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16,801 | Incorporation of prior knowledge of the signal behavior into the reconstruction to accelerate the acquisition of MR diffusion data | Diffusion MRI measurements using hyperpolarized gases are generally acquired
during patient breath hold, which yields a compromise between achievable image
resolution, lung coverage and number of b-values. In this work, we propose a
novel method that accelerates the acquisition of MR diffusion data by
undersampling in both spatial and b-value dimensions, thanks to incorporating
knowledge about the signal decay into the reconstruction (SIDER). SIDER is
compared to total variation (TV) reconstruction by assessing their effect on
both the recovery of ventilation images and estimated mean alveolar dimensions
(MAD). Both methods are assessed by retrospectively undersampling diffusion
datasets of normal volunteers and COPD patients (n=8) for acceleration factors
between x2 and x10. TV led to large errors and artefacts for acceleration
factors equal or larger than x5. SIDER improved TV, presenting lower errors and
histograms of MAD closer to those obtained from fully sampled data for
accelerations factors up to x10. SIDER preserved image quality at all
acceleration factors but images were slightly smoothed and some details were
lost at x10. In conclusion, we have developed and validated a novel compressed
sensing method for lung MRI imaging and achieved high acceleration factors,
which can be used to increase the amount of data acquired during a breath-hold.
This methodology is expected to improve the accuracy of estimated lung
microstructure dimensions and widen the possibilities of studying lung diseases
with MRI.
| 1 | 1 | 0 | 0 | 0 | 0 |
16,802 | Rabi noise spectroscopy of individual two-level tunneling defects | Understanding the nature of two-level tunneling defects is important for
minimizing their disruptive effects in various nano-devices. By exploiting the
resonant coupling of these defects to a superconducting qubit, one can probe
and coherently manipulate them individually. In this work we utilize a phase
qubit to induce Rabi oscillations of single tunneling defects and measure their
dephasing rates as a function of the defect's asymmetry energy, which is tuned
by an applied strain. The dephasing rates scale quadratically with the external
strain and are inversely proportional to the Rabi frequency. These results are
analyzed and explained within a model of interacting standard defects, in which
pure dephasing of coherent high-frequency (GHz) defects is caused by
interaction with incoherent low-frequency thermally excited defects.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,803 | Learning rate adaptation for federated and differentially private learning | We propose an algorithm for the adaptation of the learning rate for
stochastic gradient descent (SGD) that avoids the need for validation set use.
The idea for the adaptiveness comes from the technique of extrapolation: to get
an estimate for the error against the gradient flow which underlies SGD, we
compare the result obtained by one full step and two half-steps. The algorithm
is applied in two separate frameworks: federated and differentially private
learning. Using examples of deep neural networks we empirically show that the
adaptive algorithm is competitive with manually tuned commonly used
optimisation methods for differentially privately training. We also show that
it works robustly in the case of federated learning unlike commonly used
optimisation methods.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,804 | Holomorphic Hermite polynomials in two variables | Generalizations of the Hermite polynomials to many variables and/or to the
complex domain have been located in mathematical and physical literature for
some decades. Polynomials traditionally called complex Hermite ones are mostly
understood as polynomials in $z$ and $\bar{z}$ which in fact makes them
polynomials in two real variables with complex coefficients. The present paper
proposes to investigate for the first time holomorphic Hermite polynomials in
two variables. Their algebraic and analytic properties are developed here.
While the algebraic properties do not differ too much for those considered so
far, their analytic features are based on a kind of non-rotational
orthogonality invented by van Eijndhoven and Meyers. Inspired by their
invention we merely follow the idea of Bargmann's seminal paper (1961) giving
explicit construction of reproducing kernel Hilbert spaces based on those
polynomials. "Homotopic" behavior of our new formation culminates in comparing
it to the very classical Bargmann space of two variables on one edge and the
aforementioned Hermite polynomials in $z$ and $\bar{z}$ on the other. Unlike in
the case of Bargmann's basis our Hermite polynomials are not product ones but
factorize to it when bonded together with the first case of limit properties
leading both to the Bargmann basis and suitable form of the reproducing kernel.
Also in the second limit we recover standard results obeyed by Hermite
polynomials in $z$ and $\bar{z}$.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,805 | Equilibria, information and frustration in heterogeneous network games with conflicting preferences | Interactions between people are the basis on which the structure of our
society arises as a complex system and, at the same time, are the starting
point of any physical description of it. In the last few years, much
theoretical research has addressed this issue by combining the physics of
complex networks with a description of interactions in terms of evolutionary
game theory. We here take this research a step further by introducing a most
salient societal factor such as the individuals' preferences, a characteristic
that is key to understand much of the social phenomenology these days. We
consider a heterogeneous, agent-based model in which agents interact
strategically with their neighbors but their preferences and payoffs for the
possible actions differ. We study how such a heterogeneous network behaves
under evolutionary dynamics and different strategic interactions, namely
coordination games and best shot games. With this model we study the emergence
of the equilibria predicted analytically in random graphs under best response
dynamics, and we extend this test to unexplored contexts like proportional
imitation and scale free networks. We show that some theoretically predicted
equilibria do not arise in simulations with incomplete Information, and we
demonstrate the importance of the graph topology and the payoff function
parameters for some games. Finally, we discuss our results with available
experimental evidence on coordination games, showing that our model agrees
better with the experiment that standard economic theories, and draw hints as
to how to maximize social efficiency in situations of conflicting preferences.
| 1 | 1 | 0 | 0 | 0 | 0 |
16,806 | Scalable Generalized Dynamic Topic Models | Dynamic topic models (DTMs) model the evolution of prevalent themes in
literature, online media, and other forms of text over time. DTMs assume that
word co-occurrence statistics change continuously and therefore impose
continuous stochastic process priors on their model parameters. These dynamical
priors make inference much harder than in regular topic models, and also limit
scalability. In this paper, we present several new results around DTMs. First,
we extend the class of tractable priors from Wiener processes to the generic
class of Gaussian processes (GPs). This allows us to explore topics that
develop smoothly over time, that have a long-term memory or are temporally
concentrated (for event detection). Second, we show how to perform scalable
approximate inference in these models based on ideas around stochastic
variational inference and sparse Gaussian processes. This way we can train a
rich family of DTMs to massive data. Our experiments on several large-scale
datasets show that our generalized model allows us to find interesting patterns
that were not accessible by previous approaches.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,807 | Session Types for Orchestrated Interactions | In the setting of the pi-calculus with binary sessions, we aim at relaxing
the notion of duality of session types by the concept of retractable compliance
developed in contract theory. This leads to extending session types with a new
type operator of "speculative selection" including choices not necessarily
offered by a compliant partner. We address the problem of selecting successful
communicating branches by means of an operational semantics based on
orchestrators, which has been shown to be equivalent to the retractable
semantics of contracts, but clearly more feasible. A type system, sound with
respect to such a semantics, is hence provided.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,808 | An Agent-Based Approach for Optimizing Modular Vehicle Fleet Operation | Modularity in military vehicle designs enables on-base assembly, disassembly,
and reconfiguration of vehicles, which can be beneficial in promoting fleet
adaptability and life cycle cost savings. To properly manage the fleet
operation and to control the resupply, demand prediction, and scheduling
process, this paper illustrates an agent-based approach customized for highly
modularized military vehicle fleets and studies the feasibility and flexibility
of modularity for various mission scenarios. Given deterministic field demands
with operation stochasticity, we compare the performance of a modular fleet to
a conventional fleet in equivalent operation strategies and also compare fleet
performance driven by heuristic rules and optimization. Several indicators are
selected to quantify the fleet performance, including operation costs, total
resupplied resources, and fleet readiness.
When the model is implemented for military Joint Tactical Transport System
(JTTS) mission, our results indicate that fleet modularity can reduce total
resource supplies without significant losses in fleet readiness. The benefits
of fleet modularity can also be amplified through a real-time optimized
operation strategy. To highlight the feasibility of fleet modularity, a
parametric study is performed to show the impacts from working capacity on
modular fleet performance. Finally, we provide practical suggestions of modular
vehicle designs based on the analysis and other possible usage.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,809 | Delta-epsilon functions and uniform continuity on metric spaces | Under certain general conditions, an explicit formula to compute the greatest
delta-epsilon function of a continuous function is given. From this formula, a
new way to analyze the uniform continuity of a continuous function is given.
Several examples illustrating the theory are discussed.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,810 | Deterministic Dispersion of Mobile Robots in Dynamic Rings | In this work, we study the problem of dispersion of mobile robots on dynamic
rings. The problem of dispersion of $n$ robots on an $n$ node graph, introduced
by Augustine and Moses Jr. [1], requires robots to coordinate with each other
and reach a configuration where exactly one robot is present on each node. This
problem has real world applications and applies whenever we want to minimize
the total cost of $n$ agents sharing $n$ resources, located at various places,
subject to the constraint that the cost of an agent moving to a different
resource is comparatively much smaller than the cost of multiple agents sharing
a resource (e.g. smart electric cars sharing recharge stations). The study of
this problem also provides indirect benefits to the study of scattering on
graphs, the study of exploration by mobile robots, and the study of load
balancing on graphs.
We solve the problem of dispersion in the presence of two types of dynamism
in the underlying graph: (i) vertex permutation and (ii) 1-interval
connectivity. We introduce the notion of vertex permutation dynamism and have
it mean that for a given set of nodes, in every round, the adversary ensures a
ring structure is maintained, but the connections between the nodes may change.
We use the idea of 1-interval connectivity from Di Luna et al. [10], where for
a given ring, in each round, the adversary chooses at most one edge to remove.
We assume robots have full visibility and present asymptotically time optimal
algorithms to achieve dispersion in the presence of both types of dynamism when
robots have chirality. When robots do not have chirality, we present
asymptotically time optimal algorithms to achieve dispersion subject to certain
constraints. Finally, we provide impossibility results for dispersion when
robots have no visibility.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,811 | A brain signature highly predictive of future progression to Alzheimer's dementia | Early prognosis of Alzheimer's dementia is hard. Mild cognitive impairment
(MCI) typically precedes Alzheimer's dementia, yet only a fraction of MCI
individuals will progress to dementia, even when screened using biomarkers. We
propose here to identify a subset of individuals who share a common brain
signature highly predictive of oncoming dementia. This signature was composed
of brain atrophy and functional dysconnectivity and discovered using a machine
learning model in patients suffering from dementia. The model recognized the
same brain signature in MCI individuals, 90% of which progressed to dementia
within three years. This result is a marked improvement on the state-of-the-art
in prognostic precision, while the brain signature still identified 47% of all
MCI progressors. We thus discovered a sizable MCI subpopulation which
represents an excellent recruitment target for clinical trials at the prodromal
stage of Alzheimer's disease.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,812 | Deep scattering transform applied to note onset detection and instrument recognition | Automatic Music Transcription (AMT) is one of the oldest and most
well-studied problems in the field of music information retrieval. Within this
challenging research field, onset detection and instrument recognition take
important places in transcription systems, as they respectively help to
determine exact onset times of notes and to recognize the corresponding
instrument sources. The aim of this study is to explore the usefulness of
multiscale scattering operators for these two tasks on plucked string
instrument and piano music. After resuming the theoretical background and
illustrating the key features of this sound representation method, we evaluate
its performances comparatively to other classical sound representations. Using
both MIDI-driven datasets with real instrument samples and real musical pieces,
scattering is proved to outperform other sound representations for these AMT
subtasks, putting forward its richer sound representation and invariance
properties.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,813 | Gaschütz Lemma for Compact Groups | We prove the Gaschütz Lemma holds for all metrisable compact groups.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,814 | Driven flow with exclusion and spin-dependent transport in graphenelike structures | We present a simplified description for spin-dependent electronic transport
in honeycomb-lattice structures with spin-orbit interactions, using
generalizations of the stochastic non-equilibrium model known as the totally
asymmetric simple exclusion process. Mean field theory and numerical
simulations are used to study currents, density profiles and current
polarization in quasi- one dimensional systems with open boundaries, and
externally-imposed particle injection ($\alpha$) and ejection ($\beta$) rates.
