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Learning Filter Functions in Regularisers by Minimising Quotients | Learning approaches have recently become very popular in the field of inverse
problems. A large variety of methods has been established in recent years,
ranging from bi-level learning to high-dimensional machine learning techniques.
Most learning approaches, however, only aim at fitting parametrised models to
favourable training data whilst ignoring misfit training data completely. In
this paper, we follow up on the idea of learning parametrised regularisation
functions by quotient minimisation as established in [3]. We extend the model
therein to include higher-dimensional filter functions to be learned and allow
for fit- and misfit-training data consisting of multiple functions. We first
present results resembling behaviour of well-established derivative-based
sparse regularisers like total variation or higher-order total variation in
one-dimension. Our second and main contribution is the introduction of novel
families of non-derivative-based regularisers. This is accomplished by learning
favourable scales and geometric properties while at the same time avoiding
unfavourable ones.
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Riemann-Theta Boltzmann Machine | A general Boltzmann machine with continuous visible and discrete integer
valued hidden states is introduced. Under mild assumptions about the connection
matrices, the probability density function of the visible units can be solved
for analytically, yielding a novel parametric density function involving a
ratio of Riemann-Theta functions. The conditional expectation of a hidden state
for given visible states can also be calculated analytically, yielding a
derivative of the logarithmic Riemann-Theta function. The conditional
expectation can be used as activation function in a feedforward neural network,
thereby increasing the modelling capacity of the network. Both the Boltzmann
machine and the derived feedforward neural network can be successfully trained
via standard gradient- and non-gradient-based optimization techniques.
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A Joint Quantile and Expected Shortfall Regression Framework | We introduce a novel regression framework which simultaneously models the
quantile and the Expected Shortfall (ES) of a response variable given a set of
covariates. This regression is based on a strictly consistent loss function for
the pair quantile and ES, which allows for M- and Z-estimation of the joint
regression parameters. We show consistency and asymptotic normality for both
estimators under weak regularity conditions. The underlying loss function
depends on two specification functions, whose choice affects the properties of
the resulting estimators. We find that the Z-estimator is numerically unstable
and thus, we rely on M-estimation of the model parameters. Extensive
simulations verify the asymptotic properties and analyze the small sample
behavior of the M-estimator for different specification functions. This joint
regression framework allows for various applications including estimating,
forecasting, and backtesting ES, which is particularly relevant in light of the
recent introduction of ES into the Basel Accords.
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Poincaré Embeddings for Learning Hierarchical Representations | Representation learning has become an invaluable approach for learning from
symbolic data such as text and graphs. However, while complex symbolic datasets
often exhibit a latent hierarchical structure, state-of-the-art methods
typically learn embeddings in Euclidean vector spaces, which do not account for
this property. For this purpose, we introduce a new approach for learning
hierarchical representations of symbolic data by embedding them into hyperbolic
space -- or more precisely into an n-dimensional Poincaré ball. Due to the
underlying hyperbolic geometry, this allows us to learn parsimonious
representations of symbolic data by simultaneously capturing hierarchy and
similarity. We introduce an efficient algorithm to learn the embeddings based
on Riemannian optimization and show experimentally that Poincaré embeddings
outperform Euclidean embeddings significantly on data with latent hierarchies,
both in terms of representation capacity and in terms of generalization
ability.
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Randomly cross-linked polymer models | Polymer models are used to describe chromatin, which can be folded at
different spatial scales by binding molecules. By folding, chromatin generates
loops of various sizes. We present here a randomly cross-linked (RCL) polymer
model, where monomer pairs are connected randomly. We obtain asymptotic
formulas for the steady-state variance, encounter probability, the radius of
gyration, instantaneous displacement and the mean first encounter time between
any two monomers. The analytical results are confirmed by Brownian simulations.
Finally, the present results can be used to extract the minimum number of
cross-links in a chromatin region from {conformation capture} data.
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Multi-Stage Complex Contagions in Random Multiplex Networks | Complex contagion models have been developed to understand a wide range of
social phenomena such as adoption of cultural fads, the diffusion of belief,
norms, and innovations in social networks, and the rise of collective action to
join a riot. Most existing works focus on contagions where individuals' states
are represented by {\em binary} variables, and propagation takes place over a
single isolated network. However, characterization of an individual's standing
on a given matter as a binary state might be overly simplistic as most of our
opinions, feelings, and perceptions vary over more than two states. Also, most
real-world contagions take place over multiple networks (e.g., Twitter and
Facebook) or involve {\em multiplex} networks where individuals engage in
different {\em types} of relationships (e.g., acquaintance, co-worker, family,
etc.). To this end, this paper studies {\em multi-stage} complex contagions
that take place over multi-layer or multiplex networks. Under a linear
threshold based contagion model, we give analytic results for the probability
and expected size of \textit{global} cascades, i.e., cases where a randomly
chosen node can initiate a propagation that eventually reaches a {\em positive}
fraction of the whole population. Analytic results are also confirmed and
supported by an extensive numerical study. In particular, we demonstrate how
the dynamics of complex contagions is affected by the extra weight exerted by
\textit{hyper-active} nodes and by the structural properties of the networks
involved. Among other things, we reveal an interesting connection between the
assortativity of a network and the impact of \textit{hyper-active} nodes on the
cascade size.
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Secrecy and Robustness for Active Attack in Secure Network Coding and its Application to Network Quantum Key Distribution | In network coding, we discuss the effect of sequential error injection on
information leakage. We show that there is no improvement when the operations
in the network are linear operations. However, when the operations in the
network contains non-linear operations, we find a counterexample to improve
Eve's obtained information. Furthermore, we discuss the asymptotic rate in a
linear network under the secrecy and robustness conditions as well as under the
secrecy condition alone. Finally, we apply our results to network quantum key
distribution, which clarifies the type of network that enables us to realize
secure long distance communication via short distance quantum key distribution.
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SLAMBooster: An Application-aware Controller for Approximation in SLAM | Simultaneous Localization and Mapping (SLAM) is the problem of constructing a
map of an agent's environment while localizing or tracking the mobile agent's
position and orientation within the map. Algorithms for SLAM have high
computational requirements, which has hindered their use on embedded devices.
Approximation can be used to reduce the time and energy requirements of SLAM
implementations as long as the approximations do not prevent the agent from
navigating correctly through the environment. Previous studies of approximation
in SLAM have assumed that the entire trajectory of the agent is known before
the agent starts to move, and they have focused on offline controllers that use
features of the trajectory to set approximation knobs at the start of the
trajectory. In practice, the trajectory is not usually known ahead of time, and
allowing knob settings to change dynamically opens up more opportunities for
reducing computation time and energy.
We describe SLAMBooster, an application-aware online control system for SLAM
that adaptively controls approximation knobs during the motion of the agent.
SLAMBooster is based on a control technique called hierarchical proportional
control but our experiments showed this application-agnostic control led to an
unacceptable reduction in the quality of localization. To address this problem,
SLAMBooster exploits domain knowledge: it uses features extracted from input
frames and from the estimated motion of the agent in its algorithm for
controlling approximation.
We implemented SLAMBooster in the open-source SLAMBench framework. Our
experiments show that SLAMBooster reduces the computation time and energy
consumption by around half on the average on an embedded platform, while
maintaining the accuracy of the localization within reasonable bounds. These
improvements make it feasible to deploy SLAM on a wider range of devices.
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Modeling sepsis progression using hidden Markov models | Characterizing a patient's progression through stages of sepsis is critical
for enabling risk stratification and adaptive, personalized treatment. However,
commonly used sepsis diagnostic criteria fail to account for significant
underlying heterogeneity, both between patients as well as over time in a
single patient. We introduce a hidden Markov model of sepsis progression that
explicitly accounts for patient heterogeneity. Benchmarked against two sepsis
diagnostic criteria, the model provides a useful tool to uncover a patient's
latent sepsis trajectory and to identify high-risk patients in whom more
aggressive therapy may be indicated.
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Optimized Bacteria are Environmental Prediction Engines | Experimentalists have observed phenotypic variability in isogenic bacteria
populations. We explore the hypothesis that in fluctuating environments this
variability is tuned to maximize a bacterium's expected log growth rate,
potentially aided by epigenetic markers that store information about past
environments. We show that, in a complex, memoryful environment, the maximal
expected log growth rate is linear in the instantaneous predictive
information---the mutual information between a bacterium's epigenetic markers
and future environmental states. Hence, under resource constraints, optimal
epigenetic markers are causal states---the minimal sufficient statistics for
prediction. This is the minimal amount of information about the past needed to
predict the future as well as possible. We suggest new theoretical
investigations into and new experiments on bacteria phenotypic bet-hedging in
fluctuating complex environments.
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Photo-realistic Facial Texture Transfer | Style transfer methods have achieved significant success in recent years with
the use of convolutional neural networks. However, many of these methods
concentrate on artistic style transfer with few constraints on the output image
appearance. We address the challenging problem of transferring face texture
from a style face image to a content face image in a photorealistic manner
without changing the identity of the original content image. Our framework for
face texture transfer (FaceTex) augments the prior work of MRF-CNN with a novel
facial semantic regularization that incorporates a face prior regularization
smoothly suppressing the changes around facial meso-structures (e.g eyes, nose
and mouth) and a facial structure loss function which implicitly preserves the
facial structure so that face texture can be transferred without changing the
original identity. We demonstrate results on face images and compare our
approach with recent state-of-the-art methods. Our results demonstrate superior
texture transfer because of the ability to maintain the identity of the
original face image.
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Segmentation of skin lesions based on fuzzy classification of pixels and histogram thresholding | This paper proposes an innovative method for segmentation of skin lesions in
dermoscopy images developed by the authors, based on fuzzy classification of
pixels and histogram thresholding.
| 1 | 0 | 0 | 1 | 0 | 0 |
Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods | In this paper, we explore deep reinforcement learning algorithms for
vision-based robotic grasping. Model-free deep reinforcement learning (RL) has
been successfully applied to a range of challenging environments, but the
proliferation of algorithms makes it difficult to discern which particular
approach would be best suited for a rich, diverse task like grasping. To answer
this question, we propose a simulated benchmark for robotic grasping that
emphasizes off-policy learning and generalization to unseen objects. Off-policy
learning enables utilization of grasping data over a wide variety of objects,
and diversity is important to enable the method to generalize to new objects
that were not seen during training. We evaluate the benchmark tasks against a
variety of Q-function estimation methods, a method previously proposed for
robotic grasping with deep neural network models, and a novel approach based on
a combination of Monte Carlo return estimation and an off-policy correction.
Our results indicate that several simple methods provide a surprisingly strong
competitor to popular algorithms such as double Q-learning, and our analysis of
stability sheds light on the relative tradeoffs between the algorithms.
