDM cosmology and a fiducial $\Lambda$CDM cosmology as a
function of redshift, given 68\% likelihood constraints on the parameters
$P=(\Omega_{m,0}, w_0, w_a)$. The parameters are constrained using cosmic
microwave background, baryon acoustic oscillations, and type Ia supernovae data
using CosmoMC. We find that none of the tested cosmologies can deviate more
than 0.05 mag from the fiducial $\Lambda$CDM cosmology at high redshift,
implying that high redshift standard candles will not aid in discerning between
a $w_z$CDM cosmology and the fiducial $\Lambda$CDM cosmology. Conversely, this
implies that if high redshift standard candles are found to be in disagreement
with $\Lambda$CDM at high redshift, then this is a problem not only for
$\Lambda$CDM but for the entire family of $w_z$CDM cosmologies.
| 0 | 1 | 0 | 0 | 0 | 0 |
Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters | High-precision modeling of subatomic particle interactions is critical for
many fields within the physical sciences, such as nuclear physics and high
energy particle physics. Most simulation pipelines in the sciences are
computationally intensive -- in a variety of scientific fields, Generative
Adversarial Networks have been suggested as a solution to speed up the forward
component of simulation, with promising results. An important component of any
simulation system for the sciences is the ability to condition on any number of
physically meaningful latent characteristics that can effect the forward
generation procedure. We introduce an auxiliary task to the training of a
Generative Adversarial Network on particle showers in a multi-layer
electromagnetic calorimeter, which allows our model to learn an attribute-aware
conditioning mechanism.
| 1 | 0 | 0 | 0 | 0 | 0 |
Max-value Entropy Search for Efficient Bayesian Optimization | Entropy Search (ES) and Predictive Entropy Search (PES) are popular and
empirically successful Bayesian Optimization techniques. Both rely on a
compelling information-theoretic motivation, and maximize the information
gained about the $\arg\max$ of the unknown function; yet, both are plagued by
the expensive computation for estimating entropies. We propose a new criterion,
Max-value Entropy Search (MES), that instead uses the information about the
maximum function value. We show relations of MES to other Bayesian optimization
methods, and establish a regret bound. We observe that MES maintains or
improves the good empirical performance of ES/PES, while tremendously
lightening the computational burden. In particular, MES is much more robust to
the number of samples used for computing the entropy, and hence more efficient
for higher dimensional problems.
| 1 | 0 | 1 | 1 | 0 | 0 |
On stochastic differential equations with arbitrarily slow convergence rates for strong approximation in two space dimensions | In the recent article [Jentzen, A., Müller-Gronbach, T., and Yaroslavtseva,
L., Commun. Math. Sci., 14(6), 1477--1500, 2016] it has been established that
for every arbitrarily slow convergence speed and every natural number $d \in
\{4,5,\ldots\}$ there exist $d$-dimensional stochastic differential equations
(SDEs) with infinitely often differentiable and globally bounded coefficients
such that no approximation method based on finitely many observations of the
driving Brownian motion can converge in absolute mean to the solution faster
than the given speed of convergence. In this paper we strengthen the above
result by proving that this slow convergence phenomena also arises in two
($d=2$) and three ($d=3$) space dimensions.
| 0 | 0 | 1 | 0 | 0 | 0 |
Energy Dissipation in Monolayer MoS$_2$ Electronics | The advancement of nanoscale electronics has been limited by energy
dissipation challenges for over a decade. Such limitations could be
particularly severe for two-dimensional (2D) semiconductors integrated with
flexible substrates or multi-layered processors, both being critical thermal
bottlenecks. To shed light into fundamental aspects of this problem, here we
report the first direct measurement of spatially resolved temperature in
functioning 2D monolayer MoS$_2$ transistors. Using Raman thermometry we
simultaneously obtain temperature maps of the device channel and its substrate.
This differential measurement reveals the thermal boundary conductance (TBC) of
the MoS$_2$ interface (14 $\pm$ 4 MWm$^-$$^2$K$^-$$^1$) is an order magnitude
larger than previously thought, yet near the low end of known solid-solid
interfaces. Our study also reveals unexpected insight into non-uniformities of
the MoS$_2$ transistors (small bilayer regions), which do not cause significant
self-heating, suggesting that such semiconductors are less sensitive to
inhomogeneity than expected. These results provide key insights into energy
dissipation of 2D semiconductors and pave the way for the future design of
energy-efficient 2D electronics.
| 0 | 1 | 0 | 0 | 0 | 0 |
Adaptive Estimation of Nonparametric Geometric Graphs | This article studies the recovery of graphons when they are convolution
kernels on compact (symmetric) metric spaces. This case is of particular
interest since it covers the situation where the probability of an edge depends
only on some unknown nonparametric function of the distance between latent
points, referred to as Nonparametric Geometric Graphs (NGG). In this setting,
almost minimax adaptive estimation of NGG is possible using a spectral
procedure combined with a Goldenshluger-Lepski adaptation method. The latent
spaces covered by our framework encompasses (among others) compact symmetric
spaces of rank one, namely real spheres and projective spaces. For these
latter, explicit computations of the eigenbasis and of the model complexity can
be achieved, leading to quantitative non-asymptotic results. The time
complexity of our method scales cubicly in the size of the graph and
exponentially in the regularity of the graphon. Hence, this paper offers an
algorithmically and theoretically efficient procedure to estimate smooth NGG.