We investigate the influence of allowing for double site occupancy, according
to Pauli's exclusion principle, on the behavior of the quantities of interest.
We find that double occupancy shows strong signatures for specific combinations
of rates, namely high $\alpha$ and low $\beta$, but otherwise its effects are
quantitatively suppressed. Comments are made on the possible relevance of the
present results to experiments on suitably doped graphenelike structures.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,815 | MOG: Mapper on Graphs for Relationship Preserving Clustering | The interconnected nature of graphs often results in difficult to interpret
clutter. Typically techniques focus on either decluttering by clustering nodes
with similar properties or grouping edges with similar relationship. We propose
using mapper, a powerful topological data analysis tool, to summarize the
structure of a graph in a way that both clusters data with similar properties
and preserves relationships. Typically, mapper operates on a given data by
utilizing a scalar function defined on every point in the data and a cover for
scalar function codomain. The output of mapper is a graph that summarize the
shape of the space. In this paper, we outline how to use this mapper
construction on an input graphs, outline three filter functions that capture
important structures of the input graph, and provide an interface for
interactively modifying the cover. To validate our approach, we conduct several
case studies on synthetic and real world data sets and demonstrate how our
method can give meaningful summaries for graphs with various complexities
| 0 | 0 | 0 | 1 | 0 | 0 |
16,816 | Variation Evolving for Optimal Control Computation, A Compact Way | A compact version of the Variation Evolving Method (VEM) is developed for the
optimal control computation. It follows the idea that originates from the
continuous-time dynamics stability theory in the control field. The optimal
solution is analogized to the equilibrium point of a dynamic system and is
anticipated to be obtained in an asymptotically evolving way. With the
introduction of a virtual dimension, the variation time, the Evolution Partial
Differential Equation (EPDE), which describes the variation motion towards the
optimal solution, is deduced from the Optimal Control Problem (OCP), and the
equivalent optimality conditions with no employment of costates are
established. In particular, it is found that theoretically the analytic
feedback optimal control law does not exist for general OCPs because the
optimal control is related to the future state. Since the derived EPDE is
suitable to be solved with the semi-discrete method in the field of PDE
numerical calculation, the resulting Initial-value Problems (IVPs) may be
solved with mature Ordinary Differential Equation (ODE) numerical integration
methods.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,817 | Transforming Sensor Data to the Image Domain for Deep Learning - an Application to Footstep Detection | Convolutional Neural Networks (CNNs) have become the state-of-the-art in
various computer vision tasks, but they are still premature for most sensor
data, especially in pervasive and wearable computing. A major reason for this
is the limited amount of annotated training data. In this paper, we propose the
idea of leveraging the discriminative power of pre-trained deep CNNs on
2-dimensional sensor data by transforming the sensor modality to the visual
domain. By three proposed strategies, 2D sensor output is converted into
pressure distribution imageries. Then we utilize a pre-trained CNN for transfer
learning on the converted imagery data. We evaluate our method on a gait
dataset of floor surface pressure mapping. We obtain a classification accuracy
of 87.66%, which outperforms the conventional machine learning methods by over
10%.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,818 | The Price of Differential Privacy For Online Learning | We design differentially private algorithms for the problem of online linear
optimization in the full information and bandit settings with optimal
$\tilde{O}(\sqrt{T})$ regret bounds. In the full-information setting, our
results demonstrate that $\epsilon$-differential privacy may be ensured for
free -- in particular, the regret bounds scale as
$O(\sqrt{T})+\tilde{O}\left(\frac{1}{\epsilon}\right)$. For bandit linear
optimization, and as a special case, for non-stochastic multi-armed bandits,
the proposed algorithm achieves a regret of
$\tilde{O}\left(\frac{1}{\epsilon}\sqrt{T}\right)$, while the previously known
best regret bound was
$\tilde{O}\left(\frac{1}{\epsilon}T^{\frac{2}{3}}\right)$.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,819 | Simulation chain and signal classification for acoustic neutrino detection in seawater | Acoustic neutrino detection is a promising approach to extend the energy
range of neutrino telescopes to energies beyond $10^{18}$\,eV. Currently
operational and planned water-Cherenkov neutrino telescopes, most notably
KM3NeT, include acoustic sensors in addition to the optical ones. These
acoustic sensors could be used as instruments for acoustic detection, while
their main purpose is the position calibration of the detection units. In this
article, a Monte Carlo simulation chain for acoustic detectors will be
presented, covering the initial interaction of the neutrino up to the signal
classification of recorded events. The ambient and transient background in the
simulation was implemented according to data recorded by the acoustic set-up
AMADEUS inside the ANTARES detector. The effects of refraction on the neutrino
signature in the detector are studied, and a classification of the recorded
events is implemented. As bipolar waveforms similar to those of the expected
neutrino signals are also emitted from other sound sources, additional features
like the geometrical shape of the propagation have to be considered for the
signal classification. This leads to a large improvement of the background
suppression by almost two orders of magnitude, since a flat cylindrical
"pancake" propagation pattern is a distinctive feature of neutrino signals. An
overview of the simulation chain and the signal classification will be
presented and preliminary studies of the performance of the classification will
be discussed.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,820 | Parameter Space Noise for Exploration | Deep reinforcement learning (RL) methods generally engage in exploratory
behavior through noise injection in the action space. An alternative is to add
noise directly to the agent's parameters, which can lead to more consistent
exploration and a richer set of behaviors. Methods such as evolutionary
strategies use parameter perturbations, but discard all temporal structure in
the process and require significantly more samples. Combining parameter noise
with traditional RL methods allows to combine the best of both worlds. We
demonstrate that both off- and on-policy methods benefit from this approach
through experimental comparison of DQN, DDPG, and TRPO on high-dimensional
discrete action environments as well as continuous control tasks. Our results
show that RL with parameter noise learns more efficiently than traditional RL
with action space noise and evolutionary strategies individually.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,821 | Deep Illumination: Approximating Dynamic Global Illumination with Generative Adversarial Network | We present Deep Illumination, a novel machine learning technique for
approximating global illumination (GI) in real-time applications using a
Conditional Generative Adversarial Network. Our primary focus is on generating
indirect illumination and soft shadows with offline rendering quality at
interactive rates. Inspired from recent advancement in image-to-image
translation problems using deep generative convolutional networks, we introduce
a variant of this network that learns a mapping from Gbuffers (depth map,
normal map, and diffuse map) and direct illumination to any global illumination
solution. Our primary contribution is showing that a generative model can be
used to learn a density estimation from screen space buffers to an advanced
illumination model for a 3D environment. Once trained, our network can
approximate global illumination for scene configurations it has never
encountered before within the environment it was trained on. We evaluate Deep
Illumination through a comparison with both a state of the art real-time GI
technique (VXGI) and an offline rendering GI technique (path tracing). We show
that our method produces effective GI approximations and is also
computationally cheaper than existing GI techniques. Our technique has the
potential to replace existing precomputed and screen-space techniques for
producing global illumination effects in dynamic scenes with physically-based
rendering quality.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,822 | Fraternal Dropout | Recurrent neural networks (RNNs) are important class of architectures among
neural networks useful for language modeling and sequential prediction.
However, optimizing RNNs is known to be harder compared to feed-forward neural
networks. A number of techniques have been proposed in literature to address
this problem. In this paper we propose a simple technique called fraternal
dropout that takes advantage of dropout to achieve this goal. Specifically, we
propose to train two identical copies of an RNN (that share parameters) with
different dropout masks while minimizing the difference between their
(pre-softmax) predictions. In this way our regularization encourages the
representations of RNNs to be invariant to dropout mask, thus being robust. We
show that our regularization term is upper bounded by the expectation-linear
dropout objective which has been shown to address the gap due to the difference
between the train and inference phases of dropout. We evaluate our model and
achieve state-of-the-art results in sequence modeling tasks on two benchmark
datasets - Penn Treebank and Wikitext-2. We also show that our approach leads
to performance improvement by a significant margin in image captioning
(Microsoft COCO) and semi-supervised (CIFAR-10) tasks.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,823 | Finite-sample bounds for the multivariate Behrens-Fisher distribution with proportional covariances | The Behrens-Fisher problem is a well-known hypothesis testing problem in
statistics concerning two-sample mean comparison. In this article, we confirm
one conjecture in Eaton and Olshen (1972), which provides stochastic bounds for
the multivariate Behrens-Fisher test statistic under the null hypothesis. We
also extend their results on the stochastic ordering of random quotients to the
arbitrary finite dimensional case. This work can also be seen as a
generalization of Hsu (1938) that provided the bounds for the univariate
Behrens-Fisher problem. The results obtained in this article can be used to
derive a testing procedure for the multivariate Behrens-Fisher problem that
strongly controls the Type I error.
| 0 | 0 | 1 | 1 | 0 | 0 |
16,824 | Evidence for mixed rationalities in preference formation | Understanding the mechanisms underlying the formation of cultural traits,
such as preferences, opinions and beliefs is an open challenge. Trait formation
is intimately connected to cultural dynamics, which has been the focus of a
variety of quantitative models. Recently, some studies have emphasized the
importance of connecting those models to snapshots of cultural dynamics that
are empirically accessible. By analyzing data obtained from different sources,
it has been suggested that culture has properties that are universally present,
and that empirical cultural states differ systematically from randomized
counterparts. Hence, a question about the mechanism responsible for the
observed patterns naturally arises. This study proposes a stochastic structural
model for generating cultural states that retain those robust, empirical
properties. One ingredient of the model, already used in previous work, assumes
that every individual's set of traits is partly dictated by one of several,
universal "rationalities", informally postulated by several social science
theories. The second, new ingredient taken from the same theories assumes that,
apart from a dominant rationality, each individual also has a certain exposure
to the other rationalities. It is shown that both ingredients are required for
reproducing the empirical regularities. This key result suggests that the
effects of cultural dynamics in the real world can be described as an interplay
of multiple, mixing rationalities, and thus provides indirect evidence for the
class of social science theories postulating such mixing. The model should be
seen as a static, effective description of culture, while a dynamical, more
fundamental description is left for future research.
| 1 | 1 | 0 | 0 | 0 | 0 |
16,825 | A Variance Maximization Criterion for Active Learning | Active learning aims to train a classifier as fast as possible with as few
labels as possible. The core element in virtually any active learning strategy
is the criterion that measures the usefulness of the unlabeled data based on
which new points to be labeled are picked. We propose a novel approach which we
refer to as maximizing variance for active learning or MVAL for short. MVAL
measures the value of unlabeled instances by evaluating the rate of change of
output variables caused by changes in the next sample to be queried and its
potential labelling. In a sense, this criterion measures how unstable the
classifier's output is for the unlabeled data points under perturbations of the
training data. MVAL maintains, what we refer to as, retraining information
matrices to keep track of these output scores and exploits two kinds of
variance to measure the informativeness and representativeness, respectively.