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Incremental Transductive Learning Approaches to Schistosomiasis Vector Classification | The key issues pertaining to collection of epidemic disease data for our
analysis purposes are that it is a labour intensive, time consuming and
expensive process resulting in availability of sparse sample data which we use
to develop prediction models. To address this sparse data issue, we present
novel Incremental Transductive methods to circumvent the data collection
process by applying previously acquired data to provide consistent,
confidence-based labelling alternatives to field survey research. We
investigated various reasoning approaches for semisupervised machine learning
including Bayesian models for labelling data. The results show that using the
proposed methods, we can label instances of data with a class of vector density
at a high level of confidence. By applying the Liberal and Strict Training
Approaches, we provide a labelling and classification alternative to standalone
algorithms. The methods in this paper are components in the process of reducing
the proliferation of the Schistosomiasis disease and its effects.
| 1 | 0 | 0 | 0 | 0 | 0 |
Millimeter-scale layered MoSe2 grown on sapphire and evidence for negative magnetoresistance | Molecular beam epitaxy technique has been used to deposit a single layer and
a bilayer of MoSe 2 on sapphire. Extensive characterizations including in-situ
and ex-situ measurements show that the layered MoSe 2 grows in a scalable
manner on the substrate and reveals characteristics of a stoichiometric
2H-phase. The layered MoSe 2 exhibits polycrystalline features with domains
separated by defects and boundaries. Temperature and magnetic field dependent
resistivity measurements unveil a carrier hopping character described within
two-dimensional variable range hopping mechanism. Moreover, a negative
magnetoresistance was observed, stressing a fascinating feature of the charge
transport under the application of a magnetic field in the layered MoSe 2
system. This negative magnetoresistance observed at millimeter-scale is similar
to that observed recently at room temperature inWS2 flakes at a micrometer
scale [Zhang et al., Appl. Phys. Lett. 108, 153114 (2016)]. This scalability
highlights the fact that the underlying physical mechanism is intrinsic to
these two-dimensional materials and occurs at very short scale.
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Bootstrapping incremental dialogue systems from minimal data: the generalisation power of dialogue grammars | We investigate an end-to-end method for automatically inducing task-based
dialogue systems from small amounts of unannotated dialogue data. It combines
an incremental semantic grammar - Dynamic Syntax and Type Theory with Records
(DS-TTR) - with Reinforcement Learning (RL), where language generation and
dialogue management are a joint decision problem. The systems thus produced are
incremental: dialogues are processed word-by-word, shown previously to be
essential in supporting natural, spontaneous dialogue. We hypothesised that the
rich linguistic knowledge within the grammar should enable a combinatorially
large number of dialogue variations to be processed, even when trained on very
few dialogues. Our experiments show that our model can process 74% of the
Facebook AI bAbI dataset even when trained on only 0.13% of the data (5
dialogues). It can in addition process 65% of bAbI+, a corpus we created by
systematically adding incremental dialogue phenomena such as restarts and
self-corrections to bAbI. We compare our model with a state-of-the-art
retrieval model, MemN2N. We find that, in terms of semantic accuracy, MemN2N
shows very poor robustness to the bAbI+ transformations even when trained on
the full bAbI dataset.
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Learning Deep Latent Spaces for Multi-Label Classification | Multi-label classification is a practical yet challenging task in machine
learning related fields, since it requires the prediction of more than one
label category for each input instance. We propose a novel deep neural networks
(DNN) based model, Canonical Correlated AutoEncoder (C2AE), for solving this
task. Aiming at better relating feature and label domain data for improved
classification, we uniquely perform joint feature and label embedding by
deriving a deep latent space, followed by the introduction of label-correlation
sensitive loss function for recovering the predicted label outputs. Our C2AE is
achieved by integrating the DNN architectures of canonical correlation analysis
and autoencoder, which allows end-to-end learning and prediction with the
ability to exploit label dependency. Moreover, our C2AE can be easily extended
to address the learning problem with missing labels. Our experiments on
multiple datasets with different scales confirm the effectiveness and
robustness of our proposed method, which is shown to perform favorably against
state-of-the-art methods for multi-label classification.
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Encoding Multi-Resolution Brain Networks Using Unsupervised Deep Learning | The main goal of this study is to extract a set of brain networks in multiple
time-resolutions to analyze the connectivity patterns among the anatomic
regions for a given cognitive task. We suggest a deep architecture which learns
the natural groupings of the connectivity patterns of human brain in multiple
time-resolutions. The suggested architecture is tested on task data set of
Human Connectome Project (HCP) where we extract multi-resolution networks, each
of which corresponds to a cognitive task. At the first level of this
architecture, we decompose the fMRI signal into multiple sub-bands using
wavelet decompositions. At the second level, for each sub-band, we estimate a
brain network extracted from short time windows of the fMRI signal. At the
third level, we feed the adjacency matrices of each mesh network at each
time-resolution into an unsupervised deep learning algorithm, namely, a Stacked
De- noising Auto-Encoder (SDAE). The outputs of the SDAE provide a compact
connectivity representation for each time window at each sub-band of the fMRI
signal. We concatenate the learned representations of all sub-bands at each
window and cluster them by a hierarchical algorithm to find the natural
groupings among the windows. We observe that each cluster represents a
cognitive task with a performance of 93% Rand Index and 71% Adjusted Rand
Index. We visualize the mean values and the precisions of the networks at each
component of the cluster mixture. The mean brain networks at cluster centers
show the variations among cognitive tasks and the precision of each cluster
shows the within cluster variability of networks, across the subjects.
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Improper Filter Reduction | Combinatorial filters have been the subject of increasing interest from the
robotics community in recent years. This paper considers automatic reduction of
combinatorial filters to a given size, even if that reduction necessitates
changes to the filter's behavior. We introduce an algorithmic problem called
improper filter reduction, in which the input is a combinatorial filter F along
with an integer k representing the target size. The output is another
combinatorial filter F' with at most k states, such that the difference in
behavior between F and F' is minimal. We present two metrics for measuring the
distance between pairs of filters, describe dynamic programming algorithms for
computing these distances, and show that improper filter reduction is NP-hard
under these metrics. We then describe two heuristic algorithms for improper
filter reduction, one greedy sequential approach, and one randomized global
approach based on prior work on weighted improper graph coloring. We have
implemented these algorithms and analyze the results of three sets of
experiments.
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Collective Dynamics of Self-propelled Semiflexible Filaments | The collective behavior of active semiflexible filaments is studied with a
model of tangentially driven self-propelled worm-like chains. The combination
of excluded-volume interactions and self-propulsion leads to several distinct
dynamic phases as a function of bending rigidity, activity, and aspect ratio of
individual filaments. We consider first the case of intermediate filament
density. For high-aspect-ratio filaments, we identify a transition with
increasing propulsion from a state of free-swimming filaments to a state of
spiraled filaments with nearly frozen translational motion. For lower aspect
ratios, this gas-of-spirals phase is suppressed with growing density due to
filament collisions; instead, filaments form clusters similar to self-propelled
rods, as activity increases. Finite bending rigidity strongly effects the
dynamics and phase behavior. Flexible filaments form small and transient
clusters, while stiffer filaments organize into giant clusters, similarly as
self-propelled rods, but with a reentrant phase behavior from giant to smaller
clusters as activity becomes large enough to bend the filaments. For high
filament densities, we identify a nearly frozen jamming state at low
activities, a nematic laning state at intermediate activities, and an
active-turbulence state at high activities. The latter state is characterized
by a power-law decay of the energy spectrum as a function of wave number. The
resulting phase diagrams encapsulate tunable non-equilibrium steady states that
can be used in the organization of living matter.
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Two sources of poor coverage of confidence intervals after model selection | We compare the following two sources of poor coverage of post-model-selection
confidence intervals: the preliminary data-based model selection sometimes
chooses the wrong model and the data used to choose the model is re-used for
the construction of the confidence interval.
| 0 | 0 | 1 | 1 | 0 | 0 |
On the ERM Principle with Networked Data | Networked data, in which every training example involves two objects and may
share some common objects with others, is used in many machine learning tasks
such as learning to rank and link prediction. A challenge of learning from
networked examples is that target values are not known for some pairs of
objects. In this case, neither the classical i.i.d.\ assumption nor techniques
based on complete U-statistics can be used. Most existing theoretical results
of this problem only deal with the classical empirical risk minimization (ERM)
principle that always weights every example equally, but this strategy leads to
unsatisfactory bounds. We consider general weighted ERM and show new universal
risk bounds for this problem. These new bounds naturally define an optimization
problem which leads to appropriate weights for networked examples. Though this
optimization problem is not convex in general, we devise a new fully
polynomial-time approximation scheme (FPTAS) to solve it.
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Front interaction induces excitable behavior | Spatially extended systems can support local transient excitations in which
just a part of the system is excited. The mechanisms reported so far are local
excitability and excitation of a localized structure. Here we introduce an
alternative mechanism based on the coexistence of two homogeneous stable states
and spatial coupling. We show the existence of a threshold for perturbations of
the homogeneous state. Sub-threshold perturbations decay exponentially.
Super-threshold perturbations induce the emergence of a long-lived structure
formed by two back to back fronts that join the two homogeneous states. While
in typical excitability the trajectory follows the remnants of a limit cycle,
here reinjection is provided by front interaction, such that fronts slowly
approach each other until eventually annihilating. This front-mediated
mechanism shows that extended systems with no oscillatory regimes can display
excitability.
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Characterization of optimal carbon nanotubes under stretching and validation of the Cauchy-Born rule | Carbon nanotubes are modeled as point configurations and investigated by
minimizing configurational energies including two-and three-body interactions.
Optimal configurations are identified with local minima and their fine geometry
is fully characterized in terms of lower-dimensional problems. Under moderate
tension, we prove the existence of periodic local minimizers, which indeed
validates the so-called Cauchy-Born rule in this setting.
| 0 | 1 | 1 | 0 | 0 | 0 |
The Closer the Better: Similarity of Publication Pairs at Different Co-Citation Levels | We investigate the similarities of pairs of articles which are co-cited at
the different co-citation levels of the journal, article, section, paragraph,
sentence and bracket. Our results indicate that textual similarity,
intellectual overlap (shared references), author overlap (shared authors),
proximity in publication time all rise monotonically as the co-citation level
gets lower (from journal to bracket). While the main gain in similarity happens
when moving from journal to article co-citation, all level changes entail an
increase in similarity, especially section to paragraph and paragraph to
sentence/bracket levels. We compare results from four journals over the years
2010-2015: Cell, the European Journal of Operational Research, Physics Letters
B and Research Policy, with consistent general outcomes and some interesting
differences. Our findings motivate the use of granular co-citation information
as defined by meaningful units of text, with implications for, among others,
the elaboration of maps of science and the retrieval of scholarly literature.
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MP2-F12 Basis Set Convergence for the S66 Noncovalent Interactions Benchmark: Transferability of the Complementary Auxiliary Basis Set (CABS) | Complementary auxiliary basis sets for F12 explicitly correlated calculations
appear to be more transferable between orbital basis sets than has been
generally assumed. We also find that aVnZ-F12 basis sets, originally developed
with anionic systems in mind, appear to be superior for noncovalent
interactions as well, and propose a suitable CABS sequence for them.