As a by product, this paper shows a non-asymptotic concentration result on the
spectrum of integral operators defined by symmetric kernels (not necessarily
positive).
| 0 | 0 | 1 | 1 | 0 | 0 |
Angular and Temporal Correlation of V2X Channels Across Sub-6 GHz and mmWave Bands | 5G millimeter wave (mmWave) technology is envisioned to be an integral part
of next-generation vehicle-to-everything (V2X) networks and autonomous vehicles
due to its broad bandwidth, wide field of view sensing, and precise
localization capabilities. The reliability of mmWave links may be compromised
due to difficulties in beam alignment for mobile channels and due to blocking
effects between a mmWave transmitter and a receiver. To address such
challenges, out-of-band information from sub-6 GHz channels can be utilized for
predicting the temporal and angular channel characteristics in mmWave bands,
which necessitates a good understanding of how propagation characteristics are
coupled across different bands. In this paper, we use ray tracing simulations
to characterize the angular and temporal correlation across a wide range of
propagation frequencies for V2X channels ranging from 900 MHz up to 73 GHz, for
a vehicle maintaining line-of-sight (LOS) and non-LOS (NLOS) beams with a
transmitter in an urban environment. Our results shed light on increasing
sparsity behavior of propagation channels with increasing frequency and
highlight the strong temporal/angular correlation among 5.9 GHz and 28 GHz
bands especially for LOS channels.
| 1 | 0 | 0 | 0 | 0 | 0 |
Comparison of Decision Tree Based Classification Strategies to Detect External Chemical Stimuli from Raw and Filtered Plant Electrical Response | Plants monitor their surrounding environment and control their physiological
functions by producing an electrical response. We recorded electrical signals
from different plants by exposing them to Sodium Chloride (NaCl), Ozone (O3)
and Sulfuric Acid (H2SO4) under laboratory conditions. After applying
pre-processing techniques such as filtering and drift removal, we extracted few
statistical features from the acquired plant electrical signals. Using these
features, combined with different classification algorithms, we used a decision
tree based multi-class classification strategy to identify the three different
external chemical stimuli. We here present our exploration to obtain the
optimum set of ranked feature and classifier combination that can separate a
particular chemical stimulus from the incoming stream of plant electrical
signals. The paper also reports an exhaustive comparison of similar feature
based classification using the filtered and the raw plant signals, containing
the high frequency stochastic part and also the low frequency trends present in
it, as two different cases for feature extraction. The work, presented in this
paper opens up new possibilities for using plant electrical signals to monitor
and detect other environmental stimuli apart from NaCl, O3 and H2SO4 in future.
| 1 | 1 | 0 | 1 | 0 | 0 |
Interactive Reinforcement Learning for Object Grounding via Self-Talking | Humans are able to identify a referred visual object in a complex scene via a
few rounds of natural language communications. Success communication requires
both parties to engage and learn to adapt for each other. In this paper, we
introduce an interactive training method to improve the natural language
conversation system for a visual grounding task. During interactive training,
both agents are reinforced by the guidance from a common reward function. The
parametrized reward function also cooperatively updates itself via
interactions, and contribute to accomplishing the task. We evaluate the method
on GuessWhat?! visual grounding task, and significantly improve the task
success rate. However, we observe language drifting problem during training and
propose to use reward engineering to improve the interpretability for the
generated conversations. Our result also indicates evaluating goal-ended visual
conversation tasks require semantic relevant metrics beyond task success rate.
| 1 | 0 | 0 | 0 | 0 | 0 |
Adaptive Bayesian Sampling with Monte Carlo EM | We present a novel technique for learning the mass matrices in samplers
obtained from discretized dynamics that preserve some energy function. Existing
adaptive samplers use Riemannian preconditioning techniques, where the mass
matrices are functions of the parameters being sampled. This leads to
significant complexities in the energy reformulations and resultant dynamics,
often leading to implicit systems of equations and requiring inversion of
high-dimensional matrices in the leapfrog steps. Our approach provides a
simpler alternative, by using existing dynamics in the sampling step of a Monte
Carlo EM framework, and learning the mass matrices in the M step with a novel
online technique. We also propose a way to adaptively set the number of samples
gathered in the E step, using sampling error estimates from the leapfrog
dynamics. Along with a novel stochastic sampler based on Nosé-Poincaré
dynamics, we use this framework with standard Hamiltonian Monte Carlo (HMC) as
well as newer stochastic algorithms such as SGHMC and SGNHT, and show strong
performance on synthetic and real high-dimensional sampling scenarios; we
achieve sampling accuracies comparable to Riemannian samplers while being
significantly faster.
| 1 | 0 | 0 | 1 | 0 | 0 |
Neutrino Fluxes from a Core-Collapse Supernova in a Model with Three Sterile Neutrinos | The characteristics of the gravitational collapse of a supernova and the
fluxes of active and sterile neutrinos produced during the formation of its
protoneutron core have been calculated numerically. The relative yields of
active and sterile neutrinos in core matter with different degrees of
neutronization have been calculated for various input parameters and various
initial conditions. A significant increase in the fraction of sterile neutrinos
produced in superdense core matter at the resonant degree of neutronization has
been confirmed. The contributions of sterile neutrinos to the collapse dynamics
and the total flux of neutrinos produced during collapse have been shown to be
relatively small. The total luminosity of sterile neutrinos is considerably
lower than the luminosity of electron neutrinos, but their spectrum is
considerably harder at high energies.
| 0 | 1 | 0 | 0 | 0 | 0 |
Resource Allocation for Wireless Networks: A Distributed Optimization Approach | We consider the multi-cell joint power control and scheduling problem in
cellular wireless networks as a weighted sum-rate maximization problem. This
formulation is very general and applies to a wide range of applications and QoS
requirements. The problem is inherently hard due to objective's non-convexity
and the knapsack-like constraints. Moreover, practical system requires a
distributed operation. We applied an existing algorithm proposed by Scutari et
al. in distributed optimization literature to our problem. The algorithm
performs local optimization followed by consensus update repeatedly. However,
it is not fully applicable to our problem, as it requires all decision
variables to be maintained at every base station (BS), which is impractical for
large-scale networks; also, it relies on the Lipschitz continuity of the
objective function's gradient, which does not hold here. We exploited the
nature of our objective function, and proposed a localized version of the
algorithm. Furthermore, we relaxed the requirements of Lipschitz continuity
with the proximal approximation. Convergence to local optimal solutions was
proved under some conditions. Future work includes proving the above results
from a stochastic approximation perspective, and investigating non-linear
consensus schemes to speed up the convergence.