By fusing these variances, MVAL is able to select the instances which are both
informative and representative. We employ our technique both in combination
with logistic regression and support vector machines and demonstrate that MVAL
achieves state-of-the-art performance in experiments on a large number of
standard benchmark datasets.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,826 | Polarization, plasmon, and Debye screening in doped 3D ani-Weyl semimetal | We compute the polarization function in a doped three-dimensional
anisotropic-Weyl semimetal, in which the fermion energy dispersion is linear in
two components of the momenta and quadratic in the third. Through detailed
calculations, we find that the long wavelength plasmon mode depends on the
fermion density $n_e$ in the form $\Omega_{p}^{\bot}\propto n_{e}^{3/10}$
within the basal plane and behaves as $\Omega_{p}^{z}\propto n_{e}^{1/2}$ along
the third direction. This unique characteristic of the plasmon mode can be
probed by various experimental techniques, such as electron energy-loss
spectroscopy. The Debye screening at finite chemical potential and finite
temperature is also analyzed based on the polarization function.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,827 | Identifying Product Order with Restricted Boltzmann Machines | Unsupervised machine learning via a restricted Boltzmann machine is an useful
tool in distinguishing an ordered phase from a disordered phase. Here we study
its application on the two-dimensional Ashkin-Teller model, which features a
partially ordered product phase. We train the neural network with spin
configuration data generated by Monte Carlo simulations and show that distinct
features of the product phase can be learned from non-ergodic samples resulting
from symmetry breaking. Careful analysis of the weight matrices inspires us to
define a nontrivial machine-learning motivated quantity of the product form,
which resembles the conventional product order parameter.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,828 | A finite temperature study of ideal quantum gases in the presence of one dimensional quasi-periodic potential | We study the thermodynamics of ideal Bose gas as well as the transport
properties of non interacting bosons and fermions in a one dimensional
quasi-periodic potential, namely Aubry-André (AA) model at finite
temperature. For bosons in finite size systems, the effect of quasi-periodic
potential on the crossover phenomena corresponding to Bose-Einstein
condensation (BEC), superfluidity and localization phenomena at finite
temperatures are investigated. From the ground state number fluctuation we
calculate the crossover temperature of BEC which exhibits a non monotonic
behavior with the strength of AA potential and vanishes at the self-dual
critical point following power law. Appropriate rescaling of the crossover
temperatures reveals universal behavior which is studied for different
quasi-periodicity of the AA model. Finally, we study the temperature and flux
dependence of the persistent current of fermions in presence of a
quasi-periodic potential to identify the localization at the Fermi energy from
the decay of the current.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,829 | High-Frequency Analysis of Effective Interactions and Bandwidth for Transient States after Monocycle Pulse Excitation of Extended Hubbard Model | Using a high-frequency expansion in periodically driven extended Hubbard
models, where the strengths and ranges of density-density interactions are
arbitrary, we obtain the effective interactions and bandwidth, which depend
sensitively on the polarization of the driving field. Then, we numerically
calculate modulations of correlation functions in a quarter-filled extended
Hubbard model with nearest-neighbor interactions on a triangular lattice with
trimers after monocycle pulse excitation. We discuss how the resultant
modulations are compatible with the effective interactions and bandwidth
derived above on the basis of their dependence on the polarization of
photoexcitation, which is easily accessible by experiments. Some correlation
functions after monocycle pulse excitation are consistent with the effective
interactions, which are weaker or stronger than the original ones. However, the
photoinduced enhancement of anisotropic charge correlations previously
discussed for the three-quarter-filled organic conductor
$\alpha$-(bis[ethylenedithio]-tetrathiafulvalene)$_2$I$_3$
[$\alpha$-(BEDT-TTF)$_2$I$_3$] in the metallic phase is not fully explained by
the effective interactions or bandwidth, which are derived independently of the
filling.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,830 | Fast binary embeddings, and quantized compressed sensing with structured matrices | This paper deals with two related problems, namely distance-preserving binary
embeddings and quantization for compressed sensing . First, we propose fast
methods to replace points from a subset $\mathcal{X} \subset \mathbb{R}^n$,
associated with the Euclidean metric, with points in the cube $\{\pm 1\}^m$ and
we associate the cube with a pseudo-metric that approximates Euclidean distance
among points in $\mathcal{X}$. Our methods rely on quantizing fast
Johnson-Lindenstrauss embeddings based on bounded orthonormal systems and
partial circulant ensembles, both of which admit fast transforms. Our
quantization methods utilize noise-shaping, and include Sigma-Delta schemes and
distributed noise-shaping schemes. The resulting approximation errors decay
polynomially and exponentially fast in $m$, depending on the embedding method.
This dramatically outperforms the current decay rates associated with binary
embeddings and Hamming distances. Additionally, it is the first such binary
embedding result that applies to fast Johnson-Lindenstrauss maps while
preserving $\ell_2$ norms.
Second, we again consider noise-shaping schemes, albeit this time to quantize
compressed sensing measurements arising from bounded orthonormal ensembles and
partial circulant matrices. We show that these methods yield a reconstruction
error that again decays with the number of measurements (and bits), when using
convex optimization for reconstruction. Specifically, for Sigma-Delta schemes,
the error decays polynomially in the number of measurements, and it decays
exponentially for distributed noise-shaping schemes based on beta encoding.
These results are near optimal and the first of their kind dealing with bounded
orthonormal systems.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,831 | The Many Faces of Link Fraud | Most past work on social network link fraud detection tries to separate
genuine users from fraudsters, implicitly assuming that there is only one type
of fraudulent behavior. But is this assumption true? And, in either case, what
are the characteristics of such fraudulent behaviors? In this work, we set up
honeypots ("dummy" social network accounts), and buy fake followers (after
careful IRB approval). We report the signs of such behaviors including oddities
in local network connectivity, account attributes, and similarities and
differences across fraud providers. Most valuably, we discover and characterize
several types of fraud behaviors. We discuss how to leverage our insights in
practice by engineering strongly performing entropy-based features and
demonstrating high classification accuracy. Our contributions are (a)
instrumentation: we detail our experimental setup and carefully engineered data
collection process to scrape Twitter data while respecting API rate-limits, (b)
observations on fraud multimodality: we analyze our honeypot fraudster
ecosystem and give surprising insights into the multifaceted behaviors of these
fraudster types, and (c) features: we propose novel features that give strong
(>0.95 precision/recall) discriminative power on ground-truth Twitter data.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,832 | Diophantine approximation by special primes | We show that whenever $\delta>0$, $\eta$ is real and constants $\lambda_i$
satisfy some necessary conditions, there are infinitely many prime triples
$p_1,\, p_2,\, p_3$ satisfying the inequality $|\lambda_1p_1 + \lambda_2p_2 +
\lambda_3p_3+\eta|<(\max p_j)^{-1/12+\delta}$ and such that, for each
$i\in\{1,2,3\}$, $p_i+2$ has at most $28$ prime factors.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,833 | A Compositional Treatment of Iterated Open Games | Compositional Game Theory is a new, recently introduced model of economic
games based upon the computer science idea of compositionality. In it, complex
and irregular games can be built up from smaller and simpler games, and the
equilibria of these complex games can be defined recursively from the
equilibria of their simpler subgames. This paper extends the model by providing
a final coalgebra semantics for infinite games. In the course of this, we
introduce a new operator on games to model the economic concept of subgame
perfection.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,834 | Bayesian inference for spectral projectors of covariance matrix | Let $X_1, \ldots, X_n$ be i.i.d. sample in $\mathbb{R}^p$ with zero mean and
the covariance matrix $\mathbf{\Sigma^*}$. The classic principal component
analysis estimates the projector $\mathbf{P^*_{\mathcal{J}}}$ onto the direct
sum of some eigenspaces of $\mathbf{\Sigma^*}$ by its empirical counterpart
$\mathbf{\widehat{P}_{\mathcal{J}}}$. Recent papers [Koltchinskii, Lounici
(2017)], [Naumov et al. (2017)] investigate the asymptotic distribution of the
Frobenius distance between the projectors $\|
\mathbf{\widehat{P}_{\mathcal{J}}} - \mathbf{P^*_{\mathcal{J}}} \|_2$. The
problem arises when one tries to build a confidence set for the true projector
effectively. We consider the problem from Bayesian perspective and derive an
approximation for the posterior distribution of the Frobenius distance between
projectors. The derived theorems hold true for non-Gaussian data: the only
assumption that we impose is the concentration of the sample covariance
$\mathbf{\widehat{\Sigma}}$ in a vicinity of $\mathbf{\Sigma^*}$. The obtained
results are applied to construction of sharp confidence sets for the true
projector. Numerical simulations illustrate good performance of the proposed
procedure even on non-Gaussian data in quite challenging regime.
| 0 | 0 | 1 | 1 | 0 | 0 |
16,835 | Handling Incomplete Heterogeneous Data using VAEs | Variational autoencoders (VAEs), as well as other generative models, have
been shown to be efficient and accurate to capture the latent structure of vast
amounts of complex high-dimensional data. However, existing VAEs can still not
directly handle data that are heterogenous (mixed continuous and discrete) or
incomplete (with missing data at random), which is indeed common in real-world
applications.
In this paper, we propose a general framework to design VAEs, suitable for
fitting incomplete heterogenous data. The proposed HI-VAE includes likelihood
models for real-valued, positive real valued, interval, categorical, ordinal
and count data, and allows to estimate (and potentially impute) missing data
accurately. Furthermore, HI-VAE presents competitive predictive performance in
supervised tasks, outperforming super- vised models when trained on incomplete
data
| 0 | 0 | 0 | 1 | 0 | 0 |
16,836 | Geometric mean of probability measures and geodesics of Fisher information metric | The space of all probability measures having positive density function on a
connected compact smooth manifold $M$, denoted by $\mathcal{P}(M)$, carries the
Fisher information metric $G$. We define the geometric mean of probability
measures by the aid of which we investigate information geometry of
$\mathcal{P}(M)$, equipped with $G$. We show that a geodesic segment joining
arbitrary probability measures $\mu_1$ and $\mu_2$ is expressed by using the
normalized geometric mean of its endpoints. As an application, we show that any
two points of $\mathcal{P}(M)$ can be joined by a geodesic. Moreover, we prove
that the function $\ell$ defined by $\ell(\mu_1, \mu_2):=2\arccos\int_M
\sqrt{p_1\,p_2}\,d\lambda$, $\mu_i=p_i\,\lambda$, $i=1,2$ gives the distance
function on $\mathcal{P}(M)$. It is shown that geodesics are all minimal.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,837 | On automorphism groups of Toeplitz subshifts | In this article we study automorphisms of Toeplitz subshifts. Such groups are
abelian and any finitely generated torsion subgroup is finite and cyclic. When
the complexity is non superlinear, we prove that the automorphism group is,
modulo a finite cyclic group, generated by a unique root of the shift. In the
subquadratic complexity case, we show that the automorphism group modulo the
torsion is generated by the roots of the shift map and that the result of the
non superlinear case is optimal. Namely, for any $\varepsilon > 0$ we construct
examples of minimal Toeplitz subshifts with complexity bounded by $C
n^{1+\epsilon}$ whose automorphism groups are not finitely generated. Finally,
we observe the coalescence and the automorphism group give no restriction on
the complexity since we provide a family of coalescent Toeplitz subshifts with
positive entropy such that their automorphism groups are arbitrary finitely
generated infinite abelian groups with cyclic torsion subgroup (eventually
restricted to powers of the shift).