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Quantized Minimum Error Entropy Criterion | Comparing with traditional learning criteria, such as mean square error
(MSE), the minimum error entropy (MEE) criterion is superior in nonlinear and
non-Gaussian signal processing and machine learning. The argument of the
logarithm in Renyis entropy estimator, called information potential (IP), is a
popular MEE cost in information theoretic learning (ITL). The computational
complexity of IP is however quadratic in terms of sample number due to double
summation. This creates computational bottlenecks especially for large-scale
datasets. To address this problem, in this work we propose an efficient
quantization approach to reduce the computational burden of IP, which decreases
the complexity from O(N*N) to O (MN) with M << N. The new learning criterion is
called the quantized MEE (QMEE). Some basic properties of QMEE are presented.
Illustrative examples are provided to verify the excellent performance of QMEE.
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Traffic Flow Forecasting Using a Spatio-Temporal Bayesian Network Predictor | A novel predictor for traffic flow forecasting, namely spatio-temporal
Bayesian network predictor, is proposed. Unlike existing methods, our approach
incorporates all the spatial and temporal information available in a
transportation network to carry our traffic flow forecasting of the current
site. The Pearson correlation coefficient is adopted to rank the input
variables (traffic flows) for prediction, and the best-first strategy is
employed to select a subset as the cause nodes of a Bayesian network. Given the
derived cause nodes and the corresponding effect node in the spatio-temporal
Bayesian network, a Gaussian Mixture Model is applied to describe the
statistical relationship between the input and output. Finally, traffic flow
forecasting is performed under the criterion of Minimum Mean Square Error
(M.M.S.E.). Experimental results with the urban vehicular flow data of Beijing
demonstrate the effectiveness of our presented spatio-temporal Bayesian network
predictor.
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AlteregoNets: a way to human augmentation | A person dependent network, called an AlterEgo net, is proposed for
development. The networks are created per person. It receives at input an
object descriptions and outputs a simulation of the internal person's
representation of the objects. The network generates a textual stream
resembling the narrative stream of consciousness depicting multitudinous
thoughts and feelings related to a perceived object. In this way, the object is
described not by a 'static' set of its properties, like a dictionary, but by
the stream of words and word combinations referring to the object. The network
simulates a person's dialogue with a representation of the object. It is based
on an introduced algorithmic scheme, where perception is modeled by two
interacting iterative cycles, reminding one respectively the forward and
backward propagation executed at training convolution neural networks. The
'forward' iterations generate a stream representing the 'internal world' of a
human. The 'backward' iterations generate a stream representing an internal
representation of the object. People perceive the world differently. Tuning
AlterEgo nets to a specific person or group of persons, will allow simulation
of their thoughts and feelings. Thereby these nets is potentially a new human
augmentation technology for various applications.
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Modalities in homotopy type theory | Univalent homotopy type theory (HoTT) may be seen as a language for the
category of $\infty$-groupoids. It is being developed as a new foundation for
mathematics and as an internal language for (elementary) higher toposes. We
develop the theory of factorization systems, reflective subuniverses, and
modalities in homotopy type theory, including their construction using a
"localization" higher inductive type. This produces in particular the
($n$-connected, $n$-truncated) factorization system as well as internal
presentations of subtoposes, through lex modalities. We also develop the
semantics of these constructions.
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Exponentially Slow Heating in Short and Long-range Interacting Floquet Systems | We analyze the dynamics of periodically-driven (Floquet) Hamiltonians with
short- and long-range interactions, finding clear evidence for a thermalization
time, $\tau^*$, that increases exponentially with the drive frequency. We
observe this behavior, both in systems with short-ranged interactions, where
our results are consistent with rigorous bounds, and in systems with long-range
interactions, where such bounds do not exist at present. Using a combination of
heating and entanglement dynamics, we explicitly extract the effective energy
scale controlling the rate of thermalization. Finally, we demonstrate that for
times shorter than $\tau^*$, the dynamics of the system is well-approximated by
evolution under a time-independent Hamiltonian $D_{\mathrm{eff}}$, for both
short- and long-range interacting systems.
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High moments of the Estermann function | For $a/q\in\mathbb{Q}$ the Estermann function is defined as
$D(s,a/q):=\sum_{n\geq1}d(n)n^{-s}\operatorname{e}(n\frac aq)$ if $\Re(s)>1$
and by meromorphic continuation otherwise. For $q$ prime, we compute the
moments of $D(s,a/q)$ at the central point $s=1/2$, when averaging over $1\leq
a<q$.
As a consequence we deduce the asymptotic for the iterated moment of
Dirichlet $L$-functions $\sum_{\chi_1,\dots,\chi_k\mod
q}|L(\frac12,\chi_1)|^2\cdots |L(\frac12,\chi_k)|^2|L(\frac12,\chi_1\cdots
\chi_k)|^2$, obtaining a power saving error term.
Also, we compute the moments of certain functions defined in terms of
continued fractions. For example, writing $f_{\pm}(a/q):=\sum_{j=0}^r
(\pm1)^jb_j$ where $[0;b_0,\dots,b_r]$ is the continued fraction expansion of
$a/q$ we prove that for $k\geq2$ and $q$ primes one has
$\sum_{a=1}^{q-1}f_{\pm}(a/q)^k\sim2 \frac{\zeta(k)^2}{\zeta(2k)} q^k$ as
$q\to\infty$.
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On Game-Theoretic Risk Management (Part Three) - Modeling and Applications | The game-theoretic risk management framework put forth in the precursor
reports "Towards a Theory of Games with Payoffs that are
Probability-Distributions" (arXiv:1506.07368 [q-fin.EC]) and "Algorithms to
Compute Nash-Equilibria in Games with Distributions as Payoffs"
(arXiv:1511.08591v1 [q-fin.EC]) is herein concluded by discussing how to
integrate the previously developed theory into risk management processes. To
this end, we discuss how loss models (primarily but not exclusively
non-parametric) can be constructed from data. Furthermore, hints are given on
how a meaningful game theoretic model can be set up, and how it can be used in
various stages of the ISO 27000 risk management process. Examples related to
advanced persistent threats and social engineering are given. We conclude by a
discussion on the meaning and practical use of (mixed) Nash equilibria
equilibria for risk management.
| 0 | 0 | 1 | 1 | 0 | 0 |
PKS 1954-388: RadioAstron Detection on 80,000 km Baselines and Multiwavelength Observations | We present results from a multiwavelength study of the blazar PKS 1954-388 at
radio, UV, X-ray, and gamma-ray energies. A RadioAstron observation at 1.66 GHz
in June 2012 resulted in the detection of interferometric fringes on baselines
of 6.2 Earth-diameters. This suggests a source frame brightness temperature of
greater than 2x10^12 K, well in excess of both equipartition and inverse
Compton limits and implying the existence of Doppler boosting in the core. An
8.4 GHz TANAMI VLBI image, made less than a month after the RadioAstron
observations, is consistent with a previously reported superluminal motion for
a jet component. Flux density monitoring with the Australia Telescope Compact
Array confirms previous evidence for long-term variability that increases with
observing frequency. A search for more rapid variability revealed no evidence
for significant day-scale flux density variation. The ATCA light-curve reveals
a strong radio flare beginning in late 2013 which peaks higher, and earlier, at
higher frequencies. Comparison with the Fermi gamma-ray light-curve indicates
this followed ~9 months after the start of a prolonged gamma-ray high-state --
a radio lag comparable to that seen in other blazars. The multiwavelength data
are combined to derive a Spectral Energy Distribution, which is fitted by a
one-zone synchrotron-self-Compton (SSC) model with the addition of external
Compton (EC) emission.
| 0 | 1 | 0 | 0 | 0 | 0 |
Smart Assessment of and Tutoring for Computational Thinking MOOC Assignments using MindReader | One of the major hurdles toward automatic semantic understanding of computer
programs is the lack of knowledge about what constitutes functional equivalence
of code segments. We postulate that a sound knowledgebase can be used to
deductively understand code segments in a hierarchical fashion by first
de-constructing a code and then reconstructing it from elementary knowledge and
equivalence rules of elementary code segments. The approach can also be
engineered to produce computable programs from conceptual and abstract
algorithms as an inverse function. In this paper, we introduce the core idea
behind the MindReader online assessment system that is able to understand a
wide variety of elementary algorithms students learn in their entry level
programming classes such as Java, C++ and Python. The MindReader system is able
to assess student assignments and guide them how to develop correct and better
code in real time without human assistance.
| 1 | 0 | 0 | 0 | 0 | 0 |
EasyInterface: A toolkit for rapid development of GUIs for research prototype tools | In this paper we describe EasyInterface, an open-source toolkit for rapid
development of web-based graphical user interfaces (GUIs). This toolkit
addresses the need of researchers to make their research prototype tools
available to the community, and integrating them in a common environment,
rapidly and without being familiar with web programming or GUI libraries in
general. If a tool can be executed from a command-line and its output goes to
the standard output, then in few minutes one can make it accessible via a
web-interface or within Eclipse. Moreover, the toolkit defines a text-based
language that can be used to get more sophisticated GUIs, e.g., syntax
highlighting, dialog boxes, user interactions, etc. EasyInterface was
originally developed for building a common frontend for tools developed in the
Envisage project.
| 1 | 0 | 0 | 0 | 0 | 0 |
The Cramér-Rao inequality on singular statistical models I | We introduce the notion of the essential tangent bundle of a parametrized
measure model and the notion of reduced Fisher metric on a (possibly singular)
2-integrable measure model. Using these notions and a new characterization of
$k$-integrable parametrized measure models, we extend the Cramér-Rao
inequality to $2$-integrable (possibly singular) statistical models for general
$\varphi$-estimations, where $\varphi$ is a $V$-valued feature function and $V$
is a topological vector space. Thus we derive an intrinsic Cramér-Rao
inequality in the most general terms of parametric statistics.
| 0 | 0 | 1 | 1 | 0 | 0 |
Autotune: A Derivative-free Optimization Framework for Hyperparameter Tuning | Machine learning applications often require hyperparameter tuning. The
hyperparameters usually drive both the efficiency of the model training process
and the resulting model quality. For hyperparameter tuning, machine learning
algorithms are complex black-boxes. This creates a class of challenging
optimization problems, whose objective functions tend to be nonsmooth,
discontinuous, unpredictably varying in computational expense, and include
continuous, categorical, and/or integer variables. Further, function
evaluations can fail for a variety of reasons including numerical difficulties
or hardware failures. Additionally, not all hyperparameter value combinations
are compatible, which creates so called hidden constraints. Robust and
efficient optimization algorithms are needed for hyperparameter tuning. In this
paper we present an automated parallel derivative-free optimization framework
called \textbf{Autotune}, which combines a number of specialized sampling and
search methods that are very effective in tuning machine learning models
despite these challenges. Autotune provides significantly improved models over
using default hyperparameter settings with minimal user interaction on
real-world applications. Given the inherent expense of training numerous
candidate models, we demonstrate the effectiveness of Autotune's search methods
and the efficient distributed and parallel paradigms for training and tuning
models, and also discuss the resource trade-offs associated with the ability to
both distribute the training process and parallelize the tuning process.
| 0 | 0 | 0 | 1 | 0 | 0 |
Reduced fusion systems over $p$-groups with abelian subgroup of index $p$: III | We finish the classification, begun in two earlier papers, of all simple
fusion systems over finite nonabelian $p$-groups with an abelian subgroup of
index $p$. In particular, this gives many new examples illustrating the
enormous variety of exotic examples that can arise. In addition, we classify
all simple fusion systems over infinite nonabelian discrete $p$-toral groups
with an abelian subgroup of index $p$. In all of these cases (finite or
infinite), we reduce the problem to one of listing all $\mathbb{F}_pG$-modules
(for $G$ finite) satisfying certain conditions: a problem which was solved in
the earlier paper by Craven, Oliver, and Semeraro using the classification of
finite simple groups.
| 0 | 0 | 1 | 0 | 0 | 0 |
Deep learning for studies of galaxy morphology | Establishing accurate morphological measurements of galaxies in a reasonable
amount of time for future big-data surveys such as EUCLID, the Large Synoptic
Survey Telescope or the Wide Field Infrared Survey Telescope is a challenge.