| 0 | 0 | 1 | 0 | 0 | 0 |
Modular operads and Batalin-Vilkovisky geometry | This is a copy of the article published in IMRN (2007). I describe the
noncommutative Batalin-Vilkovisky geometry associated naturally with arbitrary
modular operad. The classical limit of this geometry is the noncommutative
symplectic geometry of the corresponding tree-level cyclic operad. I show, in
particular, that the algebras over the Feynman transform of a twisted modular
operad P are in one-to-one correspondence with solutions to quantum master
equation of Batalin-Vilkovisky geometry on the affine P-manifolds. As an
application I give a construction of characteristic classes with values in the
homology of the quotient of Deligne-Mumford moduli spaces. These classes are
associated naturally with solutions to the quantum master equation on affine
S[t]-manifolds, where S[t] is the twisted modular Det-operad constructed from
symmetric groups, which generalizes the cyclic operad of associative algebras.
| 0 | 0 | 1 | 0 | 0 | 0 |
Response of QD to structured beams via convolution integrals | We propose a new expression for the response of a quadrant detector using
convolution integrals. This expression is easier to evaluate by hand,
exploiting the properties of the convolution. Computationally, it is also
practicable to use since a large number of computer programs can right away
evaluate convolutions. We use the new expression to obtain an analytical form
of the response of a quadrant detector to a Gaussian beam and to
Hermite-Gaussian beams in general. We compare this analytic expression for the
response for the Gaussian beam with the approximations from previous studies
and with a response obtained through simulations. From the response, we also
obtained an analytical form for the sensitivity of the quadrant detector to a
Gaussian beam. Lastly, we demonstrate the computational ease of using our new
expression for the response calculating the sensitivity of the quadrant
detector to the Bessel beam.
| 0 | 1 | 0 | 0 | 0 | 0 |
Regularized arrangements of cellular complexes | In this paper we propose a novel algorithm to combine two or more cellular
complexes, providing a minimal fragmentation of the cells of the resulting
complex. We introduce here the idea of arrangement generated by a collection of
cellular complexes, producing a cellular decomposition of the embedding space.
The algorithm that executes this computation is called \emph{Merge} of
complexes. The arrangements of line segments in 2D and polygons in 3D are
special cases, as well as the combination of closed triangulated surfaces or
meshed models. This algorithm has several important applications, including
Boolean and other set operations over large geometric models, the extraction of
solid models of biomedical structures at the cellular scale, the detailed
geometric modeling of buildings, the combination of 3D meshes, and the repair
of graphical models. The algorithm is efficiently implemented using the Linear
Algebraic Representation (LAR) of argument complexes, i.e., on sparse
representation of binary characteristic matrices of $d$-cell bases, well-suited
for implementation in last generation accelerators and GPGPU applications.
| 1 | 0 | 0 | 0 | 0 | 0 |
Duality of deconfined quantum critical point in two dimensional Dirac semimetals | In this paper we discuss the N$\acute{e}$el and Kekul$\acute{e}$ valence bond
solids quantum criticality in graphene Dirac semimetal. Considering the quartic
four-fermion interaction $g(\bar{\psi}_i\Gamma_{ij}\psi_j)^2$ that contains
spin,valley, and sublattice degrees of freedom in the continuum field theory,
we find the microscopic symmetry is spontaneously broken when the coupling $g$
is greater than a critical value $g_c$. The symmetry breaking gaps out the
fermion and leads to semimetal-insulator transition. All possible quartic
fermion-bilinear interactions give rise to the uniform critical coupling, which
exhibits the multicritical point for various orders and the Landau-forbidden
quantum critical point. We also investigate the typical critical point between
N$\acute{e}$el and Kekul$\acute{e}$ valence bond solid transition when the
symmetry is broken. The quantum criticality is captured by the
Wess-Zumino-Witten term and there exist a mutual-duality for
N$\acute{e}$el-Kekul$\acute{e}$ VBS order. We show the emergent spinon in the
N$\acute{e}$el-Kekul$\acute{e}$ VBS transition , from which we conclude the
phase transition is a deconfined quantum critical point. Additionally, the
connection between the index theorem and zero energy mode bounded by the
topological defect in the Kekul$\acute{e}$ VBS phase is studied to reveal the
N$\acute{e}$el-Kekul$\acute{e}$ VBS duality.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Survey on Cloud Video Multicasting Over Mobile Networks | Since multimedia streaming has become very popular research topic in the
recent years, this paper surveys the state of art techniques introduced for
multimedia multicasting over mobile networks. In this paper, we give an
overview of multimedia multicasting mechanisms in respect to cloud mobile
communications, and we present some proposed solutions in perspective. We focus
on the algorithms designed specifically for the video-on-demand applications.
Our study on video-on-demand applications will eventually cover a wide range of
applications such as cloud gaming without violating the limited scope of this
survey.
| 1 | 0 | 0 | 0 | 0 | 0 |
The Brauer trees of unipotent blocks | In this paper we complete the determination of the Brauer trees of unipotent
blocks (with cyclic defect groups) of finite groups of Lie type. These trees
were conjectured by the first author. As a consequence, the Brauer trees of
principal $\ell$-blocks of finite groups are known for $\ell>71$.
| 0 | 0 | 1 | 0 | 0 | 0 |
An Empirical Bayes Approach to Regularization Using Previously Published Models | This manuscript proposes a novel empirical Bayes technique for regularizing
regression coefficients in predictive models. When predictions from a
previously published model are available, this empirical Bayes method provides
a natural mathematical framework for shrinking coefficients toward the
estimates implied by the body of existing research rather than the shrinkage
toward zero provided by traditional L1 and L2 penalization schemes. The method
is applied to two different prediction problems. The first involves the
construction of a model for predicting whether a single nucleotide polymorphism
(SNP) of the KCNQ1 gene will result in dysfunction of the corresponding voltage
gated ion channel. The second involves the prediction of preoperative serum
creatinine change in patients undergoing cardiac surgery.