| 0 | 0 | 1 | 0 | 0 | 0 |
16,838 | How to Generate Pseudorandom Permutations Over Other Groups | Recent results by Alagic and Russell have given some evidence that the
Even-Mansour cipher may be secure against quantum adversaries with quantum
queries, if considered over other groups than $(\mathbb{Z}/2)^n$. This prompts
the question as to whether or not other classical schemes may be generalized to
arbitrary groups and whether classical results still apply to those generalized
schemes. In this thesis, we generalize the Even-Mansour cipher and the Feistel
cipher. We show that Even and Mansour's original notions of secrecy are
obtained on a one-key, group variant of the Even-Mansour cipher. We generalize
the result by Kilian and Rogaway, that the Even-Mansour cipher is pseudorandom,
to super pseudorandomness, also in the one-key, group case. Using a Slide
Attack we match the bound found above. After generalizing the Feistel cipher to
arbitrary groups we resolve an open problem of Patel, Ramzan, and Sundaram by
showing that the 3-round Feistel cipher over an arbitrary group is not super
pseudorandom. We generalize a result by Gentry and Ramzan showing that the
Even-Mansour cipher can be implemented using the Feistel cipher as the public
permutation. In this result, we also consider the one-key case over a group and
generalize their bound. Finally, we consider Zhandry's result on quantum
pseudorandom permutations, showing that his result may be generalized to hold
for arbitrary groups. In this regard, we consider whether certain card shuffles
may be generalized as well.
| 1 | 0 | 1 | 0 | 0 | 0 |
16,839 | Measures of Tractography Convergence | In the present work, we use information theory to understand the empirical
convergence rate of tractography, a widely-used approach to reconstruct
anatomical fiber pathways in the living brain. Based on diffusion MRI data,
tractography is the starting point for many methods to study brain
connectivity. Of the available methods to perform tractography, most
reconstruct a finite set of streamlines, or 3D curves, representing probable
connections between anatomical regions, yet relatively little is known about
how the sampling of this set of streamlines affects downstream results, and how
exhaustive the sampling should be. Here we provide a method to measure the
information theoretic surprise (self-cross entropy) for tract sampling schema.
We then empirically assess four streamline methods. We demonstrate that the
relative information gain is very low after a moderate number of streamlines
have been generated for each tested method. The results give rise to several
guidelines for optimal sampling in brain connectivity analyses.
| 0 | 0 | 0 | 1 | 1 | 0 |
16,840 | Network Flow Based Post Processing for Sales Diversity | Collaborative filtering is a broad and powerful framework for building
recommendation systems that has seen widespread adoption. Over the past decade,
the propensity of such systems for favoring popular products and thus creating
echo chambers have been observed. This has given rise to an active area of
research that seeks to diversify recommendations generated by such algorithms.
We address the problem of increasing diversity in recommendation systems that
are based on collaborative filtering that use past ratings to predicting a
rating quality for potential recommendations. Following our earlier work, we
formulate recommendation system design as a subgraph selection problem from a
candidate super-graph of potential recommendations where both diversity and
rating quality are explicitly optimized: (1) On the modeling side, we define a
new flexible notion of diversity that allows a system designer to prescribe the
number of recommendations each item should receive, and smoothly penalizes
deviations from this distribution. (2) On the algorithmic side, we show that
minimum-cost network flow methods yield fast algorithms in theory and practice
for designing recommendation subgraphs that optimize this notion of diversity.
(3) On the empirical side, we show the effectiveness of our new model and
method to increase diversity while maintaining high rating quality in standard
rating data sets from Netflix and MovieLens.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,841 | Lattice Model for Production of Gas | We define a lattice model for rock, absorbers, and gas that makes it possible
to examine the flow of gas to a complicated absorbing boundary over long
periods of time. The motivation is to deduce the geometry of the boundary from
the time history of gas absorption. We find a solution to this model using
Green's function techniques, and apply the solution to three absorbing networks
of increasing complexity.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,842 | Adaptive Representation Selection in Contextual Bandit | We consider an extension of the contextual bandit setting, motivated by
several practical applications, where an unlabeled history of contexts can
become available for pre-training before the online decision-making begins. We
propose an approach for improving the performance of contextual bandit in such
setting, via adaptive, dynamic representation learning, which combines offline
pre-training on unlabeled history of contexts with online selection and
modification of embedding functions. Our experiments on a variety of datasets
and in different nonstationary environments demonstrate clear advantages of our
approach over the standard contextual bandit.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,843 | Algebraic surfaces with zero-dimensional cohomology support locus | Using the theory of cohomology support locus, we give a necessary condition
for the Albanese map of a smooth projective surface being a submersion. More
precisely, assuming the cohomology support locus of any finite abelian cover of
a smooth projective surface consists of finitely many points, we prove that the
surface has trivial first Betti number, or is a ruled surface of genus one, or
is an abelian surface.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,844 | Insight into the temperature dependent properties of the ferromagnetic Kondo lattice YbNiSn | Analyzing temperature dependent photoemission (PE) data of the ferromagnetic
Kondo-lattice (KL) system YbNiSn in the light of the Periodic Anderson model
(PAM) we show that the KL behavior is not limited to temperatures below a
temperature T_K, defined empirically from resistivity and specificic heat
measurements. As characteristic for weakly hybridized Ce and Yb systems, the PE
spectra reveal a 4f-derived Fermi level peak, which reflects contributions from
the Kondo resonance and its crystal electric field (CEF) satellites. In YbNiSn
this peak has an unusual temperature dependence: With decreasing temperature a
steady linear increase of intensity is observed which extends over a large
interval ranging from 100 K down to 1 K without showing any peculiarities in
the region of T_K ~ TC= 5.6 K. In the light of the single-impurity Anderson
model (SIAM) this intensity variation reflects a linear increase of 4f
occupancy with decreasing temperature, indicating an onset of Kondo screening
at temperatures above 100 K. Within the PAM this phenomenon could be described
by a non-Fermi liquid like T- linear damping of the self-energy which accounts
phenomenologically for the feedback from the closely spaced CEF-states.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,845 | Some Ageing Properties of Dynamic Additive Mean Residual Life Model | Although proportional hazard rate model is a very popular model to analyze
failure time data, sometimes it becomes important to study the additive hazard
rate model. Again, sometimes the concept of the hazard rate function is
abstract, in comparison to the concept of mean residual life function. A new
model called `dynamic additive mean residual life model' where the covariates
are time-dependent has been defined in the literature. Here we study the
closure properties of the model for different positive and negative ageing
classes under certain condition(s). Quite a few examples are presented to
illustrate different properties of the model.
| 0 | 0 | 1 | 1 | 0 | 0 |
16,846 | From Monte Carlo to Las Vegas: Improving Restricted Boltzmann Machine Training Through Stopping Sets | We propose a Las Vegas transformation of Markov Chain Monte Carlo (MCMC)
estimators of Restricted Boltzmann Machines (RBMs). We denote our approach
Markov Chain Las Vegas (MCLV). MCLV gives statistical guarantees in exchange
for random running times. MCLV uses a stopping set built from the training data
and has maximum number of Markov chain steps K (referred as MCLV-K). We present
a MCLV-K gradient estimator (LVS-K) for RBMs and explore the correspondence and
differences between LVS-K and Contrastive Divergence (CD-K), with LVS-K
significantly outperforming CD-K training RBMs over the MNIST dataset,
indicating MCLV to be a promising direction in learning generative models.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,847 | CD meets CAT | We show that if a noncollapsed $CD(K,n)$ space $X$ with $n\ge 2$ has
curvature bounded above by $\kappa$ in the sense of Alexandrov then $K\le
(n-1)\kappa$ and $X$ is an Alexandrov space of curvature bounded below by
$K-\kappa (n-2)$. We also show that if a $CD(K,n)$ space $Y$ with finite $n$
has curvature bounded above then it is infinitesimally Hilbertian.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,848 | Cost Models for Selecting Materialized Views in Public Clouds | Data warehouse performance is usually achieved through physical data
structures such as indexes or materialized views. In this context, cost models
can help select a relevant set ofsuch performance optimization structures.
Nevertheless, selection becomes more complex in the cloud. The criterion to
optimize is indeed at least two-dimensional, with monetary cost balancing
overall query response time. This paper introduces new cost models that fit
into the pay-as-you-go paradigm of cloud computing. Based on these cost models,
an optimization problem is defined to discover, among candidate views, those to
be materialized to minimize both the overall cost of using and maintaining the
database in a public cloud and the total response time ofa given query
workload. We experimentally show that maintaining materialized views is always
advantageous, both in terms of performance and cost.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,849 | Dealing with Integer-valued Variables in Bayesian Optimization with Gaussian Processes | Bayesian optimization (BO) methods are useful for optimizing functions that
are expensive to evaluate, lack an analytical expression and whose evaluations
can be contaminated by noise. These methods rely on a probabilistic model of
the objective function, typically a Gaussian process (GP), upon which an
acquisition function is built. This function guides the optimization process
and measures the expected utility of performing an evaluation of the objective
at a new point. GPs assume continous input variables. When this is not the
case, such as when some of the input variables take integer values, one has to
introduce extra approximations. A common approach is to round the suggested
variable value to the closest integer before doing the evaluation of the
objective. We show that this can lead to problems in the optimization process
and describe a more principled approach to account for input variables that are
integer-valued. We illustrate in both synthetic and a real experiments the
utility of our approach, which significantly improves the results of standard
BO methods on problems involving integer-valued variables.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,850 | Second-order constrained variational problems on Lie algebroids: applications to optimal control | The aim of this work is to study, from an intrinsic and geometric point of
view, second-order constrained variational problems on Lie algebroids, that is,
optimization problems defined by a cost functional which depends on
higher-order derivatives of admissible curves on a Lie algebroid. Extending the
classical Skinner and Rusk formalism for the mechanics in the context of Lie
algebroids, for second-order constrained mechanical systems, we derive the
corresponding dynamical equations. We find a symplectic Lie subalgebroid where,
under some mild regularity conditions, the second-order constrained variational
problem, seen as a presymplectic Hamiltonian system, has a unique solution. We
study the relationship of this formalism with the second-order constrained
Euler-Poincaré and Lagrange-Poincaré equations, among others. Our study is
applied to the optimal control of mechanical systems.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,851 | The Galactic Cosmic Ray Electron Spectrum from 3 to 70 MeV Measured by Voyager 1 Beyond the Heliopause, What This Tells Us About the Propagation of Electrons and Nuclei In and Out of the Galaxy at Low Energies | The cosmic ray electrons measured by Voyager 1 between 3-70 MeV beyond the
heliopause have intensities several hundred times those measured at the Earth
by PAMELA at nearly the same energies. This paper compares this new V1 data
with data from the earth-orbiting PAMELA experiment up to energies greater than
10 GeV where solar modulation effects are negligible. In this energy regime we
assume the main parameters governing electron propagation are diffusion and
energy loss and we use a Monte Carlo program to describe this propagation in
the galaxy. To reproduce the new Voyager electron spectrum, which is E-1.3,
together with that measured by PAMELA which is E-3.20 above 10 GeV, we require
a diffusion coefficient which is P 0.45 at energies above 0.5 GeV changing to a
P-1.00 dependence at lower rigidities. The entire electron spectrum observed at
both V1 and PAMELA from 3 MeV to 30 GeV can then be described by a simple
source spectrum, dj/dP P-2.25, with a spectral exponent that is independent of
rigidity. The change in exponent of the measured electron spectrum from -1.3 at
low energies to 3.2 at the highest energies can be explained by galactic
propagation effects related to the changing dependence of the diffusion
coefficient below 0.5 GeV, and the increasing importance above 0.5 GV of energy
loss from synchrotron and inverse Compton radiation, which are both E2, and
which are responsible for most of the changing spectral exponent above 1.0 GV.