Because of its high level of abstraction with little human intervention, deep
learning appears to be a promising approach. Deep learning is a rapidly growing
discipline that models high-level patterns in data as complex multilayered
networks. In this work we test the ability of deep convolutional networks to
provide parametric properties of Hubble Space Telescope like galaxies
(half-light radii, Sersic indices, total flux etc..). We simulate a set of
galaxies including point spread function and realistic noise from the CANDELS
survey and try to recover the main galaxy parameters using deep-learning. We
com- pare the results with the ones obtained with the commonly used profile
fitting based software GALFIT. This way showing that with our method we obtain
results at least equally good as the ones obtained with GALFIT but, once
trained, with a factor 5 hundred time faster.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Diversified Multi-Start Algorithm for Unconstrained Binary Quadratic Problems Leveraging the Graphics Processor Unit | Multi-start algorithms are a common and effective tool for metaheuristic
searches. In this paper we amplify multi-start capabilities by employing the
parallel processing power of the graphics processer unit (GPU) to quickly
generate a diverse starting set of solutions for the Unconstrained Binary
Quadratic Optimization Problem which are evaluated and used to implement
screening methods to select solutions for further optimization. This method is
implemented as an initial high quality solution generation phase prior to a
secondary steepest ascent search and a comparison of results to best known
approaches on benchmark unconstrained binary quadratic problems demonstrates
that GPU-enabled diversified multi-start with screening quickly yields very
good results.
| 1 | 0 | 0 | 0 | 0 | 0 |
Low-rank and Sparse NMF for Joint Endmembers' Number Estimation and Blind Unmixing of Hyperspectral Images | Estimation of the number of endmembers existing in a scene constitutes a
critical task in the hyperspectral unmixing process. The accuracy of this
estimate plays a crucial role in subsequent unsupervised unmixing steps i.e.,
the derivation of the spectral signatures of the endmembers (endmembers'
extraction) and the estimation of the abundance fractions of the pixels. A
common practice amply followed in literature is to treat endmembers' number
estimation and unmixing, independently as two separate tasks, providing the
outcome of the former as input to the latter. In this paper, we go beyond this
computationally demanding strategy. More precisely, we set forth a multiple
constrained optimization framework, which encapsulates endmembers' number
estimation and unsupervised unmixing in a single task. This is attained by
suitably formulating the problem via a low-rank and sparse nonnegative matrix
factorization rationale, where low-rankness is promoted with the use of a
sophisticated $\ell_2/\ell_1$ norm penalty term. An alternating proximal
algorithm is then proposed for minimizing the emerging cost function. The
results obtained by simulated and real data experiments verify the
effectiveness of the proposed approach.
| 1 | 0 | 0 | 1 | 0 | 0 |
On the Necessity of Structured Codes for Communications over MAC with Feedback | The problem of three-user multiple-access channel (MAC) with noiseless
feedback is investigated. A new coding strategy is presented. The coding scheme
builds upon the natural extension of the Cover-Leung (CL) scheme; and uses
quasi-linear codes. A new single-letter achievable rate region is derived. The
new achievable region strictly contains the CL region. This is shown through an
example. In this example, the coding scheme achieves optimality in terms of
transmission rates. It is shown that any optimality achieving scheme for this
example must have a specific algebraic structure. Particularly, the codebooks
must be closed under binary addition.
| 1 | 0 | 0 | 0 | 0 | 0 |
Historical Review of Recurrence Plots | In the last two decades recurrence plots (RPs) were introduced in many
different scientific disciplines. It turned out how powerful this method is.
After introducing approaches of quantification of RPs and by the study of
relationships between RPs and fundamental properties of dynamical systems, this
method attracted even more attention. After 20 years of RPs it is time to
summarise this development in a historical context.
| 0 | 1 | 0 | 0 | 0 | 0 |
The Meta Distribution of the SIR for Cellular Networks with Power Control | The meta distribution of the signal-to-interference ratio (SIR) provides
fine-grained information about the performance of individual links in a
wireless network. This paper focuses on the analysis of the meta distribution
of the SIR for both the cellular network uplink and downlink with fractional
power control. For the uplink scenario, an approximation of the interfering
user point process with a non-homogeneous Poisson point process is used. The
moments of the meta distribution for both scenarios are calculated. Some
bounds, the analytical expression, the mean local delay, and the beta
approximation of the meta distribution are provided. The results give
interesting insights into the effect of the power control in both the uplink
and downlink. Detailed simulations show that the approximations made in the
analysis are well justified.
| 1 | 0 | 0 | 0 | 0 | 0 |
Temperature induced transition from p-n to n-n electronic behavior in Ni0.07Zn0.93O/Mg0.21Zn0.79O heterojunction | The transport characteristics across the pulsed laser deposited
Ni0.07Zn0.93O/Mg0.21Zn0.79O heterojunction exhibits p-n type semiconducting
properties at 10 K while at 100 K, its characteristics become similar to that
of an n-n junction. The reason for the same is attributed to the role of larger
electronegativity of Ni as compared to Mg at 10 K and ionization of impurity
states at 100 K. The above behavior is confirmed by performing the Hall
measurements.
| 0 | 1 | 0 | 0 | 0 | 0 |
N-body simulations of gravitational redshifts and other relativistic distortions of galaxy clustering | Large redshift surveys of galaxies and clusters are providing the first
opportunities to search for distortions in the observed pattern of large-scale
structure due to such effects as gravitational redshift. We focus on non-linear
scales and apply a quasi-Newtonian approach using N-body simulations to predict
the small asymmetries in the cross-correlation function of two galaxy different
populations. Following recent work by Bonvin et al., Zhao and Peacock and
Kaiser on galaxy clusters, we include effects which enter at the same order as
gravitational redshift: the transverse Doppler effect, light-cone effects,
relativistic beaming, luminosity distance perturbation and wide-angle effects.
We find that all these effects cause asymmetries in the cross-correlation
functions. Quantifying these asymmetries, we find that the total effect is
dominated by the gravitational redshift and luminosity distance perturbation at
small and large scales, respectively. By adding additional subresolution
modelling of galaxy structure to the large-scale structure information, we find
that the signal is significantly increased, indicating that structure on the
smallest scales is important and should be included. We report on comparison of
our simulation results with measurements from the SDSS/BOSS galaxy redshift
survey in a companion paper.
| 0 | 1 | 0 | 0 | 0 | 0 |
Few-Shot Learning with Metric-Agnostic Conditional Embeddings | Learning high quality class representations from few examples is a key
problem in metric-learning approaches to few-shot learning. To accomplish this,
we introduce a novel architecture where class representations are conditioned
for each few-shot trial based on a target image. We also deviate from
traditional metric-learning approaches by training a network to perform
comparisons between classes rather than relying on a static metric comparison.
This allows the network to decide what aspects of each class are important for
the comparison at hand. We find that this flexible architecture works well in
practice, achieving state-of-the-art performance on the Caltech-UCSD birds
fine-grained classification task.
| 0 | 0 | 0 | 1 | 0 | 0 |
Gradient Coding from Cyclic MDS Codes and Expander Graphs | Gradient coding is a technique for straggler mitigation in distributed
learning. In this paper we design novel gradient codes using tools from
classical coding theory, namely, cyclic MDS codes, which compare favourably
with existing solutions, both in the applicable range of parameters and in the
complexity of the involved algorithms. Second, we introduce an approximate
variant of the gradient coding problem, in which we settle for approximate
gradient computation instead of the exact one. This approach enables graceful
degradation, i.e., the $\ell_2$ error of the approximate gradient is a
decreasing function of the number of stragglers. Our main result is that the
normalized adjacency matrix of an expander graph can yield excellent
approximate gradient codes, and that this approach allows us to perform
significantly less computation compared to exact gradient coding. We
experimentally test our approach on Amazon EC2, and show that the
generalization error of approximate gradient coding is very close to the full
gradient while requiring significantly less computation from the workers.
| 0 | 0 | 0 | 1 | 0 | 0 |
Commissioning of te China-ADS injector-I testing facility | The 10 MeV accelerator-driven subcritical system (ADS) Injector-I test stand
at Institute of High Energy Physics (IHEP) is a testing facility dedicated to
demonstrate one of the two injector design schemes [Injector Scheme-I, which
works at 325 MHz], for the ADS project in China. The Injector adopted a four
vane copper structure RFQ with output energy of 3.2 MeV and a superconducting
(SC) section accommodating fourteen \b{eta}g=0.12 single spoke cavities,
fourteen SC solenoids and fourteen cold BPMs. The ion source was installed
since April of 2014, periods of commissioning are regularly scheduled between
installation phases of the rest of the injector. Continuous wave (CW) beam was
shooting through the injector and 10 MeV CW proton beam with average beam
current around 2 mA was obtained recently. This contribution describe the
results achieved so far and the difficulties encountered in CW commissioning.
| 0 | 1 | 0 | 0 | 0 | 0 |
Impact of the Global Crisis on SME Internal vs. External Financing in China | Changes in the capital structure before and after the global financial crisis
for SMEs are studied, emphasizing their financing problems, distinguishing
between internal financing and external financing determinants. The empirical
research bears upon 158 small and medium-sized firms listed on Shenzhen and
Shanghai Stock Exchanges in China over the period of 2004-2014. A regression
analysis, along the lines of the Trade-Off Theory, shows that the leverage
decreases with profitability, non-debt tax shields and the liquidity, and
increases with firm size and tangibility. A positive relationship is found
between firm growth and debt ratio, though not highly significantly. It is
shown that the SMEs with high growth rates are those which will more easily
obtain external financing after a financial crisis. It is recognized that the
China government should reconsider SMEs taxation laws.
| 0 | 0 | 0 | 1 | 0 | 0 |
Dipole force free optical control and cooling of nanofiber trapped atoms | The evanescent field surrounding nano-scale optical waveguides offers an
efficient interface between light and mesoscopic ensembles of neutral atoms.