| 0 | 0 | 0 | 1 | 0 | 0 |
On minimum distance of locally repairable codes | Distributed and cloud storage systems are used to reliably store large-scale
data. Erasure codes have been recently proposed and used in real-world
distributed and cloud storage systems such as Google File System, Microsoft
Azure Storage, and Facebook HDFS-RAID, to enhance the reliability. In order to
decrease the repair bandwidth and disk I/O, a class of erasure codes called
locally repairable codes (LRCs) have been proposed which have small locality
compare to other erasure codes. Although LRCs have small locality, they have
lower minimum distance compare to the Singleton bound. Hence, seeking the
largest possible minimum distance for LRCs have been the topic of many recent
studies. In this paper, we study the largest possible minimum distance of a
class of LRCs and evaluate them in terms of achievability. Furthermore, we
compare our results with the existence bounds in the literature.
| 1 | 0 | 0 | 0 | 0 | 0 |
The MoEDAL experiment at the LHC: status and results | The MoEDAL experiment at the LHC is optimised to detect highly ionising
particles such as magnetic monopoles, dyons and (multiply) electrically charged
stable massive particles predicted in a number of theoretical scenarios.
MoEDAL, deployed in the LHCb cavern, combines passive nuclear track detectors
with magnetic monopole trapping volumes (MMTs), while spallation-product
backgrounds are being monitored with an array of MediPix pixel detectors. An
introduction to the detector concept and its physics reach, complementary to
that of the large general purpose LHC experiments ATLAS and CMS, will be given.
Emphasis is given to the recent MoEDAL results at 13 TeV, where the null
results from a search for magnetic monopoles in MMTs exposed in 2015 LHC
collisions set the world-best limits on particles with magnetic charges more
than 1.5 Dirac charge. The potential to search for heavy, long-lived
supersymmetric electrically-charged particles is also discussed.
| 0 | 1 | 0 | 0 | 0 | 0 |
Towards a population synthesis model of self-gravitating disc fragmentation and tidal downsizing II: The effect of fragment-fragment interactions | It is likely that most protostellar systems undergo a brief phase where the
protostellar disc is self-gravitating. If these discs are prone to
fragmentation, then they are able to rapidly form objects that are initially of
several Jupiter masses and larger. The fate of these disc fragments (and the
fate of planetary bodies formed afterwards via core accretion) depends
sensitively not only on the fragment's interaction with the disc, but with its
neighbouring fragments.
We return to and revise our population synthesis model of self-gravitating
disc fragmentation and tidal downsizing. Amongst other improvements, the model
now directly incorporates fragment-fragment interactions while the disc is
still present. We find that fragment-fragment scattering dominates the orbital
evolution, even when we enforce rapid migration and inefficient gap formation.
Compared to our previous model, we see a small increase in the number of
terrestrial-type objects being formed, although their survival under tidal
evolution is at best unclear. We also see evidence for disrupted fragments with
evolved grain populations - this is circumstantial evidence for the formation
of planetesimal belts, a phenomenon not seen in runs where fragment-fragment
interactions are ignored.
In spite of intense dynamical evolution, our population is dominated by
massive giant planets and brown dwarfs at large semimajor axis, which direct
imaging surveys should, but only rarely, detect. Finally, disc fragmentation is
shown to be an efficient manufacturer of free floating planetary mass objects,
and the typical multiplicity of systems formed via gravitational instability
will be low.
| 0 | 1 | 0 | 0 | 0 | 0 |
Credit Risk Meets Random Matrices: Coping with Non-Stationary Asset Correlations | We review recent progress in modeling credit risk for correlated assets. We
start from the Merton model which default events and losses are derived from
the asset values at maturity. To estimate the time development of the asset
values, the stock prices are used whose correlations have a strong impact on
the loss distribution, particularly on its tails. These correlations are
non-stationary which also influences the tails. We account for the asset
fluctuations by averaging over an ensemble of random matrices that models the
truly existing set of measured correlation matrices. As a most welcome side
effect, this approach drastically reduces the parameter dependence of the loss
distribution, allowing us to obtain very explicit results which show
quantitatively that the heavy tails prevail over diversification benefits even
for small correlations. We calibrate our random matrix model with market data
and show how it is capable of grasping different market situations.
Furthermore, we present numerical simulations for concurrent portfolio risks,
i.e., for the joint probability densities of losses for two portfolios. For the
convenience of the reader, we give an introduction to the Wishart random matrix
model.
| 0 | 0 | 0 | 0 | 0 | 1 |
Peptide-Spectra Matching from Weak Supervision | As in many other scientific domains, we face a fundamental problem when using
machine learning to identify proteins from mass spectrometry data: large ground
truth datasets mapping inputs to correct outputs are extremely difficult to
obtain. Instead, we have access to imperfect hand-coded models crafted by
domain experts. In this paper, we apply deep neural networks to an important
step of the protein identification problem, the pairing of mass spectra with
short sequences of amino acids called peptides. We train our model to
differentiate between top scoring results from a state-of-the art classical
system and hard-negative second and third place results. Our resulting model is
much better at identifying peptides with spectra than the model used to
generate its training data. In particular, we achieve a 43% improvement over
standard matching methods and a 10% improvement over a combination of the
matching method and an industry standard cross-spectra reranking tool.
Importantly, in a more difficult experimental regime that reflects current
challenges facing biologists, our advantage over the previous state-of-the-art
grows to 15% even after reranking. We believe this approach will generalize to
other challenging scientific problems.
| 0 | 0 | 0 | 1 | 0 | 0 |
Overcoming data scarcity with transfer learning | Despite increasing focus on data publication and discovery in materials
science and related fields, the global view of materials data is highly sparse.