As a result of the P-1.00 dependence of the diffusion coefficient below 0.5
GV that is required to fit the V1 electron spectrum, there is a rapid flow of
these low energy electrons out of the galaxy. These electrons in local IG space
are unobservable to us at any wave length and therefore form a dark energy
component which is 100 times the electrons rest energy.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,852 | Online Scheduling of Spark Workloads with Mesos using Different Fair Allocation Algorithms | In the following, we present example illustrative and experimental results
comparing fair schedulers allocating resources from multiple servers to
distributed application frameworks. Resources are allocated so that at least
one resource is exhausted in every server. Schedulers considered include DRF
(DRFH) and Best-Fit DRF (BF-DRF), TSF, and PS-DSF. We also consider server
selection under Randomized Round Robin (RRR) and based on their residual
(unreserved) resources. In the following, we consider cases with frameworks of
equal priority and without server-preference constraints. We first give typical
results of a illustrative numerical study and then give typical results of a
study involving Spark workloads on Mesos which we have modified and
open-sourced to prototype different schedulers.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,853 | On the representation dimension and finitistic dimension of special multiserial algebras | For monomial special multiserial algebras, which in general are of wild
representation type, we construct radical embeddings into algebras of finite
representation type. As a consequence, we show that the representation
dimension of monomial and self-injective special multiserial algebras is less
or equal to three. This implies that the finitistic dimension conjecture holds
for all special multiserial algebras.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,854 | Would You Like to Motivate Software Testers? Ask Them How | Context. Considering the importance of software testing to the development of
high quality and reliable software systems, this paper aims to investigate how
can work-related factors influence the motivation of software testers. Method.
We applied a questionnaire that was developed using a previous theory of
motivation and satisfaction of software engineers to conduct a survey-based
study to explore and understand how professional software testers perceive and
value work-related factors that could influence their motivation at work.
Results. With a sample of 80 software testers we observed that software testers
are strongly motivated by variety of work, creative tasks, recognition for
their work, and activities that allow them to acquire new knowledge, but in
general the social impact of this activity has low influence on their
motivation. Conclusion. This study discusses the difference of opinions among
software testers, regarding work-related factors that could impact their
motivation, which can be relevant for managers and leaders in software
engineering practice.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,855 | POMDP Structural Results for Controlled Sensing | This article provides a short review of some structural results in controlled
sensing when the problem is formulated as a partially observed Markov decision
process. In particular, monotone value functions, Blackwell dominance and
quickest detection are described.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,856 | Low Rank Matrix Recovery with Simultaneous Presence of Outliers and Sparse Corruption | We study a data model in which the data matrix D can be expressed as D = L +
S + C, where L is a low rank matrix, S an element-wise sparse matrix and C a
matrix whose non-zero columns are outlying data points. To date, robust PCA
algorithms have solely considered models with either S or C, but not both. As
such, existing algorithms cannot account for simultaneous element-wise and
column-wise corruptions. In this paper, a new robust PCA algorithm that is
robust to simultaneous types of corruption is proposed. Our approach hinges on
the sparse approximation of a sparsely corrupted column so that the sparse
expansion of a column with respect to the other data points is used to
distinguish a sparsely corrupted inlier column from an outlying data point. We
also develop a randomized design which provides a scalable implementation of
the proposed approach. The core idea of sparse approximation is analyzed
analytically where we show that the underlying ell_1-norm minimization can
obtain the representation of an inlier in presence of sparse corruptions.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,857 | Power-Sum Denominators | The power sum $1^n + 2^n + \cdots + x^n$ has been of interest to
mathematicians since classical times. Johann Faulhaber, Jacob Bernoulli, and
others who followed expressed power sums as polynomials in $x$ of degree $n+1$
with rational coefficients. Here we consider the denominators of these
polynomials, and prove some of their properties. A remarkable one is that such
a denominator equals $n+1$ times the squarefree product of certain primes $p$
obeying the condition that the sum of the base-$p$ digits of $n+1$ is at least
$p$. As an application, we derive a squarefree product formula for the
denominators of the Bernoulli polynomials.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,858 | A resource-frugal probabilistic dictionary and applications in bioinformatics | Indexing massive data sets is extremely expensive for large scale problems.
In many fields, huge amounts of data are currently generated, however
extracting meaningful information from voluminous data sets, such as computing
similarity between elements, is far from being trivial. It remains nonetheless
a fundamental need. This work proposes a probabilistic data structure based on
a minimal perfect hash function for indexing large sets of keys. Our structure
out-compete the hash table for construction, query times and for memory usage,
in the case of the indexation of a static set. To illustrate the impact of
algorithms performances, we provide two applications based on similarity
computation between collections of sequences, and for which this calculation is
an expensive but required operation. In particular, we show a practical case in
which other bioinformatics tools fail to scale up the tested data set or
provide lower recall quality results.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,859 | Fast learning rate of deep learning via a kernel perspective | We develop a new theoretical framework to analyze the generalization error of
deep learning, and derive a new fast learning rate for two representative
algorithms: empirical risk minimization and Bayesian deep learning. The series
of theoretical analyses of deep learning has revealed its high expressive power
and universal approximation capability. Although these analyses are highly
nonparametric, existing generalization error analyses have been developed
mainly in a fixed dimensional parametric model. To compensate this gap, we
develop an infinite dimensional model that is based on an integral form as
performed in the analysis of the universal approximation capability. This
allows us to define a reproducing kernel Hilbert space corresponding to each
layer. Our point of view is to deal with the ordinary finite dimensional deep
neural network as a finite approximation of the infinite dimensional one. The
approximation error is evaluated by the degree of freedom of the reproducing
kernel Hilbert space in each layer. To estimate a good finite dimensional
model, we consider both of empirical risk minimization and Bayesian deep
learning. We derive its generalization error bound and it is shown that there
appears bias-variance trade-off in terms of the number of parameters of the
finite dimensional approximation. We show that the optimal width of the
internal layers can be determined through the degree of freedom and the
convergence rate can be faster than $O(1/\sqrt{n})$ rate which has been shown
in the existing studies.
| 1 | 0 | 1 | 1 | 0 | 0 |
16,860 | Closed-loop field development optimization with multipoint geostatistics and statistical assessment | Closed-loop field development (CLFD) optimization is a comprehensive
framework for optimal development of subsurface resources. CLFD involves three
major steps: 1) optimization of full development plan based on current set of
models, 2) drilling new wells and collecting new spatial and temporal
(production) data, 3) model calibration based on all data. This process is
repeated until the optimal number of wells is drilled. This work introduces an
efficient CLFD implementation for complex systems described by multipoint
geostatistics (MPS). Model calibration is accomplished in two steps:
conditioning to spatial data by a geostatistical simulation method, and
conditioning to production data by optimization-based PCA. A statistical
procedure is presented to assess the performance of CLFD. Methodology is
applied to an oil reservoir example for 25 different true-model cases.
Application of a single-step of CLFD, improved the true NPV in 64%--80% of
cases. The full CLFD procedure (with three steps) improved the true NPV in 96%
of cases, with an average improvement of 37%.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,861 | Reduction and regular $t$-balanced Cayley maps on split metacyclic 2-groups | A regular $t$-balanced Cayley map (RBCM$_t$ for short) on a group $\Gamma$ is
an embedding of a Cayley graph on $\Gamma$ into a surface with some special
symmetric properties. We propose a reduction method to study RBCM$_t$'s, and as
a first practice, we completely classify RBCM$_t$'s for a class of split
metacyclic 2-groups.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,862 | Perovskite Substrates Boost the Thermopower of Cobaltate Thin Films at High Temperatures | Transition metal oxides are promising candidates for thermoelectric
applications, because they are stable at high temperature and because strong
electronic correlations can generate large Seebeck coefficients, but their
thermoelectric power factors are limited by the low electrical conductivity. We
report transport measurements on Ca3Co4O9 films on various perovskite
substrates and show that reversible incorporation of oxygen into SrTiO3 and
LaAlO3 substrates activates a parallel conduction channel for p-type carriers,
greatly enhancing the thermoelectric performance of the film-substrate system
at temperatures above 450 °C. Thin-film structures that take advantage of
both electronic correlations and the high oxygen mobility of transition metal
oxides thus open up new perspectives for thermopower generation at high
temperature.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,863 | Motion of a rod pushed at one point in a weightless environment in space | We analyze the motion of a rod floating in a weightless environment in space
when a force is applied at some point on the rod in a direction perpendicular
to its length. If the force applied is at the centre of mass, then the rod gets
a linear motion perpendicular to its length. However, if the same force is
applied at a point other than the centre of mass, say, near one end of the rod,
thereby giving rise to a torque, then there will also be a rotation of the rod
about its centre of mass, in addition to the motion of the centre of mass
itself. If the force applied is for a very short duration, but imparting
nevertheless a finite impulse, like in a sudden (quick) hit at one end of the
rod, then the centre of mass will move with a constant linear speed and
superimposed on it will be a rotation of the rod with constant angular speed
about the centre of mass. However, if force is applied continuously, say by
strapping a tiny rocket at one end of the rod, then the rod will spin faster
and faster about the centre of mass, with angular speed increasing linearly
with time. As the direction of the applied force, as seen by an external
(inertial) observer, will be changing continuously with the rotation of the
rod, the acceleration of the centre of mass would also be not in one fixed
direction. However, it turns out that the locus of the velocity vector of the
centre of mass will describe a Cornu spiral, with the velocity vector reaching
a final constant value with time. The mean motion of the centre of mass will be
in a straight line, with superposed initial oscillations that soon die down.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,864 | Dimension theory and components of algebraic stacks | We prove some basic results on the dimension theory of algebraic stacks, and
on the multiplicities of their irreducible components, for which we do not know
a reference.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,865 | When is a polynomial ideal binomial after an ambient automorphism? | Can an ideal I in a polynomial ring k[x] over a field be moved by a change of
coordinates into a position where it is generated by binomials $x^a - cx^b$
with c in k, or by unital binomials (i.e., with c = 0 or 1)? Can a variety be
moved into a position where it is toric? By fibering the G-translates of I over
an algebraic group G acting on affine space, these problems are special cases
of questions about a family F of ideals over an arbitrary base B. The main
results in this general setting are algorithms to find the locus of points in B
over which the fiber of F
- is contained in the fiber of a second family F' of ideals over B;
- defines a variety of dimension at least d;
- is generated by binomials; or
- is generated by unital binomials.