However, the thermal motion of trapped atoms, combined with the strong radial
gradients of the guided light, leads to a time-modulated coupling between atoms
and the light mode, thus giving rise to additional noise and motional dephasing
of collective states. Here, we present a dipole force free scheme for coupling
of the radial motional states, utilizing the strong intensity gradient of the
guided mode and demonstrate all-optical coupling of the cesium hyperfine ground
states and motional sideband transitions. We utilize this to prolong the trap
lifetime of an atomic ensemble by Raman sideband cooling of the radial motion,
which has not been demonstrated in nano-optical structures previously. Our work
points towards full and independent control of internal and external atomic
degrees of freedom using guided light modes only.
| 0 | 1 | 0 | 0 | 0 | 0 |
Control refinement for discrete-time descriptor systems: a behavioural approach via simulation relations | The analysis of industrial processes, modelled as descriptor systems, is
often computationally hard due to the presence of both algebraic couplings and
difference equations of high order. In this paper, we introduce a control
refinement notion for these descriptor systems that enables analysis and
control design over related reduced-order systems. Utilising the behavioural
framework, we extend upon the standard hierarchical control refinement for
ordinary systems and allow for algebraic couplings inherent to descriptor
systems.
| 1 | 0 | 0 | 0 | 0 | 0 |
Beam Based RF Voltage Measurements and Longitudinal Beam Tomography at the Fermilab Booster | Increasing proton beam power on neutrino production targets is one of the
major goals of the Fermilab long term accelerator programs. In this effort, the
Fermilab 8 GeV Booster synchrotron plays a critical role for at least the next
two decades. Therefore, understanding the Booster in great detail is important
as we continue to improve its performance. For example, it is important to know
accurately the available RF power in the Booster by carrying out beam-based
measurements in order to specify the needed upgrades to the Booster RF system.
Since the Booster magnetic field is changing continuously measuring/calibrating
the RF voltage is not a trivial task. Here, we present a beam based method for
the RF voltage measurements. Data analysis is carried out using computer
programs developed in Python and MATLAB. The method presented here is
applicable to any RCS which do not have flat-bottom and flat-top in the
acceleration magnetic ramps. We have also carried out longitudinal beam
tomography at injection and extraction energies with the data used for RF
voltage measurements. Beam based RF voltage measurements and beam tomography
were never done before for the Fermilab Booster. The results from these
investigations will be very useful in future intensity upgrades.
| 0 | 1 | 0 | 0 | 0 | 0 |
A framework for quantitative modeling and analysis of highly (re)configurable systems | This paper presents our approach to the quantitative modeling and analysis of
highly (re)configurable systems, such as software product lines. Different
combinations of the optional features of such a system give rise to
combinatorially many individual system variants. We use a formal modeling
language that allows us to model systems with probabilistic behavior, possibly
subject to quantitative feature constraints, and able to dynamically install,
remove or replace features. More precisely, our models are defined in the
probabilistic feature-oriented language QFLAN, a rich domain specific language
(DSL) for systems with variability defined in terms of features. QFLAN
specifications are automatically encoded in terms of a process algebra whose
operational behavior interacts with a store of constraints, and hence allows to
separate system configuration from system behavior. The resulting probabilistic
configurations and behavior converge seamlessly in a semantics based on
discrete-time Markov chains, thus enabling quantitative analysis. Our analysis
is based on statistical model checking techniques, which allow us to scale to
larger models with respect to precise probabilistic analysis techniques. The
analyses we can conduct range from the likelihood of specific behavior to the
expected average cost, in terms of feature attributes, of specific system
variants. Our approach is supported by a novel Eclipse-based tool which
includes state-of-the-art DSL utilities for QFLAN based on the Xtext framework
as well as analysis plug-ins to seamlessly run statistical model checking
analyses. We provide a number of case studies that have driven and validated
the development of our framework.
| 1 | 0 | 0 | 0 | 0 | 0 |
Generating large misalignments in gapped and binary discs | Many protostellar gapped and binary discs show misalignments between their
inner and outer discs; in some cases, $\sim70$ degree misalignments have been
observed. Here we show that these misalignments can be generated through a
"secular precession resonance" between the nodal precession of the inner disc
and the precession of the gap-opening (stellar or massive planetary) companion.
An evolving protostellar system may naturally cross this resonance during its
lifetime due to disc dissipation and/or companion migration. If resonance
crossing occurs on the right timescale, of order a few Myrs, characteristic for
young protostellar systems, the inner and outer discs can become highly
misaligned ($\gtrsim 60$ degrees). When the primary star has a mass of order a
solar mass, generating a significant misalignment typically requires the
companion to have a mass of $\sim 0.01-0.1$ M$_\odot$ and an orbital separation
of tens of AU. The recently observed companion in the cavity of the gapped,
highly misaligned system HD 142527 satisfies these requirements, indicating
that a previous resonance crossing event misaligned the inner and outer discs.
Our scenario for HD 142527's misaligned discs predicts that the companion's
orbital plane is aligned with the outer disc's; this prediction should be
testable with future observations as the companion's orbit is mapped out.
Misalignments observed in several other gapped disc systems could be generated
by the same secular resonance mechanism.
| 0 | 1 | 0 | 0 | 0 | 0 |
Bilinear approach to the supersymmetric Gardner equation | We study a supersymmetric version of the Gardner equation (both focusing and
defocusing) using the superbilinear formalism. This equation is new and cannot
be obtained from supersymmetric modified Korteweg-de Vries equation with a
nonzero boundary condition. We construct supersymmetric solitons and then by
passing to the long-wave limit in the focusing case obtain rational nonsingular
solutions. We also discuss the supersymmetric version of the defocusing
equation and the dynamics of its solutions.
| 0 | 1 | 0 | 0 | 0 | 0 |
The Effect of Focal Distance, Age, and Brightness on Near-Field Augmented Reality Depth Matching | Many augmented reality (AR) applications operate within near-field reaching
distances, and require matching the depth of a virtual object with a real
object. The accuracy of this matching was measured in three experiments, which
examined the effect of focal distance, age, and brightness, within distances of
33.3 to 50 cm, using a custom-built AR haploscope. Experiment I examined the
effect of focal demand, at the levels of collimated (infinite focal distance),
consistent with other depth cues, and at the midpoint of reaching distance.
Observers were too young to exhibit age-related reductions in accommodative
ability. The depth matches of collimated targets were increasingly
overestimated with increasing distance, consistent targets were slightly
underestimated, and midpoint targets were accurately estimated. Experiment II
replicated Experiment I, with older observers. Results were similar to
Experiment I. Experiment III replicated Experiment I with dimmer targets, using
young observers. Results were again consistent with Experiment I, except that
both consistent and midpoint targets were accurately estimated. In all cases,
collimated results were explained by a model, where the collimation biases the
eyes' vergence angle outwards by a constant amount. Focal demand and brightness
affect near-field AR depth matching, while age-related reductions in
accommodative ability have no effect.
| 1 | 0 | 0 | 0 | 0 | 0 |
Small-amplitude steady water waves with critical layers: non-symmetric waves | The problem for two-dimensional steady water waves with vorticity is
considered. Using methods of spatial dynamics, we reduce the problem to a
finite dimensional Hamiltonian system. As an application, we prove the
existence of non-symmetric steady water waves when the number of roots of the
dispersion equation is greater than 1.
| 0 | 0 | 1 | 0 | 0 | 0 |
Emergence of Leadership in Communication | We study a neuro-inspired model that mimics a discussion (or information
dissemination) process in a network of agents. During their interaction, agents
redistribute activity and network weights, resulting in emergence of leader(s).
The model is able to reproduce the basic scenarios of leadership known in
nature and society: laissez-faire (irregular activity, weak leadership, sizable
inter-follower interaction, autonomous sub-leaders); participative or
democratic (strong leadership, but with feedback from followers); and
autocratic (no feedback, one-way influence). Several pertinent aspects of these
scenarios are found as well---e.g., hidden leadership (a hidden clique of
agents driving the official autocratic leader), and successive leadership (two
leaders influence followers by turns). We study how these scenarios emerge from
inter-agent dynamics and how they depend on behavior rules of agents---in
particular, on their inertia against state changes.
| 0 | 1 | 0 | 0 | 0 | 0 |
Throughput-Improving Control of Highways Facing Stochastic Perturbations | In this article, we study the problem of controlling a highway segment facing
stochastic perturbations, such as recurrent incidents and moving bottlenecks.
To model traffic flow under perturbations, we use the cell-transmission model
with Markovian capacities. The control inputs are: (i) the inflows that are
sent to various on-ramps to the highway (for managing traffic demand), and (ii)
the priority levels assigned to the on-ramp traffic relative to the mainline
traffic (for allocating highway capacity). The objective is to maximize the
throughput while ensuring that on-ramp queues remain bounded in the long-run.
We develop a computational approach to solving this stability-constrained,
throughput-maximization problem. Firstly, we use the classical drift condition
in stability analysis of Markov processes to derive a sufficient condition for
boundedness of on-ramp queues. Secondly, we show that our control design
problem can be formulated as a mixed integer program with linear or bilinear
constraints, depending on the complexity of Lyapunov function involved in the
stability condition. Finally, for specific types of capacity perturbations, we
derive intuitive criteria for managing demand and/or selecting priority levels.
These criteria suggest that inflows and priority levels should be determined
simultaneously such that traffic queues are placed at locations that discharge
queues fast. We illustrate the performance benefits of these criteria through a
computational study of a segment on Interstate 210 in California, USA.
| 1 | 0 | 0 | 0 | 0 | 0 |
Deep Learning for Accelerated Ultrasound Imaging | In portable, 3-D, or ultra-fast ultrasound (US) imaging systems, there is an
increasing demand to reconstruct high quality images from limited number of
data. However, the existing solutions require either hardware changes or
computationally expansive algorithms. To overcome these limitations, here we
propose a novel deep learning approach that interpolates the missing RF data by
utilizing the sparsity of the RF data in the Fourier domain. Extensive
experimental results from sub-sampled RF data from a real US system confirmed
that the proposed method can effectively reduce the data rate without
sacrificing the image quality.
| 1 | 0 | 0 | 1 | 0 | 0 |
Adaptive Estimation for Nonlinear Systems using Reproducing Kernel Hilbert Spaces | This paper extends a conventional, general framework for online adaptive
estimation problems for systems governed by unknown nonlinear ordinary
differential equations. The central feature of the theory introduced in this
paper represents the unknown function as a member of a reproducing kernel
Hilbert space (RKHS) and defines a distributed parameter system (DPS) that
governs state estimates and estimates of the unknown function. This paper 1)
derives sufficient conditions for the existence and stability of the infinite
dimensional online estimation problem, 2) derives existence and stability of
finite dimensional approximations of the infinite dimensional approximations,
and 3) determines sufficient conditions for the convergence of finite
dimensional approximations to the infinite dimensional online estimates. A new
condition for persistency of excitation in a RKHS in terms of its evaluation
functionals is introduced in the paper that enables proof of convergence of the
finite dimensional approximations of the unknown function in the RKHS. This
paper studies two particular choices of the RKHS, those that are generated by
exponential functions and those that are generated by multiscale kernels
defined from a multiresolution analysis.
| 1 | 0 | 0 | 0 | 0 | 0 |
Weak saturation and weak amalgamation property | The two model-theoretic concepts of weak saturation and weak amalgamation
property are studied in the context of accessible categories. We relate these
two concepts providing sufficient conditions for existence and uniqueness of
weakly saturated objects of an accessible category K. We discuss the
implications of this fact in classical model theory.
| 0 | 0 | 1 | 0 | 0 | 0 |
Perturbation problems in homogenization of hamilton-jacobi equations | This paper is concerned with the behavior of the ergodic constant associated
with convex and superlinear Hamilton-Jacobi equation in a periodic environment
which is perturbed either by medium with increasing period or by a random
Bernoulli perturbation with small parameter. We find a first order Taylor's
expansion for the ergodic constant which depends on the dimension d. When d = 1
the first order term is non trivial, while for all d $\ge$ 2 it is always 0.