This sparsity encourages training models on the union of multiple datasets, but
simple unions can prove problematic as (ostensibly) equivalent properties may
be measured or computed differently depending on the data source. These hidden
contextual differences introduce irreducible errors into analyses,
fundamentally limiting their accuracy. Transfer learning, where information
from one dataset is used to inform a model on another, can be an effective tool
for bridging sparse data while preserving the contextual differences in the
underlying measurements. Here, we describe and compare three techniques for
transfer learning: multi-task, difference, and explicit latent variable
architectures. We show that difference architectures are most accurate in the
multi-fidelity case of mixed DFT and experimental band gaps, while multi-task
most improves classification performance of color with band gaps. For
activation energies of steps in NO reduction, the explicit latent variable
method is not only the most accurate, but also enjoys cancellation of errors in
functions that depend on multiple tasks. These results motivate the publication
of high quality materials datasets that encode transferable information,
independent of industrial or academic interest in the particular labels, and
encourage further development and application of transfer learning methods to
materials informatics problems.
| 1 | 0 | 0 | 1 | 0 | 0 |
Dirichlet Bayesian Network Scores and the Maximum Relative Entropy Principle | A classic approach for learning Bayesian networks from data is to identify a
maximum a posteriori (MAP) network structure. In the case of discrete Bayesian
networks, MAP networks are selected by maximising one of several possible
Bayesian Dirichlet (BD) scores; the most famous is the Bayesian Dirichlet
equivalent uniform (BDeu) score from Heckerman et al (1995). The key properties
of BDeu arise from its uniform prior over the parameters of each local
distribution in the network, which makes structure learning computationally
efficient; it does not require the elicitation of prior knowledge from experts;
and it satisfies score equivalence.
In this paper we will review the derivation and the properties of BD scores,
and of BDeu in particular, and we will link them to the corresponding entropy
estimates to study them from an information theoretic perspective. To this end,
we will work in the context of the foundational work of Giffin and Caticha
(2007), who showed that Bayesian inference can be framed as a particular case
of the maximum relative entropy principle. We will use this connection to show
that BDeu should not be used for structure learning from sparse data, since it
violates the maximum relative entropy principle; and that it is also
problematic from a more classic Bayesian model selection perspective, because
it produces Bayes factors that are sensitive to the value of its only
hyperparameter. Using a large simulation study, we found in our previous work
(Scutari, 2016) that the Bayesian Dirichlet sparse (BDs) score seems to provide
better accuracy in structure learning; in this paper we further show that BDs
does not suffer from the issues above, and we recommend to use it for sparse
data instead of BDeu. Finally, will show that these issues are in fact
different aspects of the same problem and a consequence of the distributional
assumptions of the prior.
| 0 | 0 | 1 | 1 | 0 | 0 |
Thermal lattice Boltzmann method for multiphase flows | New method to simulate heat transport in multiphase lattice Boltzmann (LB)
method is proposed. The energy transport equation needs to be solved when phase
boundaries are present. Internal energy is represented by an additional set of
distribution functions, which evolve according to a LB-like equation simulating
the transport of a passive scalar. Parasitic heat diffusion near boundaries
with large density gradient is supressed by using the interparticle
"pseudoforces" which prevent the spreading of energy. The compression work and
heat diffusion are calculated by finite differences. The latent heat of a phase
transition is released or absorbed in the inner side of a thin transition layer
between liquid and vapor. This allows one to avoide the interface tracking.
Several tests were carried out concerning all aspects of the processes. It was
shown that the Galilean invariance and the scaling of thermal conduction
process hold as well as the correct dependence of sound speed on the heat
capacity ratio. The method proposed has low scheme diffusion of the internal
energy, and it can be applied to modeling a wide range of multiphase flows with
heat and mass transfer.
| 0 | 1 | 0 | 0 | 0 | 0 |
Control for Schrödinger equation on hyperbolic surfaces | We show that the any nonempty open set on a hyperbolic surface provides
observability and control for the time dependent Schrödinger equation. The
only other manifolds for which this was previously known are flat tori. The
proof is based on the main estimate in Dyatlov-Jin and standard arguments of
control theory.
| 0 | 0 | 1 | 0 | 0 | 0 |
Grand Fujii-Fujii-Nakamoto operator inequality dealing with operator order and operator chaotic order | In this paper, we shall prove that a grand Fujii-Fujii-Nakamoto operator
inequality implies operator order and operator chaotic order under different
conditions.
| 0 | 0 | 1 | 0 | 0 | 0 |
Stability for gains from large investors' strategies in M1/J1 topologies | We prove continuity of a controlled SDE solution in Skorokhod's $M_1$ and
$J_1$ topologies and also uniformly, in probability, as a non-linear functional
of the control strategy. The functional comes from a finance problem to model
price impact of a large investor in an illiquid market. We show that
$M_1$-continuity is the key to ensure that proceeds and wealth processes from
(self-financing) càdlàg trading strategies are determined as the
continuous extensions for those from continuous strategies. We demonstrate by
examples how continuity properties are useful to solve different stochastic
control problems on optimal liquidation and to identify asymptotically
realizable proceeds.
| 0 | 0 | 1 | 0 | 0 | 0 |
Apparent and Intrinsic Evolution of Active Region Upflows | We analyze the evolution of Fe XII coronal plasma upflows from the edges of
ten active regions (ARs) as they cross the solar disk using the Hinode Extreme
Ultraviolet Imaging Spectrometer (EIS). Confirming the results of Demoulin et
al. (2013, Sol. Phys. 283, 341), we find that for each AR there is an observed
long term evolution of the upflows which is largely due to the solar rotation
progressively changing the viewpoint of dominantly stationary upflows. From
this projection effect, we estimate the unprojected upflow velocity and its
inclination to the local vertical. AR upflows typically fan away from the AR
core by 40 deg. to near vertical for the following polarity. The span of
inclination angles is more spread for the leading polarity with flows angled
from -29 deg. (inclined towards the AR center) to 28 deg. (directed away from
the AR). In addition to the limb-to-limb apparent evolution, we identify an
intrinsic evolution of the upflows due to coronal activity which is AR
dependent. Further, line widths are correlated with Doppler velocities only for
the few ARs having the largest velocities. We conclude that for the line widths
to be affected by the solar rotation, the spatial gradient of the upflow
velocities must be large enough such that the line broadening exceeds the
thermal line width of Fe XII. Finally, we find that upflows occurring in pairs
or multiple pairs is a common feature of ARs observed by Hinode/EIS, with up to
four pairs present in AR 11575. This is important for constraining the upflow
driving mechanism as it implies that the mechanism is not a local one occurring
over a single polarity. AR upflows originating from reconnection along
quasi-separatrix layers (QSLs) between over-pressure AR loops and neighboring
under-pressure loops is consistent with upflows occurring in pairs, unlike
other proposed mechanisms acting locally in one polarity.