A faster containment algorithm is also presented when the fibers of F are
prime. The big-fiber algorithm is probabilistic but likely faster than known
deterministic ones. Applications include the setting where a second group T
acts on affine space, in addition to G, in which case algorithms compute the
set of G-translates of I
- whose stabilizer subgroups in T have maximal dimension; or
- that admit a faithful multigrading by $Z^r$ of maximal rank r.
Even with no ambient group action given, the final application is an
algorithm to
- decide whether a normal projective variety is abstractly toric.
All of these loci in B and subsets of G are constructible; in some cases they
are closed.
| 1 | 0 | 1 | 0 | 0 | 0 |
16,866 | Annihilators in $\mathbb{N}^k$-graded and $\mathbb{Z}^k$-graded rings | It has been shown by McCoy that a right ideal of a polynomial ring with
several indeterminates has a non-trivial homogeneous right annihilator of
degree 0 provided its right annihilator is non-trivial to begin with. In this
note, it is documented that any $\mathbb{N}$-graded ring $R$ has a slightly
weaker property: the right annihilator of a right ideal contains a homogeneous
non-zero element, if it is non-trivial to begin with. If $R$ is a subring of a
$\mathbb{Z}^k$ -graded ring $S$ satisfying a certain non-annihilation property
(which is the case if $S$ is strongly graded, for example), then it is possible
to find annihilators of degree 0.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,867 | q-Neurons: Neuron Activations based on Stochastic Jackson's Derivative Operators | We propose a new generic type of stochastic neurons, called $q$-neurons, that
considers activation functions based on Jackson's $q$-derivatives with
stochastic parameters $q$. Our generalization of neural network architectures
with $q$-neurons is shown to be both scalable and very easy to implement. We
demonstrate experimentally consistently improved performances over
state-of-the-art standard activation functions, both on training and testing
loss functions.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,868 | Liveness-Driven Random Program Generation | Randomly generated programs are popular for testing compilers and program
analysis tools, with hundreds of bugs in real-world C compilers found by random
testing. However, existing random program generators may generate large amounts
of dead code (computations whose result is never used). This leaves relatively
little code to exercise a target compiler's more complex optimizations.
To address this shortcoming, we introduce liveness-driven random program
generation. In this approach the random program is constructed bottom-up,
guided by a simultaneous structural data-flow analysis to ensure that the
generator never generates dead code.
The algorithm is implemented as a plugin for the Frama-C framework. We
evaluate it in comparison to Csmith, the standard random C program generator.
Our tool generates programs that compile to more machine code with a more
complex instruction mix.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,869 | Modeling and optimal control of HIV/AIDS prevention through PrEP | Pre-exposure prophylaxis (PrEP) consists in the use of an antiretroviral
medication to prevent the acquisition of HIV infection by uninfected
individuals and has recently demonstrated to be highly efficacious for HIV
prevention. We propose a new epidemiological model for HIV/AIDS transmission
including PrEP. Existence, uniqueness and global stability of the disease free
and endemic equilibriums are proved. The model with no PrEP is calibrated with
the cumulative cases of infection by HIV and AIDS reported in Cape Verde from
1987 to 2014, showing that it predicts well such reality. An optimal control
problem with a mixed state control constraint is then proposed and analyzed,
where the control function represents the PrEP strategy and the mixed
constraint models the fact that, due to PrEP costs, epidemic context and
program coverage, the number of individuals under PrEP is limited at each
instant of time. The objective is to determine the PrEP strategy that satisfies
the mixed state control constraint and minimizes the number of individuals with
pre-AIDS HIV-infection as well as the costs associated with PrEP. The optimal
control problem is studied analytically. Through numerical simulations, we
demonstrate that PrEP reduces HIV transmission significantly.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,870 | STARIMA-based Traffic Prediction with Time-varying Lags | Based on the observation that the correlation between observed traffic at two
measurement points or traffic stations may be time-varying, attributable to the
time-varying speed which subsequently causes variations in the time required to
travel between the two points, in this paper, we develop a modified Space-Time
Autoregressive Integrated Moving Average (STARIMA) model with time-varying lags
for short-term traffic flow prediction. Particularly, the temporal lags in the
modified STARIMA change with the time-varying speed at different time of the
day or equivalently change with the (time-varying) time required to travel
between two measurement points. Firstly, a technique is developed to evaluate
the temporal lag in the STARIMA model, where the temporal lag is formulated as
a function of the spatial lag (spatial distance) and the average speed.
Secondly, an unsupervised classification algorithm based on ISODATA algorithm
is designed to classify different time periods of the day according to the
variation of the speed. The classification helps to determine the appropriate
time lag to use in the STARIMA model. Finally, a STARIMA-based model with
time-varying lags is developed for short-term traffic prediction. Experimental
results using real traffic data show that the developed STARIMA-based model
with time-varying lags has superior accuracy compared with its counterpart
developed using the traditional cross-correlation function and without
employing time-varying lags.
| 1 | 0 | 1 | 0 | 0 | 0 |
16,871 | Electric Vehicle Charging Station Placement Method for Urban Areas | For accommodating more electric vehicles (EVs) to battle against fossil fuel
emission, the problem of charging station placement is inevitable and could be
costly if done improperly. Some researches consider a general setup, using
conditions such as driving ranges for planning. However, most of the EV growths
in the next decades will happen in the urban area, where driving ranges is not
the biggest concern. For such a need, we consider several practical aspects of
urban systems, such as voltage regulation cost and protection device upgrade
resulting from the large integration of EVs. Notably, our diversified objective
can reveal the trade-off between different factors in different cities
worldwide. To understand the global optimum of large-scale analysis, we add
constraint one-by-one to see how to preserve the problem convexity. Our
sensitivity analysis before and after convexification shows that our approach
is not only universally applicable but also has a small approximation error for
prioritizing the most urgent constraint in a specific setup. Finally, numerical
results demonstrate the trade-off, the relationship between different factors
and the global objective, and the small approximation error. A unique
observation in this study shows the importance of incorporating the protection
device upgrade in urban system planning on charging stations.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,872 | The Steinberg linkage class for a reductive algebraic group | Let G be a reductive algebraic group over a field of positive characteristic
and denote by C(G) the category of rational G-modules. In this note we
investigate the subcategory of C(G) consisting of those modules whose
composition factors all have highest weights linked to the Steinberg weight.
This subcategory is denoted ST and called the Steinberg component. We give an
explicit equivalence between ST and C(G) and we derive some consequences. In
particular, our result allows us to relate the Frobenius contracting functor to
the projection functor from C(G) onto ST .
| 0 | 0 | 1 | 0 | 0 | 0 |
16,873 | Detection and Resolution of Rumours in Social Media: A Survey | Despite the increasing use of social media platforms for information and news
gathering, its unmoderated nature often leads to the emergence and spread of
rumours, i.e. pieces of information that are unverified at the time of posting.
At the same time, the openness of social media platforms provides opportunities
to study how users share and discuss rumours, and to explore how natural
language processing and data mining techniques may be used to find ways of
determining their veracity. In this survey we introduce and discuss two types
of rumours that circulate on social media; long-standing rumours that circulate
for long periods of time, and newly-emerging rumours spawned during fast-paced
events such as breaking news, where reports are released piecemeal and often
with an unverified status in their early stages. We provide an overview of
research into social media rumours with the ultimate goal of developing a
rumour classification system that consists of four components: rumour
detection, rumour tracking, rumour stance classification and rumour veracity
classification. We delve into the approaches presented in the scientific
literature for the development of each of these four components. We summarise
the efforts and achievements so far towards the development of rumour
classification systems and conclude with suggestions for avenues for future
research in social media mining for detection and resolution of rumours.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,874 | On harmonic analysis of spherical convolutions on semisimple Lie groups | This paper contains a non-trivial generalization of the Harish-Chandra
transforms on a connected semisimple Lie group $G,$ with finite center, into
what we term spherical convolutions. Among other results we show that its
integral over the collection of bounded spherical functions at the identity
element $e \in G$ is a weighted Fourier transforms of the Abel transform at
$0.$ Being a function on $G,$ the restriction of this integral of its spherical
Fourier transforms to the positive-definite spherical functions is then shown
to be (the non-zero constant multiple of) a positive-definite distribution on
$G,$ which is tempered and invariant on $G=SL(2,\mathbb{R}).$ These results
suggest the consideration of a calculus on the Schwartz algebras of spherical
functions. The Plancherel measure of the spherical convolutions is also
explicitly computed.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,875 | Relaxation-based viscosity mapping for magnetic particle imaging | Magnetic Particle Imaging (MPI) has been shown to provide remarkable contrast
for imaging applications such as angiography, stem cell tracking, and cancer
imaging. Recently, there is growing interest in the functional imaging
capabilities of MPI, where color MPI techniques have explored separating
different nanoparticles, which could potentially be used to distinguish
nanoparticles in different states or environments. Viscosity mapping is a
promising functional imaging application for MPI, as increased viscosity levels
in vivo have been associated with numerous diseases such as hypertension,
atherosclerosis, and cancer. In this work, we propose a viscosity mapping
technique for MPI through the estimation of the relaxation time constant of the
nanoparticles. Importantly, the proposed time constant estimation scheme does
not require any prior information regarding the nanoparticles. We validate this
method with extensive experiments in an in-house magnetic particle spectroscopy
(MPS) setup at four different frequencies (between 250 Hz and 10.8 kHz) and at
three different field strengths (between 5 mT and 15 mT) for viscosities
ranging between 0.89 mPa.s to 15.33 mPa.s. Our results demonstrate the
viscosity mapping ability of MPI in the biologically relevant viscosity range.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,876 | Detecting Statistically Significant Communities | Community detection is a key data analysis problem across different fields.
During the past decades, numerous algorithms have been proposed to address this
issue. However, most work on community detection does not address the issue of
statistical significance. Although some research efforts have been made towards
mining statistically significant communities, deriving an analytical solution
of p-value for one community under the configuration model is still a
challenging mission that remains unsolved. To partially fulfill this void, we
present a tight upper bound on the p-value of a single community under the
configuration model, which can be used for quantifying the statistical
significance of each community analytically. Meanwhile, we present a local
search method to detect statistically significant communities in an iterative
manner. Experimental results demonstrate that our method is comparable with the
competing methods on detecting statistically significant communities.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,877 | On the effectivity of spectra representing motivic cohomology theories | Let k be an infinite perfect field. We provide a general criterion for a
spectrum in the stable homotopy category over k to be effective, i.e. to be in
the localizing subcategory generated by the suspension spectra of smooth
schemes. As a consequence, we show that two recent versions of generalized
motivic cohomology theories coincide.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,878 | Aggregated Momentum: Stability Through Passive Damping | Momentum is a simple and widely used trick which allows gradient-based
optimizers to pick up speed along low curvature directions. Its performance
depends crucially on a damping coefficient $\beta$. Large $\beta$ values can
potentially deliver much larger speedups, but are prone to oscillations and
instability; hence one typically resorts to small values such as 0.5 or 0.9. We
propose Aggregated Momentum (AggMo), a variant of momentum which combines
multiple velocity vectors with different $\beta$ parameters. AggMo is trivial
to implement, but significantly dampens oscillations, enabling it to remain
stable even for aggressive $\beta$ values such as 0.999. We reinterpret
Nesterov's accelerated gradient descent as a special case of AggMo and analyze
rates of convergence for quadratic objectives. Empirically, we find that AggMo
is a suitable drop-in replacement for other momentum methods, and frequently
delivers faster convergence.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,879 | On the Number of Bins in Equilibria for Signaling Games | We investigate the equilibrium behavior for the decentralized quadratic cheap
talk problem in which an encoder and a decoder, viewed as two decision makers,
have misaligned objective functions. In prior work, we have shown that the
number of bins under any equilibrium has to be at most countable, generalizing
a classical result due to Crawford and Sobel who considered sources with
density supported on $[0,1]$. In this paper, we refine this result in the
context of exponential and Gaussian sources. For exponential sources, a
relation between the upper bound on the number of bins and the misalignment in
the objective functions is derived, the equilibrium costs are compared, and it
is shown that there also exist equilibria with infinitely many bins under
certain parametric assumptions. For Gaussian sources, it is shown that there
exist equilibria with infinitely many bins.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,880 | The Dantzig selector for a linear model of diffusion processes | In this paper, a linear model of diffusion processes with unknown drift and
diagonal diffusion matrices is discussed. We will consider the estimation
problems for unknown parameters based on the discrete time observation in
high-dimensional and sparse settings. To estimate drift matrices, the Dantzig
selector which was proposed by Candés and Tao in 2007 will be applied. Then,
we will prove two types of consistency of the estimator of drift matrix; one is
the consistency in the sense of $l_q$ norm for every $q \in [1,\infty]$ and the
other is the variable selection consistency. Moreover, we will construct an
asymptotically normal estimator of the drift matrix by using the variable
selection consistency of the Dantzig selector.