Although such questions have been looked at in the context of linear uniformly
elliptic homogenization, our results are the first of this kind in nonlinear
settings. Our arguments, which rely on viscosity solutions and the weak KAM
theory, also raise several new and challenging questions.
| 0 | 0 | 1 | 0 | 0 | 0 |
Chimera states: Effects of different coupling topologies | Collective behavior among coupled dynamical units can emerge in various forms
as a result of different coupling topologies as well as different types of
coupling functions. Chimera states have recently received ample attention as a
fascinating manifestation of collective behavior, in particular describing a
symmetry breaking spatiotemporal pattern where synchronized and desynchronized
states coexist in a network of coupled oscillators. In this perspective, we
review the emergence of different chimera states, focusing on the effects of
different coupling topologies that describe the interaction network connecting
the oscillators. We cover chimera states that emerge in local, nonlocal and
global coupling topologies, as well as in modular, temporal and multilayer
networks. We also provide an outline of challenges and directions for future
research.
| 0 | 1 | 0 | 0 | 0 | 0 |
Human Activity Recognition using Recurrent Neural Networks | Human activity recognition using smart home sensors is one of the bases of
ubiquitous computing in smart environments and a topic undergoing intense
research in the field of ambient assisted living. The increasingly large amount
of data sets calls for machine learning methods. In this paper, we introduce a
deep learning model that learns to classify human activities without using any
prior knowledge. For this purpose, a Long Short Term Memory (LSTM) Recurrent
Neural Network was applied to three real world smart home datasets. The results
of these experiments show that the proposed approach outperforms the existing
ones in terms of accuracy and performance.
| 0 | 0 | 0 | 1 | 0 | 0 |
Eigenvalues of elliptic operators with density | We consider eigenvalue problems for elliptic operators of arbitrary order
$2m$ subject to Neumann boundary conditions on bounded domains of the Euclidean
$N$-dimensional space. We study the dependence of the eigenvalues upon
variations of mass density and in particular we discuss the existence and
characterization of upper and lower bounds under both the condition that the
total mass is fixed and the condition that the $L^{\frac{N}{2m}}$-norm of the
density is fixed. We highlight that the interplay between the order of the
operator and the space dimension plays a crucial role in the existence of
eigenvalue bounds.
| 0 | 0 | 1 | 0 | 0 | 0 |
A projection pursuit framework for testing general high-dimensional hypothesis | This article develops a framework for testing general hypothesis in
high-dimensional models where the number of variables may far exceed the number
of observations. Existing literature has considered less than a handful of
hypotheses, such as testing individual coordinates of the model parameter.
However, the problem of testing general and complex hypotheses remains widely
open. We propose a new inference method developed around the hypothesis
adaptive projection pursuit framework, which solves the testing problems in the
most general case. The proposed inference is centered around a new class of
estimators defined as $l_1$ projection of the initial guess of the unknown onto
the space defined by the null. This projection automatically takes into account
the structure of the null hypothesis and allows us to study formal inference
for a number of long-standing problems. For example, we can directly conduct
inference on the sparsity level of the model parameters and the minimum signal
strength. This is especially significant given the fact that the former is a
fundamental condition underlying most of the theoretical development in
high-dimensional statistics, while the latter is a key condition used to
establish variable selection properties. Moreover, the proposed method is
asymptotically exact and has satisfactory power properties for testing very
general functionals of the high-dimensional parameters. The simulation studies
lend further support to our theoretical claims and additionally show excellent
finite-sample size and power properties of the proposed test.
| 0 | 0 | 1 | 1 | 0 | 0 |
On the impact of quantum computing technology on future developments in high-performance scientific computing | Quantum computing technologies have become a hot topic in academia and
industry receiving much attention and financial support from all sides.
Building a quantum computer that can be used practically is in itself an
outstanding challenge that has become the 'new race to the moon'. Next to
researchers and vendors of future computing technologies, national authorities
are showing strong interest in maturing this technology due to its known
potential to break many of today's encryption techniques, which would have
significant impact on our society. It is however quite likely that quantum
computing has beneficial impact on many computational disciplines.
In this article we describe our vision of future developments in scientific
computing that would be enabled by the advent of software-programmable quantum
computers. We thereby assume that quantum computers will form part of a hybrid
accelerated computing platform like GPUs and co-processor cards do today. In
particular, we address the potential of quantum algorithms to bring major
breakthroughs in applied mathematics and its applications. Finally, we give
several examples that demonstrate the possible impact of quantum-accelerated
scientific computing on society.
| 1 | 0 | 0 | 0 | 0 | 0 |
Local Monotonic Attention Mechanism for End-to-End Speech and Language Processing | Recently, encoder-decoder neural networks have shown impressive performance
on many sequence-related tasks. The architecture commonly uses an attentional
mechanism which allows the model to learn alignments between the source and the
target sequence. Most attentional mechanisms used today is based on a global
attention property which requires a computation of a weighted summarization of
the whole input sequence generated by encoder states. However, it is
computationally expensive and often produces misalignment on the longer input
sequence. Furthermore, it does not fit with monotonous or left-to-right nature
in several tasks, such as automatic speech recognition (ASR),
grapheme-to-phoneme (G2P), etc. In this paper, we propose a novel attention
mechanism that has local and monotonic properties. Various ways to control
those properties are also explored. Experimental results on ASR, G2P and
machine translation between two languages with similar sentence structures,
demonstrate that the proposed encoder-decoder model with local monotonic
attention could achieve significant performance improvements and reduce the
computational complexity in comparison with the one that used the standard
global attention architecture.
| 1 | 0 | 0 | 0 | 0 | 0 |
Unifying Value Iteration, Advantage Learning, and Dynamic Policy Programming | Approximate dynamic programming algorithms, such as approximate value
iteration, have been successfully applied to many complex reinforcement
learning tasks, and a better approximate dynamic programming algorithm is
expected to further extend the applicability of reinforcement learning to
various tasks. In this paper we propose a new, robust dynamic programming
algorithm that unifies value iteration, advantage learning, and dynamic policy
programming. We call it generalized value iteration (GVI) and its approximated
version, approximate GVI (AGVI). We show AGVI's performance guarantee, which
includes performance guarantees for existing algorithms, as special cases. We
discuss theoretical weaknesses of existing algorithms, and explain the
advantages of AGVI. Numerical experiments in a simple environment support
theoretical arguments, and suggest that AGVI is a promising alternative to
previous algorithms.
| 1 | 0 | 0 | 1 | 0 | 0 |
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting | Spatiotemporal forecasting has various applications in neuroscience, climate
and transportation domain. Traffic forecasting is one canonical example of such
learning task. The task is challenging due to (1) complex spatial dependency on
road networks, (2) non-linear temporal dynamics with changing road conditions
and (3) inherent difficulty of long-term forecasting. To address these
challenges, we propose to model the traffic flow as a diffusion process on a
directed graph and introduce Diffusion Convolutional Recurrent Neural Network
(DCRNN), a deep learning framework for traffic forecasting that incorporates
both spatial and temporal dependency in the traffic flow. Specifically, DCRNN
captures the spatial dependency using bidirectional random walks on the graph,
and the temporal dependency using the encoder-decoder architecture with
scheduled sampling. We evaluate the framework on two real-world large scale
road network traffic datasets and observe consistent improvement of 12% - 15%
over state-of-the-art baselines.
| 1 | 0 | 0 | 1 | 0 | 0 |
Machine Learning for Networking: Workflow, Advances and Opportunities | Recently, machine learning has been used in every possible field to leverage
its amazing power. For a long time, the net-working and distributed computing
system is the key infrastructure to provide efficient computational resource
for machine learning. Networking itself can also benefit from this promising
technology. This article focuses on the application of Machine Learning
techniques for Networking (MLN), which can not only help solve the intractable
old network questions but also stimulate new network applications. In this
article, we summarize the basic workflow to explain how to apply the machine
learning technology in the networking domain. Then we provide a selective
survey of the latest representative advances with explanations on their design
principles and benefits. These advances are divided into several network design
objectives and the detailed information of how they perform in each step of MLN
workflow is presented. Finally, we shed light on the new opportunities on
networking design and community building of this new inter-discipline. Our goal
is to provide a broad research guideline on networking with machine learning to
help and motivate researchers to develop innovative algorithms, standards and
frameworks.
| 1 | 0 | 0 | 0 | 0 | 0 |
The WAGGS project - I. The WiFeS Atlas of Galactic Globular cluster Spectra | We present the WiFeS Atlas of Galactic Globular cluster Spectra, a library of
integrated spectra of Milky Way and Local Group globular clusters. We used the
WiFeS integral field spectrograph on the Australian National University 2.3 m
telescope to observe the central regions of 64 Milky Way globular clusters and
22 globular clusters hosted by the Milky Way's low mass satellite galaxies. The
spectra have wider wavelength coverage (3300 {\AA} to 9050 {\AA}) and higher
spectral resolution (R = 6800) than existing spectral libraries of Milky Way
globular clusters. By including Large and Small Magellanic Cloud star clusters,
we extend the coverage of parameter space of existing libraries towards young
and intermediate ages. While testing stellar population synthesis models and
analysis techniques is the main aim of this library, the observations may also
further our understanding of the stellar populations of Local Group globular
clusters and make possible the direct comparison of extragalactic globular
cluster integrated light observations with well understood globular clusters in
the Milky Way. The integrated spectra are publicly available via the project
website.