| 0 | 1 | 0 | 0 | 0 | 0 |
Size scaling of failure strength with fat-tailed disorder in a fiber bundle model | We investigate the size scaling of the macroscopic fracture strength of
heterogeneous materials when microscopic disorder is controlled by fat-tailed
distributions. We consider a fiber bundle model where the strength of single
fibers is described by a power law distribution over a finite range. Tuning the
amount of disorder by varying the power law exponent and the upper cutoff of
fibers' strength, in the limit of equal load sharing an astonishing size effect
is revealed: For small system sizes the bundle strength increases with the
number of fibers and the usual decreasing size effect of heterogeneous
materials is only restored beyond a characteristic size. We show analytically
that the extreme order statistics of fibers' strength is responsible for this
peculiar behavior. Analyzing the results of computer simulations we deduce a
scaling form which describes the dependence of the macroscopic strength of
fiber bundles on the parameters of microscopic disorder over the entire range
of system sizes.
| 0 | 1 | 0 | 0 | 0 | 0 |
Time-dependent focusing Mean-Field Games: the sub-critical case | We consider time-dependent viscous Mean-Field Games systems in the case of
local, decreasing and unbounded coupling. These systems arise in mean-field
game theory, and describe Nash equilibria of games with a large number of
agents aiming at aggregation. We prove the existence of weak solutions that are
minimisers of an associated non-convex functional, by rephrasing the problem in
a convex framework. Under additional assumptions involving the growth at
infinity of the coupling, the Hamiltonian, and the space dimension, we show
that such minimisers are indeed classical solutions by a blow-up argument and
additional Sobolev regularity for the Fokker-Planck equation. We exhibit an
example of non-uniqueness of solutions. Finally, by means of a contraction
principle, we observe that classical solutions exist just by local regularity
of the coupling if the time horizon is short.
| 0 | 0 | 1 | 0 | 0 | 0 |
Evolution of protoplanetary disks from their taxonomy in scattered light: Group I vs. Group II | High-resolution imaging reveals a large morphological variety of
protoplanetary disks. To date, no constraints on their global evolution have
been found from this census. An evolutionary classification of disks was
proposed based on their IR spectral energy distribution, with the Group I
sources showing a prominent cold component ascribed to an earlier stage of
evolution than Group II. Disk evolution can be constrained from the comparison
of disks with different properties. A first attempt of disk taxonomy is now
possible thanks to the increasing number of high-resolution images of Herbig
Ae/Be stars becoming available. Near-IR images of six Group II disks in
scattered light were obtained with VLT/NACO in Polarimetric Differential
Imaging, which is the most efficient technique to image the light scattered by
the disk material close to the stars. We compare the stellar/disk properties of
this sample with those of well-studied Group I sources available from the
literature. Three Group II disks are detected. The brightness distribution in
the disk of HD163296 indicates the presence of a persistent ring-like structure
with a possible connection with the CO snowline. A rather compact (less than
100 AU) disk is detected around HD142666 and AK Sco. A taxonomic analysis of 17
Herbig Ae/Be sources reveals that the difference between Group I and Group II
is due to the presence or absence of a large disk cavity (larger than 5 AU).
There is no evidence supporting the evolution from Group I to Group II. Group
II are not evolved version of the Group I. Within the Group II disks, very
different geometries (both self-shadowed and compact) exist. HD163296 could be
the primordial version of a typical Group I. Other Group II, like AK Sco and
HD142666, could be smaller counterpart of Group I unable to open cavities as
large as those of Group I.
| 0 | 1 | 0 | 0 | 0 | 0 |
Sandwich semigroups in locally small categories II: Transformations | Fix sets $X$ and $Y$, and write $\mathcal{PT}_{XY}$ for the set of all
partial functions $X\to Y$. Fix a partial function $a:Y\to X$, and define the
operation $\star_a$ on $\mathcal{PT}_{XY}$ by $f\star_ag=fag$ for
$f,g\in\mathcal{PT}_{XY}$. The sandwich semigroup $(\mathcal{PT}_{XY},\star_a)$
is denoted $\mathcal{PT}_{XY}^a$. We apply general results from Part I to
thoroughly describe the structural and combinatorial properties of
$\mathcal{PT}_{XY}^a$, as well as its regular and idempotent-generated
subsemigroups, Reg$(\mathcal{PT}_{XY}^a)$ and $\mathbb E(\mathcal{PT}_{XY}^a)$.
After describing regularity, stability and Green's relations and preorders, we
exhibit Reg$(\mathcal{PT}_{XY}^a)$ as a pullback product of certain regular
subsemigroups of the (non-sandwich) partial transformation semigroups
$\mathcal{PT}_X$ and $\mathcal{PT}_Y$, and as a kind of "inflation" of
$\mathcal{PT}_A$, where $A$ is the image of the sandwich element $a$. We also
calculate the rank (minimal size of a generating set) and, where appropriate,
the idempotent rank (minimal size of an idempotent generating set) of
$\mathcal{PT}_{XY}^a$, Reg$(\mathcal{PT}_{XY}^a)$ and $\mathbb
E(\mathcal{PT}_{XY}^a)$. The same program is also carried out for sandwich
semigroups of totally defined functions and for injective partial functions.