| 0 | 0 | 1 | 1 | 0 | 0 |
16,881 | A Spatio-Temporal Multivariate Shared Component Model with an Application in Iran Cancer Data | Among the proposals for joint disease mapping, the shared component model has
become more popular. Another recent advance to strengthen inference of disease
data has been the extension of purely spatial models to include time and
space-time interaction. Such analyses have additional benefits over purely
spatial models. However, only a few proposed spatio-temporal models could
address analysing multiple diseases jointly.
In the proposed model, each component is shared by different subsets of
diseases, spatial and temporal trends are considered for each component, and
the relative weight of these trends for each component for each relevant
disease can be estimated. We present an application of the proposed method on
incidence rates of seven prevalent cancers in Iran. The effect of the shared
components on the individual cancer types can be identified. Regional and
temporal variation in relative risks is shown. We present a model which
combines the benefits of shared-components with spatio-temporal techniques for
multivariate data. We show, how the model allows to analyse geographical and
temporal variation among diseases beyond previous approaches.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,882 | The dynamo effect in decaying helical turbulence | We show that in decaying hydromagnetic turbulence with initial kinetic
helicity, a weak magnetic field eventually becomes fully helical. The sign of
magnetic helicity is opposite to that of the kinetic helicity - regardless of
whether or not the initial magnetic field was helical. The magnetic field
undergoes inverse cascading with the magnetic energy decaying approximately
like t^{-1/2}. This is even slower than in the fully helical case, where it
decays like t^{-2/3}. In this parameter range, the product of magnetic energy
and correlation length raised to a certain power slightly larger than unity, is
approximately constant. This scaling of magnetic energy persists over long time
scales. At very late times and for domain sizes large enough to accommodate the
growing spatial scales, we expect a cross-over to the t^{-2/3} decay law that
is commonly observed for fully helical magnetic fields. Regardless of the
presence or absence of initial kinetic helicity, the magnetic field experiences
exponential growth during the first few turnover times, which is suggestive of
small-scale dynamo action. Our results have applications to a wide range of
experimental dynamos and astrophysical time-dependent plasmas, including
primordial turbulence in the early universe.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,883 | Geometrically finite amalgamations of hyperbolic 3-manifold groups are not LERF | We prove that, for any two finite volume hyperbolic $3$-manifolds, the
amalgamation of their fundamental groups along any nontrivial geometrically
finite subgroup is not LERF. This generalizes the author's previous work on
nonLERFness of amalgamations of hyperbolic $3$-manifold groups along abelian
subgroups. A consequence of this result is that closed arithmetic hyperbolic
$4$-manifolds have nonLERF fundamental groups. Along with the author's previous
work, we get that, for any arithmetic hyperbolic manifold with dimension at
least $4$, with possible exceptions in $7$-dimensional manifolds defined by the
octonion, its fundamental group is not LERF.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,884 | Dining Philosophers, Leader Election and Ring Size problems, in the quantum setting | We provide the first quantum (exact) protocol for the Dining Philosophers
problem (DP), a central problem in distributed algorithms. It is well known
that the problem cannot be solved exactly in the classical setting. We then use
our DP protocol to provide a new quantum protocol for the tightly related
problem of exact leader election (LE) on a ring, improving significantly in
both time and memory complexity over the known LE protocol by Tani et. al. To
do this, we show that in some sense the exact DP and exact LE problems are
equivalent; interestingly, in the classical non-exact setting they are not.
Hopefully, the results will lead to exact quantum protocols for other important
distributed algorithmic questions; in particular, we discuss interesting
connections to the ring size problem, as well as to a physically motivated
question of breaking symmetry in 1D translationally invariant systems.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,885 | An Online Secretary Framework for Fog Network Formation with Minimal Latency | Fog computing is seen as a promising approach to perform distributed,
low-latency computation for supporting Internet of Things applications.
However, due to the unpredictable arrival of available neighboring fog nodes,
the dynamic formation of a fog network can be challenging. In essence, a given
fog node must smartly select the set of neighboring fog nodes that can provide
low-latency computations. In this paper, this problem of fog network formation
and task distribution is studied considering a hybrid cloud-fog architecture.
The goal of the proposed framework is to minimize the maximum computational
latency by enabling a given fog node to form a suitable fog network, under
uncertainty on the arrival process of neighboring fog nodes. To solve this
problem, a novel approach based on the online secretary framework is proposed.
To find the desired set of neighboring fog nodes, an online algorithm is
developed to enable a task initiating fog node to decide on which other nodes
can be used as part of its fog network, to offload computational tasks, without
knowing any prior information on the future arrivals of those other nodes.
Simulation results show that the proposed online algorithm can successfully
select an optimal set of neighboring fog nodes while achieving a latency that
is as small as the one resulting from an ideal, offline scheme that has
complete knowledge of the system. The results also show how, using the proposed
approach, the computational tasks can be properly distributed between the fog
network and a remote cloud server.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,886 | Computer-assisted proof of heteroclinic connections in the one-dimensional Ohta-Kawasaki model | We present a computer-assisted proof of heteroclinic connections in the
one-dimensional Ohta-Kawasaki model of diblock copolymers. The model is a
fourth-order parabolic partial differential equation subject to homogeneous
Neumann boundary conditions, which contains as a special case the celebrated
Cahn-Hilliard equation. While the attractor structure of the latter model is
completely understood for one-dimensional domains, the diblock copolymer
extension exhibits considerably richer long-term dynamical behavior, which
includes a high level of multistability. In this paper, we establish the
existence of certain heteroclinic connections between the homogeneous
equilibrium state, which represents a perfect copolymer mixture, and all local
and global energy minimizers. In this way, we show that not every solution
originating near the homogeneous state will converge to the global energy
minimizer, but rather is trapped by a stable state with higher energy. This
phenomenon can not be observed in the one-dimensional Cahn-Hillard equation,
where generic solutions are attracted by a global minimizer.
| 1 | 0 | 1 | 0 | 0 | 0 |
16,887 | Dust and Gas in Star Forming Galaxies at z~3 - Extending Galaxy Uniformity to 11.5 Billion Years | We present millimetre dust emission measurements of two Lyman Break Galaxies
at z~3 and construct for the first time fully sampled infrared spectral energy
distributions (SEDs), from mid-IR to the Rayleigh-Jeans tail, of individually
detected, unlensed, UV-selected, main sequence (MS) galaxies at $z=3$. The SED
modelling of the two sources confirms previous findings, based on stacked
ensembles, of an increasing mean radiation field <U> with redshift, consistent
with a rapidly decreasing gas metallicity in z > 2 galaxies. Complementing our
study with CO[3-2] emission line observations, we measure the molecular gas
mass (M_H2) reservoir of the systems using three independent approaches: 1) CO
line observations, 2) the dust to gas mass ratio vs metallicity relation and 3)
a single band, dust emission flux on the Rayleigh-Jeans side of the SED. All
techniques return consistent M_H2 estimates within a factor of ~2 or less,
yielding gas depletion time-scales (tau_dep ~ 0.35 Gyrs) and gas-to-stellar
mass ratios (M_H2/M* ~ 0.5-1) for our z~3 massive MS galaxies. The overall
properties of our galaxies are consistent with trends and relations established
at lower redshifts, extending the apparent uniformity of star-forming galaxies
over the last 11.5 billion years.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,888 | Flow speed has little impact on propulsive characteristics of oscillating foils | Experiments are reported on the performance of a pitching and heaving
two-dimensional foil in a water channel in either continuous or intermittent
motion. We find that the thrust and power are independent of the mean
freestream velocity for two-fold changes in the mean velocity (four-fold in the
dynamic pressure), and for oscillations in the velocity up to 38\% of the mean,
where the oscillations are intended to mimic those of freely swimming motions
where the thrust varies during the flapping cycle. We demonstrate that the
correct velocity scale is not the flow velocity but the mean velocity of the
trailing edge. We also find little or no impact of streamwise velocity change
on the wake characteristics such as vortex organization, vortex strength, and
time-averaged velocity profile development---the wake is both qualitatively and
quantitatively unchanged. Our results suggest that constant velocity studies
can be used to make robust conclusions about swimming performance without a
need to explore the free-swimming condition.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,889 | Switching and Data Injection Attacks on Stochastic Cyber-Physical Systems: Modeling, Resilient Estimation and Attack Mitigation | In this paper, we consider the problem of attack-resilient state estimation,
that is to reliably estimate the true system states despite two classes of
attacks: (i) attacks on the switching mechanisms and (ii) false data injection
attacks on actuator and sensor signals, in the presence of unbounded stochastic
process and measurement noise signals. We model the systems under attack as
hidden mode stochastic switched linear systems with unknown inputs and propose
the use of a multiple-model inference algorithm to tackle these security
issues. Moreover, we characterize fundamental limitations to resilient
estimation (e.g., upper bound on the number of tolerable signal attacks) and
discuss the topics of attack detection, identification and mitigation under
this framework. Simulation examples of switching and false data injection
attacks on a benchmark system and an IEEE 68-bus test system show the efficacy
of our approach to recover resilient (i.e., asymptotically unbiased) state
estimates as well as to identify and mitigate the attacks.
| 1 | 0 | 1 | 0 | 0 | 0 |
16,890 | Generating Sentence Planning Variations for Story Telling | There has been a recent explosion in applications for dialogue interaction
ranging from direction-giving and tourist information to interactive story
systems. Yet the natural language generation (NLG) component for many of these
systems remains largely handcrafted. This limitation greatly restricts the
range of applications; it also means that it is impossible to take advantage of
recent work in expressive and statistical language generation that can
dynamically and automatically produce a large number of variations of given
content. We propose that a solution to this problem lies in new methods for
developing language generation resources. We describe the ES-Translator, a
computational language generator that has previously been applied only to
fables, and quantitatively evaluate the domain independence of the EST by
applying it to personal narratives from weblogs. We then take advantage of
recent work on language generation to create a parameterized sentence planner
for story generation that provides aggregation operations, variations in
discourse and in point of view. Finally, we present a user evaluation of
different personal narrative retellings.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,891 | Detection of planet candidates around K giants, HD 40956, HD 111591, and HD 113996 | Aims. The purpose of this paper is to detect and investigate the nature of
long-term radial velocity (RV) variations of K-type giants and to confirm
planetary companions around the stars.