| 0 | 1 | 0 | 0 | 0 | 0 |
Going Viral: Stability of Consensus-Driven Adoptive Spread | The spread of new products in a networked population is often modeled as an
epidemic. However, in the case of "complex" contagion, these models are
insufficient to properly model adoption behavior. In this paper, we investigate
a model of complex contagion which allows a coevolutionary interplay between
adoption, modeled as an SIS epidemic spreading process, and social
reinforcement effects, modeled as consensus opinion dynamics. Asymptotic
stability analysis of the all-adopt as well as the none-adopt equilibria of the
combined opinion-adoption model is provided through the use of Lyapunov
arguments. In doing so, sufficient conditions are provided which determine the
stability of the "flop" state, where no one adopts the product and everyone's
opinion of the product is least favorable, and the "hit" state, where everyone
adopts and their opinions are most favorable. These conditions are shown to
extend to the bounded confidence opinion dynamic under a stronger assumption on
the model parameters. To conclude, numerical simulations demonstrate behavior
of the model which reflect findings from the sociology literature on adoption
behavior.
| 1 | 0 | 0 | 0 | 0 | 0 |
What's In A Patch, I: Tensors, Differential Geometry and Statistical Shading Analysis | We develop a linear algebraic framework for the shape-from-shading problem,
because tensors arise when scalar (e.g. image) and vector (e.g. surface normal)
fields are differentiated multiple times. The work is in two parts. In this
first part we investigate when image derivatives exhibit invariance to changing
illumination by calculating the statistics of image derivatives under general
distributions on the light source. We computationally validate the hypothesis
that image orientations (derivatives) provide increased invariance to
illumination by showing (for a Lambertian model) that a shape-from-shading
algorithm matching gradients instead of intensities provides more accurate
reconstructions when illumination is incorrectly estimated under a flatness
prior.
| 1 | 0 | 0 | 0 | 0 | 0 |
Counterexample to Gronwall's Conjecture | We present a projectively invariant description of planar linear 3-webs and
construct a counterexample to Gronwall's conjecture.
| 0 | 0 | 1 | 0 | 0 | 0 |
Urban Swarms: A new approach for autonomous waste management | Modern cities are growing ecosystems that face new challenges due to the
increasing population demands. One of the many problems they face nowadays is
waste management, which has become a pressing issue requiring new solutions.
Swarm robotics systems have been attracting an increasing amount of attention
in the past years and they are expected to become one of the main driving
factors for innovation in the field of robotics. The research presented in this
paper explores the feasibility of a swarm robotics system in an urban
environment. By using bio-inspired foraging methods such as multi-place
foraging and stigmergy-based navigation, a swarm of robots is able to improve
the efficiency and autonomy of the urban waste management system in a realistic
scenario. To achieve this, a diverse set of simulation experiments was
conducted using real-world GIS data and implementing different garbage
collection scenarios driven by robot swarms. Results presented in this research
show that the proposed system outperforms current approaches. Moreover, results
not only show the efficiency of our solution, but also give insights about how
to design and customize these systems.
| 1 | 0 | 0 | 0 | 0 | 0 |
An Algorithm of Parking Planning for Smart Parking System | There are so many vehicles in the world and the number of vehicles is
increasing rapidly. To alleviate the parking problems caused by that, the smart
parking system has been developed. The parking planning is one of the most
important parts of it. An effective parking planning strategy makes the better
use of parking resources possible. In this paper, we present a feasible method
to do parking planning. We transform the parking planning problem into a kind
of linear assignment problem. We take vehicles as jobs and parking spaces as
agents. We take distances between vehicles and parking spaces as costs for
agents doing jobs. Then we design an algorithm for this particular assignment
problem and solve the parking planning problem. The method proposed can give
timely and efficient guide information to vehicles for a real time smart
parking system. Finally, we show the effectiveness of the method with
experiments over some data, which can simulate the situation of doing parking
planning in the real world.
| 1 | 0 | 0 | 0 | 0 | 0 |
Classical Control, Quantum Circuits and Linear Logic in Enriched Category Theory | We describe categorical models of a circuit-based (quantum) functional pro-
gramming language. We show that enriched categories play a crucial role.
Following earlier work on QWire by Paykin et al., we consider both a simple
first-order linear language for circuits, and a more powerful host language,
such that the circuit language is embedded inside the host language. Our
categorical semantics for the host language is standard, and involves cartesian
closed categories and monads. We interpret the circuit language not in an
ordinary category, but in a category that is enriched in the host category. We
show that this structure is also related to linear/non-linear models. As an
extended example, we recall an earlier result that the category of W*-algebras
is dcpo-enriched, and we use this model to extend the circuit language with
some recursive types.
| 1 | 0 | 1 | 0 | 0 | 0 |
Unifying Map and Landmark Based Representations for Visual Navigation | This works presents a formulation for visual navigation that unifies map
based spatial reasoning and path planning, with landmark based robust plan
execution in noisy environments. Our proposed formulation is learned from data
and is thus able to leverage statistical regularities of the world. This allows
it to efficiently navigate in novel environments given only a sparse set of
registered images as input for building representations for space. Our
formulation is based on three key ideas: a learned path planner that outputs
path plans to reach the goal, a feature synthesis engine that predicts features
for locations along the planned path, and a learned goal-driven closed loop
controller that can follow plans given these synthesized features. We test our
approach for goal-driven navigation in simulated real world environments and
report performance gains over competitive baseline approaches.
| 1 | 0 | 0 | 0 | 0 | 0 |
Accounting for Uncertainty About Past Values In Probabilistic Projections of the Total Fertility Rate for All Countries | Since the 1940s, population projections have in most cases been produced
using the deterministic cohort component method. However, in 2015, for the
first time, in a major advance, the United Nations issued official
probabilistic population projections for all countries based on Bayesian
hierarchical models for total fertility and life expectancy. The estimates of
these models and the resulting projections are conditional on the UN's official
estimates of past values. However, these past values are themselves uncertain,
particularly for the majority of the world's countries that do not have
longstanding high-quality vital registration systems, when they rely on surveys
and censuses with their own biases and measurement errors. This paper is a
first attempt to remedy this for total fertility rates, by extending the UN
model for the future to take account of uncertainty about past values. This is
done by adding an additional level to the hierarchical model to represent the
multiple data sources, in each case estimating their bias and measurement error
variance. We assess the method by out-of-sample predictive validation. While
the prediction intervals produced by the current method have somewhat less than
nominal coverage, we find that our proposed method achieves close to nominal
coverage. The prediction intervals become wider for countries for which the
estimates of past total fertility rates rely heavily on surveys rather than on
vital registration data.
| 0 | 0 | 0 | 1 | 0 | 0 |
Periodic solutions of a perturbed Kepler problem in the plane: from existence to stability | The existence of elliptic periodic solutions of a perturbed Kepler problem is
proved. The equations are in the plane and the perturbation depends
periodically on time. The proof is based on a local description of the
symplectic group in two degrees of freedom.
| 0 | 0 | 1 | 0 | 0 | 0 |
Removal of Narrowband Interference (PLI in ECG Signal) Using Ramanujan Periodic Transform (RPT) | Suppression of interference from narrowband frequency signals play vital role
in many signal processing and communication applications. A transform based
method for suppression of narrow band interference in a biomedical signal is
proposed. As a specific example Electrocardiogram (ECG) is considered for the
analysis. ECG is one of the widely used biomedical signal. ECG signal is often
contaminated with baseline wander noise, powerline interference (PLI) and
artifacts (bioelectric signals), which complicates the processing of raw ECG
signal. This work proposes an approach using Ramanujan periodic transform for
reducing PLI and is tested on a subject data from MIT-BIH Arrhythmia database.
A sum ($E$) of Euclidean error per block ($e_i$) is used as measure to quantify
the suppression capability of RPT and notch filter based methods. The
transformation is performed for different lengths ($N$), namely $36$, $72$,
$108$, $144$, $180$. Every doubling of $N$-points results in $50{\%}$ reduction
in error ($E$).
| 0 | 0 | 1 | 1 | 0 | 0 |
Global regularity and fast small scale formation for Euler patch equation in a disk | It is well known that the Euler vortex patch in $\mathbb{R}^{2}$ will remain
regular if it is regular enough initially. In bounded domains, the regularity
theory for patch solutions is less complete. We study here the Euler vortex
patch in a disk. We prove global in time regularity by providing the upper
bound of the growth of curvature of the patch boundary. For a special symmetric
scenario, we construct an example of double exponential curvature growth,
showing that such upper bound is qualitatively sharp.
| 0 | 0 | 1 | 0 | 0 | 0 |
Effective interaction in a non-Fermi liquid conductor and spin correlations in under-doped cuprates | The effective interaction between the itinerant spin degrees of freedom in
the paramagnetic phases of hole doped quantum Heisenberg antiferromagnets is
investigated theoretically, based on the single-band t-J model on 1D lattice,
at zero temperature. The effective spin-spin interaction for this model in the
strong correlation limit, is studied in terms of the generalized spin stiffness
constant as a function of doping concentration. The plot of this generalized
spin stiffness constant against doping shows a very high value of stiffness in
the vicinity of zero doping and a very sharp fall with increase in doping
concentration, signifying the rapid decay of original coupling of
semi-localized spins in the system. Quite interestingly, this plot also shows a
maximum occurring at a finite value of doping, which strongly suggests the
tendency of the itinerant spins to couple again in the unconventional
paramagnetic phase. As the doping is further increased, this new coupling is
also suppressed and the spin response becomes analogous to almost Pauli-like.
The last two predictions of ours are quite novel and may be directly tested by
independent experiments and computational techniques in future. Our results in
general receive good support from other theoretical works and experimental
results extracted from the chains of YBa$_2$Cu$_3$O$_{6+x}$.
| 0 | 1 | 0 | 0 | 0 | 0 |
Planning with Verbal Communication for Human-Robot Collaboration | Human collaborators coordinate effectively their actions through both verbal
and non-verbal communication. We believe that the the same should hold for
human-robot teams. We propose a formalism that enables a robot to decide
optimally between doing a task and issuing an utterance. We focus on two types
of utterances: verbal commands, where the robot expresses how it wants its
human teammate to behave, and state-conveying actions, where the robot explains
why it is behaving this way. Human subject experiments show that enabling the
robot to issue verbal commands is the most effective form of communicating
objectives, while retaining user trust in the robot. Communicating why
information should be done judiciously, since many participants questioned the
truthfulness of the robot statements.
| 1 | 0 | 0 | 0 | 0 | 0 |
Magnon Condensation and Spin Superfluidity | We consider the phenomenon of Bose-Einstein condensation of quasi-equilibrium
magnons which leads to a spin superfluidity, the coherent quantum transfer of
magnetization in magnetic materials. These phenomena are beyond the classical
Landau-Lifshitz-Gilbert paradigm. The critical conditions for excited magnon
density for ferro- and antiferromagnets, bulk and thin films are estimated and
discussed. The BEC should occur in the antiferromagnetic hematite at much lower
excited magnon density compared to the ferromagnetic YIG.
| 0 | 1 | 0 | 0 | 0 | 0 |
Electroweak Vacuum Metastability and Low-scale Inflation | We study the stability of the electroweak vacuum in low-scale inflation
models whose Hubble parameter is much smaller than the instability scale of the
Higgs potential. In general, couplings between the inflaton and Higgs are
present, and hence we study effects of these couplings during and after
inflation. We derive constraints on the couplings between the inflaton and
Higgs by requiring that they do not lead to catastrophic electroweak vacuum
decay, in particular, via resonant production of the Higgs particles.