Several corollaries are obtained for various (non-sandwich) semigroups of
(partial) transformations with restricted image, domain and/or kernel.
| 0 | 0 | 1 | 0 | 0 | 0 |
Environmental feedback drives cooperation in spatial social dilemmas | Exploiting others is beneficial individually but it could also be detrimental
globally. The reverse is also true: a higher cooperation level may change the
environment in a way that is beneficial for all competitors. To explore the
possible consequence of this feedback we consider a coevolutionary model where
the local cooperation level determines the payoff values of the applied
prisoner's dilemma game. We observe that the coevolutionary rule provides a
significantly higher cooperation level comparing to the traditional setup
independently of the topology of the applied interaction graph. Interestingly,
this cooperation supporting mechanism offers lonely defectors a high surviving
chance for a long period hence the relaxation to the final cooperating state
happens logarithmically slow. As a consequence, the extension of the
traditional evolutionary game by considering interactions with the environment
provides a good opportunity for cooperators, but their reward may arrive with
some delay.
| 0 | 0 | 0 | 0 | 1 | 0 |
Finitely forcible graph limits are universal | The theory of graph limits represents large graphs by analytic objects called
graphons. Graph limits determined by finitely many graph densities, which are
represented by finitely forcible graphons, arise in various scenarios,
particularly within extremal combinatorics. Lovasz and Szegedy conjectured that
all such graphons possess a simple structure, e.g., the space of their typical
vertices is always finite dimensional; this was disproved by several ad hoc
constructions of complex finitely forcible graphons. We prove that any graphon
is a subgraphon of a finitely forcible graphon. This dismisses any hope for a
result showing that finitely forcible graphons possess a simple structure, and
is surprising when contrasted with the fact that finitely forcible graphons
form a meager set in the space of all graphons. In addition, since any finitely
forcible graphon represents the unique minimizer of some linear combination of
densities of subgraphs, our result also shows that such minimization problems,
which conceptually are among the simplest kind within extremal graph theory,
may in fact have unique optimal solutions with arbitrarily complex structure.
| 0 | 0 | 1 | 0 | 0 | 0 |
Sex-biased dispersal: a review of the theory | Dispersal is ubiquitous throughout the tree of life: factors selecting for
dispersal include kin competition, inbreeding avoidance and spatiotemporal
variation in resources or habitat suitability. These factors differ in whether
they promote male and female dispersal equally strongly, and often selection on
dispersal of one sex depends on how much the other disperses. For example, for
inbreeding avoidance it can be sufficient that one sex disperses away from the
natal site. Attempts to understand sex-specific dispersal evolution have
created a rich body of theoretical literature, which we review here. We
highlight an interesting gap between empirical and theoretical literature. The
former associates different patterns of sex-biased dispersal with mating
systems, such as female-biased dispersal in monogamous birds and male-biased
dispersal in polygynous mammals. The predominant explanation is traceable back
to Greenwood's (1980) ideas of how successful philopatric or dispersing
individuals are at gaining mates or resources required to attract them. Theory,
however, has developed surprisingly independently of these ideas: predominant
ideas in theoretical work track how immigration and emigration change
relatedness patterns and alleviate competition for limiting resources,
typically considered sexually distinct, with breeding sites and fertilisable
females limiting reproductive success for females and males, respectively. We
show that the link between mating system and sex-biased dispersal is far from
resolved: there are studies showing that mating systems matter, but the
oft-stated association between polygyny and male-biased dispersal is not a
straightforward theoretical expectation... (full abstract in the PDF)
| 0 | 0 | 0 | 0 | 1 | 0 |
Nonequilibrium quantum dynamics of partial symmetry breaking for ultracold bosons in an optical lattice ring trap | A vortex in a Bose-Einstein condensate on a ring undergoes quantum dynamics
in response to a quantum quench in terms of partial symmetry breaking from a
uniform lattice to a biperiodic one. Neither the current, a macroscopic
measure, nor fidelity, a microscopic measure, exhibit critical behavior.
Instead, the symmetry memory succeeds in identifying the point at which the
system begins to forget its initial symmetry state. We further identify a
symmetry gap in the low lying excited states which trends with the symmetry
memory.
| 0 | 1 | 0 | 0 | 0 | 0 |
Learning Hidden Quantum Markov Models | Hidden Quantum Markov Models (HQMMs) can be thought of as quantum
probabilistic graphical models that can model sequential data. We extend
previous work on HQMMs with three contributions: (1) we show how classical
hidden Markov models (HMMs) can be simulated on a quantum circuit, (2) we
reformulate HQMMs by relaxing the constraints for modeling HMMs on quantum
circuits, and (3) we present a learning algorithm to estimate the parameters of
an HQMM from data. While our algorithm requires further optimization to handle
larger datasets, we are able to evaluate our algorithm using several synthetic
datasets. We show that on HQMM generated data, our algorithm learns HQMMs with
the same number of hidden states and predictive accuracy as the true HQMMs,
while HMMs learned with the Baum-Welch algorithm require more states to match
the predictive accuracy.
| 0 | 0 | 0 | 1 | 0 | 0 |
Gradient Descent using Duality Structures | Gradient descent is commonly used to solve optimization problems arising in
machine learning, such as training neural networks. Although it seems to be
effective for many different neural network training problems, it is unclear if
the effectiveness of gradient descent can be explained using existing
performance guarantees for the algorithm. We argue that existing analyses of
gradient descent rely on assumptions that are too strong to be applicable in
the case of multi-layer neural networks. To address this, we propose an
algorithm, duality structure gradient descent (DSGD), that is amenable to a
non-asymptotic performance analysis, under mild assumptions on the training set
and network architecture. The algorithm can be viewed as a form of layer-wise
coordinate descent, where at each iteration the algorithm chooses one layer of
the network to update. The decision of what layer to update is done in a greedy
fashion, based on a rigorous lower bound of the function decrease for each
possible choice of layer. In the analysis, we bound the time required to reach
approximate stationary points, in both the deterministic and stochastic
settings. The convergence is measured in terms of a Finsler geometry that is
derived from the network architecture and designed to confirm a Lipschitz-like
property on the gradient of the training objective function. Numerical
experiments in both the full batch and mini-batch settings suggest that the
algorithm is a promising step towards methods for training neural networks that
are both rigorous and efficient.
| 1 | 0 | 0 | 0 | 0 | 0 |
On the Universal Approximation Property and Equivalence of Stochastic Computing-based Neural Networks and Binary Neural Networks | Large-scale deep neural networks are both memory intensive and
computation-intensive, thereby posing stringent requirements on the computing
platforms. Hardware accelerations of deep neural networks have been extensively
investigated in both industry and academia. Specific forms of binary neural
networks (BNNs) and stochastic computing based neural networks (SCNNs) are
particularly appealing to hardware implementations since they can be
implemented almost entirely with binary operations. Despite the obvious
advantages in hardware implementation, these approximate computing techniques
are questioned by researchers in terms of accuracy and universal applicability.