Methods. We have conducted two planet search programs by precise RV
measurement using the 1.8 m telescope at Bohyunsan Optical Astronomy
Observatory (BOAO) and the 1.88 m telescope at Okayama Astrophysical
Observatory (OAO). The BOAO program searches for planets around 55 early K
giants. The OAO program is looking for 190 G-K type giants.
Results. In this paper, we report the detection of long-period RV variations
of three K giant stars, HD 40956, HD 111591, and HD 113996. We investigated the
cause of the observed RV variations and conclude the substellar companions are
most likely the cause of the RV variations. The orbital analyses yield P =
578.6 $\pm$ 3.3 d, $m$ sin $i$ = 2.7 $\pm$ 0.6 $M_{\rm{J}}$, $a$ = 1.4 $\pm$
0.1 AU for HD 40956; P = 1056.4 $\pm$ 14.3 d, $m$ sin $i$ = 4.4 $\pm$ 0.4
$M_{\rm{J}}$, $a$ = 2.5 $\pm$ 0.1 AU for HD 111591; P = 610.2 $\pm$ 3.8 d, $m$
sin $i$ = 6.3 $\pm$ 1.0 $M_{\rm{J}}$, $a$ = 1.6 $\pm$ 0.1 AU for HD 113996.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,892 | Perfect Half Space Games | We introduce perfect half space games, in which the goal of Player 2 is to
make the sums of encountered multi-dimensional weights diverge in a direction
which is consistent with a chosen sequence of perfect half spaces (chosen
dynamically by Player 2). We establish that the bounding games of Jurdziński
et al. (ICALP 2015) can be reduced to perfect half space games, which in turn
can be translated to the lexicographic energy games of Colcombet and
Niwiński, and are positionally determined in a strong sense (Player 2 can
play without knowing the current perfect half space). We finally show how
perfect half space games and bounding games can be employed to solve
multi-dimensional energy parity games in pseudo-polynomial time when both the
numbers of energy dimensions and of priorities are fixed, regardless of whether
the initial credit is given as part of the input or existentially quantified.
This also yields an optimal 2-EXPTIME complexity with given initial credit,
where the best known upper bound was non-elementary.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,893 | Energy-efficient Analog Sensing for Large-scale, High-density Persistent Wireless Monitoring | The research challenge of current Wireless Sensor Networks~(WSNs) is to
design energy-efficient, low-cost, high-accuracy, self-healing, and scalable
systems for applications such as environmental monitoring. Traditional WSNs
consist of low density, power-hungry digital motes that are expensive and
cannot remain functional for long periods on a single charge. In order to
address these challenges, a \textit{dumb-sensing and smart-processing}
architecture that splits sensing and computation capabilities among tiers is
proposed. Tier-1 consists of dumb sensors that only sense and transmit, while
the nodes in Tier-2 do all the smart processing on Tier-1 sensor data. A
low-power and low-cost solution for Tier-1 sensors has been proposed using
Analog Joint Source Channel Coding~(AJSCC). An analog circuit that realizes the
rectangular type of AJSCC has been proposed and realized on a Printed Circuit
Board for feasibility analysis. A prototype consisting of three Tier-1 sensors
(sensing temperature and humidity) communicating to a Tier-2 Cluster Head has
been demonstrated to verify the proposed approach. Results show that our
framework is indeed feasible to support large scale high density and persistent
WSN deployment.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,894 | Computation of ground-state properties in molecular systems: back-propagation with auxiliary-field quantum Monte Carlo | We address the computation of ground-state properties of chemical systems and
realistic materials within the auxiliary-field quantum Monte Carlo method. The
phase constraint to control the fermion phase problem requires the random walks
in Slater determinant space to be open-ended with branching. This in turn makes
it necessary to use back-propagation (BP) to compute averages and correlation
functions of operators that do not commute with the Hamiltonian. Several BP
schemes are investigated and their optimization with respect to the phaseless
constraint is considered. We propose a modified BP method for the computation
of observables in electronic systems, discuss its numerical stability and
computational complexity, and assess its performance by computing ground-state
properties for several substances, including constituents of the primordial
terrestrial atmosphere and small organic molecules.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,895 | Demonstration of the Relationship between Sensitivity and Identifiability for Inverse Uncertainty Quantification | Inverse Uncertainty Quantification (UQ), or Bayesian calibration, is the
process to quantify the uncertainties of random input parameters based on
experimental data. The introduction of model discrepancy term is significant
because "over-fitting" can theoretically be avoided. But it also poses
challenges in the practical applications. One of the mostly concerned and
unresolved problem is the "lack of identifiability" issue. With the presence of
model discrepancy, inverse UQ becomes "non-identifiable" in the sense that it
is difficult to precisely distinguish between the parameter uncertainties and
model discrepancy when estimating the calibration parameters. Previous research
to alleviate the non-identifiability issue focused on using informative priors
for the calibration parameters and the model discrepancy, which is usually not
a viable solution because one rarely has such accurate and informative prior
knowledge. In this work, we show that identifiability is largely related to the
sensitivity of the calibration parameters with regards to the chosen responses.
We adopted an improved modular Bayesian approach for inverse UQ that does not
require priors for the model discrepancy term. The relationship between
sensitivity and identifiability was demonstrated with a practical example in
nuclear engineering. It was shown that, in order for a certain calibration
parameter to be statistically identifiable, it should be significant to at
least one of the responses whose data are used for inverse UQ. Good
identifiability cannot be achieved for a certain calibration parameter if it is
not significant to any of the responses. It is also demonstrated that "fake
identifiability" is possible if model responses are not appropriately chosen,
or inaccurate but informative priors are specified.
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16,896 | The GENIUS Approach to Robust Mendelian Randomization Inference | Mendelian randomization (MR) is a popular instrumental variable (IV)
approach. A key IV identification condition known as the exclusion restriction
requires no direct effect of an IV on the outcome not through the exposure
which is unrealistic in most MR analyses. As a result, possible violation of
the exclusion restriction can seldom be ruled out in such studies. To address
this concern, we introduce a new class of IV estimators which are robust to
violation of the exclusion restriction under a large collection of data
generating mechanisms consistent with parametric models commonly assumed in the
MR literature. Our approach named "MR G-Estimation under No Interaction with
Unmeasured Selection" (MR GENIUS) may be viewed as a modification to Robins'
G-estimation approach that is robust to both additive unmeasured confounding
and violation of the exclusion restriction assumption. We also establish that
estimation with MR GENIUS may also be viewed as a robust generalization of the
well-known Lewbel estimator for a triangular system of structural equations
with endogeneity. Specifically, we show that unlike Lewbel estimation, MR
GENIUS is under fairly weak conditions also robust to unmeasured confounding of
the effects of the genetic IVs, another possible violation of a key IV
Identification condition. Furthermore, while Lewbel estimation involves
specification of linear models both for the outcome and the exposure, MR GENIUS
generally does not require specification of a structural model for the direct
effect of invalid IVs on the outcome, therefore allowing the latter model to be
unrestricted. Finally, unlike Lewbel estimation, MR GENIUS is shown to equally
apply for binary, discrete or continuous exposure and outcome variables and can
be used under prospective sampling, or retrospective sampling such as in a
case-control study.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,897 | Evaluation of Trace Alignment Quality and its Application in Medical Process Mining | Trace alignment algorithms have been used in process mining for discovering
the consensus treatment procedures and process deviations. Different alignment
algorithms, however, may produce very different results. No widely-adopted
method exists for evaluating the results of trace alignment. Existing
reference-free evaluation methods cannot adequately and comprehensively assess
the alignment quality. We analyzed and compared the existing evaluation
methods, identifying their limitations, and introduced improvements in two
reference-free evaluation methods. Our approach assesses the alignment result
globally instead of locally, and therefore helps the algorithm to optimize
overall alignment quality. We also introduced a novel metric to measure the
alignment complexity, which can be used as a constraint on alignment algorithm
optimization. We tested our evaluation methods on a trauma resuscitation
dataset and provided the medical explanation of the activities and patterns
identified as deviations using our proposed evaluation methods.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,898 | Size Constraints on Majorana Beamsplitter Interferometer: Majorana Coupling and Surface-Bulk Scattering | Topological insulator surfaces in proximity to superconductors have been
proposed as a way to produce Majorana fermions in condensed matter physics. One
of the simplest proposed experiments with such a system is Majorana
interferometry. Here, we consider two possibly conflicting constraints on the
size of such an interferometer. Coupling of a Majorana mode from the edge (the
arms) of the interferometer to vortices in the centre of the device sets a
lower bound on the size of the device. On the other hand, scattering to the
usually imperfectly insulating bulk sets an upper bound. From estimates of
experimental parameters, we find that typical samples may have no size window
in which the Majorana interferometer can operate, implying that a new
generation of more highly insulating samples must be explored.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,899 | Counting Arithmetical Structures on Paths and Cycles | Let $G$ be a finite, simple, connected graph. An arithmetical structure on
$G$ is a pair of positive integer vectors $\mathbf{d},\mathbf{r}$ such that
$(\mathrm{diag}(\mathbf{d})-A)\mathbf{r}=0$, where $A$ is the adjacency matrix
of $G$. We investigate the combinatorics of arithmetical structures on path and
cycle graphs, as well as the associated critical groups (the cokernels of the
matrices $(\mathrm{diag}(\mathbf{d})-A)$). For paths, we prove that
arithmetical structures are enumerated by the Catalan numbers, and we obtain
refined enumeration results related to ballot sequences. For cycles, we prove
that arithmetical structures are enumerated by the binomial coefficients
$\binom{2n-1}{n-1}$, and we obtain refined enumeration results related to
multisets. In addition, we determine the critical groups for all arithmetical
structures on paths and cycles.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,900 | From synaptic interactions to collective dynamics in random neuronal networks models: critical role of eigenvectors and transient behavior | The study of neuronal interactions is currently at the center of several
neuroscience big collaborative projects (including the Human Connectome, the
Blue Brain, the Brainome, etc.) which attempt to obtain a detailed map of the
entire brain matrix. Under certain constraints, mathematical theory can advance
predictions of the expected neural dynamics based solely on the statistical
properties of such synaptic interaction matrix. This work explores the
application of free random variables (FRV) to the study of large synaptic
interaction matrices. Besides recovering in a straightforward way known results
on eigenspectra of neural networks, we extend them to heavy-tailed
distributions of interactions. More importantly, we derive analytically the
behavior of eigenvector overlaps, which determine stability of the spectra. We
observe that upon imposing the neuronal excitation/inhibition balance, although
the eigenvalues remain unchanged, their stability dramatically decreases due to
strong non-orthogonality of associated eigenvectors. It leads us to the
conclusion that the understanding of the temporal evolution of asymmetric
neural networks requires considering the entangled dynamics of both
eigenvectors and eigenvalues, which might bear consequences for learning and
memory processes in these models. Considering the success of FRV analysis in a
wide variety of branches disciplines, we hope that the results presented here
foster additional application of these ideas in the area of brain sciences.
| 0 | 0 | 0 | 0 | 1 | 0 |
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