| 0 | 1 | 0 | 0 | 0 | 0 |
Spectral selectivity in capillary dye lasers | We explore the spectral properties of a capillary dye laser in the highly
multimode regime. Our experiments indicate that the spectral behavior of the
laser does not conform with a simple Fabry-Perot analysis; rather, it is
strongly dictated by a Vernier resonant mechanism involving multiple modes,
which propagate with different group velocities. The laser operates over a very
broad spectral range and the Vernier effect gives rise to a free spectral range
which is orders of magnitude larger than that expected from a simple
Fabry-Perot mechanism. The presented theoretical calculations confirm the
experimental results. Propagating modes of the capillary fiber are calculated
using the finite element method (FEM) and it is shown that the optical
pathlengths resulting from simultaneous beatings of these modes are in close
agreement with the optical pathlengths directly extracted from the Fourier
Transform of the experimentally measured laser emission spectra.
| 0 | 1 | 0 | 0 | 0 | 0 |
The Mechanism of Electrolyte Gating on High-Tc Cuprates: The Role of Oxygen Migration and Electrostatics | Electrolyte gating is widely used to induce large carrier density modulation
on solid surfaces to explore various properties. Most of past works have
attributed the charge modulation to electrostatic field effect. However, some
recent reports have argued that the electrolyte gating effect in VO2, TiO2 and
SrTiO3 originated from field-induced oxygen vacancy formation. This gives rise
to a controversy about the gating mechanism, and it is therefore vital to
reveal the relationship between the role of electrolyte gating and the
intrinsic properties of materials. Here, we report entirely different
mechanisms of electrolyte gating on two high-Tc cuprates, NdBa2Cu3O7-{\delta}
(NBCO) and Pr2-xCexCuO4 (PCCO), with different crystal structures. We show that
field-induced oxygen vacancy formation in CuO chains of NBCO plays the dominant
role while it is mainly an electrostatic field effect in the case of PCCO. The
possible reason is that NBCO has mobile oxygen in CuO chains while PCCO does
not. Our study helps clarify the controversy relating to the mechanism of
electrolyte gating, leading to a better understanding of the role of oxygen
electro migration which is very material specific.
| 0 | 1 | 0 | 0 | 0 | 0 |
Invariant theory of a special group action on irreducible polynomials over finite fields | In the past few years, an action of $\mathrm{PGL}_2(\mathbb F_q)$ on the set
of irreducible polynomials in $\mathbb F_q[x]$ has been introduced and many
questions have been discussed, such as the characterization and number of
invariant elements. In this paper, we analyze some recent works on this action
and provide full generalizations of them, yielding final theoretical results on
the characterization and number of invariant elements.
| 0 | 0 | 1 | 0 | 0 | 0 |
TIP: Typifying the Interpretability of Procedures | We provide a novel notion of what it means to be interpretable, looking past
the usual association with human understanding. Our key insight is that
interpretability is not an absolute concept and so we define it relative to a
target model, which may or may not be a human. We define a framework that
allows for comparing interpretable procedures by linking them to important
practical aspects such as accuracy and robustness. We characterize many of the
current state-of-the-art interpretable methods in our framework portraying its
general applicability. Finally, principled interpretable strategies are
proposed and empirically evaluated on synthetic data, as well as on the largest
public olfaction dataset that was made recently available \cite{olfs}. We also
experiment on MNIST with a simple target model and different oracle models of
varying complexity. This leads to the insight that the improvement in the
target model is not only a function of the oracle model's performance, but also
its relative complexity with respect to the target model. Further experiments
on CIFAR-10, a real manufacturing dataset and FICO dataset showcase the benefit
of our methods over Knowledge Distillation when the target models are simple
and the complex model is a neural network.
| 1 | 0 | 0 | 1 | 0 | 0 |
Differences in 1D electron plasma wake field acceleration in MeV versus GeV and linear versus blowout regimes | In some laboratory and most astrophysical situations plasma wake-field
acceleration of electrons is one dimensional, i.e. variation transverse to the
beam's motion can be ignored. Thus, one dimensional (1D), particle-in-cell
(PIC), fully electromagnetic simulations of electron plasma wake field
acceleration are conducted in order to study the differences in electron plasma
wake field acceleration in MeV versus GeV and linear versus blowout regimes.
First, we show that caution needs to be taken when using fluid simulations, as
PIC simulations prove that an approximation for an electron bunch not to evolve
in time for few hundred plasma periods only applies when it is sufficiently
relativistic. This conclusion is true irrespective of the plasma temperature.
We find that in the linear regime and GeV energies, the accelerating electric
field generated by the plasma wake is similar to the linear and MeV regime.
However, because GeV energy driving bunch stays intact for much longer time,
the final acceleration energies are much larger in the GeV energies case. In
the GeV energy range and blowout regime the wake's accelerating electric field
is much larger in amplitude compared to the linear case and also plasma wake
geometrical size is much larger. Thus, the correct positioning of the trailing
bunch is needed to achieve the efficient acceleration. For the considered case,
optimally there should be approximately $(90-100) c/\omega_{pe}$ distance
between trailing and driving electron bunches in the GeV blowout regime.
| 0 | 1 | 0 | 0 | 0 | 0 |
Estimating network memberships by simplex vertex hunting | Consider an undirected mixed membership network with $n$ nodes and $K$
communities. For each node $1 \leq i \leq n$, we model the membership by
$\pi_{i} = (\pi_{i}(1), \pi_{i}(2), \ldots$, $\pi_{i}(K))'$, where $\pi_{i}(k)$
is the probability that node $i$ belongs to community $k$, $1 \leq k \leq K$.
We call node $i$ "pure" if $\pi_i$ is degenerate and "mixed" otherwise. The
primary interest is to estimate $\pi_i$, $1 \leq i \leq n$.
We model the adjacency matrix $A$ with a Degree Corrected Mixed Membership
(DCMM) model. Let $\hat{\xi}_1, \hat{\xi}_2, \ldots, \hat{\xi}_K$ be the first
$K$ eigenvectors of $A$. We define a matrix $\hat{R} \in \mathbb{R}^{n, K-1}$
by $\hat{R}(i,k) = \hat{\xi}_{k+1}(i)/\hat{\xi}_1(i)$, $1 \leq k \leq K-1$, $1
\leq i \leq n$. The matrix can be viewed as a distorted version of its
non-stochastic counterpart $R \in \mathbb{R}^{n, K-1}$, which is unknown but
contains all information we need for the memberships.
We reveal an interesting insight: There is a simplex ${\cal S}$ in
$\mathbb{R}^{K-1}$ such that row $i$ of $R$ corresponds to a vertex of ${\cal
S}$ if node $i$ is pure, and corresponds to an interior point of ${\cal S}$
otherwise. Vertex Hunting (i.e., estimating the vertices of ${\cal S}$) is thus
the key to our problem.
The matrix $\hat{R}$ is a row-wise normalization on the matrix of
eigenvectors $\hat{\Xi}=[\hat{\xi}_1,\ldots,\hat{\xi}_K]$, first proposed by
Jin (2015). Alternatively, we may normalize $\hat{\Xi}$ by the row-wise
$\ell^q$-norms (e.g., Supplement of Jin (2015)), but it won't give rise to a
simplex so is less convenient.
We propose a new approach $\textit{Mixed-SCORE}$ to estimating the
memberships, at the heart of which is an easy-to-use Vertex Hunting algorithm.
The approach is successfully applied to $4$ network data sets. We also derive
the rate of convergence for Mixed-SCORE.
| 0 | 0 | 0 | 1 | 0 | 0 |
Non-singular Green's functions for the unbounded Poisson equation in one, two and three dimensions | In this paper, we derive the non-singular Green's functions for the unbounded
Poisson equation in two and three dimensions using a spectral approach to
regularize the homogeneous equation. The resulting Green's functions are
relevant to applications which are restricted to a minimum resolved length
scale (e.g. a mesh size h) and thus cannot handle the singular Green's function
of the continuous Poisson equation. We furthermore derive the gradient vector
of the regularized Green's function, as this is useful in applications where
the Poisson equation represents potential functions of a vector field.
| 0 | 1 | 1 | 0 | 0 | 0 |
Evolutionary phases of gas-rich galaxies in a galaxy cluster at z=1.46 | We report a survey of molecular gas in galaxies in the XMMXCS J2215.9-1738
cluster at $z=1.46$. We have detected emission lines from 17 galaxies within a
radius of $R_{200}$ from the cluster center, in Band 3 data of the Atacama
Large Millimeter/submillimeter Array (ALMA) with a coverage of 93 -- 95 GHz in
frequency and 2.33 arcmin$^2$ in spatial direction. The lines are all
identified as CO $J$=2--1 emission lines from cluster members at $z\sim1.46$ by
their redshifts and the colors of their optical and near-infrared (NIR)
counterparts. The line luminosities reach down to $L'_{\rm
CO(2-1)}=4.5\times10^{9}$ K km s$^{-1}$ pc$^2$. The spatial distribution of
galaxies with a detection of CO(2--1) suggests that they disappear from the
very center of the cluster. The phase-space diagram showing relative velocity
versus cluster-centric distance indicates that the gas-rich galaxies have
entered the cluster more recently than the gas-poor star-forming galaxies and
passive galaxies located in the virialized region of this cluster. The results
imply that the galaxies have experienced ram-pressure stripping and/or
strangulation during the course of infall towards the cluster center and then
the molecular gas in the galaxies at the cluster center is depleted by star
formation.
| 0 | 1 | 0 | 0 | 0 | 0 |
An iterative ensemble Kalman filter in presence of additive model error | The iterative ensemble Kalman filter (IEnKF) in a deterministic framework was
introduced in Sakov et al. (2012) to extend the ensemble Kalman filter (EnKF)
and improve its performance in mildly up to strongly nonlinear cases.
However, the IEnKF assumes that the model is perfect. This assumption
simplified the update of the system at a time different from the observation
time, which made it natural to apply the IEnKF for smoothing. In this study, we
generalise the IEnKF to the case of imperfect model with additive model error.
The new method called IEnKF-Q conducts a Gauss-Newton minimisation in
ensemble space. It combines the propagated analysed ensemble anomalies from the
previous cycle and model noise ensemble anomalies into a single ensemble of
anomalies, and by doing so takes an algebraic form similar to that of the
IEnKF. The performance of the IEnKF-Q is tested in a number of experiments with
the Lorenz-96 model, which show that the method consistently outperforms both
the EnKF and the IEnKF naively modified to accommodate additive model noise.
| 0 | 1 | 0 | 1 | 0 | 0 |
A Bayesian algorithm for detecting identity matches and fraud in image databases | A statistical algorithm for categorizing different types of matches and fraud
in image databases is presented. The approach is based on a generative model of
a graph representing images and connections between pairs of identities,
trained using properties of a matching algorithm between images.
| 1 | 0 | 0 | 1 | 0 | 0 |
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