Also it is important to understand the relative pros and cons of SCNNs and BNNs
in theory and in actual hardware implementations. In order to address these
concerns, in this paper we prove that the "ideal" SCNNs and BNNs satisfy the
universal approximation property with probability 1 (due to the stochastic
behavior). The proof is conducted by first proving the property for SCNNs from
the strong law of large numbers, and then using SCNNs as a "bridge" to prove
for BNNs. Based on the universal approximation property, we further prove that
SCNNs and BNNs exhibit the same energy complexity. In other words, they have
the same asymptotic energy consumption with the growing of network size. We
also provide a detailed analysis of the pros and cons of SCNNs and BNNs for
hardware implementations and conclude that SCNNs are more suitable for
hardware.
| 0 | 0 | 0 | 1 | 0 | 0 |
Riemannian stochastic quasi-Newton algorithm with variance reduction and its convergence analysis | Stochastic variance reduction algorithms have recently become popular for
minimizing the average of a large, but finite number of loss functions. The
present paper proposes a Riemannian stochastic quasi-Newton algorithm with
variance reduction (R-SQN-VR). The key challenges of averaging, adding, and
subtracting multiple gradients are addressed with notions of retraction and
vector transport. We present convergence analyses of R-SQN-VR on both
non-convex and retraction-convex functions under retraction and vector
transport operators. The proposed algorithm is evaluated on the Karcher mean
computation on the symmetric positive-definite manifold and the low-rank matrix
completion on the Grassmann manifold. In all cases, the proposed algorithm
outperforms the state-of-the-art Riemannian batch and stochastic gradient
algorithms.
| 1 | 0 | 1 | 1 | 0 | 0 |
Higher cohomology vanishing of line bundles on generalized Springer's resolution | We give a proof of a conjecture raised by Michael Finkelberg and Andrei
Ionov. As a corollary, the coefficients of multivariable version of Kostka
functions introduced by Finkelberg and Ionov are non-negative.
| 0 | 0 | 1 | 0 | 0 | 0 |
Strong instability of ground states to a fourth order Schrödinger equation | In this note we prove the instability by blow-up of the ground state
solutions for a class of fourth order Schr\" odinger equations. This extends
the first rigorous results on blowing-up solutions for the biharmonic NLS due
to Boulenger and Lenzmann \cite{BoLe} and confirm numerical conjectures from
\cite{BaFi, BaFiMa1, BaFiMa, FiIlPa}.
| 0 | 0 | 1 | 0 | 0 | 0 |
Coalescing particle systems and applications to nonlinear Fokker-Planck equations | We study a stochastic particle system with a logarithmically-singular
inter-particle interaction potential which allows for inelastic particle
collisions. We relate the squared Bessel process to the evolution of localized
clusters of particles, and develop a numerical method capable of detecting
collisions of many point particles without the use of pairwise computations, or
very refined adaptive timestepping. We show that when the system is in an
appropriate parameter regime, the hydrodynamic limit of the empirical mass
density of the system is a solution to a nonlinear Fokker-Planck equation, such
as the Patlak-Keller-Segel (PKS) model, or its multispecies variant. We then
show that the presented numerical method is well-suited for the simulation of
the formation of finite-time singularities in the PKS, as well as PKS pre- and
post-blow-up dynamics. Additionally, we present numerical evidence that blow-up
with an increasing total second moment in the two species Keller-Segel system
occurs with a linearly increasing second moment in one component, and a
linearly decreasing second moment in the other component.
| 0 | 0 | 1 | 0 | 0 | 0 |
Periodic solution for strongly nonlinear oscillators by He's new amplitude-frequency relationship | This paper applies He's new amplitude-frequency relationship recently
established by Ji-Huan He (Int J Appl Comput Math 3 1557-1560, 2017) to study
periodic solutions of strongly nonlinear systems with odd nonlinearities. Some
examples are given to illustrate the effectiveness, ease and convenience of the
method. In general, the results are valid for small as well as large
oscillation amplitude. The method can be easily extended to other nonlinear
systems with odd nonlinearities and can therefore be found widely applicable in
engineering and other science. The method used in this paper can be applied
directly to highly nonlinear problems without any discretization, linearization
or additional requirements.
| 0 | 1 | 0 | 0 | 0 | 0 |
Singular Degenerations of Lie Supergroups of Type $D(2,1;a)$ | The complex Lie superalgebras $\mathfrak{g}$ of type $D(2,1;a)$ - also
denoted by $\mathfrak{osp}(4,2;a) $ - are usually considered for "non-singular"
values of the parameter $a$, for which they are simple. In this paper we
introduce five suitable integral forms of $\mathfrak{g}$, that are well-defined
at singular values too, giving rise to "singular specializations" that are no
longer simple: this extends the family of simple objects of type $D(2,1;a)$ in
five different ways. The resulting five families coincide for general values of
$a$, but are different at "singular" ones: here they provide non-simple Lie
superalgebras, whose structure we describe explicitly. We also perform the
parallel construction for complex Lie supergroups and describe their singular
specializations (or "degenerations") at singular values of $a$. Although one
may work with a single complex parameter $a$, in order to stress the overall
$\mathfrak{S}_3$-symmetry of the whole situation, we shall work (following
Kaplansky) with a two-dimensional parameter $\boldsymbol{\sigma} =
(\sigma_1,\sigma_2,\sigma_3)$ ranging in the complex affine plane $\sigma_1 +
\sigma_2 + \sigma_3 = 0$.
| 0 | 0 | 1 | 0 | 0 | 0 |
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